We constructed a directed network model of genes regulating Th1 and Th2 cells through text mining and manual curation. We identified four attractors in the network, three of which included genes that corresponded to Th0, Th1 and Th2 cells. The fourth attractor contained a mixture of Th1 and Th2 genes. We found that neither in silico knockouts of the Th1 and Th2 attractor genes nor gene expression microarray data from patients with immunological disorders and healthy subjects supported a counter-regulatory role of Th1 and Th2 cells. By combining network modeling with transcriptomic data analysis and in silico knockouts, we have devised a practical way to help unravel complex regulatory network topology and to increase our understanding of how network actions may differ in health and disease.Two T helper (Th) cell subsets, namely Th1 and Th2 cells, play an important role in inflammatory diseases. The two subsets are thought to counter-regulate each other, and alterations in their balance result in different diseases. This paradigm has been challenged by recent clinical and experimental data. Because of the large number of genes involved in regulating Th1 and Th2 cells, assessment of this paradigm by modeling or experiments is difficult. Novel algorithms based on formal methods now permit the analysis of large gene regulatory networks. By combining these algorithms with  in silico knockouts, and gene expression microarrays to examine if Th1 and Th2 cells had counter-regulatory roles. We constructed a directed network model of genes that regulated Th1 and Th2 cells through text mining and manual curation. We identified four cycles in the gene expression dynamics, three of which expressed genes that corresponded to Th0 (Th1/Th2 precursor), Th1 and Th2 cells. The fourth cycle contained the expression of a mixture of Th1 and Th2 genes. We found that neither in silico knockouts of the Th1 and Th2 attractor genes nor gene expression microarray data from patients and healthy subjects supported a counter-regulatory role of Th1 and Th2 cells.Different T helper (Th) cell subsets have an important role in regulating the immune response in inflammatory diseases. Th1 and Th2 cells are thought to counter-regulate each other, and alterations in their balance result in different diseases.This paradigm has been challenged by recent clinical and experimental data. Because of the large number of genes involved in regulating Th1 and Th2 cells, assessment of this paradigm by experiments or modelling is difficult. In this study, we combined novel algorithms for network analysis,  The immune system is composed of diverse cell populations, for example antigen-presenting cells, T and B lymphocytes as well as effector cells like eosinophils, mast cells and neutrophils. One type of T lymphocytes, called T helper (Th), has an important role in regulating this cellular network. Th cells can be further divided into Th1 and Th2 cells. Th1 and Th2 cells are thought to be mutually inhibitory and also to be involved in different diseases; Th1 cells are associated with autoimmune diseases, while Th2 cells are involved in allergies Although considered a simplification, the Th1/Th2 dichotomy is supported by a large body of experimental evidence in silico modeling have to some extent supported a counter-regulatory role of Th1 and Th2 cells Gene expression microarray studies evidence that hundreds of genes are involved in the Th1/Th2 cell differentiation in silico studies based on a more comprehensive gene network model, which included a larger number of genes The gene networks used have been based on a relatively small, though relevant, number of genes and interactions. In the present work we applied an algorithm previously developed to analyze large gene regulatory networks to perform www.pubmed.com) based on seed genes and protein interaction data, with manual annotation. The aim of our study was to examine if the so-constructed network model was compatible with a counter-regulatory role of Th1 and Th2 cells from healthy humans as well as patients with different inflammatory diseases.The network was constructed by combining text mining from Medline (in silico knockouts on the model dynamics To achieve this we studied the effects of We defined a gene regulatory network (GRN) model of the genes involved in Th1 and Th2 cell differentiation based on manual annotation and automated data mining of Medline abstracts. To ease inspection, this gene regulatory network was organized into four layers according to the sub-cellular localization of the genes see . AnotherThe extracellular layer included cytokines  of length one or more (up to Gene regulatory networks (GRNs) can be represented as graphs where nodes represent genes that are either active or inactive. The state of the network is given by the combination of the activation state of all genes. Starting from a certain state, the upcoming configuration is computed by applying synchronously an updating rule. In general, since the number of possible states is In what follows, we go after Kauffman Using the algorithm in in vivo, to the situation in which antigenic stimulation may be temporary or persisting, and result in different inflammatory responses The Th1 and Th2 attractors contained either Th1 or Th2 genes, an observation that was compatible with a counter-regulatory role of Th1 and Th2 cells. For example, the Th1 attractor contained the transcription factor TBET, which has been experimentally shown to induce the Th1 cytokine IFN-nd GATA3 . This suin silico knockout experiments for all genes in the network, in order to monitor the behaviour of the attractors. In so doing, we distinguished two different settings, corresponding to a different activation modality of the input nodes  for temporary stimulation whereas it was 604 (range 322–1664) for persisting stimulation, .in vivo stimulation, the network dynamics differed greatly between temporary and persisting stimulation. Next, we proceeded to examine the counter regulatory dynamics of the Th1 and Th2 cells. This was done by testing the effects of in silico knockouts of intra-cellular genes in the Th0, Th1, Th2 and ThX attractors. We started by knocking out TBET and GATA3. If TBET and GATA3 were counter-regulatory, knocking out TBET would be expected to result in attractors mainly containing IL-4, but not IFN-Therefore, as a first observation we noted that, similarly to Firstly, we applied the temporary stimulation activation modality . KnockinOn the other hand, knocking out the same genes but applying the persisting stimulation activation modality mainly resulted in attractors containing both IL-4 and IFN- .For both temporary and persisting stimulation, the knockout of other transcription factors that regulated Th1 and Th2 cells, namely IRF4, MAF, NFAT, STAT1 and STAT6, also resulted in attractors that contained IL-4 and IFN-To summarize, these findings were not compatible with a strictly counter-regulatory role of neither TBET nor GATA3 or any of the other transcription factors.in silico findings related to in vitro studies of T-cells from healthy controls and patients with different T-cell related diseases. We downloaded several sets of gene expression microarray data from the public domain to test whether Th1 and Th2 genes were inversely correlated in T-cell related diseases.We proceeded to examine how the If Th1 and Th2 cells are antagonists we would expect inverse relations between genes in the Th1 and Th2 attractors. If so, the expression levels of those genes would be negatively correlated. Instead of this, we found a highly significant positive correlation between the ratios of differentially expressed Th1-associated genes and Th2-associated genes . Successively, the connection among the proteins of the first order neighbors were retrieved. Among all the genes retrieved thus far, we selected only those associated to the Gene Ontology term (www.geneontology.org) “T cell differentiation”.Firstly, we identified two different sets of genes as a primary source: i) 17 genes from a previous network model www.genome.jp/kegg/pathway.html). Genes that were part of that pathway and had well-characterized and experimentally verified functions relevant for Th1 and Th2 cell differentiation were selected for the final network model. A detailed description of each interaction in the network, together with 126 supporting references is given in More specifically, the genes co-cited in the millions Medline abstracts together with this term were retrieved. This resulted in a set of 403 genes, that was further slimmed down and used to construct a manually annotated directed graph of gene interactions relevant for Th1 and Th2 cell differentiation. This was made by using the T-cell receptor pathway in the KEGG database as a template  which from the logical structure of the network infers the possible attractors. In simple words, the network can be seen a Boolean circuit and its attractors can be computed by using methods and largely optimised algorithms coming from modeling of Very Large Scale Integration (VLSI) circuits.In recent studies, formal methods such as symbolic computation, it is possible to unfold the dependencies between nodes that are linked together and to compose the update function as a relation among the states (activation/inhibition) of the genes/nodes. Then the algorithm uses the SAT solver to determine the values of the states that evaluate to true the relation. This process is then repeated until all attractors are identified.What is special about formal methods approach is that it enables to find attractors of large networks. The idea behind the search algorithm is that, by using We specified the network as the set of rules i.e., synchronous update). At each time step and, or and not respectively. The rule in Equation 1 states that for a gene to be activated, at least one activator and no inhibitors must be active .Analogously to In our specific case we had a set of in silico knockout experiments we anticipated two results: a) to identify the set of genes which are pivotal to the Th1/Th2 differentiation; b) to spot subsets of co-expressed genes belonging to the attractors, since from analysis of microarray data we expected these genes to be correlated.The last part of this work was the systematic characterization of the networks obtained by knocking out genes one at a time. As a consequence of these Changes in the set of the attractors were used to highlight relevant nodes. As a first approximation, differences in the mere number of attractors were considered; if a node did not affect the number of attractors, then from the point of view of the dynamics it was considered irrelevant.http://www.ncbi.nlm.nih.gov/geo/). Datasets were selected based on the criteria that they i) measured mRNA expression from CD4+ cells from healthy controls or patients with T-cell related diseases  and ii) that there were at least 5 samples per disease or per controls, Click here for additional data file.


