Tissue dyslipidemia in salmonella-infected rats treatTissue dyslipidemia in salmonella-infected rats treated with amoxillin and pefloxac

Background: This study investigated the effects of salmonella infection and its chemotherapy on lipid metabolism in tissues of rats infected orally with Salmonella typhimurium and treated intraperitoneally with pefloxacin and amoxillin. Methods: Animals were infected with Salmonella enterica serovar Typhimurium strain TA 98. After salmonellosis was confirmed, they were divided into 7 groups of 5 animals each. While one group served as infected control group, three groups were treated with amoxillin (7.14 mg/kg body weight, 8 hourly) and the remaining three groups with pefloxacin (5.71mg/kg body weight, 12 hourly) for 5 and 10 days respectively. Uninfected control animals received 0.1ml of vehicle. Rats were sacrificed 24h after 5 and 10 days of antibiotic treatment and 5 days after discontinuation of antibiotic treatment. Their corresponding controls were also sacrificed at the same time point. Blood and tissue lipids were then evaluated. Results: Salmonella infection resulted in dyslipidemia characterised by increased concentrations of free fatty acids (FFA) in plasma and erythrocyte, as well as enhanced cholesterogenesis, hypertriglyceridemia and phospholipidosis in plasma, low density lipoprotein-very low density lipoprotein (LDL-VLDL), erythrocytes, erythrocyte ghost and the organs. The antibiotics reversed the dyslipidemia but not totally. A significant correlation was observed between fecal bacterial load and plasma cholesterol (r=0.456, p<0.01), plasma triacyglycerols (r=0.485, p<0.01), plasma phospholipid (r=0.414, p<0.05), plasma free fatty acids (r=0.485, p<0.01), liver phospholipid (r=0.459, p<0.01) and brain phospholipid (r=0.343, p<0.05). Conclusion: The findings of this study suggest that salmonella infection in rats and its therapy with pefloxacin and amoxillin perturb lipid metabolism and this perturbation is characterised by cholesterogenesis

A Quick Guide for Developing Effective Bioinformatics Programming Skills

Bioinformatics programming skills are becoming a necessity across many facets of biology and medicine, owed in part to the continuing explosion of biological dat

Benchmarking atlas-level data integration in single-cell genomics.

Single-cell atlases often include samples that span locations, laboratories and conditions, leading to complex, nested batch effects in data. Thus, joint analysis of atlas datasets requires reliable data integration. To guide integration method choice, we benchmarked 68 method and preprocessing combinations on 85 batches of gene expression, chromatin accessibility and simulation data from 23 publications, altogether representing &gt;1.2 million cells distributed in 13 atlas-level integration tasks. We evaluated methods according to scalability, usability and their ability to remove batch effects while retaining biological variation using 14 evaluation metrics. We show that highly variable gene selection improves the performance of data integration methods, whereas scaling pushes methods to prioritize batch removal over conservation of biological variation. Overall, scANVI, Scanorama, scVI and scGen perform well, particularly on complex integration tasks, while single-cell ATAC-sequencing integration performance is strongly affected by choice of feature space. Our freely available Python module and benchmarking pipeline can identify optimal data integration methods for new data, benchmark new methods and improve method development

Insight into the peopling of mainland southeast Asia from thai population genetic structure.

There is considerable ethno-linguistic and genetic variation among human populations in Asia, although tracing the origins of this diversity is complicated by migration events. Thailand is at the center of Mainland Southeast Asia (MSEA), a region within Asia that has not been extensively studied. Genetic substructure may exist in the Thai population, since waves of migration from southern China throughout its recent history may have contributed to substantial gene flow. Autosomal SNP data were collated for 438,503 markers from 992 Thai individuals. Using the available self-reported regional origin, four Thai subpopulations genetically distinct from each other and from other Asian populations were resolved by Neighbor-Joining analysis using a 41,569 marker subset. Using an independent Principal Components-based unsupervised clustering approach, four major MSEA subpopulations were resolved in which regional bias was apparent. A major ancestry component was common to these MSEA subpopulations and distinguishes them from other Asian subpopulations. On the other hand, these MSEA subpopulations were admixed with other ancestries, in particular one shared with Chinese. Subpopulation clustering using only Thai individuals and the complete marker set resolved four subpopulations, which are distributed differently across Thailand. A Sino-Thai subpopulation was concentrated in the Central region of Thailand, although this constituted a minority in an otherwise diverse region. Among the most highly differentiated markers which distinguish the Thai subpopulations, several map to regions known to affect phenotypic traits such as skin pigmentation and susceptibility to common diseases. The subpopulation patterns elucidated have important implications for evolutionary and medical genetics. The subpopulation structure within Thailand may reflect the contributions of different migrants throughout the history of MSEA. The information will also be important for genetic association studies to account for population-structure confounding effects