Early Detection of Severe Acute Respiratory Syndrome Coronavirus 2 Variants Using Traveler-based Genomic Surveillance at 4 US Airports, September 2021-January 2022.

We enrolled arriving international air travelers in a severe acute respiratory syndrome coronavirus 2 genomic surveillance program. We used molecular testing of pooled nasal swabs and sequenced positive samples for sublineage. Traveler-based surveillance provided early-warning variant detection, reporting the first US Omicron BA.2 and BA.3 in North America.

Increased power from conditional bacterial genome-wide association identifies macrolide resistance mutations in Neisseria gonorrhoeae.

The emergence of resistance to azithromycin complicates treatment of Neisseria gonorrhoeae, the etiologic agent of gonorrhea. Substantial azithromycin resistance remains unexplained after accounting for known resistance mutations. Bacterial genome-wide association studies (GWAS) can identify novel resistance genes but must control for genetic confounders while maintaining power. Here, we show that compared to single-locus GWAS, conducting GWAS conditioned on known resistance mutations reduces the number of false positives and identifies a G70D mutation in the RplD 50S ribosomal protein L4 as significantly associated with increased azithromycin resistance (p-value = 1.08 × 10-11). We experimentally confirm our GWAS results and demonstrate that RplD G70D and other macrolide binding site mutations are prevalent (present in 5.42% of 4850 isolates) and widespread (identified in 21/65 countries across two decades). Overall, our findings demonstrate the utility of conditional associations for improving the performance of microbial GWAS and advance our understanding of the genetic basis of macrolide resistance.

Adaptation to the cervical environment is associated with increased antibiotic susceptibility in Neisseria gonorrhoeae.

Neisseria gonorrhoeae is an urgent public health threat due to rapidly increasing incidence and antibiotic resistance. In contrast with the trend of increasing resistance, clinical isolates that have reverted to susceptibility regularly appear, prompting questions about which pressures compete with antibiotics to shape gonococcal evolution. Here, we used genome-wide association to identify loss-of-function (LOF) mutations in the efflux pump mtrCDE operon as a mechanism of increased antibiotic susceptibility and demonstrate that these mutations are overrepresented in cervical relative to urethral isolates. This enrichment holds true for LOF mutations in another efflux pump, farAB, and in urogenitally-adapted versus typical N. meningitidis, providing evidence for a model in which expression of these pumps in the female urogenital tract incurs a fitness cost for pathogenic Neisseria. Overall, our findings highlight the impact of integrating microbial population genomics with host metadata and demonstrate how host environmental pressures can lead to increased antibiotic susceptibility.

Targeted surveillance strategies for efficient detection of novel antibiotic resistance variants.

Genotype-based diagnostics for antibiotic resistance represent a promising alternative to empiric therapy, reducing inappropriate antibiotic use. However, because such assays infer resistance based on known genetic markers, their utility will wane with the emergence of novel resistance. Maintenance of these diagnostics will therefore require surveillance to ensure early detection of novel resistance variants, but efficient strategies to do so remain undefined. We evaluate the efficiency of targeted sampling approaches informed by patient and pathogen characteristics in detecting antibiotic resistance and diagnostic escape variants in Neisseria gonorrhoeae, a pathogen associated with a high burden of disease and antibiotic resistance and the development of genotype-based diagnostics. We show that patient characteristic-informed sampling is not a reliable strategy for efficient variant detection. In contrast, sampling informed by pathogen characteristics, such as genomic diversity and genomic background, is significantly more efficient than random sampling in identifying genetic variants associated with resistance and diagnostic escape.

Surveillance to maintain the sensitivity of genotype-based antibiotic resistance diagnostics.

The sensitivity of genotype-based diagnostics that predict antimicrobial susceptibility is limited by the extent to which they detect genes and alleles that lead to resistance. As novel resistance variants are expected to emerge, such sensitivity is expected to decline unless the new variants are detected and incorporated into the diagnostic. Here, we present a mathematical framework to define how many diagnostic failures may be expected under varying surveillance regimes and thus quantify the surveillance needed to maintain the sensitivity of genotype-based diagnostics.

Evaluation of parameters affecting performance and reliability of machine learning-based antibiotic susceptibility testing from whole genome sequencing data.

Prediction of antibiotic resistance phenotypes from whole genome sequencing data by machine learning methods has been proposed as a promising platform for the development of sequence-based diagnostics. However, there has been no systematic evaluation of factors that may influence performance of such models, how they might apply to and vary across clinical populations, and what the implications might be in the clinical setting. Here, we performed a meta-analysis of seven large Neisseria gonorrhoeae datasets, as well as Klebsiella pneumoniae and Acinetobacter baumannii datasets, with whole genome sequence data and antibiotic susceptibility phenotypes using set covering machine classification, random forest classification, and random forest regression models to predict resistance phenotypes from genotype. We demonstrate how model performance varies by drug, dataset, resistance metric, and species, reflecting the complexities of generating clinically relevant conclusions from machine learning-derived models. Our findings underscore the importance of incorporating relevant biological and epidemiological knowledge into model design and assessment and suggest that doing so can inform tailored modeling for individual drugs, pathogens, and clinical populations. We further suggest that continued comprehensive sampling and incorporation of up-to-date whole genome sequence data, resistance phenotypes, and treatment outcome data into model training will be crucial to the clinical utility and sustainability of machine learning-based molecular diagnostics.

