functional inference
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Eukaryotic genomes express numerous long intergenic non-coding RNAs (lincRNAs) that do not overlap any coding genes. Some lincRNAs function in various aspects of gene regulation, but it is not clear in general to what extent lincRNAs contribute to the information flow from genotype to phenotype. To explore this question, we systematically analysed cellular roles of lincRNAs in Schizosaccharomyces pombe. Using seamless CRISPR/Cas9-based genome editing, we deleted 141 lincRNA genes to broadly phenotype these mutants, together with 238 diverse coding-gene mutants for functional context. We applied high-throughput colony-based assays to determine mutant growth and viability in benign conditions and in response to 145 different nutrient, drug, and stress conditions. These analyses uncovered phenotypes for 47.5% of the lincRNAs and 96% of the protein-coding genes. For 110 lincRNA mutants, we also performed high-throughput microscopy and flow cytometry assays, linking 37% of these lincRNAs with cell-size and/or cell-cycle control. With all assays combined, we detected phenotypes for 84 (59.6%) of all lincRNA deletion mutants tested. For complementary functional inference, we analysed colony growth of strains ectopically overexpressing 113 lincRNA genes under 47 different conditions. Of these overexpression strains, 102 (90.3%) showed altered growth under certain conditions. Clustering analyses provided further functional clues and relationships for some of the lincRNAs. These rich phenomics datasets associate lincRNA mutants with hundreds of phenotypes, indicating that most of the lincRNAs analysed exert cellular functions in specific environmental or physiological contexts. This study provides groundwork to further dissect the roles of these lincRNAs in the relevant conditions.

Abstract Deciphering microbiota functions is crucial to predict ecosystem sustainability in response to global change. High-throughput sequencing at the individual or community level has revolutionized our understanding of microbial ecology, leading to the big data era and improving our ability to link microbial diversity with microbial functions. Recent advances in bioinformatics have been key for developing functional prediction tools based on DNA metabarcoding data and using taxonomic gene information. This cheaper approach in every aspect serves as an alternative to shotgun sequencing. Although these tools are increasingly used by ecologists, an objective evaluation of their modularity, portability, and robustness is lacking. Here, we reviewed 100 scientific papers on functional inference and ecological trait assignment to rank the advantages, specificities, and drawbacks of these tools, using a scientific benchmarking. To date, inference tools have been mainly devoted to bacterial functions, and ecological trait assignment tools, to fungal functions. A major limitation is the lack of reference genomes—compared with the human microbiota—especially for complex ecosystems such as soils. Finally, we explore applied research prospects. These tools are promising and already provide relevant information on ecosystem functioning, but standardized indicators and corresponding repositories are still lacking that would enable them to be used for operational diagnosis.

Although a few studies have described the microbiome composition of TB sputa based on 16S ribosomal DNA, these studies did not compare to non-TB samples and the nature of the method does not allow any functional inference. This is the first study to apply such technology using clinical specimens and obtained functional transcriptional data on all three aspects simultaneously.

Abstract Cancer is one of the world's major causes of mortality, and it plays a most important role in the world's declining life expectancy. F-box and WD-40 domain protein 7 (FBXW7), a typical participant of the F-box family of proteins, has been considered as an antitumor protein and one of the maximum deregulated ubiquitin-proteasome system proteins in uterine carcinosarcoma, endometrial clear cell carcinoma and cervical carcinoma with the greatest prevalence of alterations. FBXW7 variants with known clinical significance, as well as nsSNP’s in the F-Box and WD40 domains, were evaluated using functionality prediction web resources. Upon analysing the seventy-three deleterious nsSNP’s impact on protein stability and function, we identified that forty-one nsSNP’s of WD40 domain and three of F-Box domain imply decreased stability of the FBXW7 structure. Next to TP53 and PTEN, FBXW7 was reported with the highest percentage of arginine substitution among mutations related to cancer. The current research concentrated on two arginine residue locations (Arg465, Arg505) within the WD40-repeat domain, which is vital for substrate binding. Computational analysis revealed that significant deviation in stability and structural configuration of mutants R505L, R465H, R465P, R505G, R505C, R465C R505S and R505L structures. Protein–protein interaction network of FBXW7 populated with promising hub proteins NOTCH1, c-Myc, CCNE1, STYX, KLG5, SREB1, NFKB2, SKP1, CUL1, thus alteration in the FBXW7 leads to aberration in their signalling pathways as well as their substrate binding ability makes this protein as attractive target for personalized therapeutic intervention.

N6-methyladenosine (m6A) is one of the most prevalent RNA modifications in mRNA and non-coding RNA. In this study, we identified 10 upregulated m6A regulators at both mRNA and protein levels, and 2,479 m6A-related lncRNAs. Moreover, the m6A-related long noncoding RNAs (lncRNAs) could clearly stratify the colon adenocarcinoma (COAD) samples into three subtypes. The subtype 2 had nearly 40% of samples with microsatellite instability (MSI), significantly higher than the two other subtypes. In accordance with this finding, the inflammatory response-related pathways were highly activated in this subtype. The subtype-3 had a shorter overall survival and a higher proportion of patients with advanced stage than subtypes 1 and 2 (p-value < 0.05). Pathway analysis suggested that the energy metabolism-related pathways might be aberrantly activated in subtype 3. In addition, we observed that most of the m6A readers and m6A-related lncRNAs were upregulated in subtype 3, suggesting that the m6A readers and the m6A-related lncRNAs might be associated with metabolic reprogramming and unfavorable outcome in COAD. Among the m6A-related lncRNAs in subtype 3, four were predicted as prognostically relevant. Functional inference suggested that CTD-3184A7.4, RP11-458F8.4, and RP11-108L7.15 were positively correlated with the energy metabolism-related pathways, further suggesting that these lncRNAs might be involved in energy metabolism-related pathways. In summary, we conducted a systematic data analysis to identify the key m6A regulators and m6A-related lncRNAs, and evaluated their clinical and functional importance in COAD, which may provide important evidences for further m6A-related researches.

