Alternative splicing is known to remodel protein-protein interaction networks (“interactomes”), yet large-scale determination of isoform-specific interactions remains challenging. We present a domain-based method to predict the isoform interactome from the reference interactome. First, we construct the domain-resolved reference interactome by mapping known domain-domain interactions onto experimentally-determined interactions between reference proteins. Then, we construct the isoform interactome by predicting that an isoform loses an interaction if it loses the domain mediating the interaction. Our prediction framework is of high-quality when assessed by experimental data. The predicted human isoform interactome reveals extensive network remodeling by alternative splicing. Protein pairs interacting with different isoforms of the same gene tend to be more divergent in biological function, tissue expression, and disease phenotype than protein pairs interacting with the same isoforms. Our prediction method complements experimental efforts, and demonstrates that integrating structural domain information with interactomes provides insights into the functional impact of alternative splicing. Protein-protein interaction networks have been extensively used in systems biology to study the role of proteins in cell function and disease. However, current network biology studies typically assume that one gene encodes one protein isoform, ignoring the effect of alternative splicing. Alternative splicing allows a gene to produce multiple protein isoforms, by alternatively selecting distinct regions in the gene to be translated to protein products. Here, we present a computational method to predict and analyze the large-scale effect of alternative splicing on protein-protein interaction networks. Starting with a reference protein-protein interaction network determined by experiments, our method annotates protein-protein interactions with domain-domain interactions, and predicts that a protein isoform loses an interaction if it loses the domain mediating the interaction as a result of alternative splicing. Our predictions reveal the central role of alternative splicing in extensively remodeling the human protein-protein interaction network, and in increasing the functional complexity of the human cell. Our prediction method complements ongoing experimental efforts by predicting isoform-specific interactions for genes not tested yet by experiments and providing insights into the functional impact of alternative splicing. Protein-protein interaction (PPI) networks  have been extensively studied in systems biology to understand genotype-phenotype relationships and several have been constructed for different model organisms such as human, yeast and bacteria –10. The It is estimated that more than 100,000 AS events occur in pre-mRNA transcripts of human multi-exon genes , 23 and In this paper we present a computational method, named DIIP: Domain-based Isoform Interactome Prediction, that predicts the isoform interactome from an experimentally determined reference interactome, with application to human . StartinWe used the 11,557 DDIs combined from the 3did database of three-dimensional interacting domains  and the From each domain-resolved reference interactome, we predicted an isoform interactome that includes experimentally determined interactions between reference proteins, as well as predicted interactions for the alternative isoforms of these proteins, where the isoform data are obtained from UniProt . We predNext, we focused on protein pairs interacting with the same target protein in the reference interactome, and classified these protein pairs into those interacting with the same subset of isoforms of the same target gene, and those interacting with different subsets of isoforms of the same target gene . The vasWe retrieved Gene Ontology (GO) associations from the UniProt-GOA database  and cons−5 each), but the similarity is not significantly different in cellular component (p = 0.25) , but less similar in molecular function (p < 10−3). However, the difference is not statistically significant in biological process (p = 0.054) and all three GO categories combined (p = 0.71)  tend to share a smaller fraction of disease subnetworks than protein pairs interacting with the same subset of isoforms of the same gene   . Our res−3, two-sided bootstrap test with 100,000 resamplings; −5, two-sided bootstrap test with 100,000 resamplings; −3, two-sided bootstrap test with 1,000 resamplings; Since tissue expression strongly correlates with biological function and disease phenotype , 42, we Here, we present case studies of two types of protein pairs predicted by our method to interact with different subsets of isoforms of the same gene. We first looked at the epidermal growth factor EGF and the growth factor receptor-bound protein GRB2, which were predicted by our method to interact with different subsets of isoforms of the epidermal growth factor receptor EGFR (UniProt ID: P00533) . Our metWe also looked at the protein pair XIAP and APAF1 that were predicted by our method to interact with different subsets of isoforms of CASP9 (UniProt ID: P55211) . Our metOverall, these two case studies highlight specific mechanisms for how AS-mediated remodeling of interactions of the isoforms of the same gene leads to divergence in their biological function and disease phenotype, and illustrate that our computational method is capable of identifying biologically relevant isoform-specific interactions.−5, two-sided Fisher’s exact test), indicating that our prediction method is of high-quality, performing significantly better than random predictions. Since the benchmark experimental dataset may contain errors, the actual TPR of our method is expected to be even higher than the observed TPR.Here, we empirically assessed the quality of our isoform-interaction prediction method by validating it against the experimental dataset of Yang et al. (2016) , which iOur computational method for predicting isoform-specific interactions is reliable because it does not aim to predict new PPIs from scratch. Rather, it starts with the annotation of experimentally determined PPIs with known DDIs, and predicts the retention or loss of PPIs based on the retention or loss of the annotated DDIs in different isoforms. All these individual steps are expected to generate high-quality predictions; thus our overall isoform-interaction predictions are expected to be of high quality as well. Indeed, the high quality of our predictions is confirmed by experimental validations. At the same time, our method is limited in that it can only predict isoform interactions from known reference PPIs. If a reference protein does not have any interactions in the reference interactome, it is not possible to predict interactions for any of its alternative isoforms. Therefore, the size of the isoform interactome predicted by our method is ultimately constrained by the size of the reference interactome.Since our method predicts isoform interactions based on experimentally determined interactions between reference proteins, it is important to ensure the high quality of these experimentally determined interactions. Interactions in the HI-II-14 reference binary interactome are of high-quality since they were subjected to multiple screening and several other quality-control measures. On the other hand, interactions in the IntAct reference interactome were curated from different sources with varying quality. Furthermore, IntAct may contain indirect interactions between proteins in the same complex. To ensure the high quality of the IntAct-derived interactions, we only included physical interactions in IntAct reported by at least two independent experimental studies. In addition, our DDI-annotated interactome significantly enriches for direct binary interactions and filters out indirect interactions, as indirect interactions are much less likely to be annotated with DDIs than direct binary interactions.For reference PPIs with multiple DDI annotations, our method imposes a strict requirement that for an interaction to be lost, all DDI annotations of that interaction must be lost, otherwise the interaction is retained. Assuming that the interactivity of different domains is independent of each other within the same protein, this strict requirement maximizes the accuracy of predicted lost interactions, at the cost of possibly reducing the accuracy of predicted retained interactions. It should be noted that this is not a significant problem in our study, as about half of the PPIs in the HI-II-14 and IntAct domain-resolved interactomes have only one DDI annotation .While our predicted isoform interactomes reveal extensive network remodeling by AS, the ratio of remodeled protein pairs  is different in the two predicted isoform interactomes . This difference is due to systematic differences in the types of interactions reported in the two reference interactomes and in the fraction of protein hubs between the two domain-resolved interactomes. Since proteins with significant medical and scientific interests are intensely studied in the literature, protein hubs are much better annotated with domains in the literature-curated IntAct interactome than in the systematic HI-II-14 interactome. Indeed, protein hubs with vertex degree >23 (~5% of proteins in both binary interactomes) are 3.4 times more likely to have a domain-annotated interaction in the IntAct interactome than in the HI-II-14 interactome, whereas average proteins are only 1.6 times more likely to have a domain-annotated interaction in the IntAct interactome than in the HI-II-14 interactome. As a result, the fraction of protein hubs is significantly larger in the IntAct domain-resolved interactome than in the HI-II-14 domain-resolved interactome . A larger fraction of protein hubs in the IntAct domain-resolved interactome is the major cause for the observed larger ratio of remodeled protein pairs in the IntAct isoform interactome, since protein pairs interacting with different subsets of isoforms of the same gene can only be created by isoforms that lose some but not all interactions (which is more likely to occur for isoforms of a protein hub with many interactions), and cannot be created by isoforms that lose all interactions at once (which is more likely to occur for isoforms of a protein non-hub with few interactions). Indeed, the observed difference  in the ratio of remodeled protein pairs between the two isoform interactomes is almost completely eliminated  upon the removal of all protein hubs with vertex degree >5. This observed difference does not significantly bias our isoform-specific interaction predictions for the following reasons: we predict isoform-specific interaction retention/loss one isoform at a time based on sequence information only; no significant differences are observed between the two isoform interactomes (IntAct and HI-II-14) in terms of the fraction of isoforms per gene losing at least one interaction (8.2% and 8.6%) and the fraction of isoform pairs per gene with different interaction profiles (14.2% and 15.3%); we only draw conclusions from comparing protein pairs in terms of their biological properties, and from observations that are consistent between the two isoform interactomes.Detailed sequence information is available for some reference proteins, but not others. When the precise sequence information is not available, we chose the reference isoform designated by UniProt to represent each reference protein in the reference interactomes. These UniProt-designated reference isoforms are typically either the longest isoform (~88%) or the most prevalent isoform; hence they are most likely the ones used in different experimental studies to map the reference interactions. In the unlikely event that some reference proteins in the reference interactomes are represented by a different isoform than the one designated by UniProt, our method only requires the knowledge that a reference PPI exists, and can still work well without knowing the exact sequence of the reference protein. Given the experimental knowledge that a reference PPI exists, our method predicts that an alternative isoform loses this interaction if and only if the isoform loses all possible interacting domains present in the UniProt-designated reference isoform with typically the longest sequence, which in half of the cases is just one interacting domain. Hence, our predictions of interaction loss and retention remain very reasonable even in the presence of minor discordances in reference isoform annotations.In summary, we developed a domain-based computational method for predicting an isoform interactome from a reference interactome by integrating structural domain information with experimentally determined interactions. Our predictions reveal extensive remodeling of the human interactome network by AS: ~22% of genes with two or more isoforms in the predicted isoform interactome have at least one isoform losing an interaction, and ~18% of isoform pairs encoded by the same gene in the isoform interactome network have different interaction profiles. Our isoform-interaction prediction framework is of high quality as it performs significantly better than random predictions when assessed by experimental data. In addition, our predicted isoform interactome is larger and probes a different part of the isoform space than the experimental isoform interactome of Yang et al. (2016) . In term−5. After annotating each protein in the reference interactome with its structural domains, each PPI in the reference interactome was annotated with a DDI if one of the interacting proteins was annotated with an interacting domain of the DDI and the other interacting protein was annotated with the other interacting domain of the same DDI. Only PPIs annotated with at least one DDI were included in the domain-resolved interactome.Domain-domain interactions (DDIs) were retrieved from the 3did Database of Three-Dimensional Interacting Domains   associations from the UniProt-GOA database  (retrievhttp://www.orpha.net), CTD [We retrieved gene-disease associations from the DisGeNET database , 40 (retet), CTD , and theet), CTD . DiseaseWe used the RNA-Seq dataset of Illumina Body Map 2.0   , which crom 3did  and DOMIrom 3did  were thehttp://bioinfo.lab.mcgill.ca/resources/diip. The code used for predictions and analysis is available at the following URL: http://github.com/MohamedGhadie/isoform_interactome_prediction.We have created a web tool called “DIIP: Domain-based Isoform Interactome Prediction” that allows users to query our predicted isoform interactome for isoform-specific interactions of a protein of interest. In addition, our web tool gives users a second advanced option to predict isoform-specific interactions using our isoform interactome prediction method (DIIP) from interactions provided by the user. Interactions provided by the user do not need to be part of our predicted isoform interactome. The web tool can be accessed at the following URL: S1 TableAll interactions in the HI-II-14 domain-resolved reference interactome with their DDI annotations.(XLSX)Click here for additional data file.S2 TableAll interactions in the IntAct domain-resolved reference interactome with their DDI annotations.(XLSX)Click here for additional data file.S3 TableAll known and predicted interactions in the predicted HI-II-14 isoform interactome. Known interactions are the experimentally observed interactions also belonging to the HI-II-14 domain-resolved interactome. Predicted interactions are isoform interactions predicted to be either retained or lost.(XLSX)Click here for additional data file.S4 TableAll known and predicted interactions in the predicted IntAct isoform interactome. Known interactions are the experimentally observed interactions also belonging to the IntAct domain-resolved interactome. Predicted interactions are isoform interactions predicted to be either retained or lost.(XLSX)Click here for additional data file.


