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

Tonic TCR Signaling Inversely Regulates the Basal Metabolism of CD4

The contribution of self-peptide-MHC signaling in CD

Generation of two iPSC lines (FAMRCi007-A and FAMRCi007-B) from patient with Emery-Dreifuss muscular dystrophy and heart rhythm abnormalities carrying genetic variant LMNA p.Arg249Gln.

Human iPSC lines were generated from peripheral blood mononuclear cells of patient carrying LMNA mutation associated with Emery–Dreifuss muscular dystrophy accompanied by atrioventricular block and paroxysmal atrial fibrillation. Reprogramming factors OCT4, KLF4, SOX2, CMYC were delivered using Sendai virus transduction. iPSCs were characterized in order to prove the pluripotency markers expression, normal karyotype, ability to differentiate into three embryonic germ layers. Generated iPSC lines would be useful model to investigate disease development associated with genetic variants in LMNA gene

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

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\u27-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 beta-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

The miR-17∼92 microRNA cluster Is a global regulator of tumor metabolism

SummaryA central hallmark of cancer cells is the reprogramming of cellular metabolism to meet the bioenergetic and biosynthetic demands of malignant growth. Here, we report that the miR-17∼92 microRNA (miRNA) cluster is an oncogenic driver of tumor metabolic reprogramming. Loss of miR-17∼92 in Myc+ tumor cells leads to a global decrease in tumor cell metabolism, affecting both glycolytic and mitochondrial metabolism, whereas increased miR-17∼92 expression is sufficient to drive increased nutrient usage by tumor cells. We mapped the metabolic control element of miR-17∼92 to the miR-17 seed family, which influences cellular metabolism and mammalian target of rapamycin complex 1 (mTORC1) signaling through negative regulation of the LKB1 tumor suppressor. miR-17-dependent tuning of LKB1 levels regulates both the metabolic potential of Myc+ lymphomas and tumor growth in vivo. Our results establish metabolic reprogramming as a central function of the oncogenic miR-17∼92 miRNA cluster that drives the progression of MYC-dependent tumors

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

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.Peer reviewe