Nathan Palmer

Comprehensive Dissection of PDGF-PDGFR Signaling Pathways in PDGFR Genetically Defined Cells

Despite the growing understanding of PDGF signaling, studies of PDGF function have encountered two major obstacles: the functional redundancy of PDGFRα and PDGFRβ in vitro and their distinct roles in vivo. Here we used wild-type mouse embryonic fibroblasts (MEF), MEF null for either PDGFRα, β, or both to dissect PDGF-PDGFR signaling pathways. These four PDGFR genetically defined cells provided us a platform to study the relative contributions of the pathways triggered by the two PDGF receptors. They were treated with PDGF-BB and analyzed for differential gene expression, in vitro proliferation and differential response to pharmacological effects. No genes were differentially expressed in the double null cells, suggesting minimal receptor-independent signaling. Protean differentiation and proliferation pathways are commonly regulated by PDGFRα, PDGFRβ and PDGFRα/β while each receptor is also responsible for regulating unique signaling pathways. Furthermore, some signaling is solely modulated through heterodimeric PDGFRα/β.

Making sense out of massive data by going beyond differential expression.

With the rapid growth of publicly available high-throughput transcriptomic data, there is increasing recognition that large sets of such data can be mined to better understand disease states and mechanisms. Prior gene expression analyses, both large and small, have been dichotomous in nature, in which phenotypes are compared using clearly defined controls. Such approaches may require arbitrary decisions about what are considered “normal” phenotypes, and what each phenotype should be compared to. Instead, we adopt a holistic approach in which we characterize phenotypes in the context of a myriad of tissues and diseases. We introduce scalable methods that associate expression patterns to phenotypes in order both to assign phenotype labels to new expression samples and to select phenotypically meaningful gene signatures. By using a nonparametric statistical approach, we identify signatures that are more precise than those from existing approaches and accurately reveal biological processes that are hidden in case vs. control studies. Employing a comprehensive perspective on expression, we show how metastasized tumor samples localize in the vicinity of the primary site counterparts and are overenriched for those phenotype labels. We find that our approach provides insights into the biological processes that underlie differences between tissues and diseases beyond those identified by traditional differential expression analyses. Finally, we provide an online resource (http://concordia.csail.mit.edu) for mapping users’ gene expression samples onto the expression landscape of tissue and disease.

A gene expression profile of stem cell pluripotentiality and differentiation is conserved across diverse solid and hematopoietic cancers

BackgroundUnderstanding the fundamental mechanisms of tumorigenesis remains one of the most pressing problems in modern biology. To this end, stem-like cells with tumor-initiating potential have become a central focus in cancer research. While the cancer stem cell hypothesis presents a compelling model of self-renewal and partial differentiation, the relationship between tumor cells and normal stem cells remains unclear.ResultsWe identify, in an unbiased fashion, mRNA transcription patterns associated with pluripotent stem cells. Using this profile, we derive a quantitative measure of stem cell-like gene expression activity. We show how this 189 gene signature stratifies a variety of stem cell, malignant and normal tissue samples by their relative plasticity and state of differentiation within Concordia, a diverse gene expression database consisting of 3,209 Affymetrix HGU133+ 2.0 microarray assays. Further, the orthologous murine signature correctly orders a time course of differentiating embryonic mouse stem cells. Finally, we demonstrate how this stem-like signature serves as a proxy for tumor grade in a variety of solid tumors, including brain, breast, lung and colon.ConclusionsThis core stemness gene expression signature represents a quantitative measure of stem cell-associated transcriptional activity. Broadly, the intensity of this signature correlates to the relative level of plasticity and differentiation across all of the human tissues analyzed. The fact that the intensity of this signature is also capable of differentiating histological grade for a variety of human malignancies suggests potential therapeutic and diagnostic implications.

