Coherence estimation has been used as an indirect measure of voluntary neurocontrol of residual motor activity following spinal cord injury (SCI). Here intramuscular Tibialis Anterior (TA) coherence estimation was performed within specific frequency bands for the 10-60 Hz bandwidth during controlled ankle dorsiflexion in subjects with incomplete SCI with and without spasticity.In the first cohort study 15 non-injured and 14 motor incomplete SCI subjects were recruited to evaluate TA coherence during controlled movement. Specifically 15-30 Hz EMG was recorded during dorsiflexion with: i) isometric activation at 50, 75 and 100% of maximal voluntary torque (MVT), ii) isokinetic activation at 60 and 120°/s and iii) isotonic dorsiflexion at 50% MVT. Following identification of the motor tasks necessary for measurement of optimal TA coherence a second cohort was analyzed within the 10-16 Hz, 15-30 Hz, 24-40 Hz and 40-60 Hz bandwidths from 22 incomplete SCI subjects, with and without spasticity.Intramuscular 40-60 Hz, but not 15-30 Hz TA, coherence calculated in SCI subjects during isometric activation at 100% of MVT was lower than the control group. In contrast only isometric activation at 100% of MVT 15-30 Hz TA coherence was higher in subjects with less severe SCI (AIS D vs. AIS C), and correlated functionally with dorsiflexion MVT. Higher TA coherence was observed for the SCI group during 120°/s isokinetic movement. In addition 15-30 Hz TA coherence calculated during isometric activation at 100% MVT or 120°/s isokinetic movement correlated moderately with walking function and time from SCI, respectively. Spasticity symptoms correlated negatively with coherence during isometric activation at 100% of MVT in all tested frequency bands, except for 15-30 Hz. Specifically, 10-16 Hz coherence correlated inversely with passive resistive torque to ankle dorsiflexion, while clinical measures of muscle hypertonia and spasm severity correlated inversely with 40-60 Hz.Analysis of intramuscular 15-30 Hz TA coherence during isometric activation at 100% of MVT is related to muscle strength and gait function following incomplete SCI. In contrast several spasticity symptoms correlated negatively with 10-16 Hz and 40-60 Hz TA coherence during isometric activation at 100% MVT. Validation of the diagnostic potential of TA coherence estimation as a reliable and comprehensive measure of muscle strength, gait and spasticity should facilitate SCI neurorehabilation. Spinal cord injury (SCI) has a devastating impact on sensorimotor function, often leading to reduced quality of life, presenting a serious socioeconomic problem for national healthcare systems. AlthougElectromyographic (EMG) muscle coherence estimation is a mathematical index that calculates the degree of synchronization of two independent EMG signal sources calculated in the frequency domain, and whiSurprisingly no systematic studies are available that demonstrate the optimal testing conditions required to analyze motor unit synchronization during controlled movement, at moderate to strong muscle contractions or at slow and fast speeds, in subjects with residual voluntary muscle strength and gait function after incomplete SCI. Several physiological studies justify the application of muscle coherence estimation as an indirect measure of voluntary motor drive with respect to specific motor tasks. A relationship between the force of isometric contraction and cortSpinal cord injury invariably leads to different degrees of corticospinal tract injury. Given tMeasurement of residual voluntary activity within the Tibialis Anterior (TA) muscle may represent an interesting diagnostic marker of function after SCI, particularly as this muscle receives strong innervation from the corticospinal system. Indeed Systematic evaluation of the optimal movement testing conditions for muscle coherence analysis and the intramuscular TA frequency band best related to residual voluntary muscle strength and gait function should provide important orientative data to guide the diagnostic potential of monitoring motor unit synchronization as a comprehensive diagnostic tool for clinical motor function after incomplete SCI.The study was divided into two trials. The objective of the first trial was to identify the optimal specific kinematic condition to measure differences in motor unit synchronization between healthy non-injured subjects and individuals with incomplete SCI Table . TherefoThe study was performed following approval by the Local Toledo Hospital Clinical Ethical Committee . All recruited subjects signed a consent form and gave their permission for data publication. Healthy non-injured subjects (n=15) and individuals with incomplete motor SCI (n=14) with a TA and Triceps Surae muscle score ≥2, were reAnalysis of TA coherence was carried out during controlled movement with the subjects comfortably seated in a dynamometer . The trunk and pelvis of the tested leg were supported using straps. The hip, knee and the ankle joints were flexed at 90°. In SCI subjects the criteria included muscle activity calculated from the global muscle score TA muscle score greater than 2. Consequently the leg was capable of generating moderate contraction levels to perform the controlled movement protocols which also avoided methodological problems associated with coherence analysis with non-rectified signals. For theIn the second trial 22 SCI subjects (15 of whom presented spasticity) performed two types of controlled TA activation i) two isometric activation at 100% of MVT maintenance for 5 s and ii) ten cycles of isokinetic movement at 60°/s and 120°/s, from 30° plantarflexion to 0° dorsiflexion. In addition several clinical tests were applied in the second trial including i) gait function measured with the WISCI II, ii) theCoherence is a measure of how closely the two EMG signals are related by a linear transformation. CoherenThere is some controversy regarding the requirement of rectifying EMG signal activity for coherence analysis with studies for-51 and aThe velocity dependence of intramuscular TA coherence was calculated by calculating the ratio of its value during isokinetic TA muscle movement at 120°/s and at 60°/s. Statistical analysis was performed with a commercial software package . Due to the non normal distribution of the data, non-parametric tests were adopted. The Kruskall Wallis test was used to compare different controlled muscle activation protocols within the first SCI cohort. The Mann–Whitney test was used to compare intramuscular TA coherence between the healthy non-injured group with both SCI cohorts (Median and 25th-75th Percentiles) and to analyze differences between SCI subjects with and without spasticity in the second cohort. The Spearman correlation test was used to identify the relationship between TA coherence with MVT, gait function and spasticity measures. Statistical significance was defined as p ≤ 0.05, with trends described at p ≤ 0.07.Fifteen healthy subjects  with a median age of 26.6 years  and 14 subjects with SCI  with a median age of 30.5 (26.4-44.3) years  isokinetic movement was related to several functional and clinical parameters in subjects with SCI, while a novel change in 40–60 Hz coherence was identified specifically in subjects with clinical measures of SCI spasticity. The identification of the optimal task and frequency bands required to support the physiological and functional significance of TA coherence activity should facilitate the diagnosis of motor recovery mediated by central motor control mechanisms, in addition to detecting early signs of adaptive and maladaptive neuroplasticity during subacute neurorehabilitation after incomplete SCI.Several clinical studies have demonstrated that 15-30 Hz muscle coherence reflect neural activation of muscle function via cortical activation of synchronous motor units in antagonist muscles,56. IndeIn the present study several consistent correlations indicated that 15-30 Hz TA coherence calculated during isometric activation at 100% of MVT correlated significantly with muscle strength following SCI. The reduction or absence of lower limb muscle coherence with an associated loss of lower limb muscle strength following damage to the corticospinal pathway has been identified at these frequencies,22,25,26Systematic analysis of TA coherence in this study also revealed consistently higher values for all the frequency bands when calculated during fast isokinetic dorsiflexion in subjects with SCI, compared to healthy subjects. However when TA motor unit synchronisation was compared within subjects with different grades of SCI, higher velocity-dependent coherence activity was observed in subjects with a more incomplete SCI, but only within the 10-16 Hz and 15-30 Hz bandwidths. In this regard, recovery of cortical motor evoked potential amplitude during subacute SCI correlated only with maximal movement velocity of dorsiflexion rather than maximal isometric muscle strength in general,32. IndeThis study also supports the relatively new identification of the physiological relevance of 40-60 Hz EMG coherence activity. Lower 4Residual voluntary motor function after SCI is initially diagnosed with the AIS scale, which characterises AIS C from AIS D with the presence of useful motor activity but without normal strength or gait. FurtherSubjects diagnosed within the AIS D classification group demonstrate different degrees of gait function, which is usually assessed by qualitative clinical tests in the rehabilitation setting,61. It iThe pathophysiology of spasticity following SCI is complex and most probably involves parallel changes in pyramidal, extrapyramidal and spinal motor control mechanisms-64, someIn contrast the association between high frequency 40-60 Hz TA coherence activity and the clinical measures of spasticity  most probably reflects central neuronal activity