Biology subjects

Bhatti, G.

Publications and source records attributed to Bhatti, G..

4 recordsLinked to original sources

bioRxiv2026

MNRR1 is required for Triple Negative Breast Cancer growth and metastasis and can be targeted by repurposed drugs.

Despite progress in recent decades, breast cancers remain the most diagnosed malignancies, and second leading cause of cancer death, in women. Although improved screening and systemic and endocrine adjuvant approaches have contributed to major declines in breast cancer mortality, current standard of care drugs are extremely toxic, and many women continue to be overtreated. Although nearly two-thirds of breast cancers are hormone-responsive, aggressive subtypes, particularly Triple Negative Breast Cancer (TNBC), still lack safe oral medications. Recently the roles that mitochondria play in TNBC carcinogenesis, metastasis, and resistance to treatment have garnered a great deal of attention. Contrary to the popular dogma that cancer cells are powered by glycolysis, metastatic breast cancer cells have enhanced mitochondrial function. Our work identified that Mitochondrial Nuclear Retrograde Regulator 1 (MNRR1; also, CHCHD2, PARK22), a key coordinator of mitochondrial-nuclear crosstalk that is physically present in both compartments, is overexpressed in TNBC cells and is an important regulator of metastasis signaling. We have identified Heat Shock Factor 1 (HSF1) as the main transcription factor that activates the MNRR1 promoter in TNBC cell lines. In the mitochondria, MNRR1 protein facilitates ATP production and inhibits apoptosis, whereas in the nucleus it regulates the transcription of stress-responsive genes including several required for epithelial to mesenchymal transition (EMT), metabolic flexibility, and cell growth. Thus, each of the bi-organellar functions of MNRR1 constitutes processes regarded as hallmarks of cancer. For reasons that are not yet fully understood, MNRR1 levels display a significant and robust ancestry bias, showing increased expression in tumor samples from Non-Hispanic Black (NHB) women when compared to disease-matched tumors from Non-Hispanic White (NHW) patients. It is possible that increased levels of MNRR1 may underlie the aggressive metastatic phenotype observed in many NHB patients. In further support of this observation, loss of MNRR1 function, either genetically or by use of inhibitors, reduces TNBC growth and metastasis. MNRR1 therefore is an attractive therapeutic target that could be exploited for design of novel therapies or as adjuncts to existing ones.

cancer biology↗

bioRxiv2025

Benchmarking Large Language Models for Predictive Modeling in Biomedical Research With a Focus on Reproductive Health

Generative AI, particularly large language models (LLMs), is increasingly being used in computational biology to support code generation for data analysis. In this study, we evaluated the ability of LLMs to generate functional R and Python code for predictive modeling tasks, leveraging standardized molecular datasets from several recent DREAM (Dialogue for Reverse Engineering Assessments and Methods) Challenges focused on reproductive health. We assessed LLM performance across four predictive tasks derived from three DREAM challenges: gestational age regression from gene expression, gestational age regression from DNA methylation profiles, and classification of preterm birth and early preterm birth from microbiome data. LLMs were prompted with task descriptions, data locations, and target outcomes. LLM-generated code was then run to fit and apply prediction models and generate graphics, and they were ranked based on their success in completing the tasks and achieving strong test set performance. Among the eight LLMs tested, o3-mini-high, 4o, DeepseekR1 and Gemini 2.0 completed at least one task without error. Overall, R code generation was more successful (14/16 tasks) than Python (7/16), attributed to the utility of Bioconductor packages for querying Gene Expression Omnibus data. OpenAIs o3-mini-high outperformed others, completing 7/8 tasks. Test set performance of the top LLM matched or exceeded top-performing teams from the original DREAM challenges. These findings underscore the potential of LLMs to enhance exploratory analysis and democratize access to predictive modeling in omics by automating key components of analysis pipelines, and highlight the potential to increase research output when conducting analyses of standardized datasets from public repositories.

bioinformatics↗

bioRxiv2020

Crowdsourcing assessment of maternal blood multi-omics for predicting gestational age and preterm birth

Identification of pregnancies at risk of preterm birth (PTB), the leading cause of newborn deaths, remains challenging given the syndromic nature of the disease. We report a longitudinal multi-omics study coupled with a DREAM challenge to develop predictive models of PTB. We found that whole blood gene expression predicts ultrasound-based gestational ages in normal and complicated pregnancies (r=0.83), as well as the delivery date in normal pregnancies (r=0.86), with an accuracy comparable to ultrasound. However, unlike the latter, transcriptomic data collected at <37 weeks of gestation predicted the delivery date of one third of spontaneous (sPTB) cases within 2 weeks of the actual date. Based on samples collected before 33 weeks in asymptomatic women we found expression changes preceding preterm prelabor rupture of the membranes that were consistent across time points and cohorts, involving, among others, leukocyte-mediated immunity. Plasma proteomic random forests predicted sPTB with higher accuracy and earlier in pregnancy than whole blood transcriptomic models (e.g. AUROC=0.76 vs. AUROC=0.6 at 27-33 weeks of gestation).

bioinformatics↗

bioRxiv2019

Predicting cellular position in the Drosophila embryo from Single-Cell Transcriptomics data

Single-cell RNA-seq technologies are rapidly evolving but while very informative, in standard scRNAseq experiments the spatial organization of the cells in the tissue of origin is lost. Conversely, spatial RNA-seq technologies designed to keep the localization of the cells have limited throughput and gene coverage. Mapping scRNAseq to genes with spatial information increases coverage while providing spatial location. However, methods to perform such mapping have not yet been benchmarked. To bridge the gap, we organized the DREAM Single-Cell Transcriptomics challenge focused on the spatial reconstruction of cells from the Drosophila embryo from scRNAseq data, leveraging as gold standard genes with in situ hybridization data from the Berkeley Drosophila Transcription Network Project reference atlas. The 34 participating teams used diverse algorithms for gene selection and location prediction, while being able to correctly localize rare subpopulations of cells. Selection of predictor genes was essential for this task and such genes showed a relatively high expression entropy, high spatial clustering and the presence of prominent developmental genes such as gap and pair-ruled genes and tissue defining markers.

systems biology↗