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We recently published two independent randomized controlled trials of vitamin D supplementation during pregnancy, both indicating a >20% reduced risk of asthma/recurrent wheeze in the offspring by 3 years of age. However, neither reached statistical significance. To perform a combined analysis of the two trials and investigate whether maternal 25-hydroxy-vitamin D (25(OH)D) level at trial entry modified the intervention effect. VDAART (N = 806) and COPSAC2010. (N = 581) randomized pregnant women to daily high-dose vitamin D3 (4,000 IU/d and 2,400 IU/d, respectively) or placebo. All women also received a prenatal vitamin containing 400 IU/d vitamin D3. The primary outcome was asthma/recurrent wheeze from 0-3yrs. Secondary end-points were specific IgE, total IgE, eczema and lower respiratory tract infections (LRTI). We conducted random effects combined analyses of the treatment effect, individual patient data (IPD) meta-analyses, and analyses stratified by 25(OH)D level at study entry. The analysis showed a 25% reduced risk of asthma/recurrent wheeze at 0-3yrs: adjusted odds ratio (aOR) = 0.74 (95% CI, 0.57-0.96), p = 0.02. The effect was strongest among women with 25(OH)D level ≥30ng/ml at study entry: aOR = 0.54 (0.33-0.88), p = 0.01, whereas no significant effect was observed among women with 25(OH)D level <30ng/ml at study entry: aOR = 0.84 (0.62-1.15), p = 0.25. The IPD meta-analyses showed similar results. There was no effect on the secondary end-points. This combined analysis shows that vitamin D supplementation during pregnancy results in a significant reduced risk of asthma/recurrent wheeze in the offspring, especially among women with 25(OH)D level ≥ 30 ng/ml at randomization, where the risk was almost halved. Future studies should examine the possibility of raising 25(OH)D levels to at least 30 ng/ml early in pregnancy or using higher doses than used in our studies. COPSAC2010: ClinicalTrials.gov NCT00856947; VDAART: ClinicalTrials.gov NCT00920621.

We conducted a literature review on the studies that investigated the relationship of preterm birth, including spontaneous preterm birth (sPTB), with vitamin D status. Overall, these studies demonstrated that the incidence of sPTB was associated with maternal vitamin D insufficiency in early pregnancy. However, the potential mechanisms and biological pathways are unknown. To investigate early pregnancy gene expression signatures associated with both vitamin D insufficiency and sPTB. We further constructed a network of these gene signatures and identified the common biological pathways involved. We conducted peripheral blood transcriptome profiling at 10-18 weeks of gestation in a nested case-control cohort of 24 pregnant women who participated in the Vitamin D Antenatal Asthma Reduction Trial (VDAART). In this cohort, 8 women had spontaneous preterm delivery (21-32 weeks of gestation) and 17 women had vitamin D insufficiency (25-hydroxyvitamin D < 30 ng/mL). We separately identified vitamin D-associated and sPTB gene signatures at 10 to 18 weeks and replicated the overlapping signatures in the mid-pregnancy peripheral blood of an independent cohort with sPTB cases. At 10-18 weeks of gestation, 146 differentially expressed genes (25 upregulated) were associated with both vitamin D insufficiency and sPTB in the discovery cohort (FDR < 0.05). Of these genes, 43 (25 upregulated) were replicated in the independent cohort of sPTB cases and controls with normal pregnancies (P < 0.05). Functional enrichment and network analyses of the replicated gene signatures suggested several highly connected nodes related to inflammatory and immune responses. Our gene expression study and network analyses suggest that the dysregulation of immune response pathways due to early pregnancy vitamin D insufficiency may contribute to the pathobiology of sPTB.

The ratio of forced expiratory volume in one second to forced vital capacity (FEV(1)/FVC) is a measure used to diagnose airflow obstruction and is highly heritable. We performed a genome-wide association study in 7,691 Framingham Heart Study participants to identify single-nucleotide polymorphisms (SNPs) associated with the FEV(1)/FVC ratio, analyzed as a percent of the predicted value. Identified SNPs were examined in an independent set of 835 Family Heart Study participants enriched for airflow obstruction. Four SNPs in tight linkage disequilibrium on chromosome 4q31 were associated with the percent predicted FEV(1)/FVC ratio with p-values of genome-wide significance in the Framingham sample (best p-value = 3.6e-09). One of the four chromosome 4q31 SNPs (rs13147758; p-value 2.3e-08 in Framingham) was genotyped in the Family Heart Study and produced evidence of association with the same phenotype, percent predicted FEV(1)/FVC (p-value = 2.0e-04). The effect estimates for association in the Framingham and Family Heart studies were in the same direction, with the minor allele (G) associated with higher FEV(1)/FVC ratio levels. Results from the Family Heart Study demonstrated that the association extended to FEV(1) and dichotomous airflow obstruction phenotypes, particularly among smokers. The SNP rs13147758 was associated with the percent predicted FEV(1)/FVC ratio in independent samples from the Framingham and Family Heart Studies producing a combined p-value of 8.3e-11, and this region of chromosome 4 around 145.68 megabases was associated with COPD in three additional populations reported in the accompanying manuscript. The associated SNPs do not lie within a gene transcript but are near the hedgehog-interacting protein (HHIP) gene and several expressed sequence tags cloned from fetal lung. Though it is unclear what gene or regulatory effect explains the association, the region warrants further investigation.

