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bioRxiv2017

Whole-cell scale dynamic organization of lysosomes revealed by spatial statistical analysis

In eukaryotic cells, lysosomes are distributed in the cytoplasm as individual membrane-bound compartments to degrade macromolecules and to control cellular metabolism. A fundamental yet unanswered question is whether and, if so, how individual lysosomes are spatially organized so that their functions can be coordinated and integrated to meet changing needs of cells. To address this question, we analyze their collective behavior in cultured cells using spatial statistical techniques. We find that in single cells, lysosomes maintain nonrandom, stable, yet distinct spatial distributions, which are mediated by the coordinated effects of the cytoskeleton and lysosomal biogenesis on different lysosomal subpopulations. Furthermore, we find that throughout the intracellular space, lysosomes form dynamic clusters that substantially increase their interactions with endosomes. Together, our findings reveal the spatial organization of lysosomes at the whole-cell scale and provide new insights into how organelle interactions are mediated and regulated over the entire intracellular space.\n\nHighlightsO_LILysosomes maintain stable yet distinct spatial distributions in single cells\nC_LIO_LIThe cytoskeleton and lysosomal biogenesis mediate stable lysosomal distributions\nC_LIO_LILysosomes form dynamic clusters that promote their interactions with endosomes\nC_LIO_LITwo subpopulations of lysosomes jointly mediate formation of lysosomal clusters\nC_LI\n\neTOC BlurbLysosomes are spatially organized at the whole-cell scale and form dynamic clusters that promote their interactions with endosomes.

systems biology↗

bioRxiv2017

Raw sequence to target gene prediction: An integrated inference pipeline for ChIP-seq and RNA-seq datasets

Gene expression patterns determine the manner whereby organisms regulate various cellular processes and therefore their organ functions.These patterns do not emerge on their own, but as a result of diverse regulatory factors such as, DNA binding proteins known as transcription factors (TF), chromatin structure and various other environmental factors. TFs play a pivotal role in gene regulation by binding to different locations on the genome and influencing the expression of their target genes. Therefore, predicting target genes and their regulation becomes an important task for understanding mechanisms that control cellular processes governing both healthy and diseased cells.In this paper, we propose an integrated inference pipeline for predicting target genes and their regulatory effects for a specific TF using next-generation data analysis tools.

systems biology↗

bioRxiv2017

Species comparison of liver proteomes reveals enhanced lipid metabolism, reduced mitochondrial respiration and enhanced expression of detoxifying enzymes in the long-lived naked mole-rat.

Mammals display wide range of variation in their lifespan. Investigating the molecular networks that distinguish long-from short-lived species has proven useful to identify determinants of longevity. Here, we compared the liver of long-lived naked mole-rats (NMRs) and the phylogenetically closely related, shorter-lived, guinea pigs using an integrated omic approach. We found that NMRs livers display a unique expression pattern of mitochondrial proteins that result in distinct metabolic features of their mitochondria. For instance, we observed a generally reduced respiration rate associated with lower protein levels of respiratory chain components, particularly complex I, and increased capacity to utilize fatty acids. Interestingly, we show that the same molecular networks are affected during aging in both NMR and humans, supporting a direct link to the extraordinary longevity of both species. Finally, we identified a novel longevity pathway and validated it experimentally in the nematode C. elegans.

systems biology↗

bioRxiv2017

Comparative omics analyses differentiate Mycobacterium tuberculosis and Mycobacterium bovis and reveal distinct macrophage responses to infection with the human and bovine tubercle bacilli