Cytomegalovirus (CMV) infection is a potentially life-threatening complication after hematopoietic cell transplantation (HCT). The potential availability of novel preventive therapies was therefore an important topic during European Congress of Clinical Microbiology and Infectious Diseases (ECCMID) 2017.A member of the β-herpesvirus family, CMV is a ubiquitous pathogen that is usually acquired in childhood or early adult life through contact with body fluids. After primary infection, the virus becomes latent and rarely causes symptoms in healthy individuals. However, in immunocompromised hosts, CMV infection can lead to invasive disease in almost any tissue, and represents one of the most common and significant complications after allogeneic HCT.Although CMV disease-related mortality has declined due to improvements in diagnosis and the use of pre-emptive antiviral treatment, CMV infection has consistently been reported to adversely affect outcomes in allogeneic HCT recipients admitted to the intensive care unit. It is an independent predictor of both in-hospital mortality and of mortality at 12 months (p = 0.005) in HCT patients who survive intensive care unit admission ), pneumonia, the central nervous system and the liver and other vital organs ,4. The iThe adverse direct and indirect effects of CMV infection are reflected in healthcare costs, Professor Ullman noted. He described a study in the USA that compared post-transplant treatment costs in 124 HCT recipients, 119 (89%) of whom were at risk of CMV due to donor or recipient seropositivity. CMV reactivation was diagnosed in 90 75.6%) of these patients at a median of 30 days (range 8–105) post-transplant. Compared with patients who did not require pre-emptive treatment, the treated group incurred additional costs of antiviral medication and longer hospital stays of $58,000–74,000  per patient within the first 6 months of HCT [.6% of thAccording to Professor Jan Styczynski (Poland), the most important risk factor for CMV infection after allogeneic HCT is the serological status of the donor and recipient. Without prophylaxis, about 80% of CMV-seropositive patients develop CMV reactivation, and about 30% of seronegative recipients with a seropositive donor develop primary infection. Other risk factors for CMV infection after allogeneic HCT include treatment with high-dose corticosteroids, T-cell depletion, acute and chronic GvHD and the use of mismatched or unrelated donors .In the past, CMV infection was most frequently diagnosed between engraftment and day 100. Following the introduction of pre-emptive antiviral therapy, the prevalence of early CMV disease declined. However, HCT recipients are now increasingly at risk of CMV infection in the later post-transplant period. According to Professor Styczynski, this is a cause for concern, since patients may now no longer be under the care of their specialist center when they develop CMV infection and this may lead to an increased risk of CMV disease later after HCT .The risk of late CMV reactivation is known to be higher in patients with delayed CMV-specific T-cell immune reconstitution following HCT . During Based on a positive change in spot counts (SPCs) for both IE-1 and pp65 (defined as week 4 SPCs greater than pretransplant SPCs), the negative predictive value for the development of first CMV reactivation was 80.0 and 83.7%, respectively. According to Professor Chemaly, while these results are preliminary, they suggest that assessing CMV-specific cell-mediated immunity at screening and 4 weeks post-HCT may in future help to guide personalized management by determining the degree of protection against CMV reactivation .Isolation of CMV by standard or rapid-culture techniques is now rarely used for monitoring of HCT patients. Guidelines recommend using quantitative nucleic acid testing (most commonly DNAemia), followed by initiation of pre-emptive antiviral therapy at first detection of CMV reactivation or primary infection . ProfessIntravenous ganciclovir is currently first line for both pre-emptive treatment and prophylaxis of CMV in HCT recipients. Ganciclovir and its oral prodrug valganciclovir are acyclic guanosine analogs, activated via a multistep triphosphorylation process catalyzed by the UL97-encoded viral kinase. Professor Michael McVoy (USA) reported that, when used in prophylaxis, ganciclovir reduces the risk of CMV infection and disease compared with placebo, but does not improve overall survival ,12.Foscarnet is a second-line treatment generally reserved for patients unresponsive to ganciclovir because of drug resistance. It is a pyrophosphate analog that inhibits viral DNA polymerase by binding to the pyrophosphate-binding site. Cidofovir is an alternative second-line therapy that is generally used for treatment-resistant CMV strains. It is a competitive inhibitor of viral DNA polymerase, resulting in early chain termination during DNA synthesis .Professor McVoy noted that all antiviral drugs currently used for pre-emptive and prophylactic treatment of CMV target DNA polymerase in order to inhibit DNA synthesis. These drugs are also all associated with significant toxicities, including myelosuppression (ganciclovir) and nephrotoxicity (foscarnet and cidofovir). In his view, such toxicities make these drugs unsuitable for prophylaxis except for high-risk HCT patients, but novel therapies – some with different modes of action – are in development and on current evidence appear to be associated with less toxicity.Brincidofovir is an oral prodrug of cidofovir. When given twice weekly as CMV prophylaxis in a double-blind dose-escalation study, the most common adverse event associated with brincidofovir was diarrhoea; there were no reports of myelosuppression or nephrotoxicity . In the According to Professor Johan Maertens (Belgium), the failure of SUPPRESS might be due to difficulty in differentiating between diarrhea associated with brincidofovir and the symptoms of GvHD. An intravenous formulation, which has the potential to reduce the likelihood of gastrointestinal adverse effects, is now in early development for CMV prophylaxis and treatment .Maribavir is an oral antiviral drug that prevents exit of new CMV virions from the nucleus by selectively inhibiting UL97-mediated phosphorylation of nuclear Lamin A/C . In a PhProfessor Maertens considered that the explanation for the negative outcome of the Phase III maribavir study might lie in the use of the rigorous end point of CMV disease in an era of pre-emptive antiviral treatment. There is also the possibility that a higher dose of maribavir might be needed for effective CMV prophylaxis. Maribavir is now being investigated in the pre-emptive treatment of CMV infection in HCT and solid-organ transplant recipients .Letermovir is a terminase inhibitor with a unique mechanism of action. It interacts with the pUL56 viral terminase subunit complex, preventing cleavage of long DNA concatemers and leading to production of noninfectious particles . In the During ECCMID 2017, Professor Chemaly reported in detail on the safety and tolerability of letermovir in the Phase III study. There was no evidence of letermovir-associated myelotoxicity or nephrotoxicity, and there was no difference between letermovir and placebo in the risk of GvHD, diarrhea, nausea, pyrexia, rash and vomiting. Adverse events more commonly seen in letermovir-treated patients compared with placebo were cardiac disorders (12.6 vs 6.3%), ear/labyrinth disorders (4.6 vs 1.0%), hyperkalemia (7.2 vs 2.1%), myalgia (5.1 vs 1.6%) and dyspnea (8.0 vs 3.1%). Most of these adverse events were mild or moderate, were not treatment related as assessed by investigators and did not lead to treatment discontinuation [Letermovir has been granted orphan designation by the European Medicines Agency, the US FDA and the Japanese Ministry of Health, Labor and Welfare for the prevention of CMV infection and disease in at-risk populations. The drug also has been granted Fast Track designation by the FDA.ASP0113 is a bivalent CMV DNA vaccine consisting of two plasmids, VCL-6368 and VCL-6365. It is currently in clinical trials involving allogeneic HCT recipients, kidney transplant recipients and dialysis patients. Professor Maertens reported that first results are expected in late 2017 of a global Phase III study in CMV-seropositive allogeneic HCT recipients (NCT01877655).Modulation of the immune system’s ability to manage CMV is an attractive option, and human CMV-specific monoclonal antibodies are now being investigated for their potential in CMV treatment. CSJ148 is a combination of two anti-CMV human monoclonal antibodies (LJP538 and LJP539) that bind to and inhibit the function of viral CMV glycoprotein B and the pentameric complex. When administered to healthy volunteers, CSJ148 was safe and well tolerated with expected pharmacokinetics . A PhaseAlthough the risk of CMV disease has fallen following allogeneic HCT, patients remain at risk of the direct and indirect effects of infection with the virus. The toxicity of current antiviral drugs means that CMV prophylaxis is at present limited to high-risk patients, and most HCT recipients receive pre-emptive therapy initiated when CMV infection is detected. The development of less toxic therapies may herald a future change in clinical practice from pre-emptive therapy to prophylaxis of CMV and its complications in HCT recipients and other at-risk groups of patients.


