scholarly journals Constructing a multiple-layer interactome for SARS-CoV-2 in the context of lung disease: Linking the virus with human genes and co-infecting microbes

2021 ◽  
Author(s):  
Shaoke Lou ◽  
Tianxiao Li ◽  
Mark Gerstein
Keyword(s):  
Viral Infection ◽  
Latent Dirichlet Allocation ◽  
Data Types ◽  
Protein Coding ◽  
Individual Level ◽  
Protein Protein Interaction ◽  
Treatment And Prevention ◽  
Multiple Layer ◽  
Network Propagation ◽  
Topic Distribution

AbstractThe severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has caused millions of deaths worldwide. Many efforts have focused on unraveling the mechanism of the viral infection to develop effective strategies for treatment and prevention. Previous studies have provided some clarity on the protein-protein interaction linkages occurring during the life cycle of viral infection; however, we lack a complete understanding of the full interactome, comprising human miRNAs and protein-coding genes and co-infecting microbes. To comprehensively determine this, we developed a statistical modeling method using latent Dirichlet allocation (called MLCrosstalk, for multiple-layer crosstalk) to fuse many types of data to construct the full interactome of SARS-CoV-2. Specifically, MLCrosstalk is able to integrate samples with multiple layers of information (e.g., miRNA and microbes), enforce a consistent topic distribution on all data types, and infer individual-level linkages (i.e., differing between patients). We also implement a secondary refinement with network propagation to allow our microbe-gene linkages to address larger network structures (e.g., pathways). Using MLCrosstalk, we generated a list of genes and microbes linked to SARS-CoV-2. Interestingly, we found that two of the identified microbes, Rothia mucilaginosa and Prevotella melaninogenica, show distinct patterns representing synergistic and antagonistic relationships with the virus, respectively. We also identified several SARS-COV-2-associated pathways, including the VEGFA-VEGFR2 and immune response pathways, which may provide potential targets for drug design.

scholarly journals Improved supervised prediction of aging-related genes via weighted dynamic network analysis

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Qi Li ◽  
Khalique Newaz ◽  
Tijana Milenković
Keyword(s):  
Aging Process ◽  
Dynamic Network ◽  
Specific Information ◽  
Related Gene ◽  
Data Types ◽  
Induced Subgraph ◽  
Protein Protein Interaction ◽  
Dynamic Aging ◽  
Equal Importance ◽  
Network Propagation

Abstract Background This study focuses on the task of supervised prediction of aging-related genes from -omics data. Unlike gene expression methods for this task that capture aging-specific information but ignore interactions between genes (i.e., their protein products), or protein–protein interaction (PPI) network methods for this task that account for PPIs but the PPIs are context-unspecific, we recently integrated the two data types into an aging-specific PPI subnetwork, which yielded more accurate aging-related gene predictions. However, a dynamic aging-specific subnetwork did not improve prediction performance compared to a static aging-specific subnetwork, despite the aging process being dynamic. This could be because the dynamic subnetwork was inferred using a naive Induced subgraph approach. Instead, we recently inferred a dynamic aging-specific subnetwork using a methodologically more advanced notion of network propagation (NP), which improved upon Induced dynamic aging-specific subnetwork in a different task, that of unsupervised analyses of the aging process. Results Here, we evaluate whether our existing NP-based dynamic subnetwork will improve upon the dynamic as well as static subnetwork constructed by the Induced approach in the considered task of supervised prediction of aging-related genes. The existing NP-based subnetwork is unweighted, i.e., it gives equal importance to each of the aging-specific PPIs. Because accounting for aging-specific edge weights might be important, we additionally propose a weighted NP-based dynamic aging-specific subnetwork. We demonstrate that a predictive machine learning model trained and tested on the weighted subnetwork yields higher accuracy when predicting aging-related genes than predictive models run on the existing unweighted dynamic or static subnetworks, regardless of whether the existing subnetworks were inferred using NP or the Induced approach. Conclusions Our proposed weighted dynamic aging-specific subnetwork and its corresponding predictive model could guide with higher confidence than the existing data and models the discovery of novel aging-related gene candidates for future wet lab validation.

