Comprehensive Functional Annotation of Metagenomes and Microbial Genomes Using a Deep Learning-Based Method.

BackgroundHigh-throughput sequencing has increased the number of available microbial genomes recovered from isolates, single cells, and metagenomes. Accordingly, fast and comprehensive functional gene annotation pipelines are needed to analyze and compare these genomes. Although several approaches exist for genome annotation, these are typically not designed for easy incorporation into analysis pipelines, do not combine results from different annotation databases or offer easy-to-use summaries of metabolic reconstructions, and typically require large amounts of computing power for high-throughput analysis not available to the average user.ResultsHere, we introduce MicrobeAnnotator, a fully automated, easy-to-use pipeline for the comprehensive functional annotation of microbial genomes that combines results from several reference protein databases and returns the matching annotations together with key metadata such as the interlinked identifiers of matching reference proteins from multiple databases [KEGG Orthology (KO), Enzyme Commission (E.C.), Gene Ontology (GO), Pfam, and InterPro]. Further, the functional annotations are summarized into Kyoto Encyclopedia of Genes and Genomes (KEGG) modules as part of a graphical output (heatmap) that allows the user to quickly detect differences among (multiple) query genomes and cluster the genomes based on their metabolic similarity. MicrobeAnnotator is implemented in Python 3 and is freely available under an open-source Artistic License 2.0 from https://github.com/cruizperez/MicrobeAnnotator.ConclusionsWe demonstrated the capabilities of MicrobeAnnotator by annotating 100 Escherichia coli and 78 environmental Candidate Phyla Radiation (CPR) bacterial genomes and comparing the results to those of other popular tools. We showed that the use of multiple annotation databases allows MicrobeAnnotator to recover more annotations per genome compared to faster tools that use reduced databases and is computationally efficient for use in personal computers. The output of MicrobeAnnotator can be easily incorporated into other analysis pipelines while the results of other annotation tools can be seemingly incorporated into MicrobeAnnotator to generate summary plots.

Advanced sequencing technologies and improvements in bioinformatics have provided a new way to study plant-associated microbial communities, including the use of host genomic sequencing. Our study focuses on the leaf microbiome of basin big sagebrush (Artemisia tridentata subsp. tridentata), a foundational shrub of western North America. We analyzed Illumina shotgun sequences from sagebrush leaves to investigate the metagenomes of leaf-associated microbes that were sequenced alongside their plant hosts. We aimed to profile the leaf microbiome across different sample sources (magenta box, greenhouse, and field/wild), reconstruct metagenome-assembled genomes (MAGs) where possible, and investigate functional gene annotations of the resulting MAGs, specifically with regard to the potential metabolism of sagebrush chemicals. To achieve this, Illumina shotgun sequence reads (containing both host and associated microbial reads) were mapped to the reference genomes of Artemisia tridentata, Artemisia annua, and the human reference genome to remove plant host and human-associated sequences. Host-cleaned reads were then analyzed using microbial metagenomics techniques. Taxonomic profiling revealed that Phyllobacterium and Sphingomonas were the most abundant microbial genera in greenhouse-grown plants, with very little variation among the samples. Wild, field-collected samples were much more variable and were dominated by Klebsiella and Aureobasidium species. From the co-assembly of greenhouse samples, we reconstructed two high-quality MAGs (a Phyllobacterium species and a Sphingomonas species) with >98% completion and <1% contamination. Functional annotation of these MAGs uncovered genes associated with the degradation and metabolism of camphor and other essential oils such as pinene, geraniol, and limonene, which are part of sagebrush leaf chemistry.IMPORTANCEBig sagebrush (Artemisia tridentata), the foundation species of the sagebrush steppe, has broad ecological importance because its evergreen leaves offer nutrients and shade that facilitate the establishment of diverse understory plants in arid environments. Sagebrush leaves contain various secondary metabolites, including terpenoids, flavonoids, and phenolic compounds. These chemicals contribute to the plant's defense mechanisms against herbivores and pathogens. Despite this, sagebrush hosts diverse bacterial and fungal communities. We found that the microbial metagenome-assembled genomes (MAGs) we recovered contained genes that have the potential to degrade some of the chemical compounds in sagebrush leaves that could inhibit the growth of other microbes. This is the first study to mine plant genome data using host-derived sequences to generate microbial MAGs. Our results showed that MAGs can be recovered from plant host-derived sequence data, providing a new way to explore the identity and functional capabilities of difficult-to-culture microbes.

