Growth performance, lipid metabolism, and systemic immunity of weaned piglets were altered by buckwheat protein through the modulation of gut microbiota.

Tartary buckwheat protein (BWP) is well known for the wide-spectrum antibacterial activity and the lipid metabolism- regulating property; therefore, BWP can be applied as feed additives to improve the animal's nutritional supply. With the aim to investigate the bioactive actions of the BWP, growth performance, lipid metabolism and systemic immunity of the weaned piglets were measured, and the alterations of pig gut microbiota were also analyzed. According to the results, the growth performances of the weaned piglets which were calculated as the average daily gain (ADG) and the average daily feed intake (ADFI) were significantly increased when compared to the control group. Simultaneously, the serum levels of the total cholesterol (TC), triglycerides (TG), and low-density lipoprotein cholesterol (LDL-C) were decreased, while the levels of high-density lipoprotein cholesterol (HDL-C) were increased in the BWP group. Moreover, the relative abundances of Lactobacillus, Prevotella_9, Subdoligranulum, Blautia, and other potential probiotics in the gut microbiota of weaned piglets were obviously increased in the BWP group. However, the relative abundances of Escherichia-Shigella, Campylobacter, Rikenellaceae_RC9_gut_group and other opportunistic pathogens were obviously decreased in the BWP group. In all, BWP was proved to be able to significantly improve the growth performance, lipid metabolism, and systemic immunity of the weaned piglets, and the specific mechanism might relate to the alterations of the gut microbiota. Therefore, BWP could be explored as a prospective antibiotic alternative for pig feed additives.

TEMINET: A Co-Informative and Trustworthy Multi-Omics Integration Network for Diagnostic Prediction.

Advancing the domain of biomedical investigation, integrated multi-omics data have shown exceptional performance in elucidating complex human diseases. However, as the variety of omics information expands, precisely perceiving the informativeness of intra- and inter-omics becomes challenging due to the intricate interrelations, thus presenting significant challenges in the integration of multi-omics data. To address this, we introduce a novel multi-omics integration approach, referred to as TEMINET. This approach enhances diagnostic prediction by leveraging an intra-omics co-informative representation module and a trustworthy learning strategy used to address inter-omics fusion. Considering the multifactorial nature of complex diseases, TEMINET utilizes intra-omics features to construct disease-specific networks; then, it applies graph attention networks and a multi-level framework to capture more collective informativeness than pairwise relations. To perceive the contribution of co-informative representations within intra-omics, we designed a trustworthy learning strategy to identify the reliability of each omics in integration. To integrate inter-omics information, a combined-beliefs fusion approach is deployed to harmonize the trustworthy representations of different omics types effectively. Our experiments across four different diseases using mRNA, methylation, and miRNA data demonstrate that TEMINET achieves advanced performance and robustness in classification tasks.

Two Enterococcus faecium Isolates Demonstrated Modulating Effects on the Dysbiosis of Mice Gut Microbiota Induced by Antibiotic Treatment.

Broad-spectrum antibiotics are frequently used to treat bacteria-induced infections, but the overuse of antibiotics may induce the gut microbiota dysbiosis and disrupt gastrointestinal tract function. Probiotics can be applied to restore disturbed gut microbiota and repair abnormal intestinal metabolism. In the present study, two strains of Enterococcus faecium (named DC-K7 and DC-K9) were isolated and characterized from the fecal samples of infant dogs. The genomic features of E. faecium DC-K7 and DC-K9 were analyzed, the carbohydrate-active enzyme (CAZyme)-encoding genes were predicted, and their abilities to produce short-chain fatty acids (SCFAs) were investigated. The bacteriocin-encoding genes in the genome sequences of E. faecium DC-K7 and DC-K9 were analyzed, and the gene cluster of Enterolysin-A, which encoded a 401-amino-acid peptide, was predicted. Moreover, the modulating effects of E. faecium DC-K7 and DC-K9 on the gut microbiota dysbiosis induced by antibiotics were analyzed. The current results demonstrated that oral administrations of E. faecium DC-K7 and DC-K9 could enhance the relative abundances of beneficial microbes and decrease the relative abundances of harmful microbes. Therefore, the isolated E. faecium DC-K7 and DC-K9 were proven to be able to alter the gut microbiota dysbiosis induced by antibiotic treatment.