scholarly journals In vivo single-cell profiling of lncRNAs during Ebola virus infection

2022 ◽  
Author(s):  
Luisa Santus ◽  
Raquel García-Pérez ◽  
Maria Sopena-Rios ◽  
Aaron E Lin ◽  
Gordon C Adams ◽  
...  
Keyword(s):  
Viral Infection ◽  
Single Cell ◽  
Ebola Virus ◽  
Cell Type ◽  
Protein Coding ◽  
Expression Variation ◽  
Lncrna Expression ◽  
Ebov Infection ◽  
Cell Type Specific

Long non-coding RNAs (lncRNAs) are pivotal mediators of systemic immune response to viral infection, yet most studies concerning their expression and functions upon immune stimulation are limited to in vitro bulk cell populations. This strongly constrains our understanding of how lncRNA expression varies at single-cell resolution, and how their cell-type specific immune regulatory roles may differ compared to protein-coding genes. Here, we perform the first in-depth characterization of lncRNA expression variation at single-cell resolution during Ebola virus (EBOV) infection in vivo. Using bulk RNA-sequencing from 119 samples and 12 tissue types, we significantly expand the current macaque lncRNA annotation. We then profile lncRNA expression variation in immune circulating single-cells during EBOV infection and find that lncRNAs' expression in fewer cells is a major differentiating factor from their protein-coding gene counterparts. Upon EBOV infection, lncRNAs present dynamic and mostly cell-type specific changes in their expression profiles especially in monocytes, the main cell type targeted by EBOV. Such changes are associated with gene regulatory modules related to important innate immune responses such as interferon response and purine metabolism. Within infected cells, several lncRNAs have positively and negatively correlated expression with viral load, suggesting that expression of some of these lncRNAs might be directly hijacked by EBOV to attack host cells. This study provides novel insights into the roles that lncRNAs play in the host response to acute viral infection and paves the way for future lncRNA studies at single-cell resolution.

scholarly journals Whole-Genome k-mer Topic Modeling Associates Bacterial Families

Genes ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 197
Author(s):  
Ernesto Borrayo ◽  
Isaias May-Canche ◽  
Omar Paredes ◽  
J. Alejandro Morales ◽  
Rebeca Romo-Vázquez ◽  
...  
Keyword(s):  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
Hierarchical Classification ◽  
Whole Genome Sequence ◽  
Whole Genome ◽  
Sequence Comparisons ◽  
Alignment Free ◽  
Biological Phenomena ◽  
Topic Distribution ◽  
Genome Comparisons

Alignment-free k-mer-based algorithms in whole genome sequence comparisons remain an ongoing challenge. Here, we explore the possibility to use Topic Modeling for organism whole-genome comparisons. We analyzed 30 complete genomes from three bacterial families by topic modeling. For this, each genome was considered as a document and 13-mer nucleotide representations as words. Latent Dirichlet allocation was used as the probabilistic modeling of the corpus. We where able to identify the topic distribution among analyzed genomes, which is highly consistent with traditional hierarchical classification. It is possible that topic modeling may be applied to establish relationships between genome’s composition and biological phenomena.

scholarly journals Venn-diaNet : venn diagram based network propagation analysis framework for comparing multiple biological experiments

2019 ◽  
Vol 20 (S23) ◽  
Author(s):  
Benjamin Hur ◽  
Dongwon Kang ◽  
Sangseon Lee ◽  
Ji Hwan Moon ◽  
Gung Lee ◽  
...  
Keyword(s):  
Interaction Network ◽  
Venn Diagram ◽  
Analysis Framework ◽  
Seed Selection ◽  
Systematic Analysis ◽  
Main Research ◽  
Protein Protein Interaction ◽  
Propagation Analysis ◽  
Network Propagation ◽  
Main Research Topic