Members of the Mycobacterium tuberculosis complex (MTBC) are the causative agents of tuberculosis in a range of mammals, including humans. A key feature of MTBC pathogens is their high degree of genetic identity, yet distinct host tropism. Notably, while Mycobacterium bovis is highly virulent and pathogenic for cattle, the human pathogen M. tuberculosis is attenuated in cattle. Previous research also suggests that host preference amongst MTBC members has a basis in host innate immune responses. To explore MTBC host tropism, we present in-depth profiling of the MTBC reference strains M. bovis AF2122/97 and M. tuberculosis H37Rv at both the global transcriptional and translational level via RNA-sequencing and SWATH mass spectrometry. Furthermore, a bovine alveolar macrophage infection time course model was employed to investigate the shared and divergent host transcriptomic response to infection with M. tuberculosis or M. bovis. Significant differential expression of virulence-associated pathways between the two bacilli was revealed, including the ESX-1 secretion system. A divergent transcriptional response was observed between M. tuberculosis and M. bovis infection of bovine alveolar macrophages, in particular cytosolic DNA-sensing pathways at 48 hours post-infection, and highlights a distinct engagement of M. bovis with the bovine innate immune system. The work presented here therefore provides a basis for the identification of host innate immune mechanisms subverted by virulent host-adapted mycobacteria to promote their survival during the early stages of infection.\n\nImportanceThe Mycobacterium tuberculosis complex (MTBC) includes the most important global pathogens for humans and animals, namely Mycobacterium tuberculosis and Mycobacterium bovis, respectively. These two exemplar mycobacterial pathogens share a high degree of genetic identity, but the molecular basis for their distinct host preference is unknown. In this work we integrated transcriptomic and proteomic analyses of the pathogens to elucidate global quantitative differences between them at the mRNA and protein level. We then integrated this data with transcriptome analysis of the bovine macrophage response to infection with either pathogen. Increased expression of the ESX-1 virulence system in M. bovis appeared a key driver of an increased cytosolic nucleic acid sensing and interferon response in bovine macrophages infected with M. bovis compared to M. tuberculosis. Our work demonstrates the specificity of host-pathogen interaction and how the subtle interplay between mycobacterial phenotype and host response may underpin host specificity amongst MTBC members.

systems biology↗

bioRxiv2017

Naked mole-rat transcriptome signatures of socially-suppressed sexual maturation and links of reproduction to aging

Naked mole-rats (NMRs) are eusocially organized in colonies. Although breeders carry the additional metabolic load of reproduction, they are extremely long-lived and remain fertile throughout their lifespan. Comparative transcriptome analysis of ten organs from breeders and non-breeders of the eusocial long-lived NMR and the polygynous shorter-lived guinea pig provide comprehensive and unbiased molecular evidence that sexual maturation in NMR is socially suppressed. After transition into breeders, transcriptomes are markedly sex-specific, show pronounced feedback signaling via gonadal steroids and have similarities to reproductive phenotypes in African cichlid fish. Further, NMRs show functional enrichment of status-related expression differences associated with aging. Lipid metabolism and oxidative phosphorylation - molecular networks known to be linked to aging - were identified among most affected gene sets. Further, a transcriptome pattern associated with longevity is reinforced in NMR breeders contradicting the disposable soma theory of aging and potentially contributing to their exceptional long life- and healthspan.

systems biology↗

bioRxiv2017

Integrating co-expression networks with GWAS detects genes driving elemental accumulation in maize seeds

BackgroundGenome wide association studies (GWAS) have identified thousands of loci linked to hundreds of traits in many different species. However, because linkage equilibrium implicates a broad region surrounding each identified locus, the causal genes often remain unknown. This problem is especially pronounced in non-human, non-model species where functional annotations are sparse and there is frequently little information available for prioritizing candidate genes.\n\nResultsTo address this issue, we developed a computational approach called Camoco (Co-Analysis of Molecular Components) that systematically integrates loci identified by GWAS with gene co-expression networks to prioritize putative causal genes. We applied Camoco to prioritize candidate genes from a large-scale GWAS examining the accumulation of 17 different elements in maize seeds. Camoco identified statistically significant subnetworks for the majority of traits examined, producing a prioritized list of high-confidence causal genes for several agronomically important maize traits. Two candidate genes identified by our approach were validated through analysis of mutant phenotypes. Strikingly, we observed a strong dependence in the performance of our approach on the type of co-expression network used: expression variation across genetically diverse individuals in a relevant tissue context (in our case, maize roots) outperformed other alternatives.\n\nConclusionsOur study demonstrates that co-expression networks can provide a powerful basis for prioritizing candidate causal genes from GWAS loci, but suggests that the success of such strategies can highly depend on the gene expression data context. Both the Camoco software and the lessons on integrating GWAS data with co-expression networks generalize to species beyond maize.

systems biology↗

bioRxiv2017

Single-cell quantitative analysis of skeletal muscle cell population dynamicsduring regeneration and ageing

