We present an analytic framework based on Self-Organizing Map (SOM) machine learning to study large scale patient data sets. The potency of the approach is demonstrated in a case study using gene expression data of more than 200 mature aggressive B-cell lymphoma patients. The method portrays each sample with individual resolution, characterizes the subtypes, disentangles the expression patterns into distinct modules, extracts their functional context using enrichment techniques and enables investigation of the similarity relations between the samples. The method also allows to detect and to correct outliers caused by contaminations. Based on our analysis, we propose a refined classification of B-cell Lymphoma into four molecular subtypes which are characterized by differential functional and clinical characteristics. Cancer is a complex disease caused by the deregulation of gene activity. Carcinogenesis and -progression is accompanied by dysfunctions on multiple layers of the cellular machinery. They are affected by a large number of different genetic and epigenetic factors. In recent years, large-scale studies such as The Cancer Genome Atlas (TCGA) ,2, The COn the one hand, high-throughput technologies such as whole genome transcriptional profiling presently revolutionize molecular biology and provide an incredible amount of data. On the other hand, these techniques pose elementary methodological challenges simply by the huge and ever increasing amount of data produced ,6,7,8: rEspecially, the latter task is very important because an intuitive visualization of massive data clearly promotes quality control, the discovery of their intrinsic structure, functional data mining and finally the generation of hypotheses. We aim at adapting a holistic ‘systems’ view on the gene activation patterns as seen by expression studies rather than to consider single genes or single pathways. This view requires methods which support an integrative and reductionist approach to disentangle the complex gene-phenotype interactions related to cancer genesis and progression. etc. [With this motivation we apply Self-Organizing Maps (SOM), a machine-learning clustering approach , to a laetc. . This moetc. . A central role in our analysis is played by the so-called expression ‘portraits’, which serve as intuitive and easy-to-interpret fingerprints of the transcriptional activity of the samples. Their analysis provides a holistic view on the expression patterns activated in a particular sample. Importantly, they also allow identification and interpretation of outlier samples and, thus, improve data quality. Our application of SOM machine learning to lymphoma expression data aims at characterizing the heterogeneity of the genome wide expression landscapes and at describing the molecular cancer subtypes. In particular, we will demonstrate the capabilities of our strategy to intuitively visualize the individual samples as well as the subtypes in terms of individual and group-averaged portraits, respectively. We show how to extract functional information from the data and appropriately incorporate it into the analysis strategy. Further, we describe how to detect and to correct outlier samples using their portraits. Finally, we propose a more detailed molecular subtype classification of the lymphoma samples.et al. [Microarray data of lymphoma are available under GEO accession number GSE4475 (data from 221 Affymetrix HG-U133A arrays). This study used biopsy specimens of mature aggressive B-cell lymphoma in which at least 70 percent of all cells were tumor cells. The classification of lymphoma samples into different subtypes is used as provided by Hummel et al. : Of all N × M where N is the number of genes measured per sample and M is the number of samples in the study. Throughout this paper a row of this matrix will be termed ‘expression profile’ of the respective gene. The columns on the other hand will be termed ‘expression states’ referring to one sample studied.For a convention we use the data as numerical matrix of dimension FC = Δi,me, the relative log-expression of gene i in sample m. Hence, a Δi,me of zero means that the gene is expressed according to its mean expression value. Positive and negative values refer to over- and under-expression in the series of samples, respectively.Raw probe intensity values of Affymetrix arrays were calibrated and summarized into one expression value per probe set using the hook method ,14. To ei,me are used to train a Self-Organizing Map (SOM). It translates the high-dimensional N × M expression data matrix into a K × M metadata matrix  of reduced dimensionality K << N . The corresponding relative log-expression values of the metagenes will be termed Δk,mmetae. The metagene expression profiles  are adapted in the iterative machine learning process to optimally cover the data space once the training is completed  is not crucial for downstream expression analysis. It provides almost identical results in terms of the expression patterns identified (see [K = 42 × 28 metagenes.Our SOM method was configured to enable the robust identification of spot modules inherent in the data (see below). Details were described previously ,12,17. Iied (see  in the sied (see ). Variatied  in this particular sample. Each spot can consequently be interpreted as a disjunct expression module of a group of metagenes (and of associated single genes) showing a unique expression profile in the data set studied.We define over-/under-expression spots by applying a simple 98th/2nd-percentile criterion as described and verified in ,21,22,23We further implemented and verified complementary methods of spot selection using different metrics and algorithms such as k-Means and hierarchical clustering based on Euclidean distance between the metagene profiles, and seed-clustering based on their pairwise correlation coefficient ,12,16. TCo-expressed genes of each spot module can be assumed to be functionally related according to the ‘guilt-by-association’ principle . FunctioWe use different approaches to estimate the enrichment of groups of predefined genes  in gene lists obtained independently, for example from SOM-spot analysis  one obtains p < 10−5. Note that Bonferroni adjustment represents a conservative approach minimizing the family wise error rate . Howevee˃set,m is the mean expression of the gene set members in sample m, ˂Δe˃ genes,mall denotes the mean expression of all genes and the denominator defines the respective standard error  combines both options of gene set overrepresentation and overexpression approaches ,32. In ption see ). We useIn addition to overrepresentation in spots and the GSZ-profiles, we generate gene set population maps. They visualize the distribution of the genes of a selected set in the SOM grid by appropriate color coding of the number of set members assigned to each metagene. It ranges from white (no gene) to maroon (maximum number of genes per tile observed for the particular gene set). Recall that each gene refers to one and the same metagene in all samples and thus occupies a fixed position in all SOM portraits allowing comparison between gene positions and spot positions in reference to specific functional modules.Sample similarity analysis aims at evaluating mutual relations between the samples studied. There are various established approaches, for example to extract a hierarchy of similarities, to estimate mutual distances between the expression states or to assess the main sources of variance in the data. Here, we use three different metrics, namely statistical dependence, Euclidean distance and Pearson’s correlation, which are applied to the metadata instead of to the original ‘single gene data’. Similarity analysis consequently compares the expression meta-states as characterised by the SOM portraits. The usage of metadata as the basal data has the advantage of improving the representativeness and resolution of the results as shown previously ,20,33.Independent component analysis (ICA)  is appliAs a second option, we apply the neighbor-joining algorithm (R-package ‘ape’ ) to visui.e., the samples), whose pairwise correlation coefficient exceeds a given threshold. Here, we chose thresholdr = 0.5, which ensures a relatively sparse but still fully connected graph structure  using a color gradient ranging from red for positive correlation to blue for negative correlation. The correlation network (CN) additionally translates the correlation matrix into a graph structure. This undirected graph is constructed by connecting the nodes .Identification of distinct molecular phenotypes is a common and important question in cancer research. A previous study of lymphoma data classifies the samples into the main subtypes molecular Burkitt’s Lymphoma (mBL), non-mBL and an intermediate group . Both, iWe applied a modified ‘prototype-guided’ k-Means clustering of the metadata to segregate the samples into these four subtypes. k-Means is an iterative algorithm which iteratively assigns the samples to so-called cluster prototypes showing the minimal mutual Euclidean distance and subsequently computes new prototypes as the centroids of the members of each cluster . k-Meansk,mmetaΔe)’ for metagenes within the spot and ‘0’ for metagenes outside. These prototypic spot patterns are then used to assign the samples to the respective clusters in the standard k-Means algorithm. Then, a bootstrapping approach is used to estimate the robustness of the assignment of samples to the subtypes. Therefore, k-Means clustering is repeatedly applied to a subset of samples chosen randomly from the complete set of samples. The mean metagene expression states of the subtypes are used as initial cluster prototypes. The fraction of proper assignments of samples in agreement with their actual class assignment then defines a robustness score of each sample: a bootstrap stability score of ‘1’ means that the respective sample is always found in the correct subtype, while a score of ‘0.5’ means that the sample is assigned properly in only 50% of the resampling repetitions.The SOM portraits now constitute another option to initialize the prototypes: they can be established using selected expression patterns observed in the portraits such as the most prominent overexpression spots. Particularly, we define initial prototypic expression portraits showing a selected spot pattern for each subclass with values ‘max. It is s number . The incin situ hybridization (FISH) for IGH, MYC, BCL6 and BCL2 loci, overall survival, age and gender.Gene expression data from germinal center B-cell line samples and tissue samples of tonsils were taken from reference . After pN = 22,283 and K = 2,500). We visualize the expression meta-state of the samples as mosaic images, consisting of 50 × 50 tiles each representing one metagene. These metagenes serve as representatives of clusters of co-expressed single genes the number of which usually varies from metagene to metagene. The color gradient of the portraits was chosen to visualize over- and under-expression of the metagenes in each particular sample: red to green colors indicate over-expression with decreasing strength, while blue to green colors indicate under-expression. The colored texture of each mosaic thus individually characterizes the gene expression landscape in each sample.SOM machine learning transforms