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bioRxiv2018

Sample Preparation by Easy Extraction and Digestion (SPEED) - A Universal, Rapid, and Detergent-free Protocol for Proteomics based on Acid Extraction

Mass spectrometry is the method of choice for deep and comprehensive analysis of proteomes and has become a key technology to support the progress in life science and biomedicine. However, sample preparation in proteomics is not standardized and contributes to a lack of reproducibility. The main challenge is to extract all proteins in a manner that enables efficient digestion into peptides and is compatible with subsequent mass spectrometric analysis. Current methods are based on the idea of removing detergents or chaotropic agents during sample processing, which are essential for protein extraction but interfere with digestion and LC-MS. These multi-step preparations are prone to losses, biases and contaminations, while being time-consuming and labor-intensive.\n\nWe report a universal detergent-free method, named Sample Preparation by Easy Extraction and Digestion (SPEED), which is based on a simple three-step procedure, acidification, neutralization and digestion. SPEED is a one-pot method for peptide generation from various sources and is easily applicable even for lysis-resistant sample types as pure trifluoroacetic acid (TFA) is used for highly efficient protein extraction. SPEED-based sample processing is highly reproducible, provides exceptional peptide yields and enables preparation even of tissue samples with less than 15 min hands-on time and without any special equipment. Evaluation of SPEED performance revealed, that the number of quantified proteins and the quantitative reproducibility are superior compared to the well-established sample processing protocols FASP, ISD-Urea and SP3 for various sample types, including human cells, bacteria and tissue, even at low protein starting amounts.

systems biology↗

bioRxiv2018

Glycine promotes longevity in Caenorhabditis elegans in a methionine cycle-dependent fashion

The deregulation of metabolism is a hallmark of aging. As such, changes in the expression of metabolic genes and the profiles of amino acid levels are features associated with aging animals. We previously reported that the levels of most amino acids decline with age in Caenorhabditis elegans (C. elegans). Glycine, in contrast, substantially accumulates in aging C. elegans. In this study we show that this is coupled to a decrease in gene expression of enzymes important for glycine catabolism. We further show that supplementation of glycine significantly prolongs C. elegans lifespan and ameliorates specific transcriptional changes that are associated with aging. Glycine feeds into the methionine cycle. We find that mutations in components of this cycle, methionine synthase (metr-1) and S-adenosylmethionine synthetase (sams-1), completely abrogate glycine-induced lifespan extension. Strikingly, the beneficial effects of glycine supplementation are conserved when we supplement with serine, also driving the methionine cycle. RNA sequencing of serine- and glycine-supplemented worms reveals similar transcriptional profiles including widespread gene suppression. Taken together, these data uncover a novel role of glycine in the deceleration of aging through its function in the methionine cycle.\n\nAuthor summaryThere are a growing number of studies showing that amino acids function as signal metabolites that influence aging and health. Although contemporary -OMICs studies have uncovered various associations between metabolite levels and aging, in many cases the directionality of the relationships is unclear. In a recent metabolomics study, we found that glycine accumulates in aged C. elegans while other amino acids decrease. The present study shows that glycine supplementation prolongs longevity and drives a genome-wide inhibition effect on C. elegans gene expression. Glycine as a one-carbon donor fuels the methyl pool of one-carbon metabolism composed of folates and methionine cycle. We find that glycine-mediated longevity effect is fully dependent on methionine cycle, and that all of these observations are conserved with supplementation of the other one-carbon amino acid, serine. These results provide a novel role for glycine as a promoter of longevity and bring new insight into the role of one-carbon amino acids in the regulation of aging that may ultimately be beneficial for humans.

systems biology↗

bioRxiv2018

Adaptive evolution of feed-forward loops versus diamonds to filter out short spurious signals

We develop a null model of the evolution of transcriptional regulatory networks, and use it to support an adaptive origin for a canonical "motif", a 3-node feed-forward loop (FFL) hypothesized to filter out short spurious signals by integrating information from a fast and a slow pathway. Our mutational model captures the intrinsically high prevalence of weak affinity transcription factor binding sites. We also capture stochasticity and delays in gene expression that distort external signals and intrinsically generate noise. Functional FFLs evolve readily under selection for the hypothesized function, but not in negative controls. Interestingly, a 4-node "diamond" motif also emerged as a short spurious signal filter. The diamond uses expression dynamics rather than path length to provide fast and slow pathways. When there is no external spurious signal to filter out, but only internally generated noise, only the diamond and not the FFL evolves.

