Single nucleotide variants in Pseudomonas aeruginosa populations from sputum correlate with baseline lung function and predict disease progression in individuals with cystic fibrosis.

The severity and progression of lung disease are highly variable across individuals with cystic fibrosis (CF) and are imperfectly predicted by mutations in the human gene CFTR, lung microbiome variation or other clinical factors. The opportunistic pathogen Pseudomonas aeruginosa (Pa) dominates airway infections in most CF adults. Here we hypothesized that within-host genetic variation of Pa populations would be associated with lung disease severity. To quantify Pa genetic variation within CF sputum samples, we used deep amplicon sequencing (AmpliSeq) of 209 Pa genes previously associated with pathogenesis or adaptation to the CF lung. We trained machine learning models using Pa single nucleotide variants (SNVs), microbiome diversity data and clinical factors to classify lung disease severity at the time of sputum sampling, and to predict lung function decline after 5 years in a cohort of 54 adult CF patients with chronic Pa infection. Models using Pa SNVs alone classified lung disease severity with good sensitivity and specificity (area under the receiver operating characteristic curve: AUROC=0.87). Models were less predictive of lung function decline after 5 years (AUROC=0.74) but still significantly better than random. The addition of clinical data, but not sputum microbiome diversity data, yielded only modest improvements in classifying baseline lung function (AUROC=0.92) and predicting lung function decline (AUROC=0.79), suggesting that Pa AmpliSeq data account for most of the predictive value. Our work provides a proof of principle that Pa genetic variation in sputum tracks lung disease severity, moderately predicts lung function decline and could serve as a disease biomarker among CF patients with chronic Pa infections.

Genome-wide association studies reveal distinct genetic correlates and increased heritability of antimicrobial resistance in Vibrio cholerae under anaerobic conditions.

The antibiotic formulary is threatened by high rates of antimicrobial resistance (AMR) among enteropathogens. Enteric bacteria are exposed to anaerobic conditions within the gastrointestinal tract, yet little is known about how oxygen exposure influences AMR. The facultative anaerobe Vibrio cholerae was chosen as a model to address this knowledge gap. We obtained V. cholerae isolates from 66 cholera patients, sequenced their genomes, and grew them under anaerobic and aerobic conditions with and without three clinically relevant antibiotics (ciprofloxacin, azithromycin, doxycycline). For ciprofloxacin and azithromycin, the minimum inhibitory concentration (MIC) increased under anaerobic conditions compared to aerobic conditions. Using standard resistance breakpoints, the odds of classifying isolates as resistant increased over 10 times for ciprofloxacin and 100 times for azithromycin under anaerobic conditions compared to aerobic conditions. For doxycycline, nearly all isolates were sensitive under both conditions. Using genome-wide association studies, we found associations between genetic elements and AMR phenotypes that varied by oxygen exposure and antibiotic concentrations. These AMR phenotypes were more heritable, and the AMR-associated genetic elements were more often discovered, under anaerobic conditions. These AMR-associated genetic elements are promising targets for future mechanistic research. Our findings provide a rationale to determine whether increased MICs under anaerobic conditions are associated with therapeutic failures and/or microbial escape in cholera patients. If so, there may be a need to determine new AMR breakpoints for anaerobic conditions.

Corrigendum: Possible roles for the hominoid-specific DSCR4 gene in human cells [Genes Genet. Syst. (2021) 96, p. 1-11].

Legends to Figures 4 and 5 (p. 7) should be exchanged. Below are the correct legends to Figure 4 and Figure 5. Fig. 4. Interconnection of DSCR4 overexpression-mediated perturbed pathways. KEGG analysis of DSCR4 overexpression-mediated DEGs shows enrichment for the tightly interconnected pathways of the coagulation cascade and the complement cascade (highlighted in red) and further confirm the connection of these cascades with cell adhesion, migration and proliferation (red circle). Fig. 5. Expression profile of DSCR4 across human cell lines and tissues. According to Roadmap Epigenomics Project data, DSCR4 and DSCR8, which share a bidirectional promoter, are highly expressed only in K562 cells, a type of leukemia cell. Analysis of transcriptome data provided by Prescott et al. (2015) showed that DSCR4 and DSCR8 also display high expression in human and chimpanzee neural crest cells, which are critical migratory cells involved in facial morphogenesis in the embryo. (1) Data from Prescott et al. (2015). (2) Samples also include esophagus, lung, spleen and fetal large intestine. (3) Samples also include brain germinal matrix, hippocampus, fetal small intestine, stomach, left ventricle, small intestine, sigmoid colon, HEPG2 cells and HMEC cells. The PDF file for DOI: https://doi.org/10.1266/ggs.20-00012 has been replaced with the corrected version as of June 17, 2021.