the whole genome expression pattern of the ‘single’ genes into metagene expression data. Thereby, the number of single genes exceeds the number of metagenes by about one order of magnitude . Samples of the intermediate subtype show more volatile patterns with over-expressed metagenes frequently tending to occupy the top-left and bottom-right corners of the SOM. The full gallery of the 221 SOM portraits is given in -right corner is over-expressed and the other one in the bottom-left corner is under-expressed in mBL samples and vice versa in non-mBL samples, revealing the antagonistic character of their expression patterns. These subtype-specific spots collect highly populated, highly variable and well resolved metagenes  whereas rare spots tend to accumulate in the central part.Please note that our spot selection algorithm neglects the abundance of each spot in the individual portraits and identifies both rare  and frequent spot modules. The over-expression heatmap in We use the spot information and the mean subtype portraits to assign subtype labels to the most prominent and specific spot modules a: Spots Please note that the training algorithm distributes the metagenes in such a way that strongly correlated profiles are located at adjacent positions in the map whereas metagenes with anti-correlated profiles tend to occupy more distant regions, e.g., in the opposite corners of the map. This rule also applies to the spots detected. In order to discover the covariance between the spot modules we calculated Pearson correlation coefficients for all pairs of spot profiles. It turned out that, as a rule of thumb, neighboring spots are strongly positively correlated and spots located in opposite corners of the map are often strongly anti-correlated. The results of this correlation analysis are visualized in For this dataset, we also detected 11 global under-expression spots emerging as blue regions in the SOM portraits. The under-expression summary map is shown in In summary, the heterogeneous expression patterns observed in the individual portraits can be condensed to a few major expression modules represented by over- and under-expression spots. This way the relevant dimension of the data set is reduced by three orders of magnitude from about 20,000 single genes to approximately 12 spot modules.p-value estimating the probability that genes of the set are found within the spot by chance.Each global overexpression spot module represents a cluster of potentially co-regulated genes. We applied gene set overrepresentation analysis to each spot-cluster taking into account a collection of more than 5,000 predefined gene sets referring to different GO-categories, pathways, diseases, human tissues and specific cell experiments . For each spot we obtained a list of gene sets ranked with increasing Based on the functional context of the overrepresented sets obtained we assign a short notation to each of the spots see a. Some svs-mBL). Neighboring spots of strongly correlated profiles can be assigned to related biological processes: the ‘cell division’ spot is surrounded by spots assigned to ‘transcription factor binding’, ‘chromatin’ and ‘transcription’ according to the most overrepresented gene sets in each of the spots. Note that, although related, these neighboring spots are usually characterized by subtle differences in their expression profiles and presumably also by fine differences in the functional context of the overrepresented gene sets. Population maps and overexpression spot maps therefore represent complementary tools for discovering the functional context of the expression landscapes. The results so far show that the lymphoma samples split into pairs of subtypes differing by the antagonistic activation of processes related to ‘inflammation’ and ‘immune response’ on one hand and to ‘cell division’ and the ‘transcriptional and translational machinery’ on the other hand  and neighbor-joining clustering (NJ), to visualize and to analyze the mutual relations between the samples. In the two-dimensional ICA-plot shown in The NJ algorithm visualizes sample similarity relations as seen by Euclidean distances. The obtained star-like dendrogram shown in In summary, the ICA-analysis allows for estimating the mutual dependence of the expression changes associated with the different subtypes. We found a one-dimensional distribution of the lymphoma subtypes, supporting the ‘longitudinal’ classification into the three subtypes considered so far. The transversal heterogeneity however remains unconsidered in this case. The NJ-dendrogram, on the other hand, reveals finer details in terms of disjunct substructures potentially reflecting a finer granularity of subtype clusters.As an alternative metric to study sample similarities, we calculated Pearson’s correlation coefficients for all pairwise combinations of samples. The pairwise correlation map (PCM) given in The correlation matrix can be transformed into the correlation network (CN) shown in A few samples are located far away from their subtype-specific cluster and/or from the majority of the other samples in the CN. Those samples are usually characterized by rare or unique spots as indicated in In summary, the correlation net of the lymphoma samples forms a ‘donut-like’ structure composed of alternating compact and more fuzzy clusters. The former ones refer to the main subtypes and the latter ones to two distinct groups of samples mainly assigned to the intermediate subtype. The mutual correlation analysis as seen by the CN in combination with the SOM portraits thus provides additional information complementing the other similarity analyses applied.Inspection of the CN in Panel a in the left part of In a simple correction step we removed the genes included in the outlier spots from the whole data set  allow judging the incremental gain of increasing the number of clusters see d. The obi.e., they represent molecular subtypes. The question arises if these molecular subtypes associate with selected genetic, clinical, or alternative molecular phenotypes collected independently [p-values are obtained from Fisher’s exact test). The full table of patient characteristics is provided as The four new subtypes are defined by their distinct expression patterns and their particular functional contexts, endently . We usedmBL* and non-mBL* one finds analogous frequency distributions of a series of characteristics as described in previous studies, e.g., the age dependency [non-mBL* and to intermediate A&B subtypes are classified as diffused, large B-cell lymphoma (DLBCL) suggesting a close similarity between these three subtypes. A series of characteristics such as the IG-MYC status and immune-phenotypes CD10, BCL6 and BCL2 support this result. For pendency , the effpendency , differependency  and the pendency . Nearly intermediate A and intermediate B subtypes also show specific properties. Interestingly, the tumors with the activated B-cell (ABC) signature are clearly overrepresented in the intermediate A subtype, whereas the alternative germinal center B-cell (GCB) signature clearly depletes in this subtype. They also show differential characteristics with respect to the appearance of genetic aberrations (MYC translocation and IGH break) and to the BCL2 immune-phenotype: Firstly, the IG-MYC translocation is more frequently found in the intermediate B subtype compared with the intermediate A and the non-mBL* lymphoma. Secondly, intermediate A lymphomas less frequently show the IGH break and the BCL2+ immuno-phenotype than the other subtypes. Thirdly, intermediate B and non-mBL* lymphomas possess slightly enriched populations of t translocations, which juxtapose the BCL2 oncogene to the immunoglobulin heavy chain locus (IGH).However, the new mBL*; activated immune response and inflammation in non-mBL*, innate immunity in the intermediate A subtype and up-regulated expression of common cancer gene signatures [intermediate B subtype. Generic, MYC-related poor prognosis gene signatures [mBL* and, to a lesser degree, intermediate A subtypes. Moreover, we found that intermediate A subtype lymphomas show expression signatures of activated B-cells and strong dissimilarity with expression landscapes of germinal center B-cells and healthy lymph node tissue suggesting different cell-of-origins. On the level of gene regulation, the decomposition of lymphoma into four subtypes obviously further diversifies into different modes which, in turn, reflect driving effects on the genetic and epigenetic levels. The understanding of these molecular mechanisms thus requires the combined analysis of genetic, epigenetic and transcriptional data. In the supplementary text , we provgnatures  in the ignatures  are assoet al., patients with mBL lymphomas show significantly better survival rates as intermediate and non-mBL patients . In connon-mBL*, intermediate A and B subtypes; however, they also differ in other properties, for example in the assignment of cell-of-origin properties and of energy metabolism signatures.A recent study also proposed new classes of B-cell lymphoma based on a correlation gene set analysis and using a larger patient collective . This stAnalysis of molecular biological data using Self-Organizing Maps (SOMs) enables a holistic view on high-dimensional data collected in large-scale studies. It provides a general framework for analytic tasks such as feature selection, integration of concepts of molecular function and systems tracking with individual resolution. The method extracts meta-features such as metagenes and spot-modules representing basal modes of systems behavior important for higher-level analysis. We applied SOM machine learning to patient expression data of mature aggressive B-cell lymphomas to characterize the specifics of the genome wide expression landscapes in different molecular subtypes of lymphoma. The expression portraits obtained by the SOM algorithm reflect the expression landscapes of the individual samples or subtypes in terms of intuitive and characteristic color textures. These spot patterns can be used to describe the underlying functional modules using gene set enrichment techniques.intermediate subtypes of heterogeneous molecular signatures are associated with poor survival prognosis compared with the more homogeneous mBL* and non-mBL* subtypes.Several sample similarity analysis methods were applied to characterize the subtype structure in detail. The correlation network approach provides a powerful representation as it visualizes multivariate relationships in a clear and accessible fashion. We presented a straightforward strategy to identify outlier samples and modules, e.g., due to contaminations of tumor samples with healthy tissue, and to correct them. Furthermore, we found indications for a finer subtype classification of aggressive B-cell lymphoma into four subtypes. Samples were classified using a spot-guided and metagene-based k-Means clustering method. The robustness and consensus-cluster stability of the new four subtypes exceeds that of previous three class approaches. The functional and clinical impact of the new subtypes was discussed. The two Our case study shows that analyzing gene expression landscapes with the tools presented here facilitates information mining in such huge data sets and eventually promotes our understanding of cancer biology.


