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Projahn EF, Fuellen G, Walter M, Möller S. Proposing candidate genes under telomeric control based on cross-species position data. NAR Genom Bioinform 2024; 6:lqae037. [PMID: 38666215 PMCID: PMC11044432 DOI: 10.1093/nargab/lqae037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Revised: 03/20/2024] [Accepted: 04/02/2024] [Indexed: 04/28/2024] Open
Abstract
In this paper, we present a comprehensive computational framework aimed at suggesting genes whose transcriptional regulation is likely to be influenced by their chromosomal position. This framework provides a user-friendly web interface, enabling researchers to explore the positional properties of all human genes and their orthologs across species, with a focus on their relation to the telomeres. Our approach involves multiple scoring methods, each adjustable by users, representing different features of the genes' positional variation across species. The resulting rankings can be combined to identify candidate genes that may be subject to position effects. Furthermore, the ranking can be tailored to a specific set of reference genes. We evaluate the method within the context of TPE-OLD, a mechanism where telomeres can exert a direct influence on gene expression across considerable genomic distances, and empower researchers to delve deeper into genes of interest, analyzing their position across species and estimating their susceptibility to position effects like TPE-OLD. We also provide simple enrichment analyses of user-provided gene lists in relation to top-ranking candidate genes.
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Affiliation(s)
- Elias F Projahn
- Institute for Biostatistics and Informatics in Medicine and Ageing Research, Rostock University Medical Center, Rostock, Germany
| | - Georg Fuellen
- Institute for Biostatistics and Informatics in Medicine and Ageing Research, Rostock University Medical Center, Rostock, Germany
| | - Michael Walter
- Institute for Clinical Chemistry and Laboratory Medicine, Rostock University Medical Center, Rostock, Germany
| | - Steffen Möller
- Institute for Biostatistics and Informatics in Medicine and Ageing Research, Rostock University Medical Center, Rostock, Germany
- Institute for Clinical Chemistry and Laboratory Medicine, Rostock University Medical Center, Rostock, Germany
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2
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Rapoport R, Greenberg A, Yakhini Z, Simon I. A Cyclic Permutation Approach to Removing Spatial Dependency between Clustered Gene Ontology Terms. BIOLOGY 2024; 13:175. [PMID: 38534445 DOI: 10.3390/biology13030175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/21/2024] [Revised: 03/04/2024] [Accepted: 03/05/2024] [Indexed: 03/28/2024]
Abstract
Traditional gene set enrichment analysis falters when applied to large genomic domains, where neighboring genes often share functions. This spatial dependency creates misleading enrichments, mistaking mere physical proximity for genuine biological connections. Here we present Spatial Adjusted Gene Ontology (SAGO), a novel cyclic permutation-based approach, to tackle this challenge. SAGO separates enrichments due to spatial proximity from genuine biological links by incorporating the genes' spatial arrangement into the analysis. We applied SAGO to various datasets in which the identified genomic intervals are large, including replication timing domains, large H3K9me3 and H3K27me3 domains, HiC compartments and lamina-associated domains (LADs). Intriguingly, applying SAGO to prostate cancer samples with large copy number alteration (CNA) domains eliminated most of the enriched GO terms, thus helping to accurately identify biologically relevant gene sets linked to oncogenic processes, free from spatial bias.
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Affiliation(s)
- Rachel Rapoport
- Microbiology and Molecular Genetics, Hebrew University of Jerusalem-IMRIC, Jerusalem 9112102, Israel
| | - Avraham Greenberg
- Microbiology and Molecular Genetics, Hebrew University of Jerusalem-IMRIC, Jerusalem 9112102, Israel
| | - Zohar Yakhini
- Efi Arazi School of Computer Science, Reichman University (IDC Herzliya), Herzliya 4610101, Israel
- Department of Computer Science, Technion-Israel Institute of Technology, Haifa 3200003, Israel
| | - Itamar Simon
- Microbiology and Molecular Genetics, Hebrew University of Jerusalem-IMRIC, Jerusalem 9112102, Israel
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3
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Baer L, Barthelson K, Postlethwait JH, Adelson DL, Pederson SM, Lardelli M. Differential allelic representation (DAR) identifies candidate eQTLs and improves transcriptome analysis. PLoS Comput Biol 2024; 20:e1011868. [PMID: 38346074 PMCID: PMC10890730 DOI: 10.1371/journal.pcbi.1011868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 02/23/2024] [Accepted: 01/29/2024] [Indexed: 02/25/2024] Open
Abstract
In comparisons between mutant and wild-type genotypes, transcriptome analysis can reveal the direct impacts of a mutation, together with the homeostatic responses of the biological system. Recent studies have highlighted that, when the effects of homozygosity for recessive mutations are studied in non-isogenic backgrounds, genes located proximal to the mutation on the same chromosome often appear over-represented among those genes identified as differentially expressed (DE). One hypothesis suggests that DE genes chromosomally linked to a mutation may not reflect functional responses to the mutation but, instead, result from an unequal distribution of expression quantitative trait loci (eQTLs) between sample groups of mutant or wild-type genotypes. This is problematic because eQTL expression differences are difficult to distinguish from genes that are DE due to functional responses to a mutation. Here we show that chromosomally co-located differentially expressed genes (CC-DEGs) are also observed in analyses of dominant mutations in heterozygotes. We define a method and a metric to quantify, in RNA-sequencing data, localised differential allelic representation (DAR) between those sample groups subjected to differential expression analysis. We show how the DAR metric can predict regions prone to eQTL-driven differential expression, and how it can improve functional enrichment analyses through gene exclusion or weighting-based approaches. Advantageously, this improved ability to identify probable eQTLs also reveals examples of CC-DEGs that are likely to be functionally related to a mutant phenotype. This supports a long-standing prediction that selection for advantageous linkage disequilibrium influences chromosome evolution. By comparing the genomes of zebrafish (Danio rerio) and medaka (Oryzias latipes), a teleost with a conserved ancestral karyotype, we find possible examples of chromosomal aggregation of CC-DEGs during evolution of the zebrafish lineage. Our method for DAR analysis requires only RNA-sequencing data, facilitating its application across new and existing datasets.