The response of rhubarb to smut infection is revealed through a comparative transcriptome and metabolome study.

MAIN CONCLUSION: Integrated transcriptome and metabolome analysis have unveiled the physiological and molecular responses of rhubarb to infection by smut fungi. Rhubarb is an important medicinal plant that is easily infected by smut fungi during its growth. Thus far, no research on the influence of smut fungi on the growth of rhubarb and its secondary metabolism has been conducted. In this study, petioles of Chinese rhubarb (Rheum officinale) [healthy or infected with smut fungus (Thecaphora schwarzmaniana)] were characterized. Microscopic structure, global gene expression profiling, global metabolic profiling, and key enzyme activity and metabolite levels in infected plants were analyzed. Infection by smut fungi resulted in numerous holes inside the petiole tissue and led to visible tumors on the external surface of the petiole. Through metabolic changes, T. schwarzmaniana induced the production of specific sugars, lipids, and amino acids, and inhibited the metabolism of phenolics and flavonoids in R. officinale. The concentrations of key medicinal compounds (anthraquinones) were decreased because of smut fungus infection. In terms of gene expression, the presence of T. schwarzmaniana led to upregulation of the genes associated with nutrient (sugar, amino acid, etc.) transport and metabolism. The gene expression profiling showed a stimulated cell division activity (the basis of tumor formation). Although plant antioxidative response was enhanced, the plant defense response against pathogen was suppressed by T. schwarzmaniana, as indicated by the expression profiling of genes involved in biotic and abiotic stress-related hormone signaling and the synthesis of plant disease resistance proteins. This study demonstrated physiological and molecular changes in R. officinale under T. schwarzmaniana infection, reflecting the survival tactics employed by smut fungus for parasitizing rhubarb.

Various mobile genetic elements carrying optrA in Enterococcus faecium and Enterococcus faecalis isolates from swine within the same farm.

OBJECTIVES: In this study, the distribution of the oxazolidinone/phenicol resistance gene optrA and the mobile genetic elements involved in its dissemination were analysed among enterococcal isolates from a farrow-to-finish swine farm. METHODS: Enterococcus faecium and Enterococcus faecalis isolates were obtained from all pig production stages in the farm. The optrA-carrying E. faecium and E. faecalis isolates were subjected to PFGE and antimicrobial susceptibility testing. Complete sequences of the genetically unrelated optrA-carrying E. faecium and E. faecalis isolates were determined using Illumina HiSeq and MinION platforms. RESULTS: The optrA gene was present in 12.2% (23/188) of the E. faecium and E. faecalis isolates, most of which originated from nursery and finishing stages. The 23 optrA-positive Enterococcus isolates represented 15 PFGE types. WGS of representative isolates of the 15 PFGE types showed that optrA was carried by diverse genetic elements either located in the chromosomal DNA or on plasmids. A novel optrA-bearing genetic element was identified on two distinct multi-resistance plasmids from E. faecium. Two new hybrid plasmids carrying several resistance genes were found in two E. faecalis isolates. pC25-1-like plasmids and chromosomally integrated Tn6674 and Tn6823-like transposons were prevalent in the remaining Enterococcus isolates. CONCLUSIONS: The gene optrA was found in genetically unrelated E. faecium and E. faecalis isolates from the same farm. Analysis of the genetic contexts of optrA suggested that horizontal transfer including different plasmids and transposons played a key role in the dissemination of optrA in this farm.

Automatic Modulation Classification for Underwater Acoustic Communication Signals Based on Deep Complex Networks.

