Diabetes translation research: where are we and where do we want to be?

Translation research transforms currently available knowledge into useful measures for everyday clinical and public health practice. We review the progress in diabetes translation research and identify future challenges and opportunities in this field. Several promising interventions to optimize implementation of efficacious diabetes treatments are available. Many of these interventions, singly or in combination, need to be more formally tested in larger randomized or quasi-experimental practical trials using outcomes of special interest to patients (for example, patient satisfaction and quality of life) and policymakers (for example, cost and cost-effectiveness). The long-term outcomes (such as morbidity, mortality, quality of life, and costs) of strategies aimed at improving diabetes care must be assessed. Translation research also needs to incorporate ways of studying complex systems of care. The challenges and opportunities offered by translation research are tremendous.     

Intensity discrimination deficits cause habituation changes in middle-aged Caenorhabditis elegans

The ability to learn and remember is critical for all animals to survive in the ever-changing environment. As we age, many of our biological faculties decay and of these, decline in learning and memory can be the most distressing. To carefully define age-dependent changes in learning during reproductive age in the nematode Caenorhabditis elegans, we performed a parametric behavioral study of habituation to nonlocalized mechanical stimuli (petri plate taps) over a range of intensities in middle-aged worms. We found that as worms age (from the onset of reproduction to the end of egg laying), response probability habituation increases (at both 10- and 60-second interstimulus intervals) and that these age-related changes were associated with a decrease in the discrimination between stimuli of different intensities. We also used optogenetics to investigate where these age-dependent changes occur. Our data suggest that the changes occur upstream of mechanosensory neuron depolarization. These data support the idea that declines in stimulus intensity discrimination abilities during aging may be one variable underlying age-related cognitive deficits.     

Kohlschütter–Tönz Syndrome: Mutations in ROGDI and Evidence of Genetic Heterogeneity

Kohlschütter–Tönz syndrome (KTS) is a rare autosomal recessive disorder characterized by amelogenesis imperfecta, psychomotor delay or regression and seizures starting early in childhood. KTS was established as a distinct clinical entity after the first report by Kohlschütter in 1974, and to date, only a total of 20 pedigrees have been reported. The genetic etiology of KTS remained elusive until recently when mutations in ROGDI were independently identified in three unrelated families and in five likely related Druze families. Herein, we report a clinical and genetic study of 10 KTS families. By using a combination of whole exome sequencing, linkage analysis, and Sanger sequencing, we identify novel homozygous or compound heterozygous ROGDI mutations in five families, all presenting with a typical KTS phenotype. The other families, mostly presenting with additional atypical features, were negative for ROGDI mutations, suggesting genetic heterogeneity of atypical forms of the disease.     

