This randomised controlled trial (RCT) with two parallel arms will evaluate the efficacy of an internet-delivered six-lesson 10-week cognitive behavioural therapy (iCBT) intervention for posttraumatic stress disorder (PTSD). It will also investigate the association between changes in PTSD symptoms, intolerance of uncertainty (IU) and emotion regulation.g of 0.47) was identified as 72, but 10 % more will be recruited to hedge against expected attrition. PTSD diagnosis will be determined using the PTSD module from the Mini International Neuropsychiatric Interview version 5.0.0. The PTSD Checklist – Civilian version (PCL-C) will be used to measure PTSD symptoms (the primary outcome measure), with the Intolerance of Uncertainty Scale 12-item version (IUS-12) and the Emotion Regulation Questionnaire (ERQ) used to measure intolerance of uncertainty and emotion regulation, respectively. The PCL-C will be administered to the treatment group before each lesson of the PTSD program and at 3-month follow-up. The IUS-12 and ERQ will be administered before lessons 1 and 4, at post-treatment and at 3-month follow-up. The waitlist control group will complete these measures at week 1, week 5 and week 11 of the waitlist period. PTSD program efficacy will be determined using intent-to-treat mixed models. Maintenance of gains will be assessed at 3-month follow-up. Mediation analyses using PROCESS will be used to examine the association between change in PTSD symptoms over treatment and change in each of IU and emotion regulation ability in separate analyses.Patients with PTSD will be recruited via the research arm of a not-for-profit clinical and research unit in Australia and randomised to a treatment group or waitlist control group. The minimum sample size for each group  guidelines.Australian New Zealand Clinical Trials Registry:  Posttraumatic stress disorder (PTSD) can follow experiencing a traumatic event in which the individual experiences or witnesses severe threat to life or physical safety. It is characterised by recurrent intrusive thoughts or memories of the traumatic event, avoidance of trauma reminders, elevated physiological arousal and negative changes in thoughts and mood . The lifCognitive behavioural therapy (CBT) is the psychological treatment of choice for PTSD and is recommended by best-practice treatment guidelines e.g. , 11. CBTAttempting to counter these difficulties, recent efforts have examined internet-based delivery of empirically supported treatment for PTSD symptomatology. Clinical research has shown that internet-based delivery of cognitive behavioural therapy (iCBT) has utility for treating PTSD symptoms to an extent approximately equivalent to face-to-face treatment –21. ThesThough iCBT appears promising in treating PTSD, variation in experimental design and course structure means further clinical trials are warranted. Moreover, the role of emotion process variables in PTSD response to iCBT has not been examined. To date, published studies have either not included a control group e.g. , not forEmerging evidence suggests intolerance of uncertainty (IU) as a transdiagnostic construct central to the expression of psychopathology –26. IU iFinally, recent work has investigated the role of emotion regulation in PTSD treatment outcome. Emotion regulation is the ability to actively influence the timing, experience and expression of emotion. Emotion regulation skills training has been shown to reduce dropout and enhance outcome from subsequent exposure-based PTSD treatment , 31. MorThe current Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT)-compliant  protocolThe proposed iCBT program aims to reduce psychological distress and PTSD symptoms in those with PTSD. It comprises psychoeducation about the trauma reaction and how it is maintained after the event by particular patterns of thinking, feeling and behaving. Dearousal skills are taught and practiced. Participants are taught ways to identify and change unhelpful thoughts about the trauma. They will be habituated to their challenging trauma memories using a series of brief structured writing assignments and taught graded exposure methods to re-enter feared situations. Finally, relapse prevention will cover continued skills practice and instruction on independent management of potential future setbacks. These elements have been incorporated into standard CBT-based treatment for PTSD both in internet and face-to-face formats , 36–38, Hypotheses are as follows: (1) patients receiving iCBT for PTSD will show greater mean symptom reduction on a standardised PTSD measure than a waitlist control group during treatment and at the end of treatment (the primary endpoint), with preserved symptom reduction at 3-month follow-up; (2) IU will be less at post-treatment than pre-treatment; (3) reduction in IU over treatment will be associated with reduction in PTSD severity; (4) emotion regulation will be greater at post-treatment than pre-treatment; and (5) increased emotion regulation after treatment will be associated with reduced PTSD severity after treatment.www.virtualclinic.org.au). The mode of internet recruitment and delivery enables potential participants from all Australian states to apply for enrolment in the current trial.The Clinical Research Unit for Anxiety and Depression (CRUfAD) is a non-profit joint initiative of St. Vincent’s Hospital and the University of New South Wales, School of Psychiatry in Sydney, Australia. CRUfAD specialises in the development, evaluation, and dissemination of evidence-based CBT programs via the internet. This RCT will be conducted within the Virtual Clinic, CRUfAD’s clinical research arm . Numbers corresponding to treatment group (1) or the waitlist control group (2) will be placed in sealed opaque envelopes bearing the sequential order number to ensure participant allocation based on the pre-determined randomisation sequence. A member of the research team will open the envelope after the diagnostic interview.Accepted participants will be randomised based on an allocation sequence generated by an independent person not involved in the study via a true randomisation process  [ (PCL-C) . The PCL (PCL-C)  will alsPTSD diagnostic status. Change in PTSD diagnostic status will be indexed using the MINI PTSD module. The MINI has sound reliability and validity psychometrics [ometrics .Patient Health Questionnaire 9-item scale (PHQ-9) [ (PHQ-9) . The PHQ (PHQ-9) , 44.Kessler-10 Psychological Distress Scale (K10) [le (K10) . The K10le (K10) , 46.Generalised Anxiety Disorder 7-Item Scale (GAD-7) [ (GAD-7) . The GAD (GAD-7) , 48.Intolerance of Uncertainty Scale – Short Form (IUS-12) [(IUS-12) . The IUS(IUS-12) . It contEmotion Regulation Questionnaire (ERQ) [re (ERQ) . The ERQre (ERQ) .Service Use and Days Out of Role Questionnaire - modified (SUDOR) [ (SUDOR) . The SUDCredibility and Expectancy/Satisfaction Questionnaire (CEQ) adapted from [ted from . At baseLife Events Checklist for DSM-IV (LEC-IV) [(LEC-IV) . The LEC(LEC-IV) .Part 2 of the Life Events Checklist for DSM-5 (LEC-5) [ (LEC-5) . The LECBeck Depression Inventory – Second Edition – Question 9 (BDI-II) [(BDI-II) . The BDI(BDI-II) . Only QuDissociative Experiences Scale (DES) [le (DES) . This isle (DES) .Skills of Cognitive Behaviour Therapy for PTSD Questionnaire. This measure has been adapted from the Skills of Cognitive Therapy – Patient version [ version . It is aLikely Comorbid Diagnostic Status. Likely comorbid generalised anxiety disorder (GAD), social phobia (SP), panic disorder, agoraphobia and obsessive compulsive disorder (OCD) status will be indexed by asking the initial enquiry question from the relevant module in the MINI.Standardised Assessment of Personality – Abbreviated Scale (SAPAS) [ (SAPAS) . The SAP (SAPAS) .The PTSD program consists of six online lessons in the form of a cartoon narrative that contains best practice CBT as well as regular homework assignments and access to supplementary resources. Lesson summaries and skills practice homework assignments are available for download by participants following each lesson. Homework tasks encourage practice of the skills covered in each lesson  or the Clinical Trials Manager (JS). If clinically indicated, or if a patient’s K10 and/or PHQ9 or PCL-C scores deteriorate, the clinician makes telephone contact. If a participant misses a lesson, a member of the research team will send an email reminder  or will make telephone contact to remind the participant to complete the lesson.The treatment group will be required to complete the iCBT program within 10 weeks to remain in the trial. Adherence is monitored throughout the program. Once enrolled, a participant can elect to discontinue at any time. A participant may be withdrawn from the trial  for the following reasons: lack of computer and internet access; change in prescribed medication for anxiety or depression; change in medication status of exclusion medications; suicidality or clinical risk; failure to complete baseline questionnaires; failure to commence the PTSD program. Lack of adherence throughout the iCBT program is not a specified reason for participant withdrawal, although a participant may withdraw voluntarily. Those in the waitlist control group will be required to complete questionnaires at weeks 1, 5 and 11 of the waitlist period to remain in the RCT. The waitlist control group will have access to the iCBT program immediately at the conclusion of the waitlist period. All reasons for withdrawal status will be documented.To promote participant retention, participants are reminded that data collection is an important aspect of research and enables the research team to track their progress and to evaluate the program. Any adverse events will be reported to the Director of CRUfAD (GA) and to the Human Research Ethics Committee (HREC) of St Vincent’s Hospital, the responsible body for initiating a clinical trial audit.All data will be collected via Virtual Clinic software and stored on a secure Virtual Clinic server. All information collected by the software is coded with either a participant identification number or an email address to facilitate data-to-patient matching. Clinical information not obtained from the online application, including diagnostic status using the MINI, is collected by interview via telephone and stored in written format in a secure location at CRUfAD. Any identifiable information collected remains confidential, except as required by law. Only members of the site (CRUfAD) research team will have access to participant information and data in order to monitor patient progress. During data analysis, re-identifiable data (i.e. coded data) will be used. At study completion, non-identifiable data will be written to a password-protected database. All data will be extracted from the Virtual Clinic servers in the form of an Excel-compatible file to be transferred to the IBM Statistical Package for the Social Sciences  by a member of the research team.Participants will be informed in writing that the research team plans to disseminate the trial results in peer-reviewed scientific publications and presentations. Participants are informed that in any such dissemination, their anonymity will be maintained.Participants will be sent (via email) a written summary of the results in lay terms following completion of the trial study phase.Power calculations were informed by calculation of effect size data from Spence et al. , providip < 0.05 (two-tailed), indicating that the effect of the independent variable on the dependent variable is contingent upon the effect of the proposed mediator [Mediation analyses will be used to examine the association between change in PTSD symptoms over treatment and change in each of IU and emotion regulation in separate analyses. Tests of the indirect effects (mediation) will be conducted using PROCESS . This memediator . In the Staff associated with the project are aware of, and will adhere to, the National Statement on Ethical Conduct in Human Research . The current trial protocol has been approved by the Human Research Ethics Committee of St Vincent’s Hospital, Sydney, Australia. The trial is registered as ACTRN12614001213639.All members of the research team who provide intellectual input to the trial design, execution, or write-up will be acknowledged as an author on any publications.The current RCT will test the efficacy of an iCBT intervention for PTSD. It seeks to replicate previous efficacy findings , 23 of iNotwithstanding the potential utility of the proposed RCT, the following potential limitations are noted. The control group in the current study is a waitlist control, rather than an active treatment control. Therefore, it will not be possible to eliminate a potential placebo effect. Moreover, the results will not speak to the relative utility of iCBT for PTSD compared to an alternative treatment approach or modality. These will be questions for future research. Further, while the trial will examine potential change in IU and emotion regulation ability, the iCBT program was not written to specifically target either of these constructs. As such, results will not permit definitive conclusions as to how treatment components impact these constructs. The current RCT will use DSM-IV PTSD diagnostic criteria and associated standardised assessment instruments, as validated DSM-5 measures were not available when recruitment began for the current project. While items reflecting the three additional DSM-5 PTSD symptoms from the PCL-5 have been included, it is important to note that formal diagnostic and associated self-report measures do not measure DSM-5 PTSD criteria specifically. We note that while having different clinicians conduct pre-treatment and follow-up interviews controls for bias, it may introduce inter-rater variability.Finally, history of child sexual and/or physical abuse is not an exclusion criterion in the current study. This approach was undertaken to maximise the generalisability of the study findings, given the prevalence of early life abuse histories in those suffering from PTSD e.g. . It is nThis article was first submitted on 19 June 2015. To date, 26 participants have met eligibility requirements. The first round of applications opened on 12 September 2014 and the first participant was enrolled on 13 September. Data collection aims to be complete in September 2016.


