Age-dependent topic modeling of comorbidities in UK Biobank identifies disease subtypes with differential genetic risk.

The analysis of longitudinal data from electronic health records (EHRs) has the potential to improve clinical diagnoses and enable personalized medicine, motivating efforts to identify disease subtypes from patient comorbidity information. Here we introduce an age-dependent topic modeling (ATM) method that provides a low-rank representation of longitudinal records of hundreds of distinct diseases in large EHR datasets. We applied ATM to 282,957 UK Biobank samples, identifying 52 diseases with heterogeneous comorbidity profiles; analyses of 211,908 All of Us samples produced concordant results. We defined subtypes of the 52 heterogeneous diseases based on their comorbidity profiles and compared genetic risk across disease subtypes using polygenic risk scores (PRSs), identifying 18 disease subtypes whose PRS differed significantly from other subtypes of the same disease. We further identified specific genetic variants with subtype-dependent effects on disease risk. In conclusion, ATM identifies disease subtypes with differential genome-wide and locus-specific genetic risk profiles.

Progress in copper-based materials for wound healing.

Chronic wounds have become the leading cause of death, particularly among diabetic patients. Chronic wounds affect ~6.5 million patients each year, according to statistics, and wound care and management incur significant financial costs. The rising prevalence of chronic wounds, combined with the limitations of current treatments, necessitates the development of new and innovative approaches to accelerate wound healing. Copper has been extensively studied for its antibacterial and anti-inflammatory activities. Copper in its nanoparticle form could have better biological properties and many applications in health care.

Topic modeling identifies novel genetic loci associated with multimorbidities in UK Biobank.

Many diseases show patterns of co-occurrence, possibly driven by systemic dysregulation of underlying processes affecting multiple traits. We have developed a method (treeLFA) for identifying such multimorbidities from routine health-care data, which combines topic modeling with an informative prior derived from medical ontology. We apply treeLFA to UK Biobank data and identify a variety of topics representing multimorbidity clusters, including a healthy topic. We find that loci identified using topic weights as traits in a genome-wide association study (GWAS) analysis, which we validated with a range of approaches, only partially overlap with loci from GWASs on constituent single diseases. We also show that treeLFA improves upon existing methods like latent Dirichlet allocation in various ways. Overall, our findings indicate that topic models can characterize multimorbidity patterns and that genetic analysis of these patterns can provide insight into the etiology of complex traits that cannot be determined from the analysis of constituent traits alone.

PHONEME-LEVEL BERT FOR ENHANCED PROSODY OF TEXT-TO-SPEECH WITH GRAPHEME PREDICTIONS.

Large-scale pre-trained language models have been shown to be helpful in improving the naturalness of text-to-speech (TTS) models by enabling them to produce more naturalistic prosodic patterns. However, these models are usually word-level or sup-phoneme-level and jointly trained with phonemes, making them inefficient for the downstream TTS task where only phonemes are needed. In this work, we propose a phoneme-level BERT (PL-BERT) with a pretext task of predicting the corresponding graphemes along with the regular masked phoneme predictions. Subjective evaluations show that our phoneme-level BERT encoder has significantly improved the mean opinion scores (MOS) of rated naturalness of synthesized speech compared with the state-of-the-art (SOTA) StyleTTS baseline on out-of-distribution (OOD) texts.

Identification of key modules and driving genes in nonalcoholic fatty liver disease by weighted gene co-expression network analysis.

BACKGROUND: Nonalcoholic fatty liver disease (NAFLD) is characterized by excessive liver fat deposition, and progresses to liver cirrhosis, and even hepatocellular carcinoma. However, the invasive diagnosis of NAFLD with histopathological evaluation remains risky. This study investigated potential genes correlated with NAFLD, which may serve as diagnostic biomarkers and even potential treatment targets. METHODS: The weighted gene co-expression network analysis (WGCNA) was constructed based on dataset E-MEXP-3291. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to evaluate the function of genes. RESULTS: Blue module was positively correlated, and turquoise module negatively correlated with the severity of NAFLD. Furthermore, 8 driving genes (ANXA9, FBXO2, ORAI3, NAGS, C/EBPα, CRYAA, GOLM1, TRIM14) were identified from the overlap of genes in blue module and GSE89632. And another 8 driving genes were identified from the overlap of turquoise module and GSE89632. Among these driving genes, C/EBPα (CCAAT/enhancer binding protein α) was the most notable. By validating the expression of C/EBPα in the liver of NAFLD mice using immunohistochemistry, we discovered a significant upregulation of C/EBPα protein in NAFLD. CONCLUSION: we identified two modules and 16 driving genes associated with the progression of NAFLD, and confirmed the protein expression of C/EBPα, which had been paid little attention to in the context of NAFLD, in the present study. Our study will advance the understanding of NAFLD. Moreover, these driving genes may serve as biomarkers and therapeutic targets of NAFLD.

Identification and verification of pivotal genes promoting the progression of atherosclerosis based on WGCNA.

As the main pathological basis for the development of cardiovascular and cerebrovascular diseases, atherosclerosis (AS) seriously affects human health. The key targets of biological information analysis of AS can help exploit therapeutic targets. The expression data of early and progressive atherosclerotic tissues were downloaded from the Gene Expression Omnibus (GEO) database. Based on GSE28829 and GSE120521, 74 key genes were obtained through differential expression analysis and weighted correlation network analysis (WGCNA) analysis, which were mainly enriched in the regulating of inflammatory response, chemokine signalling pathway, apoptosis, lipid and AS, Toll-like receptor signalling pathway and so on according to the results of the enrichment analysis. Cytoscape software was applied to screen four pivotal genes (TYROBP, ITGB2, ITGAM and TLR2) based on PPI. The results of the correlation analysis showed that the expression level of pivotal genes was positively related to macrophages M0, and was negatively related to T cells follicular helper. In addition, the expression of ITGB2 was positively related to Tregs. In this study, bioinformatics was applied to screen pivotal genes affecting the progress of AS, which were significantly related to immune-related biological functions and signal pathways of atherosclerotic tissues and the infiltration level of immune cells. Therefore, pivotal genes were expected to become therapeutic targets for AS.

