An ethylene response-related factor, GbERF1-like, from Gossypium barbadense cv. '7124' involved in the defence response to Verticillium dahliae was characterized. GbERF1-like transcripts present ubiquitously in various tissues, with higher accumulation in flower organs. GbERF1-like was also responsive to defence-related phytohormones and V. dahliae infection. The downregulation of GbERF1-like increased the susceptibility of cotton plants to V. dahliae infection, while overexpression of this gene improved disease resistance in both cotton and Arabidopsis, coupled with activation of the pathogenesis-related proteins. Further analysis revealed that genes involved in lignin synthesis, such as PAL, C4H, C3H, HCT, CCoAOMT, CCR and F5H, showed higher expression levels in the overexpressing cotton and Arabidopsis lines and lower expression levels in the RNAi cotton lines. The expression levels of these genes increased obviously when the GbERF1-like-overexpressing plants were inoculated with V. dahliae. Meanwhile, significant differences in the content of whole lignin could be found in the stems of transgenic and wild-type plants after inoculation with V. dahliae, as revealed by metabolic and histochemical analysis. More lignin could be detected in GbERF1-like-overexpressing cotton and Arabidopsis but less in GbERF1-like-silencing cotton compared with wild-type plants. The ratio of S and G monomers in GbERF1-like-overexpressing cotton and Arabidopsis increased significantly after infection by V. dahliae. Moreover, our results showed that the promoters of GhHCT1 and AtPAL3 could be transactivated by GbERF1-like in vivo based on yeast one-hybrid assays and dual-luciferase reporter assays. Knockdown of GhHCT1 in GbERF1-like over-expressing cotton decreases resistance to V. dahliae. Collectively, our results suggest that GbERF1-like acts as a positive regulator in lignin synthesis and contributes substantially to resistance to V. dahliae in plants.

Although existing computational models have identified many common driver genes, it remains challenging to identify the personalized driver genes by using samples of an individual patient. Recently, the methods of exploiting the structure-based control principles of complex networks provide new clues for identifying minimum number of driver nodes to drive the state transition of large-scale complex networks from an initial state to the desired state. However, the structure-based network control methods cannot be directly applied to identify the personalized driver genes due to the unknown network dynamics of the personalized system. Here we proposed the personalized network control model (PNC) to identify the personalized driver genes by employing the structure-based network control principle on genetic data of individual patients. In PNC model, we firstly presented a paired single sample network construction method to construct the personalized state transition network for capturing the phenotype transitions between healthy and disease states. Then, we designed a novel structure-based network control method from the Feedback Vertex Sets-based control perspective to identify the personalized driver genes. The wide experimental results on 13 cancer datasets from The Cancer Genome Atlas firstly showed that PNC model outperforms current state-of-the-art methods, in terms of F-measures for identifying cancer driver genes enriched in the gold-standard cancer driver gene lists. Furthermore, these results showed that personalized driver genes can be explored by their network characteristics even when they are hidden factors in transcription and mutation profiles. Our PNC gives novel insights and useful tools into understanding the tumor heterogeneity in cancer. The PNC package and data resources used in this work can be freely downloaded from https://github.com/NWPU-903PR/PNC.

Supplementary data are available at Bioinformatics online.

The inference of gene regulatory networks (GRNs) from expression data can mine the direct regulations among genes and gain deep insights into biological processes at a network level. During past decades, numerous computational approaches have been introduced for inferring the GRNs. However, many of them still suffer from various problems, e.g., Bayesian network (BN) methods cannot handle large-scale networks due to their high computational complexity, while information theory-based methods cannot identify the directions of regulatory interactions and also suffer from false positive/negative problems. To overcome the limitations, in this work we present a novel algorithm, namely local Bayesian network (LBN), to infer GRNs from gene expression data by using the network decomposition strategy and false-positive edge elimination scheme. Specifically, LBN algorithm first uses conditional mutual information (CMI) to construct an initial network or GRN, which is decomposed into a number of local networks or GRNs. Then, BN method is employed to generate a series of local BNs by selecting the k-nearest neighbors of each gene as its candidate regulatory genes, which significantly reduces the exponential search space from all possible GRN structures. Integrating these local BNs forms a tentative network or GRN by performing CMI, which reduces redundant regulations in the GRN and thus alleviates the false positive problem. The final network or GRN can be obtained by iteratively performing CMI and local BN on the tentative network. In the iterative process, the false or redundant regulations are gradually removed. When tested on the benchmark GRN datasets from DREAM challenge as well as the SOS DNA repair network in E.coli, our results suggest that LBN outperforms other state-of-the-art methods (ARACNE, GENIE3 and NARROMI) significantly, with more accurate and robust performance. In particular, the decomposition strategy with local Bayesian networks not only effectively reduce the computational cost of BN due to much smaller sizes of local GRNs, but also identify the directions of the regulations.

