Limits...
Development of an enhanced metaproteomic approach for deepening the microbiome characterization of the human infant gut.

Xiong W, Giannone RJ, Morowitz MJ, Banfield JF, Hettich RL - J. Proteome Res. (2014)

Bottom Line: To alleviate this restriction, we have designed a novel metaproteomic strategy based on double filtering (DF) the raw samples, a method that fractionates microbial from human cells to enhance microbial protein identification and characterization in complex fecal samples from healthy premature infants.This method dramatically improved the overall depth of infant gut proteome measurement, with an increase in the number of identified low-abundance proteins and a greater than 2-fold improvement in microbial protein identification and quantification.This enhancement of proteome measurement depth enabled a more extensive microbiome comparison between infants by not only increasing the confidence of identified microbial functional categories but also revealing previously undetected categories.

View Article: PubMed Central - PubMed

Affiliation: Chemical Sciences Division, Oak Ridge National Laboratory , Oak Ridge, Tennessee 37831, United States.

ABSTRACT
The establishment of early life microbiota in the human infant gut is highly variable and plays a crucial role in host nutrient availability/uptake and maturation of immunity. Although high-performance mass spectrometry (MS)-based metaproteomics is a powerful method for the functional characterization of complex microbial communities, the acquisition of comprehensive metaproteomic information in human fecal samples is inhibited by the presence of abundant human proteins. To alleviate this restriction, we have designed a novel metaproteomic strategy based on double filtering (DF) the raw samples, a method that fractionates microbial from human cells to enhance microbial protein identification and characterization in complex fecal samples from healthy premature infants. This method dramatically improved the overall depth of infant gut proteome measurement, with an increase in the number of identified low-abundance proteins and a greater than 2-fold improvement in microbial protein identification and quantification. This enhancement of proteome measurement depth enabled a more extensive microbiome comparison between infants by not only increasing the confidence of identified microbial functional categories but also revealing previously undetected categories.

Show MeSH
Distributions of ScanRanker scores forcollected mass spectra.ScanRanker scores are used to assess spectral quality for all collectedmass spectra. Stack histograms are generated for ScanRanker scoresof (a) infant #UN1 measured by the direct method, (b) infant #CA1by the direct method, (c) infant #UN1 by the indirect DF method, and(d) infant #CA1 by the indirect DF method. The color denotes ScanRankerscore distributions of unassigned (gray), assigned human (red), andassigned microbial (green) mass spectra in replicates. The indirectDF method enriches microbial mass spectra assignment as decreasinghuman mass spectra assignment.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4286196&req=5

fig3: Distributions of ScanRanker scores forcollected mass spectra.ScanRanker scores are used to assess spectral quality for all collectedmass spectra. Stack histograms are generated for ScanRanker scoresof (a) infant #UN1 measured by the direct method, (b) infant #CA1by the direct method, (c) infant #UN1 by the indirect DF method, and(d) infant #CA1 by the indirect DF method. The color denotes ScanRankerscore distributions of unassigned (gray), assigned human (red), andassigned microbial (green) mass spectra in replicates. The indirectDF method enriches microbial mass spectra assignment as decreasinghuman mass spectra assignment.

Mentions: The success in achieving accurateprotein identifications and deep proteome coverage in a complex communityrelies on the quality of a predicted protein sequence database thatis constructed from metagenomic data. Compared to the analysis ofa single cell type/microbial isolate, a larger portion of high-qualityspectra in a metaproteomic study remain unassigned due to the incompletenessof the proteomic database. To quantify this, we employed a spectralquality assessment tool, ScanRanker,32 toassign scores for all of the collected spectra to evaluate the qualityof the database (Table S4). Using ScanRankerscores, a distribution of total collected spectra including unassigned,assigned human, and assigned microbial spectra was plotted for eachinfant, as measured by both methods (Figure 3). For each distribution, a total of ∼280 000 spectrawere represented, as measured in duplicate runs, and ∼15% ofthose with scores below −0.6 were recognized as peptide identifications,implying that lower-quality spectra reside at the lower end of thedistribution. Although somewhat variable for microbial isolates, wetypically note that ∼60% of collected mass spectra can be assignedto peptides for an organism with a completely sequenced genome (withoutaccounting from PTMs, sequence variants, and other unknown contaminants,of course). However, due to the increased complexity of these samples,as well as the fact that the metagenomic databases used here are incomplete,approximately 27 and 29% of total collected spectra were assignedfor infant #UN1 and infant #CA1, respectively, using the direct approach(Figure 3a,b), whereas slightly higher percentagesof 30 and 33% were achieved via the indirect DF approach (Figure 3c,d). Despite having a similar spectral assignmentefficiency, one readily observable difference between the two infantsis the ratio of human versus microbial assigned spectra. For infant#UN1, the microbial peptide spectral matches (PSMs) accounted for40% of the total assigned spectra with the direct method (Figure 3a), whereas for infant #CA1, this value was muchlower (∼4%). Consequently, this suppression of microbe-derivedPSMs by the presence of abundant human proteins severely impedes theinterrogation of microbial functional activities in the gut, especiallywhen considering semiquantitation (Figure 3b). Therefore, it is a challenge to investigate the interindividualvariability through the direct approach given the relative dearthof microbial PSMs. However, compared to the direct method, our DFstrategy substantially increased microbial PSM proportions withintotal assigned spectra, from 40 to 93% for infant #UN1 and from 4to 48% for infant #CA1 (Figure 3 and Table S2).