Fold recognition and accurate sequence–structure alignment of sequences directing β-sheet proteins

The ability to predict structure from sequence is particularly important for toxins, virulence factors, allergens, cytokines, and other proteins of public health importance. Many such functions are represented in the parallel β‐helix and β‐trefoil families. A method using pairwise β‐strand interaction probabilities coupled with evolutionary information represented by sequence profiles is developed to tackle these problems for the β‐helix and β‐trefoil folds. The algorithm BetaWrapPro employs a “wrapping” component that may capture folding processes with an initiation stage followed by processive interaction of the sequence with the already‐formed motifs. BetaWrapPro outperforms all previous motif recognition programs for these folds, recognizing the β‐helix with 100% sensitivity and 99.7% specificity and the β‐trefoil with 100% sensitivity and 92.5% specificity, in crossvalidation on a database of all nonredundant known positive and negative examples of these fold classes in the PDB. It additionally aligns 88% of residues for the β‐helices and 86% for the β‐trefoils accurately (within four residues of the exact positon) to the structural template, which is then used with the side‐chain packing program SCWRL to produce 3D structure predictions. One striking result has been the prediction of an unexpected parallel β‐helix structure for a pollen allergen, and its recent confirmation through solution of its structure. A Web server running BetaWrapPro is available and outputs putative PDB‐style coordinates for sequences predicted to form the target folds. Proteins 2006.

iPSC-derived neurons as a higher-throughput readout for autism: Promises and pitfalls

The elucidation of disease etiologies and establishment of robust, scalable, high-throughput screening assays for autism spectrum disorders (ASDs) have been impeded by both inaccessibility of disease-relevant neuronal tissue and the genetic heterogeneity of the disorder. Neuronal cells derived from induced pluripotent stem cells (iPSCs) from autism patients may circumvent these obstacles and serve as relevant cell models. To date, derived cells are characterized and screened by assessing their neuronal phenotypes. These characterizations are often etiology-specific or lack reproducibility and stability. In this review, we present an overview of efforts to study iPSC-derived neurons as a model for autism, and we explore the plausibility of gene expression profiling as a reproducible and stable disease marker.

A practical platform for blood biomarker study by using global gene expression profiling of peripheral whole blood

Background Although microarray technology has become the most common method for studying global gene expression, a plethora of technical factors across the experiment contribute to the variable of genome gene expression profiling using peripheral whole blood. A practical platform needs to be established in order to obtain reliable and reproducible data to meet clinical requirements for biomarker study. Methods and Findings We applied peripheral whole blood samples with globin reduction and performed genome-wide transcriptome analysis using Illumina BeadChips. Real-time PCR was subsequently used to evaluate the quality of array data and elucidate the mode in which hemoglobin interferes in gene expression profiling. We demonstrated that, when applied in the context of standard microarray processing procedures, globin reduction results in a consistent and significant increase in the quality of beadarray data. When compared to their pre-globin reduction counterparts, post-globin reduction samples show improved detection statistics, lowered variance and increased sensitivity. More importantly, gender gene separation is remarkably clearer in post-globin reduction samples than in pre-globin reduction samples. Our study suggests that the poor data obtained from pre-globin reduction samples is the result of the high concentration of hemoglobin derived from red blood cells either interfering with target mRNA binding or giving the pseudo binding background signal. Conclusion We therefore recommend the combination of performing globin mRNA reduction in peripheral whole blood samples and hybridizing on Illumina BeadChips as the practical approach for biomarker study.

Active learning for sampling in time-series experiments with application to gene expression analysis

Many time-series experiments seek to estimate some signal as a continuous function of time. In this paper, we address the sampling problem for such experiments: determining which time-points ought to be sampled in order to minimize the cost of data collection. We restrict our attention to a growing class of experiments which measure multiple signals at each time-point and where raw materials/observations are archived initially, and selectively analyzed later, this analysis being the more expensive step. We present an active learning algorithm for iteratively choosing time-points to sample, using the uncertainty in the quality of the currently estimated time-dependent curve as the objective function. Using simulated data as well as gene expression data, we show that our algorithm performs well, and can significantly reduce experimental cost without loss of information.

