Single-cell transcriptome and metagenome profiling reveals the genetic basis of rumen functions and convergent developmental patterns in ruminants.

The rumen undergoes developmental changes during maturation. To characterize this understudied dynamic process, we profiled single-cell transcriptomes of about 308,000 cells from the rumen tissues of sheep and goats at 17 time points. We built comprehensive transcriptome and metagenome atlases from early embryonic to rumination stages, and recapitulated histomorphometric and transcriptional features of the rumen, revealing key transitional signatures associated with the development of ruminal cells, microbiota, and core transcriptional regulatory networks. In addition, we identified and validated potential cross-talk between host cells and microbiomes and revealed their roles in modulating the spatiotemporal expression of key genes in ruminal cells. Cross-species analyses revealed convergent developmental patterns of cellular heterogeneity, gene expression, and cell-cell and microbiome-cell interactions. Finally, we uncovered how the interactions can act upon the symbiotic rumen system to modify the processes of fermentation, fiber digestion, and immune defense. These results significantly enhance understanding of the genetic basis of the unique roles of rumen.

PubTator 3.0: an AI-powered literature resource for unlocking biomedical knowledge.

PubTator 3.0 (https://www.ncbi.nlm.nih.gov/research/pubtator3/) is a biomedical literature resource using state-of-the-art AI techniques to offer semantic and relation searches for key concepts like proteins, genetic variants, diseases and chemicals. It currently provides over one billion entity and relation annotations across approximately 36 million PubMed abstracts and 6 million full-text articles from the PMC open access subset, updated weekly. PubTator 3.0's online interface and API utilize these precomputed entity relations and synonyms to provide advanced search capabilities and enable large-scale analyses, streamlining many complex information needs. We showcase the retrieval quality of PubTator 3.0 using a series of entity pair queries, demonstrating that PubTator 3.0 retrieves a greater number of articles than either PubMed or Google Scholar, with higher precision in the top 20 results. We further show that integrating ChatGPT (GPT-4) with PubTator APIs dramatically improves the factuality and verifiability of its responses. In summary, PubTator 3.0 offers a comprehensive set of features and tools that allow researchers to navigate the ever-expanding wealth of biomedical literature, expediting research and unlocking valuable insights for scientific discovery.

Genomic analyses of wild argali, domestic sheep, and their hybrids provide insights into chromosome evolution, phenotypic variation, and germplasm innovation.

Understanding the genetic mechanisms of phenotypic variation in hybrids between domestic animals and their wild relatives may aid germplasm innovation. Here, we report the high-quality genome assemblies of a male Pamir argali (O ammon polii, 2n = 56), a female Tibetan sheep (O aries, 2n = 54), and a male hybrid of Pamir argali and domestic sheep, and the high-throughput sequencing of 425 ovine animals, including the hybrids of argali and domestic sheep. We detected genomic synteny between Chromosome 2 of sheep and two acrocentric chromosomes of argali. We revealed consistent satellite repeats around the chromosome breakpoints, which could have resulted in chromosome fusion. We observed many more hybrids with karyotype 2n = 54 than with 2n = 55, which could be explained by the selfish centromeres, the possible decreased rate of normal/balanced sperm, and the increased incidence of early pregnancy loss in the aneuploid ewes or rams. We identified genes and variants associated with important morphological and production traits (e.g., body weight, cannon circumference, hip height, and tail length) that show significant variations. We revealed a strong selective signature at the mutation (c.334C > A, p.G112W) in TBXT and confirmed its association with tail length among sheep populations of wide geographic and genetic origins. We produced an intercross population of 110 F2 offspring with varied number of vertebrae and validated the causal mutation by whole-genome association analysis. We verified its function using CRISPR-Cas9 genome editing. Our results provide insights into chromosomal speciation and phenotypic evolution and a foundation of genetic variants for the breeding of sheep and other animals.

Genome-wide identification of GATA transcription factor family and the effect of different light quality on the accumulation of terpenoid indole alkaloids in Uncaria rhynchophylla.

