THESIS
2021
1 online resource (xvii, 144 pages) : illustrations (chiefly color)
Abstract
Gut microbiome is perhaps the most complex microbial community, consisting of approximately
10
13 microbial cells spanning hundreds to thousands of different species. Recent researches
have revealed the myriad associations between gut microbiome and human physiology,
health and diseases. Metaproteomics is a newly developed proteomics technology
aiming at the high-throughput identification and quantification of the entire protein complement
of a microbial community. It is capable of characterizing the functional profile of the
gut microbiome in real time and establishing a more accurate correlation with the host phenotypes
or environmental factors. However, the extremely high diversity and complexity of
the gut microbiome make it difficult to identify and quantify proteins from metaproteo...[
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Gut microbiome is perhaps the most complex microbial community, consisting of approximately
10
13 microbial cells spanning hundreds to thousands of different species. Recent researches
have revealed the myriad associations between gut microbiome and human physiology,
health and diseases. Metaproteomics is a newly developed proteomics technology
aiming at the high-throughput identification and quantification of the entire protein complement
of a microbial community. It is capable of characterizing the functional profile of the
gut microbiome in real time and establishing a more accurate correlation with the host phenotypes
or environmental factors. However, the extremely high diversity and complexity of
the gut microbiome make it difficult to identify and quantify proteins from metaproteomics
samples, impeding the comparative analysis across samples. This thesis set out to develop a
metaproteomics method, circumventing the protein identifications and performing comparisons
between samples on the basis of the MS/MS spectra acquired. We employed the spectrum
clustering algorithm, SpectraST, to construct a consensus spectra library of all samples, and
then determined the similarities and differences between samples by quantifying the contribution
they made to the consensus spectra library. We successfully grouped 48 metaproteomics
samples into 4 distinct clusters, and verified them using metagenomics analysis. We then
integrated database search, de novo sequencing, and open search, trying to identify the consensus
spectra showing significant differences between clusters. A reconciliation scheme was
proposed to coordinate conflicting answers from different identification methods and from
the corresponding raw spectra of the consensus spectra. In contrast to the conventional proteomics
workflow, our method carried out unsupervised clustering and comparison between
samples priori to protein identification, whereby individual variance can be mitigated.
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