Harnessing landrace diversity empowers wheat breeding.

Harnessing genetic diversity in major staple crops through the development of new breeding capabilities is essential to ensure food security1. Here we examined the genetic and phenotypic diversity of the A.E. Watkins landrace collection2 of bread wheat (Triticum aestivum), a major global cereal, through whole-genome re-sequencing (827 Watkins landraces and 208 modern cultivars) and in-depth field evaluation spanning a decade. We discovered that modern cultivars are derived from just two of the seven ancestral groups of wheat and maintain very long-range haplotype integrity. The remaining five groups represent untapped genetic sources, providing access to landrace-specific alleles and haplotypes for breeding. Linkage disequilibrium (LD) based haplotypes and association genetics analyses link Watkins genomes to the thousands of high-resolution quantitative trait loci (QTL), and significant marker-trait associations identified. Using these structured germplasm, genotyping and informatics resources, we revealed many Watkins-unique beneficial haplotypes that can confer superior traits in modern wheat. Furthermore, we assessed the phenotypic effects of 44,338 Watkins-unique haplotypes, introgressed from 143 prioritised QTL in the context of modern cultivars, bridging the gap between landrace diversity and current breeding. This study establishes a framework for systematically utilising genetic diversity in crop improvement to achieve sustainable food security.

Systematic single-cell analysis reveals dynamic control of transposable element activity orchestrating the endothelial-to-hematopoietic transition.

BACKGROUND: The endothelial-to-hematopoietic transition (EHT) process during definitive hematopoiesis is highly conserved in vertebrates. Stage-specific expression of transposable elements (TEs) has been detected during zebrafish EHT and may promote hematopoietic stem cell (HSC) formation by activating inflammatory signaling. However, little is known about how TEs contribute to the EHT process in human and mouse. RESULTS: We reconstructed the single-cell EHT trajectories of human and mouse and resolved the dynamic expression patterns of TEs during EHT. Most TEs presented a transient co-upregulation pattern along the conserved EHT trajectories, coinciding with the temporal relaxation of epigenetic silencing systems. TE products can be sensed by multiple pattern recognition receptors, triggering inflammatory signaling to facilitate HSC emergence. Interestingly, we observed that hypoxia-related signals were enriched in cells with higher TE expression. Furthermore, we constructed the hematopoietic cis-regulatory network of accessible TEs and identified potential TE-derived enhancers that may boost the expression of specific EHT marker genes. CONCLUSIONS: Our study provides a systematic vision of how TEs are dynamically controlled to promote the hematopoietic fate decisions through transcriptional and cis-regulatory networks, and pre-train the immunity of nascent HSCs.

Development of a next generation SNP genotyping array for wheat.

High-throughput genotyping arrays have provided a cost-effective, reliable and interoperable system for genotyping hexaploid wheat and its relatives. Existing, highly cited arrays including our 35K Wheat Breeder's array and the Illumina 90K array were designed based on a limited amount of varietal sequence diversity and with imperfect knowledge of SNP positions. Recent progress in wheat sequencing has given us access to a vast pool of SNP diversity, whilst technological improvements have allowed us to fit significantly more probes onto a 384-well format Axiom array than previously possible. Here we describe a novel Axiom genotyping array, the 'Triticum aestivum Next Generation' array (TaNG), largely derived from whole genome skim sequencing of 204 elite wheat lines and 111 wheat landraces taken from the Watkins 'Core Collection'. We used a novel haplotype optimization approach to select SNPs with the highest combined varietal discrimination and a design iteration step to test and replace SNPs which failed to convert to reliable markers. The final design with 43 372 SNPs contains a combination of haplotype-optimized novel SNPs and legacy cross-platform markers. We show that this design has an improved distribution of SNPs compared to previous arrays and can be used to generate genetic maps with a significantly higher number of distinct bins than our previous array. We also demonstrate the improved performance of TaNGv1.1 for Genome-wide association studies (GWAS) and its utility for Copy Number Variation (CNV) analysis. The array is commercially available with supporting marker annotations and initial genotyping results freely available.

The miR6445-NAC029 module regulates drought tolerance by regulating the expression of glutathione S-transferase U23 and reactive oxygen species scavenging in Populus.

