A Regularized Bayesian Dirichlet-multinomial Regression Model for Integrating Single-cell-level Omics and Patient-level Clinical Study Data.

The abundance of various cell types can vary significantly among patients with varying phenotypes and even those with the same phenotype. Recent scientific advancements provide mounting evidence that other clinical variables, such as age, gender, and lifestyle habits, can also influence the abundance of certain cell types. However, current methods for integrating single-cell-level omics data with clinical variables are inadequate. In this study, we propose a regularized Bayesian Dirichlet-multinomial regression framework to investigate the relationship between single-cell RNA sequencing data and patient-level clinical data. Additionally, the model employs a novel hierarchical tree structure to identify such relationships at different cell-type levels. Our model successfully uncovers significant associations between specific cell types and clinical variables across three distinct diseases: pulmonary fibrosis, COVID-19, and non-small cell lung cancer. This integrative analysis provides biological insights and could potentially inform clinical interventions for various diseases.

Proteomics analysis of Bifidobacterium longum NCC2705 growing on glucose, fructose, mannose, xylose, ribose, and galactose.

To investigate the molecular mechanisms underlying carbohydrate uptake and connected metabolic pathways of Bifidobacterium longum NCC2705, the proteomic profiles of bacteria grown on different carbon sources including glucose, fructose, mannose, xylose, ribose, and galactose were analyzed. Our results show that all sugars tested were catabolized via the bifid shunt. Sixty-eight proteins that exhibited changes in abundance of threefold or greater were identified by MS. A striking observation was the differential expression of proteins related to the pyruvate metabolism. Further analysis of acetic acid and lactic acid in the culture supernatants by HPLC at the end of fermentation showed that more lactic acid was produced during growth on fructose, ribose, xylose, galactose and more acetic acid was produced during the fermentation of glucose and mannose. Growth experiments revealed that B. longum NCC2705 preferentially used fructose, ribose, xylose, and galactose with higher growth rates over glucose and mannose. Furthermore, five proteins (GroEL, Eno, Tal, Pgm, and BL0033) exhibited clear phosphorylation modifications at serine and/or tyrosine residues. BL0033, a component of an ATP-binding cassette (ABC) transporter, was significantly more abundant in bacteria grown on fructose and, to a lesser extent, ribose and xylose. RT-PCR analysis revealed that all genes of the ABC transporter are induced in the presence of these sugars suggesting that BL0033, BL0034, BL0035, and BL0036 constitute an ABC transporter with fructose as preferred substrate.