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The fleshy fruits of Camellia oleifera Abel are the immature fruits of C. oleifera, which are infected by Exobasidium vexans Massee and then turn to be intumescent and hollowed. They contain rich t...

Background: Accumulating evidence points to epigenetic mechanisms as essential in tumorigenesis. Treatment that targets epigenetic regulators is becoming an attractive strategy for cancer therapy. The role of epigenetic therapy in prostate cancer (PCa) remains elusive. Previously we demonstrated a correlation of levels of histone lysine demethylase KDM4B with the appearance of castration resistant prostate cancer (CRPC) and identified a small molecular inhibitor of KDM4B, B3. In this study, we aim to define the role of KDM4B in promoting PCa progression and test the efficacy of B3 using clinically relevant PCa models. Methods: KDM4B was overexpressed in LNCaP cells or knocked down (KD) in 22Rv1 cells. The specificity of B3 was determined in vitro using recombinant KDM proteins and in vivo using 22Rv1 cell lysates. The efficacy of B3 monotherapy or in combination with androgen receptor (AR) antagonist enzalutamide or the mTOR inhibitor rapamycin was tested using xenograft models in castrated mice. Comparative transcriptomic analysis was performed on KDM4B KD and B3-treated 22Rv1 cells to determine the on-target (KDM4B-dependent) and off-target (non-KDM4B-associated) effects of B3.Results: Overexpression of KDM4B in LNCaP cells enhanced its tumorigenicity whereas knockdown of KDM4B in 22Rv1 cells reduced tumor growth in castrated mice. B3 suppressed the growth of both 22Rv1 and VCaP xenografts and sensitized 22Rv1 cells to enzalutamide inhibition. B3 also inhibited 22Rv1 tumor growth synergistically with rapamycin that resulted in cell apoptosis. Mechanistically, B3 inhibited expression of AR-V7 and genes involved in epithelial-to-mesenchymal transition. DNA replication stress marker gH2A.X was upregulated by B3, which is further increased when combined with rapamycin. Based on transcriptomic and biochemical analyses, B3 inhibits both H3K9me3 and H3K27me3 demethylase activity, which is believed to underlie its anti-tumor action. Conclusions: Our studies establish KDM4B as a potent target for CRPC and B3 as a potential therapeutic agent. B3 as monotherapy or in combination with other anti-PCa therapeutics offers proof of principle for the clinical translation of epigenetic therapy targeting KDMSs for CRPC patients.

Background Fecundity is important for farm blue fox (Vulpes lagopus), who with asthenospermia have be a problem in some of farms in China. A key symptom of asthenospermia is decreased sperm motility. The decreased secreting beta-defensin108 (vBD108) of blue fox is speculated be related to asthenospermia. To clarify this idea, the mRNA expression of vBD108 in testis and epididymis of blue foxes with asthenospermia were detected and compared to the healthy one. The antibody was prepared and analyzed by immunohistochemistry. Results The vBD108 in testis and epididymis was found both in blue fox with asthenospermia and healthy group by the method of immunohistochemistry. The expression of vBD108 mRNA in testes (P P Conclusions These results suggested that vBD108 deficiency may related to blue fox asthenospermia. Meanwhile, the study on the blue fox vBD108 provides a hopeful direction to explore the pathogenesis of blue fox asthenospermia in the future.

Sickness behaviors, including anorexia, are evolutionarily conserved responses to acute infections. Inflammation-induced anorexia causes dramatic metabolic changes, of which components critical to survival are unique depending on the type of inflammation. Glucose supplementation during the anorectic period induced by bacterial inflammation suppresses adaptive fasting metabolic pathways, including fibroblast growth factor-21 (FGF21), and decreases survival. Consistent with this observation, FGF21 deficient mice are more susceptible to mortality from endotoxemia and poly-bacterial peritonitis, but not viral infection. Here we report that increased circulating FGF21 during bacterial inflammation is hepatic-derived, promotes cardiac function, and is required for survival. FGF21 signaling downstream of its obligate co-receptor beta-Klotho (KLB) is required. However, mice with central nervous system or adipose-specific deletion of Klb do not demonstrate any difference in response to bacterial inflammation, suggesting that multiple tissues and/or a novel FGF21 target tissue are required for the full protective effect of FGF21. These data suggest that hepatic FGF21 is a novel cardioprotective factor in bacterial sepsis.eTOC SummaryIn response to bacterial inflammation, hepatic fibroblast growth factor 21 (FGF21), an endocrine hormone that mediates adaptive responses to metabolic stresses such as starvation, promotes survival by supporting heart function.

B cells undergo two types of genomic alterations to increase antibody diversity: introduction of point mutations into immunoglobulin heavy- and light-chain (IgH and IgL) variable regions by somatic hypermutation (SHM) and alteration of antibody effector functions by changing the expressed IgH constant region exons through IgH class switch recombination (CSR). SHM and CSR require the B cell–specific activation-induced cytidine deaminase (AID) protein, the transcription of germline noncoding RNAs, and the activity of the 3′ regulatory region (3′RR) super-enhancer. Although many transcription regulatory elements (e.g., promoters and enhancers) reside inside the IgH and IgL sequences, the question remains whether clusters of regulatory elements outside IgH control CSR. Using RNA exosome-deficient mouse B cells where long noncoding RNAs (lncRNAs) are easily detected, we identified a cluster of three RNA-expressing elements that includes lncCSR (IgA) (that expresses lncRNA-CSR(IgA)). B cells isolated from a mouse model lacking lncRNA-CSR(IgA) transcription fail to undergo normal levels of CSR to IgA both in B cells of the Peyer’s patches and grown in ex vivo culture conditions. lncRNA-CSR(IgA) is expressed from an enhancer site (lncCSR(IgA)) to facilitate the recruitment of regulatory proteins to a nearby CTCF site (CTCF(lncCSR)) that alters the chromosomal interactions inside the TAD(lncCSRIgA) and long-range interactions with the 3′RR super-enhancer. Humans with IgA deficiency show polymorphisms in the lncCSR(IgA) locus compared with the normal population. Thus, we provide evidence for an evolutionarily conserved topologically associated domain (TAD(lncCSRIgA)) that coordinates IgA CSR in Peyer’s patch B cells through an lncRNA (lncRNA-CSR(IgA)) transcription-dependent mechanism.