Several lines of evidence indicate one’s age at exposure to radiation strongly modifies the risk of radiation-induced breast cancer. We previously reported that rat mammary carcinomas induced by pre- and post-pubertal irradiation have distinct gene expression patterns, but the changes underlying these differences have not yet been characterized. The aim of this investigation was to see if differences in CpG DNA methylation were responsible for the differences in gene expression between age at exposure groups observed in our previous study. DNA was obtained from the mammary carcinomas arising in female Sprague-Dawley rats that were either untreated or irradiated  during the pre- or post-pubertal period (3 or 7 weeks old). The DNA methylation was analyzed using CpG island microarrays and the results compared to the gene expression data from the original study. Global DNA hypomethylation in tumors was accompanied by gene-specific hypermethylation, and occasionally, by unique tumor-specific patterns. We identified methylation-regulated gene expression candidates that distinguished the pre- and post-pubertal irradiation tumors, but these represented only 2 percent of the differentially expressed genes, suggesting that methylation is not a major or primary mechanism underlying the phenotypes. Functional analysis revealed that the candidate methylation-regulated genes were enriched for stem cell differentiation roles, which may be important in mammary cancer development and worth further investigation. However, the heterogeneity of human breast cancer means that the interpretation of molecular and phenotypic differences should be cautious, and take into account the co-variates such as hormone receptor status and cell-of-origin that may influence the associations. Exposure to radiation, either accidentally or for medical reasons, is associated with an increased incidence of breast cancer , 2 and sChanges in gene expression in normal tissues are regulated at several levels such as through DNA methylation; histone modifications and other chromatin marks; and, the activity of transcription factors, repressors and other DNA-binding proteins . DNA metHere, our previously reported set of rat mammary carcinomas induced by pre- or post-pubertal irradiation were re-examined for changes in genome-wide DNA methylation patterns. The results indicate that although all of the radiation-induced tumors show dramatic changes in DNA methylation from the underlying normal mammary tissue, there are changes in CpG island DNA methylation characteristic of the age at radiation exposure which correspond to previously identified differences in gene expression. However, most of the gene expression differences identified were not associated with significant changes in DNA methylation at nearby CpG islands, implying other regulatory mechanisms are responsible for the majority of the age-specific gene expression signature.137Cs source  or were not treated. All rats were fed a standard laboratory animal diet  until 9 weeks of age and then switched to AIN-76A diet containing 23.5% corn oil (Clea Japan) to potentiate mammary tumor development. Each rat was palpated weekly to detect mammary tumors. Spontaneous mammary carcinomas were obtained from the non-irradiated rats. Detailed information of the mammary tumors and normal mammary tissues is shown in All tissues were taken from a previously reported animal experiment . Brieflywww.ncbi.nlm.nih.gov/geo) under accession number GSE62383. Briefly, genomic DNA was isolated from tumor tissues by proteinase K digestion followed by phenol/chloroform extraction. After sonication to produce DNA fragments of 200–600 bp, the MethylMiner Methylated DNA Enrichment Kit  was used to enrich methylated DNA from one half of the DNA sample while the other half acted as the reference DNA. Enriched and reference DNA fragments were fluorescently labeled using the Agilent Genomic DNA Labeling Kit , before purification and competitive hybridization using the Agilent DNA Hybridization Kit (Agilent) for 40 h to a 105K rat CpG island microarray  covering 96% of regions  that are annotated as CpG islands in the rat genome . The fluorescence data extracted from the image of the scanned slides was analyzed using the Agilent Genomic Workbench software (version 6.5).The method and the CpG island DNA methylation microarray data described here have been deposited in the Gene Expression Omnibus , the mean difference in relative gene expression between the groups was also applied to select candidates where CpG island DNA methylation differences corresponded to functional changes in gene expression (no bias was applied for any assumed direction of the correlation between methylation and gene expression). Univariate ANOVA was used to model the effect of age-at-exposure  and/or PR status (positive versus negative) on the LogOdds scores for probes which were initially identified as having age-dependence, in order to detect interactions or co-variate effects. Functional analysis of candidate gene lists was performed using the GeneMANIA online tool (available at: http://www.genemania.org/), using a false discovery rate threshold of P < 0.05.In the analysis algorithm, the degree of enrichment (or de-enrichment) by the methylation-enrichment step was compared to the overall distribution of enrichment for probes of the same melting temperature to calculate the probability that the CpG island represented by each probe was methylated or unmethylated. This was expressed ultimately as the log of the odds ratio , such that a LogOdds score of 0 is equally likely to be methylated/unmethylated, while large absolute values are increasingly likely to be methylated or unmethylated . To identify significant differences in DNA methylation, independent samples T-tests for the mean of the LogOdds between the various groups were performed probe-wise, with a further minimum difference in the mean LogOdds between the groups applied . Where multiple probes were annotated to a single gene, only a single significant probe was necessary to include the gene as a candidate. The various ion data , availabGenomic DNA was modified with sodium bisulfate using Chemicon’s CpGenome Fast DNA Modification Kit (Merck Millipore) according to the manufacturer’s instructions. Bisulfite-modified DNA was amplified by PCR using an Epitaq Kit . The genomic region and primers used for bisulfite DNA sequencing are shown in n = 7; post-pubertal irradiation, IR-7W, n = 7; spontaneous, n = 7) plus a small set of normal mammary gland tissues (n = 3) to indicate a baseline methylation level for the purposes of identifying global methylation trends in the tumors. Examination of DNA methylation in the whole set of 21 mammary tumors compared to normal mammary gland tissue revealed a general loss of methylation across the whole population of CpG island probes, consistent with the global hypomethylation frequently observed in cancer , and many of the genes associated with these CpG islands (selecting the quartile with the largest increases in LogOdds scores) were found to have >2-fold changes in gene expression levels  between the two age-at-exposure groups . The LogOdds for CpG islands using the DNA methylation array were shown to be correlated with the DNA methylation level as assessed by bisulfite-sequencing using 4 different genes spanning a wide-range of methylation levels; and, comparison of the CpG island methylation levels measured here and the previously published gene expression levels showed that the candidate methylation-regulated genes exhibited different strengths and direction of the correlation consistent with the variable role of methylation in overall transcriptional control  were very similar, with mean differences in methylation scores across all probes centered on zero (data not shown). However, there was still a group of genes which showed large, statistically significant changes  between pre- and post-pubertal tumors. Parallel analysis excluding the single tumor with marked hypermethylation across the genome confirmed that the statistical significance threshold used was already sufficient to exclude changes which might have been due to large changes in this single sample.Epha7 (1/4 significant probes), Timp2 (4/6 significant probes), and Reln (1/1 significant probe) which had probes that were significant for both age and PR status. None of the candidate probes showed a significant interaction between age and PR.Given that the balance of hormone receptor status of the tumors was known to be different between the two age-at-exposure groups, a univariate ANOVA including both PR status and age-at-exposure was conducted on these candidate genes to identify whether the DNA methylation differences were associated only with the irradiation age, only with the PR status, or both. Since none of the tumors arising after irradiation at 7 weeks of age were ER negative, it was not possible to separate the effects of age and ER status. All significant probes for the 30 candidate genes were significant only with age, except for Foxa2, Hmga2, Lef1, Msx2, Pdgfra, Tbx3, Zeb2) plus Vegfa also invoking additional gene ontology terms such as stem cell proliferation, branching morphogenesis, transcription factor regulation and muscle development  that were enriched for cellular differentiation, development and transcription factor activity. This is consistent with the idea of global demethylation coupled with specific hypermethylation of genes which are important in establishing or maintaining tumor growth , 34.Our selection criteria for age-specific methylation was chosen to limit the list to those genes with clear and large differences in the methylation levels for at least one CpG island probe, where we might expect the CpG island to have switched from methylated-to-unmethylated or vice versa . At thisP < 0.05), the expanded candidate list would still only have covered less than 10% of the >2-fold gene expression differences. Together, these data suggest that other regulatory mechanisms such as microRNA, chromatin and histone changes, transcription factor binding etc. might underlie the differences in the tumor phenotypes. So far, no obvious candidate signaling or transcription factors have been identified based on the relationships between the differentially expressed genes.Our hypothesis was that the set of genes which were differentially expressed between the tumors arising after pre- or post-pubertal irradiation might be the result of differing DNA methylation that might have existing in the cell of origin prior to tumor initiation, or that might have arisen in the changing microenvironment during puberty. The fact that only 2% of the genes with gene expression differences between the age groups were in our list of differentially-methylated genes suggests that DNA methylation is not a good explanation for the bulk of the observed differences. At the most relaxed DNA methylation criteria for the difference in the mean methylation levels between the 3 week and 7 week groups  are able to change gene expression patterns in human cancer [The 30 genes that were in the final list of candidates consisted of 8 genes which were classified within multiple, overlapping gene ontology terms which were significantly enriched  while the remaining majority of the genes were not part of enriched gene ontology categories. It is possible that the differentially-methylated genes are related in a non-functional sense, i.e. they may share common genomic position or association with chromatin marks; however, even with our strict criteria the list may include false-positive changes that do not represent genuine age-related differences. One possible example is r sample . Alternaansition , 38 and t cancer . In additastasis , 41 and tastasis , 43. Furn cancer , 45. ThuS1 Fig(DOCX)Click here for additional data file.S2 Fign = 3) and radiation-induced (n = 10) tumors are shown for the genes which were selected as candidates showing significant differential DNA methylation . The 44 genes which showed >2-fold changes in gene expression are shown within the grey boxed areas at the top (increased expression in radiation-induced tumors) and bottom of the plot (decreased expression in radiation-induced tumors).The mean gene expression levels (± standard error) in normal mammary gland ((TIF)Click here for additional data file.S1 Table(XLS)Click here for additional data file.S2 Table(XLS)Click here for additional data file.S3 Table(XLS)Click here for additional data file.


