WormCat 2.0 defines characteristics and conservation of poorly annotated genes in Caenorhabditis elegans

Genome-wide measurement of mRNA or protein levels provides broad data sets for biological discovery. However, subsequent computational methods are essential for uncovering the functional implications of the data as well as intuitively visualizing the findings. Current computational tools are biased toward well-described pathways, limiting their utility for novel discovery. Recently, we developed an annotation and category enrichment tool for Caenorhabditis elegans genomic data, WormCat, that provides an intuitive visualization output. Unlike GO, which excludes genes with no annotation information retains these genes as a special UNASSIGNED category. Here, we show that the UNASSIGNED gene category shows tissue-specific expression patterns and include genes with biological functions. Poorly annotated genes have previously been considered to lack homologs in closely related species. Instead, we find that around 3% of the UNASSIGNED genes have poorly characterized human orthologs. These human orthologs are themselves poorly characterized. A recently developed method that incorporates lineage relationships (abSENSE) indicates that failure of BLAST to detect homology explains the apparent lineage specificity for many UNASSIGNED genes, suggesting that a larger subset could be related to human genes. WormCat provides an annotation strategy that allows association of UNASSIGNED genes with specific phenotypes and known pathways. Our analysis indicates that the UNASSIGNED gene category contains candidates that merit further functional study which could yield insight into understudied areas of biology.

Taxonomic and Gene Category Analyses of Subgingival Plaques from a Group of Japanese Individuals with and without Periodontitis

Periodontitis is an inflammation of tooth-supporting tissues, which is caused by bacteria in the subgingival plaque (biofilm) and the host immune response. Traditionally, subgingival pathogens have been investigated using methods such as culturing, DNA probes, or PCR. The development of next-generation sequencing made it possible to investigate the whole microbiome in the subgingival plaque. Previous studies have implicated dysbiosis of the subgingival microbiome in the etiology of periodontitis. However, details are still lacking. In this study, we conducted a metagenomic analysis of subgingival plaque samples from a group of Japanese individuals with and without periodontitis. In the taxonomic composition analysis, genus Bacteroides and Mycobacterium demonstrated significantly different compositions between healthy sites and sites with periodontal pockets. The results from the relative abundance of functional gene categories, carbohydrate metabolism, glycan biosynthesis and metabolism, amino acid metabolism, replication and repair showed significant differences between healthy sites and sites with periodontal pockets. These results provide important insights into the shift in the taxonomic and functional gene category abundance caused by dysbiosis, which occurs during the progression of periodontal disease.

Overcoming false-positive gene-category enrichment in the analysis of spatially resolved transcriptomic brain atlas data

Transcriptomic atlases have improved our understanding of the correlations between gene-expression patterns and spatially varying properties of brain structure and function. Gene-category enrichment analysis (GCEA) is a common method to identify functional gene categories that drive these associations, using gene-to-category annotation systems like the Gene Ontology (GO). Here, we show that applying standard GCEA methodology to spatial transcriptomic data is affected by substantial false-positive bias, with GO categories displaying an over 500-fold average inflation of false-positive associations with random neural phenotypes in mouse and human. The estimated false-positive rate of a GO category is associated with its rate of being reported as significantly enriched in the literature, suggesting that published reports are affected by this false-positive bias. We show that within-category gene–gene coexpression and spatial autocorrelation are key drivers of the false-positive bias and introduce flexible ensemble-based null models that can account for these effects, made available as a software toolbox.

Time Trends in Receipt of Germline Genetic Testing and Results for Women Diagnosed With Breast Cancer or Ovarian Cancer, 2012-2019

