The phenotypical implications of immune dysregulation in fragile X syndrome.

BACKGROUND AND PURPOSE: Immune system dysfunction and inflammatory dysregulation have been shown in several animal models of fragile X syndrome (FXS). However, the phenotypical implications of this dysregulation have not been systematically evaluated in a large patient cohort. METHODS: Five thousand seven hundred thirty-six FXS patients from a nationwide health insurance database were identified and compared to 573 600 age- and sex-matched controls. The phenome-wide association studies codes of FXS patients and those without FXS were compared and the false discovery rate was controlled at 0.05 using the Benjamini-Hochberg procedure. RESULTS: In addition to the commonly reported comorbidities of FXS, an over-representation of infectious diseases, including otitis media, cellulitis and abscess of fingers or toes, viral enteritis, candidiasis and pneumonia, was discovered. In addition, there was an under-representation of autoimmune disorders in FXS patients. CONCLUSIONS: Our systematic comorbidity analyses identified immunologically-based phenotypes associated with FXS. Our findings align with previous observations of compromised immunity and phagocytic defects in animal models of FXS. These results suggest the importance of immune-related pathways in FXS patients and their relevance to the FMR1 gene.

Absence of evidence for increase in risk for autism or attention-deficit hyperactivity disorder following antidepressant exposure during pregnancy: a replication study.

Multiple studies have examined the risk of prenatal antidepressant exposure and risk for autism spectrum disorder (ASD) or attention-deficit hyperactivity disorder (ADHD), with inconsistent results. Precisely estimating such risk, if any, is of great importance in light of the need to balance such risk with the benefit of depression and anxiety treatment. We developed a method to integrate data from multiple New England health systems, matching offspring and maternal health data in electronic health records to characterize diagnoses and medication exposure. Children with ASD or ADHD were matched 1:3 with children without neurodevelopmental disorders. Association between maternal antidepressant exposure and ASD or ADHD liability was examined using logistic regression, adjusting for potential sociodemographic and psychiatric confounding variables. In new cohorts of 1245 ASD cases and 1701 ADHD cases, along with age-, sex- and socioeconomic status matched controls, neither disorder was significantly associated with prenatal antidepressant exposure in crude or adjusted models (adjusted odds ratio 0.90, 95% confidence interval 0.50-1.54 for ASD; 0.97, 95% confidence interval 0.53-1.69 for ADHD). Pre-pregnancy antidepressant exposure significantly increased risk for both disorders. These results suggest that prior reports of association between prenatal antidepressant exposure and neurodevelopmental disease are likely to represent a false-positive finding, which may arise in part through confounding by indication. They further demonstrate the potential to integrate data across electronic health records studies spanning multiple health systems to enable efficient pharmacovigilance investigation.

Prenatal antidepressant exposure is associated with risk for attention-deficit hyperactivity disorder but not autism spectrum disorder in a large health system.

Previous studies suggested that risk for Autism Spectrum Disorder (ASD) may be increased in children exposed to antidepressants during the prenatal period. The disease specificity of this risk has not been addressed and the possibility of confounding has not been excluded. Children with ASD or attention-deficit hyperactivity disorder (ADHD) delivered in a large New England health-care system were identified from electronic health records (EHR), and each diagnostic group was matched 1:3 with children without ASD or ADHD. All children were linked with maternal health data using birth certificates and EHRs to determine prenatal medication exposures. Multiple logistic regression was used to examine association between prenatal antidepressant exposures and ASD or ADHD risk. A total of 1377 children diagnosed with ASD and 2243 with ADHD were matched with healthy controls. In models adjusted for sociodemographic features, antidepressant exposure prior to and during pregnancy was associated with ASD risk, but risk associated with exposure during pregnancy was no longer significant after controlling for maternal major depression (odds ratio (OR) 1.10 (0.70-1.70)). Conversely, antidepressant exposure during but not prior to pregnancy was associated with ADHD risk, even after adjustment for maternal depression (OR 1.81 (1.22-2.70)). These results suggest that the risk of autism observed with prenatal antidepressant exposure is likely confounded by severity of maternal illness, but further indicate that such exposure may still be associated with ADHD risk. This risk, modest in absolute terms, may still be a result of residual confounding and must be balanced against the substantial consequences of untreated maternal depression.

Peripheral blood gene expression signature differentiates children with autism from unaffected siblings.