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Affiliation(s)
- Lachlan Baer
- School of Biological Sciences, University of Adelaide, Adelaide, South Australia, Australia
| | - Karissa Barthelson
- School of Biological Sciences, University of Adelaide, Adelaide, South Australia, Australia
- Childhood Dementia Research Group, College of Medicine and Public Health, Flinders Health and Medical Research Institute, Flinders University, Bedford Park, South Australia, Australia
| | - John H. Postlethwait
- Institute of Neuroscience, University of Oregon, Eugene, Oregon, United States of America
| | - David L. Adelson
- School of Biological Sciences, University of Adelaide, Adelaide, South Australia, Australia
| | - Stephen M. Pederson
- Black Ochre Data Labs, Indigenous Genomics, Telethon Kids Institute, Adelaide, South Australia, Australia
- Dame Roma Mitchell Cancer Research Laboratories, Adelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia
- John Curtin School of Medical Research, Australian National University, Canberra, Australian Capital Territory, Australia
| | - Michael Lardelli
- School of Biological Sciences, University of Adelaide, Adelaide, South Australia, Australia
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4
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Baer L, Barthelson K, Postlethwait J, Adelson D, Pederson S, Lardelli M. Differential allelic representation (DAR) identifies candidate eQTLs and improves transcriptome analysis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.02.530865. [PMID: 36945478 PMCID: PMC10028786 DOI: 10.1101/2023.03.02.530865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/08/2023]
Abstract
In comparisons between mutant and wild-type genotypes, transcriptome analysis can reveal the direct impacts of a mutation, together with the homeostatic responses of the biological system. Recent studies have highlighted that, when homozygous mutations are studied in non-isogenic backgrounds, genes from the same chromosome as a mutation often appear over-represented among differentially expressed (DE) genes. One hypothesis suggests that DE genes chromosomally linked to a mutation may not reflect true biological responses to the mutation but, instead, result from differences in representation of expression quantitative trait loci (eQTLs) between sample groups selected on the basis of mutant or wild-type genotype. This is problematic when inclusion of spurious DE genes in a functional enrichment study results in incorrect inferences of mutation effect. Here we show that chromosomally co-located differentially expressed genes (CC-DEGs) can also be observed in analyses of dominant mutations in heterozygotes. We define a method and a metric to quantify, in RNA-sequencing data, localised differential allelic representation (DAR) between groups of samples subject to differential expression analysis. We show how the DAR metric can predict regions prone to eQTL-driven differential expression, and how it can improve functional enrichment analyses through gene exclusion or weighting of gene-level rankings. Advantageously, this improved ability to identify probable eQTLs also reveals examples of CC-DEGs that are likely to be functionally related to a mutant phenotype. This supports a long-standing prediction that selection for advantageous linkage disequilibrium influences chromosome evolution. By comparing the genomes of zebrafish (Danio rerio) and medaka (Oryzias latipes), a teleost with a conserved ancestral karyotype, we find possible examples of chromosomal aggregation of CC-DEGs during evolution of the zebrafish lineage. The DAR metric provides a solid foundation for addressing the eQTL issue in new and existing datasets because it relies solely on RNA-sequencing data.
