in

Effects of environmental factors on microbiota of fruits and soil of Coffea arabica in Brazil

  • 1.

    USDA. Coffee Annual Coffee. https://gain.fas.usda.gov/RecentGAINPublications/LOCK-UPREPORT_Pretoria_SouthAfrica-Republicof_10-29-2009.pdf (2019).

  • 2.

    Carvalho Guarçoni, R. et al. Influence of solar radiation and wet processing on the final quality of Arabica coffee. J. Food Qual. https://doi.org/10.1155/2018/6408571 (2018).

    Article  Google Scholar 

  • 3.

    Iamanaka, B. T. et al. Reprint of ‘The mycobiota of coffee beans and its influence on the coffee beverage’. Food Res. Int. 61, 33–38. https://doi.org/10.1016/j.foodres.2014.05.023 (2014).

    Article  Google Scholar 

  • 4.

    Barnes, E. C., Jumpathong, J., Lumyong, S., Voigt, K. & Hertweck, C. Daldionin, an unprecedented binaphthyl derivative, and diverse polyketide congeners from a fungal orchid endophyte. Chem. A Eur. J. 22, 4551–4555. https://doi.org/10.1002/chem.201504005 (2016).

    Article  CAS  Google Scholar 

  • 5.

    Descroix, F. & Snoeck, J. Environmental factors suitable for coffee cultivation. In Coffee: Growing, Processing, Sustainable Production 164–177, https://doi.org/10.1002/9783527619627.ch6 (2008).

  • 6.

    De Bruyn, F. et al. Exploring the impacts of postharvest processing on the microbiota and metabolite profiles during green coffee bean production. Am. Soc. Microbiol. https://doi.org/10.1128/AEM.02398-16 (2016).

    Article  Google Scholar 

  • 7.

    Hamdouche, Y. et al. Discrimination of post-harvest coffee processing methods by microbial ecology analyses. Food Control 65, 112–120. https://doi.org/10.1016/j.foodcont.2016.01.022 (2016).

    Article  CAS  Google Scholar 

  • 8.

    Zhao, Q. et al. Long-term coffee monoculture alters soil chemical properties and microbial communities. Sci. Rep. 8, 1–11. https://doi.org/10.1038/s41598-018-24537-2 (2018).

    ADS  Article  CAS  Google Scholar 

  • 9.

    Júnior, P. P. et al. Agroecological coffee management increases arbuscular mycorrhizal fungi diversity. PLoS ONE 14, 1–19. https://doi.org/10.1371/journal.pone.0209093 (2019).

    Article  CAS  Google Scholar 

  • 10.

    Melloni, R. et al. Sistemas Agroflorestais cafeeiro-araucária e seu efeito na microbiota do solo e seus processos. Ciência Florest. 28, 784–795. https://doi.org/10.5902/1980509832392 (2018).

    Article  Google Scholar 

  • 11.

    Oliveira, M. N. V. et al. Endophytic microbial diversity in coffee cherries of Coffea arabica from southeastern Brazil. Can. J. Microbiol. 59, 221–230. https://doi.org/10.1139/cjm-2012-0674 (2013).

    Article  PubMed  CAS  Google Scholar 

  • 12.

    Nasanit, R. & Satayawut, K. Microbiological study during coffee fermentation of Coffea arabica var chiangmai 80 in Thailand. Kasetsart J. Nat. Sci. 49, 32–41 (2015).

    CAS  Google Scholar 

  • 13.

    Evangelista, S. R. et al. Improvement of coffee beverage quality by using selected yeasts strains during the fermentation in dry process. Food Res. Int. 61, 183–195. https://doi.org/10.1016/j.foodres.2013.11.033 (2014).

    Article  CAS  Google Scholar 

  • 14.

    Pereira, G. V. D. M. et al. Potential of lactic acid bacteria to improve the fermentation and quality of coffee during on-farm processing. Int. J. Food Sci. Technol. 51, 1689–1695. https://doi.org/10.1111/ijfs.13142 (2016).

    Article  CAS  Google Scholar 

  • 15.

    Sahu, N., Duraisamy, V., Sahu, A., Lal, N. & K. Singh, S. Strength of microbes in nutrient cycling: A key to soil health. In Agriculturally Important Microbes for Sustainable Agriculture 69–86, https://doi.org/10.1007/978-981-10-5589-8_4 (2017).

  • 16.

    Zhang, S. J. et al. Following coffee production from cherries to cup: Microbiological and metabolomic analysis of wet processing of Coffea arabica. Appl. Environ. Microbiol. 85, 1–22. https://doi.org/10.1128/AEM.02635-18 (2019).

    Article  CAS  Google Scholar 

  • 17.