Mycobacterium tuberculosis complex isolates. We collected genotyping results from Restriction Fragment Length Polymorphism (RFLP) and Mycobacterial Interspersed Repetitive Units - Variable Numbers of Tandem Repeat (MIRU-VNTR) in a geographically limited area (Stockholm) during a period of three years. The number and proportion of isolates belonging to clusters was reduced by 45 and 35% respectively when combining the two methods compared with using RFLP or MIRU-VNTR only. The mean size of the clusters was smaller when combining methods and smaller with RFLP compared to MIRU-VNTR. In clusters with confirmed epidemiological links RFLP coincided slightly better than MIRU-VNTR but where there was a difference, the variation in MIRU-VNTR pattern was only in a single locus. In isolates with few IS6110 bands in RFLP, MIRU-VNTR differentiated the isolates more, dividing the RFLP clusters. Since MIRU-VNTR is faster and less labour-intensive it is the method of choice for routine genotyping. In most cases it will be sufficient for epidemiological purposes but true clustering might still be considered if there are epidemiological links and the MIRU-VNTR results differ in only one of its 24 loci.Our aim was to analyze the difference between methods for genotyping of  Tuberculosis (TB) in Sweden is mainly restricted to risk groups, in particular immigrants from high incidence countries. From 2009 to 2011, a total of 1872 cases were reported in all of Sweden and 86% were born in another country. Of the 242 cases younger than 19 years, 28 were born in Sweden and 26 of these had at least one parent born in a high incidence country. The majority of TB cases diagnosed in Sweden are infected outside of Sweden. Since most of them are in an economically productive age they have many social contacts and this makes early treatment and thorough contact tracing very important to avoid transmission of TB.Mycobacterium tuberculosis (Mtb) complex isolates belonging to clusters has been performed at the Swedish Institute for Communicable Disease Control (SMI) since 1994. The national coverage of genotyping is currently around 85% of all cases with culture verified TB. In the county of Stockholm the coverage of genotyping is almost 100% of all culture positive cases. From 2009 to 2011, 75% of the reported cases in Stockholm were verified by a positive culture. In 2012, the reference genotyping technique was changed to Mycobacterial Interspersed Repetitive Units - Variable Numbers of Tandem Repeat (MIRU-VNTR) with additional genotyping performed on Mtb strains of the Beijing lineage. Since the methods are different, isolates belonging to clusters according to MIRU-VNTR will not always cluster when analyzed with RFLP and vice versa. An ideal method would produce genotyping results within the first few months of treatment while contact tracing is ongoing, to help clarify where the patient was most likely infected. This could then in a timely manner aid in the identification of others who are at risk of developing active TB.In order to monitor signs of domestic spread, genotyping by Restriction Fragment Length Polymorphism (RFLP) to investigate 6110 RFLP, the previous gold standard for genotyping Mtb complex isolates, has been extensively used for TB epidemiological studies because of its high discriminatory power (DP). However, the method is labor-intensive, time-consuming and is limited by the need for a large quantity of pure genomic DNA and its inability to discriminate between strains with low IS copy numbers. This has led to the development of a PCR-based typing method where 24 loci containing variable numbers of tandem repeat DNA elements called mycobacterial interspersed repetitive units (MIRU-VNTR) are analyzed Insertion sequence (IS) Mtb complex isolates from cases in Stockholm have been analyzed with both MIRU-VNTR and RFLP in combination with spoligotyping between the years 2009–2011. In this study we compared the results between the different methods and then correlated this to the epidemiological data available on the cases in Stockholm to obtain a better understanding of the variability of clustering with different methods.Within the frame of the EU TB PAN-NET project, a European network for the study and clinical management of TB drug resistance, Mtb complex isolates to SMI for disease surveillance. Epidemiological data is also collected but is not always complete. In order to access medical files to collect more complete epidemiological data when needed, ethical approval was applied for. Since the study was retrospective, these were all historical cases already cured and we assumed that accessing their medical files to complete epidemiological data would not affect them in any way. The data was de-identified before analysis. Ethical approval for the study was granted. . The Regional ethics committee Stockholm waived the need to obtain written informed consent from the patients to conduct the study.The Swedish County medical offices send clinical Reporting of TB in Sweden is mandatory according to the Communicable disease act. Demographic and clinical data of culture-verified TB cases was collected from the web-based reporting system, SmiNet, and from medical records.Mtb complex isolates obtained from patients with pulmonary or extra-pulmonary TB residing in Stockholm County during the years 2009–2011 were studied. These isolates were part of the TB PAN-NET project as previously described.All Mtb complex isolates offered by the Swedish TB reference laboratory at SMI.The isolates were tested for susceptibility to the first-line drugs isoniazid (INH), rifampicin (RIF), ethambutol (EMB) and pyrazinamide (PZA) using the MGIT 960 liquid culture and drug susceptibility testing systems according to the instructions of the manufacturer. The TB laboratory at the Karolinska University hospital in Stockholm performing the drug susceptibility testing, takes part in the external quality assurance program for drug susceptibility testing of Mtb chromosome 6110 as a probe and PvuII as the restriction enzyme The isolates were cultured on Löwenstein Jensen medium and spoligotyping was used to characterize the polymorphic direct repeat region of the 6110 RFLP fingerprints and MIRU-VNTR typing results were analyzed by the BioNumerics software version 6.6 . The IS6110 RFLP fingerprints were digitalized and compared by the unweighted pair group method of arithmetic averaging (UPGMA) using the Jaccard coefficient. MIRU-VNTR data were imported from Excel spreadsheets and analyzed in BioNumerics using the categorical coefficient and similarity trees were calculated using the UPGMA. Clusters were defined as strains showing either identical IS6110 RFLP  and spoligotyping patterns or identical MIRU-VNTR and spoligotyping patterns. The spoligotypes were also analyzed using the international MIRU-VNTRplus database website (http://www.miru-vntrplus.org/), allocated to Spoligotype International Types (SIT) and assigned to major phylogenetic lineages. In order to compare the difference in discriminatory power between the genotyping methods and the combinations of them in our material, we calculated the Hunter-Gaston index (HGI) of discriminatory ability The spoligotyping patterns, ISMtb complex strains showing either identical spoligotyping patterns and RFLP or identical spoligotyping patterns and MIRU-VNTR, but will hereafter be referred to as only RFLP or MIRU-VNTR clusters. The isolates forming clusters with RFLP versus MIRU-VNTR were compared with data on drug-susceptibility, the available epidemiological data and results from contact tracing, in order to evaluate which method could best confirm epidemiological links. Epidemiological data were classified as follows:Epidemiological data and information on contact tracing was available for all cases. A cluster was defined as two or more true link; family or social contact (friends/colleagues)possible link; the same geographical originno link; neither of the above and no other known link.In total, 406 isolates from 224 (55.2%) men and 182 (44.8%) women residing in the county of Stockholm during the years 2009–2011 were analyzed. Patients were between 2 and 93 years old with a median age of 39 years. Only 12% were of Swedish origin and the largest group were migrants from Africa (42%) and Asia (21%) . Of the Of the 406 isolates 64 were resistant to one or more of the drugs INH (n = 63), RIF (n = 13), EMB (n = 6) and PZA (n = 11). Eighteen isolates were resistant to more than one drug, and of those, 13 isolates were resistant to both INH and RIF which classified them as multidrug resistant (3.2%).Mtb complex isolates belonging to the Beijing genotype were from Asia (42%) or Eastern Europe including the former USSR (34%). More than 40% (171/406) of the patients came from Africa, mainly the Horn of Africa (n = 143), and isolates from African patients were represented among all major lineages. There were six AFRI lineage isolates in our study and of these patients three came from the Gambia, two from Somalia and one from Morocco. The Beijing lineage, in which MIRU-VNTR is known to have less discriminatory power In most cases, the isolates of foreign-born patients reflected genotypes common in their country of origin. For example, many of the patients infected with Spoligotyping of the isolates displayed 167 different patterns, of which 44 patterns were different clusters (cluster size 2–51 isolates). The largest cluster comprised 51 isolates and was of a Beijing genotype.6110 copy numbers (five bands or less), reducing the discriminatory power of RFLP. In total, 15% of the 405 Mtb isolates (n = 60) had low IS6110 copy numbers, and 31(50%) of these isolates were grouped in clusters, clearly demonstrating the reduced discriminatory power of RFLP. With RFLP, 304 isolates (75%) displayed unique patterns and 101 isolates (25%) belonged to 41 clusters containing 2–8 isolates each (average 2.24)  . Of the MIRU-VNTR was performed on 406 isolates, out of which five displayed identical MIRU-VNTR patterns but not identical spoligo patterns and hence were not considered to form a cluster. Analyses comparing RFLP-results with MIRU-VNTR results were restricted to isolates with available data from both methods (n = 405). With MIRU-VNTR, 319 isolates (79%) were unique and 86 (21%) formed 32 different clusters containing 2–10 isolates (average 2.69). Out of these 32 clusters, 16 (35 isolates) had true links according to epidemiological analysis and of the other 16 clusters, 10 had possible links and 6 had no known links. In four of the clusters with no links the lineage was Beijing.Combining RFLP (and spoligo) and MIRU-VNTR (and spoligo) gave 274 unique isolates (68%) while the remaining 131 isolates (32%) belonged to clusters by either one or both methods. For 56 (14%) isolates the clustering coincided with both methods and they formed 25 clusters comprising 2–4 isolates (average 2.24). In 16 of these 25 clusters, the cases had true links by conventional epidemiology. Seven of the remaining nine clusters with agreeing results in both RFLP and MIRU-VNTR had possible links due to shared geographical origin  while twApart from the 16 clusters with true links and coinciding RFLP and MIRU-VNTR results, there was one additional RFLP cluster with true epidemiological links where the MIRU-VNTR did not coincide. When comparing the MIRU-VNTR results in this cluster they differed in only one locus. In contrast, among the 18 RFLP clusters with only possible links and differing MIRU-VNTR results, a difference was seen