基于MiSeq高通测序技术的米酒真菌多样性分析
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摘要
为探究米酒中真菌多样性,本研究采用MiSeq高通测序技术与多元统计学手段相结合的方法对采集自恩施地区的10个农家自酿米酒样品中真菌微生物进行了研究,结果表明该地米酒样品中的真菌门主要为子囊菌门(Ascomycota)、毛霉亚门(Mucoromycotina)、担子菌门(Basidiomycota)、壶菌门(Chytridiomycota)和芽枝霉门(Blastocladiomycota),核心优势真菌属及其平均相对含量分别为复膜孢酵母属(Saccharomycopsis,62.44%)、根霉属(Rhizopus,20.28%)和淀粉菌(Amylomyces,4.42%);在对97%的相似度下划分的10627个操作分类单元(Operational taxonomic units,OTU)进行研究时发现OTU2793、OTU8553、OTU2342、OTU4367、OTU9019、OTU4274和OTU5981的累积平均相对含量高达80.64%且在各样品中均有分布,各样品中除含有大量的共有真菌菌群外亦含有独特的真菌类群;同时,在对样品差异进行分析时发现,采集的10个米酒样品大致可以分为两个聚类,且聚类Ⅰ样品真菌群落结构组内差异要显著高于聚类Ⅱ(p<0.05),而优势真菌属Saccharomycopsis、Rhizopus、Amylomyces和Poitrasia是造成米酒样品两个聚类微生物群落结构差异显著的关键真菌类群。
The fungal microbiota of rice wine samples,which collected from Enshi Hubei province,were revealed by MiSeq high throughput sequencing technologies. The results showed that Ascomycota,Mucoromycotina,Basidiomycota,Chytridiomycota and Blastocladiomycota were the main fungal phyla. At the genus level,Saccharomycopsis,Rhizopus and Amylomyces were the core dominant fungal genus,with average relative abundance of 62.44%,20.28% and 4.42%,respectively. Meanwhile,10627 OTUs were clustered with 97% similarity and OTU2793、OTU8553、OTU2342、OTU4367、OTU9019、OTU4274 and OTU5981 were shared by all samples with the cumulative average relative abundance of 80.64%.In addition to a large number of common fungal communities,the samples also contain unique fungal groups. It was also worth mentioning that 10 samples could be divided into two clusters based on fugal microflora,and the difference of fungal community structure in cluster Ⅰ was significantly higher than that in cluster Ⅱ(p<0.05). The result of Mann-Whitney test showed that Saccharomycopsis,Rhizopus,Amylomyces and Poitrasiawere identified as key fungal groups caused significant differences in the two microbial community structures of rice wine samples.
The fungal microbiota of rice wine samples,which collected from Enshi Hubei province,were revealed by MiSeq high throughput sequencing technologies. The results showed that Ascomycota,Mucoromycotina,Basidiomycota,Chytridiomycota and Blastocladiomycota were the main fungal phyla. At the genus level,Saccharomycopsis,Rhizopus and Amylomyces were the core dominant fungal genus,with average relative abundance of 62.44%,20.28% and 4.42%,respectively. Meanwhile,10627 OTUs were clustered with 97% similarity and OTU2793、OTU8553、OTU2342、OTU4367、OTU9019、OTU4274 and OTU5981 were shared by all samples with the cumulative average relative abundance of 80.64%.In addition to a large number of common fungal communities,the samples also contain unique fungal groups. It was also worth mentioning that 10 samples could be divided into two clusters based on fugal microflora,and the difference of fungal community structure in cluster Ⅰ was significantly higher than that in cluster Ⅱ(p<0.05). The result of Mann-Whitney test showed that Saccharomycopsis,Rhizopus,Amylomyces and Poitrasiawere identified as key fungal groups caused significant differences in the two microbial community structures of rice wine samples.
引文
[1]杨停,贾冬英,马浩然,等. 糯米化学成分对米酒发酵及其品质影响的研究[J]. 食品科技,2015,40(5):119-123.
[2]Wu Z,Xu E,Long J,et al. Monitoring of fermentation process parameters of Chinese rice wine using attenuated total reflectance mid-infrared spectroscopy[J]. Food Control,2015,50(4):405-412.
