Genome-edited powdery mildew resistance in wheat without growth penalties.

Disruption of susceptibility (S) genes in crops is an attractive breeding strategy for conferring disease resistance1,2. However, S genes are implicated in many essential biological functions and deletion of these genes typically results in undesired pleiotropic effects1. Loss-of-function mutations in one such S gene, Mildew resistance locus O (MLO), confers durable and broad-spectrum resistance to powdery mildew in various plant species2,3. However, mlo-associated resistance is also accompanied by growth penalties and yield losses3,4, thereby limiting its widespread use in agriculture. Here we describe Tamlo-R32, a mutant with a 304-kilobase pair targeted deletion in the MLO-B1 locus of wheat that retains crop growth and yields while conferring robust powdery mildew resistance. We show that this deletion results in an altered local chromatin landscape, leading to the ectopic activation of Tonoplast monosaccharide transporter 3 (TaTMT3B), and that this activation alleviates growth and yield penalties associated with MLO disruption. Notably, the function of TMT3 is conserved in other plant species such as Arabidopsis thaliana. Moreover, precision genome editing facilitates the rapid introduction of this mlo resistance allele (Tamlo-R32) into elite wheat varieties. This work demonstrates the ability to stack genetic changes to rescue growth defects caused by recessive alleles, which is critical for developing high-yielding crop varieties with robust and durable disease resistance.

Rationally Designed APOBEC3B Cytosine Base Editors with Improved Specificity.

Cytosine base editors (CBEs) generate C-to-T nucleotide substitutions in genomic target sites without inducing double-strand breaks. However, CBEs such as BE3 can cause genome-wide off-target changes via sgRNA-independent DNA deamination. By leveraging the orthogonal R-loops generated by SaCas9 nickase to mimic actively transcribed genomic loci that are more susceptible to cytidine deaminase, we set up a high-throughput assay for assessing sgRNA-independent off-target effects of CBEs in rice protoplasts. The reliability of this assay was confirmed by the whole-genome sequencing (WGS) of 10 base editors in regenerated rice plants. The R-loop assay was used to screen a series of rationally designed A3Bctd-BE3 variants for improved specificity. We obtained 2 efficient CBE variants, A3Bctd-VHM-BE3 and A3Bctd-KKR-BE3, and the WGS analysis revealed that these new CBEs eliminated sgRNA-independent DNA off-target edits in rice plants. Moreover, these 2 base editor variants were more precise at their target sites by producing fewer multiple C edits.

A new framework for drug-disease association prediction combing light-gated message passing neural network and gated fusion mechanism.

With the development of research on the complex aetiology of many diseases, computational drug repositioning methodology has proven to be a shortcut to costly and inefficient traditional methods. Therefore, developing more promising computational methods is indispensable for finding new candidate diseases to treat with existing drugs. In this paper, a model integrating a new variant of message passing neural network and a novel-gated fusion mechanism called GLGMPNN is proposed for drug-disease association prediction. First, a light-gated message passing neural network (LGMPNN), including message passing, aggregation and updating, is proposed to separately extract multiple pieces of information from the similarity networks and the association network. Then, a gated fusion mechanism consisting of a forget gate and an output gate is applied to integrate the multiple pieces of information to extent. The forget gate calculated by the multiple embeddings is built to integrate the association information into the similarity information. Furthermore, the final node representations are controlled by the output gate, which fuses the topology information of the networks and the initial similarity information. Finally, a bilinear decoder is adopted to reconstruct an adjacency matrix for drug-disease associations. Evaluated by 10-fold cross-validations, GLGMPNN achieves excellent performance compared with the current models. The following studies show that our model can effectively discover novel drug-disease associations.

Coronary computed tomography angiography-derived total coronary plaque burden associated with subsequent cardiovascular outcomes following percutaneous coronary intervention.

