Genome-wide association identifies novel ROP risk loci in a multiethnic cohort.

We conducted a genome-wide association study (GWAS) in a multiethnic cohort of 920 at-risk infants for retinopathy of prematurity (ROP), a major cause of childhood blindness, identifying 1 locus at genome-wide significance level (p < 5×10-8) and 9 with significance of p < 5×10-6 for ROP ≥ stage 3. The most significant locus, rs2058019, reached genome-wide significance within the full multiethnic cohort (p = 4.96×10-9); Hispanic and European Ancestry infants driving the association. The lead single nucleotide polymorphism (SNP) falls in an intronic region within the Glioma-associated oncogene family zinc finger 3 (GLI3) gene. Relevance for GLI3 and other top-associated genes to human ocular disease was substantiated through in-silico extension analyses, genetic risk score analysis and expression profiling in human donor eye tissues. Thus, we identify a novel locus at GLI3 with relevance to retinal biology, supporting genetic susceptibilities for ROP risk with possible variability by race and ethnicity.

Genome-wide association identifies novel ROP risk loci in a multi-ethnic cohort.

We conducted a genome-wide association study (GWAS) in a multiethnic cohort of 920 at-risk infants for retinopathy of prematurity (ROP), a major cause of childhood blindness, identifying 2 loci at genome-wide significance level (p<5×10-8) and 7 at suggestive significance (p<5×10-6) for ROP ≥ stage 3. The most significant locus, rs2058019, reached genome-wide significance within the full multiethnic cohort (p=4.96×10-9); Hispanic and Caucasian infants driving the association. The lead single nucleotide polymorphism (SNP) falls in an intronic region within the Glioma-associated oncogene family zinc finger 3 (GLI3) gene. Relevance for GLI3 and other top-associated genes to human ocular disease was substantiated through in-silico extension analyses, genetic risk score analysis and expression profiling in human donor eye tissues. Thus, we report the largest ROP GWAS to date, identifying a novel locus at GLI3 with relevance to retinal biology supporting genetic susceptibilities for ROP risk with possible variability by race and ethnicity.

Single-Examination Risk Prediction of Severe Retinopathy of Prematurity.

BACKGROUND AND OBJECTIVES: Retinopathy of prematurity (ROP) is a leading cause of childhood blindness. Screening and treatment reduces this risk, but requires multiple examinations of infants, most of whom will not develop severe disease. Previous work has suggested that artificial intelligence may be able to detect incident severe disease (treatment-requiring retinopathy of prematurity [TR-ROP]) before clinical diagnosis. We aimed to build a risk model that combined artificial intelligence with clinical demographics to reduce the number of examinations without missing cases of TR-ROP. METHODS: Infants undergoing routine ROP screening examinations (1579 total eyes, 190 with TR-ROP) were recruited from 8 North American study centers. A vascular severity score (VSS) was derived from retinal fundus images obtained at 32 to 33 weeks' postmenstrual age. Seven ElasticNet logistic regression models were trained on all combinations of birth weight, gestational age, and VSS. The area under the precision-recall curve was used to identify the highest-performing model. RESULTS: The gestational age + VSS model had the highest performance (mean ± SD area under the precision-recall curve: 0.35 ± 0.11). On 2 different test data sets (n = 444 and n = 132), sensitivity was 100% (positive predictive value: 28.1% and 22.6%) and specificity was 48.9% and 80.8% (negative predictive value: 100.0%). CONCLUSIONS: Using a single examination, this model identified all infants who developed TR-ROP, on average, >1 month before diagnosis with moderate to high specificity. This approach could lead to earlier identification of incident severe ROP, reducing late diagnosis and treatment while simultaneously reducing the number of ROP examinations and unnecessary physiologic stress for low-risk infants.

Impact of novel systemic therapies on the first-year costs of care for melanoma among Medicare beneficiaries.

