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Biomedical subjects

Qi Chen

Publications and source records attributed to Qi Chen.

8 recordsLinked to original sources

Dynamic and non-additive gene regulation shapes maize responses to simultaneous salt and cold stress.

Salt and cold stresses often occur together in nature and severely impact crop productivity, yet their transcriptional regulation remains poorly understood. Here, we conducted a time-series transcriptomic analysis of maize under salt, cold, and their combination at 0, 6, 12, and 24 h. Differential expression analysis revealed dynamic, condition-specific gene responses grouped into eight distinct temporal patterns. Promoter motif analysis of genes within each pattern identified 5-39 significantly enriched motifs, with over 40% lacking known counterparts, suggesting the involvement of previously uncharacterized cis-regulatory elements in stress-responsive transcriptional regulation. By comparing combined stress responses to the sum of single-stress effects, we found that about 74% of DEGs showed non-additive patterns, suggesting that combined stress triggers a distinct transcriptional program. Evolutionary analysis showed that additive DEGs tend to be more recently evolved, subject to weaker purifying selection, and enriched in transposed duplications, contrasting with the stronger constraint observed in non-additive DEGs. WGCNA identified 24 co-expression modules, among which 65 hub DEGs were detected in modules significantly correlated with specific stress conditions. Furthermore, we reconstructed 228, 20, and 200 sequential transcription factor cascades spanning 6 h, 12 h, and 24 h under cold, salt, and combined stress, respectively, with no cascade shared across all three conditions. Together, these results reveal that maize responses to combined salt and cold stress are largely non-additive and temporally dynamic, with distinct evolutionary patterns underlying different response types, offering insights and candidate regulators for enhancing crop stress resilience.

Zea mays

Aggregicoccus is a myxobacterial genus inherently deficient in fruiting genes.

Myxobacteria are fascinating and important prokaryotes with remarkable multicellular behaviors, which make them a model system for studying prokaryotic development and cooperation. Although there have been sporadic discoveries of myxobacterial species unable to fruit, it is unclear whether the non-fruiting characteristic is due to taxon-specific genetic deficiency or suboptimal cultivation conditions. Aggregicoccus is a non-fruiting myxobacterial genus typified by a single validly published species, Ag. edonensis. In this study, we report five novel Aggregicoccus strains, which are classified into three novel type species, Ag. lacus, Ag. agri, and Ag. guangxiensis, based on polyphasic taxonomic analysis. All the Aggregicoccus strains are unable to produce fruiting bodies, but can still sporulate. We compared the genome differences between Aggregicoccus and Myxococcus; both genera belong to the Myxococcaceae family, and all the genomes are of similar sizes. The results showed that the Aggregicoccus strains are inherently deficient in the fruiting body-associated genomic information (FAGI). We propose an assessment of FAGI for the classification of non-fruiting myxobacterial species.IMPORTANCEFruiting body formation is traditionally regarded as a defining trait of myxobacteria. Here, we report that Aggregicoccus spp., including six strains of four species, can sporulate but are deficient in the fruiting body-associated genomic information (FAGI). This demonstrates that the non-fruiting characteristic in Aggregicoccus stems from inherent genetic deficiencies rather than suboptimal cultivation. Our findings highlight the need to assess FAGI presence in classifying non-fruiting lineages, innovate the isolation method, and refine our understanding of the diversity and evolution of the myxobacteria.

Aggregicoccus

A homogeneous immunoassay based on AlphaLICA technology for detecting florfenicol residues in animal-derived foods.

Florfenicol (FF), a broad-spectrum amide antibiotic widely used in livestock, poultry, and aquaculture, poses potential threats to food safety and public health due to its residual accumulation. In this study, a novel homogeneous immunoassay based on Amplified Luminescent Proximity Homogeneous Assay (AlphaLICA) technology was developed for the first time for rapid screening of FF residues in milk and egg matrices. By covalently immobilizing the FF-BSA conjugate and goat anti-mouse IgG onto luminescent and photosensitive microspheres, respectively, the method achieved wash-free, homogeneous quantitative detection through a competitive immunoreaction. Under optimized conditions, the assay exhibited a linear range of 0.2-16.2 ng mL-1, with a limit of detection of 9.7 pg mL-1 and a limit of quantification of 183 pg mL-1. The intra- and inter-batch coefficients of variation ranged from 3.08% to 5.70% and 2.44% to 7.09%, respectively. Spike recovery rates in milk and egg matrices ranged from 93.18% to 107.17% (RSD &#x2264; 5.57%). Cross-reactivity with 11 other common antibiotics, including chloramphenicol and thiamphenicol, was below 0.1%, demonstrating excellent specificity. Comparative analysis with a commercial ELISA kit showed high consistency (r2 = 0.9332, p < 0.001). With high sensitivity, strong specificity, simple operation, and a detection time of only 10 min, this method provides a reliable technical platform for high-throughput, rapid monitoring of FF residues in milk and egg matrices.