Since its discovery in 1922, insulin has been thought to be required for normal metabolic homeostasis and survival. However, this view would need to be revised as recent results from different laboratories have convincingly indicated that life without insulin is possible in rodent models. These data indicate that particular neuronal circuitries, which include hypothalamic leptin-responsive neurons, are empowered with the capability of permitting life in complete absence of insulin. Here, we review the neuronal and peripheral mechanisms by which leptin signaling in the central nervous system (CNS) regulates glucose metabolism in an insulin-independent manner. The concept that “living without insulin is possible” may sound senseless as insulin signaling has long been thought to be essential for normal development and coordinated metabolic processes  in several species  Kenyon, . HoweverNobody seriously examined the possibility that “living without insulin is possible” until Dr. Roger Unger and his colleagues at University of Texas Southwestern Medical School in Dallas (USA) set out to tackle this possibility. They found that systemic overexpression of leptin ameliorates hyperglycemia in a near-complete insulin-deficient mouse model  these studies were not able to address the crucial question whether insulin is required for the anti-diabetic action of leptin -based approach to ablate the vast majority of pancreatic β-cells. STZ is a chemical that enters cells expressing glucose transporter 2 (GLUT2) and that destroys DNA in those cells Lenzen, . Using tThe action of leptin on glucose metabolism in near complete insulin-deficient mice is mediated by the CNS  of rats recapitulates the effect of i.c.v. leptin administration on glucose uptake in peripheral tissues -ergic inhibitory neurons, although recent study suggests that some particular neurons can produce and release both glutamate and GABA as neurotransmitters , in particular using a mouse strain 25MgnTg(Ins2-cre)  or 1HerrTg(Ins2-Cre) , and lateral hypothalamic area (LHA)   -axis  is a great candidate for conveying information from leptin-responsive neurons to downstream sites aimed at regulating glucose metabolism. Various neurons in the ARC including POMC, AgRP, RIPvia the leptin-responsive neurons in the ARC and/or DMH and that these neurons signal to MC4R neurons in the PVH → CRH neurons → HPA-axis to suppress hepatic glucose production , which is another arm of the autonomic nervous system, may also be involved in the mechanism underlying leptin's anti-diabetic actions in the absence of insulin. Suppressing action of leptin on glucagon secretion has been suggested to be crucial for leptin's anti-diabetic actions  attenuates leptin's insulin-sensitizing action on the liver  the biochemical identity of neuronal populations that are crucial for leptin's anti-diabetic actions in the absence of insulin; (ii) whether these effects are due to changes in neuronal membrane activities, and/or transcription, and/or neurotransmitter secretion (Williams et al., The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Despite our extensive knowledge of insulin-like growth factor 1 (IGF1) action on the growing skeleton, its role in skeletal homeostasis during aging and age-related development of certain diseases is still unclear. Advanced glycation end products (AGEs) derived from glucose are implicated in osteoporosis and a number of diabetic complications. We hypothesized that because in humans and rodents IGF1 stimulates uptake of glucose (a glycation substrate) from the bloodstream in a dose-dependent manner, the decline of IGF1 could be associated with the accumulation of glycation products and the decreasing resistance of bone to fracture. To test the aforementioned hypotheses, we used human tibial posterior cortex bone samples to perform biochemical  contents) and mechanical tests (crack initiation and propagation using compact tension specimens). Our results for the first time show a significant, age-independent association between the levels of IGF1 and AGEs. Furthermore, AGEs  predict propensity of bone to fracture (initiation and propagation) independently of age in human cortical bone. Based on these results we propose a model of IGF1-based regulation of bone fracture. Because IGF1 level increases postnatally up to the juvenile developmental phase and decreases thereafter with aging, we propose that IGF1 may play a protective role in young skeleton and its age-related decline leads to bone fragility and an increased fracture risk. Our results may also have important implications for current understanding of osteoporosis- and diabetes-related bone fragility as well as in the development of new diagnostic tools to screen for fragile bones. Decreased serum levels of IGF1 were linked to an increased risk of osteoporotic fractures in humans .Detection of pentosidine (PEN) in the analyzed bone samples as compared to the PEN standard. (DOCX)Click here for additional data file.S2 FigA. Age-related levels of PYD/collagen. B. DPD levels per collagen. Our data confirm that the content of PYD and DPD reaches a maximum concentration between 15 and 30 years of age, and then, is maintained at an approx. constant level until the age of late 80’s. In general, human cortical bone displayed approx. 3–4-fold higher level of DPD over PYD.(DOCX)Click here for additional data file.