The role of the gut microbiome in health and disease of adult honey bee workers.

The 2017 annual symposium organized by the University Medical Center Groningen in The Netherlands focused on the role of the gut microbiome in human health and disease. Experts from academia and industry examined interactions of prebiotics, probiotics, or vitamins with the gut microbiome in health and disease, the development of the microbiome in early-life and the role of the microbiome on the gut–brain axis. The gut microbiota changes dramatically during pregnancy and intrinsic factors (such as stress), in addition to extrinsic factors (such as diet, and drugs) influence the composition and activity of the gut microbiome throughout life. Microbial metabolites, e.g. short-chain fatty acids affect gut–brain signaling and the immune response. The gut microbiota has a regulatory role on anxiety, mood, cognition and pain which is exerted via the gut–brain axis. Ingestion of prebiotics or probiotics has been used to treat a range of conditions including constipation, allergic reactions and infections in infancy, and IBS. Fecal microbiota transplantation (FMT) highly effective for treating recurrent Clostridium difficile infections. The gut microbiome affects virtually all aspects of human health, but the degree of scientific evidence, the models and technologies and the understanding of mechanisms of action vary considerably from one benefit area to the other. For a clinical practice to be broadly accepted, the mode of action, the therapeutic window, and potential side effects need to thoroughly be investigated. This calls for further coordinated state-of-the art research to better understand and document the human gut microbiome’s effects on human health.

Ensuring high-quality functional annotations in newly sequenced genomes has become a fundamental problem in next-generation sequencing genomics. This problem takes additional relevance in bacteriophage genomics, where well-annotated reference genomes are scarce and often highly diverged from newly sequenced genomes. This makes automated annotation pipelines especially prone to introduce and propagate functional annotation errors, devaluing the contribution of sequencing projects to the understanding of bacteriophage biology and decreasing the likelihood that novel genetic mechanisms with industrial and clinical application can be mined from the extremely diverse genetic repertoire of bacteriophage genomes. Genome annotation by expert biocurators ensures high-quality annotations, but such approaches are hard to scale and raise financial sustainability issues. Here we show how undergraduate biology courses devoted to genome annotation can be leveraged to generate high-quality functional annotations of bacteriophage genomes by integrating targeted training within the framework of an intercampus annotation competition. Students receive advanced training on biological ontologies, orthology assessment methods, biocuration standards and critical reading of scientific manuscripts. Working in small teams, students then participate in the Community Assessment of Community Annotation with Ontologies (CACAO) intercampus competition, generating Gene Ontology (GO) annotations of bacteriophage and bacterial gene products and challenging the accuracy of annotations made by other teams in a pre-established set of innings for annotation, challenge and revision. Revised annotations are reviewed by an expert panel before submission to the Gene Ontology Consortium. This approach yields tangible benefits on many fronts. Students develop critical reading skills, get exposed to key concepts in genome biology and obtain essential hands-on experience on the use of ontologies and bioinformatics methods to assess orthology, synteny and other genetic features. Team-work in a competitive framework and intercampus rivalry motivate students to improve the accuracy of their annotations, leading to high-quality functional annotations on both reference and newly sequenced bacteriophage genomes.

Functional annotation of genomes is a prerequisite for contemporary basic and applied genomic research, yet farmed animal genomics is deficient in such annotation. To address this, the FAANG (Functional Annotation of Animal Genomes) Consortium is producing genome-wide data sets on RNA expression, DNA methylation, and chromatin modification, as well as chromatin accessibility and interactions. In addition to informing our understanding of genome function, including comparative approaches to elucidate constrained sequence or epigenetic elements, these annotation maps will improve the precision and sensitivity of genomic selection for animal improvement. A scientific community-driven effort has already created a coordinated data collection and analysis enterprise crucial for the success of this global effort. Although it is early in this continuing process, functional data have already been produced and application to genetic improvement reported. The functional annotation delivered by the FAANG initiative will add value and utility to the greatly improved genome sequences being established for domesticated animal species.

