Temporally Regulated Plant-Nematode Gene Networks Implicate Metabolic Pathways

Root-knot nematodes (RKN) (Meloidogyne spp.) constantly communicate with their host to establish and maintain specialized feeding cells. They likely regulate this interaction by monitoring host biology. As plant host biology is influenced by light and gene expression varies correspondingly, RKN gene transcription and biology likely follow similar patterns. We profiled RKN transcripts over a period of 24 h and identified approximately 1,000 differentially expressed genes (DEG) in nematode and model host Medicago truncatula, with the majority of DEG occurring in the middle of the dark period. Many of the plant DEG are involved in defense-response pathways, while the nematode DEG are involved in establishing infection, suggesting a strong host-nematode interaction occurring during the dark. To identify interacting genes, we developed a plant-nematode gene network based on DEG signals. The phenylpropanoid pathway was identified as a significant plant-nematode interacting pathway, representing four of 33 genes in the network. We further examined if this pathway interacts similarly in another host, tomato, by quantifying phenolic and flavonoid compounds produced by this pathway. Phenolic compounds showed a significant increase in production during the day in uninoculated plants as compared with during the night. However, during the dark period, there was an increase in flavonoid content in infected plants when compared with uninfected controls, indicating potential host defense mechanisms active during the height of nematode activity at night. This study elucidated cross-species interacting pathways that could be targeted to develop novel management strategies to these important pests.[Formula: see text] Copyright © 2022 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Transcriptomic Data Meta-Analysis Sheds Light on High Light Response in Arabidopsis thaliana L

The availability and intensity of sunlight are among the major factors of growth, development and metabolism in plants. However, excessive illumination disrupts the electronic balance of photosystems and leads to the accumulation of reactive oxygen species in chloroplasts, further mediating several regulatory mechanisms at the subcellular, genetic, and molecular levels. We carried out a comprehensive bioinformatic analysis that aimed to identify genetic systems and candidate transcription factors involved in the response to high light stress in Arabidopsis thaliana L. using resources GEO NCBI, string-db, ShinyGO, STREME, and Tomtom, as well as programs metaRE, CisCross, and Cytoscape. Through the meta-analysis of five transcriptomic experiments, we selected a set of 1151 differentially expressed genes, including 453 genes that compose the gene network. Ten significantly enriched regulatory motifs for TFs families ZF-HD, HB, C2H2, NAC, BZR, and ARID were found in the promoter regions of differentially expressed genes. In addition, we predicted families of transcription factors associated with the duration of exposure (RAV, HSF), intensity of high light treatment (MYB, REM), and the direction of gene expression change (HSF, S1Fa-like). We predicted genetic components systems involved in a high light response and their expression changes, potential transcriptional regulators, and associated processes.

Priming-induced alterations in histone modifications modulate transcriptional responses in soybean under salt stress

Plants that have experienced certain abiotic stress may gain tolerance to a similar stress in subsequent exposure. This phenomenon, called priming, was observed here in soybean (Glycine max) seedlings exposed to salt stress. Time-course transcriptomic profiles revealed distinctively different transcriptional responses in the primed seedlings from those in the non-primed seedlings under high salinity stress, indicating a stress response strategy of repressing unhelpful biotic stress responses and focusing on the promotion of those responses important for salt tolerance. To identify histone marks altered by the priming salinity treatment, a genome-wide profiling of histone 3 lysine 4 dimethylation (H3K4me2), H3K4me3, and histone 3 lysine 9 acetylation (H3K9ac) was performed. Our integrative analyses revealed that priming induced drastic alterations in these histone marks, which coordinately modified the stress response, ion homeostasis, and cell wall modification. Furthermore, transcriptional network analyses unveiled epigenetically modified networks which mediate the strategic downregulation of defense responses. Altering the histone acetylation status using a chemical inhibitor could elicit the priming-like transcriptional responses in non-primed seedlings, confirming the importance of histone marks in forming the priming response.

Identification and Validation of Key Genes of Differential Correlations in Gastric Cancer

Background: Gastric cancer (GC) is aggressive cancer with a poor prognosis. Previously bulk transcriptome analysis was utilized to identify key genes correlated with the development, progression and prognosis of GC. However, due to the complexity of the genetic mutations, there is still an urgent need to recognize core genes in the regulatory network of GC.