S. cerevisiae genetic interaction network and genome-wide RNAi screens in cancer cell lines. Our theory, simulations, and applications show that SANTA provides a principled statistical way to quantify the association between molecular networks and cellular functions and phenotypes. SANTA is available from http://bioconductor.org/packages/release/bioc/html/SANTA.html.Linking networks of molecular interactions to cellular functions and phenotypes is a key goal in systems biology. Here, we adapt concepts of spatial statistics to assess the functional content of molecular networks. Based on the guilt-by-association principle, our approach  quantifies the strength of association between a gene set and a network, and functionally annotates molecular networks like other enrichment methods annotate lists of genes. As a general association measure, SANTA can (i) functionally annotate experimentally derived networks using a collection of curated gene sets and (ii) annotate experimentally derived gene sets using a collection of curated networks, as well as (iii) prioritize genes for follow-up analyses. We exemplify the efficacy of SANTA in several case studies using the  Molecular networks are maps of the tens of thousands of interactions that occur between the components of biological systems. Types of interactions include physical, genetic and functional interactions between genes, gene products and metabolites. Network-based approaches to molecular biology are increasingly being used to better understand cellular functions. Currently, gene set methods can be used to functionally annotate the hits from high-throughput studies; however, no methods exist to functionally annotate molecular interaction networks. This greatly limits our ability to quantify the often subtle functional adaptions that occur in networks as they rewire to respond to external stimuli. Here, we extend well-tested concepts from spatial statistics to define a general association measure between networks and gene sets. Like Gene Set Enrichment Analysis, our approach measures concordant changes, but does this on networks, rather than on lists of genes. We validate it both in simulations and real-world case studies. We apply our approach to genetic interaction networks mapped under different conditions and created using different methods, and demonstrate how it extends the previous analyses of data sets, allowing us to better understand the high-level changes that occur within cells. PLOS Computational Biology Methods article.This is a High-throughput studies, like measuring differential expression in RNA-seq experiments or morphological changes in RNAi screens, produce genome-wide data that are often difficult to interpret. Functional annotation of hits in such studies relies mostly upon gene set analysis methods, which measure an association between hits and pre-defined gene sets S. cerevisiae genetic interaction network by Costanzo et al.et al. find that genes displaying tightly correlated profiles form discernible clusters corresponding to distinct bioprocesses and that the relative distance between distinct clusters appears to reflect shared functionality Here, we develop methodology, called SANTA, for the rigorous and unbiased functional annotation of molecular networks. The basic input to SANTA are a network and a gene set and the output is the statistical significance of their association . Like Geet al.The roadmap for functional analysis of networks provided by Costanzo et al.Enrichment analysis is a well-developed field and Khatri In summary, no approach currently exists that, like SANTA, globally assesses the functional content of a network. In the following, we describe the methodology underlying SANTA and test its efficacy by applying it to both simulated and real data. Gene set enrichment methods have had a big impact on biological research and are used in almost every analysis of experimentally derived gene lists. The case studies we present in this paper show that SANTA has the potential to have a similar impact on all functional studies of network data.Spatial statistics model spatial correlation structures between observations  where In our scenario, we measure observations at fixed locations (the nodes in the graph) instead of random locations in the plane. However, we can still adapt basic spatial statistics methodology: In the following paragraphs we will define a Distances between non-neighbouring nodes in this graph can be measured in many ways, including shortest path lengths, diffusion kernels Many of the graph distance algorithms assume that small edge-weights correspond to stronger functional association between the two nodes. Many networks, however, are built by correlation analysis, where stronger functional associations are shown by a larger weight. Thus, in practice, the edge weights in a given molecular network often need to be reweighed to be used as graph distances ance see .how many ‘hits’ are close to each node, but also how strong these hits are. We implement this notion by weighting the contribution of each node by the relative weight it carries compared to the other nodes. This results in a function Exchanging the planar distance where ding at  .The inner sum of To check how significant observed area under the AUK value. An empirical p-value for the observed AUK is calculated using a Z-test. Since we want to quantify the amount of clustering, we are interested in observed Functional annotation is an exploratory task without a general gold standard. In order to test the ability of SANTA to correctly identify clustered distributions of node weights on networks, we conducted a number of controlled simulations. In each of these simulations, we created a network containing a cluster of high weight vertices of a known strength and applied the Each of the networks contained 500 nodes and was created using the Barabasi-Albert model of preferential attachment As expected, SANTA identified more significant clustering when applied to hit sets created with smaller values of  . When no , as expeOne of the main advantages of the Many real-world networks follow a power-law degree distribution and contain nodes with both a small and large number of interacting partners Next, we compared SANTA to approaches that overlay molecular networks with additional node information and identify a high-scoring subnetwork, using simulated and real data. A widely used example is BioNet single top scoring subnetwork.We conducted a number of simulations in order to compare the abilities of We also compared the These results demonstrate that the S. cerevisiae, where there is evidence that protein function is more closely related to the global similarity between GI profiles than to individual interactions For further validation, we applied SANTA to the global genetic interaction (GI) network of S. cerevisiae GI networks under external perturbation by the DNA-damaging agent methyl methane-sulfonate (MMS) Most studies have mapped GIs in cells under normal laboratory conditions We found several GO terms that associated more strongly with the MMS-treated network than the untreated network . GO termSir and Ku, are also involved in DNA-damage repair Comparing the functional enrichment of the UV-treated network replicates the finding of the original publication as well as identifying subtle changes not reported in the publication  [38]. ThThe strength of gene set association was independent of gene set size . AssociaDifferent networks describe different aspects of cellular machinery: co-expression networks describe transcriptional effects, protein interaction networks describe complexes and genetic interaction networks describe epistatic buffering relationships. Identifying the type of network that associates most with genes of interest can point to the mechanism underlying observed phenotypes. To exemplify this idea, we used SANTA to associate RNAi screens in cancer cell lines SANTA is a general approach for functional annotation that extends enrichment analysis from gene sets to networks. SANTA combines the guilt-by-association principle, which is one of the most powerful paradigms for function prediction, with well-tested concepts adapted from spatial statistics. In this way, SANTA provides a rigorous implementation of an intuitive measure of functional annotation. We have applied SANTA to several datasets from different organisms and our results show how SANTA rigorously addresses the basic question of which functional processes are reflected in a network.In yeast, our results on genetic interactions support the idea that a strong correlation of GI profiles between two genes is a greater indicator of shared function than the presence of a single GI. The reason for this increase in functional information is most probably that individual GIs don’t bear much evidence for underlying mechanisms, while having many GI partners in common is strong evidence for genes acting in the same pathway or complex Extending these results to networks rewired under external stimulation, we show how SANTA quantifies subtle functional changes. In humans, we showed how SANTA can contribute to understanding the mechanisms underlying large RNAi screens. Testing the association of hits with many different networks  can help us to understand which cellular mechanisms underly the phenotypes. In summary, our results support that SANTA accurately quantifies the functional content of networks, points to mechanisms underlying observed phenotypes, and provides a natural way to compare functional changes across networks.We expect SANTA to contribute mostly to the functional annotation of networks derived under different environmental conditions (like the GI networks we used as case studies here). However, SANTA is a very general approach and the examples we presented here also show other uses: it can also be used to annotate RNAi hits  and prioritise individual hits over others  can also be used to compute the distances between pairs of nodes where I is the identity matrix, E is a matrix with equal dimensions containing only 1s, e is a column vector containing only 1s, A is the Markov chain transition matrix and D is a diagonal matrix with elements:where S. cerevisiae using synthetic genetic array (SGA) analysis Costanzo et al. tested for genetic interactions (GI) between 5.4 million gene pairs in The correlation network was created by computing, for each gene pair, Pearson's correlation coefficient for the respective rows of the complete GI matrix. Pairs of genes were connected in the network if their interaction profile correlation coefficient exceeded a threshold. Using a threshold of S. cerevisiae174,000 gene pairs were tested for interactions in MMS-treated and untreated S. cerevisiae treated with high doses of UV radiation  was used to collapse the shRNA-wise essentiality scores into gene-wise p-values. P-values are produced by permuting the shRNA scores 10,000 times in order to create artificial genes. The second best score of the shRNA within these artificial genes is then compared to the second best observed shRNA score. Gene-wise p-values RNAi technology can be used to identify genes essential to the survival of cancer cell lines. Cheung et al. performed genome-wide RNAi screens of 102 cell lines across 6 cancer types: oesophageal squamous cell carcinoma, glioblastoma (GBM), non-small-cell-lung cancer (NSCLC), pancreatic cancer, ovarian cancer and colon cancer Gene expression data for two subtypes of diffuse large B-cell lymphomas (DLBCL): germinal center B-like phenotype  and activated B-like phenotype , was obtained from the R package DLBCL, version 1.3.7 S. cerevisiae GO term annotations were retrieved from the Saccharomyces Genome Database (www.yeastgenome.org) using the R package org.Sc.sgd.db, version 2.10.1 The Gene Ontology (GO) database consists of a hierarchical structure of gene annotations The compactness score In order to measure the significance of the observed compactness score, we compared it to scores produced using sets of randomly permuted hits. An empirical p-value for the observed compactness score is calculated using a Z-test.http://bioconductor.org/packages/release/bioc/html/SANTA.html. This package is distributed with the code  Ripley's K-function ((TIF)Click here for additional data file.Figure S2Correlation between set size andp-value. Plot of the significance of the clustering of sets of network genes associated with a GO term against the set size on GI networks mapped in (A) untreated yeast and (B) yeast treated with the DNA-damaging agent MMS. Only those GO terms that associate with either or both networks with a strength of (TIF)Click here for additional data file.Figure S3Correlation in network-gene set association strength between distance methods. Pair-wise comparison of the association strengths of GO terms across the three distance methods. The networks tested were the MMS-treated (Top) and untreated (Bottom) S. cerevisiae GI networks created using data from Bandyopadhyay et al. Association strength correlation across networks is very high ((TIF)Click here for additional data file.Table S1GO terms differentially associated with a network of raw GIs and GI profile correlations.(PDF)Click here for additional data file.Table S2GO terms differentially associated with the untreated and MMS-treated GI networks.(PDF)Click here for additional data file.Table S3GO terms differentially associated with the untreated and UV-treated GI networks.(PDF)Click here for additional data file.Text S1Vignette containing details of how to reproduce the results given in this paper.(PDF)Click here for additional data file.