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Affiliation(s)
- Lachlan Baer
- School of Biological Sciences, University of Adelaide, Adelaide, SA, Australia
| | - Karissa Barthelson
- School of Biological Sciences, University of Adelaide, Adelaide, SA, Australia
- Childhood Dementia Research Group, College of Medicine and Public Health, Flinders Health and Medical Research Institute, Flinders University, Bedford Park, SA, Australia
| | | | - David Adelson
- School of Biological Sciences, University of Adelaide, Adelaide, SA, Australia
| | - Stephen Pederson
- Black Ochre Data Labs, Indigenous Genomics, Telethon Kids Institute, Adelaide, SA, Australia
- Dame Roma Mitchell Cancer Research Laboratories, Adelaide Medical School, University of Adelaide, Adelaide, SA, Australia
- John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia
| | - Michael Lardelli
- School of Biological Sciences, University of Adelaide, Adelaide, SA, Australia
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5
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Transcriptomics for Drought Stress Mediated by Biological Processes in-relation to Key Regulated Pathways in Gossypium darwinii. Mol Biol Rep 2022; 49:11341-11350. [PMID: 35907118 DOI: 10.1007/s11033-022-07774-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 07/05/2022] [Indexed: 10/16/2022]
Abstract
BACKGROUND Wild cotton Gossypium darwinii, an allotetraploid harbours important traits useful for tolerating abiotic stress, i.e., drought, salt and good genetic stability, hence these characteristics can be transferred to cultivated cotton for genetic improvement. MATERIALS AND METHODS In this study, we analyzed the RNA-seq transcriptomes from leaves of G. darwinii seedlings with and without drought stress. A total of 86.7 million valid reads with an average length of 95.79 bp were generated from the two samples and 58,960 transcripts with a length of more than 500 bp were assembled. We searched the known proteins on the strength of sequence similarity; these transcripts were annotated with COG, KEGG and GO functional categories. According to gene expression abundance RPKM value, we carried out RT-qPCR analysis to determine the expression pattern of the obtained transcription factors. RESULTS A total of 58,960 genes was differentially expressed (DEG), with 32,693 and 25,919 genes found to be upregulated and downregulated, respectively. Through gene ontology and KEGG pathways, the upregulated genes were found to associate with all the GO terms, molecular functions (MF), biological process (BP) and cellular components (CC), which are highly linked to enhancing drought stress tolerance. CONCLUSION The study provides an in-depth knowledge of regulation of pathways and genes involved in photosynthesis during drought stress in G. darwinii. These pathways and genes were found to be significantly downregulated and this information could be further utilized by cotton breeders in developing a more drought tolerant cotton germplasm.
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Pazos Obregón F, Silvera D, Soto P, Yankilevich P, Guerberoff G, Cantera R. Gene function prediction in five model eukaryotes exclusively based on gene relative location through machine learning. Sci Rep 2022; 12:11655. [PMID: 35803984 PMCID: PMC9270439 DOI: 10.1038/s41598-022-15329-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 06/22/2022] [Indexed: 12/13/2022] Open
Abstract
The function of most genes is unknown. The best results in automated function prediction are obtained with machine learning-based methods that combine multiple data sources, typically sequence derived features, protein structure and interaction data. Even though there is ample evidence showing that a gene's function is not independent of its location, the few available examples of gene function prediction based on gene location rely on sequence identity between genes of different organisms and are thus subjected to the limitations of the relationship between sequence and function. Here we predict thousands of gene functions in five model eukaryotes (Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster, Mus musculus and Homo sapiens) using machine learning models exclusively trained with features derived from the location of genes in the genomes to which they belong. Our aim was not to obtain the best performing method to automated function prediction but to explore the extent to which a gene's location can predict its function in eukaryotes. We found that our models outperform BLAST when predicting terms from Biological Process and Cellular Component Ontologies, showing that, at least in some cases, gene location alone can be more useful than sequence to infer gene function.
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Affiliation(s)
- Flavio Pazos Obregón
- Departamento de Biología del Neurodesarrollo, Instituto de Investigaciones Biológicas Clemente Estable, Av. Italia 3318, 11600, Montevideo, Uruguay. .,Unidad de Bioquímica y Proteómica Analíticas, Instituto Pasteur de Montevideo, Montevideo, Uruguay.