in at least three loci or more.The discriminatory power (DP) of each method and their combinations are visualized in Drug resistant isolates were not more prone to belong to a cluster in this study population. Out of 342 drug susceptible isolates, 107 (31%) belonged to a cluster in any or both of the methods compared to 64 isolates with some resistance out of which 21(33%) belonged to a cluster.Our study showed that the DP of each method may vary depending on the strain and, not surprisingly, that a combination of both methods gave a more accurate result in better accordance with what could be confirmed by conventional contact tracing. However, the difference was not large enough to recommend the use of all methods in parallel. Since MIRU-VNTR is more convenient due to faster results and is less costly than RFLP, it should be the preferred method for routine genotyping with the option of complimentary genotyping when indicated.As is logical spoligotyping alone had the lowest DP and a combination of all three methods had the highest . In our The global concordance in isolates defined as unique or belonging to clusters amounted to 82% (n = 333) and of the remaining 72 isolates, 10% (n = 42) were clustered by RFLP only, seven percent (n = 27) belonging to clusters by MIRU-VNTR only and one percent (n = 3) belonging to a different cluster depending on method. This is very similar to the result in a recent study from the Netherlands which included ten times as many isolates A limitation of our study is that RFLP was the standard method for genotyping during the study period and the results were used in the contact investigations. This might have biased the number of true links confirmed by RFLP as compared to clusters with MIRU-VNTR which may not have been as thoroughly investigated for epidemiological links. Nevertheless one of the major drawbacks with RFLP is the time delay to obtain the results, and they were mainly used for retrospective confirmation of already suspected epidemiological links. A few unsuspected links were detected where cases clustered in time and space, i.e. turned out to be neighbors. In the clusters with possible links, the majority had the same geographical origin as the only connecting factor. As most were fairly recent immigrants this was interpreted as them being infected in their country of origin with highly genetically related strains.The strength of our study was the completeness of epidemiological data available.6110 RFLP (n = 101 (25%)) was higher than with MIRU-VNTR (n = 86 (21%)), however this may be explained by 31 of the 101 isolates belonging to clusters with RFLP having low IS6110 copy numbers. Clusters identified with IS6110 RFLP coincided slightly better with epidemiological links. The lower mean number of cases per cluster with RFLP also supports our theory that RFLP has a somewhat better DP than MIRU-VNTR except for strains with low IS6110 copy numbers.The number of isolates belonging to clusters when analyzed with ISA recently published Dutch study 6110 RFLP or hypervariable MIRU-VNTR loci 6110 bands by RFLP. A German study published in 2011 6110 RFLP bands. Due to the low DP of RFLP in isolates with few IS6110 bands, a Danish study 6110 RFLP, excluded strains with less than five IS6110 bands.When analyzing the MIRU-VNTR results, a total of 16 clusters had no known epidemiological link but 11 of these 16 clusters had a possible link through geographic origin in common. In four clusters with no connection, the isolates were of the Beijing genotype and belonged to clusters by MIRU-VNTR but not by RFLP. A true difference was further supported by the strains having different drug-susceptibility patterns in two of the clusters. It has previously been reported that the DP of MIRU-VNTR is reduced when it comes to isolates of the Beijing genotype 6110 copy number isolates where RFLP is less precise. On the other hand, we also had 13% of the isolates belonging to the Beijing lineage in which MIRU-VNTR is known to be less discriminatory. Of the isolates that clustered only with RFLP, 52% were low IS6110 copy number isolates and of the isolates that clustered only with MIRU-VNTR 33% were of the Beijing lineage, clearly illustrating the weakness of each method.If RFLP has a higher DP, analysis with this method should result in a higher number of different patterns compared to MIRU-VNTR. However, in our material we had 15% low ISThere was no indication of drug resistance influencing the likelihood of clustering in our material. In a Swedish context this is reasonable. Few cases would have inadequate treatment for very long since good laboratory facilities are country-wide and readily available and line probe assay provides information on possible resistance within a week. The largest cluster ever detected in Sweden was resistant to isoniazid Genotyping is a useful tool to improve contact tracing and detect unknown connections, something that can be very helpful if the results are made available in a timely manner. A combination of all three methods resulted in confirmation of 16 of the 17 clusters with true links in Sweden.In order to get a quick and reliable genotyping result a combination of spoligotyping and 24 loci MIRU-VNTR is sufficient in the majority of cases. Thus only Beijing lineage isolates might require an additional genotyping method, for better discriminatory power. We propose that if a link is suspected but not confirmed by MIRU-VNTR, the degree of difference should be investigated before a connection is discarded as not likely. Current genotyping techniques will only give guidance to investigate connections between clinical TB cases. If isolates from TB cases do not cluster, a direct true link can usually be ruled out unless the difference is minimal. On the other hand, clustering should be explored for possible connection but is no absolute evidence for cases having a true link. When whole genome sequencing is readily available, identification of clusters is likely to be more accurate.Table S1MIRU-VNTR based dendrogram with spoligotypes and lineages.(DOCX)Click here for additional data file.


Febrile seizures, or febrile convulsions (FEB), represent the most common form of childhood seizures and are believed to be influenced by variations in several susceptibility genes. Most of the associated loci, however, remain ‘orphan’, i.e. the susceptibility genes they contain still remain to be identified. Further orphan loci have been mapped for a related disorder, genetic  epilepsy with febrile seizures plus (GEFS+).We show that both spatially mapped and ‘traditional’ gene expression data from the human brain can be successfully employed to predict the most promising candidate genes for FEB and GEFS+, apply our prediction method to the remaining orphan loci and discuss the validity of the predictions. For several of the orphan FEB/GEFS+ loci we propose excellent, and not always obvious, candidates for mutation screening in order to aid in gaining a better understanding of the genetic origin of the susceptibility to seizures. Febrile seizures, or febrile convulsions (FEB), are acute symptomatic seizures that occur in response to fever and represent the most common form of childhood seizures, affecting worldwide between 2 and 14% of infants before five years of age  and the GABAGABRG2)–four out of five known FEB susceptibility genes according to the OMIM database  images of many different brains, but instead uses a microarray technology on samples from a single individual. It does not yet provide expression data for all consecutive positions of the entire organ since the project is scheduled to be completed in 2013. Nonetheless, the current preliminary version already includes samples for most of the anatomical structures of the human brain.In contrast to the more complete MBA, the HBA is not derived from We evaluated the possibility to predict gene–phenotype associations based on 13,204 HBA-derived 3D gene expression profiles from the human brain using a large-scale leave-one-out cross validation , instead of relying on reference genes from similar OMIM phenotypes  The GEO dataset performs better than the HBA data see . This isConsequently, some cases can be successfully predicted when relying on spatial expression information while others using ‘traditional’ expression data. We therefore suggest that while the best candidates are those derived from an intersection of the prioritizations from the two data sources, those candidates obtained from only one of the datasets should still be taken into consideration. Although the GEO dataset performs generally better, it cannot replace the HBA predictions.The effectiveness of our approach for CNS-related disease phenotypes in general does not necessarily imply that it can be successfully used for FEB and GEFS+ in particular. Febrile seizures are age-dependent and occur in immature brains of infants and young children. It is therefore imperative to specifically evaluate whether in this case expression data from the adult brain, that shows important differences in gene expression patterns, can aid in prioritizing candidate genes.MASS1 ( GPR98) that was suggested by Nakayama et al MASS1 (or VLGR1 that includes most MASS1 exons), suggesting that another gene may be underlying the FEB4 locus MASS1 as a candidate, rather than a disease gene.In order to verify the performance of our approach for the specific case of FEB and GEFS+, we performed an LOOCV taking into account only the reference genes listed in As for the large-scale LOOCV performed over all CNS-related disorders, we constructed artificial loci of various sizes centered around each of the FEB or GEFS+ genes and took the remaining five genes as reference genes for the prioritization.GABRD, GABRG2, SCN1B, and SCN2A) rank in at least one of the two datasets among the first three candidates for artificial loci of similar sizes  remains the best candidate also on average. Only for four of the thirteen loci  the best average candidate did not rank first for HBA or GEO. Most important, for several of the loci , both spatially mapped and traditional expression data agree upon the best candidate. This result is especially remarkable for the GEFS+N locus, being the third largest orphan locus with a total of 89 candidates see .Overall, the good agreement between the prioritizations for FEB/GEFS+ obtained starting from HBA and GEO expression data increases the likelihood that the best candidates are functionally correlated to known FEB and GEFS+ genes and thus potentially disease relevant. Therefore, in the following sections we discuss particularly interesting candidates for the known orphan loci.The first FEB locus was mapped by Wallace et al CRH ranks 4th for the HBA expression data and 2nd for GEO, as well as 2nd with respect to the average relative rank over both datasets (see CRH gene expression by gamma-aminobutyric acid (GABA). Tran et al Wallace et al sets see . CRH is CRH is a likely candidate for this locus, and a mutation that causes an increase in CRH levels or activity would be consistent with an autosomal dominant inheritance. Our two first ranking candidates ARFGEF1 and VCPIP1, instead, have so far not been implicated in seizures or epileptic disorders, but their coexpression with the reference genes in the human brain suggests a potential functional relationship.Taken together, Johnson et al ATCAY gene, which has been found mutated in human Cayman ataxia ATCAY encodes for BNIP-H or Caytaxin, a brain-specific member of the BNIP-2 family that