The main aim of this article is to summarize the advances of the Chernobyl seed project which has the purpose to provide proteomic characterization of plants grown in the Chernobyl area. We present a summary of comparative proteomic studies on soybean and flax seeds harvested from radio-contaminated Chernobyl areas during two successive generations. Using experimental design developed for radio-contaminated areas, altered abundances of glycine betaine, seed storage proteins, and proteins associated with carbon assimilation into fatty acids were detected. Similar studies in Fukushima radio-contaminated areas might complement these data. The results from these Chernobyl experiments can be viewed in a user-friendly format at a dedicated web-based database freely available at  Radioactive minerals have accumulated on the Earth’s surface since early Achaean times (3500–4000 million year ago) and probably helped precipitate and concentrate organic carbon-rich matter . The firPlants can easily cope with increased levels of IR. This has been demonstrated in the radio-contaminated Chernobyl  and FukuArabidopsis thaliana  was modified and included (i) two non-radioactive fields (control) and one radio-contaminated experimental field , (ii) two plant species, and (iii) two successive years. An important aspect of this experimental design  is the changed location of the non-radioactive field after the first year. The logic behind this is to exclude alterations related to the differences between experimental fields .Experimental design for ecological field experiments should include several experimental fields to avoid pseudoreplication . HoweverGlycine max [L.] Merr., variety Soniachna) and flax  were sown in radio-contaminated experimental fields  located near the village Chistogalovka approximately 5 km from the Chernobyl Nuclear Power Plant (CNPP). The non-radioactive control experimental field (1350 ± 75 Bq.kg-1 for 137Cs and 490 ± 60 Bq.kg-1 for 90Sr) was established near Zhukin, a village approximately 100 km from CNPP . Soybean and flax seeds were harvested and mature seed proteomes comparatively analyzed in biological triplicate . In 2008, soybean and flax seeds harvested from the first generation of plants were sown onto the same radio-contaminated field, but a different non-radioactive field in the Chernobyl area , to obtain the second generation of seeds. A new non-radioactive experimental field was established directly in the town of Chernobyl, in an area with soil radioactivity of 1414 ± 71 Bq.kg-1 of 137Cs and 550 ± 55 Bq.kg-1 of 90Sr . The Chernobyl area is characterized by sod-podzolic soil  which is a typical soil in the Ukrainian region of Polessia. Generally, in this area, the content of aleurite (silt) and pelitic soil ranges from 20 to 30%  from the second generation which were harvested from both Chernobyl experimental fields . Protein abundance profiles are capable of comprehensively characterizing protein abundances during seed filling. The approach has been used successfully in soybean . Using this approach, it was possible to provide a detailed overview of protein abundances during seed filling in soybean  to access the data from this project in user-friendly format. At the time of the database establishment . To exclude this possibility, data were further analyzed and alterations common for both plant generations and plant species identified.Altered abundance of enzymes associated with the glycine betaine biosynthetic pathway was jointly detected in the first generation of soybean  and flaxArabidopsis thaliana resulted in mobilization of SSPs (The mobilization of seed storage proteins (SSP) and alteration of proteins associated with carbon assimilation and fatty acid metabolism were observed jointly in both generations of soybean and flax. These data support the notion that SSPs are involved in seed defense against various threats, as has been shown previously with their role in defense against Bruchids . Interes of SSPs . Further of SSPs , while s of SSPs .An interesting aspect of these Chernobyl studies are differential abundance of proteins associated with carbon assimilation and fatty acid metabolism in both generations of soybean and flax. As a result of this, the second generation of soybean  and flaxSimilar to the disaster at the CNPP in 1986, the accident at the Fukushima Daiichi Nuclear Power Plant in 2011 contaminated large areas with radioactivity . UnfortuFigure 1) could be applied.It will be interesting to compare results from the Chernobyl studies using similar experimental setups in the Fukushima radio-contaminated area. The web based database (chernobylproteomics.sav.sk) might be a good tool for quick data comparison. Ideally, follow-up studies in Fukushima should include several non-radioactive and radio-contaminated experimental fields to avoid pseudoreplication . If thisThe outcome of these Chernobyl studies was the identification of several proteins with differentially abundances in soybean and flax seeds harvested during two successive generations. It is tempting to speculate that these proteins are associated with plant growth and adaptation in radio-contaminated environments. However, follow-up studies in both the Chernobyl and Fukushima radio-contaminated areas are required to further develop these hypotheses.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Human brains are highly plastic throughout the lifespan, even after brain injuries. The possible modulation of functions due to neural plasticity in various brain regions contributes to the learning and adaptation of unlearnt as well as new behaviors. Over the past decade, research on brain-behavior relationships has grown exponentially. In addition to behavioral assessments, electrophysiology and neuroimaging methods have been widely used to examine the neural networks, structural and functional abnormalities, and the effects of the therapeutic training in aged adults or individuals with neurological disorders. The five contributions to this special issue address the relationships between brain and behavior and have implications for present and future research and practice in neurorehabilitation.Data presented in the article titled “An Influence of Birth Weight, Gestational Age, and Apgar Score on Pattern Visual Evoked Potentials in Children with History of Prematurity” by M. Michalczuk et al. suggests that low birth weight, early gestational age, and poor baseline output are possible predictors for the development rate of brain function. The article titled “Age-Related Reduced Somatosensory Gating is Associated with Altered Alpha Frequency Desynchronization” by C.-H. Cheng et al. attempts to elucidate the neural mechanisms of age-related sensory gating decline in the somatosensory system. By using a paired-pulse protocol and time frequency analysis, the authors found that insufficient stimulus 1 induced alpha oscillations lead to a less-suppressed stimulus 2 evoked response.The article titled “Coincidence Anticipation Timing Performance during an Acute Bout of Brisk Walking in Older Adults: Effects of Stimulus Speed” by M. J. Duncan et al. reported an age-related decline in anticipation timing performance when performing dual tasks. They have also reported that the stimulus speed of the secondary task plays an important role in the performance of older adults. Data from the article titled “Neural Plastic Effects on Cognitive Training on Aging Brain” by N. T. Y. Leung et al. suggested the presence of an experience-dependent neural plasticity following a thirteen-week training program of attention and working memory in older adults. This work also indicates that initial cognitive status may not limit the potential of neural plasticity at an older age. The article titled “Restoration of Central Programmed Movement Pattern by Temporal Electrical Stimulation-Assisted Training in Patients with Spinal Cerebellar Atrophy” by Y. Z. Huang et al. reported a novel 4-week training program developed to correct the aberrant triphasic EMG patterns in these patients resulting in restoration of facilitation of antagonist muscle activity.The articles published in this special issue provide new insights into the neural plasticity in terms of the brain-and-behavior relations and contribute to extension of research on neurobehavioral rehabilitation. Further research is needed to verify the value of the knowledge derived from this collection of scientific endeavors.Keh-chung LinKeh-chung LinSteven L. WolfSteven L. WolfChetwyn ChanChetwyn ChanChing-yi WuChing-yi WuChing-Po LinChing-Po Lin


This work demonstrates that mini-TyrRS has beneficial effects on cardiac repair and regeneration and offers support for the notion that elucidation of the ever expanding repertoire of noncanonical functions of aminoacyl tRNA synthetases offers unique opportunities for development of novel therapeutics.A naturally-occurring fragment of tyrosyl-tRNA synthetase (TyrRS) has been shown in higher eukaryotes to ‘moonlight’ as a pro-angiogenic cytokine in addition to its primary role in protein translation. Pro-angiogenic cytokines have previously been proposed to be promising therapeutic mechanisms for the treatment of myocardial infarction. Here, we show that systemic delivery of the natural fragment of TyRS, mini-TyrRS, improves heart function in mice after myocardial infarction. This improvement is associated with reduced formation of scar tissue, increased angiogenesis of cardiac capillaries, recruitment of c-kit In the last decade, there has been the growing realization that these enzymes have a wide range of additional cell signaling functions outside of their canonical ‘housekeeping’ roles in higher eukaryotes pos progenitor cells in hearts pos cell homing to infarcted hearts was noted in response to stromal cell-derived factor-1α Heart disease is a leading cause of death in developed countries; acute occlusion of a major coronary artery leads to cardiac ischemia due to reduction or elimination of blood flow, loss of cardiac tissue, and its subsequent replacement with non-functional scar tissue pos progenitor cells in hearts.It is universally accepted that ECs play a crucial role in regulating the survival and function of neighboring cardiomyocytes by promoting angiogenesis and vascular regeneration, but also by regulating cardiomyocyte contractility and growth via paracrine signaling Adult (12 weeks), male C57Bl/6 mice were purchased from Jackson Laboratories .Animals were used in accordance with the guideline of the National Institute of Health and for the care and use of laboratory animals .Conscious echocardiography was performed as previously described using a Vevo 2100 ultrasound biomicroscopy system Mouse surgeries were performed as previously described Triticum vulgaris) and examined by fluorescence microscopy as previously described Hearts were perfused and fixed with fresh 4% paraformaldehyde for 24 hours. The hearts were then paraffin embedded and sectioned into 5 µm sections and stained with hematoxylin and eosin or Masson’s trichrome to assess overall morphology t-test; statistical significance was defined as P<0.05.Probability values were obtained by performing a 2-tailed Student To test the hypothesis that mini-TyrRS could improve heart function after MI, we performed left anterior descending (LAD) coronary artery ligations in adult (12–14 weeks) male mice. Ligation of the LAD results in blockade of blood flow to a portion of the left ventricle (LV), thus creating a zone of ischemia. Following surgery, mice were given intraperitoneal (IP) injections of either sterile saline control or 250 ng/g mini-TyrRS for 3 days. Mice were subjected to conscious echocardiography to assess cardiac function either pre-LAD or 3 days (3d) and 7d after LAD . AlthougMyocardial infarction leads to hypoxic conditions in the heart and subsequent death of cardiomyocytes; to maintain the structural integrity of the heart after this significant tissue loss, cardiac fibroblasts migrate into the damaged infarct area and increase deposition of extracellular matrix components, including collagen, thus leading to a build up of non-contractile scar tissue in these recovering hearts Previous work with ECs showed that mini-TyrRS promotes angiogenesis by activating angiogenic signaling pathways within ECs To investigate further the mechanism by which mini-TyrRS improves cardiac function, we examined the degree of apoptosis in the infarct zone and the neighboring ischemic zone by immunostaining with anti-cleaved caspase 3 antibody (CC3). Quantitation revealed a significant reduction in the number of CC3-positive cells in the ischemic zone of mini-TyrRS-treated hearts, compared to saline-treated hearts . There wIn response to cardiomyocyte necrosis in the infracted heart, cell migration and proliferation is associated with the healing phase of cardiac remodeling pos cells to the infarcted heart. We examined the presence of c-kitpos, cells by immunostaining heart tissues with a c-kit antibody . Bar, 80 um.(DOCX)Click here for additional data file.