unrelated to the 15-30 Hz activity that approximates pyramidal activity (see above). Indeed evidence suggests that lower limb hypertonia, as assessed with the modified Ashworth scale, may also be mediated by hyperexcitability of extrapyramidal neuronal mechanisms organized at the brainstem level.The presence of both muscle hypertonia and evoked spasm activity in subjects with lower 40-60 Hz coherence following SCI also suggests that non-pyramidal adaptive mechanisms could mediate residual motor recovery, which would be masked by the development of spasticity in our cohort. Closer neurophysiological analysis of the activity within these pathways after SCI should be made to provide more detailed evidence regarding both maladaptive neuroplasticity related to spasticity and adapIt is interesting to observe the correlation between the time from SCI with 15–30 Hz and 24-40 Hz TA motor unit synchronisation estimated during fast isokinetic movement which suggests that a closer examination of velocity-dependent dorsiflexor muscle coherence activity would provide prognostic information regarding the potential for residual motor function recovery. Indeed coherence activity calculated within these mid-range frequency bands during maximal isometric contraction also correlate with gait function after SCI in our study. Whether recovery of bait function depends on the development of velocity-dependent activity of residual pyramidal and extrapyramidal motor control systems after incomplete SCI will need to be addressed with more sophisticated neurophysiological techniques.Measurement of TA motor unit synchronisation as an approximation for descending motor control activity in subjects with incomplete SCI within the neurorehabilitation setting is an obvious clinical goal. In addition the possibility that TA coherence activity could reflect residual motor control recovery with training or deterioration following the development of spasticity needs to be addressed in a closely controlled subacute longitudinal study of SCI. Further studies should more closely analyse the functional significance of a more detailed coherence spectra in the future with longer EMG signals which will lead to a more precise identification of the frequency bands. In addition analysis of EMG coherence in those subjects with SCI with low levels of muscle contraction should be preprocessed using rectification.Intramuscular TA coherence estimation calculated between 15-30 Hz or 40-60 Hz during isometric activation at 100% of MVT or during fast 120°/s isokinetic activation may provide important diagnostic information regarding the state of voluntary motor control mechanisms following incomplete SCI. In patients without spasticity these coherence bands may reflect recovery of residual motor control. In contrast the clinical diagnosis of muscle hypertonia and evoked involuntary muscle spasms negatively affect 40-60 Hz TA coherence estimation. Systematic analysis of TA motor unit synchronization during specific motor tasks within specific bandwidths provides a basis for the development of a quantitative diagnostic method that approximate adaptive and maladaptive lower limb residual motor control mechanisms and neuroplasticity during subacute SCI. Further studies in a larger cohort of subjects with incomplete SCI, using longer rectified EMG signals will be required to test this hypothesis, including corroborative data obtained from neurophysiological and functional longitudinal studies.EMG: Electromyographic activity; SCI: Spinal cord injury; TA: Tibialis anterior; AIS: American spinal injury association impairment scale; MVT: Maximal voluntary torque; SCATS: Spinal cord assessment tool for spastic reflexes scale; MUAP: Motor unit action potential.The authors declare that they have no competing interests.EBE participated in collecting the subject data, conducting data and statistical analysis, and helped to draft the manuscript. MA developed the software required to analyse EMG activity and muscle coherence analyses. CS helped collect the subject data and to draft the manuscript. DT and JLP helped to draft the manuscript and supervise the research. JT and JG-S conceived the study, interpreted the data, drafted the manuscript and critically revised it. All authors read and approved the final manuscript.


ABD-GLU = 0.89), seven of which were associated with MetS (FDR P<0.01). The strongest associated module, significantly enriched for immune response–related processes, contained 94/620 (15%) genes with inter-depot differences. In an independent cohort of 145/141 twins with ABD and WB longitudinal expression data, median variability in ABD due to familiality was greater for MetS–associated versus un-associated modules . Cis-eQTL analysis of probesets associated with MetS (FDR P<0.01) and/or inter-depot differences (FDR P<0.01) provided evidence for 32 eQTLs. Corresponding eSNPs were tested for association with MetS–related phenotypes in two GWAS of >100,000 individuals; rs10282458, affecting expression of RARRES2 (encoding chemerin), was associated with body mass index (BMI) (P = 6.0×10−4); and rs2395185, affecting inter-depot differences of HLA-DRB1 expression, was associated with high-density lipoprotein (P = 8.7×10−4) and BMI–adjusted waist-to-hip ratio (P = 2.4×10−4). Since many genes and their interactions influence complex traits such as MetS, integrated analysis of genotypes and coexpression networks across multiple tissues relevant to clinical traits is an efficient strategy to identify novel associations.Metabolic Syndrome (MetS) is highly prevalent and has considerable public health impact, but its underlying genetic factors remain elusive. To identify gene networks involved in MetS, we conducted whole-genome expression and genotype profiling on abdominal (ABD) and gluteal (GLU) adipose tissue, and whole blood (WB), from 29 MetS cases and 44 controls. Co-expression network analysis for each tissue independently identified nine, six, and zero MetS–associated modules of coexpressed genes in ABD, GLU, and WB, respectively. Of 8,992 probesets expressed in ABD or GLU, 685 (7.6%) were expressed in ABD and 51 (0.6%) in GLU only. Differential eigengene network analysis of 8,256 shared probesets detected 22 shared modules with high preservation across adipose depots  is a highly prevalent disorder with considerable public health concern, but its underlying genetic factors remain elusive. Given that most cellular components exert their functions through interactions with other cellular components, even the largest of genome-wide association (GWA) studies may often not detect their effects, nor necessarily provide insight into the complex molecular mechanisms of the disease. Rather than focusing on individual genes, the analysis of coexpression networks can be used for finding clusters (modules) of correlated expression levels across samples. In this study, we used a gene network–based approach for integrating clinical MetS, genotypic, and gene expression data from abdominal and gluteal adipose tissue and whole blood. We identified modules of genes related to MetS significantly enriched for immune response and oxidative phosphorylation pathways. We tested SNPs for association with MetS–associated expression (eSNPs), and tested prioritised eSNPs for association with MetS–related phenotypes in two large-scale GWA datasets. We identified two loci, neither of which had reached genome-wide significance levels in GWAs, associated with expression levels of  Genome-wide association (GWA) studies are routinely employed to identify common genetic variants contributing to complex diseases. Many replicated GWA signals have been found for metabolic traits including high-density lipoprotein (HDL), low-density lipoprotein, body mass index (BMI), triglycerides (TG) and blood pressure, greatly enhancing the understanding of the genetic basis of these traits eQTL (expression Quantitative Trait Loci) studies could help in understanding how known genetic variants identified by GWA studies influence clinical traits through gene expression, or suggest potential biological pathways. eQTLs have already been associated to several complex traits including Type I diabetes Metabolic syndrome (MetS) is highly prevalent, occuring in up to 22% of US individuals, and is a serious public-health problem world wide www.genome.gov/gwastudies, accessed 02-03-2011 The estimated heritability of MetS ranges from 0.10 to 0.51, whilst that of the individual traits that constitute MetS range from 0.13 to 0.72 It is likely that for a clinical entity such as MetS comprising multiple complex trait components, there is considerable genetic heterogeneity in causal pathways. Therefore examining many genes simultaneously using a systems-based approach, such as weighted gene co-expression network analysis To uncover eQTLs in tissues relevant to MetS, we analysed gene expression profiles in abdominal (ABD) and gluteal (GLU) adipose tissue, and whole blood (WB), from 73 individuals, allowing the investigation of differential regulation between different adipose depots applying both single-gene and a network approaches. Using a second independent cohort comprising 145 and 141 twins with ABD and WB expression data collected longitudinally across two visits, we demonstrate the relative contribution of familial, environmental, and experimental variability to the MetS-associated expression phenotypes. Lastly, we identify a set of SNPs associated with the expression of genes in selected MetS-associated modules showing differential expression between the adipose depots, and test these eSNPs for association with MetS-related phenotypes in two large GWA cohorts.2 (intensity) in at least 10% of individuals resulting in 8941 (ABD), 8307 (GLU) and 6909 (WB) gene expression profiles (probesets were mapped to Entrez Genes), respectively  and TG (FDR P = 0.01); waist (FDR P = 5.7 *10−3) and HDL (FDR P = 4.3 *10−3) indicating that MetS gene expression differences were more pronounced in the fat depots, which became the main focus of further analysis.None of the genes was differentially expressed between MetS cases and controls in WB although for the individual MetS components  significant gene expression differences were identified (not shown). Only two of these genes, KCTD10 and C3AR1 showed evidence for depot-specific expression changes. These findings support the hypothesis that, at least for some genes, the associations with MetS reflect depot-specific differences.The distribution of adipose tissue between ABD and GLU depots varies between individuals and this variation is associated with MetS and some of the WHR-associated loci have shown depot-specific differences in expression patterns and/or an enrichment of associations with metabolic phenotypes A Pearson correlation matrix  was calculated and transformed into a matrix of connection strengths using a power function resulting in a weighted network see . Using t−7) and GO:0006091 generation of precursor metabolites and energy (FDR P = 7.5*10−7). For these GO categories, the majority of the genes (more than 84% of genes with MM>0) were downregulated in MetS cases. The genes in these GO categories had a higher median MM (MM>0.74) than the 877 genes in the module (MM = 0.51) showing functional relevance of these genes in the module. Furthermore, among the top 10 genes with the highest rank of membership (hubgenes), eight were previously implicated in mitochondrial processes including generation of metabolites and energy , oxidation reduction  and fatty acid oxidation (ECHS1)  association with MetS 2) enrichment of Gene Ontology (GO) terms 3) hubgenes and 4) previous implicated genes. For the former, we extracted the first principal component of the gene expression profiles in each module (module ‘eigengene’) and tested its association with MetS see . From th (ECHS1) . This moith MetS  [32].CD163, C1QB, C1QC and C3AR1, which are involved in the inflammatory response and/or complement cascade were among the strongest hubgenes  with a highly significant MM (MM>0.75) from a previously identified macrophage-enriched metabolic network in subcutaneous adipose tissue in humans and mice that was associated with obesity-related traits and enriched for inflammatory response and macrophage activation pathway CD163 encodes a monocyte/macrophage specific receptor whose soluble form (sCD163) is elevated in T2D and obesity The eigengene of the ABD cyan module was upregulated for MetS cases compared to MetS controls and genes in this module were enriched for immune response related GO categories. hubgenes . This mo−3)). For the darkgreen GLU module eigengene, the strongest MetS-associated module in GLU and associated with all MetS components (FDR P<0.01), an upregulation among MetS cases was observed as compared to controls. No significant enrichment of GO terms was observed among the genes in the module (GLUL (MM = −0.89) and PHLDA2 (MM = 0.84)  . Both th−36) and the GLU darkgreen modules  most significantly associated with MetS  was calculated for each gene expression probeset see . For 862ith MetS , the corith MetS . These rith MetS  and theiMetS is a complex trait that is manifested in multiple tissues and where regulatory processes may act specific to a tissue as well as across tissues. The regulatory processes that play a role within a tissue may differ from those processes across tissues. It is likely that if modules of coexpressed genes are preserved among tissues, these modules may highlight communication between tissues and elucidate biological pathways that are shared among the tissues. Studying differential expression of individual genes in the module may reveal differences in pathway regulation across tissues.−3)) and other gene transcription terms whilst analysis of the 51 GLU-only probesets showed no enrichment. The enrichment of gene transcription categories among genes expressed in ABD only might suggest regulatory processes specific for ABD rather than genes that are able to induce gene activity changes in other tissues. The large overlap of expressed genes among ABD and GLU, however, suggest the existence of shared processes or at least communication between tissues.To examine to what extent biological processes underlying MetS are shared and differentially regulated among the different adipose depots, we first examined the overlap of expressed genes between ABD and GLU . Of 8992−10 and whole-network connectivities  between ABD and GLU  of 0.89  and the GLU dataset (FDR P = 4.6*10−6) and was upregulated in MetS cases as compared to controls in both fat depots. The genes in this yellow module were enriched for immune response related processes  and contained many common genes (310 genes).To assess the relevance of the consensus modules for MetS, we tested the consensus module eigengenes for association with MetS . Eigengerocesses . Among tand NPC2 . Consist−3)  and not differentially expressed (p = 0.64). However, 94 genes of the 620 genes (15%) were differentially expressed between depots (FDR P<0.01) which were enriched for the GO-term: GO:0009611 response to wounding (FDR P = 2.3*10−3) see . Among t−10), whole-network connectivities  and the intramodular connectivities for the brown module  were calculated using the module assignments from MolOBB to calculate the connectivities  of 154 healthy twins, we retained gene expression of 202 ABD and 191 WB samples from 145 and 141 twins after quality control, respectively . To exativities . The sigThe MolTWIN dataset allowed us to decompose the biological and experimental variation underlying an expression trait into five components: familiality (genetic and common environment effects shared by twin pairs); individual environment ; common visit and individual visit effects, which respectively measure the amount of shared (by twins within a pair) and non-shared variation occurring in the phenotype over the sampling period. The residual component of variation comprised experimental effects . Familiality and individual environment variation assess longitudinally stable, and common and individual visit variation, short-term biological components. Although, our main focus –given the size of the MolTWIN datasets– was on estimating familiality, we also included heritablity estimates for contrast and completeness . We asseFamiliality was the largest source of variation that contributed to the variance of the gene expression traits in ABD and WB , Text S2−4). This pattern was not observed for familiality estimates derived from MolTWIN WB  and GLU .We also used the MolTWIN data to characterize the sources of variation underlying the eigengenes of the MetS-associated modules from the MolOBB data. Rather than constructing networks in MolOBB and MolTWIN separately, we calculated module eigengenes in the MolTWIN study using the module assignments from MolOBB and decomposed the module eigengenes into the five variance sources as described above , Text S2lTWIN WB . For thecis eQTL analyses . For the ABD eQTL analysis, we used both ABD datasets (MolOBB and MolTWIN) comprising 189 individuals whilst for the GLU dataset we used 62 individuals from the MolOBB dataset. Out of the 8242 ABD genes tested, we found 1287 cis eQTL genes (FDR P<0.01) using a fixed-effects meta-analysis cis for 77 of the 893 probesets associated with MetS in the single-gene meta-analysis. Six of these eQTL genes showed significant inter-depot differences , we found evidence for an eQTL in cis. For 14/124 genes, a high MM (p<0.01) and a significant association with MetS was found  that were associated with MetS in a GWA of 22,161 participants HERPUD1 (rs3764261) was found  significantly associated with the ABD brown module eigengene and the 6p21 locus  associated with the GLU darkgreen module. The SNP at the 21q22.13 locus was within the Down Syndrome Critical Region in a high LD region containing the TTC3 and DSCR9 genes. Of the two genes, only TTC3 was expressed in ABD. This gene was not differentially expressed between MetS cases and controls  and assigned to the turquoise module . Remarkably, TTC3 is an E3 ligase facilitating ubiquitination and degradation of phosphorylated Akt LRFN2. This gene was however not expressed in GLU and plays a role in neuronal development. These loci may act as a master regulator of the genes in the module mediating a gene expression regulatory mechanism.Modules are groups of highly correlated genes and could be the result of transcriptional co-regulation. We examined whether we could find genomic hotspots i.e. genetic loci that regulate multiple genes that are coexpressed within the module. We tested the module eigengenes of the ABD brown, GLU darkgreen and yellow consensus modules for association with 296,017 SNPs. After multiple testing correction (FDR P<0.05), we found the 21q22.13 locus  <0.05 by correcting for 32 SNP-clinical phenotype associations. This threshold was corresponding to a FDR of 0.03 across 110 tests. Adopting a much simplified scenario given the complex correlation structure of the MetS-related traits, we found three significant associations which was more than expected by chance; assuming independence between the 110 tests, the binomial probability was 7.3*10−4. SNP rs10282458, was significantly associated with gene expression levels of the adipokine RARRES2 encoding chemerin, and was significantly associated with BMI (genomic control corrected p = 6.0*10−4). RARRES2 gene expression levels showed a familiality of 0.53 in the MolTWIN ABD dataset and were highly connected with the brown module eigengene (MM = 0.83). In MolOBB, expression levels of RARRES2 were strongly associated with MetS (p = 1.9*10−5) and with the individual components of MetS: waist (p = 1.6*10−8), HDL (p = 2.0*10−5) and diastolic