MicroRNAs are essential in plant development and stress resistance, but their specific roles in drought stress require further investigation. Here, we have uncovered that a Populus-specific microRNAs (miRNA), miR6445, targeting NAC (NAM, ATAF, and CUC) family genes, is involved in regulating drought tolerance of poplar. The expression level of miR6445 was significantly upregulated under drought stress; concomitantly, seven targeted NAC genes showed significant downregulation. Silencing the expression of miR6445 by short tandem target mimic technology significantly decreased the drought tolerance in poplar. Furthermore, 5' RACE experiments confirmed that miR6445 directly targeted NAC029. The overexpression lines of PtrNAC029 (OE-NAC029) showed increased sensitivity to drought compared with knockout lines (Crispr-NAC029), consistent with the drought-sensitive phenotype observed in miR6445-silenced strains. PtrNAC029 was further verified to directly bind to the promoters of glutathione S-transferase U23 (GSTU23) and inhibit its expression. Both Crispr-NAC029 and PtrGSTU23 overexpressing plants showed higher levels of PtrGSTU23 transcript and GST activity while accumulating less reactive oxygen species (ROS). Moreover, poplars overexpressing GSTU23 demonstrated enhanced drought tolerance. Taken together, our research reveals the crucial role of the miR6445-NAC029-GSTU23 module in enhancing poplar drought tolerance by regulating ROS homeostasis. This finding provides new molecular targets for improving the drought resistance of trees.

Aspartyl tRNA-synthetase (AspRS) gene family enhances drought tolerance in poplar through BABA-PtrIBIs-PtrVOZ signaling module.

BACKGROUND: Drought stress is a prevalent abiotic stress that significantly hinders the growth and development of plants. According to studies, ß-aminobutyric acid (BABA) can influence the ABA pathway through the AtIBI1 receptor gene to enhance cold resistance in Arabidopsis. However, the Aspartate tRNA-synthetase (AspRS) gene family, which acts as the receptor for BABA, has not yet been investigated in poplar. Particularly, it is uncertain how the AspRS gene family (PtrIBIs)r can resist drought stress after administering various concentrations of BABA to poplar. RESULTS: In this study, we have identified 12 AspRS family genes and noted that poplar acquired four PtrIBI pairs through whole genome duplication (WGD). We conducted cis-action element analysis and found a significant number of stress-related action elements on different PtrIBI genes promoters. The expression of most PtrIBI genes was up-regulated under beetle and mechanical damage stresses, indicating their potential role in responding to leaf damage stress. Our results suggest that a 50 mM BABA treatment can alleviate the damage caused by drought stress in plants. Additionally, via transcriptome sequencing, we observed that the partial up-regulation of BABA receptor genes, PtrIBI2/4/6/8/11, in poplars after drought treatment. We hypothesize that poplar responds to drought stress through the BABA-PtrIBIs-PtrVOZ coordinated ABA signaling pathway. Our research provides molecular evidence for understanding how plants respond to drought stress through external application of BABA. CONCLUSIONS: In summary, our study conducted genome-wide analysis of the AspRS family of P. trichocarpa and identified 12 PtrIBI genes. We utilized genomics and bioinformatics to determine various characteristics of PtrIBIs such as chromosomal localization, evolutionary tree, gene structure, gene doubling, promoter cis-elements, and expression profiles. Our study found that certain PtrIBI genes are regulated by drought, beetle, and mechanical damage implying their crucial role in enhancing poplar stress tolerance. Additionally, we observed that external application of low concentrations of BABA increased plant drought resistance under drought stress. Through the BABA-PtrIBIs-PtrVOZ signaling module, poplar plants were able to transduce ABA signaling and regulate their response to drought stress. These results suggest that the PtrIBI genes in poplar have the potential to improve drought tolerance in plants through the topical application of low concentrations of BABA.

α-Synuclein induces deficiency in clathrin-mediated endocytosis through inhibiting synaptojanin1 expression.