Pseudomonas aeruginosa strain PA14 and an updated, expanded reconstruction of P. aeruginosa strain PAO1. The PA14 reconstruction accounts for the activity of 112 virulence-linked genes and virulence factor synthesis pathways that produce 17 unique compounds. We integrate eight published genome-scale mutant screens to validate gene essentiality predictions in rich media, contextualize intra-screen discrepancies and evaluate virulence-linked gene distribution across essentiality datasets. Computational screening further elucidates interconnectivity between inhibition of virulence factor synthesis and growth. Successful validation of selected gene perturbations using PA14 transposon mutants demonstrates the utility of model-driven screening of therapeutic targets.Virulence-linked pathways in opportunistic pathogens are putative therapeutic targets that may be associated with less potential for resistance than targets in growth-essential pathways. However, efficacy of virulence-linked targets may be affected by the contribution of virulence-related genes to metabolism. We evaluate the complex interrelationships between growth and virulence-linked pathways using a genome-scale metabolic network reconstruction of  P. aeruginosa strain PA14 and update that of strain PAO1, which, together with mutant screens, enable them to identify genes uniquely critical for virulence factor production.Targeting virulence rather than bacterial growth is less likely to select for antibiotic resistance, but many possible targets function in both processes. Here, the authors reconstruct a genome-scale metabolic network of  There is a need for new drugs that effectively inhibit microbial infection while avoiding the development of resistance. Traditional antibiotics that inhibit growth of bacteria by targeting growth-essential functions actively select for antibiotic-resistant mutants that overtake the infection. This growth-based selection promotes the rapid development of resistance and consequently exacerbates infections13Virulence-linked genes contribute to survival and fitness within a host. Many of these genes encode the synthesis pathways of VFs, pathogen-produced small molecules that are involved in activities such as iron sequestration and bacterial communication that enable adaptation to the host environment and enhance infection potential5To study the relationship between VF synthesis and growth from a systems level perspective, we used genome-scale metabolic network models (GEMs). Assembled from annotated genomic data, GEMs are mathematical frameworks that incorporate biochemical, genetic and cell phenotypic data and account for hundreds to thousands of gene-protein-reaction (GPR) relationships and reaction stoichiometry and directionalityPseudomonas aeruginosa strain PA14 (iPau1129) as well as an updated GEM of reference strain P. aeruginosa PAO1 (iPae1146). P. aeruginosa is a Gram-negative opportunistic pathogen capable of developing multi-drug antibiotic resistance, hospital-acquired infections13141617in silico gene knockouts on synthesis of biomass versus 17 VFs and identified genes uniquely critical for VF production, genes solely important for the synthesis of biomass, as well as genes involved in both VF production and biomass production. A case study of the VF pyoverdine shows the utility of GEMs in probing network dependencies that offer novel insights into links between virulence and metabolism that may enhance design of cycled or combination drug therapies as well as reduce the development of resistance.Here, we present a new GEM of P. aeruginosa strain PAO1 (iPae1146) as well as a new GEM of P. aeruginosa strain PA14 (iPau1129) . The network reconstruction process began with previous P. aeruginosa PAO1 GEMs  on different minimal media. After extensive transport reaction curation and refinement of metabolic pathways, mPA14 and mPAO1 account for 91 and 93 carbon sources and predict utilization with accuracies of 81% and 80%, respectively .et al.et al.in silico single gene knockouts in our models and measured the subsequent effects on biomass production. Ultimately, both mPA14 and mPAO1 can be used to predict gene essentiality with an accuracy of 91% , essentiality for growth, or essentiality for both7aro operon genes contributing to aromatic amino acid synthesis are essential for the production of six VFs in the phenazine and siderophore families, while an array of genes involved in purine metabolism fully inhibit only the production of A-band-O-antigen. Ultimately, this analysis provides a novel list of genes ranked by their impact on virulence pathways in addition to growth inhibition, which may assist the design of therapeutics with broad impact on metabolic processes.To study the role of genes critical to both growth and VF synthesis, we compared the 116 genes predicted by mPA14 as essential for growth on SCFM to the genes essential for synthesis of VFs and found that 46 of the growth-essential genes are also essential for the production of at least one VF. These 46 genes critical to both biomass production and virulence are listed in x axis) and VF inhibition (y axis) relative to wild-type for a given in silico knockout. All data points are transparent such that a high density of data points results in an increase in colour intensity. Thus, the colour intensity at the origin of the plots indicates a high number of gene deletions that have no effect on production of either biomass or the indicated VF. Data points in the upper right corner of each plot represent genes essential to both VF production and growth, while data points arrayed between axes indicate the degree of biased impact on growth versus VF production by a given knockout.We then expanded our analysis to all genes in our model, plotting inhibition of each VF versus growth . Each pox axis. This result was mirrored for VF synthesis, with most gene knockouts also resulting in marginal or no VF production defects. We hypothesized that VF synthesis would be less robust to perturbation as compared to growth because these compounds rely on the catabolism of growth substrates prior to VF anabolism. We instead see that for several VFs, many genes essential for growth only partially inhibit synthesis when disrupted. The number of genes essential solely for the production of a given VF varies considerably, and is not always correlated with the complexity of the synthesis pathway or final compound. These results highlight critical differences in the degree of interconnectivity of VF synthesis and biomass production across the VFs, which we can evaluate mechanistically through the use of our computational model.This analysis enabled the identification of non-obvious relationships between growth and VF production. Unsurprisingly, most gene knockouts resulted in marginal or no growth defects, as indicated by data point clusters near the origin along the P. aeruginosa. While this integration of VF and biomass synthesis is expected for lipid A given its presence in the biomass reaction in the model as an essential component, this was a surprising result for chorismate and 1-carboxyphenazine as we do not consider these essential components. Upon closer network inspection, we find alternative pathways for the production of chorismate and multiple isozymes for the synthesis of 1-carboxyphenazine. Both instances highlight redundancies in the network that reduce the occurrence of predicted essential genes unique to these two VFs.VFs that are less sensitive to genetic perturbations than biomass production may have a high degree of redundancy in their synthesis pathways. For example, relatively few genes impact lipid A, chorismate and 1-carboxyphenazine production without also impacting growth and no gene is essential solely for the production of the respective VF. Instead, genes that are essential for VF production are also essential for growth, thus indicating the high level of integration of VF synthesis with the overall metabolism of Interestingly, B-band-O-antigen and A-band-O-antigen demonstrate the other extreme—all growth essential genes also impact the synthesis of both O-antigens to some extent. This case highlights the dependency of the production of these O-antigens on some of the biomass components themselves, namely lipid A. Since the O-antigens rely on the production of lipid A, all the genes that inhibit the synthesis of lipid A  also inhibit the synthesis of the O-antigens. This result indicates the importance of biomass function formulation; here, we retain a standard list of components for consistency with other models, but more targeted analyses may improve upon addition and removal of less integral biomass components.P. aeruginosa. VFs like alginate may be more peripheral to the general metabolic function of P. aeruginosa due to specialization. While in vivo studies have highlighted the importance of these metabolites in maintaining infections313233In contrast to these VFs, there were several VFs that displayed much less interconnectivity with the rest of the network. For example, relatively few genes maximally inhibit both growth and alginate production compared to the other VFs in the model. Most of the genes that are essential for alginate production have no impact on growth when the associated function is removed from the model, thus indicating that the synthesis pathway for alginate is less highly integrated into the metabolism of hom, encoding for homoserine dehydrogenase, is removed, there is a very slight impact on growth and only marginal impact on each of the VFs, with pyoverdine synthesis demonstrating the most inhibition as a result of a hom knockout. Similarly, while a functional disruption of pvdA, which encodes for L-ornithine N5-oxygenase, maximally inhibits pyoverdine production, it has no impact on synthesis of the other VFs accounted for in mPA14.In addition to studying the interconnectivity of individual VF synthesis pathways, this analysis also enabled the investigation of the role of individual genes. While the disruption of some gene functions results in a similar response across the VFs, other gene function disruptions produce a highly varied response, suggesting that these genes play a unique role in the synthesis of each VF  and enable easy comparison with a large collection of models created and curated by other groups125960Previously published iterations of 056 ref.  and iMO1056 ref.  were botP. aeruginosa PAO1 and P. aeruginosa PA14 and two closely related pathogens from the Burkholderia cepacia complex were compared to assist development of new, reconciled GEMs for each strain from previously built models. P. aeruginosa PA14 is a primary clinical isolate that is used as a model strain due to its substantial virulence in a variety of hosts, while P. aeruginosa PAO1, a wound isolate, is the main reference strain of this speciesBurkholderia species specifically because of their similarities to Pseudomonas as opportunistic Gram-negatives that also chronically infect cystic fibrosis patients and share similar virulence mechanisms. We also previously built and extensively curated GEMs for these species in modelSEED syntax as described further below, making them useful resources. P. aeruginosa PA14, P. aeruginosa PAO1, Burkholderia cenocepacia J2315 and Burkholderia multivorans ATCC17616 were compared using genome-scale reciprocal BLASTP with an E-value cutoff of 0.01 with no