The RNA-binding protein Msi2 regulates autophagy during myogenic differentiation.

Skeletal muscle development is a highly ordered process orchestrated transcriptionally by the myogenic regulatory factors. However, the downstream molecular mechanisms of myogenic regulatory factor functions in myogenesis are not fully understood. Here, we identified the RNA-binding protein Musashi2 (Msi2) as a myogenin target gene and a post-transcriptional regulator of myoblast differentiation. Msi2 knockdown in murine myoblasts blocked differentiation without affecting the expression of MyoD or myogenin. Msi2 overexpression was also sufficient to promote myoblast differentiation and myocyte fusion. Msi2 loss attenuated autophagosome formation via down-regulation of the autophagic protein MAPL1LC3/ATG8 (LC3) at the early phase of myoblast differentiation. Moreover, forced activation of autophagy effectively suppressed the differentiation defects incurred by Msi2 loss. Consistent with its functions in myoblasts in vitro, mice deficient for Msi2 exhibited smaller limb skeletal muscles, poorer exercise performance, and muscle fiber-type switching in vivo. Collectively, our study demonstrates that Msi2 is a novel regulator of mammalian myogenesis and establishes a new functional link between muscular development and autophagy regulation.

DNA hypomethylation characterizes genes encoding tissue-dominant functional proteins in liver and skeletal muscle.

Each tissue has a dominant set of functional proteins required to mediate tissue-specific functions. Epigenetic modifications, transcription, and translational efficiency control tissue-dominant protein production. However, the coordination of these regulatory mechanisms to achieve such tissue-specific protein production remains unclear. Here, we analyzed the DNA methylome, transcriptome, and proteome in mouse liver and skeletal muscle. We found that DNA hypomethylation at promoter regions is globally associated with liver-dominant or skeletal muscle-dominant functional protein production within each tissue, as well as with genes encoding proteins involved in ubiquitous functions in both tissues. Thus, genes encoding liver-dominant proteins, such as those involved in glycolysis or gluconeogenesis, the urea cycle, complement and coagulation systems, enzymes of tryptophan metabolism, and cytochrome P450-related metabolism, were hypomethylated in the liver, whereas those encoding-skeletal muscle-dominant proteins, such as those involved in sarcomere organization, were hypomethylated in the skeletal muscle. Thus, DNA hypomethylation characterizes genes encoding tissue-dominant functional proteins.

Transcription factor-binding k-mer analysis clarifies the cell type dependency of binding specificities and cis-regulatory SNPs in humans.

BACKGROUND: Transcription factors (TFs) exhibit heterogeneous DNA-binding specificities in individual cells and whole organisms under natural conditions, and de novo motif discovery usually provides multiple motifs, even from a single chromatin immunoprecipitation-sequencing (ChIP-seq) sample. Despite the accumulation of ChIP-seq data and ChIP-seq-derived motifs, the diversity of DNA-binding specificities across different TFs and cell types remains largely unexplored. RESULTS: Here, we applied MOCCS2, our k-mer-based motif discovery method, to a collection of human TF ChIP-seq samples across diverse TFs and cell types, and systematically computed profiles of TF-binding specificity scores for all k-mers. After quality control, we compiled a set of TF-binding specificity score profiles for 2,976 high-quality ChIP-seq samples, comprising 473 TFs and 398 cell types. Using these high-quality samples, we confirmed that the k-mer-based TF-binding specificity profiles reflected TF- or TF-family dependent DNA-binding specificities. We then compared the binding specificity scores of ChIP-seq samples with the same TFs but with different cell type classes and found that half of the analyzed TFs exhibited differences in DNA-binding specificities across cell type classes. Additionally, we devised a method to detect differentially bound k-mers between two ChIP-seq samples and detected k-mers exhibiting statistically significant differences in binding specificity scores. Moreover, we demonstrated that differences in the binding specificity scores between k-mers on the reference and alternative alleles could be used to predict the effect of variants on TF binding, as validated by in vitro and in vivo assay datasets. Finally, we demonstrated that binding specificity score differences can be used to interpret disease-associated non-coding single-nucleotide polymorphisms (SNPs) as TF-affecting SNPs and provide candidates responsible for TFs and cell types. CONCLUSIONS: Our study provides a basis for investigating the regulation of gene expression in a TF-, TF family-, or cell-type-dependent manner. Furthermore, our differential analysis of binding-specificity scores highlights noncoding disease-associated variants in humans.

