PaperPlayer biorxiv systems biology: Recent Episodes
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.27.513792v1?rss=1
Authors: Chae, S. J., Kim, D. W., Lee, S., Kim, J. K.
Abstract: The mammalian circadian (~24h) clock is based on a self-sustaining transcriptional-translational negative feedback loop (TTFL) centered around the PERIOD protein (PER), which is translated in the cytoplasm and then enters the nucleus to repress its own transcription at the right time of day. How such precise nucleus entry, critical for generating circadian rhythms, occurs is mysterious because thousands of PER molecules transit through crowded cytoplasm and arrive at the perinucleus across several hours. Here, we investigate this by developing a mathematical model that effectively describes the complex spatiotemporal dynamics of PER as a single random time delay. We find that the spatially coordinated bistable phosphoswitch of PER, which triggers the phosphorylation of accumulated PER at the perinucleus, can lead to the synchronous and precise nuclear entry of PER, and thus to precise transcriptional repression despite the heterogenous PER arrival times at the perinucleus. In particular, even when cell crowdedness, cell size, and transcriptional activator level change, and thus PER arrival times at the perinucleus are greatly perturbed, the bistable phosphoswitch allows the TTFL to maintain robust circadian rhythms. These results provide fundamental insight into how the circadian clock compensates for spatiotemporal noise from various intracellular sources.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.28.514090v1?rss=1
Authors: Krishnamurthy, M., Herron, L., Susanti, D., Volland-Munson, A., Plata, G., Dixit, P. D.
Abstract: The collective nature of the variation in host associated microbial communities suggest that they exhibit low dimensional characteristics. To identify these lower dimensional descriptors, we propose SMbiot (pronounced SIM BY OT): a Shared Latent Model for Microbiomes and their hosts. In SMbiot, latent variables embed host-specific microbial communities in a lower dimensional space and the corresponding features reflect controlling axes that dictate community compositions. Using data from different animal hosts, organ sites, and microbial kingdoms of life, we show that SMbiot identifies a small number of host-specific latent variables that accurately capture the compositional variation in host associated microbial communities. By using the same latents to describe hosts' phenotypic states and the host-associated microbiomes, we show that the latent space embedding is informed by host physiology as well as the associated microbiomes. Importantly, SMbiot enables the quantification of host phenotypic differences associated with altered microbial community compositions in a host-specific manner, underscoring the context specificity of host-microbiome associations. SMbiot can also predict missing host metadata or microbial community compositions. This way, SMbiot is a concise quantitative method to understand the low dimensional collective behavior of host-associated microbiomes.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.26.513817v1?rss=1
Authors: Allaband, C., Lingaraju, A., Ramos, S. F., Kumar, T., Javaheri, H., Tiu, M. D., Machado, A. C. D., Richter, R. A., Elijah, E., Haddad, G. G., Leone, V. A., Dorrestein, P. C., Knight, R., Zarrinpar, A.
Abstract: Although many aspects of microbiome studies have been standardized to improve experimental replicability, none account for how the daily diurnal fluctuations in the gut lumen cause dynamic changes in 16S amplicon sequencing. Here we show that sample collection time affects the conclusions drawn from microbiome studies and are larger than the effect size of a daily experimental intervention or dietary changes. The timing of divergence of the microbiome composition between experimental and control groups are unique to each experiment. Sample collection times as short as only four hours apart lead to vastly different conclusions. Lack of consistency in the time of sample collection may explain poor cross-study replicability in microbiome research. Without looking at other data, the impact on other fields is unknown but potentially significant.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.26.513808v1?rss=1
Authors: Yamagishi, J. F., Hatakeyama, T. S.
Abstract: Many previous studies have attempted to predict the metabolic states of cells assuming metabolic regulation is optimized through (sometimes artificial) evolution for some objective, e.g., growth rate or production of some metabolites. Conventional approaches, however, require identifying the microscopic details of individual metabolic reactions and the objective functions of cells, and their predictions sensitively depend on such details. In this study, we focus on the responses of metabolic systems to environmental perturbations, rather than their metabolic states themselves, and theoretically demonstrate a universal property of the responses independent of the systems' details. With the help of a microeconomic theory, we show a simple relationship between intracellular metabolic responses against nutrient abundance and metabolic inhibition due to manipulation such as drug administration: these two experimentally measurable quantities show a proportional relationship with a negative coefficient. This quantitative relationship should hold in arbitrary metabolic systems as long as the law of mass conservation holds and cells are optimized for some objectives, but the true objective functions need not be known. Through numerical calculations using large-scale metabolic networks such as the E. coli core model, we confirmed that the relationship is valid from abstract to detailed models. Because the relationship provides quantitative predictions regarding metabolic responses without prior knowledge of systems, our findings have implications for experimental applications in microbiology, systems biology, metabolic engineering, and medicine, particularly for unexplored organisms or cells.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.25.513765v1?rss=1
Authors: Grausa, K., Siddiqui, S. A., Lameyer, N., Wiesotzki, K., Smetana, S., Pentjuss, A.
Abstract: All plant and animal kingdom organisms use highly connected biochemical networks to facilitate sustaining, proliferation and growth functions. While biochemical network details are well known, the understanding of intense regulation principles is still limited. We chose to investigate Hermetia illucens fly at the larval stage as it is crucial for successful resource accumulation and allocation for the consequential organism's developmental stages. We combined the iterative wet lab experiments and innovative metabolic modeling design approaches, to simulate and explain the H. illucens larval stage resource allocation processes and biotechnology potential. We performed time-based growth and high-value chemical compound accumulation wet lab chemical analysis experiments in larvae and Gainesville diet composition. To predict diet-based alterations on fatty acid allocation potential, we built and validated the first H. illucens medium-size stoichiometric metabolic model. Using optimization methods like Flux balance and Flux variability analysis on the novel insect metabolic model, it predicted that doubled essential amino acid consumption increased the growth rate by 32%, but pure glucose consumption had no positive impact on growth. In the case of doubled pure valine consumption, the model predicted a 2% higher growth rate. In this study, we describe a new framework to research the impact of dietary alterations on the metabolism of multi-cellular organisms at different developmental stages for improved, sustainable and directed high-value chemicals.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.25.513743v1?rss=1
Authors: Rinaldi, C., Waters, C. S., Kumbier, K., Rao, L., Nichols, R. J., Jacobson, M. P., Wu, L. F., Altschuler, S. J.
Abstract: Parkinson's disease-causing LRRK2 mutations lead to varying degrees of Rab GTPase hyperphosphorylation. Puzzlingly, LRRK2 GTPase-inactivating mutations--which do not affect intrinsic kinase activity--lead to higher levels of cellular Rab phosphorylation than kinase-activating mutations. Here, we investigated whether mutation-dependent differences in LRRK2 cellular localization could explain this discrepancy. We discovered that blocking endosomal maturation leads to the rapid formation of mutant LRRK2+ endosomes on which LRRK2 phosphorylates substrate Rabs. LRRK2+ endosomes are maintained through positive feedback, which mutually reinforces membrane localization of LRRK2 and phosphorylated Rab substrates. Furthermore, across a panel of mutants, cells expressing GTPase-inactivating mutants formed strikingly more LRRK2+ endosomes than cells expressing kinase-activating mutants, resulting in higher total cellular levels of phosphorylated Rabs. Our study suggests that an increased probability of LRRK2 GTPase-inactivating mutants to be retained on intracellular membranes over the kinase-activating mutants leads to higher substrate phosphorylation.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.25.513664v1?rss=1
Authors: Riccardi, C., Calvanese, M., Ghini, V., Alonso-Vasquez, T., Perrin, E., Turano, P., Giurato, G., Weisz, A., Parrilli, E., Tutino, M. L., Fondi, M.
Abstract: Microbial communities experience continuous environmental changes, among which temperature fluctuations are arguably the most impacting. This is particularly important considering the ongoing global warming but also in the 'simpler' context of seasonal variability of sea-surface temperature. Understanding how microorganisms react at the cellular level can improve our understanding of possible adaptations of microbial communities to a changing environment. In this work, we investigated which are the mechanisms through which metabolic homeostasis is maintained in a cold-adapted bacterium during growth at temperatures that differ widely (15 and 0C). We have quantified its intracellular and extracellular central metabolomes together with changes occurring at the transcriptomic level in the same growth conditions. This information was then used to contextualize a genome-scale metabolic reconstruction and to provide a systemic understanding of cellular adaptation to growth at two different temperatures. Our findings indicate a strong metabolic robustness at the level of the main central metabolites, counteracted by a relatively deep transcriptomic reprogramming that includes changes in gene expression of hundreds of metabolic genes. We interpret this as a transcriptomic buffering of cellular metabolism, able to produce overlapping metabolic phenotypes despite the wide temperature gap. Moreover, we show that metabolic adaptation seems to be mostly played at the level of few key intermediates (e.g. phosphoenolpyruvate) and in the cross-talk between the main central metabolic pathways. Overall, our findings reveal a complex interplay at gene expression level that contributes to the robustness/resilience of core metabolism, also promoting the leveraging of state-of-the-art multi-disciplinary approaches to fully comprehend molecular adaptations to environmental fluctuations.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.25.513640v1?rss=1
Authors: Van de Graaf, M. W., Eggertsen, T. G., Zeigler, A. C., Tan, P. M., Saucerman, J. J.
Abstract: Protein interaction databases are critical resources for network bioinformatics and integrating molecular experimental data. Interaction databases may also enable construction of predictive computational models of biological networks, although their fidelity for this purpose is not clear. Here, we benchmark protein interaction databases X2K, Reactome, Pathway Commons, and Signor for their ability to recover manually curated edges from three logic-based network models of cardiac hypertrophy, mechano-signaling, and fibrosis. Pathway Commons performed best at recovering interactions from manually reconstructed hypertrophy (137 of 193 interactions, 71%), mechano-signaling (85 of 125 interactions, 68%), and fibroblast networks (98 of 142 interactions, 69%). While protein interaction databases successfully recovered central, well-conserved pathways, they performed worse at recovering tissue-specific and transcriptional regulation. This highlights a knowledge gap where manual curation is critical. Finally, we tested the ability of Signor and Pathway Commons to identify new edges that improve model predictions, revealing important roles of PKC autophosphorylation and CaMKII phosphorylation of CREB in cardiomyocyte hypertrophy. This study provides a platform for benchmarking protein interaction databases for their utility in network model construction, as well as providing new insights into cardiac hypertrophy signaling.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.24.513612v1?rss=1
Authors: Zheng, Y., Liu, Y., Yang, J., Dong, L., Zhang, R., Tian, S., Yu, Y., Ren, L., Hou, W., Zhu, F., Mai, Y., Han, J., Zhang, L., Jiang, H., Lin, L., Lou, J., Li, R., Lin, J., Liu, H., Kong, Z., Wang, D., Dai, F., Bao, D., Cao, Z., Chen, Q., Chen, Q., Chen, X., Gao, Y., Jiang, H., Li, B., Li, B., Li, J., Liu, R., Qing, T., Shang, E., Shang, J., Sun, S., Wang, H., Wang, X., Zhang, N., Zhang, P., Zhang, R., Zhu, S., Scherer, A., Wang, J., Wang, J., Xu, J., Hong, H., Xiao, W., Liang, X., Jin, L., The Quartet Project Team,, Tong, W., Ding, C., Li, J., Fang, X., Shi, L.
Abstract: Multiomics profiling is a powerful tool to characterize the same samples with complementary features orchestrating the genome, epigenome, transcriptome, proteome, and metabolome. However, the lack of ground truth hampers the objective assessment of and subsequent choice from a plethora of measurement and computational methods aiming to integrate diverse and often enigmatically incomparable omics datasets. Here we establish and characterize the first suites of publicly available multiomics reference materials of matched DNA, RNA, proteins, and metabolites derived from immortalized cell lines from a family quartet of parents and monozygotic twin daughters, providing built-in truth defined by family relationship and the central dogma. We demonstrate that the "ratio"-based omics profiling data, i.e., by scaling the absolute feature values of a study sample relative to those of a concurrently measured universal reference sample, were inherently much more reproducible and comparable across batches, labs, platforms, and omics types, thus empower the horizontal (within-omics) and vertical (cross-omics) data integration in multiomics studies. Our study identifies "absolute" feature quantitation as the root cause of irreproducibility in multiomics measurement and data integration, and urges a paradigm shift from "absolute" to "ratio"-based multiomics profiling with universal reference materials.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.24.513448v1?rss=1
Authors: Cornet, M., Najm, M., Albergante, L., Zinovyev, A., Sermet-Gaudelus, I., Stoven, V., Calzone, L., MARTIGNETTI, L.
Abstract: In many analyses of high-throughput data in systems biology, calculating the activity of a set of genes rather than focusing on the differential expression of individual genes has proven to be efficient and informative. Here, we present the rROMA software package for fast and accurate computation of the activity of gene sets with coordinated expression. We applied rROMA to cystic fibrosis, highlighting biological mechanisms potentially involved in the establishment and progression of the disease and the associated genes. Source code and documentation are available at https://github.com/sysbiocurie/rROMA.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.22.513080v1?rss=1
Authors: Mori, M., Cheng, C., Taylor, B. R., Okano, H., Hwa, T.
Abstract: Quantifying the contribution of individual molecular components to complex cellular processes is a grand challenge in systems biology. Here we establish a general theoretical framework (Functional Decomposition of Metabolism, FDM) to quantify the contribution of every metabolic reaction to metabolic functions, e.g. the biosynthesis of metabolic building blocks such as amino acids. This allows us to obtain a plethora of results for E. coli growing in different conditions. A detailed quantification of energetic costs for biosynthesis and biomass growth on glucose shows that ATP generated during de novo biosynthesis of building blocks almost balances the ATP costs of peptide chain polymerization, the single largest energy expenditure for growing cells. This leaves the bulk of energy generated by fermentation and respiration (consuming 1/3 of the glucose intake) during aerobic growth unaccounted for. FDM also enabled the quantification of protein allocated towards each metabolic function, unveiling linear enzyme-flux relations for biosynthesis. These results led us to derive a function-based coarse-grained model to capture global protein allocation and overflow metabolism, without relying on curated pathway annotation or clustering of gene expression data.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.23.513380v1?rss=1
Authors: Singh, G.
Abstract: Senescent cell accumulation and defective clearance of the senescent cells by the immune system occur with aging and increase the prevalence of diseases like cancer. Anti-tumor therapies can induce senescence in the tumor cells. Senescence Associated Secretory Phenotypes (SASP) secretion by these senescent tumor cells activates the innate NK cells which can detect and eliminate them. Mechanisms are unclear about how does it occur? A combination of immunotherapy and senotherapy has shown the possibility to reduce the tumor burden and increase the health span. The temporal and intensity dynamics of the therapeutic dose regimen remains to be studied. Therefore, a simplified therapy-induced senescence (TIS) phenomenological model is proposed to explain the mechanism of senescent tumor cell clearance by the NK immune cells and understand the possibility of a two-punch therapy technique in regulating tumors. Interaction strength changes for the cellular population within a healthy and an aged tumor microenvironment. The simulation result shows an oscillatory behavior existing between the tumor and immune cells. Tumor heterogeneity acts as inherent noise in sustaining the tumor for relapse emergence despite therapeutic clearance. The model indicates the formation of a robust oscillatory loop between the tumor, immune, and senescence cells which they can tune by modifying the phenotypic fitness landscape through secreted factors making them resistant despite selective removal of the sensitive populations by various therapies. The model highlights the importance of modified and aged tumor microenvironment by senescence tumor cells in obstructing clearance of both senescence and tumor cells by the innate immune system. Cancer therapies along with senolytics may have a robust and effective regulatory potential over tumor and senescence cells. The model also provides a preliminary analysis of the therapy temporal and intensity dosage regimen causing a therapeutic shift in tumors.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.21.513276v1?rss=1
Authors: Moschoyiannis, S., Chatzaroulas, E., Sliogeris, V., Wu, Y.
