DDX21 functions as a potential novel oncopromoter in pancreatic ductal adenocarcinoma: a comprehensive analysis of the DExD box family.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal tumor with an ill-defined pathogenesis. DExD box (DDX) family genes are widely distributed and involved in various RNA metabolism and cellular biogenesis; their dysregulation is associated with aberrant cellular processes and malignancies. However, the prognostic significance and expression patterns of the DDX family in PDAC are not fully understood. The present study aimed to explore the clinical value of DDX genes in PDAC. METHODS: Differentially expressed DDX genes were identified. DDX genes related to prognostic signatures were further investigated using LASSO Cox regression analysis. DDX21 protein expression was analyzed using the UALCAN and human protein atlas (HPA) online tools and confirmed in 40 paired PDAC and normal tissues through Tissue Microarrays (TMA). The independent prognostic significance of DDX21 in PDAC was determined through the construction of nomogram models and calibration curves. The functional roles of DDX21 were investigated using gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA). Cell proliferation, invasion, and migration were assessed using Cell Counting Kit-8, colony formation, Transwell, and wound healing assays. RESULTS: Upregulation of genes related to prognostic signatures (DDX10, DDX21, DDX60, and DDX60L) was significantly associated with poor prognosis of patients with PDAC based on survival and recurrence time. Considering the expression profile and prognostic values of the signature-related genes, DDX21 was finally selected for further exploration. DDX21 was overexpressed significantly at both the mRNA and protein levels in PDAC compared to normal pancreatic tissues. DDX21 expression, pathological stage, and residual tumor were significant independent prognostic indicators in PDAC. Moreover, functional enrichment analysis revealed that Genes co-expressed with DDX21 are predominantly involved in RNA metabolism, helicase activity, ribosome biogenesis, cell cycle, and various cancer-related pathways, such as PI3K/Akt signaling pathway and TGF-ß signaling pathway. Furthermore, in vitro experiments confirmed that the knockdown of DDX21 significantly reduced MIA PaCa-2 cell viability, proliferation, migration, and invasion. CONCLUSIONS: Four signature-related genes could relatively precisely predict the prognosis of patients with PDAC. Specifically, DDX21 upregulation may signal an unfavorable prognosis by negatively affecting the biological properties of PDAC cells. DDX21 may be considered as a candidate therapeutic target in PDAC.

Long-term annual mapping and spatial-temporal dynamic analysis of winter wheat in Shandong Province based on spatial-temporal data fusion (2000-2022).

Winter wheat, as one of the world's key staple crops, plays a crucial role in ensuring food security and shaping international food trade policies. However, there has been a relative scarcity of high-resolution, long time-series winter wheat maps over the past few decades. This study utilized Landsat and Sentinel-2 data to produce maps depicting winter wheat distribution in Google Earth Engine (GEE). We further analyzed the comprehensive spatial-temporal dynamics of winter wheat cultivation in Shandong Province, China. The gap filling and Savitzky-Golay filter method (GF-SG) was applied to address temporal discontinuities in the Landsat NDVI (Normalized Difference Vegetation Index) time series. Six features based on phenological characteristics were used to distinguish winter wheat from other land cover types. The resulting maps spanned from 2000 to 2022, featuring a 30-m resolution from 2000 to 2017 and an improved 10-m resolution from 2018 to 2022. The overall accuracy of these maps ranged from 80.5 to 93.3%, with Kappa coefficients ranging from 71.3 to 909% and F1 scores from 84.2 to 96.9%. Over the analyzed period, the area dedicated to winter wheat cultivation experienced a decline from 2000 to 2011. However, a notable shift occurred with an increase in winter wheat acreage observed from 2014 to 2017 and a subsequent rise from 2018 to 2022. This research highlights the viability of using satellite observation data for the long-term mapping and monitoring of winter wheat. The proposed methodology has long-term implications for extending this mapping and monitoring approach to other similar areas.

