Comprehensive evaluation of methods for differential expression analysis of metatranscriptomics data.

Understanding the function of the human microbiome is important but the development of statistical methods specifically for the microbial gene expression (i.e. metatranscriptomics) is in its infancy. Many currently employed differential expression analysis methods have been designed for different data types and have not been evaluated in metatranscriptomics settings. To address this gap, we undertook a comprehensive evaluation and benchmarking of 10 differential analysis methods for metatranscriptomics data. We used a combination of real and simulated data to evaluate performance (i.e. type I error, false discovery rate and sensitivity) of the following methods: log-normal (LN), logistic-beta (LB), MAST, DESeq2, metagenomeSeq, ANCOM-BC, LEfSe, ALDEx2, Kruskal-Wallis and two-part Kruskal-Wallis. The simulation was informed by supragingival biofilm microbiome data from 300 preschool-age children enrolled in a study of childhood dental disease (early childhood caries, ECC), whereas validations were sought in two additional datasets from the ECC study and an inflammatory bowel disease study. The LB test showed the highest sensitivity in both small and large samples and reasonably controlled type I error. Contrarily, MAST was hampered by inflated type I error. Upon application of the LN and LB tests in the ECC study, we found that genes C8PHV7 and C8PEV7, harbored by the lactate-producing Campylobacter gracilis, had the strongest association with childhood dental disease. This comprehensive model evaluation offers practical guidance for selection of appropriate methods for rigorous analyses of differential expression in metatranscriptomics. Selection of an optimal method increases the possibility of detecting true signals while minimizing the chance of claiming false ones.

Targeted inhibition of gut bacterial ß-glucuronidase activity enhances anticancer drug efficacy.

Irinotecan treats a range of solid tumors, but its effectiveness is severely limited by gastrointestinal (GI) tract toxicity caused by gut bacterial ß-glucuronidase (GUS) enzymes. Targeted bacterial GUS inhibitors have been shown to partially alleviate irinotecan-induced GI tract damage and resultant diarrhea in mice. Here, we unravel the mechanistic basis for GI protection by gut microbial GUS inhibitors using in vivo models. We use in vitro, in fimo, and in vivo models to determine whether GUS inhibition alters the anticancer efficacy of irinotecan. We demonstrate that a single dose of irinotecan increases GI bacterial GUS activity in 1 d and reduces intestinal epithelial cell proliferation in 5 d, both blocked by a single dose of a GUS inhibitor. In a tumor xenograft model, GUS inhibition prevents intestinal toxicity and maintains the antitumor efficacy of irinotecan. Remarkably, GUS inhibitor also effectively blocks the striking irinotecan-induced bloom of Enterobacteriaceae in immune-deficient mice. In a genetically engineered mouse model of cancer, GUS inhibition alleviates gut damage, improves survival, and does not alter gut microbial composition; however, by allowing dose intensification, it dramatically improves irinotecan's effectiveness, reducing tumors to a fraction of that achieved by irinotecan alone, while simultaneously promoting epithelial regeneration. These results indicate that targeted gut microbial enzyme inhibitors can improve cancer chemotherapeutic outcomes by protecting the gut epithelium from microbial dysbiosis and proliferative crypt damage.

Differences in the microbial profiles of early stage endometrial cancers between Black and White women.

OBJECTIVE: Black women suffer a higher mortality from endometrial cancer (EC) than White women. Potential biological causes for this disparity include a higher prevalence of obesity and more lethal histologic/molecular subtypes. We hypothesize that another biological factor driving this racial disparity could be the EC microbiome. METHODS: Banked tumor specimens of postmenopausal, Black and White women undergoing hysterectomy for early stage endometrioid EC were identified. The microbiota of the tumors were characterized by bacterial 16S rRNA sequencing. The microbial component of endometrioid ECs in The Cancer Genome Atlas (TCGA) database were assessed for comparison. RESULTS: 95 early stage ECs were evaluated: 23 Black (24%) and 72 White (76%). Microbial diversity was increased (p < 0.001), and Firmicutes, Cyanobacteria and OD1 phyla abundance was higher in tumors from Black versus White women (p < 0.001). Genus level abundance of Dietzia and Geobacillus were found to be lower in tumors of obese Black versus obese White women (p < 0.001). Analysis of early stage ECs in TCGA found that microbial diversity was higher in ECs from Black versus White women (p < 0.05). When comparing ECs from obese Black versus obese White women, 5 bacteria distributions were distinct, with higher abundance of Lactobacillus acidophilus in ECs from Black women being the most striking difference. Similarly in TCGA, Dietzia and Geobacillus were more common in ECs from White women compared to Black. CONCLUSION: Increased microbial diversity and the distinct microbial profiles between ECs of obese Black versus obese White women suggests that intra-tumoral bacteria may contribute to EC disparities and pathogenesis.