    Ramos, C. L. et al. Determination of dynamic characteristics of microbiota in a fermented beverage produced by Brazilian Amerindians using culture-dependent and culture-independent methods. Int. J. Food Microbiol. 140, 225–231. https://doi.org/10.1016/j.ijfoodmicro.2010.03.029 (2010).

    Article  PubMed  CAS  Google Scholar 

  • 18.

    Faoro, H. et al. Influence of soil characteristics on the diversity of bacteria in the Southern Brazilian Atlantic Forest. Appl. Environ. Microbiol. 76, 4744–4749. https://doi.org/10.1128/AEM.03025-09a (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • 19.

    Defelipo, B. V. & Ribeiro, A. C. Análise química do solo (metodologia). Bol. Extensão 28, 1–26 (1997).

    Google Scholar 

  • 20.

    Walters, W. et al. Improved bacterial 16S rRNA gene (V4 and V4–5) and fungal internal transcribed spacer marker gene primers for microbial community surveys. Am. Soc. Microbiol. https://doi.org/10.1128/msystems.00009-15 (2015).

    Article  Google Scholar 

  • 21.

    Pylro, V. S. et al. Data analysis for 16S microbial profiling from different benchtop sequencing platforms. J. Microbiol. Methods 107, 30–37. https://doi.org/10.1016/j.mimet.2014.08.018 (2014).

    Article  PubMed  CAS  Google Scholar 

  • 22.

    Edgar, R. C. UPARSE: Highly accurate OTU sequences from microbial amplicon reads. Nat. Methods 10, 996–998. https://doi.org/10.1038/nmeth.2604 (2013).

    Article  CAS  Google Scholar 

  • 23.

    Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336. https://doi.org/10.1038/nmeth.f.303 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • 24.

    Edgar, R. C. UCHIME2: Improved chimera prediction for amplicon sequencing. BioRxiv https://doi.org/10.1101/074252 (2016).

    Article  Google Scholar 

  • 25.

    Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596. https://doi.org/10.1093/nar/gks1219 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • 26.

    Lozupone, C., Lladser, M. E., Knights, D., Stombaugh, J. & Knight, R. UniFrac: An effective distance metric for microbial community comparison. ISME J. 5, 169–172. https://doi.org/10.1038/ismej.2010.133 (2011).

    Article  PubMed  Google Scholar 

  • 27.

    Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461. https://doi.org/10.1093/bioinformatics/btq461 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • 28.

    Bengtsson-Palme, J. et al. Improved software detection and extraction of ITS1 and ITS2 from ribosomal ITS sequences of fungi and other eukaryotes for analysis of environmental sequencing data. Methods Ecol. Evol. 4, 914–919. https://doi.org/10.1111/2041-210X.12073 (2013).

    Article  Google Scholar 

  • 29.

    Nilsson, R. H. et al. The UNITE database for molecular identification of fungi: Handling dark taxa and parallel taxonomic classifications. Nucleic Acids Res. 47, D259–D264. https://doi.org/10.1093/nar/gky1022 (2019).

    Article  PubMed  CAS  Google Scholar 

  • 30.

    Oksanen, J. et al. Community Ecology Package. 1–296, https://cran.r-project.org/web/packages/vegan/vegan.pdf (2019).

  • 31.

    R Core Team. R: A Language and Environment for Statistical Computing. https://www.r-project.org/ (2018).

  • 32.

    Borcard, D. et al. Canonical ordination. In Numerical Ecology with R 153–225, https://doi.org/10.1007/978-1-4419-7976-6_6 (2011).

  • 33.

    Gomes, D. G. E. et al. Bats perceptually weight prey cues across sensory systems when hunting in noise. Science 353, 1277–1280. https://doi.org/10.1126/science.aaf7934 (2016).

    ADS  Article  PubMed  CAS  Google Scholar 

  • 34.

    Friedman, J. & Alm, E. J. Inferring correlation networks from genomic survey data. PLOS Comput. Biol. 8, 1–11. https://doi.org/10.1371/journal.pcbi.1002687 (2012).

    Article  CAS  Google Scholar 

  • 35.

    Watts, S. C., Ritchie, S. C., Inouye, M. & Holt, K. E. FastSpar: Rapid and scalable correlation estimation for compositional data. Bioinformatics 35, 1064–1066. https://doi.org/10.1093/bioinformatics/bty734 (2019).

    Article  PubMed  CAS  Google Scholar 

  • 36.

    Csardi, G. & Nepusz, T. The igraph software package for complex network research. InterJournal Complex Syst 1695, 1–9 (2006).

    Google Scholar 

  • 37.

    Avelino, J. et al. Effects of slope exposure, altitude and yield on coffee quality in two altitude terroirs of Costa Rica, Orosi and Santa María de Dota. J. Sci. Food Agric. 85, 1869–1876. https://doi.org/10.1002/jsfa.2188 (2005).

    Article  CAS  Google Scholar 

  • 38.

