The developmental and evolutionary characteristics of transcription factor binding site clustered regions based on an explainable machine learning model.

Gene expression is temporally and spatially regulated by the interaction of transcription factors (TFs) and cis-regulatory elements (CREs). The uneven distribution of TF binding sites across the genome poses challenges in understanding how this distribution evolves to regulate spatio-temporal gene expression and consequent heritable phenotypic variation. In this study, chromatin accessibility profiles and gene expression profiles were collected from several species including mammals (human, mouse, bovine), fish (zebrafish and medaka), and chicken. Transcription factor binding sites clustered regions (TFCRs) at different embryonic stages were characterized to investigate regulatory evolution. The study revealed dynamic changes in TFCR distribution during embryonic development and species evolution. The synchronization between TFCR complexity and gene expression was assessed across species using RegulatoryScore. Additionally, an explainable machine learning model highlighted the importance of the distance between TFCR and promoter in the coordinated regulation of TFCRs on gene expression. Our results revealed the developmental and evolutionary dynamics of TFCRs during embryonic development from fish, chicken to mammals. These data provide valuable resources for exploring the relationship between transcriptional regulation and phenotypic differences during embryonic development.

dbEmbryo multi-omics database for analyses of synergistic regulation in early mammalian embryo development.

During early mammalian embryo development, different epigenetic marks undergo reprogramming and play crucial roles in the mediation of gene expression. Currently, several databases provide multi-omics information on early embryos. However, how interconnected epigenetic markers function together to coordinate the expression of the genetic code in a spatiotemporal manner remains difficult to analyze, markedly limiting scientific and clinical research. Here, we present dbEmbryo, an integrated and interactive multi-omics database for human and mouse early embryos. dbEmbryo integrates data on gene expression, DNA methylation, histone modifications, chromatin accessibility, and higher-order chromatin structure profiles for human and mouse early embryos. It incorporates customized analysis tools, such as "multi-omics visualization," "Gene&Peak annotation," "ZGA gene cluster," "cis-regulation," "synergistic regulation," "promoter signal enrichment," and "3D genome." Users can retrieve gene expression and epigenetic profile patterns to analyze synergistic changes across different early embryo developmental stages. We showed the uniqueness of dbEmbryo among extant databases containing data on early embryo development and provided an overview. Using dbEmbryo, we obtained a phase-separated model of transcriptional control during early embryo development. dbEmbryo offers web-based analytical tools and a comprehensive resource for biologists and clinicians to decipher molecular regulatory mechanisms of human and mouse early embryo development.

HDL Function and Atherosclerosis: Reactive Dicarbonyls as Promising Targets of Therapy.

Epidemiologic studies detected an inverse relationship between HDL (high-density lipoprotein) cholesterol (HDL-C) levels and atherosclerotic cardiovascular disease (ASCVD), identifying HDL-C as a major risk factor for ASCVD and suggesting atheroprotective functions of HDL. However, the role of HDL-C as a mediator of risk for ASCVD has been called into question by the failure of HDL-C-raising drugs to reduce cardiovascular events in clinical trials. Progress in understanding the heterogeneous nature of HDL particles in terms of their protein, lipid, and small RNA composition has contributed to the realization that HDL-C levels do not necessarily reflect HDL function. The most examined atheroprotective function of HDL is reverse cholesterol transport, whereby HDL removes cholesterol from plaque macrophage foam cells and delivers it to the liver for processing and excretion into bile. Indeed, in several studies, HDL has shown inverse associations between HDL cholesterol efflux capacity and ASCVD in humans. Inflammation plays a key role in the pathogenesis of atherosclerosis and vulnerable plaque formation, and a fundamental function of HDL is suppression of inflammatory signaling in macrophages and other cells. Oxidation is also a critical process to ASCVD in promoting atherogenic oxidative modifications of LDL (low-density lipoprotein) and cellular inflammation. HDL and its proteins including apoAI (apolipoprotein AI) and PON1 (paraoxonase 1) prevent cellular oxidative stress and LDL modifications. Importantly, HDL in humans with ASCVD is oxidatively modified rendering HDL dysfunctional and proinflammatory. Modification of HDL with reactive carbonyl species, such as malondialdehyde and isolevuglandins, dramatically impairs the antiatherogenic functions of HDL. Importantly, treatment of murine models of atherosclerosis with scavengers of reactive dicarbonyls improves HDL function and reduces systemic inflammation, atherosclerosis development, and features of plaque instability. Here, we discuss the HDL antiatherogenic functions in relation to oxidative modifications and the potential of reactive dicarbonyl scavengers as a therapeutic approach for ASCVD.