| | - Diego Silvera
- Departamento de Biología del Neurodesarrollo, Instituto de Investigaciones Biológicas Clemente Estable, Av. Italia 3318, 11600, Montevideo, Uruguay
| | - Pablo Soto
- Departamento de Biología del Neurodesarrollo, Instituto de Investigaciones Biológicas Clemente Estable, Av. Italia 3318, 11600, Montevideo, Uruguay
| | - Patricio Yankilevich
- Instituto de Investigación en Biomedicina de Buenos Aires (IBioBA), CONICET-Partner Institute of the Max Planck Society, Buenos Aires, Argentina
| | - Gustavo Guerberoff
- Instituto de Matemática y Estadística "Prof. Ing. Rafael Laguardia", Facultad de Ingeniería, UDELAR, Montevideo, Uruguay
| | - Rafael Cantera
- Departamento de Biología del Neurodesarrollo, Instituto de Investigaciones Biológicas Clemente Estable, Av. Italia 3318, 11600, Montevideo, Uruguay
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Genome Comparisons of the Fission Yeasts Reveal Ancient Collinear Loci Maintained by Natural Selection. J Fungi (Basel) 2021; 7:jof7100864. [PMID: 34682285 PMCID: PMC8537764 DOI: 10.3390/jof7100864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Revised: 10/06/2021] [Accepted: 10/12/2021] [Indexed: 11/30/2022] Open
Abstract
Fission yeasts have a unique life history and exhibit distinct evolutionary patterns from other yeasts. Besides, the species demonstrate stable genome structures despite the relatively fast evolution of their genomic sequences. To reveal what could be the reason for that, comparative genomic analyses were carried out. Our results provided evidence that the structural and sequence evolution of the fission yeasts were correlated. Moreover, we revealed ancestral locally collinear blocks (aLCBs), which could have been inherited from their last common ancestor. These aLCBs proved to be the most conserved regions of the genomes as the aLCBs contain almost eight genes/blocks on average in the same orientation and order across the species. Gene order of the aLCBs is mainly fission-yeast-specific but supports the idea of filamentous ancestors. Nevertheless, the sequences and gene structures within the aLCBs are as mutable as any sequences in other parts of the genomes. Although genes of certain Gene Ontology (GO) categories tend to cluster at the aLCBs, those GO enrichments are not related to biological functions or high co-expression rates, they are, rather, determined by the density of essential genes and Rec12 cleavage sites. These data and our simulations indicated that aLCBs might not only be remnants of ancestral gene order but are also maintained by natural selection.
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Das Roy R, Hallikas O, Christensen MM, Renvoisé E, Jernvall J. Chromosomal neighbourhoods allow identification of organ specific changes in gene expression. PLoS Comput Biol 2021; 17:e1008947. [PMID: 34506480 PMCID: PMC8457456 DOI: 10.1371/journal.pcbi.1008947] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 09/22/2021] [Accepted: 08/26/2021] [Indexed: 11/19/2022] Open
Abstract
Although most genes share their chromosomal neighbourhood with other genes, distribution of genes has not been explored in the context of individual organ development; the common focus of developmental biology studies. Because developmental processes are often associated with initially subtle changes in gene expression, here we explored whether neighbouring genes are informative in the identification of differentially expressed genes. First, we quantified the chromosomal neighbourhood patterns of genes having related functional roles in the mammalian genome. Although the majority of protein coding genes have at least five neighbours within 1 Mb window around each gene, very few of these neighbours regulate development of the same organ. Analyses of transcriptomes of developing mouse molar teeth revealed that whereas expression of genes regulating tooth development changes, their neighbouring genes show no marked changes, irrespective of their level of expression. Finally, we test whether inclusion of gene neighbourhood in the analyses of differential expression could provide additional benefits. For the analyses, we developed an algorithm, called DELocal that identifies differentially expressed genes by comparing their expression changes to changes in adjacent genes in their chromosomal regions. Our results show that DELocal removes detection bias towards large changes in expression, thereby allowing identification of even subtle changes in development. Future studies, including the detection of differential expression, may benefit from, and further characterize the significance of gene-gene neighbour relationships. Development of organs is typically associated with small and hard to detect changes in gene expression. Here we examined how often genes regulating specific organs are neighbours to each other in the genome, and whether this gene neighbourhood helps in the detection of changes in gene expression. We found that genes regulating individual organ development are very rarely close to each other in the mouse and human genomes. We built an algorithm, called DELocal, to detect changes in gene expression that incorporates information about neighbouring genes. Using transcriptomes of developing mouse molar teeth containing gene expression profiles of thousands of genes, we show how genes regulating tooth development are ranked high by DELocal even if their expression level changes are subtle. We propose that developmental biology studies can benefit from gene neighbourhood analyses in the detection of differential expression and identification of organ specific genes.