Left ventricular hypertrophy (LVH) is a major risk factor for cardiovascular morbidity and mortality. Pathological LVH engages transcriptional programs including reactivation of canonical fetal genes and those inducing fibrosis. Histone lysine demethylases (KDMs) are emerging regulators of transcriptional reprogramming in cancer, though their potential role in abnormal heart growth and fibrosis remains little understood. Here, we investigate gain and loss of function of an H3K9me2 specific demethylase, Kdm3a, and show it promotes LVH and fibrosis in response to pressure-overload. Cardiomyocyte KDM3A activates Timp1 transcription with pro-fibrotic activity. By contrast, a pan-KDM inhibitor, JIB-04, suppresses pressure overload-induced LVH and fibrosis. JIB-04 inhibits KDM3A and suppresses the transcription of fibrotic genes that overlap with genes downregulated in Kdm3a-KO mice versus WT controls. Our study provides genetic and biochemical evidence for a pro-hypertrophic function of KDM3A and proof-of principle for pharmacological targeting of KDMs as an effective strategy to counter LVH and pathological fibrosis., Histone lysine demethylases (KDMs) can mediate transcriptional reprogramming in disease states. Here the authors show that KDM3A promotes left ventricular hypertrophy and cardiac fibrosis by activating the transcription of Timp1, and that pharmacological inhibition of KDM3A attenuates cardiac remodeling induced by pressure overload.

Gene expression profiling techniques measure the transcriptional dynamics of thousands of genes in parallel manners. The available high-throughput transcriptomic datasets provide unprecedented opportunities of detecting biomarkers or signatures of complex diseases such as diabetes. In this work, we propose a computational method based on differential entropy to identify diabetic pathway biomarkers in rats from gene expression profiling data. We first collect the knowledgebase-documented pathways and map them with the corresponding gene expressions in control and disease samples, respectively. The pathway entropies are defined to evaluate their dysfunction-related activities and implications during the development and progression of type 2 diabetes. We rank these pathways via their differential status of entropy dynamics in the time series. The pathway biomarkers are then screened out by their classification ability of distinguishing diabetes from controls. The comparative studies with the other alternative methods demonstrate the effectiveness and advantage of our proposed strategy of biomarker identification. The classification performances on independent datasets further validate the diagnosis applicability of these identified pathway biomarkers. The functional enrichment analyses of these pathway biomarkers also indicate the pathogenesis of diabetes.

Immunoglobulin heavy chain (IgH) locus-associated G-rich long noncoding RNA (SμGLT) is important for physiological and pathological B cell DNA recombination. We demonstrate that the METTL3 enzyme-catalyzed N6-methyladenosine (m6A) RNA modification drives recognition and 3' end processing of SμGLT by the RNA exosome, promoting class switch recombination (CSR) and suppressing chromosomal translocations. The recognition is driven by interaction of the MPP6 adaptor protein with nuclear m6A reader YTHDC1. MPP6 and YTHDC1 promote CSR by recruiting AID and the RNA exosome to actively transcribe SμGLT. Direct suppression of m6A modification of SμGLT or of m6A reader YTHDC1 reduces CSR. Moreover, METTL3, an essential gene for B cell development in the bone marrow and germinal center, suppresses IgH-associated aberrant DNA breaks and prevents genomic instability. Taken together, we propose coordinated and central roles for MPP6, m6A modification, and m6A reader proteins in controlling long noncoding RNA processing, DNA recombination, and development in B cells.

// Xin Zhang 1, * , Ming-Ming Cui 1, * , Shuang Fu 1 , Lu-Lu Li 2 , Yan-Song Liu 3 , Zhi-Ping Liu 4 , Tiemin Liu 5 , Rui-Tao Wang 1 , Kai-Jiang Yu 3 1 Department of Internal Medicine, The Third Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang, 150081, China 2 Department of Geriatrics, The Second Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang, 150086, China 3 Department of Intensive Care Unit, The Third Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang, 150081, China 4 Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA 5 Division of Hypothalamic Research, Department of Internal Medicine, UT Southwestern Medical Center, Dallas, TX, 75390, USA * These authors have contributed equally to this work Correspondence to: Rui-Tao Wang, email: ruitaowang@126.com Kai-Jiang Yu, email: kaijiang_yu@yeah.net Keywords: gastric cancer, platelet distribution width, prognosis, survival Received: September 27, 2016 Accepted: January 17, 2017 Published: February 21, 2017 ABSTRACT Background: Activated platelets promote tumor cell growth, aberrant angiogenesis, and invasion. However, the value of platelet indices for predicting survival in gastric cancer remains unknown. The goal of this study was to investigate the predictive significance of platelet indices in gastric cancer. Result: Reduced platelet distribution width (PDW) was significantly correlated with age, carcinoembryonic antigen, tumor stage, nodule stage, and tumor-nodule-metastases stage. Moreover, decreased PDW correlated with a shorter overall survival in gastric cancer. Multivariate analysis identified PDW as an independent prognostic factor for overall survival (hazard ratio: 0.493, 95% confidence interval: 0.319-0.761, p = 0.001). Method: A total of 294 patients with gastric cancer were retrospectively analyzed between January 2009 and December 2009. The association between platelet indices and overall survival were evaluated. The prognostic analysis was carried out with Cox regression model. Conclusion: PDW is easily available with routine blood counts. Our data revealed that reduced PDW is unfavorable prognostic factor in gastric cancer. Further studies are warranted.

Alternative splicing is emerging as an oncogenic mechanism. In prostate cancer, generation of constitutively active forms of androgen receptor (AR) variants including AR-V7 plays an important role in progression of castration-resistant prostate cancer (CRPC). AR-V7 is generated by alternative splicing that results in inclusion of cryptic exon CE3 and translation of truncated AR protein that lacks the ligand binding domain. Whether AR-V7 can be a driver for CRPC remains controversial as the oncogenic mechanism of AR-V7 activation remains elusive. Here, we found that KDM4B promotes AR-V7 and identified a novel regulatory mechanism. KDM4B is phosphorylated by protein kinase A under conditions that promote castration-resistance, eliciting its binding to the splicing factor SF3B3. KDM4B binds RNA specifically near the 5′-CE3, upregulates the chromatin accessibility, and couples the spliceosome to the chromatin. Our data suggest that KDM4B can function as a signal responsive trans-acting splicing factor and scaffold that recruits and stabilizes the spliceosome near the alternative exon, thus promoting its inclusion. Genome-wide profiling of KDM4B-regulated genes also identified additional alternative splicing events implicated in tumorigenesis. Our study defines KDM4B-regulated alternative splicing as a pivotal mechanism for generating AR-V7 and a contributing factor for CRPC, providing insight for mechanistic targeting of CRPC.

Network biology and medicine provide unprecedented opportunities and challenges for deciphering disease mechanisms from integrative viewpoints. The disease genes and their products perform their dysfunctions via physical and biochemical interactions in the form of a molecular network. The topological parameters of these disease genes in the interactome are of prominent interest to the understanding of their functionality from a systematic perspective. In this work, we provide a systems biology analysis of the topological features of complex disease genes in an integrated biomolecular network. Firstly, we identify the characteristics of four network parameters in the ten most frequently studied disease genes and identify several specific patterns of their topologies. Then, we confirm our findings in the other disease genes of three complex disorders (i.e., Alzheimer&rsquo, s disease, diabetes mellitus, and hepatocellular carcinoma). The results reveal that the disease genes tend to have a higher betweenness centrality, a smaller average shortest path length, and a smaller clustering coefficient when compared to normal genes, whereas they have no significant degree prominence. The features highlight the importance of gene location in the integrated functional linkages.