Abstract: The ability to direct a Probabilistic Boolean Network (PBN) to a desired state is important to applications such as targeted therapeutics in cancer biology. Reinforcement Learning (RL) has been proposed as a framework that solves a discrete-time optimal control problem cast as a Markov Decision Process. We focus on an integrative framework powered by a model-free deep RL method that can address different flavours of the control problem (e.g., with or without control inputs; attractor state or a subset of the state space as the target domain). The method is agnostic to the distribution of probabilities for the next state, hence it does not use the probability transition matrix. The time complexity is only linear on the time steps, or interactions between the agent (deep RL) and the environment (PBN), during training. Indeed, we explore the scalability of the deep RL approach to (set) stabilization of large-scale PBNs and demonstrate successful control on large networks, including a metastatic melanoma PBN with 200 nodes.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.21.513303v1?rss=1
Authors: Deng, Y., Arao, K., Mantzoros, C., Karniadakis, G. E.
Abstract: Due to insufficient insulin secretion, patients with type 1 diabetes mellitus (T1DM) are prone to blood glucose fluctuations ranging from hypoglycemia to hyperglycemia. While dangerous hypoglycemia may lead to coma immediately, chronic hyperglycemia increases patients' risks for cardiorenal and vascular diseases in the long run. In principle, an artificial pancreas - a closed-loop insulin delivery system requiring patients manually input insulin dosage according to the upcoming meals - could supply exogenous insulin to control the glucose levels and hence reduce the risks from hyperglycemia. However, insulin overdosing in some type 1 diabetic patients, who are physically active, can lead to unexpected hypoglycemia beyond the control of common artificial pancreas. Therefore, it is important to take into account the glucose decrease due to physical exercise when designing the next-generation artificial pancreas. In this work, we develop a deep reinforcement learning algorithm using a T1DM dataset, containing data from wearable devices, to automate insulin dosing for patients with T1DM. In particular, we build patient-specific computational models using systems biology informed neural networks (SBINN), to mimic the glucose-insulin dynamics for a few patients from the dataset, by simultaneously considering patient-specific carbohydrate intake and physical exercise intensity.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.23.513432v1?rss=1
Authors: Leutert, M., Barente, A. S., Fukuda, N. K., Rodriguez-Mias, R. A., Villen, J.
Abstract: The cellular ability to react to environmental fluctuations depends on signaling networks that are controlled by the dynamic activities of kinases and phosphatases. To gain insight into these stress-responsive phosphorylation networks, we generated a quantitative mass spectrometry-based atlas of early phosphoproteomic responses in Saccharomyces cerevisiae exposed to 101 environmental and chemical perturbations. We report phosphosites on 59% of the yeast proteome, with 18% of the proteome harboring a phosphosite that is regulated within 5 minutes of stress exposure. We identify shared and perturbation-specific stress response programs, uncover dephosphorylation as an integral early event, and dissect the interconnected regulatory landscape of kinase-substrate networks, as we exemplify with TOR signaling. We further reveal functional organization principles of the stress-responsive phosphoproteome based on phosphorylation site motifs, kinase activities, subcellular localizations, shared functions, and pathway intersections. This information-rich map of 25,000 regulated phosphosites advances our understanding of signaling networks.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.21.513275v1?rss=1
Authors: De Meyer, S., Cruz, D. F., De Swaef, T., Lootens, P., De Block, J., Bird, K., Sprenger, H., Van de Voorde, M., Hawinkel, S., Van Hautegem, T., Inze, D., Nelissen, H., Roldan-Ruiz, I., Maere, S.
Abstract: Background: In the plant sciences, results of laboratory studies often do not translate well to the field because lab growth conditions are very different from field conditions. To help close this lab-field gap, we developed a new strategy for studying the wiring of plant traits directly in the field, based on molecular profiling and phenotyping of individual plants of the same genetic background grown in the same field. This single-plant omics strategy leverages uncontrolled micro-environmental variation across the field and stochastic variation among the individual plants as information sources, rather than controlled perturbations. Here, we use single-plant omics on winter-type Brassica napus (rapeseed) plants to investigate to what extent rosette-stage gene expression profiles can be linked to the early and late phenotypes of individual field-grown plants. Results: We find that rosette leaf gene expression in autumn has substantial predictive power for both autumnal leaf phenotypes and final yield in spring. Many of the top predictor genes are linked to developmental processes known to occur in autumn in winter-type B. napus accessions, such as the juvenile-to-adult and vegetative-to-reproductive phase transitions, indicating that the yield potential of winter-type B. napus is influenced by autumnal development. Conclusions: Our results show that profiling individual plants under uncontrolled field conditions is a valid strategy for identifying genes and processes influencing crop yield in the field.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.22.513359v1?rss=1
Authors: Pountain, A., Jiang, P., Podkowik, M., Shopsin, B., Torres, V. J., Yanai, I.
Abstract: Regulation of gene activity during the cell cycle is fundamental to bacterial replication but is challenging to study in unperturbed, asynchronous bacterial populations. Using single cell RNA-sequencing of heterogeneous Staphylococcus aureus populations, we uncovered a global gene expression pattern dominated by chromosomal position. We show that this pattern results from the effect of DNA replication on gene expression, and in Escherichia coli, changes under different growth rates and modes of replication. By constructing a quantitative model in each species that links replication to cell cycle gene expression, we identified divergent genes that may be instead subject to distinct regulation, and applied this cell cycle framework to characterize heterogeneity in responses to antibiotic stress. Our approach reveals a highly dynamic cell cycle transcriptional landscape and may be broadly applicable across species.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.21.513139v1?rss=1
Authors: Frank, A.-S., Larripa, K., Ryu, H., Roeblitz, S.
Abstract: Polarization is the process by which a macrophage cell commits to a phenotype based on external signal stimulation. To know how this process is affected by random fluctuations and events within a cell is of utmost importance to better understand the underlying dynamics and predict possible phenotype transitions. For this purpose, we develop a stochastic modeling approach for the macrophage polarization process. We classify phenotype states using the Robust Perron Cluster Analysis and quantify transition pathways and probabilities by applying Transition Path Theory. Depending on the model parameters, we identify four bistable and one tristable phenotype configuration. We find that bistable transitions are fast but their states less robust. In contrast, phenotype transitions in the tristable situation have a comparatively long time duration, which reflects the robustness of the states. The results indicate parallels in the overall transition behavior of macrophage cells with other heterogeneous and plastic cell types, such as cancer cells. Our approach allows for a probabilistic interpretation of macrophage phenotype transitions and biological inference on phenotype robustness. In general, the methodology can easily be adapted to other systems where random state switches are known to occur.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.19.512927v1?rss=1
Authors: Bouhaddou, M., Reuschl, A.-K., Polacco, B. J., Thorne, L. G., Ummadi, M. R., Ye, C., Ramirez, R. R., Pelin, A., Batra, J., Jang, G. M., Xu, J., Moen, J. M., Richards, A. L., Zhou, Y., Harjai, B., Stevenson, E., Rojc, A., Ragazzini, R., Whelan, M. V. X., Furnon, W., De Lorenzo, G., Cowton, V., Syed, A. M., Ciling, A., Deutsch, N., Pirak, D., Dowgier, G., Mesner, D., Turner, J. L., McGovern, B. L., Rodriguez, M. L., Leiva-Rebollo, R., Dunham, A. S., Zhong, X., Eckhardt, M., Fossati, A., Liotta, N., Kehrer, T., Cupic, A., Rutkowska, M., Mena, N., Aslam, S., Hoffert, A., Foussard, H., Pham, J., Ly
Abstract: A series of SARS-CoV-2 variants of concern (VOCs) have evolved in humans during the COVID-19 pandemic: Alpha, Beta, Gamma, Delta, and Omicron. Here, we used global proteomic and genomic analyses during infection to understand the molecular responses driving VOC evolution. We discovered VOC-specific differences in viral RNA and protein expression levels, including for N, Orf6, and Orf9b, and pinpointed several viral mutations responsible. An analysis of the host response to VOC infection and comprehensive interrogation of altered virus-host protein-protein interactions revealed conserved and divergent regulation of biological pathways. For example, regulation of host translation was highly conserved, consistent with suppression of VOC replication in mice using the translation inhibitor plitidepsin. Conversely, modulation of the host inflammatory response was most divergent, where we found Alpha and Beta, but not Omicron BA.1, antagonized interferon stimulated genes (ISGs), a phenotype that correlated with differing levels of Orf6. Additionally, Delta more strongly upregulated proinflammatory genes compared to other VOCs. Systematic comparison of Omicron subvariants revealed BA.5 to have evolved enhanced ISG and proinflammatory gene suppression that similarly correlated with Orf6 expression, effects not seen in BA.4 due to a mutation that disrupts the Orf6-nuclear pore interaction. Our findings describe how VOCs have evolved to fine-tune viral protein expression and protein-protein interactions to evade both innate and adaptive immune responses, offering a likely explanation for increased transmission in humans.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.18.512737v1?rss=1
Authors: D'Ambrosio, E. S., Fang, Z., Gupta, A., Khammash, M.
Abstract: Recent advances in fluorescence technologies and microscopy techniques have significantly improved scientists ability to investigate biological processes at the single-cell level. However, fluorescent reporters can only track the temporal dynamics of a limited number of critical components in a cell (e.g., fluorescent proteins), leaving other pivotal dynamic components (such as gene-state) hidden. Moreover, the nature of the interactions among intracellular biomolecular species is inevitably stochastic in the low copy number regime, which adds more difficulties to the investigation of these hidden species dynamics. Therefore developing mathematical and computational tools for inferring the behaviour of stochastic reaction networks from time-course data is urgently needed. Here we develop a finite-dimensional filter for estimating the conditional distribution of the hidden (unobserved) species given continuous-time and noise-free observations of some species (e.g. a fluorescent reporter). It was proposed that in this setting, the conditional distribution evolves in time according to a large or potentially infinite-dimensional system of coupled ordinary differential equations with jumps, known as the filtering equation. We first formally verify the validity of this filtering equation under the non-explosivity condition and then develop a Finite-State Projection method, which provides an approximate solution by truncating the infinite-dimensional system. Additionally, we give computable error bounds for the algorithm. Finally, we present several numerical examples to illustrate our method and compare its performance with an existing particle filtering method for estimating the conditional distribution.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.19.507549v1?rss=1
Authors: Yu, Y., Zhang, N., Mai, Y., Chen, Q., Cao, Z., Chen, Q., Liu, Y., Ren, L., Hou, W., Yang, J., Hong, H., Xu, J., Tong, W., Shi, L., Zheng, Y.
Abstract: Batch effects are notorious technical variations that are common in multiomic data and may result in misleading outcomes. With the era of big data, tackling batch effects in multiomic integration is urgently needed. As part of the Quartet Project for quality control and data integration of multiomic profiling, we comprehensively assess the performances of seven batch-effect correction algorithms (BECAs) for mitigating the negative impact of batch effects in multiomic datasets, including transcriptomics, proteomics, and metabolomics. Performances are evaluated based on accuracy of identifying differentially expressed features, robustness of predictive models, and the ability of accurately clustering cross-batch samples into their biological sample groups. Ratio-based method is more effective and widely applicable than others, especially in cases when batch effects are highly confounded with biological factors of interests. We further provide practical guidelines for the implementation of ratio-based method using universal reference materials profiled with study samples. Our findings show the promise for eliminating batch effects and enhancing data integration in increasingly large-scale, cross-batch multiomic studies.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.18.512662v1?rss=1
Authors: Fakih, I., Got, J., Robles-Rodriguez, C. E., Siegel, A., Forano, E., Munoz-Tamayo, R.
Abstract: Fibrobacter succinogenes is a cellulolytic predominant bacterium that plays an essential role in the degradation of plant fibers in the rumen ecosystem. It converts cellulose polymers into intracellular glycogen and the fermentation metabolites succinate, acetate, and formate. We developed dynamic models of F. succinogenes S85 metabolism on glucose, cellobiose, and cellulose on the basis of a network reconstruction done with the Automatic Reconstruction of metabolic models (AuReMe) workspace. The reconstruction was based on genome annotation, 5 templates-based orthology methods, gap-filling and manual curation. The metabolic network of F. succinogenes S85 comprises 1565 reactions with 77% linked to 1317 genes, 1586 unique metabolites and 931 pathways. The network was reduced using the NetRed algorithm and analyzed for computation of Elementary Flux Modes (EFMs). A yield analysis was further performed to select a minimal set of macroscopic reactions for each substrate. The accuracy of the models was acceptable in simulating F. succinogenes carbohydrate metabolism with an average coefficient of variation of the Root mean squared error of 19%. Resulting models are useful resources for investigating the metabolic capabilities of F. succinogenes S85, including the dynamics of metabolite production. Such an approach is a key step towards the integration of omics microbial information into predictive models of the rumen metabolism.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.20.513043v1?rss=1
Authors: Locci, E., Stocchero, M., gottardo, r., Chighine, A., De-Giorgio, F., Ferino, G., Nioi, M., Demontis, R., Tagliaro, F., d'Aloja, E.
Abstract: Introduction: The estimation of post-mortem interval remains a major challenge in forensic science. Most of the proposed approaches lack the reliability required to meet the rigorous forensic standards. Objectives: We applied 1H NMR metabolomics to estimate PMI on ovine vitreous humour comparing the results with the actual scientific gold standard, namely vitreous potassium concentrations. Methods: Vitreous humour samples were collected in a time frame ranging from 6 to 86 hours after death. Experiments were performed by using 1H NMR metabolomics and Ion Capillary Analysis. Data were submitted to multivariate statistical data analysis. Results: A multivariate calibration model was built to estimate PMI based on 47 vitreous humour samples. The model was validated with an independent test set of 24 samples, obtaining a prediction error on the entire range of 6.9 h for PMI less than 24h, 7.4 h for PMI between 24 and 48h, and 10.3 h for PMI greater than 48 h. Time-related modifications of the 1H NMR vitreous metabolomic profile could predict PMI better than potassium up to 48 hours after death, while a combination of the two is better than the single approach for higher PMIs estimation. Conclusion: The present study, although in a proof-of-concept animal model, shows that vitreous metabolomics can be a powerful tool to predict PMI providing a more accurate estimation compared to the widely studied approach based on vitreous potassium concentrations.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.16.512441v1?rss=1
Authors: Rodriguez, D., Fiengo, L., Alvarez-Kuglen, M., Qin, H., Farhy, C., Havas, A., Anderson, R. M., Adams, P. D., Sharpee, T. O., Terskikh, A.