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Affiliation(s)
- Rishi Das Roy
- Institute of Biotechnology, University of Helsinki, Helsinki, Finland
- * E-mail: (RDR); (JJ)
| | - Outi Hallikas
- Institute of Biotechnology, University of Helsinki, Helsinki, Finland
| | | | - Elodie Renvoisé
- Institute of Biotechnology, University of Helsinki, Helsinki, Finland
| | - Jukka Jernvall
- Institute of Biotechnology, University of Helsinki, Helsinki, Finland
- Department of Geosciences and Geography, University of Helsinki, Helsinki, Finland
- * E-mail: (RDR); (JJ)
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Barthelson K, Baer L, Dong Y, Hand M, Pujic Z, Newman M, Goodhill GJ, Richards RI, Pederson SM, Lardelli M. Zebrafish Chromosome 14 Gene Differential Expression in the fmr1 h u2787 Model of Fragile X Syndrome. Front Genet 2021; 12:625466. [PMID: 34135935 PMCID: PMC8203322 DOI: 10.3389/fgene.2021.625466] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Accepted: 04/14/2021] [Indexed: 11/13/2022] Open
Abstract
Zebrafish represent a valuable model for investigating the molecular and cellular basis of Fragile X syndrome (FXS). Reduced expression of the zebrafish FMR1 orthologous gene, fmr1, causes developmental and behavioural phenotypes related to FXS. Zebrafish homozygous for the hu2787 non-sense mutation allele of fmr1 are widely used to model FXS, although FXS-relevant phenotypes seen from morpholino antisense oligonucleotide (morpholino) suppression of fmr1 transcript translation were not observed when hu2787 was first described. The subsequent discovery of transcriptional adaptation (a form of genetic compensation), whereby mutations causing non-sense-mediated decay of transcripts can drive compensatory upregulation of homologous transcripts independent of protein feedback loops, suggested an explanation for the differences reported. We examined the whole-embryo transcriptome effects of homozygosity for fmr1 h u2787 at 2 days post fertilisation. We observed statistically significant changes in expression of a number of gene transcripts, but none from genes showing sequence homology to fmr1. Enrichment testing of differentially expressed genes implied effects on lysosome function and glycosphingolipid biosynthesis. The majority of the differentially expressed genes are located, like fmr1, on Chromosome 14. Quantitative PCR tests did not support that this was artefactual due to changes in relative chromosome abundance. Enrichment testing of the "leading edge" differentially expressed genes from Chromosome 14 revealed that their co-location on this chromosome may be associated with roles in brain development and function. The differential expression of functionally related genes due to mutation of fmr1, and located on the same chromosome as fmr1, is consistent with R.A. Fisher's assertion that the selective advantage of co-segregation of particular combinations of alleles of genes will favour, during evolution, chromosomal rearrangements that place them in linkage disequilibrium on the same chromosome. However, we cannot exclude that the apparent differential expression of genes on Chromosome 14 genes was, (if only in part), caused by differences between the expression of alleles of genes unrelated to the effects of the fmr1 h u2787 mutation and made manifest due to the limited, but non-zero, allelic diversity between the genotypes compared.
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Affiliation(s)
- Karissa Barthelson
- School of Biological Sciences, University of Adelaide, Adelaide, SA, Australia
| | - Lachlan Baer
- School of Biological Sciences, University of Adelaide, Adelaide, SA, Australia
| | - Yang Dong
- School of Biological Sciences, University of Adelaide, Adelaide, SA, Australia
| | - Melanie Hand
- Bioinformatics Hub, University of Adelaide, Adelaide, SA, Australia
| | - Zac Pujic
- Queensland Brain Institute, University of Queensland, Brisbane, QLD, Australia
| | - Morgan Newman
- School of Biological Sciences, University of Adelaide, Adelaide, SA, Australia
| | - Geoffrey J. Goodhill
- Queensland Brain Institute, University of Queensland, Brisbane, QLD, Australia
- School of Mathematics and Physics, University of Queensland, Brisbane, QLD, Australia
| | - Robert I. Richards
- School of Biological Sciences, University of Adelaide, Adelaide, SA, Australia
| | | | - Michael Lardelli
- School of Biological Sciences, University of Adelaide, Adelaide, SA, Australia
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Xu L, Jiang W, Jia H, Zheng L, Xing J, Liu A, Du G. Discovery of Multitarget-Directed Ligands Against Influenza A Virus From Compound Yizhihao Through a Predictive System for Compound-Protein Interactions. Front Cell Infect Microbiol 2020; 10:16. [PMID: 32117796 PMCID: PMC7026480 DOI: 10.3389/fcimb.2020.00016] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Accepted: 01/14/2020] [Indexed: 12/27/2022] Open
Abstract
Influenza A virus (IAV) is a threat to public health due to its high mutation rate and resistance to existing drugs. In this investigation, 15 targets selected from an influenza virus–host interaction network were successfully constructed as a multitarget virtual screening system for new drug discovery against IAV using Naïve Bayesian, recursive partitioning, and CDOCKER methods. The predictive accuracies of the models were evaluated using training sets and test sets. The system was then used to predict active constituents of Compound Yizhihao (CYZH), a Chinese medicinal compound used to treat influenza. Twenty-eight compounds with multitarget activities were selected for subsequent in vitro evaluation. Of the four compounds predicted to be active on neuraminidase (NA), chlorogenic acid, and orientin showed inhibitory activity in vitro. Linarin, sinensetin, cedar acid, isoliquiritigenin, sinigrin, luteolin, chlorogenic acid, orientin, epigoitrin, and rupestonic acid exhibited significant effects on TNF-α expression, which is almost consistent with predicted results. Results from a cytopathic effect (CPE) reduction assay revealed acacetin, indirubin, tryptanthrin, quercetin, luteolin, emodin, and apigenin had protective effects against wild-type strains of IAV. Quercetin, luteolin, and apigenin had good efficacy against resistant IAV strains in CPE reduction assays. Finally, with the aid of Gene Ontology biological process analysis, the potential mechanisms of CYZH action were revealed. In conclusion, a compound-protein interaction-prediction system was an efficient tool for the discovery of novel compounds against influenza, and the findings from CYZH provide important information for its usage and development.