The present study aimed to uncover the underlying mechanisms and potential intervention targets of ischemic stroke (IS). An immune cell infiltration analysis using CIBERSORT was performed on two stroke-related datasets from the Gene Expression Omnibus database to generate a competitive endogenous RNA (ceRNA) network. Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were used to predict potential biological functions of the network. Differentially expressed genes in the ceRNA network and programmed cell death-related genes were intersected to obtain common genes for a stroke ceRNA network related to programmed cell death. These genes were further verified through a middle cerebral artery occlusion rat model using reverse transcription-quantitative PCR. This built a ceRNA regulatory network based on bioinformatic analysis. In addition to the biological functions extracted from the GO and KEGG enrichment analyses, it was discovered that long non-coding (lnc)RNA-mediated ceRNA regulatory pathways were associated with programmed cell death. This included five for apoptosis (lncRNA deleted in lymphocytic leukemia 2 like (DLEU2L)/micro (miR)-4500/sulfite oxidase, lncRNA DLEU2L/miR-4500/transforming growth factor β receptor III, lncRNA DLEU2L/miR-4500/BTB and CNC homology 1 (BACH1), lncRNA DLEU2L/miR-4500/zinc finger and BTB domain containing 5 and lncRNA LINC00266-1/miR-363-3p/zinc finger protein 354B and two for ferroptosis (lncRNA DLEU2L/miR-4500/ribo-nucleotide reductase regulatory subunit M2 and lncRNA DLEU2L/miR-4500/BACH1). Based on the aforementioned results, the present study provided potential approaches for bridging programmed cell death, immune infiltration and ceRNA regulatory networks in IS. The present study may provide novel insights into the clinical diagnosis and treatment of IS, and may improve the knowledge of the regulation of pathophysiological processes for IS.

Background and Objective: Hepatocellular carcinoma (HCC) is a highly aggressive malignant tumor of the digestive system worldwide. Chronic hepatitis B virus (HBV) infection and aflatoxin exposure are predominant causes of HCC in China, whereas hepatitis C virus (HCV) infection and alcohol intake are likely the main risk factors in other countries. It is an unmet need to recognize the underlying molecular mechanisms of HCC in China. Methods: In this study, microarray datasets (GSE84005, GSE84402, GSE101685, and GSE115018) derived from Gene Expression Omnibus (GEO) database were analyzed to obtain the common differentially expressed genes (DEGs) by R software. Moreover, the gene ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed by using Database for Annotation, Visualization and Integrated Discovery (DAVID). Furthermore, the protein-protein interaction (PPI) network was constructed, and hub genes were identified by the Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape, respectively. The hub genes were verified using Gene Expression Profiling Interactive Analysis (GEPIA), UALCAN, and Kaplan-Meier Plotter online databases were performed on the TCGA HCC dataset. Moreover, the Human Protein Atlas (HPA) database was used to verify candidate genes’ protein expression levels. Results: A total of 293 common DEGs were screened, including 103 up-regulated genes and 190 down-regulated genes. Moreover, GO analysis implied that common DEGs were mainly involved in the oxidation-reduction process, cytosol, and protein binding. KEGG pathway enrichment analysis presented that common DEGs were mainly enriched in metabolic pathways, complement and coagulation cascades, cell cycle, p53 signaling pathway, and tryptophan metabolism. In the PPI network, three subnetworks with high scores were detected using the Molecular Complex Detection (MCODE) plugin. The top 10 hub genes identified were CDK1, CCNB1, AURKA, CCNA2, KIF11, BUB1B, TOP2A, TPX2, HMMR and CDC45. The other public databases confirmed that high expression of the aforementioned genes related to poor overall survival among patients with HCC. Conclusion: This study primarily identified candidate genes and pathways involved in the underlying mechanisms of Chinese HCC, which is supposed to provide new targets for the diagnosis and treatment of HCC in China.

(1) Background: The "Bider" marking refers to the symmetrical black stripes distributed on the shoulder blades of Dun Mongolian horses, representing an ancestral trait of significant genetic value. However, the molecular mechanisms underlying its formation remain unclear. This study aims to elucidate the molecular basis of these markings by comparing transcriptomic differences in skin tissues from variously pigmented areas of Mongolian horses' "Bider" patterns. (2) Methods: Using three Dun Mongolian horses as subjects, skin tissue samples were collected from their shoulders (dark-marked and light-marked areas), dorsal midline, and croup regions for transcriptome sequencing. Differentially expressed genes were identified based on sequencing data, followed by Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Key findings were validated through quantitative reverse transcription polymerase chain reaction (qRT-PCR). (3) Results: The sequencing yielded approximately 893 million high-quality clean reads, with an overall alignment rate exceeding 96%. A total of 140 to 775 differentially expressed genes were identified. GO enrichment analysis revealed that these genes were significantly enriched in biological processes related to pigment metabolism, skin and hair follicle development, signal transduction (including calcium and cyclic guanosine monophosphate (cGMP) signaling), and immune regulation. KEGG analysis further indicated that multiple pathways closely associated with pigment regulation, including the calcium signaling pathway, tyrosine metabolism, cyclic adenosine monophosphate (cAMP) signaling pathway, and melanoma pathway, were significantly enriched across different tissue comparison groups, suggesting their potential key roles in coat color phenotype formation. The reliability of the sequencing data was corroborated by the results of qRT-PCR validation. (4) Conclusions: This study conducted a transcriptome analysis of skin samples from various pigmented regions of the Dun Mongolian horse's Bider marking, revealing that the formation of this marking is associated with the differential expression of numerous genes and is co-regulated by multiple pigment-related signaling pathways.