Methods: Gene expression profiles (GSE66229) were retrieved from the GEO database. Weighted correlation network analysis (WGCNA) was employed to identify gene modules mostly correlated with GC carcinogenesis. R package ‘DiffCorr’ was applied to identify differentially correlated gene pairs in tumor and normal tissues. Cytoscape was adopted to construct and visualize the gene regulatory network.

Results: A total of 15 modules were detected in WGCNA analysis, among which three modules were significantly correlated with GC. Then genes in these modules were analyzed separately by “DiffCorr”. Multiple differentially correlated gene pairs were recognized and the network was visualized by the software Cytoscape. Moreover, GEMIN5 and PFDN2, which were rarely discussed in GC, were identified as key genes in the regulatory network and the differential expression was validated by real-time qPCR, WB and IHC in cell lines and GC patient tissues.

Conclusions: Our research has shed light on the carcinogenesis mechanism by revealing differentially correlated gene pairs during transition from normal to tumor. We believe the application of this network-based algorithm holds great potential in inferring relationships and detecting candidate biomarkers.

Prognostic Significance and Gene Co-Expression Network of PLAU and PLAUR in Gliomas

The urokinase-type plasminogen activator(PLAU) and its receptor PLAUR participate in a series of cell physiological activities on the extracellular surface. Abnormal expression of PLAU and PLAUR is associated with tumorigenesis. This study aims to evaluate the prognostic value of PLAU/PLAUR transcription expression in glioma and to explore how they affect the generation and progression of glioma. In this study, online databases are applied, such as Oncomine, GEPIA, CGGA, cBioPortal, and LinkedOmics. Overexpression of PLAU/PLAUR was found to be significantly associated with clinical variables including age, tumor type, WHO grade, histology, IDH-1 mutation, and 1p19q status. PLAU and PLAUR had a high correlation in transcriptional expression levels. High expression of PLAU and PLAUR predicted a poor prognosis in primary glioma and recurrent glioma patients, especially in lower grade gliomas. Cox regression analysis indicated that high expression of PLAU and PLAUR were independent prognostic factors for shorter overall survival in glioma patients. In gene co-expression network analysis PLAU and PLAUR and their co-expression genes were found to be involved in inflammatory activities and tumor-related signaling pathways. In conclusion, PLAU and PLAUR could be promising prognostic biomarkers and potential therapeutic targets of glioma patients.

An Augmented High-Dimensional Graphical Lasso Method to Incorporate Prior Biological Knowledge for Global Network Learning

Biological networks are often inferred through Gaussian graphical models (GGMs) using gene or protein expression data only. GGMs identify conditional dependence by estimating a precision matrix between genes or proteins. However, conventional GGM approaches often ignore prior knowledge about protein-protein interactions (PPI). Recently, several groups have extended GGM to weighted graphical Lasso (wGlasso) and network-based gene set analysis (Netgsa) and have demonstrated the advantages of incorporating PPI information. However, these methods are either computationally intractable for large-scale data, or disregard weights in the PPI networks. To address these shortcomings, we extended the Netgsa approach and developed an augmented high-dimensional graphical Lasso (AhGlasso) method to incorporate edge weights in known PPI with omics data for global network learning. This new method outperforms weighted graphical Lasso-based algorithms with respect to computational time in simulated large-scale data settings while achieving better or comparable prediction accuracy of node connections. The total runtime of AhGlasso is approximately five times faster than weighted Glasso methods when the graph size ranges from 1,000 to 3,000 with a fixed sample size (n = 300). The runtime difference between AhGlasso and weighted Glasso increases when the graph size increases. Using proteomic data from a study on chronic obstructive pulmonary disease, we demonstrate that AhGlasso improves protein network inference compared to the Netgsa approach by incorporating PPI information.