Autism spectrum disorder (ASD) is one of the most prevalent neurodevelopmental disorders with high heritability, yet a majority of genetic contribution to pathophysiology is not known. Siblings of individuals with ASD are at increased risk for ASD and autistic traits, but the genetic contribution for simplex families is estimated to be less when compared to multiplex families. To explore the genomic (dis-) similarity between proband and unaffected sibling in simplex families, we used genome-wide gene expression profiles of blood from 20 proband-unaffected sibling pairs and 18 unrelated control individuals. The global gene expression profiles of unaffected siblings were more similar to those from probands as they shared genetic and environmental background. A total of 189 genes were significantly differentially expressed between proband-sib pairs (nominal p < 0.01) after controlling for age, sex, and family effects. Probands and siblings were distinguished into two groups by cluster analysis with these genes. Overall, unaffected siblings were equally distant from the centroid of probands and from that of unrelated controls with the differentially expressed genes. Interestingly, five of 20 siblings had gene expression profiles that were more similar to unrelated controls than to their matched probands. In summary, we found a set of genes that distinguished probands from the unaffected siblings, and a subgroup of unaffected siblings who were more similar to probands. The pathways that characterized probands compared to siblings using peripheral blood gene expression profiles were the up-regulation of ribosomal, spliceosomal, and mitochondrial pathways, and the down-regulation of neuroreceptor-ligand, immune response and calcium signaling pathways. Further integrative study with structural genetic variations such as de novo mutations, rare variants, and copy number variations would clarify whether these transcriptomic changes are structural or environmental in origin.

Comparative RNA editing in autistic and neurotypical cerebella.

Adenosine-to-inosine (A-to-I) RNA editing is a neurodevelopmentally regulated epigenetic modification shown to modulate complex behavior in animals. Little is known about human A-to-I editing, but it is thought to constitute one of many molecular mechanisms connecting environmental stimuli and behavioral outputs. Thus, comprehensive exploration of A-to-I RNA editing in human brains may shed light on gene-environment interactions underlying complex behavior in health and disease. Synaptic function is a main target of A-to-I editing, which can selectively recode key amino acids in synaptic genes, directly altering synaptic strength and duration in response to environmental signals. Here, we performed a high-resolution survey of synaptic A-to-I RNA editing in a human population, and examined how it varies in autism, a neurodevelopmental disorder in which synaptic abnormalities are a common finding. Using ultra-deep (>1000 × ) sequencing, we quantified the levels of A-to-I editing of 10 synaptic genes in postmortem cerebella from 14 neurotypical and 11 autistic individuals. A high dynamic range of editing levels was detected across individuals and editing sites, from 99.6% to below detection limits. In most sites, the extreme ends of the population editing distributions were individuals with autism. Editing was correlated with isoform usage, clusters of correlated sites were identified, and differential editing patterns examined. Finally, a dysfunctional form of the editing enzyme adenosine deaminase acting on RNA B1 was found more commonly in postmortem cerebella from individuals with autism. These results provide a population-level, high-resolution view of A-to-I RNA editing in human cerebella and suggest that A-to-I editing of synaptic genes may be informative for assessing the epigenetic risk for autism.

Using electronic medical records to enable large-scale studies in psychiatry: treatment resistant depression as a model.

BACKGROUND: Electronic medical records (EMR) provide a unique opportunity for efficient, large-scale clinical investigation in psychiatry. However, such studies will require development of tools to define treatment outcome. METHOD: Natural language processing (NLP) was applied to classify notes from 127 504 patients with a billing diagnosis of major depressive disorder, drawn from out-patient psychiatry practices affiliated with multiple, large New England hospitals. Classifications were compared with results using billing data (ICD-9 codes) alone and to a clinical gold standard based on chart review by a panel of senior clinicians. These cross-sectional classifications were then used to define longitudinal treatment outcomes, which were compared with a clinician-rated gold standard. RESULTS: Models incorporating NLP were superior to those relying on billing data alone for classifying current mood state (area under receiver operating characteristic curve of 0.85-0.88 v. 0.54-0.55). When these cross-sectional visits were integrated to define longitudinal outcomes and incorporate treatment data, 15% of the cohort remitted with a single antidepressant treatment, while 13% were identified as failing to remit despite at least two antidepressant trials. Non-remitting patients were more likely to be non-Caucasian (p<0.001). CONCLUSIONS: The application of bioinformatics tools such as NLP should enable accurate and efficient determination of longitudinal outcomes, enabling existing EMR data to be applied to clinical research, including biomarker investigations. Continued development will be required to better address moderators of outcome such as adherence and co-morbidity.

