Biology subjects

Gou, W.

Publications and source records attributed to Gou, W..

9 recordsLinked to original sources

bioRxiv2026

Ratio-preserving super-resolution FLIM for quantitative live-cell lifetime imaging

Super-resolution (SR) fluorescence lifetime imaging promises nanoscale maps of molecular environment and function, but photon-limited acquisition and computational reconstruction can distort lifetime estimates, particularly for ratio-derived frequency-domain measurements under SR conditions. Here we introduce structured-illumination frequency-domain FLIM (SIM-fdFLIM), an SR lifetime imaging platform that combines commercial dual-tap fdFLIM detection with ratio-preserving reconstruction. SIM-fdFLIM uses direct-current (DC)-stabilized lifetime estimation to improve reconstruction robustness under low signal-to-noise conditions, followed by ratio-domain integrated variational estimation (RIVE) to correct channel misregistration and suppress noise amplification after SR reconstruction. The method achieves ~100 nm spatial resolution in lifetime maps and enables live-cell imaging at 2.8 Hz over a 56 x 56 {micro}m2 field of view. Across representative fluorophore standards, RIVE reduces lifetime standard deviation by approximately threefold relative to non-RIVE SIM-fdFLIM while preserving mean lifetime agreement with calibrated widefield fdFLIM. In living cells, SIM-fdFLIM resolves mitochondrial cristae-scale lifetime heterogeneity, mitochondrion-to-mitochondrion functional dynamics, nanoscale lipid-environment variations and perturbation-sensitive organelle-contact signatures. These results establish SIM-fdFLIM as a ratio-preserving platform for quantitative SR lifetime imaging, extending live-cell FLIM into a regime that combines nanoscale spatial resolution, Hz-rate acquisition, and preserved lifetime fidelity.

bioengineering↗

bioRxiv2025

Advancing Target Discovery Through Disease-Specific Integration of Multi-Modal Target Identification Models and Comprehensive Target Benchmarking System

Target identification is crucial for drug development. AI-driven approaches leveraging multi-omics and computational modeling can accelerate this process. However, integrating multi-modal data for disease-specific target identification and predicting translational potential remains challenging. Moreover, the absence of a systematic evaluation framework for model performance limits confidence in target reliability. We present a unified platform combining machine learning-based target identification with comprehensive benchmarking. As a testbed, we developed Target Identification Pro (TargetPro), a disease-specific model spanning 38 diseases across oncology, metabolic, immune, fibrotic, and neurological categories. TargetPro shows strong predictive performance for clinical-stage targets and reveals disease-specific patterns, underscoring the need for tailored target detection models. We next created Target Identification Benchmark (TargetBench 1.0) to rigorously assess target identification systems, including large language models, based on their ability to recover established targets and find high-quality novel candidates. This integrated approach offers a streamlined strategy to evaluate target discovery models, ultimately improving drug development efficiency.

bioinformatics↗

bioRxiv2025

Revisiting the Fungal ITS2 Sequencing Data from the Human Microbiome Project

While the Human Microbiome Project (HMP) significantly advanced our understanding of the human microbiome, our revisiting of its fecal ITS2 sequencing data reveals two significant issues. First, technical variations in sequencing or data processing introduce batch effects in downstream analyses. Second, we discovered a mismatch between the reported and actual amplification primers, which requires careful consideration when interpreting, reusing, or referencing HMP ITS2 data and methodology.

bioinformatics↗

bioRxiv2025

Alpha-1 Antitrypsin Overexpressing Mesenchymal Stem/Stromal Cells Reverses Type 1 Diabetes via Promoting Treg Function and CD8+ T cell exhaustion

Mesenchymal stem/stromal cell (MSC) therapy holds great promise as both a therapeutic option and as a biofactory, as cells produce therapeutic proteins to augment their efficacy in disease treatment. This study investigates the therapeutic effects and the mechanistic insights of alpha-1 antitrypsin overexpressing MSCs (AAT-MSCs) in diabetes prevention and treatment. A single infusion of AAT-MSCs not only delayed diabetes onset but reversed new-onset type 1 diabetes (T1D) in the nonobese diabetic (NOD) mice. Using single-cell RNA sequencing, flow cytometry, and functional analyses, we characterized the impact of AAT-MSCs on immune cells, particularly CD4+ and CD8+ T cells, in pancreatic lymph nodes (PLNs) and islets of NOD mice. AAT-MSCs enhanced the immunosuppressive function and the communication of regulatory T cells (Tregs) with other immune cells while reducing the numbers of T helper 1 (Th1) cells and CD8+ cytotoxic T cells. In vitro experiments further confirmed the capacity of AAT-MSCs to promote the proliferation of Tregs, which consequently fostered an exhausted phenotype in CD8+ T cells, thereby facilitating {beta} cell survival and potentially aiding in diabetes remission. Thus, our findings underscore the significant protective effects of AAT-MSCs, delineate their novel mechanistic insight on recipient immune cells, and provide evidence for the clinical application of AAT-MSCs in treating T1D.