Internal bleeding from pelvic fractures is difficult to stop with external compression and necessitates other resuscitation techniques such as endovascular balloon occlusion of the aorta (REBOA). This minimally invasive procedure stops the bleeding by inflating a balloon in the aorta, and has been used successfully in emergency departments and operations theaters. If REBOA also is feasible in the pre-hospital environment is unknown.To present two supplementary training methods for REBOA; A gelatin model for repetitive exercise of technical skills and a porcine model for “live-tissue” training and evaluation of the hemodynamic effects of the aortic occlusion.A gelatin-mannequin, containing water filled bicycle tubes to mimic the aorta and femoral arteries, was casted. A doctor-paramedic crew performed the REBOA procedure three times in a realistic pre-hospital environment. The time from arrival to balloon-inflation and skin contact to balloon-inflation was recorded.A porcine model (blood volume 3640 mL) was bled (200 mL of blood withdrawn) in eight consecutive steps to a total blood loss of 44%. The balloon was inserted through the femoral artery and into the aorta using an ultrasound guided Seldinger-technique, and inflated and deflated after each bleeding step. The systolic (SBP) and diastolic (DBP) blood pressures were measured.The mean time from arrival to inflation of the balloon was 4 minutes and 19 seconds (SD ± 58 seconds) and from skin contact to inflation 3 minutes and 12 seconds (SD ± 42 seconds). There was a clinical significant increase in SBP and DBP after balloon inflation (Figure REBOA could stop pelvic bleedings and improve central perfusion during massive bleeding. The technique appears feasible in the pre-hospital environment with an insertion time approximating 3 minutes after training. To treat the right patients at the right time requires additional decision-making skills for pre-hospital crews.The authors state no conflicts of interest.


Several task-based functional MRI (fMRI) studies have highlighted abnormal activation in specific regions involving the low-level perceptual  network in posttraumatic stress disorder (PTSD) patients. However, little is known about whether the functional connectivity of the low-level perceptual and higher-order cognitive  networks change in medication-naïve PTSD patients during the resting state.We investigated the resting state networks (RSNs) using independent component analysis (ICA) in 18 chronic Wenchuan earthquake-related PTSD patients versus 20 healthy survivors (HSs).Compared to the HSs, PTSD patients displayed both increased and decreased functional connectivity within the salience network (SN), central executive network (CEN), default mode network (DMN), somato-motor network (SMN), auditory network (AN), and visual network (VN). Furthermore, strengthened connectivity involving the inferior temporal gyrus (ITG) and supplementary motor area (SMA) was negatively correlated with clinical severity in PTSD patients.Given the absence of a healthy control group that never experienced the earthquake, our results cannot be used to compare alterations between the PTSD patients, physically healthy trauma survivors, and healthy controls. In addition, the breathing and heart rates were not monitored in our small sample size of subjects. In future studies, specific task paradigms should be used to reveal perceptual impairments.These findings suggest that PTSD patients have widespread deficits in both the low-level perceptual and higher-order cognitive networks. Decreased connectivity within the low-level perceptual networks was related to clinical symptoms, which may be associated with traumatic reminders causing attentional bias to negative emotion in response to threatening stimuli and resulting in emotional dysregulation. Posttraumatic stress disorder (PTSD) is one of the most common psychiatric disorders In recent years, the resting-state fMRI has been identified as a marker of neural connectivity to explore the alterations of coherent intrinsic neuronal activity of blood oxygen level-dependent (BOLD) fluctuations in the brain. ICA is a data-driven analysis that separates a set of signals into independent, uncorrelated, and non-Gaussian spatiotemporal components without requiring a priori specification of a seed region The primary aims of the present work were to use the task-free resting-state fMRI to investigate the two-level network effects on the pathogenesis of PTSD. Two hypotheses were considered. First, whether the functional connectivity of RSNs related to perceptual and higher cognitive processes may be aberrant in traumatized participants with PTSD. Second, whether any of the changes identified would be related to the measured clinical severity.The study was approved by the Medical Research Ethics Committee of West China Hospital, Sichuan University and all the subjects’ written informed consents were obtained before the study. We acquired whole-brain resting-state fMRI for 18 patients  and 20 non-PTSD individuals with a history of trauma exposure . All participants were recruited two years after the Wenchuan earthquake through the Mental Health Center of the Huaxi Hospital, Chengdu, China . PopulatExperiments were performed on a 3.0-T GE-Signa MRI scanner  with an eight-channel phased array head coil at the Huaxi MR Research Center. We immobilized the participants’ heads with foam padding. Functional images were acquired using a single-shot, gradient-recalled echo-planar imaging sequence . Thirty transverse slices (FOV = 240×240 mm), which had a matrix size of 64×64 and a slice thickness of 5 mm without a gap, yielded a voxel size of 3.75×3.75×5. A total of 200 volumes were acquired and images were aligned along the anterior commissure–posterior commissure (AC–PC) line. Subjects were instructed to relax, let their minds wander, keep their eyes closed, and not fall asleep.http://www.fil.ion.ucl.ac.uk/spm). All data were corrected for slice timing, and head motion correction of the functional scans was performed. Next, the functional images were coregistered to the mean functional image and normalized to the echo-planar imaging template in SPM8. The data from 1 of the 19 subjects in the PTSD group were excluded because the translation and rotation of the head motion exceeded ±1.5 mm and ±1.5°, respectively. Images were then spatially smoothed by convolution with a half-maximum isotropic Gaussian filter (FWHM = 8 mm).The data were preprocessed using SPM8 software  Briefly, ICA is a mathematical procedure to decompose, for example, a spatiotemporal signal into independent, uncorrelated, and non-Gaussian components. We performed a spatial ICA using the GIFT software . The group comparisons were restricted to the voxels within the corresponding RSNs.The spatial maps of each RSN were then gathered in each group for a random-effect analysis using one-sample t-tests ; data wePearson correlation analysis was performed between the connectivity strength  and the CAPS, HAMD, and HAMA scores to investigate the relationship between the z-values in the network maps and clinical severity. We extracted the z-values emerging from significant clusters of interest between the PTSD and HS groups as a mask consisting of several regions of interest (ROIs), which were applied to all subjects. The mean z-values of each individual within these ROIs were correlated to the clinical scales and then thresholded at a significance level of p<0.05.Group demographic characteristics and psychological and behavioral scores are shown in Our analysis indicated that the six RSNs were spatially consistent across patients and controls; the findings were consistent with previous fMRI studies on RSNs The two-sample t-tests revealed differences in the functional connectivity between the two groups ; Table 2Consistent with our hypothesis, the current study reveals the different functional connectivities in both low-level perceptual and higher-order cognitive networks in earthquake survivors with and without PTSD. Furthermore, both decreased and increased functional connectivity were found in all six networks in PTSD patients relative to HSs, whereas only changes of low-level perceptual networks were related to clinical severity, as assessed by the HAMA and HAMD scores. We also noticed that PTSD patients displayed significantly higher scores of HAMD, and HAMA when compared with the HSs, because most of PTSD patients are accompanied by physiological anxiety symptoms and negative apprehension. These findings provide further evidence that widespread changes occur in the cognitive networks of PTSD patients; these alterations may be related to stress-related impairments, possible functional reorganization, and/or a compensatory mechanism The most interesting findings of the current study were those of the deficits of the AN and SMN, which were related to clinical severity in these earthquake-related PTSD patients. The AN and SMN are the most important components of the low-level perceptual network; this network can be accessed involuntarily and forms the basis for the flashbacks and nightmares that relate to the traumatic moments in PTSD Lesions of the AN are influential for tinnitus, which was one of the most frequently reported problems among veterans returning from two recent armed conflicts, often co-occurring with PTSD The superior temporal gyrus (STG) is involved in nonverbal auditory and language processing and has been found to be activated in dissociated PTSD subjects during recall of the traumatic memory Another network showing correlations with the severity of clinical symptoms in PTSD was the SMN. The SMN is operationally defined as the region that has significant functional connectivity with the primary somato-motor cortex  The precuneus has been proposed to be involved in cognitive function, such as memory processing and spatial location encoding Excessive vigilance as a hallmark of PTSD may be associated with increased demands on the brain areas that are involved in the visual association cortex in pathological memories and planning a response to potentially threatening stimuli The present findings indicate that the Brodmann 8, 9 and 10 which are the constituents of the SN, CEN, and DMN, respectively, have decreased functional connectivity in PTSD patients when compared to HSs . These rOur results have several limitations. First, the absence of a healthy group that never experienced the earthquake limited the investigation of the discrepancy among the three groups. Alterations in cerebral function may be driven by trauma exposure rather than being unique in PTSD Future research should also use specific task paradigms that may reveal perceptual impairments. Ideally, such an optimized network metric may prove efficacious in making the critical clinical distinction regarding which at-risk subjects will develop PTSD or remain healthy.In summary, the results of our baseline study provide a comprehensive examination of the relationship between the elementary and higher cognitive networks in PTSD patients exposed to a single trauma. The lower order of the cognitive processing hierarchy is directly related to symptom severity and contributes to our understanding of the mechanisms involved in emotion regulation and memory recall from trauma in patients with PTSD. Diminished medial prefrontal function as a reliable biomarker of PTSD provides evidence for contextualization impairments, including emotion regulation, social cognition, and self-referential processing


