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/.     

Preoperative Echocardiographic-Defined Moderate–Severe Pulmonary Hypertension Predicts Prolonged Duration of Mechanical Ventilation Following Lung Transplantation for Patients with COPD

Purpose

Recent studies have suggested that pretransplant secondary pulmonary hypertension (PHT) may be associated with worse outcomes following lung transplantation. We sought to determine whether COPD patients with secondary PHT have inferior intensive care outcomes following lung transplantation.

Methods

This is a single-center, retrospective analysis of all lung transplant recipients between 2000 and 2009 for a primary diagnosis of COPD. Patients were stratified a priori into three pulmonary arterial pressure groups based on right ventricular systolic pressure (RVSP): no PHT (RVSP?<?35?mmHg), mild PHT (35????RVSP?<?45?mmHg), and moderate?Csevere PHT (RVSP????45?mmHg). Outcome measures were duration of mechanical ventilation, intensive care unit (ICU) length of stay, and PaO₂/fraction inspired oxygen (PaO₂/F_IO₂) ratio at 24?h posttransplantation.

Results

A total of 46 COPD lung transplant recipients with documented pretransplant RVSP were included in the analysis, including 18 with no PHT, 20 with mild PHT, and eight with moderate?Csevere PHT. There were no differences in baseline demographics between the three pulmonary arterial pressure groups. The presence of moderate?Csevere PHT predicted increased duration of mechanical ventilation (P?=?0.024), worse PaO₂/F_IO₂ ratio at 24?h (P?=?0.027), and a trend toward increased ICU length of stay (P?=?0.055). RVSP was the strongest risk factor for duration of mechanical ventilation and ICU length of stay. There was no difference in 1-year survival amongst the three pulmonary arterial pressure groups.

Conclusions

Preoperative moderate?Csevere PHT predicts prolonged duration of mechanical ventilation following lung transplantation in COPD subjects.     

Exploring the relation between learning style and cognitive impairment in patients with acquired brain injury

The intensity of aphasia therapy has been a key clinical question. The aim of this case-series study was to compare the outcome of intensive and non-intensive therapy in the relearning of words for people with aphasia. Eight participants took part in a study comparing the intensity of delivery of the therapy. Participants received two courses of the same therapy (each lasting 10 sessions) delivered either intensively or non-intensively. Therapy consisted of confrontation naming with progressive phonemic and orthographic cues. Post-therapy assessments were carried out immediately after the study and one month later. Performance was also monitored during each therapy session. Immediately post-therapy, both types of therapy had improved naming accuracy considerably and there was no significant difference between the two interventions. One month later, seven out of eight participants showed a small yet significant difference in naming accuracy, favouring non-intensive over intense therapy. There were no differences in the learning patterns during the therapy sessions between the intensive and non-intensive therapies. For the majority of people with aphasia post-stroke, both intense and non-intense therapy for anomia leads to improved naming performance. Retention at one-month post therapy is relatively superior after non-intensive therapy.     

Primary cutaneous mucormycosis in a lung transplant recipient: case report and concise review of the literature

Abstract: Mucormycosis (zygomycosis) is an invasive, opportunistic fungal infection caused by organisms of the class Zygomycetes. Immunocompromised individuals, including both solid organ and hematopoietic stem cell transplant recipients, are preferentially affected. Among solid organ transplant (SOT) recipients, the sinuses, with or without involvement of the orbits and cerebrum, are the most common sites of disease, although the pulmonary allograft appears to be targeted following lung transplantation. Here, we describe the unique case of a lung transplant recipient who developed multifocal cutaneous mucormycosis without involvement of the pulmonary allograft, and review the published literature regarding incidence, treatment, and prognosis of primary cutaneous mucormycosis following SOT.     

Antagonism of PDGF-BB suppresses subretinal neovascularization and enhances the effects of blocking VEGF-A

Hypoxia-inducible factor-1 (HIF-1) plays an important role in retinal and subretinal neovascularization (NV). Increased levels of HIF-1 cause increased expression of vascular endothelial growth factor (VEGF-A) and current therapies for ocular NV focus on neutralizing VEGF-A, but there is mounting evidence that other HIF-1-responsive gene products may also participate. In this study, we tested the effect of a designed ankyrin repeat protein (DARPin) that selectively binds and antagonizes the hypoxia-regulated gene product PDGF-BB in three models of subretinal NV (relevant to neovascular age-related macular degeneration) and compared its effects to a DARPin that selectively antagonizes VEGF-A. Daily intraperitoneal injections of 10 mg/kg of the anti-PDGF-BB DARPin or 1 mg/kg of the anti-VEGF DARPin significantly suppressed subretinal NV from laser-induced rupture of Bruch’s membrane. Injections of 1 mg/kg/day of the anti-PDGF-BB DARPin had no significant effect, but when combined with 1 mg/kg/day of the anti-VEGF-A DARPin there was greater suppression than injection of the anti-VEGF-A DARPin alone. In Vldlr ^?/? mice which spontaneously develop subretinal NV, intraocular injection of 1.85 μg of anti-PDGF-BB or anti-VEGF-A DARPin caused significant suppression of NV and when combined there was greater suppression than with either alone. The two DARPins also showed an additive effect in Tet/opsin/VEGF double transgenic mice, a particularly severe model of subretinal NV and exudative retinal detachment. In addition, intraocular injection of 1.85 μg of anti-PDGF-BB DARPin strongly suppressed ischemia-induced retinal NV, which is relevant to proliferative diabetic retinopathy and retinopathy of prematurity. These data demonstrate that PDGF-BB is another hypoxia-regulated gene product that along with VEGF-A contributes to ocular NV and suppression of both provides an additive effect.