Ubiquitin (Ub) can generate versatile molecular signals and lead to different celluar fates. The functional poly-valence of Ub is believed to be resulted from its ability to form distinct polymerized chains with eight linkage types. To provide a full picture of ubiquitin code, we explore the binding landscape of two free Ub monomers and also the functional landscapes of of all eight linkage types by theoretical modeling. Remarkably, we found that most of the compact structures of covalently connected dimeric Ub chains (diUbs) pre-exist on the binding landscape. These compact functional states were subsequently validated by corresponding linkage models. This leads to the proposal that the folding architecture of Ub monomer has encoded all functional states into its binding landscape, which is further selected by different topologies of polymeric Ub chains. Moreover, our results revealed that covalent linkage leads to symmetry breaking of interfacial interactions. We further propose that topological constraint not only limits the conformational space for effective switching between functional states, but also selects the local interactions for realizing the corresponding biological function. Therefore, the topological constraint provides a way for breaking the binding symmetry and reaching the functional specificity. The simulation results also provide several predictions that qualitatively and quantitatively consistent with experiments. Importantly, the K48 linkage model successfully predicted intermediate states. The resulting multi-state energy landscape was further employed to reconcile the seemingly contradictory experimental data on the conformational equilibrium of K48-diUb. Our results further suggest that hydrophobic interactions are dominant in the functional landscapes of K6-, K11-, K33- and K48 diUbs, while electrostatic interactions play a more important role in the functional landscapes of K27, K29, K63 and linear linkages. Ubiquitination, as an important post-translational modification of proteins, provides a versatile cellular signaling mechanism. This is mostly contributed by the possibility of ubiquitin units to form different polyUb chains through eight different linkages. However, it is still unclear how these linkage types determine the different functions of polyUb chains. In this study, we address this question via the theoretical modeling and molecular dynamics simulation. This allows us to obtain a full picture of topology-function relationship of polyUb chains. The theoretical results led us to propose that topology of polyUb chains selects the functional landscapes from its binding landscape and the topological constraint provides a way for breaking the binding symmetry and reaching the functional specificity. Ubiquitin (Ub) was discovered in the mid-1970s Ubiquitin tag is achieved via covalent attachment to a substrate protein with a monomeric Ub (monoubiquitination), multiple Ubs (multi-monoubiquitination) or a Ub polymer (polyubiquitination) At present, it seems clear that different polyUb chains generate distinct molecular signals and lead to different cell fates Structural characterizations can provide important local information (corresponding to energy minima or metastable states) on the functional landscape at the bottom of “energy funnel” In the present work, we will develop a flexible binding model by the introduction of electrostatic and hydrophobic interactions and employ it to explore the functional landscape of polyUb chains. Note that only two Ub units (diUb) were used in our model because this is the simplest form of a polyUb chain and the minimal structural unit for longer polyUb chains. Different polyUb chains with all seven lysine linkages and linear linkage as well as free Ub monomers (without a linkage) were investigated based on the flexible binding model. This model allows us to determine the dominant driving forces in the assembly of diUbs with different linkages. The simulation results provide several predictions that qualitatively and quantitatively consistent with experiments. Importantly, the functional landscape of K48-diUb is predicted to have three intermediate states. Inspired by the multi-state functional landscape, we employed a simple three-state model to well reconcile the seemingly contradictory experimental data on the conformational equilibrium of K48-diUb.Methods), we then introduced The electrostatic interactions in our model were calculated by the Debye-Huckel model whose parameters have been carefully tested in our previous works Methods),First, we investigated the conformational dynamics of two free Ub monomers by performing MD simulation based on the free Ub model. Hereby, we point out that in the free Ub model, the two Ub monomers are not connected by a covalent bond. This free model simulation is important not only because our work is the first to simulate the dimerization of free Ub monomers, but also because the results will provide us a benchmark to estimate and quantify the effects of linkages on the conformational behaviour of diUb chains. After calibrating the energetic parameters in the free model, we then constructed the corresponding covalent linkage models to investigate the conformational dynamics of diUbs with all linkage types. These linkage models were carried out by introducing an isopeptide or peptide bond between G76 of one Ub monomer and one of its seven lysine residues . To emphasize this point, free energy surfaces projected onto To further shed light on molecular or microscopic details of the assembly process of Ub units, we measured the interfacial interactions by counting the inter-molecular contacts. We may ask what is the physical reason  of the symmetry breaking? Or whether the symmetry breaking has its biological benefits? In contrast to the free model, the only difference lies in an extra isopeptide/peptide bond introduced between Ub units in each linkage model. The free energy contribution of the bonded constraint was further quantified on the basis of polymer theory Furthermore, to quantify the relative entropic and enthalpic contributions of the bonded constraint to the functional landscapes of different linkage types, we performed a detailed analysis of entropy-enthalpy compensation and calculated the correlation coefficients between entropy and free energy , the hydrophobic interface formed between I44 patches contains three basic residues R42, K48 and H68. More importantly, there are not any negatively charged residues located at the opposite face of these positively charged residues. This indicates that electrostatic interactions may play a negative role in the formation of hydrophobic interface between I44 patches. However, this does not rule out the possibility that electrostatic interactions contribute to form interfaces at other regions of the Ub surface. Now there are fundamental questions: which interactions are dominant for Ub-Ub binding? And what are their roles in diverse functional landscapes of diUbs?To investigate the relative contribution of hydrophobic and electrostatic interactions and their relationships with the interface formation in the binding of Ub units, we decomposed the interfacial energy of the system into two terms, that is, hydrophobic energy To quantify the differences between the conformational space of diUbs with all types of linkages, we calculated the population of conformational states based on a three-state model derived from the free energy landscape of K48-diUb (details shown in following subsection). The conformational space was coarse-grained into three states: open, closed and compact. The compact state can be further divided into I36-I36, I36–I44 and other compact state according to the role of I36 and I44 hydrophobic patches in the formation of interface between Ub units. Note that the criteria for determining the open, closed and compact states are defined according to the corresponding free energy profiles . The resIn addition, we predicted that K33-diUb is able to form I36 patches involved hydrophobic interface like K11-diUb. More precisely, the population of I36-I36 compact state for K33 is By integrating the entropy-enthalpy compensation analysis, we found that, the long-range electrostatic interactions play a remarkable role in entropy-driven cases  with significant population of open state, while the short-range hydrophobic interactions dominate in enthalpy-driven cases whose conformational spaces are tend to be compact. This implies an inherent relationship between entropy/enthalpy, electrostatic/hydrophobic interactions, and conformational distributions.Among the eight different polyUb chains, K48-linkage is the best characterized. K48-linked diUb has been suggested to have multiple distinguished structures by X-ray crystallography and NMR see . PreviouPrevious experimental data also suggested the high pH-dependence of the conformational equilibrium To emphasize the prediction ability of our flexible binding model, we show the free energy profiles as a function of RMSD from the X-ray structure of closed form of diUb  measured by smFRET is in reasonable agreement with the population difference measured by simulations (6%). This indicates our simulations not only exactly captured the small population difference but also their relative tendency to form compact conformations, that is, first K48-, then K63- and lastly M1-diUb.While polyUb chains with K48 and K63 linkages as canonical Ubs are the best studied, the K11 linkage is the most prominent among the left six atypical or non-canonical types and its relative abundance in yeast was found to be up to A partial answer to this question may be derived from the view of energy landscape.  surface  projecteMoreover, the distribution of interfacial interactions between distal unit and proximal unit in K11-diUb is also distinctly different from that in K48-diUb, as shown in The distributions of hot spot residues enable us to shed light on the binding patterns between Ub units. Our results suggest that the I36 patches  on both distal and proximal Ubs play a significant role on the formation of the interface of K11-diUb but not of K48-diUb, while the I44 patches  play a more important role on the interface of K48-diUb. In other words, we found that unlike K48-diUb in which the I44 patches can be totally buried in its closed form, for K11-diUb its I44 patches are scarcely shielded so as to be accessible to protein partners containing ubiqtitin-binding domains. Taken together, our results demonstrate that the functional landscapes and binding patterns of K11-diUb and K48-diUb have significant discrepancy. This may explain why the two share-function polyUb chains can be distinguished by proteosome, and why some proteins specifically interact with K11-linked Ubs but not with other polyUb chains Less is known about the remaining four atypical linkages involving K6, K27, K29 and K33 which are more rare in cells ibutions . Remarkaibutions , correspInterestingly, despite the close sequence and spacial positions between the three linkage residues K27, K29 and K33, the resulting functional landscapes have dramatic differences between each other. Our theoretical prediction can be validated by future experiments. This prediction further makes us to argue that, Ub as a small globule protein (with 76 residues) has been evolutionarily selected by nature, endowing it with the stable foldability and subtle binding specificity. So even a small shift of topological constraint can result in a large change of interfacial interactions. The topological sensitive of resultant functional landscape can be well explained by the highly designed local environment on the surface of Ub. From microcanonical perspectives, the topological constraint of covalent linkages is purely entropic, and dramatically reduces the