A genome-wide interaction analysis of tricyclic/tetracyclic antidepressants and RR and QT intervals: a pharmacogenomics study from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium.

BACKGROUND: Increased heart rate and a prolonged QT interval are important risk factors for cardiovascular morbidity and mortality, and can be influenced by the use of various medications, including tricyclic/tetracyclic antidepressants (TCAs). We aim to identify genetic loci that modify the association between TCA use and RR and QT intervals. METHODS AND RESULTS: We conducted race/ethnic-specific genome-wide interaction analyses (with HapMap phase II imputed reference panel imputation) of TCAs and resting RR and QT intervals in cohorts of European (n=45 706; n=1417 TCA users), African (n=10 235; n=296 TCA users) and Hispanic/Latino (n=13 808; n=147 TCA users) ancestry, adjusted for clinical covariates. Among the populations of European ancestry, two genome-wide significant loci were identified for RR interval: rs6737205 in BRE (ß=56.3, pinteraction=3.9e-9) and rs9830388 in UBE2E2 (ß=25.2, pinteraction=1.7e-8). In Hispanic/Latino cohorts, rs2291477 in TGFBR3 significantly modified the association between TCAs and QT intervals (ß=9.3, pinteraction=2.55e-8). In the meta-analyses of the other ethnicities, these loci either were excluded from the meta-analyses (as part of quality control), or their effects did not reach the level of nominal statistical significance (pinteraction>0.05). No new variants were identified in these ethnicities. No additional loci were identified after inverse-variance-weighted meta-analysis of the three ancestries. CONCLUSIONS: Among Europeans, TCA interactions with variants in BRE and UBE2E2 were identified in relation to RR intervals. Among Hispanic/Latinos, variants in TGFBR3 modified the relation between TCAs and QT intervals. Future studies are required to confirm our results.

A comparison of sequencing platforms and bioinformatics pipelines for compositional analysis of the gut microbiome.

BACKGROUND: Advancements in Next Generation Sequencing (NGS) technologies regarding throughput, read length and accuracy had a major impact on microbiome research by significantly improving 16S rRNA amplicon sequencing. As rapid improvements in sequencing platforms and new data analysis pipelines are introduced, it is essential to evaluate their capabilities in specific applications. The aim of this study was to assess whether the same project-specific biological conclusions regarding microbiome composition could be reached using different sequencing platforms and bioinformatics pipelines. RESULTS: Chicken cecum microbiome was analyzed by 16S rRNA amplicon sequencing using Illumina MiSeq, Ion Torrent PGM, and Roche 454 GS FLX Titanium platforms, with standard and modified protocols for library preparation. We labeled the bioinformatics pipelines included in our analysis QIIME1 and QIIME2 (de novo OTU picking [not to be confused with QIIME version 2 commonly referred to as QIIME2]), QIIME3 and QIIME4 (open reference OTU picking), UPARSE1 and UPARSE2 (each pair differs only in the use of chimera depletion methods), and DADA2 (for Illumina data only). GS FLX+ yielded the longest reads and highest quality scores, while MiSeq generated the largest number of reads after quality filtering. Declines in quality scores were observed starting at bases 150-199 for GS FLX+ and bases 90-99 for MiSeq. Scores were stable for PGM-generated data. Overall microbiome compositional profiles were comparable between platforms; however, average relative abundance of specific taxa varied depending on sequencing platform, library preparation method, and bioinformatics analysis. Specifically, QIIME with de novo OTU picking yielded the highest number of unique species and alpha diversity was reduced with UPARSE and DADA2 compared to QIIME. CONCLUSIONS: The three platforms compared in this study were capable of discriminating samples by treatment, despite differences in diversity and abundance, leading to similar biological conclusions. Our results demonstrate that while there were differences in depth of coverage and phylogenetic diversity, all workflows revealed comparable treatment effects on microbial diversity. To increase reproducibility and reliability and to retain consistency between similar studies, it is important to consider the impact on data quality and relative abundance of taxa when selecting NGS platforms and analysis tools for microbiome studies.

