Biology subjects

Li, G.-H.

Publications and source records attributed to Li, G.-H..

9 recordsLinked to original sources

bioRxiv2026

dbGIST: An LLM-Assisted Multi-Omics Resource for Target Exploration and Cross-Dataset Validation in Gastrointestinal Stromal Tumors

Gastrointestinal stromal tumors (GISTs) are the most common mesenchymal neoplasms of the gastrointestinal tract, yet GIST-specific omics evidence remains scattered across small cohorts and is not represented as a dedicated disease project in major cancer genomics resources, limiting reproducible target exploration. Here, we present dbGIST (https://www.dbgist.com), a dedicated GIST-focused multi-omics resource built to make dispersed GIST evidence searchable, analyzable, and reusable. dbGIST harmonizes data from 37 centers and 1,991 samples, including pathologically verified in-house cohorts, across genomics, bulk transcriptomics, proteomics, phosphoproteomics, and single-cell transcriptomics, and couples these data with curated clinical annotations covering survival, mutation status, risk stratification, metastasis or recurrence, mitotic index, tumor site and size, and imatinib response. The platform supports cohort-level molecular-clinical association, survival, enrichment, immune-infiltration, drug-sensitivity, and single-cell analyses through interactive visualizations, downloadable source data, and public APIs for programmatic access to reusable analysis outputs and visualization-ready data. An optional LLM-assisted interface helps users navigate analyses and interpret outputs. Using MCM7 as a case study, dbGIST linked a resource-derived candidate to survival, risk features, metastatic or recurrent disease, imatinib-response phenotypes, proliferative cell states, and in vitro GIST-cell behavior. dbGIST therefore provides a traceable and interoperable resource for target exploration and precision oncology research in GIST.

cancer biology↗

bioRxiv2024

Systems Modeling Reveals Shared Metabolic Dysregulation and Novel Therapeutic Treatments in ME/CFS and Long COVID

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) and Long COVID are complex, multisystem conditions that pose significant challenges in healthcare. Accumulated research evidence suggests that ME/CFS and Long COVID exhibit overlapping metabolic symptoms, indicating potential shared metabolic dysfunctions. This study aims to systematically explore shared metabolic disturbances in the muscle tissue of patients. Utilizing genome-wide metabolic modeling, we identified key metabolic irregularities in the muscle of patients with ME/CFS, notably the downregulation of the alanine and aspartate metabolism pathway and the arginine and proline metabolism pathway. Further, in silico knockout analyses suggested that supplementation with aspartate (ASP) or asparagine (ASN) could potentially ameliorate these metabolic deficiencies. In addition, assessments of metabolomic levels in Long COVID patients also showed the significant downregulation of ASP during post-exertional malaise (PEM) in both muscle and blood. Consequently, we propose that a combination of l-ornithine and l-aspartate (LOLA) is a potential candidate to alleviate metabolic symptoms in ME/CFS and Long COVID for future clinical trials.

systems biology↗

bioRxiv2023

Predictive Systems Biology Modeling: Unraveling Host Metabolic Disruptions and Potential Drug Targets in SARS-CoV-2 and Its Variants

BackgroundHost response is critical to the onset, progression, and outcome of viral infections. Since viruses hijack the host cellular metabolism for their replications, we hypothesized that restoring host cell metabolism can efficiently reduce viral production. ResultsHere, we present a viral-host Metabolic Modeling (vhMM) method to systematically evaluate the disturbances in host metabolism in viral infection and computationally identify targets for modulation by integrating genome-wide precision metabolic modeling and cheminformatics. We applied vhMM to SARS-CoV-2 infections and identified consistent changes in host metabolism and gene and endogenous metabolite targets between the original SARS-COV-2 and different variants (Alpha, Delta, and Omicron). Among six compounds predicted for repurposing, methotrexate, cinnamaldehyde, and deferiprone were tested in vitro and effective in inhibiting viral production with IC50 less than 4uM. Further, an analysis of real-world patient data showed that cinnamon usage significantly reduced the SARS-CoV-2 infection rate with an odds ratio of 0.65 [95%CI: 0.55[~]0.75]. ConclusionsThese results demonstrated that vhMM is an efficient method for predicting targets and drugs for viral infections.