The auxin signaling pathway to its PIN transporters: insights based on a meta-analysis of auxin-induced transcriptomes

Active polar transport of the plant hormone auxin carried out by its PIN transporters is a key link in the formation and maintenance of auxin distribution, which, in turn, determines plant morphogenesis. The plasticity of auxin distribution is largely realized through the molecular genetic regulation of the expression of its transporters belonging to the PIN-FORMED (PIN) protein family. Regulation of auxin-response genes occurs through the ARF-Aux/ IAA signaling pathway. However, it is not known which ARF-Aux/IAA proteins are involved in the regulation of PIN gene expression by auxin. In Arabidopsis thaliana, the PIN, ARF, and Aux/IAA families contain a larger number of members; their various combinations are possible in realization of the signaling pathway, and this is a challenge for understanding the mechanisms of this process. The use of high-throughput sequencing data on auxin-induced transcriptomes makes it possible to identify candidate genes involved in the regulation of PIN expression. To address this problem, we created an approach for the meta-analysis of auxin-induced transcriptomes, which helped us select genes that change their expression during the auxin response together with PIN1, PIN3, PIN4 and PIN7. Possible regulators of ARF-Aux/ IAA signaling pathway for each of the PINs under study were identif ied, and so were the aspects of their regulatory circuits both common for groups of PIN genes and specif ic for each PIN gene. Reconstruction of gene networks and their analysis predicted possible interactions between genes and served as an additional conf irmation of the pathways obtained in the meta-analysis. The approach developed can be used in the search for gene expression regulators in other genomewide data.

Hippocampal Transcriptome-Wide Association Study Reveals Correlations Between Impaired Glutamatergic Synapse Pathway and Age-Related Hearing Loss in BXD-Recombinant Inbred Mice

Age-related hearing loss (ARHL) is associated with cognitive dysfunction; however, the detailed underlying mechanisms remain unclear. The aim of this study is to investigate the potential underlying mechanism with a system genetics approach. A transcriptome-wide association study was performed on aged (12–32 months old) BXD mice strains. The hippocampus gene expression was obtained from 56 BXD strains, and the hearing acuity was assessed from 54 BXD strains. Further correlation analysis identified a total of 1,435 hearing-related genes in the hippocampus (p < 0.05). Pathway analysis of these genes indicated that the impaired glutamatergic synapse pathway is involved in ARHL (p = 0.0038). Further gene co-expression analysis showed that the expression level of glutamine synthetase (Gls), which is significantly correlated with ARHL (n = 26, r = −0.46, p = 0.0193), is a crucial regulator in glutamatergic synapse pathway and associated with learning and memory behavior. In this study, we present the first systematic evaluation of hippocampus gene expression pattern associated with ARHL, learning, and memory behavior. Our results provide novel potential molecular mechanisms involved in ARHL and cognitive dysfunction association.

Vitis OneGenE: A Causality-Based Approach to Generate Gene Networks in Vitis vinifera Sheds Light on the Laccase and Dirigent Gene Families

The abundance of transcriptomic data and the development of causal inference methods have paved the way for gene network analyses in grapevine. Vitis OneGenE is a transcriptomic data mining tool that finds direct correlations between genes, thus producing association networks. As a proof of concept, the stilbene synthase gene regulatory network obtained with OneGenE has been compared with published co-expression analysis and experimental data, including cistrome data for MYB stilbenoid regulators. As a case study, the two secondary metabolism pathways of stilbenoids and lignin synthesis were explored. Several isoforms of laccase, peroxidase, and dirigent protein genes, putatively involved in the final oxidative oligomerization steps, were identified as specifically belonging to either one of these pathways. Manual curation of the predicted sequences exploiting the last available genome assembly, and the integration of phylogenetic and OneGenE analyses, identified a group of laccases exclusively present in grapevine and related to stilbenoids. Here we show how network analysis by OneGenE can accelerate knowledge discovery by suggesting new candidates for functional characterization and application in breeding programs.

Systems Genetics Analysis Defines Importance Of TMEM43/LUMA For Cardiac And Metabolic Related Pathways

