Biology subjects

Jiao, T.

Publications and source records attributed to Jiao, T..

4 recordsLinked to original sources

bioRxiv2024

Building a literature knowledge base towards transparent biomedical AI

As artificial intelligence (AI) continues to advance and scale up in biomedical research, concerns about AIs trustworthiness and transparency have grown. There is a critical need to systematically bring accurate and relevant biomedical knowledge into AI applications for transparency and provenance. Knowledge graphs have emerged as a powerful tool that integrates heterogeneous knowledge by explicitly describing biomedical knowledge as entities and relationships between entities. However, PubMed, the largest and most comprehensive repository of biomedical knowledge, exists primarily as unstructured text and is under utilized for advanced machine learning tasks. To address the challenge, we developed LiteralGraph, a computational framework to extract biomedical terms and relationships from PubMed literature into a unified knowledge graph. Using this framework, we established the Genomic Literature Knowledge Base (GLKB), which consolidates 14,634,427 biomedical relationships between 3,276,336 biomedical terms from over 33 million PubMed abstracts and nine well-established biomedical repositories. The database is coupled with RESTful APIs and a user-friendly web interface that makes it accessible to researchers for various usages. We demonstrated the broad utility of GLKB towards transparent AI in three distinct application scenarios. In the LLM grounding scenario, we developed a Retrieval Augmented Generation (RAG) agent to reduce LLM hallucination in biomedical question answering. In the hypothesis generation scenario, we elucidated the potential functions of RFX6 in type 2 diabetes (T2D) using the vast evidence from PubMed articles. In the machine learning scenario, we utilized GLKB to provide semantic knowledge in predictive tasks and scientific fact-checking.

bioinformatics↗

bioRxiv2023

Mechanism of action of Buqing Granule against Diabetic retinopathy based on network pharmacology and animal experiments

ObjectiveFor this study, network pharmacology and animal experiments were used together to get a better idea of how BQKL works at the molecular level to treat DR. MethodsIn this study, we obtained the relevant action target information of BQKL from the TCMSP and also combined the GeneCards, OMIM, TTD, and PharmGKB databases with the GEO database to obtain the relevant target information of DR. The intersection of these targets was determined using a Venn diagram to identify the target of action for the treatment of DR with BQKL. The target proteins of BQKL for DR were then uploaded to the String database. The resultant data were imported into Cytoscape 3.9.0 to construct PPI networks and identify key targets of action. The DAVID database was used to do a GO and KEGG pathway enrichment analysis of target genes for treating DR with BQKL. Molecular docking was performed to validate the core action targets with the core compounds of BQKL. In addition, we induced DR production in rats by a high-fat, high-sugar diet and intraperitoneal injection of STZ and validated the results obtained from the network pharmacological analysis by changes in body weight and blood glucose, serum levels of biochemical markers, HE staining, immunohistochemistry, qRT-PCR, and Western blot experiments in rats. ResultsIn this study, quercetin, kaempferol, {beta}-sitosterol, lignanserin, and stigmasterol were identified as the key components, TP53, AKT1, JUN, CASPASE3, MAPK3, and MAPK1 as the key targets, and PI3K-Akt, AGE-RAGE, and MAPK signaling pathways as the main pathways involved. The results of animal experiments showed that BQKL could not only effectively reduce the degree of blood glucose, blood lipids, and oxidative damage in diabetic rats but also slow down the development process of DR. At the same time, it can significantly up-regulate the expression of AKT1, MAPK1, and MAPK3 and down-regulate the expression of CASPASE3, c-JUN, and TP53 in retinal tissue. ConclusionBQKL ameliorates oxidative stress, apoptosis, and inflammation due to hyperglycemia-related stress by regulating key targets of CASPASE3, AKT1, c-JUN, TP53, MAPK1, and MAPK3, thereby delaying the onset and progression of DR.

molecular biology↗

bioRxiv2022

FOXC2 marks and maintains the primitive spermatogonial stem cells subpopulation in the adult testis

In adult mammals, spermatogenesis embodies the complex developmental process from spermatogonial stem cells (SSCs) to spermatozoa. At the top of this developmental hierarchy lie a series of SSC subpopulations. Their individual identities as well as the relationships with each other, however, remain largely elusive. Using single-cell analysis and lineage tracing, we discovered both in mice and humans the quiescent adult SSC subpopulation marked specifically by forkhead box protein C2 (FOXC2). All spermatogenic progenies can be derived from FOXC2+ SSCs and the ablation of FOXC2+ SSCs led to the depletion of the undifferentiated spermatogonia pool. During germline regeneration, FOXC2+ SSCs were activated and able to completely restore the process. Germ cell specific Foxc2 knockout resulted in an accelerated exhaustion of SSCs and eventually led to male infertility. Furthermore, FOXC2 prompts the expressions of negative regulators of cell cycle thereby ensures the SSCs reside in quiescence. Thus, this work proposes that the quiescent FOXC2+ SSCs are essential for maintaining the homeostasis and regeneration of spermatogenesis in adult mammals.

developmental biology↗

bioRxiv2020

Identification of potent and safe antiviral therapeutic candidates against SARS-CoV-2

COVID-19 pandemic has infected millions of people with mortality exceeding 300,000. There is an urgent need to find therapeutic agents that can help clear the virus to prevent the severe disease and death. Identifying effective and safer drugs can provide with more options to treat the COVID-19 infections either alone or in combination. Here we performed a high throughput screen of approximately 1700 US FDA approved compounds to identify novel therapeutic agents that can effectively inhibit replication of coronaviruses including SARS-CoV-2. Our two-step screen first used a human coronavirus strain OC43 to identify compounds with anti-coronaviral activities. The effective compounds were then screened for their effectiveness in inhibiting SARS-CoV-2. These screens have identified 24 anti-SARS-CoV-2 drugs including previously reported compounds such as hydroxychloroquine, amlodipine, arbidol hydrochloride, tilorone 2HCl, dronedarone hydrochloride, and merfloquine hydrochloride. Five of the newly identified drugs had a safety index (cytotoxic/effective concentration) of >600, indicating wide therapeutic window compared to hydroxychloroquine which had safety index of 22 in similar experiments. Mechanistically, five of the effective compounds were found to block SARS-CoV-2 S protein-mediated cell fusion. These FDA approved compounds can provide much needed therapeutic options that we urgently need in the midst of the pandemic.Competing Interest StatementThe authors have declared no competing interest.View Full Text

microbiology↗