Fecal Metaproteomic Analysis Reveals Unique Changes of the Gut Microbiome Functions After Consumption of Sourdough Carasau Bread

Marcello Abbondio, Antonio Palomba, Alessandro Tanca, Cristina Fraumene, Daniela Pagnozzi, Monica Serra, Fabio Marongiu, Ezio Laconi, Sergio Uzzau, Marcello Abbondio, Antonio Palomba, Alessandro Tanca, Cristina Fraumene, Daniela Pagnozzi, Monica Serra, Fabio Marongiu, Ezio Laconi, Sergio Uzzau

Abstract

Sourdough-leavened bread (SB) is acknowledged for its great variety of valuable effects on consumer's metabolism and health, including a low glycemic index and a reduced content of the possible carcinogen acrylamide. Here, we aimed to investigate how these effects influence the gut microbiota composition and functions. Therefore, we subjected rats to a diet supplemented with SB, baker's yeast leavened bread (BB), or unsupplemented diet (chow), and, after 4 weeks of treatment, their gut microbiota was analyzed using a metaproteogenomic approach. As a result, diet supplementation with SB led to a reduction of specific members of the intestinal microbiota previously associated to low protein diets, namely Alistipes and Mucispirillum, or known as intestinal pathobionts, i.e., Mycoplasma. Concerning functions, asparaginases expressed by Bacteroides were observed as more abundant in SB-fed rats, leading to hypothesize that in their colonic microbiota the enzyme substrate, asparagine, was available in higher amounts than in BB- and chow-fed rats. Another group of protein families, expressed by Clostridium, was detected as more abundant in animal fed SB-supplemented diet. Of these, manganese catalase, small acid-soluble proteins (SASP), Ser/Thr kinase PrkA, and V-ATPase proteolipid subunit have been all reported to take part in Clostridium sporulation, strongly suggesting that the diet supplementation with SB might promote environmental conditions inducing metabolic dormancy of Clostridium spp. within the gut microbiota. In conclusion, our data describe the effects of SB consumption on the intestinal microbiota taxonomy and functions in rats. Moreover, our results suggest that a metaproteogenomic approach can provide evidence of the interplay between metabolites deriving from bread digestion and microbial metabolism.

Keywords: diet; food processes; gut microbiota; metagenomics; metaproteomics; sourdough.

Figures

Figure 1
Figure 1
Relative abundance of the genus Mycoplasma in stool and colon content samples. Each dot represents a different rat, with dots with same shape and color being referred to the same rat. For stool samples, both 16S rRNA gene sequencing (16S) and metaproteomic (MP) data are shown. BB, rats fed chow supplemented with baker's yeast leavened bread (light blue); SB, rats fed chow supplemented with sourdough leavened bread (orange); chow, rats fed chow only (green). Statistically significant differences between groups (according to edgeR test followed by SGoF adjustment) are indicated with asterisks (* = adjusted p-value < 0.05; ** = adjusted p-value < 0.01; *** = adjusted p-value < 0.00001).
Figure 2
Figure 2
Differential family-specific microbial functions in rats fed chow supplemented with bread leavened with baker's yeast (BB) vs. sourdough (SB). In each line, a dot represents a single animal, with its color intensity being proportional to the relative abundance of that given microbial protein in that subject, according to the scale depicted in the bottom-right corner. Missing values (function not identified in that animal) are in white; features with missing values in the most abundant group were filtered out. The upper part of the heatmap lists functions with higher abundance in the fecal microbiota of SB-fed animals, while the lower part lists those with higher abundance in the fecal microbiota of BB-fed animals. Functions are ordered based on the Cluster of Orthologous Groups (COG) category to which they belong (C, Energy production and conversion; E, Amino acid transport and metabolism; G, Carbohydrate transport and metabolism; J, Translation, ribosomal structure and biogenesis; M, Cell wall/membrane/envelop biogenesis; O, Posttranslational modification, protein turnover, chaperones; P, Inorganic ion transport and metabolism), and then in alphabetical order.
Figure 3
Figure 3
Differential family-specific microbial functions in rats fed chow supplemented with bread leavened with sourdough (SB) vs. chow only. In each line, a dot represents a single animal, with its color intensity being proportional to the relative abundance of that given microbial protein in that subject, according to the scale depicted in the bottom-right corner. Missing values (function not identified in that animal) are in white; features with missing values in the most abundant group were filtered out. The upper part of the heatmap lists functions with higher abundance in the fecal microbiota of SB-fed animals, while the lower part lists those with higher abundance in the fecal microbiota of chow-fed animals. Functions are ordered based on the Cluster of Orthologous Groups (COG) category to which they belong (C, Energy production and conversion; E, Amino acid transport and metabolism; G, Carbohydrate transport and metabolism; I, Lipid metabolism; J, Translation, ribosomal structure and biogenesis; M, Cell wall/membrane/envelop biogenesis; O, Posttranslational modification, protein turnover, chaperones; P, Inorganic ion transport and metabolism; T, Signal transduction mechanisms; V, Defense mechanisms), and then in alphabetical order.

