Gibson, K. M. | Search bioRxiv

bioRxiv2019

Resistome diversity in bovine clinical mastitis microbiome, a signature concurrence

The bovine clinical mastitis (CM) milk is a large reservoir for diverse groups of resistomes, which play important roles in the pathogenesis of mastitis, but little is known about the concurrence of CM microbiome signature and its associated resistomes. Here we deciphered the total resistance (antibiotics and metals resistance, biofilm formation, quorum sensing) present in CM microbiome using whole metagenome sequencing (WMS) and in vitro cultural approaches. Significant correlation (p=0.001) was found between the resistome diversity and microbiome signature. We identified the strain-level microbiome diversity in four cattle breeds, with microbiome composition represented by the phyla Proteobacteria, Bacteroidetes, Firmicutes, Actinobacteria and Fusobacteria (contributing to >95.0% of total strains). However, the resistome diversity did not vary significantly (p=0.692) across the microbiomes of cattle breeds. The in vitro investigation showed that biofilm producing CM pathogens were resistant to most of the conventional antibiotics used for CM treatment, whereas these pathogens remained sensitive to five heavy metals (Cr, Co, Ni, Cu, Zn) at varying concentrations. We also found association of some genomic functional potentials such as bacterial flagellar movement and chemotaxis, regulation and cell signaling, phages-prophages, transposable elements, plasmids and oxidative stress in the pathophysiology of bovine CM. These findings of rapid and reliable identification of CM microbiomes and associated resistomes will help improve the optimization of therapeutic schemes involving antibiotics and metals usage in the prevention and control programs of bovine CM.

microbiology↗

bioRxiv2019

Evaluation of haplotype callers for next-generation sequencing of viruses

Currently, the standard practice for assembling next-generation sequencing (NGS) reads of viral genomes is to summarize thousands of individual short reads into a single consensus sequence, thus confounding useful intra-host diversity information for molecular phylodynamic inference. It is hypothesized that a few viral strains may dominate the intra-host genetic diversity with a variety of lower frequency strains comprising the rest of the population. Several software tools currently exist to convert NGS sequence variants into haplotypes. However, previous studies suggest that current approaches of haplotype reconstruction greatly underestimate intra-host diversity. Here, we tested twelve NGS haplotype reconstruction methods using viral populations simulated under realistic evolutionary dynamics. Parameters for the simulated data spanned known fast evolving viruses (e.g., HIV-1) diversity estimates to test the limits of the haplotype reconstruction methods and ensured coverage of predicted intra-host viral diversity levels. Using those parameters, we simulated HIV-1 viral populations of 216-1,185 haplotypes per host at a frequency <7%. All twelve investigated haplotype callers showed variable performance and produced drastically different results that were mainly driven by differences in mutation rate and, to a lesser extent, in effective population size. Most methods were able to accurately reconstruct haplotypes when genetic diversity was low. However, under higher levels of diversity (e.g., those seen intra-host HIV-1 infections), haplotype reconstruction accuracy was highly variable and, on average, poor. High diversity levels led to severe underestimation of, with a few tools greatly overestimating, the true number of haplotypes. PredictHaplo and PEHaplo produced estimates close to the true number of haplotypes, although their haplotype reconstruction accuracy was worse than that of the other ten tools. We conclude that haplotype reconstruction from NGS short reads is unreliable due to high genetic diversity of fast-evolving viruses. Local haplotype reconstruction of longer reads to phase variants may provide a more reliable estimation of viral variants within a population.\n\nHighlightsO_LIHaplotype callers for NGS data vary greatly in their performance.\nC_LIO_LIHaplotype callers performance is mainly determined by mutation rate.\nC_LIO_LIHaplotype callers performance is less sensitive to effective population size.\nC_LIO_LIMost haplotype callers perform well with low diversity and poorly with high diversity.\nC_LIO_LIPredictHaplo performs best if genetic diversity is in the range of HIV diversity.\nC_LI

bioinformatics↗