Systematic optimization of host-directed therapeutic targets and preclinical validation of repositioned antiviral drugs.

Inhibition of host protein functions using established drugs produces a promising antiviral effect with excellent safety profiles, decreased incidence of resistant variants and favorable balance of costs and risks. Genomic methods have produced a large number of robust host factors, providing candidates for identification of antiviral drug targets. However, there is a lack of global perspectives and systematic prioritization of known virus-targeted host proteins (VTHPs) and drug targets. There is also a need for host-directed repositioned antivirals. Here, we integrated 6140 VTHPs and grouped viral infection modes from a new perspective of enriched pathways of VTHPs. Clarifying the superiority of nonessential membrane and hub VTHPs as potential ideal targets for repositioned antivirals, we proposed 543 candidate VTHPs. We then presented a large-scale drug-virus network (DVN) based on matching these VTHPs and drug targets. We predicted possible indications for 703 approved drugs against 35 viruses and explored their potential as broad-spectrum antivirals. In vitro and in vivo tests validated the efficacy of bosutinib, maraviroc and dextromethorphan against human herpesvirus 1 (HHV-1), hepatitis B virus (HBV) and influenza A virus (IAV). Their drug synergy with clinically used antivirals was evaluated and confirmed. The results proved that low-dose dextromethorphan is better than high-dose in both single and combined treatments. This study provides a comprehensive landscape and optimization strategy for druggable VTHPs, constructing an innovative and potent pipeline to discover novel antiviral host proteins and repositioned drugs, which may facilitate their delivery to clinical application in translational medicine to combat fatal and spreading viral infections.

Tumor-Derived Oxidative Stress Triggers Ovarian Follicle Loss in Breast Cancer.

Breast cancer is a common indication for ovarian cryopreservation. However, whether the grafting ovarian tissue meets functional requirements, as well as the need for additional interventions, remains unclear. The current study demonstrates abnormal serum hormones in breast cancer in humans and breast cancer cell line-derived tumor-bearing mice, and for the first time shows tumor-induced loss of primordial and growing follicles, and the number of follicles being lost to either growth or atresia. A gene signature of tumor-bearing mice demonstrates the disturbed regulatory network of steroidogenesis, which links to mitochondria dysfunction in oocytes and granulosa cells via the phosphatidylinositol 3-kinase signaling pathway. Notably, increased reactive oxygen species were identified in serum and ovarian tissues in tumor-bearing mice. Furthermore, supplementation with vitamin C promoted follicular quiescence, repairing tumor-induced follicle loss via inactivation of the phosphatidylinositol 3-kinase-Akt-mammalian target of rapamycin pathway, indicating the potential of antioxidants as a fertility therapy to achieve higher numbers of healthy follicles ready for ovarian cryopreservation.

Sonographic Findings of Vascular Signals for Retained Products of Conception in Women Following First-Trimester Termination of Pregnancy.

OBJECTIVES: To evaluate the occurrence of retained products of conception (RPOC) after termination of pregnancy in the first trimester and to assess the vascular signals with transvaginal ultrasonography (TVUS) examination in the detection of retained products. METHODS: A retrospective cohort study was performed using TVUS examination in patients following termination of pregnancy. In cases of RPOC, 3 scales of vascular signal were identified: type 1, no or small amount, spot flow signals; type 2, medium amount, strip-like flow signals; type 3, rich amount, circumferential-like flow signals. The correlation between vascular signals and placenta accreta spectrum (PAS) staging was proposed by sonography and histopathology findings. RESULTS: The 3 vascular patterns were differently distributed within non-RPOC as well as RPOC patients with and without PAS: type 1 vascular signal detection rates of non-RPOC and RPOC were 97.8% (262/268) and 28.1% (18/64), respectively. Of 64 cases of RPOC, 48.4% (31/64) of the patients had type 2 vascular signals. Vascular signals were enhanced in RPOC with PAS patients whose diagnosis was confirmed by histopathology. CONCLUSIONS: The vascularity (amount of flow), vascular pattern (spot, strip- or circumferential-like flow), and the flow penetrating myometrium were significant findings for distinguishing concomitant RPOC with and without PAS. Additionally, RPOC may contribute to PAS progression, or PAS and RPOC in coordination strengthen the observed vascular signals.