Abstract: Predictive biomarkers of functional or biological age are key for evaluating interventions aimed at increas-ing healthspan and / or lifespan in humans. Currently, cardiovascular performance, blood analytes, frailty indices (e.g. gait speed), and DNA methylation clocks are used to provide such estimates; each technique has its own challenges and limitations. We have developed a novel approach, microscopic imaging of bio-logical age (miBioAge), which computes multiparametric signatures based on the patterns of epigenetic landscape in single nuclei. We demonstrated that such miBioAge readouts can robustly distinguish young and old cells from multiple tissues, reveal aging progression in peripheral blood (e.g. CD3+ T cells), and reflect changes of epigenetic signatures consistent with expected slowdown or acceleration of biological age in chronologically identical mice treated with caloric restriction or chemotherapy. Because miBioAge readouts are computed from individual samples without applying linear regression, we posit that this novel biomarker may provide personalized assessment of functional aging.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.17.512406v1?rss=1
Authors: Yang, Y., Karin, O., Mayo, A., Song, X., Chen, P., Lindner, A. B., Alon, U.
Abstract: Genetically identical cells in the same stressful condition die at different times. The origin of this stochasticity is unclear; it may arise from different initial conditions that affect the time of demise, or from a stochastic damage accumulation mechanism that erases the initial conditions and instead amplifies noise to generate different lifespans. To address this requires measuring damage dynamics in individual cells over the lifespan, but this has rarely been achieved. Here, we used a microfluidic device to measure membrane damage in 648 carbon-starved E. coli cells at high temporal resolution. We find that initial conditions of damage, size or cell-cycle phase do not explain most of the lifespan variation. Instead, the data points to a stochastic mechanism in which noise is amplified by a rising production of damage that saturates its own removal. Surprisingly, the relative variation in damage drops with age: cells become more similar to each other in terms of relative damage, indicating increasing determinism with age. Thus, chance erases initial conditions and then gives way to increasingly deterministic dynamics that dominate the lifespan distribution.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.17.512470v1?rss=1
Authors: Willems, P., Huang, J., Messens, J., Van Breusegem, F.
Abstract: Deep learning algorithms such as AlphaFold2 predict three-dimensional protein structure with high confidence. The recent release of more than 200 million structural models provides an unprecedented resource for functional protein annotation. Here, we used AlphaFold2 predicted structures of fifteen plant proteomes to functionally and evolutionary analyze cysteine residues in the plant kingdom. In addition to identification of metal ligands coordinated by cysteine residues, we systematically analyzed cysteine disulfides present in these structural predictions. Our analysis demonstrates most of these predicted disulfides are trustworthy due their high agreement (~96%) with those present in X-ray and NMR protein structures, their characteristic disulfide stereochemistry, the biased subcellular distribution of their proteins and a higher degree of oxidation of their respective cysteines as measured by proteomics. Adopting an evolutionary perspective, zinc binding sites are increasingly present at the expense of iron-sulfur clusters in plants. Interestingly, disulfide formation is increased in secreted proteins of land plants, likely promoting sequence evolution to adapt to changing environments encountered by plants. In summary, Alphafold2 predicted structural models are a rich source of information for studying the role of cysteines residues in proteins of interest and for protein redox biology in general.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.13.512151v1?rss=1
Authors: Glazer, B., Lifferth, J. T., Lopez, C. F.
Abstract: Many important processes in biology, such as signaling and gene regulation, can be described using logic models. These logic models are typically built to behaviorally emulate experimentally observed phenotypes, which are assumed to be steady states of a biological system. Most models are built by hand and therefore researchers are only able to consider one or perhaps a few potential mechanisms. We present a method to automatically synthesize Boolean logic models with a specified set of steady states. Our method, called MC-Boomer, is based on Monte Carlo Tree Search (MCTS), an efficient, parallel search method using reinforcement learning. Our approach enables users to constrain the model search space using prior knowledge or biochemical interaction databases, thus leading to generation of biologically plausible mechanistic hypotheses. Our approach can generate very large numbers of data-consistent models. To help develop mechanistic insight from these models, we developed analytical tools for multi-model inference and model selection. These tools reveal the key sets of interactions that govern the behavior of the models.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.14.512281v1?rss=1
Authors: Gardner, J. J., Hodge, B.-M. S., Boyle, N.
Abstract: The open ocean is an extremely competitive environment, partially due to the dearth of nutrients. Trichodesmium erythraeum, a marine diazotrophic cyanobacterium, is a keystone species in the ocean due to its ability to fix nitrogen and leak 30-50% into the surrounding environment, providing a valuable source of a necessary macronutrient to other species. While there are other diazotrophic cyanobacteria that play an important role in the marine nitrogen cycle, Trichodesmium is unique in its ability to fix both carbon and nitrogen simultaneously during the day without the use of specialized cells called heterocysts to protect nitrogenase from oxygen. Here, we use the advanced modeling framework called Multiscale Multiobjective Systems Analysis (MiMoSA) to investigate how Trichodesmium erythraeum can reduce dimolecular nitrogen to ammonium in the presence of oxygen. Our simulations indicate that nitrogenase inhibition is best modeled as Michealis Menten competitive inhibition and that cells along the filament maintain microaerobia using high flux through Mehlers reactions in order to protect nitrogenase from oxygen. We also examined the effect of location on metabolic flux and found that cells at the end of filaments operate in distinctly different metabolic modes than internal cells despite both operating in a photoautotrophic mode. These results give us important insight into how this species is able to operate photosynthesis and nitrogen fixation simultaneously, giving it a distinct advantage over other diazotrophic cyanobacteria because they can harvest light directly to fuel the energy demand of nitrogen fixation.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.13.511953v1?rss=1
Authors: Yilmaz, S., Yorgancioglu, K., Koyuturk, M.
Abstract: In the context of biomedical applications, new link prediction algorithms are continuously being developed and these algorithms are typically evaluated computationally, using test sets generated by sampling the edges uniformly at random. However, as we demonstrate, this creates a bias in the evaluation towards "the rich nodes", i.e., those with higher degrees in the network. More concerningly, we demonstrate that this bias is prevalent even when different snapshots of the network are used for evaluation as recommended in the machine learning community. This leads to a cycle in research where newly developed algorithms generate more knowledge on well-studied biological entities while the under-studied entities are commonly ignored. To overcome this issue, we propose a weighted validation setting focusing on under-studied entities and present strategies to facilitate bias-aware evaluation of link prediction algorithms. These strategies can help researchers gain better insights from computational evaluations and promote the development of new algorithms focusing on novel findings and under-studied proteins. We provide a web tool to assess the bias in evaluation data at: less than a href="https://yilmazs.shinyapps.io/colipe/" greater than https://yilmazs.shinyapps.io/colipe/ less than /a greater than
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.13.511603v1?rss=1
Authors: Mutsuddy, A., Erdem, C., Huggins, J. R., Salim, M., Cook, D., Hobbs, N., Feltus, F. A., Birtwistle, M. R.
Abstract: Large-scale and whole-cell modeling has multiple challenges, including scalable model building and module communication bottlenecks (e.g. between metabolism, gene expression, signaling, etc). We previously developed an open-source, scalable format for a large-scale mechanistic model of proliferation and death signaling dynamics, but communication bottlenecks between gene expression and protein biochemistry modules remained. Here, we developed two solutions to communication bottlenecks that speed up simulation by ~4-fold for hybrid stochastic-deterministic simulations and by over 100-fold for fully deterministic simulations.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.11.511822v1?rss=1
Authors: Rodriguez, J. D., Iniguez, A., Jena, N., Tata, P., Liu, J., Lander, A. D., Lowengrub, J., Van Etten, R.
Abstract: Chronic myeloid leukemia (CML) is a blood cancer characterized by dysregulated production of maturing myeloid cells driven by the product of the Philadelphia chromosome, the BCR-ABL1 tyrosine kinase. Tyrosine kinase inhibitors (TKI) have proved effective in treating CML but there is still a cohort of patients who do not respond to TKI therapy even in the absence of mutations in the BCR-ABL1 kinase domain that mediate drug resistance. To discover novel strategies to improve TKI therapy in CML, we developed a nonlinear mathematical model of CML hematopoiesis that incorporates feedback control and lineage branching. Cell-cell interactions were constrained using an automated model selection method together with previous observations and new in vivo data from a chimeric BCR-ABL1 transgenic mouse model of CML. The resulting quantitative model captures the dynamics of normal and CML cells at various stages of the disease and exhibits variable responses to TKI treatment, consistent with those of CML patients. The model predicts that an increase in the proportion of CML stem cells in the bone marrow would decrease the tendency of the disease to respond to TKI therapy, in concordance with clinical data and confirmed experimentally in mice. The model further suggests that a key predictor of refractory response to TKI treatment is an increased probability of self-renewal of normal hematopoietic stem cells. We use these insights to develop a clinical prognostic criterion to predict the efficacy of TKI treatment and to design strategies to improve treatment response. The model predicts that stimulating the differentiation of leukemic stem cells while applying TKI therapy can significantly improve treatment outcomes.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.11.511763v1?rss=1
Authors: Harris, B. N., Woo, L. A., Perry, R. N., Civelek, M. J., Wolf, M. J., Saucerman, J. J.
Abstract: Cardiac diseases are characterized by the inability of adult mammalian hearts to overcome the loss of cardiomyocytes (CMs). Current knowledge in cardiac regeneration lacks a clear understanding of the molecular systems determining whether CMs will progress through the cell cycle to proliferate. Here, we developed a computational model of cardiac proliferation signaling that identifies key regulators and provides a systems-level understanding of the cardiomyocyte proliferation regulatory network. This model defines five regulatory networks (DNA replication, mitosis, cytokinesis, growth factor, hippo pathway) of cardiomyocyte proliferation, which integrates 72 nodes and 88 reactions. The model correctly predicts 72 of 76 (94.7%) independent experiments from the literature. Network analysis predicted key signaling regulators of DNA replication (e.g., AKT, CDC25A, Cyclin D/CDK4, E2F), mitosis (e.g., Cyclin B/CDK2, CDC25B/C, PLK1), and cytokinesis, whose functions varied depending on the environmental context. Regulators of DNA replication were found to be highly context-dependent, while regulators of mitosis and cytokinesis were context-independent. We also predicted that in response to the YAP-activating compound TT-10, the Hippo module crosstalks with the growth factor module via PI3K, cMyc, and FoxM1 to drive proliferation. This prediction was validated with inhibitor experiments in primary rat cardiomyocytes and further supported by re-analysis of published data on YAP-stimulated mRNA and open chromatin of Myc from mouse hearts. This study contributes a systems framework for understanding cardiomyocyte proliferation and identifies potential therapeutic regulators that induce cardiomyocyte proliferation.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.10.511622v1?rss=1
Authors: Frank, S. A.
Abstract: How do cellular regulatory networks solve the challenges of life? This article presents computer software to study that question, focusing on how transcription factor networks transform internal and external inputs into cellular response outputs. The example challenge concerns maintaining a circadian rhythm of molecular concentrations. The system must buffer intrinsic stochastic fluctuations in molecular concentrations and entrain to an external circadian signal that appears and disappears randomly. The software optimizes a stochastic differential equation of transcription factor protein dynamics and the associated mRNAs that produce those transcription factors. The cellular network takes as inputs the concentrations of the transcription factors and produces as outputs the transcription rates of the mRNAs that make the transcription factors. An artificial neural network encodes the cellular input-output function, allowing efficient search for solutions to the complex stochastic challenge. Several good solutions are discovered. The solutions differ significantly from each other, showing that overparameterized cellular networks may solve a given challenge in a variety of ways. The article concludes by drawing an analogy between overparameterized cellular networks and the dense and deeply connected overparameterized artificial neural networks that have succeeded so well in deep learning. Understanding how overparameterized networks solve challenges may provide insight into the evolutionary design of cellular regulation.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.04.510777v1?rss=1
Authors: Cheng, C.-T., Lai, J.-M., Chang, P. M.-H., Hong, Y.-R., Huang, C.-Y. F., Wang, F.-S.
Abstract: Identifying essential targets in genome-scale metabolic networks of cancer cells is a time-consuming process. This study proposed a fuzzy hierarchical optimization framework for identifying essential genes, metabolites and reactions. On the basis of four objectives, the framework can identify essential targets that lead to cancer cell death, and evaluate metabolic flux perturbations of normal cells due to treatment. Through fuzzy set theory, a multiobjective optimization problem was converted into a trilevel maximizing decision-making (MDM) problem. We applied nested hybrid differential evolution to solve the trilevel MDM problem to identify essential targets in the genome-scale metabolic models of five consensus molecular subtypes (CMSs) of colorectal cancers. We used various media to identify essential targets for each CMS, and discovered that most targets affected all five CMSs and that some genes belonged to a CMS-specific model. We used the experimental data for the lethality of cancer cell lines from the DepMap database to validate the identified essential genes. The results reveal that most of the identified essential genes were compatible to colorectal cancer cell lines from DepMap and that these genes could engender a high percentage of cell death when knocked out, except for EBP, LSS and SLC7A6. The identified essential genes were mostly involved in cholesterol biosynthesis, nucleotide metabolisms, and the glycerophospholipid biosynthetic pathway. The genes in the cholesterol biosynthetic pathway were also revealed to be determinable, if the medium used excluded a cholesterol uptake reaction. By contrast, the genes in the cholesterol biosynthetic pathway were non-essential, if a cholesterol uptake reaction was involved in the medium used. Furthermore, the essential gene CRLS1 was revealed as a medium-independent target for all CMSs irrespective of whether a medium involves a cholesterol uptake reaction.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.10.511605v1?rss=1
Authors: Nandagopal, N., Terrio, A., Vicente, F. Z., Jambhekar, A., Lahav, G.
Abstract: Stem cells integrate information from multiple signals in their environment to make fate decisions. It is unclear how signal integration is linked to the coordinated activation of a target fate program and simultaneous inactivation of competing fates. Here, we investigated this question in mouse neural stem cells, which differentiate synergistically into astrocytes in response to combined treatment with Bone Morphogenetic Protein (BMP) and Leukemia Inhibitory Factor (LIF) at the expense of alternative neuronal or oligodendrocyte fates. Analysis of the expression dynamics of Glial Fibrillary Acidic Protein (GFAP), an early astrocyte marker, showed that its synergistic activation in BMP and LIF reflects early activation by LIF which is sustained by a delayed contribution to its expression from BMP. In parallel, multiplexed RNA-FISH analysis of 14 basic helix-loop-helix (bHLH) transcription factors, known to regulate alternative fates, showed that LIF and BMP individually control different subsets of bHLHs, but together suppress all bHLHs known to promote alternative fates. Ectopic expression experiments showed that these bHLHs also inhibit GFAP induction, suggesting that suppression of alternative fates by BMP + LIF simultaneously relieves GFAP inhibition. In particular, BMP primarily affected inhibitory bHLHs indirectly, through induction of Id factors, explaining why it has a delayed contribution to GFAP transcription compared to LIF. These results show that a circuit of bHLH factors enables both synergistic astrocytic differentiation and suppression of alternative fates in NSCs. Signal integration by bHLH circuits for fate choice could be broadly relevant, given the widespread utilization of these and other bHLH factors across diverse developmental contexts.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.10.511539v1?rss=1
Authors: Nonn, O., Debnath, O., Valdes, D. S., Sallinger, K., Secener, A. K., Haider, S., Fischer, C., Tiesmeyer, S., Nimo, J., Kuenzer, T., Maxian, T., Knoefler, M., Karau, P., Bartolomaeus, H., Kroneis, T., Frolova, A., Neuper, L., Haase, N., Kraeker, K., Kedziora, S., Forstner, D., Verlohren, S., Stern, C., Coscia, F., Sugulle, M., Jones, S., Thilaganathan, B., Eils, R., Huppertz, B., El-Heliebi, A., Staff, A. C., Mueller, D. N., Dechend, R., Gauster, M., Ishaque, N., Herse, F.
