Comprehensive analysis of a TNF family based-signature in diffuse gliomas with regard to prognosis and immune significance

Background Several studies have shown that members of the tumor necrosis factor (TNF) family play an important role in cancer immunoregulation, and trials targeting these molecules are already underway. Our study aimed to integrate and analyze the expression patterns and clinical significance of TNF family-related genes in gliomas. Methods A total of 1749 gliomas from 4 datasets were enrolled in our study, including the Cancer Genome Atlas (TCGA) dataset as the training cohort and the other three datasets (CGGA, GSE16011, and Rembrandt) as validation cohorts. Clinical information, RNA expression data, and genomic profile were collected for analysis. We screened the signature gene set by Cox proportional hazards modelling. We evaluated the prognostic value of the signature by Kaplan–Meier analysis and timeROC curve. Gene Ontology (GO) and Gene set enrichment analysis (GSEA) analysis were performed for functional annotation. CIBERSORT algorithm and inflammatory metagenes were used to reveal immune characteristics. Results In gliomas, the expression of most TNF family members was positively correlated. Univariate analysis showed that most TNF family members were related to the overall survival of patients. Then through the LASSO regression model, we developed a TNF family-based signature, which was related to clinical, molecular, and genetic characteristics of patients with glioma. Moreover, the signature was found to be an independent prognostic marker through survival curve analysis and Cox regression analysis. Furthermore, a nomogram prognostic model was constructed to predict individual survival rates at 1, 3 and 5 years. Functional annotation analysis revealed that the immune and inflammatory response pathways were enriched in the high-risk group. Immunological analysis showed the immunosuppressive status in the high-risk group. Conclusions We developed a TNF family-based signature to predict the prognosis of patients with glioma.

GFAP: ultra-fast and accurate gene functional annotation software for plants

Sequence alignment is the basis of gene functional annotation for unknow sequences. Selecting closely related species as the reference species should be an effective way to improve the accuracy of gene annotation for plants, compared with only based on one or some model plants. Therefore, limited species number in previous software or website is disadvantageous for plant gene annotation. Here, we collected the protein sequences of 236 plant species with known genomic information from 63 families. After that, these sequences were annotated by pfam, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases to construct our databases. Furthermore, we developed the software, Gene Annotation Software for Plants (GFAP), to perform gene annotation using our databases. GFAP, an open-source software running on Windows and MacOS systems, is an efficient and network independent tool. GFAP can search the protein domain, GO and KEGG information for 43000 genes within 4 minutes. In addition, GFAP can also perform the sequence alignment, statistical analysis and drawing. The website of https://gitee.com/simon198912167815/gfap-database provides the software, databases, testing data and video tutorials for users. GFAP contained large amount of plant-species information. We believe that it will become a powerful tool in gene annotation using closely related species for phytologists.

De novo assembly and inferred functional annotation of the transcriptome of Heterosigma akashiwo

Heterosigma akashiwo is a eukaryotic, cosmopolitan, and unicellular alga (class: Raphidophyceae), and produces fish-killing blooms. There is a substantial scientific and practical interest in its ecophysiological characteristics that determine bloom dynamics and its adaptation to broad climate zones. A well-annotated genomic/genetic sequence information enables researchers to characterize organisms using modern molecular technology. The Chloroplast and the mitochondrial genome sequences and transcriptome sequence assembly (TSA) datasets with limited sizes for H. akashiwo are available in NCBI nucleotide database on December 2021: there is no doubt that more genetic information of the species will greatly enhance the progress of biological characterization of the species. Here, we conducted H. akashiwo RNA sequencing, a de novo transcriptome assembly (NCBI TSA ICRV01) of a large number of high-quality short-read sequences, and the functional annotation of predicted genes. Based on our transcriptome, we confirmed that the organism possesses genes that were predicted to function in phagocytosis, supporting the earlier observations of H. akashiwo bacterivory. Along with its capability for photosynthesis, the mixotrophy of H. akashiwo may partially explain its high adaptability to various environmental conditions. Our study here will provide an important toehold to decipher H. akashiwo ecophysiology at a molecular level.

