Heligmosomoides polygyrus is a helminth which naturally infects mice and is widely used as a laboratory model of chronic small intestinal helminth infection. While it is known that infection with H. polygyrus alters the composition of the host’s bacterial microbiota, the functional implications of this alteration are unclear. We investigated the impact of H. polygyrus infection on short-chain fatty acid (SCFA) levels in the mouse intestine and sera. We found that helminth infection resulted in significantly upregulated levels of the branched SCFA isovaleric acid, exclusively in the proximal small intestine, which is the site of H. polygyrus colonization. We next set out to test the hypothesis that elevating local levels of isovaleric acid was a strategy used by H. polygyrus to promote its own fitness within the mammalian host. To test this, we supplemented the drinking water of mice with isovalerate during H. polygyrus infection and examined whether this affected helminth fecundity or chronicity. We did not find that isovaleric acid supplementation affected helminth chronicity, however, we found that it did promote helminth fecundity, as measured by helminth egg output in the feces of mice. Through antibiotic-treatment of helminth-infected mice, we found that the bacterial microbiota was required in order to support elevated levels of isovaleric acid in the proximal small intestine during helminth infection. Overall, our data reveal that during H. polygyrus infection there is a microbiota-dependent localized increase in the production of isovaleric acid in the proximal small intestine and this supports helminth fecundity in the murine host.

The accurate characterization of structural variation is crucial for our understanding of how large chromosomal alterations affect phenotypic differences and contribute to genome evolution. Whole-genome sequencing is a popular approach for identifying structural variants, but the accuracy of popular tools remains unclear due to the limitations of existing benchmarks. Moreover, the performance of these tools for predicting variants in non-human genomes is less certain, as most tools were developed and benchmarked using data from the human genome. To evaluate the use of long-read data for the validation of short-read structural variant calls, the agreement between predictions from a short-read ensemble learning method and long-read tools were compared using real and simulated data from Caenorhabditis elegans. The results obtained from simulated data indicate that the best performing tool is contingent on the type and size of the variant, as well as the sequencing depth of coverage. These results also highlight the need for reference datasets generated from real data that can be used as ‘ground truth’ in benchmarks.

BackgroundNumerous quality issues may compromise genomic data's representation of its underlying organism. In this study, we compared two genomes published by different research groups for the parasitic nematode Haemonchus contortus, corresponding to divergent isolates. We analyzed differences between the genomes, attempting to ascertain which were attributable to legitimate biological differences, and which to technical error in one or both genomes. ResultsWe found discrepancies between the H. contortus genomes in both assembly and annotation. The genomes differed in representation of genes that are highly conserved across eukaryotes, with clear evidence of misassembly underlying conserved genes missing from one genome or the other. Only 45% of genes in one genome were orthologous to genes in the other genome, with one genome exhibiting almost as much orthology to C. elegans as its counterpart H. contortus strain. The two genomes differed substantially in probable causes underlying this unexpectedly low orthology. One genome included many more inparalogues than the other, and more frequently assembled inparalogues together on the same portions of contiguous sequence. It also exhibited cases of better-conserved gene position relative to C. elegans. ConclusionThe discrepancies between the two genomes far exceeded those expected as a consequence of biological differences between the two H. contortus isolates. This implies substantial quality issues in one or both genomes, suggesting that researchers must exercise caution when using genomic data for newly sequenced species. BackgroundIn the decade following publication of the human genome, the use of genomic sequence data has increased dramatically [1], with a continued exponential drop in sequencing costs pushing researchers to utilize sequencing data in sundry studies spanning biology and medicine.Genomic data production, however, is complicated, suffering from numerous issues that compromise the resulting data's reflection of its underlying organism. The precision of genomic data can easily be mistaken for accuracy-though every nucleotide, gene boundary, and splice site is denoted exactly, uncertainty in the processes producing them is poorly represented in the resulting genome [2]. Thus, researchers who draw on genomic data without understanding their potential pitfalls will place undue faith in the validity of results built atop genomic foundations.Though sequencing costs have dropped precipitously, assembly, annotation, and analysis remain complex processes [3] subject to problems affecting the quality of the resulting genome [4]. The shortread sequencing technology prevalent today struggles to resolve repetitive regions [5], resulting in repeat sequences being collapsed, or physically distant regions being mistakenly adjoined because they flank highly similar sequences. With sequencing platforms being 85.0% to 99.9% accurate in each called base [4], distinguishing misread bases from legitimate variation stemming from rare alleles is difficult. This problem is...

The accurate characterization of structural variation is crucial for our understanding of how large chromosomal alterations affect phenotypic differences and contribute to genome evolution. Whole-genome sequencing is a popular approach for identifying structural variants, but the accuracy of popular tools remains unclear due to the limitations of existing benchmarks. Moreover, the performance of these tools for predicting variants in non-human genomes is less certain, as most tools were developed and benchmarked using data from the human genome. To address this problem, multiple short- and long-read tools were benchmarked using real and simulated Caenorhabditis elegans whole-genome sequence data. To evaluate the use of long-read data for the validation of short-read predictions, the agreement between predictions from a short-read ensemble learning method and long-read tools were compared. The results obtained indicate that the best performing tool is contingent on the type and size of the variant, as well as the sequencing depth of coverage. These results also highlight the need for reference datasets generated from real data that can be used as 'ground truth' in benchmarks.