conformational search space. However, the result could be the changing of the gap and roughness of the intrinsic landscape The post-translational modification of proteins in the cell is an important theme in molecular and cell biology. Different from other post-translational modifications, such as glycosylation, methylation and phosphorylation, ubiquitination provides a more versatile cellular signaling mechanism In the present work, we explored the binding landscape of two free Ub monomers as well as the global functional landscapes of all eight linkage types by theoretical modeling. The results lead to a number of significant conclusions. In particular, the simulations from the free Ub model give a remarkable finding that most of the compact structures of covalently bonded diUbs resolved by X-ray or NMR pre-exist on the binding landscape of free Ub monomers. Additionally, all these experimental structures were validated by the corresponding linkage models. It is worth noting that our flexible binding model does not contain any prior knowledge of native protein-protein interactions in these structures. Therefore the pre-existence of native states of polyUb chains on the binding energy landscape of free Ubs is rather surprising. It also suggests the prediction ability of our flexible binding model despite at coarse grained level. Moreover, this finding leads us to speculate that the well-folded architecture of Ub monomer has embodied the information of forming polymerized functional structures on its binding energy landscape. The corresponding functional landscape of diUbs with a specific linkage type is further generated on the basis of the binding landscape on which functional states are selected by topological constraints as a form of covalent linkages between Ub monomers, as shown in In summary, there are four points revealed by theoretical data consistent well with experimental data: (1) Intermediate states exist on the functional landscape of K48-diUb, besides the known open and closed states. (2) K63- and linear diUb share a highly similar functional landscape which is significantly different from K48-diUb. (3) M1-diUb has slightly more open conformations (about 6%) than K63-diUb. (4) The functional landscapes and binding patterns of K11-diUb are significantly different from that of K48-diUb.Furthermore, our simulations predict several points that may be validated by future experiments: (1) Hydrophobic interactions play a more dominant role in K6-, K11-, K48- and K33-linked diUbs. By contrast, electrostatic interactions are more important to the association of Ub units in K27, K29 and K63 as well linear linkages. (2) The conformational distribution of K6- and K11-diUbs is highly dependent on pH like K48-diUb. Decreasing pH will increase their open conformations. (3) K33-diUb has a multi-state landscape where two functional states are similar to the compact structures of K6-diUb as well as K11-diUb. (4) The symmetry of interfacial interactions is not significantly broken in K11-diUb. (5) The functional landscapes among K27-, K29- and K33-diUbs have dramatic differences between each other despite the close sequence and spacial positions between the three linkage residues.While there are relatively abundant structures of K48-diUb, as a number of snapshots of its highly dynamic conformations, have been captured, people speculated that other linkage types also have other conformations which could be adopted besides the limited available structures Despite of ever increasing amount of three dimensional structures of protein-protein complexes and multi-domain proteins resolved by biophysical tools, especially X-ray crystallography and NMR spectroscopy, there are still big gaps between the number of experimental structures deposited in the Protein Data Bank and the number of predicted binary interactions between human proteins Traditional structure-based model (SBM) cannot be applied directly in the present work to predict the conformational dynamics of polyUb chains because several linkage types  still have not been structurally determined at present. Here, we developed a new model by extending the SBM to address the specific problem of polyUb chains. In our model, each Ub unit is built based on coarse-grained SBM in which each amino acid residue is represented by two beads  and the interactions within Ub unit are structure-based. To further describe the association between Ub units, electrostatic forces and hydrophobic effects are introduced to represent the non-native interactions. In other words, the interactions within Ub units are structure-based as traditional SBM which requires the structural information of Ub monomer, but the interactions between Ub structural units are physics-based without the need of any information of polyUb chains. Therefore, this model not only can keep the computational speed advantage inherited from coarse-grained SBM but also has the potential to predict the binding mechanism of Ub units in the context of polyUb chains. The hamiltonian energy function of our model can be given by the expression:see ref. .Simulations were performed with Gromacs 4.0.5 Each amino acid in polyUb chain is represented by one bead or two beads with or without charge which is dependent on its property. One bead named We used the structure of Ub monomer (PDB: 1UBQ) resolved by X-ray crystallography The electrostatic potential was represented by the Debye-Huckel (DH) model in which the details can be found in our previous works and elsewhere To account for the hydrophobic interface formation, we introduce nonnative hydrophobic interactions by the expression which is borrowed from Chan and coworkers' model The parameter set of b is the spring constant, r is the bond length and To model the diUb chains with different linkage types, we introduced the bond-stretching potential in the form of a harmonic potential,Figure S1Compact structures of diUb chains with different linkages can be sampled by the free Ub model in which the two Ub monomers are not connected by a covalent bond. The free energy surfaces were plotted as a function of the centroid distance between Ub units ((PDF)Click here for additional data file.Figure S2A control result to show that free Ub monomers never sample all possible conformations. The MD trajectories of the free Ub model were projected to the two-dimensional free energy surface as a function of (PDF)Click here for additional data file.Figure S3Interfacial contact maps of the free Ub model and the eight linkage models. (A) Free Ub monomers. (B) K11-diUb. (C) K27-diUb. (D) K29-diUb. (E) K33-diUb. (F) K48-diUb. (G) K63-diUb. (H) Linear diUb.(PDF)Click here for additional data file.Figure S4Estimation of entropic and enthalpic contributions of a bonded constraint by the formula.(PDF)Click here for additional data file.Figure S5Entropy-enthalpy compensation analysis by decomposing free energy F(x) into (x) and TS(x). Here x is (PDF)Click here for additional data file.Figure S6Entropy-enthalpy compensation analysis by decomposing free energy F(x) into (x) and TS(x). Here x is (PDF)Click here for additional data file.Figure S7Entropy-enthalpy compensation analysis by decomposing free energy F(x) into (x) and TS(x). Here x is (PDF)Click here for additional data file.Figure S8Hydrophobic interactions  and electrostatic interactions  at the interface between Ub units. The results are from the simulations by the free model. (A) Free energy profile as a function of (PDF)Click here for additional data file.Figure S9Criteria for determining the open, closed and compact states are defined according to the corresponding free energy profiles. It shows the free energy profiles as a function of (PDF)Click here for additional data file.Figure S10Three intermediate states revealed on the free energy surface of K48-diUb.(PDF)Click here for additional data file.Figure S11The free energy surfaces between K63-diUb and linear diUb are highly similar to each other but significantly different from K48-diUb. The free energy surfaces (PDF)Click here for additional data file.Figure S12Conformational distribution of K63-diUb based on the order parameter similar to smFRET measurement. It is difficult to monitor the conformational space of K63-diUb via this order parameter. However, the populations of open and compact states are estimated to be 24% and 76%, respectively. This is quantitatively consistent with the smFRET data. Despite of this, it is better to investigate the conformational dynamics of diUb with more than dye pairs because one-dimensional conformational distribution or free energy profile have been suggested to be not sufficient to describe the multi-state landscape of many proteins.(PDF)Click here for additional data file.Figure S13Evidence that intermediate states are possibly hidden on the one-dimensional conformational distribution or free energy profiles but can be detected by multi-dimensional free energy profiles. Note that the data is from the simulation of K48-diUb model.(PDF)Click here for additional data file.Figure S14Functional landscape of K33-diUb. K33-diUb only can form the similar conformation to the compact state of K6 with I36–I44 interface (PDB 2XK5) but also is able to sample the similar conformation of the compact state of K11 with I36-I36 interface (PDB 3NOB).(PDF)Click here for additional data file.Figure S15Convergence analysis of single independent simulation. As a typical simulation, one of the MD trajectories of K48-diUb model was chosen to show the result. The trajectory was projected onto three observables ((PDF)Click here for additional data file.Figure S16Comparison between conformational fluctuation of Ub units with experimental temperature factor.(PDF)Click here for additional data file.Figure S17The matrix of hydrophobic parameters for different sidechain-sidechain interactions in the protein-protein association model.(PDF)Click here for additional data file.Text S1Supporting Methods ; Tables 14; Supporting References.(PDF)Click here for additional data file.


Characterization of carotid artery morphology and plaque composition requires multiple measurements with different contrast-weightings, which are limited by long scan time, image misregistration, and expertise-dependency in image interpretation. Recently, a new method,3D Multi-contrast ATherosclerosis CHaracterization (MATCH) has been developed to overcome the above drawbacks by acquiring multi-contrast weighted images in one scan [1] This study was conducted to determine the accuracy of MATCH in the characterization of plaque morphology and composition in patients.Thirty patients scheduled for carotid revascularization underwent preoperative carotid MRI with MATCH and the conventional multi-contrast protocol  in the same examination with a 3T scanner, wall  and NWI  measured by MATCH and conventional protocols. Moderate to good agreement was seen between 2 protocols in the detection of plaque components .The two protocols were successfully performed in all 30 patients. The scan time was about 15 min for the conventional protocol and 5 min for the MATCH protocol.The 30 patients bilateral artery yielded 898 matched MRI cross-sectional slices for analysis. The statistical analysis shown no significant difference of mean area of lumen (36.27 vs 35.72 mmOur preliminary clinical study suggests that MATCH has similar performance in the evaluation of carotid plaque to the conventional multi-contrast protocol. Shorter scan time, less image misregistration and relatively simplified criteria for component have substantially reduced the examination failure rate and shorten the time consuming of interpretation.MATCH is a promising CMR imaging method for assessing the risk of plaque disruption in a clinical workup.Beijing health system of high-level technical personnel training project 2013-2-005.


