Gene Regulatory Network Analysis Research Articles

Bioinformatics Analysis of the MicroRNA-Metabolic Gene Regulatory Network in Neuropathic Pain and Prediction of Corresponding Potential Therapeutics.

Neuropathic pain (NP) involves metabolic processes that are regulated by metabolic genes and their non-coding regulator genes such as microRNAs (miRNAs). Here, we aimed at exploring the key miRNA signatures regulating metabolic genes involved in NP pathogenesis. We downloaded NP-related data from public databases and identified differentially expressed microRNAs (miRNAs) and mRNAs through differential gene expression analysis. The miRNA target prediction was performed, and integration with the differentially expressed metabolic genes (DEMGs) was used for constructing the miRNA-DEMG network. Subsequently, functional enrichment analysis and protein–protein interaction (PPI) analysis were performed to explore the role of DEMGs in the regulatory network. The drug prediction was performed based on the DEMGs in the miRNA-DEMG network. A total of 8251 differentially expressed mRNAs (4193 upregulated and 4058 downregulated), and 959 differentially expressed miRNAs (455 upregulated and 504 downregulated) were identified. Moreover, after target gene prediction, a miRNA-DEMG network composed of 22 miRNAs and 113 mRNAs was constructed. The network was constituted of 135 nodes and 236 edges. We found that DEMGs in the network were mainly enriched in metabolic pathways and metabolic processes. A total of 1200 drugs were predicted as potential therapeutics for NP based on the differentially expressed genes, while 170 drugs were predicted for the DEMGs in the miRNA-DEMG network. Conclusively, our study predicted drugs that may be effective against the metabolic changes induced by miRNA dysregulation in NP. This information will help further reveal the pathological mechanism of NP and provide more treatment options for NP patients.Supplementary InformationThe online version contains supplementary material available at 10.1007/s12031-021-01911-w.

Mixed [formula omitted]/passivity based stability analysis of fractional-order gene regulatory networks with variable delays

In this paper, we derive delay and fractional-order dependent sufficient conditions for fractional-order gene regulatory networks (FOGRNs) with time-varying delays to be stable with H∞/passivity performance. Distinct from the existing works on delayed FOGRNs, the Lyapunov-Krasovskii functional (LKF) is suitably structured so that the delay functions can be non-differentiable or even have jump type discontinuities. Further, the positive-definiteness condition is relaxed for the matrices involved in LKF. A new set of linear matrix inequality (LMI) conditions that ensure the stability of FOGRNs with certain H∞/passivity performance level is derived using various results on fractional derivatives/integrals and convex property of LMIs. Finally, the results obtained are verified with existing FOGRNs via numerical simulations.

Single-nucleus transcriptome analysis of human brain immune response in patients with severe COVID-19

BackgroundCoronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, has been associated with neurological and neuropsychiatric illness in many individuals. We sought to further our understanding of the relationship between brain tropism, neuro-inflammation, and host immune response in acute COVID-19 cases.MethodsThree brain regions (dorsolateral prefrontal cortex, medulla oblongata, and choroid plexus) from 5 patients with severe COVID-19 and 4 controls were examined. The presence of the virus was assessed by western blot against viral spike protein, as well as viral transcriptome analysis covering > 99% of SARS-CoV-2 genome and all potential serotypes. Droplet-based single-nucleus RNA sequencing (snRNA-seq) was performed in the same samples to examine the impact of COVID-19 on transcription in individual cells of the brain.ResultsQuantification of viral spike S1 protein and viral transcripts did not detect SARS-CoV-2 in the postmortem brain tissue. However, analysis of 68,557 single-nucleus transcriptomes from three distinct regions of the brain identified an increased proportion of stromal cells, monocytes, and macrophages in the choroid plexus of COVID-19 patients. Furthermore, differential gene expression, pseudo-temporal trajectory, and gene regulatory network analyses revealed transcriptional changes in the cortical microglia associated with a range of biological processes, including cellular activation, mobility, and phagocytosis.ConclusionsDespite the absence of detectable SARS-CoV-2 in the brain at the time of death, the findings suggest significant and persistent neuroinflammation in patients with acute COVID-19.