Abstract: Pre-eclampsia (PE) is a syndrome that affects multiple organ systems and is the most severe hypertensive disorder in pregnancy. It frequently leads to preterm delivery, maternal and fetal morbidity and mortality and life-long complications1. We currently lack efficient screening tools2,3 and early therapies4,5 to address PE. To investigate the early stages of early onset PE, and identify candidate markers and pathways, we performed spatio-temporal multi-omics profiling of human PE placentae and healthy controls and validated targets in early gestation in a longitudinal clinical cohort. We used a single-nuclei RNA-seq approach combined with spatial proteo- and transcriptomics and mechanistic in vitro signalling analyses to bridge the gap from late pregnancy disease to early pregnancy pathomechanisms. We discovered a key disruption in villous trophoblast differentiation, which is driven by the increase of transcriptional coactivator p300, that ultimately ends with a senescence-associated secretory phenotype (SASP) of trophoblasts. We found a significant increase in the senescence marker activin A in preeclamptic maternal serum in early gestation, before the development of clinical symptoms, indicating a translation of the placental syndrome to the maternal side. Our work describes a new disease progression, starting with a disturbed transition in villous trophoblast differentiation. Our study identifies potential pathophysiology-relevant biomarkers for the early diagnosis of the disease as well as possible targets for interventions, which would be crucial steps toward protecting the mother and child from gestational mortality and morbidity and an increased risk of cardiovascular disease later in life.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.10.511546v1?rss=1
Authors: Buttner, M., Hempel, F., Ryborz, T., Theis, F., Schultze, J. L.
Abstract: Flow and mass cytometry data are commonly analyzed via manual gating strategies which requires prior knowledge, expertise and time. With increasingly complex experiments with many parameters and samples, traditional manual flow and mass cytometry data analysis becomes cumbersome if not inefficient. At the same time, computational tools developed for the analysis of single-cell RNA-sequencing data have made single cell genomics analysis highly efficient, yet they are mostly inaccessible for the analysis of flow and mass cytometry data due to different data formats, noise assumptions and scales. To bring the advantages of both fields together, we developed Pytometry as an extension to the popular scanpy framework for the analysis of flow and mass cytometry data. We showcase a standard analysis workflow on healthy human bone marrow data, illustrating the applicability of tools developed for the larger feature space of single cell genomics data. Pytometry combines joint analysis of multiple samples and advanced computational applications, ranging from automated pre-processing, cell type annotation and disease classification.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.06.511161v1?rss=1
Authors: Khalilimeybodi, A., Fraley, S. I., Rangamani, P.
Abstract: Yes-associated protein (YAP) and its homolog TAZ are transducers of several biochemical and biomechanical signals, serving to integrate multiplexed inputs from the microenvironment into higher-level cellular functions such as proliferation, differentiation, apoptosis, migration, and hemostasis. Emerging evidence suggests that Ca2+ is a key second messenger that closely connects microenvironmental input signals and YAP/TAZ regulation. However, studies that directly modulate Ca2+ have reported contradictory YAP/TAZ responses: In some studies, a reduction in Ca2+ influx increases the activity of YAP/TAZ, while in others, an increase in Ca2+ influx activates YAP/TAZ. Importantly, Ca2+ and YAP/TAZ exhibit distinct spatiotemporal dynamics, making it difficult to unravel their connections from a purely experimental approach. In this study, we developed a network model of Ca2+-mediated YAP/TAZ signaling to investigate how temporal dynamics and crosstalk of signaling pathways interacting with Ca2+ can alter YAP/TAZ response, as observed in experiments. By including six signaling modules (e.g., GPCR, IP3-Ca2+, Kinases, RhoA, F-actin, and Hippo-YAP/TAZ) that interact with Ca2+, we investigated both transient and steady-state cell response to Angiotensin II and thapsigargin stimuli. The model predicts stimuli, Ca2+ transient, and frequency-dependent relationships between Ca2+ and YAP/TAZ primarily mediated by signaling species like cPKC, DAG, CaMKII, and F-actin. Model results illustrate the role of Ca2+ dynamics and CaMKII bistable response in switching the direction of changes in Ca2+-induced YAP/TAZ activity for different stimuli. Frequency-dependent YAP/TAZ response revealed the competition between upstream regulators of LATS1/2, leading to the YAP/TAZ non-monotonic response to periodic GPCR stimulation. This study provides new insights into the underlying mechanisms responsible for the controversial Ca2+-YAP/TAZ relationship observed in experiments.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.03.510723v1?rss=1
Authors: Zabaikina, I., Zhang, Z., Nieto, C., Bokes, P., Singh, A.
Abstract: The overexpression of many proteins can often have a detrimental impact on cellular growth. This expression-growth coupling leads to positive feedback - any increase of intracellular protein concentration reduces the growth rate of cell size expansion that in turn enhances the concentration via reduced dilution. We investigate how such feedback amplifies intrinsic stochasticity in gene expression to drive a skewed distribution of the protein concentration. Our results provide an exact solution to this distribution by analytically solving the Chapman-Kolmogorov equation, and we use it to quantify the enhancement of noise/skewness as a function of expression-growth coupling. This analysis has important implications for the expression of stress factors, where high levels provide protection from stress, but come at the cost of reduced cellular proliferation. Finally, we connect these analytical results to the case of an actively degraded gene product, where the degradation machinery is working close to saturation.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.06.511167v1?rss=1
Authors: Givre, A., Colman-Lerner, A., Ponce-Dawson, S.
Abstract: Cells continuously interact with their environment, detect its changes and generate responses accordingly. This requires interpreting the variations and, in many occasions, producing changes in gene expression. In this paper we use information theory and a simple transcription model to analyze the extent to which the resulting gene expression is able to identify and assess the intensity of extracellular stimuli when they are encoded in the amplitude, duration or frequency of a transcription factor's nuclear concentration. We find that the maximal information transmission is, for the three codifications between approximately 1.5 and 1.8 bits, i.e., approximately 3 ranges of input strengths can be distinguished in all cases. The types of promoters that yield maximum transmission for the three modes are all similarly fast and have a high activation threshold. The three input modulation modes differ, however, in the sensitivity to changes in the parameters that characterize the promoters, with frequency modulation being the most sensitive and duration modulation, the least. This turns out to be key for signal identification. Namely, we show that, because of this sensitivity difference, it is possible to find promoter parameters that yield an information transmission within 90% of its maximum value for duration or amplitude modulation and less than 1 bit for frequency modulation. The reverse situation cannot be found within the framework of a single promoter transcription model. This means that pulses of transcription factors in the nucleus can selectively activate the promoter that is tuned to respond to frequency modulations while prolonged nuclear accumulation would activate several promoters at the same time. Thus, frequency modulation is better suited than the other encoding modes to allow the identification of external stimuli without requiring other mediators of the transduction.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.07.510659v1?rss=1
Authors: Waters, C. S., Angenent, S. B., Altschuler, S. J., Wu, L. F.
Abstract: Mechanisms that prevent accidental degradation of healthy mitochondria by the Pink1/Parkin mitophagy pathway are poorly understood. On the surface of damaged mitochondria, Pink1 accumulates and acts as the input signal to a positive feedback loop of Parkin recruitment, which in turn promotes mitochondrial degradation via mitophagy. However, Pink1 also transiently associates with healthy mitochondria where it could errantly recruit Parkin and thereby activate this positive feedback loop. Here, we mapped the relationship between Pink1 input levels and Parkin recruitment dynamics using quantitative live-cell microscopy and mathematical modeling. We found that Parkin is recruited to the mitochondria only if Pink1 levels exceed a threshold and only after a delay that is inversely proportional to Pink1 levels. The properties of threshold and delay emerge from the Pink1/Parkin circuit topology and provide a mechanism for cells to assess damage signals before committing to mitophagy.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.07.511356v1?rss=1
Authors: Goldford, J. E., Smith, H. B., Longo, L. M., Wing, B. A., McGlynn, S. E.
Abstract: A major unresolved question in the origin of life is whether there exists a continuous path from geochemical precursors to the majority of molecules in the biosphere, due in part to the autocatalytic nature of metabolic networks in modern-day organisms and high rates of extinction throughout Earth's history. Here we simulated the emergence of ancient metabolic networks to identify a feasible path from simple geochemical precursors (e.g., phosphate, sulfide, ammonia, simple carboxylic acids, and metals) to contemporary biochemistry, using only known biochemical reactions and models of primitive coenzymes. We find that purine synthesis constitutes a bottleneck for metabolic expansion, and that non-autocatalytic phosphoryl coupling agents are sufficient to enable expansion from geochemistry to modern metabolic networks. Our model predicts distinct phases of metabolic evolution, characterized by the sequential emergence of key molecules (carboxylic acids, amino acids, sugars), purines/nucleotide cofactors (ATP, NAD+), flavins, and quinones, respectively. Early phases in the resulting expansion are associated with enzymes that are metal-dependent and structurally symmetric, consistent with models of early biochemical evolution. The production of quinones in the last phase of metabolic expansion permits oxygenic photosynthesis and the production of O2, leading to a greater than 30% increase in biomolecules. These results reveal a feasible trajectory from simple geochemical precursors to the vast majority of core biochemistry.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.07.511370v1?rss=1
Authors: Yao, H., Dahal, S., Yang, L.
Abstract: Gene expression data of cell cultures is commonly measured in biological and medical studies to understand cellular decision-making in various conditions. Metabolism, affected but not solely determined by the expression, is much more difficult to measure experimentally. Thus, finding a reliable method to predict cell metabolism for given expression data will greatly benefit model-aided metabolic engineering. We have developed such a pipeline that can explore cellular fluxomics from expression data, using only a high-quality genome-scale metabolic model. This is done through two main steps: first, construct a protein-constrained metabolic model by integrating protein and enzyme information into the metabolic model. Secondly, overlay the expression data onto the modified model using a new two-step non-convex and convex optimization formulation, resulting in context-specific models with optionally calibrated rate constants. The resulting model computes proteomes and intracellular flux states that are consistent with the measured transcriptomes. Therefore, it provides detailed cellular insights that are difficult to glean individually from the omic data or metabolic models alone. As a case study, we apply the pipeline to interpret triacylglycerol (TAG) overproduction by Chlamydomonas reinhardtii, using time-course RNA-Seq data. The pipeline allows us to compute C. reinhardtii metabolism under nitrogen deprivation and metabolic shifts after an acetate boost. We also suggest a list of possible bottlenecking proteins that need to be overexpressed to increase the TAG accumulation rate, as well as discussing other TAG-overproduction strategies.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.07.511173v1?rss=1
Authors: Sta, L., Voisinne, G., Cotari, J., Adamer, M., Molina-Paris, C., Altan-Bonnet, G.
Abstract: Cells rely on cytokines to coordinate their activation, differentiation, proliferation and survival. In particular, {gamma}C cytokines (interleukins IL-2, 4, 7, 9, 15, and 21) regulate the fate of leukocytes. The signaling cascade induced by these cytokines is relatively simple, and involves the phosphorylation of receptor-associated Janus-like kinases (JAK). Here, we explore the cell-to-cell variability of cytokine responses in primary mouse T~cells, and find a paradoxical and quantitative imprint of receptor expression levels and other signaling components. For instance, high abundance of the common $gamma_c$ chain reduces cytokine responses (both in terms of signaling amplitudes and characteristic cytokine concentrations triggering 50% of the response). We develop mathematical models to quantify how limited abundances of signaling components (e.g. JAK or other cytokine receptor subunit chains) may explain our experimental observations. We conclude by generalizing this observation of cell-to-cell signaling variability to other ligand-receptor-kinase systems.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.06.511142v1?rss=1
Authors: Chen, J.-Y., Hug, C., Reyes, J., Tian, C., Gerosa, L., Fröhlich, F., Ponsioen, B., Snippert, H. J. G., Spencer, S. L., Jambhekar, A., Sorger, P. K., Lahav, G.
Abstract: Oncogene-induced senescence (OIS) is a phenomenon in which aberrant oncogene expression causes non-transformed cells to enter a non-proliferative state. Cells undergoing OIS display phenotypic heterogeneity, with some cells senescing and others remaining proliferative. The causes of the heterogeneity remain poorly understood. We studied the sources of heterogeneity in the responses of human epithelial cells to oncogenic BRAFV600E expression. We found that a narrow expression range of BRAFV600E generated a wide range of activities of its downstream effector ERK. In population-level and single cell assays, ERK activity displayed a non-monotonic relationship to proliferation, with intermediate ERK activities leading to maximal proliferation. We profiled gene expression across a range of ERK activities over time and characterized four distinct ERK response classes, which we propose act in concert to generate the unique ERK-proliferation response. Altogether, our studies mapped the input-output relationships between ERK activity and proliferation providing important insights into how heterogeneity can be generated during OIS.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.07.511244v1?rss=1
Authors: Duppala, S. K., Yadala, R., Velingkar, A., Suravajhala, P., Pawar, S., Vuree, S.
Abstract: After breast cancer, cervical cancer (CC) is one of the most common malignancies in women globally. Over 90% of chronic infections are caused by human papillomavirus (HPV) and its subtypes. Extensive research efforts are required to identify the treatment targets and prognostic indicators for recurring and metastatic cancers. It may be possible because of omics methods, including genomes, epigenomics, transcriptomics, proteomics, and metabolomics. High throughput (HT) data on the differential mRNA and miRNA expression and their crucial interrelationships enable promising integration and interpretation of the results. Clinical data and multi-omics have risen to the top of the heap in delivering molecular and cellular activities. They aid in comparing data from different omics approaches and bridging the gap between genotype and phenotype. Therefore, multi-omic techniques may improve the knowledge of the molecular basis of the physiology and primary cause of disease, revealing a new route for the prognosis, diagnosis, prevention, and therapy of human diseases.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.05.511031v1?rss=1
Authors: Sarmah, D., Meredith, W. O., Weber, I., Price, M. R., Birtwistle, M. R.
Abstract: Cancer chemotherapy combines multiple drugs, but predicting the effects of drug combinations on cancer cell proliferation remains challenging. We hypothesized that by combining knowledge of single drug dose responses and cell state transition network dynamics, we could predict how a population of cancer cells will respond to drug combinations. We tested this hypothesis here using three targeted inhibitors of different cell cycle states in two different cell lines. We formulated a Markov model to capture temporal cell state transitions between different cell cycle phases, with single drug data constraining how drug doses affect transition rates. This model was able to predict the landscape of all three different pairwise drug combinations across all dose ranges for both cell lines with no additional data. This work shows how currently available or attainable information could be combined to predict how cancer cell populations respond to drug combinations.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.03.510720v1?rss=1
Authors: Banerjee, S., Kratz, J. C.