Association analysis and functional annotation of imputed sequence data within genomic regions influencing resistance to gastro-intestinal parasites detected by an LDLA approach in a nucleus flock of Sarda dairy sheep

Background Gastroinestinal nematodes (GIN) are one of the major health problem in grazing sheep. Although genetic variability of the resistance to GIN has been documented, traditional selection is hampered by the difficulty of recording phenotypes, usually fecal egg count (FEC). To identify causative mutations or markers in linkage disequilibrium (LD) to be used for selection, the detection of quantitative trait loci (QTL) for FEC based on linkage disequilibrium-linkage analysis (LDLA) was performed on 4097 ewes (from 181 sires) all genotyped with the OvineSNP50 Beadchip. Identified QTL regions (QTLR) were imputed from whole-genome sequences of 56 target animals of the population. An association analysis and a functional annotation of imputed polymorphisms in the identified QTLR were performed to pinpoint functional variants with potential impact on candidate genes identified from ontological classification or differentially expressed in previous studies. Results After clustering close significant locations, ten QTLR were defined on nine Ovis aries chromosomes (OAR) by LDLA. The ratio between the ANOVA estimators of the QTL variance and the total phenotypic variance ranged from 0.0087 to 0.0176. QTL on OAR4, 12, 19, and 20 were the most significant. The combination of association analysis and functional annotation of sequence data did not highlight any putative causative mutations. None of the most significant SNPs showed a functional effect on genes’ transcript. However, in the most significant QTLR, we identified genes that contained polymorphisms with a high or moderate impact, were differentially expressed in previous studies, contributed to enrich the most represented GO process (regulation of immune system process, defense response). Among these, the most likely candidate genes were: TNFRSF1B and SELE on OAR12, IL5RA on OAR19, IL17A, IL17F, TRIM26, TRIM38, TNFRSF21, LOC101118999, VEGFA, and TNF on OAR20. Conclusions This study performed on a large experimental population provides a list of candidate genes and polymorphisms which could be used in further validation studies. The expected advancements in the quality of the annotation of the ovine genome and the use of experimental designs based on sequence data and phenotypes from multiple breeds that show different LD extents and gametic phases may help to identify causative mutations.

De novo assembly and inferred functional annotation of the transcriptome of Heterosigma akashiwo

Heterosigma akashiwo is a eukaryotic, cosmopolitan, and unicellular alga (class: Raphidophyceae), and produces fish-killing blooms. There is a substantial scientific and practical interest in its ecophysiological characteristics that determine bloom dynamics and its adaptation to broad climate zones. A well-annotated genomic/genetic sequence information enables researchers to characterize organisms using modern molecular technology. The Chloroplast and the mitochondrial genome sequences and transcriptome sequence assembly (TSA) datasets with limited sizes for H. akashiwo are available in NCBI nucleotide database on December 2021: there is no doubt that more genetic information of the species will greatly enhance the progress of biological characterization of the species. Here, we conducted H. akashiwo RNA sequencing, a de novo transcriptome assembly (NCBI TSA ICRV01) of a large number of high-quality short-read sequences, and the functional annotation of predicted genes. Based on our transcriptome, we confirmed that the organism possesses genes that were predicted to function in phagocytosis, supporting the earlier observations of H. akashiwo bacterivory. Along with its capability for photosynthesis, the mixotrophy of H. akashiwo may partially explain its high adaptability to various environmental conditions. Our study here will provide an important toehold to decipher H. akashiwo ecophysiology at a molecular level.

Scripting Analyses of Genomes in Ensembl Plants

Ensembl Plants (http://plants.ensembl.org) offers genome-scale information for plants, with four releases per year. As of release 47 (April 2020) it features 79 species and includes genome sequence, gene models, and functional annotation. Comparative analyses help reconstruct the evolutionary history of gene families, genomes, and components of polyploid genomes. Some species have gene expression baseline reports or variation across genotypes. While the data can be accessed through the Ensembl genome browser, here we review specifically how our plant genomes can be interrogated programmatically and the data downloaded in bulk. These access routes are generally consistent across Ensembl for other non-plant species, including plant pathogens, pests, and pollinators.