Biological Control of Avocado Branch Blight Caused by Lasiodiplodia theobromae Using Bacillus velezensis.

In recent years, avocado branch blight has gradually become one of the major diseases causing mortality of avocado trees, which seriously affects the economic development of avocado planting regions. In order to investigate the cause of the disease, the pathogens were isolated from the interroot of avocado trees with the onset of the disease and identified as Lasiodiplodia theobromae. At the same time, three Bacillus velezensis strains, YK194, YK201, and YK268, with better antagonistic effects and high stability against L. theobromae, were isolated from the rhizospheric soil of healthy avocado plants. The results of branch experiments and field trials showed that the avocado leaves as well as branches treated with the strains YK194, YK201, and YK268 did not develop disease, and the incidence of avocado trees was significantly reduced. In the branch experiments, the biological control effect of the strains YK194, YK201, and YK268 reached 62.07, 52.70, and 72.45%, respectively. In the field experiments, it reached 63.85, 63.43, and 73.86%, respectively, which indicated that all these three strains possessed good biological control effects on avocado branch blight. Further investigation on the mechanism of action of antagonistic strains revealed that B. velezensis YK268 could produce lipopeptides, namely, surfactin, fengycin, and iturin, which could significantly inhibit the spore germination of L. theobromae. Consequently, these three isolates have potential as biocontrol agents against L. theobromae.

Computational methods for the prediction of chromatin interaction and organization using sequence and epigenomic profiles.

The exploration of three-dimensional chromatin interaction and organization provides insight into mechanisms underlying gene regulation, cell differentiation and disease development. Advances in chromosome conformation capture technologies, such as high-throughput chromosome conformation capture (Hi-C) and chromatin interaction analysis by paired-end tag (ChIA-PET), have enabled the exploration of chromatin interaction and organization. However, high-resolution Hi-C and ChIA-PET data are only available for a limited number of cell lines, and their acquisition is costly, time consuming, laborious and affected by theoretical limitations. Increasing evidence shows that DNA sequence and epigenomic features are informative predictors of regulatory interaction and chromatin architecture. Based on these features, numerous computational methods have been developed for the prediction of chromatin interaction and organization, whereas they are not extensively applied in biomedical study. A systematical study to summarize and evaluate such methods is still needed to facilitate their application. Here, we summarize 48 computational methods for the prediction of chromatin interaction and organization using sequence and epigenomic profiles, categorize them and compare their performance. Besides, we provide a comprehensive guideline for the selection of suitable methods to predict chromatin interaction and organization based on available data and biological question of interest.

Spatial density of open chromatin: an effective metric for the functional characterization of topologically associated domains.

Topologically associated domains (TADs) are spatial and functional units of metazoan chromatin structure. Interpretation of the interplay between regulatory factors and chromatin structure within TADs is crucial to understand the spatial and temporal regulation of gene expression. However, a computational metric for the sensitive characterization of TAD regulatory landscape is lacking. Here, we present the spatial density of open chromatin (SDOC) metric as a quantitative measurement of intra-TAD chromatin state and structure. SDOC sensitively reflects epigenetic properties and gene transcriptional activity in TADs. During mouse T-cell development, we found that TADs with decreased SDOC are enriched in repressed developmental genes, and the joint effect of SDOC-decreasing and TAD clustering corresponds to the highest level of gene repression. In addition, we revealed a pervasive preference for TADs with similar SDOC to interact with each other, which may reflect the principle of chromatin organization.