TR = 0.645 s) by estimating time-varying correlation coefficient (TVCC) between BOLD responses of different brain regions. We observed reliable task-related FC changes  among several visual regions of the bilateral middle occipital gyrus (MOG) and the bilateral fusiform gyrus (FuG). Importantly, only the FCs between higher visual regions (MOG) and lower visual regions (FuG) exhibited such dynamic patterns. The results suggested that simply assuming a sustained FC during a task block may be insufficient to capture distinct task-related FC changes. The investigation of FC dynamics in tasks could improve our understanding of condition shifts and the coordination between different activated brain regions.Studying task modulations of brain connectivity using functional magnetic resonance imaging (fMRI) is critical to understand brain functions that support cognitive and affective processes. Existing methods such as psychophysiological interaction (PPI) and dynamic causal modeling (DCM) usually implicitly assume that the connectivity patterns are stable over a block-designed task with identical stimuli. However, this assumption lacks empirical verification on high-temporal resolution fMRI data with reliable data-driven analysis methods. The present study performed a detailed examination of dynamic changes of functional connectivity (FC) in a simple block-designed visual checkerboard experiment with a sub-second sampling rate ( Identifying brain connectivity, especially task-related connectivity, from functional magnetic resonance imaging (fMRI) is critical to understand how brain is organized to support cognitive and affective processes  has shown that stimuli or tasks could elicit rapid changes of functional connectivity (FC) or effective connectviity  of 0.645 s . Institutional Review Board Approval was obtained for this project at the Nathan Kline Institute and at Montclair State University. Written informed consent was obtained for all study participants /Rockland sample  and followed by a 20 s checkerboard condition with three repetitions. After the third checkerboard block, there was an additional 35 s rest condition. The total scan time was about 2 m 35 s with totally 240 images acquired. The fMRI data were scanned using a multiband echo planar imaging (EPI) sequence with the following parameters: http://www.fil.ion.ucl.ac.uk/spm/) under MATLAB7.6 environment (http://www.mathworks.com/). Before processing, the first 14 functional images (around 9 s) were discarded. We have not performed slice timing since this dataset was scanned using a multiband imaging technique, in which multiple slices were excited simultaneously. The remaining images were motion corrected for each subject, and co-registered to the subject's high resolution anatomical image. The anatomical image was segmented using the new segment routine in SPM8. Then, the deformation field obtained from the segmentation step was applied to all the functional images to normalize them into standard MNI space . All functional images were smoothed using a Gaussian kernel (FWHM = 8 mm). Finally, the functional images were temporally filtered by a second order Butterworth lowpass filter with a cutoff frequency of 0.75 Hz. To quantify the amount of head motion, we calculated mean frame-wise displacement (FD) in the translational and rotational directions for each subject  were defined based on our previous study of an independent data set  was used to estimate activations relating to checkerboard presentations. A box-car function representing the checkerboard presentation block was convolved with the canonical hemodynamic response function (HRF). Other regressors in the GLM included six rigid body head motion parameters, their first order temporal derivatives, the first five eigenvariates from the signals in the white matter, and the first five eigenvariates from the signals in the cerebrospinal fluid (CSF). A default high-pass filter with a cutoff frequency of 1/128 Hz was incorporated in the GLM model. The beta maps representing activations of the visual stimuli were obtained by GLM from all subjects and then were entered into a second level analysis to assess consistent activations across subjects by using a one-sample h is the specified window size of 24 samples (16 s). This window size is shorter than the task block, so that important details of FC dynamics during the task period would not be overlooked. Also, since the fMRI data we used have a short TR of 645 ms, a window of 16 s includes around 24 samples in estimating the correlation coefficient. In literature, a window covering 24 samples is sufficient for estimating dynamic FC. For example, a window of 15 samples was used in Hutchison et al.  procedure to address the problem of multiple comparisons . TVCCs were Fisher's z-transformed before the following analyses. For each subject, BOLD responses or FCs were averaged across three block cycles, and the mean of each BOLD or FC within the pre-stimulus period (−10 to 0 s) was the baseline value of this subject. Two-tailed one sample t-test was applied to check whether slope at each time point was significantly larger than zero (indicating an increasing trend) or smaller than zero (indicating a decreasing trend). The significance threshold was corrected by the FDR procedure to address the problem of multiple comparisons , post-hoc pairwise comparisons (paired t-test) were performed between two adjacent sub-periods to identify the temporal changes of FCs. The significance threshold was corrected by the FDR procedure to address the problem of multiple comparisons  estimation bias, because FCs or slopes of BOLD and FCs at one time sample were estimated from adjacent data samples, which may not have the same properties as the data at this time sample , and the results were displayed in the right panels of Figures 0 s, and p = 0.0029 for LMOG × LFuG; p = 0.0213 for LMOG × RFuG; p = 0.0272 for RMOG × LFuG; p = 0.0342 for RMOG × RFuG). FCs with significant difference in different four experimental sub-periods  were displayed in Figure One-Way repeated measures ANOVA only identified significant FC difference among four sub-periods on those FCs between higher and lower visual ROIs , but also changed within the task block (a significant decreasing trend during the task period). Our analysis revealed that (1) the FCs during the task period had different temporal dynamic behaviors; (2) FCs between spatially distributed visual regions could not persist at a high level but decreased within the task period. This was in contrast to BOLD responses in visual regions, which exhibited sustained activations over almost the whole task condition.The transient connectivity changes between the visual areas were in line with neurophysiological evidence. At the neuronal level, neurons in visual areas showed increased synchronization within a second after stimulus onset  the predictive coding mechanism of the visual system, (2) the stimulus saliency, (3) the limited capacity of the brain for information integration, and (4) fluctuations of alpha band synchronizations.First, the predictive coding mechanism might be one of the potential causes of the decrease on FCs between different visual sub-systems  in time-varying FC estimation.It should also be noted that, regressing out task-related BOLD activities is a crucial pre-processing step in identifying task-related FC dynamics using sliding-window TVCC estimation. Correlation is a statistical metric that is only applicable for stochastic processes (spontaneous BOLD activity), but not for deterministic processes (task-activated BOLD activity). So, mathematically we need to remove task-related BOLD activities for correct estimation of TVCC. Importantly, if deterministic task-related BOLD activities were not removed before calculating TVCC, they would definitely cause dynamic changes of TVCC . But, these dynamic TVCC patterns estimated from task-related BOLD activities are not physiologically relevant because they can be explained by well-studied task-related BOLD activities. Therefore, it is important to regress out task-related BOLD activities to make sure the correctness of the FC estimation method and to ascertain the physiological significance of observed FC dynamics. In the present study, the considerable task-related FC dynamics we observed were not estimated from task-related BOLD activities, so they can provide new information about the organization of the brain that cannot be provided by task-related BOLD activities.Lastly, one possible limitation of our analysis is that, the FC dynamics of three block cycles are very large, because of the very low SNR of BOLD signal. So, we averaged the BOLD responses and FCs across three cycles before statistical comparisons across subjects and between time periods. The large variability in FC dynamics across cycles may be caused by noise, but it may also be physiologically relevant. In future, it will be interesting to study whether FC dynamics of separate cycles are stable or not from new experimental data, for example, with more cycles and more participants.The current task-related FC analyses suggested that simply dividing the samples in different task conditions and then calculating connectivity measures may not be sufficient to infer FC dynamics during a task period. We checked the sample-by-sample time-varying FC of the visual stimulation block and found significantly different changing patterns of FCs during the stimulation period. In addition, effective connectivity models that examine task related changes, such as PPI  and psychological factors  for a more comprehensive study of the task-related FC dynamics and the underlying brain mechanisms. Second, we are interested to check whether such task-related FC dynamics are different in different cohorts of individuals . Alterations in FCs have been associated with a wide range of neurological and psychiatric diseases (Greicius et al., XD, ZZ, and BB conceived and designed the study. ZF, SC, and YH analyzed the data. ZF, ZZ, and XD wrote the article. ZZ, XD, and BB made the critical revision of the article. All authors discussed the results and implications and commented on the manuscript at all stages.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Describe DWI MRI and fusion T2-DWI techniques and findings for detection and characterisation of tumours, list the current and potential applications of DWI in cancer patient management and review causes of false-positive and false-negative results in lesion detection.The detection and characterisation of malignant lesions can often be difficult, principally when the disease is small or when the tumour is combined with normal tissues.Forty five patients with malignancies underwent both DWI MRI and T2-DWI to detect and characterise primary and metastatic tumours. We acquired DWI , FAT SAT T1w imaging before and after gadolinium administration and STIR images. The standard reference was histopathology findings.b value DW-MRI with anatomic imaging have a number of utilities including data presentations to clinicians for detecting and guiding biopsy to variable tumour.DWI-MRI and fusion T2-DWI imaging is a powerful clinical tool for directing the care of patients with cancer. Three- dimensional fusion imaging of high