Comparative Metagenomic Analysis Reveals Rhizosphere Microbiome Assembly and Functional Adaptation Changes Caused by Clubroot Disease in Chinese Cabbage.

Clubroot is a major disease and severe threat to Chinese cabbage, and it is caused by the pathogen Plasmodiophora brassicae Woron. This pathogen is an obligate biotrophic protist and can persist in soil in the form of resting spores for more than 18 years, which can easily be transmitted through a number of agents, resulting in significant economic losses to global Chinese cabbage production. Rhizosphere microbiomes play fundamental roles in the occurrence and development of plant diseases. The changes in the rhizosphere microorganisms could reveal the severity of plant diseases and provide the basis for their control. Here, we studied the rhizosphere microbiota after clubroot disease infections with different severities by employing metagenomic sequencing, with the aim of exploring the relationships between plant health, rhizosphere microbial communities, and soil environments; then, we identified potential biomarker microbes of clubroot disease. The results showed that clubroot disease severity significantly affected the microbial community composition and structure of the rhizosphere soil, and microbial functions were also dramatically influenced by it. Four different microbes that had great potential in the biocontrol of clubroot disease were identified from the obtained results; they were the genera Pseudomonas, Gemmatimonas, Sphingomonas, and Nocardioides. Soil pH, organic matter contents, total nitrogen, and cation exchange capacity were the major environmental factors modulating plant microbiome assembly. In addition, microbial environmental information processing was extremely strengthened when the plant was subjected to pathogen invasion, but weakened when the disease became serious. In particular, oxidative phosphorylation and glycerol-1-phosphatase might have critical functions in enhancing Chinese cabbage's resistance to clubroot disease. This work revealed the interactions and potential mechanisms among Chinese cabbage, soil environmental factors, clubroot disease, and microbial community structure and functions, which may provide a novel foundation for further studies using microbiological or metabolic methods to develop disease-resistant cultivation technologies.

TurboID-mediated proximity labeling technologies to identify virus co-receptors.

Virus receptors determine the tissue tropism of viruses and have a certain relationship with the clinical outcomes caused by viral infection, which is of great importance for the identification of virus receptors to understand the infection mechanism of viruses and to develop entry inhibitor. Proximity labeling (PL) is a new technique for studying protein-protein interactions, but it has not yet been applied to the identification of virus receptors or co-receptors. Here, we attempt to identify co-receptor of SARS-CoV-2 by employing TurboID-catalyzed PL. The membrane protein angiotensin-converting enzyme 2 (ACE2) was employed as a bait and conjugated to TurboID, and a A549 cell line with stable expression of ACE2-TurboID was constructed. SARS-CoV-2 pseudovirus were incubated with ACE2-TurboID stably expressed cell lines in the presence of biotin and ATP, which could initiate the catalytic activity of TurboID and tag adjacent endogenous proteins with biotin. Subsequently, the biotinylated proteins were harvested and identified by mass spectrometry. We identified a membrane protein, AXL, that has been functionally shown to mediate SARS-CoV-2 entry into host cells. Our data suggest that PL could be used to identify co-receptors for virus entry.

Novel f-CaO soft sensor for cement clinker based on integrated model of dual-parallel structure.

Aiming at the problem that the cement production process is inherently affected by uncertainty, time delay, and strong coupling among variables, this paper proposed a novel soft sensor of free calcium oxide in a cement clinker. The model utilizes a dual-parallel integrated structure with an optimized integration of one-dimensional convolutional neural networks, long and short-term memory networks, graphical neural networks, and extreme gradient boosting. The proposed model can mitigate the risks associated with overfitting while incorporating the strengths of each individual model and excels in extracting both local and global features as well as temporal and spatial characteristics from the original time series data, ensuring its stability. The experimental results demonstrate that this dual-parallel integrated model exhibits superior robustness, predictive accuracy, and generalization capabilities when compared to single models or enhancements made to other deep learning algorithms.

Saracatinib prompts hemin-induced K562 erythroid differentiation but suppresses erythropoiesis of hematopoietic stem cells.