reduces the steady-state levels of glutamate by inhibiting kidney-type glutaminase (KGA) enzyme activity, affecting glutamate synthesis at synapses during neurotransmission ATCAY may lead to deregulated glutamatergic activation or even to glutamate excitotoxicity ATCAY gene may potentially lead to an imbalance between excitation and inhibition that increases the susceptibility to seizures.A potentially interesting candidate for the largest FEB/GEFS+ orphan locus is the BRUNOL5, or CELF5, with the reference genes is also intriguing. Indeed, this gene encodes for a brain-specific splicing factor  The coexpression of MASS1 ( GPR98) as the disease causing gene MASS1 (see above), so that we considered it as a regular candidate.We have considered two different linkage intervals for FEB4, since Deprez et al GPR98 ranks 16th and 20th for HBA and GEO expression data, respectively. In the larger locus identified by Deprez et al MASS1 does not seem to be a particularly strong candidate. Of course this result does not imply that this gene is not involved, as we do not expect all disease genes to be revealed by a coexpression-based approach. Its involvement in audiogenic seizures in the Frings mouse strain In our prioritization for the original FEB4 locus, PAM) gene is a particularly promising alternative. Indeed, although PAM is not a very strong candidate with the HBA dataset, it is the top scoring candidate in both intervals with the GEO dataset. The protein encoded by the PAM gene is a cuproenzyme essential for the synthesis of many neuropeptides PAM gene are more susceptible to chemically induced seizures PAM as a very strong candidate for FEB4.However, among the other candidates found in these overlapping loci, we think that the peptidylglycine GLRX and C5orf13 are potentially interesting candidates. Glutaredoxin (thioltransferase), or GLRX, regulates the activity of the copper-transporting P-type ATPases ATP7A and ATP7B GLRX may lead to increased neuronal excitability. On the other hand, C5orf13  lies outside of the original FEB4 locus but might be a candidate for the larger locus reported by Deprez et al C5orf13 knockout mice But also STX7), A kinase anchor protein 7 , putative neurotransmitter receptor ( PNR), G protein receptor 58 ( GPR58) and G protein receptor 57 ( GPR57) by sequencing their coding exons and exon–intron boundaries.Nabbout et al AKAP7 is one of the best candidates, ranking 1st for the HBA. AKAP7, that is expressed among other tissues in the cerebral cortex, targets the cAMP-dependent protein kinase (PKA) to the plasma membrane, permitting its functional coupling with L-type calcium channels AKAP7. Hence, we suggest not to exclude the possibility that a mutation in AKAP7 is the true cause for the FEB5 locus.According to our analysis, IMPA2 gene, which is located in the FEB6 locus. Moreover, Nabbout et al IMPA2 gene as most likely candidate, but found no disease-causing mutations. In our analysis, IMPA2 has no significant rankings (5th out of 11 for HBA and 8th out of 12 for GEO expression data). However, neither the lack of mutations in The haplotype analysis performed by Nakayama et al NAPG) that ranks first with both the HBA and the GEO datasets. The encoded protein belongs to the SNAP family, a group of factors involved in membrane fusion events by allowing NSF proteins to target membranes. Accordingly, the NAPG protein has been shown to mediate platelet exocytosis and to control the membrane fusion events of this process The best candidate identified by our approach is the N-ethylmaleimide-sensitive factor attachment protein gamma  have no HBA expression profiles available, but rankings obtained with the GEO dataset are rather low . KCNJ6, however, was ranked 1st for the HBA expression data and 2nd for GEO. As for AKAP7 and FEB5, a mutation in a regulatory element or in another essential non-coding region should not be excluded.The orphan FEB7 locus was mapped by Hedera et al TIAM1 gene, that ranks first with the GEO dataset, could be an interesting candidate, because studies conducted in rodents have shown that the encoded protein plays a role in axonogenesis On the other hand, even the SCL4A7 gene encoding the sodium bicarbonate cotransporter NBC3 scored best in our prioritizations .Nabbout et al The two candidates that rank first for HBA and GEO data, respectively, are difficult to link to FEB or GEFS+.GPD1L, which ranks 5th for HBA, 3rd for GEO, and 3rd with respect to its average relative rank, encodes the glycerol phosphate dehydrogenase 1-like protein, an enzyme that may modify heart excitability by regulating the activity of the sodium channel SCN5A through PKC-dependent phosphorylation SCN5A itself was one of the candidates sequenced by Nabbout et al GPD1L instead may be responsible for the phenotype through its capability to regulate the activity of SCN5A or other sodium channels in the brain, some of which have already been implicated in FEB/GEFS+ .Dai et al AADACL1 and PIK3CA that rank very favorably with both datasets. The involvement of the first gene is not supported by other evidences, because AADACL1, also known as NCHE1 , has never been studied in a neuronal context. On the other hand, PIK3CA, the p110 alpha subunit of PI3K, which is known mostly for its role in growth factor-activated signaling and for its oncogenic potential, has recently been shown to negatively regulate neuronal excitability The strongest candidates identified by our analysis are KCNS3  is one of the most attractive candidate genes in the GEFS+4 locus, since it encodes a subunit of the voltage-gated potassium channel of the delayed rectifier type and is abundantly expressed in the brain Audenaert et al VSNL1  as a potential alternative candidate. Indeed, besides ranking first for both the HBA and the GEO dataset, this gene may be strongly involved in regulating neuronal excitability. VSNL1 is a member of the visinin/recoverin subfamily of neuronal calcium sensor proteins According to our prioritization, we propose The locus for GEFS+6 was mapped by Baulac et al LOC157627, MTMR9, PRAGMIN–rank 2nd, 4th, and 3rd for family 15173, respectively  and might thus be taken as an additional interesting candidate.However, it is in principle possible that the genetic causes are distinct for the two families and hence we have performed the prioritization for both intervals. The results of these prioritizations are in good agreement, since the top three genes (with respect to average relative rank) for the narrow locus– vely see . LOC1576MTMR9, MTMR7, CTSB, SGCZ, SG223 ( PRAGMIN), and ATP6V1B–according to their expression in the brain and putative function, but all identified variants were either known polymorphisms or found in 100 matched French controls. Two of these candidates are suggested as interesting by our analysis: MTMR9 and PRAGMIN . The locus contains no known genes associated with ion channels or neurotransmitter receptors, therefore Poduri et al KPNA5 obtained the highest average relative rank in our prioritization .Of the 16 candidates sequenced and excluded by Poduri et al TSPYL1 and TSPYL4 genes come out as outstanding candidates from our analysis. Indeed, they are found as the first and second ranking genes, respectively, for both the HBA and GEO dataset; a very strong result if considering that the locus contains a total of 89 candidates. The two genes are closely related members of the Nucleosome Assembly Protein (NAP) family. Both genes have been investigated as candidates for a genetic syndrome characterized by sudden infant death from cardiac and respiratory arrest, associated with dysgenesis of the testes, and null mutations have been found in TSPYL1The In this study, we have shown that the preliminary Human Brain Atlas (HBA), although not yet complete, is already a powerful tool for disease gene prediction and provides results that are complementary to those obtained from a traditional microarray dataset.Despite the fact that the HBA and GEO gene expression data represent mostly the adult brain and not the immature brain, an application to FEB/GEFS+ seemed particularly promising, given that, in an LOOCV with artificial loci of approximately the same size as the largest FEB or GEFS+ orphan loci, four out of six known disease genes ranked among the first three candidates for at least one of the two human brain gene expression atlases. This allowed us to propose several strong candidates for the true orphan loci and to discuss their possible involvement in FEB/GEFS+. Several of them would be excellent candidates for mutation screenings, however also high ranking candidates that are less obviously related to neuronal excitability should not be excluded from further analysis. In fact, the purpose and strength of computational approaches like the one presented here is to potentially uncover so far unknown and non obvious functional relationships.We downloaded the spatial gene-expression data of the Human Brain Atlas (HBA) from For comparison with the spatially mapped gene expression data we compiled a microarray dataset from GEO The evaluation and prioritization of positional candidates was performed as in our previous study. The following is a brief summary of the procedure, for a more detailed description, please see First, a set of ‘reference genes’ is defined as the genes that are involved in the given phenotype or in similar phenotypes see . Then, fThe relative ranks of the positional candidates within these reference coexpression lists are determined (i.e. the ranks divided by the number of genes in the lists) and overall scores for the single candidates are determined as the product of their relative ranks.Finally, positional candidates are sorted, i.e. prioritized, according to their increasing overall scores, since lower scores indicate a higher correlation with the reference genes and thus a higher probability of being functionally related to (and hence more likely involved in) the given phenotype.We performed large-scale LOOCVs over all CNS-related Mendelian disorders for both the spatially mapped expression data from the HBA and the GEO dataset. For each known gene–disease association, we constructed an artificial locus comprising the disease gene itself and the To identify candidate genes (in terms of Entrez gene IDs) residing between or at the markers flanking the mapped linkage intervals, reported in the respective publications see , we usedTable S1List of GEO accessions of the ‘traditional’ microarray expression data set.(PDF)Click here for additional data file.Table S2Results of the leave-one-out tests for human CNS-related OMIM phenotypes. Legend: (PDF)Click here for additional data file.Table S3Overlap of correct leave-one-out predictions  for reference genes from similar phenotypes. Legend: (PDF)Click here for additional data file.Table S4Candidate gene prioritizations/predictions for orphan FEB and GEFS+ loci. Legend: locus  =  orphan FEB/GEFS+ locus; candidate_id/ symbol  =  Entrez gene ID and symbol of the positional candidate; rank_HBA/ GEO  =  rank among the prioritized positional candidates for the HBA/GEO dataset; relRank_HBA/GEO  =  relative rank among the prioritized positional candidates for the HBA/GEO dataset (rank divided by number of candidates with expression data); avRelRank_HBA+GEO  =  average relative rank, i.e. (XLS)Click here for additional data file.Text S1Supplementary Methods. Definition of CNS-related Mendelian disorders; similarity of human disease phenotypes; leave-one-out cross validation. (PDF)Click here for additional data file.