MALBoost: a web-based application for gene regulatory network analysis in Plasmodium falciparum

BackgroundGene Regulatory Networks (GRN) produce powerful insights into transcriptional regulation in cells. The power of GRNs has been underutilized in malaria research. The Arboreto library was incorporated into a user-friendly web-based application for malaria researchers (http://malboost.bi.up.ac.za). This application will assist researchers with gaining an in depth understanding of transcriptomic datasets.MethodsThe web application for MALBoost was built in Python-Flask with Redis and Celery workers for queue submission handling, which execute the Arboreto suite algorithms. A submission of 5–50 regulators and total expression set of 5200 genes is permitted. The program runs in a point-and-click web user interface built using Bootstrap4 templates. Post-analysis submission, users are redirected to a status page with run time estimates and ultimately a download button upon completion. Result updates or failure updates will be emailed to the users.ResultsA web-based application with an easy-to-use interface is presented with a use case validation of AP2-G and AP2-I. The validation set incorporates cross-referencing with ChIP-seq and transcriptome datasets. For AP2-G, 5 ChIP-seq targets were significantly enriched with seven more targets presenting with strong evidence of validated targets.ConclusionThe MALBoost application provides the first tool for easy interfacing and efficiently allows gene regulatory network construction for Plasmodium. Additionally, access is provided to a pre-compiled network for use as reference framework. Validation for sexually committed ring-stage parasite targets of AP2-G, suggests the algorithm was effective in resolving “traditionally” low-level signatures even in bulk RNA datasets.

Comparative Transcriptomics Reveals Distinct Patterns of Gene Expression Conservation through Vertebrate Embryogenesis.

Despite life’s diversity, studies of variation often remind us of our shared evolutionary past. Abundant genome sequencing and analyses of gene regulatory networks illustrate that genes and entire pathways are conserved, reused, and elaborated in the evolution of diversity. Predating these discoveries, 19th-century embryologists observed that though morphology at birth varies tremendously, certain stages of vertebrate embryogenesis appear remarkably similar across vertebrates. In the mid to late 20th century, anatomical variability of early and late-stage embryos and conservation of mid-stages embryos (the “phylotypic” stage) was named the hourglass model of diversification. This model has found mixed support in recent analyses comparing gene expression across species possibly owing to differences in species, embryonic stages, and gene sets compared. We compare 186 microarray and RNA-seq data sets covering embryogenesis in six vertebrate species. We use an unbiased clustering approach to group stages of embryogenesis by transcriptomic similarity and ask whether gene expression similarity of clustered embryonic stages deviates from a null expectation. We characterize expression conservation patterns of each gene at each evolutionary node after correcting for phylogenetic nonindependence. We find significant enrichment of genes exhibiting early conservation, hourglass, late conservation patterns in both microarray and RNA-seq data sets. Enrichment of genes showing patterned conservation through embryogenesis indicates diversification of embryogenesis may be temporally constrained. However, the circumstances under which each pattern emerges remain unknown and require both broad evolutionary sampling and systematic examination of embryogenesis across species.

Bioinformatics Analysis of Choriocarcinoma-Related MicroRNA-Transcription Factor-Target Gene Regulatory Networks and Validation of Key miRNAs.

