A sex-specific association of common variants of neuroligin genes (NLGN3 and NLGN4X) with autism spectrum disorders in a Chinese Han cohort

<p>Abstract</p> <p>Background</p> <p>Synaptic genes, <it>NLGN3 </it>and <it>NLGN4X</it>, two homologous members of the neuroligin family, have been supposed as predisposition loci for autism spectrum disorders (ASDs), and defects of these two genes have been identified in a small fraction of individuals with ASDs. But no such rare variant in these two genes has as yet been adequately replicated in Chinese population and no common variant has been further investigated to be associated with ASDs.</p> <p>Methods</p> <p>7 known ASDs-related rare variants in <it>NLGN3 </it>and <it>NLGN4X </it>genes were screened for replication of the initial findings and 12 intronic tagging single nucleotide polymorphisms (SNPs) were genotyped for case-control association analysis in a total of 229 ASDs cases and 184 control individuals in a Chinese Han cohort, using matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry.</p> <p>Results</p> <p>We found that a common intronic variant, SNP rs4844285 in <it>NLGN3 </it>gene, and a specific 3-marker haplotype X^A-X^G-X^T(rs11795613-rs4844285-rs4844286) containing this individual SNP were associated with ASDs and showed a male bias, even after correction for multiple testing (SNP allele: P = 0.048, haplotype:P = 0.032). Simultaneously, none of these 7 known rare mutation of <it>NLGN3</it> and <it>NLGN4X</it> genes was identified, neither in our patients with ASDs nor controls, giving further evidence that these known rare variants might be not enriched in Chinese Han cohort.</p> <p>Conclusion</p> <p>The present study provides initial evidence that a common variant in <it>NLGN3 </it>gene may play a role in the etiology of ASDs among affected males in Chinese Han population, and further supports the hypothesis that defect of synapse might involvement in the pathophysiology of ASDs.</p

Annotation and analysis of 10,000 expressed sequence tags from developing mouse eye and adult retina

Abstract Background As a biomarker of cellular activities, the transcriptome of a specific tissue or cell type during development and disease is of great biomedical interest. We have generated and analyzed 10,000 expressed sequence tags (ESTs) from three mouse eye tissue cDNA libraries: embryonic day 15.5 (M15E) eye, postnatal day 2 (M2PN) eye and adult retina (MRA). Results Annotation of 8,633 non-mitochondrial and non-ribosomal high-quality ESTs revealed that 57% of the sequences represent known genes and 43% are unknown or novel ESTs, with M15E having the highest percentage of novel ESTs. Of these, 2,361 ESTs correspond to 747 unique genes and the remaining 6,272 are represented only once. Phototransduction genes are preferentially identified in MRA, whereas transcripts for cell structure and regulatory proteins are highly expressed in the developing eye. Map locations of human orthologs of known genes uncovered a high density of ocular genes on chromosome 17, and identified 277 genes in the critical regions of 37 retinal disease loci. In silico expression profiling identified 210 genes and/or ESTs over-expressed in the eye; of these, more than 26 are known to have vital retinal function. Comparisons between libraries provided a list of temporally regulated genes and/or ESTs. A few of these were validated by qRT-PCR analysis. Conclusions Our studies present a large number of potentially interesting genes for biological investigation, and the annotated EST set provides a useful resource for microarray and functional genomic studies.http://deepblue.lib.umich.edu/bitstream/2027.42/112906/1/13059_2003_Article_574.pd

Feature Selection and Molecular Classification of Cancer Using Genetic Programming