low-complexity filter using CLC Main Workbench . Hits with E-values below 1E-40 were considered high confidence hits and automatically matched. Genes with hits that received a higher E-value score were manually evaluated based on predicted function, gene descriptions and PseudoCAP category (custom system/pathway annotations) on the PGD .Updated, strain-specific biomass formulas were created using a field standard approach that approximates the biomass composition by accounting for DNA, RNA, protein, cell wall components, lipids and organism-specific compounds whose production is required for growth9Burkholderia species. We built on prior curation efforts while maintaining consistent modelSEED syntax to enable future cross-species comparisons and community modelling. If SEED reactions in Burkholderia models were not present in the new Pseudomonas SEED model, the high confidence BLASTP results were used in conjunction with the PGD and Burkholderia Genome DatabasePseudomonas models (from only the PM1 substrate set to both PM1 and PM2a substrate sets); this effort was guided by previous work we performed for the highly catabolically flexible BurkholderiaBurkholderia pathways as a guide. Other new reactions were added to expand lipid metabolism pathways using literature regarding Pseudomonas-specific lipid composition and the increased specificity of SEED reactions in this subsystem. Reactions implemented in the other well-curated SEED model available during our build work, B. subtilis iBsu1103, as well as reactions included in the MetaCyc and MetRxn databases were also used as resources5969To fill gaps and improve predictions, additional model components were first derived from iMO1086 and recently published GEMs of Models were validated using new, comprehensive assessments of experimental data from genome-scale transposon libraries and carbon utilization screening. Similar data had been used with prior models, but unexpected discrepancies identified in comparisons between PAO1 and PA14 measurements motivated careful re-assessment of data sets and experimental confirmation of results.in silico deletions of single genes while optimizing for production of biomass using flux balance analysis via the COBRA ToolboxP. aeruginosa PAO1 . Production levels were normalized by maximum possible production of a component under wild-type conditions for comparison within VFs.https://inkscape.org/en/).mPA14 was visualized using a command line implementation of MetDrawP. aeruginosa PAO1 and PA14 and PA14 single gene knock-out mutants from the PA14 non-redundant genome-scale transposon library−1 gentamycin as necessary at 37 °C with aeration for liquid cultures.Wild-type strains of To measure pyoverdine production, strains were grown in synthetic cystic fibrosis mediaP. aeruginosa PAO1 and PA14, iPae1146 and iPau1129, respectively, are provided in spreadsheet format (http://bme.virginia.edu/csbl/Downloads1-pseudomonas.html). The lab website also includes files detailing the development of strain-specific biomass formulas. Experimental data sets are available by request to the corresponding author.The new metabolic network reconstructions for t format  that incHow to cite this article: Bartell, J. A. et al. Reconstruction of the metabolic network of Pseudomonas aeruginosa to interrogate virulence factor synthesis. Nat. Commun.8, 14631 doi: 10.1038/ncomms14631 (2017).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary figures.Curation of putative proteins added to mPA14 and mPAO1We present information on every gene included in the models with hypothetical, putative, and probable annotations in the Pseudomonas Genome Database as well as genes which we explicitly use for an alternate function than that annotated. We show how these genes were implemented functionally in the model and include notes and any relevant literature supporting our curationdecisions.Integration of virulence-linked genes from the Pseudomonas Genome DatabaseWe present our reconciliation of the lists of virulence-linked genes from PAO1 versus PA14 from the Pseudomonas Genome Database. The Virulence-linked genes spreadsheet includes mainly data provided by the PGD for each gene including database or study source and infection model. We organized this information such that genes matched between strains are paired. We used both BLASTP, manual evaluation of similar operons, and literature in creating these pairings - genes missing in one strain genome are noted as ‘not available'. The summary count information provides insight into the bias of virulence-linked annotations towards PAO1 versus PA14 despite nearly all genes being duplicated in each genome.Genome-scale metabolic network model of Pseudomonas aeruginosa PA14, iPau1129.Our model accounts for the function of 1129 genes and is provided in spreadsheetformat consistent with usage of COBRA Toolbox 2.0 using SEED notation for reactions and metabolites.Genome-scale metabolic network model of Pseudomonas aeruginosa PAO1, iPae1146.Our model accounts for the function of 1146 genes and is provided in spreadsheetformat consistent with usage of COBRA Toolbox 2.0 using SEED notation for reactions and metabolites.


Different studies indicated that the prion protein induces hybridization of complementary DNA strands. Cell culture studies showed that the scrapie isoform of prion protein remained bound with the chromosome. In present work, we used an oxazole dye, YOYO, as a reporter to quantitative characterization of the DNA condensation by prion protein. We observe that the prion protein induces greater fluorescence quenching of YOYO intercalated in DNA containing only GC bases compared to the DNA containing four bases whereas the effect of dye bound to DNA containing only AT bases is marginal. DNA-condensing biological polyamines are less effective than prion protein in quenching of DNA-bound YOYO fluorescence. The prion protein induces marginal quenching of fluorescence of the dye bound to oligonucleotides, which are resistant to condensation. The ultrastructural studies with electron microscope also validate the biophysical data. The GC bases of the target DNA are probably responsible for increased condensation in the presence of prion protein. To our knowledge, this is the first report of a human cellular protein inducing a sequence-dependent DNA condensation. The increased condensation of GC-rich DNA by prion protein may suggest a biological function of the prion protein and a role in its pathogenesis. C, is a predominantly α-helical soluble glycoprotein which remains attached to the outer membrane of the cells through a glycol-phosphatidyl inositol linkage  is linear for the dye intercalated in gcDNA with a slope of 0.11 indicating that the dye is poorly accessible to acrylamide  analysis of different DNA molecules with prion protein. The images from SEM system indicated that the DNA molecules with GC nucleotides form higher order globular structures in the presence of ructures .α-PrP induces condensation of the nucleic acid containing only GC and GC and AT base pairs. However a small percentage of the decrease in the fluorescence intensity of the dye also results from the dissociation of the dye intercalated in nucleic acid condensed structure in prion protein-nucleic acid complex , does not condense nucleic acid. Therefore, the N-terminal unstructured highly basic 23–120 segment containing seven lysine, three arginine, and six histidine residues is responsible for the condensation. Although full-length prion protein and its 121–231 fragment bind nucleic acids with comparable affinities (unpublished results), the inability of the protein fragment to condense nucleic acids suggests that condensation of the DNA is independent of the binding energy between the protein and nucleic acid. Similar conclusion has been arrived from the DNA-condensing property of various NCp7 peptides [C derived peptide is sufficient to induce a Gpr126-dependent cAMP response in cells and mice and improved myelination in hypomorphic gpr126 mutant zebrafish [A comparison of the results presented in peptides . The prepeptides . It is apeptides . Besidesebrafish .It is known that oligonucleotides having less than 300 base pairs are not amenable to condensation by any DNA-condensing agent , 48. Thiα-PrP induced condensation exposed the GC bases of gcDNA more than those of mDNA containing both AT and GC bases. The extent of base exposure of atDNA by the protein was minimum among the three nucleic acids studied and the extent of base exposure did not change with the increase in protein concentration  showed KSV = 0.6 M−1 [α-PrP (Ksv = 2.32 M−1 for gcDNA) is much higher than the NCp7 of HIV-1. The current observation indicated that the extent of reduction in the fluorescence intensity of YOYO-1  and 55% (mDNA), which is more than 50% and 30% reduction, respectively, observed with spermine -poly(dA-dT) results in Ψ-type condensation . The N-tin cells . It is pin cells , 60–62. in cells , 64. It C in neuronal cells in the presence of copper ions shows that the protein is present in the perinuclear compartment as well [In terms of interaction between histone and the different domains of chromatin, the GC-rich and AT-rich chromatin domains display distinct chromatin conformations and are marked by distinct patterns of histone modifications. The previous study identified the histone deacetylase Rpd3p as an attenuator of these base composition-dependent differences in chromatin status . The res as well . It has  as well . The bin as well , 69. PriSc has been found to accumulate in the nuclei of prion-infected cells and is associated with chromatin independently of proteasome inhibition [Proteinase K-resistant PrPhibition , 71. Thehibition . It has It is assumed that the prion protein-nucleic acids complex regulates different cellular functions which correlate with the biophysical properties of the complex. Previous studies indicated that there may be sequence specific interaction between GC-rich oligonucleotide and prion protein , 36. It