ObjectiveThe aim of the current research was to construct a miRNA-transcription factor (TF)-target gene regulatory network in order to investigate the mechanism underlying choriocarcinoma and to verify the network through the overexpression or silencing of hub miRNAs in vitro.Materials and MethodsA mRNA expression dataset and two miRNA expression datasets were analysed to identify differentially expressed genes (DEGs) and miRNAs (DEMs) between normal cells and choriocarcinoma cells. The top 400 upregulated and downregulated DEGs were identified as candidate DEGs, which were then mapped to construct protein–protein interaction (PPI) networks and select hub genes. Moreover, the DGIdb database was utilized to select candidate drugs for hub genes. Moreover, DEM target genes were predicted through the miRWalk2.0 database and overlaid with candidate DEGs to identify the differentially expressed target genes (DETGs). Furthermore, we established miRNA-TF-target gene regulatory networks and performed functional enrichment analysis of hub DEMs. Finally, we transfected mimics or inhibitors of hub DEMs into choriocarcinoma cells and assessed cell proliferation and migration to verify the vital role of hub DEMs in choriocarcinoma.ResultsA total of 140 DEMs and 400 candidate DEGs were screened from choriocarcinoma cells and normal cells. A PPI network of 400 candidate DEGs was established. Twenty-nine hub genes and 99 associated small molecules were identified to provide potential target drugs for choriocarcinoma treatment. We obtained 70 DETGs of DEMs derived from the intersection between predicted miRNA target genes and candidate DEGs. Subsequently, 3 hub DEMs were selected, and miRNA-TF-target gene regulatory networks containing 4 TFs, 3 TFs and 3 TFs for each network were constructed. The RT-PCR results confirmed that miR-29b-3p was highly expressed and that miR-519c-3p and miR-520a-5p were expressed at low levels in choriocarcinoma cells. The overexpression or silencing results suggested that 3 dysregulated hub DEMs jointly accelerated the proliferation and migration of choriocarcinoma.ConclusionAssociation of miRNA-TF-target gene regulatory networks may help us explore the underlying mechanism and provide potential targets for the diagnosis and treatment of choriocarcinoma.

Gene regulatory networks analysis of muscle-invasive bladder cancer subtypes using differential graphical model

BackgroundRecently, erdafitinib (Balversa), the first targeted therapy drug for genetic alteration, was approved to metastatic urothelial carcinoma. Cancer genomics research has been greatly encouraged. Currently, a large number of gene regulatory networks between different states have been constructed, which can reveal the difference states of genes. However, they have not been applied to the subtypes of Muscle-invasive bladder cancer (MIBC).ResultsIn this paper, we propose a method that construct gene regulatory networks under different molecular subtypes of MIBC, and analyse the regulatory differences between different molecular subtypes. Through differential expression analysis and the differential network analysis of the top 100 differential genes in the network, we find that SERPINI1, NOTUM, FGFR1 and other genes have significant differences in expression and regulatory relationship between MIBC subtypes.ConclusionsFurthermore, pathway enrichment analysis and differential network analysis demonstrate that Neuroactive ligand-receptor interaction and Cytokine-cytokine receptor interaction are significantly enriched pathways, and the genes contained in them are significant diversity in the subtypes of bladder cancer.

1173-P: Sex Differences in NASH Pathways Informed by Multiomics

Nonalcoholic fatty liver disease (NAFLD) has presented itself as a significant health burden, with increasing prevalence globally and evidence of a sexually dimorphic disease course. Particularly, it is of critical importance to understand the latter stage of disease, nonalcoholic steatohepatitis (NASH) with fibrosis, which is more prevalent and severe in males, in order to provide better prevention, treatment, and biomarker options to reduce the health burden. Our mechanistic understanding of NASH progression is far from elucidated, and thus, we present a multi-omics pathway and network analysis utilizing genomic, transcriptomic, and lipidomic data from the Hybrid Mouse Diversity Panel (HMDP) to uncover key biological processes and key driver genes causal to liver fibrosis and to dissect sex differences. Analyzing these multi-omics datasets by sex, we present mechanistic differences important to fibrosis development between males and females. We find that males present with greater enrichment of immune processes as well as abnormalities in protein and lipid metabolism, whereas females present with significant abnormalities linked with carbohydrate metabolism processes, suggesting differential causal mechanisms of fibrosis progression between sexes. Comparing males and females, we also find consistent processes such as extracellular matrix development, clearly vital for fibrosis to develop. Using gene regulatory network analysis of the liver tissue, we further identified key network genes driving the sex-specific processes as well as those consistent between sexes that are important for liver fibrosis. Our multi-omics integrative studies offer comprehensive understanding of NASH in a sex-specific manner. Disclosure M. Blencowe: None. A. Lusis: None. F. Norheim: None. Z. Saleem: None. N. S. Hsu: None. S. Hui: None. C. Pan: None. K. Chella krishnan: None. C. Edillor: None. X. Yang: None. Funding National Institutes of Health (R01DK117850)

Elevated Expression of PDZD11 Is Associated With Poor Prognosis and Immune Infiltrates in Hepatocellular Carcinoma.

