Insights into Butyrate Production in a Controlled Fermentation System via Gene Predictions.
Insights into Butyrate Production in a Controlled Fermentation System via Gene Predictions.
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DOI：
10.1128/msystems.00051-17
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发表时间：
2017-07
期刊：
影响因子：
6.4
通讯作者：
Krajmalnik-Brown R
中科院分区：
文献类型：
--
作者：
Esquivel-Elizondo S;Ilhan ZE;Garcia-Peña EI;Krajmalnik-Brown R
这项研究展示了生物信息学工具，如来自16S rRNA基因的元基因组功能预测，如何帮助理解生物系统并揭示受控系统(如生物反应器)中的微生物相互作用。从受控系统获得的结果比从人类/动物研究中获得的结果更容易解释，因为观察到的变化可能具体归因于强加于系统的设计条件。当我们在一个严格控制的发酵系统中系统地改变PH2时，生物信息学分析使我们能够识别潜在的丁酸系统类型和相关的丁酸代谢途径。我们的见解可能适用于生物氢系统和肠道模型中丁酸盐生产的研究，因为丁酸盐是一种主要产品，也是人/动物结肠健康中的一种关键脂肪酸。丁酸是一种常见的脂肪酸，在重要的发酵系统中产生，如人/动物肠道和其他氢气生产系统。尽管丁酸酯很重要，但关于丁酸酯生产商与其他细菌之间的合作伙伴关系的信息很少。这项工作的目的是揭示丁酸生产微生物群落和以丁酸生产为目标的受控发酵系统中可能的代谢途径。该反应器在37℃、pH=5.5、水力停留时间为31h、低氢分压(PH2)条件下运行。16S rRNA数据的高通量测序和元基因组功能预测表明，在间歇(封闭)和连续操作模式下，丁酸酯的生产途径和微生物群落不同。在没有pH_2控制的封闭系统中，乳杆菌科、乳杆菌科和肠球菌科是最丰富的系统类型，而在低pH_2连续运行条件下，普氏杆菌科、瘤胃球菌科和放线菌科是最丰富的系统类型。在我们的系统中鉴定的推定的丁酸酯生产者来自普雷沃泰科、梭菌科、瘤胃球菌科和乳杆菌科。元基因组预测分析表明，非产丁酸微生物通过产生丁酸前体，如醋酸酯、乳酸和琥珀酸来影响丁酸的生产。16S rRNA基因分析表明，在低PH2连续流操作下，已鉴定的产酪菌与丁二酸(即放线菌科)、乙酸酯(即瘤胃球菌科和放线菌科)以及乳酸盐产生菌(即瘤胃球菌科和乳杆菌科)之间发生了伙伴关系。重要性这项研究展示了生物信息学工具，如16S rRNA基因的元基因组功能预测，如何帮助理解生物系统并揭示受控系统(如生物反应器)中的微生物相互作用。从受控系统获得的结果比从人类/动物研究中获得的结果更容易解释，因为观察到的变化可能具体归因于强加于系统的设计条件。当我们在一个严格控制的发酵系统中系统地改变PH2时，生物信息学分析使我们能够识别潜在的丁酸系统类型和相关的丁酸代谢途径。我们的见解可能适用于生物氢系统和肠道模型中丁酸盐生产的研究，因为丁酸盐是一种主要产品，也是人/动物结肠健康中的一种关键脂肪酸。
This study demonstrates how bioinformatics tools, such as metagenome functional prediction from 16S rRNA genes, can help understand biological systems and reveal microbial interactions in controlled systems (e.g., bioreactors). Results obtained from controlled systems are easier to interpret than those from human/animal studies because observed changes may be specifically attributed to the design conditions imposed on the system. Bioinformatics analysis allowed us to identify potential butyrogenic phylotypes and associated butyrate metabolism pathways when we systematically varied the PH2 in a carefully controlled fermentation system. Our insights may be adapted to butyrate production studies in biohydrogen systems and gut models, since butyrate is a main product and a crucial fatty acid in human/animal colon health. Butyrate is a common fatty acid produced in important fermentative systems, such as the human/animal gut and other H2 production systems. Despite its importance, there is little information on the partnerships between butyrate producers and other bacteria. The objective of this work was to uncover butyrate-producing microbial communities and possible metabolic routes in a controlled fermentation system aimed at butyrate production. The butyrogenic reactor was operated at 37°C and pH 5.5 with a hydraulic retention time of 31 h and a low hydrogen partial pressure (PH2). High-throughput sequencing and metagenome functional prediction from 16S rRNA data showed that butyrate production pathways and microbial communities were different during batch (closed) and continuous-mode operation. Lactobacillaceae, Lachnospiraceae, and Enterococcaceae were the most abundant phylotypes in the closed system without PH2 control, whereas Prevotellaceae, Ruminococcaceae, and Actinomycetaceae were the most abundant phylotypes under continuous operation at low PH2. Putative butyrate producers identified in our system were from Prevotellaceae, Clostridiaceae, Ruminococcaceae, and Lactobacillaceae. Metagenome prediction analysis suggests that nonbutyrogenic microorganisms influenced butyrate production by generating butyrate precursors such as acetate, lactate, and succinate. 16S rRNA gene analysis suggested that, in the reactor, a partnership between identified butyrogenic microorganisms and succinate (i.e., Actinomycetaceae), acetate (i.e., Ruminococcaceae and Actinomycetaceae), and lactate producers (i.e., Ruminococcaceae and Lactobacillaceae) took place under continuous-flow operation at low PH2. IMPORTANCE This study demonstrates how bioinformatics tools, such as metagenome functional prediction from 16S rRNA genes, can help understand biological systems and reveal microbial interactions in controlled systems (e.g., bioreactors). Results obtained from controlled systems are easier to interpret than those from human/animal studies because observed changes may be specifically attributed to the design conditions imposed on the system. Bioinformatics analysis allowed us to identify potential butyrogenic phylotypes and associated butyrate metabolism pathways when we systematically varied the PH2 in a carefully controlled fermentation system. Our insights may be adapted to butyrate production studies in biohydrogen systems and gut models, since butyrate is a main product and a crucial fatty acid in human/animal colon health.