OBJECTIVE: To investigate the association of coronary plaque burden variables derived from coronary computed tomography angiography (CCTA) before patients underwent their first percutaneous coronary intervention (PCI) procedure and major adverse cardiovascular events (MACEs) after PCI. METHODS: Patients who underwent CCTA before their first PCI were included retrospectively. A radiologist and a cardiologist analyzed CCTA images on a dedicated workstation. The coronary plaque burden variables included total plaque volume, total percent atheroma volume, volumes and fractions of total low-attenuation plaque, total fibrous plaque, and total calcified plaque. The primary outcomes were MACEs, a composite of all-cause death, nonfatal myocardial infarction, nonfatal stroke, and unscheduled coronary revascularization. RESULTS: A total of 230 patients were included in the final analysis. During a median follow-up of 4.8 years, 67 MACEs occurred. Total plaque volume, total percent atheroma volume, volumes of total low-attenuation plaque and total fibrous plaque but not their fractions were independent predictors for MACEs. Compared with the first tertiles, the hazard ratio of the third tertile of total plaque volume, total percent atheroma volume, total low-attenuation plaque volume, and total fibrous plaque volume were 2.06 (95% CI: 1.03-4.15), 2.15 (95% CI: 1.02-4.51), 3.04 (95% CI: 1.45-6.36), and 2.23 (95% CI: 1.11-4.46), respectively. Neither total calcified plaque volume nor fraction was associated with MACEs independently. CONCLUSION: Selected pre-PCI CCTA-derived variables, including total percent atheroma volume, volumes of total plaque, total low-attenuation plaque and total fibrous plaque, were significantly associated with MACEs after PCI, suggesting that CCTA before PCI reveals the residual risk after revascularization. CLINICAL RELEVANCE STATEMENT: The coronary plaque burden variables derived from coronary computed tomography angiography before percutaneous coronary intervention are independently associated with major adverse cardiovascular events, which could be instrumental in optimizing patient management. KEY POINTS: Coronary plaque burden is associated with cardiovascular events in patients with coronary artery disease. Selected total plaque burden variables derived from coronary computed tomography angiography before percutaneous coronary intervention were associated with poor prognosis. Routine coronary computed tomography angiography before percutaneous coronary intervention might be helpful in reducing future risks.

Assessing the relationship between gut microbiota and endometriosis: a bidirectional two-sample mendelian randomization analysis.

BACKGROUND: An increasing body of observational studies have indicated an association between gut microbiota and endometriosis. However, the causal relationship between them is not yet clear. In this study, we employed Mendelian randomization method to investigate the causal relationship between 211 gut microbiota taxa and endometriosis. METHODS: Independent genetic loci significantly associated with the relative abundance of 211 gut microbiota taxa, based on predefined thresholds, were extracted as instrumental variables. The primary analytical approach employed was the IVW method. Effect estimates were assessed primarily using the odds ratio and 95% confidence intervals. Supplementary analyses were conducted using MR-Egger regression, the weighted median method, the simple mode and the weighted mode method to complement the IVW results. In addition, we conducted tests for heterogeneity, horizontal pleiotropy, sensitivity analysis, and MR Steiger to assess the robustness of the results and the strength of the causal relationships. RESULTS: Based on the IVW method, we found that the family Prevotellaceae, genus Anaerotruncus, genus Olsenella, genus Oscillospira, and order Bacillales were identified as risk factors for endometriosis, while class Melainabacteria and genus Eubacterium ruminantium group were protective factors. Additionally, no causal relationship was observed between endometriosis and gut microbiota. Heterogeneity tests, pleiotropy tests, and leave-one-out sensitivity analyses did not detect any significant heterogeneity or pleiotropic effects. CONCLUSIONS: Our MR study has provided evidence supporting a potential causal relationship between gut microbiota and endometriosis, and it suggests the absence of bidirectional causal effects. These findings could potentially offer new insights for the development of novel strategies for the prevention and treatment of endometriosis.

The effect of deep and awake extubation on emergence agitation after nasal surgery: a randomized controlled trial.