This SNP is located in DnaJ homolog subfamily C member 5B (DNAJC5B) and merits investigation in additional randomized studies of pravastatin and other statins.Statin therapy reduces the risk of coronary heart disease (CHD), however, the person-to-person variability in response to statin therapy is not well understood. We have investigated the effect of genetic variation on the reduction of CHD events by pravastatin. First, we conducted a genome-wide association study of 682 CHD cases from the Cholesterol and Recurrent Events (CARE) trial and 383 CHD cases from the West of Scotland Coronary Prevention Study (WOSCOPS), two randomized, placebo-controlled studies of pravastatin. In a combined case-only analysis, 79 single nucleotide polymorphisms (SNPs) were associated with differential CHD event reduction by pravastatin according to genotype (P<0.0001), and these SNPs were analyzed in a second stage that included cases as well as non-cases from CARE and WOSCOPS and patients from the PROspective Study of Pravastatin in the Elderly at Risk/PHArmacogenomic study of Statins in the Elderly at risk for cardiovascular disease (PROSPER/PHASE), a randomized placebo controlled study of pravastatin in the elderly. We found that one of these SNPs (rs13279522) was associated with differential CHD event reduction by pravastatin therapy in all 3 studies: P = 0.002 in CARE, P = 0.01 in WOSCOPS, P = 0.002 in PROSPER/PHASE. In a combined analysis of CARE, WOSCOPS, and PROSPER/PHASE, the hazard ratio for CHD when comparing pravastatin with placebo decreased by a factor of 0.63 (95% CI: 0.52 to 0.75) for each extra copy of the minor allele (P = 4.8×10 Statins, inhibitors of 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGCR), are widely prescribed to reduce low-density lipoprotein cholesterol (LDL-C) levels and cardiovascular events. In an analysis of 14 randomized clinical trials, statin therapy was associated with about 20% reduction of major cardiovascular events for each mmol/L (38.7 mg/dL) reduction of LDL-C APOE is associated with differential LDL-C lowering by statin therapy. Additionally, variants of the HMGCR gene have been also been shown to be associated with differential LDL-C reduction by statin treatment KIF6 variant (rs20455) and differential event reduction by pravastatin Multiple studies investigated whether genetic variants are associated with differential LDL-C reduction by statin therapy To investigate the effect of genetic variation on the reduction of CHD events by pravastatin we conducted a genome wide association study (GWAS) in two large randomized controlled trials that used the same dose of pravastatin: Cholesterol and Recurrent Events (CARE) trial, and the West of Scotland Coronary Prevention Study (WOSCOPS) trial and replicated our findings in a third randomized control trial of pravastatin: PROspective Study of Pravastatin in the Elderly at Risk/PHArmacogenomic study of Statins in the Elderly at risk for cardiovascular disease (PROSPER/PHASE).A summary of the baseline characteristics of the patients included in the genetic analyses of CARE, WOSCOPS, and PROSPER is provided in −5) were found around QTRRTD1 and KIAA1407 on chromosome 3, near LINC00474 on chromosome 9, and near FAM9C on chromosome X (−4) associated with differential event reduction by pravastatin therapy . The minor allele (C) of the rs13279522 SNP had a frequency of 16% among 2775 self-described Caucasian of the CARE study and the genotype distribution of this SNPs did not deviate from Hardy-Weinberg equilibrium expectations (P = 0.3). The frequency of the C allele was 61% among 76 self-described African Americans in CARE, 23% among 83 self-described Hispanics and 19% among 31 self-described Asians or Pacific islanders. We estimated the risk reduction by pravastatin in each genotype group in each study and in a combined analysis of all three studies (−10) and among major homozygotes was 13% . This SNP was not associated with baseline LDL-C levels among cases and non-cases in CARE (P>0.7) or in PROSPER/PHASE (P>0.18). Neither was it associated with change in LDL-C from baseline to 3 month visit in the pravastatin groups of CARE (P>0.4) or PROSPER/PHASE (P>0.09).In the second stage of this investigation, we analyzed 74 of these 79 SNPs in PROSPER/PHASE (genotypes were not available in PROSPER/PHASE for the remaining 5 SNPs). We have also determined the genotypes for the remaining patients from CARE and WOSCOPS (with or without CHD events) for most of these 74 SNPs and conducted an analysis of these 74 SNPs in the combined CARE, WOSCOPS, and PROSPER/PHASE studies . We foun studies . The ris2 = 1) in PROSPER/PHASE. Our analysis indicated that the major allele of this variant in HMGCR had greater risk reduction from pravastatin therapy . This finding is consistent with the report by Chasman et al. (8) and Krauss et al. (5) who found that the major allele is associated with greater LDL-C reduction by statin therapy. We have also investigated the rs7412 SNP in APOE. We had no data for this SNP (or an LD SNP) in the PROSPER/PHASE study. However, a combined analysis of the case-only studies of CARE and WOSCOPS found that the major allele of this SNP  is associated with greater risk reduction from pravastatin therapy . This result is inconsistent with a previous report by Thompson et al. (9) who found that the minor allele of this SNP was associated with greater LDL-C reduction from atorvastatin therapy.We have also investigated the association of SNPs that have been previously reported to be associated with differential response to statin therapy. We investigated a variation in HMGCR gene by combining the analysis of rs17238540 in the case-only studies of CARE and WOSCOPS, and of a SNP in LD with rs17238540 . Heterozygotes of rs13279522 (∼26% of the population) had 45% event reduction in a combined analysis of CARE, WOSCOPS and PROSPER/PHASE, while major homozygotes (∼71% of the population) had 13% event reduction by pravastatin.We have conducted a genome-wide association study designed to identify genetic variants associated with differential CHD event reduction by pravastatin therapy and found that event reduction by therapy differed according to genotype for a SNP in the DNAJC5B gene . DnaJ/Hsp40 family proteins are co-chaperones characterized by a highly conserved 70 residue J-domain, homologous to the Thus, DNAJC5B as well as other genes in the same linkage region , such asWe found no association between rs13279522 and baseline LDL-C levels or change in LDL-C levels by pravastatin therapy. If event reduction by pravastatin is at least partially mediated through non-LDL-C effects–such as reduction in protein isoprenylation and subsequent effect on inflammatory pathway −8. However, this observation is sufficiently intriguing to encourage other investigators to study this SNP in other randomized trials of statin therapy. This study was powered to detect SNPs associated with a 1.7 fold differential event reduction by pravastatin therapy, allele frequency greater than 0.15 at alpha = 10−4. Therefore, some SNPs were likely to be misclassified as non-associated SNPs . Although one of the strengths of this study is that patients were randomized to 40 mg pravastatin therapy daily in all of the trials examined, the risk profile of the patients included in these studies varied. CARE is a secondary prevention trial which recruited patients who had had a myocardial infarction prior to the beginning of the study. WOSCOPS is a primary prevention trial who recruited patients with no history of myocardial infarction, but who had high risk profile for CHD. The PROSPER/PHASE study recruited patients who were elderly, either with or without history of CHD. Other potential differences between the studies, such as concomitant medication use and environmental exposures have not been accounted for. However, the randomized nature of these studies goes a long way toward addressing these potential biases.This study has several limitations. The case-only analysis used in the first stage of this investigation is valid only if genotype and treatment are independent of each other; however, in a randomized trial they are independent by design. The minimal P value observed in this is greater than the commonly used genome-wide significance threshold of 5×10In conclusion, in this genome wide association analysis of event reduction by pravastatin therapy, we have identified a SNP in the DNAJC5B gene that was associated with differential event reduction by pravastatin therapy in CARE, WOSCOPS, and PROSPER. Widespread replication of this association in multiple additional studies and potentially a clinical trial demonstrating improved outcomes or an investigation supporting the biological rationale for the mechanism of action of this variant are needed before the testing of this variant is introduced into clinical practice.This investigation was designed to identify genetic polymorphisms that were associated with differential CHD event reduction by pravastatin therapy. The individuals in the study were drawn from three randomized, placebo-controlled trials of pravastatin: CARE, WOSCOPS, and PROSPER/PHASE. We conducted the investigation in two stages . In the The population for this investigation was derived from three randomized, placebo-controlled trials of pravastatin: CARE, WOSCOPS, and PROSPER/PHASE. All patients in this investigation provided a written informed consent and the study protocol was approved by the institutional ethics review boards. CARE has been described previously WOSCOPS has been previously described PROSPER has been previously described Genotyping for the whole-genome scan of the first stage was conducted using Infinium HumanOMNI-1 Quad V1 Beadchip from Illumina (San Diego CA) which interrogates 1.14 million markers. Quality control was performed according to Fellay et al. In order to make efficient use of the available resources for genotyping, in the first stage of this investigation, a case-only design was used to estimate whether event reduction during pravastatin therapy differed according to genotype. Since the genotype and treatment are independent of each