blood pressure (p = 1.5*10−4). SNP rs2395185, which affected expression levels of HLA-DRB1, was significantly associated with HDL (genomic-control corrected p = 8.7*10−4). Expression levels of HLA-DRB1 showed a familiality of 0.59 in MolTWIN ABD and were correlated with waist circumference (p = 2.9*10−5) and HDL (p = 5.3*10−5) in MolOBB.To validate our results, we tested our prioritised eSNPs for association with MetS-related phenotypes using data from two large GWA cohorts. Based on the eQTL analyses, we prioritised a set of 32 eSNPs that were associated with MetS-associated probesets/modules : 15 eSNPHLA-DRB1 expression levels in ABD and GLU, and WHR-adjBMI (p = 2.4*10−4). These results may suggest that differential regulation of the HLA-DRB1 region is associated with WHR-adjBMI.Next, we tested the 14 eSNPs that were associated with ABD-GLU inter-depot differences and rs10282458 for association with WHR-adjBMI in the GIANT consortium. By focusing on WHR after adjustment for BMI, we anticipated to detect associations with body fat distribution independent of those influencing overall adiposity. SNP rs10282458 was indeed associated with BMI but not with WHR-adjBMI . We founRARRES2 and HLA-DRB1. Thus, by constructing networks across and within different adipose depots combined with single-gene analysis, two signals which had not reached genome-wide significance in GWA datasets of more than 100,000 individuals were identified.Given that many molecular processes in multiple tissues could be involved in the onset of MetS, we genotyped and profiled gene expression in WB and two different adipose depots, ABD and GLU, from 73 individuals. After constructing coexpression networks for each tissue independently and between tissues, we identified MetS-associated modules of coexpressed genes enriched for immune response and oxidative phosphorylation pathways in adipose depots but not in WB. By testing eSNPs, that were associated with expression of the genes in the MetS-associated modules, for association with MetS-related phenotypes in large scale GWA datasets, we found associations with RARRES2, is an adipokine known to play an important role in adipogenesis and metabolic homeostasis and modulating chemotaxis and activation of dendritic cells and macrophages EIDL3 gene and serum chemerin levels supporting a potential role for chemerin in angiogenesis Adipose tissue is a dynamic endocrine organ that secretes proteins such as cytokines and hormones, collectively named adipokines. Adipokines may regulate energy and vascular homeostasis, as well as inflammatory processes, and are involved in glucose and lipid metabolism. Chemerin, encoded by HLA-DRB1 expression levels in ABD and GLU, and WHR-adjBMI was found. The HLA-associated SNP found in our study has also been linked to ulcerative colitis Cis eQTLs of the HLA-DRB1 locus with other SNPs have previously been associated with Type 1 diabetes in liver tissue In humans, variations in adipose tissue distribution is associated to different metabolic consequences, with abdominal increase of fat producing a much greater risk for metabolic traits than gluteofemoral fat, suggesting differential regulation between the two adipose depots The specific identified genetic associations were found with a network approach rather than with single-gene association between expression and clinical traits, even though there was substantial concordance in results between the two strategies. Investigating coexpression networks may be a more powerful approach than a single-gene association analysis since most cellular components are connected to each other through regulatory, metabolic and protein-protein interactions and summarising coexpressed genes in a single eigengene reduces the number of tests significantly. In our study, we tested a set of SNPs, associated with expression of genes in MetS-associated expression modules in relevant tissues, with the hypothesis that these eSNPs are enriched for SNP-MetS associations. Testing this small SNP set for disease association in large GWAS cohorts revealed two SNP-disease associations, the signals of which would have been relatively weak in a genome-wide multiple-testing context.An additional motivation for utilising a network-based approach is that it is unlikely that MetS is a consequence of an abnormality in a single gene product, but reflects the perturbations of a particular functional module in the gene network by a complex interaction of genetic and environmental interactions We found an enrichment of oxidative phosphorylation genes in the most significant ABD gene network module, which is consistent with previous studies showing compelling evidence for mitochondrial dysfunction in association with insulin resistance and obesity CD163, one of the hubgenes found this study, encodes a monocyte/macrophage specific receptor whose soluble form (sCD163) is elevated in T2D and obesity The network topology-based approach helps to uncover potential mechanisms that contribute to the shared pathophysiology of the multiple components of MetS. Defects in gene products that are part of the same pathway, may also affect other cellular functions, resulting in potential comorbidity effects. Consistent with this view, our results and those of other studies support the idea that the chronic low-grade inflammatory condition that is associated with obesity plays a role in the etiology of MetS It is however not clear whether obesity is the origin or whether inflammation is proximal to metabolic dysfunction In any expression study, many expression traits associated with the disease will not necessarily be causative, but instead be mostly reactive to disease. In addition, expression levels represent measurements from a heterogeneous mixtures of cells. It is important to distinguish the effect of genetic variation on gene expression from other factors that are reactive to the disease or confounding factors that also correlate with expression variability. Both our single-gene and network-based results showed that the expression probesets and modules filtered by association with MetS had increasing familiality and heritability levels, which may suggest an enrichment of genetically relevant signals.Our results arose principally from the analysis of gene expression in ABD and GLU adipose depots, and not WB. In addition, the heritability of the genes associated with MetS in both ABD and GLU was high in ABD tissue of the twins but not in WB. Moreover, the proportion of variance that was explained by the fact that the twins attended the hospital together (common visit effect) was high in WB, indicating stronger short-term environmental effects on WB than ABD expression levels. These observations suggest that WB is not necessarily the tissue of choice to detect eQTLs that are of direct relevance to MetS.In conclusion, we performed an eQTL study in WB, ABD and GLU and highlighted two genetic loci associated with MetS mediated by gene expression variation. Considering many genes and their interactions influence complex traits such as MetS, the integrated analysis of genotype data and expression networks across multiple tissues relevant to the clinical traits under study is an efficient strategy to identify novel genetic associations, and may offer better targets for drug development.The MolTWIN study was approved by St. Thomas' Hospital Research Ethics Committee (EC04/015 Twins UK). The MolOBB study received ethical approval from Oxfordshire REC C (08/H0606/107). All participants gave informed consent.www.idf.org), MetS was assigned as central obesity (waist circumference (or BMI>30 kg/m2) plus any two of the following four factors: raised triglycerides, reduced HDL cholesterol, raised blood pressure or raised fasting plasma glucose The MolOBB study consists of 44 healthy controls  and 29 cases with MetS  between 39 and 56 years old that were collected from the Oxford Biobank as part of the MolPAGE consortium A total of 154 twins (56 monozygotic (MZ) pairs and 21 dizygotic (DZ) pairs), were ascertained from the UK Adult Twin registry at St. Thomas' Hospital et al.For the WB samples, PAXgene tubes were used and RNA was extracted according to the manufacturer's protocol . Total RNA was extracted with TRIreagent  from the fat biopsies and quantified using a NanoDrop. For six of the MolOBB subjects and 30 of the MolTWIN subjects from the first visit (15 MZ pairs), RNA was split into two aliquots before labelling . RNA was labelled using the MessageAmp II 96-well amplification kit . Labelled RNA was hybridized onto Affymetrix hgu133plus2 arrays washed, stained, and scanned for fluorescence intensity according to manufacturers protocols . For each tissue, RNA samples were randomised and extracted in batches of 12 samples, rerandomised before labelling in 96-well plates and hybridised in batches of 12 samples on a plate-row by plate-row basis. Quality control checks involved signal intensities, background intensity, expression of control genes and spike-ins, as well as a spatial representation of the intensities. To identify outliers further, principal components analysis was performed on the normalised gene expression dataset using the NIPALS algorithm. The majority of the probes on the hgu133plus2 arrays were collected into 17,726 non-overlapping probesets according to Entrez Gene annotations provided by Dai 2 (intensity) in at least 10% of individuals. After this filtering stage in the MolOBB, there remained 8619 probesets in the ABD-GLU dataset, 8941 in the ABD dataset, 8307 in the GLU and 6909 probesets in the WB dataset. In the MolTWIN study, 8928 probesets in the ABD dataset and 9071 probesets in the WB dataset remained.After outlying arrays had been removed, there remained data in the MolOBB study from 54 ABD samples , 65 GLU samples  and 68 WB samples. During 202 visits, 231 MolTWIN samples from ABD  were successfully profiled from 145 individuals. From the WB samples, 217 MolTWIN gene expression profiles for 141 individuals were generated during 191 visits . All arrays were normalised concurrently across datasets for comparisons between tissues and separately for comparisons within a single tissue using GC robust multi-array analysis DNA was successfully extracted from WB samples using GeneCatcher  according to manufacturer's protocol. 