Prediction of protein-protein interactions between fungus (Magnaporthe grisea) and rice (Oryza sativa L.).

Rice blast disease caused by the fungus Magnaporthe grisea (M. grisea) is one of the most serious diseases for the cultivated rice Oryza sativa (O. sativa). A key factor causing rice blast disease and defense might be protein-protein interactions (PPIs) between rice and fungus. In this research, we have developed a computational pipeline to predict PPIs between blast fungus and rice. After cross-prediction by interolog-based and domain-based method, we achieved 532 potential PPIs between 27 fungus proteins and 236 rice proteins. Accuracy of jackknife test, 10-fold cross-validation test and independent test for these PPIs were 90.43, 93.85 and 84.67%, respectively, by using support vector machine classification method. Meanwhile, the pathogenic genes of blast fungus were enriched in the predicted PPIs network when compared with 1000 random interaction networks. The rice regulatory network was downloaded and divided into 228 subnetworks with over six nodes, and the top seven subnetworks affected by blast fungus through PPIs were investigated. The results indicated that 34 upregulated and 12 downregulated master regulators in rice interacting with the fungus proteins in response to the infection of blast fungus. The common master regulators in rice in response to the infection of M. grisea, Xanthomonas oryzae pv.oryzae and rice stripe virus were analyzed. The ubiquitin proteasome pathway was the common pathway in rice regulated by these three pathogens, while apoptosis signaling pathway was induced by fungus and bacteria. In summary, the results in this article provide insight into the process of blast fungus infection.

Causes of death and effect of non-cancer-specific death on rates of overall survival in adult classic Hodgkin lymphoma: a populated-based competing risk analysis.

BACKGROUND: The improved prognosis of classic Hodgkin lymphoma (cHL) has been accompanied by elevated risks of non-cancer-specific death (non-CSD). The aim of this study was to verify the occurrence of non-CSD and its effect on rates of overall survival among adult patients with cHL. METHODS: To ensure sufficient follow-up time, we analyzed retrospective data from patients aged ≥20 years with cHL that was diagnosed between 1983 and 2005 in the Surveillance, Epidemiology, and End Results (SEER) database. Logistic regression was applied to analyze the non-CSD occurrence in relation to all factors. Using Fine-Gray's method, we calculated the cumulative incidences of CSD and non-CSD. Stacked cumulative incidence plots and ratio of non-CSD to all causes of death were applied to evaluate the effect of non-CSD on rates of overall survival. Finally, we analyzed long-term mortality through Cox proportional hazard regression analysis and competing risk regression analysis to emphasize a more appropriate model of survival for patients with cHL. RESULTS: Among the 18,518 patients included, there were 3768 cases of CSD (20.3%) and 3217 of non-CSD (17.4%). Older age, earlier period, male sex, unmarried status, mixed cellularity (MC) and lymphocyte-depletion (LD) histological subtype, and patients received radiotherapy (RT) only were associated with more non-CSD according to binary logistic analysis. The cumulative incidence of non-CSD exceeded CSD after approximately 280 months follow-up. The most common causes of non-CSDs were cardiovascular disease, subsequent primary neoplasms, infectious diseases, accidents, and suicide. In a Cox proportional hazards model, patients who were black, unmarried, at an advanced stage or underwent chemotherapy (CT) alone were at greater risk of mortality than were white patients, who were married, at an early stage, and underwent combined modality; these populations were also found to be at greater risk for CSD in a competing risk model, but the risk of non-CSD did not differ significantly according to race and marital status, patients with early-stage disease and who underwent RT only were found to be at higher risk of non-CSD instead. CONCLUSIONS: Lymphoma was the cause of death in most patients who died, but non-CSD was not unusual. Patients with cHL should be monitored closely for signs of cardiovascular disease and malignant tumors. Rates of overall survival of patients were diminished by non-CSD, and a competing risk model was more suitable for establishing the prognosis than was the Cox proportional hazards model.

Myeloid-Specific Deletion of Epsins 1 and 2 Reduces Atherosclerosis by Preventing LRP-1 Downregulation.