Epithelial cells are held together by tight and adherent junctions, which are destroyed by the activation of epithelial-to-mesenchymal transition (EMT). The PLEKHA7-PDZD11 complex has been reported to be important for epithelial cell adhesion and connecting tissues. However, there is no research regarding the expression and role of PDZD11 in liver hepatocellular carcinoma (LIHC) progression. Here, we analyzed PDZD11 mRNA expression and its clinical results in LIHC patient RNA sequencing data based on different open databases. Furthermore, we examined differences in PDZD11 expression in LIHC tissues and cell lines using western blotting and real-time qPCR. These results are the first to report that the mRNA and protein levels of PDZD11 are significantly overexpressed in LIHC. Moreover, high expression of PDZD11 was correlated with poor overall survival in patients with LIHC. Gene regulatory network analysis suggested that PDZD11 is mainly involved in copper ion homeostasis, proteasome, and oxidative phosphorylation pathways. Interestingly, we found that PDZD11 levels were positively correlated with the abundance of immune infiltrates. In particular, higher infiltration levels of CD4+ T cells and macrophage subsets significantly affected LIHC patient prognosis. Taken together, these results demonstrate that PDZD11 could be a potential diagnostic and prognostic biomarker in LIHC.

Role of Regulatory Non-Coding RNAs in Aggressive Thyroid Cancer: Prospective Applications of Neural Network Analysis.

Thyroid cancer (TC) is the most common endocrine malignancy. Most TCs have a favorable prognosis, whereas anaplastic thyroid carcinoma (ATC) is a lethal form of cancer. Different genetic and epigenetic alterations have been identified in aggressive forms of TC such as ATC. Non-coding RNAs (ncRNAs) represent functional regulatory molecules that control chromatin reprogramming, including transcriptional and post-transcriptional mechanisms. Intriguingly, they also play an important role as coordinators of complex gene regulatory networks (GRNs) in cancer. GRN analysis can model molecular regulation in different species. Neural networks are robust computing systems for learning and modeling the dynamics or dependencies between genes, and are used for the reconstruction of large data sets. Canonical network motifs are coordinated by ncRNAs through gene production from each transcript as well as through the generation of a single transcript that gives rise to multiple functional products by post-transcriptional modifications. In non-canonical network motifs, ncRNAs interact through binding to proteins and/or protein complexes and regulate their functions. This article overviews the potential role of ncRNAs GRNs in TC. It also suggests prospective applications of deep neural network analysis to predict ncRNA molecular language for early detection and to determine the prognosis of TC. Validation of these analyses may help in the design of more effective and precise targeted therapies against aggressive TC.

Integrated DNA Methylation and Gene Expression Analysis Identified S100A8 and S100A9 in the Pathogenesis of Obesity.

Background: To explore the association of DNA methylation and gene expression in the pathology of obesity.Methods: (1) Genomic DNA methylation and mRNA expression profile of visceral adipose tissue (VAT) were performed in a comprehensive database of gene expression in obese and normal subjects. (2) Functional enrichment analysis and construction of differential methylation gene regulatory networks were performed. (3) Validation of the two different methylation sites and corresponding gene expression was done in a separate microarray dataset. (4) Correlation analysis was performed on DNA methylation and mRNA expression data.Results: A total of 77 differentially expressed mRNAs matched with differentially methylated genes. Analysis revealed two different methylation sites corresponding to two unique genes—s100a8-cg09174555 and s100a9-cg03165378. Through the verification test of two interesting different expression positions [differentially methylated positions (DMPs)] and their corresponding gene expression, we found that methylation in these genes was negatively correlated to gene expression in the obesity group. Higher S100A8 and S100A9 expressions in obese subjects were validated in a separate microarray dataset.Conclusion: This study confirmed the relationship between DNA methylation and gene expression and emphasized the important role of S100A8 and S100A9 in the pathogenesis of obesity.

Single-Cell Transcriptional Heterogeneity of Neutrophils During Acute Pulmonary Cryptococcus neoformans Infection.