Congenital heart disease (CHD)is a serious disorder with the heart's structure and function presenting at birth. It has become the leading cause of infant morbidity worldwide. Thus, finding the disease genes causing CHD is crucial in clinical diagnosis and medical treatment. Currently, network-based method has become a powerful tool of systematically analyzing complex diseases, including the identification of candidate disease genes. In this paper, we propose a bioinformatics framework of prioritizing CHD genes by leveraging the modified PageRank algorithm on an integrative protein-protein interaction network. Specifically, we weigh the interactions by mutual information of gene expressions for measuring the context specificities between genes. After formulating the network into two components of known disease genes and normal healthy genes, we rank the disease-related genes by bringing the order in the network via PageRank. We conclude that these known disease genes achieve significantly higher ranks compared to the normal genes, and these high-ranked genes perform relevant dysfunctions of CHD risks.

Rationale: Inflammation in post–myocardial infarction (MI) is necessary for myocyte repair and wound healing. Unfortunately, it is also a key component of subsequent heart failure pathology. Transcription factor forkhead box O4 (FoxO4) regulates a variety of biological processes, including inflammation. However, its role in MI remains unknown. Objective: To test the hypothesis that FoxO4 promotes early post-MI inflammation via endothelial arginase 1 (Arg1). Methods and Results: We induced MI in wild-type and FoxO4 −/− mice. FoxO4 −/− mice had a significantly higher post-MI survival, better cardiac function, and reduced infarct size. FoxO4 −/− hearts had significantly fewer neutrophils, reduced expression of cytokines, and competitive nitric oxide synthase inhibitor Arg1. We generated conditional FoxO4 knockout mice with FoxO4 deleted in cardiac mycoytes or endothelial cells. FoxO4 endothelial cell–specific knockout mice showed significant post-MI improvement of cardiac function and reduction of neutrophil accumulation and cytokine expression, whereas FoxO4 cardiac mycoyte–specific knockout mice had no significant difference in cardiac function and post-MI inflammation from those of control littermates. FoxO4 binds the Foxo-binding site in the Arg1 promoter and activates Arg1 transcription. FoxO4 knockdown in human aortic endothelial cells upregulated nitric oxide on ischemia and suppressed monocyte adhesion that can be reversed by ectopic-expression of Arg1. Furthermore, chemical inhibition of Arg1 in wild-type mice had similar cardioprotection and reduced inflammation after MI as FoxO4 inactivation and administration of nitric oxide synthase inhibitor to FoxO4 KO mice reversed the beneficial effects of FoxO4 deletion on post-MI cardiac function. Conclusions: FoxO4 activates Arg1 transcription in endothelial cells in response to MI, leading to downregulation of nitric oxide and upregulation of neutrophil infiltration to the infarct area.

Lung cancer is the leading cause of cancer-related death in the United States, and metastatic behavior is largely responsible for this mortality. Mutations in multiple 'driver' oncogenes and tumor suppressors are known to contribute to the lung tumorigenesis and in some cases represent therapeutic targets. Leucine Zipper Transcription Factor-like 1 (LZTFL1) is located in the chromosome region 3p21.3 where allelic loss and genetic alterations occur early and frequently in lung cancers. Previously, we found that LZTFL1 is downregulated in epithelial tumors, including lung cancer, and functions as a tumor suppressor in gastric cancers. However, the functional role of LZTFL1 in lung oncogenesis is undefined. We show here that downregulation of LZTFL1 expression in non-small cell lung cancer is associated with recurrence and poor survival, whereas re-expression of LZTFL1 in lung tumor cells inhibited extravasation/colonization of circulating tumor cells to the lung and inhibited tumor growth in vivo. Mechanistically, we found that LZTFL1 is expressed in ciliated human bronchial epithelial cells (HBECs) and its expression correlates with HBEC differentiation. LZTFL1 inhibits transforming growth factor β-activated mitogen-activated protein kinase and hedgehog signaling. Alteration of intracellular levels of LZTFL1 resulted in changes of expression of genes associated with epithelial-to-mesenchymal transition (EMT). We conclude that LZTFL1 inhibits lung tumorigenesis, possibly by maintaining epithelial cell differentiation and/or inhibition of signalings that lead to EMT and suggest that reactivation of LZTFL1 expression in tumor cells may be a novel lung cancer therapeutic approach.

SummaryWe have ablated the cellular RNA degradation machinery in differentiated B cells and pluripotent embryonic stem cells (ESCs) by conditional mutagenesis of core (Exosc3) and nuclear RNase (Exosc10) components of RNA exosome and identified a vast number of long non-coding RNAs (lncRNAs) and enhancer RNAs (eRNAs) with emergent functionality. Unexpectedly, eRNA-expressing regions accumulate R-loop structures upon RNA exosome ablation, thus demonstrating the role of RNA exosome in resolving deleterious DNA/RNA hybrids arising from active enhancers. We have uncovered a distal divergent eRNA-expressing element (lncRNA-CSR) engaged in long-range DNA interactions and regulating IgH 3′ regulatory region super-enhancer function. CRISPR-Cas9-mediated ablation of lncRNA-CSR transcription decreases its chromosomal looping-mediated association with the IgH 3′ regulatory region super-enhancer and leads to decreased class switch recombination efficiency. We propose that the RNA exosome protects divergently transcribed lncRNA expressing enhancers by resolving deleterious transcription-coupled secondary DNA structures, while also regulating long-range super-enhancer chromosomal interactions important for cellular function.

Protein-RNA interactions play critical roles in numerous biological processes such as posttranscriptional regulation and protein synthesis. However, experimental screening of protein-RNA interactions is usually laborious and time-consuming. It is therefore desirable to develop efficient bioinformatics methods to predict protein-RNA interactions, which can provide valuable hints for future experimental design and advance our understanding of the interaction mechanisms. In this study, we propose a novel method for predicting protein-RNA interactions based on both sequence and structure descriptors of protein and RNA (e.g., the sequence-based physicochemical features, the secondary and three-dimensional structure-based features). We train and compare several classifiers using these descriptors on several benchmark datasets, and the random forest method is selected to build an efficient predictor of protein-RNA interactions. We conduct further cross-validations and the results clearly suggest the efficacy of the proposed method.