    Wei, L., Wai, M., Curran, P., Yu, B. & Quan, S. Coffee fermentation and flavor—An intricate and delicate relationship. Food Chem. 185, 182–191. https://doi.org/10.1016/j.foodchem.2015.03.124 (2015).

    Article  CAS  Google Scholar 

  • 39.

    Fulthorpe, R., Martin, A. R. & Isaac, M. E. Root endophytes of coffee ( Coffea arabica): Variation across climatic gradients and relationships with functional traits. Phytobiomes J. 4, 27–39. https://doi.org/10.1094/PBIOMES-04-19-0021-R (2020).

    Article  Google Scholar 

  • 40.

    Chu, H. et al. Effects of slope aspects on soil bacterial and arbuscular fungal communities in a boreal forest in China. Pedosphere 26, 226–234. https://doi.org/10.1016/S1002-0160(15)60037-6 (2016).

    Article  Google Scholar 

  • 41.

    Karungi, J. et al. Elevation and cropping system as drivers of microclimate and abundance of soil macrofauna in coffee farmlands in mountainous ecologies. Appl. Soil Ecol. 132, 126–134. https://doi.org/10.1016/J.APSOIL.2018.08.003 (2018).

    Article  Google Scholar 

  • 42.

    Ferreira, W. P. M., Queiroz, D. M., Silvac, S. A., Tomaz, R. S. & Corrêa, P. C. Effects of the orientation of the mountainside, altitude and varieties on the quality of the coffee beverage from the “Matas de Minas” region, Brazilian Southeast. Am. J. Plant Sci. 7, 1291–1303. https://doi.org/10.4236/ajps.2016.78124 (2016).

    Article  Google Scholar 

  • 43.

    Velmourougane, K. Impact of organic and conventional systems of coffee farming on soil properties and culturable microbial diversity. Scientifica 1–9, 2016. https://doi.org/10.1155/2016/3604026 (2016).

    Article  CAS  Google Scholar 

  • 44.

    Siles, J. A. & Margesin, R. Abundance and diversity of bacterial, archaeal, and fungal communities along an altitudinal gradient in alpine forest soils: What are the driving factors?. Soil Microbiol. 72, 207–220. https://doi.org/10.1007/s00248-016-0748-2 (2016).

    Article  Google Scholar 

  • 45.

    Frank, A., Saldierna Guzmán, J. & Shay, J. Transmission of bacterial endophytes. Microorganisms 5, 70. https://doi.org/10.3390/microorganisms5040070 (2017).

    Article  PubMed Central  CAS  Google Scholar 

  • 46.

    Haile, M. & Kang, W. H. The role of microbes in coffee fermentation and their impact on coffee quality. J. Food Qual. 2019, 6. https://doi.org/10.1155/2019/4836709 (2019).

    Article  CAS  Google Scholar 

  • 47.

    Decazy, F. et al. Quality of different Honduran coffees in relation to several environments. J. Food Sci. 68, 2356–2361. https://doi.org/10.1111/j.1365-2621.2003.tb05772.x (2003).

    Article  CAS  Google Scholar 

  • 48.

    de Melo Pereira, G. V. et al. Conducting starter culture-controlled fermentations of coffee beans during on-farm wet processing: Growth, metabolic analyses and sensorial effects. Food Res. Int. 75, 348–356. https://doi.org/10.1016/j.foodres.2015.06.027 (2015).

    Article  PubMed  CAS  Google Scholar 

  • 49.

    Zhang, W. et al. Microbial diversity in two traditional bacterial douchi from Gansu province in northwest China using Illumina sequencing. PLoS ONE 13, 1–16. https://doi.org/10.1371/journal.pone.0194876 (2018).

    Article  CAS  Google Scholar 

  • 50.

    Tolessa, K., D’heer, J., Duchateau, L. & Boeckx, P. Influence of growing altitude, shade and harvest period on quality and biochemical composition of Ethiopian specialty coffee. J. Sci. Food Agric. 97, 2849–2857. https://doi.org/10.1002/jsfa.8114 (2017).

    Article  PubMed  CAS  Google Scholar 

  • 51.

    Batista, D. et al. Legitimacy and implications of reducing Colletotrichum kahawae to subspecies in plant pathology. Front. Plant Sci. 7, 1–9. https://doi.org/10.3389/fpls.2016.02051 (2017).

    Article  CAS  Google Scholar 

  • 52.

    Wei, Z. et al. Trophic network architecture of root-associated bacterial communities determines pathogen invasion and plant health. Nat. Commun. 6, 1–9. https://doi.org/10.1038/ncomms9413 (2015).

    ADS  Article  CAS  Google Scholar 


  • Source: Ecology - nature.com

    Environmental stability impacts the differential sensitivity of marine microbiomes to increases in temperature and acidity

    Assessing the effect of wind farms in fauna with a mathematical model