Influenza classification from short reads with VAPOR facilitates robust mapping pipelines and zoonotic strain detection for routine surveillance applications

Prospecting Human Milk Oligosaccharides as a Defense Against Viral Infections

In addition to providing maximal nutritional value for neonatal growth and development, human milk functions as an early defense mechanism against invading pathogens. Human milk oligosaccharides (HMOs), which are abundant in human milk, are a diverse group of heterogeneous carbohydrates with wide ranging protective effects. In addition to promoting the colonization of beneficial intestinal flora, HMOs serve as decoy receptors, effectively blocking the attachment of pathogenic bacteria. HMOs also function as bacteriostatic agents, inhibiting the growth of gram-positive bacteria. Based on this precedence, an emerging area in the field has focused on characterizing the antiviral properties of HMOs. Indeed, HMOs have been evaluated as antiviral agents, with many possessing activity against life-threatening infections. This targeted review provides insight into the known glycan-binding interactions between select HMOs and influenza, rotavirus, respiratory syncytial virus, human immunodeficiency virus, and norovirus. Additionally, we review the role of HMOs in preventing necrotizing enterocolitis, an intestinal disease linked to viral infections. We close with a discussion of what is known broadly regarding human milk oligosaccharides and their interactions with coronaviruses.

Evaluation of methods for detecting human reads in microbial sequencing datasets

Sequencing data from host-associated microbes can often be contaminated by the body of the investigator or research subject. Human DNA is typically removed from microbial reads either by subtractive alignment (dropping all reads that map to the human genome) or by using a read classification tool to predict those of human origin, and then discarding them. To inform best practice guidelines, we benchmarked eight alignment-based and two classification-based methods of human read detection using simulated data from 10 clinically prevalent bacteria and three viruses, into which contaminating human reads had been added. While the majority of methods successfully detected >99 % of the human reads, they were distinguishable by variance. The most precise methods, with negligible variance, were Bowtie2 and SNAP, both of which misidentified few, if any, bacterial reads (and no viral reads) as human. While correctly detecting a similar number of human reads, methods based on taxonomic classification, such as Kraken2 and Centrifuge, could misclassify bacterial reads as human, although the extent of this was species-specific. Among the most sensitive methods of human read detection was BWA, although this also made the greatest number of false positive classifications. Across all methods, the set of human reads not identified as such, although often representing <0.1 % of the total reads, were non-randomly distributed along the human genome with many originating from the repeat-rich sex chromosomes. For viral reads and longer (>300 bp) bacterial reads, the highest performing approaches were classification-based, using Kraken2 or Centrifuge. For shorter (c. 150 bp) bacterial reads, combining multiple methods of human read detection maximized the recovery of human reads from contaminated short read datasets without being compromised by false positives. A particularly high-performance approach with shorter bacterial reads was a two-stage classification using Bowtie2 followed by SNAP. Using this approach, we re-examined 11 577 publicly archived bacterial read sets for hitherto undetected human contamination. We were able to extract a sufficient number of reads to call known human SNPs, including those with clinical significance, in 6 % of the samples. These results show that phenotypically distinct human sequence is detectable in publicly archived microbial read datasets.