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MADS-box family transcription factors play essential roles in the growth and development of plants, and some MADS-box genes have also been found to participate in the stress-response. At present, little information regarding stress-related MADS-box genes is available in pepper. We isolated a MADS-box transcription factor gene from Capsicum annuum, which we named CaMADS. CaMADS expression is induced by low and high temperature, salt, and osmotic stress, and also by abscisic acid (ABA), salicylic acid (SA), methyl-jasmonic acid (MeJA), and CaCl2. To understand the function of CaMADS in the abiotic stress response, we generated pepper plants in which CaMADS expression was down-regulated using VIGS (Virus-induced Gene Silencing), and also transgenic Arabidopsis plants overexpressing CaMADS. We found that CaMADS-down-regulated seedlings were more seriously injured than WT after cold, NaCl, and mannitol treatment, and showed increased electrolyte leakage, malondialdehyde (MDA) levels, and lower chlorophyll content. CaMADS-overexpressing Arabidopsis plants were more tolerant to these stresses than WT, and showed significantly high survival rates and lower H2O2 and super oxide radical contents after cold treatment. CaMADS-overexpressing plants had higher germination rates and percentages of green cotyledons following NaCl and mannitol treatment. Root lengths and fresh weight in CaMADS-overexpressing plants were also significantly longer and higher, respectively, than in WT plants. Taken together, our results suggest that CaMADS functions as a positive stress-responsive transcription factor in the cold, salt, and osmotic stress signaling pathways.

Squamosa promoter binding protein (SBP)-box genes are plant-specific transcription factors involved in plant growth and development, morphogenesis and biotic and abiotic stress responses. However, these genes have been understudied in pepper, especially with respect to defense responses to Phytophthora capsici infection. CaSBP11 is a SBP-box family gene in pepper that was identified in our previous research. Silencing CaSBP11 enhanced the defense response of pepper plants to Phytophthora capsici. Without treatment, the expression of defense-related genes (CaBPR1,CaPO1,CaSAR8.2 and CaDEF1) increased in CaSBP11-silenced plants. However, the expression levels of these genes were inhibited under transient CaSBP11 expression. CaSBP11 overexpression in transgenic Nicotiana benthamiana decreased defense responses, while in Arabidopsis, it induced or inhibited the expression of genes in the salicylic acid and jasmonic acid signaling pathways. CaSBP11 overexpression in sid2-2 mutants induced AtNPR1, AtNPR3, AtNPR4, AtPAD4, AtEDS1, AtEDS5, AtMPK4 and AtNDR1 expression, while AtSARD1 and AtTGA6 expression was inhibited. CaSBP11 overexpression in coi1-21 and coi1-22 mutants, respectively, inhibited AtPDF1.2 expression and induced AtPR1 expression. These results indicate CaSBP11 has a negative regulatory effect on defense responses to Phytophthora capsici. Moreover, it may participate in the defense response of pepper to Phytophthora capsici by regulating defense-related genes and the salicylic and jasmonic acid-mediated disease resistance signaling pathways.

Powdery mildew (PM), caused by Podosphaera xanthii, is a major threat to the global cucurbit yield. The molecular mechanisms underlying the PM resistance of pumpkin (Cucurbita moschata Duch.) are largely unknown. A homolog of the basic helix-loop-helix (bHLH) transcription factor was previously identified through a transcriptomic analysis of a PM-resistant pumpkin. In this study, this bHLH homolog in pumpkin has been functionally characterized. CmbHLH87 is present in the nucleus. CmbHLH87 expression in the PM-resistant material was considerably downregulated by PM; and abscisic acid, methyl jasmonate, ethephon, and NaCl treatments induced CmbHLH87 expression. Ectopic expression of CmbHLH87 in tobacco plants alleviated the PM symptoms on the leaves, accelerated cell necrosis, and enhanced H2O2 accumulation. The expression levels of PR1a, PR5, and NPR1 were higher in the PM-infected transgenic plants than in PM-infected wild-type plants. Additionally, the chlorosis and yellowing of plant materials were less extensive and the concentration of bacteria at infection sites was lower in the transgenic tobacco plants than in the wild-type plants in response to bacterial wilt and scab pathogens. CmbHLH87 may be useful for genetic engineering of novel pumpkin cultivars in the future.

