Expanding the molecular landscape of childhood apraxia of speech: evidence from a single-center experience.

Background: Childhood apraxia of speech (CAS) is a genetically heterogeneous pediatric motor speech disorder. The advent of whole exome sequencing (WES) and whole genome sequencing techniques has led to increased identification of pathogenic variants in CAS genes. In an as yet uncharacterized Italian cohort, we aimed both to identify new pathogenic gene variants associated with CAS, and to confirm the disease-related role of genes already reported by others. We also set out to refine the clinical and neurodevelopmental characterization of affected children, with the aim of identifying specific, gene-related phenotypes. Methods: In a single-center study aiming to explore the genetic etiology of CAS in a cohort of 69 Italian children, WES was performed in the families of the 34 children found to have no copy number variants. Each of these families had only one child affected by CAS. Results: High-confidence (HC) gene variants were identified in 7/34 probands, in two of whom they affected KAT6A and CREBBP, thus confirming the involvement of these genes in speech impairment. The other probands carried variants in low-confidence (LC) genes, and 20 of these variants occurred in genes not previously reported as associated with CAS. UBA6, ZFHX4, and KAT6A genes were found to be more enriched in the CAS cohort compared to control individuals. Our results also showed that most HC genes are involved in epigenetic mechanisms and are expressed in brain regions linked to language acquisition processes. Conclusion: Our findings confirm a relatively high diagnostic yield in Italian patients.

Transcriptome and RNA sequencing analysis of H9C2 cells exposed to diesel particulate matter.

Although air pollution has been classified as a risk factor for heart disease, the underlying mechanisms remain nebulous. Therefore, this study investigated the effect of diesel particulate matter (DPM) exposure on cardiomyocytes and identified differentially expressed genes (DEGs) induced by DPM. DPM treatment decreased H9C2 cell viability and increased cytotoxicity. Ten genes showed statistically significant differential expression following treatment with DPM at 25 and 100 µg/ml for 3 h. A total of 273 genes showed statistically significant differential expression following treatment with DPM at 25 and 100 µg/ml for 24 h. Signaling pathway analysis revealed that the DEGs were related to the 'reactive oxygens species,' 'IL-17,' and 'fluid shear stress and atherosclerosis' signaling pathways. Hmox1, Fos, and Fosb genes were significantly upregulated among the selected DEGs. This study identified DPM-induced DEGs and verified the selected genes using qRT-PCR and western blotting. The findings provide insights into the molecular events in cardiomyocytes following exposure to DPM.

Transcriptome Analysis of Porphyromonas gingivalis Lipopolysaccharide-Induced Early Gene Expression in Human Gingival Keratinocytes.

AIM: Porphyromonas gingivalis lipopolysaccharide (PgLPS) is a significant virulence factor and a driver of early innate immune responses in epithelial cells. The presence of PgLPS in immediate proximity to gingival epithelium induces significant inflammatory responses. In primary human gingival keratinocytes (HGK), we utilized transcriptome analysis to elucidate the change in early gene expression induced by PgLPS. METHODS: HGK cell cultures were treated with PgLPS (4 h), and RNA was extracted and prepared for RNA sequence (RNAseq) analysis. Differentially expressed genes (DEGs) were identified, and potential interactions between these genes were subsequently examined using gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analytic approaches to identify significantly enriched pathways. Expression of genes associated with relevant pathways was evaluated using real-time quantitative reverse-transcription polymerase chain reaction (RT-qPCR). RESULTS: RNAseq analysis identified 25 DEGs, and GO and KEGG analytic approaches showed related genes expressed in two general pathways. First, pathways broadly related to urokinase and coagulation included the genes PLAU, PLAUR, and SerpinB2. In RT-qPCR analysis, these genes were induced by PgLPS over time (4-24 h), and these data were consistent with PgLPS induction of cell migration. Second, interleukin-1 (IL-1) receptor binding and cytokine-activity pathways were also enriched. Genes associated with these pathways included IL36G, IL1B, IL1RN, and CXCL14. RT-qPCR analysis confirmed PgLPS induction of genes associated with the IL-1family. When expression of IL1B and IL36G genes was examined in relation to their respective antagonists, only IL36G gene expression was increased. CXCL14 gene expression was reduced over time, and this was consistent with RNAseq analysis. CONCLUSIONS: Genes associated with significantly enriched GO and KEGG pathways are relevant to aspects of periodontal disease (PDD) pathogenesis. First, PgLPS induced expression of PLAU, PLAUR, and SerpinB2, and these changes were consistent with an increase in cell migration that was found. Second, both IL36G and IL1B gene expression was significantly induced, but only IL36G in relation to its selective antagonist (IL36RN) was increased. These data support that early upregulation of IL36G may serve as an alarmin that can drive early innate immune inflammatory responses in HGK. Further in vivo testing of these findings is ongoing.