RATIONALE: Atherosclerosis is, in part, caused by immune and inflammatory cell infiltration into the vascular wall, leading to enhanced inflammation and lipid accumulation in the aortic endothelium. Understanding the molecular mechanisms underlying this disease is critical for the development of new therapies. Our recent studies demonstrate that epsins, a family of ubiquitin-binding endocytic adaptors, are critical regulators of atherogenicity. Given the fundamental contribution lesion macrophages make to fuel atherosclerosis, whether and how myeloid-specific epsins promote atherogenesis is an open and significant question. OBJECTIVE: We will determine the role of myeloid-specific epsins in regulating lesion macrophage function during atherosclerosis. METHODS AND RESULTS: We engineered myeloid cell-specific epsins double knockout mice (LysM-DKO) on an ApoE-/- background. On Western diet, these mice exhibited marked decrease in atherosclerotic lesion formation, diminished immune and inflammatory cell content in aortas, and reduced necrotic core content but increased smooth muscle cell content in aortic root sections. Epsins deficiency hindered foam cell formation and suppressed proinflammatory macrophage phenotype but increased efferocytosis and anti-inflammatory macrophage phenotype in primary macrophages. Mechanistically, we show that epsin loss specifically increased total and surface levels of LRP-1 (LDLR [low-density lipoprotein receptor]-related protein 1), an efferocytosis receptor with antiatherosclerotic properties. We further show that epsin and LRP-1 interact via epsin's ubiquitin-interacting motif domain. ox-LDL (oxidized LDL) treatment increased LRP-1 ubiquitination, subsequent binding to epsin, and its internalization from the cell surface, suggesting that epsins promote the ubiquitin-dependent internalization and downregulation of LRP-1. Crossing ApoE-/-/LysM-DKO mice onto an LRP-1 heterozygous background restored, in part, atherosclerosis, suggesting that epsin-mediated LRP-1 downregulation in macrophages plays a pivotal role in propelling atherogenesis. CONCLUSIONS: Myeloid epsins promote atherogenesis by facilitating proinflammatory macrophage recruitment and inhibiting efferocytosis in part by downregulating LRP-1, implicating that targeting epsins in macrophages may serve as a novel therapeutic strategy to treat atherosclerosis.

DeepHiC: A generative adversarial network for enhancing Hi-C data resolution.

Hi-C is commonly used to study three-dimensional genome organization. However, due to the high sequencing cost and technical constraints, the resolution of most Hi-C datasets is coarse, resulting in a loss of information and biological interpretability. Here we develop DeepHiC, a generative adversarial network, to predict high-resolution Hi-C contact maps from low-coverage sequencing data. We demonstrated that DeepHiC is capable of reproducing high-resolution Hi-C data from as few as 1% downsampled reads. Empowered by adversarial training, our method can restore fine-grained details similar to those in high-resolution Hi-C matrices, boosting accuracy in chromatin loops identification and TADs detection, and outperforms the state-of-the-art methods in accuracy of prediction. Finally, application of DeepHiC to Hi-C data on mouse embryonic development can facilitate chromatin loop detection. We develop a web-based tool (DeepHiC, http://sysomics.com/deephic) that allows researchers to enhance their own Hi-C data with just a few clicks.

Occurrence and emission of phthalates, bisphenol A, and oestrogenic compounds in concentrated animal feeding operations in Southern China.

Phthalates (PAEs), bisphenol A (BPA), and oestrogenic compounds have become major concerns due to their endocrine-disrupting effect. However, few studies related to the occurrence of PAEs, BPA, and oestrogen in food and compost from different growth age livestock have been conducted. In this study, faeces, urine and food samples were collected from a typical livestock (cow) and a special livestock (pigeon) from concentrated animal feeding operations (CAFOs). The daily total oestrogen excretion of a single cow ranged from 192 µg/day to 671 µg/day, which was significantly higher than that of a single pigeon (0-0.01 µg/day). Conjugated oestrogens represented 22.0-46.0% of the total oestrogens excreted from cow faeces and 80.7-91.8% of those from cow urine, indicating that the form of the excreted oestrogens depends on the livestock species and type of excrement. BPA was all detected in all livestock manure and food, and the concentration in pigeon was 9.2-40.2 ng/g and 23.1 ng/g respectively, while that in cattle was 50.5-72.0 ng/g and 41.1 ng/g respectively. The results indicated that the food is significant sources of BPA entering the process of cow and pigeon breeding. Diethyl phthalate (DEP) was detected at high frequency in pigeon faeces samples, suggesting that pigeons were highly exposed to these plasticisers. The total oestradiol equivalent quantity (EEQt) of livestock origin in aquatic environments was estimated to be 2.99 ng/L, which was higher than the baseline hazard value (1 ng/L) (Xu et al., 2018). The study provides data on the emissions and sources of PAEs, BPA, and oestrogenic compounds from different livestock in CAFOs and demonstrates that food is a significant source of BPA entering livestock.

