Biology subjects

Sudhakar, M.

Publications and source records attributed to Sudhakar, M..

6 recordsLinked to original sources

bioRxiv2025

Enhancing CRISPR Education: A Plug-And-Play Framework for Teaching Gene Editing in Plant Biology

Modern molecular biology tools and technologies such as CRISPR have sped up scientific discovery. From an educational perspective, these advancements are both exciting and overwhelming. Educators shaping these future scientists face the ongoing challenge of staying compliant with the latest developments in molecular biology while finding effective ways to teach these discoveries. As the use of CRISPR gene editing technology continues to expand globally, there is an increasing need for a workforce that is both knowledgeable about its theoretical foundations and trained in its practical use. While advanced, technology-driven STEM courses have the potential to improve student retention, they are often lecture-heavy and lack intentional engagement strategies that support deeper learning. Moreover, agriculture is the second most impacted sector by this technology, yet there is a significant lack of teaching materials focused on CRISPR in plant biology. To address these gaps, we developed a framework for teaching gene editing that incorporates multiple engagement strategies beyond traditional lecture-based instruction. This framework was implemented over two semesters in an Introduction to Gene Editing course at Tennessee State University, offered to both undergraduate and graduate students enrolled in a degree in Agricultural Sciences. This manuscript outlines the various strategies used in the course including active learning, multimodal instructional approaches and experiential learning strategies that can be adopted in other classrooms to effectively teach gene editing. Survey-based results from the course indicate a measurable increase in student comfort with designing and executing CRISPR-Cas based experiments. Societal Impact StatementPlant biology lacks accessible teaching materials for CRISPR, a powerful gene-editing technology widely used to improve agriculture. We developed an engaging framework to teach CRISPR concepts to advanced undergraduates and graduate students in plant sciences, which can be readily adopted by other instructors. The approach increased self-reported confidence in students and comfort in explaining CRISPR. Since instructors often have limited time to design interactive lessons, this framework offers a ready-to-use, effective strategy that makes CRISPR more widely available in classrooms, ultimately strengthening CRISPR literacy in the future agricultural workforce.

scientific communication and education↗

bioRxiv2025

Gender-specific MetS prediction using pathophysiological determinants: Beyond diagnostic constraints

Metabolic syndrome (MetS), constituted by obesity, hyperglycemia, hypertension, and dyslipidemia, is a growing public health concern due to its associated risk with cardiovascular and other metabolic disorders. Early-stage detection and prevention of MetS are key factors in an effective management strategy. Thus, the current study aimed to develop a risk score for MetS progression based on features pertaining to its pathophysiology, that can facilitate early detection and treatment strategies. A multivariate logistic regression model was developed using a representative feature from MetS pathophysiological pathways of inflammation, endothelial dysfunction, and hepatic dysregulation. The model was built on NHANES dataset and validated in Chinese and Indian datasets. The model performance evaluated using ROC resulted in an AUC of 0.81 in training and 0.87 and 0.79 for Chinese and Indian validation datasets, respectively. In conclusion, a MetS predictive model built on three MetS pathophysiological determinants was developed and validated in distinct datasets. HighlightsO_LIA MetS prediction model, trained on NHANES data, is validated with two datasets of varied ethnicity. C_LIO_LIThe three parameter model, independent of MetS diagnostic criteria, is reflective of inflammation, endothelial dysfunction, and hepatic dysregulation. C_LIO_LIThe model achieved an AUC of 0.81 in NHANES data; 0.87 and 0.79 for Chinese and Indian validation datasets respectively. C_LI

bioinformatics↗

bioRxiv2022

Multi-omic data helps improve prediction of personalised tumor suppressors and oncogenes

The progression of tumorigenesis starts with a few mutational and structural driver events in the cell. Various cohort-based computational tools exist to identify driver genes but require a large number of samples to produce reliable results. Many studies use different methods to identify driver mutations/genes from mutations that have no impact on tumour progression; however, a small fraction of patients show no mutational events in any known driver genes. Current unsupervised methods map somatic and expression data onto a network to identify the perturbation in the network. Our method is the first machine learning model to classify genes as tumour suppressor gene (TSG), oncogene (OG) or neutral, thus assigning the functional impact of the gene in the patient. In this study, we develop a multi-omic approach, PIVOT (Personalised Identification of driVer OGs and TSGs), to train on experimentally or computationally validated mutational and structural driver events. Given the lack of any gold standards for the identification of personalised driver genes, we label the data using four strategies and, based on classification metrics, show gene-based labelling strategies perform best. We build different models using SNV, RNA, and multi-omic features to be used based on the data available. Our models trained on multi-omic data improved predictions compared to mutation and expression data, achieving an accuracy [≥] 0.99 for BRCA, LUAD and COAD datasets. We show network and expression-based features contribute the most to PIVOT. Our predictions on BRCA, COAD and LUAD cancer types reveal commonly altered genes such as TP53, and PIK3CA, which are predicted drivers for multiple cancer types. Along with known driver genes, our models also identify new driver genes such as PRKCA, SOX9 and PSMD4. Our multi-omic model labels both CNV and mutations with a more considerable contribution by CNV alterations. While predicting labels for genes mutated in multiple samples, we also label rare driver events occurring in as few as one sample. We also identify genes with dual roles within the same cancer type. Overall, PIVOT labels personalised driver genes as TSGs and OGs and also identifies rare driver genes. PIVOT is available at https://github.com/RamanLab/PIVOT.