other in a randomized clinical trial, the genotype frequencies among treated and untreated cases can be used to estimate the interaction between pravastatin treatment and genotype and to calculate the corresponding P value.The P value for each SNP in the first stage of this investigation was evaluated in a logistic regression model with treatment status as the dependent variable and SNP coded in an additive model as the predictor variable. The rationale for this design is that if one of the alleles is associated with better event reduction by pravastatin treatment compared with placebo, we would expect that this allele frequency would be lower among cases of the treatment group compared with the placebo group. This expectation is justified if drug allocation is independent of genotype, a valid assumption by design in a randomized study. The odds ratio for the SNP from this logistic regression model was referred to as the synergy index by Davis et al. In the second stage of this investigation, a time to event analysis was performed using Cox proportional hazards models and the Wald test was used to assess the effect of pravastatin, compared with placebo, on coronary events. Interaction between SNP and pravastatin treatment was assessed from models that included an interaction term between SNP and pravastatin therapy and included the following covariates: age for WOSCOPS, age, sex, and country of clinical center for PROSPER/PHASE, and age, sex, and principal components of the genetic variability using genotypes of 746 SNPs as previously described


Ketamine, a known antagonist of N-methyl-D-aspartic (NMDA) glutamate receptors, had been used as an anesthetic particularly for pediatric or for cardiac patients. Unfortunately, ketamine has become an abusive drug in many parts of the world while chronic and prolonged usage led to damages of many organs including the brain. However, no studies on possible damages in the brains induced by chronic ketamine abuse have been documented in the human via neuroimaging. This paper described for the first time via employing magnetic resonance imaging (MRI) the changes in ketamine addicts of 0.5–12 years and illustrated the possible brain regions susceptible to ketamine abuse. Twenty-one ketamine addicts were recruited and the results showed that the lesions in the brains of ketamine addicts were located in many regions which appeared 2–4 years after ketamine addiction. Cortical atrophy was usually evident in the frontal, parietal or occipital cortices of addicts. Such study confirmed that many brain regions in the human were susceptible to chronic ketamine injury and presented a diffuse effect of ketamine on the brain which might differ from other central nervous system (CNS) drugs, such as cocaine, heroin, and methamphetamine. Ketamine, a known antagonist of N-methyl-D-aspartic (NMDA) glutamate receptors, had been used as an anesthetic, particularly for pediatric or for cardiac patients. Ketamine employed in prescribed medical conditions had its advantages as it did not increase intracranial pressure during neurosurgery . They all had no previous medical history of brain trauma or neurological diseases. The dosage used by the patients was from 0.2 to 3 g a day but majority dosage was 1 g a day. Among them, 19 of these patients took ketamine daily, while only two took it twice or three times a week. The durations of drug addiction ranged from 0.5 to 12 years. The break down of addicts with three years addiction or below was 2 with 8 channel SENSE head coil. T1-weighted images were obtained with 2000 ms repetition time (TR) and 20 ms echo time (TE). T2-weighted images were obtained with TR and TE at 3000 ms and 20 ms and fluid attenuated inversion recovery (FLAIR) images were obtained at 11,000 ms TR and 20 ms TE. The total acquisition time for the sequences was about 30 min.MRI was performed with a 3.0-Tesla imager . The images were obtained with 5.0 mm section thickness (ST). The field of view (FOV) was 23 × 18 cmThe results of lesions observed in all the 21 ketamine addicts were depicted in Table Medical histories indicated that addicts over 4 year of ketamine addiction displayed memory deficits and anxiety depression while those from the 5 to 7 years developed definitive ataxia and by 7 years and above, dyskinesia became obvious.This study revealed the lesions in many regions of the brain of ketamine addicts. These lesions appeared as minute patches in the first year and became larger sites of atrophy by 4 years of addiction. The majority of the addicts was on dosage of 1 g per day and used ketamine daily for several years. In this work, since the volunteers were mostly below 30 years old and only 2 individuals above 30 years old, a comparison of the effect of age upon addiction was not conclusive in this stage, even though we had seen no worse in the aged group (above 30 years old) when compared with the slightly younger old. A study of age response would definitely be conducted in future. However, it is well-known that ketamine addicts were usually young as represented in this cohort. The brain regions affected were prefrontal, parietal, occipital, limbic, brainstem, and corpus striatum. The lesions affected both the gray and white matter, i.e., neurons and nerve fibers in the human; these were similar to those reported earlier by us in the mice and the monkey  scan usually pointed to demyelination or metabolic changes (Brismar and Ozand, There were some studies on the neuroimaging of addicts using different drugs. Reneman et al. , for exaThis piece of work further suggests that addicts even on a single drug alone like ketamine might lead to atrophy of the brain after a few years of addiction. As addicts usually take ketamine at different time and ad lib, it was very difficult to compare dosages. In this work, there were hints that increasing dosage or combination of ketamine with other abusive drug would hasten the damages. It is important therefore to compare the years of duration and with a certain level of dosage which at least led some usable data. Further work should emphasize on the comparison of the interaction of these abuse drugs.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 contrast, maps created from LA-MRSA isolates obtained during a proven LA-MRSA outbreak were nearly indistinguishable showing that transmission of LA-MRSA can be detected by whole genome mapping. Finally, whole genome maps of LA-MRSA isolates originating from two unrelated veterinarians and their household members showed that veterinarians may carry and transmit different LA-MRSA strains at the same time. No such conclusions could be drawn based spa-typing and MLVA. Although PFGE seems to be suitable for molecular typing of LA-MRSA, WGM provides a much higher discriminatory power. Furthermore, whole genome mapping can provide a comparison with other maps within 2 days after the bacterial culture is received, making it suitable to investigate transmission events and outbreaks caused by LA-MRSA.After its emergence in 2003, a livestock-associated (LA-)MRSA clade (CC398) has caused an impressive increase in the number of isolates submitted for the Dutch national MRSA surveillance and now comprises 40% of all isolates. The currently used molecular typing techniques have limited discriminatory power for this MRSA clade, which hampers studies on the origin and transmission routes. Recently, a new molecular analysis technique named whole genome mapping was introduced. This method creates high-resolution, ordered whole genome restriction maps that may have potential for strain typing. In this study, we assessed and validated the capability of whole genome mapping to differentiate LA-MRSA isolates. Multiple validation experiments showed that whole genome mapping produced highly reproducible results. Assessment of the technique on two well-documented MRSA outbreaks showed that whole genome mapping was able to confirm one outbreak, but revealed major differences between the maps of a second, indicating that not all isolates belonged to this outbreak. Whole genome mapping of LA-MRSA isolates that were epidemiologically unlinked provided a much higher discriminatory power than  Staphylococcus aureus and in particular methicillin resistant S. aureus (MRSA) is a bacterial pathogen that is associated with serious hospital- and community-acquired infections SmaI recognition site Cfr9I, a neoschizomer of SmaI, can be used to overcome the problems with DNA-methylation Cfr9I yields a relatively high discriminatory power for LA-MRSA isolates, PFGE remains a time-consuming, laborious and non-portable method. Other typing techniques, such as staphylococcal protein A (spa)-typing and multiple-locus variable number of tandem repeat analysis (MLVA), can be used to characterize LA-MRSA, but yield very limited discrimination within this clade. From 2008–2012, 17,869 MRSA isolates were characterized by spa-typing and MLVA within our national MRSA surveillance. The predominant MLVA complex was MC398, representing LA-MRSA and comprising of 7,066 isolates. Although 96 different spa-types and 78 different MLVA-types (MT) were found within this clade, the 2 dominant types, spa-type t011, MT398 (n = 4093) and spa-type t108, MT572 (n = 1282), accounted for 76% of all LA-MRSA isolates. In contrast, MC5 , the most isolated clade after MC398, yielded 113 different spa-types and 205 different MLVA-types with spa-type t003, MT130 (n = 182) and spa-type t002, MT5 (n = 126) as the predominant types, but accounting only for 12% of all MC5 isolates. Strains within MC398 show limited variability and the absence of a highly discriminating typing method to characterize MC398 (LA-MRSA) isolates has hampered studies on the origin and transmission routes of this MRSA clade.Typing of LA-MRSA however, has turned out to be a challenge. One of its characteristics is that LA-MRSA are non-typeable with regular pulsed-field gel electrophoresis (PFGE) due to methylation of the S. aureus isolates these maps consists of 200–300 restriction fragments. In contrast, in PFGE of S. aureus only 10–15 non-ordered restriction fragments are used for the analysis In 