BACKGROUND: Post-anesthetic emergence agitation is common after general anesthesia and may cause adverse consequences, such as injury as well as respiratory and circulatory complications. Emergence agitation after general anesthesia occurs more frequently in nasal surgery than in other surgical procedures. This study aimed to assess the occurrence of emergence agitation in patients undergoing nasal surgery who were extubated under deep anesthesia or when fully awake. METHODS: A total of 202 patients (18-60 years, American Society of Anesthesiologists classification: I-II) undergoing nasal surgery under general anesthesia were randomized 1:1 into two groups: a deep extubation group (group D) and an awake extubation group (group A). The primary outcome was the incidence of emergence agitation. The secondary outcomes included number of emergence agitations, sedation score, vital signs, and incidence of adverse events. RESULTS: The incidence of emergence agitation was lower in group D than in group A (34.7% vs. 72.8%; p < 0.001). Compared to group A, patients in group D had lower Richmond Agitation-Sedation Scale scores, higher Ramsay sedation scores, fewer agitation episodes, and lower mean arterial pressure when extubated and 30 min after surgery, whereas these indicators did not differ 90 min after surgery. There was no difference in the incidence of adverse events between the two groups. CONCLUSIONS: Extubation under deep anesthesia can significantly reduce emergence agitation after nasal surgery under general anesthesia without increasing the incidence of adverse events. TRIAL REGISTRATION: Registered in Clinicaltrials.gov (NCT04844333) on 14/04/2021.

[Relationship Between Tim-3 and Galectin-9 Expression Levels, Clinical Pathological Characteristics, and Prognosis in Patients After Radical Resection of Colorectal Cancer]. / ç»“ç›´è‚ ç™Œæ ¹æ²»æœ¯åŽæ‚£è€ Tim-3、galectin-9è¡¨è¾¾æ°´å¹³ä¸Žå ¶ä¸´åºŠç— ç†ç‰¹å¾åŠé¢„åŽçš„å ³ç³».

Objective: Some colorectal cancer patients still face high recurrence rates and poor prognoses even after they have undergone the surgical treatment of radical resection. Identifying potential biochemical markers and therapeutic targets for the prognostic evaluation of patients undergoing radical resection of colorectal cancer is crucial for improving their clinical outcomes. Recently, it has been reported that the T cell immunoglobulin and mucin domain protein 3 (Tim-3) and its ligand galactose lectin 9 (galectin-9) play crucial roles in immune dysfunction caused by various tumors, such as colorectal cancer. However, their expressions, biological functions, and prognostic value in colorectal cancer are still unclear. This study aims to investigate the relationship between Tim-3 and galectin-9 expression levels and the clinicopathological characteristics and prognosis of patients undergoing radical resection of colorectal cancer. Methods: A total of 171 patients who underwent radical resection of colorectal cancer at Chengdu Fifth People's Hospital between February 2018 and March 2019 were selected. Immunohistochemistry was performed to assess the expression levels of Tim-3 and galectin-9 in the cancer tissue samples and the paracancerous tissue samples of the patients. The relationship between Tim-3 and galectin-9 expression levels and the baseline clinical parameters of the patients was analyzed accordingly. Kaplan-Meier analysis was performed to assess the association between Tim-3 and galectin-9 expression levels and the relapse-free survival (RFS) and the overall survival (OS) of colorectal cancer patients. Cox regression analysis was conducted to identify factors associated with adverse prognosis in the patients. Results: The immunohistochemical results showed that the high expression levels of Tim-3 and galectin-9 were observed in 70.18% (120/171) and 32.16% (55/171), respectively, of the colorectal cancer tissues, whereas the low expression levels were 29.82% (51/171) and 67.84% (116/171), respectively. Furthermore, the expression score of Tim-3 was significantly higher in colorectal cancer tissues than that in the paracancerous tissues, while the expression score of galectin-9 was lower than that in the paracancerous tissues (P<0.05). Further analysis revealed that the expression of Tim-3 and galectin-9 was associated with the depth of tumor infiltration, vascular infiltration, and clinical staging (P<0.05). During the follow-up period of 14-63 months, 7 out of 171 patients were lost to follow-up. Among the remaining patients, 49 and 112 cases presented abnormally low expression of Tim-3 and galectin-9, respectively, whereas 115 and 52 cases presented high expression of Tim-3 and galectin-9, respectively. Kaplan-Meier survival analysis demonstrated that patients with high Tim-3 expression in colorectal cancer tissues had significantly lower RFS and OS than those with low expression did (RFS: log-rank=22.66, P<0.001; OS: log-rank=19.71, P<0.001). Conversely, patients with low galectin-9 expression had significantly lower RFS and OS than those with high expression did (RFS: log-rank=19.45, P<0.001; OS: log-rank=22.24, P<0.001). Cox multivariate analysis indicated that TNM stage Ⅲ (HR=2.26, 95% CI: 1.20-5.68), high expression of Tim-3 (HR=0.80, 95% CI: 0.33-0.91), and low expression of galectin-9 (HR=1.80, 95% CI: 1.33-4.70) were independent risk factors affecting RFS and OS in patients (P<0.05). Conclusion: Aberrant expression of Tim-3 and galectin-9 is observed in colorectal cancer tissues. High expression of Tim-3 and low expression of galectin-9 are closely associated with adverse clinico-pathological characteristics and prognosis. They are identified as independent influencing factors that may trigger adverse prognostic events in patients. These findings suggest that Tim-3 and galectin-9 have potential as new therapeutic targets and clinical indicators.