Automatic modulation classification (AMC) is an important method for monitoring and identifying any underwater communication interference. Since the underwater acoustic communication scenario is full of multi-path fading and ocean ambient noise (OAN), coupled with the application of modern communication technology, which is usually susceptible to environmental influences, automatic modulation classification (AMC) becomes particularly difficult when it comes to an underwater scenario. Motivated by the deep complex networks (DCN), which have an innate ability to process complex data, we explore DCN for AMC of underwater acoustic communication signals. To integrate the signal processing method with deep learning and overcome the influences of underwater acoustic channels, we propose two complex physical signal processing layers based on DCN. The proposed layers include a deep complex matched filter (DCMF) and deep complex channel equalizer (DCCE), which are designed to remove noise and reduce the influence of multi-path fading for the received signals, respectively. Hierarchical DCN is constructed using the proposed method to achieve better performance of AMC. The influence of the real-world underwater acoustic communication scenario is taken into account; two underwater acoustic multi-path fading channels are conducted using the real-world ocean observation dataset, white Gaussian noise, and real-world OAN are used as the additive noise, respectively. Contrastive experiments show that the AMC based on DCN can achieve better performance than the traditional deep neural network based on real value (the average accuracy of the DCN is 5.3% higher than real-valued DNN). The proposed method based on DCN can effectively reduce the influence of underwater acoustic channels and improve the AMC performance in different underwater acoustic channels. The performance of the proposed method was verified on the real-world dataset. In the underwater acoustic channels, the proposed method outperforms a series of advanced AMC method.

Feature Fusion Based on Graph Convolution Network for Modulation Classification in Underwater Communication.

Automatic modulation classification (AMC) of underwater acoustic communication signals is of great significance in national defense and marine military. Accurate modulation classification methods can make great contributions to accurately grasping the parameters and characteristics of enemy communication systems. While a poor underwater acoustic channel makes it difficult to classify the modulation types correctly. Feature extraction and deep learning methods have proven to be effective methods for the modulation classification of underwater acoustic communication signals, but their performance is still limited by the complex underwater communication environment. Graph convolution networks (GCN) can learn the graph structured information of the data, making it an effective method for processing structured data. To improve the stability and robustness of AMC in underwater channels, we combined the feature extraction and deep learning methods by fusing the multi-domain features and deep features using GCN. The proposed method takes the relationships among the different multi-domain features and deep features into account. Firstly, a feature graph was built using the properties of the features. Secondly, multi-domain features were extracted from the received signals and deep features were extracted from the signals using a deep neural network. Thirdly, we constructed the input of GCN using these features and the graph. Then, the multi-domain features and deep features were fused by the GCN. Finally, we classified the modulation types using the output of GCN by way of a softmax layer. We conducted the experiments on a simulated dataset and a real-world dataset, respectively. The results show that the AMC based on GCN can achieve a significant improvement in performance compared to the current state-of-the-art methods. Our approach is robust in underwater acoustic channels.

Expression and Clinical Significance of MDM2 in Non-Functioning PitNETs.

Background and Objective: Non-functioning pituitary neuroendocrine tumors (NF-PitNETs) represent a heterogeneous tumor type that lacks effective medical treatment. MDM2, the main negative regulator of p53, binds to and forms a stable complex with p53 to regulate its activity. In this study, we measured the expression levels and role of MDM2 in non-functioning PitNET patients' combined clinical features and investigated the effect of etoposide on the cell bioactivity of the GT1-1 cell line in vivo and in vitro. Methods: RT-PCR and immunochemistry measured the expression levels and role of MDM2 in 103 NF-PitNET patients' combined clinical features. Cell proliferation, migration, colony and apoptosis experiments measured the effect of etoposide on the GT1-1 cell line in vivo and in vitro. Results: There was more invasive behavior (p = 0.013) in patients with high MDM2, who were also younger (p = 0.007), were more frequently female (p = 0.049) and had larger tumor sizes (p = 0.018) compared with patients with low MDM2. Patients with high p53 were younger (p = 0.017) and had larger tumor sizes (p = 0.034) compared with patients with low p53. Univariate (p = 0.018) and multivariate (p = 0.023) Cox regression analysis showed that MDM2 was the independent factor for invasive behavior in NF-PitNET patients. Log-rank analysis showed that the average progression-free survival (PFS) time in the low MDM2 patients was longer than that in the high MDM2 patients (p = 0.044). Functional studies indicated that etoposide inhibited cell proliferation and cell migration and induced apoptosis in p53 independence in GT1-1 cells. Furthermore, etoposide significantly inhibited the growth of GT1-1-xenograft in BALB/c nude mice. The tumor growth inhibition rate of etoposide was 67.4 ± 4.6% after 14 d of treatment, which suggested the anti-tumor activity of etoposide. Conclusions: MDM2 played the role of tumorigenesis of NF-PitNET in a p53 independence manner, and an MDM2 inhibitor could be a potential choice for the treatment of NF-PitNET patients.