Abstract: Bacteria dynamically regulate cell size and growth rate to thrive in changing environments. While much work has been done to characterize bacterial growth physiology and cell size control during steady-state exponential growth, a quantitative understanding of how bacteria dynamically regulate cell size and growth in time-varying nutrient environments is lacking. Here we develop a dynamic coarse-grained proteome sector model which connects growth rate and division control to proteome allocation in time-varying environments in both exponential and stationary phase. In such environments, growth rate and size control is governed by trade-offs between prioritization of biomass accumulation or division, and results in the uncoupling of single-cell growth rate from population growth rate out of steady-state. Specifically, our model predicts that cells transiently prioritize ribosome production, and thus biomass accumulation, over production of division machinery during nutrient upshift, explaining experimentally observed size control behaviors. Strikingly, our model predicts the opposite behavior during downshift, namely that bacteria temporarily prioritize division over growth, despite needing to upregulate costly division machinery and increasing population size when nutrients are scarce. Importantly, when bacteria are subjected to pulsatile nutrient concentration, we find that cells exhibit a transient memory of the previous metabolic state due to the slow dynamics of proteome reallocation. This phenotypic memory allows for faster adaptation back to previously-seen environments when nutrient fluctuations are short-lived.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.02.510525v1?rss=1
Authors: Claeys, T., Menu, M., Bouwmeester, R., Gevaert, K., Martens, L.
Abstract: Using data from 183 public human data sets from PRIDE, a machine learning model was trained to identify tissue and cell-type specific protein patterns. PRIDE projects were searched with ionbot and tissue/cell type annotation was manually added. Data from physiological samples were used to train a Random Forest model on protein abundances to classify samples into tissues and cell types. Subsequently, a one-vs-all classification and feature importance were used to analyse the most discriminating protein abundances per class. Based on protein abundance alone, the model was able to predict tissues with 98% accuracy, and cell types with 99% accuracy. The F-scores describe a clear view on tissue-specific proteins and tissue-specific protein expression patterns. In-depth feature analysis shows slight confusion between physiologically similar tissues, demonstrating the capacity of the algorithm to detect biologically relevant patterns. These results can in turn inform downstream uses, from identification of the tissue of origin of proteins in complex samples such as liquid biopsies, to studying the proteome of tissue-like samples such as organoids and cell lines.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.01.510467v1?rss=1
Authors: McGrail, D. J., Li, Y., Smith, R. S., Feng, B., Dai, H., Li, Y., Hu, L., Dennehey, B., Awasthi, S., Mendillo, M. L., Mills, G. B., Lin, S.-Y., Yi, S. S., Sahni, N.
Abstract: Since the discovery of BRCA1 and BRCA2 mutations as cancer risk factors, we have gained substantial insight into their role in maintaining genomic stability through homologous recombination (HR) DNA repair. However, upon pan-cancer analysis of tumors from The Cancer Genome Atlas (TCGA), we found that mutations in BRCA1/2 and other classical HR genes only identified 10-20% of tumors that display genomic evidence of HR deficiency (HRD), suggesting that the cause of the vast majority of HR defects in tumors is unknown. As HRD both predisposes individuals to cancer development and leads to therapeutic vulnerabilities, it is critical to define the spectrum of genetic events that drive HRD. Here, we employed a network-based approach leveraging the abundance of molecular characterization data from TCGA to identify novel drivers of HRD. We discovered that over half of putative genes driving HRD originated outside of canonical DNA damage response genes, with a particular enrichment for RNA binding protein (RBP)-encoding genes. These novel drivers of HRD were cross-validated using an independent ICGC cohort, and were enriched in GWAS loci associated with cancer risk. Experimental approaches validated over 90% of our predictions in a panel of 50 genes tested by siRNA and 31 additional engineered mutations identified from TCGA patient tumors. Moreover, genetic suppression of identified RBPs or pharmacological inhibition of RBPs induced PARP inhibition. Further mechanistic studies indicate that some RBPs are recruited to sites of DNA damage to facilitate repair, whereas others control the expression of canonical HR genes. Overall, this study greatly expands the repertoire of known drivers of HRD and their contributions to DNA damage repair, which has implications for not only future mechanistic studies, but also for genetic screening and therapy stratification.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.01.510438v1?rss=1
Authors: Batish, I., Zarei, M., Nitin, N., Ovissipour, R.
Abstract: The use of fetal bovine serum (FBS) and the price of the cell culture media are the key constraints for developing serum-free cost-effective media. This study aims to replace or reduce the typical 10% serum application in fish cell culture media by applying protein hydrolysates from insects and marine invertebrate species for the growth of Zebrafish embryonic stem cells (ESC) as the model organism. Protein hydrolysates were produced from Black soldier fly (BSF), cricket, oyster, mussel, and lugworm with high protein content, suitable functional properties, adequate amino acids composition, and the degree of hydrolysis from 18.24 to 33.52%. Protein hydrolysates at low concentrations from 0.001 to 0.1 mg/mL in combination with 1 and 2.5% serum significantly increased cell growth compared to the control groups (5 and 10% serum) (P less than 0.05). All protein hydrolysates with concentrations of 1 and 10 mg/mL were found to be toxic to cells and significantly reduced cell growth and performance (P less than 0.05). However, except for cricket, all hydrolysates were able to restore or significantly increase cell growth and viability with 50% less serum at a concentration of 0.001, 0.01, and 0.1 mg/mL. Although cell growth was enhanced at lower concentrations of protein hydrolysates, cell morphology was altered due to the lack of serum. Lactate dehydrogenase (LDH) activity results indicated that BSF and lugworm hydrolysates did not alter the cell membrane. In addition, light and fluorescence imaging revealed that cell morphological features were comparable to the 10% serum control group. Overall, lugworm and BSF hydrolysates reduced serum by up to 90% while preserving excellent cell health.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.10.03.510635v1?rss=1
Authors: Padiadpu, J., Garcia-Jaramillo, M., Newman, N., Pederson, J., Rodrigues, R., Li, Z., Singh, S., Monnier, P., Trinchieri, G., Brown, K., Dzutsev, A. K., Shulzhenko, N., Jump, D. B., Morgun, A.
Abstract: Nonalcoholic steatohepatitis (NASH) is currently the only prevalent metabolic disease with no FDA-approved treatment strategy. Supplementation of omega-3 polyunsaturated fatty acids (PUFA) represent a promising treatment as it can attenuate fibrosis and inflammation, but the mechanisms are poorly defined. We employed a causal inference approach for multi-omics network analysis which revealed critical cellular and molecular processes responsible for the effects of omega-3 PUFA (Docosahexaenoic acid, DHA; Eicosapentaenoic acid, EPA) in a preclinical mouse model of NASH. Because NASH is one of the leading causes of liver cancer, we also performed a meta-analysis of 7 cancer datasets and integrated these results with the mouse gene expression network. The overlap of the NASH network with meta-analysis identified betacellulin (BTC)-EGFR-ERBB as a central pathway of hepatocellular carcinoma. In mice, DHA inhibits this pathway. Using two cell lines, we confirmed that BTC acts at several levels of pathogenesis by: 1) promoting proliferation of quiescent hepatic stellate cells; 2) stimulating transforming growth factor-beta 2 (TGFB-2), which increases collagen production, and 3) upregulation of integrins in macrophages together with TLR2/4 agonists. Strikingly, these pathogenic processes were attenuated by DHA and to a much lesser degree by EPA. We also found that DHA restores hepatic cardiolipin precursors and mitochondrial pathways. Together, our results suggest that inhibition of BTC by DHA is a key mechanism behind its beneficial effects on liver health, and that administration of DHA may prevent progression of NASH to liver cancer by averting the BTC-EGFR-ERBB pathway.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.09.30.510319v1?rss=1
Authors: Wuchty, S., White, A. K., Olthof, A. M., Drake, K., Hume, A. J., Olejnik, J., Muehlberger, E., Aguiar-Pulido, V., Kanadia, R. N.
Abstract: The pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) revealed the worlds unpreparedness to deal with the emergence of novel pathogenic viruses, pointing to the urgent need to identify targets for broad-spectrum antiviral strategies. Here, we report that proteins encoded by Minor Intron-containing Genes (MIGs) are significantly enriched in datasets of cellular proteins that are leveraged by SARS-CoV-2 and other viruses. Pointing to a general gateway for viruses to tap cellular machinery, MIG-encoded proteins (MIG-Ps) that react to the disruption of the minor spliceosome are most important points of viral attack, suggesting that MIG-Ps may pan-viral drug targets. While contemporary anti-viral drugs shun MIG-Ps, we surprisingly found that anti-cancer drugs that have been repurposed to combat SARS-CoV-2, indeed target MIG-Ps, suggesting that such genes can potentially be tapped to efficiently fight viruses.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.09.30.510412v1?rss=1
Authors: Sarabipour, S., Kinghorn, K., Quigley, K. M., Kovacs-Kasa, A., Annex, B. H., Bautch, V. L., Mac Gabhann, F.
Abstract: The vascular endothelial growth factor (VEGF) family of cytokines are key drivers of blood vessel growth and remodeling. These ligands act via multiple VEGF receptors (VEGFR) and co-receptors such as Neuropilin (NRP) expressed on endothelial cells. These membrane-associated receptors are not solely expressed on the cell surface, they move between the surface and intracellular locations, where they can function differently. The location of the receptor alters its ability to 'see' (access and bind to) its ligands, which regulates receptor activation; location also alters receptor exposure to subcellularly localized phosphatases, which regulates its deactivation. Thus, receptors in different subcellular locations initiate different signaling, both in terms of quantity and quality. Similarly, the local levels of co-expression of other receptors alters competition for ligands. Subcellular localization is controlled by intracellular trafficking processes, which thus control VEGFR activity; therefore, to understand VEGFR activity, we must understand receptor trafficking. Here, for the first time, we simultaneously quantify the trafficking of VEGFR1, VEGFR2, and NRP1 on the same cells - specifically human umbilical vein endothelial cells (HUVECs). We build a computational model describing the expression, interaction, and trafficking of these receptors, and use it to simulate cell culture experiments. We use new quantitative experimental data to parameterize the model, which then provides mechanistic insight into the trafficking and localization of this receptor network. We show that VEGFR2 and NRP1 trafficking is not the same on HUVECs as on non-human ECs; and we show that VEGFR1 trafficking is not the same as VEGFR2 trafficking, but rather is faster and weighted to intracellular expression, due to faster internalization and lower recycling, resulting in increased overall degradation. As a consequence, the VEGF receptors are not evenly distributed between the cell surface and intracellular locations, with a very low percentage of VEGFR1 being on the cell surface. Our findings have implications both for the sensing of extracellular ligands and for the composition of signaling complexes at the cell surface versus inside the cell.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.09.29.510179v1?rss=1
Authors: Naseem, M. T., Beaven, R., Koyama, T., Naz, S., Su, M., Leader, D., Klaerke, D., Calloe, K., Denholm, B., Halberg, K. V.
Abstract: More than half of all extant metazoan species on earth are insects. The evolutionary success of insects is intrinsically linked with their ability to osmoregulate, suggesting that they have evolved unique physiological mechanisms to maintain water balance. In beetles (Coleoptera)-the largest group of insects-a specialized rectal (cryptonephridial) complex has evolved that recovers water from the rectum destined for excretion and recycles it back to the body. However, the molecular mechanisms underpinning the remarkable water-conserving functions of this system are unknown. Here, we introduce a transcriptomic resource, BeetleAtlas.org, for red flour beetle Tribolium castaneum, and demonstrate its utility by identifying a cation/H+ antiporter (NHA1) that is enriched and functionally significant in the Tribolium rectal complex. NHA1 localizes exclusively to a specialized cell type, the leptophragmata, in the distal region of the Malpighian tubules associated with the rectal complex. Computational modelling and electrophysiological characterization in Xenopus oocytes show that NHA1 acts as an electroneutral K+/H+ antiporter. Furthermore, genetic silencing of Nha1 dramatically increases excretory water loss and reduces organismal survival during desiccation stress, implying that NHA1 activity is essential for maintaining systemic water balance. Finally, we show that Tiptop, a conserved transcription factor, regulates NHA1 expression in leptophragmata and controls leptophragmata maturation, illuminating the developmental mechanism that establishes the novel functions of this cell. Together, our work provides the first insights into the molecular architecture underpinning the function of one most powerful water-conserving mechanisms in nature, the beetle rectal complex.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.09.28.509970v1?rss=1
Authors: Conole, D. P., Cao, F., Am Ende, C. W., Xue, L., Kantesaria, S., Kang, D., Jin, J., Owen, D. P., Lohr, L. L., Schenone, M., Majmudar, J. D., Tate, E. W.
Abstract: Deubiquitinases (DUBs) are proteases that hydrolyze isopeptide bonds linking ubiquitin to protein substrates, which can lead to reduced substrate degradation through the ubiquitin proteasome system. Deregulation of DUB activity has been implicated in many disease states, including cancer, neurodegeneration and inflammation, making them potentially attractive targets for therapeutic intervention. The greater than 100 known DUB enzymes have been classified primarily by their con-served active sites, but we are still building our understanding of their substrate profiles, localization and regulation of DUB activity in diverse contexts. Ubiquitin-derived covalent activity-based probes (ABPs) are the premier tool for DUB activity profiling, but their large recognition element impedes cellular permeability and presents an unmet need for small molecule ABPs which account for local DUB concentration, protein interactions, complexes, and organelle compartmentalization in intact cells or organisms. Here, through comprehensive warhead profiling we identify cyanopyrrolidine (CNPy) probe IMP-2373 (12), a small molecule pan-DUB ABP to monitor DUB activity in physiologically relevant live cell systems. Through chemical proteomics and targeted assays we demonstrate that IMP-2373 quantitatively engages more than 35 DUBs in live cells across a range of non-toxic concentrations, and in diverse cell lines and disease models, and we demonstrate its application to quantification of changes in intracellular DUB activity during MYC deregulation in a model of B cell lymphoma. IMP-2373 thus offers a complementary tool to ubiquitin ABPs to monitor dynamic DUB activity in the context of disease-relevant phenotypes.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.09.29.510183v1?rss=1
Authors: Halu, A., Baek, S.-H., Lo, I., Martini, L., Silverman, E. K., Weiss, S. T., Glass, K.
Abstract: The versatility of cellular response arises from the communication, or crosstalk, of signaling pathways in a complex network of signaling and transcriptional regulatory interactions. Understanding the various mechanisms underlying crosstalk on a global scale requires untargeted computational approaches. We present a network-based statistical approach, MuXTalk, that uses high-dimensional edges called multilinks to model the unique ways in which signaling and regulatory interactions can interface. We demonstrate that the signaling-regulatory interface is located primarily in the intermediary region between signaling pathways where crosstalk occurs, and that multilinks can differentiate between distinct signaling-transcriptional mechanisms. Using statistically over-represented multilinks as proxies of crosstalk, we predict crosstalk among 60 signaling pathways, expanding currently available crosstalk databases by more than five-fold. MuXTalk surpasses existing methods in terms of prediction performance, identifies additions to manual curation efforts, and pinpoints potential mediators of crosstalk for each prediction. Moreover, it accommodates the inherent context-dependence of crosstalk, allowing future applications to cell type- and disease-specific crosstalk.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.09.28.509990v1?rss=1
Authors: Shamsi, F., Zheng, R., Ho, L.-L., Chen, K., Tseng, Y.-H.