Statistical evaluation of the Rodin-Ohno hypothesis: sense/antisense coding of ancestral class I and II aminoacyl-tRNA synthetases.

We tested the idea that ancestral class I and II aminoacyl-tRNA synthetases arose on opposite strands of the same gene. We assembled excerpted 94-residue Urgenes for class I tryptophanyl-tRNA synthetase (TrpRS) and class II Histidyl-tRNA synthetase (HisRS) from a diverse group of species, by identifying and catenating three blocks coding for secondary structures that position the most highly conserved, active-site residues. The codon middle-base pairing frequency was 0.35 ± 0.0002 in all-by-all sense/antisense alignments for 211 TrpRS and 207 HisRS sequences, compared with frequencies between 0.22 ± 0.0009 and 0.27 ± 0.0005 for eight different representations of the null hypothesis. Clustering algorithms demonstrate further that profiles of middle-base pairing in the synthetase antisense alignments are correlated along the sequences from one species-pair to another, whereas this is not the case for similar operations on sets representing the null hypothesis. Most probable reconstructed sequences for ancestral nodes of maximum likelihood trees show that middle-base pairing frequency increases to approximately 0.42 ± 0.002 as bacterial trees approach their roots; ancestral nodes from trees including archaeal sequences show a less pronounced increase. Thus, contemporary and reconstructed sequences all validate important bioinformatic predictions based on descent from opposite strands of the same ancestral gene. They further provide novel evidence for the hypothesis that bacteria lie closer than archaea to the origin of translation. Moreover, the inverse polarity of genetic coding, together with a priori α-helix propensities suggest that in-frame coding on opposite strands leads to similar secondary structures with opposite polarity, as observed in TrpRS and HisRS crystal structures.

Accurate sampling and deep sequencing of the HIV-1 protease gene using a Primer ID.

Viruses can create complex genetic populations within a host, and deep sequencing technologies allow extensive sampling of these populations. Limitations of these technologies, however, potentially bias this sampling, particularly when a PCR step precedes the sequencing protocol. Typically, an unknown number of templates are used in initiating the PCR amplification, and this can lead to unrecognized sequence resampling creating apparent homogeneity; also, PCR-mediated recombination can disrupt linkage, and differential amplification can skew allele frequency. Finally, misincorporation of nucleotides during PCR and errors during the sequencing protocol can inflate diversity. We have solved these problems by including a random sequence tag in the initial primer such that each template receives a unique Primer ID. After sequencing, repeated identification of a Primer ID reveals sequence resampling. These resampled sequences are then used to create an accurate consensus sequence for each template, correcting for recombination, allelic skewing, and misincorporation/sequencing errors. The resulting population of consensus sequences directly represents the initial sampled templates. We applied this approach to the HIV-1 protease (pro) gene to view the distribution of sequence variation of a complex viral population within a host. We identified major and minor polymorphisms at coding and noncoding positions. In addition, we observed dynamic genetic changes within the population during intermittent drug exposure, including the emergence of multiple resistant alleles. These results provide an unprecedented view of a complex viral population in the absence of PCR resampling.

Selenomonas sputigena acts as a pathobiont mediating spatial structure and biofilm virulence in early childhood caries.