CCPLS reveals cell-type-specific spatial dependence of transcriptomes in single cells.

MOTIVATION: Cell-cell communications regulate internal cellular states, e.g. gene expression and cell functions, and play pivotal roles in normal development and disease states. Furthermore, single-cell RNA sequencing methods have revealed cell-to-cell expression variability of highly variable genes (HVGs), which is also crucial. Nevertheless, the regulation of cell-to-cell expression variability of HVGs via cell-cell communications is still largely unexplored. The recent advent of spatial transcriptome methods has linked gene expression profiles to the spatial context of single cells, which has provided opportunities to reveal those regulations. The existing computational methods extract genes with expression levels influenced by neighboring cell types. However, limitations remain in the quantitativeness and interpretability: they neither focus on HVGs nor consider the effects of multiple neighboring cell types. RESULTS: Here, we propose CCPLS (Cell-Cell communications analysis by Partial Least Square regression modeling), which is a statistical framework for identifying cell-cell communications as the effects of multiple neighboring cell types on cell-to-cell expression variability of HVGs, based on the spatial transcriptome data. For each cell type, CCPLS performs PLS regression modeling and reports coefficients as the quantitative index of the cell-cell communications. Evaluation using simulated data showed our method accurately estimated the effects of multiple neighboring cell types on HVGs. Furthermore, applications to the two real datasets demonstrate that CCPLS can extract biologically interpretable insights from the inferred cell-cell communications. AVAILABILITY AND IMPLEMENTATION: The R package is available at https://github.com/bioinfo-tsukuba/CCPLS. The data are available at https://github.com/bioinfo-tsukuba/CCPLS_paper. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

In vivo transomic analyses of glucose-responsive metabolism in skeletal muscle reveal core differences between the healthy and obese states.

Metabolic regulation in skeletal muscle is essential for blood glucose homeostasis. Obesity causes insulin resistance in skeletal muscle, leading to hyperglycemia and type 2 diabetes. In this study, we performed multiomic analysis of the skeletal muscle of wild-type (WT) and leptin-deficient obese (ob/ob) mice, and constructed regulatory transomic networks for metabolism after oral glucose administration. Our network revealed that metabolic regulation by glucose-responsive metabolites had a major effect on WT mice, especially carbohydrate metabolic pathways. By contrast, in ob/ob mice, much of the metabolic regulation by glucose-responsive metabolites was lost and metabolic regulation by glucose-responsive genes was largely increased, especially in carbohydrate and lipid metabolic pathways. We present some characteristic metabolic regulatory pathways found in central carbon, branched amino acids, and ketone body metabolism. Our transomic analysis will provide insights into how skeletal muscle responds to changes in blood glucose and how it fails to respond in obesity.

Identifying potential regulators of JAGGED1 expression in portal mesenchymal cells.

OBJECTIVE: Portal mesenchymal cells induce the epithelial differentiation of the bile ducts in the developing liver via one of the Delta-Notch signaling components, JAGGED1. Although this differential induction is crucial for normal liver physiology as its genetic disorder (Alagille syndrome) causes jaundice, the molecular mechanism behind JAGGED1 expression remains unknown. Here, we searched for upstream regulatory transcription factors of JAGGED1 using an integrated bioinformatics method. RESULTS: According to the DoRothEA database, which integrates multiple lines of evidence on the relationship between transcription factors and their downstream target genes, three transcription factors were predicted to be upstream of JAGGED1: SLUG, SOX2, and EGR1. Among these, SLUG and EGR1 were enriched in ACTA2-expressing portal mesenchymal cells in two previously reported human fetal liver single-cell RNA-seq datasets. JAGGED1-expressing portal mesenchymal cells tended to express SLUG rather than EGR1, supporting that SLUG induced JAGGED1 expression. Together with the higher confidentiality of SLUG (DoRothEA level A) over EGR1 (DoRothEA level D), we concluded that SLUG was one of the most important candidate transcription factors upstream of JAGGED1. These results add mechanistic insights into the developmental biology of how portal mesenchymal cells support biliary development in the liver.

Trans-omic analysis reveals obesity-associated dysregulation of inter-organ metabolic cycles between the liver and skeletal muscle.

