Solitary extramedullary plasmacytomas are plasma cell tumors that tend to develop in mucosa-associated lymphoid tissues including the sinonasal or nasopharyngeal regions. Primary plasmacytoma of the lung is exceedingly rare and often presents as a solitary mass or nodule in mid-lung or hilar areas and diagnosed after resection. Herein, we report a case of primary pulmonary plasmacytoma that presented with diffuse alveolar consolidation and diagnosed by transbronchial lung biopsy. Extramedullary plasmacytoma (EMP) comprises roughly 3%–5% of all plasma cell neoplasms. Eighty percent of EMP occurs in the head and neck and most cases involve the upper aerodigestive tract . PrimaryA 60-year-old nonsmoker female with a chief complaint of dyspnea (function class III to IV) from three days before referred to our center. She had a 6-month history of dry cough which changed to purulent productive cough 20 days prior to admission, in addition to solid meal dysphagia, fever, chills, sweating, weight loss, and loss of appetite.She was hospitalized with a diagnosis of unspecified pneumonia. She had a history of coronary artery angioplasty eight months ago and vague history of rheumatoid arthritis. She used corticosteroids, atorvastatin, verapamil, and digoxin for the past 6 months. Her vital signs were: BP 140/80 mmHg, PR 85/min, RR 23/min, T 38°C. She had respiratory distress and central cyanosis. Pulmonary exam showed diffuse wheezing and fine and coarse crackles were heard over the lungs. Chest X-ray demonstrated bilateral alveolar consolidation which was more prominent in the lower part of the left lung. Bilateral hilar prominency, mild increase in mediastinal diameter without a gross bone lesion was also apparent.Arterial blood gas showed: pH 7.42, Pco2 43.9 mmHg, Hco3 27.6, Po2 40.2 mmHg, O2 sat 77.1%. Routine laboratory exams including biochemistry tests and complete blood count were normal. Angiotensin converting enzyme (ACE) and rheumatological tests were normal as well, but ESR was elevated to 94 mm in the first hour. Echocardiography showed mildly-reduced left ventricle function with an ejection fraction of 40%, right-sided wall motion abnormality and mild mitral valve regurgitation. Doppler sonography of the lower extremity veins did not show evidence of deep vein thrombosis. Spiral computed tomography of the thorax with intravenous contrast revealed no intraarterial filling defect, but hilar and mediastinal lymphadenopathies with right-sided parenchymal alveolar consolidation and nodular infiltration were seen in the left lung. Nodular opacity in the right middle lobe with pericardiac lymphadenopathy was also noted. A bleb about one centimeter in diameter was seen in the midzone of the right lung Figures  and 2. Bronchoscopy revealed diffuse hemorrhagic erythematous mucosa in the right and left bronchi without an endobronchial lesion. Transbronchial lung biopsy was done and pathologic examination of the specimen demonstrated diffuse plasmacytoid cell proliferation in the alveolar spaces and interstitium. The plasma cells had fine chromatin with mild pleomorphism and mitotic activity without evidence of fibrosis or any other inflammatory cell infiltration. Immunohistochemistry (IHC) revealed diffuse reactivity with CD79a and CD138 and was negative for CD20 and cytokeratin (CK) Figures . Polymerase chain reaction (PCR) analysis of the paraffin embedded block of transbronchial lung biopsy (TBLB) specimen revealed the presence of clonally-rearranged Ig heavy chain gene. According to above findings the diagnosis of plasmacytoma was confirmed and prompt investigation to rule out multiple myeloma was undertaken.γ region. No Bence-Jones protein was detected in urine. Serum calcium and phosphorus were within normal range. Bone survey revealed no abnormality. Serum total protein value was 9 gr/dl  with an IgG value of 39.1 gr/L . Levels of other serum immunoglobulins were within normal limit. Serum electrophoresis revealed the M component in the Bone marrow examinations on two occasions were unremarkable with less than 5% plasma cells and no dyscrasia. The patient was treated with melphalan and prednisolone. After 4 monthly courses, the chest X-ray became normal and the patient was free of symptoms.Presence of clonal plasma cell infiltrates in an extramedullary site can be a representation of multiple myeloma or solitary extramedullary plasmacytoma (SEP).Solitary extramedullary plasmacytoma of the lung is extremely rare, although pulmonary involvement with multiple myeloma is more common .To differentiate solitary EMP from multiple myeloma, bone marrow examination is needed and the patient should have lower than 5% plasma cells with no dyscrasia and a normal skeletal survey .γ region on electrophoresis and gene rearrangement study with PCR showed a monoclonal band on electrophoresis.Unlike multiple myeloma, EMPs may not have serum M protein or Bence Jones light chains in urine. However, up to 25% of patients with EMP will show a monoclonal gammopathy (M component). Our patient had M component within the It is reported that PPP have an equal sex ratio and most of them are asymptomatic . HoweverThe radiographic presentation of PPP is that of a solitary mass or nodule mostly in hilar areas . Diffuse+, CD79α+, CD5−, CD10−, CD23−, and CD43−. Our case revealed diffuse monotonous plasma cell infiltration, some with open chromatin pattern, and low mitotic activity without fibrosis or other secondary features which was totally negative for CD20, but diffusely positive for CD138 and CD79a which is consistent with diagnosis of plasmacytoma. Gene rearrangement study on paraffin embedded block specimens via PCR revealed the monoclonal band.The pathologic differential diagnoses include MALT lymphoma with plasma cell differentiation. Extra nodal marginal zone lymphoma of lung has polymorphous microscopic appearance and cellular heterogeneity is a hallmark of these lymphomas. The most characteristic cells are small- to medium-size lymphocytes with irregular nuclear outline (centrocyte-like) and plasma cell differentiation is common. However, other characteristic features including lymphoid follicles with reactive germinal center, lymphoepithelial lesion and fibrosis in addition to varying proportion of plasma cells which are either reactive or neoplastic are also present in involved tissue . IHC finAlso, plasmacytoma should be distinguished from plasma cell granulomas in which there is an admixture of inflammatory cells (including lymphocytes and macrophages) within fibrous stroma without evidence of monoclonality by immunohistochemistry or molecular study.Most of the patients with PPP have been treated with surgery and/or radiotherapy . However


Pathogens such as fungi, bacteria and especially viruses, are highly variable even within an individual host, intensifying the difficulty of distinguishing and accurately quantifying numerous allelic variants co-existing in a single nucleic acid sample. The majority of currently available techniques are based on real-time PCR or primer extension and often require multiplexing adjustments that impose a practical limitation of the number of alleles that can be monitored simultaneously at a single locus.Here, we describe a novel method that allows the simultaneous quantification of numerous allelic variants in a single reaction tube and without multiplexing. Quantitative Single-letter Sequencing (QSS) begins with a single PCR amplification step using a pair of primers flanking the polymorphic region of interest. Next, PCR products are submitted to single-letter sequencing with a fluorescently-labelled primer located upstream of the polymorphic region. The resulting monochromatic electropherogram shows numerous specific diagnostic peaks, attributable to specific variants, signifying their presence/absence in the DNA sample. Moreover, peak fluorescence can be quantified and used to estimate the frequency of the corresponding variant in the DNA population.Cauliflower mosaic virus, we reliably monitored six different viral genotypes in DNA extracted from infected plants. Evaluation of the intrinsic variance of this method, as applied to both artificial plasmid DNA mixes and viral genome populations, demonstrates that QSS is a robust and reliable method of detection and quantification for variants with a relative frequency of between 0.05 and 1.Using engineered allelic markers in the genome of This simple method is easily transferable to many other biological systems and questions, including those involving high throughput analysis, and can be performed in any laboratory since it does not require specialized equipment. The need to analyze genetic variation within populations of various organisms has engendered a wide variety of techniques designed to identify genetic differences between related genomes ,2. The vFor pathogenic microorganisms, such as fungi, bacteria and especially viruses, populations