Journal Article

Revealing potential biomarkers and metabolic mechanisms of ovarian aging in hens during late laying period based on machine learning and metabolomics.

Ovarian function decline during the late laying period represents a major bottleneck for the economic efficiency of the global poultry industry. However, the underlying metabolic mechanisms and reliable early-warning biomarkers for ovarian aging remain poorly understood. In this study, we performed the first untargeted LC-MS/MS metabolomics analysis of ovarian tissues from Taihe silky fowls at peak laying (30&#xa0;weeks) and late laying (50&#xa0;weeks) stages, and employed an ensemble machine learning strategy integrating LASSO, random forest, and support vector machine (SVM) algorithms to identify high-confidence core biomarkers of ovarian aging. Gene expression analysis was further conducted to validate the potential molecular mechanisms. Our results showed that the metabolic profiles of ovarian tissues differed significantly between the two groups. A total of 6 core biomarkers were identified, 4 of which were long-chain acylcarnitines. Mechanistic analysis revealed that downregulation of key genes in the carnitine shuttle system led to impaired mitochondrial fatty acid &#x3b2;-oxidation, which in turn triggered excessive oxidative stress and compromised ovarian endocrine function. In conclusion, this study identifies long-chain acylcarnitines as potential metabolic biomarkers for ovarian aging in Taihe silky fowls. These findings provide novel insights into the metabolic basis of poultry ovarian aging and lay a theoretical foundation for the precise regulation of reproductive performance in indigenous poultry breeds.

Animals

Novel biallelic FSIP2 variants cause male infertility with multiple morphological abnormalities of sperm flagella in humans.

Biallelic variants in fibrous sheath-interacting protein 2 ( FSIP2 ) gene are a known cause of multiple morphological abnormalities of the sperm flagella (MMAF). This study aimed to identify novel FSIP2 variants and evaluate their impact on sperm ultrastructure and intracytoplasmic sperm injection (ICSI) outcomes. Whole-exome sequencing (WES) was employed to screen a cohort of 92 MMAF patients, with candidate variants validated via Sanger sequencing and third-generation sequencing. We identified one homozygous variant in a proband from a consanguineous family and two pairs of compound heterozygous variants in two unrelated, non-consanguineous families. Routine semen analysis demonstrated markedly reduced motility across all probands. Detailed morphological and ultrastructural assessments using Papanicolaou staining, scanning electron microscopy (SEM), and transmission electron microscopy (TEM) demonstrated that approximately 80.0% of spermatozoa exhibited pathological elongation of the mitochondrial sheath in the midpiece. Furthermore, 50.0%-70.0% of spermatozoa displayed fibrous sheath dysplasia or loss in the principal piece. Immunofluorescence assays and Western blotting confirmed that FSIP2 protein localization was disrupted, and the expression of key axonemal assembly factors was dysregulated. Notably, successful pregnancies were achieved via ICSI in the partners of two probands. This study expands the mutational spectrum of FSIP2 in both consanguineous and non-consanguineous populations. Ultrastructural abnormalities, such as mitochondrial sheath elongation and fibrous sheath disassembly, highlight FSIP2 's critical role in flagellar assembly. Clinical results further support ICSI as an effective therapeutic intervention for affected individuals.

Humans

Predicting host tropism in influenza a viruses: insights from multi-segment nucleotide signatures.

BACKGROUND: Influenza A virus (IAV) poses a significant public health threat due to its cross-species transmission and complex host adaptation mechanisms. This study integrated whole-genome data from avian, human, swine, and bovine IAV strains, using machine learning to predict viral host tropism based on nucleotide site features and to identify key sites driving host adaptation along with their synergistic effects. METHODS: A total of 64,000 IAV sequences from avian, human, swine, and bovine hosts were analyzed to build host-prediction models. A four-class classification framework (avian, human, swine, bovine) was constructed using nucleotide site features from all eight genomic segments (PB2, PB1, PA, HA, NP, NA, MP, NS). Eight machine learning algorithms (logistic regression, decision tree, random forest, SVM, KNN, gradient boosting, XGBoost, LightGBM) were benchmarked via 10-fold stratified cross-validation. Model performance was evaluated using accuracy, precision, recall, F1-score, AUPRC, and AUC. SHAP (SHapley Additive exPlanations) analysis prioritized critical nucleotide sites, while bivariate association tests identified synergistic/antagonistic interactions between sites. Nucleotide composition profiles were compared across host groups using hierarchical clustering and heatmap visualization. RESULTS: The XGBoost algorithm demonstrated the best and most stable performance, achieving an AUC value of over 0.95 in distinguishing human-derived sequences from non-human ones. SHAP analysis identified the top 20 critical nucleotide sites for each gene segment, such as sites 46 and 698 in the NS segment. Nucleotide composition analysis revealed high similarity between human and swine sequences in the HA and PB2 segments, and between avian and bovine sequences. The HA segment was particularly challenging in differentiating human from swine strains. Bivariate site association analysis uncovered significant synergistic or antagonistic effects between key sites within gene segments, forming complex networks. For instance, in the NS segment, a positive prediction contribution was observed when sites 371, 698, and 419 were all G. CONCLUSIONS: This study advances our mechanistic understanding of IAV host adaptation, identifies molecular determinants for zoonotic risk stratification, and establishes a scalable machine learning framework for predicting viral host tropism through nucleotide signature analysis, thereby enhancing surveillance strategies and informing preventive measures against emerging viral threats.

