Aberrant newborn T cell and microbiota developmental trajectories predict respiratory compromise during infancy.

Neonatal immune-microbiota co-development is poorly understood, yet age-appropriate recognition of - and response to - pathogens and commensal microbiota is critical to health. In this longitudinal study of 148 preterm and 119 full-term infants from birth through one year of age, we found that postmenstrual age or weeks from conception is a central factor influencing T cell and mucosal microbiota development. Numerous features of the T cell and microbiota functional development remain unexplained; however, by either age metric and are instead shaped by discrete perinatal and postnatal events. Most strikingly, we establish that prenatal antibiotics or infection disrupt the normal T cell population developmental trajectory, influencing subsequent respiratory microbial colonization and predicting respiratory morbidity. In this way, early exposures predict the postnatal immune-microbiota axis trajectory, placing infants at later risk for respiratory morbidity in early childhood.

Temporal Dysbiosis of Infant Nasal Microbiota Relative to Respiratory Syncytial Virus Infection.

BACKGROUND: Respiratory syncytial virus (RSV) is a leading cause of infant respiratory disease. Infant airway microbiota has been associated with respiratory disease risk and severity. The extent to which interactions between RSV and microbiota occur in the airway, and their impact on respiratory disease susceptibility and severity, are unknown. METHODS: We carried out 16S rRNA microbiota profiling of infants in the first year of life from (1) a cross-sectional cohort of 89 RSV-infected infants sampled during illness and 102 matched healthy controls, and (2) a matched longitudinal cohort of 12 infants who developed RSV infection and 12 who did not, sampled before, during, and after infection. RESULTS: We identified 12 taxa significantly associated with RSV infection. All 12 taxa were differentially abundant during infection, with 8 associated with disease severity. Nasal microbiota composition was more discriminative of healthy vs infected than of disease severity. CONCLUSIONS: Our findings elucidate the chronology of nasal microbiota dysbiosis and suggest an altered developmental trajectory associated with RSV infection. Microbial temporal dynamics reveal indicators of disease risk, correlates of illness and severity, and impact of RSV infection on microbiota composition.

Broadening our understanding of the genetics of Juvenile Idiopathic Arthritis (JIA): Interrogation of three dimensional chromatin structures and genetic regulatory elements within JIA-associated risk loci.

OBJECTIVE: The risk loci for juvenile idiopathic arthritis (JIA) consist of extended haplotypes that include functional elements in addition to canonical coding genes. As with most autoimmune diseases, the risk haplotypes for JIA are highly enriched for H3K4me1/H3K27ac histone marks, epigenetic signatures that typically identify poised or active enhancers. In this study, we test the hypothesis that genetic risk for JIA is exerted through altered enhancer-mediated gene regulation. METHODS: We mined publically available HiC and other chromatin conformation data to determine whether H3K27ac-marked regions in 25 JIA risk loci showed physical evidence of contact with gene promoters. We also used in vitro reporter assays to establish as proof-of-concept the idea that genetic variants in linkage disequilibrium with GWAS-identified tag SNPs alter enhancer function. RESULTS: All 25 loci examined showed multiple contact sites in the 4 different cell lines that we queried. These regions were characterized by HiC-defined loop structures that included 237 immune-related genes. Using in vitro assays, we found that a 657 bp, H3K4me1/H3K27-marked region within the first intron of IL2RA shows enhancer activity in reporter assays, and this activity is attenuated by SNPs on the IL2RA haplotype that we identified using whole genome sequencing of children with JIA. Similarly, we identified a 1,669 bp sequence in an intergenic region of the IL6R locus where SNPs identified in children with JIA increase enhancer function in reporter assays. CONCLUSIONS: These studies provide evidence that altered enhancer function contributes to genetic risk in JIA. Further studies to identify the specific target genes of genetically altered enhancers are warranted.

Neonatal gut and respiratory microbiota: coordinated development through time and space.

