Publications by authors named "V Ramachandran"

752 Publications

Validated In Silico Model for Biofilm Formation in .

ACS Synth Biol 2022 Jan 13. Epub 2022 Jan 13.

Bugworks Research India Pvt. Ltd., Centre for Cellular and Molecular Platforms, National Centre for Biological Sciences, GKVK, Bellary Road, Bengaluru, Karnataka 560065, India.

Using as the representative biofilm former, we report here the development of an in silico model built by simulating events that transform a free-living bacterial entity into self-encased multicellular biofilms. Published literature on ∼300 genes associated with pathways involved in biofilm formation was curated, static maps were created, and suitably interconnected with their respective metabolites using ordinary differential equations. Precise interplay of genetic networks that regulate the transitory switching of bacterial growth pattern in response to environmental changes and the resultant multicomponent synthesis of the extracellular matrix were appropriately represented. Subsequently, the in silico model was analyzed by simulating time-dependent changes in the concentration of components by using the R and python environment. The model was validated by simulating and verifying the impact of key gene knockouts (KOs) and systematic knockdowns on biofilm formation, thus ensuring the outcomes were comparable with the reported literature. Similarly, specific gene KOs in laboratory and pathogenic were constructed and assessed. MiaA, YdeO, and YgiV were found to be crucial in biofilm development. Furthermore, qRT-PCR confirmed the elevation of expression in biofilm-forming clinical isolates. Findings reported in this study offer opportunities for identifying biofilm inhibitors with applications in multiple industries. The application of this model can be extended to the health care sector specifically to develop novel adjunct therapies that prevent biofilms in medical implants and reduce emergence of biofilm-associated resistant polymicrobial-chronic infections. The in silico framework reported here is open source and accessible for further enhancements.
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http://dx.doi.org/10.1021/acssynbio.1c00445DOI Listing
January 2022

Using Machine Learning to Identify Metabolomic Signatures of Pediatric Chronic Kidney Disease Etiology.

J Am Soc Nephrol 2022 Jan 11. Epub 2022 Jan 11.

Division of Nephrology, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania.

Background: Untargeted plasma metabolomic profiling combined with machine learning (ML) may lead to discovery of metabolic profiles that inform our understanding of pediatric CKD causes. We sought to identify metabolomic signatures in pediatric CKD based on diagnosis: FSGS, obstructive uropathy (OU), aplasia/dysplasia/hypoplasia (A/D/H), and reflux nephropathy (RN).

Methods: Untargeted metabolomic quantification (GC-MS/LC-MS, Metabolon) was performed on plasma from 702 Chronic Kidney Disease in Children study participants (: FSGS=63, OU=122, A/D/H=109, and RN=86). Lasso regression was used for feature selection, adjusting for clinical covariates. Four methods were then applied to stratify significance: logistic regression, support vector machine, random forest, and extreme gradient boosting. ML training was performed on 80% total cohort subsets and validated on 20% holdout subsets. Important features were selected based on being significant in at least two of the four modeling approaches. We additionally performed pathway enrichment analysis to identify metabolic subpathways associated with CKD cause.

Results: ML models were evaluated on holdout subsets with receiver-operator and precision-recall area-under-the-curve, F1 score, and Matthews correlation coefficient. ML models outperformed no-skill prediction. Metabolomic profiles were identified based on cause. FSGS was associated with the sphingomyelin-ceramide axis. FSGS was also associated with individual plasmalogen metabolites and the subpathway. OU was associated with gut microbiome-derived histidine metabolites.

Conclusion: ML models identified metabolomic signatures based on CKD cause. Using ML techniques in conjunction with traditional biostatistics, we demonstrated that sphingomyelin-ceramide and plasmalogen dysmetabolism are associated with FSGS and that gut microbiome-derived histidine metabolites are associated with OU.
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http://dx.doi.org/10.1681/ASN.2021040538DOI Listing
January 2022

Genome-Scale Metabolic Modelling of Lifestyle Changes in Rhizobium leguminosarum.

mSystems 2022 Jan 11:e0097521. Epub 2022 Jan 11.