Deep multiview learning to identify imaging-driven subtypes in mild cognitive impairment.

BACKGROUND: In Alzheimer's Diseases (AD) research, multimodal imaging analysis can unveil complementary information from multiple imaging modalities and further our understanding of the disease. One application is to discover disease subtypes using unsupervised clustering. However, existing clustering methods are often applied to input features directly, and could suffer from the curse of dimensionality with high-dimensional multimodal data. The purpose of our study is to identify multimodal imaging-driven subtypes in Mild Cognitive Impairment (MCI) participants using a multiview learning framework based on Deep Generalized Canonical Correlation Analysis (DGCCA), to learn shared latent representation with low dimensions from 3 neuroimaging modalities. RESULTS: DGCCA applies non-linear transformation to input views using neural networks and is able to learn correlated embeddings with low dimensions that capture more variance than its linear counterpart, generalized CCA (GCCA). We designed experiments to compare DGCCA embeddings with single modality features and GCCA embeddings by generating 2 subtypes from each feature set using unsupervised clustering. In our validation studies, we found that amyloid PET imaging has the most discriminative features compared with structural MRI and FDG PET which DGCCA learns from but not GCCA. DGCCA subtypes show differential measures in 5 cognitive assessments, 6 brain volume measures, and conversion to AD patterns. In addition, DGCCA MCI subtypes confirmed AD genetic markers with strong signals that existing late MCI group did not identify. CONCLUSION: Overall, DGCCA is able to learn effective low dimensional embeddings from multimodal data by learning non-linear projections. MCI subtypes generated from DGCCA embeddings are different from existing early and late MCI groups and show most similarity with those identified by amyloid PET features. In our validation studies, DGCCA subtypes show distinct patterns in cognitive measures, brain volumes, and are able to identify AD genetic markers. These findings indicate the promise of the imaging-driven subtypes and their power in revealing disease structures beyond early and late stage MCI.

Integrative analysis of summary data from GWAS and eQTL studies implicates genes differentially expressed in Alzheimer's disease.

BACKGROUND: Although genome-wide association studies (GWAS) have successfully located various genetic variants susceptible to Alzheimer's Disease (AD), it is still unclear how specific variants interact with genes and tissues to elucidate pathologies associated with AD. Summary-data-based Mendelian Randomization (SMR) addresses this problem through an instrumental variable approach that integrates data from independent GWAS and expression quantitative trait locus (eQTL) studies in order to infer a causal effect of gene expression on a trait. RESULTS: Our study employed the SMR approach to integrate a set of meta-analytic cis-eQTL information from the Genotype-Tissue Expression (GTEx), CommonMind Consortium (CMC), and Religious Orders Study and Rush Memory and Aging Project (ROS/MAP) consortiums with three sets of meta-analysis AD GWAS results. CONCLUSIONS: Our analysis identified twelve total gene probes (associated with twelve distinct genes) with a significant association with AD. Four of these genes survived a test of pleiotropy from linkage (the HEIDI test).Three of these genes - RP11-385F7.1, PRSS36, and AC012146.7 - have not yet been reported differentially expressed in the brain in the context of AD, and thus are the novel findings warranting further investigation.

Genome-Wide association study of quantitative biomarkers identifies a novel locus for alzheimer's disease at 12p12.1.

