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AI-guided multi-omics analysis identifies NPC1-modulated susceptibility to SARS-CoV-2 infection under PM(2.5) exposure

Nat Commun. 2026 Mar 30. doi: 10.1038/s41467-026-71196-3. Online ahead of print.

ABSTRACT

Exposure to airborne fine particulate matter (PM2.5) has been linked to increased risk of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, yet the underlying mechanisms remain unclear. Here, by leveraging a fine-tuned foundation model of single-cell transcriptomics, we uncover shared transcriptional signatures between PM2.5 exposure and SARS-CoV-2 infection. We further validate this association using population-level epidemiological analyses and perform genome-wide association studies (GWAS) to identify genetic variants that modulate infection risk under PM2.5 exposure. In addition, we identify NPC1 as a key modulator involved in SARS-CoV-2 infection efficiency under virus-laden PM2.5 exposure through integrative functional genomic analyses and in vitro experiments. Our findings suggest that PM2.5 facilitates viral entry through an NPC1-modulated endo-lysosomal pathway, providing a mechanistic explanation for observed pollution-related susceptibility. By integrating artificial intelligence (AI)-guided transcriptomics, epidemiology, GWAS, functional genomics, and in vitro verification, our study elucidates how environmental and genetic factors jointly influence SARS-CoV-2 susceptibility. This work highlights how AI-assisted multi-omics integration systematically decodes the health impacts of environmental exposures from molecular to population levels and informs air quality policy and infectious disease preparedness.

PMID:41912520 | DOI:10.1038/s41467-026-71196-3

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Mechanisms Underlying Drought Adaptability in Duolang Sheep Based on Metabolomic and Transcriptomic Analyses

Biology (Basel). 2026 Mar 12;15(6):461. doi: 10.3390/biology15060461.

ABSTRACT

This study investigates the mechanisms underlying drought adaptability in Duolang sheep, a local breed from two distinct habitats in Xinjiang-an arid southern region and a grassland northern region-aiming to identify key factors driving differential environmental adaptation. Integrated multi-omics analyses were performed, including serum biochemical assays, untargeted metabolomics of perirenal and tail fat tissues, and transcriptomic profiling of lung, liver, and kidney samples. Our results revealed notable differences: (1) serum levels of GSH-Px, IL-2, and IgG were significantly higher in the southern group (p < 0.01); (2) metabolomic analysis identified key differential metabolites, including EPA (involved in unsaturated fatty acid biosynthesis), choline (glycerophospholipid metabolism), L-serine and glutathione (cofactor biosynthesis), and taurine (sulfur metabolism); and (3) transcriptomic analysis revealed significant differential expression of genes such as FGF21 (thermogenesis), CD14 and DUSP2 (MAPK signaling pathway), GOT1 (arginine biosynthesis), and AVPR2 (vasopressin-regulated water reabsorption). Integrative correlation analysis further indicated that glutathione, EPA, GOT1, and CD14 are involved in energy and lipid metabolism, while taurine, AVPR2, and DUSP2 contribute to oxidative stress resistance and immune regulation. These molecular and metabolic adjustments collectively enhance drought adaptability in southern Xinjiang Duolang sheep. In conclusion, adaptation to arid environments requires enhanced antioxidant capacity and immune function, with metabolites such as EPA supporting lipid metabolism and genes such as FGF21 regulating fatty acid oxidation to limit triglyceride accumulation.

PMID:41892221 | PMC:PMC13023672 | DOI:10.3390/biology15060461

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Step 3.5 Flash: Open Frontier-Level Intelligence with 11B Active Parameters

arXiv:2602.10604v2 Announce Type: replace-cross Abstract: We introduce Step 3.5 Flash, a sparse Mixture-of-Experts (MoE) model that bridges frontier-level agentic intelligence and computational efficiency. We focus on what matters most when building agents: sharp reasoning and fast, reliable execution. Step 3.5 Flash pairs a 196B-parameter foundation with 11B active parameters for efficient inference. It is optimized with interleaved 3:1 sliding-window/full attention and Multi-Token Prediction (MTP-3) to reduce the latency and cost of multi-round agentic interactions. To reach frontier-level intelligence, we design a scalable reinforcement learning framework that combines verifiable signals with preference feedback, while remaining stable under large-scale off-policy training, enabling consistent self-improvement across mathematics, code, and tool use. Step 3.5 Flash demonstrates strong performance across agent, coding, and math tasks, achieving 85.4% on IMO-AnswerBench, 86.4% on LiveCodeBench-v6 (2024.08-2025.05), 88.2% on tau2-Bench, 69.0% on BrowseComp (with context management), and 51.0% on Terminal-Bench 2.0, comparable to frontier models such as GPT-5.2 xHigh and Gemini 3.0 Pro. By redefining the efficiency frontier, Step 3.5 Flash provides a high-density foundation for deploying sophisticated agents in real-world industrial environments.
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