Protein structure fundamentally underpins the function and processes of numerous biological systems. Fold recognition algorithms offer a sensitive and robust tool to detect structural, and thereby functional, similarities between distantly related homologs. In the era of accurate structure prediction owing to advances in machine learning techniques, previously curated sequence databases have become a rich source of biological information. Here, we use bioinformatic fold recognition algorithms to scan the entire AlphaFold structure database to identify novel protein family members, infer function and group predicted protein structures. As an example of the utility of this approach, we identify novel, previously unknown members of various pore-forming protein families, including MACPFs, GSDMs and aerolysin-like proteins. Further, we explore the use of structure-based mining for functional inference.

Cells require coordinated control over gene expression when responding to environmental stimuli. Here, we apply scATAC-seq and scRNA-seq in resting and stimulated human blood cells. Collectively, we generate ~91,000 single-cell profiles, allowing us to probe the cis -regulatory landscape of immunological response across cell types, stimuli and time. Advancing tools to integrate multi-omic data, we develop FigR - a framework to computationally pair scATAC-seq with scRNA-seq cells, connect distal cis -regulatory elements to genes, and infer gene regulatory networks (GRNs) to identify candidate TF regulators. Utilizing these paired multi-omic data, we define Domains of Regulatory Chromatin (DORCs) of immune stimulation and find that cells alter chromatin accessibility prior to production of gene expression at time scales of minutes. Further, the construction of the stimulation GRN elucidates TF activity at disease-associated DORCs. Overall, FigR enables the elucidation of regulatory interactions across single-cell data, providing new opportunities to understand the function of cells within tissues.

Abstract Background Arthropoda, the most numerous and diverse metazoan phylum, has species in many habitats where they encounter various microorganisms and, as a result, mechanisms for pathogen recognition and elimination have evolved. The Toll pathway, involved in the innate immune system, was first described as part of the developmental pathway for dorsal-ventral differentiation in Drosophila. Its later discovery in vertebrates suggested that this system was extremely conserved. However, there is variation in presence/absence, copy number and sequence divergence in various genes along the pathway. As most studies have only focused on Diptera, for a comprehensive and accurate homology-based approach it is important to understand gene function in a number of different species and, in a group as diverse as insects, the use of species belonging to different taxonomic groups is essential. Results We evaluated the diversity of Toll pathway gene families in 39 Arthropod genomes, encompassing 13 different Insect Orders. Through computational methods, we shed some light into the evolution and functional annotation of protein families involved in the Toll pathway innate immune response. Our data indicates that: 1) intracellular proteins of the Toll pathway show mostly species-specific expansions; 2) the different Toll subfamilies seem to have distinct evolutionary backgrounds; 3) patterns of gene expansion observed in the Toll phylogenetic tree indicate that homology based methods of functional inference might not be accurate for some subfamilies; 4) Spatzle subfamilies are highly divergent and also pose a problem for homology based inference; 5) Spatzle subfamilies should not be analyzed together in the same phylogenetic framework; 6) network analyses seem to be a good first step in inferring functional groups in these cases. We specifically show that understanding Drosophila’s Toll functions might not indicate the same function in other species. Conclusions Our results show the importance of using species representing the different orders to better understand insect gene content, origin and evolution. More specifically, in intracellular Toll pathway gene families the presence of orthologues has important implications for homology based functional inference. Also, the different evolutionary backgrounds of Toll gene subfamilies should be taken into consideration when functional studies are performed, especially for TOLL9, TOLL, TOLL2_7, and the new TOLL10 clade. The presence of Diptera specific clades or the ones lacking Diptera species show the importance of overcoming the Diptera bias when performing functional characterization of Toll pathways.

Background Pharmacogenetic variation is important to drug responses through diverse and complex mechanisms. Predictions of the functional impact of missense pharmacogenetic variants primarily rely on the degree of sequence conservation between species as a primary discriminator. However, idiosyncratic or off-target drug-variant interactions sometimes involve effects that are peripheral or accessory to the central systems in which a gene functions. Given the importance of sequence conservation to functional prediction tools—these idiosyncratic pharmacogenetic variants may violate the assumptions of predictive software commonly used to infer their effect. Methods Here we exhaustively assess the effectiveness of eleven missense mutation functional inference tools on all known pharmacogenetic missense variants contained in the Pharmacogenomics Knowledgebase (PharmGKB) repository. We categorize PharmGKB entries into sub-classes to catalog likely off-target interactions, such that we may compare predictions across different variant annotations. Results As previously demonstrated, functional inference tools perform variably across the complete set of PharmGKB variants, with large numbers of variants incorrectly classified as ‘benign’. However, we find substantial differences amongst PharmGKB variant sub-classes, particularly in variants known to cause off-target, type B adverse drug reactions, that are largely unrelated to the main pharmacological action of the drug. Specifically, variants associated with off-target effects (hence referred to as off-target variants) were most often incorrectly classified as ‘benign’. These results highlight the importance of understanding the underlying mechanism of pharmacogenetic variants and how variants associated with off-target effects will ultimately require new predictive algorithms. Conclusion In this work we demonstrate that functional inference tools perform poorly on pharmacogenetic variants, particularly on subsets enriched for variants causing off-target, type B adverse drug reactions. We describe how to identify variants associated with off-target effects within PharmGKB in order to generate a training set of variants that is needed to develop new algorithms specifically for this class of variant. Development of such tools will lead to more accurate functional predictions and pave the way for the increased wide-spread adoption of pharmacogenetics in clinical practice.