infecting an individual host are often highly variable, intensifying the difficulties involved in distinguishing and accurately quantifying numerous alleles or variants co-existing in a single nucleic acid sample. This is best illustrated by considering the properties of viral populations, which exist as a swarm of related mutants commonly designated as a quasispecies . One remCauliflower mosaic virus (CaMV) during systemic invasion of plant host tissues (to be described elsewhere). For this purpose, and because of concerns related to the available genotyping techniques mentioned above, we developed a novel analysis method based on classical detection of genetic variants by dideoxy fingerprinting [We faced exactly this challenge when evaluating the effective population size of printing ,24. ThisHere, we describe the QSS method and its application to samples consisting of mixtures of purified plasmids or viral genomes extracted from a multiply infected host plant. We test the accuracy, reproducibility, robustness and limits of this method, and discuss its potential application to other biological systems in any standard laboratory setting. Since QSS applies to known sequences only, it will be useful for monitoring experimental populations, as well as natural populations where the different variants at the targeted locus have been previously identified.Genetic markers were engineered into the CaMV genomes at exactly the same position. Thus, examining single letter (A) sequence traces from a mixed population should, in theory, reveal some common peaks at positions where more than one marker harbours a A, and discriminating peaks at positions where only one marker does so. Figure The actual observed positions of A-peaks from the amplified and single-letter-sequenced pCaVIT1-6 plasmids showed slight differences from their theoretical positions relative to a co-electrophoresed labeled DNA ladder Figure . SequencThis experimental list of discriminating A-positions was validated when all six markers co-existed in the DNA sample and were analyzed together. A mix of equal amounts of all pCaVIT1-6 plasmids was used as a template for PCR and subsequent single-letter sequencing. All experimentally-confirmed discriminating A-peaks were easily distinguished at their specific position on the electropherogram Figure , demonstIn all subsequent experiments, only those peaks that perfectly matched their expected position  to each discriminating-peak, corresponding to its height relative to that of the normalizing-peak. For each given discriminating-peak, a standard curve was constructed by plotting the known frequencies of the corresponding marker against the observed I values Figure . One sta2 values obtained for all standard curves were remarkably high, and two examples  are shown in Figure The polynomial regression function corresponding to each standard curve was calculated using Excel (Microsoft). The RThe standard curves shown in Figure 2 correlation coefficient was excellent for all VIT markers, being between 0.984 and 0.999.The accuracy of the quantification was assessed by comparing the measured frequencies to their expected values on plasmid mixes prepared from the reference stocks. Several mixes were tested (compositions listed in Table The name Quantitative Single-letter Sequencing (QSS) is used to describe the overall process of identifying discriminating peaks, recording their normalized height, and converting them into frequencies of genetic markers in a mixed DNA population.We performed five independent PCR-amplifications of plasmid mix N°9 Table , containA similar reproducibility evaluation was also performed on a more realistic biological sample. A mixed population of viral DNA genomes was extracted from a single infected plant, originally inoculated with the CaMV Mix6VIT virus suspension as described in Methods. The extracted viral DNA was used in 19 independent PCR amplifications followed by QSS analysis Table . SeveralIn the analysis of viral samples from the CaMV Mix6VIT-infected plant Table , repetitThis report describes the development of a novel technique – Quantitative Single-letter Sequencing (QSS) – allowing simultaneous quantification of the frequencies of numerous individual allelic genetic markers in a mixed DNA population without the need for multiplexing adjustments. Although several techniques for the simultaneous detection and quantification of multiple variants at a single locus have been described previously -27, the 2) and the very small standard deviation (SD) between repeats, make QSS equally, if not more, reliable than other techniques for which we could extract similar parameters from the literature  would likely avoid partially overlapping peaks , and duplicating each analysis in two different spectrophotometers . A 1 mL solution, designated as the reference stock, was immediately prepared for each plasmid at a concentration of 50 ng·μLBrassica rapa var. "Just Right") were maintained in an insect-proof greenhouse under controlled conditions (25/19°C day/night with a photoperiod of 16/8 hours day/night).Turnip plants , as previously described [Seedlings at the three-leaf stage were mechanically inoculated with escribed . SymptomAt 21 dpi, each CaMV-VIT1-6 virus clone was transferred to new healthy plantlets by mechanical sap inoculation. After two successive plant-to-plant passages (21 days each), viral DNA was extracted from single plants as described below, and the integrity of all six genetic markers was verified by sequencing, confirming their stability.2), submitted to final clarification at 15,000 × g for 20 min. and stored at -20°C until use.Leaves systemically infected with CaMV viral clones were collected at 21dpi, ground in buffer 1 , and stirred overnight with 1.5 M urea and 2% triton-X-100 . These crude extracts were then clarified by centrifugation at 3,000 × g for 15 min., and the supernatants were further ultra-centrifuged through a 15% sucrose cushion at 200,000 × g for 2 hours. The virus particles in the pellet were suspended in 600 μl of buffer 2  of virus particle preparations from plants infected with each of the 6 CaMV-VIT clones were pooled to produce a mixed population designated Mix6VIT. Note that we were aware that the different CaMV-VIT clones are not necessarily present in equal amounts in this mix at this point. Young healthy plantlets were then mechanically inoculated by carefully rubbing 20 μl of Mix6VIT on the total surface of three young leaves previously powdered with the abrasive carborundum. Symptoms indicative of CaMV infection appeared within 8 to 11 days, and all plants were considered systemically infected at 13 dpi.For unknown reasons, CaMV symptoms always appear much earlier on plants inoculated with viral particles than on those inoculated with plasmid DNA, as in the previous section.CaMV genomic DNA was purified from infected plants at 13 dpi according to the protocol described previously , plus an® DNA Polymerase (New England Biolabs) according to the supplier's recommendations. Samples (2–5 ng) of plasmid mix or purified viral DNA were amplified by 35 PCR cycles, using the CaMV-specific primers F748 (5'-CTTGGAGCGGTCAAAATATTG-3') and Ris7 (5'-GTTGGGTACCTAAGGCTTCTAATATCTC-3'), generating a 1301 bp fragment spanning 601 and 660 bp upstream and downstream, respectively, of the introduced genetic marker. The primers were designed relatively far away from the polymorphic region to alleviate putative problems related to allele specificity during PCR amplification. The amplified DNA fragment was finally purified with a double phenol/chloroform extraction and ethanol precipitation, suspended in 45 μl water and processed for single-letter sequencing as described below.PCR amplification of viral sequences was performed in a total volume of 100 μl, using VENT© technology [The single-letter sequencing reactions were carried out by COGENICS Genome Express according to a proprietary protocol initially developed for DACSchnology . Brieflychnology , thus laThe 6FAM-1236bis sequencing primer was positioned 112 bases upstream of the marker region – a compromise between too long a distance engendering a decrease in fluorescence sequencing signals, and too short a distance increasing the risk of putative allele specificity problems. In our case, the electropherogram was exploitable without significant decrease in signal intensity from 75 to 200 bp downstream of the fluorescent sequencing primer.Nucleic Acid Analysis Software . The poBM performed research except single-letter sequencing reactions which were carried out by HD. SB had the idea to exploit single-letter sequencing for distinguishing and quantifying several virus variants in a mixed population. BM, HD and SB extracted data from single-letter sequencing electropherograms. HD helped to draft the manuscript. BM and SB wrote the manuscript. All authors have read and approved the final version of this manuscript.