Abstract Background The main research topic in this paper is how to compare multiple biological experiments using transcriptome data, where each experiment is measured and designed to compare control and treated samples. Comparison of multiple biological experiments is usually performed in terms of the number of DEGs in an arbitrary combination of biological experiments. This process is usually facilitated with Venn diagram but there are several issues when Venn diagram is used to compare and analyze multiple experiments in terms of DEGs. First, current Venn diagram tools do not provide systematic analysis to prioritize genes. Because that current tools generally do not fully focus to prioritize genes, genes that are located in the segments in the Venn diagram (especially, intersection) is usually difficult to rank. Second, elucidating the phenotypic difference only with the lists of DEGs and expression values is challenging when the experimental designs have the combination of treatments. Experiment designs that aim to find the synergistic effect of the combination of treatments are very difficult to find without an informative system. Results We introduce Venn-diaNet, a Venn diagram based analysis framework that uses network propagation upon protein-protein interaction network to prioritizes genes from experiments that have multiple DEG lists. We suggest that the two issues can be effectively handled by ranking or prioritizing genes with segments of a Venn diagram. The user can easily compare multiple DEG lists with gene rankings, which is easy to understand and also can be coupled with additional analysis for their purposes. Our system provides a web-based interface to select seed genes in any of areas in a Venn diagram and then perform network propagation analysis to measure the influence of the selected seed genes in terms of ranked list of DEGs. Conclusions We suggest that our system can logically guide to select seed genes without additional prior knowledge that makes us free from the seed selection of network propagation issues. We showed that Venn-diaNet can reproduce the research findings reported in the original papers that have experiments that compare two, three and eight experiments. Venn-diaNet is freely available at: http://biohealth.snu.ac.kr/software/venndianet

scholarly journals Topic Predictions and Optimized Recommendation Mechanism Based on Integrated Topic Modeling and Deep Neural Networks in Crowdfunding Platforms

2019 ◽  
Vol 9 (24) ◽  
pp. 5496 ◽  
Author(s):  
Wafa Shafqat ◽  
Yung-Cheol Byun
Keyword(s):  
Neural Networks ◽  
Network Architecture ◽  
Latent Dirichlet Allocation ◽  
Performance Metrics ◽  
Short Term Memory ◽  
Language Modeling ◽  
Optimal Number ◽  
Internet Users ◽  
Topic Distribution ◽  
Baseline Algorithm

The accelerated growth rate of internet users and its applications, primarily e-business, has accustomed people to write their comments and reviews about the product they received. These reviews are remarkably competent to shape customers’ decisions. However, in crowdfunding, where investors finance innovative ideas in exchange for some rewards or products, the comments of investors are often ignored. These comments can play a markedly significant role in helping crowdfunding platforms to battle against the bitter challenge of fraudulent activities. We take advantage of the language modeling techniques and aim to merge them with neural networks to identify some hidden discussion patterns in the comments. Our objective is to design a language modeling based neural network architecture, where Recurrent Neural Networks (RNN) Long Short-Term Memory (LSTM) is used to predict discussion trends, i.e., either towards scam or non-scam. LSTM layers are fed with latent topic distribution learned from the pre-trained Latent Dirichlet Allocation (LDA) model. In order to optimize the recommendations, we used Particle Swarm Optimization (PSO) as a baseline algorithm. This module helps investors find secure projects to invest in (with the highest chances of delivery) within their preferred categories. We used prediction accuracy, an optimal number of identified topics, and the number of epochs, as metrics of performance evaluation for the proposed approach. We compared our results with simple Neural Networks (NNs) and NN-LDA based on these performance metrics. The strengths of both integrated models suggest that the proposed model can play a substantial role in a better understanding of crowdfunding comments.

scholarly journals Omics Data Complementarity Underlines Functional Cross-Communication in Yeast

2017 ◽  
Vol 14 (2) ◽  
Author(s):  
Noël Malod-Dognin ◽  
Nataša Pržulj
Keyword(s):  
Functional Organization ◽  
Genetic Interaction ◽  
Molecular Data ◽  
Omics Data ◽  
Functional Communication ◽  
Data Types ◽  
Protein Protein Interaction ◽  
Exact Answer ◽  
A Cell ◽  
Computational Intractability

