Biomedical subjects

Sitong Liu

Publications and source records attributed to Sitong Liu.

4 recordsLinked to original sources

PubMed2026

Large-scale discovery platform enables identification of peptides targeting drug-resistant candidiasis.

Natural products have an unparalleled track record as sources of clinical drugs. Among them, nonribosomal peptides (NRPs) stand as one of the most therapeutically significant classes, encompassing numerous approved anti-infective and anticancer agents. Yet, discovering bioactive NRPs remains profoundly challenging due to their complex biosynthesis and chemical architecture. Here, we present NPDiscover, a pathogen-oriented, scalable bioinformatics platform that integrates genome mining, metabolomics, and machine learning to identify NRPs active against drug-resistant pathogens. Applying NPDiscover to Actinobacteria datasets, we discovered edaphochelin A, a previously unreported NRP that kills multi-drug-resistant Candida auris and Candida glabrata by disrupting respiratory chain proteins. Structural elucidation via nuclear magnetic resonance and mass spectrometry, alongside in vitro and in vivo validation, confirmed its efficacy, safety, and a mode of action distinct from existing antifungals-establishing edaphochelin A as a compelling drug candidate and NPDiscover as a powerful engine for scalable natural product discovery.

CP: biotechnology↗

PubMed2026

Reconstructing the early spatial spread of pandemic respiratory viruses in the United States.

Understanding the geographic spread of emerging respiratory viruses is critical for pandemic preparedness, yet the early spatiotemporal dynamics of the 2009 H1N1 pandemic influenza and severe acute respiratory syndrome coronavirus 2 in the United States remain unclear. While mobility and genomic data have revealed important aspects of pandemic spatial spread, several key questions remain: Did the two pandemics follow similar spatial transmission routes? How rapidly did they spread across the United States? What role did stochastic processes play in early spatial transmission? To address these questions, we integrated high-resolution disease data with a robust, data-efficient inference framework combining air travel, commuting flows, and pathogen superspreading potentials to reconstruct their spatial spread across US metropolitan areas. The two pandemics exhibited distinct transmission pathways across locations; however, both pandemics established local circulation in most metropolitan areas within weeks, driven by several shared transmission hubs. Early spatial spread was more strongly associated with air travel than with commuting, though stochastic dynamics introduced substantial uncertainty in transmission routes, creating challenges for timely detection and control. Simulations indicate that broad wastewater surveillance coverage beyond top transmission hubs coupled with effective infection control may slow initial spatial expansion. Our findings highlight the rapid, stochastic spread of pandemic respiratory pathogens and the difficulties of early outbreak containment.

Humans↗

PubMed2025

Seq2Saccharide: Discovering Oligosaccharides and Aminoglycosides Natural Products by Integrating Computational Mass Spectrometry and Genome Mining.

Natural oligosaccharides and aminoglycosides are important sources of new drug candidates, especially in the development of antibiotics. In the past, discovering novel saccharides has been time-consuming and costly. However, the rapid expansion of high-throughput data, including genomic and mass spectrometry data sets, has greatly increased opportunities for natural saccharide discovery. Yet, due to the complex biosynthesis pathways of saccharides, no existing method can predict their structures with high precision. To address this, we introduce Seq2Saccharide, a tool designed to automate saccharide natural product discovery by integrating both genomic and mass spectrometry data. To enhance accuracy, Seq2Saccharide predicts hundreds or thousands of putative structures for each gene cluster. The correct structure is then identified from these predictions using a mass spectral search. Benchmarks against saccharides in the MiBIG database show that Seq2Saccharide outperforms existing methods in predicting the structure of saccharides. Furthermore, mass spectrometry analysis indicates that the variable search module can correct mispredictions from genome mining. By searching genomic and mass spectrometry data of microbial strains, Seq2Saccharide correctly identified the biosynthetic gene cluster for the polysaccharide oligosaccharide trestatin B.

Aminoglycosides↗

PubMed2025

Genome-wide association study reveals that TaODORANT1 negatively contributes to thousand grain weight by affecting starch synthesis in wheat.

Thousand grain weight (TGW) is one of the most important factors that control grain weight and crop yield. To date, dozens of wheat genes related to TGW have been isolated; however, the underlying molecular mechanisms governing grain development in wheat (Triticum aestivum) remain largely unknown. Benefiting from whole-genome resequencing and genome-wide association study, we identified an R2R3-type myeloblastosis (MYB) transcription factor, TaODORANT1, which was tightly associated with TGW. TaODORANT1 was specifically and highly expressed during the wheat grain developing stage. Knockout of TaODORANT1 led to an increase in TGW and starch content, as well as affected the expression of starch synthesis-related genes. Loss of function of TaODORANT1 altered the molecular structure and physiochemical properties of grain starch. Haplotype analysis showed that favorable Hap IV of TaODORANT1-A and favorable Hap I of TaODORANT1-B were significantly associated with the production of larger grains and higher TGW, respectively. Moreover, TaODORANT1 was a crucial targeted gene continuously selected in wheat domestication and breeding, and its orthologous genes might have retained similar functions in response to grain development. Our results highlight the importance of TaODORANT1 in affecting TGW, presenting potential targets for improving yield in wheat.

Triticum↗