Human myeloid leukemia cells (such as K562) could be used for the study of erythropoiesis, and mature erythroid markers and globins could be induced during leukemia cell differentiation; however, the pathways involved are different compared with those of hematopoietic stem cells (HSCs).We identified the differentially expressed genes (DEGs) of K562 cells and HSCs associated with stem cells and erythroid differentiation. Furthermore, we showed that hemin-induced differentiation of K562 cells could be induced by serum starvation or treatment with the tyrosine kinase inhibitor saracatinib. However, erythroid differentiation of HSCs was inhibited by the deprivation of the important serum component erythropoietin (EPO) or treatment with saracatinib. Finally, we found that the mRNA expression of K562 cells and HSCs was different during saracatinib-treated erythroid differentiation, and the DEGs of K562 cells and HSCs associated with tyrosine-protein kinase were identified.These findings elucidated the cellular phenomenon of saracatinib induction during erythroid differentiation of K562 cells and HSCs, and the potential mechanism is the different mRNA expression profile of tyrosine-protein kinase in K562 cells and HSCs.

Similarity of Chinese and Pakistani oral microbiome.

Oral microbiota is vital for human health and can be affected by various factors (i.e. diets, ethnicity). However, few studies have compared oral microbiota of individuals from different nationalities in the same environment. Here, we explored the assembly and interaction of oral microbial communities of Chinese and Pakistanis in one university. Firmicutes and Proteobacteria were the predominant microorganisms in the oral cavity of Chinese and Pakistanis. Streptococcus and Neisseria were the dominant genera of China, while Streptococcus and Haemophilus were the dominant genera of Pakistanis. In addition, the oral community membership and structure were not influenced by season, Chinese/Pakistani student and gender, reflecting the stability of the human oral microbiome. The beta diversity of oral microbiomes between Chinese and Pakistanis significantly differed in winter, but not in spring. The alpha diversity of Chinese students and Pakistani students was similar. Moreover, oral microbial community of both Chinese and Pakistani students was mainly driven by stochastic processes. The microbial network of Chinese was more complexity and stability than that of Pakistanis. Our study uncovers the characteristics of human oral microbiota, which is of great significance for oral and human health.

Prenatal diagnosis of dent disease type I with a nonsense pathogenic variant in CLCN5: a case study.

INTRODUCTION: Dent disease type I is a rare X-linked recessive renal tubular disease resulting from pathogenic variants in the CLCN5 gene. Due to the rarity of Dent disease type I and the diversity of its phenotypes, its clinical diagnosis is complex and poses a challenge to clinicians. METHODS: A foetus and a child from a 36-year-old pregnant woman with a birth history of abnormal children were enrolled in this study. Pregnant women undergo amniocentesis for prenatal diagnosis at the gestational age of 12+ 3 weeks. Chromosomal microarray (CMA) analysis and whole-exome sequencing (WES) were employed to investigate the chromosomal copy number and single gene variants. Literature retrieval and data analysis were performed for genotype and phenotype collection analysis. RESULTS: No chromosomal abnormalities or CNVs were detected in the entire family through karyotype and familial CMA analyses. WES identified a nonsense pathogenic variant in CLCN5 of the X chromosome, c.1942 C > T (exon 11, NM_000084), which was inherited from his mother, who exhibited regular clinical features. CONCLUSION: This study suggests that children with low-molecular-weight proteinuria and hypercalciuria should undergo prompt genetic testing to exclude Dent disease.

Tissue-specific populations from amniotic fluid-derived mesenchymal stem cells manifest variant in vitro and in vivo properties.

