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Naidu, S.

Publications and source records attributed to Naidu, S..

4 recordsLinked to original sources
bioRxiv2026

Light-cycle time-restricted feeding remodels a hidden layer of the cardiac transcriptome through sex-specific transcript switching

Light-cycle time-restricted feeding disrupts daily cardiovascular and thermoregulatory rhythms, but the molecular effects of light-cycle time-restricted feeding on the heart have been measured only at the level of total gene expression. We used Oxford Nanopore long-read RNA sequencing to resolve the full-length ventricular transcriptome from male and female mice under ad libitum feeding or light-cycle time-restricted feeding across the 24-hour cycle. Greater than 20% of cardiac transcripts represent unannotated variants of known genes absent from the current GENCODE reference annotation. Light-cycle time-restricted feeding reorganizes transcript usage across hundreds of genes, including genes encoding splicing regulators, largely without changing total gene expression. The genes affected are sex-specific, with fewer than 2% of changes shared at the gene, transcript, and transcript-usage levels. We show that transcript-level regulation is a previously underrecognized component of the cardiac response to altered feeding behavior, undetected by conventional short-read approaches.

physiology↗
bioRxiv2026

Housing Mice in Thermoneutrality Causes Tissue-specific Changes in Number, Identity, and Phase of Circadian-expressed mRNA Transcripts

Most laboratory mice are housed at room temperature (20-25{degrees}C), which exposes them to chronic mild cold stress because it is below their thermoneutral temperature (30{degrees}C). We hypothesized that mild cold stress suppresses circadian gene expression in peripheral tissues. We performed RNA sequencing on hearts, livers, and diaphragms collected every 4 hours over 48 hours in constant darkness from male mice to identify transcripts with approximately 24-hour rhythms. Thermoneutral housing produced tissue-specific changes in the number, identity, and timing of rhythmic transcripts without altering the expression of core circadian clock genes. In the heart, the number of rhythmic transcripts increased fourfold, whereas the diaphragm showed a 1.5-fold increase. In the liver, the overall number of rhythmic transcripts showed little change, but their identity changed by 30%. Gene Ontology analysis revealed coordinated changes in the temporal organization of metabolic pathways in the heart and liver. Together, these findings demonstrate that ambient housing temperature is a major determinant of tissue-specific circadian gene expression, altering the abundance, identity, and timing of rhythmic transcripts independently of the core circadian clock. SignificanceScientists typically house laboratory mice at room temperature, below their thermoneutrality, forcing them to increase their metabolic rate to maintain core body temperature. Since ambient temperature plays an important role in metabolism, cold stress could disrupt circadian gene expression. Comparing mice housed at room temperature with a warmer, thermoneutral temperature, we found that housing temperature causes tissue-specific differences in rhythmically expressed genes in the heart, liver, and diaphragm, without altering core clock genes. The heart was especially sensitive, with rhythmic genes peaking at the transition between subjective light and dark cycles, increasing fourfold. These results identify ambient housing temperature as an underrecognized variable that biases circadian gene expression in cardio-metabolic tissues, affecting interpretation of preclinical studies of metabolism and disease.

physiology↗
bioRxiv2021

Artificial Intelligence uncovers carcinogenic human metabolites

The genome of a eukaryotic cell is often vulnerable to both intrinsic and extrinsic threats due to its constant exposure to a myriad of heterogeneous compounds. Despite the availability of innate DNA damage response pathways, some genomic lesions trigger cells for malignant transformation. Accurate prediction of carcinogens is an ever-challenging task due to the limited information about bona fide (non)carcinogens. We developed Metabokiller, an ensemble classifier that accurately recognizes carcinogens by quantitatively assessing their electrophilicity as well as their potential to induce proliferation, oxidative stress, genomic instability, alterations in the epigenome, and anti-apoptotic response. Concomitant with the carcinogenicity prediction, Metabokiller is fully interpretable since it reveals the contribution of the aforementioned biochemical properties in imparting carcinogenicity. Metabokiller outperforms existing best-practice methods for carcinogenicity prediction. We used Metabokiller to unravel cells endogenous metabolic threats by screening a large pool of human metabolites and predicted a subset of these metabolites that could potentially trigger malignancy in normal cells. To cross-validate Metabokiller predictions, we performed a range of functional assays using Saccharomyces cerevisiae and human cells with two Metabokiller-flagged human metabolites namely 4-Nitrocatechol and 3,4-Dihydroxyphenylacetic acid and observed high synergy between Metabokiller predictions and experimental validations.

bioinformatics↗