Biology subjects

Manda, S.

Publications and source records attributed to Manda, S..

3 recordsLinked to original sources

bioRxiv2025

Examining the composition of gut microbiota in a South African population: a comparative study between type 2 diabetes mellitus patients and non-diabetic individuals

BackgroundLiterature has highlighted the gut microbiotas role in metabolic functions, suggesting a potential link between gut microbiota composition and T2DM. The purpose of the study was to identify microbial signatures unique to T2DM patients and non-diabetic individuals, to compare microbial profiles between the two groups and to investigate how gut microbiota may be related to inflammation associated with T2DM. MethodsA cross-sectional study was conducted involving 51 T2DM patients and 99 non-diabetic South African individuals. Faecal samples were collected and analysed using 16S rRNA gene sequencing to characterize the gut microbiota. Blood samples were obtained to perform HbA1c, CRP and ferritin tests. Bioinformatic and statistical analyses were performed to identify differences in microbial composition and diversity between the two groups. ResultsThe gut microbiota in T2DM patients was predominantly composed of Firmicutes (47.7%), Bacteroidota (37.5%), and Proteobacteria (11.4%), while the non-diabetic group showed a slightly different microbial profile with higher Bacteroidota (41.9%) and a notable presence of Actinobacteriota (4.5%). Abundant families in the T2DM group included Bacteroidaceae (22.8%), Prevotellaceae (7.4%), Enterobacteriaceae (7.4%), Erysipelotrichaceae (6.0%) and Lachnospiraceae (5.2%). The non-diabetic group exhibited dominant families such as Lachnospiraceae 26.7%, Prevotellaceae (25.3%), Bacteroidaceae (12.7%), Ruminococcaceae (9.5%) and Oscillospiraceae (3.8%). At the genus level, Bacteroides (22.8%), Escherichia-Shigella (5.0%), Holdemanella (4.8%), Phascolarctobacterium (3.2%) and Blautia (2.8%) were prevalent in the T2DM group, while Prevotella_9 (22.1%), Bacteroides (12.7%), Agathobacter (6.7%), Blautia (6.3%) and Faecalibacterium (5.1%) were dominant in the non-diabetic group. Differential abundance testing revealed 5 phyla, 16 families, and 25 genera that were either enriched/depleted in T2DM patients relative to non-diabetic individuals. The comparison of alpha diversity metrics between the two groups revealed significant differences across all four measures (P < 0.001), with non-diabetic individuals showing higher values than T2DM patients. HbA1c and CRP levels showed correlations with the relative abundance of various gut microbes at various phyla, family, and genus levels, as well as with all alpha diversity metrics. ConclusionThe study revealed distinct differences in gut microbiota composition between T2DM patients and non-diabetic individuals, with T2DM patients showing a higher prevalence of certain phyla, families, and genera linked to metabolic dysregulation. Non-diabetic individuals exhibited greater microbial diversity and beneficial taxa, highlighting a potential protective microbial profile.

microbiology↗

bioRxiv2023

DNA nanopores as artificial membrane channels for origami based bioelectronics

Biological membrane channels mediate information exchange between cells and facilitate molecular recognition1-4. While tuning the shape and function of membrane channels for precision molecular sensing via de-novo routes is complex, an even more significant challenge is interfacing membrane channels with electronic devices for signal readout5-8. This challenge at the biotic-abiotic interface results in low efficiency of information transfer - one of the major barriers to the continued development of high-performance bioelectronic devices9. To this end, we integrate membrane spanning DNA nanopores with bioprotonic contacts to create programmable, modular, and efficient artificial ion-channel interfaces that resolve the iono-electronic disparity between the biotic environment and electronics. Through simulations and experiments, we show that cholesterol modified DNA nanopores spontaneously and with remarkable affinity span the lipid bilayer formed over the planar bio-protonic electrode surface and mediate proton transport across the bilayer. Using the ability to easily modify DNA nanostructures, we illustrate that this bioelectronic device can be programmed for electronic recognition of biomolecular signals such as presence of Streptavidin, without disrupting the native environment of the biomolecule. We anticipate this robust biotic-abiotic interface will allow facile electronic measurement of inter-cellular ionic communication and also open the door for active control of cell behavior through externally controlled selective gating of the channels.

bioengineering↗

bioRxiv2022

Pan-cancer proteomic map of 949 human cell lines reveals principles of cancer vulnerabilities

The proteome provides unique insights into biology and disease beyond the genome and transcriptome. Lack of large proteomic datasets has restricted identification of new cancer biomarkers. Here, proteomes of 949 cancer cell lines across 28 tissue types were analyzed by mass spectrometry. Deploying a clinically-relevant workflow to quantify 8,498 proteins, these data capture evidence of cell type and post-transcriptional modifications. Integrating multi-omics, drug response and CRISPR-Cas9 gene essentiality screens with a deep learning-based pipeline revealed thousands of protein-specific biomarkers of cancer vulnerabilities. Proteomic data had greater power to predict drug response than the equivalent portion of the transcriptome. Further, random downsampling to only 1,500 proteins had limited impact on predictive power, consistent with protein networks being highly connected and co-regulated. This pan-cancer proteomic map (ProCan-DepMapSanger), available at https://cellmodelpassports.sanger.ac.uk, is a comprehensive resource revealing principles of protein regulation with important implications for future clinical studies.

systems biology↗