scholarly journals Model-based quantification of metabolic interactions from dynamic microbial-community data

PLoS ONE ◽  
2017 ◽  
Vol 12 (3) ◽  
pp. e0173183 ◽  
Author(s):  
Mark Hanemaaijer ◽  
Brett G. Olivier ◽  
Wilfred F. M. Röling ◽  
Frank J. Bruggeman ◽  
Bas Teusink
Author(s):  
Reza Barati Rashvanlou ◽  
Mahdi Farzadkia ◽  
Abbas Ali Moserzadeh ◽  
Asghar Riazati ◽  
Chiang Wei ◽  
...  

Introduction: One of biological wastewater treatment methods that utilizes to both digesting waste activated sludge and methane production is anaerobic digestion (AD). It is believed to be most effective solution in terms of energy crisis and environmental pollution issues. Materials and Methods: In this study the sludge was digested anaerobically sampled from a full-scale WWTP, located at south of Tehran, Iran for evaluation. To study the microbial community within the sludge the MiSeq Sequencing method utilized. Based on our field data (data not shown) and microbial community data, a schematic diagram of probable leading pathways was made in the studied digester. Results: At first, the community variety in the bulk sludge and richness were enhanced followed by loading increasing. Meanwhile, the loading change enhanced the community richness and variety of the sludge. By comparing the rank-abundance distributions, a shallow gradient would show high evenness since the abundances of diverse species are alike. The results showed all the communities were extremely diverse and 15 phyla were distinguished in the sludge sample. The dominant phyla of the community were Bacteroidetes and Firmicutes and quantity of the two phyla were 21% and 11%, respectively. Anaerobaculum, Acinetobacter, Syntrophomonas, and Coprothermobacter were the chief genera for the microbial communities and the sum of four genera were 7%, 3%, 3%, and 2%, respectively. Conclusion: It was shown that syntrophic acetate oxidizing bacterias (SAOBs) metabolized acetate through hydrogen trophic methanogenesis in the digester. Generally, the findings may be useful to help the wastewater operators to utilize an effective method that able to treat waste sludge plus methane production, simultaneously.


2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Gemma Henderson ◽  
◽  
Faith Cox ◽  
Siva Ganesh ◽  
Arjan Jonker ◽  
...  

Abstract Ruminant livestock are important sources of human food and global greenhouse gas emissions. Feed degradation and methane formation by ruminants rely on metabolic interactions between rumen microbes and affect ruminant productivity. Rumen and camelid foregut microbial community composition was determined in 742 samples from 32 animal species and 35 countries, to estimate if this was influenced by diet, host species, or geography. Similar bacteria and archaea dominated in nearly all samples, while protozoal communities were more variable. The dominant bacteria are poorly characterised, but the methanogenic archaea are better known and highly conserved across the world. This universality and limited diversity could make it possible to mitigate methane emissions by developing strategies that target the few dominant methanogens. Differences in microbial community compositions were predominantly attributable to diet, with the host being less influential. There were few strong co-occurrence patterns between microbes, suggesting that major metabolic interactions are non-selective rather than specific.


2019 ◽  
Vol 133 ◽  
pp. 64-71 ◽  
Author(s):  
Wenfang Cai ◽  
Keaton Larson Lesnik ◽  
Matthew J. Wade ◽  
Elizabeth S. Heidrich ◽  
Yunhai Wang ◽  
...  

mSystems ◽  
2019 ◽  
Vol 4 (3) ◽  
Author(s):  
Karoline Faust

ABSTRACT The investigation of microbial community dynamics is hampered by low resolution, a lack of control, and a small number of replicates. These deficiencies can be tackled with defined communities grown under well-controlled conditions in high-throughput automated cultivation devices. Besides delivering high-quality microbial community data, automated cultivation will also ease measurement of the basic parameters needed to parameterize mathematical models of microbial communities. Better experimental data will allow revisiting classical ecological questions, such as the impact of community structure on dynamics. In addition, such data will allow validation and comparison of community models and benchmarking of microbial data analysis software. In summary, high-throughput automated cultivation will lead to a deeper understanding of microbial community dynamics through better models and software.


2018 ◽  
Vol 18 (1) ◽  
Author(s):  
Deni Ribicic ◽  
Kelly Marie McFarlin ◽  
Roman Netzer ◽  
Odd Gunnar Brakstad ◽  
Anika Winkler ◽  
...  

2018 ◽  
Author(s):  
Jacob R. Price ◽  
Stephen Woloszynek ◽  
Gail Rosen ◽  
Christopher M. Sales

Abstracttheseus is a collection of functions within the R programming framework [1] to assist microbiologists and molecular biologists in the interpretation of microbial community composition data.


mSphere ◽  
2017 ◽  
Vol 2 (3) ◽  
Author(s):  
Zehra Esra Ilhan ◽  
Andrew K. Marcus ◽  
Dae-Wook Kang ◽  
Bruce E. Rittmann ◽  
Rosa Krajmalnik-Brown

