Systems Immunology Approaches to Metabolism.

Over the last decade, immunometabolism has emerged as a novel interdisciplinary field of research and yielded significant fundamental insights into the regulation of immune responses. Multiple classical approaches to interrogate immunometabolism, including bulk metabolic profiling and analysis of metabolic regulator expression, paved the way to appreciating the physiological complexity of immunometabolic regulation in vivo. Studying immunometabolism at the systems level raised the need to transition towards the next-generation technology for metabolic profiling and analysis. Spatially resolved metabolic imaging and computational algorithms for multi-modal data integration are new approaches to connecting metabolism and immunity. In this review, we discuss recent studies that highlight the complex physiological interplay between immune responses and metabolism and give an overview of technological developments that bear the promise of capturing this complexity most directly and comprehensively.

TREM2 Maintains Microglial Metabolic Fitness in Alzheimer's Disease.

Elevated risk of developing Alzheimer's disease (AD) is associated with hypomorphic variants of TREM2, a surface receptor required for microglial responses to neurodegeneration, including proliferation, survival, clustering, and phagocytosis. How TREM2 promotes such diverse responses is unknown. Here, we find that microglia in AD patients carrying TREM2 risk variants and TREM2-deficient mice with AD-like pathology have abundant autophagic vesicles, as do TREM2-deficient macrophages under growth-factor limitation or endoplasmic reticulum (ER) stress. Combined metabolomics and RNA sequencing (RNA-seq) linked this anomalous autophagy to defective mammalian target of rapamycin (mTOR) signaling, which affects ATP levels and biosynthetic pathways. Metabolic derailment and autophagy were offset in vitro through Dectin-1, a receptor that elicits TREM2-like intracellular signals, and cyclocreatine, a creatine analog that can supply ATP. Dietary cyclocreatine tempered autophagy, restored microglial clustering around plaques, and decreased plaque-adjacent neuronal dystrophy in TREM2-deficient mice with amyloid-ß pathology. Thus, TREM2 enables microglial responses during AD by sustaining cellular energetic and biosynthetic metabolism.

Oxylipins and metabolites from pyroptotic cells act as promoters of tissue repair.

Pyroptosis is a lytic cell death mode that helps limit the spread of infections and is also linked to pathology in sterile inflammatory diseases and autoimmune diseases1-4. During pyroptosis, inflammasome activation and the engagement of caspase-1 lead to cell death, along with the maturation and secretion of the inflammatory cytokine interleukin-1ß (IL-1ß). The dominant effect of IL-1ß in promoting tissue inflammation has clouded the potential influence of other factors released from pyroptotic cells. Here, using a system in which macrophages are induced to undergo pyroptosis without IL-1ß or IL-1α release (denoted Pyro-1), we identify unexpected beneficial effects of the Pyro-1 secretome. First, we noted that the Pyro-1 supernatants upregulated gene signatures linked to migration, cellular proliferation and wound healing. Consistent with this gene signature, Pyro-1 supernatants boosted migration of primary fibroblasts and macrophages, and promoted faster wound closure in vitro and improved tissue repair in vivo. In mechanistic studies, lipidomics and metabolomics of the Pyro-1 supernatants identified the presence of both oxylipins and metabolites, linking them to pro-wound-healing effects. Focusing specifically on the oxylipin prostaglandin E2 (PGE2), we find that its synthesis is induced de novo during pyroptosis, downstream of caspase-1 activation and cyclooxygenase-2 activity; further, PGE2 synthesis occurs late in pyroptosis, with its release dependent on gasdermin D pores opened during pyroptosis. As for the pyroptotic metabolites, they link to immune cell infiltration into the wounds, and polarization to CD301+ macrophages. Collectively, these data advance the concept that the pyroptotic secretome possesses oxylipins and metabolites with tissue repair properties that may be harnessed therapeutically.

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.