Development of an enhanced metaproteomic approach for deepening the microbiome characterization of the human infant gut.

Xiong W, Giannone RJ, Morowitz MJ, Banfield JF, Hettich RL - J. Proteome Res. (2014)

Distributions of ScanRanker scores forcollected mass spectra.ScanRanker scores are used to assess spectral quality for all collectedmass spectra. Stack histograms are generated for ScanRanker scoresof (a) infant #UN1 measured by the direct method, (b) infant #CA1by the direct method, (c) infant #UN1 by the indirect DF method, and(d) infant #CA1 by the indirect DF method. The color denotes ScanRankerscore distributions of unassigned (gray), assigned human (red), andassigned microbial (green) mass spectra in replicates. The indirectDF method enriches microbial mass spectra assignment as decreasinghuman mass spectra assignment.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4286196&req=5

fig3: Distributions of ScanRanker scores forcollected mass spectra.ScanRanker scores are used to assess spectral quality for all collectedmass spectra. Stack histograms are generated for ScanRanker scoresof (a) infant #UN1 measured by the direct method, (b) infant #CA1by the direct method, (c) infant #UN1 by the indirect DF method, and(d) infant #CA1 by the indirect DF method. The color denotes ScanRankerscore distributions of unassigned (gray), assigned human (red), andassigned microbial (green) mass spectra in replicates. The indirectDF method enriches microbial mass spectra assignment as decreasinghuman mass spectra assignment.
Mentions: The success in achieving accurateprotein identifications and deep proteome coverage in a complex communityrelies on the quality of a predicted protein sequence database thatis constructed from metagenomic data. Compared to the analysis ofa single cell type/microbial isolate, a larger portion of high-qualityspectra in a metaproteomic study remain unassigned due to the incompletenessof the proteomic database. To quantify this, we employed a spectralquality assessment tool, ScanRanker,32 toassign scores for all of the collected spectra to evaluate the qualityof the database (Table S4). Using ScanRankerscores, a distribution of total collected spectra including unassigned,assigned human, and assigned microbial spectra was plotted for eachinfant, as measured by both methods (Figure 3). For each distribution, a total of ∼280 000 spectrawere represented, as measured in duplicate runs, and ∼15% ofthose with scores below −0.6 were recognized as peptide identifications,implying that lower-quality spectra reside at the lower end of thedistribution. Although somewhat variable for microbial isolates, wetypically note that ∼60% of collected mass spectra can be assignedto peptides for an organism with a completely sequenced genome (withoutaccounting from PTMs, sequence variants, and other unknown contaminants,of course). However, due to the increased complexity of these samples,as well as the fact that the metagenomic databases used here are incomplete,approximately 27 and 29% of total collected spectra were assignedfor infant #UN1 and infant #CA1, respectively, using the direct approach(Figure 3a,b), whereas slightly higher percentagesof 30 and 33% were achieved via the indirect DF approach (Figure 3c,d). Despite having a similar spectral assignmentefficiency, one readily observable difference between the two infantsis the ratio of human versus microbial assigned spectra. For infant#UN1, the microbial peptide spectral matches (PSMs) accounted for40% of the total assigned spectra with the direct method (Figure 3a), whereas for infant #CA1, this value was muchlower (∼4%). Consequently, this suppression of microbe-derivedPSMs by the presence of abundant human proteins severely impedes theinterrogation of microbial functional activities in the gut, especiallywhen considering semiquantitation (Figure 3b). Therefore, it is a challenge to investigate the interindividualvariability through the direct approach given the relative dearthof microbial PSMs. However, compared to the direct method, our DFstrategy substantially increased microbial PSM proportions withintotal assigned spectra, from 40 to 93% for infant #UN1 and from 4to 48% for infant #CA1 (Figure 3 and Table S2).

Bottom Line: To alleviate this restriction, we have designed a novel metaproteomic strategy based on double filtering (DF) the raw samples, a method that fractionates microbial from human cells to enhance microbial protein identification and characterization in complex fecal samples from healthy premature infants.This method dramatically improved the overall depth of infant gut proteome measurement, with an increase in the number of identified low-abundance proteins and a greater than 2-fold improvement in microbial protein identification and quantification.This enhancement of proteome measurement depth enabled a more extensive microbiome comparison between infants by not only increasing the confidence of identified microbial functional categories but also revealing previously undetected categories.

View Article: PubMed Central - PubMed

Affiliation: Chemical Sciences Division, Oak Ridge National Laboratory , Oak Ridge, Tennessee 37831, United States.

ABSTRACT
The establishment of early life microbiota in the human infant gut is highly variable and plays a crucial role in host nutrient availability/uptake and maturation of immunity. Although high-performance mass spectrometry (MS)-based metaproteomics is a powerful method for the functional characterization of complex microbial communities, the acquisition of comprehensive metaproteomic information in human fecal samples is inhibited by the presence of abundant human proteins. To alleviate this restriction, we have designed a novel metaproteomic strategy based on double filtering (DF) the raw samples, a method that fractionates microbial from human cells to enhance microbial protein identification and characterization in complex fecal samples from healthy premature infants. This method dramatically improved the overall depth of infant gut proteome measurement, with an increase in the number of identified low-abundance proteins and a greater than 2-fold improvement in microbial protein identification and quantification. This enhancement of proteome measurement depth enabled a more extensive microbiome comparison between infants by not only increasing the confidence of identified microbial functional categories but also revealing previously undetected categories.

Show MeSH