Streptococcus mutans has been implicated as the primary pathogen in childhood caries (tooth decay). While the role of polymicrobial communities is appreciated, it remains unclear whether other microorganisms are active contributors or interact with pathogens. Here, we integrate multi-omics of supragingival biofilm (dental plaque) from 416 preschool-age children (208 males and 208 females) in a discovery-validation pipeline to identify disease-relevant inter-species interactions. Sixteen taxa associate with childhood caries in metagenomics-metatranscriptomics analyses. Using multiscale/computational imaging and virulence assays, we examine biofilm formation dynamics, spatial arrangement, and metabolic activity of Selenomonas sputigena, Prevotella salivae and Leptotrichia wadei, either individually or with S. mutans. We show that S. sputigena, a flagellated anaerobe with previously unknown role in supragingival biofilm, becomes trapped in streptococcal exoglucans, loses motility but actively proliferates to build a honeycomb-like multicellular-superstructure encapsulating S. mutans, enhancing acidogenesis. Rodent model experiments reveal an unrecognized ability of S. sputigena to colonize supragingival tooth surfaces. While incapable of causing caries on its own, when co-infected with S. mutans, S. sputigena causes extensive tooth enamel lesions and exacerbates disease severity in vivo. In summary, we discover a pathobiont cooperating with a known pathogen to build a unique spatial structure and heighten biofilm virulence in a prevalent human disease.

Maternal precarity and HPA axis functioning shape infant gut microbiota and HPA axis development in humans.

BACKGROUND: Early life exposure to adverse environments, and maternal stress in particular, has been shown to increase risk for metabolic diseases and neurobehavioral disorders. While many studies have examined the hypothalamic-pituitary-adrenal axis (HPA axis) as the primary mechanism behind these relationships, emerging research on the brain-gut axis suggests that the microbiome may play a role. In this study, we tested the relationships among maternal precarity and HPA axis dysregulation during the peripartum period, infant gut microbiome composition, and infant HPA axis functioning. METHODS: Data come from 25 mother-infant dyads in the Galápagos, Ecuador. Women completed surveys on precarity measures (food insecurity, low social support, depression, and stress) and gave salivary cortisol samples during and after pregnancy. Infant salivary cortisol and stool were collected in the postpartum. Statistical significance of differences in microbial diversity and relative abundance were assessed with respect to adjusted linear regression models. RESULTS: Maternal precarity was associated with lower diversity and higher relative abundance of Enterobacteriaceae and Streptococcaceae and a lower relative abundance of Bifidobacterium and Lachnospiraceae. These patterns of colonization for Enterobacteriaceae and Bifidobacterium mirrored those found in infants with HPA axis dysregulation. Maternal HPA axis dysregulation during pregnancy was also associated with a greater relative abundance of Veillonella. CONCLUSIONS: Overall, exposures to precarity and HPA axis dysregulation were associated with an increase in groups that include potentially pathogenic bacteria, including Enterobacteriaceae, Streptococcaceae, and Veillonella, and a decrease in potentially protective bacteria, including Bifidobacterium and Lachnospiraceae, as well as a decrease in overall diversity.

Improved Metabolite Prediction Using Microbiome Data-Based Elastic Net Models.

Microbiome data are becoming increasingly available in large health cohorts, yet metabolomics data are still scant. While many studies generate microbiome data, they lack matched metabolomics data or have considerable missing proportions of metabolites. Since metabolomics is key to understanding microbial and general biological activities, the possibility of imputing individual metabolites or inferring metabolomics pathways from microbial taxonomy or metagenomics is intriguing. Importantly, current metabolomics profiling methods such as the HMP Unified Metabolic Analysis Network (HUMAnN) have unknown accuracy and are limited in their ability to predict individual metabolites. To address this gap, we developed a novel metabolite prediction method, and we present its application and evaluation in an oral microbiome study. The new method for predicting metabolites using microbiome data (ENVIM) is based on the elastic net model (ENM). ENVIM introduces an extra step to ENM to consider variable importance (VI) scores, and thus, achieves better prediction power. We investigate the metabolite prediction performance of ENVIM using metagenomic and metatranscriptomic data in a supragingival biofilm multi-omics dataset of 289 children ages 3-5 who were participants of a community-based study of early childhood oral health (ZOE 2.0) in North Carolina, United States. We further validate ENVIM in two additional publicly available multi-omics datasets generated from studies of gut health. We select gene family sets based on variable importance scores and modify the existing ENM strategy used in the MelonnPan prediction software to accommodate the unique features of microbiome and metabolome data. We evaluate metagenomic and metatranscriptomic predictors and compare the prediction performance of ENVIM to the standard ENM employed in MelonnPan. The newly developed ENVIM method showed superior metabolite predictive accuracy than MelonnPan when trained with metatranscriptomics data only, metagenomics data only, or both. Better metabolite prediction is achieved in the gut microbiome compared with the oral microbiome setting. We report the best-predictable compounds in all these three datasets from two different body sites. For example, the metabolites trehalose, maltose, stachyose, and ribose are all well predicted by the supragingival microbiome.