Amniotic fluid derived mesenchymal stem cells (AFMSCs), shed along the fetal development, exhibit superior multipotency and immunomodulatory properties compared to MSCs derived from other somatic tissues (e.g., bone marrow and fat). However, AFMSCs display heterogeneity due to source ambiguity, making them an underutilized stem cells source for translational clinical trials. Consequently, there is an urgent need to identify a method to purify the AFMSCs for clinical use. We found that the AFMSCs can be categorized into three distinct groups: kidney-specific AFMSCs (AFMSCs-K), lung-specific AFMSCs (AFMSCs-L), and AFMSCs with an undefined tissue source (AFMSCs-X). This classification was based on tissue-specific gene expression pattern of single cell colony. Additionally, we observed that AFMSCs-X, a minority population within the AFMSCs, exhibited the highest multipotency, proliferation, resistance to senescence and immuno-modulation. Our results showed that AFMSCs-X significantly improved survival rates and reduced bacterial colony forming units (CFU) in cecal ligation and puncture (CLP)-induced septic mice. Therefore, our study introduces a novel classification method to enhance the consistency and efficacy of AFMSCs. These subpopulations, originating from different tissue source, may offer a valuable and innovative resource of cells for regenerative medicine purposes.

Multi-Granularity Graph Convolution Network for Major Depressive Disorder Recognition.

Major depressive disorder (MDD) is the most common psychological disease. To improve the recognition accuracy of MDD, more and more machine learning methods have been proposed to mining EEG features, i.e. typical brain functional patterns and recognition methods that are closely related to depression using resting EEG signals. Most existing methods typically utilize threshold methods to filter weak connections in the brain functional connectivity network (BFCN) and construct quantitative statistical features of brain function to measure the BFCN. However, these thresholds may excessively remove weak connections with functional relevance, which is not conducive to discovering potential hidden patterns in weak connections. In addition, statistical features cannot describe the topological structure features and information network propagation patterns of the brain's different functional regions. To solve these problems, we propose a novel MDD recognition method based on a multi-granularity graph convolution network (MGGCN). On the one hand, this method applies multiple sets of different thresholds to build a multi-granularity functional neural network, which can remove noise while fully retaining valuable weak connections. On the other hand, this method utilizes graph neural network to learn the topological structure features and brain saliency patterns of changes between brain functional regions on the multi-granularity functional neural network. Experimental results on the benchmark datasets validate the superior performance and time complexity of MGGCN. The analysis shows that as the granularity increases, the connectivity defects in the right frontal(RF) and right temporal (RT) regions, left temporal(LT) and left posterior(LP) regions increase. The brain functional connections in these regions can serve as potential biomarkers for MDD recognition.

External validation of the FAMISH predicting score for early gastric cancer with endoscopic submucosal dissection.

OBJECTIVE: To externally validate the robustness of the FAMISH predicting score designed to estimate the risk of metachronous gastric lesions (MGLs) after endoscopic submucosal dissection (ESD) for early gastric cancer (EGC). METHODS: This multicenter, retrospective study included 618 patients with EGC who underwent curative ESD at two tertiary referral teaching hospitals between January 2014 and December 2019. FAMISH score was a composite indicator of age, sex, family history, corpus intestinal metaplasia, synchronous lesions, and H. pylori infection. Discrimination, calibration, and risk stratification of these scores were assessed. Associations between MGL characteristics and FAMISH scores were also explored. RESULTS: After a median follow-up period of 60 months, 83 of 618 patients (13.4%) developed MGL. The discrimination ability according to the area under the curve was 0.708 (95% CI, 0.645-0.772) for predicting the 5-year MGL. The calibration results showed good consistency between the predicted and actual MGL (Hosmer-Lemeshow, P  > 0.05). In terms of risk stratification, the 5-year MGL rates were 4.1% (95% CI, 1.6%-6.5%), 10.8% (95% CI, 7.2%-14.3%), and 32.1% (95% CI, 20.9%-41.7%) in the low-, intermediate-, and high-risk groups, respectively ( P  < 0.001). For patients with MGL, the curative resection rate of ESD was significantly higher in the low- and intermediate-risk groups than in the high-risk group (100% vs. 80%, P  = 0.037). CONCLUSION: The FAMISH predicting score was externally validated and can be generalized to an independent patient population. This adjuvant tool can assist in individual clinical decision-making.