BACKGROUND: Genetic study of quantitative biomarkers in Alzheimer's Disease (AD) is a promising method to identify novel genetic factors and relevant endophenotypes, which provides valuable information to deconvolute mechanistic complexity and better understand disease subtypes. RESULTS: Using the data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we performed a genome-wide association study (GWAS) between 565,373 single nucleotide polymorphisms (SNPs) and 16 key AD biomarkers from 1,576 subjects at four visits. We identified a novel locus rs5011804 at 12p12.1 significantly associated with several AD biomarkers, including three cognitive traits (CDRSB, FAQ, ADAS13) and one imaging trait (fusiform volume). Additional mediation and interaction analyses investigated the relationships among this SNP, relevant biomarkers, and clinical diagnosis, confirming and further elaborating the genetic effects seen in the GWAS. CONCLUSION: Our GWAS not only affirms key AD genes but also suggests the promising role of the SNP rs5011804 due to its associations with several AD cognitive and imaging outcomes. The SNP rs5011804 has a reported association with adult asthma and slightly affects intracranial volume but has not been associated with AD before. Our novel findings contribute to a more comprehensive view of the molecular mechanism behind AD.

The Integrated Stress Response Is Tumorigenic and Constitutes a Therapeutic Liability in Somatotroph Adenomas.

Somatotroph adenomas are the leading cause of acromegaly, with the nearly sparsely granulated somatotroph subtype belonging to high-risk adenomas, and they are less responsive to medical treatment. The integrated stress response (ISR) is an essential stress-support pathway increasingly recognized as a determinant of tumorigenesis. In this study, we identified the characteristic profiling of the integrated stress response in translocation and translation initiation factor activity in somatotroph adenomas, normal pituitary, or other adenoma subtypes through proteomics. Immunohistochemistry exhibited the differential significance and the priority of eukaryotic translation initiation factor 2ß (EIF2ß) in somatotroph adenomas compared with gonadotroph and corticotroph adenomas. Differentially expressed genes based on the level of EIF2ß in somatotroph adenomas were revealed. MetaSape pathways showed that EIF2ß was involved in regulating growth and cell activation, immune system, and extracellular matrix organization processes. The correlation analysis showed Spearman correlation coefficients of r = 0.611 (p < 0.001) for EIF2ß and eukaryotic translation initiation factor 2 alpha kinase 1 (HRI), r = 0.765 (p < 0.001) for eukaryotic translation initiation factor 2 alpha kinase 2 (PKR), r = 0.813 (p < 0.001) for eukaryotic translation initiation factor 2 alpha kinase 3 (PERK), r = 0.728 (p < 0.001) for GCN2, and r = 0.732 (p < 0.001) for signal transducer and activator of transcription 3 (STAT3). Furthermore, the invasive potential in patients with a high EIF2ß was greater than that in patients with a low EIF2ß (7/10 vs. 4/18, p = 0.038), with a lower immune-cell infiltration probability (p < 0.05). The ESTIMATE algorithm showed that the levels of activation of the EIF2 pathway were negatively correlated with the immune score in somatotroph adenomas (p < 0.001). In in vitro experiments, the knockdown of EIF2ß changed the phenotype of somatotroph adenomas, including cell proliferation, migration, and the secretion ability of growth hormone/insulin-like growth factor-1. In this study, we demonstrate that the ISR is pivotal in somatotroph adenomas and provide a rationale for implementing ISR-based regimens in future treatment strategies.

Evaluation of the Chemical Composition and Antioxidant Activity of Mulberry (Morus alba L.) Fruits from Different Varieties in China.

Mulberry (Morus alba L.) fruit is a fruit with nutritional and medicinal value. It is widely cultivated in different regions of China, which may result in differences in its chemical composition. In this research, 25 mulberry fruit samples from six provinces in China were investigated. The contents of anthocyanins were evaluated by high-performance liquid chromatography (HPLC). The contents of two main anthocyanins, cyanidin-3-O-glucoside (C3G) and cyanidin-3-O-rutinoside (C3R), ranged from 0.656 ± 0.006 mg/g to 4.367 ± 0.243 mg/g and from 0.226 ± 0.007 mg/g to 1.649 ± 0.013 mg/g, respectively. Additionally, the contents of total phenolic, total flavonoid, vitamin C, titratable acids, reducing sugars and antioxidant capacity (FRAP, DPPH, scavenging and hydroxyl radical scavenging activity) were also assessed. The results and principal component analysis showed that the Zhongsang 5801 variety from Sichuan, Dechang had the greatest health value with the highest active compound contents. Based on our analysis, the variety from Sichuan, Dechang is a high-quality plant source for mulberry fruit cultivation. This research provides a basis for the rational development and utilization of mulberry fruit resources in China.