AbstractMapping the complete functional layout of a cell and understanding the cross-talk between different processes are fundamental challenges. They elude us because of the incompleteness and noisiness of molecular data and because of the computational intractability of finding the exact answer. We perform a simple integration of three types of baker’s yeast omics data to elucidate the functional organization and lines of cross-functional communication. We examine protein–protein interaction (PPI), co-expression (COEX) and genetic interaction (GI) data, and explore their relationship with the gold standard of functional organization, the Gene Ontology (GO). We utilize a simple framework that identifies functional cross-communication lines in each of the three data types, in GO, and collectively in the integrated model of the three omics data types; we present each of them in our new Functional Organization Map (FOM) model. We compare the FOMs of the three omics datasets with the FOM of GO and find that GI is in best agreement with GO, followed COEX and PPI. We integrate the three FOMs into a unified FOM and find that it is in better agreement with the FOM of GO than those of any omics dataset alone, demonstrating functional complementarity of different omics data.

scholarly journals Male differentiation in the marine copepod Oithona nana reveals the development of a new nervous ganglion linked to Lin12-Notch-Repeat protein-associated proteolysis

2021 ◽  
Author(s):  
Kevin Sugier ◽  
Romuald Laso-Jadart ◽  
Benoit Vacherie ◽  
Jos Kafer ◽  
Laurie Bertrand ◽  
...  
Keyword(s):  
Extracellular Matrix ◽  
Protein Interaction ◽  
System Development ◽  
Interaction Network ◽  
Nervous System Development ◽  
Protein Coding ◽  
Protein Protein Interaction ◽  
Marine Copepod ◽  
Male Specific

Background: Copepods are among the most numerous animals, and play an essential role in the marine trophic web and biogeochemical cycles. The genus Oithona is described as having the highest density of copepods, and as being the most cosmopolite copepods. The Oithona male paradox describes the activity states of males, which are obliged to alternate between immobile and mobile phases for ambush feeding and mate searching, respectively, while the female is typically less mobile and often feeding. To characterize the molecular basis of this sexual dimorphism, we combined immunofluorescence, genomics, transcriptomics, and protein-protein interaction approaches. Results: Immunofluorescence of β3- and α-tubulin revealed two male-specific nervous ganglia in the lateral first segment of the Oithona nana male's prosome. In parallel, transcriptomic analysis showed male-specific enrichment for nervous system development-related transcripts. Twenty-seven Lin12-Notch Repeat domain-containing protein coding genes (LDPGs) of the 75 LDPGs identified in the genome were specifically expressed only in males. Furthermore, most of the LDPGs (27%) coded for proteins having predicted proteolytic activity, and non-LDPG proteolysis-associated transcripts showed a male-specific enrichment. Using yeast double-hybrid assays, we constructed a protein-protein interaction network involving two LDPs with proteases, extracellular matrix proteins, and neurogenesis-related proteins. Conclusions: For the first time, our study describes the lateral nervous ganglia of O. nana males, unique to copepods. We also demonstrated a role of LDPGs and their associated proteolysis in male-specific physiology, and we hypothesize a role of the LDPGs in the development of the lateral ganglia through directed lysis of the extracellular matrix for the growth of neurites and genesis of synapses.

scholarly journals Comprehensive Analysis Reveals the Potential Regulatory Mechanism Between Ub–Proteasome System and Cell Cycle in Colorectal Cancer

2021 ◽  
Vol 9 ◽  
Author(s):  
Zhiyuan Zhang ◽  
Jingwen Chen ◽  
Wentao Tang ◽  
Qingyang Feng ◽  
Jianmin Xu ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Cell Cycle ◽  
Regulatory Mechanism ◽  
Differentially Expressed ◽  
Potential Mechanism ◽  
Ppi Network ◽  
Protein Coding ◽  
Protein Protein Interaction ◽  
Differentially Expressed Protein ◽  
Cycle System