The skeletal muscle is populated by a variety of different mononuclear cell types that actively contribute to tissue homeostasis. When the tissue is stressed by exercise or by an acute or chronic insult, the different cell types are activated, exchange signals and initiate a finely-orchestrated regeneration process to prevent the loss of muscle mass. This cell variety is exacerbated by an additional intra population heterogeneity, where the cell population boundaries often lose their significance. Here, we applied a high-dimensional single-cell mass cytometry analysis to solve the cellular and molecular complexity of the muscle tissue in different physiological and pathological conditions. Taken together, our results provide a comprehensive picture both of muscle cell population homeostasis during ageing and of the changes induced by a perturbation of the system, be it chronic (dystrophy) or acute (cardiotoxin).

systems biology↗

bioRxiv2017

deepNF: Deep network fusion for protein function prediction

The prevalence of high-throughput experimental methods has resulted in an abundance of large-scale molecular and functional interaction networks. The connectivity of these networks provide a rich source of information for inferring functional annotations for genes and proteins. An important challenge has been to develop methods for combining these heterogeneous networks to extract useful protein feature representations for function prediction. Most of the existing approaches for network integration use shallow models that cannot capture complex and highly-nonlinear network structures. Thus, we propose deepNF, a network fusion method based on Multimodal Deep Autoencoders to extract high-level features of proteins from multiple heterogeneous interaction networks. We apply this method to combine STRING networks to construct a common low-dimensional representation containing high-level protein features. We use separate layers for different network types in the early stages of the multimodal autoencoder, later connecting all the layers into a single bottleneck layer from which we extract features to predict protein function. We compare the cross-validation and temporal holdout predictive performance of our method with state-of-the-art methods, including the recently proposed method Mashup. Our results show that our method outperforms previous methods for both human and yeast STRING networks. We also show substantial improvement in the performance of our method in predicting GO terms of varying type and specificity.\n\nAvailabilitydeepNF is freely available at: https://github.com/VGligorijevic/deepNF

systems biology↗

bioRxiv2017

Quantifying relationships between chromosome organization and chromosomal aberrations

The question of to what extent large scale structure of interphase chromosomes contributes to chromosomal exchange aberrations is discussed for a long time but still remains unclear. We designed the polymer model of 3D organization of a mouse chromosome and simulated X-ray induced chromosomal aberrations exploring two alternative hypotheses: the probability of contacted damaged loci entailing a chromosomal rearrangement is (a) a constant value or (b) not constant, depending on the distribution of DNAsel-hypersensitivity peaks along the chromosome. The latter hypothesis proved to explain the experimental data better than the former, meaning that not only large-scale structure but also local chromatin alterations play a role.

systems biology↗

bioRxiv2017

Variance reduction for antithetic integral control of stochastic reaction networks

The antithetic integral feedback motif recently introduced in [6] is known to ensure robust perfect adaptation for the mean dynamics of a given molecular species involved in a complex stochastic biomolecular reaction network. However, it was observed that it also leads to a higher variance in the controlled network than that obtained when using a constitutive (i.e. open-loop) control strategy. This was interpreted as the cost of the adaptation property and may be viewed as a performance deterioration for the overall controlled network. To decrease this variance and improve the performance, we propose to combine the antithetic integral feedback motif with a negative feedback strategy. Both theoretical and numerical results are obtained. The theoretical ones are based on a tailored moment closure method allowing one to obtain approximate expressions for the stationary variance for the controlled network and predict that the variance can indeed be decreased by increasing the strength of the negative feedback. Numerical results verify the accuracy of this approximation and show that the controlled species variance can indeed be decreased, sometimes below its constitutive level. Three molecular networks are considered in order to verify the wide applicability of two types of negative feedback strategies. The main conclusion is that there is a trade-off between the speed of the settling-time of the mean trajectories and the stationary variance of the controlled species; i.e. smaller variance is associated with larger settling-time.\n\nAuthor summaryHomeostasis, the ability of living organisms to regulate their internal state, is of fundamental importance for their adaptation to environmental changes and their survival. This is the reason why complex regulatory genetic networks evolved and allowed for the emergence of more and more complex organisms. Recently, the theoretical study of those regulatory networks using ideas and concepts from control theory and the design of novel ones have gained a lot of attention. Synthetic regulatory circuits are seen as elementary building blocks for the design of complex networks that need to incorporate some regulating elements to be fully functional. This is for instance the case in metabolic engineering where the production of biomolecules, such as drugs or biofuels, need to be optimized and tightly regulated. A particular circuit, the so-called antithetic integral controller, is now known to ensure homeostasis even when regulatory circuits are subject to randomness. However, it is also known that this circuit increases variability in the network. The effects of a correcting negative feedback loop on the variance are discussed here and it is shown that variability can be reduced this way. Notably, we show that there is a tradeoff between speed of the network and variability.