[3]郭壮,汤尚文,蔡宏宇,等. 市售与农家自酿孝感米酒滋味品质的比较研究[J]. 食品工业,2015,36(11):185-188.
[4]单艺,张兰威,崔宏斌. 传统法酿造糯米酒中酵母菌的筛选及发酵特性研究[J]. 食品工业科技,2007,28(8):88-90.
[5]宁洁,赵新淮. 发酵米酒中一些霉菌和酵母的性质与应用研究[J]. 食品科技,2008,2008(10):5-9.
[6]苗乘源,郑琳,程雅韵,等. 朝鲜族传统米酒中的乳酸菌多样性分析[J]. 延边大学农学学报,2016,38(3):248-250,270.
[7]赵婷婷,卢倩文,宋菲菲,等. 1 株产香真菌的筛选及其协同米根霉对米酒发酵的影响[J]. 食品科学,2017,38(14):42-48.
[8]王艳萍,程巧玲,张阳,等. 米酒醪中优势微生物菌相组成的初步研究[J]. 中国酿造,2008,27(5):12-14.
[9]Cocolin L,Alessandria V,Dolci P,et al. Culture independent methods to assess the diversity and dynamics of microbiota during food fermentation[J]. International Journal of Food Microbiology,2013,167(1):29-43.
[10]Hu X,Du H,Ren C,et al. Illuminating anaerobic microbial community and cooccurrence patterns across a quality gradient in Chinese liquor fermentation pit muds[J]. Applied and Environmental Microbiology,2016,82(8):2506-2515.
[11]刘茂柯,唐玉明,赵珂,等. 浓香型白酒窖泥微生物群落结构及其选育应用研究进展[J]. 微生物学通报,2017,44(5):1222-1229.
[12]Yang H,Wu H,Gao L,et al. Effects of Lactobacillus curvatus and Leuconostoc mesenteroides on suan cai fermentation in Northeast China[J]. Journal of Microbiology and Biotechnology,2016,26(12):2148-2158.
[13]沈馨,尚雪娇,董蕴,等. 基于MiSeq高通量测序技术对 3 个孝感凤窝酒曲细菌多样性的评价[J]. 中国微生态学杂志,2018,30(5):525-530.
[14]Sun W,Xiao H,Peng Q,et al. Analysis of bacterial diversity of Chinese Luzhou-flavor liquor brewed in different seasons by illumina MiSeq sequencing[J]. Annals of Microbiology,2016,66(3):1293-1301.
[15]王丹丹,沈馨,董蕴,等. 孝感凤窝酒曲真菌多样性评价[J]. 中国酿造,2017,36(11):38-42.
[16]陈庆金,黄丽,滕建文,等. 基于MiSeq测序分析六堡茶陈化初期真菌多样性[J]. 食品科技,2015,41(8):67-71.
[17]张振东,尚雪娇,董蕴,等. 丢糟窖窖泥细菌多样性评价[J]. 中国微生态学杂志,2018,30(8):882-888.
[18]Caporaso J G,Kuczynski J,Stombaugh J,et al. QIIME allows analysis of high-throughput community sequencing data[J]. Nature Methods,2010,7(5):335-336.
[19]Edgar R C. Search and clustering orders of magnitude faster than blast[J]. Bioinformatics,2010,26(19):2460-2461.
[20]Quast C,Pruesse E,Yilmaz P,et al. The SILVA ribosomal RNA gene database project:Improved data processing and web-based tools[J]. Nucleic Acids Research,2012,41(1):D590-D596.
[21]Yilmaz P,Parfrey L W,Yarza P,et al. The SILVA and “all-species living tree project(LTP)” taxonomic frameworks[J]. Nucleic Acids Research,2014,42(D1):643-648.
[22]焦晶凯. 传统酿造米酒微生物多样性及优势菌特性的研究[D]. 哈尔滨:哈尔滨工业大学,2012.
[23]Lozupone C A,Knight R. The unifrac significance test is sensitive to tree topology[J]. BMC Bioinformatics,2015,16(1):211-213.