BACKGROUND: Since 2011, the therapeutic landscape of melanoma has changed dramatically because of the adoption of immune checkpoint inhibitor and targeted therapies. The authors sought to quantify the effects of these changes on short-term treatment costs by comparing the first-year cancer-attributable costs in novel (2011-2015) and historical (2004-2010) treatment eras. METHODS: The authors estimated the first-year cancer-attributable and out-of-pocket (OOP) costs by cancer stage at diagnosis by using a case-control approach. Patients aged ≥67 years with melanoma results were used to calculate the total direct costs of treatment during the first year after the diagnosis of melanoma in the US Medicare population older than 65 years. Costs were reported in 2018 dollars. RESULTS: Costs increased with the stage at diagnosis. Average first-year cancer-attributable costs per patient for stage IV patients increased significantly by 61.7% from $45,952 to $74,297 after the adoption of novel treatments. Per-patient OOP responsibility decreased by almost 30.8% across all stages of cancer but increased by 16.5% for stage IV patients from 2004 ($7646) to 2015 ($8911). The total direct cost of treatment for persons with melanoma older than 65 years increased by $16.03 million (4.93%) from $324.68 million in 2010 to $340.71 million in 2015. The largest increase in yearly total cost, $23.64 million (56.53%), was observed among stage IV patients. CONCLUSIONS: The direct cost of melanoma increased significantly in the Medicare population, particularly for advanced-stage disease. Prevention and early detection initiatives may reduce the economic burden of melanoma.

Identification of candidate genes and pathways in retinopathy of prematurity by whole exome sequencing of preterm infants enriched in phenotypic extremes.

Retinopathy of prematurity (ROP) is a vasoproliferative retinal disease affecting premature infants. In addition to prematurity itself and oxygen treatment, genetic factors have been suggested to predispose to ROP. We aimed to identify potentially pathogenic genes and biological pathways associated with ROP by analyzing variants from whole exome sequencing (WES) data of premature infants. As part of a multicenter ROP cohort study, 100 non-Hispanic Caucasian preterm infants enriched in phenotypic extremes were subjected to WES. Gene-based testing was done on coding nonsynonymous variants. Genes showing enrichment of qualifying variants in severe ROP compared to mild or no ROP from gene-based tests with adjustment for gestational age and birth weight were selected for gene set enrichment analysis (GSEA). Mean BW of included infants with pre-plus, type-1 or type 2 ROP including aggressive posterior ROP (n = 58) and mild or no ROP (n = 42) were 744 g and 995 g, respectively. No single genes reached genome-wide significance that could account for a severe phenotype. GSEA identified two significantly associated pathways (smooth endoplasmic reticulum and vitamin C metabolism) after correction for multiple tests. WES of premature infants revealed potential pathways that may be important in the pathogenesis of ROP and in further genetic studies.

Deep Learning for the Diagnosis of Stage in Retinopathy of Prematurity: Accuracy and Generalizability across Populations and Cameras.

PURPOSE: Stage is an important feature to identify in retinal images of infants at risk of retinopathy of prematurity (ROP). The purpose of this study was to implement a convolutional neural network (CNN) for binary detection of stages 1, 2, and 3 in ROP and to evaluate its generalizability across different populations and camera systems. DESIGN: Diagnostic validation study of CNN for stage detection. PARTICIPANTS: Retinal fundus images obtained from preterm infants during routine ROP screenings. METHODS: Two datasets were used: 5943 fundus images obtained by RetCam camera (Natus Medical, Pleasanton, CA) from 9 North American institutions and 5049 images obtained by 3nethra camera (Forus Health Incorporated, Bengaluru, India) from 4 hospitals in Nepal. Images were labeled based on the presence of stage by 1 to 3 expert graders. Three CNN models were trained using 5-fold cross-validation on datasets from North America alone, Nepal alone, and a combined dataset and were evaluated on 2 held-out test sets consisting of 708 and 247 images from the Nepali and North American datasets, respectively. MAIN OUTCOME MEASURES: Convolutional neural network performance was evaluated using area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), sensitivity, and specificity. RESULTS: Both the North American- and Nepali-trained models demonstrated high performance on a test set from the same population: AUROC, 0.99; AUPRC, 0.98; sensitivity, 94%; and AUROC, 0.97; AUPRC, 0.91; and sensitivity, 73%; respectively. However, the performance of each model decreased to AUROC of 0.96 and AUPRC of 0.88 (sensitivity, 52%) and AUROC of 0.62 and AUPRC of 0.36 (sensitivity, 44%) when evaluated on a test set from the other population. Compared with the models trained on individual datasets, the model trained on a combined dataset achieved improved performance on each respective test set: sensitivity improved from 94% to 98% on the North American test set and from 73% to 82% on the Nepali test set. CONCLUSIONS: A CNN can identify accurately the presence of ROP stage in retinal images, but performance depends on the similarity between training and testing populations. We demonstrated that internal and external performance can be improved by increasing the heterogeneity of the training dataset features of the training dataset, in this case by combining images from different populations and cameras.

