Succession and determinants of the early life nasopharyngeal microbiota in a South African birth cohort

Background: Bacteria colonizing the nasopharynx play a key role as gatekeepers of respiratory health. Yet, dynamics of early life nasopharyngeal (NP) bacterial profiles remain understudied in low- and middle-income countries (LMICs), where children have a high prevalence of risk factors for lower respiratory tract infection. We investigated longitudinal changes in NP bacterial profiles, and associated exposures, among healthy infants from low-income households in South Africa. Methods: We used short fragment (V4 region) 16S rRNA gene amplicon sequencing to characterize NP bacterial profiles from 103 infants in a South African birth cohort, at monthly intervals from birth through the first 12 months of life and six monthly thereafter until 30 months. Results: Corynebacterium and Staphylococcus were dominant colonizers at 1 month of life; however, these were rapidly replaced by Moraxella- or Haemophilus-dominated profiles by 4 months. This succession was almost universal and largely independent of a broad range of exposures. Warm weather (summer), lower gestational age, maternal smoking, no day-care attendance, antibiotic exposure, or low height-for-age z score at 12 months were associated with higher alpha and beta diversity. Summer was also associated with higher relative abundances of Staphylococcus, Streptococcus, Neisseria, or anaerobic gram-negative bacteria, whilst spring and winter were associated with higher relative abundances of Haemophilus or Corynebacterium, respectively. Maternal smoking was associated with higher relative abundances of Porphyromonas. Antibiotic therapy (or isoniazid prophylaxis for tuberculosis) was associated with higher relative abundance of anerobic taxa (Porphyromonas, Fusobacterium, and Prevotella) and with lower relative abundances of health associated-taxa Corynebacterium and Dolosigranulum. HIV-exposure was associated with higher relative abundances of Klebsiella or Veillonella and lower relative abundances of an unclassified genus within the family Lachnospiraceae. Conclusions: In this intensively sampled cohort, there was rapid and predictable replacement of early profiles dominated by health-associated Corynebacterium and Dolosigranulum with those dominated by Moraxella and Haemophilus, independent of exposures. Season and antibiotic exposure were key determinants of NP bacterial profiles. Understudied but highly prevalent exposures prevalent in LMICs, including maternal smoking and HIV-exposure, were associated with NP bacterial profiles

A systematic evaluation of high-dimensional, ensemble-based regression for exploring large model spaces in microbiome analyses.

BackgroundMicrobiome studies incorporate next-generation sequencing to obtain profiles of microbial communities. Data generated from these experiments are high-dimensional with a rich correlation structure but modest sample sizes. A statistical model that utilizes these microbiome profiles to explain a clinical or biological endpoint needs to tackle high-dimensionality resulting from the very large space of variable configurations. Ensemble models are a class of approaches that can address high-dimensionality by aggregating information across large model spaces. Although such models are popular in fields as diverse as economics and genetics, their performance on microbiome data has been largely unexplored.ResultsWe developed a simulation framework that accurately captures the constraints of experimental microbiome data. Using this setup, we systematically evaluated a selection of both frequentist and Bayesian regression modeling ensembles. These are represented by variants of stability selection in conjunction with elastic net and spike-and-slab Bayesian model averaging (BMA), respectively. BMA ensembles that explore a larger space of models relative to stability selection variants performed better and had lower variability across simulations. However, stability selection ensembles were able to match the performance of BMA in scenarios of low sparsity where several variables had large regression coefficients.ConclusionsGiven a microbiome dataset of interest, we present a methodology to generate simulated data that closely mimics its characteristics in a manner that enables meaningful evaluation of analytical strategies. Our evaluation demonstrates that the largest ensembles yield the strongest performance on microbiome data with modest sample sizes and high-dimensional measurements. We also demonstrate the ability of these ensembles to identify microbiome signatures that are associated with opportunistic Candida albicans colonization during antibiotic exposure. As the focus of microbiome research evolves from pilot to translational studies, we anticipate that our strategy will aid investigators in making evaluation-based decisions for selecting appropriate analytical methods

HIV-exposure, early life feeding practices and delivery mode impacts on faecal bacterial profiles in a South African birth cohort

CITATION: Claassen-Weitz, S., et al. 2018. HIV-exposure, early life feeding practices and delivery mode impacts on faecal bacterial profiles in a South African birth cohort. Scientific Reports, 8:5078, doi:10.1038/s41598-018-22244-6.The original publication is available at https://www.nature.comThere are limited data on meconium and faecal bacterial profiles from African infants and their mothers. We characterized faecal bacterial communities of infants and mothers participating in a South African birth cohort. Stool and meconium specimens were collected from 90 mothers and 107 infants at birth, and from a subset of 72 and 36 infants at 4–12 and 20–28 weeks of age, respectively. HIV-unexposed infants were primarily exclusively breastfed at 4–12 (49%, 26/53) and 20–28 weeks (62%, 16/26). In contrast, HIV-exposed infants were primarily exclusively formula fed at 4–12 (53%; 10/19) and 20–28 weeks (70%, 7/10). Analysis (of the bacterial 16S rRNA gene sequences of the V4 hypervariable region) of the 90 mother-infant pairs showed that meconium bacterial profiles [dominated by Proteobacteria (89%)] were distinct from those of maternal faeces [dominated by Firmicutes (66%) and Actinobacteria (15%)]. Actinobacteria predominated at 4–12 (65%) and 20–28 (50%) weeks. HIV-exposed infants had significantly higher faecal bacterial diversities at both 4–12 (p = 0.026) and 20–28 weeks (p = 0.002). HIV-exposed infants had lower proportions of Bifidobacterium (p = 0.010) at 4–12 weeks. Maternal faecal bacterial profiles were influenced by HIV status, feeding practices and mode of delivery. Further longitudinal studies are required to better understand how these variables influence infant and maternal faecal bacterial composition.https://www.nature.com/articles/s41598-018-22244-6Publisher's versio