Graphene oxide chronic exposure enhanced perfluorooctane sulfonate mediated toxicity through oxidative stress generation in freshwater clam Corbicula fluminea.

Graphene oxide (GO), a frequently utilized graphene family nanomaterial, is inevitably released into the aquatic environment and interacts with organic pollutants, including perfluorooctane sulfonate (PFOS), a well-known persistent organic pollutant. To determine the adverse effects of GO chronic exposure on PFOS bioaccumulation and toxicity, adult freshwater bivalves, namely Asian clams (Corbicula fluminea) were treated for 28 days with PFOS (500 ng/L) and different concentrations of GO (0.2, 1, 5 mg/L) as PFOS single and GO single exposure groups, as well as PFOS-GO mixture exposure groups. Our results demonstrated that the bioaccumulation of PFOS was significantly enhanced by co-exposure in gills and visceral masses, which was 1.64-2.91 times higher in gills than in visceral masses. Both single, as well as co-exposure, caused a significant reduction in clams' siphoning behavior, compared to the controls. Further, the co-exposure significantly increased the production of reactive oxygen species (ROS), exacerbating malondialdehyde (MDA) content, enhancing superoxide dismutase (SOD) and catalase (CAT), while decreasing glutathione reductase (GR) and glutathione S-transferase (GST) enzymatic activities in clam tissues. And co-exposure significantly altered the expressions of se-gpx, sod, cyp30, hsp40, and hsp22 genes (associated with oxidative stress and xenobiotic metabolism) both in gills and visceral masses. Moreover, co-exposure caused significant histopathological changes such as cilia degradation in the gills, expansion of tubule lumens in digestive glands, and oocyte shrinkage in gonads. Finally, the enhanced integrated biomarker response (EIBR) index revealed that co-exposure to 500 ng/L PFOS + 1 mg/L/5 mg/L GO was the most stressful circumstance. Overall, our findings suggested that the presence of GO increased PFOS bioaccumulation in tissues, inducing multifaceted negative implications at molecular and behavioral levels through oxidative stress generation in Asian clams.

The impact of age on genetic risk for common diseases.

Inherited genetic variation contributes to individual risk for many complex diseases and is increasingly being used for predictive patient stratification. Previous work has shown that genetic factors are not equally relevant to human traits across age and other contexts, though the reasons for such variation are not clear. Here, we introduce methods to infer the form of the longitudinal relationship between genetic relative risk for disease and age and to test whether all genetic risk factors behave similarly. We use a proportional hazards model within an interval-based censoring methodology to estimate age-varying individual variant contributions to genetic relative risk for 24 common diseases within the British ancestry subset of UK Biobank, applying a Bayesian clustering approach to group variants by their relative risk profile over age and permutation tests for age dependency and multiplicity of profiles. We find evidence for age-varying relative risk profiles in nine diseases, including hypertension, skin cancer, atherosclerotic heart disease, hypothyroidism and calculus of gallbladder, several of which show evidence, albeit weak, for multiple distinct profiles of genetic relative risk. The predominant pattern shows genetic risk factors having the greatest relative impact on risk of early disease, with a monotonic decrease over time, at least for the majority of variants, although the magnitude and form of the decrease varies among diseases. As a consequence, for diseases where genetic relative risk decreases over age, genetic risk factors have stronger explanatory power among younger populations, compared to older ones. We show that these patterns cannot be explained by a simple model involving the presence of unobserved covariates such as environmental factors. We discuss possible models that can explain our observations and the implications for genetic risk prediction.

Complete mitochondrial genome of a leaf-mining beetle, Podagricomela nigricollis (Coleoptera: Chrysomelidae).

Podagricomela nigricollis is a citrus pest that distributes in South China. Currently, there was no complete mitochondrial genome of Podagricomela species available in GenBank. Here, we reported the complete circular mitogenome of P. nigricollis. It had a total length of 16,756 bp, including 13 protein-coding genes (PCGs), 22 tRNA genes, two rRNA genes, and one A + T-rich region. Among the 13 PCGs, only four (NAD5, NAD4, NAD4l, NAD1) located on the L-strand, whereas the other nine (NAD2, COX1, COX2, ATP8, ATP6, COX3, NAD3, NAD6, COB) located on the H-strand. Phylogenetic analysis using nucleotide sequences of the 13 PCGs indicated that P. nigricollis were clustered with six Galerucinae species, which was consistent with previous morphological classification.

Complete mitochondrial genome of a leaf-mining beetle, Agonita chinensis Weise (Coleoptera: Chrysomelidae).

The complete circular mitochondrial genome of Agonita chinensis was 16,395 bp in length, which contained two ribosomal RNA genes, 22 transfer RNAs, 13 protein-coding genes (PCGs) and one non-coding AT-rich region with the length of 2001 bp. All of the 22 tRNA genes displayed a typical clover-leaf structure, with the exception of tRNASer (TCT). Twelve PCGs were initiated by ATN codons, except that nad1 started with TTG. Only four PCGs used the typical stop codon 'TAA' and 'TGA', while nine PCGs terminated with incomplete stop codons (TA or T). Phylogenetic analysis based on 13 PCGs of Chrysomelidae mitogenomes showed that A. chinensis was closely related to Cassida viridis.