EAPR: explainable and augmented patient representation learning for disease prediction.

Patient representation learning aims to encode meaningful information about the patient's Electronic Health Records (EHR) in the form of a mathematical representation. Recent advances in deep learning have empowered Patient representation learning methods with greater representational power, allowing the learned representations to significantly improve the performance of disease prediction models. However, the inherent shortcomings of deep learning models, such as the need for massive amounts of labeled data and inexplicability, limit the performance of deep learning-based Patient representation learning methods to further improvements. In particular, learning robust patient representations is challenging when patient data is missing or insufficient. Although data augmentation techniques can tackle this deficiency, the complex data processing further weakens the inexplicability of patient representation learning models. To address the above challenges, this paper proposes an Explainable and Augmented Patient Representation Learning for disease prediction (EAPR). EAPR utilizes data augmentation controlled by confidence interval to enhance patient representation in the presence of limited patient data. Moreover, EAPR proposes to use two-stage gradient backpropagation to address the problem of unexplainable patient representation learning models due to the complex data enhancement process. The experimental results on real clinical data validate the effectiveness and explainability of the proposed approach.

Activating transcription factor (ATF) 6 upregulates cystathionine ß synthetase (CBS) expression and hydrogen sulfide (H2S) synthesis to ameliorate liver metabolic damage.

Activating transcription factor 6 (ATF6) is an endoplasmic reticulum stress responsive gene. We previously reported that conditional knockout of hepatic ATF6 exacerbated liver metabolic damage by repressing autophagy through mTOR pathway. However, the mechanism by which ATF6 influence liver metabolism has not been well established. Hydrogen sulfide (H2S) is a gaseous signaling molecule that plays an important role in regulating inflammation, and suppress nonalcoholic fatty liver in mice. Based on the previous study, we assumed that ATF6 may regulate H2S production to participate in liver metabolism. In order to clarify the mechanism by which ATF6 regulates H2S synthesis to ameliorate liver steatosis and inflammatory environment, we conducted the present study. We used the liver specific ATF6 knockout mice and fed on high-fat-diet, and found that H2S level was significantly downregulated in hepatic ATF6 knockout mice. Restoring H2S by the administration of slow H2S releasing agent GYY4137 ameliorated the hepatic steatosis and glucose tolerance. ATF6 directly binds to the promoter of cystathionine ß synthetase (CBS), an important enzyme in H2S synthesis. Thus, ATF6 could upregulate H2S production through CBS. Sulfhydrated Sirtuin-1 (SIRT1) was downregulated in ATF6 knockout mice. The expression of pro-inflammatory factor IL-17A was upregulated and anti-inflammatory factor IL-10 was downregulated in ATF6 knockout mice. Our results suggest that ATF6 can transcriptionally enhance CBS expression as well as H2S synthesis. ATF6 increases SIRT1 sulfhydration and ameliorates lipogenesis and inflammation in the fatty liver. Therefore, ATF6 could be a novel therapeutic strategy for high-fat diet induced fatty liver metabolic abnormalities.

LSM2 is associated with a poor prognosis and promotes cell proliferation, migration, and invasion in skin cutaneous melanoma.

BACKGROUND: Skin cutaneous melanoma (SKCM) is an extremely malignant tumor that is associated with a poor prognosis. LSM2 has been found to be related to different types of tumors; however, its role in SKCM is poorly defined. We aimed to determine the value of LSM2 as a prognostic biomarker for SKCM. METHODS: The expression profile of LSM2 mRNA was compared between tumor and normal tissues in public databases, such as TCGA, GEO, and BioGPS. LSM2 protein expression was explored using immunohistochemistry (IHC) on a tissue microarray containing 44 SKCM tissues and 8 normal samples collected at our center. Kaplan-Meier analysis was performed to assess the prognostic value of LSM2 expression in patients with SKCM. SKCM cell lines with LSM2 knockdown were used to determine the effects of LSM2. Cell counting kit-8 (CCK8) and colony formation assays were conducted to assess cell proliferation, whereas wound healing and transwell assays were carried out to assess the migration and invasion abilities of SKCM cells. RESULTS: LSM2 was more highly expressed at the mRNA and protein levels in SKCM than that in normal skin. Moreover, elevated expression of LSM2 was associated with shorter survival time and early recurrence in patients with SKCM. The in vitro results revealed that the silencing of LSM2 in SKCM cells significantly inhibited cell proliferation, migration, and invasion. CONCLUSION: Overall, LSM2 contributes to malignant status and poor prognosis in patients with SKCM and may be identified as a novel prognostic biomarker and therapeutic target.