[Distribution characteristics of 46 elements in drinking water in Ankang area in 2020].

OBJECTIVE: To understand the distribution characteristics of 46 elements in drinking water in Ankang area of Shanxi Province. METHODS: A total of 46 elements in drinking water samples collected in Ankang area during dry and wet seasons in 2020 were determined by inductively coupled plasma mass spectrometry(ICP-MS). According to the "drinking water hygiene standards"(GB 5749-2022) and "food safety national standards". the 46 elements were classified As general chemical indexes(Al, Mn, Fe, Cu, Zn), toxicological indexes(Cr, As, Cd, Hg, Pb, Ba, B, Mo, Ni, Sb, Be, Ag, Tl), new reference indexes(U, V), and major elements(K, Ca, N) A, Mg), trace elements(Li, Co, Se, Sr, Sn) and rare earth elements(Sc, Y, La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu, Th) were analyzed and described. RESULTS: The maximum values of Al and Fe in drinking water in Ankang area were 1.21 and 0.98 mg/L, exceeding the limits of 0.2 and 0.3 mg/L. In dry season, the median content of Fe in drinking water of different water sources was higher in groundwater than in surface water. The Al and Fe of surface water were higher than that of groundwater in wet season. The toxicological indexes all met the standard requirements, and there was no significant difference among districts and counties. The median content of Na in drinking water of different water sources was higher in groundwater than in surface water, while Mg, K and Ca were higher in surface water than in groundwater. The maximum value of the newly added reference index U was 0.015 mg/L lower than the limit standard 0.03 mg/L, and the maximum value of V was 0.019 mg/L higher than the limit standard 0.01 mg/L, but the median of both indexes were low. The median content of Li in drinking water of different water sources was that surface water was higher than groundwater, while Se and Sr were higher in groundwater than surface water. The maximum content of Se was 0.016 mg/L in Ziyang County, and Sr content was generally higher. The content of rare earth elements was low, mostly below the detection. CONCLUSION: The drinking water quality in Ankang area is excellent and rich in strontium. However, the general chemical indexes Al, Fe and the newly added appendix index V in some sampling points exceed the limit standard, so supervision and monitoring should be strengthened.

OASL Triggered by Novel Goose Astrovirus via ORF2 Restricts Its Replication.

Although astroviruses causes enteric diseases and encephalitis in humans and nephritis and hepatitis in poultry, astrovirus infection is thought to be self-limiting. However, little is known about its molecular mechanism. In this study, we found that a novel goose astrovirus (GAstV), GAstV-GD, and its open reading frame 2 (ORF2) could efficiently activate the innate immune response and induce a high level of OASL in vitro and in vivo The truncation assay for ORF2 further revealed that the P2 domain of ORF2 contributed to stimulating OASL, whereas the acidic C terminus of ORF2 attenuated such activation. Moreover, the overexpression and knockdown of OASL could efficiently restrict and promote the viral replication of GAstV-GD, respectively. Our data not only give novel insights for elucidating self-limiting infection by astrovirus but also provide virus and host targets for fighting against astroviruses.IMPORTANCE Astroviruses cause gastroenteritis and encephalitis in human, and nephritis, hepatitis, and gout disease in poultry. However, the host immune response activated by astrovirus is mostly unknown. Here, we found that a novel goose astrovirus, GAstV-GD, and its ORF2 protein could efficiently induce a high level of OASL in vitro and in vivo, which could feed back to restrict the replication of GAstV-GD, revealing novel innate molecules triggered by astroviruses and highlighting that the ORF2 of GAstV-GD and OASL can be potential antiviral targets for astroviruses.

Gp37 Regulates the Pathogenesis of Avian Leukosis Virus Subgroup J via Its C Terminus.