Quantification of Early Neonatal Oxygen Exposure as a Risk Factor for Retinopathy of Prematurity Requiring Treatment.

Purpose: Retinopathy of prematurity (ROP) is a leading cause of childhood blindness related to oxygen exposure in premature infants. Since oxygen monitoring protocols have reduced the incidence of treatment-requiring ROP (TR-ROP), it remains unclear whether oxygen exposure remains a relevant risk factor for incident TR-ROP and aggressive ROP (A-ROP), a severe, rapidly progressing form of ROP. The purpose of this proof-of-concept study was to use electronic health record (EHR) data to evaluate early oxygen exposure as a predictive variable for developing TR-ROP and A-ROP. Design: Retrospective cohort study. Participants: Two hundred forty-four infants screened for ROP at a single academic center. Methods: For each infant, oxygen saturations and fraction of inspired oxygen (FiO2) were extracted manually from the EHR until 31 weeks postmenstrual age (PMA). Cumulative minimum, maximum, and mean oxygen saturation and FiO2 were calculated on a weekly basis. Random forest models were trained with 5-fold cross-validation using gestational age (GA) and cumulative minimum FiO2 at 30 weeks PMA to identify infants who developed TR-ROP. Secondary receiver operating characteristic (ROC) curve analysis of infants with or without A-ROP was performed without cross-validation because of small numbers. Main Outcome Measures: For each model, cross-validation performance for incident TR-ROP was assessed using area under the ROC curve (AUC) and area under the precision-recall curve (AUPRC) scores. For A-ROP, we calculated AUC and evaluated sensitivity and specificity at a high-sensitivity operating point. Results: Of the 244 infants included, 33 developed TR-ROP, of which 5 developed A-ROP. For incident TR-ROP, random forest models trained on GA plus cumulative minimum FiO2 (AUC = 0.93 ± 0.06; AUPRC = 0.76 ± 0.08) were not significantly better than models trained on GA alone (AUC = 0.92 ± 0.06 [P = 0.59]; AUPRC = 0.74 ± 0.12 [P = 0.32]). Models using oxygen alone showed an AUC of 0.80 ± 0.09. ROC analysis for A-ROP found an AUC of 0.92 (95% confidence interval, 0.87-0.96). Conclusions: Oxygen exposure can be extracted from the EHR and quantified as a risk factor for incident TR-ROP and A-ROP. Extracting quantifiable clinical features from the EHR may be useful for building risk models for multiple diseases and evaluating the complex relationships among oxygen exposure, ROP, and other sequelae of prematurity.

Strategizing Screening for Melanoma in an Era of Novel Treatments: A Model-Based Approach.

BACKGROUND: Benefit-harm tradeoffs of melanoma screening depend on disease risk and treatment efficacy. We developed a model to project outcomes of screening for melanoma in populations with different risks under historic and novel systemic treatments. METHODS: Computer simulation model of a screening program with specified impact on overall and advanced-stage incidence. Inputs included meta-analyses of treatment trials, cancer registry data, and a melanoma risk prediction study RESULTS: Assuming 50% reduction in advanced stage under screening, the model projected 59 and 38 lives saved per 100,000 men under historic and novel treatments, respectively. With 10% increase in stage I, the model projects 2.9 and 4.7 overdiagnosed cases per life saved and number needed to be screened (NNS) equal to 1695 and 2632 under historical and novel treatments. When screening was performed only for the 20% of individuals with highest predicted risk, 34 and 22 lives per 100,000 were saved under historic and novel treatments. Similar results were obtained for women, but lives saved were lower. CONCLUSIONS: Melanoma early detection programs must shift a substantial fraction of cases from advanced to localized stage to be sustainable. Advances in systemic therapies for melanoma might noticeably reduce benefits of screening, but restricting screening to individuals at highest risk will likely reduce intervention efforts and harms while preserving >50% of the benefit of nontargeted screening. IMPACT: Our accessible modeling framework will help to guide population melanoma screening programs in an era of novel treatments for advanced disease.

The genetics of retinopathy of prematurity: a model for neovascular retinal disease.