Uncaria rhynchophylla is an evergreen vine plant, belonging to the Rubiaceae family, that is rich in terpenoid indole alkaloids (TIAs) that have therapeutic effects on hypertension and Alzheimer's disease. GATA transcription factors (TF) are a class of transcription regulators that participate in the light response regulation, chlorophyll synthesis, and metabolism, with the capability to bind to GATA cis-acting elements in the promoter region of target genes. Currently the charactertics of GATA TFs in U. rhynchophylla and how different light qualities affect the expression of GATA and key enzyme genes, thereby affecting the changes in U. rhynchophylla alkaloids have not been investigated. In this study, 25 UrGATA genes belonging to four subgroups were identified based on genome-wide analysis. Intraspecific collinearity analysis revealed that only segmental duplications were identified among the UrGATA gene family. Collinearity analysis of GATA genes between U. rhynchophylla and four representative plant species, Arabidopsis thaliana, Oryza sativa, Coffea Canephora, and Catharanthus roseus was also performed. U. rhynchophylla seedlings grown in either red lights or under reduced light intensity had altered TIAs content after 21 days. Gene expression analysis reveal a complex pattern of expression from the 25 UrGATA genes as well as a number of key TIA enzyme genes. UrGATA7 and UrGATA8 were found to have similar expression profiles to key enzyme TIA genes in response to altered light treatments, implying that they may be involved in the regulation TIA content. In this research, we comprehensively analyzed the UrGATA TFs, and offered insight into the involvement of UrGATA TFs from U. rhynchophylla in TIAs biosynthesis.

BioRED: a rich biomedical relation extraction dataset.

Automated relation extraction (RE) from biomedical literature is critical for many downstream text mining applications in both research and real-world settings. However, most existing benchmarking datasets for biomedical RE only focus on relations of a single type (e.g. protein-protein interactions) at the sentence level, greatly limiting the development of RE systems in biomedicine. In this work, we first review commonly used named entity recognition (NER) and RE datasets. Then, we present a first-of-its-kind biomedical relation extraction dataset (BioRED) with multiple entity types (e.g. gene/protein, disease, chemical) and relation pairs (e.g. gene-disease; chemical-chemical) at the document level, on a set of 600 PubMed abstracts. Furthermore, we label each relation as describing either a novel finding or previously known background knowledge, enabling automated algorithms to differentiate between novel and background information. We assess the utility of BioRED by benchmarking several existing state-of-the-art methods, including Bidirectional Encoder Representations from Transformers (BERT)-based models, on the NER and RE tasks. Our results show that while existing approaches can reach high performance on the NER task (F-score of 89.3%), there is much room for improvement for the RE task, especially when extracting novel relations (F-score of 47.7%). Our experiments also demonstrate that such a rich dataset can successfully facilitate the development of more accurate, efficient and robust RE systems for biomedicine. Availability: The BioRED dataset and annotation guidelines are freely available at https://ftp.ncbi.nlm.nih.gov/pub/lu/BioRED/.

GNorm2: an improved gene name recognition and normalization system.

MOTIVATION: Gene name normalization is an important yet highly complex task in biomedical text mining research, as gene names can be highly ambiguous and may refer to different genes in different species or share similar names with other bioconcepts. This poses a challenge for accurately identifying and linking gene mentions to their corresponding entries in databases such as NCBI Gene or UniProt. While there has been a body of literature on the gene normalization task, few have addressed all of these challenges or make their solutions publicly available to the scientific community. RESULTS: Building on the success of GNormPlus, we have created GNorm2: a more advanced tool with optimized functions and improved performance. GNorm2 integrates a range of advanced deep learning-based methods, resulting in the highest levels of accuracy and efficiency for gene recognition and normalization to date. Our tool is freely available for download. AVAILABILITY AND IMPLEMENTATION: https://github.com/ncbi/GNorm2.

AIONER: all-in-one scheme-based biomedical named entity recognition using deep learning.

MOTIVATION: Biomedical named entity recognition (BioNER) seeks to automatically recognize biomedical entities in natural language text, serving as a necessary foundation for downstream text mining tasks and applications such as information extraction and question answering. Manually labeling training data for the BioNER task is costly, however, due to the significant domain expertise required for accurate annotation. The resulting data scarcity causes current BioNER approaches to be prone to overfitting, to suffer from limited generalizability, and to address a single entity type at a time (e.g. gene or disease). RESULTS: We therefore propose a novel all-in-one (AIO) scheme that uses external data from existing annotated resources to enhance the accuracy and stability of BioNER models. We further present AIONER, a general-purpose BioNER tool based on cutting-edge deep learning and our AIO schema. We evaluate AIONER on 14 BioNER benchmark tasks and show that AIONER is effective, robust, and compares favorably to other state-of-the-art approaches such as multi-task learning. We further demonstrate the practical utility of AIONER in three independent tasks to recognize entity types not previously seen in training data, as well as the advantages of AIONER over existing methods for processing biomedical text at a large scale (e.g. the entire PubMed data). AVAILABILITY AND IMPLEMENTATION: The source code, trained models and data for AIONER are freely available at https://github.com/ncbi/AIONER.