PURPOSE Genetic testing is important for breast and ovarian cancer risk reduction and treatment, yet little is known about its evolving use. METHODS SEER records of women of age ≥ 20 years diagnosed with breast or ovarian cancer from 2013 to 2017 in California or Georgia were linked to the results of clinical germline testing through 2019. We measured testing trends, rates of variants of uncertain significance (VUS), and pathogenic variants (PVs). RESULTS One quarter (25.2%) of 187,535 patients with breast cancer and one third (34.3%) of 14,689 patients with ovarian cancer were tested; annually, testing increased by 2%, whereas the number of genes tested increased by 28%. The prevalence of test results by gene category for breast cancer cases in 2017 were BRCA1/2 , PVs 5.2%, and VUS 0.8%; breast cancer–associated genes or ovarian cancer–associated genes ( ATM, BARD1, BRIP1, CDH1, CHEK2, EPCAM, MLH1, MSH2, MSH6, NBN, NF1, PALB2, PMS2, PTEN, RAD51C, RAD51D, STK11, and TP53), PVs 3.7%, and VUS 12.0%; other actionable genes ( APC, BMPR1A, MEN1, MUTYH, NF2, RB1, RET, SDHAF2, SDHB, SDHC, SDHD, SMAD4, TSC1, TSC2, and VHL) PVs 0.6%, and VUS 0.5%; and other genes, PVs 0.3%, and VUS 2.6%. For ovarian cancer cases in 2017, the prevalence of test results were BRCA1/2, PVs 11.0%, and VUS 0.9%; breast or ovarian genes, PVs 4.0%, and VUS 12.6%; other actionable genes, PVs 0.7%, and VUS 0.4%; and other genes, PVs 0.3%, and VUS 0.6%. VUS rates doubled over time (2013 diagnoses: 11.2%; 2017 diagnoses: 26.8%), particularly for racial or ethnic minorities (47.8% Asian and 46.0% Black, v 24.6% non-Hispanic White patients; P < .001). CONCLUSION A testing gap persists for patients with ovarian cancer (34.3% tested v nearly all recommended), whereas adding more genes widened a racial or ethnic gap in VUS results. Most PVs were in 20 breast cancer–associated genes or ovarian cancer–associated genes; testing other genes yielded mostly VUS. Quality improvement should focus on testing indicated patients rather than adding more genes.

Clinicopathologic characterisation of myeloid neoplasms with concurrent spliceosome mutations and myeloproliferative-neoplasm-associated mutations

AimsSpliceosome genes (SF3B1, SRSF2, U2AF1 and ZRSR2) are commonly mutated in myeloid neoplasms, particularly in myelodysplastic syndromes (MDS). JAK2, MPL and CALR mutations are associated with myeloproliferative neoplasms (MPN). Although SF3B1 and MPN-associated mutations frequently co-occur in the rare entity MDS/MPN with ring sideroblasts and thrombocytosis (MDS/MPN-RS-T), myeloid neoplasms with concurrent spliceosome and MPN-associated mutations encompass many disease entities and are not well characterised.MethodsSpecimens from 2016 to 2019 with concurrent spliceosome and MPN-associated mutations were identified, and the clinicopathologic features were assessed.ResultsThe 36 cases were divided into mutational categories based on their spliceosome mutation. At diagnosis, cases with concurrent U2AF1 and MPN-associated mutations had lower leucocyte counts and platelet counts than did the other groups. Cases with mutant SRSF2 were more likely to have ASXL1 and IDH2 mutations, while U2AF1-mutated neoplasms were more likely to have an abnormal karyotype. The most common SF3B1 K700 and U2AF1 S34 mutational hotspots were underrepresented in our cohort of myeloid neoplasms with concurrent spliceosome and MPN-associated mutations, as SF3B1 and U2AF1 mutations tended to involve other codons. Numerous WHO-defined disease entities were represented in each spliceosome gene category; although MDS/MPN-RS-T were only identified in the group with SF3B1 mutations, they constituted only 1/4 of the neoplasms in the category.ConclusionsMyeloid neoplasms with different mutant splicing factor and concurrent MPN-associated mutations demonstrate somewhat different clinical and pathologic features, but t he association between genotypes and phenotypes in these overlapping neoplasms is not straightforward.