Background Asthma is a heterogeneous disease with high morbidity. Advancement in high-throughput multi-omics approaches has enabled the collection of molecular assessments at different layers, providing a complementary perspective of complex diseases. Numerous computational methods have been developed for the omics-based patient classification or disease outcome prediction. Yet, a systematic benchmarking of those methods using various combinations of omics data for the prediction of asthma development is still lacking. Objective We aimed to investigate the computational methods in disease status prediction using multi-omics data. Method We systematically benchmarked 18 computational methods using all the 63 combinations of six omics data (GWAS, miRNA, mRNA, microbiome, metabolome, DNA methylation) collected in The Vitamin D Antenatal Asthma Reduction Trial (VDAART) cohort. We evaluated each method using standard performance metrics for each of the 63 omics combinations. Results Our results indicate that overall Logistic Regression, Multi-Layer Perceptron, and MOGONET display superior performance, and the combination of transcriptional, genomic and microbiome data achieves the best prediction. Moreover, we find that including the clinical data can further improve the prediction performance for some but not all the omics combinations. Conclusions Specific omics combinations can reach the optimal prediction of asthma development in children. And certain computational methods showed superior performance than other methods.

Although the taxonomic composition of the human microbiome varies tremendously across individuals, its gene composition or functional capacity is highly conserved — implying an ecological property known as functional redundancy. Such functional redundancy has been hypothesized to underlie the stability and resilience of the human microbiome, but this hypothesis has never been quantitatively tested. The origin of functional redundancy is still elusive. Here, we investigate the basis for functional redundancy in the human microbiome by analyzing its genomic content network — a bipartite graph that links microbes to the genes in their genomes. We find that this network exhibits several topological features that favor high functional redundancy. Furthermore, we develop a simple genome evolution model to generate genomic content network, finding that moderate selection pressure and high horizontal gene transfer rate are necessary to generate genomic content networks with key topological features that favor high functional redundancy. Finally, we analyze data from two published studies of fecal microbiota transplantation (FMT), finding that high functional redundancy of the recipient’s pre-FMT microbiota raises barriers to donor microbiota engraftment. This work elucidates the potential ecological and evolutionary processes that create and maintain functional redundancy in the human microbiome and contribute to its resilience. Here, the authors develop a genome evolution model to investigate the origin of functional redundancy in the human microbiome by analyzing its genomic content network and illustrate potential ecological and evolutionary processes that may contribute to its resilience.

The identification and understanding of gene-environment interactions can provide insights into the pathways and mechanisms underlying complex diseases. However, testing for gene-environment interaction remains a challenge since a.) statistical power is often limited and b.) modeling of environmental effects is nontrivial and such model misspecifications can lead to false positive interaction findings. To address the lack of statistical power, recent methods aim to identify interactions on an aggregated level using, for example, polygenic risk scores. While this strategy can increase the power to detect interactions, identifying contributing genes and pathways is difficult based on these relatively global results. Here, we propose RITSS (Robust Interaction Testing using Sample Splitting), a gene-environment interaction testing framework for quantitative traits that is based on sample splitting and robust test statistics. RITSS can incorporate sets of genetic variants and/or multiple environmental factors. Based on the user’s choice of statistical/machine learning approaches, a screening step selects and combines potential interactions into scores with improved interpretability. In the testing step, the application of robust statistics minimizes the susceptibility to main effect misspecifications. Using extensive simulation studies, we demonstrate that RITSS controls the type 1 error rate in a wide range of scenarios, and we show how the screening strategy influences statistical power. In an application to lung function phenotypes and human height in the UK Biobank, RITSS identified highly significant interactions based on subcomponents of genetic risk scores. While the contributing single variant interaction signals are weak, our results indicate interaction patterns that result in strong aggregated effects, providing potential insights into underlying gene-environment interaction mechanisms.