Thus, the interaction data are a composite of the individual networks that may operate in specific tissues or developmental stages. Genome-wide expression data may be useful for filtering interaction data to identify the subnetworks that operate in specific spatial or temporal contexts. Here we take advantage of the extensive interaction and expression data available for Drosophila to analyze how interaction networks may be unique to specific tissues and developmental stages.Networks of interacting genes and gene products mediate most cellular and developmental processes. High throughput screening methods combined with literature curation are identifying many of the protein-protein interactions (PPI) and protein-DNA interactions (PDI) that constitute these networks. Most of the detection methods, however, fail to identify the We ranked genes on a scale from ubiquitously expressed to tissue or stage specific and examined their interaction patterns. Interestingly, ubiquitously expressed genes have many more interactions among themselves than do non-ubiquitously expressed genes both in PPI and PDI networks. While the PDI network is enriched for interactions between tissue-specific transcription factors and their tissue-specific targets, a preponderance of the PDI interactions are between ubiquitous and non-ubiquitously expressed genes and proteins. In contrast to PDI, PPI networks are depleted for interactions among tissue- or stage- specific proteins, which instead interact primarily with widely expressed proteins. In light of these findings, we present an approach to filter interaction data based on gene expression levels normalized across tissues or developmental stages. We show that this filter (the percent maximum or pmax filter) can be used to identify subnetworks that function within individual tissues or developmental stages.These observations suggest that protein networks are frequently organized into hubs of widely expressed proteins to which are attached various tissue- or stage-specific proteins. This is consistent with earlier analyses of human PPI data and suggests a similar organization of interaction networks across species. This organization implies that tissue or stage specific networks can be best identified from interactome data by using filters designed to include both ubiquitously expressed and specifically expressed genes and proteins. A majority of available PPI, for example, have come from two methods: the yeast two-hybrid system, which detects interactions between proteins expressed in a yeast nucleus [in vivo interactions. However, since only a fraction of these interactions may be active in a particular spatial or temporal context, filters are needed to identify the regulatory networks that are relevant to specific cells, tissues, or developmental time points.The phenotypic identities of cells and tissues are governed in part by the particular regulatory networks that are active in them. Steady progress has been made to map the molecular interactions that constitute these networks, including the interactions among proteins and between transcription factors (TFs) and the genes that they regulate. Tens of thousands of protein-protein interactions (PPI) and TF-gene interactions have been identified for human and several model organisms, providing a foundation for identifying cell or tissue specific regulatory networks -4. Most  nucleus -7, and p nucleus ,9, which nucleus ,11). Thein vivo interactions [in vivo PPI and thus may not be useful for filtering interactome data to identify relevant subnetworks. An alternative approach would be to search the interactome data for subnetworks of genes that are specifically expressed in cells or tissues of interest. Recent studies on human PPI and transcriptome data, however, have suggested that tissue-specific proteins are involved in relatively few interactions, most of which are with house-keeping or ubiquitous proteins [To identify spatially or temporally relevant subnetworks, composite interactome networks can be filtered using gene expression or transcriptome data. It has been shown, for example, that interactions between proteins encoded by genes with similar or correlated expression patterns are more likely than those with dissimilar expression patterns to be genuine ractions -14. Thisractions ,16, to sractions -19, and ractions ,21. Corrproteins -24. ThesDrosophila. We took advantage of the extensive PPI and protein-DNA interaction (PDI) data available for Drosophila[Drosophila, tissue-specific proteins infrequently interacted among themselves but instead interacted primarily with widely expressed proteins. In addition, we show that stage-specific proteins have many more interactions with ubiquitously expressed proteins than with other stage-specific proteins. In contrast, we find that the Drosophila PDI network is enriched for interactions between tissue- and stage-specific TFs and their relevant tissue- and stage-specific targets, yet there is a preponderance of interactions between specifically expressed TFs and non-specifically expressed targets.In this study we examined the relationship between the interactome and transcriptome of rosophila, and recrosophila and deverosophila. As suggDrosophila, for example, mRNA abundances of different genes at their maximal level of expression can range over four orders of magnitude [The specific interaction networks active in particular cells or tissues will be determined in part by the genes that are expressed in them. The problem of filtering an interactome network based on quantitative gene expression data, however, is particularly challenging because fully active genes can be expressed at widely different levels. In agnitude ,27. ThisDrosophila genes expressed in different patterns we classified genes based on the specificity of their expression across tissues or developmental stages (Methods). We used tissue expression data from FlyAtlas [Drosophila genes as tissue ubiquitous and 22% as tissue-specific in this study. This is comparable to a study of human genes [Drosophila common ubiquitous genes are enriched for genes involved in basic cellular processes, including protein synthesis, trafficking and degradation, RNA transcription and processing, and cytoskeleton organization . The ubiquitously expressed genes are also enriched for intracellular proteins  while the tissue- and stage-specific genes are enriched for extracellular proteins  (Methods). This is in partial agreement with the results of a human study where the tissue-specific proteins were found to be enriched for extracellular and membrane proteins [Drosophila ubiquitously expressed genes are also more evolutionarily conserved than the tissue- or stage-specific genes  available in the DroID database [Drosophila proteins, and a partially overlapping set of 144,171 potentially conserved interactions (interologs) that were predicted from experimental data in other species. For simplicity we refer to proteins encoded by ubiquitously expressed genes as “ubiquitous proteins”, and similarly to “tissue-specific proteins” and “stage-specific proteins”, keeping in mind that measured mRNA abundance may be an imperfect surrogate for protein abundance [Drosophila tissue-specific proteins frequently interact with a core set of ubiquitously expressed proteins is consistent with analyses of the human interactome [To determine the interaction properties of database . These ibundance . We founeractome ,23. CombDrosophila proteins we calculated fold difference between the number of interactions among or between these groups and among random sets of other proteins of the same sizes (Methods). We found that both the tissue- and stage-ubiquitous proteins had about 8-fold  more interactions among themselves compared to other random proteins in the network  fewer interactions than the random proteins , then they will likely have few interactions among themselves just by chance. Likewise, proteins with many overall interactions are more likely to interact with each other just by chance. Moreover, since protein interaction networks generally follow a power law distribution of interactions per protein (degree) , the aveIt is perhaps not surprising to find few interactions among all of the tissue- or stage-specific proteins, since many of them are never expressed together in the same tissue or at the same developmental stage. To determine whether the tissue- or stage- specific proteins interacted with each other within each tissue or stage, we built networks of genes expressed in each of the 23 adult and larval tissues and performed the same comparisons. The tissue-specific proteins within every tissue except the ovary had many fold fewer interactions among themselves than did random sets of other proteins expressed in the same tissue . In addition, approximately half of the regulatory interactions of ubiquitous TFs involve non-ubiquitous targets. The PDI network is more limited than the PPI network because of the relatively small number of TFs that have been experimentally shown to bind target genes [Next we determined the interaction tendencies of the ubiquitous and specific genes in the PDI. Not surprisingly, the ubiquitous TFs bound to ubiquitous targets more than expected by random chance  in specific tissues. We compared these filters to one that selects genes expressed above the average value for all genes in a given tissue. We chose a set of six tissues  and applied the three different filters to genes expressed in those tissues; the pmax filtered gene lists had in some cases more and in some cases fewer genes than the corresponding lists of genes expressed above the average level , while the testis subnetwork is enriched for the phenotype ‘male sterile’  . Likewise, many of the PPI subnetworks made from genes expressed above 75 pmax in specific stages were enriched for stage relevant phenotypes   to diacylglycerol (DAG) and inositol 1,4,5-triphosphate (IP3), which in turn leads to opening of the TRP channels and influx of Ca2+. The pathway also includes genes for a protein kinase C or PKC (inaC), calmodulin (CaM), a G-protein-coupled receptor kinase (Gprk1), a protein that blocks the rhodopsin-Gq interactions called arrestin 2 (Arr2), a myosin III protein (ninaC), and a scaffold protein (inaD). We used these 25 genes to search the DroID database [−5), ‘retina’ (p < 1.265 × 10−6), ‘visual behavior defective’ (p < 8.934 × 10−10), ‘phototaxis defective’ (p < 0.008) or ‘sensory perception defective’ (p < 0.01), in addition to ‘neuroanatomy defective’ (p < 8.021 × 10−4) and ‘neurophysiology defective’ (p < 1.335 × 10−13). In total 55 (16%) of the genes have eye-related phenotype terms, including 11 of the KEGG pathway genes . In These findings indicate that protein networks frequently consist of a core of evolutionarily conserved, widely-expressed proteins to which are attached a set of relatively new, tissue-specific proteins. Many of the functions of specific tissues may emerge via a modification of core non-specific cell networks. This is in contrast to the notion of cell- or tissue-specific systems that consist primarily of cell or tissue-specific proteins. The Drosophila phototransduction network Figure  is a gooThe identification and characterization of cellular pathways and other functional modules is a major challenge in post-genome research. Clues about the constituents of pathways can be obtained from the huge amount of protein interaction data that is becoming available from high throughput screens and collections of data from published low throughput experiments. Since most of the available data comes from context-independent assays, many of the available interactions