Possible roles for the hominoid-specific DSCR4 gene in human cells.

Down syndrome in humans is caused by trisomy of chromosome 21. DSCR4 (Down syndrome critical region 4) is a de novo-originated protein-coding gene present only in human chromosome 21 and its homologous chromosomes in apes. Despite being located in a medically critical genomic region and an abundance of evidence indicating its functionality, the roles of DSCR4 in human cells are unknown. We used a bioinformatic approach to infer the biological importance and cellular roles of this gene. Our analysis indicates that DSCR4 is likely involved in the regulation of interconnected biological pathways related to cell migration, coagulation and the immune system. We also showed that these predicted biological functions are consistent with tissue-specific expression of DSCR4 in migratory immune system leukocyte cells and neural crest cells (NCCs) that shape facial morphology in the human embryo. The immune system and NCCs are known to be affected in Down syndrome individuals, who suffer from DSCR4 misregulation, which further supports our findings. Providing evidence for the critical roles of DSCR4 in human cells, our findings establish the basis for further experimental investigations that will be necessary to confirm the roles of DSCR4 in the etiology of Down syndrome.

Predicting Vibrio cholerae Infection and Disease Severity Using Metagenomics in a Prospective Cohort Study.

BACKGROUND: Susceptibility to Vibrio cholerae infection is affected by blood group, age, and preexisting immunity, but these factors only partially explain who becomes infected. A recent study used 16S ribosomal RNA amplicon sequencing to quantify the composition of the gut microbiome and identify predictive biomarkers of infection with limited taxonomic resolution. METHODS: To achieve increased resolution of gut microbial factors associated with V. cholerae susceptibility and identify predictors of symptomatic disease, we applied deep shotgun metagenomic sequencing to a cohort of household contacts of patients with cholera. RESULTS: Using machine learning, we resolved species, strains, gene families, and cellular pathways in the microbiome at the time of exposure to V. cholerae to identify markers that predict infection and symptoms. Use of metagenomic features improved the precision and accuracy of prediction relative to 16S sequencing. We also predicted disease severity, although with greater uncertainty than our infection prediction. Species within the genera Prevotella and Bifidobacterium predicted protection from infection, and genes involved in iron metabolism were also correlated with protection. CONCLUSION: Our results highlight the power of metagenomics to predict disease outcomes and suggest specific species and genes for experimental testing to investigate mechanisms of microbiome-related protection from cholera.

Benchmarking bacterial genome-wide association study methods using simulated genomes and phenotypes.

Genome-wide association studies (GWASs) have the potential to reveal the genetics of microbial phenotypes such as antibiotic resistance and virulence. Capitalizing on the growing wealth of bacterial sequence data, microbial GWAS methods aim to identify causal genetic variants while ignoring spurious associations. Bacteria reproduce clonally, leading to strong population structure and genome-wide linkage, making it challenging to separate true 'hits' (i.e. mutations that cause a phenotype) from non-causal linked mutations. GWAS methods attempt to correct for population structure in different ways, but their performance has not yet been systematically and comprehensively evaluated under a range of evolutionary scenarios. Here, we developed a bacterial GWAS simulator (BacGWASim) to generate bacterial genomes with varying rates of mutation, recombination and other evolutionary parameters, along with a subset of causal mutations underlying a phenotype of interest. We assessed the performance (recall and precision) of three widely used single-locus GWAS approaches (cluster-based, dimensionality-reduction and linear mixed models, implemented in plink, pyseer and gemma) and one relatively new multi-locus model implemented in pyseer, across a range of simulated sample sizes, recombination rates and causal mutation effect sizes. As expected, all methods performed better with larger sample sizes and effect sizes. The performance of clustering and dimensionality reduction approaches to correct for population structure were considerably variable according to the choice of parameters. Notably, the multi-locus elastic net (lasso) approach was consistently amongst the highest-performing methods, and had the highest power in detecting causal variants with both low and high effect sizes. Most methods reached the level of good performance (recall >0.75) for identifying causal mutations of strong effect size [log odds ratio (OR) ≥2] with a sample size of 2000 genomes. However, only elastic nets reached the level of reasonable performance (recall=0.35) for detecting markers with weaker effects (log OR ~1) in smaller samples. Elastic nets also showed superior precision and recall in controlling for genome-wide linkage, relative to single-locus models. However, all methods performed relatively poorly on highly clonal (low-recombining) genomes, suggesting room for improvement in method development. These findings show the potential for multi-locus models to improve bacterial GWAS performance. BacGWASim code and simulated data are publicly available to enable further comparisons and benchmarking of new methods.