References

    1. Agans R., Gordon A., Kramer D. L., Perez-Burillo S., Rufian-Henares J. A., Paliy O. (2018). Dietary fatty acids sustain the growth of the human gut microbiota. Appl. Environ. Microbiol. 84, 1–15. 10.1128/AEM.01525-18
    1. Bartkiene E., Bartkevics V., Krungleviciute V., Pugajeva I., Zadeike D., Juodeikiene G. (2017). Lactic acid bacteria combinations for wheat sourdough preparation and their influence on wheat bread quality and acrylamide formation. J. Food Sci. 82, 2371–2378. 10.1111/1750-3841.13858
    1. Bartkiene E., Jakobsone I., Juodeikiene G., Vidmantiene D., Pugajeva I., Bartkevics V. (2013). Effect of lactic acid fermentation of lupine wholemeal on acrylamide content and quality characteristics of wheat-lupine bread. Int. J. Food Sci. Nutr. 64, 890–896. 10.3109/09637486.2013.805185
    1. Boeck L. D., Sires R. W., Wilson M. W., Ho P. P. (1970). Effect of glucose and low oxygen tension on L-asparaginase production by a strain of Escherichia coli B. Appl. Microbiol. 20, 964–969.
    1. Buchfink B., Xie C., Huson D. H. (2015). Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60. 10.1038/nmeth.3176
    1. Caporaso J. G., Kuczynski J., Stombaugh J., Bittinger K., Bushman F. D., Costello E. K., et al. . (2010). QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336. 10.1038/nmeth.f.303
    1. Carvajal-Rodriguez A., de Una-Alvarez J. (2011). Assessing significance in high-throughput experiments by sequential goodness of fit and q-value estimation. PLoS ONE 6:e24700. 10.1371/journal.pone.0024700
    1. Carvajal-Rodriguez A., de Una-Alvarez J., Rolan-Alvarez E. (2009). A new multitest correction (SGoF) that increases its statistical power when increasing the number of tests. BMC Bioinformatics 10:209. 10.1186/1471-2105-10-209
    1. Cox L. M., Sohn J., Tyrrell K. L., Citron D. M., Lawson P. A., Patel N. B., et al. . (2017). Description of two novel members of the family Erysipelotrichaceae: Ileibacterium valens gen. nov., sp. nov. and Dubosiella newyorkensis, gen. nov., sp. nov., from the murine intestine, and emendation to the description of Faecalibaculum rodentium. Int. J. Syst. Evol. Microbiol. 67, 1247–1254. 10.1099/ijsem.0.001793
    1. De Vuyst L., Van Kerrebroeck S., Harth H., Huys G., Daniel H.-M., Weckx S. (2014). Microbial ecology of sourdough fermentations: diverse or uniform? Food Microbiol. 37, 11–29. 10.1016/j.fm.2013.06.002
    1. DeSantis T. Z., Hugenholtz P., Larsen N., Rojas M., Brodie E. L., Keller K., et al. . (2006). Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Env. Microbiol. 72, 5069–5072. 10.1128/aem.03006-05
    1. Dhariwal A., Chong J., Habib S., King I. L., Agellon L. B., Xia J. (2017). MicrobiomeAnalyst: a web-based tool for comprehensive statistical, visual and meta-analysis of microbiome data. Nucleic Acids Res. 45, W180–W188. 10.1093/nar/gkx295
    1. Di Nunzio M., Bordoni A., Aureli F., Cubadda F., Gianotti A. (2018). Sourdough fermentation favorably influences selenium biotransformation and the biological effects of flatbread. Nutrients 10:1898. 10.3390/nu10121898
    1. Edgar R. C. (2010). Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461. 10.1093/bioinformatics/btq461
    1. El-Naggar N. E., Deraz S. F., Soliman H. M., El-Deeb N. M., El-Ewasy S. M. (2016). Purification, characterization, cytotoxicity and anticancer activities of L-asparaginase, anti-colon cancer protein, from the newly isolated alkaliphilic Streptomyces fradiae NEAE-82. Sci. Rep. 6:32926. 10.1038/srep32926