B4GALT1 gene. B4GALT1 encodes a glycosyltransferase that functions in the glycosylation of the Immunoglobulin G (IgG) antibody in such a way as to convert its activity from pro- to anti-inflammatory. The disruption of the B4GALT1 enhancer by the SVA insertion is associated with down-regulation of the gene in B-cells, which would serve to keep the IgG molecule in a pro-inflammatory state. Consistent with this idea, the B4GALT1 enhancer SVA insertion is linked to a genomic region implicated by GWAS in both inflammatory conditions and autoimmune diseases, such as systemic lupus erythematosus and Crohn’s disease. We explore this example and the other cases uncovered by our genome-wide screen in an effort to illuminate how retrotransposon insertion polymorphisms can impact human health and disease by causing changes in gene expression.The human genome hosts several active families of transposable elements (TEs), including the Alu, LINE-1, and SVA retrotransposons that are mobilized via reverse transcription of RNA intermediates. We evaluated how insertion polymorphisms generated by human retrotransposon activity may be related to common health and disease phenotypes that have been previously interrogated through genome-wide association studies (GWAS). To address this question, we performed a genome-wide screen for retrotransposon polymorphism disease associations that are linked to TE induced gene regulatory changes. Our screen first identified polymorphic retrotransposon insertions found in linkage disequilibrium (LD) with single nucleotide polymorphisms that were previously associated with common complex diseases by GWAS. We further narrowed this set of candidate disease associated retrotransposon polymorphisms by identifying insertions that are located within tissue-specific enhancer elements. We then performed expression quantitative trait loci analysis on the remaining set of candidates in order to identify polymorphic retrotransposon insertions that are associated with gene expression changes in B-cells of the human immune system. This progressive and stringent screen yielded a list of six retrotransposon insertions as the strongest candidates for TE polymorphisms that lead to disease via enhancer-mediated changes in gene regulation. For example, we found an SVA insertion within a cell-type specific enhancer located in the second intron of the  At least one half of the human genome sequence is derived from the replication and insertion of retrotransposons – RNA agents that transpose among chromosomal locations via the reverse transcription of RNA intermediates . The vasSequences from active retrotransposon families generate insertional polymorphisms within and between human populations by means of germline transposition events. In this way, ongoing retrotranspositional activity of these RNA agents serves as an important source of human genetic variation. Retrotransposons are further distinguished by the fact that they are known to impact the regulation of human genes in a number of different ways . NeverthIn one sense, a link between retrotransposon activity and disease is already well established. Active human retrotransposons were originally discovered due to the deleterious effects of element insertions . There aGiven the known connection between retrotransposon activity and genetic disease, we hypothesized that retrotransposon insertion polymorphisms may also contribute to inter-individual phenotypic differences that are associated with common diseases that have complex, multi-factorial genetic etiology. Since we previously showed that retrotransposon insertions contribute to inter-individual and population-specific differences in human gene regulation , we alsoPreviously, it has only been possible to investigate the impact of retrotransposon polymorphisms on disease phenotypes for a limited number of individuals owing to the number of genomes that were available . For theFigure 1). PolyTE insertion genotypes were characterized by the 1KGP Structural Variation Group using the program MELT as previously described , 1-a single polyTE insertion (heterozygote), and 2-two polyTE insertions (homozygote present). PolyTE genotypes were used for eQTL analysis as described in section “Expression Quantitative Trait Locus (eQTL) Analysis”. For each of the two continental population groups, only polyTE insertions and SNPs with greater than 5% minor allele frequencies (MAF) were used for the downstream analysis to ensure both the confidence of genotype calls and the reliability of the association analyses. Minor alleles for TEs are assumed to be the insertion present allele, since the ancestral state for any polyTE insertion site corresponds to the absence of an insertion (Human polymorphic TE (polyTE) insertion presence/absence genotypes for whole genome sequences of 445 individuals from five human populations were accessed from the phase 3 variant release of the 1000 Genomes Project (1KGP) . Whole gescribed . Previouescribed . The polnsertion .r) between the target polyTE insertion alleles and all SNP alleles in the same LD block were computed across all individual genome samples. A correlation (r) significance P-value threshold of 0.05 was used to identify all SNPs considered to be in LD with each polyTE insertion. Pairwise distances between polyTE insertion sites and linked SNPs were calculated as the number of base pairs between each polyTE insertion site and all linked SNP locations.The GCTA program (version 1.25.0) was used to estimate the linkage disequilibrium (LD) structure for polyTEs and SNPs in genomic regions centered at each polyTE insertion site. For each polyTE insertion site, pairwise correlations  and health- or disease-related phenotypes were explored using the NHGRI-EBI GWAS database . GWAS dahttp://mitra.stanford.edu/kundaje/leepc12/web_portal/chr_state_learning.html. The genomic locations of polyTE insertions that are in LD with disease-associated SNPs were compared with the genomic locations for enhancers from the 127 epigenomes, and polyTE insertions found to be located within active enhancer elements were considered to have regulatory potential. A subset of 27 epigenomes characterized for cells and tissues related to blood and the immune system – such as T cells, B cells and hematopoietic stem cells – were selected for downstream eQTL analysis [see Expression Quantitative Trait Locus (eQTL) Analysis].The regulatory potential for polyTE insertions was evaluated by considering their co-location with known enhancer sequences. Active enhancers for 127 cell-types and tissues were characterized by the Roadmap Epigenomics Project using the ChromHMM program . ChromHMi is quantified as:The overall relative regulatory potential of polyTE insertions in a given epigenome i is the proportion of polyTEs that are co-located with an enhancer element in a given epigenome i, and si is the proportion of SNPs from polyTE LD blocks that overlap with an enhancer element in the same epigenome i.where thttp://songyanglab.sysu.edu.cn/ccsi/  database at cn/ccsi/ .Figure 1). PolyTE insertion presence/absence genotypes were characterized as described in section “Polymorphic Transposable Element (polyTE) and SNP Genotypes”. RNA-seq gene expression data for the same 445 individual genome samples used for polyTE genotype characterization were taken from the GEUVADIS RNA sequencing project. Genome-wide expression levels were measured for the same lymphoblastoid cell lines, i.e., Epstein–Barr virus (EBV) transformed B-lymphocytes (B cells), as used for DNA-seq analysis in the 1KGP. RNA isolation, library preparation, sequencing and read-to-genome mapping were performed as previously described  or outside (trans) 1 megabase from gene boundaries. P-values were calculated for all TE-eQTL associations, and FDR q-values were then calculated to correct for multiple statistical tests. The genome-wide significant TE-eQTL association threshold was set at FDR q < 0.05, corresponding to P = 4.7 × 10-7 (AFR) and P = 2.6 × 10-7 (EUR).PolyTE insertions that are (1) linked to at least one disease-associated SNP, and (2) located within a blood- or immune system-related enhancer were taken as a candidate set for eQTL analysis with the lymphoblastoid cell line RNA-seq data. PolyTE insertion presence/absence genotypes were regressed against gene expression levels to identify eQTLs (TE-eQTLs) using the program Matrix eQTL . Matrix Table 1). For each gene in the final set, its tissue-specific expression levels across 18 tissues, including 4 blood- and immune-related tissues, were taken from the Illumina BodyMap and GTEx projects . This approach entailed an integrative analysis of (1) TE insertion polymorphisms, (2) SNPs, (3) SNP-disease associations, (4) tissue-specific enhancers, (5) eQTL, and (6) gene function/expression profiles. The rationale behind this approach was to employ a series of successive genome-wide filters, which would converge on a set of high-confidence TE insertion polymorphisms that are most likely to impact health- or disease-related phenotypes. Our analysis started with 5,845 polyTE insertions, with MAF > 0.05 for two continental population groups (European and African), and converged on a final set of seven high-confidence TE disease-association candidates . The final set of seven health/disease-implicated TE insertion polymorphisms that we found are distinguished by their linkage to disease-associated SNPs as well as their regulatory and functional properties. We describe the results and implications for each step in our TE disease-association screen in the sections below.We used a genome-scale data analysis approach to explore the potential impact of human genetic variation generated by the activity of TEs on health and disease  and four European (EUR) populations as described in section “Polymorphic Transposable Element (polyTE) and SNP Genotypes” of the Materials and Methods. This was done for the most common families of active human TEs: Alu, L1, and SVA. For each polyTE insertion location, individual genotypes were characterized as homozygous absent (0), heterozygous (1), or homozygous present (2). The distributions of polyTE insertion genotypes among individuals from each population were used to screen for polyTEs that are found at relatively high MAF > 0.05. The LD structure of the resulting common polyTE insertions, with adjacent common SNPs , was then defined using correlation analysis across individual genome samples . In addition, the genomic locations of common polyTE insertion variants and their linked SNPs were compared to the locations of disease-associated SNPs reported in the NHGRI-EBI GWAS database [see Materials and Methods Genome Wide Association Studies (GWAS) for Disease]. Linkage correlation coefficients between all polyTE insertions analyzed here and GWAS SNPs are shown in Figure 3A). In addition, polyTE variants are located closer to disease-associated SNPs than non-disease-associated SNPs for the EUR population group . A similar enrichment was not seen for the AFR population group, which may be attributed to the lower number of samples available for analysis for this group (AFR = 87 vs. EUR = 358). Indeed, when a larger number of AFR samples, which do not have matched RNA-seq data, were used for the same linkage analysis, the results were qualitatively identical to those seen for the EUR samples analyzed here. Taken together, these results indicate that polyTEs are more likely to be tightly linked to disease-associated SNPs compared to adjacent linked SNPs from the same LD blocks, suggesting a possible role in disease etiology for some TE variants. This is notable in light of the facts that (1) the TE genotypes were not considered in the initial association studies, and (2) TE insertions entail substantially larger-scale genetic variants than SNPs. Thus, polyTEs found on the same haplotypes as disease-associated SNPs may be expected to have an even greater impact on health- and disease-outcomes in some cases.Distributions of LD correlations between polyTEs and adjacent SNPs were compared separately for non-disease-associated vs. disease-associated SNPs. For all three families of active human TEs, in both the AFR and EUR population groups, polyTEs are found in significantly higher LD with disease-associated SNPs compared to non-disease-associated SNPs (IGHV2-26) encoding gene . We found a total 607 disease-linked polyTEs co-located with enhancers in the AFR population group and 437 in EUR group; 391 (AFR) and 336 (EUR) of those enhancers correspond to blood- or immune-related tissues . Details on the co-localization of polyTE insertions and the enhancers characterized for each epigenome are shown in Supplementary Table S2.Given the fact that TEs are known to participate in human gene regulation via a wide variety