Parkinson's disease (PD) is an age-related chronic neurological disorder, mainly characterized by the pathological feature of α-synuclein (α-syn) aggregation, with the exact disease pathogenesis unclear. During the onset and progression of PD, synaptic dysfunction, including dysregulation of axonal transport, impaired exocytosis, and endocytosis are identified as crucial events of PD pathogenesis. It has been reported that over-expression of α-syn impairs clathrin-mediated endocytosis (CME) in the synapses. However, the underlying mechanisms still needs to be explored. In this study, we investigated the molecular events underlying the synaptic dysfunction caused by over-expression of wild-type human α-syn and its mutant form, involving series of proteins participating in CME. We found that excessive human α-syn causes impaired fission and uncoating of clathrin-coated vesicles during synaptic vesicle recycling, leading to reduced clustering of synaptic vesicles near the active zone and increased size of plasma membrane and number of endocytic intermediates. Furthermore, over-expressed human α-syn induced changes of CME-associated proteins, among which synaptojanin1 (SYNJ1) showed significant reduction in various brain regions. Over-expression of SYNJ1 in primary hippocampal neurons from α-syn transgenic mice recovered the synaptic vesicle density, clustering and endocytosis. Using fluorescence-conjugated transferrin, we demonstrated that SYNJ1 re-boosted the CME activity by restoring the phosphatidylinositol-4,5-bisphosphate homeostasis. Our data suggested that over-expression of α-syn disrupts synaptic function through interfering with vesicle recycling, which could be alleviated by re-availing of SYNJ1. Our study unrevealed a molecular mechanism of the synaptic dysfunction in PD pathogenesis and provided a potential therapeutic target for treating PD.

An efficient RNA-seq-based segregation analysis identifies the sex chromosomes of Cannabis sativa.

Cannabis sativa-derived tetrahydrocannabinol (THC) production is increasing very fast worldwide. C. sativa is a dioecious plant with XY Chromosomes, and only females (XX) are useful for THC production. Identifying the sex chromosome sequence would improve early sexing and better management of this crop; however, the C. sativa genome projects have failed to do so. Moreover, as dioecy in the Cannabaceae family is ancestral, C. sativa sex chromosomes are potentially old and thus very interesting to study, as little is known about old plant sex chromosomes. Here, we RNA-sequenced a C. sativa family (two parents and 10 male and female offspring, 576 million reads) and performed a segregation analysis for all C. sativa genes using the probabilistic method SEX-DETector. We identified >500 sex-linked genes. Mapping of these sex-linked genes to a C. sativa genome assembly identified the largest chromosome pair being the sex chromosomes. We found that the X-specific region (not recombining between X and Y) is large compared to other plant systems. Further analysis of the sex-linked genes revealed that C. sativa has a strongly degenerated Y Chromosome and may represent the oldest plant sex chromosome system documented so far. Our study revealed that old plant sex chromosomes can have large, highly divergent nonrecombining regions, yet still be roughly homomorphic.

Sequence repetitiveness quantification and de novo repeat detection by weighted k-mer coverage.

DNA repeats are abundant in eukaryotic genomes and have been proved to play a vital role in genome evolution and regulation. A large number of approaches have been proposed to identify various repeats in the genome. Some de novo repeat identification tools can efficiently generate sequence repetitive scores based on k-mer counting for repeat detection. However, we noticed that these tools can still be improved in terms of repetitive score calculation, sensitivity to segmental duplications and detection specificity. Therefore, here, we present a new computational approach named Repeat Locator (RepLoc), which is based on weighted k-mer coverage to quantify the genome sequence repetitiveness and locate the repetitive sequences. According to the repetitiveness map of the human genome generated by RepLoc, we found that there may be relationships between sequence repetitiveness and genome structures. A comprehensive benchmark shows that RepLoc is a more efficient k-mer counting based tool for de novo repeat detection. The RepLoc software is freely available at http://bis.zju.edu.cn/reploc.

Bioinformatics resources facilitate understanding and harnessing clinical research of SARS-CoV-2.

The coronavirus disease 2019 (COVID-19) pandemic, caused by the coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has created an unprecedented threat to public health. The pandemic has been sweeping the globe, impacting more than 200 countries, with more outbreaks still lurking on the horizon. At the time of the writing, no approved drugs or vaccines are available to treat COVID-19 patients, prompting an urgent need to decipher mechanisms underlying the pathogenesis and develop curative treatments. To fight COVID-19, researchers around the world have provided specific tools and molecular information for SARS-CoV-2. These pieces of information can be integrated to aid computational investigations and facilitate clinical research. This paper reviews current knowledge, the current status of drug development and various resources for key steps toward effective treatment of COVID-19, including the phylogenetic characteristics, genomic conservation and interaction data. The final goal of this paper is to provide information that may be utilized in bioinformatics approaches and aid target prioritization and drug repurposing. Several SARS-CoV-2-related tools/databases were reviewed, and a web-portal named OverCOVID (http://bis.zju.edu.cn/overcovid/) is constructed to provide a detailed interpretation of SARS-CoV-2 basics and share a collection of resources that may contribute to therapeutic advances. These information could improve researchers' understanding of SARS-CoV-2 and help to accelerate the development of new antiviral treatments.