Term-BLAST-like alignment tool for concept recognition in noisy clinical texts.

MOTIVATION: Methods for concept recognition (CR) in clinical texts have largely been tested on abstracts or articles from the medical literature. However, texts from electronic health records (EHRs) frequently contain spelling errors, abbreviations, and other nonstandard ways of representing clinical concepts. RESULTS: Here, we present a method inspired by the BLAST algorithm for biosequence alignment that screens texts for potential matches on the basis of matching k-mer counts and scores candidates based on conformance to typical patterns of spelling errors derived from 2.9 million clinical notes. Our method, the Term-BLAST-like alignment tool (TBLAT) leverages a gold standard corpus for typographical errors to implement a sequence alignment-inspired method for efficient entity linkage. We present a comprehensive experimental comparison of TBLAT with five widely used tools. Experimental results show an increase of 10% in recall on scientific publications and 20% increase in recall on EHR records (when compared against the next best method), hence supporting a significant enhancement of the entity linking task. The method can be used stand-alone or as a complement to existing approaches. AVAILABILITY AND IMPLEMENTATION: Fenominal is a Java library that implements TBLAT for named CR of Human Phenotype Ontology terms and is available at https://github.com/monarch-initiative/fenominal under the GNU General Public License v3.0.

Cu-Catalyzed Direct Enantioselective Vinylogous Mannich Reaction between ß,γ-Unsaturated Pyrazoleamides and Ketimines.

A catalytic asymmetric vinylogous Mannich-type reaction between ß,γ-unsaturated amides and ketimines has been developed in excellent regio-, diastereo-, and enantioselectivities. The methodology provides an efficient approach to construct chiral homoallylic amines with a 3-amino-2-oxindole scaffold. Moreover, the transformations of the chiral products, including the removal of the pyrazole group or Boc group, the reduction of the C-C double bond, and Suzuki coupling, have been investigated.

P2X7 receptor is essential for ST36-attenuated cardiac fibrosis upon beta-adrenergic insult.

P2X7 receptor (P2X7R) plays an important role in modulating inflammation and fibrosis, but information is limited whether Zusanli (ST36) can inhibit inflammation and fibrosis by regulating P2X7R. Isoprenaline at 5 mg/kg was subcutaneously injected to wild-type and P2X7R knockout mice for 7 days, while treatment groups received electroacupuncture (EA) stimulation at ST36 for 7 sessions. Following 7-session treatment, Masson's trichrome staining was performed to assess the fibrosis. Morphology, electrocardiogram, and echocardiography were carried out to evaluate the cardiac function and structure. Western blotting, hematoxylin and eosin staining, immunohistochemistry, and biochemical analysis of inflammatory cytokine and transmission electron microscopy were carried out to characterize the effect of ST36 on inflammation. P2X7R was overexpressed in ISO-treated mice. EA at ST36, but not at non-points, reduced ISO-induced cardiac fibrosis, increases in HW/BW, R+S wave relative to mice in ISO groups. In addition, EA at ST36 downregulated ISO-upregulated P2X7R and NLRP3 in ventricle. Moreover, EA reduced cytokines of IL-1ß, IL-6, and IL-18 in serum, and inhibited foam cell gathering, inflammatory cell infiltration, and autophagy. However, EA at ST36 failed to attenuate the cardiac fibrosis and hypertrophy in P2X7R knockout mice. In conclusion, EA at ST36 attenuated ISO-induced fibrosis possibly via P2X7R.

Anti-inflammatory diet reduces risk of metabolic dysfunction-associated fatty liver disease among US adults: a nationwide survey.