of mechanisms , we hypoFigure 4B) and all other tissues from which enhancers were characterized. Enhancer co-located disease-linked polyTEs from blood/immune cell-types show higher overall regulatory potential than ones that are co-located with enhancers characterized for the other tissue-types . These results suggest that the set of disease-linked polyTEs studied here may have a disproportionate impact on immune-related diseases, and we focused our subsequent efforts on this subset of health conditions.We estimated the overall regulatory potential for disease-linked polyTEs in all cell- and tissue-types by computing the relative ratio of enhancer co-located insertions as described in section “Evaluating polyTE Regulatory Potential” of the Materials and Methods. The results of this analysis are considered separately for the blood- and immune-related tissues  and 42 (EUR) genome-wide significant TE-eQTL (Supplementary Table S3), and they are enriched in genomic regions that encode immune-related genes . We narrowed this list further by selecting the strongest TE-eQTL association for each individual polyTE, resulting in a final list of 37 (AFR) and 24 (EUR) immune-related TE-eQTL .We further evaluated the regulatory potential of the polyTEs that were found to be both disease-linked and co-located with blood- and immune-related enhancers using an eQTL approach [see Materials and Methods section Expression Quantitative Trait Locus (eQTL) analysis]. Genotypes for this subset of polyTEs from the 445 genome samples analyzed here were regressed against gene expression levels characterized from lymphoblastoid cell lines for the same individuals. Quantile–quantile (Q–Q) plots comparing the observed vs. expected Figure 2 and Table 1). Four of these disease-associated polyTEs are illustrated in Figure 6, and we provide additional information on two examples in the following section “Effects of polyTE Insertions on Immune- and Blood-Related Conditions.” The four examples shown in Figure 6 all correspond to polyTEs that are linked to disease-associated SNPs and co-located with enhancers characterized from blood- or immune system-related tissues; in addition, the genes that these polyTE insertions regulate are all known to function in the immune system.The results of the TE-eQTL analysis further underscore the regulatory potential of the disease-linked polyTEs characterized here and also allowed us to narrow down the list of candidate insertions. Starting with the list of TE-eQTL, we searched for ‘consistent’ examples where the disease-linked polyTE is associated with the expression of a gene that is functionally related to the annotated disease phenotype. This allowed us to converge on a final set of seven high-confidence disease-associated TE insertion polymorphisms . However, two of the six cases considered to be population-specific using the eQTL criterion do show consistent trends across populations but failed to reach genome-wide significance when controls for multiple statistical tests were implemented .Six of the seven disease-associated polyTE insertions are considered to be population-specific, based on significant eQTL results in only one population, whereas a single case is shared between both the AFR and EUR population groups  encoding gene, which is normally expressed at high levels in immune-related tissues. Chromatin interaction maps characterized for several different cell types – CD34, GM12878, and Mcf7 – show that this B4GALT1 intronic enhancer physically associates with the gene’s promoter region. The disruption of the B4GALT1 enhancer by the SVA insertion is associated with down-regulation of the gene in B-cells, for both AFR and EUR population groups . B4GALT1 encodes a glycosyltransferase that functions in the glycosylation of the Immunoglobulin G (IgG) antibody in such a way as to convert its activity from pro- to anti-inflammatory   . Down-re disease .STAM) and Transmembrane Protein 236 (TMEM236) encoding genes. The SVA-438 insertion is co-located with an enhancer in the first intron of the STAM gene , but its presence is associated with changes in expression of the nearby TMEM236 gene . TMEM236 is located ∼100 kbp downstream of the SVA-438 insertion and is most highly expressed in pancreatic islet α-cells   of the SNP rs6602203, which is associated with a reduced metabolic clearance rate of insulin (MCRI), an endophenotype that is associated with the risk of type 2 diabetes  . Islet αgure 7F) . The SVAdiabetes . In otheFigures 7). Nevertheless, it is worth noting that our conservative approach could be prone to false negatives as it would not uncover novel enhancer activity provided by element insertions at new locations in the genome.The results reported here underscore the influence that retrotransposon insertion polymorphisms can exert on human health- and disease-related phenotypes. The integrative data analysis approach that we took for this study also revealed how polyTE disease-associations are mediated by the gene regulatory properties of retrotransposon insertions. We adopted a conservative approach to screen for the potential regulatory effects of retrotransposon insertions by choosing candidate elements as those that were inserted into regions previously defined as tissue-specific enhancers in blood/immune cells. Retrotransposons that insert into enhancer sequences could entail loss-of-function mutants by virtue of disrupting enhancer sequences, or they could serve as gain-of-function mutants by altering enhancer activity. Our results can be considered to show instances of both loss- and gain-of-function enhancer mutations with respect to the decrease or increase, respectively, of gene expression levels that are associated with element insertion genotypes  locations in the genome sequences of all human individuals. In other words, previously described TE-derived regulatory sequences are uniformly present among individual human genomes and thereby do not represent a source of structural genetic variation. Such fixed TE-derived regulatory sequences may not be expected to provide for gene regulatory variation among individuals or for that matter to contribute to inter-individual differences related to health and disease.Nevertheless, we recently showed that TE insertion polymorphisms also exert regulatory effects on the human genome . SpecifiLW performed all of the analyses described in the study. EN contributed all GWAS data used in the study. IJ conceived of, designed and supervised the study. All authors contributed to the drafting and revision of the manuscript.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.


In the empirical RSNs, higher metastability and synchrony were respectively associated with greater cohesion and lower integration. Consistent with their dual role in supporting both sustained and diverse mental operations, higher-order RSNs had lower metastability and synchrony. Sensory and motor RSNs showed greater cohesion and metastability, likely to respectively reflect their functional specialization and their greater capacity for altering network states in response to multiple and diverse external demands. Our findings suggest that functional and dynamic RSN properties are closely linked and expand our understanding of the neural architectures that support optimal brain function.Spontaneous brain activity is organized into resting-state networks (RSNs) involved in internally-guided, higher-order mental functions  and externally-driven, specialized sensory and motor processing . RSNs are characterized by their functional connectivity in terms of within-network cohesion and between-network integration, and by their dynamic properties in terms of synchrony and metastability. We examined the relationship between functional connectivity and dynamic network features using fMRI data and an anatomically constrained Kuramoto model. Extrapolating from simulated data, synchrony and metastability across the RSNs emerged at coupling strengths of 5 ≤  The default mode network (DMN)5, the central executive network (CEN)6 and the salience network (SAL)7 support diverse and mostly internally-guided processes. The DMN is implicated in self-referential and integrative processes5, the CEN is involved in goal-directed selection of stimuli and responses6, and the SAL processes information relating to varied forms of cognitive, affective and homeostatic salience7. Further networks, particularly the auditory (AN), visual (VN) and sensorimotor (SMN) networks, are known to support more specialized and mostly externally-driven functions8.Brain activity at rest is organized into functional resting-state networks (RSNs) defined by their spatiotemporal configuration and functional roles9. Similarly, RSNs with high between-network connectivity can be designated as connector networks while those with low between-network connectivity can be designated as provincial networks9. In the adult brain, the DMN has a unique role as a cohesive connector, the CEN and SAL behave as incohesive connectors while the AN, VN and SMN function as cohesive provincial networks9. These functional properties are considered essential components of the optimal functional configuration of the human brain.Using functional connectivity metrics, it is possible to characterize RSNs based on their internal cohesion and integration. RSNs with high within-network connectivity can be considered cohesive while those with low within-network connectivity can be considered incohesive11. Optimal brain function is thought to occur at levels of synchrony and metastability that allow both efficient information flow and the adaptive changes to network states in response to external or internal demands18. Previous studies have found that synchrony and metastability differ between RSNs19. However, it is unclear whether such differences are associated with the functional connectivity profiles of the RSNs. The aim of the current paper is to test whether the functional and dynamic properties of RSNs are indeed linked. Specifically, we hypothesized that the role of internally-guided RSNs as connector networks would be associated with lower metastability and synchrony; lower metastability would enable connector networks to maintain relatively stable configurations over time to sustain ongoing mental operations, consistent with the dynamic behavior of these networks during tasks13, while lower synchrony at rest would enable connector networks to adopt variable configurations in order to support diverse mental operations13. In contrast, we also hypothesized that the specialized and externally-driven function of provincial RSNs would be associated with greater synchrony and metastability that will enable them to change their dynamic states in response to variable and competing inputs while maintaining internal network cohesion consistent with their more circumscribed functional role.More recently, the functional behavior of brain networks has been considered in terms of their synchrony and metastability that attempt to capture dynamic changes in network configuration. Synchrony is a measure of the synchronization of the individual network regions while metastability refers to the degree of variability in network states over timeAn overview of the study workflow is shown in Fig. p > 0.1). None of these measures was significantly influenced by age, sex and IQ based on a series of multiple regression