DeepTrio: a ternary prediction system for protein-protein interaction using mask multiple parallel convolutional neural networks.

MOTIVATION: Protein-protein interaction (PPI), as a relative property, is determined by two binding proteins, which brings a great challenge to design an expert model with an unbiased learning architecture and a superior generalization performance. Additionally, few efforts have been made to allow PPI predictors to discriminate between relative properties and intrinsic properties. RESULTS: We present a sequence-based approach, DeepTrio, for PPI prediction using mask multiple parallel convolutional neural networks. Experimental evaluations show that DeepTrio achieves a better performance over several state-of-the-art methods in terms of various quality metrics. Besides, DeepTrio is extended to provide additional insights into the contribution of each input neuron to the prediction results. AVAILABILITY AND IMPLEMENTATION: We provide an online application at http://bis.zju.edu.cn/deeptrio. The DeepTrio models and training data are deposited at https://github.com/huxiaoti/deeptrio.git. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

CoGO: a contrastive learning framework to predict disease similarity based on gene network and ontology structure.

MOTIVATION: Quantifying the similarity of human diseases provides guiding insights to the discovery of micro-scope mechanisms from a macro scale. Previous work demonstrated that better performance can be gained by integrating multiview data sources or applying machine learning techniques. However, designing an efficient framework to extract and incorporate information from different biological data using deep learning models remains unexplored. RESULTS: We present CoGO, a Contrastive learning framework to predict disease similarity based on Gene network and Ontology structure, which incorporates the gene interaction network and gene ontology (GO) domain knowledge using graph deep learning models. First, graph deep learning models are applied to encode the features of genes and GO terms from separate graph structure data. Next, gene and GO features are projected to a common embedding space via a nonlinear projection. Then cross-view contrastive loss is applied to maximize the agreement of corresponding gene-GO associations and lead to meaningful gene representation. Finally, CoGO infers the similarity between diseases by the cosine similarity of disease representation vectors derived from related gene embedding. In our experiments, CoGO outperforms the most competitive baseline method on both AUROC and AUPRC, especially improves 19.57% in AUPRC (0.7733). The prediction results are significantly comparable with other disease similarity studies and thus highly credible. Furthermore, we conduct a detailed case study of top similar disease pairs which is demonstrated by other studies. Empirical results show that CoGO achieves powerful performance in disease similarity problem. AVAILABILITY AND IMPLEMENTATION: https://github.com/yhchen1123/CoGO.

Novel models by machine learning to predict prognosis of breast cancer brain metastases.

BACKGROUND: Breast cancer brain metastases (BCBM) are the most fatal, with limited survival in all breast cancer distant metastases. These patients are deemed to be incurable. Thus, survival time is their foremost concern. However, there is a lack of accurate prediction models in the clinic. What's more, primary surgery for BCBM patients is still controversial. METHODS: The data used for analysis in this study was obtained from the SEER database (2010-2019). We made a COX regression analysis to identify prognostic factors of BCBM patients. Through cross-validation, we constructed XGBoost models to predict survival in patients with BCBM. Meanwhile, a BCBM cohort from our hospital was used to validate our models. We also investigated the prognosis of patients treated with surgery or not, using propensity score matching and K-M survival analysis. Our results were further validated by subgroup COX analysis in patients with different molecular subtypes. RESULTS: The XGBoost models we created had high precision and correctness, and they were the most accurate models to predict the survival of BCBM patients (6-month AUC = 0.824, 1-year AUC = 0.813, 2-year AUC = 0.800 and 3-year survival AUC = 0.803). Moreover, the models still exhibited good performance in an externally independent dataset (6-month: AUC = 0.820; 1-year: AUC = 0.732; 2-year: AUC = 0.795; 3-year: AUC = 0.936). Then we used Shiny-Web tool to make our models be easily used from website. Interestingly, we found that the BCBM patients with an annual income of over USD$70,000 had better BCSS (HR = 0.523, 95%CI 0.273-0.999, P < 0.05) than those with less than USD$40,000. The results showed that in all distant metastasis sites, only lung metastasis was an independent poor prognostic factor for patients with BCBM (OS: HR = 1.606, 95%CI 1.157-2.230, P < 0.01; BCSS: HR = 1.698, 95%CI 1.219-2.365, P < 0.01), while bone, liver, distant lymph nodes and other metastases were not. We also found that surgical treatment significantly improved both OS and BCSS in BCBM patients with the HER2 + molecular subtypes and was beneficial to OS of the HR-/HER2- subtype. In contrast, surgery could not help BCBM patients with HR + /HER2- subtype improve their prognosis (OS: HR = 0.887, 95%CI 0.608-1.293, P = 0.510; BCSS: HR = 0.909, 95%CI 0.604-1.368, P = 0.630). CONCLUSION: We analyzed the clinical features of BCBM patients and constructed 4 machine-learning prognostic models to predict their survival. Our validation results indicate that these models should be highly reproducible in patients with BCBM. We also identified potential prognostic factors for BCBM patients and suggested that primary surgery might improve the survival of BCBM patients with HER2 + and triple-negative subtypes.

