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

Метод совместной кластеризации в графовом и корреляционном пространствах

Network algorithms are often used to analyze and interpret the biological data. One of the widely used approaches is to solve the problem of identifying an active module, where a connected subnetwork of a biological network is selected which best reflects the difference between the two considered biological conditions. In this work this approach is extended to the case of a larger number of biological conditions and the problem of the joint clustering in network and correlation spaces is formulated.To solve this problem, an iterative method is proposed at takes as the input graph G and matrix X, in which the rows correspond to the vertices of the graph. As the output, the algorithm produces a set of subgraphs of the graph G so that each subgraph is connected and the rows corresponding to its vertices have a high pairwise correlation. The efficiency of the method is confirmed by an experimental study on the simulated data.Алгоритмы на графах часто используются для анализа и интерпретации биологических данных. Одним из широко используемых подходов является решение задачи поиска активного модуля, в которой в графе биологических взаимодействий выделяется связный подграф, лучше всего отражающий разницу между двумя рассматриваемыми биологическими состояниями. В настоящей работе этот подход расширяется на случай большего числа биологических состояний и формулируется задача совместной кластеризации в графовом и корреляционном пространстве.Для решения этой задачи предлагается итеративный метод, принимающий на вход граф G и матрицу X, в которой строки соответствуют вершинам графа. На выходе алгоритм выдает набор подграфов графа G так, что каждый подграф является связным и строки, соответствующие его вершинам, обладают высокой попарной корреляцией.Эффективность метода подтверждается экспериментальным исследованием на смоделированных данных

Dynamic shifts in the composition of resident and recruited macrophages influence tissue remodeling in NASH

Macrophage-mediated inflammation is critical in the pathogenesis of non-alcoholic steatohepatitis (NASH). Here, we describe that, with high-fat, high-sucrose-diet feeding, mature TIM

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

Method of the Joint Clustering in Network and Correlation Spaces

Network algorithms are often used to analyze and interpret the biological data. One of the widely used approaches is to solve the problem of identifying an active module, where a connected subnetwork of a biological network is selected which best reflects the difference between the two considered biological conditions. In this work this approach is extended to the case of a larger number of biological conditions and the problem of the joint clustering in network and correlation spaces is formulated.To solve this problem, an iterative method is proposed at takes as the input graph G and matrix X, in which the rows correspond to the vertices of the graph. As the output, the algorithm produces a set of subgraphs of the graph G so that each subgraph is connected and the rows corresponding to its vertices have a high pairwise correlation. The efficiency of the method is confirmed by an experimental study on the simulated data

iPSC-Derived Macrophages: The Differentiation Protocol Affects Cell Immune Characteristics and Differentiation Trajectories

The generation of human macrophages from induced pluripotent stem cells (iMacs) is a rapidly developing approach used to create disease models, screen drugs, study macrophage–pathogen interactions and develop macrophage-based cell therapy. To generate iMacs, different types of protocols have been suggested, all thought to result in the generation of similar iMac populations. However, direct comparison of iMacs generated using different protocols has not been performed. We have compared the productivity, the differentiation trajectories and the characteristics of iMacs generated using two widely used protocols: one based on the formation of embryoid bodies and the induction of myeloid differentiation by only two cytokines, interleukin-3 and macrophage colony-stimulating factor, and the other utilizing multiple exogenous factors for iMac generation. We report inter-protocol differences in the following: (i) protocol productivity; (ii) dynamic changes in the expression of genes related to inflammation and lipid homeostasis following iMac differentiation and (iii) the transcriptomic profiles of terminally differentiated iMacs, including the expression of genes involved in inflammatory response, antigen presentation and lipid homeostasis. The results document the dependence of fine iMac characteristics on the type of differentiation protocol, which is important for further development of the field, including the development of iMac-based cell therapy

LYVE1+ macrophages of murine peritoneal mesothelium promote omentum-independent ovarian tumor growth

Two resident macrophage subsets reside in peritoneal fluid. Macrophages also reside within mesothelial membranes lining the peritoneal cavity, but they remain poorly characterized. Here, we identified two macrophage populations (LYVE1hi MHC IIlo-hi CX3CR1gfplo/- and LYVE1lo/- MHC IIhi CX3CR1gfphi subsets) in the mesenteric and parietal mesothelial linings of the peritoneum. These macrophages resembled LYVE1+ macrophages within surface membranes of numerous organs. Fate-mapping approaches and analysis of newborn mice showed that LYVE1hi macrophages predominantly originated from embryonic-derived progenitors and were controlled by CSF1 made by Wt1+ stromal cells. Their gene expression profile closely overlapped with ovarian tumor-associated macrophages previously described in the omentum. Indeed, syngeneic epithelial ovarian tumor growth was strongly reduced following in vivo ablation of LYVE1hi macrophages, including in mice that received omentectomy to dissociate the role from omental macrophages. These data reveal that the peritoneal compartment contains at least four resident macrophage populations and that LYVE1hi mesothelial macrophages drive tumor growth independently of the omentum

LYVE1+ macrophages of murine peritoneal mesothelium promote omentum-independent ovarian tumor growth

Two resident macrophage subsets reside in peritoneal fluid. Macrophages also reside within mesothelial membranes lining the peritoneal cavity, but they remain poorly characterized. Here, we identified two macrophage populations (LYVE1hi MHC IIlo-hi CX3CR1gfplo/- and LYVE1lo/- MHC IIhi CX3CR1gfphi subsets) in the mesenteric and parietal mesothelial linings of the peritoneum. These macrophages resembled LYVE1+ macrophages within surface membranes of numerous organs. Fate-mapping approaches and analysis of newborn mice showed that LYVE1hi macrophages predominantly originated from embryonic-derived progenitors and were controlled by CSF1 made by Wt1+ stromal cells. Their gene expression profile closely overlapped with ovarian tumor-associated macrophages previously described in the omentum. Indeed, syngeneic epithelial ovarian tumor growth was strongly reduced following in vivo ablation of LYVE1hi macrophages, including in mice that received omentectomy to dissociate the role from omental macrophages. These data reveal that the peritoneal compartment contains at least four resident macrophage populations and that LYVE1hi mesothelial macrophages drive tumor growth independently of the omentum

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

Summary: 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