Trends in research on dietary behavior and cardiovascular disease from 2002 to 2022: a bibliometric analysis.

Background: Dietary behaviors and cardiovascular disease are two major health issues that have attracted a lot of attention from researchers worldwide. In this study, we aimed to provide a comprehensive analysis of the publication trends, authorship patterns, institutional affiliations, country/region contributions, journal outlets, highly cited documents, and keyword clusters in the field of dietary behaviors and cardiovascular disease research over the past two decades. Methods: We conducted a systematic literature review of peer-reviewed articles published from 2002 to 2022 in the Web of Science Core Collection database. We extracted and analyzed data on the annual publication volume, authorship patterns, institutional affiliations, country/region contributions, journal outlets, highly cited documents, and keyword clusters using bibliometric methods and visualization tools. Results: Our study analyzed 3,904 articles, including 702 reviews and 3,202 research articles. The results revealed a continuous increase in the number of publications in this field over the past two decades. The top 10 authors, institutions, and countries/regions with the highest publication output were identified, indicating the leading contributors to this field. Moreover, the most frequently cited documents and highly clustered keywords were identified, providing insights into the research themes and topics in this field. Conclusion: Our study provides a comprehensive analysis of the publication trends, authorship patterns, institutional affiliations, country/region contributions, journal outlets, highly cited documents, and keyword clusters in the field of dietary behaviors and cardiovascular disease research over the past two decades. The findings provide valuable information for researchers, policymakers, and stakeholders to understand the research landscape, identify research gaps, and develop future research directions in this field.

Mammalian carcass decay increases carbon storage and temporal turnover of carbon-fixing microbes in alpine meadow soil.

Corpse decomposition is of great significance to the carbon cycle of natural ecosystem. Carbon fixation is a carbon conversion process that converts carbon dioxide into organic carbon, which greatly contributes to carbon emission reduction. However, the effects of wild animal carcass decay on carbon-fixing microbes in grassland soil environment are still unknown. In this research, thirty wild mammal (Ochotona curzoniae) corpses were placed on alpine meadow soil to study the carbon storage and carbon-fixing microbiota succession for a 94-day decomposition using next-generation sequencing. Our results revealed that 1) the concentration of total carbon increased approximately 2.24-11.22% in the corpse group. 2) Several carbon-fixing bacterial species (Calothrix parietina, Ancylobacter rudongensis, Rhodopseudomonas palustris) may predict the concentration of total carbon. 3) Animal cadaver degradation caused the differentiation of carbon-fixing microbiota structures during succession and made the medium-stage networks of carbon-fixing microbes more complicated. 4) The temporal turnover rate in the experimental groups was higher than that in the control groups, indicating a quick change of gravesoil carbon-fixing microbiota. 5) The deterministic process dominates the assembly mechanism of experimental groups (ranging from 53.42% to 94.94%), which reflects that the carbon-fixing microbial community in gravesoil can be regulated. Under global climate change, this study provides a new perspective for understanding the effects of wild animal carcass decay on soil carbon storage and carbon-fixing microbes.

Haplotype-specific MAPK3 expression in 16p11.2 deletion contributes to variable neurodevelopment.