Using Bayesian modelling to investigate factors governing antibiotic-induced Candida albicans colonization of the GI tract.

Receipt of broad-spectrum antibiotics enhances Candida albicans colonization of the GI tract, a risk factor for haematogenously-disseminated candidiasis. To understand how antibiotics influence C. albicans colonization, we treated mice orally with vancomycin or a combination of penicillin, streptomycin, and gentamicin (PSG) and then inoculated them with C. albicans by gavage. Only PSG treatment resulted in sustained, high-level GI colonization with C. albicans. Furthermore, PSG reduced bacterial diversity in the colon much more than vancomycin. Both antibiotic regimens significantly reduced IL-17A, IL-21, IL-22 and IFN-γ mRNA levels in the terminal ileum but had limited effect on the GI fungal microbiome. Through a series of models that employed Bayesian model averaging, we investigated the associations between antibiotic treatment, GI microbiota, and host immune response and their collective impact on C. albicans colonization. Our analysis revealed that bacterial genera were typically associated with either C. albicans colonization or altered cytokine expression but not with both. The only exception was Veillonella, which was associated with both increased C. albicans colonization and reduced IL-21 expression. Overall, antibiotic-induced changes in the bacterial microbiome were much more consistent determinants of C. albicans colonization than either the GI fungal microbiota or the GI immune response

A systematic evaluation of high-dimensional, ensemble-based regression for exploring large model spaces in microbiome analyses.

BackgroundMicrobiome studies incorporate next-generation sequencing to obtain profiles of microbial communities. Data generated from these experiments are high-dimensional with a rich correlation structure but modest sample sizes. A statistical model that utilizes these microbiome profiles to explain a clinical or biological endpoint needs to tackle high-dimensionality resulting from the very large space of variable configurations. Ensemble models are a class of approaches that can address high-dimensionality by aggregating information across large model spaces. Although such models are popular in fields as diverse as economics and genetics, their performance on microbiome data has been largely unexplored.ResultsWe developed a simulation framework that accurately captures the constraints of experimental microbiome data. Using this setup, we systematically evaluated a selection of both frequentist and Bayesian regression modeling ensembles. These are represented by variants of stability selection in conjunction with elastic net and spike-and-slab Bayesian model averaging (BMA), respectively. BMA ensembles that explore a larger space of models relative to stability selection variants performed better and had lower variability across simulations. However, stability selection ensembles were able to match the performance of BMA in scenarios of low sparsity where several variables had large regression coefficients.ConclusionsGiven a microbiome dataset of interest, we present a methodology to generate simulated data that closely mimics its characteristics in a manner that enables meaningful evaluation of analytical strategies. Our evaluation demonstrates that the largest ensembles yield the strongest performance on microbiome data with modest sample sizes and high-dimensional measurements. We also demonstrate the ability of these ensembles to identify microbiome signatures that are associated with opportunistic Candida albicans colonization during antibiotic exposure. As the focus of microbiome research evolves from pilot to translational studies, we anticipate that our strategy will aid investigators in making evaluation-based decisions for selecting appropriate analytical methods

Longitudinal changes in the nasopharyngeal resistome of South African infants using shotgun metagenomic sequencing.

IntroductionNasopharyngeal (NP) colonization with antimicrobial-resistant bacteria is a global public health concern. Antimicrobial-resistance (AMR) genes carried by the resident NP microbiota may serve as a reservoir for transfer of resistance elements to opportunistic pathogens. Little is known about the NP antibiotic resistome. This study longitudinally investigated the composition of the NP antibiotic resistome in Streptococcus-enriched samples in a South African birth cohort.MethodsAs a proof of concept study, 196 longitudinal NP samples were retrieved from a subset of 23 infants enrolled as part of broader birth cohort study. These were selected on the basis of changes in serotype and antibiogram over time. NP samples underwent short-term enrichment for streptococci prior to total nucleic acid extraction and whole metagenome shotgun sequencing (WMGS). Reads were assembled and aligned to pneumococcal reference genomes for the extraction of streptococcal and non-streptococcal bacterial reads. Contigs were aligned to the Antibiotic Resistance Gene-ANNOTation database of acquired AMR genes.ResultsAMR genes were detected in 64% (125/196) of the samples. A total of 329 AMR genes were detected, including 36 non-redundant genes, ranging from 1 to 14 genes per sample. The predominant AMR genes detected encoded resistance mechanisms to beta-lactam (52%, 172/329), macrolide-lincosamide-streptogramin (17%, 56/329), and tetracycline antibiotics (12%, 38/329). MsrD, ermB, and mefA genes were only detected from streptococcal reads. The predominant genes detected from non- streptococcal reads included blaOXA-60, blaOXA-22, and blaBRO-1. Different patterns of carriage of AMR genes were observed, with only one infant having a stable carriage of mefA, msrD and tetM over a long period.ConclusionThis study demonstrates that WMGS can provide a broad snapshot of the NP resistome and has the potential to provide a comprehensive assessment of resistance elements present in this niche