Gut microbiomes of wild great apes fluctuate seasonally in response to diet.

The microbiome is essential for extraction of energy and nutrition from plant-based diets and may have facilitated primate adaptation to new dietary niches in response to rapid environmental shifts. Here we use 16S rRNA sequencing to characterize the microbiota of wild western lowland gorillas and sympatric central chimpanzees and demonstrate compositional divergence between the microbiotas of gorillas, chimpanzees, Old World monkeys, and modern humans. We show that gorilla and chimpanzee microbiomes fluctuate with seasonal rainfall patterns and frugivory. Metagenomic sequencing of gorilla microbiomes demonstrates distinctions in functional metabolic pathways, archaea, and dietary plants among enterotypes, suggesting that dietary seasonality dictates shifts in the microbiome and its capacity for microbial plant fiber digestion versus growth on mucus glycans. These data indicate that great ape microbiomes are malleable in response to dietary shifts, suggesting a role for microbiome plasticity in driving dietary flexibility, which may provide fundamental insights into the mechanisms by which diet has driven the evolution of human gut microbiomes.

Global patterns in coronavirus diversity.

Since the emergence of Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV) and Middle East Respiratory Syndrom Coronavirus (MERS-CoV) it has become increasingly clear that bats are important reservoirs of CoVs. Despite this, only 6% of all CoV sequences in GenBank are from bats. The remaining 94% largely consist of known pathogens of public health or agricultural significance, indicating that current research effort is heavily biased towards describing known diseases rather than the 'pre-emergent' diversity in bats. Our study addresses this critical gap, and focuses on resource poor countries where the risk of zoonotic emergence is believed to be highest. We surveyed the diversity of CoVs in multiple host taxa from twenty countries to explore the factors driving viral diversity at a global scale. We identified sequences representing 100 discrete phylogenetic clusters, ninety-one of which were found in bats, and used ecological and epidemiologic analyses to show that patterns of CoV diversity correlate with those of bat diversity. This cements bats as the major evolutionary reservoirs and ecological drivers of CoV diversity. Co-phylogenetic reconciliation analysis was also used to show that host switching has contributed to CoV evolution, and a preliminary analysis suggests that regional variation exists in the dynamics of this process. Overall our study represents a model for exploring global viral diversity and advances our fundamental understanding of CoV biodiversity and the potential risk factors associated with zoonotic emergence.

Maternal Antibiotic Treatment Impacts Development of the Neonatal Intestinal Microbiome and Antiviral Immunity.

Microbial colonization of the infant gastrointestinal tract (GIT) begins at birth, is shaped by the maternal microbiota, and is profoundly altered by antibiotic treatment. Antibiotic treatment of mothers during pregnancy influences colonization of the GIT microbiota of their infants. The role of the GIT microbiota in regulating adaptive immune function against systemic viral infections during infancy remains undefined. We used a mouse model of perinatal antibiotic exposure to examine the effect of GIT microbial dysbiosis on infant CD8(+) T cell-mediated antiviral immunity. Maternal antibiotic treatment/treated (MAT) during pregnancy and lactation resulted in profound alterations in the composition of the GIT microbiota in mothers and infants. Streptococcus spp. dominated the GIT microbiota of MAT mothers, whereas Enterococcus faecalis predominated within the MAT infant GIT. MAT infant mice subsequently exhibited increased and accelerated mortality following vaccinia virus infection. Ag-specific IFN-γ-producing CD8(+) T cells were reduced in sublethally infected MAT infant mice. MAT CD8(+) T cells from uninfected infant mice also demonstrated a reduced capacity to sustain IFN-γ production following in vitro activation. We additionally determined that control infant mice became more susceptible to infection if they were born in an animal facility using stricter standards of hygiene. These data indicate that undisturbed colonization and progression of the GIT microbiota during infancy are necessary to promote robust adaptive antiviral immune responses.

Non-random patterns in viral diversity.

It is currently unclear whether changes in viral communities will ever be predictable. Here we investigate whether viral communities in wildlife are inherently structured (inferring predictability) by looking at whether communities are assembled through deterministic (often predictable) or stochastic (not predictable) processes. We sample macaque faeces across nine sites in Bangladesh and use consensus PCR and sequencing to discover 184 viruses from 14 viral families. We then use network modelling and statistical null-hypothesis testing to show the presence of non-random deterministic patterns at different scales, between sites and within individuals. We show that the effects of determinism are not absolute however, as stochastic patterns are also observed. In showing that determinism is an important process in viral community assembly we conclude that it should be possible to forecast changes to some portion of a viral community, however there will always be some portion for which prediction will be unlikely.