Chiropteran types I and II interferon genes inferred from genome sequencing traces by a statistical gene-family assembler.

BACKGROUND: The rate of emergence of human pathogens is steadily increasing; most of these novel agents originate in wildlife. Bats, remarkably, are the natural reservoirs of many of the most pathogenic viruses in humans. There are two bat genome projects currently underway, a circumstance that promises to speed the discovery host factors important in the coevolution of bats with their viruses. These genomes, however, are not yet assembled and one of them will provide only low coverage, making the inference of most genes of immunological interest error-prone. Many more wildlife genome projects are underway and intend to provide only shallow coverage. RESULTS: We have developed a statistical method for the assembly of gene families from partial genomes. The method takes full advantage of the quality scores generated by base-calling software, incorporating them into a complete probabilistic error model, to overcome the limitation inherent in the inference of gene family members from partial sequence information. We validated the method by inferring the human IFNA genes from the genome trace archives, and used it to infer 61 type-I interferon genes, and single type-II interferon genes in the bats Pteropus vampyrus and Myotis lucifugus. We confirmed our inferences by direct cloning and sequencing of IFNA, IFNB, IFND, and IFNK in P. vampyrus, and by demonstrating transcription of some of the inferred genes by known interferon-inducing stimuli. CONCLUSION: The statistical trace assembler described here provides a reliable method for extracting information from the many available and forthcoming partial or shallow genome sequencing projects, thereby facilitating the study of a wider variety of organisms with ecological and biomedical significance to humans than would otherwise be possible.

Functional gene categories differentiate maize leaf drought-related microbial epiphytic communities.

The phyllosphere epiphytic microbiome is composed of microorganisms that colonize the external aerial portions of plants. Relationships of plant responses to specific microorganisms-both pathogenic and beneficial-have been examined, but the phyllosphere microbiome functional and metabolic profile responses are not well described. Changing crop growth conditions, such as increased drought, can have profound impacts on crop productivity. Also, epiphytic microbial communities provide a new target for crop yield optimization. We compared Zea mays leaf microbiomes collected under drought and well-watered conditions by examining functional gene annotation patterns across three physically disparate locations each with and without drought treatment, through the application of short read metagenomic sequencing. Drought samples exhibited different functional sequence compositions at each of the three field sites. Maize phyllosphere functional profiles revealed a wide variety of metabolic and regulatory processes that differed in drought and normal water conditions and provide key baseline information for future selective breeding.

Relative abundance of Akkermansia spp. and other bacterial phylotypes correlates with anxiety- and depressive-like behavior following social defeat in mice.

As discussion of stress and stress-related disorders rapidly extends beyond the brain, gut microbiota have emerged as a promising contributor to individual differences in the risk of illness, disease course, and treatment response. Here, we employed chronic mild social defeat stress and 16S rRNA gene metagenomic sequencing to investigate the role of microbial composition in mediating anxiety- and depressive-like behavior. In socially defeated animals, we found significant reductions in the overall diversity and relative abundances of numerous bacterial genera, including Akkermansia spp., that positively correlated with behavioral metrics of both anxiety and depression. Functional analyses predicted a reduced frequency of signaling molecule pathways, including G-protein-coupled receptors, in defeated animals. Collectively, our data suggest that shifts in microbial composition may play a role in the pathogenesis of anxiety and depression.

Correction: Comparative evaluation of a new magnetic bead-based DNA extraction method from fecal samples for downstream next-generation 16S rRNA gene sequencing.

[This corrects the article DOI: 10.1371/journal.pone.0202858.].

Hindgut microbiota in laboratory-reared and wild Triatoma infestans.