The providing multidisciplinary ILD diagnoses (PROMISE) study - study design of the national registry of Japan facilitating interactive online multidisciplinary discussion diagnosis.

BACKGROUND: Multidisciplinary discussion (MDD), in which physicians, radiologists, and pathologists communicate and diagnose together, has been reported to improve diagnostic accuracy compared to diagnoses made solely by physicians. However, even among experts, diagnostic concordance of MDD is not always good, and some patients may not receive a specific diagnosis due to insufficient findings. A provisional diagnosis based on the ontology with a diagnostic confidence level has recently been proposed. Additionally, we developed an artificial intelligence model to differentiate idiopathic pulmonary fibrosis (IPF) from other chronic interstitial lung diseases (ILD)s, which needs validation in a broader population. METHODS: This prospective nationwide ILD registry has recruited patients with newly diagnosed ILD at the referral respiratory hospitals in Japan and provides rapid MDD diagnoses and treatment recommendations through a central online MDD platform with a 3-year follow-up period. A modified diagnostic ontology is used. If no diagnosis reaches more than 50% certainty, the diagnosis is unclassifiable ILD. If multiple diseases are expected, the diagnosis with a high probability takes precedence. If the confidence levels for the top two possible diagnoses are equal, the diagnosis can be unclassifiable. The registry uses tentative diagnostic criteria for nonspecific interstitial pneumonia with organising pneumonia and smoking-related ILD not otherwise specified as possible new entities. Central MDD diagnosticians review the clinical data, test results, radiology images, and pathological specimens on a dedicated website and conduct MDD diagnoses using online meetings with a cloud-based reporting system. This study aims to (1) provide MDD diagnoses with treatment recommendations; (2) determine the overall ILD rates in Japan; (3) clarify the reasons for unclassifiable ILDs; (4) evaluate possible new disease entities; (5) identify progressive phenotypes and create a clinical prediction model; (6) measure the agreement rate between institutional and central diagnoses in ILD referral and non-referral centres; (7) identify key factors for each specific ILD diagnosis; and (8) create a new disease classification system based on treatment strategies, including the use of antifibrotic drugs. DISCUSSION: This study will provide ILD frequencies, including new entities, using central MDD on dedicated online systems, and develop a machine learning model for ILD diagnosis and prognosis prediction. TRIAL REGISTRATION: UMIN-CTR Clinical Trial Registry (UMIN000040678).

Exploring Differentially Expressed Genes and Immune Modulation in Diffuse Large B-Cell Lymphoma through RNA Sequencing Analysis.

Background: Diffuse large B-cell lymphoma (DLBCL) is globally recognized as the most prevalent and aggressive subtype of non-Hodgkin lymphoma. While conventional treatments are effective initially, the disease can become resistant or relapse over time. This study aimed to examine the differentially expressed genes at the transcriptome level and molecular pathways in DLBCL patients. Methods: This investigation utilized RNA sequencing analysis to compare differentially expressed gene samples from five diffuse large B-cell lymphoma patients with two healthy volunteers. These participants were admitted to UKM Medical Center, Kuala Lumpur between 2019 and 2020. The differentially expressed genes were identified using the DESeq2 R package (version 1.10.1) using a negative binomial distribution model. The obtained P values were corrected with the Benjamin and Hochberg method and identified using a False Discovery Rate threshold of <0.05, with log2 fold change (FC) of ≥2 or ≤-2. Results: Results showed 73 differentially expressed genes between the two groups, among which 70 genes were downregulated, and three genes were upregulated. The differentially expressed genes analyzed with the Reactome pathway were significantly associated with the downregulation of antimicrobial humoral response (P<0.001), neutrophil degranulation (P<0.001), chemokine receptors bind chemokines (P=0.028), defensins (P=0.028) and metabolism of angiotensinogen (P=0.040). Conclusion: These findings suggest that the identified pathways may contribute to cancer progression and weaken the immune response in diffuse large B-cell lymphoma patients. This study offers fresh insights into previously undiscovered downstream targets and pathways modulated by diffuse large B-cell lymphoma.