Systemic metabolic homeostasis is regulated by inter-organ metabolic cycles involving multiple organs. Obesity impairs inter-organ metabolic cycles, resulting in metabolic diseases. The systemic landscape of dysregulated inter-organ metabolic cycles in obesity has yet to be explored. Here, we measured the transcriptome, proteome, and metabolome in the liver and skeletal muscle and the metabolome in blood of fasted wild-type and leptin-deficient obese (ob/ob) mice, identifying components with differential abundance and differential regulation in ob/ob mice. By constructing and evaluating the trans-omic network controlling the differences in metabolic reactions between fasted wild-type and ob/ob mice, we provided potential mechanisms of the obesity-associated dysfunctions of metabolic cycles between liver and skeletal muscle involving glucose-alanine, glucose-lactate, and ketone bodies. Our study revealed obesity-associated systemic pathological mechanisms of dysfunction of inter-organ metabolic cycles.

Transomics analysis reveals allosteric and gene regulation axes for altered hepatic glucose-responsive metabolism in obesity.

Impaired glucose tolerance associated with obesity causes postprandial hyperglycemia and can lead to type 2 diabetes. To study the differences in liver metabolism in healthy and obese states, we constructed and analyzed transomics glucose-responsive metabolic networks with layers for metabolites, expression data for metabolic enzyme genes, transcription factors, and insulin signaling proteins from the livers of healthy and obese mice. We integrated multiomics time course data from wild-type and leptin-deficient obese (ob/ob) mice after orally administered glucose. In wild-type mice, metabolic reactions were rapidly regulated within 10 min of oral glucose administration by glucose-responsive metabolites, which functioned as allosteric regulators and substrates of metabolic enzymes, and by Akt-induced changes in the expression of glucose-responsive genes encoding metabolic enzymes. In ob/ob mice, the majority of rapid regulation by glucose-responsive metabolites was absent. Instead, glucose administration produced slow changes in the expression of carbohydrate, lipid, and amino acid metabolic enzyme-encoding genes to alter metabolic reactions on a time scale of hours. Few regulatory events occurred in both healthy and obese mice. Thus, our transomics network analysis revealed that regulation of glucose-responsive liver metabolism is mediated through different mechanisms in healthy and obese states. Rapid changes in allosteric regulators and substrates and in gene expression dominate the healthy state, whereas slow changes in gene expression dominate the obese state.

Stability of RNA sequences derived from the coronavirus genome in human cells.

Most viruses inhibit the innate immune system and/or the RNA degradation processes of host cells to construct an advantageous intracellular environment for their survival. Characteristic RNA sequences within RNA virus genomes or RNAs transcribed from DNA virus genomes contribute toward this inhibition. In this study, we developed a method called "Fate-seq" to comprehensively identify the RNA sequences derived from RNA and DNA viruses, contributing RNA stability in the cells. We examined the stabilization activity of 5,924 RNA fragments derived from 26 different viruses (16 RNA viruses and 10 DNA viruses) using next-generation sequencing of these RNAs fused 3' downstream of GFP reporter RNA. With the Fate-seq approach, we detected multiple virus-derived RNA sequences that stabilized GFP reporter RNA, including sequences derived from severe acute respiratory syndrome-related coronavirus (SARS-CoV). Comparative genomic analysis revealed that these RNA sequences and their predicted secondary structures are highly conserved between SARS-CoV and the novel coronavirus, SARS-CoV-2, which is responsible for the global outbreak of the coronavirus-associated disease that emerged in December 2019 (COVID-19). These sequences have the potential to enhance the stability of viral RNA genomes, thereby augmenting viral replication efficiency and virulence.

System identification of signaling dependent gene expression with different time-scale data.

Cells decode information of signaling activation at a scale of tens of minutes by downstream gene expression with a scale of hours to days, leading to cell fate decisions such as cell differentiation. However, no system identification method with such different time scales exists. Here we used compressed sensing technology and developed a system identification method using data of different time scales by recovering signals of missing time points. We measured phosphorylation of ERK and CREB, immediate early gene expression products, and mRNAs of decoder genes for neurite elongation in PC12 cell differentiation and performed system identification, revealing the input-output relationships between signaling and gene expression with sensitivity such as graded or switch-like response and with time delay and gain, representing signal transfer efficiency. We predicted and validated the identified system using pharmacological perturbation. Thus, we provide a versatile method for system identification using data with different time scales.

