Biomedical subjects

Xiaoling Wu

Publications and source records attributed to Xiaoling Wu.

5 recordsLinked to original sources

PubMed2026

Branching plasticity and candidate gene-hormone networks associated with shade responses in soybean under relay strip intercropping.

BACKGROUND: Branching is a key determinant of high-yield plant architecture in soybean, particularly in maize- soybean relay strip intercropping where plants experience an "initially shaded-then fully illuminated" light regime. However, the genetic regulation of branching responses to shading remains poorly understood. METHODS: We evaluated 11 branching-related traits across 202 soybean accessions grown under monoculture (SS) and relay strip intercropping (RI). Branch number (BN), branching incidence (BI), and total branch length (TBL) were assessed together with stress tolerance indices (STI) and relative distance plasticity index (RDPI). Genome-wide association studies (GWAS) using mixed linear model (MLM) and three-variance-component MLM (3VmrMLM) were combined with haplotype and protein structural analyses to refine candidate genes. RESULTS: Based on Pearson correlation analysis of all 11 traits, BN, BI, and TBL measured before maize harvest showed the strongest and most consistent associations with branch seed weight within the corresponding cropping system (BSW_SS under SS and BSW_RI under RI), whereas other traits showed weaker or environment-dependent associations. Higher STI values calculated from these traits during the co-growth phase were negatively associated with BSW_RI, suggesting weaker compensatory recovery after light restoration in genotypes with more stable early branching patterns between SS and RI. In contrast, mediation analysis indicated that RDPI was positively associated with BSW_RI mainly through improved mature branching architecture (MB_index), which accounted for approximately 70% of the total positive effect. GWAS identified 57 and 74 significant QTNs using MLM and 3VmrMLM, respectively, and LD-window genes were filtered for exonic nonsynonymous or premature stop-codon variants, yielding 883 genes with putative functional variants. Two high-confidence genes emerged: Glyma.02G058600 (PP2C55), exhibiting shading-specific haplotype effects likely linked to GA-mediated branch-stem balance, and Glyma.02G059900 (DA1-related protein), showing stable effects across environments and implicated in ABA-mediated suppression of axillary meristems. CONCLUSIONS: These results provide insight into the genetic and physiological basis of soybean branching responses under relay strip intercropping, clarify that branching plasticity and relative shade tolerance represent distinct response dimensions in this system, and identify putative loci that may be useful for breeding soybean cultivars with improved shade adaptation and yield stability.

Glycine max↗

PubMed2025

Metagenome-Based Characterization of the Gut Virome Signatures in Patients With Gout.

The gut microbiome has been implicated in the development of autoimmune diseases, including gout. However, the role of the gut virome in gout pathogenesis remains underexplored. We employed a reference-dependent virome approach to analyze fecal metagenomic data from 102 gout patients (77 in the discovery cohort and 25 in the validation cohort) and 86 healthy controls (HCs) (63 and 23 in each cohort). A subset of gout patients in the discovery cohort provided longitudinal samples at Weeks 2, 4, and 24. Our analysis revealed significant alterations in the gut virome of gout patients, including reduced viral richness and shifts in viral family composition. Notably, Siphoviridae, Myoviridae, and Podoviridae were depleted, while Quimbyviridae, Retroviridae, and Schitoviridae were enriched in gout patients. We identified 359 viral operational taxonomic units (vOTUs) associated with gout. Enriched vOTUs in gout patients predominantly consisted of Fusobacteriaceae, Bacteroidaceae, and Selenomonadaceae phages, while control-enriched vOTUs included Ruminococcaceae, Oscillospiraceae, and Enterobacteriaceae phages. Longitudinal analysis revealed that a substantial proportion of these virome signatures remained stable over 6 months. Functional profiling highlighted the enrichment of viral auxiliary metabolic genes, suggesting potential metabolic interactions between viruses and host bacteria. Notably, gut virome signatures effectively discriminated gout patients from HCs, with high classification performance in the validation cohort. This study provides the first comprehensive characterization of the gut virome in gout, revealing its potential role in disease pathogenesis and highlighting virome-based signatures as promising biomarkers for gout diagnosis and future therapeutic strategies.