Prevalence of Inflammatory Bowel Disease among Patients with Autism Spectrum Disorders

Background:The objective of this study was to measure the prevalence of inflammatory bowel disease (IBD) among patients with autism spectrum disorders (ASD), which has not been well described previously. Methods:The rates of IBD among patients with and without ASD were measured in 4 study populations with distinct modes of ascertainment: a health care benefits company, 2 pediatric tertiary care centers, and a national ASD repository. The rates of IBD (established through International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9-CM] codes) were compared with respective controls and combined using a Stouffer meta-analysis. Clinical charts were also reviewed for IBD among patients with ICD-9-CM codes for both IBD and ASD at one of the pediatric tertiary care centers. This expert-verified rate was compared with the rate in the repository study population (where IBD diagnoses were established by expert review) and in nationally reported rates for pediatric IBD. Results:In all of case–control study populations, the rates of IBD-related ICD-9-CM codes for patients with ASD were significantly higher than that of their respective controls (Stouffer meta-analysis, P < 0.001). Expert-verified rates of IBD among patients with ASD were 7 of 2728 patients in one study population and 16 of 7201 in a second study population. The age-adjusted prevalence of IBD among patients with ASD was higher than their respective controls and nationally reported rates of pediatric IBD. Conclusions:Across each population with different kinds of ascertainment, there was a consistent and statistically significant increased prevalance of IBD in patients with ASD than their respective controls and nationally reported rates for pediatric IBD.

Postsurgical prescriptions for opioid naive patients and association with overdose and misuse: retrospective cohort study

Abstract Objective To quantify the effects of varying opioid prescribing patterns after surgery on dependence, overdose, or abuse in an opioid naive population. Design Retrospective cohort study. Setting Surgical claims from a linked medical and pharmacy administrative database of 37 651 619 commercially insured patients between 2008 and 2016. Participants 1 015 116 opioid naive patients undergoing surgery. Main outcome measures Use of oral opioids after discharge as defined by refills and total dosage and duration of use. The primary outcome was a composite of misuse identified by a diagnostic code for opioid dependence, abuse, or overdose. Results 568 612 (56.0%) patients received postoperative opioids, and a code for abuse was identified for 5906 patients (0.6%, 183 per 100 000 person years). Total duration of opioid use was the strongest predictor of misuse, with each refill and additional week of opioid use associated with an adjusted increase in the rate of misuse of 44.0% (95% confidence interval 40.8% to 47.2%, P<0.001), and 19.9% increase in hazard (18.5% to 21.4%, P<0.001), respectively. Conclusions Each refill and week of opioid prescription is associated with a large increase in opioid misuse among opioid naive patients. The data from this study suggest that duration of the prescription rather than dosage is more strongly associated with ultimate misuse in the early postsurgical period. The analysis quantifies the association of prescribing choices on opioid misuse and identifies levers for possible impact.

Integrative analysis of genetic data sets reveals a shared innate immune component in autism spectrum disorder and its co-morbidities

BackgroundAutism spectrum disorder (ASD) is a common neurodevelopmental disorder that tends to co-occur with other diseases, including asthma, inflammatory bowel disease, infections, cerebral palsy, dilated cardiomyopathy, muscular dystrophy, and schizophrenia. However, the molecular basis of this co-occurrence, and whether it is due to a shared component that influences both pathophysiology and environmental triggering of illness, has not been elucidated. To address this, we deploy a three-tiered transcriptomic meta-analysis that functions at the gene, pathway, and disease levels across ASD and its co-morbidities.ResultsOur analysis reveals a novel shared innate immune component between ASD and all but three of its co-morbidities that were examined. In particular, we find that the Toll-like receptor signaling and the chemokine signaling pathways, which are key pathways in the innate immune response, have the highest shared statistical significance. Moreover, the disease genes that overlap these two innate immunity pathways can be used to classify the cases of ASD and its co-morbidities vs. controls with at least 70 % accuracy.ConclusionsThis finding suggests that a neuropsychiatric condition and the majority of its non-brain-related co-morbidities share a dysregulated signal that serves as not only a common genetic basis for the diseases but also as a link to environmental triggers. It also raises the possibility that treatment and/or prophylaxis used for disorders of innate immunity may be successfully used for ASD patients with immune-related phenotypes.