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 rewiring 1 marked by the production of substantial amounts of itaconate, which has recently been described as an immunoregulatory metabolite 2 . Itaconate and its membrane-permeable derivative dimethyl itaconate (DI) selectively inhibit a subset of cytokines 2 , 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.

Shiny GATOM: omics-based identification of regulated metabolic modules in atom transition networks.

Multiple high-throughput omics techniques provide different angles on systematically quantifying and studying metabolic regulation of cellular processes. However, an unbiased analysis of such data and, in particular, integration of multiple types of data remains a challenge. Previously, for this purpose we developed GAM web-service for integrative metabolic network analysis. Here we describe an updated pipeline GATOM and the corresponding web-service Shiny GATOM, which takes as input transcriptional and/or metabolomic data and finds a metabolic subnetwork most regulated between the two conditions of interest. GATOM features a new metabolic network topology based on atom transition, which significantly improves interpretability of the analysis results. To address computational challenges arising with the new network topology, we introduce a new variant of the maximum weight connected subgraph problem and provide a corresponding exact solver. To make the used networks up-to-date we upgraded the KEGG-based network construction pipeline and developed one based on the Rhea database, which allows analysis of lipidomics data. Finally, we simplified local installation, providing R package mwcsr for solving relevant graph optimization problems and R package gatom, which implements the GATOM pipeline. The web-service is available at https://ctlab.itmo.ru/shiny/gatom and https://artyomovlab.wustl.edu/shiny/gatom.

Mitochondrial Phosphoenolpyruvate Carboxykinase Regulates Metabolic Adaptation and Enables Glucose-Independent Tumor Growth.

Cancer cells adapt metabolically to proliferate under nutrient limitation. Here we used combined transcriptional-metabolomic network analysis to identify metabolic pathways that support glucose-independent tumor cell proliferation. We found that glucose deprivation stimulated re-wiring of the tricarboxylic acid (TCA) cycle and early steps of gluconeogenesis to promote glucose-independent cell proliferation. Glucose limitation promoted the production of phosphoenolpyruvate (PEP) from glutamine via the activity of mitochondrial PEP-carboxykinase (PCK2). Under these conditions, glutamine-derived PEP was used to fuel biosynthetic pathways normally sustained by glucose, including serine and purine biosynthesis. PCK2 expression was required to maintain tumor cell proliferation under limited-glucose conditions in vitro and tumor growth in vivo. Elevated PCK2 expression is observed in several human tumor types and enriched in tumor tissue from non-small-cell lung cancer (NSCLC) patients. Our results define a role for PCK2 in cancer cell metabolic reprogramming that promotes glucose-independent cell growth and metabolic stress resistance in human tumors.

Markov chain Monte Carlo for active module identification problem.

BACKGROUND: Integrative network methods are commonly used for interpretation of high-throughput experimental biological data: transcriptomics, proteomics, metabolomics and others. One of the common approaches is finding a connected subnetwork of a global interaction network that best encompasses significant individual changes in the data and represents a so-called active module. Usually methods implementing this approach find a single subnetwork and thus solve a hard classification problem for vertices. This subnetwork inherently contains erroneous vertices, while no instrument is provided to estimate the confidence level of any particular vertex inclusion. To address this issue, in the current study we consider the active module problem as a soft classification problem. RESULTS: We propose a method to estimate probabilities of each vertex to belong to the active module based on Markov chain Monte Carlo (MCMC) subnetwork sampling. As an example of the performance of our method on real data, we run it on two gene expression datasets. For the first many-replicate expression dataset we show that the proposed approach is consistent with an existing resampling-based method. On the second dataset the jackknife resampling method is inapplicable due to the small number of biological replicates, but the MCMC method can be run and shows high classification performance. CONCLUSIONS: The proposed method allows to estimate the probability that an individual vertex belongs to the active module as well as the false discovery rate (FDR) for a given set of vertices. Given the estimated probabilities, it becomes possible to provide a connected subgraph in a consistent manner for any given FDR level: no vertex can disappear when the FDR level is relaxed. We show, on both simulated and real datasets, that the proposed method has good computational performance and high classification accuracy.