Leptin receptor (LepRb) signaling pathway in the hypothalamus of the forebrain controls food intake and energy expenditure in response to an altered energy state. Defects in the LepRb signaling pathway can result in leptin-resistance and obesity. Leucine zipper transcription factor like 1 (Lztfl1)/BBS17 is a member of the Bardet–Biedl syndrome (BBS) gene family. Human BBS patients have a wide range of pathologies including obesity. The cellular and molecular mechanisms underlying Lztfl1-regulated obesity are unknown. Here, we generated Lztfl1f/f mouse model in which Lztfl1 can be deleted globally and in tissue-specific manner. Global Lztfl1 deficiency resulted in pleiotropic phenotypes including obesity. Lztfl1−/− mice are hyperphagic and showed similar energy expenditure as WT littermates. The obese phenotype of Lztfl1−/− mice is caused by the loss of Lztfl1 in the brain but not in the adipocytes. Lztfl1−/− mice are leptin-resistant. Inactivation of Lztfl1 abolished phosphorylation of Stat3 in the LepRb signaling pathway in the hypothalamus upon leptin stimulation. Deletion of Lztfl1 had no effect on LepRb membrane localization. Furthermore, we observed that Lztfl1−/− mouse embryonic fibroblasts (MEFs) have significantly longer cilia than WT MEFs. We identified several proteins that potentially interact with Lztfl1. As these proteins are known to be involved in regulation of actin/cytoskeleton dynamics, we suggest that Lztfl1 may regulate leptin signaling and ciliary structure via these proteins. Our study identified Lztfl1 as a novel player in the LepRb signaling pathway in the hypothalamus that controls energy homeostasis.

BACKGROUND Squamous cell cervical carcinoma is the most common gynecological malignant disorder worldwide. Early detection of squamous cell cervical carcinoma is needed for proper clinical management. Squamous cell carcinoma antigen (SCCA) is commonly used as a tumor marker for squamous cell cervical carcinoma. Platelet distribution width (PDW) is an indicator of platelet activation. Prealbumin is a negative acute-phase protein. OBJECTIVE The aim of this study was to investigate the ability of SCCA, PDW, and prealbumin individually or in combination, to distinguish between cervical carcinoma and control subjects. MEHTODS Two hundred and twenty patients with squamous cell cervical carcinoma and 211 control subjects were included in the study. Patients' characteristics and hematologic tests data at initial diagnosis were collected. RESULTS Our results showed that SCCA and PDW were higher, and prealbumin was lower in cervical carcinoma patients than in control subjects. Single biomarker had AUC value ranging from 0.753 for SCCA to 0.845 for PDW. The combination of SCCA and PDW increased the AUC to 0.900 (p< 0.0001). In addition, the combination of SCCA, PDW and prealbumin exhibited a significantly larger AUC of 0.917 (0.887-0.942), significantly higher than those of any single marker. CONCLUSIONS The combined use of SCCA, PDW and prealbumin can accurately distinguish squamous cell cervical carcinoma from control subjects. This promising approach could be helpful in early detection of squamous cell cervical carcinoma.

Identifying effective cancer biomarkers is crucial in precision medicine. Based on the high-throughput available omics data such as microarray, this paper aims to identify potential biomarker genes for hepatocellular carcinoma by bioinformatics and machine learning. We describe the gene coexpressions with network model and detect out the genes that are closely related to liver cancer infected by hepatitis virus. We cluster these genes by the network topology and then evaluate their classification performance of distinguishing controls from disease samples by support vector machine classification. The functional enrichments of the gene group are also implemented and analyzed. These genes with good classification power and dysfunctional implications are identified as candidate biomarkers for hepatocellular carcinoma.

Diabetes is one of the most prevalent diseases worldwide, and hundreds of millions of patients are suffering from diabetes and its serious complications. Early detection and early treatment are urgent needed for clinical diagnosis of diabetics. In this work, we establish a gene coexpression network framework to identify biomarkers of transcripts with highly different gene coexpression patterns in disease and control samples. We category the differentially expressed genes into four groups by their pair-wise gene coexpression dynamics, i.e., the gene pairs with (and/or without) high correlation in disease samples, while without (and/or with) high correlation in control samples. And we then identify the key genes significantly associated with the pathogenesis of diabetes mellitus. We evaluate the classification abilities of these gene groups by a support vector machine based method. The genes with good classification accuracy are further investigated in functional enrichment analysis, which are served as identified biomarkers.

Some rheumatoid arthritis patients are not clinical response to Rituximab treatment, so the identification of candidate genes for Rituximab response is very important which might contribute to improve the response chance. This paper identifies the candidate genes in a module view, which is more robust and biologically meaningful compared to the conventional gene differentially expressed analysis. The Weighted Gene Co-expression Network Analysis algorithm is firstly used to construct the gene co-expression network of rheumatoid arthritis. The co-expression network is then divided into several gene modules that might correspond to some biological functions with the dynamic tree cut algorithm. Gene expression profile based method and module eigen-genes based method are proposed to calculate the significance of gene modules that respond to Rituximab treatment. The experiment is conducted on a real dataset (GSE54629) from GEO, on which the co-expression network is constructed. The network finally is separated into six gene modules (blue, brown, green, grey, turquoise, yellow), of which the green module that consists of 61 genes is consistently confirmed most significant by both of the aforementioned two methods. The encouraging experimental results in comparison with several other representative papers demonstrate the effectiveness of our method to identify the candidate genes for Rituximab response.

Glycogen synthase kinase-3β (GSK-3β), a serine/threonine protein kinase, has been reported to show essential roles in molecular pathophysiology of many diseases. Mitochondrion is a dynamic organelle for producing cellular energy and determining cell fates. Stress-induced translocated GSK-3β may interact with mitochondrial proteins, including PI3K-Akt, PGC-1α, HK II, PKCε, components of respiratory chain, and subunits of mPTP. Mitochondrial pool of GSK-3β has been implicated in mediation of mitochondrial functions. GSK-3β exhibits the regulatory effects on mitochondrial biogenesis, mitochondrial bioenergetics, mitochondrial permeability, mitochondrial motility, and mitochondrial apoptosis. The versatile functions of GSK-3β might be associated with its wide range of substrates. Accumulative evidence demonstrates that GSK-3β inactivation may be potentially developed as the promising strategy in management of many diseases, such as Alzheimer’s disease (AD) and Parkinson’s disease (PD). Intensive efforts have been made for exploring GSK-3β inhibitors. Natural products provide us a great source for screening new lead compounds in inactivation of GSK-3β. The key roles of GSK-3β in mediation of mitochondrial functions are discussed in this review.