DIAMOND2GO (D2GO) is a high-speed toolset for assigning Gene Ontology (GO) terms to genes or proteins based on sequence similarity. Leveraging the ultra-fast alignment capabilities of DIAMOND, which is 100 to 20,000 times faster than BLAST, D2GO enables rapid functional annotation of large-scale datasets. D2GO maps GO terms from pre-annotated sequences in the NCBI non-redundant database to query sequences. During benchmarking, D2GO assigned over 2 million GO terms to 98% of 130,184 predicted human protein isoforms in under 13 min on a standard laptop. In addition to annotation, D2GO includes an enrichment analysis tool that allows users to identify significantly overrepresented GO terms between subsets of sequences. We compared D2GO against two widely used tools, Blast2GO and eggNOG-mapper, and observed substantial differences in the number and type of annotations produced. These discrepancies reflect varying sensitivities and specificities across tools and suggest that using multiple methods in tandem may improve overall annotation coverage. D2GO is open-source and freely available under the MIT license at https://github.com/rhysf/DIAMOND2GO.

BackgroundThe chicken genome was sequenced because of its phylogenetic position as a non-mammalian vertebrate, its use as a biomedical model especially to study embryology and development, its role as a source of human disease organisms and its importance as the major source of animal derived food protein. However, genomic sequence data is, in itself, of limited value; generally it is not equivalent to understanding biological function. The benefit of having a genome sequence is that it provides a basis for functional genomics. However, the sequence data currently available is poorly structurally and functionally annotated and many genes do not have standard nomenclature assigned.ResultsWe analysed eight chicken tissues and improved the chicken genome structural annotation by providing experimental support for the in vivo expression of 7,809 computationally predicted proteins, including 30 chicken proteins that were only electronically predicted or hypothetical translations in human. To improve functional annotation (based on Gene Ontology), we mapped these identified proteins to their human and mouse orthologs and used this orthology to transfer Gene Ontology (GO) functional annotations to the chicken proteins. The 8,213 orthology-based GO annotations that we produced represent an 8% increase in currently available chicken GO annotations. Orthologous chicken products were also assigned standardized nomenclature based on current chicken nomenclature guidelines.ConclusionWe demonstrate the utility of high-throughput expression proteomics for rapid experimental structural annotation of a newly sequenced eukaryote genome. These experimentally-supported predicted proteins were further annotated by assigning the proteins with standardized nomenclature and functional annotation. This method is widely applicable to a diverse range of species. Moreover, information from one genome can be used to improve the annotation of other genomes and inform gene prediction algorithms.

The widespread occurrence of sulfate-reducing microorganisms (SRMs) in temporarily oxic/hypoxic aquatic environments indicates an intriguing possibility that SRMs can prevail in constantly oxic/hypoxic terrestrial sulfate-rich environments. However, little attention has been given to this possibility, leading to an incomplete understanding of microorganisms driving the terrestrial part of the global sulfur (S) cycle. In this study, genome-centric metagenomics and metatranscriptomics were employed to explore the diversity, metabolic potential, and gene expression profile of SRMs in a revegetated acidic mine wasteland under constantly oxic/hypoxic conditions. We recovered 16 medium- to high-quality metagenome-assembled genomes (MAGs) containing reductive dsrAB. Among them, 12 and four MAGs belonged to Acidobacteria and Deltaproteobacteria, respectively, harboring three new SRM genera. Comparative genomic analysis based on seven high-quality MAGs (completeness >90% and contamination <10%; including six acidobacterial and one deltaproteobacterial) and genomes of three additional cultured model species showed that Acidobacteria-related SRMs had more genes encoding glycoside hydrolases, oxygen-tolerant hydrogenases, and cytochrome c oxidases than Deltaproteobacteria-related SRMs. The opposite pattern was observed for genes encoding superoxide reductases and thioredoxin peroxidases. Using VirSorter, viral genome sequences were found in five of the 16 MAGs and in all three cultured model species. These prophages encoded enzymes involved in glycoside hydrolysis and antioxidation in their hosts. Moreover, metatranscriptomic analysis revealed that 15 of the 16 SRMs reported here were active in situ. An acidobacterial MAG containing a prophage dominated the SRM transcripts, expressing a large number of genes involved in its response to oxidative stress and competition for organic matter.

