High-throughput sequencing technology has enabled population-based studies of the role of the human microbiome in disease etiology and exposure response. Microbiome data are summarized as counts or composition of the bacterial taxa at different taxonomic levels. An important problem is to identify the bacterial taxa that are associated with a response. One method is to test the association of specific taxon with phenotypes in a linear mixed effect model, which incorporates phylogenetic information among bacterial communities. Another type of approaches consider all taxa in a joint model and achieves selection via penalization method, which ignores phylogenetic information. In this paper, we consider regression analysis by treating bacterial taxa at different level as multiple random effects. For each taxon, a kernel matrix is calculated based on distance measures in the phylogenetic tree and acts as one variance component in the joint model. Then taxonomic selection is achieved by the lasso (least absolute shrinkage and selection operator) penalty on variance components. Our method integrates biological information into the variable selection problem and greatly improves selection accuracies. Simulation studies demonstrate the superiority of our methods versus existing methods, for example, group-lasso. Finally, we apply our method to a longitudinal microbiome study of Human Immunodeficiency Virus (HIV) infected patients. We implement our method using the high performance computing language Julia. Software and detailed documentation are freely available at https://github.com/JingZhai63/VCselection.

In metagenomic studies, testing the association between microbiome composition and clinical outcomes translates to testing the nullity of variance components. Motivated by a lung human immunodeficiency virus (HIV) microbiome project, we study longitudinal microbiome data by using variance component models with more than two variance components. Current testing strategies only apply to models with exactly two variance components and when sample sizes are large. Therefore, they are not applicable to longitudinal microbiome studies. In this paper, we propose exact tests (score test, likelihood ratio test, and restricted likelihood ratio test) to (a) test the association of the overall microbiome composition in a longitudinal design and (b) detect the association of one specific microbiome cluster while adjusting for the effects from related clusters. Our approach combines the exact tests for null hypothesis with a single variance component with a strategy of reducing multiple variance components to a single one. Simulation studies demonstrate that our method has a correct type I error rate and superior power compared to existing methods at small sample sizes and weak signals. Finally, we apply our method to a longitudinal pulmonary microbiome study of HIV-infected patients and reveal two interesting genera Prevotella and Veillonella associated with forced vital capacity. Our findings shed light on the impact of the lung microbiome on HIV complexities. The method is implemented in the open-source, high-performance computing language Julia and is freely available at https://github.com/JingZhai63/VCmicrobiome.

Investigation of the human microbiome has become an important field of research facilitated by advances in sequencing technologies. The lung, which is one of the latest body sites being explored for the characterization of human-associated microbial communities, has a microbiome that is suspected to play a substantial role in health and disease. In this review, we provide an overview of the basics of microbiome studies. Challenges in the study of the lung microbiome are highlighted, and further attention is called to the optimization and standardization of methodologies to explore the role of the lung microbiome in health and disease. We also provide examples of lung microbial communities associated with disease or infection status and discuss the role of fungal species in the lung. Finally, we review studies demonstrating that the environmental microbiome can influence lung health and disease, such as the finding that the diversity of microbial exposure correlates inversely with the development of childhood asthma.

The National Heart, Lung, and Blood Institute (NHLBI) AIDS Program's goal is to provide direction and support for research and training programs in areas of HIV-related heart, lung, blood, and sleep (HLBS) diseases. To better define NHLBI current HIV-related scientific priorities and with the goal of identifying new scientific priorities and gaps in HIV-related HLBS research, a wide group of investigators gathered for a scientific NHLBI HIV Working Group on December 14-15, 2015, in Bethesda, MD. The core objectives of the Working Group included discussions on: (1) HIV-related HLBS comorbidities in the antiretroviral era; (2) HIV cure; (3) HIV prevention; and (4) mechanisms to implement new scientific discoveries in an efficient and timely manner so as to have the most impact on people living with HIV. The 2015 Working Group represented an opportunity for the NHLBI to obtain expert advice on HIV/AIDS scientific priorities and approaches over the next decade.

Rationale

Results from 16S rDNA-encoding gene sequence-based, culture-independent techniques have led to conflicting conclusions about the composition of the lower respiratory tract microbiome.

Objectives

To compare the microbiome of the upper and lower respiratory tract in healthy HIV-uninfected nonsmokers and smokers in a multicenter cohort.