DNA hypermethylation in lung cancer is targeted at differentiation-associated genes.

Aberrant DNA hypermethylation of tumor suppressor genes is thought to be an early event in tumorigenesis. Many studies have reported the methylation status of individual genes with known involvement in cancer, but an unbiased assessment of the biological function of the collective of hypermethylated genes has not been conducted so far. Based on the observation that a variety of human cancers recapitulate developmental gene expression patterns (that is activate genes normally expressed in early development and suppress late developmental genes), we hypothesized that the silencing of differentiation-associated genes in cancer could be attributed in part to DNA hypermethylation. To this end, we investigated the developmental expression patterns of genes with hypermethylated CpG islands in primary human lung carcinomas and lung cancer cell lines. We found that DNA hypermethylation primarily affects genes that are expressed in late stages of murine lung development. Gene ontology characterization of these genes shows that they are almost exclusively involved in morphogenetic differentiation processes. Our results indicate that DNA hypermethylation in cancer functions as a selective silencing mechanism of genes that are required for the maintenance of a differentiated state. The process of cellular de-differentiation that is evident on both the microscopic and transcriptional level in cancer might at least partly be mediated by these epigenetic events. Our observations provide a mechanistic explanation for induction of differentiation upon treatment with DNA methyltransferase inhibitors.

Identification of autoimmune gene signatures in autism.

The role of the immune system in neuropsychiatric diseases, including autism spectrum disorder (ASD), has long been hypothesized. This hypothesis has mainly been supported by family cohort studies and the immunological abnormalities found in ASD patients, but had limited findings in genetic association testing. Two cross-disorder genetic association tests were performed on the genome-wide data sets of ASD and six autoimmune disorders. In the polygenic score test, we examined whether ASD risk alleles with low effect sizes work collectively in specific autoimmune disorders and show significant association statistics. In the genetic variation score test, we tested whether allele-specific associations between ASD and autoimmune disorders can be found using nominally significant single-nucleotide polymorphisms. In both tests, we found that ASD is probabilistically linked to ankylosing spondylitis (AS) and multiple sclerosis (MS). Association coefficients showed that ASD and AS were positively associated, meaning that autism susceptibility alleles may have a similar collective effect in AS. The association coefficients were negative between ASD and MS. Significant associations between ASD and two autoimmune disorders were identified. This genetic association supports the idea that specific immunological abnormalities may underlie the etiology of autism, at least in a number of cases.

Comparative analysis of neurological disorders focuses genome-wide search for autism genes.

The behaviors of autism overlap with a diverse array of other neurological disorders, suggesting common molecular mechanisms. We conducted a large comparative analysis of the network of genes linked to autism with those of 432 other neurological diseases to circumscribe a multi-disorder subcomponent of autism. We leveraged the biological process and interaction properties of these multi-disorder autism genes to overcome the across-the-board multiple hypothesis corrections that a purely data-driven approach requires. Using prior knowledge of biological process, we identified 154 genes not previously linked to autism of which 42% were significantly differentially expressed in autistic individuals. Then, using prior knowledge from interaction networks of disorders related to autism, we uncovered 334 new genes that interact with published autism genes, of which 87% were significantly differentially regulated in autistic individuals. Our analysis provided a novel picture of autism from the perspective of related neurological disorders and suggested a model by which prior knowledge of interaction networks can inform and focus genome-scale studies of complex neurological disorders.

Side population cells isolated from different tissues share transcriptome signatures and express tissue-specific markers.

Side Population (SP) cells, isolated from murine adult bone marrow (BM) based on the exclusion of the DNA dye Hoechst 33342, exhibit potent hematopoietic stem cell (HSC) activity when compared to Main Population (MP) cells. Furthermore, SP cells derived from murine skeletal muscle exhibit both hematopoietic and myogenic potential in vivo. The multipotential capacity of SP cells isolated from variable tissues is supported by an increasing number of studies. To investigate whether the SP phenotype is associated with a unique transcriptional profile, we characterized gene expression of SP cells isolated from two biologically distinct tissues, bone marrow and muscle. Comparison of SP cells with differentiated MP cells within a tissue revealed that SP cells are in an active transcriptional and translational status and underexpress genes reflecting tissue-specific functions. Direct comparison of gene expression of SP cells isolated from different tissues identified genes common to SP cells as well as genes specific to SP cells within a particular tissue and further define a muscle and bone marrow environment. This study reports gene expression of muscle SP cells, common features and differences between SP cells isolated from muscle and bone marrow, and further identifies common signaling pathways that might regulate SP cell functions.