Therapeutic, Clinicopathological, and Molecular Correlates of PRKACA Expression in Gastrointestinal Cancers.

BACKGROUND/OBJECTIVES: PRKACA alterations have clear diagnostic and biological roles in the fibrolamellar variant of hepatocellular carcinoma and a potential predictive role in that cancer type. However, the roles of PRKACA have not been comprehensively examined in gastric and colorectal cancers (GC and CRC). This study, therefore, sought to investigate the roles of PRKACA expression in GC and CRC. METHODS: The clinico-genomic data of 441 GC and 629 CRC cases were analyzed for therapeutic, clinicopathological, and biological correlates using appropriate bioinformatics and statistical tools. Furthermore, the deregulation of PRKACA expression in GC and CRC was investigated using correlative and regression analyses. RESULTS: The results showed that PRKACA expression subsets were enriched for gene targets of chemotherapeutics, tyrosine kinase, and ß-adrenergic inhibitors. Moreover, high PRKACA expression was associated with adverse clinicopathological and genomic features of GC and CRC. Gene Ontology Enrichment Analysis also showed that PRKACA-high subsets of the GI cancers were enriched for the biological and molecular functions that are associated with cell motility, invasion, and metastasis but not cell proliferation. Finally, multiple regression analyses identified multiple methylation loci, transcription factors, miRNA species, and PRKACA copy number changes that deregulated PRKACA expression in GC and CRC. CONCLUSIONS: This study has identified potential predictive and clinicopathological roles for PRKACA expression in GI cancers and has added to the growing body of knowledge on the deregulation of PRKACA in cancer.

In Silico Insights Reveal Fibronectin 1 as a Theranostic Marker in Gastric Cancer.

Gastric cancer (GC) is a complex and highly variable disease, ranking among the top five cancers diagnosed globally, and a leading cause of cancer-related deaths. Emerging from stomach lining cells amid chronic inflammation, it often advances to preneoplastic stages. Late-stage diagnoses and treatment challenges highlight the critical need for early detection and innovative biomarkers, motivating this study's focus on identifying theranostic markers through gene ontology analysis. By exploring deregulated biological processes, this study aims to uncover insights into cancer progression and associated markers, potentially identifying novel theranostic candidates in GC. Using public data from The Human Protein Atlas, this study pinpointed 299 prognostic genes, delineating 171 with unfavorable prognosis and 128 with favorable prognosis. Functional enrichment and protein-protein interaction analyses, supported by RNAseq results and conducted via Metascape and Cytoscape, highlighted five genes (vWF, FN1, THBS1, PCDH7, and F5) with promising theranostic potential. Notably, FN1 and THBS1 exhibited significant promise, with FN1 showing a 370% expression increase in cancerous tissue, and it is possible that FN1 can also indicate the stratification status in GC. While further validation is essential, these findings provide new insights into molecular alterations in GC and potential avenues for clinical application of theranostic markers.

Annotation of Functions of Sequences of Concern and Its Relevance to the New Biosecurity Regulatory Framework in the United States.