In this work, we provide a comparative study of the main available association measures for characterizing gene regulatory strengths. Detecting the association between genes (as well as RNAs, proteins, and other molecules) is very important to decipher their functional relationship from genomic data in bioinformatics. With the availability of more and more high-throughput datasets, the quantification of meaningful relationships by employing association measures will make great sense of the data. There are various quantitative measures have been proposed for identifying molecular associations. They are depended on different statistical assumptions, for different intentions, as well as with different computational costs in calculating the associations in thousands of genes. Here, we comprehensively summarize these association measures employed and developed for describing gene regulatory relationships. We compare these measures in their consistency and specificity of detecting gene regulations from both simulation and real gene expression profiling data. Obviously, these measures used in genes can be easily extended in other biological molecules or across them.

Child maltreatment is a significant public health problem. Violence and injuries are serious threats to the health and wellbeing of children, as children are at high risk of disability or even mortality due to these injuries. Childhood victimization has long term negative impacts such as low educational performance, becoming more suicidal prone, fear of other sex and acquiring violent behavior. Child maltreatment by burning is a common means of assault that may be difficult to prove. The aim of the present work was to highlight the problem of pediatric burn injuries, and to study the recorded cases of these injuries to find out if the recorded data are sufficient for diagnosis of child maltreatment, as well as to investigate a fresh case of child maltreatment and the complex medico-legal problems affecting the management of such cases. Results: an analysis of records revealed 112 cases of pediatric burn injuries (48.9%) among 229 burned cases admitted to the Burn Unit of Alexandria Main University Hospital during a period of six months from first of January 2013 till the end of June 2013. Scalds were the commonest type especially in children below 5 years (77.3%). Perineal burns were mostly found in females (72.2%). The commonest cause of death was multi-organ failure (63.1%). The records gave no data about the circumstances of burn injuries, thus it was difficult to detect cases of child maltreatment, in spite of finding many clues in the records that may point to this diagnosis. In this work, a case of severe maltreatment was examined, investigated and followed up till discharge. As she was a minor and her caretakers were the main offenders, the question was from whom we would take the consent? This created surgical and medico-legal problems in managing the case.

To improve the accuracy and genetic progress of blue fox breeding, the relationships between genetic polymorphisms and growth and reproductive traits of the blue fox were investigated. MC4R, MC3R, INHA and INHBA were selected as candidate genes for molecular evolution and statistical analyses. Single-factor variance analyses showed that the MC4R (g.267C T, g.423C T, and g.731C A) and MC3R (g.677C T) genotypes had significant impacts on body weight, chest circumference, abdominal perimeter and body mass index (BMI) (P 0.05) in blue fox. The MC4R and MC3R combined genotypes had significant effects on the body weight and abdominal circumference. The different genotypes of INHA g.75G A had significant effects on female fecundity, whereas the different genotypes of INHBA g.404G T and g.467G T and the INHA and INHBA combined genotypes had significant effects on male fecundity. The proteins encoded by the open reading frames (ORFs) of different polymorphic loci were predicted and analysed. The aims of this study were to identify genetic markers related to growth and reproduction in the blue fox and to provide an efficient, economical and accurate theoretical approach for auxiliary fox breeding.

The study of gene regulatory network and protein-protein interaction network is believed to be fundamental to the understanding of molecular processes and functions in systems biology. In this study, the authors are interested in single nucleotide polymorphism (SNP) level and construct SNP-SNP interaction network to understand genetic characters and pathogenetic mechanisms of complex diseases. The authors employ existing methods to mine, model and evaluate a SNP sub-network from SNP-SNP interactions. In the study, the authors employ the two SNP datasets: Parkinson disease and coronary artery disease to demonstrate the procedure of construction and analysis of SNP-SNP interaction networks. Experimental results are reported to demonstrate the procedure of construction and analysis of such SNP-SNP interaction networks can recover some existing biological results and related disease genes.

Protein-RNA interactions are fundamentally important in understanding cellular processes. In particular, non-coding RNA-protein interactions play an important role to facilitate biological functions in signalling, transcriptional regulation, and even the progression of complex diseases. However, experimental determination of protein-RNA interactions remains time-consuming and labour-intensive. Here, we develop a novel extended naïve-Bayes-classifier for de novo prediction of protein-RNA interactions, only using protein and RNA sequence information. Specifically, we first collect a set of known protein-RNA interactions as gold-standard positives and extract sequence-based features to represent each protein-RNA pair. To fill the gap between high dimensional features and scarcity of gold-standard positives, we select effective features by cutting a likelihood ratio score, which not only reduces the computational complexity but also allows transparent feature integration during prediction. An extended naïve Bayes classifier is then constructed using these effective features to train a protein-RNA interaction prediction model. Numerical experiments show that our method can achieve the prediction accuracy of 0.77 even though only a small number of protein-RNA interaction data are available. In particular, we demonstrate that the extended naïve-Bayes-classifier is superior to the naïve-Bayes-classifier by fully considering the dependences among features. Importantly, we conduct ncRNA pull-down experiments to validate the predicted novel protein-RNA interactions and identify the interacting proteins of sbRNA CeN72 in C. elegans, which further demonstrates the effectiveness of our method.

Greater understanding of the complex host responses induced by type 1 interferon (IFN) cytokines could allow new therapeutic approaches for diseases in which these cytokines are implicated. We found that in response to the Toll-like receptor-9 agonist CpGA, plasmacytoid dendritic cells (pDC) produced type 1 IFNs, which, through an autocrine type 1 IFN receptor-dependent pathway, induced changes in cellular metabolism characterized by increased fatty acid oxidation (FAO) and oxidative phosphorylation (OXPHOS). Direct inhibition of FAO and of pathways that support this process, such as fatty acid synthesis, prevented full pDC activation. Type 1 IFNs also induced increased FAO and OXPHOS in non-hematopoietic cells and were found to be responsible for increased FAO and OXPHOS in virus-infected cells. Increased FAO and OXPHOS in response to type 1 IFNs was regulated by PPARα. Our findings reveal FAO, OXPHOS and PPARα as potential targets to therapeutically modulate downstream effects of type 1 IFNs.

In this work, we review the main available computational methods of identifying biomarkers of complex diseases from high-throughput data. The emerging omics techniques provide powerful alternatives to measure thousands of molecules in cells in parallel manners. The generated genomic, transcriptomic, proteomic, metabolomic and phenomic data provide comprehensive molecular and cellular information for detecting critical signals served as biomarkers by classifying disease phenotypic states. Networks are often employed to organize these profiles in the identification of biomarkers to deal with complex diseases in diagnosis, prognosis and therapy as well as mechanism deciphering from systematic perspectives. Here, we summarize some representative network-based bioinformatics methods in order to highlight the importance of computational strategies in biomarker discovery.