DM-MOGA: a multi-objective optimization genetic algorithm for identifying disease modules of non-small cell lung cancer.

BACKGROUND: Constructing molecular interaction networks from microarray data and then identifying disease module biomarkers can provide insight into the underlying pathogenic mechanisms of non-small cell lung cancer. A promising approach for identifying disease modules in the network is community detection. RESULTS: In order to identify disease modules from gene co-expression networks, a community detection method is proposed based on multi-objective optimization genetic algorithm with decomposition. The method is named DM-MOGA and possesses two highlights. First, the boundary correction strategy is designed for the modules obtained in the process of local module detection and pre-simplification. Second, during the evolution, we introduce Davies-Bouldin index and clustering coefficient as fitness functions which are improved and migrated to weighted networks. In order to identify modules that are more relevant to diseases, the above strategies are designed to consider the network topology of genes and the strength of connections with other genes at the same time. Experimental results of different gene expression datasets of non-small cell lung cancer demonstrate that the core modules obtained by DM-MOGA are more effective than those obtained by several other advanced module identification methods. CONCLUSIONS: The proposed method identifies disease-relevant modules by optimizing two novel fitness functions to simultaneously consider the local topology of each gene and its connection strength with other genes. The association of the identified core modules with lung cancer has been confirmed by pathway and gene ontology enrichment analysis.

Network embedding framework for driver gene discovery by combining functional and structural information.

Comprehensive analysis of multiple data sets can identify potential driver genes for various cancers. In recent years, driver gene discovery based on massive mutation data and gene interaction networks has attracted increasing attention, but there is still a need to explore combining functional and structural information of genes in protein interaction networks to identify driver genes. Therefore, we propose a network embedding framework combining functional and structural information to identify driver genes. Firstly, we combine the mutation data and gene interaction networks to construct mutation integration network using network propagation algorithm. Secondly, the struc2vec model is used for extracting gene features from the mutation integration network, which contains both gene's functional and structural information. Finally, machine learning algorithms are utilized to identify the driver genes. Compared with the previous four excellent methods, our method can find gene pairs that are distant from each other through structural similarities and has better performance in identifying driver genes for 12 cancers in the cancer genome atlas. At the same time, we also conduct a comparative analysis of three gene interaction networks, three gene standard sets, and five machine learning algorithms. Our framework provides a new perspective for feature selection to identify novel driver genes.

ETGPDA: identification of piRNA-disease associations based on embedding transformation graph convolutional network.