Coronavirus disease 2019 (COVID-19), primarily a respiratory disease caused by infection with Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), is often accompanied by gastrointestinal symptoms. However, little is known about the relation between the human microbiome and COVID-19, largely due to the fact that most previous studies fail to provide high taxonomic resolution to identify microbes that likely interact with SARS-CoV-2 infection. Here we used whole-metagenome shotgun sequencing data together with assembly and binning strategies to reconstruct metagenome-assembled genomes (MAGs) from 514 COVID-19 related nasopharyngeal and fecal samples in six independent cohorts. We reconstructed a total of 11,584 medium-and high-quality microbial MAGs and obtained 5403 non-redundant MAGs (nrMAGs) with strain-level resolution. We found that there is a significant reduction of strain richness for many species in the gut microbiome of COVID-19 patients. The gut microbiome signatures can accurately distinguish COVID-19 cases from healthy controls and predict the progression of COVID-19. Moreover, we identified a set of nrMAGs with a putative causal role in the clinical manifestations of COVID-19 and revealed their functional pathways that potentially interact with SARS-CoV-2 infection. Finally, we demonstrated that the main findings of our study can be largely validated in three independent cohorts. The presented results highlight the importance of incorporating the human gut microbiome in our understanding of SARS-CoV-2 infection and disease progression. Here, using whole-metagenome shotgun sequencing data from patients with COVID-19 and controls, the authors reconstruct 11,584 microbial metagenome-assembled genomes (MAGs) including 5,403 non-redundant MAGs, revealing microbiota and metabolic pathways associations with SARS-CoV-2 infection at strain-level resolution.

Globally, asthma is a chronic inflammatory respiratory disease affecting over 300 million people. Some asthma patients remain poorly controlled by conventional therapies and experience more life-threatening exacerbations. Vitamin D, as an adjunct therapy, may improve disease control in severe asthma patients since vitamin D enhances glucocorticoid responsiveness and mitigates airway smooth muscle (ASM) hyperplasia. We sought to characterize differences in transcriptome responsiveness to vitamin D between fatal asthma- and non-asthma-derived ASM by using RNA-Seq to measure ASM transcript expression in five donors with fatal asthma and ten non-asthma-derived donors at baseline and with vitamin D treatment. Based on a Benjamini-Hochberg corrected p-value <0.05, 838 genes were differentially expressed in fatal asthma vs. non-asthma-derived ASM at baseline, and vitamin D treatment compared to baseline conditions induced differential expression of 711 and 867 genes in fatal asthma- and non-asthma-derived ASM, respectively. Functional gene categories that were highly represented in all groups included extracellular matrix, and responses to steroid hormone stimuli and wounding. Genes differentially expressed by vitamin D also included cytokine and chemokine activity categories. Follow-up qPCR and individual analyte ELISA experiments were conducted for four cytokines (i.e. CCL2, CCL13, CXCL12, IL8) to measure TNFα-induced changes by asthma status and vitamin D treatment. Vitamin D inhibited TNFα-induced IL8 protein secretion levels to a comparable degree in fatal asthma- and non-asthma-derived ASM even though IL8 had significantly higher baseline levels in fatal asthma-derived ASM. Our findings identify vitamin D-specific gene targets and provide transcriptomic data to explore differences in the ASM of fatal asthma- and non-asthma-derived donors.

Mapping the ecological networks of microbial communities is a necessary step toward understanding their assembly rules and predicting their temporal behavior. However, existing methods require assuming a particular population dynamics model, which is not known a priori. Moreover, those methods require fitting longitudinal abundance data, which are often not informative enough for reliable inference. To overcome these limitations, here we develop a new method based on steady-state abundance data. Our method can infer the network topology and inter-taxa interaction types without assuming any particular population dynamics model. Additionally, when the population dynamics is assumed to follow the classic Generalized Lotka–Volterra model, our method can infer the inter-taxa interaction strengths and intrinsic growth rates. We systematically validate our method using simulated data, and then apply it to four experimental data sets. Our method represents a key step towards reliable modeling of complex, real-world microbial communities, such as the human gut microbiota. Understanding ecological interactions in microbial communities is limited by lack of informative longitudinal abundance data necessary for reliable inference. Here, Xiao et al . develop a method to infer the interactions between microbes based on their abundances in steady-state samples.

Previous studies on the differences in gut microbiota between exclusively breastfed (EBF) and non-EBF infants have provided highly variable results. Here we perform a meta-analysis of seven microbiome studies (1825 stool samples from 684 infants) to compare the gut microbiota of non-EBF and EBF infants across populations. In the first 6 months of life, gut bacterial diversity, microbiota age, relative abundances of Bacteroidetes and Firmicutes, and predicted microbial pathways related to carbohydrate metabolism are consistently higher in non-EBF than in EBF infants, whereas relative abundances of pathways related to lipid metabolism, vitamin metabolism, and detoxification are lower. Variation in predicted microbial pathways associated with non-EBF infants is larger among infants born by Caesarian section than among those vaginally delivered. Longer duration of exclusive breastfeeding is associated with reduced diarrhea-related gut microbiota dysbiosis. Furthermore, differences in gut microbiota between EBF and non-EBF infants persist after 6 months of age. Our findings elucidate some mechanisms of short and long-term benefits of exclusive breastfeeding across different populations. Studies on the effects of breastfeeding on the infant gut microbiota have provided inconsistent results. Here, Ho et al. perform a meta-analysis of seven studies across different populations, supporting that exclusive breastfeeding is associated with short-term and long-term alterations in the infant gut microbiota.