Neutrophils are critical as the first-line defense against fungal pathogens. Yet, previous studies indicate that neutrophil function is complex during Cryptococcus neoformans (Cn) infection. To better understand the role of neutrophils in acute pulmonary cryptococcosis, we analyzed neutrophil heterogeneity by single-cell transcriptional analysis of immune cells in the lung of Cn-infected mice from a published dataset. We identified neutrophils by reference-based annotation and identified two distinct neutrophil subsets generated during acute Cn infection: A subset with an oxidative stress signature (Ox-PMN) and another with enhanced cytokine gene expression (Cyt-PMN). Based on gene regulatory network and ligand-receptor analysis, we hypothesize that Ox-PMNs interact with the fungus and generate ROS, while Cyt-PMNs are longer-lived neutrophils that indirectly respond to Cn-derived ligands and cytokines to modulate cell-cell communication with dendritic cells and alveolar macrophages. Based on the data, we hypothesized that, during in vivo fungal infection, there is a division of labor in which each activated neutrophil becomes either Ox-PMN or Cyt-PMN.

Single human oocyte transcriptome analysis reveals distinct maturation stage-dependent pathways impacted by age.

Female fertility is inversely correlated with maternal age due to a depletion of the oocyte pool and a reduction in oocyte developmental competence. Few studies have addressed the effect of maternal age on the human mature oocyte (MII) transcriptome, which is established during oocyte growth and maturation, however, the pathways involved remain unclear. Here, we characterize and compare the transcriptomes of a large cohort of fully grown germinal vesicle stage (GV) and in vitro matured (IVM‐MII) oocytes from women of varying reproductive age. First, we identified two clusters of cells reflecting the oocyte maturation stage (GV and IVM‐MII) with 4445 and 324 putative marker genes, respectively. Furthermore, we identified genes for which transcript representation either progressively increased or decreased with age. Our results indicate that the transcriptome is more affected by age in IVM‐MII oocytes (1219 genes) than in GV oocytes (596 genes). In particular, we found that transcripts of genes involved in chromosome segregation and RNA splicing significantly increased representation with age, while genes related to mitochondrial activity showed a lower representation. Gene regulatory network analysis facilitated the identification of potential upstream master regulators of the genes involved in those biological functions. Our analysis suggests that advanced maternal age does not globally affect the oocyte transcriptome at GV or IVM‐MII stages. Nonetheless, hundreds of genes displayed altered transcript representation, particularly in IVM‐MII oocytes, which might contribute to the age‐related quality decline in human oocytes.

Gene Regulation Network Analysis on Human Prostate Orthografts Highlights a Potential Role for the JMJD6 Regulon in Clinical Prostate Cancer.

Simple SummaryProstate cancer is a very common malignancy worldwide. Treatment resistant prostate cancer poses a big challenge to clinicians and is the second most common cause of premature death in men with cancer. Gene expression analysis has been performed on clinical tumours but to date none of the gene expression-based biomarkers for prostate cancer have been successfully integrated to into clinical practice to improve patient management and treatment choice. We applied a novel laboratory prostate cancer model to mimic clinical hormone responsive and resistant prostate cancer and tested whether a network of genes similarly regulated by transcription factors (gene products that control the expression of target genes) are associated with patient outcome. We identified regulons (networks of genes similarly regulated) from our preclinical prostate cancer models and further evaluated the top ranked JMJD6 gene related regulated network in three independent clinical patient cohorts.Background: Prostate cancer (PCa) is the second most common tumour diagnosed in men. Tumoral heterogeneity in PCa creates a significant challenge to develop robust prognostic markers and novel targets for therapy. An analysis of gene regulatory networks (GRNs) in PCa may provide insight into progressive PCa. Herein, we exploited a graph-based enrichment score to integrate data from GRNs identified in preclinical prostate orthografts and differentially expressed genes in clinical resected PCa. We identified active regulons (transcriptional regulators and their targeted genes) associated with PCa recurrence following radical prostatectomy. Methods: The expression of known transcription factors and co-factors was analysed in a panel of prostate orthografts (n = 18). We searched for genes (as part of individual GRNs) predicted to be regulated by the highest number of transcriptional factors. Using differentially expressed gene analysis (on a per sample basis) coupled with gene graph enrichment analysis, we identified candidate genes and associated GRNs in PCa within the UTA cohort, with the most enriched regulon being JMJD6, which was further validated in two additional cohorts, namely EMC and ICGC cohorts. Cox regression analysis was performed to evaluate the association of the JMJD6 regulon activity with disease-free survival time in the three clinical cohorts as well as compared to three published prognostic gene signatures (TMCC11, BROMO-10 and HYPOXIA-28). Results: 1308 regulons were correlated to transcriptomic data from the three clinical prostatectomy cohorts. The JMJD6 regulon was identified as the top enriched regulon in the UTA cohort and again validated in the EMC cohort as the top-ranking regulon. In both UTA and EMC cohorts, the JMJD6 regulon was significantly associated with cancer recurrence. Active JMJD6 regulon also correlated with disease recurrence in the ICGC cohort. Furthermore, Kaplan–Meier analysis confirmed shorter time to recurrence in patients with active JMJD6 regulon for all three clinical cohorts (UTA, EMC and ICGC), which was not the case for three published prognostic gene signatures (TMCC11, BROMO-10 and HYPOXIA-28). In multivariate analysis, the JMJD6 regulon status significantly predicted disease recurrence in the UTA and EMC, but not ICGC datasets, while none of the three published signatures significantly prognosticate for cancer recurrence. Conclusions: We have characterised gene regulatory networks from preclinical prostate orthografts and applied transcriptomic data from three clinical cohorts to evaluate the prognostic potential of the JMJD6 regulon.