Broad cellular functions and diseases including muscular dystrophy, arrhythmogenic right ventricular cardiomyopathy (ARVC5) and cancer are associated with transmembrane protein43 (TMEM43/LUMA). The study aimed to investigate biological roles of TMEM43 through genetic regulation, gene pathways and gene networks, candidate interacting genes, and up- or downstream regulators. Cardiac transcriptomes from 40 strains of recombinant inbred BXD mice and two parental strains representing murine genetic reference population (GRP) were applied for genetic correlation, functional enrichment, and coexpression network analysis using systems genetics approach. The results were validated in a newly created knock-in Tmem43-S358L mutation mouse model (Tmem43^S358L) that displayed signs of cardiac dysfunction, resembling ARVC5 phenotype seen in humans. We found high Tmem43 levels among BXDs with broad variability in expression. Expression of Tmem43 highly negatively correlated with heart mass and heart rate among BXDs, whereas levels of Tmem43 highly positively correlated with plasma high-density lipoproteins (HDL). Through finding differentially expressed genes (DEGs) between Tmem43^S358L mutant and wild-type (Tmem43^WT) lines, 18 pathways (out of 42 found in BXDs GRP) that are involved in ARVC, hypertrophic cardiomyopathy, dilated cardiomyopathy, nonalcoholic fatty liver disease, Alzheimer’s disease, Parkinson’s disease, and Huntington’s disease were verified. We further constructed Tmem43-mediated gene network, in which Ctnna1, Adcy6, Gnas, Ndufs6, and Uqcrc2 were significantly altered in Tmem43^S358L mice versus Tmem43^WT controls. Our study defined the importance of Tmem43 for cardiac- and metabolism-related pathways, suggesting that cardiovascular disease-relevant risk factors may also increase risk of metabolic and neurodegenerative diseases via TMEM43-mediated pathways.

A phosphate starvation response-centered network regulates mycorrhizal symbiosis

To secure phosphorus (P) from soil, most land plants use a direct phosphate uptake pathway via root hairs and epidermis and an indirect phosphate uptake pathway via mycorrhizal symbiosis. The interaction between these two pathways is unclear. Here, we mapped a network between transcription factors and mycorrhizal symbiosis-related genes using Y1H. Intriguingly, this gene regulatory network is governed by the conserved P-sensing pathway, centered on phosphate starvation response (PHR) transcription factors. PHRs are required for mycorrhizal symbiosis and regulate symbiosis-related genes via the P1BS motif. SPX-domain proteins suppress OsPHR2-mediated induction of symbiosis-related genes and inhibit mycorrhizal infection. In contrast, plants overexpressing OsPHR2 show improved mycorrhizal infection and are partially resistant to P-mediated inhibition of symbiosis. Functional analyses of network nodes revealed co-regulation of hormonal signaling and mycorrhizal symbiosis. This network deciphers extensive regulation of mycorrhizal symbiosis by endogenous and exogenous signals and highlights co-option of the P-sensing pathway for mycorrhizal symbiosis.

Identification of Gene Signature as Diagnostic and Prognostic Blood Biomarker for Early Hepatocellular Carcinoma Using Integrated Cross-Species Transcriptomic and Network Analyses

Background: Hepatocellular carcinoma (HCC) is considered the most common type of liver cancer and the fourth leading cause of cancer-related deaths in the world. Since the disease is usually diagnosed at advanced stages, it has poor prognosis. Therefore, reliable biomarkers are urgently needed for early diagnosis and prognostic assessment. Methods: We used genome-wide gene expression profiling datasets from human and rat early HCC (eHCC) samples to perform integrated genomic and network-based analyses, and discovered gene markers that are expressed in blood and conserved in both species. We then used independent gene expression profiling datasets for peripheral blood mononuclear cells (PBMCs) for eHCC patients and from The Cancer Genome Atlas (TCGA) database to estimate the diagnostic and prognostic performance of the identified gene signature. Furthermore, we performed functional enrichment, interaction networks and pathway analyses. Results: We identified 41 significant genes that are expressed in blood and conserved across species in eHCC. We used comprehensive clinical data from over 600 patients with HCC to verify the diagnostic and prognostic value of 41-gene-signature. We developed a prognostic model and a risk score using the 41-geneset that showed that a high prognostic index is linked to a worse disease outcome. Furthermore, our 41-gene signature predicted disease outcome independently of other clinical factors in multivariate regression analysis. Our data reveals a number of cancer-related pathways and hub genes, including EIF4E, H2AFX, CREB1, GSK3B, TGFBR1, and CCNA2, that may be essential for eHCC progression and confirm our gene signature's ability to detect the disease in its early stages in patients' biological fluids instead of invasive procedures and its prognostic potential. Conclusion: Our findings indicate that integrated cross-species genomic and network analysis may provide reliable markers that are associated with eHCC that may lead to better diagnosis, prognosis, and treatment options.