TOPIC: Retinopathy of prematurity (ROP) is a proliferative retinal vascular disease in premature infants, and is a major cause of childhood blindness worldwide. In addition to known clinical risk factors such as low birth weight and gestational age, there is a growing body of evidence supporting a genetic basis for ROP. CLINICAL RELEVANCE: While comorbidities and environmental factors have been identified as contributing to ROP outcomes in premature infants, most notably gestational age and oxygen, some infants progress to severe disease despite absence of these clinical risk factors. The contribution of genetic factors may explain these differences and allow better detection and treatment of infants at risk for severe ROP. METHODS: To comprehensively review genetic factors that potentially contribute to the development and severity of ROP, we conducted a literature search focusing on the genetic basis for ROP. Terms related to other heritable retinal vascular diseases like "familial exudative vitreoretinopathy", as well as to genes implicated in animal models of ROP, were also used to capture research in diseases with similar pathogenesis to ROP in humans with known genetic components. RESULTS: Contributions across several genetic domains are described including vascular endothelial growth factor, the Wnt signaling pathway, insulin-like growth factor 1, inflammatory mediators, and brain-derived neurotrophic factor. CONCLUSIONS: Most candidate gene studies of ROP have limitations such as inability to replicate results, conflicting results from various studies, small sample size, and differences in clinical characterization. Additional difficulty arises in separating the contribution of genetic factors like Wnt signaling to ROP and prematurity. Although studies have implicated involvement of multiple signaling pathways in ROP, the genetics of ROP have not been clearly elucidated. Next-generation sequencing and genome-wide association studies have potential to expand future understanding of underlying genetic risk factors and pathophysiology of ROP.

Plus Disease in Retinopathy of Prematurity: A Continuous Spectrum of Vascular Abnormality as a Basis of Diagnostic Variability.

PURPOSE: To identify patterns of interexpert discrepancy in plus disease diagnosis in retinopathy of prematurity (ROP). DESIGN: We developed 2 datasets of clinical images as part of the Imaging and Informatics in ROP study and determined a consensus reference standard diagnosis (RSD) for each image based on 3 independent image graders and the clinical examination results. We recruited 8 expert ROP clinicians to classify these images and compared the distribution of classifications between experts and the RSD. PARTICIPANTS: Eight participating experts with more than 10 years of clinical ROP experience and more than 5 peer-reviewed ROP publications who analyzed images obtained during routine ROP screening in neonatal intensive care units. METHODS: Expert classification of images of plus disease in ROP. MAIN OUTCOME MEASURES: Interexpert agreement (weighted κ statistic) and agreement and bias on ordinal classification between experts (analysis of variance [ANOVA]) and the RSD (percent agreement). RESULTS: There was variable interexpert agreement on diagnostic classifications between the 8 experts and the RSD (weighted κ, 0-0.75; mean, 0.30). The RSD agreement ranged from 80% to 94% for the dataset of 100 images and from 29% to 79% for the dataset of 34 images. However, when images were ranked in order of disease severity (by average expert classification), the pattern of expert classification revealed a consistent systematic bias for each expert consistent with unique cut points for the diagnosis of plus disease and preplus disease. The 2-way ANOVA model suggested a highly significant effect of both image and user on the average score (dataset A: P < 0.05 and adjusted R2 = 0.82; and dataset B: P < 0.05 and adjusted R2 = 0.6615). CONCLUSIONS: There is wide variability in the classification of plus disease by ROP experts, which occurs because experts have different cut points for the amounts of vascular abnormality required for presence of plus and preplus disease. This has important implications for research, teaching, and patient care for ROP and suggests that a continuous ROP plus disease severity score may reflect more accurately the behavior of expert ROP clinicians and may better standardize classification in the future.

Plus Disease in Retinopathy of Prematurity: Improving Diagnosis by Ranking Disease Severity and Using Quantitative Image Analysis.