Research on Classification Model of Panax notoginseng Taproots Based on Machine Vision Feature Fusion.

The existing classification methods for Panax notoginseng taproots suffer from low accuracy, low efficiency, and poor stability. In this study, a classification model based on image feature fusion is established for Panax notoginseng taproots. The images of Panax notoginseng taproots collected in the experiment are preprocessed by Gaussian filtering, binarization, and morphological methods. Then, a total of 40 features are extracted, including size and shape features, HSV and RGB color features, and texture features. Through BP neural network, extreme learning machine (ELM), and support vector machine (SVM) models, the importance of color, texture, and fusion features for the classification of the main roots of Panax notoginseng is verified. Among the three models, the SVM model performs the best, achieving an accuracy of 92.037% on the prediction set. Next, iterative retaining information variables (IRIVs), variable iterative space shrinkage approach (VISSA), and stepwise regression analysis (SRA) are used to reduce the dimension of all the features. Finally, a traditional machine learning SVM model based on feature selection and a deep learning model based on semantic segmentation are established. With the model size of only 125 kb and the training time of 3.4 s, the IRIV-SVM model achieves an accuracy of 95.370% on the test set, so IRIV-SVM is selected as the main root classification model for Panax notoginseng. After being optimized by the gray wolf optimizer, the IRIV-GWO-SVM model achieves the highest classification accuracy of 98.704% on the test set. The study results of this paper provide a basis for developing online classification methods of Panax notoginseng with different grades in actual production.

Cloning and characterization of a novel amylopullulanase from Bacillus megaterium Y103 with transglycosylation activity.

OBJECTIVE: To obtain a novel pullulanase with synthetic ability from a microorganism and characterize its substrates specificity. RESULTS: A novel pullulanase, PulY103A, from Bacillus megaterium Y103 was purified, characterized and expressed in Escherichia coli. PulY103A contained the signature sequences of type I pullulanases and showed 94.7% identity with a type I pullulanase (BmPul) from B. megaterium WW1210, showing similar molecular weight (110.8 kDa) and optimal pH (6.5). However, PulY103A had an optimal temperature of of 45 °C and exhibited relatively higher activity toward amylose (48.3%) compared with pullulan (100%), soluble starch (67.5%), and amylopectin (23.1%). The thin-layer chromatography results showed that the major pullulan hydrolysis products were maltotriose and maltohexaose, which differed from those reported in other pullulanases. On the basis of enzyme specificity, PulY103A was an amylopullulanase, which presented transglycosylation activity by forming α-1,4-glucosidic linkages. CONCLUSIONS: A novel amylopullulanase with transglycosylation activity was characterized. The features of this enzyme suggested its potential to produce maltohexaose.

The characteristics of oestrone mobility in water and soil by the addition of Ca-biochar and Fe-Mn-biochar derived from Litchi chinensis Sonn.