BACKGROUND: While dietary intervention was an important public health strategy for the prevention and intervention of metabolic dysfunction-associated fatty liver disease (MAFLD), the effect of diet-induced inflammation on MAFLD has not been studied in detail. Therefore, we aimed to analyze the relationship between dietary inflammatory index (DII) and MAFLD. METHODS: This study included data from the National Health and Nutrition Examination Survey 2017-2018. MAFLD was diagnosed based on the presence of hepatic steatosis, as determined by transient elastography, along with evidence of either overweight/obesity, type 2 diabetes mellitus, or metabolic dysfunction. DII was calculated using 27 dietary components collected through 24-hour dietary recall questionnaire. Weighted logistic regression was used to analyze the relationship between DII and MAFLD and its main components in three different models. Subgroup analyses were performed by age, sex, and alcohol use. RESULTS: A total of 1991 participants were included, and the MAFLD group had higher DII scores. After adjusting for age, sex, race, physical activity, smoking status, and alcohol use, the highest quartile of DII was associated with increased risk of MAFLD (OR:2.90, 95% CIs: 1.46, 5.75). Overweight/obesity, central obesity, low high density lipoprotein cholesterol (HDL-C) and high C-reactive protein (CRP) also shared the same characteristics in the main components of MAFLD. Results were consistent across subgroups (age, sex, and alcohol use). CONCLUSIONS: A higher DII diet was positively associated with the risk of MAFLD in American adults, particularly as related to overweight/obesity, central obesity, high CRP level, and low HDL-C level.

A carbon dot nanozyme hydrogel enhances pulp regeneration activity by regulating oxidative stress in dental pulpitis.

Preserving pulp viability and promoting pulp regeneration in pulpitis have attracted widespread attention. Restricted by the oxidative stress microenvironment of dental pulpitis, excessive reactive oxygen and nitrogen species (RONS) trigger uncontrolled inflammation and exacerbate pulp tissue destruction. However, modulating redox homeostasis in inflamed pulp tissue to promote pulp regeneration remains a great challenge. Herein, this work proposes an effective antioxidative system (C-NZ/GelMA) consisting of carbon dot nanozymes (C-NZ) with gelatin methacryloyl (GelMA) to modulate the pulpitis microenvironment for dental pulp regeneration by utilizing the antioxidant properties of C-NZ and the mechanical support of an injectable GelMA hydrogel. This system effectively scavenges RONS to normalize intracellular redox homeostasis, relieving oxidative stress damage. Impressively, it can dramatically enhance the polarization of regenerative M2 macrophages. This study revealed that the C-NZ/GelMA hydrogel promoted pulp regeneration and dentin repair through its outstanding antioxidant, antiapoptotic, and anti-inflammatory effects, suggesting that the C-NZ/GelMA hydrogel is highly valuable for pulpitis treatment.

Ultrasound evaluation of cardiac and diaphragmatic function at different positions during a spontaneous breathing trial predicting extubation outcomes: a retrospective cohort study.

BACKGROUND: The ratio (E/Ea) of mitral Doppler inflow velocity to annular tissue Doppler wave velocity by transthoracic echocardiography and diaphragmatic excursion (DE) by diaphragm ultrasound have been confirmed to predict extubation outcomes. However, few studies focused on the predicting value of E/Ea and DE at different positions during a spontaneous breathing trial (SBT), as well as the effects of △E/Ea and △DE (changes in E/Ea and DE during a SBT). METHODS: This study was a reanalysis of the data of 60 difficult-to-wean patients in a previous study published in 2017. All eligible participants were organized into respiratory failure (RF) group and extubation success (ES) group within 48 h after extubation, or re-intubation (RI) group and non-intubation (NI) group within 1 week after extubation. The risk factors for respiratory failure and re-intubation including E/Ea and △E/Ea, DE and △DE at different positions were analyzed by multivariate logistic regression, respectively. The receiver operating characteristic (ROC) curves of E/Ea (septal, lateral, average) and DE (right, left, average) were compared with each other, respectively. RESULTS: Of the 60 patients, 29 cases developed respiratory failure within 48 h, and 14 of those cases required re-intubation within 1 week. Multivariate logistic regression showed that E/Ea were all associated with respiratory failure, while only DE (right) and DE (average) after SBT were related to re-intubation. There were no statistic differences among the ROC curves of E/Ea at different positions, nor between the ROC curves of DE. No statistical differences were shown in △E/Ea between RF and ES groups, while △DE (average) was remarkably higher in NI group than that in RI group. However, multivariate logistic regression analysis showed that △DE (average) was not associated with re-intubation. CONCLUSIONS: E/Ea at different positions during a SBT could predict postextubation respiratory failure with no statistical differences among them. Likewise, only DE (right) and DE (average) after SBT might predict re-intubation with no statistical differences between each other.