PURPOSE: To determine expert agreement on relative retinopathy of prematurity (ROP) disease severity and whether computer-based image analysis can model relative disease severity, and to propose consideration of a more continuous severity score for ROP. DESIGN: We developed 2 databases of clinical images of varying disease severity (100 images and 34 images) as part of the Imaging and Informatics in ROP (i-ROP) cohort study and recruited expert physician, nonexpert physician, and nonphysician graders to classify and perform pairwise comparisons on both databases. PARTICIPANTS: Six participating expert ROP clinician-scientists, each with a minimum of 10 years of clinical ROP experience and 5 ROP publications, and 5 image graders (3 physicians and 2 nonphysician graders) who analyzed images that were obtained during routine ROP screening in neonatal intensive care units. METHODS: Images in both databases were ranked by average disease classification (classification ranking), by pairwise comparison using the Elo rating method (comparison ranking), and by correlation with the i-ROP computer-based image analysis system. MAIN OUTCOME MEASURES: Interexpert agreement (weighted κ statistic) compared with the correlation coefficient (CC) between experts on pairwise comparisons and correlation between expert rankings and computer-based image analysis modeling. RESULTS: There was variable interexpert agreement on diagnostic classification of disease (plus, preplus, or normal) among the 6 experts (mean weighted κ, 0.27; range, 0.06-0.63), but good correlation between experts on comparison ranking of disease severity (mean CC, 0.84; range, 0.74-0.93) on the set of 34 images. Comparison ranking provided a severity ranking that was in good agreement with ranking obtained by classification ranking (CC, 0.92). Comparison ranking on the larger dataset by both expert and nonexpert graders demonstrated good correlation (mean CC, 0.97; range, 0.95-0.98). The i-ROP system was able to model this continuous severity with good correlation (CC, 0.86). CONCLUSIONS: Experts diagnose plus disease on a continuum, with poor absolute agreement on classification but good relative agreement on disease severity. These results suggest that the use of pairwise rankings and a continuous severity score, such as that provided by the i-ROP system, may improve agreement on disease severity in the future.

Patient-specific factors influence somatic variation patterns in von Hippel-Lindau disease renal tumours.

Cancer development is presumed to be an evolutionary process that is influenced by genetic background and environment. In laboratory animals, genetics and environment are variables that can largely be held constant. In humans, it is possible to compare independent tumours that have developed in the same patient, effectively constraining genetic and environmental variation and leaving only stochastic processes. Patients affected with von Hippel-Lindau disease are at risk of developing multiple independent clear cell renal carcinomas. Here we perform whole-genome sequencing on 40 tumours from six von Hippel-Lindau patients. We confirm that the tumours are clonally independent, having distinct somatic single-nucleotide variants. Although tumours from the same patient show many differences, within-patient patterns are discernible. Single-nucleotide substitution type rates are significantly different between patients and show biases in trinucleotide mutation context. We also observe biases in chromosome copy number aberrations. These results show that genetic background and/or environment can influence the types of mutations that occur.

A Network-Based Model of Oncogenic Collaboration for Prediction of Drug Sensitivity.

Tumorigenesis is a multi-step process, involving the acquisition of multiple oncogenic mutations that transform cells, resulting in systemic dysregulation that enables proliferation, invasion, and other cancer hallmarks. The goal of precision medicine is to identify therapeutically-actionable mutations from large-scale omic datasets. However, the multiplicity of oncogenes required for transformation, known as oncogenic collaboration, makes assigning effective treatments difficult. Motivated by this observation, we propose a new type of oncogenic collaboration where mutations in genes that interact with an oncogene may contribute to the oncogene's deleterious potential, a new genomic feature that we term "surrogate oncogenes." Surrogate oncogenes are representatives of these mutated subnetworks that interact with oncogenes. By mapping mutations to a protein-protein interaction network, we determine the significance of the observed distribution using permutation-based methods. For a panel of 38 breast cancer cell lines, we identified a significant number of surrogate oncogenes in known oncogenes such as BRCA1 and ESR1, lending credence to this approach. In addition, using Random Forest Classifiers, we show that these significant surrogate oncogenes predict drug sensitivity for 74 drugs in the breast cancer cell lines with a mean error rate of 30.9%. Additionally, we show that surrogate oncogenes are predictive of survival in patients. The surrogate oncogene framework incorporates unique or rare mutations from a single sample, and therefore has the potential to integrate patient-unique mutations into drug sensitivity predictions, suggesting a new direction in precision medicine and drug development. Additionally, we show the prevalence of significant surrogate oncogenes in multiple cancers from The Cancer Genome Atlas, suggesting that surrogate oncogenes may be a useful genomic feature for guiding pancancer analyses and assigning therapies across many tissue types.

A system biology approach to identify regulatory pathways underlying the neuroendocrine control of female puberty in rats and nonhuman primates.