BACKGROUND: Piwi-interacting RNAs (piRNAs) have been proven to be closely associated with human diseases. The identification of the potential associations between piRNA and disease is of great significance for complex diseases. Traditional "wet experiment" is time-consuming and high-priced, predicting the piRNA-disease associations by computational methods is of great significance. METHODS: In this paper, a method based on the embedding transformation graph convolution network is proposed to predict the piRNA-disease associations, named ETGPDA. Specifically, a heterogeneous network is constructed based on the similarity information of piRNA and disease, as well as the known piRNA-disease associations, which is applied to extract low-dimensional embeddings of piRNA and disease based on graph convolutional network with an attention mechanism. Furthermore, the embedding transformation module is developed for the problem of embedding space inconsistency, which is lightweighter, stronger learning ability and higher accuracy. Finally, the piRNA-disease association score is calculated by the similarity of the piRNA and disease embedding. RESULTS: Evaluated by fivefold cross-validation, the AUC of ETGPDA achieves 0.9603, which is better than the other five selected computational models. The case studies based on Head and neck squamous cell carcinoma and Alzheimer's disease further prove the superior performance of ETGPDA. CONCLUSIONS: Hence, the ETGPDA is an effective method for predicting the hidden piRNA-disease associations.

Establishment of a prognostic model for melanoma based on necroptosis-related genes.

In this study, the clinical implications and potential functions of necroptosis-related genes (NRGs) in melanoma were systematically characterized. A novel NRG signature was then constructed to analyze the immune status and prognosis of patients with melanoma. The NRG signatures for melanoma prognosis were searched using the Cancer Genome Atlas (TCGA) dataset and followed by stepwise Cox regression analysis. Patients with melanoma were divided into two groups, and survival analysis, receiver operating characteristic (ROC), and univariate and multivariate analyses were subsequently performed. The correlation of risk score (RS) with tumor immunity and RT-polymerase chain reaction (PCR) was analyzed to further verify the gene signatures. Data on tumor mutational burden (TMB) and chromosomal copy number variation (CNV) were analyzed. Three NRGs were identified as prognostic risk signatures and were significantly related to overall survival (OS) in melanoma. The signatures had better diagnostic accuracy. Furthermore, analysis of mutations in the NRGs and the incidence of chromosomal CNV helped to reveal the relationship between mutations and melanoma occurrence. A nomogram was established based on RSs. The risk characteristics were significantly associated with immunity and high risk is closely correlated with melanoma development. In vitro experiments revealed that necrostatin-1 (Nec-1) promoted cell viability and repressed the expression levels of interleukin (IL)12A and proprotein convertase subtilisin/kexin type (PCSK)1. Additionally, the expression levels of IL12A, CXCL10, and PCSK1 decreased in tumor tissues of melanoma patients. NRGs exert vital roles in immunity and might be applied as a prognostic factor of melanoma.

Effects of selenium supplementation on Polycystic Ovarian Syndrome: a systematic review and meta-analysis on randomized clinical trials.

BACKGROUND: This study provides a systematic review and meta-analysis of randomized controlled trials, which examined the effect of the selenium supplementation on polycystic ovary syndrome (PCOS). METHODS: Confirmed studies related to selenium supplementation and PCOS were searched from the databases of EMBASE, PubMed and Web of Science. Data were reported as weighted mean difference (WMD) or standard mean difference (SMD) and associated 95% confidence intervals (CIs). Analysis was performed with Stata version 12.0. RESULTS: A total of 389 cases (selenium group n = 195, control group n = 194) were included in this studies. This meta-analysis showed that selenium supplementation has a positive effect on TAC, and supplementation of selenium does not significantly improve the level of BMI, Weight, LDL, HDL, Triglycerides, Total Testosterone, HOMA-IR, NO, GSH, MDA and FPG. CONCLUSION: Although selenium can improve TAC in PCOS patients, it has no significant effect on BMI, Total Testosterone, et al. In terms of the results of this meta-analysis, it is not recommended for patients with PCOS to use selenium as a regular trace element supplement. Based on the improving effect of selenium on TAC, supplementation of selenium may have a positive effect on improving follicle quality for some PCOS patients who have poor follicle quality caused by oxidative stress or who want to undergo IVF.

