@article {163997, title = {EGFR and AR expression and co-expression in Indian triple-negative breast cancer with patient outcome association.}, journal = {Sci Rep}, year = {2026}, month = {2026 Feb 21}, keywords = {AR, Basal TNBC, co-expression of EGFR and AR., EGFR, Molecular profile}, issn = {2045-2322}, doi = {10.1038/s41598-026-40913-9}, url = {https://pubmed.ncbi.nlm.nih.gov/41723216/}, author = {Vaid, Pooja and Puntambekar, Anirudha and Kanse, Pranali and Kadu, Shweta and Agrawal, Piyush and Khatpe, Aditi and Banale, Rituja and Reddy, Ruhi and Kelkar, Devaki A and Hannenhalli, Sridhar and Shashidhara, L S and Koppiker, Chaitanyanand and Kulkarni, Madhura} } @article {152142, title = {Non-coding genetic variants underlying higher prostate cancer risk in men of African ancestry.}, journal = {Res Sq}, year = {2024}, month = {2024 Dec 05}, abstract = {

Incidence and severity of prostate cancer (PrCa) substantially varies across ancestries. American men of African ancestry (AA) are more likely to be diagnosed with and die from PrCa than the those of European ancestry (EA). Published polygenic risk scores for developing prostate cancer, even those based on multi-ancestry genome-wide association studies, do not address population-specific genetic mechanisms underlying PrCa risk in men of African ancestry. Specifically, the role of non-coding regulatory polymorphisms in driving inter-ancestry variation in PrCa has not been suficiently explored. Here, by employing a sequence-based deep learning model of prostate regulatory enhancers, we identified ~ 2,000 SNPs with higher alternate allele frequency in AA men that potentially affect enhancer function associated with PrCa susceptibility, as supported by our experimental validation. The identified enhancer SNPs (eSNPs) may influence PrCa development through two complementary mechanisms: 1) the alternate allele that increase enhancer activity result in immune suppression and telomere elongation, and 2) the alternate alleles that decrease enhancer activity, lead to de-differentiation and inhibition of apoptosis. Notably, the eSNPs tend to disrupt the binding of known prostate transcription factors including FOX, AR and HOX families. Lastly, the identified eSNPs can be combined into a polygenic risk score that adds value to current GWAS-based risk variants in assessing PrCa risk in independent cohorts.

}, issn = {2693-5015}, doi = {10.21203/rs.3.rs-5485172/v1}, author = {Hannenhalli, Sridhar and Li, Shan and Fatema, Kaniz and Nidharshan, Sundarraj and Singh, Arashdeep and Rajagopal, Padma Sheila and Notani, Dimple and Takeda, David} } @article {71098, title = {Ligand dependent gene regulation by transient ERα clustered enhancers.}, journal = {PLoS Genet}, volume = {16}, year = {2020}, month = {2020 Jan 06}, pages = {e1008516}, abstract = {

Unliganded Estrogen receptor alpha (ERα) has been implicated in ligand-dependent gene regulation. Upon ligand exposure, ERα binds to several EREs relatively proximal to the pre-marked, or persistent, ERα-bound sites and affects transient but robust gene expression. However, the underlying mechanisms are not fully understood. Here we demonstrate that upon ligand stimulation, persistent sites interact extensively, via chromatin looping, with the proximal transiently ERα-bound sites, forming Ligand Dependent ERα Enhancer Cluster in 3D (LDEC). The E2-target genes are regulated by these clustered enhancers but not by the H3K27Ac super-enhancers. Further, CRISPR-based deletion of TFF1 persistent site disrupts the formation of its LDEC resulting in the loss of E2-dependent expression of TFF1 and its neighboring genes within the same TAD. The LDEC overlap with nuclear ERα condensates that coalesce in a ligand and persistent site dependent manner. Furthermore, formation of clustered enhancers, as well as condensates, coincide with the active phase of signaling and their later disappearance results in the loss of gene expression even though persistent sites remain bound by ERα. Our results establish, at TFF1 and NRIP1 locus, a direct link between ERα condensates, ERα enhancer clusters, and transient, but robust, gene expression in a ligand-dependent fashion.

}, issn = {1553-7404}, doi = {10.1371/journal.pgen.1008516}, author = {Saravanan, Bharath and Soota, Deepanshu and Islam, Zubairul and Majumdar, Sudeshna and Mann, Rajat and Meel, Sweety and Farooq, Umer and Walavalkar, Kaivalya and Gayen, Srimonta and Singh, Anurag Kumar and Hannenhalli, Sridhar and Notani, Dimple} } @article {54455, title = {ChIPulate: A comprehensive ChIP-seq simulation pipeline.}, journal = {PLoS Comput Biol}, volume = {15}, year = {2019}, month = {2019 Mar 21}, pages = {e1006921}, abstract = {

ChIP-seq (Chromatin Immunoprecipitation followed by sequencing) is a high-throughput technique to identify genomic regions that are bound in vivo by a particular protein, e.g., a transcription factor (TF). Biological factors, such as chromatin state, indirect and cooperative binding, as well as experimental factors, such as antibody quality, cross-linking, and PCR biases, are known to affect the outcome of ChIP-seq experiments. However, the relative impact of these factors on inferences made from ChIP-seq data is not entirely clear. Here, via a detailed ChIP-seq simulation pipeline, ChIPulate, we assess the impact of various biological and experimental sources of variation on several outcomes of a ChIP-seq experiment, viz., the recoverability of the TF binding motif, accuracy of TF-DNA binding detection, the sensitivity of inferred TF-DNA binding strength, and number of replicates needed to confidently infer binding strength. We find that the TF motif can be recovered despite poor and non-uniform extraction and PCR amplification efficiencies. The recovery of the motif is, however, affected to a larger extent by the fraction of sites that are either cooperatively or indirectly bound. Importantly, our simulations reveal that the number of ChIP-seq replicates needed to accurately measure in vivo occupancy at high-affinity sites is larger than the recommended community standards. Our results establish statistical limits on the accuracy of inferences of protein-DNA binding from ChIP-seq and suggest that increasing the mean extraction efficiency, rather than amplification efficiency, would better improve sensitivity. The source code and instructions for running ChIPulate can be found at https://github.com/vishakad/chipulate.

}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1006921}, author = {Datta, Vishaka and Hannenhalli, Sridhar and Siddharthan, Rahul} }