Evolution and diversity of copy number variation in the great ape lineage

Copy number variation (CNV) contributes to disease and has restructured the genomes of great apes. The diversity and rate of this process, however, have not been extensively explored among great ape lineages. We analyzed 97 deeply sequenced great ape and human genomes and estimate 16% (469 Mb) of the hominid genome has been affected by recent CNV. We identify a comprehensive set of fixed gene deletions (n = 340) and duplications (n = 405) as well as >13.5 Mb of sequence that has been specifically lost on the human lineage. We compared the diversity and rates of copy number and single nucleotide variation across the hominid phylogeny. We find that CNV diversity partially correlates with single nucleotide diversity (r² = 0.5) and recapitulates the phylogeny of apes with few exceptions. Duplications significantly outpace deletions (2.8-fold). The load of segregating duplications remains significantly higher in bonobos, Western chimpanzees, and Sumatran orangutans—populations that have experienced recent genetic bottlenecks (P = 0.0014, 0.02, and 0.0088, respectively). The rate of fixed deletion has been more clocklike with the exception of the chimpanzee lineage, where we observe a twofold increase in the chimpanzee–bonobo ancestor (P = 4.79 × 10⁻⁹) and increased deletion load among Western chimpanzees (P = 0.002). The latter includes the first genomic disorder in a chimpanzee with features resembling Smith-Magenis syndrome mediated by a chimpanzee-specific increase in segmental duplication complexity. We hypothesize that demographic effects, such as bottlenecks, have contributed to larger and more gene-rich segments being deleted in the chimpanzee lineage and that this effect, more generally, may account for episodic bursts in CNV during hominid evolution.Sequence and assembly of great ape reference genomes have consistently revealed that copy number variation (CNV) affects more base pairs than single nucleotide variation (SNV) (Cheng et al. 2005; The Chimpanzee Sequencing and Analysis Consortium 2005; Locke et al. 2011). Segmental duplications, in particular, have disproportionately affected the African great ape (human, chimpanzee, and gorilla) lineages, where they appear to have accumulated at an accelerated rate (Cheng et al. 2005; Marques-Bonet et al. 2009). This has led to speculation that differences in fixation and copy number polymorphism may have contributed to the phenotypic “plasticity” and species-specific differences between humans and great apes (Olson 1999; Varki et al. 2008). While there is some evidence that fixed deletions and duplications contribute to morphological differences between humans and great apes (McLean et al. 2011; Charrier et al. 2012; Dennis et al. 2012), a comprehensive assessment of these differences at the level of the genome has not yet been performed. Previous studies of CNV have been predominated by array comparative genomic hybridization (CGH) experiments (Fortna et al. 2004; Perry et al. 2006; Dumas et al. 2007; Gazave et al. 2011; Locke et al. 2011), which provide limited size resolution, are imprecise in absolute copy number differences, and are biased by probes derived from the human reference genome. Comparisons of reference genomes have been complicated by assessments of a single individual and distinguishing CNVs from assembly errors (The Chimpanzee Sequencing and Analysis Consortium 2005; Locke et al. 2011; Ventura et al. 2011; Prüfer et al. 2012). Here, we compare the evolution and diversity of deletions, duplications, and SNVs in 97 great ape individuals sequenced to high coverage (median ∼25×) (Prado-Martinez et al. 2013). The set includes multiple individuals from the four great ape genera, including Bornean and Sumatran orangutans, each of the four recognized chimpanzee subspecies, bonobos, and both Eastern and Western gorillas, in addition to 10 diverse humans and a high-coverage archaic Denisovan individual. This data set provides unprecedented genome-wide resolution to interrogate multiple forms of genetic variation and a unique opportunity to directly compare mutational processes and patterns of diversity in great apes.     

Pathoscope: Species identification and strain attribution with unassembled sequencing data