Circ_0000566 contributes oxygen-glucose deprivation and reoxygenation (OGD/R)-induced human brain microvascular endothelial cell injury via regulating miR-18a-5p/ACVR2B axis.

Circular RNAs (circRNAs) exert regulatory roles in cerebrovascular disease. Human brain microvascular endothelial cells (HBMECs) participated in brain vascular dysfunction in ischemic stroke. Herein, the functions of circ_0000566 in oxygen-glucose deprivation and reoxygenation (OGD/R)-induced HBMECs were investigated. The expression of circ_0000566, miR-18a-5p, and Activin receptor type 2B (ACVR2B) was measured via quantitative real-time PCR (qRT-PCR). Cell Counting Kit-8 (CCK-8) and flow cytometry assays were utilized to detect cell viability and cell apoptosis. Western blot assay was employed to measure the levels of apoptotic-related proteins and ACVR2B. The secretion of IL-1ß, IL-6, and TNF-α was detected via corresponding kits. The relationship between miR-18a-5p and circ_0000566 or ACVR2B was examined via dual-luciferase reporter assay and RNA immunoprecipitation (RIP) assay. Circ_0000566 and ACVR2B were highly expressed, while miR-18a-5p was down-regulated in OGD/R-treated HBMECs. OGD/R treatment promoted HBMECs apoptosis and inflammation and suppressed cell viability, which could be attenuated by silencing of circ_0000566. Circ_0000566 acted as a miR-18a-5p sponge to contribute to OGD/R-induced HBMECs injury. ACVR2B served as a direct target of miR-18a-5p, and ACVR2B overexpression might abolish the inhibitory role of miR-18a-5p on OGD/R-treated HBMEC injury. Circ_0000566 sponged miR-18a-5p to regulate OGD/R-induced HBMECs injury via regulating ACVR2B expression.

A binary biclustering algorithm based on the adjacency difference matrix for gene expression data analysis.

Biclustering algorithm is an effective tool for processing gene expression datasets. There are two kinds of data matrices, binary data and non-binary data, which are processed by biclustering method. A binary matrix is usually converted from pre-processed gene expression data, which can effectively reduce the interference from noise and abnormal data, and is then processed using a biclustering algorithm. However, biclustering algorithms of dealing with binary data have a poor balance between running time and performance. In this paper, we propose a new biclustering algorithm called the Adjacency Difference Matrix Binary Biclustering algorithm (AMBB) for dealing with binary data to address the drawback. The AMBB algorithm constructs the adjacency matrix based on the adjacency difference values, and the submatrix obtained by continuously updating the adjacency difference matrix is called a bicluster. The adjacency matrix allows for clustering of gene that undergo similar reactions under different conditions into clusters, which is important for subsequent genes analysis. Meanwhile, experiments on synthetic and real datasets visually demonstrate that the AMBB algorithm has high practicability.

BLNIMDA: identifying miRNA-disease associations based on weighted bi-level network.

BACKGROUND: MicroRNAs (miRNAs) have been confirmed to be inextricably linked to the emergence of human complex diseases. The identification of the disease-related miRNAs has gradually become a routine way to unveil the genetic mechanisms of examined disorders. METHODS: In this study, a method BLNIMDA based on a weighted bi-level network was proposed for predicting hidden associations between miRNAs and diseases. For this purpose, the known associations between miRNAs and diseases as well as integrated similarities between miRNAs and diseases are mapped into a bi-level network. Based on the developed bi-level network, the miRNA-disease associations (MDAs) are defined as strong associations, potential associations and no associations. Then, each miRNA-disease pair (MDP) is assigned two information properties according to the bidirectional information distribution strategy, i.e., associations of miRNA towards disease and vice-versa. Finally, two affinity weights for each MDP obtained from the information properties and the association type are then averaged as the final association score of the MDP. Highlights of the BLNIMDA lie in the definition of MDA types, and the introduction of affinity weights evaluation from the bidirectional information distribution strategy and defined association types, which ensure the comprehensiveness and accuracy of the final prediction score of MDAs. RESULTS: Five-fold cross-validation and leave-one-out cross-validation are used to evaluate the performance of the BLNIMDA. The results of the Area Under Curve show that the BLNIMDA has many advantages over the other seven selected computational methods. Furthermore, the case studies based on four common diseases and miRNAs prove that the BLNIMDA has good predictive performance. CONCLUSIONS: Therefore, the BLNIMDA is an effective method for predicting hidden MDAs.