Association between standing height and physical disability among U.S. adults aged 60 years and older: findings from NHANES 2015-2018.

BACKGROUND: Physical disability is an important cause of affecting the quality of life in the elderly. The association between standing height and physical disability is less studied. PURPOSE: The purpose of this study is to investigate the possible link between standing height and physical disability among U.S. adults aged 60 years and older. METHODS: The cross-sectional data were obtained from the US National Health and Nutrition Examination Survey (NHANES) 2015-2018. Physical disability was assessed by six questions: "Have serious difficulty hearing (SDH)?", "Have serious difficulty seeing (SDS)?", "Have serious difficulty concentrating (SDC)?", "Have serious difficulty walking (SDW)?", "Have difficulty dressing or bathing (DDB)?" and "Have difficulty doing errands alone (DDEA)?". Responses to these questions were "yes" or "no". Answer yes to one of the above six questions was identified as physical disability. Standing height (cm) was measured with an altimeter. Multivariate logistic regression was performed to examine the possible link between standing height and physical disability after adjustment for all covariates. RESULTS: A total of 2624 participants aged ≥ 60 years were included in our study, including 1279 (48.7%) females and 1345 (51.3%) males. The mean age of participants was 69.41 ± 6.82 years. After adjusting for all potential confounders, the inverse relationship between standing height and all physical disability (APD) was statistically significant (OR = 0.976, 95%CI:0.957-0.995). In addition, among six types of physical disability (SDH, SDS, SDC, SDW, DDB, DDEA), standing height was also a protective factor for SDW (OR = 0.961, 95%CI:0.939-0.983) and DDEA (OR = 0.944, 95%CI:0.915-0.975) in the full-adjusted model. CONCLUSION: The cross-sectional population based study demonstrates that standing height is a protective factor for physical disability among U.S. adults aged 60 years and older.

Bayesian network analysis of factors influencing type 2 diabetes, coronary heart disease, and their comorbidities.

OBJECTIVE: Bayesian network (BN) models were developed to explore the specific relationships between influencing factors and type 2 diabetes mellitus (T2DM), coronary heart disease (CAD), and their comorbidities. The aim was to predict disease occurrence and diagnose etiology using these models, thereby informing the development of effective prevention and control strategies for T2DM, CAD, and their comorbidities. METHOD: Employing a case-control design, the study compared individuals with T2DM, CAD, and their comorbidities (case group) with healthy counterparts (control group). Univariate and multivariate Logistic regression analyses were conducted to identify disease-influencing factors. The BN structure was learned using the Tabu search algorithm, with parameter estimation achieved through maximum likelihood estimation. The predictive performance of the BN model was assessed using the confusion matrix, and Netica software was utilized for visual prediction and diagnosis. RESULT: The study involved 3,824 participants, including 1,175 controls, 1,163 T2DM cases, 982 CAD cases, and 504 comorbidity cases. The BN model unveiled factors directly and indirectly impacting T2DM, such as age, region, education level, and family history (FH). Variables like exercise, LDL-C, TC, fruit, and sweet food intake exhibited direct effects, while smoking, alcohol consumption, occupation, heart rate, HDL-C, meat, and staple food intake had indirect effects. Similarly, for CAD, factors with direct and indirect effects included age, smoking, SBP, exercise, meat, and fruit intake, while sleeping time and heart rate showed direct effects. Regarding T2DM and CAD comorbidities, age, FBG, SBP, fruit, and sweet intake demonstrated both direct and indirect effects, whereas exercise and HDL-C exhibited direct effects, and region, education level, DBP, and TC showed indirect effects. CONCLUSION: The BN model constructed using the Tabu search algorithm showcased robust predictive performance, reliability, and applicability in forecasting disease probabilities for T2DM, CAD, and their comorbidities. These findings offer valuable insights for enhancing prevention and control strategies and exploring the application of BN in predicting and diagnosing chronic diseases.

The distribution and key influential factors of antibiotic resistance genes in agricultural soils polluted by multiple heavy metals.