systems biology↗

bioRxiv2017

Enumerating all possible biosynthetic pathways from metabolic networks

Exhaustive identification of all alternate possible pathways that exist within metabolic networks can provide valuable insights into cellular metabolism. With the growing number of metabolic reconstructions, there is a need for an efficient method to enumerate pathways, which can also scale well to large metabolic networks, such as those corresponding to microbial communities.\n\nWe developed MetQuest, an efficient graph-theoretic algorithm to enumerate all possible pathways of a particular length between a given set of source and target molecules. Our algorithm employs a guided breadth-first search to identify all feasible reactions based on the availability of the precursor molecules, followed by a novel dynamic-programming based enumeration, which assembles these reactions into pathways producing the target from the source. We demonstrate several interesting applications of our algorithm, ranging from predicting amino acid biosynthesis pathways to identifying the most diverse pathways involved in degradation of complex molecules. We also illustrate the scalability of our algorithm, by studying larger graphs such as those corresponding to microbial communities, and identify several metabolic interactions happening therein.\n\nA Python-based implementation of MetQuest is available at https://github.com/RamanLab/MetQuest

systems biology↗

bioRxiv2017

Catalytic potential and disturbance rejection of glycolytic kinases in the human red blood cell

The allosteric regulation of metabolic enzymes plays a key role in controlling the flux through metabolic pathways. The activity of such enzymes is traditionally described by allosteric rate laws in complex kinetic models of metabolic network function. As an alternative, we describe the fraction of the regulated enzyme that is in an active form by developing a detailed reaction network of all known ligand binding events to the enzyme. This fraction is the fundamental result of metabolic regulation as it represents the \"tug of war\" among the various regulators and substrates that determine the utilization of the enzyme. The active fraction corresponds to the utilization of the catalytic potential of the enzyme. Using well developed kinetic models of human red blood cell (RBC) glycolysis, we characterize the catalytic potential of its three key kinases: hexokinase (HEX), phosphofructokinase (PFK), and pyruvate kinase (PYK). We then compute their time-dependent interacting catalytic potentials. We show how detailed kinetic models of the management of the catalytic potential of the three kinases elucidates disturbance rejection capabilities of glycolysis. Further, we examine the sensitivity of the catalytic potential through an examination of existing personalized RBC models, providing a physiologically-meaningful sampling of the feasible parameter space. The graphical representation of the dynamic interactions of the individual kinase catalytic potential adjustment provides an easy way to understand how a robust homeostatic state is maintained through interacting allosteric regulatory mechanisms.

systems biology↗

bioRxiv2017

Deciphering microbial interactions in synthetic human gut microbiome communities

The human gut microbiota comprises a dynamic ecological system that contributes significantly to human health and disease. The ecological forces that govern community assembly and stability in the gut microbiota remain unresolved. We developed a generalizable model-guided framework to predict higher-order consortia from time-resolved measurements of lower-order assemblages. This method was employed to decipher microbial interactions in a diverse 12-member human gut microbiome synthetic community. We show that microbial growth parameters and pairwise interactions are the major drivers of multi-species community dynamics, as opposed to context-dependent (conditional) interactions. The inferred microbial interaction network as well as a top-down approach to community assembly pinpointed both ecological driver and responsive species that were significantly modulated by microbial inter-relationships. Our model demonstrated that negative pairwise interactions could generate history-dependent responses of initial species proportions on physiological timescales that frequently does not originate from bistability. The model elucidated a topology for robust coexistence in pairwise assemblages consisting of a negative feedback loop that balances disparities in monospecies fitness levels. Bayesian statistical methods were used to evaluate the constraint of model parameters by the experimental data. Measurements of extracellular metabolites illuminated the metabolic capabilities of monospecies and potential molecular basis for competitive and cooperative interactions in the community. However, these data failed to predict influential organisms shaping community assembly. In sum, these methods defined the ecological roles of key species shaping community assembly and illuminated network design principles of microbial communities.

