Natural selection differences detected in key protein domains between non-pathogenic and pathogenic feline coronavirus phenotypes.

Feline coronaviruses (FCoVs) commonly cause mild enteric infections in felines worldwide (termed feline enteric coronavirus [FECV]), with around 12 per cent developing into deadly feline infectious peritonitis (FIP; feline infectious peritonitis virus [FIPV]). Genomic differences between FECV and FIPV have been reported, yet the putative genotypic basis of the highly pathogenic phenotype remains unclear. Here, we used state-of-the-art molecular evolutionary genetic statistical techniques to identify and compare differences in natural selection pressure between FECV and FIPV sequences, as well as to identify FIPV- and FECV-specific signals of positive selection. We analyzed full-length FCoV protein coding genes thought to contain mutations associated with FIPV (Spike, ORF3abc, and ORF7ab). We identified two sites exhibiting differences in natural selection pressure between FECV and FIPV: one within the S1/S2 furin cleavage site (FCS) and the other within the fusion domain of Spike. We also found fifteen sites subject to positive selection associated with FIPV within Spike, eleven of which have not previously been suggested as possibly relevant to FIP development. These sites fall within Spike protein subdomains that participate in host cell receptor interaction, immune evasion, tropism shifts, host cellular entry, and viral escape. There were fourteen sites (twelve novel sites) within Spike under positive selection associated with the FECV phenotype, almost exclusively within the S1/S2 FCS and adjacent to C domain, along with a signal of relaxed selection in FIPV relative to FECV, suggesting that furin cleavage functionality may not be needed for FIPV. Positive selection inferred in ORF7b was associated with the FECV phenotype and included twenty-four positively selected sites, while ORF7b had signals of relaxed selection in FIPV. We found evidence of positive selection in ORF3c in FCoV-wide analyses, but no specific association with the FIPV or FECV phenotype. We hypothesize that some combination of mutations in FECV may contribute to FIP development, and that it is unlikely to be one singular 'switch' mutational event. This work expands our understanding of the complexities of FIP development and provides insights into how evolutionary forces may alter pathogenesis in coronavirus genomes.

Natural selection differences detected in key protein domains between non-pathogenic and pathogenic Feline Coronavirus phenotypes.

Feline Coronaviruses (FCoVs) commonly cause mild enteric infections in felines worldwide (termed Feline Enteric Coronavirus [FECV]), with around 12% developing into deadly Feline Infectious Peritonitis (FIP; Feline Infectious Peritonitis Virus [FIPV]). Genomic differences between FECV and FIPV have been reported, yet the putative genotypic basis of the highly pathogenic phenotype remains unclear. Here, we used state-of-the-art molecular evolutionary genetic statistical techniques to identify and compare differences in natural selection pressure between FECV and FIPV sequences, as well as to identify FIPV and FECV specific signals of positive selection. We analyzed full length FCoV protein coding genes thought to contain mutations associated with FIPV (Spike, ORF3abc, and ORF7ab). We identified two sites exhibiting differences in natural selection pressure between FECV and FIPV: one within the S1/S2 furin cleavage site, and the other within the fusion domain of Spike. We also found 15 sites subject to positive selection associated with FIPV within Spike, 11 of which have not previously been suggested as possibly relevant to FIP development. These sites fall within Spike protein subdomains that participate in host cell receptor interaction, immune evasion, tropism shifts, host cellular entry, and viral escape. There were 14 sites (12 novel) within Spike under positive selection associated with the FECV phenotype, almost exclusively within the S1/S2 furin cleavage site and adjacent C domain, along with a signal of relaxed selection in FIPV relative to FECV, suggesting that furin cleavage functionality may not be needed for FIPV. Positive selection inferred in ORF7b was associated with the FECV phenotype, and included 24 positively selected sites, while ORF7b had signals of relaxed selection in FIPV. We found evidence of positive selection in ORF3c in FCoV wide analyses, but no specific association with the FIPV or FECV phenotype. We hypothesize that some combination of mutations in FECV may contribute to FIP development, and that is unlikely to be one singular "switch" mutational event. This work expands our understanding of the complexities of FIP development and provides insights into how evolutionary forces may alter pathogenesis in coronavirus genomes.

Genomes of endangered great hammerhead and shortfin mako sharks reveal historic population declines and high levels of inbreeding in great hammerhead.

Despite increasing threats of extinction to Elasmobranchii (sharks and rays), whole genome-based conservation insights are lacking. Here, we present chromosome-level genome assemblies for the Critically Endangered great hammerhead (Sphyrna mokarran) and the Endangered shortfin mako (Isurus oxyrinchus) sharks, with genetic diversity and historical demographic comparisons to other shark species. The great hammerhead exhibited low genetic variation, with 8.7% of the 2.77 Gbp genome in runs of homozygosity (ROH) > 1 Mbp and 74.4% in ROH >100 kbp. The 4.98 Gbp shortfin mako genome had considerably greater diversity and <1% in ROH > 1 Mbp. Both these sharks experienced precipitous declines in effective population size (Ne) over the last 250 thousand years. While shortfin mako exhibited a large historical Ne that may have enabled the retention of higher genetic variation, the genomic data suggest a possibly more concerning picture for the great hammerhead, and a need for evaluation with additional individuals.

Characterizing infectious disease progression through discrete states using hidden Markov models.

Infectious disease management relies on accurate characterization of disease progression so that transmission can be prevented. Slowly progressing infectious diseases can be difficult to characterize because of a latency period between the time an individual is infected and when they show clinical signs of disease. The introduction of Mycobacterium avium ssp. paratuberculosis (MAP), the cause of Johne's disease, onto a dairy farm could be undetected by farmers for years before any animal shows clinical signs of disease. In this time period infected animals may shed thousands of colony forming units. Parameterizing trajectories through disease states from infection to clinical disease can help farmers to develop control programs based on targeting individual disease state, potentially reducing both transmission and production losses due to disease. We suspect that there are two distinct progression pathways; one where animals progress to a high-shedding disease state, and another where animals maintain a low-level of shedding without clinical disease. We fit continuous-time hidden Markov models to multi-year longitudinal fecal sampling data from three US dairy farms, and estimated model parameters using a modified Baum-Welch expectation maximization algorithm. Using posterior decoding, we observed two distinct shedding patterns: cows that had observations associated with a high-shedding disease state, and cows that did not. This model framework can be employed prospectively to determine which cows are likely to progress to clinical disease and may be applied to characterize disease progression of other slowly progressing infectious diseases.