Abstract: Brown adipose tissue (BAT) is responsible for regulating body temperature through adaptive thermogenesis. The ability of thermogenic adipocytes to dissipate chemical energy as heat counteracts weight gain and has gained considerable attention as a strategy against obesity. BAT undergoes major remodeling in a cold environment. This remodeling results from changes in the number and function of brown adipocytes, expanding the network of blood vessels and sympathetic nerves, and changes in the makeup and function of immune cells. All these processes are essential for enhanced BAT thermogenesis to maintain euthermia in the cold. Such synergistic adaptation requires extensive crosstalk between the individual cells in tissues to coordinate their responses. To understand the mechanisms of intercellular communication in BAT, we applied the CellChat algorithm to single-cell transcriptomic data of mouse BAT. We constructed an integrative network of ligand-receptor interactome in BAT and identified the major signaling input and output of each cell type. By comparing the ligand-receptor interactions in BAT of mice housed at different environmental temperatures, we found that cold exposure enhances the intercellular interactions among the major cell types in BAT, including adipocytes, adipocyte progenitors, lymphatic and vascular endothelial cells, myelinated Schwann cells (MSC), non-myelinated Schwann cells (NMSC), and immune cells. Furthermore, we identified the ligands and receptors that are regulated at the transcriptional level by temperature. These interactions are predicted to regulate the remodeling of extracellular matrix (ECM), inflammatory response, angiogenesis, and neurite growth. Together, our integrative analysis of intercellular communications in BAT and their dynamic regulation in response to housing temperatures establishes a holistic understanding of the mechanisms involved in BAT thermogenesis. The resources presented in this study provide a valuable platform for future investigations of BAT development and thermogenesis.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.09.29.510032v1?rss=1
Authors: Wang, J., He, X.
Abstract: Genotype and phenotype are both the themes of modern biology. Despite the elegant protein coding rules recognized decades ago in genotype, little is known on how traits are coded in a phenotype space (P). Mathematically, P can be partitioned into a subspace determined by genetic factors (PG) and a subspace affected by non-genetic factors (PNG). Evolutionary theory predicts PG is composed of limited dimensions while PNG may have infinite dimensions, which suggests a dimension decomposition method, termed as uncorrelation-based high-dimensional dependence (UBHDD), to separate them. We applied UBHDD to a yeast phenotype space comprising ~400 traits in ~1,000 individuals. The obtained tentative PG matches the actual genetic components of the yeast traits, explains the broad-sense heritability, and facilitates the mapping of quantitative trait loci, suggesting the tentative PG be the yeast genetic subspace. A limited number of latent dimensions in the PG were found to be recurrently used for coding the diverse yeast traits, while dimensions in the PNG tend to be trait specific and increase constantly with trait sampling. A similar separation success was achieved when applying UBHDD to the UK Biobank human brain phenotype space that comprises ~700 traits in ~26,000 individuals. The obtained PG helped elucidate the genetic versus non-genetic origins of the left-right asymmetry of human brain, and reveal several hundred novel genetic correlations between brain regions and dozens of mental traits/diseases. In sum, by developing a dimension decomposition method we show that phenotypic traits are coded by a limited number of genetically determined common dimensions and unlimited trait-specific dimensions shaped by non-genetic factors, a rule fundamental to the emerging field of phenomics.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.09.27.509403v1?rss=1
Authors: Roemer, M. G. M., van de Brug, T., Bosch, E., Berry, D., Hijmering, N., Stathi, P., Weijers, K., Doorduijn, J., Broemberg, J., van de Wiel, M., Ylstra, B., de Jong, D., Kim, Y.
Abstract: To understand the clinical significance of the tumor microenvironment (TME), it is essential to study the interactions between malignant and non-malignant cells in clinical specimens. Here, we established a computational framework for a multiplex imaging system to comprehensively characterize spatial contexts of the TME at multiple scales, including close and long-distance spatial interactions between cell type pairs. We applied this framework to a total of 1,393 multiplex imaging data newly generated from 88 primary central nervous system lymphomas with complete follow-up data and identified significant prognostic subgroups mainly shaped by the spatial context. A supervised analysis confirmed a significant contribution of spatial context in predicting patient survival. In particular, we found an opposite prognostic value of macrophage infiltration depending on its proximity to specific cell types. Altogether, we provide a comprehensive framework to analyze spatial cellular interaction that can be broadly applied to other technologies and tumor contexts.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.09.26.507202v1?rss=1
Authors: Yang, J., Liu, Y., Shang, J., Chen, Q., Chen, Q., Ren, L., Zhang, N., Yu, Y., Li, Z., Song, Y., Yang, S., Scherer, A., Tong, W., Hong, H., Shi, L., Xiao, W., Zheng, Y.
Abstract: The implementation of quality control for multiomic data requires the widespread use of well-characterized reference materials, reference datasets, and related resources. The Quartet Data Portal was built to facilitate community access to such rich resources established in the Quartet Project. A convenient platform is provided for users to request the DNA, RNA, protein, and metabolite reference materials, as well as multi-level datasets generated across omics, platforms, labs, protocols, and batches. Interactive visualization tools are offered to assist users to gain a quick understanding of the reference datasets. Crucially, the Quartet Data Portal continuously collects, evaluates, and integrates the community-generated data of the distributed Quartet multiomic reference materials. In addition, the portal provides analysis pipelines to assess the quality of user-submitted multiomic data. Furthermore, the reference datasets, performance metrics, and analysis pipelines will be improved through periodic review and integration of multiomic data submitted by the community. Effective integration of the evolving technologies via active interactions with the community will help ensure the reliability of multiomics-based biological discoveries. The Quartet Data Portal is accessible at https://chinese-quartet.org.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.09.27.509541v1?rss=1
Authors: Paquette, A. G., Ahuna, K., Hwang, Y. M., Pearl, J., Liao, H., Shannon, P., Kadam, L., Lapehn, S., Bucher, M., Roper, R., Funk, C. C., MacDonald, J., Bammler, T., Baloni, P., Brockway, H., Mason, W. A., Bush, N., LeWinn, K. Z., Carr, C. J., Stamatoyannopoulos, J., Muglia, L. J., Jones, H. M., Sadovsky, Y., Myatt, L., Sathyanarayana, S., Price, N., Environmental influences on Child Health Outcomes
Abstract: Gene regulation is essential to placental function and fetal development. We report a genome-scale transcriptional regulatory network (TRN) of the human placenta built using digital genomic footprinting and transcriptomic data. We integrated 475 transcriptomes and 12 DNase hypersensitivity datasets from placental samples to globally and quantitatively map transcription factor (TF)-target gene interactions. In an independent dataset, the TRN model predicted target gene expression with an out of sample R2 value greater than 0.25 for 74% of target genes. We performed siRNA knockdowns of 4 TFs and achieved concordance between the predicted gene targets in our TRN and differences in expression of knockdowns with an accuracy of greater than 0.7 for 3 of the 4 TFs. Our final model contained 113,158 interactions across 391 TFs and 7,712 target genes and is publicly available. We identified six TFs which were significantly enriched as regulators for genes previously associated with preterm birth.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.09.27.509770v1?rss=1
Authors: Kumbier, K., Roth, M., Li, Z., Lazzari-Dean, J., Waters, C., Huang, P., Korobeynikov, V., Consortium, N. Y. G. C., Phatnani, H., Schneider, N., Jacobson, M., Wu, L., Altschuler, S.
Abstract: A major challenge for understanding and treating Amyotrophic Lateral Sclerosis (ALS) is that most patients have no known genetic cause. Even within defined genetic subtypes, patients display considerable clinical heterogeneity. It is unclear how to identify subsets of ALS patients that share common molecular dysregulation or could respond similarly to treatment. Here, we developed a scalable microscopy and machine learning platform to phenotypically subtype readily available, primary patient-derived fibroblasts. Application of our platform identified robust signatures for the genetic subtype FUS-ALS, allowing cell lines to be scored along a spectrum from FUS-ALS to non-ALS. Our FUS-ALS phenotypic score negatively correlates with age of diagnosis and provides information that is distinct from transcript profiling. Interestingly, the FUS-ALS phenotypic score can be used to identify sporadic patient fibroblasts that have consistent pathway dysregulation with FUS-ALS. Further, we showcase how the score can be used to evaluate the effects of ASO treatment on patient fibroblasts. Our platform provides an approach to move from genetic to phenotypic subtyping and a first step towards rational selection of patient subpopulations for targeted therapies.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.09.26.509592v1?rss=1
Authors: Tarkhov, A. E., Lindstrom-Vautrin, T., Zhang, S., Ying, K., Moqri, M., Zhang, B., Gladyshev, V. N.
Abstract: Age-related changes in DNA methylation (DNAm) form the basis for the development of most robust predictors of age, epigenetic clocks, but a clear mechanistic basis for what exactly they quantify is lacking. Here, to clarify the nature of epigenetic aging, we analyzed the aging dynamics of bulk-tissue and single-cell DNAm, together with single-cell DNAm changes during early development. We show that aging DNAm changes are widespread, but are relatively slow and small in amplitude, with DNAm levels trending towards intermediate values and showing increased heterogeneity with age. By considering dominant types of DNAm changes, we find that aging manifests in the exponential decay-like loss or gain of methylation with a universal rate, independent of the initial level of DNAm. We further show that aging is dominated by the stochastic component, yet co-regulated changes are also present during both development and adulthood. We support the finding of stochastic epigenetic aging by direct single-cell DNAm analyses and modeling of aging DNAm trajectories with a stochastic process akin to radiocarbon decay. Finally, we describe a single-cell algorithm for the identification of co-regulated CpG clusters that may provide new opportunities for targeting aging and evaluating longevity interventions.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.09.27.509819v1?rss=1
Authors: Zhang, F., Luna, A., Tan, T., Chen, Y., Sander, C., Guo, T.
Abstract: Background: The ongoing pandemic of the coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) still has limited treatment options partially due to our incomplete understanding of the molecular dysregulations of the COVID-19 patients. We aimed to generate a repository and data analysis tools to examine the modulated proteins underlying COVID-19 patients for the discovery of potential therapeutic targets and diagnostic biomarkers. Methods: We built a web server containing proteomic expression data from COVID-19 patients with a toolset for user-friendly data analysis and visualization. The web resource covers expert-curated proteomic data from COVID-19 patients published before May 2022. The data were collected from ProteomeXchange and from select publications via PubMed searches and aggregated into a comprehensive dataset. Protein expression by disease subgroups across projects was compared by examining differentially expressed proteins. We also visualize differentially expressed pathways and proteins. Moreover, circulating proteins that differentiated severe cases were nominated as predictive biomarkers. Findings: We built and maintain a web server COVIDpro (https://www.guomics.com/covidPro/) containing proteomics data generated by 41 original studies from 32 hospitals worldwide, with data from 3077 patients covering 19 types of clinical specimens, the majority from plasma and sera. 53 protein expression matrices were collected, for a total of 5434 samples and 14,403 unique proteins. Our analyses showed that the lipopolysaccharide-binding protein, as identified in the majority of the studies, was highly expressed in the blood samples of patients with severe disease. A panel of significantly dysregulated proteins was identified to separate patients with severe disease from non-severe disease. Classification of severe disease based on these proteomic signatures on five test sets reached a mean AUC of 0.87 and ACC of 0.80. Interpretation: COVIDpro is an online database with an integrated analysis toolkit. It is a unique and valuable resource for testing hypotheses and identifying proteins or pathways that could be targeted by new treatments of COVID-19 patients.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.09.25.509381v1?rss=1
Authors: Abrar, M. A., Kaykobad, M., Rahman, M. S., Samee, M. A. H.
Abstract: Spatial transcriptomics (ST) holds the promise to identify the existence and extent of spatial variation of gene expression in complex tissues. Such analyses could help identify gene expression signatures that distinguish between healthy and disease samples. Existing tools to detect spatially variable genes assume a constant noise variance across location. This assumption might miss important biological signals when the variance could change across spatial locations, e.g., in the tumor microenvironment. In this paper, we propose NoVaTeST, a framework to identify genes with location-dependent noise variance in ST data. NoVaTeST can model gene expression as a function of spatial location with a spatially variable noise. We then compare the model to one with constant noise to detect genes that show significant spatial variation in noise. Our results show genes detected by NoVaTeST provide complimentary information to existing tools while providing important biological insights.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.09.23.509189v1?rss=1
Authors: Som, A. M., Mohd Sohadi, N. A., Mohd Nor, N. S., Ali, S. A., Ahmad, M. A.
Abstract: BackgroundHovorka model is one of the diabetic models which is widely used in the artificial pancreas device (APD) also known as closed loop system, meant for people with type 1 diabetes (T1D). Previous workers had modified some equations in the sub-sections of the Hovorka model, which is also known as improved Hovorka equations, in regulating the blood glucose level (BGL) within normoglycemic range (4.0 to 7.0 mmol/L). However, the improved Hovorka equations have not been tested yet in terms of its usability to regulate and control the BGL in safe range for two or more people with T1D. This study aims to simulate their BGL with meal disturbances for 24 hours using the improved Hovorka equations.
MethodsData for people with T1D were obtained from Clinic 1, Clinical Training Centre (CTC), UiTM Medical Specialist Centre, Sungai Buloh, Selangor. Data collected include gender, age, body weight, mealtimes, meal amount, and duration. Three patients whose ages range from 11 to 14 years old were selected. All patients consumed three meals daily: breakfast, lunch, and dinner. The simulation (in-silico work) was done using MATLAB software, and the BGL profile from both in-silico and clinical works were compared and analysed.
ResultsIt was revealed that the BGLs for all three people with T1D were far better in the in-silico work compared to the clinical work. The BGL for patient 1 was able to achieve normoglycaemia 73% of the time in the in-silico work. Meanwhile, patient 2 managed to stay in the normoglycemic range for 85% of the time in the in-silico work compared to clinical work, which was merely 31%. For Patient 3, the time duration spent in the normoglycemic range was only 16% in the in-silico work compared to none as in the clinical work. The p-values obtained in the study were less than 0.05, indicating that the in-silico work using the improved Hovorka equations was acceptable for predicting the BGL for people with T1D.
ConclusionsIt can be concluded that the improved Hovorka equations are reliable in simulating the meal disturbances effect on BGL and increasing people in T1D times duration in the normoglycemic range compared to the clinical work.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.09.23.509157v1?rss=1
Authors: Bhattacharya, P., Raman, K., Tangirala, A. K.
