Alexey Sergushichev

Network Integration of Parallel Metabolic and Transcriptional Data Reveals Metabolic Modules that Regulate Macrophage Polarization

Macrophage polarization involves a coordinated metabolic and transcriptional rewiring that is only partially understood. By using an integrated high-throughput transcriptional-metabolic profiling and analysis pipeline, we characterized systemic changes during murine macrophage M1 and M2 polarization. M2 polarization was found to activate glutamine catabolism and UDP-GlcNAc-associated modules. Correspondingly, glutamine deprivation or inhibition of N-glycosylation decreased M2 polarization and production of chemokine CCL22. In M1 macrophages, we identified a metabolic break at Idh, the enzyme that converts isocitrate to alpha-ketoglutarate, providing mechanistic explanation for TCA cycle fragmentation. (13)C-tracer studies suggested the presence of an active variant of the aspartate-arginosuccinate shunt that compensated for this break. Consistently, inhibition of aspartate-aminotransferase, a key enzyme of the shunt, inhibited nitric oxide and interleukin-6 production in M1 macrophages, while promoting mitochondrial respiration. This systems approach provides a highly integrated picture of the physiological modules supporting macrophage polarization, identifying potential pharmacologic control points for both macrophage phenotypes.

Homeostatic Control of Innate Lung Inflammation by Vici Syndrome Gene Epg5 and Additional Autophagy Genes Promotes Influenza Pathogenesis

Mutations in the autophagy gene EPG5 are linked to the multisystem human disease Vici syndrome, which is characterized in part by pulmonary abnormalities, including recurrent infections. We found that Epg5-deficient mice exhibited elevated baseline innate immune cellular and cytokine-based lung inflammation and were resistant to lethal influenza virus infection. Lung transcriptomics, bone marrow transplantation experiments, and analysis of cellular cytokine expression indicated that Epg5 plays a role in lung physiology through its function in macrophages. Deletion of other autophagy genes including Atg14, Fip200, Atg5, and Atg7 in myeloid cells also led to elevated basal lung inflammation and influenza resistance. This suggests that Epg5 and other Atg genes function in macrophages to limit innate immune inflammation in the lung. Disruption of this normal homeostatic dampening of lung inflammation results in increased resistance to influenza, suggesting that normal homeostatic mechanisms that limit basal tissue inflammation support some infectious diseases.

An algorithm for fast preranked gene set enrichment analysis using cumulative statistic calculation

Gene set enrichment analysis is a widely used tool for analyzing gene expression data. However, current implementations are slow due to a large number of required samples for the analysis to have a good statistical power. In this paper we present a novel algorithm, that efficiently reuses one sample multiple times and thus speeds up the analysis. We show that it is possible to make hundreds of thousands permutations in a few minutes, which leads to very accurate p-values. This, in turn, allows applying standard FDR correction procedures, which are more accurate than the ones currently used. The method is implemented in a form of an R package and is freely available at https://github.com/ctlab/fgsea.Abstract Preranked gene set enrichment analysis (GSEA) is a widely used method for interpretation of gene expression data in terms of biological processes. Here we present FGSEA method that is able to estimate arbitrarily low GSEA P-values with a higher accuracy and much faster compared to other implementations. We also present a polynomial algorithm to calculate GSEA P-values exactly, which we use to practically confirm the accuracy of the method.

Integrating immunometabolism and macrophage diversity

Macrophages are heterogeneous cells that play a key role in inflammatory and tissue reparative responses. Over the past decade it has become clear that shifts in cellular metabolism are important determinants of macrophage function and phenotype. At the same time, our appreciation of macrophage diversity in vivo has also been increasing. Factors such as cell origin and tissue localization are now recognized as important variables that influence macrophage biology. Whether different macrophage populations also have unique metabolic phenotypes has not been extensively explored. In this article, we will discuss the importance of understanding how macrophage origin can modulate metabolic programming and influence inflammatory responses.

Targeted next-generation sequencing (NGS) of nine candidate genes with custom AmpliSeq in patients and a cardiomyopathy risk group

BACKGROUND Hypertrophic cardiomyopathy is a common genetic cardiac disease. Prevention and early diagnosis of this disease are very important. Because of the large number of causative genes and the high rate of mutations involved in the pathogenesis of this disease, traditional methods of early diagnosis are ineffective. METHODS We developed a custom AmpliSeq panel for NGS sequencing of the coding sequences of ACTC1, MYBPC3, MYH7, MYL2, MYL3, TNNI3, TNNT2, TPM1, and CASQ2. A genetic analysis of student cohorts (with and without cardiomyopathy risk in their medical histories) and patients with cardiomyopathies was performed. For the statistical and bioinformatics analysis, Polyphen2, SIFT, SnpSift and PLINK software were used. To select genetic markers in the patients with cardiomyopathy and in the students of the high risk group, four additive models were applied. RESULTS Our AmpliSeq custom panel allowed us to efficiently explore targeted sequences. Based on the score analysis, we detected three substitutions in the MYBPC3 and CASQ2 genes and six combinations between loci in the MYBPC3, MYH7 and CASQ2 genes that were responsible for cardiomyopathy risk in our cohorts. We also detected substitutions in the TNNT2 gene that can be considered as protective against cardiomyopathy. CONCLUSION We used NGS with AmpliSeq libraries and Ion PGM sequencing to develop improved predictive information for patients at risk of cardiomyopathy.

