Biology subjects

Sachdev, N.

Publications and source records attributed to Sachdev, N..

3 recordsLinked to original sources

bioRxiv2026

Genetic depletion in zebrafish uncovers requirement for septins in haematopoiesis

Haematopoiesis and differentiation of immune cells from haematopoietic stem and progenitor cells (HSPCs) are essential to core aspects of health and disease. A key player in haematopoiesis and HSPC differentiation is the cytoskeleton, which governs cell division and lineage bias. Despite insights using mouse models, regulation of haematopoiesis by the septin cytoskeleton is mostly unknown. Septins are unconventional filament forming proteins best known for roles in cell division and host defence. To investigate septin-mediated host defence in vivo, we generated septin-deficient zebrafish models for infection with Mycobacterium marinum. Unexpectedly, septin-deficient larvae were protected from mycobacterial infection due to significantly increased macrophage numbers, reduced cell death, and enhanced inflammatory responses. Underlying this, we found that septin-deficient larvae produce significantly more HSPCs and show myeloid lineage bias, establishing a requirement for septins in haematopoiesis. In agreement with classical HSPC hierarchy, increased myeloid production in septin-deficient larvae is at the expense of erythroid lineage production. Our findings that septins play a role in haematopoiesis is consistent with hallmarks of haematological disorders in which septin dysfunction has been implicated, including acute myeloid leukaemia, myelodysplastic syndrome, and platelet disorder Bernard-Soulier syndrome. These results highlight zebrafish as a new model to investigate septin-mediated haematopoiesis and application of septin-based medicines to treat blood disorders.

cell biology↗

bioRxiv2025

Neanderthal introgressed ancestry reveals human genomic regions enriched with recessive deleterious mutations

Negative selection on deleterious mutations plays a key role in shaping human genetic variation, yet the dominance effects of these mutations remain poorly understood because existing statistical methods often cannot distinguish dominance effects from the overall selective effects. In this work, we take a fundamentally different approach by leveraging the distribution of Neanderthal ancestry across the human genome. Simulations show that recessive deleterious mutations can increase archaic introgressed ancestry through heterosis in the absence of positive selection, in contrast to the depletion expected under additive effects. We use this signal to develop DominL, a machine learning classifier trained on simulations of human demography with Neanderthal introgression to identify megabase-scale genomic windows enriched with recessive mutations. DominL demonstrates robust accuracy, with particularly high power in exon-dense regions. Applied to 7 non-African populations from the 1000 Genomes Project, DominL identifies approximately 3-9% of the genome as enriched for recessive mutations, with most high-confidence regions shared across populations. Predicted regions show patterns consistent with expected signatures from recessive mutations, including weakened background selection, depletion of runs of homozygosity, and enrichment of non-additive trait-associated variants. These regions also contain genes associated with metabolic and immune-related functions.

evolutionary biology↗

bioRxiv2023

The cell type composition of the adult mouse brain revealed by single cell and spatial genomics

The function of the mammalian brain relies upon the specification and spatial positioning of diversely specialized cell types. Yet, the molecular identities of the cell types, and their positions within individual anatomical structures, remain incompletely known. To construct a comprehensive atlas of cell types in each brain structure, we paired high-throughput single-nucleus RNA-seq with Slide-seq-a recently developed spatial transcriptomics method with near-cellular resolution-across the entire mouse brain. Integration of these datasets revealed the cell type composition of each neuroanatomical structure. Cell type diversity was found to be remarkably high in the midbrain, hindbrain, and hypothalamus, with most clusters requiring a combination of at least three discrete gene expression markers to uniquely define them. Using these data, we developed a framework for genetically accessing each cell type, comprehensively characterized neuropeptide and neurotransmitter signaling, elucidated region-specific specializations in activity-regulated gene expression, and ascertained the heritability enrichment of neurological and psychiatric phenotypes. These data, available as an online resource (BrainCellData.org) should find diverse applications across neuroscience, including the construction of new genetic tools, and the prioritization of specific cell types and circuits in the study of brain diseases.

neuroscience↗