Diagnosis of bacteria from the CMNR group in farm animals.

The CMNR group comprises bacteria of the genera Corynebacterium, Mycobacterium, Nocardia, and Rhodococcus and share cell wall and DNA content characteristics. Many pathogenic CMNR bacteria cause diseases such as mastitis, lymphadenitis, and pneumonia in farmed animals, which cause economic losses for breeders and represent a threat to public health. Traditional diagnosis in CMNR involves isolating target bacteria on general or selective media and conducting metabolic analyses with the assistance of laboratory biochemical identification systems. Advanced mass spectrometry may also support diagnosing these bacteria in the clinic's daily routine despite some challenges, such as the need for isolated bacteria. In difficult identification among some CMNR members, molecular methods using polymerase chain reaction (PCR) emerge as reliable options for correct specification that is sometimes achieved directly from clinical samples such as tracheobronchial aspirates and feces. On the other hand, immunological diagnostics such as the skin test or Enzyme-Linked Immunosorbent Assay (ELISA) for Mycobacterium tuberculosis yield promising results in subclinical infections with no bacterial growth involved. In this review, we present the methods most commonly used to diagnose pathogenic CMNR bacteria and discuss their advantages and limitations, as well as challenges and perspectives on adopting new technologies in diagnostics.

Application of Gut Bacterial Profiling Information in Precision Nutrition for Obesity and Weight Loss Management.

BACKGROUND: It has been suggested that the dysfunction of the gut microbiome can have deleterious effects on the regulation of body weight and adiposity by affecting energy metabolism. In this context, gut bacterial profiling studies have contributed to characterize specific bacteria associated with obesity. This review covers the information driven by gut bacterial profiling analyses and emphasizes the potential application of this knowledge in precision nutrition strategies for obesity understanding and weight loss management. SUMMARY: Gut bacterial profiling studies have identified bacterial families that are more abundant in obese than in nonobese individuals (i.e., Prevotellaeae, Ruminococcaceae, and Veillonellaceae) as well as other families that have been repeatedly found more abundant in nonobese people (i.e., Christensenellaceae and Coriobacteriaceae), suggesting that an increase in their relative amount could be an interesting target in weight-loss treatments. Also, some gut-derived metabolites have been related to the regulation of body weight, including short-chain fatty acids, trimethylamine-N-oxide, and branched-chain and aromatic amino acids. Moreover, gut microbiota profiles may play a role in determining weight loss responses to specific nutritional treatments for the precise management of obesity. Thus, incorporating gut microbiota features may improve the performance of integrative models to predict weight loss outcomes. KEY MESSAGES: The application of gut bacterial profiling information is of great value for precision nutrition in metabolic diseases since it contributes to the understanding of the role of the gut microbiota in obesity onset and progression, facilitates the identification of potential microorganism targets, and allows the personalization of tailored weight loss diets as well as the prediction of adiposity outcomes based on the gut bacterial profiling of each individual. Integrating microbiota information with other omics knowledge (genetics, epigenetics, transcriptomics, proteomics, and metabolomics) may provide a more comprehensive understanding of the molecular and physiological events underlying obesity and adiposity outcomes for precision nutrition.

GeM-Pro: a tool for genome functional mining and microbial profiling.

Gem-Pro is a new tool for gene mining and functional profiling of bacteria. It initially identifies homologous genes using BLAST and then applies three filtering steps to select orthologous gene pairs. The first one uses BLAST score values to identify trivial paralogs. The second filter uses the shared identity percentages of found trivial paralogs as internal witnesses of non-orthology to set orthology cutoff values. The third filtering step uses conditional probabilities of orthology and non-orthology to define new cutoffs and generate supportive information of orthology assignations. Additionally, a subsidiary tool, called q-GeM, was also developed to mine traits of interest using logistic regression (LR) or linear discriminant analysis (LDA) classifiers. q-GeM is more efficient in the use of computing resources than Gem-Pro but needs an initial classified set of homologous genes in order to train LR and LDA classifiers. Hence, q-GeM could be used to analyze new set of strains with available genome sequences, without the need to rerun a complete Gem-Pro analysis. Finally, Gem-Pro and q-GeM perform a synteny analysis to evaluate the integrity and genomic arrangement of specific pathways of interest to infer their presence. The tools were applied to more than 2 million homologous pairs encoded by Bacillus strains generating statistical supported predictions of trait contents. The different patterns of encoded traits of interest were successfully used to perform a descriptive bacterial profiling.