AbstractDespite important advances in microarray-based molecular classification of tumors, its application in clinical settings remains formidable. This is in part due to the limitation of current analysis programs in discovering robust biomarkers and developing classifiers with a practical set of genes. Genetic programming (GP) is a type of machine learning technique that uses evolutionary algorithm to simulate natural selection as well as population dynamics, hence leading to simple and comprehensible classifiers. Here we applied GP to cancer expression profiling data to select feature genes and build molecular classifiers by mathematical integration of these genes. Analysis of thousands of GP classifiers generated for a prostate cancer data set revealed repetitive use of a set of highly discriminative feature genes, many of which are known to be disease associated. GP classifiers often comprise five or less genes and successfully predict cancer types and subtypes. More importantly, GP classifiers generated in one study are able to predict samples from an independent study, which may have used different microarray platforms. In addition, GP yielded classification accuracy better than or similar to conventional classification methods. Furthermore, the mathematical expression of GP classifiers provides insights into relationships between classifier genes. Taken together, our results demonstrate that GP may be valuable for generating effective classifiers containing a practical set of genes for diagnostic/ prognostic cancer classification

Mouse eye gene microarrays for investigating ocular development and disease

AbstractMicroarray technology can facilitate simultaneous expression analysis of thousands of genes and assist in delineating cellular pathways involved in development or disease pathogenesis. Since public databases and commercial cDNA microarrays have an under-representation of eye-expressed genes, we generated over 3000 expressed sequence tags from three unamplified mouse eye/retina cDNA libraries. These eye-expressed genes were used to produce cDNA microarrays. Methodology for printing of slides, hybridization, scanning and data analysis has been optimized. The I-gene microarrays will be useful for establishing expression profiles of the mouse eye/retina and provide a resource for defining molecular pathways involved in development, aging and disease

Exclusive Enteral Nutrition versus Infliximab in Inducing Therapy of Pediatric Crohn’s Disease

Aim. To compare the effectiveness of exclusive enteral nutrition (EEN) and infliximab (IFX) therapy in pediatric Crohn’s disease (CD). Methods. In a prospective study of children initiating EEN or infliximab therapy for CD, we compared clinical outcomes using the pediatric Crohn’s disease activity index (PCDAI), growth improvement, endoscopic mucosal healing, and adverse effects. Data were measured at baseline and after 8 weeks of therapy. Results. We enrolled 26 children with CD; of whom, 13 were treated with infliximab, 13 with EEN. Clinical response (PCDAI) reduction ≥ 15 or final PCDAI ≤ 10 was achieved by 83.3% in the EEN group and 90.9% in the IFX group. Body mass index for age (BMIFA) z-scores were significantly increased in both groups (P<0.05). No significant differences were observed in PCDAI, height for age (HFA), or BMI recovery between two groups. Adverse effects were detected in 30.7% on infliximab and 0% on EEN. Mucosal healing was achieved in 71.4% cases in the EEN group versus 85.7% in the IFX group. Conclusion. EEN provided similar improvements as IFX in clinical symptoms, mucosal healing, and BMI. EEN therapy has less adverse effects when compared with IFX. This trial is registered with the Clinical Registration Number: ChiCTR-OON-17010834

Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression

Multiple, complex molecular events characterize cancer development and progression(1,2). Deciphering the molecular networks that distinguish organ- confined disease from metastatic disease may lead to the identification of critical biomarkers for cancer invasion and disease aggressiveness. Although gene and protein expression have been extensively profiled in human tumours, little is known about the global metabolomic alterations that characterize neoplastic progression. Using a combination of high- throughput liquid- and- gas- chromatography- based mass spectrometry, we profiled more than 1,126 metabolites across 262 clinical samples related to prostate cancer ( 42 tissues and 110 each of urine and plasma). These unbiased metabolomic profiles were able to distinguish benign prostate, clinically localized prostate cancer and metastatic disease. Sarcosine, an N- methyl derivative of the amino acid glycine, was identified as a differential metabolite that was highly increased during prostate cancer progression to metastasis and can be detected non- invasively in urine. Sarcosine levels were also increased in invasive prostate cancer cell lines relative to benign prostate epithelial cells. Knockdown of glycine- N- methyl transferase, the enzyme that generates sarcosine from glycine, attenuated prostate cancer invasion. Addition of exogenous sarcosine or knockdown of the enzyme that leads to sarcosine degradation, sarcosine dehydrogenase, induced an invasive phenotype in benign prostate epithelial cells. Androgen receptor and the ERG gene fusion product coordinately regulate components of the sarcosine pathway. Here, by profiling the metabolomic alterations of prostate cancer progression, we reveal sarcosine as a potentially important metabolic intermediary of cancer cell invasion and aggressivity.Early Detection Research Network ; National Institutes of Health ; MTTC ; Clinical Translational Science Award ; Fund for Discovery of the University of Michigan Comprehensive Cancer Center ; University of Michigan Cancer Biostatistics Training Grant ; Doris Duke Charitable FoundationWe thank J. Granger for help in manuscript preparation, J. Siddiqui and R. Varambally for help with the clinical database, and A. Vellaichamy and S. Pullela for technical assistance. We thank K. Pienta for access to metastatic prostate cancer samples from the University of Michigan Prostate SPORE rapid autopsy programme. This work is supported in part by the Early Detection Research Network (A.M.C., J.T.W.), National Institutes of Health (A.S., S.P., J.B., T.M.R., D.G., G.S.O. and A.M.C.) and an MTTC grant (G.S.O. and A.S.). A.M.C. is supported by a Clinical Translational Science Award from the Burroughs Welcome Foundation. A. S. is supported by a grant from the Fund for Discovery of the University of Michigan Comprehensive Cancer Center. L. M. P. is supported by the University of Michigan Cancer Biostatistics Training Grant. A. M. C and S. P. are supported by the Doris Duke Charitable Foundation.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/62661/1/nature07762.pd

On the detection and refinement of transcription factor binding sites using ChIP-Seq data

Coupling chromatin immunoprecipitation (ChIP) with recently developed massively parallel sequencing technologies has enabled genome-wide detection of protein–DNA interactions with unprecedented sensitivity and specificity. This new technology, ChIP-Seq, presents opportunities for in-depth analysis of transcription regulation. In this study, we explore the value of using ChIP-Seq data to better detect and refine transcription factor binding sites (TFBS). We introduce a novel computational algorithm named Hybrid Motif Sampler (HMS), specifically designed for TFBS motif discovery in ChIP-Seq data. We propose a Bayesian model that incorporates sequencing depth information to aid motif identification. Our model also allows intra-motif dependency to describe more accurately the underlying motif pattern. Our algorithm combines stochastic sampling and deterministic ‘greedy’ search steps into a novel hybrid iterative scheme. This combination accelerates the computation process. Simulation studies demonstrate favorable performance of HMS compared to other existing methods. When applying HMS to real ChIP-Seq datasets, we find that (i) the accuracy of existing TFBS motif patterns can be significantly improved; and (ii) there is significant intra-motif dependency inside all the TFBS motifs we tested; modeling these dependencies further improves the accuracy of these TFBS motif patterns. These findings may offer new biological insights into the mechanisms of transcription factor regulation

Proteomic Interrogation of Androgen Action in Prostate Cancer Cells Reveals Roles of Aminoacyl tRNA Synthetases

Prostate cancer remains the most common malignancy among men in United States, and there is no remedy currently available for the advanced stage hormone-refractory cancer. This is partly due to the incomplete understanding of androgen-regulated proteins and their encoded functions. Whole-cell proteomes of androgen-starved and androgen-treated LNCaP cells were analyzed by semi-quantitative MudPIT ESI- ion trap MS/MS and quantitative iTRAQ MALDI- TOF MS/MS platforms, with identification of more than 1300 high-confidence proteins. An enrichment-based pathway mapping of the androgen-regulated proteomic data sets revealed a significant dysregulation of aminoacyl tRNA synthetases, indicating an increase in protein biosynthesis- a hallmark during prostate cancer progression. This observation is supported by immunoblot and transcript data from LNCaP cells, and prostate cancer tissue. Thus, data derived from multiple proteomics platforms and transcript data coupled with informatics analysis provides a deeper insight into the functional consequences of androgen action in prostate cancer