Large-Scale Phylogenetic Analysis of Emerging Infectious Diseases

Microorganisms that cause infectious diseases present critical issues of national security, public health, and economic welfare.  For example, in recent years, highly pathogenic strains of avian influenza have emerged in Asia, spread through Eastern Europe and threaten to become pandemic. As demonstrated by the coordinated response to Severe Acute Respiratory Syndrome (SARS) and influenza, agents of infectious disease are being addressed via large-scale genomic sequencing.  The goal of genomic sequencing projects are to rapidly put large amounts of data in the public domain to accelerate research on disease surveillance, treatment, and prevention. However, our ability to derive information from large comparative genomic datasets lags far behind acquisition.  Here we review the computational challenges of comparative genomic analyses, specifically sequence alignment and reconstruction of phylogenetic trees.  We present novel analytical results on from two important infectious diseases, Severe Acute Respiratory Syndrome (SARS) and influenza.SARS and influenza have similarities and important differences both as biological and comparative genomic analysis problems.  Influenza viruses (Orthymxyoviridae) are RNA based.  Current evidence indicates that influenza viruses originate in aquatic birds from wild populations. Influenza has been studied for decades via well-coordinated international efforts.  These efforts center on surveillance via antibody characterization of the hemagglutinin (HA) and neuraminidase (N) proteins of the circulating strains to inform vaccine design. However we still do not have a clear understanding of: 1) various transmission pathways such as the role of intermediate hosts such as swine and domestic birds and 2) the key mutation and genomic recombination events that underlie periodic pandemics of influenza.  In the past 30 years, sequence data from HA and N loci has become an important data type. In the past year, full genomic data has become prominent.  These data present exciting opportunities to address unanswered questions in influenza pandemics.SARS is caused by a previously unrecognized lineage of coronavirus, SARS-CoV, which like influenza has an RNA based genome.  Although SARS-CoV is widely believed to have originated in animals there remains disagreement over the candidate animal source that lead to the original outbreak of SARS.  In contrast to the long history of the study of influenza, SARS was only recognized in late 2002 and the virus that causes SARS has been documented primarily by genomic sequencing.In the past, most studies of influenza were performed on a limited number of isolates and genes suited to a particular problem.  Major goals in science today are to understand emerging diseases in broad geographic, environmental, societal, biological, and genomic contexts. Synthesizing diverse information brought together by various researchers is important to find out what can be done to prevent future outbreaks {JON03}.  Thus comprehensive means to organize and analyze large amounts of diverse information are critical.  For example, the relationships of isolates and patterns of genomic change observed in large datasets might not be consistent with hypotheses formed on partial data.  Moreover when researchers rely on partial datasets, they restrict the range of possible discoveries.Phylogenetics is well suited to the complex task of understanding emerging infectious disease. Phylogenetic analyses can test many hypotheses by comparing diverse isolates collected from various hosts, environments, and points in time and organizing these data into various evolutionary scenarios.  The products of a phylogenetic analysis are a graphical tree of ancestor-descendent relationships and an inferred summary of mutations, recombination events, host shifts, geographic, and temporal spread of the viruses.  However, this synthesis comes at a price.  The cost of computation of phylogenetic analysis expands combinatorially as the number of isolates considered increases. Thus, large datasets like those currently produced are commonly considered intractable.  We address this problem with synergistic development of heuristics tree search strategies and parallel computing.Fil: Janies, D.. Ohio State University; Estados UnidosFil: Pol, Diego. Ohio State University; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Seasonal Dynamics of Arbuscular Mycorrhizal Fungal Communities in Roots in a Seminatural Grassland▿ †

Symbiotic arbuscular mycorrhizal fungi (AMF) have been shown to influence both the diversity and productivity of grassland plant communities. These effects have been postulated to depend on the differential effects of individual mycorrhizal taxa on different plant species; however, so far there are few detailed studies of the dynamics of AMF colonization of different plant species. In this study, we characterized the communities of AMF colonizing the roots of two plant species, Prunella vulgaris and Antennaria dioica, in a Swedish seminatural grassland at different times of the year. The AMF small subunit rRNA genes were subjected to PCR, cloning, sequencing, and phylogenetic analysis. Nineteen discrete sequence types belonging to Glomus groups A and B and to the genus Acaulospora were distinguished. No significant seasonal changes in the species compositions of the AMF communities as a whole were observed. However, the two plant species hosted significantly different AMF communities. P. vulgaris hosted a rich AMF community throughout the entire growing season. The presence of AMF in A. dioica decreased dramatically in autumn, while an increased presence of Ascomycetes species was detected

Computational modeling of lung deposition of inhaled particles in chronic obstructive pulmonary disease (COPD) patients : identification of gaps in knowledge and data

Computational modeling together with experimental data are essential to assess the risk for particulate matter mediated lung toxicity and to predict the efficacy, safety and fate of aerosolized drug molecules used in inhalation therapy. In silico models are widely used to understand the deposition, distribution, and clearance of inhaled particles and aerosols in the human lung. Exacerbations of chronic obstructive pulmonary disease (COPD) have been reported due to increased particulate matter related air pollution episodes. Considering the profound functional, anatomical and structural changes occurring in COPD lungs, the relevance of the existing in silico models for mimicking diseased lungs warrants reevaluation. Currently available computational modeling tools were developed for the healthy adult (male) lung. Here, we analyze the major alterations occurring in the airway structure, anatomy and pulmonary function in the COPD lung, as compared to the healthy lung. We also scrutinize the various physiological and particle characteristics that influence particle deposition, distribution and clearance in the lung. The aim of this review is to evaluate the availability of the fundamental knowledge and data required for modeling particle deposition in a COPD lung departing from the existing healthy lung models. The extent to which COPD pathophysiology may affect aerosol deposition depends on the relative contribution of several factors such as altered lung structure and function, bronchoconstriction, emphysema, loss of elastic recoil, altered breathing pattern and altered liquid volumes that warrant consideration while developing physiologically relevant in silico models

Profiling of 2'-<i>O</i>-Me in human rRNA reveals a subset of fractionally modified positions and provides evidence for ribosome heterogeneity

Ribose methylation is one of the two most abundant modifications in human ribosomal RNA and is believed to be important for ribosome biogenesis, mRNA selectivity and translational fidelity. We have applied RiboMeth-seq to rRNA from HeLa cells for ribosome-wide, quantitative mapping of 2′-O-Me sites and obtained a comprehensive set of 106 sites, including two novel sites, and with plausible box C/D guide RNAs assigned to all but three sites. We find approximately two-thirds of the sites to be fully methylated and the remainder to be fractionally modified in support of ribosome heterogeneity at the level of RNA modifications. A comparison to HCT116 cells reveals similar 2′-O-Me profiles with distinct differences at several sites. This study constitutes the first comprehensive mapping of 2′-O-Me sites in human rRNA using a high throughput sequencing approach. It establishes the existence of a core of constitutively methylated positions and a subset of variable, potentially regulatory positions, and paves the way for experimental analyses of the role of variations in rRNA methylation under different physiological or pathological settings