    1. Fraumene C., Manghina V., Cadoni E., Marongiu F., Abbondio M., Serra M., et al. . (2018). Caloric restriction promotes rapid expansion and long-lasting increase of Lactobacillus in the rat fecal microbiota. Gut Microbes 9, 104–114. 10.1080/19490976.2017.1371894
    1. Gobbetti M., De Angelis M., Di Cagno R., Calasso M., Archetti G., Rizzello C. G. (2018a). Novel insights on the functional/nutritional features of the sourdough fermentation. Int. J. Food Microbiol. 302, 103–113. 10.1016/j.ijfoodmicro.2018.05.018
    1. Gobbetti M., Minervini F., Pontonio E., Di Cagno R., De Angelis M. (2016). Drivers for the establishment and composition of the sourdough lactic acid bacteria biota. Int. J. Food Microbiol. 239, 3–18. 10.1016/j.ijfoodmicro.2016.05.022
    1. Gobbetti M., Pontonio E., Filannino P., Rizzello C. G., De Angelis M., Di Cagno R. (2018b). How to improve the gluten-free diet: The state of the art from a food science perspective. Food Res. Int. 110, 22–32. 10.1016/j.foodres.2017.04.010
    1. Huson D. H., Beier S., Flade I., Gorska A., El-Hadidi M., Mitra S., et al. . (2016). MEGAN community edition - interactive exploration and analysis of large-scale microbiome sequencing data. PLoS Comput. Biol. 12:e1004957. 10.1371/journal.pcbi.1004957
    1. Kang Y., Li Y., Du Y., Guo L., Chen M., Huang X., et al. . (2018). Konjaku flour reduces obesity in mice by modulating the composition of the gut microbiota. Int. J. Obes. 10.1038/s41366-018-0187-x. [Epub ahead of print].
    1. Keramat J., LeBail A., Prost C., Jafari M. (2011). Acrylamide in baking products: a review article. Food Bioprocess Technol. 4, 530–543. 10.1007/s11947-010-0495-1
    1. Korem T., Zeevi D., Zmora N., Weissbrod O., Bar N., Lotan-Pompan M., et al. . (2017). Bread affects clinical parameters and induces gut microbiome-associated personal glycemic responses. Cell Metab. 25, 1243.e5–1253.e5. 10.1016/j.cmet.2017.05.002
    1. Liu H., Ray W. K., Helm R. F., Popham D. L., Melville S. B. (2016). Analysis of the spore membrane proteome in Clostridium perfringens implicates cyanophycin in spore assembly. J. Bacteriol. 198, 1773–1782. 10.1128/JB.00212-16
    1. Maioli M., Pes G. M., Sanna M., Cherchi S., Dettori M., Manca E., et al. . (2008). Sourdough-leavened bread improves postprandial glucose and insulin plasma levels in subjects with impaired glucose tolerance. Acta Diabetol. 45, 91–96. 10.1007/s00592-008-0029-8
    1. McLaughlin P. A., McClelland M., Yang H.-J., Porwollik S., Bogomolnaya L., Chen J.-S., et al. . (2017). Contribution of asparagine catabolism to Salmonella virulence. Infect. Immun. 85, e00740–e007416. 10.1128/IAI.00740-16
    1. Mesuere B., Van der Jeugt F., Willems T., Naessens T., Devreese B., Martens L., et al. . (2017). High-throughput metaproteomics data analysis with Unipept: a tutorial. J Proteomics. 171:11–22, 10.1016/j.jprot.2017.05.022
    1. Miyo M., Konno M., Nishida N., Sueda T., Noguchi K., Matsui H., et al. . (2016). Metabolic adaptation to nutritional stress in human colorectal cancer. Sci. Rep. 6:38415. 10.1038/srep38415
    1. Nasiri Esfahani B., Kadivar M., Shahedi M., Soleimanian-Zad S. (2017). Reduction of acrylamide in whole-wheat bread by combining lactobacilli and yeast fermentation. Food Addit. Contam. Part A Chem. Anal. Control. Expo. Risk Assess. 34, 1904–1914. 10.1080/19440049.2017.1378444