Our genome-wide analysis shows conclusively that in yeast, relative exchange is a novel consistent feature of coding regions. Outside of replication, each coding region has a characteristic pattern of histone H3 exchange that is either higher or lower than what was expected by its RNAPII transcription rate alone. Histone H3 exchange in coding regions might be a way to add or remove certain histone modifications that are important for transcription elongation. Therefore, our results that gene-specific coding region histone H3 exchange is decoupled from transcription rate might hint at a new epigenetic mechanism of transcription regulation.In eukaryotic organisms, histones are dynamically exchanged independently of DNA replication. Recent reports show that different coding regions differ in their amount of replication-independent histone H3 exchange. The current paradigm is that this histone exchange variability among coding regions is a consequence of transcription rate. Here we put forward the idea that this variability might be also modulated in a gene-specific manner independently of transcription rate. To that end, we study transcription rate–independent replication-independent coding region histone H3 exchange. We term such events  relative exchange, is a reproducible and consistent feature of the entire coding region and cannot be explained by regional effects. Next, we characterize the relations between relative exchange and a variety of histone H3 modifications, as well as the histone chaperone Asf1. Taken together, our analysis shows that gene-specific replication-independent histone H3 exchange in coding regions is mediated independently of transcription rate, thus constituting a new mechanism in epigenetic transcription regulation.During nucleosome disassembly and reassembly, evicted histones are exchanged with newly synthesized histones. Histone exchange occurs in several DNA metabolism processes, including replication, transcription, and repair. Recent reports from several labs show that replication-independent histone H3 exchange in yeast coding regions is tightly correlated with transcription rate. We have computationally shown that histone exchange variability among genes is not only a consequence of transcription rate. Instead, each coding region has a characteristic amount of replication-independent histone exchange, even when excluding the confounding effect of transcription rate. We show that this transcription rate–independent exchange, referred to as  The nucleosome is the basic repeating unit of the chromatin and comprises 147 bp of DNA wrapped around an octamer of histone proteins . Nucleosome disassembly and reassembly is tightly coupled with replication, transcription, DNA repair and heterochromatin silencing e.g., . Under nCoding region replication-independent H3 exchange varies from gene to gene. Recent studies have shown that this variation among coding region is linked to differences in transcription rate Although it is widely accepted that coding region replication-independent H3 exchange differences are a consequence of transcription rate, it is still unknown whether this variability is also controlled independently of transcription rate. In other words, it is not clear whether different coding regions can have substantially different replication-independent H3 exchange even if they have the same transcription rate. This leads us to investigate genome-wide coding region H3 exchange independently of both replication and transcription rate. We address two key questions: First, is there evidence that transcription rate–independent and replication-independent H3 exchange in coding regions is a consistent feature of genes? Second, is there evidence for an active regulation of this feature?total exchange, whereas the calculated transcription rate–independent total exchange is referred to as relative exchange. Positive (negative) relative exchange implies that the total exchange is higher (lower) than what is expected based on transcription rate alone. Importantly, although relative exchange is independent of transcription rate, it is still likely to be influenced by the transcription process. We found that relative exchange varies from gene to gene and is a reproducible feature of genes. Elevated or reduced relative exchange occurs along the entire coding region and not only in a specific part of it. Moreover, relative exchange is a gene-specific property rather than a regional effect. Finally, we revealed that H3K79 trimethylation is depleted in coding regions with hyper relative exchange and enriched in coding regions with hypo relative exchange. Taken together, our data provides evidence that coding regions have a characteristic relative exchange, a new feature of genes. Genes might have either hyper or hypo relative exchange, irrespective of their total exchange or transcription rate. Histone exchange in coding regions might be a way to add or remove certain histone modifications that are important for transcription elongation. Therefore, decoupling replication-independent histone exchange from transcription rate is a process with potential for epigenetic gene regulation.In this study, we analyzed published data sets of replication-independent histone H3 exchange in yeast Asf1 is a histone H3/H4 chaperone that has been implicated in histone H3/H4 exchange during elongation transcription rate is defined as RNAPII density averaged over the coding region. Total exchange is the measured replication-independent histone H3 exchange averaged over the coding region.The present work is focused on the understanding of replication-independent histone H3 exchange in coding regions. For our study, we used published genome-wide measurements of histone H3 exchange and RNAPII densities Relative exchange is the distance of total exchange to a running average of the total exchange along the transcription rate axis (probe's relative exchange), we used measurements of total exchange and RNAPII density in a single probe without averaging over the entire coding region  gene. For comparison, we have computed the difference between relative exchange replicates ]. This observation is particularly significant because of the bias toward both negative and positive differences between similar-transcription genes. Whereas the bias toward positive values can be attributed to the global relationships with transcription rate, this effect cannot explain the bias toward negative values.The observed relative exchange variation among genes might be a consequence of biological or experimental noise. To exclude the latter, we grouped genes into pairs with minimal difference in transcription rate. Such a gene pair is termed −200, Next, we have investigated the reproducibility of relative exchange in different laboratories. Total exchange (together with transcription rate) was measured in two different laboratories To examine whether relative exchange is a general property of genes, we wished to analyze the reproducibility of relative exchange along the entire coding region. To that end, we divided the genes into five subsets according to their relative exchange based on Dion et al. For each subset, we plotted a profile of probe's relative exchange throughout the coding region based on Rufiange et al. . Note thsegmental relative exchange) was calculated using only probes located within this segment (see within genes) tend to be smaller than relative exchange differences between segments from neighboring genes (denoted between genes). In agreement, relative exchange variation between genes is significantly larger than the variation within genes [P<10−200 (F-test for significance of difference between variances)], indicating the consistency of relative exchange along the coding region. Notably, the distribution of relative exchange differences between neighboring genes is similar to the distribution of relative exchange differences between random genes (hyper relative exchange (or hypo relative exchange), based on their total exchange that is relatively higher (or lower) from what can be expected from their transcription rate alone. A genome-wide mapping shows that hyper relative exchange coding regions are scattered throughout the genome , indicating that H3K79me3 is specifically associated with relative exchange. In agreement, Unexpectedly, we found that relative exchange is tightly related to H3K79 trimethylation H3K79me3 . H3K79mePrevious studies show that in coding regions, there is a correlation between H3K79me3 and transcription rate e.g., . We aske−35, Figure S4 in −3). Therefore, our analysis indicates that although both H3K36me3 and H3K36Ac are inversely related, they are associated differentially with relative exchange.Histone H3K36 can be a target of acetylation, mono-, di- and trimethylation . Recent report shows that H3K36Ac pattern is inversely related to H3K36me2 and H3K36me3 patterns in coding regions, suggesting that H3K36 is an ‘acetyl/methyl switch’ asf1Δ, denoted Asf1-mediated exchange . However, given total exchange, the conditional correlation is insignificant . This can be clearly seen in We next sought to investigate the relations between Asf1-mediated exchange and transcription rate. In agreement with previous reports, we found that Asf1-mediated exchange indeed correlates with transcription rate . The presence of Asf1 at promoters is important for disassembly of H3/H4 upon activation and for reassembly of H3/H4 upon loss of activation  but is not associated with total exchange . To avoid biases related to genes that are not transcribed by RNAPII and global effects on histone H3 exchange, we removed non-coding genes, 25-kbp regions near the telomeres and centromeres, and 1-kbp regions near rRNA, tRNA, ARS and mitochondrial DNA . To calculate relative exchange, we needed the running average curve of total exchange along the RNAPII density. To avoid complications arising from different total exchange along the coding region .In x-axis), total exchange (y-axis) and an additional molecular feature  exchange and an additional molecular feature, were calculated independently of total (relative) exchange and transcript length. This way, the correlation with any exchange measure is calculated only after factoring out the contribution of the other exchange measure and after eliminating potential effects of transcript length. For each coding region, we used the molecular feature value averaged over the coding region. Transcript lengths were computed based on sequencing of cDNA library (Text S1(0.74 MB PDF)Click here for additional data file.Text S2(0.03 MB PDF)Click here for additional data file.Text S3(0.02 MB PDF)Click here for additional data file.Text S4(0.04 MB PDF)Click here for additional data file.Text S5(0.28 MB PDF)Click here for additional data file.