[2]Wu Z,Xu E,Long J,et al. Monitoring of fermentation process parameters of Chinese rice wine using attenuated total reflectance mid-infrared spectroscopy[J]. Food Control,2015,50(4):405-412.
[3]郭壮,汤尚文,蔡宏宇,等. 市售与农家自酿孝感米酒滋味品质的比较研究[J]. 食品工业,2015,36(11):185-188.
[4]单艺,张兰威,崔宏斌. 传统法酿造糯米酒中酵母菌的筛选及发酵特性研究[J]. 食品工业科技,2007,28(8):88-90.
[5]宁洁,赵新淮. 发酵米酒中一些霉菌和酵母的性质与应用研究[J]. 食品科技,2008,2008(10):5-9.
[6]苗乘源,郑琳,程雅韵,等. 朝鲜族传统米酒中的乳酸菌多样性分析[J]. 延边大学农学学报,2016,38(3):248-250,270.
[7]赵婷婷,卢倩文,宋菲菲,等. 1 株产香真菌的筛选及其协同米根霉对米酒发酵的影响[J]. 食品科学,2017,38(14):42-48.
[8]王艳萍,程巧玲,张阳,等. 米酒醪中优势微生物菌相组成的初步研究[J]. 中国酿造,2008,27(5):12-14.
[9]Cocolin L,Alessandria V,Dolci P,et al. Culture independent methods to assess the diversity and dynamics of microbiota during food fermentation[J]. International Journal of Food Microbiology,2013,167(1):29-43.
[10]Hu X,Du H,Ren C,et al. Illuminating anaerobic microbial community and cooccurrence patterns across a quality gradient in Chinese liquor fermentation pit muds[J]. Applied and Environmental Microbiology,2016,82(8):2506-2515.
[11]刘茂柯,唐玉明,赵珂,等. 浓香型白酒窖泥微生物群落结构及其选育应用研究进展[J]. 微生物学通报,2017,44(5):1222-1229.
[12]Yang H,Wu H,Gao L,et al. Effects of Lactobacillus curvatus and Leuconostoc mesenteroides on suan cai fermentation in Northeast China[J]. Journal of Microbiology and Biotechnology,2016,26(12):2148-2158.
[13]沈馨,尚雪娇,董蕴,等. 基于MiSeq高通量测序技术对 3 个孝感凤窝酒曲细菌多样性的评价[J]. 中国微生态学杂志,2018,30(5):525-530.
[14]Sun W,Xiao H,Peng Q,et al. Analysis of bacterial diversity of Chinese Luzhou-flavor liquor brewed in different seasons by illumina MiSeq sequencing[J]. Annals of Microbiology,2016,66(3):1293-1301.
[15]王丹丹,沈馨,董蕴,等. 孝感凤窝酒曲真菌多样性评价[J]. 中国酿造,2017,36(11):38-42.
[16]陈庆金,黄丽,滕建文,等. 基于MiSeq测序分析六堡茶陈化初期真菌多样性[J]. 食品科技,2015,41(8):67-71.
[17]张振东,尚雪娇,董蕴,等. 丢糟窖窖泥细菌多样性评价[J]. 中国微生态学杂志,2018,30(8):882-888.
[18]Caporaso J G,Kuczynski J,Stombaugh J,et al. QIIME allows analysis of high-throughput community sequencing data[J]. Nature Methods,2010,7(5):335-336.
[19]Edgar R C. Search and clustering orders of magnitude faster than blast[J]. Bioinformatics,2010,26(19):2460-2461.
[20]Quast C,Pruesse E,Yilmaz P,et al. The SILVA ribosomal RNA gene database project:Improved data processing and web-based tools[J]. Nucleic Acids Research,2012,41(1):D590-D596.
[21]Yilmaz P,Parfrey L W,Yarza P,et al. The SILVA and “all-species living tree project(LTP)” taxonomic frameworks[J]. Nucleic Acids Research,2014,42(D1):643-648.
[22]焦晶凯. 传统酿造米酒微生物多样性及优势菌特性的研究[D]. 哈尔滨:哈尔滨工业大学,2012.
[23]Lozupone C A,Knight R. The unifrac significance test is sensitive to tree topology[J]. BMC Bioinformatics,2015,16(1):211-213.