166 samples ) are genotyped with Illumina 317K BeadChip SNP arrays . Genotype data (EGAS00000000102) is available at EGA.−4. After QC, genotypes of the ungenotyped MZ twin were copied from genotyped MZ twin. Finally, we included 69 MolOBB individuals and 144 twins genotyped for 296,017 autosomal SNPs.Quality control on the genotyped subjects was performed applying slightly changed quality control filters as described previously by the Wellcome Trust Case Control Consortium For the differential expression analysis between MetS cases and controls, we investigated the effect of gender, RNA Integrity Number, age and plate on gene expression measurements. Several probesets were associated with gender and plate but none with age or RNA Integrity Number. We then tested all detected probesets for association with MetS adjusting for gender and plate-effects using linear models. To investigate whether genes are differentially expressed between ABD and GLU, we used a linear mixed model on in which tissue (ABD versus GLU), MetS case-control status, gender, and plate were fitted as fixed effects, and subject (sample donor) as a random effect. Models were fitted using the Rpackage Maanova http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/Rpackages/WGCNA/Tutorials/index.html). A weighted gene co-expression network reconstruction algorithm was used to reconstruct co-expression networks for each of the three MolOBB datasets  We used the Rpackage WGCNA for network construction. To construct single tissue and consensus eigengene networks, we adjusted the tutorials from the WGCNA website : To determine whether the MetS-associated modules are biologically meaningful, GO enrichment analyses were conducted using DAVID th consensus module in ABD and GLU, respectively. High levels of the preservation network indicate a strong preservation between I and J across the two networks. The density D is an aggregate measure of adjacency preservation between the To assess the preservation of network properties across the ABD and GLU datasets, we applied differential eigengene network analysis to identify consensus modules th aliquot  from a biological sample taken at the kth visit  of the jth twin  from the ith pair :Maximum likelihood estimation of variance components from the MolTWIN dataset was performed by fitting a linear mixed-effects model using the lme4 package To estimate the familiality  among twin pairs, we calculated: cis. We defined an association between a SNP and gene expression level as cis-acting if the SNP was located within 500 kb from the start or stop position of the annotated gene (NCBI build 36). Genotypes were coded as 0, 1, and 2 corresponding to the counts of the minor allele and an additive model was fitted in all models. For each of the genes, each SNP in the cis region was tested independently.We selected subjects who had complete data on plate, gender, casecontrol status, gene expression and genotypes. We set out to identify SNPs that affect gene expression levels in In the MolOBB study, SNP associations were calculated by regressing expression level against using a linear regression model adjusting for gender and plate-effects. In the MolTWIN study, we fitted linear mixed models, for which twin pairing, zygosity, individual visit, common visit, individual environment and residual effect were fitted as random effects and genotype and plate as fixed effects. eQTL associations in MolOBB and MolTWIN were calculated with the Rpackages lm and lme4, respectively. In the ABD meta-analysis, we combined study-specific allelic effect-estimates on 8242 probesets expressed in both ABD MolOBB (N = 52) and MolTWIN (N = 137) using a fixed effect model, using the inverse of the variance of the study-specific allelic effect-estimates to weight the contribution of the two studies −3 is chosen such that Pr <0.05. Exact binomial probabilities were calculated through repeated applications of the standard binomial formula.In order to followup eSNPs in independent cohort we assembled a set of 32 eSNPs based on two criteria: i. Expression levels had to be differentially expressed between MetS cases and controls and showed a high MM (p<0.01) with the brown module in ABD or the darkgreen module in GLU. ii. Expression levels in yellow consensus module or gene-by-gene model had to be differentially expressed between depots. In order to confirm the associations between the 32 eSNPs and the MetS related phenotypes in independent cohorts, we obtained results of relevant meta-analyses conducted by two consortia. For the BMI associations, 32 eSNPs were tested for association in the GIANT consortium consisting of >120,000 individuals. To confirm associations with TG and HDL, we used previously published GWA results comprising >100,000 individuals 2>0.5) previously associated to MetS or a pair of MetS traits We selected 29 variants and their proxies Click here for additional data file.Figure S2Heatmap plot of the topological overlap in the ABD gene network of 1000 random selected genes. In the heatmap, each row and column corresponds to a gene, light color displays low topological overlap, and darker color displays higher topological overlap. The gene dendogram and module assignment are shown along the left and top. The cyan and brown modules are clearly seen by the darker squares along the diagonal.(TIF)Click here for additional data file.Figure S310 pvalue of the probeset-clinical trait association for each gene in the darkgreen module. The red indicated genes show genes with evidence for a cis eQTL and their eSNPs are examined for association with BMI.Scatterplot between MM (x-axis) and gene significance (y-axis) for MetS and the six MetS components in the GLU darkgreen module. Gene significance (y-axis) is defined as −log(TIF)Click here for additional data file.Figure S4Venn diagram of number of expressed genes in MolOBB ABD and GLU.(TIF)Click here for additional data file.Figure S5Preservation of genes across fat depots. A. Scatterplot of 8256 median expression levels for ABD versus GLU. B. Scatterplot of whole-network connectivities for ABD versus GLU. Each point corresponds to a gene expression level. Spearman correlations and the corresponding p-values are displayed in the title of each plot. The whole-network connectivity is strongly preserved between ABD and GLU.(TIF)Click here for additional data file.Figure S6Differential eigengene network analysis across different depots. A, B Dendrograms of consensus module eigengenes in ABD and GLU. C, F.The heatmap plots of eigengene adjacencies in each eigengene network. Each row and column corresponds to one eigengene (labeled by consensus module color). Within each heatmap, red indicates high adjacency and green displays a low adjacency as shown by the color legend. D. Barplot shows the preservation of relationships of consensus eigengenes of the consensus modules. D represents the overall preservation measure. E. Adjacency heatmap for the pairwise preservation network of ABD (rows) and GLU (columns). Red indicates the adjacency.(TIF)Click here for additional data file.Figure S7Preservation of genes across MolOBB and MolTWIN study. A. Scatterplot of 8242 median expression levels for MolOBB ABD versus MolTWIN ABD. B. Scatterplot of whole-network connectivities for MolOBB ABD versus MolTWIN ABD. C. Scatterplot of intramodular connectivities of genes in the brown module for MolOBB ABD versus MolTWIN ABD. Each point corresponds to a gene expression level. Spearman correlations and the corresponding p-values are displayed in the title of each plot. The whole-network connectivity and intramodular connectivities of genes in the brown module are preserved between MolTWIN and MolOBB.(TIF)Click here for additional data file.Table S1Genes differentially expressed between MetS cases and controls in ABD .(DOC)Click here for additional data file.Table S2Genes differentially expressed between MetS cases and controls in GLU .(DOC)Click here for additional data file.Table S3et al.Module membership and association with MetS and BMI in the MolOBB ABD dataset of genes identified by Emilsson (DOC)Click here for additional data file.Table S4Evaluation of GWA SNPs previously associated with MetS.(DOC)Click here for additional data file.Table S5Hubgenes (genes with highest rank of module membership) in the modules strongest associated with MetS in the ABD and GLU single-tissue networks.(DOC)Click here for additional data file.Table S6Modules for which eigengenes were significantly correlated (FDR p<0.01) with MetS in ABD (N = 6) and GLU (N = 6). FDR corrected pvalues for the associations with MetS and six quantitative metabolic traits are shown.(DOC)Click here for additional data file.Table S7Enrichment of Biological Processes GO terms in yellow consensus modules.(DOC)Click here for additional data file.Table S8Hubgenes (genes with highest rank of module membership) in the yellow consensus module in both the ABD and GLU networks.(DOC)Click here for additional data file.Table S9Median familiality estimates, assessed in MolTWIN ABD and WB, of four groups of MolOBB MetS-associated probesets identified using the single-gene approach, compared with probesets not associated with MetS.(DOC)Click here for additional data file.Text S1Members of GIANT consortium and members of MolPAGE consortium.(DOC)Click here for additional data file.Text S2Variability of MetS-associated gene expression.(DOC)Click here for additional data file.


