The Puerto Rican Infant Metagenomic and Epidemiologic Study of Respiratory Outcomes (PRIMERO): Design and Baseline Characteristics for a Birth Cohort Study of Early-life Viral Respiratory Illnesses and Airway Dysfunction in Puerto Rican Children.

Epidemiologic studies demonstrate an association between early-life respiratory illnesses (RIs) and the development of childhood asthma. However, it remains uncertain whether these children are predisposed to both conditions or if early-life RIs induce alterations in airway function, immune responses, or other human biology that contribute to the development of asthma. Puerto Rican children experience a disproportionate burden of early-life RIs and asthma, making them an important population for investigating this complex interplay. PRIMERO, the Puerto Rican Infant Metagenomics and Epidemiologic Study of Respiratory Outcomes , recruited pregnant women and their newborns to investigate how the airways develop in early life among infants exposed to different viral RIs, and will thus provide a critical understanding of childhood asthma development. As the first asthma birth cohort in Puerto Rico, PRIMERO will prospectively follow 2,100 term healthy infants. Collected samples include post-term maternal peripheral blood, infant cord blood, the child's peripheral blood at the year two visit, and the child's nasal airway epithelium, collected using minimally invasive nasal swabs, at birth, during RIs over the first two years of life, and at annual healthy visits until age five. Herein, we describe the study's design, population, recruitment strategy, study visits and procedures, and primary outcomes.

phyloFlash: Rapid Small-Subunit rRNA Profiling and Targeted Assembly from Metagenomes.

The small-subunit rRNA (SSU rRNA) gene is the key marker in molecular ecology for all domains of life, but it is largely absent from metagenome-assembled genomes that often are the only resource available for environmental microbes. Here, we present phyloFlash, a pipeline to overcome this gap with rapid, SSU rRNA-centered taxonomic classification, targeted assembly, and graph-based binning of full metagenomic assemblies. We show that a cleanup of artifacts is pivotal even with a curated reference database. With such a filtered database, the general-purpose mapper BBmap extracts SSU rRNA reads five times faster than the rRNA-specialized tool SortMeRNA with similar sensitivity and higher selectivity on simulated metagenomes. Reference-based targeted assemblers yielded either highly fragmented assemblies or high levels of chimerism, so we employ the general-purpose genomic assembler SPAdes. Our optimized implementation is independent of reference database composition and has satisfactory levels of chimera formation. phyloFlash quickly processes Illumina (meta)genomic data, is straightforward to use, even as part of high-throughput quality control, and has user-friendly output reports. The software is available at https://github.com/HRGV/phyloFlash (GPL3 license) and is documented with an online manual.IMPORTANCE To track organisms across all domains of life, the SSU rRNA gene is the gold standard. Many environmental microbes are known only from high-throughput sequence data, but the SSU rRNA gene, the key to visualization by molecular probes and link to existing literature, is often missing from metagenome-assembled genomes (MAGs). The easy-to-use phyloFlash software suite tackles this gap with rapid, SSU rRNA-centered taxonomic classification, targeted assembly, and graph-based linking to MAGs. Starting from a cleaned reference database, phyloFlash profiles the taxonomic diversity and assembles the sorted SSU rRNA reads. The phyloFlash design is domain agnostic and covers eukaryotes, archaea, and bacteria alike. phyloFlash also provides utilities to visualize multisample comparisons and to integrate the recovered SSU rRNAs in a metagenomics workflow by linking them to MAGs using assembly graph parsing.

Type 2 and interferon inflammation regulate SARS-CoV-2 entry factor expression in the airway epithelium.

Coronavirus disease 2019 (COVID-19) is caused by SARS-CoV-2, an emerging virus that utilizes host proteins ACE2 and TMPRSS2 as entry factors. Understanding the factors affecting the pattern and levels of expression of these genes is important for deeper understanding of SARS-CoV-2 tropism and pathogenesis. Here we explore the role of genetics and co-expression networks in regulating these genes in the airway, through the analysis of nasal airway transcriptome data from 695 children. We identify expression quantitative trait loci for both ACE2 and TMPRSS2, that vary in frequency across world populations. We find TMPRSS2 is part of a mucus secretory network, highly upregulated by type 2 (T2) inflammation through the action of interleukin-13, and that the interferon response to respiratory viruses highly upregulates ACE2 expression. IL-13 and virus infection mediated effects on ACE2 expression were also observed at the protein level in the airway epithelium. Finally, we define airway responses to common coronavirus infections in children, finding that these infections generate host responses similar to other viral species, including upregulation of IL6 and ACE2. Our results reveal possible mechanisms influencing SARS-CoV-2 infectivity and COVID-19 clinical outcomes.

