Cofitness network connectivity determines a fuzzy essential zone in open bacterial pangenome

Most in silico evolutionary studies commonly assumed that core genes are essential for cellular function,while accessory genes are dispensable,particularly in nutrient-rich environments.However,this assumption is seldom tested genetically within the pangenome context.In this study,we conducted a robust pangenomic Tn-seq analysis of fitness genes in a nutrient-rich medium for Sinorhizobium strains with a canonical open pangenome.To evaluate the robustness of fitness category assignment,Tn-seq data for three independent mutant libraries per strain were analyzed by three methods,which indicates that the Hidden Markov Model(HMM)-based method is most robust to variations between mutant libraries and not sensitive to data size,outperforming the Bayesian and Monte Carlo simulation-based methods.Consequently,the HMM method was used to classify the fitness category.Fitness genes,categorized as essential(ES),advantage(GA),and disadvantage(GD)genes for growth,are enriched in core genes,while nonessential genes(NE)are over-represented in accessory genes.Accessory ES/GA genes showed a lower fitness effect than core ES/GA genes.Connectivity degrees in the cofitness network decrease in the order of ES,GD,and GA/NE.In addition to accessory genes,1599 out of 3284 core genes display differential essentiality across test strains.Within the pangenome core,both shared quasi-essential(ES and GA)and strain-dependent fitness genes are enriched in similar functional categories.Our analysis demonstrates a considerable fuzzy essential zone determined by cofitness connectivity degrees in Sinorhizobium pangenome and highlights the power of the cofitness network in understanding the genetic basis of ever-increasing prokaryotic pangenome data.

PlaD： A Transcriptomics Database for Plant Defense Responses to Pathogens, Providing New Insights into Plant Immune System

High-throughput transcriptomics technologies have been widely used to study plant transcriptional reprogramming during the process of plant defense responses, and a large quantity of gene expression data have been accumulated in public repositories. However, utilization of these data is often hampered by the lack of standard metadata annotation. In this study, we curated2444 public pathogenesis-related gene expression samples from the model plant Arabidopsis and three major crops （maize, rice, and wheat）. We organized the data into a user-friendly database termed as PlaD. Currently, PlaD contains three key features. First, it provides large-scale curated data related to plant defense responses, including gene expression and gene functional annotation data.Second, it provides the visualization of condition-specific expression profiles. Third, it allows users to search co-regulated genes under the infections of various pathogens. Using PlaD, we conducted a large-scale transcriptome analysis to explore the global landscape of gene expression in the curated data. We found that only a small fraction of genes were differentially expressed under multiple conditions, which might be explained by their tendency of having more network connections and shorter network distances in gene networks. Collectively, we hope that PlaD can serve as an important and comprehensive knowledgebase to the community of plant sciences, providing insightful clues to better understand the molecular mechanisms underlying plant immune responses. PlaD is freely available at http：//systbio.cau.edu.cn/plad/index.php or http：//zzdlab.com/plad/index.php.

Systems understanding of plant-pathogen interactions through genome-wide protein-protein interaction networks

Plants are frequently affected by pathogen infections.To effectively defend against such infections,two major modes of innate immunity have evolved in plants;pathogen-associated molecular pattern-triggered immunity and effector-triggered immunity.Although the molecular components as well as the corresponding pathways involved in these two processes have been identified,many aspects of the molecular mechanisms of the plant immune system remain elusive.Recently,the rapid development of omics techniques(e.g.,genomics,proteomics and transcriptomics) has provided a great opportunity to explore plant–pathogen interactions from a systems perspective and studies on protein–protein interactions(PPIs) between plants and pathogens have been carried out and characterized at the network level.In this review,we introduce experimental and computational identification methods of PPIs,popular PPI network analysis approaches,and existing bioinformatics resources/tools related to PPIs.Then,we focus on reviewing the progress in genome-wide PPI networks related to plant–pathogen interactions,including pathogen-centric PPI networks,plant-centric PPI networks and interspecies PPI networks between plants and pathogens.We anticipate genome-wide PPI network analysis will provide a clearer understanding of plant–pathogen interactions and will offer some new opportunities for crop protection and improvement.

Prediction and analysis of human-herpes simplex virus type 1 protein-protein interactions by integrating multiple methods

Background:Herpes simplex virus type 1(HSV-1)is a ubiquitous infectious pathogen that widely affects human health.To decipher the complicated human-HSV-1 interactions,a comprehensive protein-protein interaction(PPI)network between human and HSV-1 is highly demanded.Methods:To complement the experimental identification of human-HSV-1 PPIs,an integrative strategy to predict proteome-wide PPIs between human and HSV-1 was developed.For each human-HSV-1 protein pair,four popular PPI inference methods,including interolog mapping,the domain-domain interaction-based method,the domain-motif interaction-based method,and the machine learning-based method,were optimally implemented to generate four interaction probability scores,which were further integrated into a final probability score.Results:As a result,a comprehensive high-confidence PPI network between human and HSV-1 was established,covering 10,432 interactions between 4,546 human proteins and 72 HSV-1 proteins.Functional and network analyses of the HSV-1 targeting proteins in the context of human interactome can recapitulate the known knowledge regarding the HSV-1 replication cycle,supporting the overall reliability of the predicted PPI network.Considering that HSV-1 infections are implicated in encephalitis and neurodegenerative diseases,we focused on exploring the biological significance of the brain-specific human-HSV-1 PPIs.In particular,the predicted interactions between HSV-1 proteins and Alzheimer's-disease-related proteins were intensively investigated.Conclusion:The current work can provide testable hypotheses to assist in the mechanistic understanding of the human-HSV-1 relationship and the anti-HSV-1 pharmaceutical target discovery.To make the predicted PPI network and the datasets freely accessible to the scientific community,a user-friendly database browser was released at http://www.zzdlab.com/HintHSV/index.php.