systems biology↗

bioRxiv2022

Natural selection and neutral mutations through the lens of viruses

The Neutral Theory and the Modern Synthesis, a modified version of Darwins theory, have been arguing for decades about the influence of natural selection on molecular evolution1-10. Here we elucidate through the lens of viruses that a frequently used method11-17 employing the ratio of nonsynonymous versus synonymous substitution rates has dramatically underestimated the influence of natural selection on molecular evolution. We also find novel evidence from viral sequences to support the co-existence of the crucial role of natural selection in molecular evolution and the ubiquity of neutral mutations. The co-existence has perplexed biologists for decades2,5,7. We then elucidate for the first time the causality between natural selection and the ubiquity of neutral mutations with a novel interpretation of natural selection. This novel interpretation incorporates biochemistry, genetics, epigenetics, physiology, and dynamics. It holds that natural selection acts directly on the overall phenotypic performance of organisms and indirectly on each genomic site or phenotypic trait. It highlights not only restrictions and competitions but also freedom and diversity, besides the overall harmonious development of organisms and human societies. Therefore, this novel interpretation could have far-reaching implications in the natural and social sciences.

evolutionary biology↗

bioRxiv2022

Origins of monkeypox viruses and their strains circulating in 2022

Monkeypox has spread unprecedentedly to nearly 100 countries and infected more than 51,000 people since 1 May 2022. This large-scale outbreak constituted a public health emergency of international concern (PHEIC), as declared by the World Health Organization. To better recognize and control this outbreak, we explore here through phylogenetic analysis the origins of MPXVs and their strains circulating in 2022, which remain unclear so far. Our results suggest that MPXVs possibly originated from some cowpox viruses and three lineages of MPXVs within Clade IIb circulated in 2022. Our results also suggest that two MPXVs respectively similar to the two MPXVs exported from Nigeria to the USA in 2021, ON676708/USA/2021 and ON676707/USA/2021, evolved into two lineages and sparked the large-scale outbreak in 2022, after their unknown evolutionary and epidemiological journeys possibly in Nigeria, the USA, or other countries before May 2022. This view does not stigmatize any country, because monkeypox is an endemic zoonosis in west Africa with wildlife reservoirs, and its large-scale outbreak in 2022 also resulted from the global decline of population immunity against smallpox due to the cease of vaccination against smallpox decades ago.

microbiology↗

bioRxiv2021

GREPore-seq: A Robust Workflow to Detect Changes after Gene Editing through Long-range PCR and Nanopore Sequencing

To achieve the enormous potential of gene-editing technology in clinical therapies, both the on-target and unintended editing consequences need to be thoroughly evaluated. However, there is a lack of a comprehensive, pipelined, large-scale and economical workflow for detecting genome editing outcomes, in particular insertion or deletion of a large fragment. Here, we describe an approach for efficient and accurate detection of multiple genetic changes after CRISPR-Cas9 editing by pooled nanopore sequencing of barcoded long-range PCR products. To overcome the high error rates and indels of nanopore sequencing, we developed a pipeline to capture the barcoded sequences by grepping reads of nanopore amplicon sequencing (GREPore-seq). GREPore-seq can detect NHEJ-mediated double-stranded oligodeoxynucleotide (dsODN) insertions with comparable accuracy to Illumina next-generation sequencing (NGS). GREPore-seq also identifies HDR-mediated large gene knock-in, which excellently correlates with FACS analysis data. Low-level plasmid backbone insertion after HDR editing was also detected. We have established a practical workflow to identify genetic changes, including quantifying dsODN insertions, knock-ins, plasmid backbone insertions, and large fragment deletions after CRISPR editing. This toolkit for nanopore sequencing of pooled long amplicons should have broad applications in assessing on-target HDR editing and inadvertent large indels of over 1 kb. GREPore-seq is freely available at GitHub (https://github.com/lisiang/GREPore-seq).