The ubiquitin (Ub)–proteasome system (UPS) is an important regulatory component in colorectal cancer (CRC), and the cell cycle is also characterized to play a significant role in CRC. In this present study, we firstly identified UPS-associated differentially expressed genes and all the differentially expressed protein-coding genes in CRC through three differential analyses. UPS-associated genes were also further analyzed via survival analysis. A weighted gene co-expression network analysis (WGCNA) was used to identify the cell cycle-associated genes. We used protein–protein interaction (PPI) network to comprehensively mine the potential mechanism of the UPS–cell cycle regulatory axis. Moreover, we constructed a signature based on UPS-associated genes to predict the overall survival of CRC patients. Our research provides a novel insight view of the UPS and cell cycle system in CRC.

scholarly journals Genomic associations with poxvirus across divergent island populations in Berthelot's pipit

2021 ◽  
Author(s):  
Eleanor Sheppard ◽  
Claudia Martin ◽  
Claire Armstrong ◽  
Catalina González-Quevedo ◽  
Juan Carlos Illera ◽  
...  
Keyword(s):  
Viral Infection ◽  
Population Level ◽  
Genomic Variation ◽  
Nucleotide Polymorphisms ◽  
Individual Level ◽  
Island Populations ◽  
Bird Populations ◽  
Animal Populations ◽  
Infection Pressure ◽  
Avian Pox

Understanding the mechanisms and genes that enable animal populations to adapt to pathogens is important from an evolutionary, health and conservation perspective. Berthelot’s pipit (Anthus berthelotii) experiences extensive and consistent spatial heterogeneity in avian pox infection pressure across its range of island populations, thus providing an excellent system with which to examine how pathogen-mediated selection drives spatial variation in immunogenetic diversity. Here we test for evidence of genetic variation associated with avian pox at both an individual and population-level. At the individual level, we find no evidence that variation in MHC class I and TLR4 (both known to be important in recognising viral infection) was associated with pox infection within two separate populations. However, using genotype-environment association (Bayenv) in conjunction with genome-wide (ddRAD-seq) data, we detected strong associations between population-level avian pox prevalence and allele frequencies of single nucleotide polymorphisms (SNPs) at a number of sites across the genome. These sites were located within genes involved in cellular stress signalling and immune responses, many of which have previously been associated with responses to viral infection in humans and other animals. Consequently, our analyses provide evidence that pathogen-mediated selection has shaped genomic variation among relatively recently colonised island bird populations, and highlights the utility of genotype-environment associations for identifying candidate genes involved in adaption to local pathogen pressures.

scholarly journals Key protein-coding genes related to microglia in immune regulation and inflammatory response induced by epilepsy

2012 ◽  
Vol 18 (6) ◽  
pp. 9563-9578
Author(s):  
Jing Cao ◽  
◽  
Hui Gan ◽  
Han Xiao ◽  
Hui Chen ◽  
...  
Keyword(s):  
Immune Cell ◽  
Quantitative Polymerase Chain Reaction ◽  
Expression Patterns ◽  
Polymerase Chain Reaction Analysis ◽  
Protein Coding ◽  
Protein Protein Interaction ◽  
Inflammatory Processes ◽  
The Central Nervous System ◽  
And Migration ◽  
New Biomarkers

<abstract> <p>Several studies have shown a link between immunity, inflammatory processes, and epilepsy. Active neuroinflammation and marked immune cell infiltration occur in epilepsy of diverse etiologies. Microglia, as the first line of defense in the central nervous system, are the main effectors of neuroinflammatory processes. Discovery of new biomarkers associated with microglia activation after epileptogenesis indicates that targeting specific molecules may help control seizures. In this research, we used a combination of several bioinformatics approaches, including RNA sequencing, to explore differentially expressed genes (DEGs) in epileptic lesions and control samples, and to construct a protein-protein interaction (PPI) network for DEGs, which was examined utilizing plug-ins in Cytoscape software. Finally, we aimed to identify 10 hub genes in immune and inflammation-related sub-networks, which were subsequently validated in real-time quantitative polymerase chain reaction analysis in a mouse model of kainic acid-induced epilepsy. The expression patterns of nine genes were consistent with sequencing outcomes. Meanwhile, several genes, including CX3CR1, CX3CL1, GPR183, FPR1, P2RY13, P2RY12 and LPAR5, were associated with microglial activation and migration, providing novel candidate targets for immunotherapy in epilepsy and laying the foundation for further research.</p> </abstract>

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