Biomedical subjects

Smita Krishnaswamy

Publications and source records attributed to Smita Krishnaswamy.

3 recordsLinked to original sources

PubMed2026

Beta cell-derived cholecystokinin drives obesity-associated pancreatic adenocarcinoma development.

Pancreatic endocrine-exocrine crosstalk plays a key role in normal physiology and disease and can be altered by host metabolic states, such as obesity. Classically, endocrine islet beta (β) cell secretion of insulin is thought to promote the development of obesity-associated pancreatic adenocarcinoma (PDAC), an exocrine cell-derived tumor. Here, we show that β cell expression of the peptide hormone cholecystokinin (CCK) is necessary and sufficient for obesity-associated PDAC progression in mice and that CCK expression - rather than insulin - correlates strongly with enhanced tumorigenesis. Single-cell RNA-sequencing, in silico latent-space archetypal and trajectory analysis, and experimental lineage tracing in vivo reveal that obesity induces the expansion of postnatal immature β cells, which adapt to express CCK via stress-responsive JNK/cJun signaling. Finally, obesity perturbs CCK-dependent peri-islet exocrine cell transcriptional states and enhances islet-proximal tumor formation. These results define endocrine-exocrine CCK signaling as a bona fide driver of obesity-associated PDAC development and uncover avenues to target the endocrine pancreas to subvert exocrine tumorigenesis.

Animals↗

PubMed2025

In vivo differentiation of embryonic cells devoid of key reprogramming factors.

Embryonic cell differentiation depends on reprogramming of the oocyte and sperm nucleus into a transient totipotent state. In zebrafish, this coincides with genome activation, which is regulated by the pioneer factors Nanog, Pou5f3, and Sox19b (NPS). Here, we investigate the role of NPS in developmental reprogramming and differentiation by analyzing the fate of NPS mutant cells in a wild-type embryo using single-cell RNA-seq. We find that many cells fail to activate transcription or undergo cell death, while others acquire gene expression profiles that resemble germ cells, neural progenitors, and motoneuron states. These cells achieve intermediate transcriptional states, revealing the essential role of NPS in coordinating nuclear and cytoplasmic reprogramming and preventing the premature activation of lineage-specific differentiation programs. These results demonstrate that most developmental programs require developmental reprogramming by NPS, yet some cells can bypass transient totipotency to achieve intermediate developmental states resembling wild-type states in vivo.

Animals↗

PubMed2023

Inferring Metabolic States from Single Cell Transcriptomic Data via Geometric Deep Learning.

The ability to measure gene expression at single-cell resolution has elevated our understanding of how biological features emerge from complex and interdependent networks at molecular, cellular, and tissue scales. As technologies have evolved that complement scRNAseq measurements with things like single-cell proteomic, epigenomic, and genomic information, it becomes increasingly apparent how much biology exists as a product of multimodal regulation. Biological processes such as transcription, translation, and post-translational or epigenetic modification impose both energetic and specific molecular demands on a cell and are therefore implicitly constrained by the metabolic state of the cell. While metabolomics is crucial for defining a holistic model of any biological process, the chemical heterogeneity of the metabolome makes it particularly difficult to measure, and technologies capable of doing this at single-cell resolution are far behind other multiomics modalities. To address these challenges, we present GEFMAP (Gene Expression-based Flux Mapping and Metabolic Pathway Prediction), a method based on geometric deep learning for predicting flux through reactions in a global metabolic network using transcriptomics data, which we ultimately apply to scRNAseq. GEFMAP leverages the natural graph structure of metabolic networks to learn both a biological objective for each cell and estimate a mass-balanced relative flux rate for each reaction in each cell using novel deep learning models.

Preprint↗