Different from other subgroups of avian leukosis viruses (ALVs), ALV-J is highly pathogenic. It is the main culprit causing myeloid leukemia and hemangioma in chickens. The distinctiveness of the env gene of ALV-J, with low homology to those of other ALVs, is linked to its unique pathogenesis, but the underlying mechanism remains unclear. Previous studies show that env of ALV-J can be grouped into three species based on the tyrosine motifs in the cytoplasmic domain (CTD) of Gp37, i.e., the inhibitory, bifunctional, and active groups. To explore whether the C terminus or the tyrosine motifs in the CTD of Gp37 affect the pathogenicity of ALV-J, a set of ALV-J infectious clones containing different C termini of Gp37 or the mutants at the tyrosine sites were tested in vitro and in vivo Viral growth kinetics indicated not only that ALV-J with active env is the fastest in replication and ALV-J with inhibitory env is the lowest but also that the tyrosine sites essentially affected the replication of ALV-J. Moreover, in vivo studies demonstrated that chickens infected by ALV-J with active or bifunctional env showed higher viremia, cloacal viral shedding, and viral tissue load than those infected by ALV-J with inhibitory env Notably, the chickens infected by ALV-J with active or bifunctional env showed significant loss of body weight compared with the control chickens. Taken together, these findings reveal that the C terminus of Gp37 plays a vital role in ALV-J pathogenesis, and change from inhibitory env to bifunctional or active env increases the pathogenesis of ALV-J.IMPORTANCE ALV-J can cause severe immunosuppression and myeloid leukemia in infected chickens. However, no vaccine or antiviral drug is available against ALV-J, and the mechanism for ALV-J pathogenesis needs to be elucidated. It is generally believed that gp85 and LTR of ALV contribute to its pathogenesis. Here, we found that the C terminus and the tyrosine motifs (YxxM, ITIM, and ITAM-like) in the CTD of Gp37 of ALV-J could affect the pathogenicity of ALV-J in vitro and in vivo The pathogenicity of ALV-J with Gp37 containing ITIM only was significantly less than ALV-J with Gp37 containing both YxxM and ITIM and ALV-J with Gp37 containing both YxxM and ITAM-like. This study highlights the vital role of the C terminus of Gp37 in the pathogenesis of ALV-J and thus provides a new perspective to elucidate the interaction between ALV-J and its host and a molecular basis to develop efficient strategies against ALV-J.

Identifying diagnosis-specific genotype-phenotype associations via joint multitask sparse canonical correlation analysis and classification.

MOTIVATION: Brain imaging genetics studies the complex associations between genotypic data such as single nucleotide polymorphisms (SNPs) and imaging quantitative traits (QTs). The neurodegenerative disorders usually exhibit the diversity and heterogeneity, originating from which different diagnostic groups might carry distinct imaging QTs, SNPs and their interactions. Sparse canonical correlation analysis (SCCA) is widely used to identify bi-multivariate genotype-phenotype associations. However, most existing SCCA methods are unsupervised, leading to an inability to identify diagnosis-specific genotype-phenotype associations. RESULTS: In this article, we propose a new joint multitask learning method, named MT-SCCALR, which absorbs the merits of both SCCA and logistic regression. MT-SCCALR learns genotype-phenotype associations of multiple tasks jointly, with each task focusing on identifying one diagnosis-specific genotype-phenotype pattern. Meanwhile, MT-SCCALR cannot only select relevant SNPs and imaging QTs for each diagnostic group alone, but also allows the selection of those shared by multiple diagnostic groups. We derive an efficient optimization algorithm whose convergence to a local optimum is guaranteed. Compared with two state-of-the-art methods, MT-SCCALR yields better or similar canonical correlation coefficients and classification performances. In addition, it owns much better discriminative canonical weight patterns of great interest than competitors. This demonstrates the power and capability of MTSCCAR in identifying diagnostically heterogeneous genotype-phenotype patterns, which would be helpful to understand the pathophysiology of brain disorders. AVAILABILITY AND IMPLEMENTATION: The software is publicly available at https://github.com/dulei323/MTSCCALR. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Regional imaging genetic enrichment analysis.

