Kemal Sonmez

Gibbon genome and the fast karyotype evolution of small apes.

Gibbons are small arboreal apes that display an accelerated rate of evolutionary chromosomal rearrangement and occupy a key node in the primate phylogeny between Old World monkeys and great apes. Here we present the assembly and analysis of a northern white-cheeked gibbon (Nomascus leucogenys) genome. We describe the propensity for a gibbon-specific retrotransposon (LAVA) to insert into chromosome segregation genes and alter transcription by providing a premature termination site, suggesting a possible molecular mechanism for the genome plasticity of the gibbon lineage. We further show that the gibbon genera (Nomascus, Hylobates, Hoolock and Symphalangus) experienced a near-instantaneous radiation ∼5 million years ago, coincident with major geographical changes in southeast Asia that caused cycles of habitat compression and expansion. Finally, we identify signatures of positive selection in genes important for forelimb development (TBX5) and connective tissues (COL1A1) that may have been involved in the adaptation of gibbons to their arboreal habitat.

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‐NH2) 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 (ED50<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.—Toll, L., Khroyan, T. V., Sonmez, K., Ozawa, A., Lindberg, I., McLaughlin, J. P., Eans, S. O., Shahien, A. A., Kapusta, D. R. Peptides derived from the prohormone proNPQ/spexin are potent central modulators of cardiovascular and renal function and nociception. FASEB J. 26, 947–954 (2012). www.fasebj.org

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.

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.

Gene networks, epigenetics and the control of female puberty

Puberty is a major developmental milestone set in motion by the interaction of genetic factors and environmental cues. The pubertal process is initiated by an increased pulsatile release of gonadotropin releasing hormone (GnRH) from neurosecretory neurons of the hypothalamus. Although single genes have been identified that are essential for puberty to occur, it appears clear now that many genes controlling diverse cellular functions contribute to the process. The polygenic nature of the neuroendocrine complex controlling puberty has prompted two important questions: are these genes functionally connected and, if they are, is their activity subject to a dynamic level of control independent of changes in DNA sequence? In this article we will discuss emerging evidence suggesting that the onset of puberty is controlled at the transcriptional level by interactive gene networks subjected to epigenetic regulation. At least two modes of epigenetic regulation provide coordination and transcriptional plasticity to these networks: changes in DNA methylation and differential association of histone modifications to genomic regions controlling gene activity. Architecturally, puberty-controlling networks are endowed with “activators,” which move the process along by setting in motion key developmental events, and “repressors,” which play a central role in preventing the untimely unfolding of these events.

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.

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.