Department of Plant Sciences, University of Oxfordgrid.4991.5, Oxford, UK.

Biological nitrogen fixation in rhizobium-legume symbioses is of major importance for sustainable agricultural practices. To establish a mutualistic relationship with their plant host, rhizobia transition from free-living bacteria in soil to growth down infection threads inside plant roots and finally differentiate into nitrogen-fixing bacteroids. We reconstructed a genome-scale metabolic model for Rhizobium leguminosarum and integrated the model with transcriptome, proteome, metabolome, and gene essentiality data to investigate nutrient uptake and metabolic fluxes characteristic of these different lifestyles. Synthesis of leucine, polyphosphate, and AICAR is predicted to be important in the rhizosphere, while inositol catabolism is active in undifferentiated nodule bacteria in agreement with experimental evidence. The model indicates that bacteroids utilize xylose and glycolate in addition to dicarboxylates, which could explain previously described gene expression patterns. Histidine is predicted to be actively synthesized in bacteroids, consistent with transcriptome and proteome data for several rhizobial species. These results provide the basis for targeted experimental investigation of metabolic processes specific to the different stages of the rhizobium-legume symbioses. Rhizobia are soil bacteria that induce nodule formation on plant roots and differentiate into nitrogen-fixing bacteroids. A detailed understanding of this complex symbiosis is essential for advancing ongoing efforts to engineer novel symbioses with cereal crops for sustainable agriculture. Here, we reconstruct and validate a genome-scale metabolic model for Rhizobium leguminosarum bv. 3841. By integrating the model with various experimental data sets specific to different stages of symbiosis formation, we elucidate the metabolic characteristics of rhizosphere bacteria, undifferentiated bacteria inside root nodules, and nitrogen-fixing bacteroids. Our model predicts metabolic flux patterns for these three distinct lifestyles, thus providing a framework for the interpretation of genome-scale experimental data sets and identifying targets for future experimental studies.
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http://dx.doi.org/10.1128/msystems.00975-21DOI Listing
January 2022

Analysis of OCT1, OCT2 and OCT3 gene polymorphisms among Type 2 diabetes mellitus subjects in Indian ethnicity, Malaysia.

Saudi J Biol Sci 2022 Jan 14;29(1):453-459. Epub 2021 Sep 14.

Department of Medicine, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang 43400, Selangor DE, Malaysia.

Background: Type 2 Diabetes mellitus (T2DM) is a chronic metabolic disorder. It is a major non-communicable disease affecting 463 million people globally in 2019 and is expected to be double to about 700 million by 2045. The majority are Asians with Indian ethnicity in Malaysia reported as the highest prevalence of T2DM. Cardiovascular disease, renal failure, blindness and neuropathy, as well as premature death are the known morbidity and mortality resulted from T2DM. T2DM is characterized by the dysfunctional insulin physiology that causes reduction of glucose transport into the cells which lead to hyperglycaemia. Hence, one of the important treatments is an oral antidiabetic drug that lowers the serum glucose level in patients with T2DM. This drug will be transported across cell membranes by organic cation transporters (OCT). Therefore, it is important to identify the OCT candidate gene polymorphisms related to T2DM especially among the Indian ethnicity in Malaysia.

Methods: Blood samples were collected from 132 T2DM patients and 133 controls. Genotyping of OCT1 (rs628031), OCT2 (rs145450955), OCT3 (rs3088442 and rs2292334) was performed using (PCR-RFLP).

Results: No association was observed for genotypic and allelic distributions in all the gene polymorphisms of OCT genes ( > 0.05). However, a logistic regression analysis stratified by gender in a dominant model showed a significant difference for OCT3 among males with T2DM ( = 0.006). Significant association was also observed for OCT3 when stratified to subjects aged > 45 years old ( 0.009).

Conclusion: Based on these findings, the association of OCT3 (rs2292334) could be considered as a possible genetic risk factor for the development of T2DM among Indian males alone.
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http://dx.doi.org/10.1016/j.sjbs.2021.09.008DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8716931PMC
January 2022
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