Cardiovascular development and homeostasis are regulated by a group of core cardiac transcription factors in a coordinated temporal and spatial manner. Histone methylation is an emerging epigenetic mechanism for regulating gene transcription. Interplay among transcription factors and histone lysine modifiers plays important role in cardiovascular development and diseases. Aberrant expression and mutation of the histone lysine modifiers during development and in adult life can cause either embryonic lethality or congenital heart diseases, and influences the response of adult hearts to pathological stresses. In this review, we describe basics of histone methylation, and current body of literature on the role of several common histone methylations and their modifying enzymes in cardiovascular development, congenital and adult heart diseases.

As smoking rates decrease, proportionally more cases with lung adenocarcinoma occur in never-smokers, while aberrant DNA methylation has been suggested to contribute to the tumorigenesis of lung adenocarcinoma. It is extremely difficult to distinguish which genes play key roles in tumorigenic processes via DNA methylation-mediated gene silencing from a large number of differentially methylated genes. By integrating gene expression and DNA methylation data, a pipeline combined with the differential network analysis is designed to uncover driver methylation genes and responsive modules, which demonstrate distinctive expressions and network topology in tumors with aberrant DNA methylation. Totally, 135 genes are recognized as candidate driver genes in early stage lung adenocarcinoma and top ranked 30 genes are recognized as driver methylation genes. Functional annotation and the differential network analysis indicate the roles of identified driver genes in tumorigenesis, while literature study reveals significant correlations of the top 30 genes with early stage lung adenocarcinoma in never-smokers. The analysis pipeline can also be employed in identification of driver epigenetic events for other cancers characterized by matched gene expression data and DNA methylation data.

Cardiac hypertrophy and failure are accompanied by a reprogramming of gene expression that involves transcription factors and chromatin remodeling enzymes. Little is known about the roles of histone methylation and demethylation in this process. To understand the role of JMJD2A, a histone trimethyl demethylase, in cardiac hypertrophy, we generated mouse lines with heart-specific Jmjd2a deletion (hKO) and overexpression (Jmjd2a-Tg). Jmjd2a hKO and Jmjd2a-Tg mice had no overt baseline phenotype, but did demonstrate altered responses to cardiac stresses. While inactivation of Jmjd2a resulted in an attenuated hypertrophic response to transverse aortic constriction–induced (TAC-induced) pressure overload, Jmjd2a-Tg mice displayed exacerbated cardiac hypertrophy. We identified four-and-a-half LIM domains 1 (FHL1), a key component of the mechanotransducer machinery in the heart, as a direct target of JMJD2A. JMJD2A bound to the FHL1 promoter in response to TAC, upregulated FHL1 expression, and downregulated H3K9 trimethylation. Upregulation of FHL1 by JMJD2A was mediated through SRF and myocardin and required its demethylase activity. The expression of JMJD2A was upregulated in human hypertrophic cardiomyopathy patients. Our studies reveal that JMJD2A promotes cardiac hypertrophy under pathological conditions and suggest what we believe to be a novel mechanism for JMJD2A in reprogramming of gene expression involved in cardiac hypertrophy.

Detecting thermophilic proteins is an important task for designing stable protein engineering in interested temperatures. In this work, we develop a simple but efficient method to classify thermophilic proteins from mesophilic ones using the amino acid and dipeptide compositions. Since most of the amino acid and dipeptide compositions are redundant, we propose a new forward floating selection technique to select only a useful subset of these compositions as features for support vector machine-based classification. We test the proposed method on a benchmark data set of 915 thermophilic and 793 mesophilic proteins. The results show that our method using 28 amino acid and dipeptide compositions achieves an accuracy rate of 93.3% evaluated by the jackknife cross-validation test, which is higher not only than the existing methods but also than using all amino acid and dipeptide compositions.

Motivation: Protein–RNA interactions play a key role in a number of biological processes, such as protein synthesis, mRNA processing, mRNA assembly, ribosome function and eukaryotic spliceosomes. As a result, a reliable identification of RNA binding site of a protein is important for functional annotation and site-directed mutagenesis. Accumulated data of experimental protein–RNA interactions reveal that a RNA binding residue with different neighbor amino acids often exhibits different preferences for its RNA partners, which in turn can be assessed by the interacting interdependence of the amino acid fragment and RNA nucleotide. Results: In this work, we propose a novel classification method to identify the RNA binding sites in proteins by combining a new interacting feature (interaction propensity) with other sequence- and structure-based features. Specifically, the interaction propensity represents a binding specificity of a protein residue to the interacting RNA nucleotide by considering its two-side neighborhood in a protein residue triplet. The sequence as well as the structure-based features of the residues are combined together to discriminate the interaction propensity of amino acids with RNA. We predict RNA interacting residues in proteins by implementing a well-built random forest classifier. The experiments show that our method is able to detect the annotated protein–RNA interaction sites in a high accuracy. Our method achieves an accuracy of 84.5%, F-measure of 0.85 and AUC of 0.92 prediction of the RNA binding residues for a dataset containing 205 non-homologous RNA binding proteins, and also outperforms several existing RNA binding residue predictors, such as RNABindR, BindN, RNAProB and PPRint, and some alternative machine learning methods, such as support vector machine, naive Bayes and neural network in the comparison study. Furthermore, we provide some biological insights into the roles of sequences and structures in protein–RNA interactions by both evaluating the importance of features for their contributions in predictive accuracy and analyzing the binding patterns of interacting residues. Availability: All the source data and code are available at http://www.aporc.org/doc/wiki/PRNA or http://www.sysbio.ac.cn/datatools.asp Contact: lnchen@sibs.ac.cn Supplementary information: Supplementary data are available at Bioinformatics online.

To investigate the proteasome inhibitor MG132-induced apoptosis pathway in HL-60 cells and the role of allogeneic mixed lymphocyte reaction. Cell apoptosis was analyzed by flow cytometry. The expressions of p21 protein, p27 protein and p53 protein in HL-60 cells treated with MG132 were measured by Western blot. The proliferation of, peripheral blood mononuclear cells (PBMNCs) after treatment with 75 Gy irradiated HL-60 cells treated with MG132 was measured with CCK-8. High-dose MG132 induced apoptosis in HL-60 cells. No significant change was observed in MG132-induced apoptosis after inhibiting caspase-8 and caspase-9 pathway. The expressions of p21 protein and p27 protein increased in MG132-induced apoptosis. HL-60 cells treated with low-dose MG132 improved the proliferation of PBMNCs from healthy volunteers. High-dose MG132 induced apoptosis and directly killed HL-60 cells. MG132 induced apoptosis in a caspase-8- and caspase-9-independent pathway. p21 protein and p27 protein were involved in MG132-induced apoptosis in HL-60 cells. HL-60 cells treated with Low-dose MG132 improved the effect of promoting the proliferation of PBMNCs from healthy volunteers.