cell biology↗

bioRxiv2025

A reference genome assembly of the alpine forage grass Elymus nutans

Elymus nutans Griseb. (Poaceae: Triticeae, 2n=6x=42) is a dominant perennial plant species in the Qinghai-Tibetan Plateau in China, which is an important forage resource in high-altitude and cold regions and is the most popular species used for high yield artificial grassland planting because of its rich nutritional value and good palatability for herbivorous ruminant animals. In this study, using advanced sequencing technology, we generated an allohexaploid reference genome for E. nutans, representing the three sets of chromosomes (subgenomes St, Y, and H). This is the first study to our knowledge to confirm the origin of the Y subgenome, which shares a close relationship with the V subgenome. We predict that E. nutans arose via two-step hybridization and that its speciation occurred [~]3.16 MYA. The whole genome in this study will update the Triticeae family genomics information and provide theoretical basis for the evolutionary of its related speicies.

plant biology↗

bioRxiv2025

DORA AI Scientist: Multi-agent Virtual Research Team for Scientific Exploration Discovery and Automated Report Generation

Modern goal-oriented scientific research process involves hierarchical teams of researchers of diverse backgrounds performing generalist and domain-specific tasks. Many of these tasks include hypothesis generation, literature review, data collection, cleanup, processing and analysis, experimental design, virtual and physical experiments, research report and academic paper writing, reference management, bibliography and quality control. Most of these tasks can be performed automatically or in a co-pilot mode by the generative reinforcement learning systems. In this paper, we introduce a versatile multi-agent scientific exploration and draft outline research assistant (DORA), which provides multiple templates and workflows for automated or semi-automated research studies and report generation. Under user guidance, it employs hierarchical teams of AI agents based on the plug-and-play generalist and domain-specific large language models (LLMs) exploiting a variety of specialized research tools and open data repositories and generates high-quality research outputs publication drafts with maximally-accurate references. DORA is designed to minimize the time and effort required for manuscript preparation, thereby enabling researchers to devote more attention to high-value discovery tasks. The system is constantly evolving with user feedback with regular feature and resource updates. The platform is available at https://dora.insilico.com.

bioinformatics↗

bioRxiv2024

A high-continuity and annotated reference genome of allotetraploid Siberian wildrye (Elymus sibiricus L., Poaceae: Triticeae)

Elymus sibiricus L. (Siberian wildrye, Es), a species belonging to the wheat tribe, is extensively employed as forage and for the reclamation of degraded grasslands within the Qinghai-Tibet Plateau (QTP). This study provides a high-quality reference genome assembly for the allotetraploid Es, which is composed of 14 pseudomolecules with the total genome size of 6.57 Gb. Our finding suggest that large-scale bursts of retrotransposons are critical for the genome expansion of Es. We discovered a translocation event between the Es4H and Es6H chromosomes with a low frequency of combination. Phylogenetic analyses of 90 Es accessions and 25 diploid accessions representing proposed ancestors from various habitats revealed the existence of four distinct populations. We further provided support for the hypothesis that the QTP is the center of origin and genetic diversity for Es. Collectively, our study offers valuable insights into the evolution of Es, as well as providing genomic resources for genetic enhancement in the Elymus genus and wheat tribe.

genomics↗

bioRxiv2020

Interpretable machine learning framework reveals novel gut microbiome features in predicting type 2 diabetes

Gut microbiome targets for type 2 diabetes (T2D) prevention among human cohorts have been controversial. Using an interpretable machine learning-based analytic framework, we identified robust human gut microbiome features, with their optimal threshold, in predicting T2D. Based on the results, we constructed a microbiome risk score (MRS), which was consistently associated with T2D across 3 independent Chinese cohorts involving 9111 participants (926 T2D cases). The MRS could also predict future glucose increment, and was correlated with a variety of gut microbiota-derived blood metabolites. Faecal microbiota transplantation from humans to germ-free mice demonstrated a causal role of the identified combination of microbes in the T2D development. We further identified adiposity and dietary factors which could prospectively modulate the MRS, and found that body fat distribution may be the key factor modulating the gut microbiome-T2D relationship. Taken together, we proposed a new analytical framework for the investigation of microbiome-disease relationship. The identified microbiota may serve as potential drug targets for T2D in future.

microbiology↗

bioRxiv2019

The interplay between host genetics and the gut microbiome reveals common and distinct microbiome features for human complex diseases

There is increasing interest about the interplay between host genetics and gut microbiome on human complex diseases, with prior evidence mainly derived from animal models. In addition, the shared and distinct microbiome features among human complex diseases remain largely unclear. We performed a microbiome genome-wide association study to identify host genetic variants associated with gut microbiome in a Chinese population with 1475 participants. We then conducted bi-directional Mendelian randomization analyses to examine the potential causal associations between gut microbiome and human complex diseases. We found that Saccharibacteria (also known as TM7 phylum) could potentially improve renal function by affecting renal function biomarkers (i.e., creatinine and estimated glomerular filtration rate). In contrast, atrial fibrillation, chronic kidney disease and prostate cancer, as predicted by the host genetics, had potential causal effect on gut microbiome. Further disease-microbiome feature analysis suggested that gut microbiome features revealed novel relationship among human complex diseases. These results suggest that different human complex diseases share common and distinct gut microbiome features, which may help re-shape our understanding about the disease etiology in humans.

microbiology↗