systems biology↗

bioRxiv2017

Estimating global enzyme abundance levels from cofactor requirements: a model-based analysis of the iron metabolism in yeast

Metabolic networks adapt to changes in their environment by modulating the activity of their enzymes and transporters; often by changing their abundance. Understanding such quantitative changes can shed light onto how metabolic adaptation works, or how it can fail and lead to a metabolically dysfunctional state. Unfortunately such data are only available on a fraction of the enzyme and transporter pools, and this leaves us distant from generating the full picture. We here propose a strategy to quantify metabolic protein requirements for cofactor-bearing enzymes and transporters through constraint-based modelling. In this work, we constructed the first eukaryotic genome-scale metabolic model with a comprehensive iron metabolism and investigated partial functional impairment of the genes involved in iron-sulphur (Fe-S) cluster maturation, which revealed extensive rewiring of the fluxes in silico, despite only marginal phenotypic dissimilarities. The yeast metabolism at steady state was determined to employ a constant iron-recruiting enzyme turnover at a rate of 3.02 x 10-11 mM / (g biomass)-1 h-1. Furthermore, we employed this model to identify and study a mutualistic relationship between Streptomyces coelicolor and Saccharomyces cerevisiae, which involves iron and folate sharing economies between the two microbes.

systems biology↗

bioRxiv2017

Personalized modeling of the human gut microbiome reveals distinct bile acid deconjugation and biotransformation potential in healthy and IBD individuals

The human gut microbiome performs important functions human health and disease. Intestinal microbes are capable of deconjugation and biotransformation of human primary bile acids to secondary bile acids. Alterations of the bile acid pool as a result of microbial dysbiosis have been linked to multifactorial diseases, such as inflammatory bowel disease (IBD).\n\nConstraint-based modeling is a powerful approach for the mechanistic, systems-level analysis of metabolic interactions in microbial communities. Recently, we constructed a resource of 773 curated genome-scale reconstructions of human gut microbes, AGORA. Here, we performed a comparative genomic analysis of bile acid deconjugation and biotransformation pathways in 693 human gut microbial genomes to expand these AGORA reconstructions accordingly (available at http://vmh.life).\n\nTo elucidate the metabolic potential of individual microbiomes, publicly available metagenomic data from a cohort of healthy Western individuals, as well as two cohorts of IBD patients and healthy controls, were mapped onto the reference set of AGORA genomes. We constructed for each individual a large-scale personalized microbial community model that take strain-level abundances into account. Using flux balance analysis, we found that distinct potential to deconjugate and tranform primary bile acids between the gut microbiomes of healthy individuals. Moreover, the microbiomes of pediatric IBD patients were significantly depleted in their bile acid production potential compared with controls. The contributions of each strain to overall bile acid production potential across individuals were found to be distinct between IBD patients and controls. IBD microbiomes were depleted in contributions of Bacteroidetes strains but enriched in contributions of Proteobacteria. Finally, bottlenecks limiting secondary bile acid production potential were identified in each microbiome model. For ursodeoxycholate, the abundance of strains producing the precursor rather than of strains directly producing this secondary bile acid was synthesis-limiting in certain microbiomes.\n\nIn summary, we integrated for the first-time metagenomics data with large-scale personalized metabolic modeling of microbial communities. We provided mechanistic insight into the link between dysbiosis and metabolic potential in IBD microbiomes. This large-scale modeling approach provides a novel way of analyzing metagenomics data to accelerate our understanding of the metabolic interactions between human host and gut microbiomes in health and diseases states.