Identification of a novel nidovirus in an outbreak of fatal respiratory disease in ball pythons (Python regius).

BACKGROUND: Respiratory infections are important causes of morbidity and mortality in reptiles; however, the causative agents are only infrequently identified. FINDINGS: Pneumonia, tracheitis and esophagitis were reported in a collection of ball pythons (Python regius). Eight of 12 snakes had evidence of bacterial pneumonia. High-throughput sequencing of total extracted nucleic acids from lung, esophagus and spleen revealed a novel nidovirus. PCR indicated the presence of viral RNA in lung, trachea, esophagus, liver, and spleen. In situ hybridization confirmed the presence of intracellular, intracytoplasmic viral nucleic acids in the lungs of infected snakes. Phylogenetic analysis based on a 1,136 amino acid segment of the polyprotein suggests that this virus may represent a new species in the subfamily Torovirinae. CONCLUSIONS: This report of a novel nidovirus in ball pythons may provide insight into the pathogenesis of respiratory disease in this species and enhances our knowledge of the diversity of nidoviruses.

Cell tropism predicts long-term nucleotide substitution rates of mammalian RNA viruses.

The high rates of RNA virus evolution are generally attributed to replication with error-prone RNA-dependent RNA polymerases. However, these long-term nucleotide substitution rates span three orders of magnitude and do not correlate well with mutation rates or selection pressures. This substitution rate variation may be explained by differences in virus ecology or intrinsic genomic properties. We generated nucleotide substitution rate estimates for mammalian RNA viruses and compiled comparable published rates, yielding a dataset of 118 substitution rates of structural genes from 51 different species, as well as 40 rates of non-structural genes from 28 species. Through ANCOVA analyses, we evaluated the relationships between these rates and four ecological factors: target cell, transmission route, host range, infection duration; and three genomic properties: genome length, genome sense, genome segmentation. Of these seven factors, we found target cells to be the only significant predictors of viral substitution rates, with tropisms for epithelial cells or neurons (P<0.0001) as the most significant predictors. Further, one-tailed t-tests showed that viruses primarily infecting epithelial cells evolve significantly faster than neurotropic viruses (P<0.0001 and P<0.001 for the structural genes and non-structural genes, respectively). These results provide strong evidence that the fastest evolving mammalian RNA viruses infect cells with the highest turnover rates: the highly proliferative epithelial cells. Estimated viral generation times suggest that epithelial-infecting viruses replicate more quickly than viruses with different cell tropisms. Our results indicate that cell tropism is a key factor in viral evolvability.

One misdated sequence of rabbit hemorrhagic disease virus prevents accurate estimation of its nucleotide substitution rate.

BACKGROUND: The literature is ripe with phylogenetic estimates of nucleotide substitution rates, especially of measurably evolving species such as RNA viruses. However, it is not known how robust these rate estimates are to inaccuracies in the data, particularly in sampling dates that are used for molecular clock calibration. Here we report on the rate of evolution of the emerging pathogen Rabbit hemorrhagic disease virus (RHDV), which has significantly different rates of evolution for the same outer capsid (VP60) gene published in the literature. In an attempt to reconcile the conflicting data and further elucidate details of RHDV 's evolutionary history, we undertook fresh Bayesian analyses and employed jackknife control methods to produce robust substitution rate and time to most recent common ancestor (TMRCA) estimates for RHDV based on the VP60 and RNA-dependent RNA polymerase genes. RESULTS: Through these control methods, we were able to identify a single misdated taxon, a passaged lab strain used for vaccine production, which was responsible for depressing the RHDV capsid gene's rate of evolution by 65%. Without this isolate, the polymerase and the capsid protein genes had nearly identical rates of evolution: 1.90x10-3 nucleotide substitutions/site/year, ns/s/y, (95% highest probability density (HPD) 1.25x10-3-2.55x10-3) and 1.91x10-3 ns/s/y (95% HPD 1.50x10-3-2.34x10-3), respectively. CONCLUSIONS: After excluding the misdated taxon, both genes support a significantly higher substitution rate as well as a relatively recent emergence of RHDV, and obviate the need for previously hypothesized decades of unobserved diversification of the virus. The control methods show that using even one misdated taxon in a large dataset can significantly skew estimates of evolutionary parameters and suggest that it is better practice to use smaller datasets composed of taxa with unequivocal isolation dates. These jackknife controls would be useful for future tip-calibrated rate analyses that include taxa with ambiguous dates of isolation.

Genus-specific substitution rate variability among picornaviruses.

Picornaviruses have some of the highest nucleotide substitution rates among viruses, but there have been no comparisons of evolutionary rates within this broad family. We combined our own Bayesian coalescent analyses of VP1 regions from four picornaviruses with 22 published VP1 rates to produce the first within-family meta-analysis of viral evolutionary rates. Similarly, we compared our rate estimates for the RNA polymerase 3D(pol) gene from five viruses to four published 3D(pol) rates. Both a structural and a nonstructural gene show that enteroviruses are evolving, on average, a half order of magnitude faster than members of other genera within the Picornaviridae family.