analyses with bootstrapping . Analyses of variance with bootstrapping (n = 1000) showed that the networks differed in metastability  and synchrony . The results remained significant after co-varying for network size (number of component regions), age, sex and IQ. Bonferroni corrected post-hoc pairwise comparisons showed that metastability was comparable between connector networks  and between provincial networks . However, connector networks  had significantly lower metastability than all other networks . Bonferroni corrected post-hoc pairwise comparisons showed that synchrony was comparable between connector networks  but all other pairwise comparisons were significant .Overall, externally-driven provincial networks  had numerically higher synchrony and metastability than internally-guided connector networks (DMN and CEN). The variance of metastability and synchrony was not statistically different between networks despite differences in network size  and negatively associated with network integration (between-network connectivity) . Similar analyses showed that network synchrony was positively associated with network cohesion  and negatively associated with network integration . The relationship between the functional and dynamic properties of the empirical RSNs is visualized in Fig. Correlation analyses with bootstrapping (n = 1000) revealed that network metastability was positively associated with network cohesion (within-network connectivity)  of the empirical RSNs. We examined the relationship between simulated metastability and synchrony versus empirical within- and between-network connectivity. We compared the empirical data to the simulated data at k equal to the maximal metastability of each RSN. As shown in Fig. The simulated metastability and synchrony of each RSN as a function of structural coupling strength is depicted in Fig. We examined the relationship between the functional and dynamic properties of the DMN, CEN and SAL, which represent internally-guided, connector networks, and the AN, VN and SMN, which represent externally-driven provincial networks concerned with processing specialized functions. Across RSNs, metastability and synchrony were positively correlated with within-network connectivity and negatively correlated with between-network connectivity, indicating that increased metastability and synchrony were associated with elevated cohesion and decreased functional integration.20, had the lowest metastability and synchrony. The salience network (SAL), which is considered important in tuning neural activity from internal to salient external stimuli7, occupied an intermediate position. The lower metastability of the internally-guided networks may reflect their role as a stable core of mental processing while their lower synchrony may reflect the ability of their component regions to show asynchronous activity profiles, potentially enabling more diverse functions.Functional cohesion, synchrony and metastability were higher in provincial than connector RSNs. This dynamic profile suggests that provincial networks show both high-levels of coordination and greater capacity for altering their network states in order to adapt to multiple and diverse external demands. On the other hand, the DMN and CEN, which are implicated mostly in internal and goal-directed processing21 to investigate the alignment of simulated and empirical dynamic RSN properties. There are alternative computational models that could be used to simulate the dynamics of both amplitudes and phases of oscillations, such as the Hopf model24, the Wilson-Cowan model26, the FitzHugh-Nagumo model27, and the Jansen-Rit model28. We chose the Kuramoto model because of its relative simplicity and ability to capture essential aspects of phase dynamics33. Additionally, the Kuramoto model has been successfully used in neuroscience research for the purpose of modeling slow35 and fast39 cortical oscillations. Here, we sought to evaluate the performance of the model with fast local gamma-band oscillations, in agreement with experimental43 and theoretical models of neural networks44. We found that the alignment between empirical and simulated metastability and synchrony occurred within anatomical coupling strengths (k) ranging from 5 to 12. Simulated and empirical synchrony were most closely aligned around the point of criticality, defined as the point of transition between asynchronous (dominated by noise that prevents information flow) and globally synchronized states . These findings confirm our previous report that empirical and simulated fMRI data align within the critical range of coupling strengths33, and are in line with the model of Kitzbichler and colleagues who proposed that simulated time series generate behaviors that most resemble those of empirical datasets when their global coupling strength reaches criticality45.We used the Kuramoto model of coupled oscillatorsIn summary, this study demonstrates that functional and dynamic RSN properties are connected and that the Kuramoto model provides a viable option for the investigation of the dynamic properties of empirical RSNs.46. Their mean intelligence quotient (IQ) was 119.3 (SD = 14.9) based on the Wechsler Abbreviated Scale of Intelligence (WASI-II)47. The study protocol was approved by the Institutional Review Board of the Icahn School of Medicine at Mount Sinai (ISMMS). All experimental procedures were performed in accordance with standard protocols for magnetic resonance imaging studies. All participants provided written informed consent.Thirty healthy adults  were recruited following advertisements in the local press. They were interviewed to exclude any past or current medical disorder or head trauma and any psychiatric disorder based on the Mini-International Neuropsychiatric Interview (M.I.N.I.)2 were applied in 64 non-collinear directions (left-to-right phase encoding) with 5 non-diffusion weighted (b0) images. This scan was repeated with phase encoding gradients of reverse polarity to correct for b0- and eddy current-induced distortions and to improve signal-to-noise ratio.All imaging data were acquired on a Siemens Magnetom Skyra 3 T scanner  at the ISMMS. Participants were asked to keep their eyes open during the scan. Rs-fMRI data were acquired with a multi-band accelerated gradient-echo echo-planar imaging (EPI) sequence . T1-weighted structural MRI images were obtained with a 3-D magnetization-prepared rapid gradient-echo (MPRAGE) . DTI data were acquired using a single-shot spin-echo EPI sequence with a multi-band factor of 3 and monopolar gradient pulse . The diffusion sensitizing gradients with a b-value of 1200 s/mmhttp://www.fil.ion.ucl.ac.uk/spm/) and the Data Processing Assistant for rs-fMRI toolbox48. Steps included slice timing correction, re-alignment, coregistration of functional images to the anatomical T1-weighted images followed by spatial normalization to the Montreal Neurological Institute (MNI) stereotaxic standard space, and spatial smoothing with a Gaussian kernel with a full-width half-maximum of 6 mm. We then applied a wavelet despiking technique for denoising signal transients related to small amplitude head movements (<1 mm)49. In all participants, head motion was less than 2 mm of displacement and 0.5 degrees of rotation in any direction. We regressed out 24 motion re-alignment parameters  to compensate for head movement inside the MRI scanner50. Linear trends were removed and white matter and cerebrospinal fluid (CSF) signals were regressed out from the data using a component based noise reduction method 51. These preprocessing steps minimized the impact of head motion and physiological noise on the rs-fMRI data. Subsequently, for each subject we averaged the blood-oxygen-level dependent (BOLD) time series from each of 90 brain regions of interest (ROIs) (45 in each hemisphere) defined using the Automated Anatomical Labeling (AAL) template52  software 59, the DSI Studio60 and custom routines written in Matlab . Artifacts and spatial distortions due to eddy currents and head motion were corrected by deploying an affine registration between diffusion-weighted images and non-diffusion-weighted (b0) images. Non-brain tissue was removed using the brain extraction tool (BET) with a fractional intensity threshold of 0.1. Then, diffusion eigenvectors, eigenvalues, and fractional anisotropy (FA) for each voxel were computed using the dtifit function. Voxel-wise estimates of the angular distribution of local tract direction were calculated using the bedpostx function, which estimated a 2-fiber model using Markov Chain Monte Carlo sampling. Next, a total of 100,000 whole brain tracks were obtained using a deterministic fiber tracking algorithm60. The anisotropy threshold and step size were 0.1 and 0.9 mm, respectively, determined automatically in DSI Studio (http://dsi-studio.labsolver.org/). The angular threshold was 60°. Fiber tracks with lengths less than 10 mm were discarded to prevent the tracking process from being overwhelmed by short association fibers. The parcellation was performed by warping the subject space to a standard space using nonlinear registration61. For each subject, the AAL template was used to parcellate the entire brain into 90 ROIs identical to those used for the rs-fMRI data. We generated a symmetric weighted structural connectivity matrix representing the density of white matter fiber tracts (streamline density) connecting any two ROIs and a tract length matrix representing the average length across all the fibers connecting them. The individual structural connectome data of all participants were averaged to obtain a group-averaged structural connectivity matrix and a tract length matrix. The edge weight of the structural connectivity matrix, representing the structural connectivity network organization of the brain, was normalized by the volume of the interconnected ROIs to account for different size of the ROIs62.We analyzed the DTI data using an in-house image processing pipeline that combines tools from the FSL’s diffusion toolbox  power is coupled to the BOLD fMRI signal and is considered representative of the overall neuronal activity43. Simulations were run for 410 seconds  with a time-step of 0.1 ms for a range of global coupling strengths (1 ≤ k ≤ 25 at a resolution of 1) using an Euler scheme.We ran the Kuramoto simulations with a mean delay of https://findlab.stanford.edu/functional_ROIs.html)  to examine the effect of (a) rs-fMRI scan duration (volumes/time points) on metastability and synchrony (SI section 2.1); (b) setting the intrinsic frequencies of the oscillators to be normally distributed with mean 0.045 Hz and SD = 0.01 Hz, corresponding to the center frequency of empirical fMRI signal (SI section 2.2); (c) using phase-locking value-based connectivity as an alternate method for defining RSN cohesion and integration conditions (SI section 2.3); (d) estimating the metastability and synchrony using rs-fMRI datasets from a sample of 30 participants of the Human Connectome Project (SI section 2.4) that were demographically matched to the original study sample. Finally, to ensure robustness of the simulation results, we conducted 5 additional runs of the Kuramoto simulation with varying random initial conditions (SI section 2.5).The datasets generated during the current study are available from the corresponding author on reasonable request.Supplemental Information