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Anindya Dutta

Publications and source records attributed to Anindya Dutta.

7 recordsLinked to original sources
PubMed2026

tRF-3021a, a tRNA-Ala-TGC derived 3' fragment, promotes glioblastoma cell invasion, suppresses apoptosis, and is required for normal levels of protein synthesis.

UNLABELLED: tRNA-derived fragments (tRFs) are relatively recently discovered class of small RNAs implicated in gene-regulatory processes in diverse biological contexts but there have been very few reports of a clear phenotypic role of these small RNAs in cancer progression. By analyzing small RNA-seq data from The Cancer Genome Atlas (TCGA), we found that high expression of three 3' tRFs (tRF-3a), tRF-3009a, tRF-3021a or tRF-3030a, is significantly associated with poor overall survival in low-grade glioma (LGG). In glioblastoma cells, tRF-3009a, tRF-3021a and tRF-3030a enhance cell invasion and migration but tRF-3021a was uniquely required for cell proliferation and suppression of apoptosis. Interestingly, tRF-3021a knockdown decreases global protein synthesis prior to and independent of apoptosis. These data indicate that tRF-3021a supports glioma cell survival and particularly protein synthesis while promoting cellular invasion and migration. Given its association with poor outcome in LGG patients, tRF-3021a represents a promising biomarker and potential therapeutic target in gliomas and these results provide a foundation for future studies to define its molecular interactors and downstream pathways controlling protein synthesis and apoptosis in cancer cells. IMPLICATION: tRF-3021a promotes malignant glioma phenotypes, sustains global protein synthesis and prevents spontaneous apoptosis, motivating efforts to evaluate it as a biomarker and therapeutic target.

Journal Article↗
PubMed2025

PATTY corrects open-chromatin bias for improved bulk and single-cell CUT&Tag profiling.

Precise profiling of epigenomes is essential for better understanding chromatin biology and gene regulation. Cleavage Under Targets & Tagmentation (CUT&Tag) is an efficient epigenomic profiling technique that can be performed on a low number of cells and at the single-cell level. With its growing adoption, CUT&Tag datasets spanning diverse biological systems are rapidly accumulating in the field. CUT&Tag assays use the hyperactive transposase Tn5 for DNA tagmentation. Tn5's preference toward accessible chromatin alters CUT&Tag sequence read distributions in the genome and introduces open-chromatin bias that can confound downstream analysis, an issue more substantial in sparse single-cell data. We show that open-chromatin bias extensively exists in published CUT&Tag datasets, including those generated with recently optimized high-salt protocols. To address this challenge, we present PATTY (Propensity Analyzer for Tn5 Transposase Yielded bias), a comprehensive computational method that corrects open-chromatin bias in CUT&Tag data by leveraging accompanying ATAC-seq. By integrating transcriptomic and epigenomic data using machine learning and integrative modeling, we demonstrate that PATTY enables accurate and robust detection of occupancy sites for both active and repressive histone modifications, including H3K27ac, H3K27me3, and H3K9me3, with experimental validation. We further develop a single-cell CUT&Tag analysis framework built on PATTY and show improved cell clustering when using bias-corrected single-cell CUT&Tag data compared to using uncorrected data. Beyond CUT&Tag, PATTY sets a foundation for further development of bias correction methods for improving data analysis for all Tn5-based high-throughput assays.

Journal Article↗
PubMed2025

PATTY corrects open chromatin bias for improved bulk and single-cell CUT&Tag profiling.

Precise profiling of epigenomes is essential for better understanding chromatin biology and gene regulation. Cleavage Under Targets & Tagmentation (CUT&Tag) is an efficient epigenomic profiling technique that can be performed on a low number of cells and at the single-cell level. With its growing adoption, CUT&Tag datasets spanning diverse biological systems are rapidly accumulating in the field. CUT&Tag assays use the hyperactive transposase Tn5 for DNA tagmentation. Tn5's preference toward accessible chromatin alters CUT&Tag sequence read distributions in the genome and introduces open chromatin bias that can confound downstream analysis, an issue more substantial in sparse single-cell data. We show that open chromatin bias extensively exists in published CUT&Tag datasets, including those generated with recently optimized high-salt protocols. To address this challenge, we present PATTY (Propensity Analyzer for Tn5 Transposase Yielded bias), a comprehensive computational method that corrects open chromatin bias in CUT&Tag data by leveraging accompanying ATAC-seq. By integrating transcriptomic and epigenomic data using machine learning and integrative modeling, we demonstrate that PATTY enables accurate and robust detection of occupancy sites for both active and repressive histone modifications, including H3K27ac, H3K27me3, and H3K9me3, with experimental validation. We further develop a single-cell CUT&Tag analysis framework built on PATTY and show improved cell clustering when using bias-corrected single-cell CUT&Tag data compared to using uncorrected data. Beyond CUT&Tag, PATTY sets a foundation for further development of bias correction methods for improving data analysis for all Tn5-based high-throughput assays.