bioinformatics↗

bioRxiv2020

Genome assembly and annotation of Macadamia tetraphylla

Macadamia is a kind of evergreen nut trees which belong to the Proteaceae family. The two commercial macadamia species, Macadamia integrifolia and M. tetraphylla, are highly prized for their edible kernels. Catherine et al. reported M. integrifolia genome using NGS sequencing technology. However, the lack of a high-quality assembly for M. tetraphylla hinders the progress in biological research and breeding program. In this study, we report a high-quality genome sequence of M. tetraphylla using the Oxford Nanopore Technologies (ONT) technology. We generated an assembly of 750.54 Mb with a contig N50 length of 1.18 Mb, which is close to the size estimated by flow cytometry and k-mer analysis. Repetitive sequence represent 58.57% of the genome sequence, which is strikingly higher compared with M. integrifolia. A total of 31,571 protein-coding genes were annotated with an average length of 6,055 bp, of which 92.59% were functionally annotated. The genome sequence of M. tetraphylla will provide novel insights into the breeding of novel strains and genetic improvement of agronomic traits.

genomics↗

bioRxiv2017

Anti-HIV activity prediction and enrichment identify novel human targets for Anti-HIV therapy

Human immunodeficiency virus (HIV) relies heavily on the host proteins to facilitate its entry and replication. Currently, more than 4000 human proteins are recorded to be involved in the HIV-1 life cycle. Identifying appropriate anti-HIV targets from so many host proteins is crucial to anti-HIV drug development, but a challenging work. Here we combined anti-HIV activity prediction and enrichment analysis to identify novel human targets for anti-HIV therapy. We firstly developed an accurate prediction tool named Anti-HIV-Predictor (AUC>0.96) to predict the anti-HIV activity of given compounds. Using this tool, we predicted 10488 anti-HIV compounds from ChEMBLdb. Then, based on this result and relationships of targets and compounds, we inferred 73 anti-HIV targets that enriched with anti-HIV compounds. The functional annotation and network analysis revealed that they directly or indirectly interact with 20 HIV proteins through neuropeptide signaling, GPCR signaling, cell surface signaling pathway, and so on. Nearly half of these targets overlap with the NCBI HIV dataset. However, the percentage of known therapeutic targets in these targets is significantly higher than that in the NCBI HIV dataset. After a series of feature analysis, we identified 13 novel human targets with high potential as anti-HIV targets, the inhibitors of which have experimentally confirmed anti-HIV activity. It is noteworthy that the inhibitors of REN and CALCA have better anti-HIV activity than CCR5 inhibitors. Taken together, our findings provide novel human targets for the host-oriented anti-HIV drug development and should significantly advance current anti-HIV research.

bioinformatics↗

bioRxiv2016

Efficient repositioning of approved drugs as anti-HIV agents using Anti-HIV-Predictor

Development of new, effective and affordable drugs against HIV is urgently needed. In this study, we developed a worlds first web server called Anti-HIV-Predictor (http://bsb.kiz.ac.cn:70/hivpre) for predicting anti-HIV activity of given compounds. This server is rapid and accurate (accuracy >93% and AUC > 0.958). We applied the server to screen 1835 approved drugs for anti-HIV therapy. Totally 67 drugs were predicted to have anti-HIV activity, 25 of which are anti-HIV drugs. Then we experimentally evaluated 35 predicted new anti-HIV compounds by assays of syncytia formation, p24 quantification, cytotoxicity. Finally, we repurposed 7 approved drugs (cetrorelix, dalbavancin, daunorubicin, doxorubicin, epirubicin, idarubicin and valrubicin) as new anti-HIV agents. The original indication of these drugs is involved in a variety of diseases such as female infertility and cancer. Anti-HIV-Predictor and the 7 repurposed anti-HIV agents provided here demonstrate the efficacy of this strategy for discovery of new anti-HIV agents.

bioinformatics↗