Glioblastoma multiforme (GBM) is the most common primary intracranial tumor and despite recent advances in treatment regimens, prognosis for affected patients remains poor. Active cell migration and invasion of GBM cells ultimately lead to ubiquitous tumor recurrence and patient death.To further understand the genetic mechanisms underlying the ability of glioma cells to migrate, we compared the matched transcriptional profiles of migratory and stationary populations of human glioma cells. Using a monolayer radial migration assay, motile and stationary cell populations from seven human long term glioma cell lines and three primary GBM cultures were isolated and prepared for expression analysis.a priori knowledge with expression data. Principal component analysis (PCA) revealed two discriminating patterns between migrating and stationary glioma cells: i) global down-regulation and ii) global up-regulation profiles that were used in a proband-based rule function implemented in GABRIEL to find subsets of genes having similar expression patterns. Genes with up-regulation pattern in migrating glioma cells were found to be overexpressed in 75% of human GBM biopsy specimens compared to normal brain. A 22 gene signature capable of classifying glioma cultures based on their migration rate was developed. Fidelity of this discovery algorithm was assessed by validation of the invasion candidate gene, connective tissue growth factor (CTGF). siRNA mediated knockdown yielded reduced in vitro migration and ex vivo invasion; immunohistochemistry on glioma invasion tissue microarray confirmed up-regulation of CTGF in invasive glioma cells.Gene expression signatures of stationary and migratory populations across all cell lines were identified using a pattern recognition approach that integrates in vitro affords discovery of genomic signatures; selected candidates were validated clinically at the transcriptional and translational levels as well as through functional assays thereby underscoring the fidelity of the discovery algorithm.Gene expression profiling of migratory glioma cells induced to disperse  Gene expression profiles of a panel of seven established glioma cell lines and three primary glioma cultures, induced to migrate for 24 hours, were established. They revealed two signatures of migrating and stationary glioma cells and selected candidates were validated clinically on a comprehensive glioma expression data set, a glioma invasion tissue microarray (TMA) and functionally in migration assays and ex vivo rat brain slice assays.Glioblastoma multiforme (GBM) is the most common primary brain tumor, affecting 20,000 patients per year; the peak age of occurrence is between 50–60 years of age. Despite advances in diagnosis and treatment, life expectancy for patients suffering from this disease still remains at 18 months -5. We haMycoplasma sp. infection by DAPI staining regularly.Ten GBM cell lines were selected for this study: seven established , U87ΔEGFR , and thrThe radial migration assay was performed as described previously ,12. To sFor microarray experiments, stationary (core) and migratory (rim), cells were harvested under an inverse microscope  using P2 pipette in three independent biological replicates. Thirty individual dispersion assays (three 10-well slides) were collected per cell line and all materials were stored at -20°C until RNA isolation. Core and rim cells were separately lysed with Trizol .Total RNA was extracted from Trizol solution, according to manufacturer's instructions and followed with purification through RNeasy mini kit . Total RNA quality and quantity were assessed by Bioanalyzer RNA 6000 Nano LabChip Kit . Samples selected for the study contained intact RNA as determined by visual inspection of electropherograms for distinct 18S and 28S ribosomal peaks. Samples that failed this assessment were not included in the study.Sample (either rim or core) and human universal reference RNA  underwent one round of linear amplification and fluorescent labeling in Agilent's Low-Input Linear Amplification and Labeling Kits yielding fluorescent cRNA. Sample RNA was labeled with Cyanine 5 (Cy5) while reference RNA was labeled with Cyanine 3(Cy3) – CTP  and cRNA concentration was assessed spectro-photometrically . One hundred nanograms of total RNA from each sample were labeled for hybridization to microarray chips and remaining unamplified sample RNA was saved for validation studies. Reference RNA was amplified/labeled in replicate reactions and pooled.2 (S/R) (S = sample (rim or core) and R = universal reference) for each gene was calculated.Each Cy5-labeled core and rim sample  was hybridized against Cy3-labeled universal reference RNA on whole human genome 40 K oligonucleotide microarray chips (Agilent) according to manufacture's protocol . Agilent2 ratio of ratios, which is calculated by dividing rim ratio  by core ratio .Technical variation  was assessed with two sets of technical triplicates. One collected at the beginning of the study and one at the end. A comparison of variation in technical and biological triplicates from the same cell line (T98G) was done to determine if technical variation was the same or greater than biological variation. Variation was qualitatively assessed by overlaying standard deviation versus average gene expression for each set of technical triplicates with their corresponding biological replicates. Expression values were logA minimum fold change threshold was established by calculating the median standard deviation of rim/core expression for each biological replicate (n = 3). Genes that were not statistically (p > 0.1) differentially expressed were identified with ANOVA and removed. Genes that are not differentially expressed are considered non-informative and removed to reduce the complexity of the data.a priori knowledge about transcriptional differences between rim and core populations was employed. Principal component analysis (PCA) was used to determine components of variation in the data whereby patterns can be identified that discriminate samples (i.e. rim from core) [In order to identify genes whose expression was consistently up- or down-regulated in rim cells as compared to their corresponding core cells across all cell lines, a pattern recognition approach integrating om core) ,16. Geneom core) . One of om core) . ProbandMigration rates from the replicates of the 7 glioma cell lines and 3 primary cultures were averaged and binned into fast (>average migration rate) and slow migration (<average migration). Significance analysis of microarrays (SAM) was carried out on the union of migratory and stationary signatures derived from GABRIEL in order to identify significantly different genes between fast and slow migration groups. Support vector machines (SVM) with a linear kernel using leave-1-out crossvalidation were used to predict migration rate (e.g. fast or slow) based on gene signatures. A permutation test  was performed to assess the performance of the classification.Clustering of the migration rate signature in the malignant astrocytoma cases was performed using k-means and visualized using multidimensional scaling (MDS) plots. Kaplan-Meier analysis were used to assess survival differences between clusters.TM4  was used® MAS5 (Microarray Suite 5) algorithm implemented in Affymetrix® GCOS (GeneChip Operating Software). Text files exported from GCOS were uploaded into GeneSpring®7.2 for data management .Gene expression data of 111 glial tumors and 24 normal brain specimens was kindly provided by Dr. T. Mikkelsen, Henry Ford Hospital, Detroit MI and Dr. H. Fine Neuro Oncology Branch, National Cancer Institute, National Cancer Institute, Bethesda, MD. Array data was processed according to AffymetrixGene symbols and accession numbers were used to map Agilent probes to Affymetrix probes using Affymetrix's Netaffx tool.QRT-PCR was performed with the unamplified RNA collected for use in microarray experiments. One hundred nanograms of RNA from each biological replicate were reverse transcribed  using oligo-dT primers. cDNA was diluted 1:4 in water and aliquoted until use. Primers were designed to hybridize in the 3'prime region of the genes and to produce amplicons ranging from 100 to 300 base pairs. Relative quantification was performed on the LightCycler Instrument  using LightCycler FastStart DNA Master SYBR Green I and normalized to Histone 3A as a housekeeping gene. Specificity of PCR amplicons was confirmed by melting curve analysis  and agarIH-IG)-(CH-CG) for 30 minutes, and incubated at room temperature for one hour with anti-CTGF antibody  at 1:100. Addition of Cy3 conjugated secondary antibody  for one hour at room temperature (1:2000) was completed and cells were viewed with a LSM 5 Pascal laser scanning confocal microscope .A glioma invasion specific TMA  was usedNM_001901) were GTCCCGGAGACAATGACATCT (C1) and ATCGGAATCCTGTCGATTAGACT (C2). The sequences were designed to be unique when compared with the sequence of other CCN members. The sequence targeting luciferase was AACGTACGCGGAATACTTCGATT. Glioma cells (8 × 105) were transfected with 20 nM of siRNA using lipofectamine 2000 (Invitrogen) and cultured for 48 hours prior to use.Purified, duplexed siRNAs for CTGF and for luciferase (control) were purchased from Qiagen . The siRNA sequences targeting human CTGF (GenBank accession number In-vitro migration assays were performed on 10-well slides coated with 10 μg/ml laminin.ex vivo invasion assay on rat brain slices was carried out as described previously [5 glioma cells stably expressing green fluorescence protein (GFP) were gently placed (0.5 μl transfer volume) on the brain slice in 4–6 replicates per condition. After 72 hours, glioma cell invasion into the rat brain slices was quantitated using a LSM 5 Pascal Laser scanning confocal microscope . Serial optical sections were obtained