Causal effects of COVID-19 on cancer risk: A Mendelian randomization study.

In contemporary literature, little attention has been paid to the association between coronavirus disease-2019 (COVID-19) and cancer risk. We performed the Mendelian randomization (MR) to investigate the causal associations between the three types of COVID-19 exposures (critically ill COVID-19, hospitalized COVID-19, and respiratory syndrome coronavirus 2 (SARS-CoV-2) infection) and 33 different types of cancers of the European population. The results of the inverse-variance-weighted model indicated that genetic liabilities to critically ill COVID-19 had suggestive causal associations with the increased risk for HER2-positive breast cancer (odds ratio [OR] = 1.0924; p-value = 0.0116), esophageal cancer (OR = 1.0004; p-value = 0.0226), colorectal cancer (OR = 1.0010; p-value = 0.0242), stomach cancer (OR = 1.2394; p-value = 0.0331), and colon cancer (OR = 1.0006; p-value = 0.0453). The genetic liabilities to hospitalized COVID-19 had suggestive causal associations with the increased risk for HER2-positive breast cancer (OR = 1.1096; p-value = 0.0458), esophageal cancer (OR = 1.0005; p-value = 0.0440) as well as stomach cancer (OR = 1.3043; p-value = 0.0476). The genetic liabilities to SARS-CoV-2 infection had suggestive causal associations with the increased risk for stomach cancer (OR = 2.8563; p-value = 0.0019) but with the decreasing risk for head and neck cancer (OR = 0.9986, p-value = 0.0426). The causal associations of the above combinations were robust through the test of heterogeneity and pleiotropy. Together, our study indicated that COVID-19 had causal effects on cancer risk.

Integrated bioinformatics analysis and experimental validation reveal ISG20 as a novel prognostic indicator expressed on M2 macrophage in glioma.

BACKGROUND: Glioma is the most common malignant primary brain tumor and is characterized by a poor prognosis and limited therapeutic options. ISG20 expression is induced by interferons or double-stranded RNA and is associated with poor prognosis in several malignant tumors. Nevertheless, the expression of ISG20 in gliomas, its impact on patient prognosis, and its role in the tumor immune microenvironment have not been fully elucidated. METHODS: Using bioinformatics, we comprehensively illustrated the potential function of ISG20, its predictive value in stratifying clinical prognosis, and its association with immunological characteristics in gliomas. We also confirmed the expression pattern of ISG20 in glioma patient samples by immunohistochemistry and immunofluorescence staining. RESULTS: ISG20 mRNA expression was higher in glioma tissues than in normal tissues. Data-driven results showed that a high level of ISG20 expression predicted an unfavorable clinical outcome in glioma patients, and revealed that ISG20 was possibly expressed on tumor-associated macrophages and was significantly associated with immune regulatory processes, as evidenced by its positive correlation with the infiltration of regulatory immune cells (e.g., M2 macrophages and regulatory T cells), expression of immune checkpoint molecules, and effectiveness of immune checkpoint blockade therapy. Furthermore, immunohistochemistry staining confirmed the enhanced expression of ISG20 in glioma tissues with a higher WHO grade, and immunofluorescence assay verified its cellular localization on M2 macrophages. CONCLUSIONS: ISG20 is expressed on M2 macrophages, and can serve as a novel indicator for predicting the malignant phenotype and clinical prognosis in glioma patients.