may not occur in any given biological setting. Gene expression information could be used as a first pass filter to identify interacting proteins that are expressed in the same cell or tissue. The finding that specifically expressed proteins frequently interact with ubiquitously expressed proteins shows that a filter based on tissue-specific expression would not be effective. Another implication of this finding is that genes encoding pathway members are not necessarily coordinately regulated across tissues. Thus, while correlated expression has been useful for finding groups of genes that may function together, the expression patterns of many members of specific pathways are not correlated. Another approach that has been used effectively in some studies is to first find genes that are expressed at high levels in a particular tissue in order to enrich for genes belonging to pathways that are active in that tissue . Such aWe set out to develop a method for using gene expression data to identify context-relevant networks from interactome data. We reasoned that a gene is likely to be active in a tissue where it is expressed at its maximal level relative to other tissues and that the closer a gene is to its maximal expression level the more likely it is to be active. The availability of quantitative gene expression data from a wide range of tissues or developmental stages has made it possible to test this idea. We used a normalization procedure that scaled  each gene’s expression based on its maximum expression level in any tissue or stage. Each gene in each tissue or stage is expressed at a percent of its maximum value, or pmax. We showed that genes expressed at higher pmax values in a tissue or stage are more enriched for genes that function in that tissue. Moreover, filtering composite protein interaction networks using this scale generates biologically relevant subnetworks. Such a gene expression filter will be useful for generating hypotheses about the composition of pathways and other functional modules in cells. The pmax filter along with other gene expression filters should also be useful for understanding how the interactome changes from cell to cell and in different conditions , particularly as additional expression data with better time and spatial resolution becomes available.Drosophila[Drosophila interologs predicted from experimental data in yeast, worm and human [C. elegans, or human [Drosophila PDI data include 158,508 unique regulatory PDI for 149 transcription factors (TF) and 12,441 of their target genes that were inferred using TF binding and correlated expression of targets [Drosophila expression arrays. Data was included for probes that mapped to single Flybase gene identifiers (FBgn). Probe data was included only where Affymetrix called the mRNA present for all four replicates. The mean signal from four replicates was used for all subsequent calculations. The stage-wide temporal gene expression data was obtained from Graveley et al. [Protein-protein interaction (PPI) and protein-DNA interaction (PDI) data were downloaded from DroID ,25 versirosophila,62. The nd human ,25. In tnd human ,58, yeasnd human , interolor human . The Dro targets ,63. We s targets . Tissue- targets . This day et al. . This daEsi) of a gene in tissue or stage i is simply a fraction of the total abundance across all tissues or stages and was calculated as follows:Genes were classified according to the specificity of their expression levels across all tissues or developmental stages. The expression specificity ; genes that were not tissue-specific and that had non-zero expression values across all adult tissues were labeled as ubiquitous (3960 genes); the remaining genes were labeled as tissue non-specific-non-ubiquitous (5830 genes). For the analyses in Additional file Esi values of > =0.19 were labeled as stage-specific (3566 genes); genes that were not stage-specific and that had developmental stage Esi values >0.005 across all time points were labeled as ubiquitous (4972 genes); the remaining genes were labeled as stage non-ubiquitous, non-specific (6064 genes). The stage-specific bin included genes that showed a transient and sharp change in abundance that spanned a maximum of four consecutive developmental time points in a majority of the cases. The overlap among genes classified by the two specificity scales (tissue specificity and stage specificity) is shown in Additional file We placed genes into three non-overlapping bins based on their specificity of expression across all 15 adult tissues: Genes with i at a percent (pmaxi) of its value in the tissue or stage where it is maximally expressed. pmaxi is calculated by:We created a normalized gene expression scale to indicate for each gene the extent of its expression in a particular tissue or stage relative to its maximum level of expression in any tissue or stage, respectively. We refer to this normalized expression scale as the percent of maximum or pmax. To normalize gene expression based on this scale, the expression of a gene is calculated as the percent of its maximum expression level across all tissues or stages. For example, if a gene is maximally expressed in the ovary compared with all other tissues, then its pmax value in the mid-gut is its expression level in the midgut as a percentage of its expression in the ovary. A gene is expressed in tissue or stage abundancei is the raw expression value in tissue or stage i, and abundancemax is the raw expression value in the tissue or stage where it is maximally expressed. For the analysis in Figure http://www.droidb.org) [where idb.org) . The datidb.org) , which iDrosophila orthologs of yeast, worm and human proteins were identified using data downloaded from InParanoid version 7.0 database [Drosophila gene as a unique reference to each orthology group.Potential database . InParanC. elegans interologs, respectively; light blue and pink are from large co-complex studies [To compare the numbers of interactions among different groups of genes we calculated the fold difference in interactions over random expectation. First we counted the number of interactions among genes in the test set. Then we picked the same number of random genes from the PPI network minus the test set and counted interactions among them. The fold difference is the number of interactions among genes in the test set divided by the number of interactions among genes in the random set. We repeated this 5000 times for each test set. To calculate a p-value for each test case, we performed 100,000 Monte Carlo simulations by picking gene sets of the same size randomly and counting the number of interactions between the genes in each of these random sets. We computed the number of times the interactions in the random sets were lower, or higher, depending on the test case. This number was used to calculate the binomial confidence interval using binom.confint in R to calculate the p-value at a confidence of 99.99% (CI = 0.9999) ,67. We u studies ; dark bl studies -58; oran studies -61; red Drosophila phenotype controlled vocabulary terms from the FlyBase phenotype ontology [i.Gene Ontology enrichment analysis was performed using DAVID 6.7  and the ontology . The phehttp://www.droidb.org).All interaction data used in this study is available along with calculated gene expression pmax values in DroID, the Drosophila Interactions Database . For tissue expression data only the adult tissues were used. Numbers in parentheses are total number of genes in each bin.Click here for fileConservation of ubiquitously expressed genes. Bars indicate the percentage of ubiquitous, tissue-specific, and stage-specific Drosophila genes that are conserved in each organism. Conservation is based on identification of close sequence homology (Methods).Click here for fileWithin tissue- or stage-specific subnetworks, tissue- or stage-specific proteins rarely interact with each other. For each protein set the average fold difference between the number of direct interactions in the test set and the number of interactions among other proteins in each of 5000 random sets of proteins expressed in the relevant tissue or stage is shown. Standard deviations are shown as error bars. The log p-values for each comparison are shown as red dots (right axis). (A) Interactions among the tissue-ubiquitous proteins or among each set of tissue-specific proteins in each of 15 adult tissues. (B) Interactions among the stage-ubiquitous proteins or among each set of stage-specific proteins for each of 30 developmental stages.Click here for fileWithin tissue- or stage-specific subnetworks, tissue- or stage-specific proteins rarely interact with each other indirectly through a third protein. For each protein set the average fold difference between the number of indirect interactions in the test set and number of indirect interactions among proteins in each of 5000 random sets of proteins expressed in the relevant tissue or stage is shown. Standard deviations are shown as error bars. The log p-values for each comparison are shown as red dots (right axis). > > indicates more than 200-fold less than random sets. (A) Indirect interactions among the tissue-ubiquitous proteins or among each set of tissue-specific proteins in each of 15 adult tissues. (B) Indirect interactions among the stage-ubiquitous proteins or among each set of stage-specific proteins for each of 30 developmental stages.Click here for fileWithin larval tissue subnetworks, tissue-specific proteins rarely interact with each other directly or indirectly. For each protein set the average fold difference between the number of direct (A) or indirect (B) interactions in the test set and number of direct or indirect interactions among proteins in each of 5000 random sets of proteins expressed in the relevant tissue is shown. Standard deviations are shown as error bars. The log p-values for each comparison are shown as red dots (right axis). (A) Direct interactions among the larval tissue-ubiquitous proteins or among each set of larval tissue-specific proteins. (B) Indirect interactions among each set of larval tissue-specific proteins.Click here for fileTissue- and stage-specific transcription factors (TFs) regulate tissue- and stage-specific targets in the PDI networks more than expected by chance. For specific TFs in each tissue or stage 100,000 random networks were built by assigning random targets while keeping the node degree constant. P-values  were computed by counting the number of times specific interactions in the random networks were lower than the number of specific interactions in the tissue and stage PDI networks. The protein-DNA interaction network (PDI) and the predicted PDI network (Methods) were analyzed separately.Click here for fileNumber of genes in the tissue gene lists after applying different expression filters. Number of genes expressed at average, greater than 50 pmax and greater than 75 pmax in six different tissues.Click here for fileHeat map of enriched phenotypes in variously filtered gene lists. Heat map of enriched mutant phenotypes obtained in lists of genes because they were expressed above the average, greater than 50 pmax, or greater than 75 pmax in six different tissues. The corrected p-values for enrichment were log transformed, scaled, and then plotted.Click here for fileHeat map of phenotypes enriched in tissue-relevant subnetworks. Heat map of enriched mutant phenotypes for genes in subnetworks containing only genes expressed above 75 pmax in each indicated tissue. The corrected p-values for enrichment were log transformed, scaled, and then plotted. Tissues