Streptococcus suis serotype 2 (S. suis 2) is an important zoonotic
pathogen that presents a significant threat both to pigs
and to workers in the pork industry. The initial steps of S. suis
2 pathogenesis are unclear. In this study, we found that the
type II histidine triad protein HtpsC from the highly virulent
Chinese isolate 05ZYH33 is structurally similar to internalin
A (InlA) from Listeria monocytogenes, which plays an important
role in mediating listerial invasion of epithelial cells. To
determine if HtpsC and InlA function similarly, an isogenic
htpsC mutant (ΔhtpsC) was generated in S. suis by homologous
recombination. The htpsC deletion strain exhibited a
diminished ability to adhere to and invade epithelial cells from
different sources. Double immunofluorescence microscopy
also revealed reduced survival of the ΔhtpsC mutant after cocultivation
with epithelium. Adhesion to epithelium and invasion
by the wild type strain was inhibited by a monoclonal
antibody against E-cadherin. In contrast, the htpsC-deficient
mutant was unaffected by the same treatment, suggesting that
E-cadherin is the host-cell receptor that interacts with HtpsC
and facilitates bacterial internalization. Based on these results,
we propose that HtpsC is involved in the process by which
S. suis 2 penetrates host epithelial cells, and that this protein
is an important virulence factor associated with cell adhesion
and invasion.
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Our understanding of the interactions between microbial communities
and their niche in the host gut has improved owing
to recent advances in environmental microbial genomics.
Integration of metagenomic and metataxonomic sequencing
data with other omics data to study the gut microbiome
has become increasingly common, but downstream analysis
after data integration and interpretation of complex omics
data remain challenging. Here, we review studies that have
explored the gut microbiome signature using omics approaches,
including metagenomics, metataxonomics, metatranscriptomics,
and metabolomics. We further discuss recent
analytics programs to analyze and integrate multi-omics datasets
and further utilization of omics data with other advanced
techniques, such as adaptive immune receptor repertoire sequencing,
microbial culturomics, and machine learning, to
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