Introduction: Recent regulations from United States Government agencies reshape the screening of synthetic nucleic acids. These take a step away from categorizing hazard on the basis of "bad" taxa and invoke the function of the sequence in pathogenesis or intoxication. Ascertaining functions related to pathogenesis and distinguishing these from other molecular abilities that are unproblematic is not simple. Some have suggested that this information can be readily obtained from existing databases of pathogens. Objectives: We evaluate how virulence factors are described in current databases of pathogens and their adequacy for biothreat data science. We discuss limitations of how virulence factors have been conceived and propose using the sequence of concern (SoC) term to distinguish sequences with biothreat from those without. We discuss ways in which databases of SoCs might be implemented for research and regulatory purposes. We describe ongoing work improving functional descriptions of SoCs. Methods: We assess the adequacy of descriptions of virulence factors in pathogen databases following extensive engagement with the literature in microbial pathogenesis. Results/Conclusions: Descriptions of virulence factors in pathogen databases are inadequate for understanding biothreats. Many are not biothreats and would not be concerning if transferred to another pathogen. New gene ontology terms have been authored, and those specific to pathogenic viral processes are being generalized to make them relevant to other pathogenic taxa. This allows better understanding by humans and better recognition by machines. A database of annotated functions of SoCs could benefit the evolving biosecurity regulatory framework in the United States.

Empowering healthcare education: A multilingual ontology for medical informatics and digital health (MIMO) integrated to artificial intelligence powered training in smart hospitals.

Objective: A comprehensive understanding of professional and technical terms is essential to achieving practical results in multidisciplinary projects dealing with health informatics and digital health. The medical informatics multilingual ontology (MIMO) initiative has been created through international cooperation. MIMO is continuously updated and comprises over 3700 concepts in 37 languages on the Health Terminology/Ontology Portal (HeTOP). Methods: We conducted case studies to assess the feasibility and impact of integrating MIMO into real-world healthcare projects. In HosmartAI, MIMO is used to index technological tools in a dedicated marketplace and improve partners' communication. Then, in SaNuRN, MIMO supports the development of a "Catalog and Index of Digital Health Teaching Resources" (CIDHR) backing digital health resources retrieval for health and allied health students. Results: In HosmartAI, MIMO facilitates the indexation of technological tools and smooths partners' interactions. In SaNuRN within CIDHR, MIMO ensures that students and practitioners access up-to-date, multilingual, and high-quality resources to enhance their learning endeavors. Conclusion: Integrating MIMO into training in smart hospital projects allows healthcare students and experts worldwide with different mother tongues and knowledge to tackle challenges facing the health informatics and digital health landscape to find innovative solutions improving initial and continuous education.

gGN: Representing the Gene Ontology as low-rank Gaussian distributions.

Computational representations of knowledge graphs are critical for several tasks in bioinformatics, including large-scale graph analysis and gene function characterization. In this study, we introduce gGN, an unsupervised neural network for learning node representations as Gaussian distributions. Unlike prior efforts, where the covariance matrices of these distributions are simplified to diagonal, we propose representing them with a low-rank approximation. This representation not only maintains manageable learning complexity, allowing for scaling to large graphs, but is also more effective for modeling the structural features of knowledge graphs, such as their hierarchical and directional relationships between nodes. To learn the low-rank Gaussian distributions, we introduce a semantic-based loss function that effectively preserves these structural features. Systematic experiments reveal that gGN preserves structural features more effectively than existing approaches and scales efficiently on large knowledge graphs. Furthermore, applying gGN to represent the Gene Ontology, a widely used knowledge graph in bioinformatics, outperformed multiple baseline methods in ubiquitous gene characterization tasks. Altogether, the proposed low-rank Gaussian distributions not only effectively represent knowledge graphs but also open new avenues for enhancing bioinformatics tasks. gGN is publicly available as an easily installable package at https://github.com/aedera/ggn.

Leveraging Clinical Intuition to Improve Accuracy of Phenotype-Driven Prioritization.