Non-negative low-rank representation based on dictionary learning for single-cell RNA-sequencing data analysis.

In the analysis of single-cell RNA-sequencing (scRNA-seq) data, how to effectively and accurately identify cell clusters from a large number of cell mixtures is still a challenge. Low-rank representation (LRR) method has achieved excellent results in subspace clustering. But in previous studies, most LRR-based methods usually choose the original data matrix as the dictionary. In addition, the methods based on LRR usually use spectral clustering algorithm to complete cell clustering. Therefore, there is a matching problem between the spectral clustering method and the affinity matrix, which is difficult to ensure the optimal effect of clustering. Considering the above two points, we propose the DLNLRR method to better identify the cell type. First, DLNLRR can update the dictionary during the optimization process instead of using the predefined fixed dictionary, so it can realize dictionary learning and LRR learning at the same time. Second, DLNLRR can realize subspace clustering without relying on spectral clustering algorithm, that is, we can perform clustering directly based on the low-rank matrix. Finally, we carry out a large number of experiments on real single-cell datasets and experimental results show that DLNLRR is superior to other scRNA-seq data analysis algorithms in cell type identification.

Wheat AGAMOUS LIKE 6 transcription factors function in stamen development by regulating the expression of Ta APETALA3.

Previous studies have revealed the functions of rice and maize AGAMOUS LIKE 6 (AGL6) genes OsMADS6 and ZAG3, respectively, in floral development; however, the functions of three wheat (Triticum aestivum) AGL6 genes are still unclear. Here, we report the main functions of wheat AGL6 homoeologous genes in stamen development. In RNAi plants, stamens showed abnormality in number and morphology, and a tendency to transform into carpels. Consistently, the expression of the B-class gene TaAPETALA3 (AP3) and the auxin-responsive gene TaMGH3 was downregulated, whereas the wheat ortholog of the rice carpel identity gene DROOPING LEAF was ectopically expressed in RNAi stamens. TaAGL6 proteins bind to the promoter of TaAP3 directly. Yeast one-hybrid and transient expression assays further showed that TaAGL6 positively regulates the expression of TaAP3 in vivo. Wheat AGL6 transcription factors interact with TaAP3, TaAGAMOUS and TaMADS13. Our findings indicate that TaAGL6 transcription factors play an essential role in stamen development through transcriptional regulation of TaAP3 and other related genes. We propose a model to illustrate the function and probable mechanism of this regulation. This study extends our understanding of AGL6 genes.

Multiscale part mutual information for quantifying nonlinear direct associations in networks.

MOTIVATION: For network-assisted analysis, which has become a popular method of data mining, network construction is a crucial task. Network construction relies on the accurate quantification of direct associations among variables. The existence of multiscale associations among variables presents several quantification challenges, especially when quantifying nonlinear direct interactions. RESULTS: In this study, the multiscale part mutual information (MPMI), based on part mutual information (PMI) and nonlinear partial association (NPA), was developed for effectively quantifying nonlinear direct associations among variables in networks with multiscale associations. First, we defined the MPMI in theory and derived its five important properties. Second, an experiment in a three-node network was carried out to numerically estimate its quantification ability under two cases of strong associations. Third, experiments of the MPMI and comparisons with the PMI, NPA and conditional mutual information were performed on simulated datasets and on datasets from DREAM challenge project. Finally, the MPMI was applied to real datasets of glioblastoma and lung adenocarcinoma to validate its effectiveness. Results showed that the MPMI is an effective alternative measure for quantifying nonlinear direct associations in networks, especially those with multiscale associations. AVAILABILITY AND IMPLEMENTATION: The source code of MPMI is available online at https://github.com/CDMB-lab/MPMI. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MicroRNA Expression Profiles of Epicardial Adipose Tissue-Derived Exosomes in Patients with Coronary Atherosclerosis.