Pederin belongs to a group of antitumor compounds found in terrestrial beetles and marine sponges. It is apparently used by some members of the rove beetle Paederus as a chemical defense against predators. A recent cluster analysis of the putative pederin biosynthesis gene (ped) strongly suggests that pederin is produced by bacterial symbionts. This paper reviewed the criteria for proving symbiontic origin of bioactive metabolite, indirect and molecular evidences for pederin bacterial origin, as well as three sets of ped clusters and putative biosynthesis process of pederin.

One of the major goals of molecular and evolutionary biology is to understand the functions of proteins by extracting functional information from protein sequences, structures and interactions. In this review, we summarize the repertoire of methods currently being applied and report recent progress in the field of in silico annotation of protein function based on the accumulation of vast amounts of sequence and structure data. In particular, we emphasize the newly developed structure-based methods, which are able to identify locally structural motifs and reveal their relationship with protein functions. These methods include computational tools to identify the structural motifs and reveal the strong relationship between these pre-computed local structures and protein functions. We also discuss remaining problems and possible directions for this exciting and challenging area.

Homeodomain-only protein/not expressed in choriocarcinoma clone 1 (HOP/NECC1) is a newly identified gene that modifies the expression of cardiac-specific genes and thereby regulates heart development. More recently, HOP/NECC1 was reported to be a suppressor of choriocarcinogenesis. Here, we examined the temporal expression profile of HOP/NECC1 in wild-type mouse placenta. We found that E8.5-E9.5 wild-type placenta expressed HOP/NECC1 in the giant cell and spongiotrophoblast layers. HOP/NECC1 (-/-) placenta exhibited marked propagation of giant cell layers and, in turn reduction of spongiotrophoblast formation. We demonstrated SRF transcriptional activity increased in the differentiating trophoblasts and forced expression of SRF in a trophoblast stem (TS) cell line induces the differentiation into giant cells. Negative regulation of SRF (serum response factor) by the binding of HOP/NECC1 protein contributed at least in part to the generation of these placental defects. Gradual induction of HOP/NECC1 in response to differentiation stimuli may result in the decision to differentiate into a particular type of trophoblastic cell lineage and result in non-lethal defects shown by the HOP/NECC1 (-/-) placentas.

Fabry disease is caused by deficient activity of α-galactosidase A and subsequent intracellular accumulation of glycosphingolipids, mainly globotriaosylceramide (Gb3). Vascular endothelial cells may play important roles in disease pathogenesis, and are one of the main target cell types in therapeutic interventions. In this study, we generated immortalized aortic endothelial cell lines from a mouse model of Fabry disease. These cells retained endothelial cell-specific markers and functions. Gb3 expression level in one of these clones (referred to as FMEC2) was highly susceptible to culture media, and appeared to be regulated by glucosylceramide synthase. Results also showed that Gb3 could be upregulated by hydrocortisone. FMEC2 express the mannose 6-phosphate receptor and sortilin but not the mannose receptor. Uptake studies suggested that sortilin plays a role in the binding and internalization of mammalian cell-produced α-galactosidase A. Moss-aGal (a plant-made enzyme) was endocytosed by FMEC2 via a receptor other than the aforementioned receptors. In conclusion, this study suggests that glucosylceramide synthase and hydrocortisone may play important roles in modulating Gb3 levels in Fabry mouse aortic endothelial cells, and that endocytosis of recombinant α-galactosidase A involves a combination of multiple receptors depending on the properties of the enzyme.

Smooth muscle cells (SMCs) and the extracellular matrix (ECM) are intimately associated in the aortic wall. Fbln4SMKO mice with a smooth muscle cell-specific deletion of the Fbln4 gene, which encodes the vascular ECM component fibulin-4, develop ascending aortic aneurysms that have increased abundance of angiotensin converting enzyme (ACE); inhibiting angiotensin II signaling within the first month of life prevents aneurysm development. We used comparative proteomics analysis of Fbln4SMKO aortas from postnatal day (P) 1 to P30 mice to identify key molecules involved in aneurysm initiation and expansion. At P14, the actin depolymerizing factor cofilin was dephosphorylated and thus activated, and at P7, the abundance of slingshot-1 phosphatase (SSH1), an activator of cofilin, was increased, leading to actin cytoskeletal remodeling. Also by P7, biomechanical changes and underdeveloped elastic lamina-SMC connections were evident and the abundance of early growth response-1 (Egr1), a mechanosensitive transcription factor that stimulates ACE expression, was increased, which was before the increases in ACE abundance and cofilin activation. Postnatal deletion of Fbln4 in SMCs at P7 prevented cofilin activation and aneurysm formation, suggesting that these processes required disruption of elastic lamina-SMC connections. Phosphoinsitide 3-kinase (PI3K) is involved in the angiotensin II-mediated activation of SSH1 and administration of PI3K inhibitors from P7 to P30 decreased SSH1 abundance and prevented aneurysms. These results suggest that aneurysm formation arises from abnormal mechanosensing of SMCs resulting from the loss of elastic lamina-SMC connections and from increased SSH1 and cofilin activity, which may be potential therapeutic targets for treating ascending aortic aneurysms.

Transcriptional and post-transcriptional regulation of gene expression is of fundamental importance to numerous biological processes. Nowadays, an increasing amount of gene regulatory relationships have been documented in various databases and literature. However, to more efficiently exploit such knowledge for biomedical research and applications, it is necessary to construct a genome-wide regulatory network database to integrate the information on gene regulatory relationships that are widely scattered in many different places. Therefore, in this work, we build a knowledge-based database, named 'RegNetwork', of gene regulatory networks for human and mouse by collecting and integrating the documented regulatory interactions among transcription factors (TFs), microRNAs (miRNAs) and target genes from 25 selected databases. Moreover, we also inferred and incorporated potential regulatory relationships based on transcription factor binding site (TFBS) motifs into RegNetwork. As a result, RegNetwork contains a comprehensive set of experimentally observed or predicted transcriptional and post-transcriptional regulatory relationships, and the database framework is flexibly designed for potential extensions to include gene regulatory networks for other organisms in the future. Based on RegNetwork, we characterized the statistical and topological properties of genome-wide regulatory networks for human and mouse, we also extracted and interpreted simple yet important network motifs that involve the interplays between TF-miRNA and their targets. In summary, RegNetwork provides an integrated resource on the prior information for gene regulatory relationships, and it enables us to further investigate context-specific transcriptional and post-transcriptional regulatory interactions based on domain-specific experimental data. Database URL: http://www.regnetworkweb.org.