PURPOSE: Clinical intuition is commonly incorporated into the differential diagnosis as an assessment of the likelihood of candidate diagnoses based either on the patient population being seen in a specific clinic or on the signs and symptoms of the initial presentation. Algorithms to support diagnostic sequencing in individuals with a suspected rare genetic disease do not yet incorporate intuition and instead assume that each Mendelian disease has an equal pretest probability. METHODS: The LIRICAL algorithm calculates the likelihood ratio of clinical manifestations represented by Human Phenotype Ontology (HPO) terms to rank candidate diagnoses. The initial version of LIRICAL assumed an equal pretest probability for each disease in its calculation of the posttest probability (where the test is diagnostic exome or genome sequencing). We introduce Clinical Intuition for Likelihood Ratios (ClintLR), an extension of the LIRICAL algorithm that boosts the pretest probability of groups of related diseases deemed to be more likely. RESULTS: The average rank of the correct diagnosis in simulations using ClintLR showed a statistically significant improvement over a range of adjustment factors. CONCLUSION: ClintLR successfully encodes clinical intuition to improve ranking of rare diseases in diagnostic sequencing. ClintLR is freely available at https://github.com/TheJacksonLaboratory/ClintLR.

Healing with Ayahuasca the Plant Teacher: Psychedelic Metaphoricity and Polyontologies.

Shamans, neo-shamans, atheists, and others describe gaining special knowledge from drinking ayahuasca, supporting the cross-cultural idea of ayahuasca as a plant teacher. While secular enthusiasts interpret this metaphorically, animists and others take it literally. This article examines ontological collisions at a healing retreat in the Peruvian Amazon, considering Shipibo shamans and their international clients. It explores how embodied experiences, such as purging and visions, inform both literal and metaphorical views of healing and illness. By addressing incommensurable ontologies, the article highlights how a polyontological framework approaches ontological collision without necessarily privileging specific ways of knowing.

Assessment of inbreeding coefficients and inbreeding depression on complex traits from genomic and pedigree data in Nelore cattle.

BACKGROUND: Nelore cattle play a key role in tropical production systems due to their resilience to harsh conditions, such as heat stress and seasonally poor nutrition. Monitoring their genetic diversity is essential to manage the negative impacts of inbreeding. Traditionally, inbreeding and inbreeding depression are assessed by pedigree-based coefficients (F), but recently, genetic markers have been preferred for their precision in capturing the inbreeding level and identifying animals at risk of reduced productive and reproductive performance. Hence, we compared the inbreeding and inbreeding depression for productive and reproductive performance traits in Nelore cattle using different inbreeding coefficient estimation methods from pedigree information (FPed), the genomic relationship matrix (FGRM), runs of homozygosity (FROH) of different lengths (> 1 Mb (genome), between 1 and 2 Mb - FROH 1-2; 2-4 Mb FROH 2-4 or > 8 Mb FROH >8) and excess homozygosity (FSNP). RESULTS: The correlation between FPed and FROH was lower when the latter was based on shorter segments (r = 0.15 with FROH 1-2, r = 0.20 with FROH 2-4 and r = 0.28 with FROH 4-8). Meanwhile, the FPed had a moderate correlation with FSNP (r = 0.47) and high correlation with FROH >8 (r = 0.58) and FROH-genome (r = 0.60). The FROH-genome was highly correlated with inbreeding based on FROH>8 (r = 0.93) and FSNP (r = 0.88). The FGRM exhibited a high correlation with FROH-genome (r = 0.55) and FROH >8 (r = 0.51) and a lower correlation with other inbreeding estimators varying from 0.30 for FROH 2-4 to 0.37 for FROH 1-2. Increased levels of inbreeding had a negative impact on the productive and reproductive performance of Nelore cattle. The unfavorable inbreeding effect on productive and reproductive traits ranged from 0.12 to 0.51 for FPed, 0.19-0.59 for FGRM, 0.21-0.58 for FROH-genome, and 0.19-0.54 for FSNP per 1% of inbreeding scaled on the percentage of the mean. When scaling the linear regression coefficients on the standard deviation, the unfavorable inbreeding effect varied from 0.43 to 1.56% for FPed, 0.49-1.97% for FGRM, 0.34-2.2% for FROH-genome, and 0.50-1.62% for FSNP per 1% of inbreeding. The impact of the homozygous segments on reproductive and performance traits varied based on the chromosomes. This shows that specific homozygous chromosome segments can be signs of positive selection due to their beneficial effects on the traits. CONCLUSIONS: The low correlation observed between FPed and genomic-based inbreeding estimates suggests that the presence of animals with one unknown parent (sire or dam) in the pedigree does not account for ancient inbreeding. The ROH hotspots surround genes related to reproduction, growth, meat quality, and adaptation to environmental stress. Inbreeding depression has adverse effects on productive and reproductive traits in Nelore cattle, particularly on age at puberty in young bulls and heifer calving at 30 months, as well as on scrotal circumference and body weight when scaled on the standard deviation of the trait.