In this study, the effect of biochar (BC) derived from Litchi chinensis Sonn. and its modification, including Ca-biochar (Ca-BC) and Fe-Mn-biochar (Fe-Mn-BC), on the transportation of oestrone (E1) in water and soil was investigated. Fe-Mn-BC showed better adsorption ability than other types of biochar (BC, Ca-BC) under different conditions (humic acid, pH, ionic strength) in an aqueous environment. The maximum mass of sorbent at 298 K increased from 1.12 mg g-1 (BC) to 4.18 mg g-1 (Fe-Mn-BC). Humic acid had a greater impact on aqueous E1 adsorption on these biochars than did the pH and ionic strength. Fe-Mn-BC as a soil amendment had a great control of E1 transport in soil, and no leachate of E1 was observed in the column experiment. E1 mobility showed strong retardation in amended soil with Ca-BC (Rf = 11.2) compared with raw soil (Rf = 7.1). These results suggested that Fe-Mn-BC was more effective in controlling E1 transportation, and Fe-Mn-BC could be used as an alternative and inexpensive adsorbent to reduce E1 contaminants from water and soil.

WITHDRAWN: Very low density lipoprotein receptor deficiency prevents obesity-induced cardiac lipotoxicity.

This article has been withdrawn by the authors. We have become aware of errors in the construction of Figs 1 and 2. In Fig 1D, the tIRS1 immunoblot from untreated cardiomyocytes was inadvertently reused from Fig 2A of PLOS One 2016 May 19;11(5):e0155611. Also in Fig 1D, there were undeclared gel splices with no line indicating the assemblage of two parts in the pIRS1 immunoblot from insulin-stimulated cardiomyocytes. In Fig 2B, lanes 1-3 and lanes 5-7 of the actin immunoblot were mistakenly duplicated. Because some of the original data are no longer available, the authors wish to withdraw this article. However, the authors have full confidence in the findings and conclusions of this paper, and will repeat the missing immunoblots to complete the paper.

Gene co-expression network analysis identifies trait-related modules in Arabidopsis thaliana.

MAIN CONCLUSION: A comprehensive network of the Arabidopsis transcriptome was analyzed and may serve as a valuable resource for candidate gene function investigations. A web tool to explore module information was also provided. Arabidopsis thaliana is a widely studied model plant whose transcriptome has been substantially profiled in various tissues, development stages and other conditions. These data can be reused for research on gene function through a systematic analysis of gene co-expression relationships. We collected microarray data from National Center for Biotechnology Information Gene Expression Omnibus, identified modules of co-expressed genes and annotated module functions. These modules were associated with experiments/traits, which provided potential signature modules for phenotypes. Novel heat shock proteins were implicated according to guilt by association. A higher-order module networks analysis suggested that the Arabidopsis network can be further organized into 15 meta-modules and that a chloroplast meta-module has a distinct gene expression pattern from the other 14 meta-modules. A comparison with the rice transcriptome revealed preserved modules and KEGG pathways. All the module gene information was available from an online tool at http://bioinformatics.fafu.edu.cn/arabi/ . Our findings provide a new source for future gene discovery in Arabidopsis.

Prediction and identification of the effectors of heterotrimeric G proteins in rice (Oryza sativa L.).

Heterotrimeric G protein signaling cascades are one of the primary metazoan sensing mechanisms linking a cell to environment. However, the number of experimentally identified effectors of G protein in plant is limited. We have therefore studied which tools are best suited for predicting G protein effectors in rice. Here, we compared the predicting performance of four classifiers with eight different encoding schemes on the effectors of G proteins by using 10-fold cross-validation. Four methods were evaluated: random forest, naive Bayes, K-nearest neighbors and support vector machine. We applied these methods to experimentally identified effectors of G proteins and randomly selected non-effector proteins, and tested their sensitivity and specificity. The result showed that random forest classifier with composition of K-spaced amino acid pairs and composition of motif or domain (CKSAAP_PROSITE_200) combination method yielded the best performance, with accuracy and the Mathew's correlation coefficient reaching 74.62% and 0.49, respectively. We have developed G-Effector, an online predictor, which outperforms BLAST, PSI-BLAST and HMMER on predicting the effectors of G proteins. This provided valuable guidance for the researchers to select classifiers combined with different feature selection encoding schemes. We used G-Effector to screen the effectors of G protein in rice, and confirmed the candidate effectors by gene co-expression data. Interestingly, one of the top 15 candidates, which did not appear in the training data set, was validated in a previous research work. Therefore, the candidate effectors list in this article provides both a clue for researchers as to their function and a framework of validation for future experimental work. It is accessible at http://bioinformatics.fafu.edu.cn/geffector.