This article is part of a Special Issue "Puberty and Adolescence". Puberty is a major developmental milestone controlled by the interaction of genetic factors and environmental cues of mostly metabolic and circadian nature. An increased pulsatile release of the decapeptide gonadotropin releasing hormone (GnRH) from hypothalamic neurosecretory neurons is required for both the initiation and progression of the pubertal process. This increase is brought about by coordinated changes that occur in neuronal and glial networks associated with GnRH neurons. These changes ultimately result in increased neuronal and glial stimulatory inputs to the GnRH neuronal network and a reduction of transsynaptic inhibitory influences. While some of the major players controlling pubertal GnRH secretion have been identified using gene-centric approaches, much less is known about the system-wide control of the overall process. Because the pubertal activation of GnRH release involves a diversity of cellular phenotypes, and a myriad of intracellular and cell-to-cell signaling molecules, it appears that the overall process is controlled by a highly coordinated and interactive regulatory system involving hundreds, if not thousands, of gene products. In this article we will discuss emerging evidence suggesting that these genes are arranged as functionally connected networks organized, both internally and across sub-networks, in a hierarchical fashion. According to this concept, the core of these networks is composed of transcriptional regulators that, by directing expression of downstream subordinate genes, provide both stability and coordination to the cellular networks involved in initiating the pubertal process. The integrative response of these gene networks to external inputs is postulated to be coordinated by epigenetic mechanisms.

Virk: an active learning-based system for bootstrapping knowledge base development in the neurosciences.

The frequency and volume of newly-published scientific literature is quickly making manual maintenance of publicly-available databases of primary data unrealistic and costly. Although machine learning (ML) can be useful for developing automated approaches to identifying scientific publications containing relevant information for a database, developing such tools necessitates manually annotating an unrealistic number of documents. One approach to this problem, active learning (AL), builds classification models by iteratively identifying documents that provide the most information to a classifier. Although this approach has been shown to be effective for related problems, in the context of scientific databases curation, it falls short. We present Virk, an AL system that, while being trained, simultaneously learns a classification model and identifies documents having information of interest for a knowledge base. Our approach uses a support vector machine (SVM) classifier with input features derived from neuroscience-related publications from the primary literature. Using our approach, we were able to increase the size of the Neuron Registry, a knowledge base of neuron-related information, by a factor of 90%, a knowledge base of neuron-related information, in 3 months. Using standard biocuration methods, it would have taken between 1 and 2 years to make the same number of contributions to the Neuron Registry. Here, we describe the system pipeline in detail, and evaluate its performance against other approaches to sampling in AL.

Computing graphlet signatures of network nodes and motifs in Cytoscape with GraphletCounter.

UNLABELLED: Biological network analysis can be enhanced by examining the connections between nodes and the rest of the network. For this purpose we have developed GraphletCounter, an open-source software tool for computing graphlet degree signatures that can operate on its own or as a plug-in to the network analysis environment Cytoscape. A unique characteristic of GraphletCounter is its ability to compute the graphlet signatures of network motifs, which can be specified by files generated by the motif-finding tool mfinder. GraphletCounter displays graphlet signatures for visual inspection within Cytoscape, and can output graphlet data for integration with larger workflows. AVAILABILITY AND IMPLEMENTATION: GraphletCounter is implemented in Java. It can be downloaded from the Cytoscape plugin repository, and is also available at http://sonmezsysbio.org/software/ graphletcounter.

Peptides derived from the prohormone proNPQ/spexin are potent central modulators of cardiovascular and renal function and nociception.

Computational methods have led two groups to predict the endogenous presence of a highly conserved, amidated, 14-aa neuropeptide called either spexin or NPQ. NPQ/spexin is part of a larger prohormone that contains 3 sets of RR residues, suggesting that it could yield more than one bioactive peptide; however, no in vivo activity has been demonstrated for any peptide processed from this precursor. Here we demonstrate biological activity for two peptides present within proNPQ/spexin. NPQ/spexin (NWTPQAMLYLKGAQ-NH(2)) and NPQ 53-70 (FISDQSRRKDLSDRPLPE) have differing renal and cardiovascular effects when administered intracerebroventricularly or intravenously into rats. Intracerebroventricular injection of NPQ/spexin produced a 13 ± 2 mmHg increase in mean arterial pressure, a 38 ± 8 bpm decrease in heart rate, and a profound decrease in urine flow rate. Intracerebroventricular administration of NPQ 53-70 produced a 26 ± 9 bpm decrease in heart rate with no change in mean arterial pressure, and a marked increase in urine flow rate. Intraventricular NPQ/spexin and NPQ 53-70 also produced antinociceptive activity in the warm water tail withdrawal assay in mice (ED(50)<30 and 10 nmol for NPQ/spexin and NPQ 53-70, respectively). We conclude that newly identified peptides derived from the NPQ/spexin precursor contribute to CNS-mediated control of arterial blood pressure and salt and water balance and modulate nociceptive responses.

