Literature-based discovery of diabetes- and ROS-related targets

Abstract Background Reactive oxygen species (ROS) are known mediators of cellular damage in multiple diseases including diabetic complications. Despite its importance, no comprehensive database is currently available for the genes associated with ROS. Methods We present ROS- and diabetes-related targets (genes/proteins) collected from the biomedical literature through a text mining technology. A web-based literature mining tool, SciMiner, was applied to 1,154 biomedical papers indexed with diabetes and ROS by PubMed to identify relevant targets. Over-represented targets in the ROS-diabetes literature were obtained through comparisons against randomly selected literature. The expression levels of nine genes, selected from the top ranked ROS-diabetes set, were measured in the dorsal root ganglia (DRG) of diabetic and non-diabetic DBA/2J mice in order to evaluate the biological relevance of literature-derived targets in the pathogenesis of diabetic neuropathy. Results SciMiner identified 1,026 ROS- and diabetes-related targets from the 1,154 biomedical papers (http://jdrf.neurology.med.umich.edu/ROSDiabetes/). Fifty-three targets were significantly over-represented in the ROS-diabetes literature compared to randomly selected literature. These over-represented targets included well-known members of the oxidative stress response including catalase, the NADPH oxidase family, and the superoxide dismutase family of proteins. Eight of the nine selected genes exhibited significant differential expression between diabetic and non-diabetic mice. For six genes, the direction of expression change in diabetes paralleled enhanced oxidative stress in the DRG. Conclusions Literature mining compiled ROS-diabetes related targets from the biomedical literature and led us to evaluate the biological relevance of selected targets in the pathogenesis of diabetic neuropathy.http://deepblue.lib.umich.edu/bitstream/2027.42/78315/1/1755-8794-3-49.xmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/78315/2/1755-8794-3-49-S7.XLShttp://deepblue.lib.umich.edu/bitstream/2027.42/78315/3/1755-8794-3-49-S10.XLShttp://deepblue.lib.umich.edu/bitstream/2027.42/78315/4/1755-8794-3-49-S8.XLShttp://deepblue.lib.umich.edu/bitstream/2027.42/78315/5/1755-8794-3-49-S3.XLShttp://deepblue.lib.umich.edu/bitstream/2027.42/78315/6/1755-8794-3-49-S1.XLShttp://deepblue.lib.umich.edu/bitstream/2027.42/78315/7/1755-8794-3-49-S4.XLShttp://deepblue.lib.umich.edu/bitstream/2027.42/78315/8/1755-8794-3-49-S2.XLShttp://deepblue.lib.umich.edu/bitstream/2027.42/78315/9/1755-8794-3-49-S12.XLShttp://deepblue.lib.umich.edu/bitstream/2027.42/78315/10/1755-8794-3-49-S11.XLShttp://deepblue.lib.umich.edu/bitstream/2027.42/78315/11/1755-8794-3-49-S9.XLShttp://deepblue.lib.umich.edu/bitstream/2027.42/78315/12/1755-8794-3-49-S5.XLShttp://deepblue.lib.umich.edu/bitstream/2027.42/78315/13/1755-8794-3-49-S6.XLShttp://deepblue.lib.umich.edu/bitstream/2027.42/78315/14/1755-8794-3-49.pdfPeer Reviewe

Are genetic risk factors for psychosis also associated with dimension-specific psychotic experiences in adolescence?

Psychosis has been hypothesised to be a continuously distributed quantitative phenotype and disorders such as schizophrenia and bipolar disorder represent its extreme manifestations. Evidence suggests that common genetic variants play an important role in liability to both schizophrenia and bipolar disorder. Here we tested the hypothesis that these common variants would also influence psychotic experiences measured dimensionally in adolescents in the general population. Our aim was to test whether schizophrenia and bipolar disorder polygenic risk scores (PRS), as well as specific single nucleotide polymorphisms (SNPs) previously identified as risk variants for schizophrenia, were associated with adolescent dimension-specific psychotic experiences. Self-reported Paranoia, Hallucinations, Cognitive Disorganisation, Grandiosity, Anhedonia, and Parent-rated Negative Symptoms, as measured by the Specific Psychotic Experiences Questionnaire (SPEQ), were assessed in a community sample of 2,152 16-year-olds. Polygenic risk scores were calculated using estimates of the log of odds ratios from the Psychiatric Genomics Consortium GWAS stage-1 mega-analysis of schizophrenia and bipolar disorder. The polygenic risk analyses yielded no significant associations between schizophrenia and bipolar disorder PRS and the SPEQ measures. The analyses on the 28 individual SNPs previously associated with schizophrenia found that two SNPs in TCF4 returned a significant association with the SPEQ Paranoia dimension, rs17512836 (p-value=2.57x10-4) and rs9960767 (p-value=6.23x10-4). Replication in an independent sample of 16-year-olds (N=3,427) assessed using the Psychotic-Like Symptoms Questionnaire (PLIKS-Q), a composite measure of multiple positive psychotic experiences, failed to yield significant results. Future research with PRS derived from larger samples, as well as larger adolescent validation samples, would improve the predictive power to test these hypotheses further. The challenges of relating adult clinical diagnostic constructs such as schizophrenia to adolescent psychotic experiences at a genetic level are discussed