bioinformatics↗

bioRxiv2021

iCOMIC: a graphical interface-driven bioinformatics pipeline for analyzing cancer omics data

Despite the tremendous increase in omics data generated by modern sequencing technologies, their analysis can be tricky and often requires substantial expertise in bioinformatics. To address this concern, we have developed a user-friendly pipeline to analyze (cancer) genomic data that takes in raw sequencing data (FASTQ format) as input and outputs insightful statistics on the nature of the data. Our iCOMIC toolkit pipeline can analyze whole-genome and transcriptome data and is embedded in the popular Snakemake workflow management system. iCOMIC is characterized by a user-friendly GUI that offers several advantages, including executing analyses with minimal steps, eliminating the need for complex command-line arguments. The toolkit features many independent core workflows for both whole genomic and transcriptomic data analysis. Even though all the necessary, well-established tools are integrated into the pipeline to enable out-of-the-box analysis, we provide the user with the means to replace modules or alter the pipeline as needed. Notably, we have integrated algorithms developed in-house for predicting driver and passenger mutations based on mutational context and tumor suppressor genes and oncogenes from somatic mutation data. We benchmarked our tool against Genome In A Bottle (GIAB) benchmark dataset (NA12878) and got the highest F1 score of 0.971 and 0.988 for indels and SNPs, respectively, using the BWA MEM - GATK HC DNA-Seq pipeline. Similarly, we achieved a correlation coefficient of r=0.85 using the HISAT2-StringTie-ballgown and STAR-StringTie-ballgown RNA-Seq pipelines on the human monocyte dataset (SRP082682). Overall, our tool enables easy analyses of omics datasets, with minimal steps, significantly ameliorating complex data analysis pipelines. Availability: https://github.com/RamanLab/iCOMIC

bioinformatics↗

bioRxiv2020

Novel ratio-metric features enable the identification of new driver genes across cancer types

An emergent area of cancer genomics has been the identification of driver genes. Driver genes confer a selective growth advantage to the cell and push it towards tumorigenesis. Functionally, driver genes can be divided into two categories, tumour suppressor genes (TSGs) and oncogenes (OGs), which have distinct mutation type profiles. While several driver genes have been discovered, many remain undiscovered, especially those that are mutated at a low frequency across samples. The current methods are not sufficient to predict all driver genes because the underlying characteristics of these genes are not yet well understood. Thus, to predict novel genes, we need to define new features and models that are not biased and identify genes that might otherwise be overshadowed by mutation profiles of recurrent driver genes. In this study, we define new features and build a model to identify novel driver genes. We overcome overfitting and show that certain mutation types such as nonsense mutations are more important for classification. Some known cancer driver genes, which are predicted by the model as TSGs with high probability are ARID1A, TP53, and RB1. In addition to these known genes, potential driver genes predicted are CD36, ZNF750 and ARHGAP35 as TSGs and TAB3 as an oncogene. Overall, our approach surmounts the issue of low recall and bias towards genes with high mutation rates and predicts potential novel driver genes for further experimental screening.

cancer biology↗

bioRxiv2019

Robust lysosomal rewiring in Mtb infected macrophages mediated by Mtb lipids restricts the intracellular bacterial survival

Intracellular pathogens commonly manipulate the host lysosomal system for their survival, however whether this affects the organization and functioning of the lysosomal system itself is not known. Here, we show using in vitro and in vivo infections that the lysosomal content and activity is globally elevated in M. tuberculosis infected macrophages. The enhanced lysosomal state is sustained over time and defines an adaptive homeostasis of the infected cell. Lysosomal alterations are caused by mycobacterial surface components, notably the cell wall lipid SL-1, which functions through the mTORC1-TFEB axis. Mtb mutant defective for SL-1 levels shows reduced lysosomal content and activity compared to wild type. Importantly, this phenotype is conserved during in vivo infection. The alteration in lysosomal phenotype in mutant Mtb lead to decreased lysosomal delivery of Mtb, and importantly, increased survival of intracellular Mtb. These results define the global alterations in the host lysosomal system as a crucial distinguishing feature of Mtb infected macrophages that is host protective and contribute to the containment of the pathogen.

cell biology↗