Dual function of GhATAF1 in the responses to salinity stress and Verticillium dahliae infection in cotton. NAC (NAM/ATAF1/2/CUC2) is a large plant-specific transcription factor family that plays important roles in the response to abiotic stresses. We previously isolated a cotton NAC transcription factor gene, GhATAF1, which was up-regulated by ABA, cold and salt stresses and classified into AFAT1/2, a sub-family of NAC. Here, we report that GhATAF1 was also highly induced by MeJA, SA and Verticillium dahliae inoculation, which implied that GhATAF1 was involved not only in the response to abiotic stress but also in the response to biotic stress. GhATAF1 was localized in the nucleus and possessed transactivation activity. Overexpression of GhATAF1 enhanced cotton plant tolerance to salt stress by enhancing the expression of various stress-related genes, including the ABA response gene GhABI4; the transporter gene GhHKT1, involved in Na(+)/K(+) homeostasis; and several stress-response genes (GhAVP1, GhRD22, GhDREB2A, GhLEA3, and GhLEA6). Additionally, overexpressing GhATAF1 increased cotton plant susceptibility to the fungal pathogens V. dahliae and Botrytis cinerea, coupled with the suppression of JA-mediated signaling and the activation of SA-mediated signaling. Our results suggested that GhATAF1, the cotton stress-responsive NAC transcription factor, plays important roles in the response to both abiotic stress and biotic stress by coordinating the phytohormone signaling networks.

There is growing awareness of a link between the gut and cardiovascular disease. Constipation is common among individuals who have had a stroke, and it negatively affects social functioning and quality of life. However, no systematic study on the incidence of constipation in stroke patients has been reported.We selected studies included in Medline, Embase, Cochrane database, and Web of Science. Studies were included if they reported the incidence in stroke patients. Two authors selected the studies, extracted the data independently, and assessed these. Subgroup analyses were conducted according to the stroke subtype and stage of stroke.After detailed evaluations, 8 studies (n  =  1385 participants) were found that contained data that were suitable for meta-analytic synthesis. A forest plot showed that the incidence of constipation was 48% (95% confidence interval [CI]  =  33%–63%). In the analysis of the type of stroke subgroup, the incidence of constipation in patients who had had a hemorrhagic stroke (66% [95% CI  =  40–91%]) was higher than that in patients who had experienced an ischemic stroke (51% [95% CI  =  27%–75%]). The incidence in the acute stage (45% [95% CI  =  36%–54%]) was lower than that in the rehabilitation stage (48% [95% CI  =  23%–73%]).Constipation after a stroke event occurs frequently. This finding may raise awareness about bowel complications to allow correct evaluation and proper management.

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BackgroundThe advances in target control of complex networks not only can offer new insights into the general control dynamics of complex systems, but also be useful for the practical application in systems biology, such as discovering new therapeutic targets for disease intervention. In many cases, e.g. drug target identification in biological networks, we usually require a target control on a subset of nodes (i.e., disease-associated genes) with minimum cost, and we further expect that more driver nodes consistent with a certain well-selected network nodes (i.e., prior-known drug-target genes).ResultsTherefore, motivated by this fact, we pose and address a new and practical problem called as target control problem with objectives-guided optimization (TCO): how could we control the interested variables (or targets) of a system with the optional driver nodes by minimizing the total quantity of drivers and meantime maximizing the quantity of constrained nodes among those drivers. Here, we design an efficient algorithm (TCOA) to find the optional driver nodes for controlling targets in complex networks. We apply our TCOA to several real-world networks, and the results support that our TCOA can identify more precise driver nodes than the existing control-fucus approaches. Furthermore, we have applied TCOA to two bimolecular expert-curate networks. Source code for our TCOA is freely available from http://sysbio.sibcb.ac.cn/cb/chenlab/software.htm or https://github.com/WilfongGuo/guoweifeng.ConclusionsIn the previous theoretical research for the full control, there exists an observation and conclusion that the driver nodes tend to be low-degree nodes. However, for target control the biological networks, we find interestingly that the driver nodes tend to be high-degree nodes, which is more consistent with the biological experimental observations. Furthermore, our results supply the novel insights into how we can efficiently target control a complex system, and especially many evidences on the practical strategic utility of TCOA to incorporate prior drug information into potential drug-target forecasts. Thus applicably, our method paves a novel and efficient way to identify the drug targets for leading the phenotype transitions of underlying biological networks.Electronic supplementary materialThe online version of this article (doi: 10.1186/s12864-017-4332-z) contains supplementary material, which is available to authorized users.