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Affiliation(s)
- Lvjie Xu
- Institute of Materia Medica, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Wen Jiang
- The Sixth Clinical Hospital of Xinjiang Medical University, Ürümqi, China
| | - Hao Jia
- Institute of Materia Medica, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Lishu Zheng
- Chinese Center for Disease Control and Prevention, National Institute for Viral Disease Control and Prevention, Beijing, China
| | - Jianguo Xing
- Xinjiang Institute of Materia Medica, Ürümqi, China
| | - Ailin Liu
- Institute of Materia Medica, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Guanhua Du
- Institute of Materia Medica, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
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11
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Monitoring the prolonged Tnf stimulation in space and time with topological-functional networks. Comput Struct Biotechnol J 2020; 18:220-229. [PMID: 32021663 PMCID: PMC6994266 DOI: 10.1016/j.csbj.2020.01.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 12/18/2019] [Accepted: 01/03/2020] [Indexed: 11/21/2022] Open
Abstract
Genes in linear proximity often share regulatory inputs, expression and evolutionary patterns, even in complex eukaryote genomes with extensive intergenic sequences. Gene regulation, on the other hand, is effected through the co-ordinated activation (or suppression) of genes participating in common biological pathways, which are often transcribed from distant loci. Existing approaches for the study of gene expression focus on the functional aspect, taking positional constraints into account only marginally. In this work we propose a novel concept for the study of gene expression, through the combination of topological and functional information into bipartite networks. Starting from genome-wide expression profiles, we define extended chromosomal regions with consistent patterns of differential gene expression and then associate these domains with enriched functional pathways. By analyzing the resulting networks in terms of size, connectivity and modularity we can draw conclusions on the way genome organization may underlie the gene regulation program. Implementation of this approach in a detailed RNASeq profiling of sustained Tnf stimulation of mouse synovial fibroblasts, allowed us to identify unexpected regulatory changes taking place in the cells after 24 h of stimulation. Bipartite network analysis suggests that the cytokine response set by Tnf, progresses through two distinct transitions. An early generalization of the inflammatory response, that is followed by a late shutdown of immune-related functions and the redistribution of expression to developmental and cell adhesion pathways and distinct chromosomal regions. We show that the incorporation of topological information may provide additional insights in the complex propagation of Tnf activation.
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12
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Patterns of diverse gene functions in genomic neighborhoods predict gene function and phenotype. Sci Rep 2019; 9:19537. [PMID: 31863070 PMCID: PMC6925100 DOI: 10.1038/s41598-019-55984-0] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Accepted: 12/02/2019] [Indexed: 01/01/2023] Open
Abstract
Genes with similar roles in the cell cluster on chromosomes, thus benefiting from coordinated regulation. This allows gene function to be inferred by transferring annotations from genomic neighbors, following the guilt-by-association principle. We performed a systematic search for co-occurrence of >1000 gene functions in genomic neighborhoods across 1669 prokaryotic, 49 fungal and 80 metazoan genomes, revealing prevalent patterns that cannot be explained by clustering of functionally similar genes. It is a very common occurrence that pairs of dissimilar gene functions – corresponding to semantically distant Gene Ontology terms – are significantly co-located on chromosomes. These neighborhood associations are often as conserved across genomes as the known associations between similar functions, suggesting selective benefits from clustering of certain diverse functions, which may conceivably play complementary roles in the cell. We propose a simple encoding of chromosomal gene order, the neighborhood function profiles (NFP), which draws on diverse gene clustering patterns to predict gene function and phenotype. NFPs yield a 26–46% increase in predictive power over state-of-the-art approaches that propagate function across neighborhoods, thus providing hundreds of novel, high-confidence gene function inferences per genome. Furthermore, we demonstrate that copy number-neutral structural variation that shapes gene function distribution across chromosomes can predict phenotype of individuals from their genome sequence.
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The Bioinformatic Analysis of the Dysregulated Genes and MicroRNAs in Entorhinal Cortex, Hippocampus, and Blood for Alzheimer's Disease. BIOMED RESEARCH INTERNATIONAL 2017; 2017:9084507. [PMID: 29359159 PMCID: PMC5735586 DOI: 10.1155/2017/9084507] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2017] [Revised: 08/15/2017] [Accepted: 08/29/2017] [Indexed: 02/08/2023]
Abstract
Aim The incidence of Alzheimer's disease (AD) has been increasing in recent years, but there exists no cure and the pathological mechanisms are not fully understood. This study aimed to find out the pathogenesis of learning and memory impairment, new biomarkers, potential therapeutic targets, and drugs for AD. Methods We downloaded the microarray data of entorhinal cortex (EC) and hippocampus (HIP) of AD and controls from Gene Expression Omnibus (GEO) database, and then the differentially expressed genes (DEGs) in EC and HIP regions were analyzed for functional and pathway enrichment. Furthermore, we utilized the DEGs to construct coexpression networks to identify hub genes and discover the small molecules which were capable of reversing the gene expression profile of AD. Finally, we also analyzed microarray and RNA-seq dataset of blood samples to find the biomarkers related to gene expression in brain. Results We found some functional hub genes, such as ErbB2, ErbB4, OCT3, MIF, CDK13, and GPI. According to GO and KEGG pathway enrichment, several pathways were significantly dysregulated in EC and HIP. CTSD and VCAM1 were dysregulated significantly in blood, EC, and HIP, which were potential biomarkers for AD. Target genes of four microRNAs had similar GO_terms distribution with DEGs in EC and HIP. In addtion, small molecules were screened out for AD treatment. Conclusion These biological pathways and DEGs or hub genes will be useful to elucidate AD pathogenesis and identify novel biomarkers or drug targets for developing improved diagnostics and therapeutics against AD.