Increasing evidence suggests an association between neuronal cell cycle (CCL) events and the processes that underlie neurodegeneration in Alzheimer’s disease (AD). Elevated levels of oxidative stress markers and mitochondrial dysfunction are also among early events in AD. Recent studies have reported the role of CCL checkpoint proteins and tumor suppressors, such as ATM and p53 in the control of glycolysis and oxidative metabolism in cancer, but their involvement in AD remains uncertain.In this postmortem study, we measured gene expression levels of eight CCL checkpoint proteins in the superior temporal cortex (STC) of persons with varying severities of AD dementia and compare them to those of cognitively normal controls. To assess whether the CCL changes associated with cognitive impairment in AD are specific to dementia, gene expression of the same proteins was also measured in STC of persons with schizophrenia (SZ), which is also characterized by mitochondrial dysfunction. The expression of CCL-checkpoint and DNA damage response genes: MDM4, ATM and ATR was strongly upregulated and associated with progression of dementia , appearing as early as questionable or mild dementia (CDRs 0.5–1). In addition to gene expression changes, the downstream target of ATM-p53 signaling - TIGAR, a p53-inducible protein, the activation of which can regulate energy metabolism and protect against oxidative stress was progressively decreased as severity of dementia evolved, but it was unaffected in subjects with SZ. In contrast to AD, different CCL checkpoint proteins, which include p53, CHEK1 and BRCA1 were significantly downregulated in SZ.These results support the activation of an ATM signaling and DNA damage response network during the progression of AD dementia, while the progressive decrease in the levels of TIGAR suggests loss of protection initiated by ATM-p53 signaling against intensifying oxidative stress in AD. The most prominent characteristics of AD are the presence of amyloid plaques, neurofibrillary tangles and neuronal cell death Experimental evidence has also shown an interaction between the accumulation of AD neuropathology hallmarks - Aβ peptide and hyperphosphorylated tau protein - with the activation of CCL and mitosis. Among these are the increased tau phosphorylation and microtubular destabilization that accompanies mitosis Ataxia-telangiectasia mutated (ATM) gene, a serine/threonine kinase is one of the well-characterized cell cycle checkpoint proteins, the substrates of which are involved in double-stranded DNA break responses To study the potential dysregulation of CCL in AD and to map the relationship between CCL checkpoint dysregulation and the progression of AD, we characterize the gene expression of several checkpoint proteins  in postmortem brain samples from persons with varying severities of AD-dementia and compare them to cognitively normal controls. We additionally report dementia related changes in the expression of TIGAR and show its neuronal localization. To access disease-specificity of the CCL-associated changes in AD-dementia their expression levels were measured in the postmortem brain of similarly aged persons with schizophrenia, a disease where CCL and oxidative stress related changes have also been reported p≤0.01; ranging from 3,912–11,322 genes per CDR group) with corresponding individual t-score values, which were uploaded to the MetaCore database and software suite (Thomson Reuter) and subjected to the multiple experiments comparison analysis according to the MetaCore guidelines. Most relevant common and unique gene networks identified from the 3 dementia groups are shown in MAS5 normalized samples from 15 cortical regions in the microarray dataset were stratified into the four groups based on CDR score ratings in order to analyze gene expression changes in clinically distinct groups with different severities of dementia and controls. Control subjects were classified as non-demented (CDR = 0). Cases with different severity of dementia were stratified as mild dementia, which includes persons with CDRs of 0.5 or 1, moderate dementia (CDR = 2) and those with CDR scores of ≥3 were categorized as severely demented and combined in a single group. These groups were subjected to contrast analysis p<1E-14) with the upstream converging nodes including the following genes: ATM, ATR, BRCA1, CHEK1 and NBN, and thus implicating DNA damage ATM/ATR regulation of G1/S transition pathway in the progression of dementia.Overall, the CCL related gene ontology categories were represented by 832 probe sets targeting 293 genes, which were differentially expressed in groups with varying degrees of dementia severity  compared to controls. In order to understand possible interactions between differentially expressed genes enriched in the CCL related categories, we built potential networks of CCL genes using MetaCore software. As shown in 2, 112 = 4.037, p = 0.02 (MDM4); F2,112 = 4.357, p = 0.015 (ATM) and F2,112 = 3.038, p = 0.052 (ATR); see 3,112 = 3.601, p = 0.016 and F3,112 = 4.802, p = 0.009, respectively) and Braak neuropathological stages . Changes in ATR gene expression as a function of NP density or Braak neuropathological stages were not significant, but showed nominal increases. These results suggest that expression of MDM4, ATM and ATR genes is dysregulated in the earliest recognizable stages of AD-dementia. Levels of MDM4 and ATM were also upregulated early during progression of AD-associated neuropathology and remain elevated throughout the course of AD. Partial correlations of MDM4 and ATM gene expression controlling for Age, pH and PMI demonstrated significant associations with CDR . In ANCOVAs controlling for age, brain tissue pH, and PMI, only ATM mRNA expression showed significant associations with CDR , NP density  and Braak neuropathological stages . MDM4 and ATR gene expression did not show significant association with NP density and Braak neuropathological stages.Differentially expressed genes in the microarray analysis that encode critical proteins involved in ATM-ATR signaling in response to DNA damage: ataxia telangiectasia mutated (ATM) and RAD3 related (ATR); Abelson murine leukemia viral oncogene homolog 1 (ABL1); checkpoint homolog (CHEK1); mouse doubled minute 4 homolog (MDM4); nibrin (NBN); tumor protein p53 (TP53) and breast cancer 1 gene (BRCA1) were selected for conformational qPCR analysis in the STG of an independent cohort of cases with varying severity of AD dementia, SZ and cognitively normal controls. Comparison of individuals without dementia (NL = CDR 0), with questionable-mild (CDR 0.5-1) and with moderate to severe dementia (CDR 2-5) showed higher levels of MDM4, ATM and ATR gene expression in individuals with dementia ; F1,120 = 5.79, p = 0.018 (TP53) and F1,120 = 5.98, p = 0.016 (CHEK1), see Comparison of cognitively normal individuals (NL) and individuals with SZ showed lower levels of BRCA1, TP53 and CHEK1 gene expression in individuals with SZ (Fp<0.01). Using Western blotting, we measured protein levels of TIGAR in homogenates from STG of cognitively normal and cognitively impaired persons with varying degrees of severity and from individuals with SZ (p = 0.004), but not with Braak scores  or NP density . Comparison of individuals without dementia (NL = CDR 0), to groups with increased severity of dementia from questionable (CDR 0.5), mild (CDR 1) to severe dementia (CDR 5) showed significant decrease of TIGAR protein expression . ANCOVAs corrected for age, PMI and tissue pH did not affect the significant association with CDR . Significantly less TIGAR protein expression was observed in cases with dementia (CDR ≥1) relative to controls (CDR = 0) . However, comparisons of individuals with and without AD-associated neuropathology did not show significant changes of TIGAR expression either as a function of NP density , or Braak scores . These findings suggest strong downregulation of TIGAR expression associated with measures of dementia severity but not conventional measures of AD neuropathology (NP density and Braak scores). Comparison of cognitively normal individuals (NL) and individuals with SZ showed no difference for TIGAR protein levels in SZ .To further elucidate responses induced by ATM signaling we evaluated gene expression changes for the broad range of downstream effectors of this pathway in the microarray dataset. Microarray data showed that gene expression of one of these effectors, TIGAR (C12orf5 gene), a p53-inducible gene which regulates the complex balance of ROS-inducing and ROS- decreasing signals propagated by p53  with SZ . Westernpression  in indivAs TIGAR expression in specialized brain cells has never being examined before, we studied localization of TIGAR protein in human cerebral cortex using immunohistochemistry. Human brain tissue sections from STG containing both grey and the underlining white matter were subjected to immunocytochemistry for TIGAR protein . Large pThis study documents gene and protein expression changes for markers associated with CCL activation indicative of cell cycle re-entry in the superior temporal gyrus during the progression of AD-dementia. Many of these CCL-associated changes occur during the early stages of the development of dementia and typical AD-neuropathology. ATM signaling is essential for the CCL checkpoint mechanism that ensures DNA integrity and repair Although previous analyses of postmortem AD brains have revealed increased p53 expression in overlapping populations of cortical neurons, and cortical and white matter glial cells in regions damaged by neurodegeneration The changes in the expression of CCL genes observed in AD dementia were specific to AD since they did not generalize to the brains of persons of similar age diagnosed with SZ, the majority of whom were cognitively impaired (average CDR = 2.3). Thus, in contrast to AD dementia, the proteins eliciting DNA damage response signaling were unaffected in the STG of individuals with SZ, whereas some of the downstream key effectors of the ATM signaling cascade associated with the activity of checkpoint proteins were significantly downregulated in SZ. The divergence in the expression of different classes of CCL genes affected in AD and SZ gives credence to the specificity of changes in each disease state and suggests a difference in the etiological substrates of AD and SZ. Given the elevated activity of some CCL indices in the brains of individual with SZ Taken as whole, the present findings suggest that ATM upregulation and progressive decrease of TIGAR protein levels are distinctive features of dementia progression in AD. Although CCL abnormalities are prevalent in both AD and SZ, these abnormalities are disease specific and distinct from one another. Whether the AD-associated DNA-damage/repair changes reported here are unique to AD or present also in other diseases characterized by eventual neurodegeneration needs to be further elucidated.In conclusion, ATM may be a crucial link between the damaging effect of ROS and reactivation of mitotic cell cycle in dementia. These data indicate that ATM signaling aimed at reducing the damaging effect of ROS and inducing protective mechanism against DNA damage fails