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.

End Sequence Analysis Toolkit (ESAT) expands the extractable information from single-cell RNA-seq data.

RNA-seq protocols that focus on transcript termini are well suited for applications in which template quantity is limiting. Here we show that, when applied to end-sequencing data, analytical methods designed for global RNA-seq produce computational artifacts. To remedy this, we created the End Sequence Analysis Toolkit (ESAT). As a test, we first compared end-sequencing and bulk RNA-seq using RNA from dendritic cells stimulated with lipopolysaccharide (LPS). As predicted by the telescripting model for transcriptional bursts, ESAT detected an LPS-stimulated shift to shorter 3'-isoforms that was not evident by conventional computational methods. Then, droplet-based microfluidics was used to generate 1000 cDNA libraries, each from an individual pancreatic islet cell. ESAT identified nine distinct cell types, three distinct ß-cell types, and a complex interplay between hormone secretion and vascularization. ESAT, then, offers a much-needed and generally applicable computational pipeline for either bulk or single-cell RNA end-sequencing.

Clinical Response to Personalized Exercise Therapy in Heart Failure Patients with Reduced Ejection Fraction is Accompanied by Skeletal Muscle Histological Alterations.

Abstract: Heart failure (HF) is associated with skeletal muscle wasting and exercise intolerance. This study aimed to evaluate the exercise-induced clinical response and histological alterations. One hundred and forty-four HF patients were enrolled. The individual training program was determined as a workload at or close to the lactate threshold (LT1); clinical data were collected before and after 12 weeks/6 months of training. The muscle biopsies from eight patients were taken before and after 12 weeks of training: histology analysis was used to evaluate muscle morphology. Most of the patients demonstrated a positive response after 12 weeks of the physical rehabilitation program in one or several parameters tested, and 30% of those showed improvement in all four of the following parameters: oxygen uptake (VO2) peak, left ventricular ejection fraction (LVEF), exercise tolerance (ET), and quality of life (QOL); the walking speed at LT1 after six months of training showed a significant rise. Along with clinical response, the histological analysis detected a small but significant decrease in both fiber and endomysium thickness after the exercise training course indicating the stabilization of muscle mechanotransduction system. Together, our data show that the beneficial effect of personalized exercise therapy in HF patients depends, at least in part, on the improvement in skeletal muscle physiological and biochemical performance.

De novo mutations in FLNC leading to early-onset restrictive cardiomyopathy and congenital myopathy.

Mutations in FLNC for a long time are known in connection to neuromuscular disorders and only recently were described in association with various cardiomyopathies. Here, we report a new clinical phenotype of filaminopathy in four unrelated patients with early-onset restrictive cardiomyopathy (RCM) in combination with congenital myopathy due to FLNC mutations (NM_001458.4:c.3557C>T, p.A1186V, rs1114167361 in three probands and c.[3547G>C; 3548C>T], p.A1183L, rs1131692185 in one proband). In all cases, concurrent myopathy was confirmed by neurological examination, electromyography, and morphological studies. Three of the patients also presented with arthrogryposis. The pathogenicity of the described missense variants was verified by cellular and morphological studies and by in vivo modeling in zebrafish. Combination of in silico and experimental approaches revealed that FLNC missense variants localized in Ig-loop segments often lead to development of RCM. The described FLNC mutations associated with early-onset RCMP extend cardiac spectrum of filaminopathies and facilitate the differential diagnosis of restrictive cardiac phenotype associated with neuromuscular involvement in children.

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/.

Phantasus, a web application for visual and interactive gene expression analysis.