GWAS Meta-Analysis Reveals Shared Genes and Biological Pathways between Major Depressive Disorder and Insomnia

Major depressive disorder (MDD) is one of the most prevalent and disabling mental disorders worldwide. Among the symptoms of MDD, sleep disturbance such as insomnia is prominent, and the first reason patients may seek professional help. However, the underlying pathophysiology of this comorbidity is still elusive. Recently, genome-wide association studies (GWAS) have begun to unveil the genetic background of several psychiatric disorders, including MDD and insomnia. Identifying the shared genomic risk loci between comorbid psychiatric disorders could be a valuable strategy to understanding their comorbidity. This study seeks to identify the shared genes and biological pathways between MDD and insomnia based on their shared genetic variants. First, we performed a meta-analysis based on the GWAS summary statistics of MDD and insomnia obtained from Psychiatric Genomics Consortium and UK Biobank, respectively. Next, we associated shared genetic variants to genes using two gene mapping strategies: (a) positional mapping based on genomic proximity and (b) expression quantitative trait loci (eQTL) mapping based on gene expression linkage across multiple tissues. As a result, a total of 719 shared genes were identified. Over half (51%) of them are protein-coding genes. Functional enrichment analysis shows that the most enriched biological pathways are related to epigenetic modification, sensory perception, and immunologic signatures. We also identified druggable targets using a network approach. Together, these results may provide insights into understanding the genetic predisposition and underlying biological pathways of comorbid MDD and insomnia symptoms.

The genomes of nucleocytoplasmic large DNA viruses: viral evolution writ large

The nucleocytoplasmic large DNA viruses (NCLDVs) are a diverse group that currently contain the largest known virions and genomes, also called giant viruses. The first giant virus was isolated and described nearly 20 years ago. Their genome sizes were larger than for any other known virus at the time and it contained a number of genes that had not been previously described in any virus. The origin and evolution of these unusually complex viruses has been puzzling, and various mechanisms have been put forward to explain how some NCLDVs could have reached genome sizes and coding capacity overlapping with those of cellular microbes. Here we critically discuss the evidence and arguments on this topic. We have also updated and systematically reanalysed protein families of the NCLDVs to further study their origin and evolution. Our analyses further highlight the small number of widely shared genes and extreme genomic plasticity among NCLDVs that are shaped via combinations of gene duplications, deletions, lateral gene transfers and de novo creation of protein-coding genes. The dramatic expansions of the genome size and protein-coding gene capacity characteristic of some NCLDVs is now increasingly understood to be driven by environmental factors rather than reflecting relationships to an ancient common ancestor among a hypothetical cellular lineage. Thus, the evolution of NCLDVs is writ large viral, and their origin, like all other viral lineages, remains unknown.

Geometry of gene regulatory dynamics

Embryonic development leads to the reproducible and ordered appearance of complexity from egg to adult. The successive differentiation of different cell types that elaborate this complexity results from the activity of gene networks and was likened by Waddington to a flow through a landscape in which valleys represent alternative fates. Geometric methods allow the formal representation of such landscapes and codify the types of behaviors that result from systems of differential equations. Results from Smale and coworkers imply that systems encompassing gene network models can be represented as potential gradients with a Riemann metric, justifying the Waddington metaphor. Here, we extend this representation to include parameter dependence and enumerate all three-way cellular decisions realizable by tuning at most two parameters, which can be generalized to include spatial coordinates in a tissue. All diagrams of cell states vs. model parameters are thereby enumerated. We unify a number of standard models for spatial pattern formation by expressing them in potential form (i.e., as topographic elevation). Turing systems appear nonpotential, yet in suitable variables the dynamics are low dimensional and potential. A time-independent embedding recovers the original variables. Lateral inhibition is described by a saddle point with many unstable directions. A model for the patterning of the Drosophila eye appears as relaxation in a bistable potential. Geometric reasoning provides intuitive dynamic models for development that are well adapted to fit time-lapse data.

Molecular Biology Networks and Key Gene Regulators for Inflammatory Biomarkers Shared by Breast Cancer Development: Multi-Omics Systems Analysis