are clustered based on the similarity of their enriched mutant phenotypes.Click here for fileHeat map of phenotypes enriched in stage-relevant subnetworks. Heat map of enriched mutant phenotypes for genes in subnetworks containing only genes expressed above 75 pmax in each indicated stage. The corrected p-values for enrichment were log transformed, scaled, and then plotted. Stages are clustered based on the similarity of their enriched mutant phenotypes.Click here for fileHeat map of depleted phenotypes in tissue-relevant subnetworks. Heat map of depleted mutant phenotypes for genes in subnetworks containing only genes expressed above 75 pmax in each indicated tissue. The corrected p-values for depletion were log transformed, scaled, and then plotted.Click here for fileHeat map of depleted phenotypes in stage-relevant subnetworks. Mutant phenotypes for genes in subnetworks containing only genes expressed above 75 pmax in each indicated stage. The corrected p-values for depletion were log transformed, scaled, and then plotted.Click here for fileNetworks filtered for genes expressed at higher pmax values are more enriched for context-relevant genes. Top panel: shows fold enrichment for genes with the indicated phenotypes in protein interaction networks filtered for genes expressed above 25, 45, 65, or 85 pmax in brain (neurophysiology defective and behavior defective), ovary , eye , or testis . Fold enrichment is relative to the frequency of finding genes with those phenotypes in the unfiltered network. Bottom panel: Bonferroni-corrected p values for enrichment of the indicated phenotypes in the filtered networks relative to the frequency of those phenotypes in the proteome. Note that the unfiltered network is enriched for genes with some phenotypes, yet the enrichment increases with higher pmax filters.Click here for fileFiltered eye network interactions and genes. List of all protein interactions in the filtered eye network in Figure Click here for file


The irreproducibility problem seriously hinders the studies on transcriptional signatures for predicting relapse risk of early stage colorectal cancer (CRC) patients. Through reviewing recently published 34 literatures for the development of CRC prognostic signatures based on gene expression profiles, we revealed a surprising phenomenon that 33 of these studies analyzed CRC samples with and without adjuvant chemotherapy together in the training and/or validation datasets. This data misuse problem could be partially attributed to the unclear and incomplete data annotation in public data sources. Furthermore, all the signatures proposed by these studies were based on risk scores summarized from gene expression levels, which are sensitive to experimental batch effects and risk compositions of the samples analyzed together. To avoid the above-mentioned problems, we carefully selected three qualified large datasets to develop and validate a signature consisting of three pairs of genes. The within-sample relative expression orderings of these gene pairs could robustly predict relapse risk of stage II CRC samples assessed in different laboratories. The transcriptional and functional analyses provided clear evidence that the high risk patients predicted by the proposed signature represent patients with micro-metastases. Colorectal cancer (CRC) is the third most common cancer and the fourth leading cause of cancer death worldwide . The maiHigh-throughput gene expression profiling has emerged as a powerful tool to identify stage II CRC patients with potential relapse risk , 14. HowIn this work . The heterogeneous risk compositions could be due to many factors such as the differences in diagnosis criteria, surgery quality and location of CRC. For signatures based on risk scores summarized from gene expression measurements of a set of signature genes, this problem would induce spurious risk classification and difficulty in clinical settings because the risk classification of a sample would change when different samples are adopted for analysis together [CRC is clinically and pathologically highly heterogeneous with a large variation in 5-year survival rates in different countries and even different cities in the same country . As showtogether .For example, ColoGuideEx , a 13-gen = 203) dataset with the largest sample size to train a GPS of the relapse risk for stage II CRC and validated it in the GSE14333 and GSE17536 datasets (shown in the Table n = 55) included 35 samples, which were technical replicates of the samples of GSE14333, we considered it as a validation dataset for technical reproducibility of the signature.We used the GSE39582  from the GSE39582 and GSE14333 datasets, respectively, and then found 149472 and 1154605 significantly reversed gene pairs , each consisting at least one of the Metastatic-DE genes, between the metastatic samples (stage III and IV) and the non-metastatic samples (stage I) in the two datasets, respectively. The two lists of gene pairs had 6386 overlaps and 99.86% of them had the same reversal patterns in the two datasets, which was unlikely to be observed by chance . Finally, from the 6377 metastasis-associated gene pairs consistently detected in the two datasets, we extracted 15 prognosis-associated gene pairs based on 203 stage II CRC samples from GSE39582 dataset, by univariate Cox proportional-hazards regression model with p < 0.01. The 15 prognosis-associated gene pairs are listed in p-value as a seed, we performed a forward selection procedure and obtained an optimal set consisting of three gene pairs reached the smallest p-value  dataset. The RFS of the patients in the low-risk groups was significantly longer than that of the patients in the high-risk group . The 3-GPS was also validated in the stage II CRC samples without CTX included in the GSE17536 (n = 55) dataset .The 3-GPS was validated in the stage II CRC samples without CTX included in the GSE14333 . Functional enrichment analysis showed that the 1003 Risk-DE genes were significantly enriched in six KEGG pathways , indicating that the CTX was not beneficial to patients in the low risk group and even shortened their RFS. In contrast, for the patients predicted to the high relapse risk group, the patients with CTX tended to have significantly longer RFS than the patients without CTX , indicating that the CTX tended to increase the RFS of patients at high relapse risk.Due to the small size of the stage II CRC patients with adjuvant CTX, we integrated the samples in these two datasets. The 3-GPS signature assigned 68 (21 samples with CTX and 47 samples without CTX) and 285 (57 samples with CTX and 228 samples without CTX) patients to the high- and low-risk groups, respectively. For the patients predicted to the low relapse risk group, the RFS of the patients with CTX was significantly shorter than that of the patients without CTX  and that the random chance of a person to develop cancer ab initio is negligible. Our results supported this hypothesis, as evidenced by the observation the Risk-DE genes between the predicted high- and low-risk samples significantly overlapped with the Metastatic-DE genes detected between the metastatic samples (the stage III-IV samples) and non-metastatic samples (stage I samples) and the deregulation directions of the overlapped DE genes in the high-risk samples compared with the low-risk samples were significantly concordant with the deregulation directions in the metastatic samples compared with the non-metastatic samples. Additionally, the Risk-DE genes were significantly enriched in six well-known metastasis-associated pathways, including “ECM-receptor interaction” , “Focal Currently, adjuvant chemotherapy (CTX) is the most common therapeutic regimen prescribed for patients with stage III CRC after surgical resection , while ihttp://www.ncbi.nlm.nih.gov/pubmed/) to identify articles on the analyses of gene expression data for developing prognostic signatures of CRC, published in English between January 1, 2010 and June1, 2015.We searched the PubMed medical literature database (http://www.ncbi.nlm.nih.gov/geo/) [Three large datasets generated on the GPL570 platform were analyzed in this work Table , which sov/geo/) .The raw data (.CEL files) for each dataset was processed using the RMA algorithm for background adjustment without quantile normalization . Then, eSurvival curves were estimated using the Kaplan-Meier method and were compared using the log-rank test . The unit-test, we selected differential expression genes between metastatic samples (stage III and IV CRC) and non-metastatic samples (stage I CRC), denoted as Metastatic-DE genes. From gene pairs including at least one Metastatic-DE genes, we selected gene pairs whose REOs were associated with metastasis. Let Ea and Eb represent the expression levels of gene a and gene b, respectively, we compared the frequency of samples with the REO pattern Ea > Eb between the metastatic CRC samples and the stage I CRC samples using Fisher exact test with 20% FDR control. The p-values were adjusted using the Benjamini-Hochberg (BH) procedure [Firstly, using Student's rocedure . The oveEa > Eb REO was associated with worse outcome, then we considered that this REO and the reversal REO (Ea < Eb) in a cancer sample votes for high and low risk, respectively. Gene pairs with p values less than 0.01 were considered as candidate prognosis-associated gene pairs. We chose the gene pair with the smallest p-value as a seed and added a prognosis-related gene pair at each iteration until the p-value did not decrease based on the classification rule as follows: a sample was classified into the high-risk group if the majority of the REOs of a set of gene pairs within this sample voted for high risk; otherwise, into the low risk group. The optimal set was defined as the gene pair signature (GPS).Then, for each of the metastasis-associated gene pairs, we classified stage II CRC samples without CTX into two groups according to the REO of this gene pair in each sample and compared RFS between the two groups using the univariate Cox proportional-hazards regression model. A gene pair was defined as a prognosis-associated gene pair if the two groups of samples had significantly different RFS. If the The GPS was validated by two independent datasets GSE14333 and GSE17536. Figure k overlapped genes, among which s genes showed the same deregulation directions (up- or down-regulation) in the two DE gene lists, then the concordance score was calculated as s/k. The probability of observing a concordance score of s/k by chance was evaluated by the cumulative binomial distribution model as following [eP is the probability of one gene having the concordant relationship between the two lists of genes by chance .If two lists of DE genes between the high- and low-risk groups detected separately from two datasets had ollowing :P=1−∑i=The significance of a score indicated that DE genes extracted from independent datasets were significantly consistent.http://www.genome.jp/kegg/) in July, 2014. The hypergeometric distribution model was used to test whether the number of DE genes annotated in a functional category was significantly more than what expected by random chance:N is the total number of the measured genes with functional annotation; n is the number of DE genes with functional annotation; M is the number of the measured genes in a functional category and m is the number of the DE genes in the functional category. The p-values were adjusted using the Benjamini-Hochberg (BH) procedure [The gene categories for functional enrichment analysis were downloaded from KEGG (rocedure .