Type 2 and interferon inflammation strongly regulate SARS-CoV-2 related gene expression in the airway epithelium.

Coronavirus disease 2019 (COVID-19) outcomes vary from asymptomatic infection to death. This disparity may reflect different airway levels of the SARS-CoV-2 receptor, ACE2, and the spike protein activator, TMPRSS2. Here we explore the role of genetics and co-expression networks in regulating these genes in the airway, through the analysis of nasal airway transcriptome data from 695 children. We identify expression quantitative trait loci (eQTL) for both ACE2 and TMPRSS2, that vary in frequency across world populations. Importantly, we find TMPRSS2 is part of a mucus secretory network, highly upregulated by T2 inflammation through the action of interleukin-13, and that interferon response to respiratory viruses highly upregulates ACE2 expression. Finally, we define airway responses to coronavirus infections in children, finding that these infections upregulate IL6 while also stimulating a more pronounced cytotoxic immune response relative to other respiratory viruses. Our results reveal mechanisms likely influencing SARS-CoV-2 infectivity and COVID-19 clinical outcomes.

Uniting the classification of cultured and uncultured bacteria and archaea using 16S rRNA gene sequences.

Publicly available sequence databases of the small subunit ribosomal RNA gene, also known as 16S rRNA in bacteria and archaea, are growing rapidly, and the number of entries currently exceeds 4 million. However, a unified classification and nomenclature framework for all bacteria and archaea does not yet exist. In this Analysis article, we propose rational taxonomic boundaries for high taxa of bacteria and archaea on the basis of 16S rRNA gene sequence identities and suggest a rationale for the circumscription of uncultured taxa that is compatible with the taxonomy of cultured bacteria and archaea. Our analyses show that only nearly complete 16S rRNA sequences give accurate measures of taxonomic diversity. In addition, our analyses suggest that most of the 16S rRNA sequences of the high taxa will be discovered in environmental surveys by the end of the current decade.

The SILVA and "All-species Living Tree Project (LTP)" taxonomic frameworks.

SILVA (from Latin silva, forest, http://www.arb-silva.de) is a comprehensive resource for up-to-date quality-controlled databases of aligned ribosomal RNA (rRNA) gene sequences from the Bacteria, Archaea and Eukaryota domains and supplementary online services. SILVA provides a manually curated taxonomy for all three domains of life, based on representative phylogenetic trees for the small- and large-subunit rRNA genes. This article describes the improvements the SILVA taxonomy has undergone in the last 3 years. Specifically we are focusing on the curation process, the various resources used for curation and the comparison of the SILVA taxonomy with Greengenes and RDP-II taxonomies. Our comparisons not only revealed a reasonable overlap between the taxa names, but also points to significant differences in both names and numbers of taxa between the three resources.

Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies.

16S ribosomal RNA gene (rDNA) amplicon analysis remains the standard approach for the cultivation-independent investigation of microbial diversity. The accuracy of these analyses depends strongly on the choice of primers. The overall coverage and phylum spectrum of 175 primers and 512 primer pairs were evaluated in silico with respect to the SILVA 16S/18S rDNA non-redundant reference dataset (SSURef 108 NR). Based on this evaluation a selection of 'best available' primer pairs for Bacteria and Archaea for three amplicon size classes (100-400, 400-1000, ≥ 1000 bp) is provided. The most promising bacterial primer pair (S-D-Bact-0341-b-S-17/S-D-Bact-0785-a-A-21), with an amplicon size of 464 bp, was experimentally evaluated by comparing the taxonomic distribution of the 16S rDNA amplicons with 16S rDNA fragments from directly sequenced metagenomes. The results of this study may be used as a guideline for selecting primer pairs with the best overall coverage and phylum spectrum for specific applications, therefore reducing the bias in PCR-based microbial diversity studies.

The SILVA ribosomal RNA gene database project: improved data processing and web-based tools.

SILVA (from Latin silva, forest, http://www.arb-silva.de) is a comprehensive web resource for up to date, quality-controlled databases of aligned ribosomal RNA (rRNA) gene sequences from the Bacteria, Archaea and Eukaryota domains and supplementary online services. The referred database release 111 (July 2012) contains 3 194 778 small subunit and 288 717 large subunit rRNA gene sequences. Since the initial description of the project, substantial new features have been introduced, including advanced quality control procedures, an improved rRNA gene aligner, online tools for probe and primer evaluation and optimized browsing, searching and downloading on the website. Furthermore, the extensively curated SILVA taxonomy and the new non-redundant SILVA datasets provide an ideal reference for high-throughput classification of data from next-generation sequencing approaches.