Protein-RNA interactions play crucial roles in numerous biological processes. However, detecting the interactions and binding sites between protein and RNA by traditional experiments is still time consuming and labor costing. Thus, it is of importance to develop bioinformatics methods for predicting protein-RNA interactions and binding sites. Accurate prediction of protein-RNA interactions and recognitions will highly benefit to decipher the interaction mechanisms between protein and RNA, as well as to improve the RNA-related protein engineering and drug design. In this work, we summarize the current bioinformatics strategies of predicting protein-RNA interactions and dissecting protein-RNA interaction mechanisms from local structure binding motifs. In particular, we focus on the feature-based machine learning methods, in which the molecular descriptors of protein and RNA are extracted and integrated as feature vectors of representing the interaction events and recognition residues. In addition, the available methods are classified and compared comprehensively. The molecular descriptors are expected to elucidate the binding mechanisms of protein-RNA interaction and reveal the functional implications from structural complementary perspective.

Background Identifying diagnosis and prognosis biomarkers from expression profiling data is of great significance for achieving personalized medicine and designing therapeutic strategy in complex diseases. However, the reproducibility of identified biomarkers across tissues and experiments is still a challenge for this issue. Results We propose a strategy based on discriminative area of module activities to identify gene biomarkers which interconnect as a subnetwork or module by integrating gene expression data and protein-protein interactions. Then, we implement the procedure in T2DM as a case study and identify a module biomarker with 32 genes from mRNA expression data in skeletal muscle for T2DM. This module biomarker is enriched with known causal genes and related functions of T2DM. Further analysis shows that the module biomarker is of superior performance in classification, and has consistently high accuracies across tissues and experiments. Conclusion The proposed approach can efficiently identify robust and functionally meaningful module biomarkers in T2DM, and could be employed in biomarker discovery of other complex diseases characterized by expression profiles. Electronic supplementary material The online version of this article (doi:10.1186/s12859-015-0519-y) contains supplementary material, which is available to authorized users.

Fabry disease is caused by deficient activity of lysosomal enzyme α-galactosidase A. The enzyme deficiency results in intracellular accumulation of glycosphingolipids, leading to a variety of clinical manifestations including hypertrophic cardiomyopathy and renal insufficiency. The mechanism through which glycosphingolipid accumulation causes these manifestations remains unclear. Current treatment, especially when initiated at later stage of the disease, does not produce completely satisfactory results. Elucidation of the pathogenesis of Fabry disease is therefore crucial to developing new treatments. We found increased activity of androgen receptor (AR) signaling in Fabry disease. We subsequently also found that blockade of AR signaling either through castration or AR-antagonist prevented and reversed cardiac and kidney hypertrophic phenotype in a mouse model of Fabry disease. Our findings implicate abnormal AR pathway in the pathogenesis of Fabry disease and suggest blocking AR signaling as a novel therapeutic approach.

Using the mean spherical approximation (MSA), an explicit analytical equation of state (EOS) with non-dimensional variables for one-component two-Yukawa fluid is established based on the work of Blum, L., and Ubriaco, M., 2000, Molec. Phys., 98, 829. A simple and directly iterative method is found for obtaining an acceptable solution. The strict two-Yukawa EOS is used to correlate the experimental osmotic pressure data of aqueous bovine serum albumin (BSA)-NaCl solutions in the one-component assumption. Considering the experimental error, the correlation results are good, with only one regressed parameter (disperse energy parameter ∊). The deviation of correlation is discussed in detail. A concept of effective diameter, which obviously can decrease the deviation of correlation, is given.

We have discovered an unusual homeodomain protein, called HOP, which is comprised simply of a homeodomain. HOP is highly expressed in the developing heart where its expression is dependent on the cardiac-restricted homeodomain protein Nkx2.5. HOP does not bind DNA and acts as an antagonist of serum response factor (SRF), which regulates the opposing processes of proliferation and myogenesis. Mice homozygous for a HOP null allele segregate into two phenotypic classes characterized by an excess or deficiency of cardiac myocytes. We propose that HOP modulates SRF activity during heart development; its absence results in an imbalance between cardiomyocyte proliferation and differentiation with consequent abnormalities in cardiac morphogenesis.

Addition of a small amount of ganglioside GM(1) to phosphatidylserine (PS) liposomes, a gradual increase of protein kinase C (PKC) activity was recorded up to about 2 mol% GM(1) where the maximal enzyme activity was obtained. Then the activity of PKC began to decline and even turned to be inhibited with the further increase of GM(1) content. It was also indicated that GM(1)/PS binary liposomes had the highest membrane fluidity and very low spatial density of lipid headgroups which was demonstrated in the MC-540 studies due to the interposition of GM(1) when the liposomes contained about 2 mol% GM(1). Besides, the liposomes containing about 2 mol% GM(1) provided a more hydrophobic environment for PKC than the liposomes containing less or more GM(1) which was indicated in the Acrylodan experiments. These factors commonly induced PKC to be stimulated maximally. Whether at the lower or higher GM(1) content, the membrane structure was not the most suitable to support the activity of PKC, which declined as a consequence.

Background Respiratory epithelial cells are the primary target of influenza virus infection in human. However, the molecular mechanisms of airway epithelial cell responses to viral infection are not fully understood. Revealing genome-wide transcriptional and post-transcriptional regulatory relationships can further advance our understanding of this problem, which motivates the development of novel and more efficient computational methods to simultaneously infer the transcriptional and post-transcriptional regulatory networks. Results Here we propose a novel framework named SITPR to investigate the interactions among transcription factors (TFs), microRNAs (miRNAs) and target genes. Briefly, a background regulatory network on a genome-wide scale (~23,000 nodes and ~370,000 potential interactions) is constructed from curated knowledge and algorithm predictions, to which the identification of transcriptional and post-transcriptional regulatory relationships is anchored. To reduce the dimension of the associated computing problem down to an affordable size, several topological and data-based approaches are used. Furthermore, we propose the constrained LASSO formulation and combine it with the dynamic Bayesian network (DBN) model to identify the activated regulatory relationships from time-course expression data. Our simulation studies on networks of different sizes suggest that the proposed framework can effectively determine the genuine regulations among TFs, miRNAs and target genes; also, we compare SITPR with several selected state-of-the-art algorithms to further evaluate its performance. By applying the SITPR framework to mRNA and miRNA expression data generated from human lung epithelial A549 cells in response to A/Mexico/InDRE4487/2009 (H1N1) virus infection, we are able to detect the activated transcriptional and post-transcriptional regulatory relationships as well as the significant regulatory motifs. Conclusion Compared with other representative state-of-the-art algorithms, the proposed SITPR framework can more effectively identify the activated transcriptional and post-transcriptional regulations simultaneously from a given background network. The idea of SITPR is generally applicable to the analysis of gene regulatory networks in human cells. The results obtained for human respiratory epithelial cells suggest the importance of the transcriptional, post-transcriptional regulations as well as their synergies in the innate immune responses against IAV infection. Electronic supplementary material The online version of this article (doi:10.1186/1471-2105-15-336) contains supplementary material, which is available to authorized users.