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Slizovskiy, I.

Publications and source records attributed to Slizovskiy, I..

2 recordsLinked to original sources

Postpartum Systemic Inflammation is Reflected in Early Distinct Fecal Microbiome Differences in Dairy Cows

A tightly regulated inflammatory response occurs during and following parturition; however, excessive or prolonged inflammation negatively affects herd health and productivity. The gut microbiota plays an important role in host immunity and metabolism and undergoes substantial changes during the transition period. However, the relationship between systemic inflammation, measured by serum acute-phase proteins, and gut microbial dynamics during early lactation remains poorly understood. We investigated fecal microbiota dynamics in relation to systemic inflammation in early postpartum dairy cows. Fecal and blood samples were collected from 71 Holstein cows on days 1 and 3 in milk (DIM). The V3-V4 region of the 16S rRNA gene was sequenced and microbial diversity, differential abundance, and microbial interaction networks were evaluated. Inflammatory status, defined by fibrinogen, haptoglobin, and their combined classification, was associated with significant alterations in fecal microbial composition during the immediate postpartum period, independent of body condition score, parity, DIM, and DNA extraction parameters. Differential abundance analyses revealed extensive taxonomic restructuring, while network analyses identified increased modularity, altered keystone taxa distribution, and greater compartmentalization of microbial interactions in animals with elevated inflammatory markers. Several taxa were consistently associated with inflammatory status across analytical approaches. Notably, Ruminococcaceae UCG-002 and Dielma were associated with inflammatory states, whereas Xylanibacter, Marvinbryantia, Akkermansia, and Oscillibacter were associated with non-inflammatory status. This study identifies an association between systemic inflammation and fecal microbiota composition in early transition dairy cows, providing a foundation for future microbiome-based biomarkers of inflammatory status. IMPORTANCESubclinical systemic inflammation during early postpartum can negatively affect dairy cow health and productivity, yet current monitoring relies on repeated blood sampling and transient inflammatory biomarkers. For the first time association of systemic inflammation, assessed using fibrinogen, haptoglobin, and their combined classification, with alterations in fecal microbial composition, microbial interaction networks, and keystone taxa distribution during the early postpartum period was established. Several bacterial taxa were consistently associated with either elevated or normal inflammatory states across differential abundance, network, and odds ratio analyses. Many of these taxa remain poorly characterized in dairy cattle, highlighting the need for future mechanistic studies. This study demonstrates that systemic inflammation during early postpartum is associated with measurable alterations in the fecal microbiota. This work provides a foundation for developing microbiome-based biomarkers for detecting and monitoring subclinical systemic inflammation in dairy cattle.

microbiology↗

Syotti: Scalable Bait Design for DNA Enrichment

Bait-enriched sequencing is a relatively new sequencing protocol that is becoming increasingly ubiquitous as it has been shown to successfully amplify regions of interest in metagenomic samples. In this method, a set of synthetic probes ("baits") are designed, manufactured, and applied to fragmented metagenomic DNA. The probes bind to the fragmented DNA and any unbound DNA is rinsed away, leaving the bound fragments to be amplified for sequencing. This effectively enriches the DNA for which the probes were designed. Most recently, Metsky et al. (Nature Biotech 2019) demonstrated that bait-enrichment is capable of detecting a large number of human viral pathogens within metagenomic samples. In this work, we formalize the problem of designing baits by defining the Minimum Bait Cover problem, which aims to find the smallest possible set of bait sequences that cover every position of a set of reference sequences under an approximate matching model. We show that the problem is NP-hard, and that it remains NP-hard under very restrictive assumptions. This indicates that no polynomial-time exact algorithm exists for the problem, and that the problem is intractable even for small and deceptively simple inputs. In light of this, we design an efficient heuristic that takes advantage of succinct data structures. We refer to our method as syotti. The running time of syotti shows linear scaling in practice, running at least an order of magnitude faster than state-of-the-art methods, including the recent method of Metsky et al. At the same time, our method produces bait sets that are smaller than the ones produced by the competing methods, while also leaving fewer positions uncovered. Lastly, we show that syotti requires only 25 minutes to design baits for a dataset comprised of 3 billion nucleotides from 1000 related bacterial substrains, whereas the method of Metsky et al. shows clearly super-linear running time and fails to process even a subset of 8% of the data in 24 hours. Our implementation is publicly available at https://github.com/jnalanko/syotti.

bioinformatics↗