SINA: accurate high-throughput multiple sequence alignment of ribosomal RNA genes.

MOTIVATION: In the analysis of homologous sequences, computation of multiple sequence alignments (MSAs) has become a bottleneck. This is especially troublesome for marker genes like the ribosomal RNA (rRNA) where already millions of sequences are publicly available and individual studies can easily produce hundreds of thousands of new sequences. Methods have been developed to cope with such numbers, but further improvements are needed to meet accuracy requirements. RESULTS: In this study, we present the SILVA Incremental Aligner (SINA) used to align the rRNA gene databases provided by the SILVA ribosomal RNA project. SINA uses a combination of k-mer searching and partial order alignment (POA) to maintain very high alignment accuracy while satisfying high throughput performance demands. SINA was evaluated in comparison with the commonly used high throughput MSA programs PyNAST and mothur. The three BRAliBase III benchmark MSAs could be reproduced with 99.3, 97.6 and 96.1 accuracy. A larger benchmark MSA comprising 38 772 sequences could be reproduced with 98.9 and 99.3% accuracy using reference MSAs comprising 1000 and 5000 sequences. SINA was able to achieve higher accuracy than PyNAST and mothur in all performed benchmarks. AVAILABILITY: Alignment of up to 500 sequences using the latest SILVA SSU/LSU Ref datasets as reference MSA is offered at http://www.arb-silva.de/aligner. This page also links to Linux binaries, user manual and tutorial. SINA is made available under a personal use license.

Analysis of 23S rRNA genes in metagenomes - a case study from the Global Ocean Sampling Expedition.

As an evolutionary marker, 23S ribosomal RNA (rRNA) offers more diagnostic sequence stretches and greater sequence variation than 16S rRNA. However, 23S rRNA is still not as widely used. Based on 80 metagenome samples from the Global Ocean Sampling (GOS) Expedition, the usefulness and taxonomic resolution of 23S rRNA were compared to those of 16S rRNA. Since 23S rRNA is approximately twice as large as 16S rRNA, twice as many 23S rRNA gene fragments were retrieved from the GOS reads than 16S rRNA gene fragments, with 23S rRNA gene fragments being generally about 100bp longer. Datasets for 16S and 23S rRNA sequences revealed similar relative abundances for major marine bacterial and archaeal taxa. However, 16S rRNA sequences had a better taxonomic resolution due to their significantly larger reference database. Reevaluation of the specificity of previously published PCR amplification primers and group specific fluorescence in situ hybridization probes on this metagenomic set of non-amplified 23S rRNA sequences revealed that out of 16 primers investigated, only two had more than 90% target group coverage. Evaluations of two probes, BET42a and GAM42a, were in accordance with previous evaluations, with a discrepancy in the target group coverage of the GAM42a probe when evaluated against the GOS metagenomic dataset.

SILVA: a comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB.

Sequencing ribosomal RNA (rRNA) genes is currently the method of choice for phylogenetic reconstruction, nucleic acid based detection and quantification of microbial diversity. The ARB software suite with its corresponding rRNA datasets has been accepted by researchers worldwide as a standard tool for large scale rRNA analysis. However, the rapid increase of publicly available rRNA sequence data has recently hampered the maintenance of comprehensive and curated rRNA knowledge databases. A new system, SILVA (from Latin silva, forest), was implemented to provide a central comprehensive web resource for up to date, quality controlled databases of aligned rRNA sequences from the Bacteria, Archaea and Eukarya domains. All sequences are checked for anomalies, carry a rich set of sequence associated contextual information, have multiple taxonomic classifications, and the latest validly described nomenclature. Furthermore, two precompiled sequence datasets compatible with ARB are offered for download on the SILVA website: (i) the reference (Ref) datasets, comprising only high quality, nearly full length sequences suitable for in-depth phylogenetic analysis and probe design and (ii) the comprehensive Parc datasets with all publicly available rRNA sequences longer than 300 nucleotides suitable for biodiversity analyses. The latest publicly available database release 91 (August 2007) hosts 547 521 sequences split into 461 823 small subunit and 85 689 large subunit rRNAs.