Biphasic effect of extracellular ATP on human and rat airways is due to multiple P2 purinoceptor activation

BACKGROUND: Extracellular ATP may modulate airway responsiveness. Studies on ATP-induced contraction and [Ca(2+)](i )signalling in airway smooth muscle are rather controversial and discrepancies exist regarding both ATP effects and signalling pathways. We compared the effect of extracellular ATP on rat trachea and extrapulmonary bronchi (EPB) and both human and rat intrapulmonary bronchi (IPB), and investigated the implicated signalling pathways. METHODS: Isometric contraction was measured on rat trachea, EPB and IPB isolated rings and human IPB isolated rings. [Ca(2+)](i )was monitored fluorimetrically using indo 1 in freshly isolated and cultured tracheal myocytes. Statistical comparisons were done with ANOVA or Student's t tests for quantitative variables and χ(2 )tests for qualitative variables. Results were considered significant at P < 0.05. RESULTS: In rat airways, extracellular ATP (10(-6)–10(-3 )M) induced an epithelium-independent and concentration-dependent contraction, which amplitude increased from trachea to IPB. The response was transient and returned to baseline within minutes. Similar responses were obtained with the non-hydrolysable ATP analogous ATP-γ-S. Successive stimulations at 15 min-intervals decreased the contractile response. In human IPB, the contraction was similar to that of rat IPB but the time needed for the return to baseline was longer. In isolated myocytes, ATP induced a concentration-dependent [Ca(2+)](i )response. The contractile response was not reduced by thapsigargin and RB2, a P2Y receptor inhibitor, except in rat and human IPB. By contrast, removal of external Ca(2+), external Na(+ )and treatment with D600 decreased the ATP-induced response. The contraction induced by α-β-methylene ATP, a P2X agonist, was similar to that induced by ATP, except in IPB where it was lower. Indomethacin and H-89, a PKA inhibitor, delayed the return to baseline in extrapulmonary airways. CONCLUSION: Extracellular ATP induces a transient contractile response in human and rat airways, mainly due to P2X receptors and extracellular Ca(2+ )influx in addition with, in IPB, P2Y receptors stimulation and Ca(2+ )release from intracellular Ca(2+ )stores. Extracellular Ca(2+ )influx occurs through L-type voltage-dependent channels activated by external Na(+ )entrance through P2X receptors. The transience of the response cannot be attributed to ATP degradation but to purinoceptor desensitization and, in extrapulmonary airways, prostaglandin-dependent PKA activation

A new pairwise kernel for biological network inference with support vector machines

International audienceBACKGROUND: Much recent work in bioinformatics has focused on the inference of various types of biological networks, representing gene regulation, metabolic processes, protein-protein interactions, etc. A common setting involves inferring network edges in a supervised fashion from a set of high-confidence edges, possibly characterized by multiple, heterogeneous data sets (protein sequence, gene expression, etc.). RESULTS: Here, we distinguish between two modes of inference in this setting: direct inference based upon similarities between nodes joined by an edge, and indirect inference based upon similarities between one pair of nodes and another pair of nodes. We propose a supervised approach for the direct case by translating it into a distance metric learning problem. A relaxation of the resulting convex optimization problem leads to the support vector machine (SVM) algorithm with a particular kernel for pairs, which we call the metric learning pairwise kernel. This new kernel for pairs can easily be used by most SVM implementations to solve problems of supervised classification and inference of pairwise relationships from heterogeneous data. We demonstrate, using several real biological networks and genomic datasets, that this approach often improves upon the state-of-the-art SVM for indirect inference with another pairwise kernel, and that the combination of both kernels always improves upon each individual kernel. CONCLUSION: The metric learning pairwise kernel is a new formulation to infer pairwise relationships with SVM, which provides state-of-the-art results for the inference of several biological networks from heterogeneous genomic data

Neither loss of Bik alone, nor combined loss of Bik and Noxa, accelerate murine lymphoma development or render lymphoma cells resistant to DNA damaging drugs

