SARS-CoV-2 and Prevotella spp.: friend or foe? A systematic literature review.

During this global pandemic of the COVID-19 disease, a lot of information has arisen in the media and online without scientific validation, and among these is the possibility that this disease could be aggravated by a secondary bacterial infection such as Prevotella, as well as the interest or not in using azithromycin, a potentially active antimicrobial agent. The aim of this study was to carry out a systematic literature review, to prove or disprove these allegations by scientific arguments. The search included Medline, PubMed, and Pubtator Central databases for English-language articles published 1999-2021. After removing duplicates, a total of final eligible studies (n=149) were selected. There were more articles showing an increase of Prevotella abundance in the presence of viral infection like that related to Human Immunodeficiency Virus (HIV), Papillomavirus (HPV), Herpesviridae and respiratory virus, highlighting differences according to methodologies and patient groups. The arguments for or against the use of azithromycin are stated in light of the results of the literature, showing the role of intercurrent factors, such as age, drug consumption, the presence of cancer or periodontal diseases. However, clinical trials are lacking to prove the direct link between the presence of Prevotella spp. and a worsening of COVID-19, mainly those using azithromycin alone in this indication.

BioMeKe: an ontology-based biomedical knowledge extraction system devoted to transcriptome analysis.

Semantic interoperability between knowledge bases in medicine, and knowledge base in genomics and molecular biology will lead to advances in fundamental research as well as to improved patient care. DNA chips strategy is used for transcriptome analysis in order to identify deregulated genes in physio-pathological conditions. The objective of the BioMedical Knowledge Extraction project (BioMeKe) is to develop a knowledge warehouse in the context of transcriptome analysis during liver diseases. Knowledge sources include ontologies, related terminologies and annotations linked towards public databases (e.g., SWISSPROT). BioMeKe has been developed to have access to information using systematic investigation upon a concept, gene, gene products, pathology, or any target keyword, and is based on the combination of several relevant resources: UMLS, GeneOntology, MeSH supplementary terms, GOA, and HUGO. Current efforts are focusing on exploiting this ontology-based Knowledge Extractor, to enrich the expression data on genes delivered by a liver specific DNA microarray for better assistance of analysis.

Representation of roles in biomedical ontologies: a case study in functional genomics.

OBJECTIVE: Representing roles, i.e. functions of proteins, sequences and structures, is the cornerstone of knowledge representation in functional genomics. The objective of this study is to investigate representation of roles as functional categories or associative relations. We focus on GeneOntology (GO) and the UMLS and take examples from iron metabolism. METHODS: The terms corresponding to the main proteins involved in iron metabolism were mapped to GO (including the annotations) and the UMLS. The representation of their biological roles was then analyzed. RESULTS: Functional aspects are represented in both GO and the UMLS. However, the granularity may not be appropriate. DISCUSSION: Advantages and limits of functional categories and associative relations are discussed.