systems biology↗

bioRxiv2018

Minor isozymes tailor yeast metabolism to carbon availability

Isozymes are enzymes that differ in sequence but catalyze the same chemical reactions. Despite their apparent redundancy, isozymes are often retained over evolutionary time for reasons that can be unclear. We find that, in yeast, isozymes are strongly enriched in central carbon metabolism. Using a gene expression compendium, we find that many isozyme pairs show anticorrelated expression during the respirofermentative shift, suggesting roles in adapting to changing carbon availability. Building on this observation, we assign function to two minor central carbon isozymes, aconitase 2 (ACO2) and pyruvate kinase 2 (PYK2). ACO2 is expressed during fermentation and proves advantageous when glucose is limiting. PYK2 is expressed during respiration and proves advantageous for growth on three-carbon substrates. PYK2s deletion is rescued by expressing the major pyruvate kinase, but only if that enzyme carries mutations mirroring PYK2s allosteric regulation. Thus, central carbon isozymes enable more precise tailoring of metabolism to changing nutrient availability.\n\nImportanceGene duplication is one of the main evolutionary drivers of new protein function. However, some gene duplicates have nevertheless persisted long-term without apparent divergence in biochemical function. Further, under standard lab conditions, many isozymes have subtle or no knockout phenotypes. These factors make it hard to assess the unique contributions of individual isozymes to fitness. We therefore developed a method to identify experimental perturbations that could expose such contributions, and applied it to yeast gene expression data, revealing a potential role for a set of yeast isozymes in adapting to changing carbon sources. Our experimental confirmation of distinct roles for two \"minor\" yeast isozymes, including one with no previously described knockout phenotype, highlight that even apparently redundant paralogs can have important and unique functions, with implications for genome-scale metabolic modeling and systems-level studies of quantitative genetics.

systems biology↗

bioRxiv2018

Global identification of functional microRNA::mRNA interactions in Drosophila

MicroRNAs (miRNAs) are key mediators of post-transcriptional gene expression silencing. Although Drosophila has been of critical importance for miRNA discovery, biogenesis and function, there has been no comprehensive experimental annotation of functional miRNA target sites. To close this gap, we generated the first in vivo map of miRNA::mRNA interactions in Drosophila melanogaster, making use of crosslinked nucleotides in Argonaute (AGO) crosslinking and immunoprecipitation (CLIP) experiments that enable an unambiguous assignment of miRNAs to AGO binding sites at much higher signal-to-noise ratio than computational predictions alone.\n\nAbsolute quantification of cellular miRNA levels showed the miRNA pool in Drosophila cell lines to be more diverse than previously reported. Benchmarking two different CLIP approaches, we identified a similar predictive potential to unambiguously assign thousands of miRNA::mRNA pairs from AGO1 interaction data at unprecedented depth. Quantitative RNA-Seq and subcodon-resolution ribosomal footprinting data upon AGO1 depletion enabled the determination of miRNA-mediated effects on target expression and translation. We thus provide the first comprehensive resource of miRNA target sites as well as their quantitative functional impact in Drosophila.

systems biology↗

bioRxiv2018

Modeling of CD16, 2B4 and NKG2D stimulation in natural killer cell activation

Natural killer (NK) cells are part of the innate immune system and are capable of killing diseased cells. As a result, NK cells are being used for adoptive cell therapies for cancer patients. The activation of NK cell stimulatory receptors leads to a cascade of intracellular phosphorylation reactions, which activates key signaling species that facilitate the secretion of cytolytic molecules required for cell killing. Strategies that maximize the activation of such intracellular species can increase the likelihood of NK cell activation upon contact with a cancer cell, and thereby improve efficacy of NK cell-based therapies. However, due to the complexity of intracellular signaling, it is difficult to deduce a priori which strategies can enhance species activation. Therefore, we constructed a mechanistic model of the CD16, 2B4 and NKG2D signaling pathways in NK cells to simulate strategies that enhance signaling. The model predictions were fit to published data and validated with a separate dataset. Model simulations demonstrate strong network activation when the CD16 pathway is stimulated. The magnitude of species activation is most sensitive to the receptor concentration and the rate at which the receptor is deactivated. Co-stimulation of CD16 and NKG2D in silico required fewer ligands to achieve half-maximal activation than other combinations, suggesting co-stimulating these pathways is most effective in activating the species. We applied the model to predict the effects of perturbing the signaling network and found two strategies that can potently enhance network activation. When the availability of ligands is low, it is more influential to engineer NK cell receptors that are resistant to proteolytic cleavage. In contrast, for high ligand concentrations, inhibiting phosphatase activity leads to more activation. The work presented here establishes a framework for understanding the complex, nonlinear aspects of NK cell signaling and provides detailed strategies for enhancing NK cell activation.