The pro-apoptotic BH3-only protein, BIK, is widely expressed and although many critical functions in developmental or stress-induced death have been ascribed to this protein, mice lacking Bik display no overt abnormalities. It has been postulated that Bik can serve as a tumour suppressor, on the basis that its deficiency and loss of apoptotic function have been reported in many human cancers, including lymphoid malignancies. Evasion of apoptosis is a major factor contributing to c-Myc-induced tumour development, but despite this, we found that Bik deficiency did not accelerate Eμ-Myc-induced lymphomagenesis. Co-operation between BIK and NOXA, another BH3-only protein, has been previously described, and was attributed to their complementary binding specificities to distinct subsets of pro-survival BCL-2 family proteins. Nevertheless, combined deficiency of Bik and Noxa did not alter the onset of Eμ-Myc transgene induced lymphoma development. Moreover, although p53-mediated induction of Bik has been reported, neither Eμ-Myc/Bik−/− nor Eμ-Myc/Bik−/−Noxa−/− lymphomas were more resistant than control Eμ-Myc lymphomas to killing by DNA damaging drugs, either in vitro or in vivo. These results suggest that Bik, even in combination with Noxa, is not a potent suppressor of c-Myc-driven tumourigenesis or critical for chemotherapeutic drug-induced killing of Myc-driven tumours

Critical assessment of sequence-based protein-protein interaction prediction methods that do not require homologous protein sequences

<p>Abstract</p> <p>Background</p> <p>Protein-protein interactions underlie many important biological processes. Computational prediction methods can nicely complement experimental approaches for identifying protein-protein interactions. Recently, a unique category of sequence-based prediction methods has been put forward - unique in the sense that it does not require homologous protein sequences. This enables it to be universally applicable to all protein sequences unlike many of previous sequence-based prediction methods. If effective as claimed, these new sequence-based, universally applicable prediction methods would have far-reaching utilities in many areas of biology research.</p> <p>Results</p> <p>Upon close survey, I realized that many of these new methods were ill-tested. In addition, newer methods were often published without performance comparison with previous ones. Thus, it is not clear how good they are and whether there are significant performance differences among them. In this study, I have implemented and thoroughly tested 4 different methods on large-scale, non-redundant data sets. It reveals several important points. First, significant performance differences are noted among different methods. Second, data sets typically used for training prediction methods appear significantly biased, limiting the general applicability of prediction methods trained with them. Third, there is still ample room for further developments. In addition, my analysis illustrates the importance of complementary performance measures coupled with right-sized data sets for meaningful benchmark tests.</p> <p>Conclusions</p> <p>The current study reveals the potentials and limits of the new category of sequence-based protein-protein interaction prediction methods, which in turn provides a firm ground for future endeavours in this important area of contemporary bioinformatics.</p

Disc1 variation leads to specific alterations in adult neurogenesis

Disrupted in schizophrenia 1 (DISC1) is a risk factor for a spectrum of neuropsychiatric illnesses including schizophrenia, bipolar disorder, and major depressive disorder. Here we use two missense Disc1 mouse mutants, described previously with distinct behavioural phenotypes, to demonstrate that Disc1 variation exerts differing effects on the formation of newly generated neurons in the adult hippocampus. Disc1 mice carrying a homozygous Q31L mutation, and displaying depressive-like phenotypes, have fewer proliferating cells while Disc1 mice with a homozygous L100P mutation that induces schizophrenia-like phenotypes, show changes in the generation, placement and maturation of newly generated neurons in the hippocampal dentate gyrus. Our results demonstrate Disc1 allele specific effects in the adult hippocampus, and suggest that the divergence in behavioural phenotypes may in part stem from changes in specific cell populations in the brain

PESCADOR, a web-based tool to assist text-mining of biointeractions extracted from PubMed queries

BACKGROUND: Biological function is greatly dependent on the interactions of proteins with other proteins and genes. Abstracts from the biomedical literature stored in the NCBI's PubMed database can be used for the derivation of interactions between genes and proteins by identifying the co-occurrences of their terms. Often, the amount of interactions obtained through such an approach is large and may mix processes occurring in different contexts. Current tools do not allow studying these data with a focus on concepts of relevance to a user, for example, interactions related to a disease or to a biological mechanism such as protein aggregation. RESULTS: To help the concept-oriented exploration of such data we developed PESCADOR, a web tool that extracts a network of interactions from a set of PubMed abstracts given by a user, and allows filtering the interaction network according to user-defined concepts. We illustrate its use in exploring protein aggregation in neurodegenerative disease and in the expansion of pathways associated to colon cancer. CONCLUSIONS: PESCADOR is a platform independent web resource available at: http://cbdm.mdc-berlin.de/tools/pescador