GAM: a web-service for integrated transcriptional and metabolic network analysis

Novel techniques for high-throughput steady-state metabolomic profiling yield information about changes of nearly thousands of metabolites. Such metabolomic profiles, when analyzed together with transcriptional profiles, can reveal novel insights about underlying biological processes. While a number of conceptual approaches have been developed for data integration, easily accessible tools for integrated analysis of mammalian steady-state metabolomic and transcriptional data are lacking. Here we present GAM (‘genes and metabolites’): a web-service for integrated network analysis of transcriptional and steady-state metabolomic data focused on identification of the most changing metabolic subnetworks between two conditions of interest. In the web-service, we have pre-assembled metabolic networks for humans, mice, Arabidopsis and yeast and adapted exact solvers for an optimal subgraph search to work in the context of these metabolic networks. The output is the most regulated metabolic subnetwork of size controlled by false discovery rate parameters. The subnetworks are then visualized online and also can be downloaded in Cytoscape format for subsequent processing. The web-service is available at: https://artyomovlab.wustl.edu/shiny/gam/

Electrophilic properties of itaconate and derivatives regulate the IκBζ–ATF3 inflammatory axis

Metabolic regulation has been recognized as a powerful principle guiding immune responses. Inflammatory macrophages undergo extensive metabolic rewiring1 marked by the production of substantial amounts of itaconate, which has recently been described as an immunoregulatory metabolite2. Itaconate and its membrane-permeable derivative dimethyl itaconate (DI) selectively inhibit a subset of cytokines2, including IL-6 and IL-12 but not TNF. The major effects of itaconate on cellular metabolism during macrophage activation have been attributed to the inhibition of succinate dehydrogenase2,3, yet this inhibition alone is not sufficient to account for the pronounced immunoregulatory effects observed in the case of DI. Furthermore, the regulatory pathway responsible for such selective effects of itaconate and DI on the inflammatory program has not been defined. Here we show that itaconate and DI induce electrophilic stress, react with glutathione and subsequently induce both Nrf2 (also known as NFE2L2)-dependent and -independent responses. We find that electrophilic stress can selectively regulate secondary, but not primary, transcriptional responses to toll-like receptor stimulation via inhibition of IκBζ protein induction. The regulation of IκBζ is independent of Nrf2, and we identify ATF3 as its key mediator. The inhibitory effect is conserved across species and cell types, and the in vivo administration of DI can ameliorate IL-17–IκBζ-driven skin pathology in a mouse model of psoriasis, highlighting the therapeutic potential of this regulatory pathway. Our results demonstrate that targeting the DI–IκBζ regulatory axis could be an important new strategy for the treatment of IL-17–IκBζ-mediated autoimmune diseases.The immunoregulatory metabolite itaconate and its dimethyl derivative induce electrophilic stress and react with glutathione to induce both Nrf2-dependent and Nrf2-independent responses, resulting in AF3-mediated inhibition of the inflammation-related protein IκBζ.

The use of evolutionary programming based on training examples for the generation of finite state machines for controlling objects with complex behavior

It is proposed to use evolutionary programming to generate finite state machines (FSMs) for controlling objects with complex behavior. The well-know approach in which the FSM performance is evaluated by simulation, which is typically time consuming, is replaced with comparison of the object’s behavior controlled by the FSM with the behavior of this object controlled by a human. A feature of the proposed approach is that it makes it possible to deal with objects that have not only discrete but also continuous parameters. The use of this approach is illustrated by designing an FSM controlling a model aircraft executing a loop-the-loop maneuver.

Genetic algorithm for induction of finite automata with continuous and discrete output actions

In this paper, we describe a genetic algorithm for induction of finite automata with continuous and discrete output actions. Input data for the algorithm is a set of tests. Each test consists of two sequences: input events and output actions. In previous works output actions were discrete, i.e. selected from the finite set, in this work output actions can also be continuous, i.e. represented by real numbers. Only the structure of automaton transitions graph is evolved by the genetic algorithm. Values of output actions are found using transition labeling algorithm, which aim is to maximize the value of fitness function. New transition labeling algorithm is proposed. It also works with continuous output actions and is based on equations system solving. In case of proper selection of fitness function, equations in this system are linear and it can be solved by the Gaussian elimination method. The unmanned airplane performing the loop is considered as an example of the controlled object.

Solving Generalized Maximum-Weight Connected Subgraph Problem for Network Enrichment Analysis

Network enrichment analysis methods allow to identify active modules without being biased towards a priori defined pathways. One of mathematical formulations of such analysis is a reduction to a maximum-weight connected subgraph problem. In particular, in analysis of metabolic networks a generalized maximum-weight connected subgraph (GMWCS) problem, where both nodes and edges are scored, naturally arises. Here we present the first to our knowledge practical exact GMWCS solver. We have tested it on real-world instances and compared to similar solvers. First, the results show that on node-weighted instances GMWCS solver has a similar performance to the best solver for that problem. Second, GMWCS solver is faster compared to the closest analogue when run on GMWCS instances with edge weights.