Transcriptomic profiling became a standard approach to quantify a cell state, which led to the accumulation of huge amount of public gene expression datasets. However, both reuse of these datasets or analysis of newly generated ones requires significant technical expertise. Here, we present Phantasus: a user-friendly web application for interactive gene expression analysis which provides a streamlined access to more than 96,000 public gene expression datasets, as well as allows analysis of user-uploaded datasets. Phantasus integrates an intuitive and highly interactive JavaScript-based heatmap interface with an ability to run sophisticated R-based analysis methods. Overall Phantasus allows users to go all the way from loading, normalizing, and filtering data to doing differential gene expression and downstream analysis. Phantasus can be accessed online at https://alserglab.wustl.edu/phantasus or can be installed locally from Bioconductor (https://bioconductor.org/packages/phantasus). Phantasus source code is available at https://github.com/ctlab/phantasus under an MIT license.

Gene, cell type, and drug prioritization analysis suggest genetic basis for the utility of diuretics in treating Alzheimer disease.

We introduce a user-friendly tool for risk gene, cell type, and drug prioritization for complex traits: GCDPipe. It uses gene-level GWAS-derived data and gene expression data to train a model for the identification of disease risk genes and relevant cell types. Gene prioritization information is then coupled with known drug target data to search for applicable drug agents based on their estimated functional effects on the identified risk genes. We illustrate the utility of our approach in different settings: identification of the cell types, implicated in disease pathogenesis, was tested in inflammatory bowel disease (IBD) and Alzheimer disease (AD); gene target and drug prioritization was tested in IBD and schizophrenia. The analysis of phenotypes with known disease-affected cell types and/or existing drug candidates shows that GCDPipe is an effective tool to unify genetic risk factors with cellular context and known drug targets. Next, analysis of the AD data with GCDPipe suggested that gene targets of diuretics, as an Anatomical Therapeutic Chemical drug subgroup, are significantly enriched among the genes prioritized by GCDPipe, indicating their possible effect on the course of the disease.

CR1 variants contribute to FSGS susceptibility across multiple populations.

Focal segmental glomerulosclerosis (FSGS) is a common cause of nephrotic syndrome with an annual incidence in the United States in African-Americans compared to European-Americans of 24 cases and 5 cases per million, respectively. Among glomerular diseases in Europe and Latin-America, FSGS was the second most frequent diagnosis, and in Asia the fifth. We expand previous efforts in understanding genetics of FSGS by performing a case-control study involving ethnically-diverse groups FSGS cases (726) and a pool of controls (13,994), using panel sequencing of approximately 2,500 podocyte-expressed genes. Through rare variant association tests, we replicated known risk genes - KANK1, COL4A4, and APOL1. A novel significant association was observed for the gene encoding complement receptor 1 (CR1). High-risk rare variants in CR1 in the European-American cohort were commonly observed in Latin- and African-Americans. Therefore, a combined rare and common variant analysis was used to replicate the CR1 association in non-European populations. The CR1 risk variant, rs17047661, gives rise to the Sl1/Sl2 (R1601G) allele that was previously associated with protection against cerebral malaria. Pleiotropic effects of rs17047661 may explain the difference in allele frequencies across continental ancestries and suggest a possible role for genetically-driven alterations of adaptive immunity in the pathogenesis of FSGS.

Tumor-associated macrophages respond to chemotherapy by detrimental transcriptional reprogramming and suppressing stabilin-1 mediated clearance of EGF.