in the cortical grey matter at the earliest stages of AD dementia and may predate the development of the classical neuropathological hallmarks of AD. Futile responses to DNA damage likely further promote progressive deterioration of genome stability and energy metabolism in neural cells.Postmortem brains, donated by the next of kin of deceased subjects participating in studies of aging, early dementia and schizophrenia, were received over a period of 20 years by the Mount Sinai School of Medicine Department of Psychiatry Brain Bank. All assessments were approved by governing institutional review boards including the Mount Sinai School of Medicine and the next of kin of all tissue donors gave formal written consent for research use of the brain tissue. The specimen handling, neuropathology and diagnostic systems used for classifying human brains have been described extensively 2. The density of NPs in the superior temporal gyrus  was used in the analyses below. Cognitively intact controls had no known history of any psychiatric or neurologic disorders and no discernable neuropathologic lesions. The demographic characteristics of the study cohorts are shown in The demographic characteristics of the SZ cohort are shown in 2 and 4 = ≥11 per mm2. Neuritic plaque groups reflect a composite score of neuritic plaques counts in 5 cortical regions. Five high power fields (0.5 mm2) were examined in each of 5 slides from the cortical region of interest and an average density score was calculated for each region and expressed as mean plaque density per mm2. Only neuritic plaques (with and without cores) were included in the NP counts reported here. When plaques were unevenly distributed in each slide, plaques in the region with the highest density were counted.The Clinical Dementia Rating (CDR) scale https://harouv01.u.hpc.mssm.edu/.The group composition, demographic characteristics and the procedures for RNA isolation and preparation for the microarrays were as described previously The mRNA levels of selected cell cycle genes were measured in STG of 173 donors  and S2 bProtein abundance was measured in STG using Western blotting in a subset of randomly selected cases used for qPCR  and S2. Human brain tissue was paraffin-embedded and sectioned at 8 µm thickness. All sections were de-paraffinizing with CitriSolv clearing agent  for 5 minutes, washed and then soaked in 0.3% hydrogen peroxide to remove endogenous peroxidase activity. Primary antibody - rabbit anti-human TIGAR  was diluted in 10% goat serum with 0.5% Tween-20 (1∶50 v/v). Primary antibody staining was detected with biotinylated goat anti-rabbit secondary antibody (1∶400 v/v), avidin-biotin complex horseradish peroxidase  and peroxidase substrate – DAB/Metal concentrate . Some sections were counterstained with Harris modified hematoxylin . Stained sections were viewed and photographed at a final magnification x40 using Carl Zeiss Axio Imager Z1 microscope and AxioVision Digital Image Processing System version 4.8.2.t-scores, Pearson correlation coefficients and corresponding p-values (ANOVA) p≤0.01) Pearson correlation coefficients meant that the pattern of variation of expression values between sample sets closely follows the pattern represented by the contrast vectors, indicating upregulation of gene expression. Large negative t-score values together with significant (p≤0.01) negative Pearson correlation coefficients meant that the pattern of variation is the inverse to the pattern represented by the contrast vectors, indicating downregulation of gene expression. Finally, t-scores close to zero meant that the gene's expression pattern matches neither the contrast pattern nor its inverse, or that the amount of variation between sample sets was comparable to or smaller than the variation within sample sets. Datasets of differentially expressed genes were subjected to the multiple experiments comparison analysis using MetaCore database and software suite (Thomson Reuter) according to the MetaCore guidelines. Most relevant gene networks with high G-scores were identified in various dementia groups. Highly positive G-scores meant that the networks were saturated with genes from the analyzed gene set and the network contained few to none high degree nodes that were not represented by the analyzed gene set p-values represent the probability that particular mapping may arise by chance, given the numbers of genes in the set of all genes within the network, genes within a particular network, and genes in the analyzed gene set.Multiple statistical procedures were employed for different aspects of the study. Max post hoc analysis were used to compare relative mRNA expression of analyzed genes in experimental groups stratified based on their clinical and neuropathology characteristics. ANCOVA was performed for each categorical variable controlling for sex, PMI, RIN and any other potential covariates. Comparison of the demographic variables and relative abundance of TIGAR protein in Western blots was performed using separate one-way ANOVAs. All procedures were performed using SPSS (IBM ver.19).For qPCR experiments, a preliminary analysis assessed linear associations of gene expression with gender, pH and PMI to evaluate their use as covariates. In addition, age, the most significant risk factor for dementia and a critical determinant of the extent of AD associated neuropathology, was used as a covariate in all analyses regardless of its association with the dependent variables. We determined the linear association of gene expression with CDR, Braak stages and NP density by partial correlation analyses, controlling for potential covariates if preliminary analyses showed significant correlation with the gene expression levels under analysis. One way ANOVAs with Tukey’s Table S1Controls and AD groups’ classifications for gene and protein expression studies.(DOC)Click here for additional data file.Table S2Demographic characteristics of study cohorts used for gene and protein expression analyses.(DOC)Click here for additional data file.Table S3TaqMan gene expression assays used in the study.(DOC)Click here for additional data file.


Microeukaryotes have vital roles for the functioning of marine ecosystems, but still some general characteristics of their current diversity and phylogeny remain unclear. Here we investigated both aspects in major oceanic microeukaryote lineages using 18S rDNA (V4–V5 hypervariable regions) sequences from public databases that derive from various marine environmental surveys. A very carefully and manually curated dataset of 8291 Sanger sequences was generated and subsequently split into 65 taxonomic groups (roughly to Class level based on KeyDNATools) prior to downstream analyses. First, we calculated genetic distances and clustered sequences into Operational Taxonomic Units (OTUs) using different distance cut-off levels. We found that most taxonomic groups had a maximum pairwise genetic distance of 0.25. Second, we used phylogenetic trees to study general evolutionary patterns. These trees confirmed our taxonomic classification and served to run Lineage Through Time (LTT) plots. LTT results indicated different cladogenesis dynamics across groups, with some displaying an early diversification and others a more recent one. Overall, our study provides an improved description of the microeukaryote diversity in the oceans in terms of genetic differentiation within groups as well as in the general phylogenetic structure. These results will be important to interpret the large amount of sequence data that is currently generated by High Throughput Sequencing technologies. Decoding the complexity of marine microeukaryotic diversity is one of the biggest challenges of modern microbial ecology, given the astonishingly large diversity detected in molecular surveys The clustering pattern, based on pair-wise genetic distances, has the advantage of being easily comparable among datasets and strongly related to sequence similarity. Indeed, OTU counts provide an estimate of present diversity in each taxonomic group. Alternatively, the phylogenetic structure derived from the branching pattern of a tree gives a complementary view that contains imprints of evolutionary events occurring within given lineages. The phylogenetic structure is the result of the interplay between speciation and extinction through time, processes that are driven by factors such as geographical isolation, environmental restrictions, reproduction modes and intraspecific interactions An important issue when clustering sequences in OTUs is the meaning of the clustering level applied. Several studies have attempted to identify the threshold fitting species definitions, to establish a countable unit in biodiversity inventories. Sequences sharing a similarity above 98% of the 18S rDNA gene have been proposed to derive from the same species Here we used publicly available 18S rDNA Sanger sequences obtained from molecular surveys aimed to study the diversity of marine planktonic protists by a culture-independent approach. We classified these sequences into separate taxonomic groups, combining classical taxonomy (Class level) with ribogrouping, and analyzed the genetic diversity in each group by OTU clustering and phylogeny. Our main objective was to get an improved representation of marine protist diversity. This will serve as a frame for interpretation and comparison with data obtained by High Throughput Sequencing (HTS) technologies like 454 or Illumina In this study we carried out an analysis of very carefully curated 18S rDNA environmental sequences derived from marine surveys both from oxic and anoxic water samples see . A firstThe rationale of choosing the V4–V5 region (∼550 bp) for most analyses was to maximize the number of sequences with shared positions, since many clone libraries targeted this region. We investigated how well this partial region represented the variability of the complete 18S rDNA gene. This test also included the V9 region (∼160 bp). For the three separate datasets  we plotted the pair-wise distances calculated with the two partial regions (V4–V5 and V9) with respect to the distances computed using the full-length gene . The V4–Supergroup maximum-likelihood phylogenetic trees were computed to validate the taxonomic assignment of the environmental sequences. The Alveolata tree  includedThe number of OTUs after clustering sequences at three different cut-off distance levels was estimated for each taxonomic group . At 0 diTo report the genetic distance encompassed within groups, we calculated the average, maximum, and maximum corrected pair-wise distances among all sequences within each group . The disThe clustering pattern was defined as the representation of the number of OTUs obtained in each group when clustering at different cut-off levels . In ordeLineages Through Time (LTT) plots can be compared using the γ value, which is zero if the rate of cladogenesis was constant through time, negative if it was faster at the origin of the lineage, or positive if it was faster towards the present. Graphically, this is represented by a straight, a concave and a convex line, respectively In order to further explore additional features contained