2007, a molecular analysis technique was introduced initially called optical mapping and now designated as whole genome mapping (WGM), although whole chromosome mapping would be more appropriate. A whole genome map is a high-resolution, ordered, whole genome restriction map and for In this study, we assessed and validated the capability of whole genome mapping to differentiate LA-MRSA isolates. For this purpose, we used epidemiologically related and non-related MRSA and LA-MRSA isolates.in silico maps. In addition, two LA-MRSA (MC398) isolated from Dutch veterinarians (VET78 (t = 0 m) and VET35 (t = 0 m)) were used for reproducibility experiments. The capability to identify transmission events was studied using isolates obtained during three well-documented outbreaks in the Netherlands of CA-MRSA (USA300), HA-MRSA (MC45) and LA-MRSA (MC398) and 22 LA-MRSA isolates from Dutch veterinarians and their household members spa-typing and PFGE as described previously For this study a total of 18 MRSA and 45 LA-MRSA isolates were selected to create 84 different whole genome maps (WGMs). Two MRSA strains  and one LA-MRSA strain (S0385), which are often used as reference strains and for which published genomes are available S. aureus this protocol required small but essential adaptations. First, we doubled the amount of lysostaphine (15 units/sample) used during the spheroplasting step and tripled the incubation time (3 hrs) recommended by the manufacturer. Furthermore, to obtain sufficient yield of HMW DNA for WGM we empirically determined that the isolated DNA required to relax and go into solution for at least 24 hrs at room temperature before proceeding to the dilution step. In our protocol, a 1∶40 dilution was usually optimal for S. aureus. The quality (e.g. the average molecule size (AMS)) and the concentration of the DNA samples were checked using so-called Quality Control cards . We found that for S. aureus a minimum of 5–10 DNA molecules of approximately 250,000 bp should be present per image in order to obtain good WGMs.Whole genome mapping requires the input of high molecular weight DNA (HMW DNA) with an average molecule size of approximately 250,000 bp. The Argus™ HMW DNA isolation kit  provides reagents and a protocol specifically designed for the isolation of HMW DNA. Briefly, a single colony was picked from a plate and suspended in cell wash buffer. Bacteria were treated to form spheroplasts and subsequently lysed to release the HMW DNA. For the isolation of HMW DNA of AflII and stained with a fluorescent agent in a micro fluids system. The restriction fragments were sized in the whole genome mapper and assembled into a whole genome map in which the restriction sites are mapped in the order in which they occur in the chromosome using MapManager software version 1.1 (OpGen). For assembly, only DNA molecules larger than 150,000 bp and with a minimum of 12 restriction sites were included. In a complete map, each assigned restriction site should have been found in at least 30 single molecules (coverage) and typically an average depth of 50 to 80 molecules is found. To assess whether assembly using the settings recommended by OpGen resulted in reproducible maps, the same raw data obtained from DNA of reference strains NCTC8325 and S0385 were assembled into WGMs 5 times each. Comparison of the generated maps revealed identical WGMs demonstrating the reproducibility of the assembly under the recommended conditions. After assembly, the generated restriction maps were imported into MapSolver software version 3.0  to create the final circular whole genome map and without further manipulation the map was subsequently saved in .xml file format.Whole genome maps were created using the manufacturer’s instructions. Shortly, HMW DNA was applied to Mapcards containing micro channels in which DNA molecules were stretched, bound to a glass surface, and subsequently digested with in silico whole genome map of LA-MRSA strain S0385 (AM990992) was used as a template and for regular MRSA strains an in silico map of NCTC8325 (CP000253) was utilized. The BioNumerics software allowed alignment and clustering of WGMs using filtering of small fragments and size tolerance settings. The background on the algorithms used for the comparisons of whole genome maps in BioNumerics will be described in detail elsewhere. In the alignment, two fragments were considered to be identical if their sizes differed no more than the value set for the absolute tolerance. If this criterion was not met, two fragments were still considered identical if they met the relative tolerance criterion. This relative tolerance is defined as the difference between two fragments dived by their average; (size fragment 1– size fragment 2)/((size fragment 1+ size fragment 2)/2). The similarity between the whole genome maps was calculated by dividing the number of matched fragments by the total number of fragments. The method we chose for cluster analysis was UPGMA. In the BioNumerics software WGMs are represented as linear maps displaying the fragments in randomly chosen colours, with matching fragments sharing the same colour. Fragments of maps that were excluded from the comparison as a result from the fragment filtering were not deleted from the maps, but displayed as blocks with reduced height in the maps.The .xml files containing the ordered maps were imported into a database created with an alpha version of BioNumerics version 7.0 . For clustering and alignment of the maps, we chose the first restriction fragment after the origin of replication in the chromosome as the starting point of the map, using a map rotation plugin in BioNumerics. For rotation of LA-MRSA maps, the The whole genome mapping encompasses procedures in which the restriction fragments are sized and subsequently assembled into ordered restriction maps. However, there is experimental variation in the sizing and also smaller restriction fragments may variably be lost during the procedure. The reason for this loss is that smaller DNA fragments have a relatively low net charge and as a result a weaker bond to the glass surface of the Mapcard. To compensate for both the variation in sizing and the presence or absence of smaller fragments during clustering and alignment, tolerance and filtering were employed in the BioNumerics software. In order to assess the optimal filtering and tolerance settings, DNA isolated from a MRSA and a LA-MRSA isolate was repeatedly used to create WGMs on 4 consecutive days and analyzed in BioNumerics using an range of filtering and tolerance settings. A combination of a filtering setting that excluded fragments <3,000 bp from the comparison and a relative tolerance of 15% combined with an absolute tolerance of 1,000 bp resulted in maps with a similarity of >99% for replicates of the DNA samples, whilst unrelated samples yielded distinct maps. Settings that were more stringent resulted in artificial differences between maps of the replicates while less stringent settings resulted in loss of the ability to discriminate unrelated samples.To assess the temporal stability of MRSA genomes as reflected in whole genome maps under laboratory conditions, single colonies from 2 LA-MRSA and 2 MRSA isolates were sub-cultured for 30 consecutive days. The similarity between the WGMs obtained from the DNA isolated on day 1 and day 30 ranged between 99.4% and 100%, showing that under laboratory conditions the MRSA genome was stable enough to yield virtually identical maps.in silico counterparts generated in the Mapsolver software. The similarity between the WGMs created in the laboratory and their in silico counterparts varied between 95.5% and 98.7% for these isolates. Close inspection of the differences between the real WGMs and the in silico maps revealed that in general, sizing of the restriction fragments by the whole genome mapper in general results in slightly smaller fragments than those predicted based on the whole genome sequences. Furthermore, there were several locations where the composition of the predicted maps clearly differed from the maps generated in the laboratory  boratory .spa-type t024 and MT308 (MC8). The whole genome maps of the isolates were also highly similar with a similarity of 97.9% between the most distinct maps. Remarkably, the 3 isolates that had an additional band in PFGE also carried an additional, approximately 40 kb DNA segment which was absent in the 5 other isolates  isolates obtained during an outbreak in a Dutch beauty salon in 2006 isolates .Based on the results with the replicates, that yielded >99% similar profiles, and the result of the above described outbreak we chose to set the cut-off value at 98% for indistinguishable profiles. This will allow for the variation in WGMs due to the presence or absence of mobile elements.spa-type t445 and MT512 (MC45) and indistinguishable PFGE) from one facility for whole genome mapping. Although the WGMs of 4 of the isolates were closely related with similarities of >98.5%, there were major differences with the maps of the other isolates resulting in only 90.4% similarity between the most distinct maps  and originated from an outbreak in two large medical care facilities in the Netherlands that started in 2001 and persisted for a period of 20 months nct maps .Spa-typing and MLVA could hardly discriminate these isolates yielding only 5 different spa- and 4 MLVA-types caused by variations in the number of spa-repeats only. In contrast, whole genome mapping was able to discriminate these isolates and the average similarity between maps ranging from 77.0% to 98.3%. However, 2 of the 16 WGMs were nearly identical, with a similarity of 99.6%. Considerable variation was seen in the SCCmec region of these isolates with 11 different compositions of the SCCmec region among the 16 isolates . The spa and MLVA profiles from the 3 other isolates, all originating from the family of VET66, differed only slightly from the 21 other isolates. PFGE yielded indistinguishable banding patterns for 11 of the 13 isolates obtained from the family of VET45 and the profiles of the other 2 isolates, differed from that of the 11 isolates in a single band