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Adhikari, I.

Publications and source records attributed to Adhikari, I..

2 recordsLinked to original sources

Intrinsic DNA codes govern distinct modes of nucleosome-transcription factor interactions

The interplay between transcription factors (TFs) and nucleosomes is central to gene regulation, yet most studies adopt a protein-centric perspective that largely overlooks DNA sequence contributions. Here, we identify four distinct nucleosomal DNA classes, including the canonical WW/SS pattern (W = A/T, S = G/C). All four nucleosome types are widespread across the human genome both in vivo and in vitro. Nucleosomes sharing the same pattern are rotationally in phase, whereas different patterns exhibit specific rotational offsets, revealing distinct DNA-encoded codes for nucleosome positioning. Analysis of 747 TFs across eukaryotes shows that the WW/SS and anti-WW/SS patterns are strongly associated with TF binding in chromatin. Differences in the proportions of these two patterns, quantified as {Delta}NPS values, correlate closely with in vitro nucleosome occupancy. Integrating {Delta}NPS with in vivo nucleosome occupancy uncovers four distinct modes of nucleosome-TF interaction and provides new insight into nucleosome-depleted regions around TF binding sites.

bioinformatics↗

Machine-learning-based determination of sex-related bladder cancer biomarkers

Bladder cancer exhibits sex-specific behavior, occurring more frequently in males but progressing to advanced stages more commonly in females. The activation of sex hormone receptors may explain these differences, but the exact genetic drivers remain poorly understood. Furthermore, current bladder cancer biomarkers have inconsistent sensitivities and specificities in practice, making early diagnosis a challenge. This study approaches bladder cancer biomarker discovery through machine learning techniques on gender and disease-stratified RNA-seq data. Training sets limited to differentially expressed genes were subjected to four different feature selection methods: differential gene expression analysis adjusted p-value, recursive feature elimination with support vector machine, logistic regression, and an optimized random forest procedure. Gene panels were compared and aggregated across selection strategies and cross-validation folds to identify robust biomarkers for sex-specific bladder cancer development and progression. When applied to unseen datasets and limited to 50 genes or less, male and female-specific panels achieved areas under the receiver operating characteristic curve of 0.932 and 0.914, respectively, in distinguishing bladder cancer samples from non-tumor controls. Genes such as PRAC1 and PCDH11Y were identified as high-impact predictors related to sex hormones or chromosomes for male tumor development. In the female-specific panel, genes related to aberrant androgen signaling across tumor types like AR, PLXNA1, USP54, and PMEPA1 were influential. These results offer potential targets for further in vivo/vitro experimentation and provide a framework for constructing generalizable, high-performance gene panels for bladder cancer diagnosis and prognosis.

bioinformatics↗