Identification of hub molecules of FUS-ALS by Bayesian gene regulatory network analysis of iPSC model: iBRN

Fused in sarcoma/translated in liposarcoma (FUS) is a causative gene of amyotrophic lateral sclerosis (ALS). Mutated FUS causes accumulation of DNA damage and cytosolic stress granule (SG) formation, thereby motor neuron (MN) death. However, key molecular aetiology remains unclear. Here, we applied a novel platform technology, iBRN, "Non- biased" Bayesian gene regulatory network analysis based on induced pluripotent stem cell (iPSC)-derived cell model, to elucidate the molecular aetiology using transcriptome of iPSC-derived MNs harboring FUSH517D. iBRN revealed "hub molecules", which strongly influenced transcriptome network, such as miR-125b-5p-TIMELESS axis and PRKDC for the molecular aetiology. Next, we confirmed miR-125b-5p-TIMELESS axis in FUSH517D MNs such that miR-125b-5p regulated several DNA repair-related genes including TIMELESS. In addition, we validated both introduction of miR-125b-5p and knocking down of TIMELESS caused DNA damage in the cell culture model. Furthermore, PRKDC was strongly associated with FUS mis-localization into SGs by DNA damage under impaired DNA-PK activity. Collectively, our iBRN strategy provides the first compelling evidence to elucidate molecular aetiology in neurodegenerative diseases.

PGC1/PPAR drive cardiomyocyte maturation at single cell level via YAP1 and SF3B2

Cardiomyocytes undergo significant structural and functional changes after birth, and these fundamental processes are essential for the heart to pump blood to the growing body. However, due to the challenges of isolating single postnatal/adult myocytes, how individual newborn cardiomyocytes acquire multiple aspects of the mature phenotype remains poorly understood. Here we implement large-particle sorting and analyze single myocytes from neonatal to adult hearts. Early myocytes exhibit wide-ranging transcriptomic and size heterogeneity that is maintained until adulthood with a continuous transcriptomic shift. Gene regulatory network analysis followed by mosaic gene deletion reveals that peroxisome proliferator-activated receptor coactivator-1 signaling, which is active in vivo but inactive in pluripotent stem cell-derived cardiomyocytes, mediates the shift. This signaling simultaneously regulates key aspects of cardiomyocyte maturation through previously unrecognized proteins, including YAP1 and SF3B2. Our study provides a single-cell roadmap of heterogeneous transitions coupled to cellular features and identifies a multifaceted regulator controlling cardiomyocyte maturation.

Encoding Threshold Boolean Networks into Reaction Systems for the Analysis of Gene Regulatory Networks

Gene regulatory networks represent the interactions among genes regulating the activation of specific cell functionalities and they have been successfully modeled using threshold Boolean networks. In this paper we propose a systematic translation of threshold Boolean networks into reaction systems. Our translation produces a non redundant set of rules with a minimal number of objects. This translation allows us to simulate the behavior of a Boolean network simply by executing the (closed) reaction system we obtain. This can be very useful for investigating the role of different genes simply by “playing” with the rules. We developed a tool able to systematically translate a threshold Boolean network into a reaction system. We use our tool to translate two well known Boolean networks modelling biological systems: the yeast-cell cycle and the SOS response in Escherichia coli. The resulting reaction systems can be used for investigating dynamic causalities among genes.