As key inflammatory biomarkers C-reactive protein (CRP) and interleukin-6 (IL6) play an important role in the pathogenesis of non-inflammatory diseases, including specific cancers, such as breast cancer (BC). Previous genome-wide association studies (GWASs) have neither explained the large proportion of genetic heritability nor provided comprehensive understanding of the underlying regulatory mechanisms. We adopted an integrative genomic network approach by incorporating our previous GWAS data for CRP and IL6 with multi-omics datasets, such as whole-blood expression quantitative loci, molecular biologic pathways, and gene regulatory networks to capture the full range of genetic functionalities associated with CRP/IL6 and tissue-specific key drivers (KDs) in gene subnetworks. We applied another systematic genomics approach for BC development to detect shared gene sets in enriched subnetworks across BC and CRP/IL6. We detected the topmost significant common pathways across CRP/IL6 (e.g., immune regulatory; chemokines and their receptors; interferon γ, JAK-STAT, and ERBB4 signaling), several of which overlapped with BC pathways. Further, in gene–gene interaction networks enriched by those topmost pathways, we identified KDs—both well-established (e.g., JAK1/2/3, STAT3) and novel (e.g., CXCR3, CD3D, CD3G, STAT6)—in a tissue-specific manner, for mechanisms shared in regulating CRP/IL6 and BC risk. Our study may provide robust, comprehensive insights into the mechanisms of CRP/IL6 regulation and highlight potential novel genetic targets as preventive and therapeutic strategies for associated disorders, such as BC.

Differentially Regulated miRNAs and Their Related Molecular Pathways in Lichen Sclerosus

Lichen sclerosus (LS) is a chronic inflammatory skin disorder with unknown pathogenesis. The aberrant expression of microRNAs (miRNAs) is considered to exert a crucial role in LS. We used the next-generation sequencing technology (RNASeq) for miRNA profiling and Ingenuity Pathway Analysis (IPA) for molecular network analysis. We performed qRT-PCR, miRNA transfection and Matrigel assays for functional studies. We identified a total of 170 differentially expressed miRNAs between female LS and matched adjacent normal tissue using RNASeq, with 119 upregulated and 51 downregulated. Bioinformatics analysis revealed molecular networks that may shed light on the pathogenesis of LS. We verified the expression of a set of miRNAs that are related to autoimmunity, such as upregulated miR-326, miR-142-5p, miR-155 and downregulated miR-664a-3p and miR-181a-3p in LS tissue compared to the matched adjacent normal tissue. The differentially expressed miRNAs were also verified in blood samples from LS patients compared to healthy female volunteers. Functional studies demonstrated that a forced expression of miR-142-5p in human dermal fibroblast PCS-201-010 cells resulted in decreased cell proliferation and migration. These findings suggest that differentially expressed miRNAs may play an important role in LS pathogenesis; therefore, they could serve as biomarkers for LS management.

The integrative biology of genetic dominance

Dominance is a basic property of inheritance systems describing the link between a diploid genotype at a single locus and the resulting phenotype. Models for the evolution of dominance have long been framed as an opposition between the irreconcilable views of Fisher in 1928 supporting the role of largely elusive dominance modifiers and Wright in 1929, who viewed dominance as an emerging property of the structure of enzymatic pathways. Recent theoretical and empirical advances however suggest that these opposing views can be reconciled, notably using models investigating the regulation of gene expression and developmental processes. In this more comprehensive framework, phenotypic dominance emerges from departures from linearity between any levels of integration in the genotype‐to‐phenotype map. Here, we review how these different models illuminate the emergence and evolution of dominance. We then detail recent empirical studies shedding new light on the diversity of molecular and physiological mechanisms underlying dominance and its evolution. By reconciling population genetics and functional biology, we hope our review will facilitate cross‐talk among research fields in the integrative study of dominance evolution.

KnetMiner: a comprehensive approach for supporting evidence-based gene discovery and complex trait analysis across species

The generation of new ideas and scientific hypotheses is often the result of extensive literature and database searches, but, with the growing wealth of public and private knowledge, the process of searching diverse and interconnected data to generate new insights into genes, gene networks, traits and diseases is becoming both more complex and more time-consuming. To guide this technically challenging data integration task and to make gene discovery and hypotheses generation easier for researchers, we have developed a comprehensive software package called KnetMiner which is open-source and containerized for easy use. KnetMiner is an integrated, intelligent, interactive gene and gene network discovery platform that supports scientists explore and understand the biological stories of complex traits and diseases across species. It features fast algorithms for generating rich interactive gene networks and prioritizing candidate genes based on knowledge mining approaches. KnetMiner is used in many plant science institutions and has been adopted by several plant breeding organizations to accelerate gene discovery. The software is generic and customizable and can therefore be readily applied to new species and data types; for example, it has been applied to pest insects and fungal pathogens; and most recently repurposed to support COVID-19 research. Here, we give an overview of the main approaches behind KnetMiner and we report plant-centric case studies for identifying genes, gene networks and trait relationships in Triticum aestivum (bread wheat), as well as, an evidence-based approach to rank candidate genes under a large Arabidopsis thaliana QTL. KnetMiner is available at: https://knetminer.org.