DNA synthesis during replication or repair is a fundamental cellular process that is catalyzed by a set of evolutionary conserved polymerases. Despite a large body of research, the DNA polymerases of Drosophila melanogaster have not yet been systematically reviewed, leading to inconsistencies in their nomenclature, shortcomings in their functional (Gene Ontology, GO) annotations and an under-appreciation of the extent of their characterization. Here, we describe the complete set of DNA polymerases in D. melanogaster, applying nomenclature already in widespread use in other species, and improving their functional annotation. A total of 19 genes encode the proteins comprising three replicative polymerases (alpha-primase, delta, epsilon), five translesion/repair polymerases (zeta, eta, iota, Rev1, theta) and the mitochondrial polymerase (gamma). We also provide an overview of the biochemical and genetic characterization of these factors in D. melanogaster. This work, together with the incorporation of the improved nomenclature and GO annotation into key biological databases, including FlyBase and UniProtKB, will greatly facilitate access to information about these important proteins.

The process of functional annotation of human genetic variants is important for some significant biomedical tasks like understanding functional genomics and gene-disease associations. Moreover, genetic variants that lead to pathogenic mutations can have harmful consequences which may lead to certain medical conditions or diseases. This paper investigates and presents two methodologies utilizing the Gene Ontology (GO) for functional annotation of genetic variants (and human gene mutations): (1) using the concept of term enrichment from the biological process taxonomy in the gene ontology, and (2) using the concept of least common subsumer (LCS) within the gene ontology tree. The second methodology induces the most significant and accurate functions for a given set of variants having one common aspect, e.g. specific disease. This method is based on the structure of the directed acyclic graph of GO to induce and identify the most significant LCS’s to be used for functionally annotating the variants under investigation. We applied the methods on a large sets of genetic variants causing mutations. Our method can determine important functions and with significance level of p<0.02, the outcomes and results of the method are biologically significant. We found that certain ontology annotations are more related to mutations through certain genes compared with normal bp functions (biological process terms) in GO; for example, one of these bp functions is {GO:0031325; positive regulation of cellular metabolic process}. The results are basically important and suggest that a mutation can be annotated with functions from the gene ontology, e.g. biological process aspect, just like the genes. This outcome may contribute into a more complete understanding of mutation-gene-disease relationships, mutation pathogenicity, and understanding disease mechanisms..

Gymnosperms are a clade of non-flowering plants that include about 1000 living species. Due to their complex genomes and lack of genomic resources, functional annotation in genomics and transcriptomics on gymnosperms suffers from limitations. Here we present gymnotoa-db, which is a novel, publicly accessible relational database designed to facilitate functional annotation in gymnosperms. This database stores non-redundant records of gymnosperm proteins, encompassing taxonomic and functional information. The complementary software, gymnotoa-app, enables users to download gymnotoa-db and execute a comprehensive functional annotation pipeline for high-throughput sequencing-derived DNA or cDNA sequences. gymnotoa-app’s user-friendly interface and efficient algorithms streamline the functional annotation process, making it an invaluable tool for researchers studying gymnosperms. We compared gymnotoa-app’s performance against other annotation tools utilizing disparate reference databases. Our results demonstrate gymnotoa-app’s superior ability to accurately annotate gymnosperm transcripts, recovering a greater number of transcripts and unique, non-redundant Gene Ontology terms. gymnotoa-db’s distinctive features include comprehensive coverage with a non-redundant dataset of gymnosperm protein sequences, robust functional information that integrates data from multiple ontology systems, including GO, KEGG, EC, and MetaCYC, while keeping the taxonomic context, including Arabidopsis homologs.Database URL: https://blogs.upm.es/gymnotoa-db/gymnotoa-db/