systems biology↗

bioRxiv2018

Excitable dynamics through toxin-induced mRNA cleavage in bacteria

Toxin-antitoxin (TA) systems in bacteria and archaea are small genetic elements consisting of the genes coding for an intracellular toxin and an antitoxin that can neutralize this toxin. In various cases, the toxins cleave the mRNA. In this theoretical work we use deterministic and stochastic modeling to explain how toxin-induced cleavage of mRNA in TA systems can lead to excitability, allowing large transient spikes in toxin levels to be triggered. By using a simplified network where secondary complex formation and transcriptional regulation are not included, we show that a two-dimensional, deterministic model captures the origin of such toxin excitations. Moreover, it allows to increase our understanding by examining the dynamics in the phase plane. By systematically comparing the deterministic results with Gillespie simulations we demonstrate that even though the real TA system is intrinsically stochastic, toxin excitations can be accurately described deterministically. A bifurcation analysis of the system shows that the excitable behavior is due to a nearby Hopf bifurcation in the parameter space, where the system becomes oscillatory. The influence of stress is modeled by varying the degradation rate of the antitoxin and the translation rate of the toxin. We find that stress increases the frequency of toxin excitations and decreases the excitation time. The inclusion of secondary complex formation and transcriptional regulation does not fundamentally change the mechanism of toxin excitations. Therefore, the deterministic model used in this work provides a simple and intuitive explanation of toxin excitations in TA systems.\n\nAuthor summary\n\nThe genomes of most bacteria and archaea encode several toxin-antitoxin (TA) systems, small genetic elements consisting of the genes coding for an intracellular toxin and an antitoxin that can neutralize this toxin. For several toxins, the target is the mRNA. In this theoretical work we use deterministic and stochastic modeling to explain how toxin-induced cleavage of mRNA in TA systems can trigger large transient spikes in toxin levels. By using a simplified network where secondary complex formation and transcriptional regulation are not included, we show that a two-dimensional, deterministic model captures the origin of such toxin excitations. By systematically comparing our findings with more complex models, we find that the intrinsically stochastic TA system shows similar toxin excitations that are accurately predicted by the underlying simplified theory. This has allowed us to understand how the TA system responds in the presence of stress conditions, finding that toxin excitations become shorter and more frequent.

systems biology↗

bioRxiv2018

Computations performed by shadow enhancers and enhancer duplications vary across the Drosophila embryo

Transcription of developmental genes is controlled by multiple enhancers. Frequently, more than one enhancer can activate transcription from the same promoter in the same cells. In these cases, how is regulatory information from multiple enhancers combined to determine the overall expression output of their shared promoter? To investigate this question, we quantified nascent transcription driven by a pair shadow enhancers, each individual of the pair, and their duplications in Drosophila embryos using live imaging. This set of constructs allows us to quantify the \"computation\" made by the pairs of enhancers: their combined output expression as a function of the expression that they drive separately. We show that the computation performed by these shadow enhancers and duplications varies across the expression pattern, implying that how their activities are combined depends on the transcriptional regulators bound to the enhancers in different parts of the embryo. Characterizing the computation made by multiple enhancers is a critical first step in developing conceptual and computational models of gene expression at the locus level, where multiple enhancers collaborate.