BACKGROUND: Postnatal development of early life microbiota influences immunity, metabolism, neurodevelopment, and infant health. Microbiome development occurs at multiple body sites, with distinct community compositions and functions. Associations between microbiota at multiple sites represent an unexplored influence on the infant microbiome. Here, we examined co-occurrence patterns of gut and respiratory microbiota in pre- and full-term infants over the first year of life, a period critical to neonatal development. RESULTS: Gut and respiratory microbiota collected as longitudinal rectal, throat, and nasal samples from 38 pre-term and 44 full-term infants were first clustered into community state types (CSTs) on the basis of their compositional profiles. Multiple methods were used to relate the occurrence of CSTs to temporal microbiota development and measures of infant maturity, including gestational age (GA) at birth, week of life (WOL), and post-menstrual age (PMA). Manifestation of CSTs followed one of three patterns with respect to infant maturity: (1) chronological, with CST occurrence frequency solely a function of post-natal age (WOL), (2) idiosyncratic to maturity at birth, with the interval of CST occurrence dependent on infant post-natal age but the frequency of occurrence dependent on GA at birth, and (3) convergent, in which CSTs appear first in infants of greater maturity at birth, with occurrence frequency in pre-terms converging after a post-natal interval proportional to pre-maturity. The composition of CSTs was highly dissimilar between different body sites, but the CST of any one body site was highly predictive of the CSTs at other body sites. There were significant associations between the abundance of individual taxa at each body site and the CSTs of the other body sites, which persisted after stringent control for the non-linear effects of infant maturity. Canonical correlations exist between the microbiota composition at each pair of body sites, with the strongest correlations between proximal locations. CONCLUSION: These findings suggest that early microbiota is shaped by neonatal innate and adaptive developmental responses. Temporal progression of CST occurrence is influenced by infant maturity at birth and post-natal age. Significant associations of microbiota across body sites reveal distal connections and coordinated development of the infant microbial ecosystem.

Using Chromatin Architecture to Understand the Genetics and Transcriptomics of Juvenile Idiopathic Arthritis.

The presence of abnormal gene expression signatures is a well-described feature of the oligoarticular and polyarticular forms of juvenile idiopathic arthritis. In this review, we discuss how new insights into genetic risk for JIA and the three dimensional architecture of the genome may be used to develop a better understanding of the mechanisms driving these gene expression patterns.

Impact of prematurity and nutrition on the developing gut microbiome and preterm infant growth.

BACKGROUND: Identification of factors that influence the neonatal gut microbiome is urgently needed to guide clinical practices that support growth of healthy preterm infants. Here, we examined the influence of nutrition and common practices on the gut microbiota and growth in a cohort of preterm infants. RESULTS: With weekly gut microbiota samples spanning postmenstrual age (PMA) 24 to 46 weeks, we developed two models to test associations between the microbiota, nutrition and growth: a categorical model with three successive microbiota phases (P1, P2, and P3) and a model with two periods (early and late PMA) defined by microbiota composition and PMA, respectively. The more significant associations with phase led us to use a phase-based framework for the majority of our analyses. Phase transitions were characterized by rapid shifts in the microbiota, with transition out of P1 occurring nearly simultaneously with the change from meconium to normal stool. The rate of phase progression was positively associated with gestational age at birth, and delayed transition to a P3 microbiota was associated with growth failure. We found distinct bacterial metabolic functions in P1-3 and significant associations between nutrition, microbiota phase, and infant growth. CONCLUSION: The phase-dependent impact of nutrition on infant growth along with phase-specific metabolic functions suggests a pioneering potential for improving growth outcomes by tailoring nutrient intake to microbiota phase.

Annotation of proteins of unknown function: initial enzyme results.

Working with a combination of ProMOL (a plugin for PyMOL that searches a library of enzymatic motifs for local structural homologs), BLAST and Pfam (servers that identify global sequence homologs), and Dali (a server that identifies global structural homologs), we have begun the process of assigning functional annotations to the approximately 3,500 structures in the Protein Data Bank that are currently classified as having "unknown function". Using a limited template library of 388 motifs, over 500 promising in silico matches have been identified by ProMOL, among which 65 exceptionally good matches have been identified. The characteristics of the exceptionally good matches are discussed.

Estimation of protein function using template-based alignment of enzyme active sites.

BACKGROUND: The accumulation of protein structural data occurs more rapidly than it can be characterized by traditional laboratory means. This has motivated widespread efforts to predict enzyme function computationally. The most useful/accurate strategies employed to date are based on the detection of motifs in novel structures that correspond to a specific function. Functional residues are critical components of predictively useful motifs. We have implemented a novel method, to complement current approaches, which detects motifs solely on the basis of distance restraints between catalytic residues. RESULTS: ProMOL is a plugin for the PyMOL molecular graphics environment that can be used to create active site motifs for enzymes. A library of 181 active site motifs has been created with ProMOL, based on definitions published in the Catalytic Site Atlas (CSA). Searches with ProMOL produce better than 50% useful Enzyme Commission (EC) class suggestions for level 1 searches in EC classes 1, 4 and 5, and produce some useful results for other classes. 261 additional motifs automatically translated from Jonathan Barker's JESS motif set [Bioinformatics 19:1644-1649, 2003] and a set of NMR motifs is under development. Alignments are evaluated by visual superposition, Levenshtein distance and root-mean-square deviation (RMSD) and are reasonably consistent with related search methods. CONCLUSION: The ProMOL plugin for PyMOL provides ready access to template-based local alignments. Recent improvements to ProMOL, including the expanded motif library, RMSD calculations and output selection formatting, have greatly increased the program's usability and speed, and have improved the way that the results are presented.