Direct formylation of phenols using difluorocarbene as a safe CO surrogate.

A convenient method to prepare aryl formates is reported herein that exploits difluorocarbene to serve as a CO surrogate. This reaction is proposed to occur through a sequential O-difluoromethylation of phenol, followed by α-C-F bond functionalization of the resulting aryl difluoromethyl ether intermediate by phenol or moisture through fluorosemiacetal or orthoformate intermediates. Late-stage modification of biologically and materially active compounds is demonstrated.

Inhibitory Effects of Corydalis saxicola Bunting Total Alkaloids on Macrophage Pyroptosis.

This study investigated the effect of Corydalis saxicola Bunting total alkaloids (CSBTA) on pyroptosis in macrophages (Mϕ). In the Mϕ pyroptosis model, an inverted fluorescence microscope was used to assess cell pyroptosis, while a scanning electron microscope was used to observe morphological changes in Mϕ. NLR family pyrin domain-containing 3 (NLRP3), caspase-1, and gasdermin D (GSDMD) expression levels were detected by polymerase chain reaction and western blotting, whereas interleukin-1 (IL-1) and interleukin-18 (IL-18) expression levels were measured by an enzyme-linked immunosorbent assay. After pretreatment with CSBTA or the caspase-1 inhibitor, acetyl-tyrosyl-valyl-alanyl-aspartyl-chloromethylketone (Ac-YVAD-cmk), it was discovered that NLRP3, caspase-1, and GSDMD expressions were significantly reduced at both the mRNA and protein levels, as were IL-1 and IL-18 levels. The inhibitory effects of CSBTA and Ac-YVAD-cmk did not differ significantly. These findings indicate that CSBTA blocks Porphyromonas gingivalis-lipopolysaccharide-induced Mϕ pyroptosis.

Genome-Wide Analysis of the FBA Subfamily of the Poplar F-Box Gene Family and Its Role under Drought Stress.

F-box proteins are important components of eukaryotic SCF E3 ubiquitin ligase complexes, which specifically determine protein substrate proteasomal degradation during plant growth and development, as well as biotic and abiotic stress. It has been found that the FBA (F-box associated) protein family is one of the largest subgroups of the widely prevalent F-box family and plays significant roles in plant development and stress response. However, the FBA gene family in poplar has not been systematically studied to date. In this study, a total of 337 F-box candidate genes were discovered based on the fourth-generation genome resequencing of P. trichocarpa. The domain analysis and classification of candidate genes revealed that 74 of these candidate genes belong to the FBA protein family. The poplar F-box genes have undergone multiple gene replication events, particularly in the FBA subfamily, and their evolution can be attributed to genome-wide duplication (WGD) and tandem duplication (TD). In addition, we investigated the P. trichocarpa FBA subfamily using the PlantGenIE database and quantitative real-time PCR (qRT-PCR); the results showed that they are expressed in the cambium, phloem and mature tissues, but rarely expressed in young leaves and flowers. Moreover, they are also widely involved in the drought stress response. At last, we selected and cloned PtrFBA60 for physiological function analysis and found that it played an important role in coping with drought stress. Taken together, the family analysis of FBA genes in P. trichocarpa provides a new opportunity for the identification of P. trichocarpa candidate FBA genes and elucidation of their functions in growth, development and stress response, thus demonstrating their utility in the improvement of P. trichocarpa.

Internal and external validation of machine learning-assisted prediction models for mechanical ventilation-associated severe acute kidney injury.