Influenza A virus

The suprachiasmatic nucleus regulates brown fat thermogenesis in male mice through an adrenergic receptor ADRB3-S100B signaling pathway.

The suprachiasmatic nucleus (SCN), the central circadian pacemaker, orchestrates daily metabolic rhythms, yet its role in substrate selection and thermogenic adaptation under stress remains insufficiently understood. Here, we show that SCN lesioning abolishes the adaptive suppression of brown adipose tissue (BAT) thermogenesis typically observed during time-restricted feeding in subthermoneutral environments (TRF-STE), a paradigm that imposes concurrent nutrient and thermal stress. Contrary to wild-type responses, SCN-lesioned mice maintain elevated BAT thermogenic activity, despite impaired lipolysis, instead shifting toward glucose-driven heat production. This phenotype is accompanied by sustained sympathetic tone and &#x3b2;3-adrenergic receptor (ADRB3) signaling in BAT. Mechanistically, we identify a SCN-regulated ADRB3-S100B signaling axis underlying this metabolic reprogramming. S100B, a nutrient-sensitive calcium-binding protein, is upregulated in BAT following SCN disruption, where it promotes thermogenesis by stimulating brown adipocyte proliferation and suppressing senescence. Functional studies reveal that S100B is both necessary and sufficient for sustaining BAT thermogenesis under TRF-STE. Furthermore, diverse SCN disruption models, including light-induced circadian arrhythmia, N-Methyl-D-aspartic acid (NMDA) excitotoxicity, and Caspase-3-mediated ablation, consistently elevate S100B expression in BAT, reinforcing its role as a convergent effector of SCN-regulated metabolic adaptation. Thus, in intact animal, the SCN restrains the ADRB3-S100B module, gating BAT thermogenic output in accordance with energetic availability. Disruption of SCN output lifts this restraint, unmasking a latent ADRB3-S100B program that preserves thermogenesis when lipid fuel is limited. These findings reveal a previously unrecognized role of the SCN in governing thermogenic flexibility and fuel partitioning, and position the ADRB3-S100B axis as a potential target for mitigating circadian misalignment and metabolic disease.

Animals

ZEB family is a prognostic biomarker and correlates with anoikis and immune infiltration in kidney renal clear cell carcinoma.

BACKGROUND: Zinc finger E-box binding homEeobox 1 (ZEB1) and ZEB2 are two anoikis-related transcription factors. The mRNA expressions of these two genes are significantly increased in kidney renal clear cell carcinoma (KIRC), which are associated with poor survival. Meanwhile, the mechanisms and clinical significance of ZEB1 and ZEB2 upregulation in KIRC remain unknown. METHODS: Through the Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database, expression profiles, prognostic value and receiver operating characteristic curves (ROCs) of ZEB1 and ZEB2 were evaluated. The correlations of ZEB1 and ZEB2 with anoikis were further assessed in TCGA-KIRC database. Next, miRTarBase, miRDB, and TargetScan were used to predict microRNAs targeting ZEB1 and ZEB2, and TCGA-KIRC database was utilized to discern differences in microRNAs and establish the association between microRNAs and ZEBs. TCGA, TIMER, TISIDB, and TISCH were used to analyze tumor immune infiltration. RESULTS: It was found that ZEB1 and ZEB2 expression were related with histologic grade in KIRC patient. Kaplan-Meier survival analyses showed that KIRC patients with low ZEB1 or ZEB2 levels had a significantly lower survival rate. Meanwhile, ZEB1 and ZEB2 are closely related to anoikis and are regulated by microRNAs. We constructed a risk model using univariate Cox and LASSO regression analyses to identify two microRNAs (hsa-miR-130b-3p and hsa-miR-138-5p). Furthermore, ZEB1 and ZEB2 regulate immune cell invasion in KIRC tumor microenvironments. CONCLUSIONS: Anoikis, cytotoxic immune cell infiltration, and patient survival outcomes were correlated with ZEB1 and ZEB2 mRNA upregulation in KIRC. ZEB1 and ZEB2 are regulated by microRNAs.

Humans