Abstract: Biological adaptation, the tendency of every living organism to regulate its essential activities in environmental fluctuations, is a well-studied functionality in systems and synthetic biology. In this work, we present a generic methodology inspired by systems theory to discover the design principles for robust adaptation, perfect and imperfect, in two different contexts: (1) in the presence of deterministic external disturbance and (2) in a stochastic setting. In all the cases, firstly, we translate the necessary qualitative conditions for adaptation to mathematical constraints using the language of systems theory, which we then map back as design requirements for the underlying networks. Thus, contrary to the existing approaches, the proposed methodologies provide an exhaustive set of admissible network structures without resorting to computationally burdensome brute-force techniques. Further, the proposed frameworks do not assume prior knowledge about the particular rate kinetics, thereby validating the conclusions for a large class of biological networks. In the deterministic setting, we show that unlike the incoherent feed-forward network structures (IFFLP), the modules containing negative feedback with buffer action (NFBLB) are robust to parametric fluctuations when a specific part of the network is assumed to remain unaffected. To this end, we propose a sufficient condition for imperfect adaptation and show that adding negative feedback in an IFFLP topology improves the robustness concerning parametric fluctuations. Further, we propose a stricter set of necessary conditions for imperfect adaptation. Turning to the stochastic scenario, we adopt a Wiener-Kolmogorov filter strategy to tune the parameters of a given network structure towards minimum output variance. We show that both NFBLB and IFFLP can be used as a reduced order W-K filter. Further, we define the notion of nearest neighboring motifs to compare the output variances across different network structures. We argue that the NFBLB achieves adaptation at the cost of a variance higher than its nearest neighboring motifs whereas the IFFLP topology produces locally minimum variance while compared with its nearest neighboring motifs. We present numerical simulations to support the theoretical results. Overall, our results present a generic, systematic, and robust framework for advancing the understanding of complex biological networks.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.09.24.509324v1?rss=1
Authors: Wieder, F., Henk, M., Bockmayr, A.
Abstract: Elementary flux modes (EFMs) play an important role in metabolic network analysis. Here we study geometric properties of EFMs. In particular, we are interested in the distribution of EFMs in the face lattice of the steady-state flux cone of a metabolic network. The number of EFMs can be exponentially large in the number of reactions. Geometric insight may help to better understand the structure of the set of all EFMs, which is important both from the mathematical and the biological viewpoint.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.09.23.509266v1?rss=1
Authors: Molversmyr, H., Oyas, O., Rotnes, F., Vik, J. O.
Abstract: MotivationConstraint-based models (CBMs) are used to study the metabolic networks of organisms ranging from microbes to multicellular eukaryotes. Published CBMs are usually generic rather than context-specific, meaning that they do not capture metabolic differences between cell types, tissues, environments, or other conditions. However, only a subset of reactions in a model are likely to be active in any given context, and several methods have therefore been developed to extract context-specific models from generic CBMs through integration of omics data.
ResultsWe tested the ability of six model extraction methods (MEMs) to create functionally accurate context-specific models of Atlantic salmon using a generic CBM (SALARECON) and liver transcriptomics data from contexts differing in water salinity (life stage) and dietary lipids. Reaction contents and metabolic task feasibility predictions of context-specific CBMs were mainly determined by the MEM that was used, but life stage explained significant variance in both contents and predictions for some MEMs. Three MEMs clearly outperformed the others in terms of their ability to capture context-specific metabolic activities inferred directly from the data, and one of these (GIMME) was much faster than the others. Context-specific versions of SALARECON consistently outperformed the generic version, showing that context-specific modeling captures more realistic representations of Atlantic salmon metabolism.
Contactjon.vik@nmbu.no
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.09.23.509185v1?rss=1
Authors: Grohens, T., Beslon, G., Meyer, S.
Abstract: DNA supercoiling, the level of twist and writhe of the DNA molecule around itself, plays a major role in the regulation of gene expression in bacteria by modulating promoter activity. The level of supercoiling is a dynamic property of the chromosome, and it changes in response to external and internal stimuli including many environmental perturbations but also, importantly, in response to gene transcription. As transcription itself depends on the level of supercoiling, the interplay between these two factors results in a coupling between the transcription rates, and expression levels, of neighboring genes.
In this work, we study how the regulation of gene expression by the transcription-supercoiling coupling shapes the organization of bacterial genomes. We present an evolutionary model of gene transcription and DNA supercoiling at the whole-genome scale, in which individuals must adjust their gene expression levels to face different environments. We show that, in this model, whole-genome regulatory networks that provide fine control over gene expression evolve, and that these networks are grounded in the local organization of the genome in order to leverage the transcription-supercoiling coupling. Our results provide therefore important insight into the role of supercoiling in jointly shaping gene regulation and genome organization.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.09.23.509138v1?rss=1
Authors: den Ridder, M., Daran-Lapujade, P., Pabst, M.
Abstract: The yeast Saccharomyces cerevisiae is a widely used eukaryotic model organism and a promising cell factory for industry. However, despite decades of research, the regulation of its metabolism is not yet fully understood, and its complexity represents a major challenge for engineering and optimising biosynthetic routes. Recent studies have demonstrated the potential of resource and proteomic allocation data in enhancing models for metabolic processes. However, comprehensive and accurate proteome dynamics data that can be used for such approaches are still very limited. Therefore, we performed a quantitative proteome dynamics study to comprehensively cover the transition from exponential to stationary phase for both aerobically and anaerobically grown yeast cells. The combination of highly controlled reactor experiments, biological replicates and standardised sample preparation procedures ensured reproducibility and accuracy. Additionally, we selected the CEN.PK lineage for our experiments because of its relevance for both fundamental and applied research. Together with the prototrophic, standard haploid strain CEN.PK113-7D, we also investigated an engineered strain with genetic minimisation of the glycolytic pathway, resulting in the quantitative assessment of over 1700 proteins across 54 proteomes. These proteins account for nearly 40% of the overall yeast proteome and approximately 99% of the total protein biomass. The anaerobic cultures showed remarkably less proteome-level changes compared to the aerobic cultures, during transition from the exponential to the stationary phase as a consequence of the lack of the diauxic shift in the absence of oxygen. These results support the notion that anaerobically growing cells lack time and resources to adapt to changes in the environment. This proteome dynamics study constitutes an important step towards better understanding of the impact of glucose exhaustion and oxygen on the complex proteome allocation process in yeast. Finally, the established proteome dynamics data provide a valuable resource for the development of resource allocation models as well as for metabolic engineering efforts.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.09.23.509038v1?rss=1
Authors: Meindl, A., Romberger, M., Lehmann, G., Eichner, N., Kleemann, L., Wu, J., Danner, J., Boesl, M., Mesitov, M., Meister, G., Koenig, J., Leidel, S., Medenbach, J.
Abstract: Ribosome profiling provides quantitative, comprehensive, and high-resolution snapshots of cellular translation by the high-throughput sequencing of short mRNA fragments that are protected from nucleolytic digestion by ribosomes. While the overall principle is simple, the workflow of ribosome profiling experiments is complex and challenging, and typically requires large amounts of sample, limiting its broad applicability. Here, we present a new protocol for ultra-rapid ribosome profiling from low-input samples. It features a robust strategy for sequencing library preparation within one day that employs solid phase purification of reaction intermediates, allowing to reduce the input to as little as 0.1 pmol of RNA. Hence, it is particularly suited for the analyses of small samples or targeted ribosome profiling. Its high sensitivity and its ease of implementation will foster the generation of higher quality data from small samples, which opens new opportunities in applying ribosome profiling.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.09.20.508759v1?rss=1
Authors: Qiu, Y., Hou, Y., Zhou, Y., Xu, J., Bykova, M., Leverenz, J. B., Pieper, A. P., Nussinov, R., Caldwell, J. Z. K., Brown, M., Cheng, F.
Abstract: Accumulating evidence suggests that gut-microbiota metabolites contribute to human disease pathophysiology, yet the host receptors that sense these metabolites are largely unknown. Here, we developed a systems pharmacogenomics framework that integrates machine learning (ML), AlphaFold2-derived structural pharmacology, and multi-omics to identify disease-relevant metabolites derived from gut-microbiota with non-olfactory G-protein-coupled receptors (GPCRome). Specifically, we evaluated 1.68 million metabolite-protein pairs connecting 408 human GPCRs and 516 gut metabolites using an Extra Trees algorithm-improved structural pharmacology strategy. Using genetics-derived Mendelian randomization and multi-omics (including transcriptomic and proteomic) analyses, we identified likely causal GPCR targets (C3AR, FPR1, GALR1 and TAS2R60) in Alzheimers disease (AD). Using three-dimensional structural fingerprint analysis of the metabolite-GPCR complexome, we identified over 60% of the allosteric pockets of orphan GPCR models for gut metabolites in the GPCRome, including AD-related orphan GPCRs (GPR27, GPR34, and GPR84). We additionally identified the potential targets (e.g., C3AR) of two AD-related metabolites (3-hydroxybutyric acid and Indole-3-pyruvic acid) and four metabolites from AD-related bacterium Eubacterium rectale, and also showed that tridecylic acid is a candidate ligand for orphan GPR84 in AD. In summary, this study presents a systems pharmacogenomics approach that serves to uncover the GPCR molecular targets of gut microbiota in AD and likely many other human diseases if broadly applied.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.09.20.508736v1?rss=1
Authors: Suresh, H., Crow, M., Jorstad, N., Hodge, R., Lein, E., Dobin, A., Bakken, T., Gillis, J.
Abstract: Enhanced cognitive function in humans is hypothesized to result from cortical expansion and increased cellular diversity. However, the mechanisms that drive these phenotypic differences remain poorly understood, in part due to the lack of high-quality cellular resolution data in human and non-human primates. Here, we take advantage of single cell expression data from the middle temporal gyrus of five primates (human, chimp, gorilla, macaque and marmoset) to identify 57 homologous cell types and generate cell-type specific gene coexpression networks for comparative analysis. While ortholog expression patterns are generally well conserved, we find 24% of genes with extensive differences between human and non-human primates (3383/14,131), which are also associated with multiple brain disorders. To validate these observations, we perform a meta-analysis of coexpression networks across 19 animals, and find that a subset of these genes have deeply conserved coexpression across all non-human animals, and strongly divergent coexpression relationships in humans (139/3383, less than 1% of primate orthologs). Genes with human-specific cellular expression and coexpression networks (like NHEJ1, GTF2H2, C2 and BBS5) typically evolve under relaxed selective constraints and may drive rapid evolutionary change in brain function.
One Sentence SummaryCross-primate middle temporal gyrus single cell expression data reveals patterns of conservation and divergence that can be validated with population coexpression networks.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.09.21.508820v1?rss=1
Authors: Garcia Blay, O., Verhagen, P., Martin, B., Hansen, M. M. K.
Abstract: Co-expression of genes measured with single-cell RNA sequencing is extensively utilized to understand the principles of gene regulation within and across cell types and species. It is assumed that the presence of correlation in gene expression values at the single-cell level demonstrates the existence of common regulatory mechanisms. However, the regulatory mechanisms that should lead to observed co-expression at an mRNA level often remain unexplored. Here we investigate the relationship between processes upstream and downstream of transcription (i.e., promoter architecture and coordination, DNA contact frequencies and mRNA degradation) and pairwise gene expression correlations at an mRNA level. We identify that differences in mRNA degradation (i.e., half-life) is a pivotal source of single-cell correlations in mRNA levels independently of the presence of common regulatory mechanisms. These findings reinforce the necessity of including post-transcriptional regulation mechanisms in the analysis of gene expression in mammalian cells.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.09.20.508795v1?rss=1
Authors: He, W., Demas, D. M., Shajahan-Haq, A. N., Baumann, W. T.
Abstract: Estrogen receptor positive (ER+) breast cancer is responsive to a number of targeted therapies used clinically. Unfortunately, the continuous application of targeted therapy often results in resistance. Mathematical modeling of the dynamics of cancer cell drug responses can help find better therapies that not only hold proliferation in check but also potentially stave off resistance. Toward this end, we developed a mathematical model that can simulate various mono, combination and alternating therapies for ER+ breast cancer cells at different doses over long time scales. The model is used to look for optimal drug combinations and predicts a significant synergism between Cdk4/6 inhibitors in combination with the anti-estrogen fulvestrant, which may help explain the clinical success of adding CDK4/6 inhibitors to anti-estrogen therapy. Lastly, the model is used to optimize an alternating treatment protocol that works as well as monotherapy while using less total drug dose.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.09.20.508244v1?rss=1
Authors: Xu, Z., Omar, M., Benedetti, E., Rosenthal, J., Umeton, R., Krumsiek, J., Pomerantz, M., Imada, E., Loda, M., Marchionni, L.
Abstract: Effective biomarkers and diagnostic tools are urgently needed in clinical settings for improved management of prostate cancer patients, especially to reduce over-treatment of indolent tumors and for early identification of aggressive disease. Gene expression signatures are currently the "gold standard" to provide guide clinical decision, however their clinical utility and interpretability is questionable. Multi-modal molecular profiling provides an holistic approach to systematically unravel the biological complexity underlying cancer pathogenesis, hence biomarkers developed using such an integrated approach hold the potential to more accurately capture cancer-driving alterations than signatures based on a single omics modality. Currently, however, robust and reproducible multi-omics biomarkers are still lacking for prostate cancer. In this study, we analyzed transcriptomics and metabolomics profiles jointly in a prostate cancer cohort and identified two prognostic signatures with high statistical powers (signature 1: EGLN3, succinate, trans-4-hydroxyprolin; and signature 2: IL6, SLC22A2, histamine). Our approach leveraged a priori biological knowledge of the cellular metabolism and gene circuitry, enabling the identification of dysregulated network modules. Functional bioinformatics analyses suggest that these signatures can capture relevant molecular alterations in prostate cancer tissues, including dysregulations of cellular signaling, cell cycle progression, and immune system modulation, stratifying patients in distinct risk groups. Next, we trained two gene expression signatures as a proxy for the multi-omics ones, extending our investigation to publicly available data, further confirming their prognostic values in independent patient cohorts. In summary, the analysis of multi-modal molecular grounded in cellular network biology represents a promising approach for the development of robust prognostic biomarkers of detecting and discriminating high grade disease.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.09.21.508930v1?rss=1
Authors: McWhite, C. D., Sae-Lee, W., Yuan, Y., Mallam, A., Gort-Frietas, N. A., Ramundo, S., Onishi, M., Marcotte, E. M.
Abstract: Variability of proteins at the sequence level creates an enormous potential for proteome complexity. Exploring the depths and limits of this complexity is an ongoing goal in biology. Here, we systematically survey human and plant high-throughput bottom-up native proteomics data for protein truncation variants, where substantial regions of the full-length protein are missing from an observed protein product. In humans, Arabidopsis, and the green alga Chlamydomonas, approximately one percent of observed proteins show a short form, which we can assign by comparison to RNA isoforms as either likely deriving from transcript-directed processes or limited proteolysis. While some detected protein fragments align with known splice forms and protein cleavage events, multiple examples are previously undescribed, such as our observation of fibrocystin proteolysis and nuclear translocation in a green alga. We find that truncations occur almost entirely between structured protein domains, even when short forms are derived from transcript variants. Intriguingly, multiple endogenous protein truncations of phase-separating translational proteins resemble cleaved proteoforms produced by enteroviruses during infection. Some truncated proteins are also observed in both humans and plants, suggesting that they date to the last eukaryotic common ancestor. Finally, we describe novel proteoform-specific protein complexes, where loss of a domain may accompany complex formation.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.09.21.508950v1?rss=1
Authors: Inoue, K.-i., Kishimoto, S., Mogami, T., Toyoda, S., Hariyama, M.
Abstract: BackgroundIntercellular communication is a critical innovation during multicellular organismal evolution. Cells release / receive cytokines and utilize them as intercellular signal entities. How a well-orchestrated communication emerges from individual cell behavior remains largely unknown. Here we abstracted the biological phenomenon and developed multi-agent-simulation to investigate the intracellular communication.