Improving gene network inference with graph wavelets and making insights about ageing-associated regulatory changes in lungs

Using gene-regulatory-networks-based approach for single-cell expression profiles can reveal unprecedented details about the effects of external and internal factors. However, noise and batch effect in sparse single-cell expression profiles can hamper correct estimation of dependencies among genes and regulatory changes. Here, we devise a conceptually different method using graphwavelet filters for improving gene network (GWNet)-based analysis of the transcriptome. Our approach improved the performance of several gene network-inference methods. Most Importantly, GWNet improved consistency in the prediction of gene regulatory network using single-cell transcriptome even in the presence of batch effect. The consistency of predicted gene network enabled reliable estimates of changes in the influence of genes not highlighted by differential-expression analysis. Applying GWNet on the single-cell transcriptome profile of lung cells, revealed biologically relevant changes in the influence of pathways and master regulators due to ageing. Surprisingly, the regulatory influence of ageing on pneumocytes type II cells showed noticeable similarity with patterns due to the effect of novel coronavirus infection in human lung.

FOSL2 Is Involved in the Regulation of Glycogen Content in Chicken Breast Muscle Tissue

The glycogen content in muscle of livestock and poultry animals affects the homeostasis of their body, growth performance, and meat quality after slaughter. FOS-like 2, AP-1 transcription factor subunit (FOSL2) was identified as a candidate gene related to muscle glycogen (MG) content in chicken in our previous study, but the role of FOSL2 in the regulation of MG content remains to be elucidated. Differential gene expression analysis and weighted gene coexpression network analysis (WGCNA) were performed on differentially expressed genes (DEGs) in breast muscle tissues from the high-MG-content (HMG) group and low-MG-content (LMG) group of Jingxing yellow chickens. Analysis of the 1,171 DEGs (LMG vs. HMG) identified, besides FOSL2, some additional genes related to MG metabolism pathway, namely PRKAG3, CEBPB, FOXO1, AMPK, and PIK3CB. Additionally, WGCNA revealed that FOSL2, CEBPB, MAP3K14, SLC2A14, PPP2CA, SLC38A2, PPP2R5E, and other genes related to the classical glycogen metabolism in the same coexpressed module are associated with MG content. Also, besides finding that FOSL2 expression is negatively correlated with MG content, a possible interaction between FOSL2 and CEBPB was predicted using the STRING (Search Tool for the Retrieval of Interacting Genes) database. Furthermore, we investigated the effects of lentiviral overexpression of FOSL2 on the regulation of the glycogen content in vitro, and the result indicated that FOSL2 decreases the glycogen content in DF1 cells. Collectively, our results confirm that FOSL2 has a key role in the regulation of the MG content in chicken. This finding is helpful to understand the mechanism of MG metabolism regulation in chicken and provides a new perspective for the production of high-quality broiler and the development of a comprehensive nutritional control strategy.

Srag Regulates Autophagy via Integrating into a Preexisting Autophagy Pathway in Testis

Spermatogenesis is an essential process for producing sperm cells. Reproductive strategy is successfully evolved for a species to adapt to a certain ecological system. However, roles of newly evolved genes in testis autophagy remain unclear. In this study, we found that a newly evolved gene srag (Sox9-regulated autophagy gene) plays an important role in promoting autophagy in testis in the lineage of the teleost Monopterus albus. The gene integrated into an interaction network through a two-way strategy of evolution, via Sox9-binding in its promoter and interaction with Becn1 in the coding region. Its promoter region evolved a cis element for binding of Sox9, a transcription factor for male sex determination. Both in vitro and in vivo analyses demonstrated that transcription factor Sox9 could bind to and activate the srag promoter. Its coding region acquired ability to interact with key autophagy initiation factor Becn1 via the conserved C-terminal, indicating that srag integrated into preexisting autophagy network. Moreover, we determined that Srag enhanced autophagy by interacting with Becn1. Notably, srag transgenic zebrafish revealed that Srag exerted the same function by enhancing autophagy through the Srag–Becn1 pathway. Thus, the new gene srag regulated autophagy in testis by integrated into preexisting autophagy network.