ABSTRACT The human gut is a dynamic environment in which microorganisms consistently interact with the host via their metabolic products. Some of the most important microbial metabolic products are fermentation products such as short-chain fatty acids. Production of these fermentation products and the prevalence of fermenting microbiota depend on pH, alkalinity, and available dietary sugars, but details about their metabolic interactions are unknown. Here, we show that, for in vitro conditions, pH was the strongest driver of microbial community structure and function and microbial and metabolic interactions among pH-sensitive fermentative species. The balance between bicarbonate alkalinity and formation of fatty acids by fermentation determined the pH, which controlled microbial community structure. Our results underscore the influence of pH balance on microbial function in diverse microbial ecosystems such as the human gut. pH and fermentable substrates impose selective pressures on gut microbial communities and their metabolisms. We evaluated the relative contributions of pH, alkalinity, and substrate on microbial community structure, metabolism, and functional interactions using triplicate batch cultures started from fecal slurry and incubated with an initial pH of 6.0, 6.5, or 6.9 and 10 mM glucose, fructose, or cellobiose as the carbon substrate. We analyzed 16S rRNA gene sequences and fermentation products. Microbial diversity was driven by both pH and substrate type. Due to insufficient alkalinity, a drop in pH from 6.0 to ~4.5 clustered pH 6.0 cultures together and distant from pH 6.5 and 6.9 cultures, which experienced only small pH drops. Cellobiose yielded more acidity than alkalinity due to the amount of fermentable carbon, which moved cellobiose pH 6.5 cultures away from other pH 6.5 cultures. The impact of pH on microbial community structure was reflected by fermentative metabolism. Lactate accumulation occurred in pH 6.0 cultures, whereas propionate and acetate accumulations were observed in pH 6.5 and 6.9 cultures and independently from the type of substrate provided. Finally, pH had an impact on the interactions between lactate-producing and -consuming communities. Lactate-producing Streptococcus dominated pH 6.0 cultures, and acetate- and propionate-producing Veillonella, Bacteroides, and Escherichia dominated the cultures started at pH 6.5 and 6.9. Acid inhibition on lactate-consuming species led to lactate accumulation. Our results provide insights into pH-derived changes in fermenting microbiota and metabolisms in the human gut. IMPORTANCE The human gut is a dynamic environment in which microorganisms consistently interact with the host via their metabolic products. Some of the most important microbial metabolic products are fermentation products such as short-chain fatty acids. Production of these fermentation products and the prevalence of fermenting microbiota depend on pH, alkalinity, and available dietary sugars, but details about their metabolic interactions are unknown. Here, we show that, for in vitro conditions, pH was the strongest driver of microbial community structure and function and microbial and metabolic interactions among pH-sensitive fermentative species. The balance between bicarbonate alkalinity and formation of fatty acids by fermentation determined the pH, which controlled microbial community structure. Our results underscore the influence of pH balance on microbial function in diverse microbial ecosystems such as the human gut.


2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Joachim Ludwig ◽  
Christian Höner zu Siederdissen ◽  
Zishu Liu ◽  
Peter F. Stadler ◽  
Susann Müller

Abstract Background Flow cytometry (FCM) is a powerful single-cell based measurement method to ascertain multidimensional optical properties of millions of cells. FCM is widely used in medical diagnostics and health research. There is also a broad range of applications in the analysis of complex microbial communities. The main concern in microbial community analyses is to track the dynamics of microbial subcommunities. So far, this can be achieved with the help of time-consuming manual clustering procedures that require extensive user-dependent input. In addition, several tools have recently been developed by using different approaches which, however, focus mainly on the clustering of medical FCM data or of microbial samples with a well-known background, while much less work has been done on high-throughput, online algorithms for two-channel FCM. Results We bridge this gap with , a model-based clustering tool based on multivariate Gaussian mixture models with subsampling and foreground/background separation. These extensions provide a fast and accurate identification of cell clusters in FCM data, in particular for microbial community FCM data that are often affected by irrelevant information like technical noise, beads or cell debris. outperforms other available tools with regard to running time and information content of the clustering results and provides near-online results and optional heuristics to reduce the running-time further. Conclusions is a useful tool for the automated cluster analysis of microbial FCM data. It overcomes the user-dependent and time-consuming manual clustering procedure and provides consistent results with ancillary information and statistical proof.


2006 ◽  
Vol 54 (1) ◽  
pp. 157-166 ◽  
Author(s):  
G. Sin ◽  
R. Govoreanu ◽  
N. Boon ◽  
G. Schelstraete ◽  
P.A. Vanrolleghem

Impact of model-based operation of nutrient removing SBRs on the stability of activated sludge population was studied in this contribution. The optimal operation scenario found by the systematic model-based optimisation protocol of Sin et al. (Wat. Sci. Tech., 2004, 50(10), 97–105) was applied to a pilot-scale SBR and observed to considerably improve the nutrient removal efficiency in the system. Further, the process dynamics was observed to change under the optimal operation scenario, e.g. the nitrite route prevailed and also filamentous bulking was provoked in the SBR system. At the microbial community level as monitored by DGGE, a transient shift was observed to gradually take place parallel to the shift into the optimal operation scenario. This implies that the model-based optimisation of a nutrient removing SBR causes changes at the microbial community level. This opens future perspectives to incorporate the valuable information from the molecular monitoring of activated sludge into the model-based optimisation methodologies. In this way, it is expected that model-based optimisation approaches will better cover complex and dynamic aspects of activated sludge systems.


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