in phylogenetic trees, we chose the Stramenopiles supergroup, since all taxonomic groups within this tree appeared monophyletic . This waThis study is an effort to advance in the understanding of the diversity of marine protists by using publicly available 18S rDNA Sanger environmental sequences. Substantial advances have been gained by sequencing environmental genes using traditional Sanger methods, and the new High Throughput Sequencing (HTS) technologies (e.g. Illumina and 454) are now used to continue exploring marine microbial diversity 2 = 0.40) and higher slope (m = 1.86), probably because this study did not perform a separate analysis per supergroup as we did here. The third step was to find out specific clustering cut-off levels that define taxonomic ranks. While some studies have investigated the level corresponding to the rank species Our aim was to report for each taxonomic group 1) the number of OTUs and its maximum genetic distance, and 2) the evolutionary patterns inferred from phylogenetic trees. Yet, some preliminary validations were necessary before this analysis. The first step was a proper classification of environmental sequences into classical taxonomic groups or ribogroups. Phylogenetic trees indicated that chimeras or misclassified sequences, which would artificially increase intragroup diversity, were accurately removed. The second step was identifying a useful 18S rDNA region. The V4–V5 hypervariable region, widely used in environmental surveys Once the dataset was manually curated and all sequences assigned to one of the 65 taxonomic groups, we started to analyze the diversity of the whole dataset of marine microeukaryotes. Overall, we detected 3,677 OTUs at 0.01 distance, mostly within Alveolata (63% of OTUs), Stramenopiles (15%), Rhizaria (9%) and CCTH (6%). Almost half of these OTUs belonged to taxonomically undefined ribogroups. The poor representation of the supergroups Amoebozoa and Excavata probably reflects their lower relative abundance as compared with the other supergroups in the marine plankton. This taxonomic distribution was similar to previously reviewed data Whereas the clustering pattern  allowed Phylogenetic supergroup trees displayed a branch distance that was not used in LTT plots, the trunk at the base of each monophyletic group. The trunk length represents the evolutionary time between the first appearance of the group and its observed diversification (putative diversifying lineages during this time are extinct). In a complete phylogeny, this trunk is a key feature to understand the intergroup diversity and complements the information given by MPD (Mean Phylogenetic Distance). Using the Stramenopiles tree as model for this analysis, it became evident that the MPD was not enough to describe the genetic isolation of a group, as confirmed by the minimum intergroup distance . For insIn summary, a good approximation to the evolutionary history of a given group could be reached by combining LTT plots and trunk lengths. This provided an overview of when most diversification occurred and what was the uniqueness of each group. The phylogenetic structure enriched and complemented the picture drawn by clustering pattern, which allowed reasonable comparisons among groups in terms of OTU numbers and maximum distances. Together, these two structural features gave a reasonable characterization of the diversity of the main microeukaryote clades. New sequencing technologies  are already providing a huge amount of sequences, and a good phylogenetic and clustering pattern overview based on a robust technique is required to ensure a solid backbone for interpreting and manipulating future high-throughput datasets.http://www.keydnatools.com)  .The resultant 13,270 sequences were taxonomically classified with KeyDNATools . Sequenc5′-CCAGCA(G/C)C(C/T)GCGGTAATTCC-3′, S. cerevisiae [U53879] positions 565–584) and TAReukREV3 (5′-ACTTTCGTTCTTGAT(C/T)(A/G)A-3′, positions 964–981) 5′-GTACACACCGCCCGTC-3′, positions 1629–1644), and EukB . The V4 forward and V9 reverse primers were excluded from the alignments.Full-length 18S rDNA sequences were prepared from three major supergroups: Rhizaria (72 sequences), Stramenopiles (60 sequences) and Alveolata (232 sequences). These were aligned with MAFFT as before and two regional alignments were extracted from the full gene alignments. The V4–V5 region was composed by the V4 region delimited by primers TAReuk454FWD1 , using the GTR-GAMMA evolutionary model and performing 100 alternative searches for topology and bootstrap using distinct random starting trees. Phylogenetic trees were visualized with the online tool iTOL Phylogenetic trees were constructed using one representative sequence from each OTU, generated using a clustering threshold of 0.01  or 0.05 (Alveolata). OTU clustering was done separately for each taxonomic group, then representative sequences from the same supergroup were combined and aligned with MAFFT. Maximum-likelihood phylogenetic trees were done with RAxML http://www.r-project.org/) using APE For each taxonomic group within Stramenopiles, the mean phylogenetic distance (MPD) was calculated with PHYLOCOM Figure S1Pipeline for database treatment. Processing of environmental 18S rDNA sequences from initial database to working dataset, showing the number of sequences left after each filtering step.(EPS)Click here for additional data file.Figure S2Pipeline for sequence treatment. Dark grey boxes are analyses performed on the entire dataset to split sequences into 65 taxonomic groups (plus the unassigned sequences as “Novel”). Light grey boxes are analyses performed on each of the 65 groups.(EPS)Click here for additional data file.Figure S3Genetic distances. Distribution of Average, Maximum and Maximum corrected distances within the 20 classes that have more than 30 sequences.(EPS)Click here for additional data file.Figure S4Phylogenetic structure of several groups of marine protists. Lineage Through Time (LTT) plots are based on the trees shown in (EPS)Click here for additional data file.Table S1List of all studies from which we have retrieved the 18S rDNA environmental sequences.(DOC)Click here for additional data file.Table S2Classification of environmental 18S rDNA sequences in 23 taxonomic groups. In this table are shown groups with less than 10 sequences. The groups are coded according to their taxonomic rank . The table shows the number of sequences per group (Seq), the average (Avg), maximum (Max) and maximum corrected (Maxc) pair-wise distances, and the number of OTUs at three cut-off levels.(DOC)Click here for additional data file.Table S3Matrix of mean distances among sequences belonging to different stramenopiles. In bold there is the minimum distance between groups.(DOC)Click here for additional data file.Nexus File S1Nexus files of the four phylogenetic trees.(TXT)Click here for additional data file.


Membrane protein research is frequently hampered by the low natural abundance of these proteins in cells and typically relies on recombinant gene expression. Different expression systems, like mammalian cells, insect cells, bacteria and yeast are being used, but very few research efforts have been directed towards specific host cell customization for enhanced expression of membrane proteins. Here we show that by increasing the intracellular membrane production by interfering with a key enzymatic step of lipid synthesis, enhanced expression of membrane proteins in yeast is achieved.Yarrowia lipolytica, by deleting the phosphatidic acid phosphatase, PAH1, which led to massive proliferation of endoplasmic reticulum (ER) membranes. For all eight tested representatives of different integral membrane protein families, we obtained enhanced protein accumulation levels and in some cases enhanced proteolytic integrity in the ∆pah1 strain. We analysed the adenosine A2AR G-protein coupled receptor case in more detail and found that concomitant induction of the unfolded protein response in the ∆pah1 strain enhanced the specific ligand binding activity of the receptor. These data indicate an improved quality control mechanism for membrane proteins accumulating in yeast cells with proliferated ER.We engineered the oleotrophic yeast, PAH1 gene inactivation, provides a valuable approach to enhance eukaryotic membrane protein production. Complementary to this improvement in membrane protein quantity, UPR co-induction further enhances the quality of the membrane protein in terms of its proper folding and biological activity. Importantly, since these pathways are conserved in all eukaryotes, it will be of interest to investigate similar engineering approaches in other cell types of biotechnological interest, such as insect cells and mammalian cells.We conclude that redirecting the metabolic flux of fatty acids away from triacylglycerol- and sterylester-storage towards membrane phospholipid synthesis by  Membrane proteins play critically important roles in a huge diversity of physiological processes. However, structural information on these proteins is scarce because of the multitude of experimental problems that need to be overcome to obtain a sufficient quantity of a detergent-solubilized, stable, homogeneous and monodisperse protein preparation from which well-ordered crystals for X-ray structural analysis can be grown. Only few membrane proteins have a natural abundance that is high enough to warrant purification from their native source, and heterologous overexpression is therefore required in most other cases. The expression systems that have been successfully used to produce membrane proteins for structure determination include mammalian cell lines, insect cells, bacterial cells and yeast. However, few expression systems have been specifically designed for the purpose of producing integral membrane proteins . In partPAH1, which encodes phosphatidic acid phosphatase Pah1p and which is a gate keeper for directing the flux of FA into TAG . We generated a construct that includes the promoter and terminator fragment of the PAH1 gene and a LEU2 marker for selection. A knockout is obtained after double homologous recombination at the promoter and terminator sites of PAH1. C. After transformation with the PAH1 knockout construct (P-LEU2-T), transformants were isolated and genotyped for the PAH1 gene locus. Lane 1, 1 kb DNA marker (Promega). Lanes 2 and 3, PCR amplificate of the PAH1 gene locus in, respectively, the wild type PO1d strain and an empty vector plasmid strain (expected wild type PAH1 amplificate size is 3268 bp). Lane 4, PCR amplificate of the disrupted PAH1 gene locus in a knockout strain (expected amplificate size is 2966 bp).Click here for file2A transgene mRNA expression in the wild type and PAH1 deletion strains. Graph showing the normalized relative quantities of the adenosine A2A receptor transgene mRNA in the wild type and PAH1 deletion strains. A trend towards more transgene mRNA in the knockout strain can be observed, which however did not reach statistical significance (p = 0.083), even with 8 biological replicates. (Mann-Whitney U-test). Horizontal bars represent median with the interquartile ranges.Adenosine AClick here for fileqPCR primers of protein-coding genes.Click here for file