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Tsochatzidou M, Malliarou M, Papanikolaou N, Roca J, Nikolaou C. Genome urbanization: clusters of topologically co-regulated genes delineate functional compartments in the genome of Saccharomyces cerevisiae. Nucleic Acids Res 2017; 45:5818-5828. [PMID: 28369650 PMCID: PMC5449599 DOI: 10.1093/nar/gkx198] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2017] [Accepted: 03/15/2017] [Indexed: 02/01/2023] Open
Abstract
The eukaryotic genome evolves under the dual constraint of maintaining coordinated gene transcription and performing effective DNA replication and cell division, the coupling of which brings about inevitable DNA topological tension. DNA supercoiling is resolved and, in some cases, even harnessed by the genome through the function of DNA topoisomerases, as has been shown in the concurrent transcriptional activation and suppression of genes upon transient deactivation of topoisomerase II (topoII). By analyzing a genome-wide transcription run-on experiment upon thermal inactivation of topoII in Saccharomyces cerevisiae we were able to define 116 gene clusters of consistent response (either positive or negative) to topological stress. A comprehensive analysis of these topologically co-regulated gene clusters reveals pronounced preferences regarding their functional, regulatory and structural attributes. Genes that negatively respond to topological stress, are positioned in gene-dense pericentromeric regions, are more conserved and associated to essential functions, while upregulated gene clusters are preferentially located in the gene-sparse nuclear periphery, associated with secondary functions and under complex regulatory control. We propose that genome architecture evolves with a core of essential genes occupying a compact genomic ‘old town’, whereas more recently acquired, condition-specific genes tend to be located in a more spacious ‘suburban’ genomic periphery.
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Affiliation(s)
- Maria Tsochatzidou
- Computational Genomics Group, Department of Biology, University of Crete, Herakleion 70013, Greece
| | - Maria Malliarou
- Computational Genomics Group, Department of Biology, University of Crete, Herakleion 70013, Greece
| | - Nikolas Papanikolaou
- Computational Genomics Group, Department of Biology, University of Crete, Herakleion 70013, Greece
| | - Joaquim Roca
- Molecular Biology Institute of Barcelona (IBMB), Spanish National Research Council (CSIC), Barcelona 08028, Spain
| | - Christoforos Nikolaou
- Computational Genomics Group, Department of Biology, University of Crete, Herakleion 70013, Greece
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Xu L, Yin L, Tao X, Qi Y, Han X, Xu Y, Song S, Li L, Sun P, Peng J. Dioscin, a potent ITGA5 inhibitor, reduces the synthesis of collagen against liver fibrosis: Insights from SILAC-based proteomics analysis. Food Chem Toxicol 2017; 107:318-328. [DOI: 10.1016/j.fct.2017.07.014] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2017] [Revised: 07/04/2017] [Accepted: 07/05/2017] [Indexed: 11/26/2022]
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López Villar E, Madero L, A López-Pascual J, C Cho W. Study of phosphorylation events for cancer diagnoses and treatment. Clin Transl Med 2015; 4:59. [PMID: 26055493 PMCID: PMC4460185 DOI: 10.1186/s40169-015-0059-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2015] [Accepted: 05/19/2015] [Indexed: 02/07/2023] Open
Abstract
The activation of signaling cascades in response to extracellular and intracellular stimuli to control cell growth, proliferation and survival, is orchestrated by protein kinases via phosphorylation. A critical issue is the study of the mechanisms of cancer cells for the development of more effective drugs. With the application of the new proteomic technologies, together with the advancement in the sequencing of the human proteome, patients will therefore be benefited by the discovery of novel therapeutic and/or diagnostic protein targets. Furthermore, the advances in proteomic approaches and the Human Proteome Organization (HUPO) have opened a new door which is helpful in the identification of patients at risk and towards improving current therapies. Modification of the signaling-networks via mutations or abnormal protein expression underlies the cause or consequence of many diseases including cancer. Resulting data is used to reveal connections between genes proteins and compounds and the related molecular pathways for underlining disease states. As a delegate of HUPO, for human proteome on children assays and studies, we, at Hospital Universitario Niño Jesús, are seeking to support the human proteome in this context. Clinical goals have to be clearly established and proteomics experts have to set up the appropriate proteomic strategy, which coupled to bioinformatics will make it possible to achieve new therapies for patients with poor prognosis. We envision to combine our up-coming data to the HUPO organization in order to support international efforts to advance the cure of cancer disease.