and in 2 bands, respectively. Nine of the 11 isolates from VET66 and his household members had identical PFGE patterns, while the remaining 2 isolates were clearly different. The PFGE profiles of the two families represented two distinct groups, which was corroborated by WGM. Within the isolates from VET45 and his household members, two different WGM-clusters were identified. The first cluster (cluster A) was comprised of 8 isolates with a similarity of 97.9% between the most distinct WGMs and 6 of the maps were more than 99.5% similar. The second cluster (cluster B) was comprised of WGMs from 4 isolates and was distinct from the first cluster with a similarity between the first and second cluster of 93.3%. PFGE profiles of 11 of the 12 isolates belonging to these clusters were identical, showing the high discriminatory power of WGM. The remaining WGM obtained from the first isolate (t = 0 m) cultured from VET45, was quite distinct and did not belong to cluster A or cluster B. This isolate also had a distinct PFGE profile, indicating that the veterinarian carried a different strain at this time. Within the 11 LA-MRSA isolates obtained from the second family (VET66), the maps of 9 isolates were nearly identical with a similarity of 96.2% between the most distinct maps within this cluster (cluster C). These 9 isolates were also indistinguishable with PFGE. The remaining 2 isolates differed considerably in all typing analyses , sampling took place during home visits after 2 months (t = 2 m) and by the veterinarians themselves after 6, 10 and 14 months . During this study period, 24 LA-MRSA isolates were cultured from the two families. Thirteen LA-MRSA isolates were obtained from veterinarian VET45 and his household members, while 11 isolates were cultured from veterinarian VET66 and his household members. analyses .The sample from veterinarian VET66 taken at t = 0 m contained LA-MRSA with a WGM that was identical to those found for the LA-MRSA from the same veterinarian up to 14 months later. At sampling points from 6 to 14 months this LA-MRSA was also isolated from the household members at sampling moments 6 to 14 months. Remarkably, the veterinarian apparently carried 2 distinct LA-MRSA strains in nose and throat at the 6 month sampling point and it was the strain found in the nose that had been transmitted to its household members. In the family of VET45, WGM suggested that transmission with 2 different strains had taken place at different time points. The first strain (cluster A) was isolated from VET45 and his household members at the 6 months sampling point and thereafter. The second strain (cluster B) was isolated from VET45 at the 2 months (nose) and 10 months sampling point (nose and throat). This strain was also found in contact45.1 (HHM45.1) at 10 months (throat), but remarkably, a different strain was isolated from the same contact at the same sample moment, albeit in a different anatomic site (nose) .spa-typing, MLST and MLVA. Although the PFGE using Cfr9I provides a much better differentiation of MC398 isolates this method is laborious and yields data that are not easily electronically exchanged. This hampers the study of possible transmission events and outbreaks caused by this MRSA clade. The whole genome mapping presented here provides a typing method with high discriminatory power that appears to be suitable to identify LA-MRSA transmission events.LA-MRSA isolates are hard to discriminate when using current molecular typing techniques, such as spa- and MLVA-typing failed to provide clear distinction. Among these unrelated isolates many different compositions of the SCCmec region existed indicating that variation in this locus significantly contributes to observed genomic diversity among LA-MRSA. Furthermore, WGM was able to confirm well documented CA-MRSA, HA-MRSA and LA-MRSA transmission events. In the first outbreak, involving transmission of a USA300 strain in a beauty salon, WGM identified a DNA segment of approximately 40 kb present in only 3 of the 8 isolates. Although this was not reported as such in the original paper, the additional fragment was also detected by PFGE. This additional fragment most likely represents a bacteriophage which usually has a genome size of approximately 40 kb and has the ability to jump in and out of bacterial genomes The discriminatory power of WGM was illustrated by the ability to type and differentiate LA-MRSA isolates obtained from epidemiologically unrelated veterinarians frequently visiting livestock farms for which spa-typing, MLVA and PFGE) were circulating at the time of the outbreak. Indeed in 2002, 19% (n = 265) of all MRSA isolates sent to the RIVM had the PFGE-type that was identified as the outbreak-type. Of these isolates one-third originated from health care centers other than the two that were identified as the outbreak centers. This shows that the higher discriminatory power of WGM makes it possible to better assess whether isolates belong to an outbreak or not.WGM of isolates presumably belonging to a HA-MRSA outbreak in the Netherlands revealed that several isolates did not belong to the outbreak. Although, this multi-center outbreak expanded over a long period of time and involved many different patients, the isolates we selected for this study originated from one center and were received within 2 weeks of each other. We believe that we were unable to assign all isolates as part of the outbreak because multiple strains yielding the same molecular characteristics (spa-typing or MLVA and with much more uncertainty when using PFGE. We are currently conducting WGM of isolates obtained from a larger number of veterinarians and their household members to study LA-MRSA transmission among this group in further detail.We employed WGM to assess whether the technique is suitable to identify transmission events of LA-MRSA in a community setting (i.e. transmission from veterinarians to their household members). Indeed, we obtained virtually identical WGMs of the isolates obtained from the veterinarians and their household members. However, not all isolates were identical and two different clusters were identified among the isolates of one veterinarian and his household members. These results suggest that veterinarians may pick up different LA-MRSA strains during their visits to animal farms and become colonized for a longer time period. Apparently, veterinarians may carry different LA-MRSA strains in their nose and throat at the same time and both may be transmitted to their direct contacts. We conclude that WGM now enables us to identify transmission events of LA-MRSA which would be impossible using S. aureus was described and where a map distance of 5% was chosen as a cut-off point to define a WGM cluster XbaI as restriction enzyme, but seems to be valid for LA-MRSA and other restriction enzymes as well.Based on the comparisons made of replicates of both MRSA and LA-MRSA isolates in this report and allowing for the presence of occasional mobile elements we now consider MRSA isolates with WGMs that have similarities of 98% or higher as indistinguishable. Isolates with WGMs with similarities between 95–98% may represent the same strain or should be regarded as highly related strains and those with maps that have similarities below 95% are deemed different strains. These cut-off values are supported by a recent report of Shukla et al. in which WGM of in silico maps and the maps created in our lab would not be designated as identical as would be expected. Although showing good concordance, we found inconsistencies in the size, number and order of fragments between the WGMs created in the lab and the in silico maps. The fragments present in the in silico maps were generally larger, but that should have been compensated for by the tolerance settings. The subcultures of the reference strains from which DNA was isolated to generate the whole genome sequences which were used to create the in silico maps and the subcultures used in our lab to create the real WGMs were not identical. Since the isolates used for comparison of in silico and real maps may have been subcultured for many years in various laboratories, changes in the genome may have occurred over time and this might explain for some of the observed differences. Another possible explanation could be that either the whole genome sequences of the isolates used for generating in silico maps contain sequence errors or that the assembly of the WGMs is inaccurate. However, the reproducibility of the WGM procedure assessed by repeated mapping of the same DNA sample on 4 consecutive days turned out to be excellent. To exclude that subculture of the reference isolates caused the observed differences we are currently assessing the complete genome sequences of 3 LA-MRSA isolates and we will compare the in silico maps based on these sequences with those obtained in the lab.We do acknowledge that based on our cut-off criteria the Molecular typing is used to characterize pathogens like MRSA in order to provide evidence that will support epidemiological studies on transmission and outbreaks. Furthermore, it is used to study changes in bacterial population structures e.g. to assess the effects of human intervention such as widespread antibiotic treatment and vaccination. WGM seems to be suitable to type LA-MRSA and identify transmission events where existing typing methods usually fail. An alternative that was not included in the analysis, but is rapidly gaining interest as typing tool, is whole genome sequencing (WGS). WGS has many advantages over WGM e.g. it will be very difficult to infer phylogenetic relationships and assess population structures using WGM, whereas WGS is very well suited for these purposes. Furthermore, sequencing can potentially reveal all details on gene composition, the presence of virulence factors, etc. Although we believe that WGS is the ultimate typing method, there may be a number of drawbacks for outbreak investigations leaving a niche for methods like WGM. WGS has been suggested as the best tool for typing during outbreak investigations and a number of papers have been published supporting this claim