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Peng Jiang

Publications and source records attributed to Peng Jiang.

2 recordsLinked to original sources
PubMed2026

Conditional eIF2A Deletion Suggests Extra-Adipose Mechanisms Underlying Metabolic Syndrome in Total-Body eIF2A Knockout Mice.

Dynamic regulation of protein synthesis is essential for metabolic homeostasis, with translation initiation playing a key role in this process. Emerging evidence strongly indicates that in addition to canonical eukaryotic initiation factors (e.g., eIF2, eIF4E) non-canonical factors, such as eukaryotic initiation factor 2A can modulate metabolic homeostasis. eIF2A is a highly conserved eukaryotic protein originally proposed to function analogously to bacterial IF2, promoting initiator Met-tRNAi recruitment to the 40S ribosomal subunit, though its precise mechanism remains debated. To investigate its organismal role, we have previously generated the total-body eIF2A knockout mouse, which revealed eIF2A functions in lipid homeostasis, glucose tolerance, insulin sensitivity, and susceptibility to metabolic syndrome. To further determine whether adipose tissue drives these phenotypes, we presently generated adipose-specific eIF2A knockout mice. Despite dysregulation of some key adipokines, including for example, adiponectin, these mice did not develop metabolic syndrome, even under high-fat diet conditions, indicating that adipose tissue specific deficiency of eIF2A is insufficient to reproduce the metabolic defects observed in total-body knockout. However, we found that eIF2A deficiency in the liver of the total body eIF2A-KO mice can independently drive metabolic syndrome components via translational control of Lpin1 (a phosphatidate phosphatase and a transcriptional coactivator) that controls hepatic lipid storage and metabolism. eIF2A deficiency in the liver leads to disruption of fatty acid oxidation and the production of ketone bodies, not observed in adipose-specific eIF2A knockout mice. Our findings suggest that systemic metabolic effects observed in the total body eIF2A-KO mice may arise from coordinated functions across multiple organs.

adipose tissue↗
PubMed2026

SIVA: diagonal integration of spatial multi-omics data via spatially informed variational autoencoders and anchor guidance.

MOTIVATION: Understanding cellular states and regulatory programs requires integrative analysis of multiple omics layers. Although recent spatial sequencing technologies allow molecular profiling of cells within their tissue context, paired spatial multi-omics assays are still limited by technical complexity and cost. This creates a pressing need for diagonal integration methods that enable joint analysis of unpaired spatial omics datasets. RESULTS: We propose SIVA, a deep generative framework based on Spatially-Informed Variational Autoencoders with Anchor Guidance, for diagonal integration of spatial multi-modal data. SIVA employs modality-specific variational autoencoders (VAEs) with a hybrid latent embedding that integrates Gaussian process and standard Gaussian priors, enabling joint modeling of spatially structured variation and dominant underlying data distributions across modalities. To facilitate cross-modal alignment in the absence of one-to-one cell correspondence, SIVA adopts a dual integration strategy combining global distribution alignment via Maximum Mean Discrepancy and local correspondence guidance using mutual nearest neighbor anchors. Extensive experiments across multiple cross-slice integration scenarios demonstrate that SIVA achieves robust and accurate integration of unpaired spatial omics datasets, consistently outperforming existing methods. AVAILABILITY AND IMPLEMENTATION: The source codes are available at https://github.com/PelenJiang/SIVA.

Autoencoder↗