systems biology↗

bioRxiv2018

How to design an optimal sensor network for the unfolded protein response

Cellular protein homeostasis requires continuous monitoring of stress in the endoplasmic reticulum (ER). Stress detection networks control protein homeostasis by mitigating the deleterious effects of protein accumulation, such as aggregation and misfolding, with precise modulation of chaperone production. Here, we develop a coarse model of the unfolded protein response in yeast, and use multi-objective optimization to determine which sensing and activation strategies optimally balance the trade-off between unfolded protein accumulation and chaperone production. By comparing a stress-sensing mechanism that responds directly to the level of unfolded protein in the ER to a mechanism that is negatively regulated by unbound chaperones, we show that chaperone-mediated sensors are more efficient than sensors that detect unfolded proteins directly. This results from the chaperone-mediated sensor having separate thresholds for activation and deactivation. Lastly, we demonstrate that a sensor responsive to both unfolded protein and unbound chaperone does not further optimize homeostatic control. Our results suggest a strategy for designing stress sensors and may explain why BiP-mitigated ER stress sensing networks have evolved.

systems biology↗

bioRxiv2018

Systems therapeutics analyses identify genomic signatures defining responsiveness to allopurinol and combination therapy for lung cancer

The ability to predict responsiveness to drugs in individual patients is limited. We hypothesized that integrating molecular information from databases would yield predictions that could be experimentally tested to develop genomic signatures for sensitivity or resistance to specific drugs. We analyzed TCGA data for lung adenocarcinoma (LUAD) patients and identified a subset where xanthine dehydrogenase expression correlated with decreased survival. We tested allopurinol, a FDA approved drug that inhibits xanthine dehydrogenase on a library of human Non Small Cell Lung Cancer (NSCLC) cell lines from CCLE and identified sensitive and resistant cell lines. We utilized the gene expression profiles of these cell lines to identify six-gene signatures for allopurinol sensitive and resistant cell lines. Network building and analyses identified JAK2 as an additional target in allopurinol-resistant lines. Treatment of resistant cell lines with allopurinol and CEP-33779 (a JAK2 inhibitor) resulted in cell death. The effectiveness of allopurinol alone or allopurinol and CEP-33779 were verified in vivo using tumor formation in NCR-nude mice. We utilized the six-gene signatures to predict five additional allopurinolsensitive NSCLC lines, and four allopurinol-resistant lines susceptible to combination therapy. We found that drug treatment of all cell lines yielded responses as predicted by the genomic signatures. We searched the library of patient derived NSCLC tumors from Jackson Laboratory to identify tumors that would be predicted to be sensitive or resistant to allopurinol treatment. Both patient derived tumors predicted to be allopurinol sensitive showed the predicted sensitivity, and the predicted resistant tumors were sensitive to combination therapy. These data indicate that we can use integrated molecular information from cancer databases to predict drug responsiveness in individual patients and thus enable precision medicine.

systems biology↗

bioRxiv2018

Understanding the killing mechanism of action by virus-infected yeasts

Killer yeasts are microorganisms, which can produce and secrete proteinaceous toxins, a characteristic gained via infection by a virus. These toxins are able to kill sensitive cells of the same or a related species. From a biotechnological perspective, killer yeasts have been considered as beneficial due to their antifungal/antimicrobial activity, but also regarded as problematic for large-scale fermentation processes, whereby those yeasts would kill species off starter cultures and lead to stuck fermentations. Here, we propose a mechanistic model of the toxin-binding kinetics pertaining to the killer population coupled with the toxin-induced death kinetics of the sensitive population to study toxic action in silico. Our deterministic model explains how killer Saccharomyces cerevisiae cells distress and consequently kill the sensitive members of the species, accounting for the K1, K2 and K28 toxin mode of action at high or low concentrations. The dynamic model captured the transient toxic activity starting from the introduction of killer cells into the culture at the time of inoculation through to induced cell death, and allowed us to gain novel insight on these mechanisms. The kinetics of K1/K2 activity via its primary pathway of toxicity was 5.5 times faster than its activity at low concentration inducing the apoptotic pathway in sensitive cells. Conversely, we showed that the primary pathway for K28 was approximately 3 times slower than its equivalent apoptotic pathway, indicating the particular relevance of K28 in biotechnological applications where the toxin concentration is rarely above those limits to trigger the primary pathway of killer activity.

systems biology↗

bioRxiv2018

Agent-Based Modeling Predicts HDL-independent Pathway of Removal of Excess Surface Lipids from Very Low Density Lipoprotein