every 5 μm downward from the surface plane to the bottom of the slice. The invasion rate was calculated as described previously [An eviously ,23. Brieeviously . In brieGlioma cells or glioma tissue specimens were lysed in sample buffer as described previously  and sepaRNA was isolated from migratory and stationary glioma cells harvested as three biological replicates from the radial migration assay. RNA integrity was confirmed with the Bioanalyzer by the presence of well-defined 18s and 28s ribosomal RNA bands; the median yield per biological replicate was 700 ng. Microarray analysis of migratory glioma cells compared to their stationary cognates was performed using the three biological replicates derived from seven glioma cell lines and three primary cultures. Median standard deviation of rim/core expression for three biological replicates was ± 1.23-fold with a 95% confidence interval at ± 1.5-fold, leading to a detection limit of ± 1.5-fold change between rim and core samples for this study. Hierarchical clustering of median expression for each core or rim sample in this study revealed close similarity between core and rim samples derived from the same cell line Figure .An ANOVA-filter removed 25,221 genes that were not statistically differentially expressed across all the samples leaving a subset of 16,454 differentially regulated genes (p <= 0.1) in at least one sample.PCA was performed on a 16,454 genes × 20 samples matrix. Expert analysis of principal components (PC) revealed that PCs 1–8, comprising the majority of the variation in the data, described the variation between cell lines. PC 9 and 10 exhibited patterns matching anticipated differences in expression between migratory and stationary cells (i.e. up- or down-regulation). PC 9 displayed a down-regulation profile (i.e. rim sample compared to corresponding core) and PC10 displayed an up-regulation profile (rim to core). In addition, plotting the scores of PC 9 and PC 10 against each other formed two clusters, which discriminated rim from core samples Figure . ProbandThe dataset of 16,454 genes was up-loaded into GABRIEL and proband analysis using a correlation coefficient of 0.8 identified a subset of 105 genes correlating with down-regulation pattern of AK098354 (FDR = 0.003 and FNR = 0.889) and will be referred to as "stationary signature"; a subset of 50 genes was found to correlate with up-regulation pattern of CYR61 (FDR = 0.004 and FNR = 0.025) and will be referred to as "migratory signature" [see Additional file migratory and stationary signatures genes were selected for validation by quantitative RT-PCR because they exhibited 1.5-fold or greater differential expression between rim and core in a majority of the cell lines and are annotated as being engaged in cancer, migration, and/or invasion (literature mining). Cyr61 and CTGF were found to exhibit total concordance of directionality of expression between microarray and QRT-PCR expression. RRAS and RhoA were found to be up-regulated in eight and six, respectively, of the cell lines examined. Integral membrane protein 2B (ITM2B) and zinc finger protein 436 (ZNF436) showed concordant down-regulation between microarray data and QRT-PCR in 8 out of 10 cell lines, while p300/CBP-associated factor (PCAF) and mothers against decapentaplegic homolog 1 (MADH1) showed down-regulation in seven and six out of 10 cell lines, respectively  and 24 non-tumor tissues. 75% of genes in the migratory signature   Figure .migratory and stationary signature to select features that discriminate between fast  and slow migrating glioma cells   and classifiers developed using support vector machines correctly predicted migration rate in 9 out of 10 glioma cultures in this study (permutation test P = 0.03). Cox hazard analysis identified CTGF as the gene with the highest regression coefficient with migration rate .Significance analysis of microarrays (SAM) was carried out on the union of ) Figure . A migraMapping the 22 genes (Agilent platform) from the migration rate signature to the clinical data set (Affymetrix) found that 19 genes were represented in the clinical data. Clinical samples were binarized according to patient by age (young < median age < old) and K-mean (k = 3) identified three distinct populations in each age group. Multidimensional scaling was used to visualize the clusters with 3D scatterplots Figure . In younin vivo protein expression of CTGF. IHC staining intensity in seventeen GBM samples represented by matched tumor core and invasive rim from the same sample were evaluated by a pathologist (S.N.). Invasive glioma cells were found to exhibit significantly stronger median immuno-positivity for CTGF than stationary core cells (P = 0.025). Ten percent of invasive glioma cells showed strong staining for CTGF, 70% exhibited medium and 20% weak immuno-positivity while 80% of glioma cells in core samples exhibited weak staining and 20% exhibited medium staining. Normal brain astrocytes and neurons showed no staining for CTGF  compared to normal brain specimens (n = 24) (p < 0.004) Figure .in vitro glioma migration  as compared to luciferase control . QRT-PCR revealed 90% reduction of CTGF mRNA levels as compared to control (data not shown) and immunoblotting confirmed significant reduction of protein levels compared to control in three glioma cell lines Figure . siRNAs l Figure .To evaluate biological effects of CTGF on glioma cell invasion through physiologically and anatomically relevant tissue, we determined whether RNA interference-mediated depletion of CTGF inhibits the invasion of GFP-expressing SNB19 and U251 human glioma cells into vital rat brain slices, a well-established organotypic model for glioma invasion. Cells treated with two independent siRNAs against CTGF or luciferase were implanted in 4 to 6 replicates on contralateral sides of the same rat brain slice and invasion was quantified by confocal microscopy. Depletion of CTGF in SNB19 causes significantly reduced cell invasion  compared with luciferase treatment (100% ± 24). Likewise, depletion of CTGF in U251 causes significant reduction in invasion  compared to cells treated with luciferase siRNA for control (100% ± 20) Figure .In the present study we aimed at shedding light on the transcriptional mechanisms activated in glioma cells under migratory conditions and identifying gene candidates that can be translated into targets for anti-invasive therapies. A comprehensive panel of human glioma cell lines and primary cultures was employed for this study. To reflect already well-established genetic properties of gliomas, cell lines were selected according to their p53 status. In greater than 65% of secondary GBM (recurring) cases tumors present with p53 mutations . Primaryin vitro and in situ [in vitro to allow for candidate discovery and functional validation. Whole human genome oligonucleotide microarray was employed to analyze gene expression profiles of migratory and stationary cells isolated from 7 established glioma cell lines and three primary cultures of human glioma biopsies. To ensure statistical rigor of this study, samples presenting migratory and stationary phenotype were collected in a randomized fashion as three independent biological replicates. These replicates provided sufficient statistical power for a 1.5-fold sensitivity between migratory and stationary cells. Quality control through technical replicates ensured that, as expected, technical variation in the array data was lower than biological variation between samples or cell lines.While gene expression profiling has been widely used to identify subtypes of gliomas and to discern genes related to tumor progression and patient survival ,28, only in situ . Here, astationary and migratory signatures. Based on low false discovery rates and concurrent high false negative rates it was concluded that the selected subsets contained genes that truly exhibited the desired up- or down-regulation pattern and merited further investigation.As a first step of data analysis, genes that were not differentially expressed between phenotypes (i.e. stationary vs. migratory) were filtered through one-way ANOVA performed across all samples. We sought a pattern of individual genes responsible for differences between these phenotypes that may be involved in directing cell behavior. In a simplistic approach, we identified up-regulation and down-regulation patterns to find subsets of genes with correlated expression. PCA was employed to reduce high dimensional array data into components that represent the majority of variation within the data. All 20 PC scores were examined even though the patterns of interest (rim vs. core) were thought to represent only a small proportion of variation, which should materialize in higher components. Visual analysis of PCA scores yielded anticipated signatures in the higher PCs, specifically PC 9 and 10 representing a small fraction  of the total variation. This observation indicates that the majority of variation within the expression changes in the dataset is a consequence of intrinsic differences between cell-lines as manifested by the hierarchical clustering of cell lines, in which core and rim samples of individual cell lines cluster together before biological (phenotypic) replicates of core and rim cluster together. A pattern describing down regulation in migratory cells compared to stationary cells was observed in PC9 and AK098354 was selected as a proband exhibiting strong correlation with this pattern. Similarly, CYR61 was chosen as a proband that significantly correlates with the pattern (PC10) for genes up-regulated in migratory cells. In a pattern-based approach implemented in GABRIEL, two gene signatures highly correlated (r > 0.8) with either proband, AK098354 or CYR61, were identified and termed in vivo gene expression study where stationary and invasive glioma cells were