MOTIVATION: Brain imaging genetics aims to reveal genetic effects on brain phenotypes, where most studies examine phenotypes defined on anatomical or functional regions of interest (ROIs) given their biologically meaningful interpretation and modest dimensionality compared with voxelwise approaches. Typical ROI-level measures used in these studies are summary statistics from voxelwise measures in the region, without making full use of individual voxel signals. RESULTS: In this article, we propose a flexible and powerful framework for mining regional imaging genetic associations via voxelwise enrichment analysis, which embraces the collective effect of weak voxel-level signals and integrates brain anatomical annotation information. Our proposed method achieves three goals at the same time: (i) increase the statistical power by substantially reducing the burden of multiple comparison correction; (ii) employ brain annotation information to enable biologically meaningful interpretation and (iii) make full use of fine-grained voxelwise signals. We demonstrate our method on an imaging genetic analysis using data from the Alzheimer's Disease Neuroimaging Initiative, where we assess the collective regional genetic effects of voxelwise FDG-positron emission tomography measures between 116 ROIs and 565 373 single-nucleotide polymorphisms. Compared with traditional ROI-wise and voxelwise approaches, our method identified 2946 novel imaging genetic associations in addition to 33 ones overlapping with the two benchmark methods. In particular, two newly reported variants were further supported by transcriptome evidences from region-specific expression analysis. This demonstrates the promise of the proposed method as a flexible and powerful framework for exploring imaging genetic effects on the brain. AVAILABILITY AND IMPLEMENTATION: The R code and sample data are freely available at https://github.com/lshen/RIGEA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The Aquaporin TaPIP2;10 Confers Resistance to Two Fungal Diseases in Wheat.

Plants employ aquaporins (AQPs) of the plasma membrane intrinsic protein (PIP) family to import environmental substrates, thereby affecting various processes, such as the cellular responses regulated by the signaling molecule hydrogen peroxide (H2O2). Common wheat (Triticum aestivum) contains 24 candidate members of the PIP family, designated as TaPIP1;1 to TaPIP1;12 and TaPIP2;1 to TaPIP2;12. None of these TaPIP candidates have been characterized for substrate selectivity or defense responses in their source plant. Here, we report that T. aestivum AQP TaPIP2;10 facilitates the cellular uptake of H2O2 to confer resistance against powdery mildew and Fusarium head blight, two devastating fungal diseases in wheat throughout the world. In wheat, the apoplastic H2O2 signal is induced by fungal attack, while TaPIP2;10 is stimulated to translocate this H2O2 into the cytoplasm, where it activates defense responses to restrict further attack. TaPIP2;10-mediated transport of H2O2 is essential for pathogen-associated molecular pattern-triggered plant immunity (PTI). Typical PTI responses are induced by the fungal infection and intensified by overexpression of the TaPIP2;10 gene. TaPIP2;10 overexpression causes a 70% enhancement in wheat resistance to powdery mildew and an 86% enhancement in resistance to Fusarium head blight. By reducing the disease severities, TaPIP2;10 overexpression brings about >37% increase in wheat grain yield. These results verify the feasibility of using an immunity-relevant AQP to concomitantly improve crop productivity and immunity.

Multivariate genome wide association and network analysis of subcortical imaging phenotypes in Alzheimer's disease.

BACKGROUND: Genome-wide association studies (GWAS) have identified many individual genes associated with brain imaging quantitative traits (QTs) in Alzheimer's disease (AD). However single marker level association discovery may not be able to address the underlying biological interactions with disease mechanism. RESULTS: In this paper, we used the MGAS (Multivariate Gene-based Association test by extended Simes procedure) tool to perform multivariate GWAS on eight AD-relevant subcortical imaging measures. We conducted multiple iPINBPA (integrative Protein-Interaction-Network-Based Pathway Analysis) network analyses on MGAS findings using protein-protein interaction (PPI) data, and identified five Consensus Modules (CMs) from the PPI network. Functional annotation and network analysis were performed on the identified CMs. The MGAS yielded significant hits within APOE, TOMM40 and APOC1 genes, which were known AD risk factors, as well as a few new genes such as LAMA1, XYLB, HSD17B7P2, and NPEPL1. The identified five CMs were enriched by biological processes related to disorders such as Alzheimer's disease, Legionellosis, Pertussis, and Serotonergic synapse. CONCLUSIONS: The statistical power of coupling MGAS with iPINBPA was higher than traditional GWAS method, and yielded new findings that were missed by GWAS. This study provides novel insights into the molecular mechanism of Alzheimer's Disease and will be of value to novel gene discovery and functional genomic studies.