Due to anthropogenic activities such as mining, several agricultural soils are polluted by multiple heavy metals. However, it is still unclear whether multiple heavy metals could affect the distribution of antibiotic resistance genes (ARGs), and how metals affect ARGs. To understand ARGs' distribution in heavy metal-polluted soils, we chose soils contaminated by different types and contents of heavy metals to determine the ARGs' number and abundance through high-throughput quantitative real-time PCR (HT-qPCR) in this study. Additionally, the factors affecting ARGs' distribution, such as soil properties, mobile genetic genes (MGEs), and bacterial communities, were explored. The results demonstrated that the sampled soils were primarily contaminated by Cd, As, Pb, and Zn, and the pollution load index (PLI) values of these metals ranged from 1.3 to 2.7, indicating a low to moderate degree of heavy metal contamination. The number and abundance of ARGs ranged from 44 to 113 and from 2.74 × 107 copies/g to 1.07 × 108 copies/g, respectively. Besides, abundant MGEs in soils, ranging from 1.84 × 106 copies/g to 5.82 × 106 copies/g, were observed. The pathway analysis suggested that MGEs were the most important factor directly affecting ARG abundance (0.89). Notably, heavy metals also affected the ARG abundance. Proteobacteria and Actinobacteria, the main heavy metal tolerant bacteria, were found to be the main hosts of ARGs through network analysis. ARG-carrying pathogens (ACPs) in agricultural soils were found to carry MGEs, indicating a high risk of dissemination. This study provided important information for understanding the ARGs' fate and also the key factors affecting ARGs' spread in multiple heavy metal-contaminated soils.

Transcriptomic analysis reveals the early body wall regeneration mechanism of the sea cucumber Holothuria leucospilota after artificially induced transverse fission.

BACKGROUND: Sea cucumbers exhibit a remarkable ability to regenerate damaged or lost tissues and organs, making them an outstanding model system for investigating processes and mechanisms of regeneration. They can also reproduce asexually by transverse fission, whereby the anterior and posterior bodies can regenerate independently. Despite the recent focus on intestinal regeneration, the molecular mechanisms underlying body wall regeneration in sea cucumbers still remain unclear. RESULTS: In this study, transverse fission was induced in the tropical sea cucumber, Holothuria leucospilota, through constrainment using rubber bands. Histological examination revealed the degradation and loosening of collagen fibers on day-3, followed by increased density but disorganization of the connective tissue on day-7 of regeneration. An Illumina transcriptome analysis was performed on the H. leucospilota at 0-, 3- and 7-days after artificially induced fission. The differential expression genes were classified and enriched by GO terms and KEGG database, respectively. An upregulation of genes associated with extracellular matrix remodeling was observed, while a downregulation of pluripotency factors Myc, Klf2 and Oct1 was detected, although Sox2 showed an upregulation in expression. In addition, this study also identified progressively declining expression of transcription factors in the Wnt, Hippo, TGF-ß, and MAPK signaling pathways. Moreover, changes in genes related to development, stress response, apoptosis, and cytoskeleton formation were observed. The localization of the related genes was further confirmed through in situ hybridization. CONCLUSION: The early regeneration of H. leucospilota body wall is associated with the degradation and subsequent reconstruction of the extracellular matrix. Pluripotency factors participate in the regenerative process. Multiple transcription factors involved in regulating cell proliferation were found to be gradually downregulated, indicating reduced cell proliferation. Moreover, genes related to development, stress response, apoptosis, and cell cytoskeleton formation were also involved in this process. Overall, this study provides new insights into the mechanisms of whole-body regeneration and uncover potential cross-species regenerative-related genes.

Whole-Genome Resequencing of Worldwide Wild and Domestic Sheep Elucidates Genetic Diversity, Introgression, and Agronomically Important Loci.