In-silico characterization and structure-based functional annotation of a hypothetical protein from Campylobacter jejuni involved in propionate catabolism

Campylobacter jejuni is one of the most prevalent organisms associated with foodborne illness across the globe causing campylobacteriosis and gastritis. Many proteins of C. jejuni are still unidentified. The purpose of this study was to determine the structure and function of a non-annotated hypothetical protein (HP) from C. jejuni. A number of properties like physiochemical characteristics, 3D structure, and functional annotation of the HP (accession No. CAG2129885.1) were predicted using various bioinformatics tools followed by further validation and quality assessment. Moreover, the protein-protein interactions and active site were obtained from the STRING and CASTp server, respectively. The hypothesized protein possesses various characteristics including an acidic pH, thermal stability, water solubility, and cytoplasmic distribution. While alpha-helix and random coil structures are the most prominent structural components of this protein, most of it is formed of helices and coils. Along with expected quality, the 3D model has been found to be novel. This study has identified the potential role of the HP in 2-methylcitric acid cycle and propionate catabolism. Furthermore, protein-protein interactions revealed several significant functional partners. The in-silico characterization of this protein will assist to understand its molecular mechanism of action better. The methodology of this study would also serve as the basis for additional research into proteomic and genomic data for functional potential identification.

3DFAACTS-SNP: Using Regulatory T Cell-specific Epigenomics Data to Uncover Candidate Mechanisms of Type-1 Diabetes (T1D) risk

BackgroundGenome-wide association studies (GWAS) have enabled the discovery of single nucleotide polymorphisms (SNPs) that are significantly associated with many autoimmune diseases including type 1 diabetes (T1D). However, many of the identified variants lie in non-coding regions, limiting the identification of mechanisms that contribute to autoimmune disease progression. To address this problem, we developed a variant filtering workflow called 3DFAACTS-SNP to link genetic variants to target genes in a cell specific manner. Here we use 3DFAACTS-SNP to identify candidate SNPs and target genes associated with the loss of immune tolerance in regulatory T cells (Treg) in T1D. ResultsUsing 3DFAACTS-SNP we identified from a list of 1,228 previously fine-mapped variants, 36 SNPs with plausible Treg-specific mechanisms of action. The integration of cell-type specific chromosome conformation capture data in 3DFAACTS-SNP, identified 119 regulatory regions and 51 candidate target genes that interact with these variant-containing regions in Treg cells. We further demonstrated the utility of the workflow by applying it to three other SNP autoimmune datasets, identifying 17 Treg-centric candidate variants and 35 interacting genes. Finally, we demonstrate the broad utility of 3DFAACTS-SNP for functional annotation of all known common (>10% allele frequency) variants from the Genome Aggregation Database (gnomAD). We identified 7,900 candidate variants and 3,245 candidate target genes, generating a list of potential sites for future T1D or autoimmune research. ConclusionsWe demonstrate that it is possible to further prioritise variants that contribute to T1D based on regulatory function and illustrate the power of using cell type specific multi-omics datasets to determine disease mechanisms. Our workflow can be customised to any cell type for which the individual datasets for functional annotation have been generated, giving broad applicability and utility.

Bridging functional annotation gaps in non-model plant genes with AlphaFold, DeepFRI and small molecule docking

Background: Functional annotation assigns descriptive biological meaning to genetic sequences. Limited availability of manually curated or experimentally validated plant genes from a diverse range of taxa poses a significant challenge for functional annotation in non-model organisms. Accurate computational approaches are required. We argue that recent breakthroughs in deep learning have the potential to not only narrow the functional annotation gap between non-model and model plant organisms, but also annotate and reveal novel functions even for genes with no homologs in public databases. Results: Deep learning models were applied to functionally annotate a set of previously published differentially expressed genes. Predicted protein structures and functional annotations were generated using the AlphaFold protein structure and DeepFRI protein language inference models respectively. The resulting structures and functional annotations were validated using small molecule docking experiments. DeepFRI and AlphaFold models not only correctly annotated differentially expressed genes, but also revealed detailed mechanisms involving protein-protein interactions. Conclusions: Deep learning models are capable of inferring novel functions and achieving high accuracy in functional annotation. Their increased use in plant research will result in major improvements in annotations for non-model plants that are underrepresented in genome databases. We illustrate how integrating protein structure prediction, functional residue prediction, and small molecule docking can infer plausible protein-protein interactions and yield additional mechanistic insights. This approach will aid in the selection of candidate genes for further study from differential expression studies that generate large gene lists.