Framing the Multi-Centre Qualitative Research Design as a Novel Methodology for Nursing and Healthcare Research: Reflections and A Methodological Discussion.

AIM: To discuss the multi-centre qualitative methodology as a unique design, articulate its guiding paradigm/theoretical perspectives, and highlight its methodological and methodical issues. A secondary objective is to generate further scholarly discourse regarding the multi-centre approach within the broader qualitative research tradition. DESIGN: Methodological discussion. FINDINGS: Rather than an emphasis on only experiences, the multi-centre approach is presented as a unique design which also focuses on uncovering why a phenomenon or problem exists and perceptions regarding the phenomenon/problem. With its focus on capturing multiple subjective realities, the multi-centre qualitative design is arguably underpinned by pragmatist constructivism which offers a robust framework for researching phenomenon in a way that is both theoretically informed and practically relevant. Methodologically, the multi-centre qualitative research design emphasises a problem-centred enquiry, collaborative approach and rigorous study protocols, systematic site selection, contextual immersion and sensitivity and methodical flexibility. CONCLUSION: With the rapidly evolving nursing and global health landscape, the multi-centre design lends itself to exploring and capturing perceptions on a larger scale compared to single site studies. Careful planning, availability of adequate resources, rigorous protocols and quality assurance plans are critical to ensuring its success. IMPLICATIONS FOR PROFESSION AND PATIENT CARE: The multi-centre approach offers the possibility of undertaking the same study across multiple settings/locations which has the potential to improve representation and strengthen transferability. IMPACT: This methodological discussion offers clarity regarding the use of the multi-centre approach and offering strategies for its subsequent uptake in nursing and healthcare research. REPORTING METHOD: Not applicable. PATIENT AND PUBLIC CONTRIBUTION: No patient or public contribution.

The clinical and genetic spectrum of paediatric speech and language disorders.

Speech and language disorders are known to have a substantial genetic contribution. Although frequently examined as components of other conditions, research on the genetic basis of linguistic differences as separate phenotypic subgroups has been limited so far. Here, we performed an in-depth characterization of speech and language disorders in 52 143 individuals, reconstructing clinical histories using a large-scale data-mining approach of the electronic medical records from an entire large paediatric healthcare network. The reported frequency of these disorders was the highest between 2 and 5 years old and spanned a spectrum of 26 broad speech and language diagnoses. We used natural language processing to assess the degree to which clinical diagnoses in full-text notes were reflected in ICD-10 diagnosis codes. We found that aphasia and speech apraxia could be retrieved easily through ICD-10 diagnosis codes, whereas stuttering as a speech phenotype was coded in only 12% of individuals through appropriate ICD-10 codes. We found significant comorbidity of speech and language disorders in neurodevelopmental conditions (30.31%) and, to a lesser degree, with epilepsies (6.07%) and movement disorders (2.05%). The most common genetic disorders retrievable in our analysis of electronic medical records were STXBP1 (n = 21), PTEN (n = 20) and CACNA1A (n = 18). When assessing associations of genetic diagnoses with specific linguistic phenotypes, we observed associations of STXBP1 and aphasia (P = 8.57 × 10-7, 95% confidence interval = 18.62-130.39) and MYO7A with speech and language development delay attributable to hearing loss (P = 1.24 × 10-5, 95% confidence interval = 17.46-infinity). Finally, in a sub-cohort of 726 individuals with whole-exome sequencing data, we identified an enrichment of rare variants in neuronal receptor pathways, in addition to associations of UQCRC1 and KIF17 with expressive aphasia, MROH8 and BCHE with poor speech, and USP37, SLC22A9 and UMODL1 with aphasia. In summary, our study outlines the landscape of paediatric speech and language disorders, confirming the phenotypic complexity of linguistic traits and novel genotype-phenotype associations. Subgroups of paediatric speech and language disorders differ significantly with respect to the composition of monogenic aetiologies.