Genetic Analysis Using an Isogenic Mating Pair of <i>Aspergillus fumigatus</i> Identifies Azole Resistance Genes and Lack of <i>MAT</i> Locus’s Role in Virulence

<div><p>Invasive aspergillosis (IA) due to <i>Aspergillus fumigatus</i> is a major cause of mortality in immunocompromised patients. The discovery of highly fertile strains of <i>A</i>. <i>fumigatus</i> opened the possibility to merge classical and contemporary genetics to address key questions about this pathogen. The merger involves sexual recombination, selection of desired traits, and genomics to identify any associated loci. We constructed a highly fertile isogenic pair of <i>A</i>. <i>fumigatus</i> strains with opposite mating types and used them to investigate whether mating type is associated with virulence and to find the genetic loci involved in azole resistance. The pair was made isogenic by 9 successive backcross cycles of the foundational strain AFB62 (<i>MAT1-1</i>) with a highly fertile (<i>MAT1-2</i>) progeny. Genome sequencing showed that the F₉<i>MAT1-2</i> progeny was essentially identical to the AFB62. The survival curves of animals infected with either strain in three different animal models showed no significant difference, suggesting that virulence in <i>A</i>. <i>fumigatus</i> was not associated with mating type. We then employed a relatively inexpensive, yet highly powerful strategy to identify genomic loci associated with azole resistance. We used traditional <i>in vitro</i> drug selection accompanied by classical sexual crosses of azole-sensitive with resistant isogenic strains. The offspring were plated under varying drug concentrations and pools of resulting colonies were analyzed by whole genome sequencing. We found that variants in 5 genes contributed to azole resistance, including mutations in <i>erg11A</i> (<i>cyp51A</i>), as well as multi-drug transporters, <i>erg25</i>, and in HMG-CoA reductase. The results demonstrated that with minimal investment into the sequencing of three pools from a cross of interest, the variation(s) that contribute any phenotype can be identified with nucleotide resolution. This approach can be applied to multiple areas of interest in <i>A</i>. <i>fumigatus</i> or other heterothallic pathogens, especially for virulence associated traits.</p></div

Total number of SNP of sequenced AFB62 or AFB62F9 intermediate (bold) and high level resistant isolates in itraconazole, posaconazole, or voriconazole after in vitro selection.

<p>Number of mutations in relevant genes <i>erg11A</i>, <i>erg25A</i>, <i>hmg1</i>, <i>ssc70</i>, <i>ganA</i>, and the ABC transporter (AFUA_92.m00226) are presented. Strains in <b>bold</b> were selected under intermediate drug concentrations, strain in <i>italics</i> were direct derivatives of strains in bold selected at high drug concentrations. Numbers in parentheses refer to the number of individual SNPs identified in each gene. Details on the specific mutations are found in <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1004834#ppat.1004834.s006" target="_blank">S4 Table</a>. Itra—itraconazole, Posa—posaconazole, Vori—voriconazole.</p><p>Total number of SNP of sequenced AFB62 or AFB62F9 intermediate (bold) and high level resistant isolates in itraconazole, posaconazole, or voriconazole after in vitro selection.</p

Genomic comparison of AFB62F9 and AFB62.

<p>(A) Whole genome coverage plot using AFB62F9 reads mapped to the AFB62 genome showing percent identity (y-axis) per chromosome (x-axis). The red line and dots represents regions of the AFB62 genome covered by AFB62F9 reads at greater than 90% identity. (B) Close-up of mapped reads to the region surrounding the <i>MAT1-2</i> locus in AFB62. Numbers on top are coordinates on contig 677 (coordinates 1495670–1582078 on chromosome III). Top track in dark blue: average coverage of the genome was 48X with a maximum of 56 read coverage. The <i>MAT1-2</i> locus in AFB62F9 had no mapped reads so the coverage drops to 0X (~27 Kb in this figure). Second track: SNP density per 1,000 bp is depicted as a red histogram. Maximum SNP/Kb was 10. Note the sharp increase surrounding the <i>MAT1-1</i> locus and extending in the 3’ direction for an additional ~230Kb not depicted in this figure. Bottom track: annotated protein-coding genes in the AFB62 assembly. Direction of arrows depicts the coding strand.</p

Azole resistance in spontaneous and engineered strains.

<p>MIC determined by broth dilution method.</p><p>Azole resistance in spontaneous and engineered strains.</p