collected by laser-capture microdissection from 19 glioblastoma multiforme biopsy samples (unpublished data). We found that 51% (54 out of 105) of the genes in the stationary signature were down-regulated and 39% (21 out of 54) of genes in the migratory signature showed up-regulation in invasive tumor cells. CTGF was found to be up-regulated in invasive cells from 8 out of 19 tumor samples. Expression of the migratory signatures was further examined in these samples. A slightly lower number of stationary signature genes was found to be down-regulated (52 out of 105 – 49%), and 50% of migration signature genes were up-regulated (27 out of 54). This correlation between in vitro and in vivo motility gene expression profiles suggests a parallel between migration and invasion.The expression of genes in the stationary and migratory signatures was investigated in a more recently collected and larger Literature mining was performed to identify genes whose functions were linked to cancer, migration and invasion; these were further evaluated in clinical datasets and in empiric testing for involvement in migration and invasion. Technical validation of 8 candidate genes by QRT-PCR confirmed directionality of array data (i.e. up- or down-regulation with migration). Incomplete concordance of RRAS, RhoA, PCAF and MADH1 regulation across all cell lines studied, was interpreted as biological variation of gene expression between cell lines reflecting the genetic diversity of the disease Figure .migratory and stationary signatures is underscored by discovering that 75% of genes in the up-regulation signature were also found to be differentially regulated in expression datasets of human glial tumors compared to normal brain. In conjunction with overexpression of TGFβ, Ephrin receptor and Wnt/β-catenin signaling in migrating glioma cells we postulate that genes from the migratory signature act as drivers of invasion, a hallmark of astrocytic tumors. Also, these pathways have been linked to glioma migration in hypothesis driven experiments and therefore underscore the validity of our discovery approach . Cliynthesis  and is synthesis ,35. Enhaynthesis . Based oynthesis ,36. Instmigratory and stationary signatures we could develop a classifier that predicts the migration rate of cell lines studied. It underscores that a gene signature might directly govern the invasive and metastatic potential of a tumor. While it has been demonstrated before, that modifications of the extracellular matrix and changes in the expression of single genes result in changes to migratory behavior [Interestingly, from the behavior ,37, to omigration rate signature identified groups of patients that had significant differences in survival, underscoring the clinical significance of the migration rate signature. Hazard analysis revealed CTGF as the gene with the highest contribution to the migration rate. CTGF was initially identified as a mitogen, produced by vascular endothelial cells promoting proliferation and differentiation of chondrocytes [After stratification for age, the most important predictor for survival , the migdrocytes . CTGF hadrocytes , as welldrocytes . While Cdrocytes ,43. Our drocytes  the obsein vitro migration in three human glioma cell lines. This finding is further supported by the observation that CTGF knockdown results in decreased invasion of two human glioma cell lines in ex vivo organotypic rat brain slice assay, a system closely resembling extracellular matrix environment present in the brain. The observation that CTGF knockdown does not completely inhibit migration and invasion in our model systems underscores the finding in this manuscript that a network of genes, the migratory signature rather than a single gene, is the driver of the motile phenotype [To test the functional role of CTGF in glioma migration, we performed knockdown studies with two independent siRNAs that yielded significant reduction of henotype ,44. An ahenotype  and thermigratory and stationary glioma cells as well as a migration rate signature. The most prominent contributor to the migratory phenotype was validated clinically and functionally by siRNA. Taken together these findings provide strong rationale for anti-invasive therapies targeting CTGF, as this molecule was found to be significantly over-expressed in invasive glioma cells where knock-down studies confirmed biological activity.The novel discovery approach presented here identified signatures of TD and JLR conceived, designed, carried out the experiments, analyzed the data and drafted the manuscript. DBH, LBR and MN designed and carried out experiments, analyzed data and drafted the manuscript. CB, SN, EMA, ANH and AJ carried out experiments and analyzed data. WS carried out migration assays. RL, KHP and CJL discussed data analysis, SNC discussed data analysis and the manuscript. MEB conceived and designed the study and drafted the manuscript. All authors read and approved the final manuscript.Expression signatures of stationary and migratory glioma cells. Provided are details for stationary, migratory and migration rate signature derived from 10 human glioma lines.Click here for file


EEG studies of working memory (WM) have demonstrated load dependent frequency band modulations. FMRI studies have localized load modulated activity to the dorsolateral prefrontal cortex (DLPFC), medial prefrontal cortex (MPFC), and posterior parietal cortex (PPC). Recently, an EEG-fMRI study found that low frequency band  activity negatively correlated with the BOLD signal during the retention phase of a WM task. However, the coupling of higher (beta and gamma) frequencies with the BOLD signal during WM is unknown.In 16 healthy adult subjects, we first investigated EEG-BOLD signal correlations for theta (5–7 Hz), alpha1 (8–10), alpha2 (10–12 Hz), beta1 (13–20), beta2 (20–30 Hz), and gamma (30–40 Hz) during the retention period of a WM task with set size 2 and 5. Secondly, we investigated whether load sensitive brain regions are characterised by effects that relate frequency bands to BOLD signals effects.We found negative theta-BOLD signal correlations in the MPFC, PPC, and cingulate cortex (ACC and PCC). For alpha1 positive correlations with the BOLD signal were found in ACC, MPFC, and PCC; negative correlations were observed in DLPFC, PPC, and inferior frontal gyrus (IFG). Negative alpha2-BOLD signal correlations were observed in parieto-occipital regions. Beta1-BOLD signal correlations were positive in ACC and negative in precentral and superior temporal gyrus. Beta2 and gamma showed only positive correlations with BOLD, e.g., in DLPFC, MPFC (gamma) and IFG (beta2/gamma). The load analysis revealed that theta and—with one exception—beta and gamma demonstrated exclusively positive load effects, while alpha1 showed only negative effects.We conclude that the directions of EEG-BOLD signal correlations vary across brain regions and EEG frequency bands. In addition, some brain regions show both load sensitive BOLD and frequency band effects. Our data indicate that lower as well as higher frequency brain oscillations are linked to neurovascular processes during WM. Functional magnetic resonance imaging (fMRI) studies have identified working memory (WM) related activities in a widely-distributed network involving the medial prefrontal cortex (MPFC), dorsolateral prefrontal cortex (DLPFC), posterior parietal cortex (PPC), hippocampus, insula, and occipito-temporal cortex To date, only few EEG-fMRI studies of WM examined the relationship between EEG band dynamics and fMRI responses, i.e. the Blood Oxygen Level Dependency (BOLD) signal, during the retention period of a WM task In the present study we first examined load independent EEG-BOLD signal correlations during the retention period for low as well as for high frequency bands. In the same time window we performed a load sensitive analysis (BOLD contrast: set size 5> set size 2) to investigate whether regions related to WM are further characterised by frequency band related load effects.We hypothesized that during the retention period theta and alpha would mainly show negative correlations with the BOLD signal as previously reported, and that beta and gamma exhibit positive correlations. We further expect that load sensitive WM areas should demonstrate both BOLD and frequency band related load effects.http://www.kek.zh.ch). All participants gave written informed consent prior to participation.Sixteen healthy volunteers  participated in this study. All subjects were right-handed and had normal or corrected-to-normal vision. The study falls under the ethical approval of the ‘Kantonale Ethikkommission Zürich’ (baseline condition consisted of a fixation star (*) and was presented in six blocks, each with a duration of 24.5 s. Subjects were instructed to indicate by button press (‘yes/no’ forced choice decision task) whether the probe was part of the stimulus. Response button assignment (‘right/left’) was counterbalanced across subjects.We used a Sternberg WM task in which the encoding of memory items, retention and retrieval period are temporally separated The stimuli were projected via a LCD projector onto a screen placed in front of the MR scanner. Subjects viewed the stimuli via a mirror attached to the MR-coil. Responses were made via a fibre-optics response button box . Stimulus delivery and response registration was controlled by Presentation . To become familiar with the task, the subjects were given a short practice version (with different stimuli) of the task outside the scanner.The simultaneous EEG was recorded using a 64-channel EEG cap . Electrode positions included all 10–20 system electrodes and the additional electrodes Afz, Fpz, FCz, CPz, POz, Oz, Iz, F5/6, FC1/2/3/4/5/6, FT7/8/9/10, C1/2/5/6, CP1/2/3/4/5/6,TP7/8/9/10, P5/6, PO1/2/9/10, OI1/2 plus two electrodes below the outer canthus of