    1. Navarro G., Sharma A., Dugas L. R., Forrester T., Gilbert J. A., Layden B. T. (2018). Gut microbial features can predict host phenotype response to protein deficiency. Physiol. Rep. 6:e13932. 10.14814/phy2.13932
    1. Olsen J. V., de Godoy L. M., Li G., Macek B., Mortensen P., Pesch R., et al. . (2005). Parts per million mass accuracy on an Orbitrap mass spectrometer via lock mass injection into a C-trap. Mol. Cell Proteomics 4, 2010–2021. 10.1074/mcp.T500030-MCP200
    1. Permpoonpattana P., Phetcharaburanin J., Mikelsone A., Dembek M., Tan S., Brisson M.-C., et al. . (2013). Functional characterization of Clostridium difficile spore coat proteins. J. Bacteriol. 195, 1492–1503. 10.1128/JB.02104-12
    1. Pompeo F., Foulquier E., Galinier A. (2016). Impact of serine/threonine protein kinases on the regulation of sporulation in Bacillus subtilis. Front. Microbiol. 7:568. 10.3389/fmicb.2016.00568
    1. Poutanen K., Flander L., Katina K. (2009). Sourdough and cereal fermentation in a nutritional perspective. Food Microbiol. 26, 693–699. 10.1016/j.fm.2009.07.011
    1. Pundir S., Martin M. J., O'Donovan C. (2016). UniProt tools. Curr. Protoc. Bioinform. 53, 1.29.1–1.29.15. 10.1002/0471250953.bi0129s53
    1. Robinson M. D., McCarthy D. J., Smyth G. K. (2010). edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140. 10.1093/bioinformatics/btp616
    1. Sanna M., Fois S., Falchi G., Campus M., Roggio T., Catzeddu P. (2018). Effect of liquid sourdough technology on the pre-biotic, texture, and sensory properties of a crispy flatbread. Food Sci. Biotechnol. 28, 721–730. 10.1007/s10068-018-0530-y
    1. Scazzina F., Del Rio D., Pellegrini N., Brighenti F. (2009). Sourdough bread: starch digestibility and postprandial glycemic response. J. Cereal Sci. 49, 419–421. 10.1016/J.JCS.2008.12.008
    1. Scotti C., Sommi P., Pasquetto M. V., Cappelletti D., Stivala S., Mignosi P., et al. . (2010). Cell-cycle inhibition by Helicobacter pylori L-asparaginase. PLoS ONE 5:e13892. 10.1371/journal.pone.0013892
    1. Shen A. (2015). A gut odyssey: the impact of the microbiota on Clostridium difficile spore formation and germination. PLoS Pathog. 11:e1005157. 10.1371/journal.ppat.1005157
    1. Spicher G., Nierle W. (1988). Proteolytic activity of sourdough bacteria. Appl. Microbiol. Biotechnol. 28, 487–492. 10.1007/BF00268220
    1. Stamataki N. S., Yanni A. E., Karathanos V. T. (2017). Bread making technology influences postprandial glucose response: a review of the clinical evidence. Br. J. Nutr. 117, 1001–1012. 10.1017/S0007114517000770
    1. Tanca A., Abbondio M., Palomba A., Fraumene C., Marongiu F., Serra M., et al. . (2018). Caloric restriction promotes functional changes involving short-chain fatty acid biosynthesis in the rat gut microbiota. Sci Rep. 8:14778. 10.1038/s41598-018-33100-y
    1. Tanca A., Biosa G., Pagnozzi D., Addis M. F., Uzzau S. (2013). Comparison of detergent-based sample preparation workflows for LTQ-Orbitrap analysis of the Escherichia coli proteome. Proteomics 13, 2597–2607. 10.1002/pmic.201200478
    1. Tanca A., Manghina V., Fraumene C., Palomba A., Abbondio M., Deligios M., et al. . (2017). Metaproteogenomics reveals taxonomic and functional changes between cecal and fecal microbiota in mouse. Front. Microbiol. 8:391. 10.3389/fmicb.2017.00391
