Biomedical subjects

Xiang Ma

Publications and source records attributed to Xiang Ma.

4 recordsLinked to original sources

PubMed2026

Functional Characterization of the Oat (Avena sativa L.) TCP Transcription Factor AsTCP38 Reveals Its Role in Low-Nitrogen Stress Tolerance.

Nitrogen limitation restricts plant growth, development, and yield in crops and forage species. Although TCP transcription factors are implicated in diverse abiotic-stress responses, the functions of most TCP genes in oat remain unclear. Here, we cloned and characterized the AsTCP38 gene, which is 1215 bp long and encodes a 405-amino-acid protein. The predicted protein contains a conserved TCP domain and shares its highest sequence similarity with Arabidopsis thaliana (A. thaliana) AtTCP15. The AsTCP38 protein localized to the nucleus, and promoter analysis identified cis-elements associated with light, hormone, and stress responses. We generated AsTCP38-overexpressing A. thaliana and wheat plants and screened an oat leaf yeast cDNA library for candidate interacting proteins. In these heterologous overexpression lines, AsTCP38 overexpression was associated with greater abscisic acid (ABA) sensitivity and improved seedling growth under low-nitrogen conditions. Changes in antioxidant-enzyme activities, nitrogen-metabolism-related enzyme activities, and endogenous hormone contents were also observed. Together, these findings suggest that AsTCP38 may participate in low-nitrogen responses and provide a basis for further functional studies in oat. Direct regulatory targets and the contribution of AsTCP38 to low-nitrogen adaptation in oat remain to be established.

Avena↗

PubMed2026

Exploring the mechanism of Acanthopanax in treating vertigo: A network pharmacology and molecular docking study.

Acanthopanax has therapeutic efficacy against vertigo; however, the underlying mechanism remains unclear. This study aimed to elucidate the mechanism by which Acanthopanax treats vertigo through integrated network pharmacology and molecular docking techniques, and retrieved all target genes of Acanthopanax for vertigo treatment from July to October 2025. Vertigo-related target genes were subsequently identified from public databases, including GeneCards and Online Mendelian Inheritance in Man. The intersection between Acanthopanax-derived targets and vertigo-related targets was analyzed to identify candidate target genes. Using the STRING platform, we constructed protein-protein interaction networks for the identified candidate targets and mined the core functional modules within these networks. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed on candidate targets via the clusterProfiler package. A carp bile poisoning-liver injury target-pathway network was constructed via Cytoscape 3.8.2 software, network topology analysis was conducted, and the core components and targets were screened. The results found that A total of 295 candidate targets for the treatment of vertigo caused by Eleutherococcus senticosus were identified. Pathway enrichment analysis revealed that Eleutherococcus senticosus treatment for vertigo may be closely associated with pathways related to IL-17, TNF, phosphoinositide 3-kinase (PI3K)-Akt, p53, HIF-1, and Forkhead box O signaling. The core targets for the treatment of A. senticosus vertigo include TP53, AKT1, STAT3, TNF, and JUN. Network pharmacology and molecular docking studies suggest that A. senticosus may treat vertigo by regulating targets such as JUN, TNF, AKT1, STAT3, and STAT3 through pathways such as the IL-17, TNF, phosphoinositide 3-kinase-Akt, p53, HIF-1, and Forkhead box O signaling pathways. These mechanisms warrant further investigation in future o and in vitro studies.

Molecular Docking Simulation↗

PubMed2026

How not to be seen: predicting unseen enzyme functions using contrastive learning.

MOTIVATION: Predicting enzyme function from its sequence is still an unsolved problem in the life sciences. Moreover, with the explosion of annotated genome data, we are inundated with potential enzymatic sequences that have not yet been biochemically characterized. While it is not possible to assign a not-yet-existing label to such a sequence, there is high value in placing the sequence as accurately as possible in known function space. Doing so can help provide more accurate falsifiable hypotheses for experimentalists wishing to characterize enzymes from specific functional families. RESULTS: Here we present a contrastive learning algorithm for predicting enzyme function from sequence. Our method, EnzPlacer, predicts the third, second, and first EC numbers for a protein whose fourth EC number is not in the training corpus. This novel prediction mechanism accurately places a protein sequence within a narrowed-down functional context, even if the precise function remains unknown. AVAILABILITY AND IMPLEMENTATION: EnzPlacer and data is available at https://github.com/drxiangma/EnzPlacer under a GPL3 license.

Enzymes↗

PubMed2026

How Not to be Seen: Predicting Unseen Enzyme Functions using Contrastive Learning.

MOTIVATION: Predicting enzyme function from its sequence is still an unsolved problem in the life sciences. Moreover, with the explosion of annotated genome data, we are inundated with potential enzymatic sequences that have not yet been biochemically characterized. While it is not possible to assign a not-yet-existing label to such a sequence, there is high value in placing the sequence as accurately as possible in known function space. Doing so can help provide more accurate falsifiable hypotheses for experimentalists wishing to characterize enzymes from specific functional families. RESULTS: Here we present a contrastive learning algorithm for predicting enzyme function from sequence. Our method, EnzPlacer, predicts the third, second, and first EC numbers for a protein whose fourth EC number is not in the training corpus. This novel prediction mechanism accurately places a protein sequence within a narrowed-down functional context, even if the precise function remains unknown. AVAILABILITY: EnzPlacer is available from https://github.com/drxiangma/EnzPlacer under a GPL3 license.

Contrastive learning↗