each eye and two electrocardiogram (ECG) electrodes. One ECG electrode was placed on the sternum and the other close to the heart. All channels were sampled at 5 kHz with 32 mV input range using a MR-conditioned amplifier  . O1/2 and Fp1/2 were placed 5% more laterally for even coverage http://www.cnl.salk.edu). ECG related components were defined by time delay parameters as implemented in Brain Vision Analyzer EEG data were analyzed offline using the Brain Vision Analyzer Software . The data were corrected for MR imaging gradient and ECG artefacts by template subtraction The spectral analysis focused on the last 2.5 s of the retention period , because it has been demonstrated previously that WM related alpha band power effects are suppressed in the first second of the retention period To compare the group results with previous studies using EEG spectral power, we calculated an index of global spectral power for our data Load independent analysis: EEG power during the retention period:Load dependent analysis: The power in trials with ss5 was compared to trials with ss2 (‘ss5–ss2’) for each frequency point. Load effects were calculated as follows:The more exploratory load independent- and dependent EEG effects were assessed by one sample t-tests . However, load dependent EEG effects were also computed with the false discovery rate  correction approach −4  was chosen for the LORETA transformation matrix. Since sLORETA explicitly takes into account that scalp electric potentials are determined up to an arbitrary additive constant, the final sLORETA solution is independent of the electrical reference used. We calculated tomographic sLORETA images corresponding to the estimated neuronal generators of brain activity We used standardized low resolution brain electromagnetic tomography (sLORETA) to localize the generators of the scalp EEG power spectra for the contrast ‘ss5–ss2’. The sLORETA solution space is restricted to the cortical grey matter in the digitized MNI atlas with a total of 6239 voxels at 5 mm spatial resolution 3; flip angle  = 75°). The first 4 scans were discarded to allow for equilibration effects. Participants were fitted with earplugs. Particular care was taken to stabilize with vacuum cushions and custom made padding. Functional MRI data pre-processing and statistical analysis was done using SPM5 . The data were first motion corrected and the images were then normalized using a 4th degree B-Spline interpolation method to match the Montreal Neurological Institute (MNI) EPI template. Two of eighteen subjects were excluded from the analysis due to major head movements  during scanning. Finally, functional volumes were resampled to isotropic 3 mm3 voxels and spatially smoothed with a 9 mm full width at half maximum isotropic Gaussian kernel. Functional images were temporally high-pass filtered with a cut-off period of 128 s, and serial correlations were accounted for by using an autoregressive model of the first order. No slice time correction was applied, because it has been demonstrated for event-related designs that slice time correction results in poor fMRI model fits and thus in type 2 errors MRI data was acquired on a 3.0 T  whole-body scanner. For fMRI, 558 scans sensitive to BOLD contrast with 33 axial slices covering the whole brain were acquired with a T2*-sensitive multi-slice echo planar imaging (EPI) sequence  were included in the design matrix. A random effect second-level analyses was calculated for each task phase. Results were shown with both directions for each contrast  - baseline’ and ‘baseline - retention’) at a corrected voxel threshold of p<0.05 using family-wise error correction http://www2.bc.edu/~slotnics/scripts.htmStatistical fMRI analysis at the individual subject level was performed using the general linear model (GLM), as implemented in SPM5. Separate regressors were constructed for each phase of the task  and convolved with the hemodynamic response function for each of the two load conditions (ss2 and ss5). In addition, the frequency EEG regressors for the retention interval . Both correct and incorrect trials were merged for the statistical analysis, because the overall accuracy was high . Results are reported in the MNI coordinate system .parametric modulators of the retention period in the GLM design matrix, resulting in nine regressors per load condition . To test for the influence of shared variance, we also performed the same type of analysis for the three fMRI task phases and a single frequency . Because we found a strong overlap between the EEG-BOLD signal correlations patterns for the two analyses we will report only results from the analysis with 9 regressors.To study load independent trial-by-trial EEG-BOLD signal correlations related to the retention period, first separate EEG regressors for each of the 40 trials from each load condition and frequency band were constructed from the 2.5 s retention interval. The EEG regressor was calculated by averaging frequency points in the band of interest  for the RMS of all channels. Next, the EEG regressors for the six frequency bands were introduced as For all frequency bands, EEG-BOLD signal correlations were considered significant at an uncorrected voxel threshold of p<0.001 (t = 3.1) and at a corrected cluster threshold of p<0.01 (k = 27 resampled voxels using Monte Carlo simulations). The same voxel threshold has been used in a recent EEG-fMRI WM study not a correlation analysis and was also not performed in regions that showed EEG-BOLD signal correlations. For each activated region, contrast estimates for each load condition and each frequency band were extracted for all participants using MarsBar [For the load specific analysis first load sensitive regions from the BOLD contrast ‘ss5> ss2’ were identified (FDR with p<0.005). Activated regions at this threshold served as a mask for further processing. Therefore, this analysis was p = 0.08). Longer reaction times were found for ss5 in comparison to ss2 , consistent with results from a recent EEG-fMRI WM study Overall the response accuracy was high  and tended to decrease with memory set size  increase of theta activity with load at the border of the MPFC and the ACC as shown in Results for the fMRI analysis are displayed in The load dependent analysis  revealed bilateral activations in the PPC (BA 7), superior temporal gyrus (BA 22/42), IFG, superior frontal gyrus , MPFC, thalamus, right insula, left lingual gyrus, DLPFC (BA 9/46), precentral gyrus, middle temporal gyrus (BA 21/37), putamen, and nucleus caudatus as shown in The EEG-BOLD correlations in higher frequency bands were overall spatially smaller and less extended than in lower frequency bands. Nevertheless significant correlations occurred in several grey matter regions as shown in The load dependent analysis revealed that some brain regions demonstrated not only significant load BOLD effects (BOLD contrast: ‘ss5> ss2’) but also frequency bands related load effects . It has been demonstrated by fMRI and EEG-fMRI studies that the DMN consists of the MPFC, insula, thalamus, ACC, PCC, PPC, occipito-temporal regions and the pre- and postcentral gyri Our fMRI data confirm earlier fMRI findings of dominant WM activations in a fronto-parietal network Global measures summarizing EEG activity across channels like RMS and global synchronization have the advantage of representing the full frequency band of interest without preselected electrodes, topographic components, or sources. Spectral RMS has been successfully used to investigate alpha-BOLD signal correlations during rest in healthy subjects Of course, the RMS approach might be a more conservative way to isolate WM related effects with a known, constant topography than an ICA or single-electrode approach. However, the two latter approaches will work only for effects with a consistent (ICA) topography across subjects, such as in the theta band The first main finding of the EEG-BOLD signal correlation analysis is that theta and alpha show predominantly negative correlations with the BOLD signal. Specifically, we demonstrate that theta showed exclusively negative correlations with BOLD signal in the DMN. These theta frequency-BOLD signal correlations are in line with a recent EEG-fMRI study that reported predominantly negative correlations in the DMN In contrast, alpha revealed dominant negative BOLD signal correlations in parieto-occipital regions including the precuneus  or in the early visual cortex including the cuneus  in line with other brain imaging studies The DLPFC and other frontal regions showed significant negative EEG-BOLD signal correlations in alpha1. The DLPFC is a core area of the WM network, because it is known that this region is activated as soon as manipulation of information is required, such as different load conditions In summary, our results demonstrate that (negative) low frequency-BOLD signal correlations are not only a marker for DMN The second main finding of the EEG-BOLD signal correlation analysis showed exclusively significant positive (grey and white matter) correlations between beta2/gamma and the BOLD signal. Interestingly, these correlations were manifested focally than the correlations in lower frequency bands. For example, EEG-BOLD signal correlations for theta and alpha were localized in a large cortical network, e.g. in the DMN, whereas beta/gamma-BOLD signal correlations were restricted to only few cortical  regions. This finding is in line with earlier EEG findings that reported long-range  connections in lower frequency bands and short-range connections in the gamma band during a WM task It has been demonstrated that the BOLD signal mainly measures the hemodynamic correlate of neuronal LFP activity We found that the DLPFC, MPFC, SFG, and IFG showed exclusively positive beta2 or gamma-BOLD signal correlations. This first indicates that the regions