Prediction of Causative Genes in Inherited Retinal Disorders from Spectral-Domain Optical Coherence Tomography Utilizing Deep Learning Techniques

Purpose. To illustrate a data-driven deep learning approach to predicting the gene responsible for the inherited retinal disorder (IRD) in macular dystrophy caused by ABCA4 and RP1L1 gene aberration in comparison with retinitis pigmentosa caused by EYS gene aberration and normal subjects. Methods. Seventy-five subjects with IRD or no ocular diseases have been ascertained from the database of Japan Eye Genetics Consortium; 10 ABCA4 retinopathy, 20 RP1L1 retinopathy, 28 EYS retinopathy, and 17 normal patients/subjects. Horizontal/vertical cross-sectional scans of optical coherence tomography (SD-OCT) at the central fovea were cropped/adjusted to a resolution of 400 pixels/inch with a size of 750 × 500 pix2 for learning. Subjects were randomly split following a 3 : 1 ratio into training and test sets. The commercially available learning tool, Medic mind was applied to this four-class classification program. The classification accuracy, sensitivity, and specificity were calculated during the learning process. This process was repeated four times with random assignment to training and test sets to control for selection bias. For each training/testing process, the classification accuracy was calculated per gene category. Results. A total of 178 images from 75 subjects were included in this study. The mean training accuracy was 98.5%, ranging from 90.6 to 100.0. The mean overall test accuracy was 90.9% (82.0–97.6). The mean test accuracy per gene category was 100% for ABCA4, 78.0% for RP1L1, 89.8% for EYS, and 93.4% for Normal. Test accuracy of RP1L1 and EYS was not high relative to the training accuracy which suggests overfitting. Conclusion. This study highlighted a novel application of deep neural networks in the prediction of the causative gene in IRD retinopathies from SD-OCT, with a high prediction accuracy. It is anticipated that deep neural networks will be integrated into general screening to support clinical/genetic diagnosis, as well as enrich the clinical education.

Distribution, structure and diversity of “bacterial” genes encoding two-component proteins in the Euryarchaeota

The publicly available annotated archaeal genome sequences (23 complete and three partial annotations, October 2005) were searched for the presence of potential two-component open reading frames (ORFs) using gene category lists and BLASTP. A total of 489 potential two-component genes were identified from the gene category lists and BLASTP. Two-component genes were found in 14 of the 21 Euryarchaeal sequences (October 2005) and in neither the Crenarchaeota nor the Nanoarchaeota. A total of 20 predicted protein domains were identified in the putative two-component ORFs that, in addition to the histidine kinase and receiver domains, also includes sensor and signalling domains. The detailed structure of these putative proteins is shown, as is the distribution of each class of two-component genes in each species. Potential members of orthologous groups have been identified, as have any potential operons containing two or more two-component genes. The number of two-component genes in those Euryarchaeal species which have them seems to be linked more to lifestyle and habitat than to genome complexity, with most examples being found inMethanospirillum hungatei,Haloarcula marismortui,Methanococcoides burtoniiand the mesophilic Methanosarcinales group. The large numbers of two-component genes in these species may reflect a greater requirement for internal regulation. Phylogenetic analysis of orthologous groups of five different protein classes, three probably involved in regulating taxis, suggests that most of these ORFs have been inherited vertically from an ancestral Euryarchaeal species and point to a limited number of key horizontal gene transfer events.

Antioncogenes and human cancer

The antioncogenes, or tumor suppressor genes, as negative regulators of cell division, stand in contrast to oncogenes. For most human cancers, the more frequently mutated genes are the antioncogenes, the principal exception being the leukemias and lymphomas. Persons heterozygous for germ-line mutations in antioncogenes are strongly predisposed to one or more kinds of cancer, and most dominantly inherited cancer is attributable to such heterozygosity. Seven antioncogenes have been cloned through the study of these persons, and several others have been mapped. An eighth one was mapped and cloned through the investigation of tumors and is not yet known in hereditary form. Three dominantly inherited forms of cancer are not attributable to mutations in antioncogenes. The corresponding nonhereditary forms of most cancers generally reveal abnormalities of the same antioncogenes that are found in the hereditary forms but may also show additional ones. Some cancers, especially the embryonal tumors of children, have a small number of antioncogene mutations; some others, such as most sarcomas, have more, and the common carcinomas have the most, reflecting a hierarchy of controls over growth of stem cell populations. Still more members of this gene category remain to be mapped and cloned through the study of cancer families and of tumors. The genes that have been cloned act at diverse points in the signal transduction pathway in cells, from the outer cell membranes to sites of gene transcription, in some cases as negative regulators of oncogene expression.