Journal Article↗
PubMed2025

Short-term diet intervention comprising of olive oil, vitamin D, and omega-3 fatty acids alters the small non-coding RNA (sncRNA) landscape of human sperm.

Offspring health outcomes are often linked with epigenetic alterations triggered by maternal nutrition and intrauterine environment. Strong experimental data also link paternal preconception nutrition with pathophysiology in the offspring, but the mechanism(s) routing effects of paternal exposures remain elusive. Animal experimental models have highlighted small non-coding RNAs (sncRNAs) as potential regulators of paternal effects. Here, we characterised the baseline sncRNA landscape of human sperm and the effect of a 6-week dietary intervention on their expression profile. This study involves sncRNAseq profiling, that was performed on a subset (n = 17) of the participants enrolled in the PREPARE trial: 9 from the control group and 8 from the intervention group. 5'tRFs, miRNAs and piRNAs were the most abundant sncRNA subtypes identified; their expression was associated with age, BMI, and sperm quality. Nutritional intervention with olive oil, vitamin D and omega-3 fatty acids altered expression of 3 tRFs, 15 miRNAs and 112 piRNAs, targeting genes involved in fatty acid metabolism and transposable elements in the sperm genome. PREPARE Trial registration number: ISRCTN50956936, Trial registration date: 10/02/2014.

Humans↗
PubMed2025

A general framework to over-express tRNA-derived fragments from their parental tRNAs in mammalian cells.

tRNA-derived fragments (tRFs), generated from the cleavage of mature or precursor tRNAs are a category of regulatory noncoding RNAs with diverse functions in physiological or pathophysiological conditions. Here we describe a framework for the over-expression of tRFs from their parental tRNAs in mammalian cells. The process involves bioinformatics analysis to identify specific tRNAs that produce the tRF, PCR amplification of corresponding tRNA genes, and insertion into expression vectors. Transfection is carried out in HEK293T cells and detection of tRFs is achieved through northern blotting and dual luciferase reporter assays. In the latter, a complementary sequence to the tRF of interest is inserted into the luciferase reporter. By observing the reduction in luciferase activity, we can validate the expression of tRFs. This method enables precise study of tRF functions and their roles in cellular processes.

Humans↗
PubMed2024

TGIRT-seq to profile tRNA-derived RNAs and associated RNA modifications.

RNA modifications are key regulators for RNA processes. tRNA-derived RNAs are small RNAs with size between 15 and 50 bases long that are processed from mature or precursor tRNAs. Despite their more recent discovery, tRNA-derived RNAs have been found to play regulatory roles in many cellular processes including gene silencing, protein synthesis, stress response, and transgenerational inheritance. Furthermore, tRNA-derived RNAs are highly abundant in bodily fluids, posing as potential biomarkers. A unique feature of tRNA-derived RNAs is that they are rich in RNA modifications. Many of the RNA modifications on tRNA-derived RNAs disrupt Watson-Crick base pairing and will thus stall reverse transcriptase, such as N1-methyladenosine (m1A), N1-methylguanosine (m1G) and N2, N2-dimethylguanosine (m22G). These RNA modifications add another layer of regulation onto tRNA-derived RNAs' functions and are of interests for future research. However, these RNA modifications could also lead to lower detection of modification-containing RNAs in genome-wide small RNA sequencing analysis due to reverse transcriptase stall. To circumvent this bias, TGIRT (Thermostable Group II Intron Reverse Transcriptase) has been used to readthrough RNA modifications inserting mismatches. These mismatch signatures can then be used to precisely map the modification sites at base resolution. Here we describe the step-by-step experimental protocol to start with purified RNAs from cells or tissues and use TGIRT to make small RNA sequencing library for Illumina sequencing to profile the abundance of tRNA-derived RNAs and the associated RNA modifications.

RNA, Transfer↗