Domestic sheep and their wild relatives harbor substantial genetic variants that can form the backbone of molecular breeding, but their genome landscapes remain understudied. Here, we present a comprehensive genome resource for wild ovine species, landraces and improved breeds of domestic sheep, comprising high-coverage (∼16.10×) whole genomes of 810 samples from 7 wild species and 158 diverse domestic populations. We detected, in total, ∼121.2 million single nucleotide polymorphisms, ∼61 million of which are novel. Some display significant (P < 0.001) differences in frequency between wild and domestic species, or are private to continent-wide or individual sheep populations. Retained or introgressed wild gene variants in domestic populations have contributed to local adaptation, such as the variation in the HBB associated with plateau adaptation. We identified novel and previously reported targets of selection on morphological and agronomic traits such as stature, horn, tail configuration, and wool fineness. We explored the genetic basis of wool fineness and unveiled a novel mutation (chr25: T7,068,586C) in the 3'-UTR of IRF2BP2 as plausible causal variant for fleece fiber diameter. We reconstructed prehistorical migrations from the Near Eastern domestication center to South-and-Southeast Asia and found two main waves of migrations across the Eurasian Steppe and the Iranian Plateau in the Early and Late Bronze Ages. Our findings refine our understanding of genome variation as shaped by continental migrations, introgression, adaptation, and selection of sheep.

Study on the role of calcium channel protein TRPV4 in the inflammatory pathway of type 2 diabetic adipose tissue based on gene databases.

BACKGROUND: Chronic inflammation of adipose tissue may be one of the key factors contributing to the development of insulin resistance in T2DM adipose tissue. Transient receptor potential vanilloid type 4 (TRPV4) can be involved in a variety of cellular inflammatory responses. In this study, we evaluated the role of TRPV4 channelin in the T2DM adipose tissue inflammatory pathway. METHODS: Based on the gene expression profiling data of the public database, bioinformatics methods were used to screen the target gene population of the TRPV4 channel protein involved in the regulation of T2DM fat cells. A mature adipocyte model was constructed to verify the expression level of target genes and to evaluate the regulatory effect of TRPV4 channel inhibition on target genes of inflammation-related pathways. RESULTS: In shTRPV4 adipocytes, 144 genes with downregulation expression were screened, a PPI network was constructed and a core module containing 15 genes was screened out, and the core genes were mainly enriched in the Toll-like receptor signaling pathway through enrichment analysis. Constructing a mature adipocyte model found that the TRPV4 inhibitor HC067047 inhibited the effect of upregulation of the expression level of the relevant gene in the signaling pathway. CONCLUSIONS: Our findings suggest that the expression of highly expressed pro-inflammatory cytokines and chemokines in T2DM adipose tissue decreases after inhibiting the expression of TRPV4 in adipocytes, suggesting that TRPV4 may become a potential drug target for the treatment of T2DM.

China's wetland soil organic carbon pool: New estimation on pool size, change, and trajectory.

Robust estimates of wetland soil organic carbon (SOC) pools are critical to understanding wetland carbon dynamics in the global carbon cycle. However, previous estimates were highly variable and uncertain, due likely to the data sources and method used. Here we used machine learning method to estimate SOC storage and their changes over time in China's wetlands based on wetland SOC density database, associated geospatial environmental data, and recently published wetland maps. We built a database of wetland SOC density in China that contains 809 samples from 181 published studies collected over the last 20 years as presented in the published literature. All samples were extended and standardized to a 1-m depth, on the basis of the relationship between SOC density data from soil profiles of different depths. We used three different machine learning methods to evaluate their robustness in estimating wetland SOC storage and changes in China. The results indicated that random forest model achieved accurate wetland SOC estimation with R2 being .65. The results showed that average SOC density of top 1 m in China's wetlands was 25.03 ± 3.11 kg C m-2 in 2000 and 26.57 ± 3.73 kg C m-2 in 2020, an increase of 6.15%. SOC storage change from 4.73 ± 0.58 Pg in 2000 to 4.35 ± 0.61 Pg in 2020, a decrease of 8.03%, due to 13.6% decreased in wetland area from 189.12 × 103 to 162.8 × 103 km2 in 2020, despite the increase in SOC density during the same time period. The carbon accumulation rate was 107.5 ± 12.4 g C m-2 year-1 since 2000 in wetlands with no area changes. Climate change caused variations in wetland SOC density, and a future warming and drying climate would lead to decreases in wetland SOC storage. Estimates under Shared Socioeconomic Pathway 1-2.6 (low-carbon emissions) suggested that wetland SOC storage in China would not change significantly by 2100, but under Shared Socioeconomic Pathway 5-8.5 (high-carbon emissions), it would decrease significantly by approximately 5.77%. In this study, estimates of wetland SOC storage were optimized from three aspects, including sample database, wetland extent, and estimation method. Our study indicates the importance of using consistent SOC density and extent data in estimating and projecting wetland SOC storage.