    1. Tanca A., Palomba A., Fraumene C., Pagnozzi D., Manghina V., Deligios M., et al. . (2016). The impact of sequence database choice on metaproteomic results in gut microbiota studies. Microbiome 4:51. 10.1186/s40168-016-0196-8
    1. Tanca A., Palomba A., Pisanu S., Addis M. F., Uzzau S. (2015). Enrichment or depletion? The impact of stool pretreatment on metaproteomic characterization of the human gut microbiota. Proteomics 15, P3474–P3485. 10.1002/pmic.201400573
    1. Tanca A., Palomba A., Pisanu S., Deligios M., Fraumene C., Manghina V., et al. . (2014). A straightforward and efficient analytical pipeline for metaproteome characterization. Microbiome 2:49. 10.1186/s40168-014-0049-2
    1. Tareke E., Rydberg P., Karlsson P., Eriksson S., Tornqvist M. (2002). Analysis of acrylamide, a carcinogen formed in heated foodstuffs. J. Agric. Food Chem. 50, 4998–5006. 10.1021/jf020302f
    1. Toda K., Kawada K., Iwamoto M., Inamoto S., Sasazuki T., Shirasawa S., et al. . (2016). Metabolic alterations caused by KRAS mutations in colorectal cancer contribute to cell adaptation to glutamine depletion by upregulation of asparagine synthetase. Neoplasia 18, 654–665. 10.1016/j.neo.2016.09.004
    1. Turnbaugh P. J., Backhed F., Fulton L., Gordon J. I. (2008). Diet-induced obesity is linked to marked but reversible alterations in the mouse distal gut microbiome. Cell Host Microbe 3, 213–223. 10.1016/j.chom.2008.02.015
    1. van der Beek C. M., Dejong C. H. C., Troost F. J., Masclee A. A. M., Lenaerts K. (2017). Role of short-chain fatty acids in colonic inflammation, carcinogenesis, and mucosal protection and healing. Nutr. Rev. 75, 286–305. 10.1093/nutrit/nuw067
    1. Vizcaino J. A., Csordas A., Del-Toro N., Dianes J. A., Griss J., Lavidas I., et al. (2016). 2016 update of the PRIDE database and its related tools. Nucleic Acids Res. 44, D447–D456. 10.1093/nar/gkv1145
    1. Wang J., Mei H., Zheng C., Qian H., Cui C., Fu Y., et al. . (2013). The metabolic regulation of sporulation and parasporal crystal formation in Bacillus thuringiensis revealed by transcriptomics and proteomics. Mol. Cell. Proteomics 12, 1363–1376. 10.1074/mcp.M112.023986
    1. Wei S., Han R., Zhao J., Wang S., Huang M., Wang Y., et al. . (2018). Intermittent administration of a fasting-mimicking diet intervenes in diabetes progression, restores beta cells and reconstructs gut microbiota in mice. Nutr. Metab. 15:80. 10.1186/s12986-018-0318-3
    1. Wetzel D., Fischer R.-J. (2015). Small acid-soluble spore proteins of Clostridium acetobutylicum are able to protect DNA in vitro and are specifically cleaved by germination protease GPR and spore protease YyaC. Microbiology 161, 2098–2109. 10.1099/mic.0.000162
    1. Wisniewski J. R., Zougman A., Nagaraj N., Mann M. (2009). Universal sample preparation method for proteome analysis. Nat. Methods 6, 359–362. 10.1038/nmeth.1322
    1. Xiao L., Feng Q., Liang S., Sonne S. B., Xia Z., Qiu X., et al. . (2015). A catalog of the mouse gut metagenome. Nat. Biotechnol. 33, 1103–1108. 10.1038/nbt.3353
    1. Zhai X., Lin D., Zhao Y., Li W., Yang X. (2018). Effects of dietary fiber supplementation on fatty acid metabolism and intestinal microbiota diversity in C57BL/6J mice fed with a high-fat diet. J. Agric. Food Chem. 66, 12706–12718. 10.1021/acs.jafc.8b05036

Source: PubMed

3
Tilaa