76 research outputs found

    A Diverse Group of Previously Unrecognized Human Rhinoviruses Are Common Causes of Respiratory Illnesses in Infants

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    Human rhinoviruses (HRVs) are the most prevalent human pathogens, and consist of 101 serotypes that are classified into groups A and B according to sequence variations. HRV infections cause a wide spectrum of clinical outcomes ranging from asymptomatic infection to severe lower respiratory symptoms. Defining the role of specific strains in various HRV illnesses has been difficult because traditional serology, which requires viral culture and neutralization tests using 101 serotype-specific antisera, is insensitive and laborious.To directly type HRVs in nasal secretions of infants with frequent respiratory illnesses, we developed a sensitive molecular typing assay based on phylogenetic comparisons of a 260-bp variable sequence in the 5' noncoding region with homologous sequences of the 101 known serotypes. Nasal samples from 26 infants were first tested with a multiplex PCR assay for respiratory viruses, and HRV was the most common virus found (108 of 181 samples). Typing was completed for 101 samples and 103 HRVs were identified. Surprisingly, 54 (52.4%) HRVs did not match any of the known serotypes and had 12-35% nucleotide divergence from the nearest reference HRVs. Of these novel viruses, 9 strains (17 HRVs) segregated from HRVA, HRVB and human enterovirus into a distinct genetic group ("C"). None of these new strains could be cultured in traditional cell lines.By molecular analysis, over 50% of HRV detected in sick infants were previously unrecognized strains, including 9 strains that may represent a new HRV group. These findings indicate that the number of HRV strains is considerably larger than the 101 serotypes identified with traditional diagnostic techniques, and provide evidence of a new HRV group

    A Unique Signal Distorts the Perception of Species Richness and Composition in High-Throughput Sequencing Surveys of Microbial Communities: a Case Study of Fungi in Indoor Dust

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    Sequence-based surveys of microorganisms in varied environments have found extremely diverse assemblages. A standard practice in current high-throughput sequence (HTS) approaches in microbial ecology is to sequence the composition of many environmental samples at once by pooling amplicon libraries at a common concentration before processing on one run of a sequencing platform. Biomass of the target taxa, however, is not typically determined prior to HTS, and here, we show that when abundances of the samples differ to a large degree, this standard practice can lead to a perceived bias in community richness and composition. Fungal signal in settled dust of five university teaching laboratory classrooms, one of which was used for a mycology course, was surveyed. The fungal richness and composition in the dust of the nonmycology classrooms were remarkably similar to each other, while the mycology classroom was dominated by abundantly sporulating specimen fungi, particularly puffballs, and appeared to have a lower overall richness based on rarefaction curves and richness estimators. The fungal biomass was three to five times higher in the mycology classroom than the other classrooms, indicating that fungi added to the mycology classroom swamped the background fungi present in indoor air. Thus, the high abundance of a few taxa can skew the perception of richness and composition when samples are sequenced to an even depth. Next, we used in silico manipulations of the observed data to confirm that a unique signature can be identified with HTS approaches when the source is abundant, whether or not the taxon identity is distinct. Lastly, aerobiology of indoor fungi is discussed. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s00248-013-0266-4) contains supplementary material, which is available to authorized users

    Genetic architecture of human plasma lipidome and its link to cardiovascular disease

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    Understanding genetic architecture of plasma lipidome could provide better insights into lipid metabolism and its link to cardiovascular diseases (CVDs). Here, we perform genome-wide association analyses of 141 lipid species (n = 2,181 individuals), followed by phenome-wide scans with 25 CVD related phenotypes (n = 511,700 individuals). We identify 35 lipid-species-associated loci (P <5 x10(-8)), 10 of which associate with CVD risk including five new loci-COL5A1, GLTPD2, SPTLC3, MBOAT7 and GALNT16 (false discovery rate<0.05). We identify loci for lipid species that are shown to predict CVD e.g., SPTLC3 for CER(d18:1/24:1). We show that lipoprotein lipase (LPL) may more efficiently hydrolyze medium length triacylglycerides (TAGs) than others. Polyunsaturated lipids have highest heritability and genetic correlations, suggesting considerable genetic regulation at fatty acids levels. We find low genetic correlations between traditional lipids and lipid species. Our results show that lipidomic profiles capture information beyond traditional lipids and identify genetic variants modifying lipid levels and risk of CVD
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