Background and Aims: Epicardial adipose tissue, exosomes, and miRNAs have important activities in atherosclerosis. The purpose of this study was to establish miRNA expression profiles of epicardial adipose tissue-derived exosomes in patients with coronary atherosclerosis. Methods: Biopsies of epicardial adipose tissue were obtained from patients with and without coronary artery disease (CAD, n = 12 and NCAD, n = 12) during elective open-heart surgeries. Tissue was incubated with DMEM-F12 for 24 hours. Exosomes were isolated, then nanoparticle tracking analysis, transmission electron microscopy, and immunoblotting were performed to confirm the existence of exosomes. Total RNA in exosomes was subjected to high-throughput sequencing to identify differentially expressed miRNAs. MicroRNA target gene prediction was performed, and target genes were analyzed by Gene Ontology (GO), the Kyoto Encyclopedia of Genes and Genomes (KEGG), and mirPath to identify function. Reverse transcription quantitative PCR was performed to confirm the differentially expressed miRNAs. Results: Fifty-three unique miRNAs were identified (adjusted p < 0.05, fold of change > 2), among which 32 miRNAs were upregulated and 21 miRNAs were downregulated in coronary artery disease patients. Reverse transcription quantitative PCR validated the results for seven miRNAs including miR-141-3p, miR-183-5p, miR-200a-5p, miR-205-5p, miR-429, miR-382-5p and miR-485-3p, with the last two downregulated. GO and KEGG analysis by mirPath indicated that these differentially expressed miRNAs were enriched in cell survival, apoptosis, proliferation, and differentiation. Conclusions: Coronary artery disease patients showed differential epicardial adipose tissue exosomal miRNA expression compared with patients without coronary artery disease. The results provide clues for further studies of mechanisms of atherosclerosis.

COVID-19 vaccine and menstrual conditions in female: data analysis of the Vaccine Adverse Event Reporting System (VAERS).

BACKGROUND: In reports of adverse reactions following vaccination with the coronavirus disease 2019(COVID-19) vaccines, there have been fewer reports of concern for menstrual disorders in female. OBJECTIVE: Our study employed Vaccine Adverse Event Reporting System (VAERS) to investigate and analyze the relationship between COVID-19 Vaccines and menstrual disorders in female. METHODS: We collected reports of menstrual disorders in VAERS from July 2, 1990 to November 12, 2021, and performed a stratified analysis. The potential relationship between COVID-19 vaccine and reports of menstrual disorders was evaluated using the Reporting Odds Ratio (ROR) method. RESULTS: A total of 14,431 reports of menstrual disorders were included in the study, and 13,118 were associated with COVID-19 vaccine. The ROR was 7.83 (95% confidence interval [95%CI]: 7.39-8.28). The most commonly reported event was Menstruation irregular (4998 reports), and a higher percentage of female aged 30-49 years reported menstrual disorders (42.55%) after exposure to COVID-19 Vaccines. Both for all reports of menstrual disorders (ROR = 5.82; 95%CI: 4.93-6.95) and excluding reports of unknown age (ROR = 13.02; 95%CI: 10.89-15.56),suggest that female age may be associated with menstrual disorders after vaccination with the COVID-19 Vaccines. CONCLUSION: There is a potential safety signal when the COVID-19 vaccine is administered to young adult female (30-49 years old), resulting in menstrual disorders in. However, due to the well-known limitations of spontaneous reporting data, it is challenging to explicity classify menstrual disorders as an adverse event of the COVID-19 Vaccines, and reports of adverse reactions to COVID-19 Vaccines in this age group should continue to be tracked.