Advancing mechanistic explanations through natural and artificial embodied cognitive systems.

Mougenot and Matheson propose that mechanistic models can explain behavior by describing the complex interactions among components of the brain, body, and environment as an integrated system, which aligns with embodied cognition. However, we suggest incorporating cognitive ontology theory and addressing degeneracy and neuronal reuse. We also recommend studying natural embodied cognition through artificial systems to develop a comprehensive mechanistic framework.

Ruminant microbiome data are skewed and unFAIR, undermining their usefulness for sustainable production improvement.

The ruminant microbiome plays a key role in the health, feed utilization and environmental impact of ruminant production systems. Microbiome research provides insights to reduce the environmental footprint and improve meat and milk production from ruminants. However, the microbiome composition depends on the ruminant species, habitat and diet, highlighting the importance of having a good representation of ruminant microbiomes in their local environment to translate research findings into beneficial approaches. This information is currently lacking. In this study, we examined the metadata of farmed ruminant microbiome studies to determine global representativeness and summarized information by ruminant species, geographic location, body site, and host information. We accessed data from the International Nucleotide Sequence Database Collaboration via the National Center for Biotechnology Information database. We retrieved 47,628 sample metadata, with cattle accounting for more than two-thirds of the samples. In contrast, goats, which have a similar global population to cattle, were underrepresented with less than 4% of the total samples. Most samples originated in Western Europe, North America, Australasia and China but countries with large ruminant populations in South America, Africa, Asia, and Eastern Europe were underrepresented. Microbiomes from the gastrointestinal tract were the most frequently studied, comprising about 87% of all samples. Additionally, the number of samples from other body sites such as the respiratory tract, milk, skin, reproductive tract, and fetal tissue, has markedly increased over the past decade. More than 40% of the samples lacked basic information and many were retrieved from generic taxonomic classifications where the ruminant species was manually recovered. The lack of basic information such as age, breed or sex can limit the reusability of the data for further analysis and follow-up studies. This requires correct taxonomic assignment of the ruminant host and basic metadata information using accepted ontologies adapted to host-associated microbiomes. Repositories should require this information as a condition of acceptance. The results of this survey highlight the need to encourage studies of the ruminant microbiome from underrepresented ruminant species and countries worldwide. This shortfall in information poses a challenge for the development of microbiome-based strategies to meet sustainability requirements, particularly in areas with expanding livestock production systems.

Representation of non-coding RNA-mediated regulation of gene expression using the Gene Ontology.

Regulatory non-coding RNAs (ncRNAs) are increasingly recognized as integral to the control of biological processes. This is often through the targeted regulation of mRNA expression, but this is by no means the only mechanism through which regulatory ncRNAs act. The Gene Ontology (GO) has long been used for the systematic annotation of protein-coding and ncRNA gene function, but rapid progress in the understanding of ncRNAs meant that the ontology needed to be revised to accurately reflect current knowledge. Here, a targeted effort to revise GO terms used for the annotation of regulatory ncRNAs is described, focusing on microRNAs (miRNAs), long non-coding RNAs (lncRNAs), small interfering RNAs (siRNAs) and PIWI-interacting RNAs (piRNAs). This paper provides guidance to biocurators annotating ncRNA-mediated processes using the GO and serves as background for researchers wishing to make use of the GO in their studies of ncRNAs and the biological processes they regulate.

Bioassay protocol metadata annotation: Proposed standards adoption.

We present a standardized metadata template for assays used in pharmaceutical drug discovery research, according to the FAIR principles. We also describe the use of an automated tool for annotating assays from a variety of sources, including PubChem, commercial assay providers, and the peer-reviewed literature, to this metadata template. Adoption of a standardized metadata template will allow drug discovery scientists to better understand and compare the increasing amounts of assay data becoming available, and will facilitate the use of artificial intelligence tools and other computational methods for analysis and prediction. Since bioassays drive advances in biomedical research, improvements in assay metadata can improve productivity in discovery of new therapeutics, platform technologies, and assay methods.