Cellular plasticity balances the metabolic and proliferation dynamics of a regenerating liver.

The adult liver has an exceptional ability to regenerate, but how it maintains its specialized functions during regeneration is unclear. Here, we used partial hepatectomy (PHx) in tandem with single-cell transcriptomics to track cellular transitions and heterogeneities of ∼22,000 liver cells through the initiation, progression, and termination phases of mouse liver regeneration. Our results uncovered that, following PHx, a subset of hepatocytes transiently reactivates an early-postnatal-like gene expression program to proliferate, while a distinct population of metabolically hyperactive cells appears to compensate for any temporary deficits in liver function. Cumulative EdU labeling and immunostaining of metabolic, portal, and central vein–specific markers revealed that hepatocyte proliferation after PHx initiates in the midlobular region before proceeding toward the periportal and pericentral areas. We further demonstrate that portal and central vein proximal hepatocytes retain their metabolically active state to preserve essential liver functions while midlobular cells proliferate nearby. Through combined analysis of gene regulatory networks and cell–cell interaction maps, we found that regenerating hepatocytes redeploy key developmental regulons, which are guided by extensive ligand-receptor-mediated signaling events between hepatocytes and nonparenchymal cells. Altogether, our study offers a detailed blueprint of the intercellular crosstalk and cellular reprogramming that balances the metabolic and proliferative requirements of a regenerating liver.

Tolerogenic and immunogenic states of Langerhans cells are orchestrated by epidermal signals acting on a core maturation gene module.

Langerhans cells (LCs), residing in the epidermis, are able to induce potent immunogenic responses and also to mediate immune tolerance. We propose that tolerogenic and immunogenic responses of LCs are directed by signaling from the epidermis and involve counter-acting gene circuits that are coupled to a core maturation gene module. We base our analysis on recent genetic and genomic findings facilitating the understanding of the molecular mechanisms controlling these divergent immune functions. Comparing gene regulatory network (GRN) analyses of various types of dendritic cells (DCs) including LCs we integrate signaling-dependent (TGFβ, EpCAM, β-Catenin) and transcription factor (IRF4, IRF1, NFκB) regulated gene circuits that appear to orchestrate the distinctive LC functional states. Our model proposes, that while epidermal signaling in the steady-state promotes LC tolerogenic function, the disruption of cell-cell contacts coupled with inflammatory signaling induces LC immunogenic programing. The conceptual framework emphasizes the sensing of discrete epidermal and inflammatory cues by resident LCs in dictating their genomic programing and cell state dynamics.

OsTPR boosts the superior grains through increase in upper secondary rachis branches without incurring a grain quality penalty.

SummaryTo address the future food security in Asia, we need to improve the genetic gain of grain yield while ensuring the consumer acceptance. This study aimed to identify novel genes influencing the number of upper secondary rachis branches (USRB) to elevate superior grains without compromising grain quality by studying the genetic variance of 310 diverse O. sativa var. indica panel using single‐ and multi‐locus genome‐wide association studies (GWAS), gene set analyses and gene regulatory network analysis. GWAS of USRB identified 230 significant (q‐value < 0.05) SNPs from chromosomes 1 and 2. GWAS targets narrowed down using gene set analyses identified large effect association on an important locus LOC_Os02g50790/LOC_Os02g50799 encoding a nuclear‐pore anchor protein (OsTPR). The superior haplotype derived from non‐synonymous SNPs identified in OsTPR was specifically associated with increase in USRB with superior grains being low chalk. Through haplotype mining, we further demonstrated the synergy of offering added yield advantage due to superior allele of OsTPR in elite materials with low glycaemic index (GI) property. We further validated the importance of OsTPR using recombinant inbred lines (RILs) population by introgressing a superior allele of OsTPR into elite materials resulted in raise in productivity in high amylose background. This confirmed a critical role for OsTPR in influencing yield while maintaining grain and nutritional quality.