A hallmark of the metabolic syndrome is low HDL-cholesterol coupled with high plasma triglycerides (TG), but it is unclear what drives this close association. Plasma triglycerides and HDL cholesterol are thought to communicate through two distinct mechanisms. Firstly, excess surface lipids from VLDL released during lipolysis are transferred to HDL, thereby contributing to HDL directly but also indirectly through providing substrate for LCAT. Secondly, high plasma TG increases clearance of HDL through core-lipid exchange between VLDL and HDL via CETP and subsequent hydrolysis of the TG in HDL, resulting in smaller HDL and thus increased clearance rates.\n\nTo test our understanding of how high plasma TG induces low HDL-cholesterol, making use of established knowledge, we developed a comprehensive agent-based model of lipoprotein metabolism which was validated using monogenic disorders of lipoprotein metabolism.\n\nBy perturbing plasma TG in the model, we tested whether the current theoretical framework reproduces experimental findings. Interestingly, while increasing plasma TG through simulating decreased lipolysis of VLDL resulted in the expected decrease in HDL cholesterol, perturbing plasma TG through simulating increased VLDL production rates did not result in the expected HDL-TG relation at physiological lipid fluxes. However, model perturbations and experimental findings can be reconciled if we assume a pathway removing excess surface-lipid from VLDL that does not contribute to HDL cholesterol ester production through LCAT. In conclusion, our model simulations suggest that excess surface lipid from VLDL is cleared in part independently from HDL.\n\nAuthor summaryWhile it has long been known that high plasma triglycerides are associated with low HDL cholesterol, the reason for this association has remained unclear. One of the proposed mechanisms is that during catabolism of VLDL, lipoproteins rich in triglyceride, the excess surface of these particles become a source for the production of HDL cholesterol, and that therefore decreased catabolism of VLDL will lead to both higher plasma triglyceride and low HDL cholesterol. Another proposed mechanism is that during increased production of VLDL, there will be increased exchange of core lipids between VLDL and HDL, with subsequent hydrolysis of the triglyceride in HDL, leading to smaller HDL that is cleared more rapidly. To investigate these mechanisms further we developed a computational model based on established knowledge concerning lipoprotein metabolism and validated the model with known findings in monogenetic disorders. Upon perturbing the plasma triglycerides within the model by increasing the VLDL production rate, we unexpectedly found an increase in both triglyceride and HDL cholesterol. However, upon assuming that less excess surface lipid is available to HDL, HDL decreases in response to increased VLDL production. We therefore propose that there must be a pathway removing excess surface lipids that is independent from HDL.\n\nAbbreviations

systems biology↗

bioRxiv2018

Infection dynamics in L. major autophagic machinery is regulated by ATG9-PI3P negative feedback loop

Autophagy is a self-destructing mechanism of cell via lysosomal degradation, which helps to degrade/ destroy hazardous substances, proteins, degenerating organelles and recycling nutrient. It plays an important role in cellular homeostasis and regulates internal environment of cell, moreover, when needed causes non-apoptotic programmed death of cell. Its usually detailed with ageing and nutritional stress, but recent researches provide support for its role in innate as well as adaptive immunity. Autophagy has been observed as one of the major factors in parasite clearance in leishmaniasis. Due to intra-cellular pathogen, the cell mediated response is only alternative for adaptive immunity against Leishmania in host. T-cells have been observed as main component of cell mediated immunity in leishmaniasis. The differentiation of T-cells generates either destruction or proliferation of parasite as different kind of cytokine mediate different results. Pro-inflammatory cytokine IL12 and TNF generate Th2 response which helps in active phagocytosis of parasite whereas an anti-inflammatory cytokine like IL10 mediate parasite promotion by blocking autophagic pathways and inhibiting phagocytic actions. TLR2/6 mediated signaling stimulated by LPG produces many pro-inflammatory cytokines like IL12, TNF and IL6 etc. In macrophages, it is found that TNF via autocrine signaling induce autophagy and help in parasite killing of Toxoplasma gondii via JNK phosphorylation in macrophages. The phosphorylated JNK induces Beclin by phosphorylating Bcl2, which inhibits Beclin action, thus releasing Beclin and promoting VpS34 which recruits PI3P for autophagosome formation. Recruitment of PI3P is controlled by ATG9 protein, autophagy related gene protein, which in normal condition is inhibited by mTOR via ULK/ ATG1. IL10 has been observed to inhibit autophagy via starvation induced AKT-PI3K pathway in macrophages and result in inducing mTOR. Thus, we can conclude that IL10 inhibits recruitment of PI3P via mTOR. Through systems aspect, here, we decipher that Atg9-PI3P acts as a negative feedback loop in autophagic machinery of leishmaniasis.