Triatomine vectors transmit Trypanosoma cruzi, the etiological agent of Chagas disease in humans. Transmission to humans typically occurs when contaminated triatomine feces come in contact with the bite site or mucosal membranes. In the Southern Cone of South America, where the highest burden of disease exists, Triatoma infestans is the principal vector for T. cruzi. Recent studies of other vector-borne illnesses have shown that arthropod microbiota influences the ability of infectious agents to colonize the insect vector and transmit to the human host. This has garnered attention as a potential control strategy against T. cruzi, as vector control is the main tool of Chagas disease prevention. Here we characterized the microbiota in T. infestans feces of both wild-caught and laboratory-reared insects and examined the relationship between microbial composition and T. cruzi infection using highly sensitive high-throughput sequencing technology to sequence the V3-V4 region of the 16S ribosomal RNA gene on the MiSeq Illumina platform. We collected 59 wild (9 with T. cruzi infection) and 10 lab-reared T. infestans (4 with T. cruzi infection) from the endemic area of Arequipa, Perú. Wild T. infestans had greater hindgut bacterial diversity than laboratory-reared bugs. Microbiota of lab insects comprised a subset of those identified in their wild counterparts, with 96 of the total 124 genera also observed in laboratory-reared insects. Among wild insects, variation in bacterial composition was observed, but time and location of collection and development stage did not explain this variation. T. cruzi infection in lab insects did not affect α- or ß-diversity; however, we did find that the ß-diversity of wild insects differed if they were infected with T. cruzi and identified 10 specific taxa that had significantly different relative abundances in infected vs. uninfected wild T. infestans (Bosea, Mesorhizobium, Dietzia, and Cupriavidus were underrepresented in infected bugs; Sporosarcina, an unclassified genus of Porphyromonadaceae, Nestenrenkonia, Alkalibacterium, Peptoniphilus, Marinilactibacillus were overrepresented in infected bugs). Our findings suggest that T. cruzi infection is associated with the microbiota of T. infestans and that inferring the microbiota of wild T. infestans may not be possible through sampling of T. infestans reared in the insectary.

Intra-species Genomic and Physiological Variability Impact Stress Resistance in Strains of Probiotic Potential.

Large-scale microbiome studies have established that most of the diversity contained in the gastrointestinal tract is represented at the strain level; however, exhaustive genomic and physiological characterization of human isolates is still lacking. With increased use of probiotics as interventions for gastrointestinal disorders, genomic and functional characterization of novel microorganisms becomes essential. In this study, we explored the impact of strain-level genomic variability on bacterial physiology of two novel human Lactobacillus rhamnosus strains (AMC143 and AMC010) of probiotic potential in relation to stress resistance. The strains showed differences with known probiotic strains (L. rhamnosus GG, Lc705, and HN001) at the genomic level, including nucleotide polymorphisms, mutations in non-coding regulatory regions, and rearrangements of genomic architecture. Transcriptomics analysis revealed that gene expression profiles differed between strains when exposed to simulated gastrointestinal stresses, suggesting the presence of unique regulatory systems in each strain. In vitro physiological assays to test resistance to conditions mimicking the gut environment (acid, alkali, and bile stress) showed that growth of L. rhamnosus AMC143 was inhibited upon exposure to alkaline pH, while AMC010 and control strain LGG were unaffected. AMC143 also showed a significant survival advantage compared to the other strains upon bile exposure. Reverse transcription qPCR targeting the bile salt hydrolase gene (bsh) revealed that AMC143 expressed bsh poorly (a consequence of a deletion in the bsh promoter and truncation of bsh gene in AMC143), while AMC010 had significantly higher expression levels than AMC143 or LGG. Insertional inactivation of the bsh gene in AMC010 suggested that bsh could be detrimental to bacterial survival during bile stress. Together, these findings show that coupling of classical microbiology with functional genomics methods for the characterization of bacterial strains is critical for the development of novel probiotics, as variability between strains can dramatically alter bacterial physiology and functionality.

Comparative evaluation of a new magnetic bead-based DNA extraction method from fecal samples for downstream next-generation 16S rRNA gene sequencing.

