SNP variable selection by generalized graph domination

Vocal divergence is concordant with genomic evidence for strong reproductive isolation in grasshopper mice (Onychomys)

Behavioral barriers to gene flow often evolve faster than intrinsic incompatibilities and can eliminate the opportunity for hybridization between interfertile species. While acoustic signal divergence is a common driver of premating isolation in birds and insects, its contribution to speciation in mammals is less studied. Here we characterize the incidence of, and potential barriers to, hybridization among three closely related species of grasshopper mice (genus Onychomys). All three species use long-distance acoustic signals to attract and localize mates; Onychomys arenicola and Onychomys torridus are acoustically similar and morphologically cryptic whereas Onychomys leucogaster is larger and acoustically distinct. We used genotyping-by-sequencing (GBS) to test for evidence of introgression in 227 mice from allopatric and sympatric localities in the western United States and northern Mexico. We conducted laboratory mating trials for all species pairs to assess reproductive compatibility, and recorded vocalizations from O. arenicola and O. torridus in sympatry and allopatry to test for evidence of acoustic character displacement. Hybridization was rare in nature and, contrary to prior evidence for O. torridus/O. arenicola hybrids, only involved O. leucogaster and O. arenicola. In contrast, laboratory crosses between O. torridus and O. arenicola produced litters whereas O. leucogaster and O. arenicola crosses did not. Call fundamental frequency in O. torridus and O. arenicola was indistinguishable in allopatry but significantly differentiated in sympatry, a pattern consistent with reproductive character displacement. These results suggest that assortative mating based on a long-distance signal is an important isolating mechanism between O. torridus and O. arenicola and highlight the importance of behavioral barriers in determining the permeability of species boundaries.

Pan-Genomic and Polymorphic Driven Prediction of Antibiotic Resistance in Elizabethkingia

The Elizabethkingia are a genetically diverse genus of emerging pathogens that exhibit multidrug resistance to a range of common antibiotics. Two representative species, Elizabethkingia bruuniana and E. meningoseptica, were phenotypically tested to determine minimum inhibitory concentrations (MICs) for five antibiotics. Ultra-long read sequencing with Oxford Nanopore Technologies (ONT) and subsequent de novo assembly produced complete, gapless circular genomes for each strain. Alignment based annotation with Prokka identified 5,480 features in E. bruuniana and 5,203 features in E. meningoseptica, where none of these identified genes or gene combinations corresponded to observed phenotypic resistance values. Pan-genomic analysis, performed with an additional 19 Elizabethkingia strains, identified a core-genome size of 2,658,537 bp, 32 uniquely identifiable intrinsic chromosomal antibiotic resistance core-genes and 77 antibiotic resistance pan-genes. Using core-SNPs and pan-genes in combination with six machine learning (ML) algorithms, binary classification of clindamycin and vancomycin resistance achieved f1 scores of 0.94 and 0.84, respectively. Performance on the more challenging multiclass problem for fusidic acid, rifampin and ciprofloxacin resulted in f1 scores of 0.70, 0.75, and 0.54, respectively. By producing two sets of quality biological predictors, pan-genome genes and core-genome SNPs, from long-read sequence data and applying an ensemble of ML techniques, our results demonstrated that accurate phenotypic inference, at multiple AMR resolutions, can be achieved.