MethodsTwo dimensional MAS platform was developed using Artisoc 4.2.1 standard software. We focused on intercellular communication via cytokines and extracellular matrices. Three agents, "cells", "cytokines" and "extracellular matrices" were defined and the interaction rules among the agents were designed. Two different mathematical models of cytokine-gradient determination were tested: spatial derivative and temporal derivative models. As a case study, neutrophil swarming was modeled and the cell swarming was defined as an evaluation criterion. Moreover, a surgically injured mouse model and a fluorescent time-lapse imaging were used to observe neutrophil swarming.
ResultsWe performed multiple simulations with six different virtual conditions, changing multiple parameters simultaneously and randomly. After 400 simulations for each condition, we counted the number of successful trials (i.e. neutrophil swarming within 10000 steps). Spatial derivative model showed more successes compared to temporal derivative model. Among eight parameters randomly assigned, cells exploration speed by random walk most remarkably influenced on success rate of neutrophil swarming. In in vivo model, bone marrow derived neutrophils gather towards the clumps with various migration speed. The mode of approaching resembles to spatial derivative model, rather than temporal derivative model.
ConclusionsMAS could be a useful approach to investigate the emergence of intercellular communication during multicellular evolution. Neutrophil could adopt the spatial derivative model as a sensing mechanism of cytokine gradient.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.09.20.506926v1?rss=1
Authors: Millard, P., Uttenweiler-Joseph, S., Enjalbert, B.
Abstract: Acetate is a major by-product of glycolytic metabolism in Escherichia coli and many other microorganisms. It has long been considered a toxic waste compound that inhibits microbial growth, but this counterproductive auto-inhibition, which represents a major problem in biotechnology, has puzzled the scientific community for decades. Recent studies have revealed that acetate is also a co-substrate of glycolytic nutrients and a global regulator of E. coli metabolism and physiology. However, most of these insights were obtained at high glycolytic flux and little is known about the role of acetate at lower glycolytic fluxes, conditions that are nevertheless frequently experienced by E. coli in natural, industrial and laboratory environments. Here, we used a systems biology strategy to investigate the mutual regulation of glycolytic and acetate metabolism. Computational and experimental results demonstrate that reducing the glycolytic flux enhances co-utilization of acetate and glucose through the Pta-AckA pathway. Enhanced acetate metabolism compensates for the reduction in glycolytic flux and eventually buffers carbon uptake so that acetate, far from being toxic, actually enhances E. coli growth under these conditions. The same mechanism of increased growth was also observed on glycerol and galactose, two nutrients with a natively low glycolytic flux. Therefore, acetate makes E. coli more robust to glycolytic perturbations and is a valuable nutrient, with a beneficial effect on microbial growth. Finally, we show that some evolutionarily conserved design principles of eukaryotic fermentative metabolism are also present in bacteria.
Significance StatementAcetate, a by-product of glycolytic metabolism in many microorganisms including Escherichia coli, is traditionally viewed as a toxic waste compound. Here, we demonstrate that this is only the case at high glycolytic fluxes. At low glycolytic fluxes in contrast, acetate acts as a co-substrate of glycolytic nutrients and boosts E. coli growth. Acetate also improves E. colis robustness to glycolytic perturbations. We clarify the functional relationship between glycolytic and acetate metabolisms, show that acetate is a beneficial co-substrate of glycolytic nutrients used by E. coli in bioprocesses and in the gut, and provide insights into the underlying biochemical and regulatory mechanisms.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.09.19.508598v1?rss=1
Authors: Wei, L., Dankwa, S., Vijayan, K., Smith, J. D., Kaushansky, A.
Abstract: Breakdown of the blood-brain barrier is triggered by a range of physiological and pathological stimuli and is detrimental for brain function. Yet, the underlying signaling networks regulating barrier integrity are incompletely understood. Here, we present a novel and generalizable tool, Temporally REsolved KInase Network Generation (TREKING), that combines machine learning and network reconstruction to build time-resolved, functional phosphosignaling networks. We investigated kinase-driven pathways that modulate barrier permeability in brain endothelial cells in the presence of inflammatory stimuli. Our results reveal that greater than 100 kinases are functional during barrier insult and provide time-resolved molecular insights into the differential networks that drive barrier disruption and recovery. The resulting models suggest a multi-layered rewiring of thrombin-induced barrier disruption following TNF priming, including the timing of common signaling pathways, condition-specific phosphosignaling networks, and a skewing at major signaling hubs towards barrier-weakening activities. TREKING provides a novel tool for dissecting temporal phosphosignaling networks in biological systems.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.09.20.508694v1?rss=1
Authors: Dinh, H. V., Maranas, C. D.
Abstract: Saccharomyces cerevisiae is an important model organism and a workhorse in biochemical production. Here, we reconstructed a compact and tractable genome-scale resource balance analysis (RBA) model (i.e., scRBA) to analyze metabolic fluxes and proteome allocation in a computationally efficient manner. Resource capacity models such as scRBA provide the quantitative means to identify bottlenecks in biosynthetic pathways due to enzyme and/or ribosome availability limitations. ATP maintenance rate and in vivo apparent turnover numbers (kapp) were regressed from metabolic flux and protein concentration data to capture observed physiological growth yield and proteome efficiency and allocation, respectively. Estimated parameter values were found to vary with oxygen and nutrient availability. Overall, this work (i) provides condition-specific model parameters to recapitulate phenotypes corresponding to different extracellular environments, (ii) alludes to the enhancing effect of substrate channeling and post-translational activation on in vivo enzyme efficiency in glycolysis and electron transport chain, and (iii) reveals that the Crabtree effect is underpinned by ribosome availability limitations and reserved protein capacity.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.09.17.508184v1?rss=1
Authors: Trudeau, S. J., Hwang, H., Mathur, D., Begum, K., Petrey, D., Murray, D., Honig, B.
Abstract: We describe the Predicting Protein Compound Interactions (PrePCI) database which comprises over 5 billion predicted interactions between nearly 7 million chemical compounds and 19,797 human proteins. PrePCI relies on a proteome-wide database of structural models based on both traditional modeling techniques and the AlphaFold Protein Structure Database. Sequence and structural similarity-based metrics are established between template proteins in the Protein Data Bank, T, that bind small molecules, C, and proteins in the models database, Q. When these metrics pass a sequence threshold value, it is assumed that C also binds to Q with a probability derived from machine learning. If the relationship is based on structure, this probability is based on a scoring function that measures the extent to which C is compatible with the binding site of Q as described in the LT-scanner algorithm. For every predicted complex derived in this way, chemical similarity based on the Tanimoto Coefficient identifies other small molecules that may bind to Q. A likelihood ratio for the binding of C to Q is obtained from naive Bayesian statistics. The PrePCI algorithm performs well under different validations. It can be queried by entering a UniProt ID for a protein and obtaining a list of compounds predicted to bind to it along with associated probabilities. Alternatively, entering an identifier for the compound outputs a list of proteins it is predicted to bind. Specific applications of the database are described and a strategy is introduced to use PrePCI as a first step in a docking screen.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.09.19.508486v1?rss=1
Authors: Keles, M., Grein, S., Froese, N., Wirth, D., Trogisch, F. A., Wardman, R., Hemanna, S., Weinzierl, N., Uhlig, S., Lomada, S., Dittrich, G. M., Szaroszyk, M., Haustein, R., Hegermann, J., Martin Garrido, A., Bauersachs, J., Frank, D., Frey, N., Bieback, K., Cordero, J., Dobreva, G., Wieland, T., Heineke, J.
Abstract: BackgroundPathological cardiac overload triggers maladaptive myocardial remodeling that predisposes to the development of heart failure. The contribution of long non-coding RNAs (lncRNAs) to intercellular signaling during cardiac remodeling is largely unknown.
MethodsWe analyzed the expression of Gadlor 1 and Gadlor2 lncRNAs in mouse hearts, mouse cardiac cells, extracellular vesicles (EVs), human failing hearts as well as patient serum. Gadlor knock-out (KO) mice were generated and analyzed. The effect of Gadlor knock-out and Gadlor overexpression during cardiac pressure overload induced by transverse aortic constriction (TAC) was analyzed by echocardiography, histological analyses and RNA sequencing in isolated cardiac cells. Gadlor1/2 interaction partners were identified by RNA antisense purification coupled with mass-spectrometry (RAP-MS).
ResultsIn the heart, the related lncRNAs Gadlor1 and 2 are mainly expressed in endothelial cells and to a lesser extent in fibroblasts. Gadlor1/2 are upregulated in failing mouse hearts as well as in the myocardium and in serum of heart failure patients. Interestingly, Gadlor1 and 2 are secreted from endothelial cells within EVs, which are taken up by cardiomyocytes. Gadlor-KO mice exerted reduced cardiomyocyte hypertrophy, diminished myocardial fibrosis and improved cardiac function, but paradoxically suffered from sudden death during prolonged overload. Gadlor overexpression, in turn, triggered hypertrophy, fibrosis and cardiac dysfunction. Mechanistically, Gadlor1 and Gadlor2 inhibit angiogenic gene expression in endothelial cells, while promoting the expression of pro-fibrotic genes in cardiac fibroblasts. In cardiomyocytes, Gadlor1/2 upregulate mitochondrial genes, but downregulate angiogenesis genes, while interacting with the transcriptional regulator Glyr1 and the calcium/calmodulin-dependent protein kinase type II (CaMKII), entailing cardiomyocyte hypertrophy and perturbed cardiomyocyte calcium dynamics.
ConclusionsWe describe that Gadlor1 and 2, two related, novel lncRNAs, are upregulated in cardiac pathological overload and are secreted from endothelial cells within EVs. Gadlor1/2 induce cardiac dysfunction, cardiomyocyte hypertrophy and myocardial fibrosis by exerting heterocellular effects on cardiac cellular gene-expression and by affecting calcium dynamics in cardiomyocytes, which take up the Gadlor1/2 by EV mediated transfer from endothelial cells. Targeted inhibition of Gadlor lncRNAs in endothelial cells or fibroblasts might serve as a therapeutic strategy in the future.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.09.16.508345v1?rss=1
Authors: Hromada, S., Venturelli, O. S.
Abstract: In the human gut, the growth of Clostridioides difficile is impacted by a complex web of inter-species interactions with members of human gut microbiota. We investigate the contribution of inter-species interactions on the antibiotic response of C. difficile to clinically relevant antibiotics using bottom-up assembly of human gut communities. We discover two classes of microbial interactions that alter C. difficiles antibiotic susceptibility: infrequent increases in tolerance at high antibiotic concentrations and frequent growth enhancements at low antibiotic concentrations. Based on genome-wide transcriptional profiling data, we demonstrate that metal sequestration due to hydrogen sulfide production by the prevalent gut species Desulfovibrio piger increases metronidazole tolerance of C. difficile. Competition with species that display higher sensitivity to the antibiotic than C. difficile leads to enhanced growth of C. difficile at low antibiotic concentrations. A dynamic computational model identifies the ecological design principles driving this effect. Our results provide a deeper understanding of ecological and molecular principles shaping C. difficiles response to antibiotics, which could inform therapeutic interventions.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.09.14.508054v1?rss=1
Authors: Tham, N., Langley, S. R.
Abstract: Drug repurposing is an approach to identify new therapeutic applications for existing drugs and small molecules. It is a field of growing research interest due to its time and cost effectiveness as compared with de novo drug discovery. One method for drug repurposing is to adopt a systems biology approach to associate molecular signatures of drug and disease. Drugs which have an inverse relationship with the disease signature may be able to reverse the molecular effects of the disease and thus be candidates for repurposing. Conversely, drugs which mimic the disease signatures can inform on potential molecular mechanisms of disease. The relationship between these disease and drug signatures are quantified through connectivity scores. Identifying a suitable drug-disease scoring method is key for in silico drug repurposing, so as to obtain an accurate representation of the true drug-disease relationship. There are several methods to calculate these connectivity scores, notably the Kolmogorov-Smirnov (KS), Zhang and eXtreme Sum (XSum). However, these methods can provide discordant estimations of the drug-disease relationship and this discordance can affect the drug-disease indication. Using the gene expression profiles from the Library of Integrated Network-Based Cellular Signatures (LINCS) database, we evaluated the methods based on their drug-disease connectivity scoring performance. In this first-of-its-kind analysis, we varied the quality of disease signatures by using only highly differential genes or by the inclusion of non-differential genes. Further, we simulated noisy disease signatures by introducing varying levels of noise into the gene expression signatures. Overall, we found that there was not one method that outperformed the others in all instances, but the Zhang method performs well in a majority of our analyses. Our results provide a framework to evaluate connectivity scoring methods, and considerations for deciding which scoring method to apply in future systems biology studies for drug repurposing.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.09.14.507938v1?rss=1
Authors: Kugler, A., Stensjö, K.
Abstract: Cyanobacteria represent an attractive platform for the sustainable production of chemicals and fuels. However, the obtained rates, yields, and titers are below those required for commercial application. Carbon metabolism alone cannot achieve maximal accumulation of end-products, since an efficient production of target molecules entails energy and redox balance, in addition to carbon flow. The interplay between cofactor regeneration and heterologous metabolite overproduction in cyanobacteria is not fully explored. Here, we applied stoichiometric metabolic modelling of the cyanobacterium Synechocystis sp. PCC 6803, in order to investigate the optimality of energy and redox metabolism, while overproducing bio-alkenes - isobutene, isoprene, ethylene and 1-undecene. Our network-wide analysis indicates that the rate of NADP+ reduction, rather than ATP synthesis, controls ATP/NADPH ratio, and thereby chemical production. The simulation implies that energy and redox balance necessitates gluconeogenesis, and that acetate metabolism via phosphoketolase serves as an efficient carbon- and energy-recycling pathway. Furthermore, we show that an auxiliary pathway, composed of serine, one-carbon and glycine metabolism, supports cellular redox homeostasis and ATP cycling, and that the Synechocystis metabolism is controlled by few key reactions carrying a high flux. The study also revealed non-intuitive metabolic pathways to enhance isoprene, ethylene and 1-undecene production. We conclude that metabolism of ATP and NAD(P)H is entwined with carbon and nitrogen metabolism, and cannot be assessed in isolation. We envision that the presented here in-depth metabolic analysis will guide the a priori design of Synechocystis as a host strain for an efficient manufacturing of target products.
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2022.09.15.508006v1?rss=1
Authors: Munyoki, S. K., Goff, J. P., Mullett, S. J., Burns, J. K., Jenkins, A. K., DePoy, L. M., Wendell, S. G., McClung, C. A., Morrison, K. E., Jasarevic, E.
Abstract: Circadian rhythms in microbial communities regulate a variety of essential homeostatic functions in the intestinal tract and distal tissues. Circadian disruption is often associated with sex-specific disease risk, but studies on circadian rhythms, the microbiome, and health outcomes primarily use male mice or collapse both sexes into one experimental condition. Here, we identify sex differences in diurnal rhythms in the intestinal microbiota, the metabolites they produce, and the expression of host genes, with more pronounced effects in females. The magnitude of these sex differences also varies by time of day, suggesting that time of collection may influence the capacity to detect sex differences in mice. Further, transitioning female mice to high-fat and low-fiber diet abolished circadian rhythms in microbiota, metabolites, and host gene expression that is entrained by a chow diet. As a result, consumption of a high-fat and low-fiber diet generated new diurnal rhythms in the microbiota and host transcriptome in females. Together, we show that circadian rhythms in the crosstalk between microbiota and their hosts are sex-specific and that diet plays an essential role in maintaining these sex differences.
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