Emerging next-generation sequencing technologies have revolutionized the collection of genomic data for applications in bioforensics, biosurveillance, and for use in clinical settings. However, to make the most of these new data, new methodology needs to be developed that can accommodate large volumes of genetic data in a computationally efficient manner. We present a statistical framework to analyze raw next-generation sequence reads from purified or mixed environmental or targeted infected tissue samples for rapid species identification and strain attribution against a robust database of known biological agents. Our method, Pathoscope, capitalizes on a Bayesian statistical framework that accommodates information on sequence quality, mapping quality, and provides posterior probabilities of matches to a known database of target genomes. Importantly, our approach also incorporates the possibility that multiple species can be present in the sample and considers cases when the sample species/strain is not in the reference database. Furthermore, our approach can accurately discriminate between very closely related strains of the same species with very little coverage of the genome and without the need for multiple alignment steps, extensive homology searches, or genome assembly—which are time-consuming and labor-intensive steps. We demonstrate the utility of our approach on genomic data from purified and in silico “environmental” samples from known bacterial agents impacting human health for accuracy assessment and comparison with other approaches.The accurate and rapid identification of species and strains of pathogens is an essential component of biosurveillance from both human health and biodefense perspectives (Vaidyanathan 2011). For example, misidentification was among the issues that resulted in a 3-wk delay in accurate diagnosis of the recent outbreak of hemorrhagic Escherichia coli being due to strain O104:H4, resulting in over 3800 infections across 13 countries in Europe with 54 deaths (Frank et al. 2011). The most accurate diagnostic information, necessary for species identification and strain attribution, comes from the most refined level of biological data—genomic DNA sequences (Eppinger et al. 2011). Advances in DNA-sequencing technologies allows for the rapid collection of extraordinary amounts of genomic data, yet robust approaches to analyze this volume of data are just developing, from both statistical and algorithmic perspectives.Next-generation sequencing approaches have revolutionized the way we collect DNA sequence data, including for applications in pathology, bioforensics, and biosurveillance. Given a particular clinical or metagenomic sample, our goal is to identify the specific species, strains, or substrains present in the sample, as well as accurately estimate the proportions of DNA originating from each source genome in the sample. Current approaches for next-gen sequencing usually have read lengths between 25 and 1000 bp; however, these sequencing technologies include error rates that vary by approach and by samples. Such variation is typically less important for species identification given the relatively larger genetic divergences among species than among individuals within species. But for strain attribution, sequencing error has the potential to swamp out discriminatory signal in a data set, necessitating highly sensitive and refined computational models and a robust database for both species identification and strain attribution.Current methods for classifying metagenomic samples rely on one or more of three general approaches: composition or pattern matching (McHardy et al. 2007; Brady and Salzberg 2009; Segata et al. 2012), taxonomic mapping (Huson et al. 2007; Meyer et al. 2008; Monzoorul Haque et al. 2009; Gerlach and Stoye 2011; Patil et al. 2012; Segata et al. 2012), and whole-genome assembly (Kostic et al. 2011; Bhaduri et al. 2012). Composition and pattern-matching algorithms use predetermined patterns in the data, such as taxonomic clade markers (Segata et al. 2012), k-mer frequency, or GC content, often coupled with sophisticated classification algorithms such as support vector machines (McHardy et al. 2007; Patil et al. 2012) or interpolated Markov Models (Brady and Salzberg 2009) to classify reads to the species of interest. These approaches require intensive preprocessing of the genomic database before application. In addition, the classification rule and results can often change dramatically depending on the size and composition of the genome database.Taxonomy-based approaches typically rely on a “lowest common ancestor” approach (Huson et al. 2007), meaning that they identify the most specific taxonomic group for each read. If a read originates from a genomic region that shares homology with other organisms in the database, the read is assigned to the lowest taxonomic group that contains all of the genomes that share the homologous region. These methods are typically highly accurate for higher-level taxonomic levels (e.g., phylum and family), but experience reduced accuracy at lower levels (e.g., species and strain) (Gerlach and Stoye 2011). Furthermore, these approaches are not informative when the reads originate from one or more species or strains that are closely related to each other or different organisms in the database. In these cases, all of the reads can be reassigned to higher-level taxonomies, thus failing to identify the specific species or strains contained in the sample.Assembly-based algorithms can often lead to the most accurate strain identification. However, these methods also require the assembly of a whole genome from a sample, which is a computationally difficult and time-consuming process that requires large numbers of reads to achieve an adequate accuracy—often on the order of 50–100× coverage of the target genome (Schatz et al. 2010). Given current sequencing depths, obtaining this level of coverage is usually possible for purified samples, but coverage levels may not be sufficient for mixed samples or in multiplexed sequencing runs. Assembly approaches are further complicated by the fact that data collection at a crime scene or hospital might include additional environmental components in the biological sample (host genome or naturally occurring bacterial and viral species), thus requiring multiple filtering and alignment steps in order to obtain reads specific to the pathogen of interest.Here we describe an accurate and efficient approach to analyze next-generation sequence data for species identification and strain attribution that capitalizes on a Bayesian statistical framework implemented in the new software package Pathoscope v1.0. Our approach accommodates information on sequence quality, mapping quality, and provides posterior probabilities of matches to a known database of reference genomes. Importantly, our approach incorporates the possibility that multiple species can be present in the sample or that the target strain is not even contained within the reference database. It also accurately discriminates between very closely related strains of the same species with much less than 1× coverage of the genome and without the need for sequence assembly or complex preprocessing of the database or taxonomy. No other method in the literature can identify species or substrains in such a direct and automatic manner and without the need for large numbers of reads. We demonstrate our approach through application to next-generation DNA sequence data from a recent outbreak of the hemorrhagic E. coli (O104:H4) strain in Europe (Frank et al. 2011; Rohde et al. 2011; Turner 2011) and on purified and in silico mixed samples from several other known bacterial agents that impact human health. Software and data examples for our approach are freely available for download at https://sourceforge.net/projects/pathoscope/.