BACKGROUND: Currently, very few preventive or therapeutic strategies are used for mechanical ventilation (MV)-associated severe acute kidney injury (AKI). OBJECTIVES: We developed clinical prediction models to detect the onset of severe AKI in the first week of intensive care unit (ICU) stay during the initiation of MV. METHODS: A large ICU database Medical Information Mart for Intensive Care IV (MIMIC-IV) was analysed retrospectively. Data were collected from the clinical information recorded at the time of ICU admission and during the initial 12 h of MV. Using univariate and multivariate analyses, the predictors were selected successively. For model development, two machine learning algorithms were compared. The primary goal was to predict the development of AKI stage 2 or 3 (AKI-23) and AKI stage 3 (AKI-3) in the first week of patients' ICU stay after initial 12 h of MV. The developed models were externally validated using another multicentre ICU database (eICU Collaborative Research Database, eICU) and evaluated in various patient subpopulations. RESULTS: Models were developed using data from the development cohort (MIMIC-IV: 2008-2016; n = 3986); the random forest algorithm outperformed the logistic regression algorithm. In the internal (MIMIC-IV: 2017-2019; n = 1210) and external (eICU; n = 1494) validation cohorts, the incidences of AKI-23 were 154 (12.7%) and 119 (8.0%), respectively, with areas under the receiver operator characteristic curve of 0.78 (95% confidence interval [CI]: 0.74-0.82) and 0.80 (95% CI: 0.76-0.84); the incidences of AKI-3 were 81 (6.7%) and 67 (4.5%), with areas under the receiver operator characteristic curve of 0.81 (95% CI: 0.76-0.87) and 0.80 (95% CI: 0.73-0.86), respectively. CONCLUSIONS: Models driven by machine learning and based on routine clinical data may facilitate the early prediction of MV-associated severe AKI. The validated models can be found at: https://apoet.shinyapps.io/mv_aki_2021_v2/.

JAK/STAT signaling pathway and its regulation on insect immunity.

JAK/STAT signaling pathway is involved in important biological processes, such as cell growth, differentiation, apoptosis and immune regulation, and is one of the most important signal transduction pathways mediated by cytokines. JAK/STAT is a relatively conserved signaling pathway in insects, which is the main immune pathway together with Toll signaling pathway and Imd signaling pathway to resist the invasion of pathogens. It plays a vital role in insect immunity, hormone regulation and other physiological regulation processes. In this review, we summarize the cytokine receptor superfamily, JAKs family, STATs family, JAK/STAT signaling pathway and its mechanism of negative feedback regulation. We analyze the important functions and the latest research progress of JAK/STAT signaling pathway in insect infected by parasites, viruses and fungi. Finally, we propose the remaining problems in the study of JAK/STAT signaling pathway, so as to provide direction and reference for further research in this field.

Time-course RNA-seq analysis reveals stage-specific and melatonin-triggered gene expression patterns during the hair follicle growth cycle in Capra hircus.

BACKGROUND: Cashmere goat is famous for its high-quality fibers. The growth of cashmere in secondary hair follicles exhibits a seasonal pattern arising from circannual changes in the natural photoperiod. Although several studies have compared and analyzed the differences in gene expression between different hair follicle growth stages, the selection of samples in these studies relies on research experience or morphological evidence. Distinguishing hair follicle growth cycle according to gene expression patterns may help to explore the regulation mechanisms related to cashmere growth and the effect of melatonin from a molecular level more accurately. RESULTS: In this study, we applied RNA-sequencing to the hair follicles of three normal and three melatonin-treated Inner Mongolian cashmere goats sampled every month during a whole hair follicle growth cycle. A total of 3559 and 988 genes were subjected as seasonal changing genes (SCGs) in the control and treated groups, respectively. The SCGs in the normal group were divided into three clusters, and their specific expression patterns help to group the hair follicle growth cycle into anagen, catagen and telogen stages. Some canonical pathways such as Wnt, TGF-beta and Hippo signaling pathways were detected as promoting the hair follicle growth, while Cell adhesion molecules (CAMs), Cytokine-cytokine receptor interaction, Jak-STAT, Fc epsilon RI, NOD-like receptor, Rap1, PI3K-Akt, cAMP, NF-kappa B and many immune-related pathways were detected in the catagen and telogen stages. The PI3K-Akt signaling, ECM-receptor interaction and Focal adhesion were found in the transition stage between telogen to anagen, which may serve as candidate biomarkers for telogen-anagen regeneration. A total of 16 signaling pathways, 145 pathway mRNAs, and 93 lncRNAs were enrolled to construct the pathway-mRNA-lncRNA network, which indicated the function of lncRNAs through interacting with their co-expressed mRNAs. Pairwise comparisons between the control and melatonin-treated groups also indicated 941 monthly differentially expressed genes (monthly DEGs). These monthly DEGs were mainly distributed from April and September, which revealed a potential signal pathway map regulating the anagen stage triggered by melatonin. Enrichment analysis showed that Wnt, Hedgehog, ECM, Chemokines and NF-kappa B signaling pathways may be involved in the regulation of non-quiescence and secondary shedding under the influence of melatonin. CONCLUSIONS: Our study decoded the key regulators of the whole hair follicle growth cycle, laying the foundation for the control of hair follicle growth and improvement of cashmere yield.