SNPper: retrieval and analysis of human SNPs.

MOTIVATION: Single Nucleotide Polymorphisms (SNPs) are an increasingly important tool for the study of the human genome. SNPs can be used as markers to create high-density genetic maps, as causal candidates for diseases, or to reconstruct the history of our genome. SNP-based studies rely on the availability of large numbers of validated, high-frequency SNPs whose position on the chromosomes is known with precision. Although large collections of SNPs exist in public databases, researchers need tools to effectively retrieve and manipulate them. RESULTS: We describe the implementation and usage of SNPper, a web-based application to automate the tasks of extracting SNPs from public databases, analyzing them and exporting them in formats suitable for subsequent use. Our application is oriented toward the needs of candidate-gene, whole-genome and fine-mapping studies, and provides several flexible ways to present and export the data. The application has been publicly available for over a year, and has received positive user feedback and high usage levels.

CHIP TUNER: a web tool for evidence-based noise reduction in gene discovery.

The potential for gene discovery, fueled by DNA microchip technology and the sequencing of hundreds of genomes, is unprecedented. In this context, trying to discover genes that are actually of significance rather than merely appearing so due to noise is of utmost importance. We present a web application, CHIP TUNER, which assists in this gene discovery process. Our system uses evidence-based noise reduction to help delineate candidate target genes of biological importance. Specifically, CHIP TUNER learns from redundant experiments an "identity mask" that defines a region of noise inherent to biological sampling and DNA microarray processing; it then takes this into account during actual sample comparisons. The goal of CHIP TUNER is to improve the chances that newly discovered "important" genes are actually of importance before large amounts of time and resources are invested.

Molecular profiles of inflammatory myopathies.

OBJECTIVE: To describe the use of large-scale gene expression profiles to distinguish broad categories of myopathy and subtypes of inflammatory myopathies (IM) and to provide insight into the pathogenesis of inclusion body myositis (IBM), polymyositis, and dermatomyositis. METHODS: Using Affymetrix GeneChip microarrays, the authors measured the simultaneous expression of approximately 10,000 genes in muscle specimens from 45 patients in four major disease categories (dystrophy, congenital myopathy, inflammatory myopathy, and normal). The authors separately analyzed gene expression in 14 patients limited to the three major subtypes of IM. Bioinformatics techniques were used to classify specimens with similar expression profiles based on global patterns of gene expression and to identify genes with significant differential gene expression compared with normal. RESULTS: Ten of 11 patients with IM, all normals and nemaline myopathies, and 10 of 12 patients with Duchenne muscular dystrophy were correctly classified by this approach. The various subtypes of inflammatory myopathies have distinct gene expression signatures. Specific sets of immune-related genes allow for molecular classification of patients with IBM, polymyositis, and dermatomyositis. Analysis of differential gene expression identifies as relevant to disease pathogenesis previously reported cytokines, major histocompatibility complex class I and II molecules, granzymes, and adhesion molecules, as well as newly identified members of these categories. Increased expression of actin cytoskeleton genes is also identified. CONCLUSIONS: The molecular profiles of muscle tissue in patients with inflammatory myopathies are distinct and represent molecular signatures from which diagnostic insight may follow. Large numbers of differentially expressed genes are rapidly identified.

Corticotropin releasing hormone increases apparent potency of adrenocorticotropic hormone stimulation of cortisol secretion.

HYPOTHESIS: Corticotropin releasing hormone (CRH) has a regulatory effect on cortisol secretion in addition to its classic effect of stimulating adrenocorticotropic hormone (ACTH) secretion. REVIEW: There is growing evidence of "long-loop" and paracrine adrenal stimulation by CRH. Data from a study of the ovine-corticotropin releasing hormone (oCRH) stimulation test in 13 sexually abused girls and 13 normal controls was used in Montecarlo simulations of the hypothalamic-pituitary-adrenal axis, to get estimates of adrenal sensitivity to ACTH and cortisol elimination kinetics before and after oCRH administration. In both controls and sexually abused girls, ACTH had an apparent greater effect on cortisol secretion after administration of oCRH compared to its effect during the baseline period. This lends support to the hypothesis and suggests that it should be tested experimentally.

Optimal data entry by patients: effects of interface structure and design.

