Biomedical subjects

Tiannan Guo

Publications and source records attributed to Tiannan Guo.

5 recordsLinked to original sources

PubMed2026

AI proteomics: from protein identification to virtual cells.

Artificial intelligence (AI) is transforming scientific research, including proteomics. In this Perspective, we highlight key mass spectrometry (MS)-based proteomics areas where AI is driving innovation, ranging from protein identification to building AI virtual cells. These include improving peptide and protein identification and quantification; characterizing protein-protein interactions and protein complexes; advancing spatial and perturbation proteomics; integrating multi-omics data; and, ultimately, enabling AI virtual cells. Finally, we call for global collaboration among data producers, data consumers and other stakeholders to establish an AI-friendly ecosystem for MS-based proteomics, laying the foundation for transformative advancements in proteomics driven by AI.

Proteomics↗

PubMed2026

Allelic variation in UVR8 modulates thermotolerance-yield tradeoffs in plants.

Industrial activities have driven stratospheric ozone depletion, increasing surface UV-B radiation while exacerbating global warming. These changes limit crop productivity, alter species distributions, and disrupt plant metabolic processes, but the mechanisms linking energy signaling to heat-stress responses remain unclear. Here, we identify the photoreceptor UV RESISTANCE LOCUS 8b (OsUVR8b) as a substrate of SNF1-related protein kinase 1 (SnRK1) in rice and reveal a natural variation at its SnRK1-mediated phosphorylation site (Ser177) that is correlated with adaptation to tropical climates. The thermotolerant OsUVR8bAla177 accessions show geographic enrichment in low-latitude regions with elevated temperatures. Functional validation through prime editing demonstrated that a Ser177-to-Ala177 substitution enhances heat tolerance, whereas the reverse edit compromises it. Mechanistically, OsUVR8bSer177 exhibits reduced stability and an impaired capacity for scavenging reactive oxygen species under heat stress. The regulatory function of the OsUVR8b Ser177 phosphorylation site, a molecular switch that governs UVR8 stability and thermotolerance, can be functionally re-established across rice, Arabidopsis, tobacco, and soybean, indicating its preservation during domestication. Notably, OsUVR8bSer177 maintains higher fertility and yield under non-stress conditions, indicating a tradeoff between heat adaptation and productivity. Our findings thus establish this switch as a key regulator of the yield-resilience balance and a promising target for breeding of climate-resilient crops.

Thermotolerance↗

PubMed2025

The suprachiasmatic nucleus regulates brown fat thermogenesis in male mice through an adrenergic receptor ADRB3-S100B signaling pathway.

The suprachiasmatic nucleus (SCN), the central circadian pacemaker, orchestrates daily metabolic rhythms, yet its role in substrate selection and thermogenic adaptation under stress remains insufficiently understood. Here, we show that SCN lesioning abolishes the adaptive suppression of brown adipose tissue (BAT) thermogenesis typically observed during time-restricted feeding in subthermoneutral environments (TRF-STE), a paradigm that imposes concurrent nutrient and thermal stress. Contrary to wild-type responses, SCN-lesioned mice maintain elevated BAT thermogenic activity, despite impaired lipolysis, instead shifting toward glucose-driven heat production. This phenotype is accompanied by sustained sympathetic tone and β3-adrenergic receptor (ADRB3) signaling in BAT. Mechanistically, we identify a SCN-regulated ADRB3-S100B signaling axis underlying this metabolic reprogramming. S100B, a nutrient-sensitive calcium-binding protein, is upregulated in BAT following SCN disruption, where it promotes thermogenesis by stimulating brown adipocyte proliferation and suppressing senescence. Functional studies reveal that S100B is both necessary and sufficient for sustaining BAT thermogenesis under TRF-STE. Furthermore, diverse SCN disruption models, including light-induced circadian arrhythmia, N-Methyl-D-aspartic acid (NMDA) excitotoxicity, and Caspase-3-mediated ablation, consistently elevate S100B expression in BAT, reinforcing its role as a convergent effector of SCN-regulated metabolic adaptation. Thus, in intact animal, the SCN restrains the ADRB3-S100B module, gating BAT thermogenic output in accordance with energetic availability. Disruption of SCN output lifts this restraint, unmasking a latent ADRB3-S100B program that preserves thermogenesis when lipid fuel is limited. These findings reveal a previously unrecognized role of the SCN in governing thermogenic flexibility and fuel partitioning, and position the ADRB3-S100B axis as a potential target for mitigating circadian misalignment and metabolic disease.

Animals↗

PubMed2025

Oncogenic roles of young human de novo genes and their potential as neoantigens in cancer immunotherapy.

Young human de novo genes, recently emerging from non-coding regions, are expected to contribute to human-specific traits and diseases. However, systematic explorations of this connection have been lacking. Here, we report 37 recently originated de novo genes in humans, with their evolution and characteristics defined within an updated genomic context. The expression of these genes is significantly upregulated and temporospatially expanded in tumors, partially associated with extrachromosomal DNA amplification. Depletion of 57.1% of these genes suppresses tumor cell proliferation, underscoring their roles in tumorigenesis. As a proof of concept, we developed mRNA vaccines expressing ELFN1-AS1 and TYMSOS-young genes specifically expressed during early development but reactivated exclusively in tumors. In humanized mice, these vaccines triggered specific T cell activation and inhibited tumor growth. The antigens derived from these genes are immunogenic and capable of eliciting antigen-specific T cell activation in colorectal cancer patients. These findings underscore young human de novo genes as neoantigens in cancer immunotherapy.

Humans↗

PubMed2024

metaExpertPro: A Computational Workflow for Metaproteomics Spectral Library Construction and Data-Independent Acquisition Mass Spectrometry Data Analysis.

Analysis of large-scale data-independent acquisition mass spectrometry metaproteomics data remains a computational challenge. Here, we present a computational pipeline called metaExpertPro for metaproteomics data analysis. This pipeline encompasses spectral library generation using data-dependent acquisition MS, protein identification and quantification using data-independent acquisition mass spectrometry, functional and taxonomic annotation, as well as quantitative matrix generation for both microbiota and hosts. By integrating FragPipe and DIA-NN, metaExpertPro offers compatibility with both Orbitrap and timsTOF MS instruments. To evaluate the depth and accuracy of identification and quantification, we conducted extensive assessments using human fecal samples and benchmark tests. Performance tests conducted on human fecal samples indicated that metaExpertPro quantified an average of 45,000 peptides in a 60-min diaPASEF injection. Notably, metaExpertPro outperformed three existing software tools by characterizing a higher number of peptides and proteins. Importantly, metaExpertPro maintained a low factual false discovery rate of approximately 5% for protein groups across four benchmark tests. Applying a filter of five peptides per genus, metaExpertPro achieved relatively high accuracy (F-score = 0.67-0.90) in genus diversity and showed a high correlation (rSpearman = 0.73-0.82) between the measured and true genus relative abundance in benchmark tests. Additionally, the quantitative results at the protein, taxonomy, and function levels exhibited high reproducibility and consistency across the commonly adopted public human gut microbial protein databases IGC and UHGP. In a metaproteomic analysis of dyslipidemia patients, metaExpertPro revealed characteristic alterations in microbial functions and potential interactions between the microbiota and the host.

Proteomics↗