Bacterial antibiotic resistance represents a public health concern that will remain relevant for the foreseeable future. Antibiotic resistant bacterial infections can occur in two ways: (1) a host is infected by a resistant bacterial strain (due to between-host transmission of resistance), or (2) a host is infected infection by a susceptible strain, followed by the de novo evolution or acquisition of resistance (due to within-host evolution of resistance). While both are critical to understanding how the evolution of resistance happens in natural settings, the relative rate at which they occur is unclear. Here, we employ phylogenetic comparative methods to examine the evolutionary dynamics of resistance in Escherichia coli for multiple common antibiotics. We report evolutionary patterns consistent with common de novo evolution of resistance for some antibiotics and sustained transmission of resistant strains for others. For example, we observe 79 putative de novo resistance evolution events for resistance to Cefuroxime but only 31 for resistance to Ciprofloxacin, despite similar numbers of observed infections (239 and 267 respectively). We find that clusters of resistance are generally larger for Ciprofloxacin, Ceftazidima and AmoxiClav, which suggests that for these drugs, resistance is often transmitted from patient to patient. In contrast, we find that cluster sizes for resistance are generally smaller for PipTaz, Cefuroxime and Gentamicin, suggesting that resistance to these drugs is less often transmitted from patient to patient and instead evolves de novo. In addition to differences between drugs, we also find that cluster sizes were generally larger in phylogroup B2 compared to the other phylogroups, suggesting that transmission of resistant strains is more common in this phylogroup compared to the others. Our study proposes new approaches for determining the importance of de novo evolution or acquisition (within-host evolution) from resistance from infection with an already resistant strain (between-host transmission). Significantly, this work also bridges an important gap between evolutionary genomics and epidemiology, opening up a range of opportunities for studying the evolutionary dynamics of bacterial antibiotic resistance.

Like many viruses, Hepatitis C Virus (HCV) has a high nutation rate, which helps the virus adapt quickly, but mutations come with fitness costs. Fitness costs can be studied by different approaches, such as experimental or frequency-based approaches. The frequency-based approach is particularly useful to estimate in vivo fitness costs, but this approach works best with deep sequencing data from many hosts are. In this study, we applied the frequency-based approach to a large dataset of 195 patients and estimated the fitness costs of mutations at 7957 sites along the HCV genome. We used beta regression and random forest models to better understand how different factors influenced fitness costs. Our results revealed that costs of nonsynonymous mutations were three times higher than those of synonymous mutations, and mutations at nucleotides A or T had higher costs than those at C or G. Genome location had a modest effect, with lower costs for mutations in HVR1 and higher costs for mutations in Core and NS5B. Resistance mutations were, on average, costlier than other mutations. Our results show that in vivo fitness costs of mutations can be site and virus specific, reinforcing the utility of constructing in vivo fitness cost maps of viral genomes.

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Psoriasis is an immune-mediated inflammatory skin disease typically characterized by erythematous and scaly plaques. It affects 3% of the Newfoundland population while only affecting 1.7% of the general Canadian population. Recent genome-wide association studies (GWAS) in psoriasis have identified more than 63 genetic susceptibility loci that individually have modest effects. Prior studies have shown that a genetic risk score (GRS) combining multiple loci can improve psoriasis disease prediction. However, these prior GRS studies have not fully explored the association of GRS with patient clinical characteristics. In this study, we calculated three types of GRS: one using all known GWAS SNPs (GRS-ALL), one using a subset of SNPs from the HLA region (GRS-HLA), and the last using non-HLA SNPs (GRS-noHLA). We examined the relationship between these GRS and a number of psoriasis features within a well characterized Newfoundland psoriasis cohort. We found that both GRS-ALL and GRS-HLA were significantly associated with early age of psoriasis onset, psoriasis severity, first presentation of psoriasis at the elbow or knee, and the total number of body locations affected, while only GRS-ALL was associated with a positive family history of psoriasis. GRS-noHLA was uniquely associated with genital psoriasis. These findings clarify the relationship of the HLA and non-HLA components of GRS with important clinical features of psoriasis.