systems biology↗

bioRxiv2018

Loss of Trans Regulation in Breast Cancer Molecular Subtypes

Breast carcinomas are characterized by anomalous gene regulatory programs. As is well known, gene expression programs are able to shape phenotypes. Hence, the understanding of gene co-expression may shed light on the underlying mechanisms behind the transcriptional regulatory programs affecting tumor development and evolution. For instance, in breast cancer, there is a clear loss of inter-chromosomal (trans-) co-expression, compared with healthy tissue. At the same time cis- (intra-chromosomal) interactions are favored in breast tumors. In order to have a deeper understanding of regulatory phenomena in cancer, here, we constructed Gene Co-expression Networks by using 848 RNA-seq whole-genome samples corresponding to the four breast cancer molecular subtypes, as well as healthy tissue. We quantify the cis-/trans- co-expression imbalance in all phenotypes. Additionally, we measured the association between co-expression and physical distance between genes, and characterized the proportion of intra/inter-cytoband interactions per phenotype. We confirmed loss of trans- co-expression in all molecular subtypes. We also observed that gene cisco-expression decays abruptly with distance in all tumors in contrast with healthy tissue. We observed co-expressed gene hotspots, that tend to be connected at cytoband regions, and coincide accurately with already known copy number altered regions, such as Chr17q12, or Chr8q24.3 for all subtypes. Our methodology recovered different alterations already reported for specific breast cancer subtypes, showing how co-expression network approaches might help to capture distinct events that modify the cell regulatory program.

systems biology↗

bioRxiv2018

Automated sample preparation for high-throughput single-cell proteomics

A major limitation to applying quantitative LC-MS/MS proteomics to small samples, such as single cells, are the losses incured during sample cleanup. To relieve this limitation, we developed a Minimal ProteOmic sample Preparation (mPOP) method for culture-grown mammalian cells. mPOP obviates cleanup and thus eliminates cleanup-related losses while expediting sample preparation and simplifying its automation. Bulk SILAC samples processed by mPOP or by conventional urea-based methods indicated that mPOP results in complete cell lysis and accurate relative quantification. We integrated mPOP lysis with the Single Cell ProtEomics by Mass Spectrometry (SCoPE-MS) sample preparation, and benchmarked the quantification of such samples on a Q-exactive instrument. The results demonstrate low noise and high technical reproducibility. Then, we FACS sorted single U-937, HEK-293, and mouse ES cells into 96-well plates and analyzed them by automated mPOP and SCoPE-MS. The quantified proteins enabled separating the single cells by cell-type and cell-division-cycle phase.

systems biology↗

bioRxiv2018

Gene-Specific Predictability of Protein Levels from mRNA Data in Humans

Transcriptomic data are widely available, and the extent to which they are predictive of protein abundances remains debated. Using multiple public databases, we calculate mRNA and mRNA-to-protein ratio variability across human tissues to quantify and classify genes for protein abundance predictability confidence. We propose that such predictability is best understood as a spectrum. A gene-specific, tissue-independent mRNA-to-protein ratio plus mRNA levels explains [~]80% of protein abundance variance for more predictable genes, as compared to [~]55% for less predictable genes. Protein abundance predictability is consistent with independent mRNA and protein data from two disparate cell lines, and mRNA-to-protein ratios estimated from publicly-available databases have predictive power in these independent datasets. Genes with higher predictability are enriched for metabolic function, tissue development/cell differentiation roles, and transmembrane transporter activity. Genes with lower predictability are associated with cell adhesion, motility and organization, the immune system, and the cytoskeleton. Surprisingly, many genes that regulate mRNA-to-protein ratios are constitutively expressed but also exhibit ratio variability, suggesting a general autoregulation mechanism whereby protein expression profile changes can be implemented quickly, or homeostatic sensing stabilizes protein abundances under fluctuating conditions. Gene classifications and their mRNA-to-protein ratios are provided as a resource to facilitate protein abundance predictions by others.

systems biology↗

bioRxiv2018

Investigating the deregulation of metabolic tasks via Minimum Network Enrichment Analysis (MiNEA) as applied to nonalcoholic fatty liver disease using mouse and human omics data