We are colonized by a vast population of genetically diverse microbes, the majority of which are unculturable bacteria that reside within the gastrointestinal tract. As affordable, advanced next-generation sequencing technologies become more widely available, important discoveries about the composition and function of these microbes become increasingly possible. In addition to rapid advancement in sequencing technologies, automated systems have been developed for nucleic acid extraction; however, these methods have yet to be widely used for the isolation of bacterial DNA from fecal samples. Here, we adapted Promega's Maxwell® RSC PureFood GMO and Authentication kit for use with fecal samples and compared it to the commonly used Qiagen QIAamp® PowerFecal® kit. Results showed that the two approaches yielded similar measures of DNA purity and successful next-generation sequencing amplification and produced comparable composition of microbial communities. However, DNA extraction with the Maxwell® RSC kit produced higher concentrations with a lower fecal sample input weight and took a fraction of the time compared to the QIAamp® PowerFecal® protocol. The results of this study demonstrate that the Promega Maxwell® RSC system can be used for medium-throughput DNA extraction in a time-efficient manner without compromising the quality of the downstream sequencing.

Genome-wide association study and meta-analysis identify loci associated with ventricular and supraventricular ectopy.

The genetic basis of supraventricular and ventricular ectopy (SVE, VE) remains largely uncharacterized, despite established genetic mechanisms of arrhythmogenesis. To identify novel genetic variants associated with SVE/VE in ancestrally diverse human populations, we conducted a genome-wide association study of electrocardiographically identified SVE and VE in five cohorts including approximately 43,000 participants of African, European and Hispanic/Latino ancestry. In thirteen ancestry-stratified subgroups, we tested multivariable-adjusted associations of SVE and VE with single nucleotide polymorphism (SNP) dosage. We combined subgroup-specific association estimates in inverse variance-weighted, fixed-effects and Bayesian meta-analyses. We also combined fixed-effects meta-analytic t-test statistics for SVE and VE in multi-trait SNP association analyses. No loci reached genome-wide significance in trans-ethnic meta-analyses. However, we found genome-wide significant SNPs intronic to an apoptosis-enhancing gene previously associated with QRS interval duration (FAF1; lead SNP rs7545860; effect allele frequency = 0.02; P = 2.0 × 10-8) in multi-trait analysis among European ancestry participants and near a locus encoding calcium-dependent glycoproteins (DSC3; lead SNP rs8086068; effect allele frequency = 0.17) in meta-analysis of SVE (P = 4.0 × 10-8) and multi-trait analysis (P = 2.9 × 10-9) among African ancestry participants. The novel findings suggest several mechanisms by which genetic variation may predispose to ectopy in humans and highlight the potential value of leveraging pleiotropy in future studies of ectopy-related phenotypes.

Erratum: "Genome-wide Association Study of Susceptibility to Particulate Matter-Associated QT Prolongation".

[This corrects the article DOI: 10.1289/EHP347.].

Genome-wide Association Study of Susceptibility to Particulate Matter-Associated QT Prolongation.

BACKGROUND: Ambient particulate matter (PM) air pollution exposure has been associated with increases in QT interval duration (QT). However, innate susceptibility to PM-associated QT prolongation has not been characterized. OBJECTIVE: To characterize genetic susceptibility to PM-associated QT prolongation in a multi-racial/ethnic, genome-wide association study (GWAS). METHODS: Using repeated electrocardiograms (1986­2004), longitudinal data on PM<10 µm in diameter (PM10), and generalized estimating equations methods adapted for low-prevalence exposure, we estimated approximately 2.5×106 SNP×PM10 interactions among nine Women's Health Initiative clinical trials and Atherosclerosis Risk in Communities Study subpopulations (n=22,158), then combined subpopulation-specific results in a fixed-effects, inverse variance-weighted meta-analysis. RESULTS: A common variant (rs1619661; coded allele: T) significantly modified the QT-PM10 association (p=2.11×10−8). At PM10 concentrations >90th percentile, QT increased 7 ms across the CC and TT genotypes: 397 (95% confidence interval: 396, 399) to 404 (403, 404) ms. However, QT changed minimally across rs1619661 genotypes at lower PM10 concentrations. The rs1619661 variant is on chromosome 10, 132 kilobase (kb) downstream from CXCL12, which encodes a chemokine, stromal cell-derived factor 1, that is expressed in cardiomyocytes and decreases calcium influx across the L-type Ca2+ channel. CONCLUSIONS: The findings suggest that biologically plausible genetic factors may alter susceptibility to PM10-associated QT prolongation in populations protected by the U.S. Environmental Protection Agency's National Ambient Air Quality Standards. Independent replication and functional characterization are necessary to validate our findings. https://doi.org/10.1289/EHP347