The impact of sequence database choice on metaproteomic results in gut microbiota studies.
BACKGROUND
Elucidating the role of gut microbiota in physiological and pathological processes has recently emerged as a key research aim in life sciences. In this respect, metaproteomics, the study of the whole protein complement of a microbial community, can provide a unique contribution by revealing which functions are actually being expressed by specific microbial taxa. However, its wide application to gut microbiota research has been hindered by challenges in data analysis, especially related to the choice of the proper sequence databases for protein identification.


RESULTS
Here, we present a systematic investigation of variables concerning database construction and annotation and evaluate their impact on human and mouse gut metaproteomic results. We found that both publicly available and experimental metagenomic databases lead to the identification of unique peptide assortments, suggesting parallel database searches as a mean to gain more complete information. In particular, the contribution of experimental metagenomic databases was revealed to be mandatory when dealing with mouse samples. Moreover, the use of a "merged" database, containing all metagenomic sequences from the population under study, was found to be generally preferable over the use of sample-matched databases. We also observed that taxonomic and functional results are strongly database-dependent, in particular when analyzing the mouse gut microbiota. As a striking example, the Firmicutes/Bacteroidetes ratio varied up to tenfold depending on the database used. Finally, assembling reads into longer contigs provided significant advantages in terms of functional annotation yields.


CONCLUSIONS
This study contributes to identify host- and database-specific biases which need to be taken into account in a metaproteomic experiment, providing meaningful insights on how to design gut microbiota studies and to perform metaproteomic data analysis. In particular, the use of multiple databases and annotation tools has to be encouraged, even though this requires appropriate bioinformatic resources.
Porto Conte Ricerche, Science and Technology Park of Sardinia, Tramariglio, Alghero, Italy.
Alessandro
A
Tanca
Porto Conte Ricerche, Science and Technology Park of Sardinia, Tramariglio, Alghero, Italy.
Antonio
A
Palomba
Porto Conte Ricerche, Science and Technology Park of Sardinia, Tramariglio, Alghero, Italy.
Cristina
C
Fraumene
Porto Conte Ricerche, Science and Technology Park of Sardinia, Tramariglio, Alghero, Italy.
Daniela
D
Pagnozzi
Department of Biomedical Sciences, University of Sassari, Sassari, Italy.
Valeria
V
Manghina
Department of Biomedical Sciences, University of Sassari, Sassari, Italy.
Massimo
M
Deligios
Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany.|Research Group Bioinformatics (NG 4), Robert Koch Institute, Berlin, Germany.
Thilo
T
Muth
Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany.
Erdmann
E
Rapp
Department of Biochemistry, Ghent University, Ghent, Belgium.|Medical Biotechnology Center, VIB, Ghent, Belgium.|Bioinformatics Institute Ghent, Ghent University, Zwijnaarde, Ghent, Belgium.
Lennart
L
Martens
Porto Conte Ricerche, Science and Technology Park of Sardinia, Tramariglio, Alghero, Italy.
Maria Filippa
MF
Addis
Porto Conte Ricerche, Science and Technology Park of Sardinia, Tramariglio, Alghero, Italy. uzzau@portocontericerche.it.|Department of Biomedical Sciences, University of Sassari, Sassari, Italy. uzzau@portocontericerche.it.
Sergio
http://orcid.org/0000-0001-6246-2794
S
Uzzau
Bioinformatics; Gut microbiota; Mass spectrometry; Metagenomics; Metaproteomics
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