This paper addresses the problem of accounting for confounding factors and expression quantitative trait loci (eQTL) mapping in the study of SNP-gene associations. The existing convex penalty based algorithm has limited capacity to keep main information of matrix in the process of reducing matrix rank. We present an algorithm, which use nonconvex penalty based low-rank representation to account for confounding factors and make use of sparse regression for eQTL mapping (NCLRS). The efficiency of the presented algorithm is evaluated by comparing the results of 18 synthetic datasets given by NCLRS and presented algorithm, respectively. The experimental results or biological dataset show that our approach is an effective tool to account for non-genetic effects than currently existing methods.

Powdery mildew (PM), which is mainly caused by Podosphaera xanthii, is a serious biotrophic pathogen disease affecting field-grown and greenhouse-grown cucurbit crops worldwide. Because fungicides poorly control PM, the development and cultivation of PM-resistant varieties is critical. A homolog of SGT1 (suppressor of the G2 allele of skp1), which encodes a key component of the plant disease-associated signal transduction pathway, was previously identified through a transcriptomic analysis of a PM-resistant pumpkin (Cucurbita moschata) inbred line infected with PM. In this study, we have characterized this SGT1 homolog in C. moschata, and investigated its effects on biotic stress resistance. Subcellular localization results revealed that CmSGT1 is present in the nucleus. Additionally, CmSGT1 expression levels in the PM-resistant material was strongly induced by PM, salicylic acid (SA) and hydrogen peroxide (H2O2). In contrast, SA and H2O2 downregulated CmSGT1 expression in the PM-susceptible material. The ethephon (Eth) and methyl jasmonate (MeJA) treatments upregulated CmSGT1 expression in both plant materials. The constitutive overexpression of CmSGT1 in Nicotiana benthamiana (N. benthamiana) minimized the PM symptoms on the leaves of PM-infected seedlings, accelerated the onset of cell necrosis, and enhanced the accumulation of H2O2. Furthermore, the expression levels of PR1a and PR5, which are SA signaling transduction markers, were higher in the transgenic plants than in wild-type plants. Thus, the transgenic N. benthamiana plants were significantly more resistant to Erysiphe cichoracearum than the wild-type plants. This increased resistance was correlated with cell death, H2O2 accumulation, and upregulated expression of SA-dependent defense genes. However, the chlorosis and yellowing of plant materials and the concentration of bacteria at infection sites were greater in the transgenic N. benthamiana plants than in the wild-type plants in response to infections by the pathogens responsible for bacterial wilt and scab. Therefore, CmSGT1-overexpressing N. benthamiana plants were hypersensitive to these two diseases. The results of this study may represent valuable genetic information for the breeding of disease-resistant pumpkin varieties, and may also help to reveal the molecular mechanism underlying CmSGT1 functions.

Transcription factors (TFs) are DNA-binding proteins that have a central role in regulating gene expression. Identification of DNA-binding sites of TFs is a key task in understanding transcriptional regulation, cellular processes and disease. Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) enables genome-wide identification of in vivo TF binding sites. However, it is still difficult to map every TF in every cell line owing to cost and biological material availability, which poses an enormous obstacle for integrated analysis of gene regulation. To address this problem, we propose a novel computational approach, TFBSImpute, for predicting additional TF binding profiles by leveraging information from available ChIP-seq TF binding data. TFBSImpute fuses the dataset to a 3-mode tensor and imputes missing TF binding signals via simultaneous completion of multiple TF binding matrices with positional consistency. We show that signals predicted by our method achieve overall similarity with experimental data and that TFBSImpute significantly outperforms baseline approaches, by assessing the performance of imputation methods against observed ChIP-seq TF binding profiles. Besides, motif analysis shows that TFBSImpute preforms better in capturing binding motifs enriched in observed data compared with baselines, indicating that the higher performance of TFBSImpute is not simply due to averaging related samples. We anticipate that our approach will constitute a useful complement to experimental mapping of TF binding, which is beneficial for further study of regulation mechanisms and disease.