Methods

Participants were nonsmokers and smokers without significant comorbidities. Oral washes and bronchoscopic alveolar lavages were collected in a standardized manner. Sequence analysis of bacterial 16S rRNA-encoding genes was performed, and the neutral model in community ecology was used to identify bacteria that were the most plausible members of a lung microbiome.

Measurements and main results

Sixty-four participants were enrolled. Most bacteria identified in the lung were also in the mouth, but specific bacteria such as Enterobacteriaceae, Haemophilus, Methylobacterium, and Ralstonia species were disproportionally represented in the lungs compared with values predicted by the neutral model. Tropheryma was also in the lung, but not the mouth. Mouth communities differed between nonsmokers and smokers in species such as Porphyromonas, Neisseria, and Gemella, but lung bacterial populations did not.

Conclusions

This study is the largest to examine composition of the lower respiratory tract microbiome in healthy individuals and the first to use the neutral model to compare the lung to the mouth. Specific bacteria appear in significantly higher abundance in the lungs than would be expected if they originated from the mouth, demonstrating that the lung microbiome does not derive entirely from the mouth. The mouth microbiome differs in nonsmokers and smokers, but lung communities were not significantly altered by smoking.

Rationale

Lung infections caused by opportunistic or virulent pathogens are a principal cause of morbidity and mortality in HIV infection. It is unknown whether HIV infection leads to changes in basal lung microflora, which may contribute to chronic pulmonary complications that increasingly are being recognized in individuals infected with HIV.

Objectives

To determine whether the immunodeficiency associated with HIV infection resulted in alteration of the lung microbiota.

Methods

We used 16S ribosomal RNA targeted pyrosequencing and shotgun metagenomic sequencing to analyze bacterial gene sequences in bronchoalveolar lavage (BAL) and mouths of 82 HIV-positive and 77 HIV-negative subjects.

Measurements and main results

Sequences representing Tropheryma whipplei, the etiologic agent of Whipple's disease, were significantly more frequent in BAL of HIV-positive compared with HIV-negative individuals. T. whipplei dominated the community (>50% of sequence reads) in 11 HIV-positive subjects, but only 1 HIV-negative individual (13.4 versus 1.3%; P = 0.0018). In 30 HIV-positive individuals sampled longitudinally, antiretroviral therapy resulted in a significantly reduced relative abundance of T. whipplei in the lung. Shotgun metagenomic sequencing was performed on eight BAL samples dominated by T. whipplei 16S ribosomal RNA. Whole genome assembly of pooled reads showed that uncultured lung-derived T. whipplei had similar gene content to two isolates obtained from subjects with Whipple's disease.

Conclusions

Asymptomatic subjects with HIV infection have unexpected colonization of the lung by T. whipplei, which is reduced by effective antiretroviral therapy and merits further study for a potential pathogenic role in chronic pulmonary complications of HIV infection.

Rationale

Improved understanding of the lung microbiome in HIV-infected individuals could lead to better strategies for diagnosis, therapy, and prophylaxis of HIV-associated pneumonias. Differences in the oral and lung microbiomes in HIV-infected and HIV-uninfected individuals are not well defined. Whether highly active antiretroviral therapy influences these microbiomes is unclear.

Objectives

We determined whether oral and lung microbiomes differed in clinically healthy groups of HIV-infected and HIV-uninfected subjects.

Methods

Participating sites in the Lung HIV Microbiome Project contributed bacterial 16S rRNA sequencing data from oral washes and bronchoalveolar lavages (BALs) obtained from HIV-uninfected individuals (n = 86), HIV-infected individuals who were treatment naive (n = 18), and HIV-infected individuals receiving antiretroviral therapy (n = 38).

Measurements and main results

Microbial populations differed in the oral washes among the subject groups (Streptococcus, Actinomyces, Rothia, and Atopobium), but there were no individual taxa that differed among the BALs. Comparison of oral washes and BALs demonstrated similar patterns from HIV-uninfected individuals and HIV-infected individuals receiving antiretroviral therapy, with multiple taxa differing in abundance. The pattern observed from HIV-infected individuals who were treatment naive differed from the other two groups, with differences limited to Veillonella, Rothia, and Granulicatella. CD4 cell counts did not influence the oral or BAL microbiome in these relatively healthy, HIV-infected subjects.

Conclusions

The overall similarity of the microbiomes in participants with and without HIV infection was unexpected, because HIV-infected individuals with relatively preserved CD4 cell counts are at higher risk for lower respiratory tract infections, indicating impaired local immune function.