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PLoS Comput Biol
2017[Oct]; 13
(10
): e1005579
PMID29023448
show ga
Hundreds of different species colonize multicellular organisms making them
"metaorganisms". A growing body of data supports the role of microbiota in health
and in disease. Grasping the principles of host-microbiota interactions (HMIs) at
the molecular level is important since it may provide insights into the
mechanisms of infections. The crosstalk between the host and the microbiota may
help resolve puzzling questions such as how a microorganism can contribute to
both health and disease. Integrated superorganism networks that consider host and
microbiota as a whole-may uncover their code, clarifying perhaps the most
fundamental question: how they modulate immune surveillance. Within this
framework, structural HMI networks can uniquely identify potential microbial
effectors that target distinct host nodes or interfere with endogenous host
interactions, as well as how mutations on either host or microbial proteins
affect the interaction. Furthermore, structural HMIs can help identify master
host cell regulator nodes and modules whose tweaking by the microbes promote
aberrant activity. Collectively, these data can delineate pathogenic mechanisms
and thereby help maximize beneficial therapeutics. To date, challenges in
experimental techniques limit large-scale characterization of HMIs. Here we
highlight an area in its infancy which we believe will increasingly engage the
computational community: predicting interactions across kingdoms, and mapping
these on the host cellular networks to figure out how commensal and pathogenic
microbiota modulate the host signaling and broadly cross-species consequences.
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