Laguerre Filter Analysis with Partial Least Square Regression Reveals a Priming Effect of ERK and CREB on c-FOS Induction.

Signaling networks are made up of limited numbers of molecules and yet can code information that controls different cellular states through temporal patterns and a combination of signaling molecules. In this study, we used a data-driven modeling approach, the Laguerre filter with partial least square regression, to describe how temporal and combinatorial patterns of signaling molecules are decoded by their downstream targets. The Laguerre filter is a time series model used to represent a nonlinear system based on Volterra series expansion. Furthermore, with this approach, each component of the Volterra series expansion is expanded by Laguerre basis functions. We combined two approaches, application of a Laguerre filter and partial least squares (PLS) regression, and applied the combined approach to analysis of a signal transduction network. We applied the Laguerre filter with PLS regression to identify input and output (IO) relationships between MAP kinases and the products of immediate early genes (IEGs). We found that Laguerre filter with PLS regression performs better than Laguerre filter with ordinary regression for the reproduction of a time series of IEGs. Analysis of the nonlinear characteristics extracted using the Laguerre filter revealed a priming effect of ERK and CREB on c-FOS induction. Specifically, we found that the effects of a first pulse of ERK enhance the subsequent effects on c-FOS induction of treatment with a second pulse of ERK, a finding consistent with prior molecular biological knowledge. The variable importance of projections and output loadings in PLS regression predicted the upstream dependency of each IEG. Thus, a Laguerre filter with partial least square regression approach appears to be a powerful method to find the processing mechanism of temporal patterns and combination of signaling molecules by their downstream gene expression.

Robustness of MEK-ERK Dynamics and Origins of Cell-to-Cell Variability in MAPK Signaling.

Cellular signaling processes can exhibit pronounced cell-to-cell variability in genetically identical cells. This affects how individual cells respond differentially to the same environmental stimulus. However, the origins of cell-to-cell variability in cellular signaling systems remain poorly understood. Here, we measure the dynamics of phosphorylated MEK and ERK across cell populations and quantify the levels of population heterogeneity over time using high-throughput image cytometry. We use a statistical modeling framework to show that extrinsic noise, particularly that from upstream MEK, is the dominant factor causing cell-to-cell variability in ERK phosphorylation, rather than stochasticity in the phosphorylation/dephosphorylation of ERK. We furthermore show that without extrinsic noise in the core module, variable (including noisy) signals would be faithfully reproduced downstream, but the within-module extrinsic variability distorts these signals and leads to a drastic reduction in the mutual information between incoming signal and ERK activity.

Robustness and compensation of information transmission of signaling pathways.

Robust transmission of information despite the presence of variation is a fundamental problem in cellular functions. However, the capability and characteristics of information transmission in signaling pathways remain poorly understood. We describe robustness and compensation of information transmission of signaling pathways at the cell population level. We calculated the mutual information transmitted through signaling pathways for the growth factor-mediated gene expression. Growth factors appeared to carry only information sufficient for a binary decision. Information transmission was generally more robust than average signal intensity despite pharmacological perturbations, and compensation of information transmission occurred. Information transmission to the biological output of neurite extension appeared robust. Cells may use information entropy as information so that messages can be robustly transmitted despite variation in molecular activities among individual cells.

Temporal decoding of MAP kinase and CREB phosphorylation by selective immediate early gene expression.

A wide range of growth factors encode information into specific temporal patterns of MAP kinase (MAPK) and CREB phosphorylation, which are further decoded by expression of immediate early gene products (IEGs) to exert biological functions. However, the IEG decoding system remain unknown. We built a data-driven based on time courses of MAPK and CREB phosphorylation and IEG expression in response to various growth factors to identify how signal is processed. We found that IEG expression uses common decoding systems regardless of growth factors and expression of each IEG differs in upstream dependency, switch-like response, and linear temporal filters. Pulsatile ERK phosphorylation was selectively decoded by expression of EGR1 rather than c-FOS. Conjunctive NGF and PACAP stimulation was selectively decoded by synergistic JUNB expression through switch-like response to c-FOS. Thus, specific temporal patterns and combinations of MAPKs and CREB phosphorylation can be decoded by selective IEG expression via distinct temporal filters and switch-like responses. The data-driven modeling is versatile for analysis of signal processing and does not require detailed prior knowledge of pathways.