Nonalcoholic fatty liver disease (NAFLD) is associated with metabolic syndromes spanning a wide spectrum of diseases, from simple steatosis to the more complex nonalcoholic steatohepatitis. To identify the deregulation that occurs in metabolic processes at the molecular level that give rise to these various NAFLD phenotypes, algorithms such as pathway enrichment analysis (PEA) can be used. These analyses require the use of predefined pathway maps, which are composed of reactions describing metabolic processes/subsystems. Unfortunately, the annotation of the metabolic subsystems can differ depending on the pathway database used, making these approaches subject to biases associated with different pathway annotations, and these methods cannot capture the balancing of cofactors and byproducts through the complex nature and interactions of genome-scale metabolic networks (GEMs). Here, we introduce a framework entitled Minimum Network Enrichment Analysis (MiNEA) that is applied to GEMs to generate all possible alternative minimal networks (MiNs), which are possible and feasible networks composed of all the reactions pertaining to various metabolic subsystems that can synthesize a target metabolite. We applied MiNEA to investigate deregulated MiNs and to identify key regulators in different NAFLD phenotypes, such as a fatty liver and liver inflammation, in both humans and mice by integrating condition-specific transcriptomics data from liver samples. We identified key deregulations in the synthesis of cholesteryl esters, cholesterol, and hexadecanoate in both humans and mice, and we found that key regulators of the hydrogen peroxide synthesis network were regulated differently in humans and mice. We further identified which MiNs demonstrate the general and specific characteristics of the different NAFLD phenotypes. MiNEA is applicable to any GEM and to any desired target metabolite, making MiNEA flexible enough to study condition-specific metabolism for any given disease or organism.\n\nAuthor SummaryThis work aims to introduce a network-based enrichment analysis using metabolic networks and transcriptomics data. Previous pathways/subsystems enrichment methods use predefined gene annotations of metabolic processes and gene annotations can differ based on different resources and can produce bias in pathways definitions. Thus, we introduce a framework, Minimum Network Enrichment Analysis (MiNEA), which first finds all possible minimal-size networks for a given metabolic process/task and then identifies deregulated minimal networks using deregulated genes between two conditions. MiNEA also identifies the deregulation in key reactions that are overlapped across all possible minimal-size networks. We applied MiNEA to identify deregulated metabolic tasks and their synthesis networks in the steatosis and nonalcoholic steatohepatitis (NASH) disease using a metabolic network and transcriptomics data of mouse and human liver samples. We identified key regulators of NASH form the synthesis networks of hydrogen peroxide and ceramide in both humans and mice. We also identified opposite deregulation in NASH for the phosphatidylserine synthesis network between humans and mice. MiNEA finds synthesis networks for a given target metabolite and due to this it is flexible to study deregulation in different phenotypes. MiNEA can be widely applicable for studying context-specific metabolism for any organism because the metabolic networks and context-specific gene expression data are now available for many organisms.

systems biology↗

bioRxiv2018

Enterotype-like microbiome stratification as emergent structure in complex adaptive systems: A mathematical model.

BackgroundMetagenomics has provided valuable insight into human gut microbiome composition together with its structure and function. Studies suggest that the adult gut microbial community possess a certain amount of stability and resilience. The architecture of host-microbial symbiotic states suggested by microbiome clustering findings and co-occurrence functional networks might be involved in these properties. Models for understanding the underlying structure (including enterotypes) and derived properties are in demand by researchers.\n\nPrincipal findingsWe propose a simple random function system as a model approach of the adaption and self-organization of the microbiome space when fostering the optimal functioning of the system. The construction of this model is based on key facts of microbiota functioning reported in recent studies. We aim to demonstrate the existence of a probability distribution as a microbiome attractor resulting from an intermittent adaption process. Its mathematical structural properties explain the stability of gut microbiota and its ability for restoration after occasional perturbations. The model is consistent with microbiome clustering results and provides precise mathematical meaning to the gradient among enterotypes previously reported. The model also explains how intermittent perturbations, such as long-term dietary patterns, might affect the microbiome structure, and these results are consistent with previously reported experimental results.\n\nConclusions/significanceThese mathematical facts implied by the model unveil an underlying mechanism that may explain gut microbiome structure and related experimental findings. Within this framework, stability and resilience properties of human gut microbiota are explained as a consequence of the model.

systems biology↗