Atherosclerosis and its associated diseases are constantly increasing in developed countries. Aortic stiffness is an indicator for atherosclerosis and is associated with mortality and morbidity especially in aortic abdominal aneurysms. For further evaluation of aortic stiffness, we examined n = 13 healthy volunteers using real-time magnetic-resonance imaging (RT-MRI) with highly undersampled radial fast low-angle shot (FLASH) acquisitions, phase-sensitive image reconstructions and regularized nonlinear inversion (NLINV). We hypothesized that RT-MRI is able to determine pulse wave velocity (PWV) and flow data using just one transverse view of the ascending and descending aorta. This method could be superior to complex known MRI methods using velocity projection today.We assessed PWV as surrogate parameter for aortic stiffness by velocity-encoded RT-MRI. Time lag between the ascending and descending aortic pulse wave was calculated and divided by the mean length of the aortic arch for each individual in detail. RT-MRI can determine PWV during normal breathing, physical strain and recovery from strain using the Valsalva (VM) and Mueller maneuvers (MM). During strain/maneuvers volunteers had visual feedback of intra-thoracic pressure via a mouthpiece to ensure adequate strain performance. We calculated PWV in normal breathing, at the end of strain and at the end of the recovery phase. Due to the advantage of a single heart beat-to-beat variability with RT-MRI, the average PWV of 4 heartbeats could be calculated in each phase with Standard deviation (SD).While changes during MM were very subtle , increase and especially decrease during recovery from VM was more pronounced .Successful measurements of PWV in one transverse view during physical strain have not been shown before to our knowledge and prove to be an easy alternative to previous MR attempts determining PWV. In addition, the significant PWV decrease measured in RT-MRI from VM to recovery resembles the normal variability of aortic stiffness in healthy subjects due to change in intra-thoracic pressure. Real time MRI is able to measure these data under stress maneuvers such as the VM or MM. A combined score of PWV and its variability might be helpful for risk assessment in the development of aortic aneurysmsThis work was funded by the Deutsche Forschungsgemeinschaft (LO 1773/1-1).


Background. HIV infection is associated with systemic inflammation that can increase risk for cardiovascular events. Acupuncture has been shown to have immunomodulatory effects and to improve symptoms in persons with inflammatory conditions. Objective. To test the anti-inflammatory effects of an acupuncture protocol that targets the cholinergic anti-inflammatory pathway (CAIP), a neural mechanism whose activation has been shown to reduce the release of proinflammatory cytokines, in persons with HIV-associated inflammation. Design, Setting, Participants, and Interventions. Double-blind, placebo-controlled clinical trial conducted in an outpatient clinic located in a medically underserved urban neighborhood. Twenty-five clinically-stable HIV-infected persons on antiretroviral therapy were randomized to receive once weekly CAIP-based acupuncture or sham acupuncture. Main Outcome Measures. Outcomes included plasma concentrations of high sensitivity C-reactive protein and D-dimer and fasting lipids. Results. Twenty-five participants completed the protocol . No adverse events related to the acupuncture protocol were observed. Compared to baseline values, the two groups did not significantly differ in any outcome measures at the end of the acupuncture protocol. Conclusions. CAIP-based acupuncture did not favorably modulate inflammatory or lipid parameters. Additional studies are warranted of CAIP-based protocols of different frequencies/durations. Persistent elevation of inflammatory markers is common in HIV-infected persons and has been associated with the development of dyslipidemia and cardiovascular events, as well as all-cause mortality , 2. WhilThe study used a randomized, placebo-controlled, double-blind clinical trial design to evaluate the efficacy and safety of an 8-week course of CAIP-based acupuncture in persons with HIV-related inflammation. Participants were randomized 1 : 1 to the CAIP-based or placebo conditions using computer-generated random numbers. Data were collected at baseline and at the eighth acupuncture session. All personnel, except the statistician and acupuncturist, were blinded to participant assignment.The study was conducted between July 2011 and October 2012 at two outpatient HIV/AIDS clinics located in a federally designated medically underserved neighborhood in Chicago. Institutional review board approval was granted and the ethical standards of the Helsinki Declaration of 1975 were followed. All participants gave written informed consent before enrollment into the study.3, and a viral load of <75 copies/mL and had been on stable antiretroviral therapy for at least two months.Participants were eligible if they were HIV-infected adults between 18 and 60 years of age with an hsCRP level of ≥3.0 mg/L, a CD4 T cell count of at least 250 cells/mm3; had a chronic condition known to affect lipid status ; had a chronic inflammatory condition ; used within the previous month medications or herbal therapies reported to influence bleeding or inflammatory parameters ; ever received acupuncture; had lymphedema in any body part; had documented active opportunistic infections or malignancies; had serum creatinine >2.0 mg/dL or liver enzyme elevations >three times the upper limit of normal; has body mass index ≥30; used lipid-lowering medications; or were currently using illicit drugs or consuming ≥three alcoholic drinks/day.Participants were excluded if they had a history of heart attack or stroke; had a platelet count <150,000 cells/mmParticipants were recruited via flyers and outreach efforts by the clinics' research coordinators. Participants completed a total of 10 study visits. At the first two visits (one to two weeks apart), medical history data and blood samples were collected to determine eligibility. At the third visit (one to two weeks later), participants completed a Traditional Chinese Medicine (TCM) history form after which the acupuncturist performed a TCM assessment and assigned a TCM diagnosis. The acupuncturist then opened the randomization envelope and introduced the participant to the concept of needle insertion using either actual acupuncture needles (for participants randomized to the CAIP-based group) or the placebo needles (for participants randomized to the placebo group). The acupuncturist then performed the assigned 30-minute acupuncture treatment. Participants returned to the clinic for seven additional visits to receive the assigned 30-minute acupuncture treatment. We selected this protocol because 6–8 weeks of acupuncture treatment has been shown to reduce symptoms in inflammatory conditions, such as arthritis  and prosAcupuncture treatments were performed by two licensed acupuncturists who were certified in “Clean Needle Technique” by the Council of Colleges of Acupuncture and Oriental Medicine (CCAOM). To promote treatment fidelity, both acupuncturists followed a procedure manual that detailed the steps in the TCM assessment and the two acupuncture protocols.To control for the effects of social interaction on the study outcomes, the acupuncturist's conversation with the participant was limited to treatment-specific issues. The acupuncturist swabbed each acupuncture point location with an alcohol swab and inserted the needles. Throughout the 30-minute treatment period, the acupuncturist returned at 10-minute intervals to check on the participant. At the end of 30 minutes, the needles were removed. Two acupuncture protocols were delivered: CAIP-based and placebo. de-Qi needle sensation. This procedure, including point selection, was repeated at each of the eight acupuncture sessions.Each acupuncture treatment was delivered using disposable, sterile, stainless steel 36-gauge Serin needles . The acupuncturist placed a single needle bilaterally at the following four points: (1) LU1; (2) ST 36; (3) PC6; and (4) LI4. These points have been shown to correspond to vagal stimulation –25. EachParticipants randomized to the placebo group did not receive real acupuncture. Instead, the acupuncturist used a needle called a “Park Sham Device”  that does not penetrate the skin because it is encased in a cartridge . The carAll primary and secondary outcome measurements were performed at a CLIA-certified commercial laboratory .High sensitivity C-reactive protein (hsCRP) concentration was measured using a latex immunoturbidimetry assay. Due to biological variability, two baseline measurements of hsCRP were obtained and the mean was used in the analyses. Plasma concentration of D-dimer was quantified at the baseline and final study visits using an immunoturbidimetry assay. We chose these markers because elevated serum levels of hsCRP and D-dimer are indicative of systemic inflammation, are common in HIV infection , and areThe standard lipid profile was obtained following an overnight fast at the baseline and final study visits and consisted of triglycerides and total, high-density lipoprotein (HDL), and low-density lipoprotein (LDL) cholesterol. Lipids were quantified using spectrophotometric methods. Safety parameters were measured at the baseline and final study visits. CD4+ T lymphocytes were quantified using two-color (anti-CD3/anti-CD4) flow cytometry and plasma HIV-1 RNA was quantified using a polymerase chain reaction assay.t-tests and chi-square analyses were used to test for baseline group differences. For each outcome, the differences in the two groups' change scores  were determined and independent samples t-tests were performed. Effect sizes (Cohen's D) for the CAIP-based acupuncture were calculated by dividing the treatment group's change score for each inflammatory and lipid outcome by the standard deviation for that change score.Descriptive statistics were used to characterize the groups' demographic and clinical data. Independent samples P values > .5).A total of 80 participants were screened for eligibility, 27 were randomized and entered into the analyses, and 25 completed the protocol . Most ofNo statistically significant differences were found between the groups' change scores for any of the primary or secondary outcomes Tables  and 4. Ed range = .11 to .45).This pilot study is the first to explore the safety and efficacy of an eight-week CAIP-based acupuncture protocol to favorably modulate inflammatory and lipid parameters in adults with HIV-related inflammation. While the acupuncture protocol was well-tolerated, we found no significant differences between the two groups on any outcome measures and all effect sizes were in the low range  , 23, 24 Measurement issues may have contributed to a type II error. It is possible that anti-inflammatory effects were present immediately after the completion of each acupuncture session but had subsided by the time of the posttreatment measurements. Potential lack of sensitivity of our outcome measures may have precluded detecting treatment effects. We have previously shown that nuclear magnetic resonance- (NMR-) derived lipoprotein particle profiles are more sensitive than the standard lipid profile for identifying HIV-infected persons at risk for coronary heart disease . AdditioOur findings suggest that CAIP-based acupuncture is feasible and not associated with adverse events when administered to clinically-stable HIV-infected adults with evidence of systemic inflammation. Additional studies with larger samples, more sensitive measures of inflammation and lipid metabolism, and objective measures of vagal activation are needed to determine if CAIP-based acupuncture is efficacious for reducing inflammation and/or normalizing lipid profiles.