Recurrent proximal 16p11.2 deletion (16p11.2del) is a risk factor for diverse neurodevelopmental disorders with incomplete penetrance and variable expressivity. Although investigation with human induced pluripotent stem cell models has confirmed disruption of neuronal development in 16p11.2del neuronal cells, which genes are responsible for abnormal cellular phenotypes and what determines the penetrance of neurodevelopmental abnormalities are unknown. We performed haplotype phasing of the 16p11.2 region in a 16p11.2del neurodevelopmental disorders cohort and generated human induced pluripotent stem cells for two 16p11.2del families with distinct residual haplotypes and variable neurodevelopmental disorder phenotypes. Using transcriptomic profiles and cellular phenotypes of the human induced pluripotent stem cell-differentiated cortex neuronal cells, we revealed MAPK3 to be a contributor to dysfunction in multiple pathways related to early neuronal development, with altered soma and electrophysiological properties in mature neuronal cells. Notably, MAPK3 expression in 16p11.2del neuronal cells varied on the basis of a 132 kb 58 single nucleotide polymorphism (SNP) residual haplotype, with the version composed entirely of minor alleles associated with reduced MAPK3 expression. Ten SNPs on the residual haplotype were mapped to enhancers of MAPK3. We functionally validated six of these SNPs by luciferase assay, implicating them in the residual haplotype-specific differences in MAPK3 expression via cis-regulation. Finally, the analysis of three different cohorts of 16p11.2del subjects showed that this minor residual haplotype is associated with neurodevelopmental disorder phenotypes in 16p11.2del carriers.

PAK2 is essential for chromosome alignment in metaphase I oocytes.

As a highly conserved and ubiquitously expressed serine/threonine kinase, p21-activated kinase 2 (PAK2) participates in diverse biologic events. However, its roles in mouse oocyte meiotic maturation remain unclear. The present study revealed that mouse oocytes depleted of Pak2 were unable to completely progress through meiosis and that a majority were arrested at metaphase I. Pak2 depletion thus prompted MI arrest and induced meiotic chromosome alignment defects in mouse oocytes, in part due to a reduction in polo-like kinase (PLK1). We demonstrated that PAK2's interaction with PLK1 protected it from degradation by APC/CCdh1, and that it promoted meiotic progression and bipolar spindle formation. Our data collectively display critical functions for PAK2 in meiotic progression and chromosome alignment in mouse oocytes.

Metagenomics reveals the response of antibiotic resistance genes to elevated temperature in the Yellow River.

Climate warming may aggravate the threat of antibiotic resistance genes (ARGs) to environmental and human health. However, whether temperature can predict ARGs and influence their assembly processes remains unknown. Here, we used metagenomic sequencing to explore how gradually elevated water temperature (23 °C, 26 °C, 29 °C, 32 °C, 35 °C) influences ARG and mobile genetic element (MGE) profiles in the Yellow River. In total, 30 ARG types including 679 subtypes were detected in our water samples. Gradually increased temperature remarkably reduced ARG diversity but increased ARG abundance. Approximately 37 % of ARGs and 42 % of MGEs were predicted by temperature, while most others were not sensitive to temperature. For each 1 °C increase in temperature, the ARG abundance rose by 2133 TPM (Transcripts Per kilobase of exon model per Million mapped reads) abundance, and multidrug, tetracycline and peptide resistance genes had the fastest increases. Proteobacteria and Actinobacteria were the primary ARG hosts, with 558 and 226 ARG subtypes, respectively. Although ARG profiles were mainly governed by stochastic process, elevated temperature increased the deterministic process of ARGs in the Yellow River. The abundance of five high-risk ARGs (tetM, mecA, bacA, vatE and tetW) significantly increased with elevated water temperature, and these ARGs co-occurred with several opportunistic pathogens (Delftia, Legionella and Pseudomonas), implying that antibiotic resistance risk may increase under climate warming. Our study explored the possibility of predicting resistomes and their health risks through temperature, providing a novel approach to predict and control ARGs in water environments under climate warming.