UNLABELLED: Direct electronic acquisition of data from patients possesses accuracy and diagnostic value. The mechanics of how best to capture historical information from patients using electronic interfaces are not well studied. We undertook an iterative usability experiment to answer 2 questions: 1) How can maximal electronic data input from a patient be achieved, and 2) Do varying structures for data entry promote differential documentation of specified data elements? METHODS: A series of four trials comprised the testing cycle. Unstructured text entry, directed text entry, and closed ended questions were tested in combination against outcomes of word count, time to task completion, and user preferences. Covariates of interest included participants' technologic experience and ergonomic experience with keyboards, as well as self-report of educational status, literacy, and primary language. RESULTS: Participants clearly preferred the order of initial closed-ended questions followed by unstructured text entry, and this ordering was not associated with decrements in word count or increase in time. When compared to unstructured text entry, directed text entry provided higher documentation of data for past medical history and questions which parents wished to discuss with the clinician. A closed-end question structure, when compared to directed text entry, provided higher capture of parents' questions for discussion. CONCLUSIONS: Optimal design of an electronic interview for the capture of medical histories will benefit from a mixed structure of directed text entry and closed-ended questions. For historical or clinically relevant items where maximal capture of data is desired, a structure with closed-ended questions would be preferred.

The personal internetworked notary and guardian.

In this paper, we propose a secure, distributed and scaleable infrastructure for a lifelong personal medical record system. We leverage on existing and widely available technologies, like the Web and public-key cryptography, to define an architecture that allows patients to exercise full control over their medical data. This is done without compromising patients' privacy and the ability of other interested parties (e.g. physicians, health-care institutions, public-health researchers) to access the data when appropriately authorized. The system organizes the information as a tree of encrypted plain-text XML files, in order to ensure platform independence and durability, and uses a role-based authorization scheme to assign access privileges. In addition to the basic architecture, we describe tools to populate the patient's record with data from hospital databases and the first testbed applications we are deploying.

The ever imminent electronic medical record.

The electronic medical record (EMR) remains an elusive holy grail. The reasons include limited electronic and voice recognition capabilities, as well as established medical practice patterns. There is also some question as to the time and cost efficacy of inputs into an EMR.

Extracting knowledge from dynamics in gene expression.

Most investigations of coordinated gene expression have focused on identifying correlated expression patterns between genes by examining their normalized static expression levels. In this study, we focus on the dynamics of gene expression by seeking to identify correlated patterns of changes in genetic expression level. In doing so, we build upon methods developed in clinical informatics to detect temporal trends of laboratory and other clinical data. We construct relevance networks from Saccharomyces cerevisiae gene-expression dynamics data and find genes with related functional annotations grouped together. While some of these associations are also found using a standard expression level analysis, many are identified exclusively through the dynamic analysis. These results strongly suggest that the analysis of gene expression dynamics is a necessary and important tool for studying regulatory and other functional relationships among genes. The source code developed for this investigation is freely available to all non-commercial investigators by contacting the authors.

Unsupervised learning from complex data: the matrix incision tree algorithm.

Analysis of large-scale gene expression data requires novel methods for knowledge discovery and predictive model building as well as clustering. Organizing data into meaningful structures is one of the most fundamental modes of learning. DNA microarray data set can be viewed as a set of mutually associated genes in a high-dimensional space. This paper describes a novel method to organize a complex high-dimensional space into successive lower-dimensional spaces based on the geometric properties of the data structure in the absence of a priori knowledge. The matrix incision tree algorithm reveals the hierarchical structural organization of observed data by determining the successive hyperplanes that 'optimally' separate the data hyperspace. The algorithm was tested against published data sets yielding promising results.

On reporting fold differences.

As we enter an age in which genomics and bioinformatics make possible the discovery of new knowledge about the biological characteristics of an organism, it is critical that we attempt to report newly discovered "significant" phenotypes only when they are actually of significance. With the relative youth of genome-scale gene expression technologies, how to make such distinctions has yet to be better defined. We present a "mask technology" by which to filter out those levels of gene expression that fall within the noise of the experimental techniques being employed. Conversely, our technique can lend validation to significant fold differences in expression level even when the fold value may appear quite small (e.g. 1.3). Given array-organized expression level results from a pair of identical experiments, our ID Mask Tool enables the automated creation of a two-dimensional "region of insignificance" that can then be used with subsequent data analyses. Fundamentally, this should enable researchers to report on findings that are more likely to be in nature truly meaningful. Moreover, this can prevent major investments of time, energy, and biological resources into the pursuit of candidate genes that represent false positives.