Although musical training has been shown to facilitate both native and non-native phonetic perception, it remains unclear whether and how musical experience affects native speakers’ categorical perception (CP) of speech at the suprasegmental level. Using both identification and discrimination tasks, this study compared Chinese-speaking musicians and non-musicians in their CP of a lexical tone continuum . While the identification functions showed similar steepness and boundary location between the two subject groups, the discrimination results revealed superior performance in the musicians for discriminating within-category stimuli pairs but not for between-category stimuli. These findings suggest that musical training can enhance sensitivity to subtle pitch differences between within-category sounds in the presence of robust mental representations in service of CP of lexical tonal contrasts. Categorical perception (CP), which refers to the phenomenon that gradually morphed sounds in a stimulus continuum tend to be perceived as discrete representations, has been studied for more than 50 years. While most of the early CP studies focused on segmental features  and 32 non-musicians . The musicians had undergone at least seven years of continuous formal western instrumental music training, and they had regular practice and current opportunities to play an instrument. The non-musicians were selected with the inclusion criterion that they had never received formal musical training within the last five years and less than 2 years of musical experience prior to that . The twoFigure 1). The speech stimuli were the Chinese monosyllables /pa/ that differed in their lexical tones . The original stimuli were first recorded at a sampling rate of 44.1 kHz from a female native Chinese speaker. The syllables were then digitally edited to have a duration of 200 ms using Sound-Forge . In order to isolate the lexical tones and keep the rest of the acoustic features equivalent, pitch tier transfer was performed using the Praat software (http://www.fon.hum.uva.nl/praat/). This procedure generated two stimuli, /pa1/ and /pa4/, which were identical with each other except for the pitch contour difference. The /pa1/ and /pa4/ stimuli were then taken as the endpoint stimuli to create a lexical tone continuum. A morphing technique was performed in Matlab  using STRAIGHT  presented in random order. The rate of presentation was self-paced with 1-s pause after response.In the discrimination task, two stimuli were presented consecutively with a 500-ms interstimulus interval (ISI) and participants were instructed to judge whether the two sounds were identical or not by pressing the “1” (same) or 2 (different) key on the keyboard. There were three occurrences of each of the fourteen 2-step pairs  randomly presented, either in forward  or reverse order . Foil trials involving identical stimuli in the pairwise presentation were also included. The rate of presentation was self-paced as in the identification task.To investigate the effects of group (musician vs. non-musician) and stimulus type (speech vs. non-speech) on identification and discrimination performance, five essential characteristics of CP, i.e., sharpness and location of category boundary, between- and within-category discrimination accuracy, and peakedness of discrimination function were calculated .Xcb) corresponding to the 50% identification score.Logistic regression between identification score and step number was used to obtain the identification function. The Generalized Estimating Equations estimated regression coefficient b1 was used to evaluate the slope of the fitted logistic curve which is an indication of the sharpness of the categorical boundary . The catPbc), which was measured from the comparison unit corresponding to the categorical boundary (Xcb) determined from the subgroup identification functions (Pbc = P46); within-category discrimination accuracy (Pwc), which was the average of the two comparison units at the ends of the continuum (P13 and P79); and peakedness of the discrimination function (Ppk), estimated by Pbc minus Pwc. All the discrimination measures were obtained by computing the proportion of “different” responses.The obtained discrimination data were examined by three different measures: between-category discrimination accuracy  = 6.575, p = 0.013], indicating that CP of the speech continuum is stronger than non-speech. There was a significant main effect of stimulus type  and group × stimulus type interaction effect  for the location of category boundary (Xcb). The category boundary of the speech continuum shifted to the high level end compared with the non-speech continuum for both groups. This boundary shift phenomenon was more obvious in the non-musician group.Each of the five measures was analyzed by a two-way ANOVA model for group (musician vs. non-musician) and stimulus type (speech vs. non-speech) effects. The steepness of the category boundary (b1) had a significant main effect of stimulus type  and stimulus type . The group × stimulus type interaction effect was not significant, indicating that musicians were superior to non-musicians in discriminating the within-category pitch difference in both the speech and non-speech continua. For both groups, non-speech yielded better within-category discrimination accuracy than speech. For both the between-category discrimination accuracy (Pbc) and peakedness of the discrimination function (Ppk), there was no significant main effect or interaction .The within-category discrimination accuracy  performing initial acoustic analysis and the downstream regions  performing higher-level phonological processing . The impFinally, our study revealed an effect of stimulus type (speech vs. non-speech) in both identification and discrimination tasks. Compared with speech, perception of the non-speech continuum had a shallower category boundary, higher within-category discrimination accuracy and shifted the boundary location toward the high falling end of the continuum. These results are consistent with an earlier study  adopting


Variation within splicing regulatory sequences often leads to differences in gene models among individuals within a species. Two alleles of the same gene may express transcripts with different exon/intron structures and consequently produce functionally different proteins. Matching genomic and transcriptomic data allows us to identify putative regulatory variants associated with changes in splicing patterns.Drosophila melanogaster with known genotypes. We identified dozens of genotype-specific splicing patterns associated with putative cis-splicing quantitative trait loci (sQTL). The majority of changes can be explained by mutations in splice sites. Allelic-imbalance in splicing patterns confirmed that the majority are regulated mainly by cis-genetic effects. Remarkably, allele-specific splicing changes often lead to qualitative changes in gene models, yielding many isoforms not previously annotated. The observed alterations are typically outside protein-coding regions or affect only very short protein segments.Here we analyzed natural variation of splicing patterns in the transcriptomes of 81 natural strains of D. melanogaster populations. The observed variation in splicing patterns are predicted to have limited effects on the encoded protein sequences. To our knowledge, this is the first sQTL mapping study in Drosophila.Overall, the sets of gene models appear to be flexible within The online version of this article (doi:10.1186/s12864-015-1415-6) contains supplementary material, which is available to authorized users. The latter determine the precise recognition of exon/intron boundaries and the structure of mature transcripts . The flies were crossbred following a common reference design [w[1118]). The resulting transcriptomes of F1-hybrids were subsequently sequenced  and qualitative (i.e. expression of new isoforms). To be able to detect new genotype-specific isoforms, we annotated all alternative splicing events observed in the analyzed dataset. To this end we extended the base set of annotated introns with newly detected splice junctions and used them to annotate and quantify alternative splicing events see . We concAlternative splicing events were quantified in each genotype by a Ψ-value  which is a natural representation of the fraction of one of the isoforms among two major alternate transcripts see b.cis-variants )  with SNPs from the parental lines . We used only biallelic SNPs that were called and were in homozygous state in both parental genomes. Thus, for each F1-hybrid we created two separate parental genomes  by update of the common reference genome at the same set of genomic positions. This allowed us to avoid spurious allelic imbalance in the case when the genomic coverage of parental genotypes differed substantially.For each F1-hybrid we created two parental genome references. To this end, we updated the http://broadinstitute.github.io/picard/).RNA-Seq reads were aligned to the parental genomes using STAR . AlignmeMost of the reads mapped to the both parental genomes with same number of mismatches. In most of the cases, two alignments of the read with the same number of mismatches to the both parental genomes are identical, but potentially they may represent mapping to different positions. Thus, we randomly choose one of them. For each read that was aligned to the two parental genome references with different number of mismatches, the alignment with fewer mismatches was selected. For reads that were aligned to only one of the parental genomes we used the obtained alignment. All the selected alignments  were used to annotate and quantify splicing events.Additionally, the reads that differentially aligned between two parental genomes  were considered as allele-specific reads. The reads, which aligned to the genome of a natural line with fewer mismatches, were considered to be natural allele-specific reads and were used to estimate ns-Ψ-values. The reads aligned to the genome of the tester line with fewer mismatches, were considered to be tester-allele specific reads and were used to estimate ts-Ψ-values.The obtained fraction of allele-specific reads was less than that for interspecies hybrids ,33 and iReads aligned to splicing junctions (SJs), were used to annotate and quantify alternative splicing events. We extended the set of annotated SJs (BDGP 5.72) with high-confident unannotated SJs, which shared at least one of splice sites with annotated introns. Most of them had low coverage and are likely to represent splicing errors, captured due to the high overall coverage in our data set. We filtered out weakly covered introns and kept SJs that were covered by at least 10 junction reads in at least one analyzed F1-hybrid. The majority of unannotated SJs together with parts of annotated introns were excluded by this filtering and the remaining set contained 43571 introns of protein-coding genes  and cassette exons Figure b. We alsAfter estimation of Ψ-values we additionally filtered out alternative splicing events with almost constitutive splicing patterns in all samples . We obtained this value for each of the detected genotype-specific splicing event, by performing association tests with 10000 random SNPs from other chromosomes. Then, λ was estimated as the ratio of medians of the observed and expected uniform log-p-value distributions.Statistical significance of associations between variants in canonical splicing dinucleotides and changes in splicing patterns was calculated using Fisher’s exact test. To this end, we constructed 2 by 2 contingency tables for inclusion and exclusion reads for two alternative alleles of tested SNPs. Read counts from all genotypes that carried the same allele and had estimated Ψ-values were merged.cis-sQTL. RT-PCR was performed for F1-hybrids and for their parental lines .RT-PCR validations were performed for several representative examples of genotype-specific splicing events that affected long gene segments. For each example, we selected one heterozygous F1-hybrid that carried both derived and ancestral alleles of associated Primers were designed such that PCR products would cover alternative segments of an analyzed genotype-specific splicing events. Total RNA from adult female heads was reverse transcribed using SuperScript III First-Strand Synthesis SuperMix (Invitrogen). PCR was performed using PCR Master Mix (2X) (Thermo Scientific). PCR products were confirmed by running in 1.5% agarose gel and by UV-inspection. Images of gels were photographed by Gel Doc XR+ System (BIO-RAD). List of F1-hybrids, primer sequences and specific annealing conditions are provided in Additional file Genomic and transcriptomic data analyzed in this study is available at NCBI SRA .


Immediate selective hypersensitivity reactions to pyrazolones are thought to be mediated by specific-IgE antibodies. The diagnosis of selective reactions to pyrazolones is mainly based on the clinical history, skin testing and/or drug provocation test (DPT). Skin testing has low sensitivity and has potential risk of eliciting an anaphylactic response as well as DPT. Therefore the use of in vitro tests could be a safe and straightforward approach, although till now no well-validated test is available. Basophil activation test (BAT) has been proposed as a new in vitro diagnostic tool for different drug reactions although its sensibility is not optimal. The aim of this study was to analyze the potential usefulness of dipyrone metabolites to improve BAT sensitivity.The study included a group of 8 patients with a selective immediate hypersensitivity reaction to dipyrone, plus a group of 15 non-atopic controls tolerating dipyrone. BAT was done with dipyrone and four metabolites purified from human urine samples (4-methylamino antipyrine (MAA), 4-aminoantipyrine (AA), 4-acetylamino antipyrine (AAA) and 4-formylamino antipyrine (FAA). To confirm IgE-mediated basophil activation, the phosphatidylinositol 3-kinase (PI3-K) inhibitor wortmannin (WTM) was used in the BAT.All patients were skin test positive in the immediate reading to dipyrone and had good tolerance to acetyl salicylic acid in drug provocation test. BAT results were positive in 2 patients to both dipyrone and MAA, in 2 patients to MAA alone and in 1 patient to AA alone. We confirmed that positive BAT results were an IgE-mediated response by using the WTM. All healthy controls showed negative BAT to dipyrone and metabolites.This study demonstrates the role of dipyrone metabolites in immediate reactions and supports the hypothesis that drug metabolites may underlie some allergic reactions. The additional use of dipyrone metabolites, besides the parent drug, increases BAT sensitivity, thus improving and refining the BAT-based diagnosis of selective reactions to dipyrone. These results deserve further studies using a higher number of patients and other dipyrone metabolites to gain deeper insight in the potential use of metabolite-based BAT as a target biomarker for allergic reactions to dipyrone.