systems biology↗

bioRxiv2017

Modeling and simulating networks of interdependent protein interactions

Protein interactions are fundamental building blocks of biochemical reaction systems underlying cellular functions. The complexity and functionality of these systems emerge not only from the protein interactions themselves but also from the dependencies between these interactions, e.g., allosteric effects, mutual exclusion or steric hindrance. Therefore, formal models for integrating and using information about such dependencies are of high interest. We present an approach for endowing protein networks with interaction dependencies using propositional logic, thereby obtaining constrained protein interaction networks (\"constrained networks\"). The construction of these networks is based on public interaction databases and known as well as text-mined interaction dependencies. We present an efficient data structure and algorithm to simulate protein complex formation in constrained networks. The efficiency of the model allows a fast simulation and enables the analysis of many proteins in large networks. Therefore, we are able to simulate perturbation effects (knockout and overexpression of single or multiple proteins, changes of protein concentrations). We illustrate how our model can be used to analyze a partially constrained human adhesome network. Comparing complex formation under known dependencies against without dependencies, we find that interaction dependencies limit the resulting complex sizes. Further we demonstrate that our model enables us to investigate how the interplay of network topology and interaction dependencies influences the propagation of perturbation effects. Our simulation software CPINSim (for Constrained Protein Interaction Network Simulator) is available under the MIT license at http://github.com/BiancaStoecker/cpinsimandviaBioconda (https://bioconda.github.io).\n\nAuthor summaryProteins are the main molecular tools of cells. They do not act individually, but rather collectively in order to peform complex cellular actions. Recent years have led to a relatively good understanding about which proteins may interact, both in general and in specific conditions, leading to the definition of protein interaction networks. However, the reality is more complex, and protein interactions are not independent of each other. Instead, several potential interaction partners of a specific protein may compete for the same binding domain, making all of these interactions mutually exclusive. Additionally, a binding of a protein to another one can enable or prevent their interactions with other proteins, even if those interactions are mediated by different domains. Hence, understanding how the dependencies (or constraints) of protein interactions affect the behaviour of the system is an important and timely goal, as data is now becoming available. Here we present a mathematical framework to formalize such interaction constraints and incorporate them into the simulation of protein complex formation. With our framework, we are able to better understand how perturbations of single proteins (knockout or overexpression) impact other proteins in the network.

systems biology↗

bioRxiv2017

Metabolic and Hormonal Alterations Induced by Roux-en-Y Gastric Bypass

Roux-en-Y gastric bypass (RYGB) is an effective way to lose weight and reverse type 2 diabetes. We profiled the metabolome of 18 obese patients (nine euglycemic and nine diabetics) that underwent RYGB surgery and seven lean subjects. Plasma samples from the obese patients were collected before the surgery and one week and three months after the surgery. We analyzed the metabolome in association to five hormones (Adiponectin, Insulin, Ghrelin, Leptin, and Resistin), four peptide hormones (GIP, Glucagon, GLP1, and PYY), and two cytokines (IL-6 and TNF). PCA showed samples cluster by surgery time and many microbially driven metabolites (indoles in particular) correlated with the three months after the surgery. Network analysis of metabolites revealed a connection between carbohydrate (mannosamine and glucosamine) and glyoxylate and confirms glyoxylate association to diabetes. Only leptin and IL-6 had a significant association with the measured metabolites. Leptin decreased immediately after RYGB (before significant weight loss), whereas IL-6 showed no consistent response to RYGB. Moreover, leptin associated with tryptophan in support of the possible role of leptin in the regulation of serotonin synthesis pathways in the gut. These results suggest a potential link between gastric leptin and microbial-derived metabolites in the context of obesity and diabetes.

systems biology↗

bioRxiv2017

Cell Lysate Microarray for Mapping the Network of Genetic Regulators for Histone Marks

Protein, as the major executer for cell progresses and functions, its abundance and the level of post-translational modifications, are tightly monitored by regulators. Genetic perturbation could help us to understand the relationships between genes and protein functions. Herein, we developed a cell lysate microarray on kilo-conditions (CLICK) from 4,837 yeast knockout (YKO) strains and 322 temperature-sensitive mutant strains to explore the impact of the genome-wide interruption on certain protein. Taking histone marks as examples, a general workflow was established for the global identification of upstream regulators. Through a single CLICK array test, we obtained a series of regulators for H3K4me3 which covers most of the known regulators in Saccharomyces cerevisiae. We also noted that several group of proteins that are linked to negatively regulation of H3K4me3. Further, we discovered that Cab4p and Cab5p, two key enzymes of CoA biosynthesis, play central roles in histone acylation. Because of its general applicability, CLICK array could be easily adopted to rapid and global identification of upstream protein/enzyme(s) that regulate/modify the level of a protein or the posttranslational modification of a non-histone protein.

systems biology↗