Occupancy classification of position weight matrix-inferred transcription factor binding sites.

BACKGROUND: Computational prediction of Transcription Factor Binding Sites (TFBS) from sequence data alone is difficult and error-prone. Machine learning techniques utilizing additional environmental information about a predicted binding site (such as distances from the site to particular chromatin features) to determine its occupancy/functionality class show promise as methods to achieve more accurate prediction of true TFBS in silico. We evaluate the Bayesian Network (BN) and Support Vector Machine (SVM) machine learning techniques on four distinct TFBS data sets and analyze their performance. We describe the features that are most useful for classification and contrast and compare these feature sets between the factors. RESULTS: Our results demonstrate good performance of classifiers both on TFBS for transcription factors used for initial training and for TFBS for other factors in cross-classification experiments. We find that distances to chromatin modifications (specifically, histone modification islands) as well as distances between such modifications to be effective predictors of TFBS occupancy, though the impact of individual predictors is largely TF specific. In our experiments, Bayesian network classifiers outperform SVM classifiers. CONCLUSIONS: Our results demonstrate good performance of machine learning techniques on the problem of occupancy classification, and demonstrate that effective classification can be achieved using distances to chromatin features. We additionally demonstrate that cross-classification of TFBS is possible, suggesting the possibility of constructing a generalizable occupancy classifier capable of handling TFBS for many different transcription factors.

Designing antimicrobial peptides with weighted finite-state transducers.

The design of novel antimicrobial peptides (AMPs) is an important problem given the rise of drug-resistant bacteria. However, the large size of the sequence search space, combined with the time required to experimentally test or simulate AMPs at the molecular level makes computational approaches based on sequence analysis attractive. We propose a method for designing novel AMPs based on learning from n-gram counts of classes of amino acid residues, and then using weighted finite-state machines to produce sequences that incorporate those features that are strongly associated with AMP sequences. Finite-state machines are able to generate sequences that include desired n-gram features. We use this approach to generate candidate novel AMPs, which we test using third-party prediction servers. We demonstrate that our framework is capable of producing large numbers of novel peptide sequences that share features with known antimicrobial peptides.

Evolutionary sequence modeling for discovery of peptide hormones.

There are currently a large number of "orphan" G-protein-coupled receptors (GPCRs) whose endogenous ligands (peptide hormones) are unknown. Identification of these peptide hormones is a difficult and important problem. We describe a computational framework that models spatial structure along the genomic sequence simultaneously with the temporal evolutionary path structure across species and show how such models can be used to discover new functional molecules, in particular peptide hormones, via cross-genomic sequence comparisons. The computational framework incorporates a priori high-level knowledge of structural and evolutionary constraints into a hierarchical grammar of evolutionary probabilistic models. This computational method was used for identifying novel prohormones and the processed peptide sites by producing sequence alignments across many species at the functional-element level. Experimental results with an initial implementation of the algorithm were used to identify potential prohormones by comparing the human and non-human proteins in the Swiss-Prot database of known annotated proteins. In this proof of concept, we identified 45 out of 54 prohormones with only 44 false positives. The comparison of known and hypothetical human and mouse proteins resulted in the identification of a novel putative prohormone with at least four potential neuropeptides. Finally, in order to validate the computational methodology, we present the basic molecular biological characterization of the novel putative peptide hormone, including its identification and regional localization in the brain. This species comparison, HMM-based computational approach succeeded in identifying a previously undiscovered neuropeptide from whole genome protein sequences. This novel putative peptide hormone is found in discreet brain regions as well as other organs. The success of this approach will have a great impact on our understanding of GPCRs and associated pathways and help to identify new targets for drug development.