Prostate cancer is one of the most common complex diseases with high leading cause of death in men. Identifications of prostate cancer associated genes and biomarkers are thus essential as they can gain insights into the mechanisms underlying disease progression and advancing for early diagnosis and developing effective therapies.In this study, we presented an integrative analysis of gene expression profiling and protein interaction network at a systematic level to reveal candidate disease-associated genes and biomarkers for prostate cancer progression. At first, we reconstructed the human prostate cancer protein-protein interaction network (HPC-PPIN) and the network was then integrated with the prostate cancer gene expression data to identify modules related to different phases in prostate cancer. At last, the candidate module biomarkers were validated by its predictive ability of prostate cancer progression.Different phases-specific modules were identified for prostate cancer. Among these modules, transcription Androgen Receptor (AR) nuclear signaling and Epidermal Growth Factor Receptor (EGFR) signalling pathway were shown to be the pathway targets for prostate cancer progression. The identified candidate disease-associated genes showed better predictive ability of prostate cancer progression than those of published biomarkers. In context of functional enrichment analysis, interestingly candidate disease-associated genes were enriched in the nucleus and different functions were encoded for potential transcription factors, for examples key players as AR, Myc, ESR1 and hidden player as Sp1 which was considered as a potential novel biomarker for prostate cancer.http://www.ibio-cn.org/HPC-PPIN/.The successful results on prostate cancer samples demonstrated that the integrative analysis is powerful and useful approach to detect candidate disease-associate genes and modules which can be used as the potential biomarkers for prostate cancer progression. The data, tools and supplementary files for this integrative analysis are deposited at  Prostate cancer is the second leading cause of morbidity and mortality in men ,2. In reIn the post-genomics era, there is an explosion of biological data and information generated from high-throughput technologies which have rapidly provided an unprecedented multi-level omics data . Such trFocusing on prostate cancer, there were some reports on identifying disease-related gene modules, sub-networks or dysfunctional pathways focused on global characteristics of interactome together with gene expression data by different novel algorithms and methods development -23. NoneIn this study, we therefore aim to reveal candidate disease- associated genes and biomarkers for prostate cancer progression by integrative gene expression profiling and network analysis at a systematic level. We first reconstructed human prostate cancer protein-protein interaction network and used this network as a scaffold for further integrative analysed with gene expression data of prostate cancer. Here, analysis of gene expression profiling of prostate cancer was performed at different disease phases. Through modular analysis, the different modules associated with disease phases were then identified. Last but not least, we could identify significant genes through these modules which were supposed to be the gene expression signatures with highly relevant to specific phases of prostate cancer. Once the common genes identified in each of different modules were overlapped, expectedly these common genes were beneficial for uncovering of novel prostate cancer-related pathways and transcription factors which could be candidate biomarkers for prostate cancer progression. Our study hereby demonstrated a practical workflow for integrative analysis of prostate cancer at the systematic level. For the genome-wide studies, this will be a basic effort for future development and evolution in aspects of the translational biomedical informatics, which ultimately intend to improve patient outcomes and diagnostics with omics dataset through integrative systems biology .The human prostate cancer protein-protein interaction network (HPC-PPIN) was initially reconstructed in order to be further used for integrative analysis as a diagram illustrated in Figure http://www.genome.jp/kegg/pathway.html).For the second type of the dataset, it was the human protein-protein interactions data (Homo sapiens) which was downloaded from the BioGRID database . ConcernThe gene expression profiles based different platform arrays from different stages of prostate cancer  were collected from various laboratories. Table -1) for scoring and ranking [In order to perform modular analysis for study of disease progression, three main steps were necessarily performed. At the first step, the analysed gene expression data previously derived from pairwise disease phase comparison of prostate cancer was integrated with the reconstructed HPC-PPIN. Hereafter core sub-networks analysis and overlapping analysis as second and the third steps were then performed, respectively. Regarding on the core sub-networks analysis, they were investigated for which were shown highly active scores and top ranks based on the greedy algorithm. In this investigation, the greedy algorithm was selected for searching the core sub-networks in a large network of interactions from any pairwise disease phases comparison, where refers to a connected sub-graph of the interactome that has high significance of differential expression values . To elab ranking . Afterwa ranking ,42 was u ranking , yeast S ranking , worm C. ranking  and huma ranking ,46.To finalize the modular analysis, the overlapping analysis was carried out. The overlapping analysis at gene level was applied to show the number of enriched genes shared by all gene expression profiles (see Table 1) and the final core sub-network2 as (CS2). The overlapping percentage between the final core sub-networks was designated as the number of overlapping genes (G) divided by the number of genes in the union of (CS1) and (CS2) with subtracted (G). It can be calculated as follows in following formula (1):in the final core sub-network1 as , three different modules associated with disease progression were eventually identified. It is very possible that each of these three modules plays important roles in dynamic changes of molecular interactions at a specific phase of the disease progression. The identified unique genes in each module were regarded as signatures at a specific phase of prostate cancer. The identified common genes in all three modules were regarded as candidate disease-associated genes.To validate the identified prostate cancer associated genes as putative module biomarker, we used them as a module biomarker to discriminate between control and prostate cancer samples. Support vector machine (SVM) regression proposed by Cortes and Vapnik  was seleFor the validation of candidate prostate cancer associated genes, known related genes obtained from the Cancer Gene Census database   illustrates the distributing numbers of prostate cancer- related genes from different databases with assigned and unassigned in HPC-PPIN.The HPC-PPIN was reconstructed from different prostate cancer databases and other relevant resources along with one directional interaction and repeat interactions removed, hereafter resulting in 5,827 interactions among 1,100 proteins. The characteristics of the reconstructed HPC-PPIN are shown in Table 1Concerning on the annotated functions of HPC-PPIN in DAVID system, we found that the major of GO terms involved in biological process 36.78%) as illustrated in Figure 6.78% as As described in modular analysis for prostate cancer progression , three different modules were obtained. The results are shown in Figure To further elaborate functions of unique gene expression signatures, literature search using PubMed was performed. Our finding clearly showed that unique gene expression signatures play important roles in progression of prostate cancer at a specific phase. For examples, SMAD3 and TGFB2 were reported as androgen- independent prostate cancer-specific genes  which weRegarding on pathway enrichment analysis associated in prostate cancer, interestingly Transcription Androgen Receptor nuclear signaling was found to be the enriched pathway as illustrated in Figure In order to identify candidate disease-associated genes in prostate cancer progression, the gene members in each module were overlapped among three modules. With repeat interactions removed, 94 genes were found as the common members and regarded as the candidate disease-associated genes  and the independent gene expression dataset [To further validate the ability of the candidate disease- associated genes to distinguish cancer samples from controls, the gene expression dataset in series of GSE6919 for prostate cancer obtained from GEO database  nuclear signaling and Epidermal Growth Factor Receptor (EGFR) signalling pathway were clearly shown to be the pathway targets for prostate cancer progression. Transcription factor AR plays an important role in Transcription relationship and acts as a hub for regulating a lot of genes in the Transcription AR nuclear signaling. EGFR signalling regulates cell proliferation, cell differentiation, cell cycle, and cell migration and therefore it has been a potential interested target for effective cancer therapies. Last but not least, we successfully found an interesting transcription factor Sp1 which could be regarded as a potential novel biomarker for prostate cancer. For a future work, we will further study the experimental validation of potential disease genes and pathways during prostate cancer progression.The authors declare that they have no competing interests.YL performed integrative data analysis. WV analyzed the gene expression data. YL and WV wrote the paper. and wrote the paper. BS and LC conceived and designed the overall study and revised the manuscript.Supplementary file 1. All the Supplementary Figures and Tables mentioned in the paperClick here for fileSupplementary file 2. The reconstructed HPC-PPIN with annotated functions is shown in it.Click here for fileSupplementary file 3. A graphic representation of HPC-PPIN by Cytoscape is presented in it. The file is in .cys format which can be opened by Cytoscape software.Click here for file


For every thousand children born in the world, there is one who is born with a hereditary form of hearing loss. In Saudi Arabia, the rate of children affected with sensineuronal hearing loss (SNHL) was estimated to be approximately 26 children out of 1000 [Using a combination of targeted linkage analysis and PCR-sequencing that allows analyzing up to 10 genetic loci associated with SNHL.Our results showed excluding LRTOMT, TMHS and TMIE as major genetic loci underlining SNHL in a cohort of 100 patients from the Western region of Saudi Arabia.As there are over 60 genetic loci responsible of SNHL, traditional methods will prove cumbersome and time-consuming exercise. We are currently applying whole-exome sequencing in order to identify the genetic causes of SNHL.The authors would like to thank the KACST, Riyadh, Saudi Arabia for funding this research Project (ID: ARP-30-304).