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Affiliation(s)
- Elena López Villar
- Oncohematology of Children Department, Hospital Universitario Infantil Niño Jesús, Av. Menéndez Pelayo 65, Madrid, Spain,
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Huang L, Chen J, Cao P, Pan H, Ding C, Xiao T, Zhang P, Guo J, Su Z. Anti-obese effect of glucosamine and chitosan oligosaccharide in high-fat diet-induced obese rats. Mar Drugs 2015; 13:2732-56. [PMID: 25942093 PMCID: PMC4446603 DOI: 10.3390/md13052732] [Citation(s) in RCA: 81] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2015] [Revised: 04/07/2015] [Accepted: 04/22/2015] [Indexed: 01/09/2023] Open
Abstract
Objective: This study is to evaluate the anti-obese effects of glucosamine (GLC) and chitosan oligosaccharide (COS) on high-fat diet-induced obese rats. Methods: The rats were randomly divided into twelve groups: a normal diet group (NF), a high-fat diet group (HF), Orlistat group, GLC high-, middle-, and low-dose groups (GLC-H, GLC-M, GLC-L), COS1 (COS, number-average molecular weight ≤1000) high-, middle-, and low-dose groups (COS1-H, COS1-M, COS1-L), and COS2 (COS, number-average molecular weight ≤3000) high-, middle-, and low-dose groups (COS2-H, COS2-M, COS2-L). All groups received oral treatment by gavage once daily for a period of six weeks. Results: Rats fed with COS1 gained the least weight among all the groups (P < 0.01), and these rats lost more weight than those treated with Orlistat. In addition to the COS2-H and Orlistat groups, the serum total cholesterol (CHO) and low-density lipoprotein cholesterol (LDL-C) levels were significantly reduced in all treatment groups compared to the HF group (P < 0.01). The various doses of GLC, COS1 and COS2 reduced the expression levels of PPARγ and LXRα mRNA in the white adipose tissue. Conclusions: The results above demonstrated that GLC, COS1, and COS2 improved dyslipidemia and prevented body weight gains by inhibiting the adipocyte differentiation in obese rats induced by a high-fat diet. Thus, these agents may potentially be used to treat obesity.
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Affiliation(s)
- Lanlan Huang
- Key Research Center of Liver Regulation for Hyperlipidemia SATCM/Class III Laboratory of Metabolism SATCM, Guangdong TCM Key Laboratory for Metabolic Diseases, Guangdong Pharmaceutical University, Guangzhou 510006, China.
| | - Jian Chen
- Key Research Center of Liver Regulation for Hyperlipidemia SATCM/Class III Laboratory of Metabolism SATCM, Guangdong TCM Key Laboratory for Metabolic Diseases, Guangdong Pharmaceutical University, Guangzhou 510006, China.
| | - Peiqiu Cao
- Key Research Center of Liver Regulation for Hyperlipidemia SATCM/Class III Laboratory of Metabolism SATCM, Guangdong TCM Key Laboratory for Metabolic Diseases, Guangdong Pharmaceutical University, Guangzhou 510006, China.
| | - Haitao Pan
- Key Research Center of Liver Regulation for Hyperlipidemia SATCM/Class III Laboratory of Metabolism SATCM, Guangdong TCM Key Laboratory for Metabolic Diseases, Guangdong Pharmaceutical University, Guangzhou 510006, China.
| | - Chen Ding
- Key Research Center of Liver Regulation for Hyperlipidemia SATCM/Class III Laboratory of Metabolism SATCM, Guangdong TCM Key Laboratory for Metabolic Diseases, Guangdong Pharmaceutical University, Guangzhou 510006, China.
| | - Tiancun Xiao
- Inorganic Chemistry Laboratory, Oxford University, South Parks Road, OX1 3QR Oxford, UK.
- Guangzhou Boxabio Technology Ltd., Guangzhou Hi-Tech Development Zone, Guangzhou 510663, China.
| | - Pengfei Zhang
- Guangzhou Boxabio Technology Ltd., Guangzhou Hi-Tech Development Zone, Guangzhou 510663, China.
| | - Jiao Guo
- Key Research Center of Liver Regulation for Hyperlipidemia SATCM/Class III Laboratory of Metabolism SATCM, Guangdong TCM Key Laboratory for Metabolic Diseases, Guangdong Pharmaceutical University, Guangzhou 510006, China.
| | - Zhengquan Su
- Key Research Center of Liver Regulation for Hyperlipidemia SATCM/Class III Laboratory of Metabolism SATCM, Guangdong TCM Key Laboratory for Metabolic Diseases, Guangdong Pharmaceutical University, Guangzhou 510006, China.
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