Introduction: Tumor resistance to chemotherapy and metastatic relapse account for more than 90% of cancer specific mortality. Tumor-associated macrophages (TAMs) can process chemotherapeutic agents and impair their action. Little is known about the direct effects of chemotherapy on TAMs. Methods: The effect of chemotherapeutic platinum agent cisplatin was assessed in the model system of human ex vivo TAMs. Whole-transcriptome sequencing for paired TAMs stimulated and not stimulated by cisplatin was analysed by NGS. Endocytic uptake of EGF was quantified by flow cytometry. Confocal microscopy was used to visualize stabilin-1-mediated internalization and endocytic trafficking of EGF in CHO cells expressing ectopically recombinant stabilin-1 and in stabilin-1+ TAMs. In cohort of patients with breast cancer, the effect of platinum therapy on the transcriptome of TAMs was validated, and differential expression of regulators of endocytosis was identified. Results: Here we show that chemotherapeutic agent cisplatin can initiate detrimental transcriptional and functional programs in TAMs, without significant impairment of their viability. We focused on the clearance function of TAMs that controls composition of tumor microenvironment. For the first time we demonstrated that TAMs' scavenger receptor stabilin-1 is responsible for the clearance of epidermal growth factor (EGF), a potent stimulator of tumor growth. Cisplatin suppressed both overall and EGF-specific endocytosis in TAMs by bidirectional mode: suppression of positive regulators and stimulation of negative regulators of endocytosis, with strongest effect on synaptotagmin-11 (SYT11), confirmed in patients with breast cancer. Conclusion: Our data demonstrate that synergistic action of cytostatic agents and innovative immunomodulators is required to overcome cancer therapy resistance.

Network analysis of large-scale ImmGen and Tabula Muris datasets highlights metabolic diversity of tissue mononuclear phagocytes.

The diversity of mononuclear phagocyte (MNP) subpopulations across tissues is one of the key physiological characteristics of the immune system. Here, we focus on understanding the metabolic variability of MNPs through metabolic network analysis applied to three large-scale transcriptional datasets: we introduce (1) an ImmGen MNP open-source dataset of 337 samples across 26 tissues; (2) a myeloid subset of ImmGen Phase I dataset (202 MNP samples); and (3) a myeloid mouse single-cell RNA sequencing (scRNA-seq) dataset (51,364 cells) assembled based on Tabula Muris Senis. To analyze such large-scale datasets, we develop a network-based computational approach, genes and metabolites (GAM) clustering, for unbiased identification of the key metabolic subnetworks based on transcriptional profiles. We define 9 metabolic subnetworks that encapsulate the metabolic differences within MNP from 38 different tissues. Obtained modules reveal that cholesterol synthesis appears particularly active within the migratory dendritic cells, while glutathione synthesis is essential for cysteinyl leukotriene production by peritoneal and lung macrophages.

Genotype imputation and polygenic score estimation in northwestern Russian population.

Numerous studies demonstrated the lack of transferability of polygenic score (PGS) models across populations and the problem arising from unequal presentation of ancestries across genetic studies. However, even within European ancestry there are ethnic groups that are rarely presented in genetic studies. For instance, Russians, being one of the largest, diverse, and yet understudied group in Europe. In this study, we evaluated the reliability of genotype imputation for the Russian cohort by testing several commonly used imputation reference panels (e.g. HRC, 1000G, HGDP). HRC, in comparison with two other panels, showed the most accurate results based on both imputation accuracy and allele frequency concordance between masked and imputed genotypes. We built polygenic score models based on GWAS results from the UK biobank, measured the explained phenotypic variance in the Russian cohort attributed to polygenic scores for 11 phenotypes, collected in the clinic for each participant, and finally explored the role of allele frequency discordance between the UK biobank and the study cohort in the resulting PGS performance.

Case Report: Supernormal Vascular Aging in Leningrad Siege Survivors.

Age-related changes in the vascular system play an important role in the biological age and lifespan of a person and maybe affected from an early age onward. One of the indicators of changes in the vascular system is arterial wall stiffness and its main measure, i.e., carotid-femoral pulse wave velocity (cfPWV). We examined arterial wall stiffness in a sample of 305 Leningrad Siege survivors to assess how hunger and stressful conditions during fetal development and early childhood affected the state of the cardiovascular system at a later age and what factors may neutralize the negative impact sustained in early childhood. Here, we presented an evaluation of two unique patients with supernormal vascular aging (SUPERNOVA) phenotype from this cohort and described the details of congruence between hereditary resistance and practiced lifestyle yielding slower biological aging rate.