Humans↗

PubMed2007

Daxx cooperates with the Axin/HIPK2/p53 complex to induce cell death.

Daxx, a death domain-associated protein, has been implicated in proapoptosis, antiapoptosis, and transcriptional regulation. Many factors known to play critically important roles in controlling apoptosis and gene transcription have been shown to associate with Daxx, including the Ser/Thr protein kinase HIPK2, promyelocytic leukemia protein, histone deacetylases, and the chromatin remodeling protein ATRX. Although it is clear that Daxx may exert multiple functions, the underlying mechanisms remain far from clear. Here, we show that Axin, originally identified for its scaffolding role to control beta-catenin levels in Wnt signaling, strongly associates with Daxx at endogenous levels. The Daxx/Axin complex formation is enhanced by UV irradiation. Axin tethers Daxx to the tumor suppressor p53, and cooperates with Daxx, but not DaxxDeltaAxin, which is unable to interact with Axin, to stimulate HIPK2-mediated Ser(46) phosphorylation and transcriptional activity of p53. Interestingly, Axin and Daxx seem to selectively activate p53 target genes, with strong activation of PUMA, but not p21 or Bax. Daxx-stimulated p53 transcriptional activity was significantly diminished by small interfering RNA against Axin; Daxx fails to inhibit colony formation in Axin(-/-) cells. Moreover, UV-induced cell death was attenuated by the knockdown of Axin and Daxx. All these results show that Daxx cooperates with Axin to stimulate p53, and implicate a direct role for Axin, HIPK2, and p53 in the proapoptotic function of Daxx. We have hence unraveled a novel aspect of p53 activation and shed new light on the ultimate understanding of the Daxx protein, perhaps most pertinently, in relation to stress-induced cell death.

Adaptor Proteins, Signal Transducing↗

PubMed2006

[Allelopathy of decomposing pepper stalk on pepper growth].

With decomposing pepper stalk as test material, this paper studied its allelopathy on the growth of pepper plants. The results showed that after 60 days of decomposition, the decomposed pepper stalk could decrease the plant height, stem diameter, dry weights of above-and underground biomass, leaf area, and chlorophyll content of pepper plants by 0.0374 - 0.0646, 0.0020 - 0.0097, 0.0050 - 0.0355 and 0.0916 - 0.3584, 0.0016 - 0.0251, and 0.0043 - 0.0242 respectively. These inhibitory effects were enhanced after 120 days of decomposition, but the difference with CK was not significant. The root vigor and its SOD, POD and CAT activities of pepper plants were decreased, while the MDA content and relative conductivity were increased with the increasing concentration of decomposed pepper stalk and with the prolong of treating time. The allelopathic effects of decomposed pepper stalk on the physiological indices of pepper root activity ranged from 0.0163 to 0.6507, which was significantly higher than that of plant growth index.

Biomass↗

PubMed2005

p-Value simulation for affected sib pair multiple testing.

A standard approach to calculation of critical values for affected sib pair multiple testing is based on: (a) fully informative markers, (b) Haldane map function assumptions leading to a Markov chain model for inheritance vectors, (c) central limit approximation to averages of sampled inheritance vectors leading to an Ornstein-Uhlenbeck process approximation, and (d) simple approximations to the maximum of such a process. Under these assumptions, assuming equispaced or close to equispaced markers, if the sample size is large, an approximation is available that is easy to calculate and performs well. However, for small sample sizes, a large number of markers, and for small p-values, there is good reason to be cautious about the use of the Gaussian approximation. We develop an algorithm for calculation of multiple testing p-values based on the standard Markov chain model, avoiding the use of Gaussian (large sample) approximation. We illustrate the use of this algorithm by demonstrating some inadequacies of the Gaussian approximation.

Algorithms↗