of the PFC must contain neurons that are modulated not only by slow but also by fast oscillatory activity during cognitive processing. During the retention period, spatially tuned elevated power in the beta and gamma band in LFP have been found in electrophysiological recordings from monkeys in parietal However, one might ask why we observed retention period related EEG-BOLD effects in low as well as in higher frequency bands in frontal regions. During long-term memory, there is evidence that the hippocampus and other regions of the cortex have dual oscillations, in which a theta oscillation is subdivided into subcycles by a high-frequency oscillation in the gamma range In addition to frontal areas, the nucleus caudatus demonstrated positive gamma BOLD signal correlations. This region is part of a functional network involving the DLPFC and is activated during WM tasks The majority of studies relating electrophysiological brain signals to hemodynamics support the notion of a systematic relation between frequency and direction of the BOLD signal, in the sense that lower frequencies are coupled to BOLD signal decreases, whereas higher frequencies are linked to BOLD signal increases. Although our study lends support for this finding , we found both positive and negative effects in beta1, the latter of which were manifested in the precentral gyrus and at the border of the STG and IPL. These areas are also active during WM tasks The investigation of the coupling between the BOLD signal and different EEG frequency bands enables the differentiation of neuronal activity based on their specific oscillatory activity. The load independent EEG-BOLD signal correlation analysis demonstrates that the interpretation of task-related neurovascular changes  is incomplete without incorporating the role of oscillatory activity. Our results indicate that lower and higher frequency bands are coupled to BOLD signal changes, extending findings from fMRI and EEG/MEG studies.The next question is then whether EEG-BOLD interactions are further modulated under varying demanding conditions (contrast ‘ss5–ss2’). This will be discussed in the following section.The load specific analysis additionally revealed that some load sensitive areas (identified by the BOLD contrast: ‘ss5–ss2’) also exhibit frequency related load effects .Interestingly, theta, gamma and (mostly) beta demonstrated only positive load modulations, whereas alpha showed either no  or negative  load-effects. This is in line with human intracranial EEG findings of positive gamma load effects in the occipital lobe but also in other regions including the MPFC Our study adds significantly to these findings by showing that load dependence in some regions of the WM network is not only reflected by BOLD signal- or EEG/MEG changes alone, but by closely coupled EEG-BOLD signal changes. Our data also indicates that this coupling generally has opposite directions for low and higher frequencies, but that the coupling may also vary for a given frequency across different brain regions. For example, the left DLPC showed negative and positive load effects in different frequency bands. In contrast, the SPL showed exclusively positive load modulations in lower and higher frequency bands. These two general findings might indicate firstly that different neuronal populations must be engaged within the same cortical region operating at different frequencies. Second, it may suggest that negative EEG-BOLD signal correlations in lower frequencies do not automatically reflect processing of less cognitively demanding conditions, because we found negative theta-BOLD signal correlations during the retention period but positive load effects in (similar) frontal regions.We also found load effects in sub-cortical regions such as the thalamus. Several studies demonstrated that in the thalamus alpha band power is positively linked to the BOLD signal during rest in healthy subject but also in patients with epilepsy One could argue that a load specific EEG-BOLD analysis should be either performed in regions that show retention period related BOLD effects or significant EEG-BOLD signal correlations. However, BOLD effects during the retention period differ substantially from the observed load specific BOLD effects  and are Our data demonstrate that simultaneous EEG-fMRI recordings are a useful tool for investigating state-dependent brain activity, e.g. during the retention period of a WM task. We suggest that the observed state-dependent dynamics of positive and negative activity fits with the view of the brain being driven by momentary environmental demands. From the load independent analysis we conclude that the directions of EEG-BOLD signal correlations vary for EEG frequency bands. In addition we suggest that both negative and positive EEG-BOLD signal correlations can characterize the same area.From the load dependent analysis we further conclude that the coupling of frequency band oscillations and the BOLD signal in task-related areas changes with cognitive load, i.e., predominantly positive load effects occur across frequency bands . In summary, our results indicate that low as well as high oscillatory activity is linked to neuronal activity during cognitive demanding processes.Figure S1Illustration of the steps performed for the load dependent EEG-BOLD signal analysis. The first step (panels on the top) is the calculation of the BOLD signal contrast ss5-ss2 . The second step is the extraction of the contrast estimates for the different frequency bands and load conditions in the activated region . For each region statistical comparisons (paired t-tests) are calculated  which results in a colour-coded significance map . For example the left DLPFC (red arrow) shows a negative load effects (contrast estimates set size 5>contrast estimate set size 2) in alpha1 as well as a positive load effect in the gamma band .(1.94 MB EPS)Click here for additional data file.Figure S2Group averaged grey- and white matter beta2-BOLD (A) and gamma-BOLD (B) signal correlations . Red areas indicating positive EEG-BOLD signal correlations.(1.92 MB EPS)Click here for additional data file.Figure S3(A) EEG-BOLD signal correlations  for the RMS and electrodes Afz, C3 and O2. Blue areas indicating negative EEG-BOLD signal correlations, red areas indicating positive EEG-BOLD signal correlations. (B) Statistical differences between the RMS and the single electrode approach . For example, the two bottom panels on the left indicate regions in which Afz theta-BOLD signal correlations are stronger than RMS-BOLD signal correlations (green colour code).(18.19 MB EPS)Click here for additional data file.


Protein domains present some of the most useful information that can be used to understand protein structure and functions. Recent research on protein domain boundary prediction has been mainly based on widely known machine learning techniques, such as Artificial Neural Networks and Support Vector Machines. In this study, we propose a new machine learning model (IGRN) that can achieve accurate and reliable classification, with significantly reduced computations. The IGRN was trained using a PSSM (Position Specific Scoring Matrix), secondary structure, solvent accessibility information and inter-domain linker index to detect possible domain boundaries for a target sequence.The proposed model achieved average prediction accuracy of 67% on the Benchmark_2 dataset for domain boundary identification in multi-domains proteins and showed superior predictive performance and generalisation ability among the most widely used neural network models. With the CASP7 benchmark dataset, it also demonstrated comparable performance to existing domain boundary predictors such as DOMpro, DomPred, DomSSEA, DomCut and DomainDiscovery with 70.10% prediction accuracy.The performance of proposed model has been compared favourably to the performance of other existing machine learning based methods as well as widely known domain boundary predictors on two benchmark datasets and excels in the identification of domain boundaries in terms of model bias, generalisation and computational requirements. Since proteins provide some of the most fundamental information about many processes in almost all organisms, the ability to predict protein structure and function has become one of the most important goals in bioinformatics research ,2. ProteOver the last three decade, a large number of methods using the three dimensional coordinates of the protein structure have been proposed for more accurate delineation of domains boundaries . Recentlab initio protein folding method; DomSSEA  by using following algorithm.wherep = R × Q matrix of input vectors,minp = R × 1 vector containing minimums for each p,maxp = R × 1 vector containing maximums for each p.10 possibilities and requires a network of 200 inputs  to be fed into networks.(4) Assign target levels: positive (1) for boundary residues and negative (-1) for non-boundary residues.(5) Hold-out method was performed to divide the combined dataset into 7 subsets (training and testing sets).(6) Train each model on training set to create a model.(7) Simulate each model on test set to obtain predicted outputs.(8) Post-processing was performed to find predicted boundary locations.ANN: Artificial Neural NetworkGRNN: General Regression Neural NetworkIGRN: Improved General Regression NetworkMLP: Multi-Layered PerceptronPSI-BLAST Position Specific Iterated Basic Local Alignment Search ToolPSSM: Position Specific Scoring MatrixRBFN: Radial Basis Function Neural NetworkThe authors declare that they have no competing interests.PDY developed and implemented the new model (IGRN) and drafted the manuscript. ARS prepared datasets, programming in C++, Shell and Perl scripts, and interpreted the results with PDY. BBZ and AYZ edited the manuscript and introduced the problem initially.