Tissue-specificity is important for the function of human body. However, it is still not clear how the functional diversity of different tissues is achieved. Here we construct gene regulatory networks in 13 human tissues by integrating large-scale transcription factor (TF)-gene regulations with gene and protein expression data. By comparing these regulatory networks, we find many tissue-specific regulations that are important for tissue identity. In particular, the tissue-specific TFs are found to regulate more genes than those expressed in multiple tissues, and the processes regulated by these tissue-specific TFs are closely related to tissue functions. Moreover, the regulations that are present in certain tissue are found to be enriched in the tissue associated disease genes, and these networks provide the molecular context of disease genes. Therefore, recognizing tissuespecific regulatory networks can help better understand the molecular mechanisms underlying diseases and identify new disease genes.

Gene regulatory networks in different tissues offer insight into the mechanism of tissue identity and function. Here we construct regulatory networks in 10 human tissues including regulations among miRNAs, transcription factors and genes. The results reveal that TS miRNAs are regulated largely by non-tissue specific TFs. TS miRNAs connect with more TFs compared with trivial miRNAs, inferring tight co-regulation of gene expression for TS miRNAs and TFs. Both TS miRNAs and TSTFs tend to regulate broad sets of genes involved in tissue specific functions. In particular, we identified tissue specific regulations instrumental to defining tissue specific functions, and some pathways important to tissue identity or disease, which cannot be explained by only tissue specific genes, can be captured in our tissue specific regulations.

In the past few years, ChIP-seq data have emerged as a powerful source to discover regulatory functional elements as well as disease related genetic reasons. However, owing to cost, or biological material availability, it is hard to do experiment for every transcription factor in every cell line, which restricts analysis requiring completed data to only those with existed experiments. The imputation of missing ChIP-seq data can help to solve the problem, while because of the massive scale of ChIP-seq data, traditional methods is unsuitable to treat such huge amount of data or it is time-consuming. In this paper, we proposed a tensor completion-based method for the imputation of ChIP-seq data by modeling the ChIP-seq dataset as a 3-way tensor pattern. The results show that the proposed method is better than state-of-the-art baseline methods with respect to imputation accuracy.

Although the newly available ChIP-seq data provides immense opportunities for comparative study of regulatory activities across different biological conditions, due to cost, time or sample material availability, it is not always possible for researchers to obtain binding profiles for every protein in every sample of interest, which considerably limits the power of integrative studies. Recently, by leveraging related information from measured data, Ernst et al. proposed ChromImpute for predicting additional ChIP-seq and other types of datasets, it is demonstrated that the imputed signal tracks accurately approximate the experimentally measured signals, and thereby could potentially enhance the power of integrative analysis. Despite the success of ChromImpute, in this paper, we reexamine its learning process, and show that its performance may degrade substantially and sometimes may even fail to output a prediction when the available data is scarce. This limitation could hurt its applicability to important predictive tasks, such as the imputation of TF binding data. To alleviate this problem, we propose a novel method called Local Sensitive Unified Embedding (LSUE) for imputing new ChIP-seq datasets. In LSUE, the ChIP-seq data compendium are fused together by mapping proteins, samples, and genomic positions simultaneously into the Euclidean space, thereby making their underling associations directly evaluable using simple calculations. In contrast to ChromImpute which mainly makes use of the local correlations between available datasets, LSUE can better estimate the overall data structure by formulating the representation learning of all involved entities as a single unified optimization problem. Meanwhile, a novel form of local sensitive low rank regularization is also proposed to further improve the performance of LSUE. Experimental evaluations on the ENCODE TF ChIP-seq data illustrate the performance of the proposed model. The code of LSUE is available at https://github.com/ekffar/LSUE .