An Integrative Gene Expression Microarray Meta-analysis Identifies Host Factors and Key Signatures Involved in Hepatitis B Virus Infection

Hepatitis B virus (HBV) is a global health concern. Viral and host factors orchestrate the natural history of HBV infection, but the impact of host factors that influence the clinical course of the disease remains poorly understood. The aim of this study was to identify host factors crucial to the HBV life cycle by conducting a meta-analysis utilizing public microarray datasets.

METHODS

An integrative meta-analysis of expression data from two microarray datasets of HBVinfected liver tissues and healthy uninfected livers was conducted to identify gene expression signatures and overlapping biological processes modulating infection/disease.

RESULTS

Using integrative meta-analysis of expression data (INMEX), we identified across two datasets a total of 841 genes differentially expressed during HBV infection, including 473 upregulated and 368 downregulated genes. In addition, through functional enrichment and pathway analysis, we observed that Jak-STAT, TLR, and NF-κB are the most relevant signaling pathways in chronic HBV infection. The network-based meta-analysis identified NEDD8, SKP2, JUN, and HIF1A as the most highly ranked hub genes.

CONCLUSION

Thus, these results may provide valuable information about novel potential host factors modulating chronic HBV infection. Such factors may serve as potential targets for the development of novel therapeutics such as activin receptor-like kinase inhibitors.

KLF4, a Key Regulator of a Transitive Triplet, Acts on the TGF-β Signaling Pathway and Contributes to High-Altitude Adaptation of Tibetan Pigs

Tibetan pigs are native mammalian species on the Tibetan Plateau that have evolved distinct physiological traits that allow them to tolerate high-altitude hypoxic environments. However, the genetic mechanism underlying this adaptation remains elusive. Here, based on multitissue transcriptional data from high-altitude Tibetan pigs and low-altitude Rongchang pigs, we performed a weighted correlation network analysis (WGCNA) and identified key modules related to these tissues. Complex network analysis and bioinformatics analysis were integrated to identify key genes and three-node network motifs. We found that among the six tissues (muscle, liver, heart, spleen, kidneys, and lungs), lung tissue may be the key organs for Tibetan pigs to adapt to hypoxic environment. In the lung tissue of Tibetan pigs, we identified KLF4, BCL6B, EGR1, EPAS1, SMAD6, SMAD7, KDR, ATOH8, and CCN1 genes as potential regulators of hypoxia adaption. We found that KLF4 and EGR1 genes might simultaneously regulate the BCL6B gene, forming a KLF4-EGR1-BCL6B complex. This complex, dominated by KLF4, may enhance the hypoxia tolerance of Tibetan pigs by mediating the TGF-β signaling pathway. The complex may also affect the PI3K-Akt signaling pathway, which plays an important role in angiogenesis caused by hypoxia. Therefore, we postulate that the KLF4-EGR1-BCL6B complex may be beneficial for Tibetan pigs to survive better in the hypoxia environments. Although further molecular experiments and independent large-scale studies are needed to verify our findings, these findings may provide new details of the regulatory architecture of hypoxia-adaptive genes and are valuable for understanding the genetic mechanism of hypoxic adaptation in mammals.

ScanBious: Survey for Obesity Genes Using PubMed Abstracts and DisGeNET

We used automatic text-mining of PubMed abstracts of papers related to obesity, with the aim of revealing that the information used in abstracts reflects the current understanding and key concepts of this widely explored problem. We compared expert data from DisGeNET to the results of an automated MeSH (Medical Subject Heading) search, which was performed by the ScanBious web tool. The analysis provided an overview of the obesity field, highlighting major trends such as physiological conditions, age, and diet, as well as key well-studied genes, such as adiponectin and its receptor. By intersecting the DisGeNET knowledge with the ScanBious results, we deciphered four clusters of obesity-related genes. An initial set of 100+ thousand abstracts and 622 genes was reduced to 19 genes, distributed among just a few groups: heredity, inflammation, intercellular signaling, and cancer. Rapid profiling of articles could drive personalized medicine: if the disease signs of a particular person were superimposed on a general network, then it would be possible to understand which are non-specific (observed in cohorts and, therefore, most likely have known treatment solutions) and which are less investigated, and probably represent a personalized case.