Model-based evidence for the relevance of microbial community variability to the efficiency of the anaerobic reductive dechlorination of TCE

Author(s):  
Kyriakos Kandris ◽  
M. Pantazidou ◽  
D. Mamais
PLoS ONE ◽  
2017 ◽  
Vol 12 (3) ◽  
pp. e0173183 ◽  
Author(s):  
Mark Hanemaaijer ◽  
Brett G. Olivier ◽  
Wilfred F. M. Röling ◽  
Frank J. Bruggeman ◽  
Bas Teusink

2016 ◽  
Vol 19 (3) ◽  
pp. 968-981 ◽  
Author(s):  
Siavash Atashgahi ◽  
Yue Lu ◽  
Ying Zheng ◽  
Edoardo Saccenti ◽  
Maria Suarez-Diez ◽  
...  

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.


2011 ◽  
Vol 46 (2) ◽  
pp. 1044-1054 ◽  
Author(s):  
Patrick K. H. Lee ◽  
F. Warnecke ◽  
Eoin L. Brodie ◽  
Tamzen W. Macbeth ◽  
Mark E. Conrad ◽  
...  

2021 ◽  
Author(s):  
Andrea J. Jani ◽  
Jessie Bushell ◽  
Cédric G. Arisdakessian ◽  
Mahdi Belcaid ◽  
Daniel M. Boiano ◽  
...  

AbstractInfectious pathogens can disrupt the microbiome in addition to directly affecting the host. Impacts of disease may be dependent on the ability of the microbiome to recover from such disturbance, yet remarkably little is known about microbiome recovery after disease, particularly in nonhuman animals. We assessed the resilience of the amphibian skin microbial community after disturbance by the pathogen, Batrachochytrium dendrobatidis (Bd). Skin microbial communities of laboratory-reared mountain yellow-legged frogs were tracked through three experimental phases: prior to Bd infection, after Bd infection (disturbance), and after clearing Bd infection (recovery period). Bd infection disturbed microbiome composition and altered the relative abundances of several dominant bacterial taxa. After Bd infection, frogs were treated with an antifungal drug that cleared Bd infection, but this did not lead to recovery of microbiome composition (measured as Unifrac distance) or relative abundances of dominant bacterial groups. These results indicate that Bd infection can lead to an alternate stable state in the microbiome of sensitive amphibians, or that microbiome recovery is extremely slow—in either case resilience is low. Furthermore, antifungal treatment and clearance of Bd infection had the additional effect of reducing microbial community variability, which we hypothesize results from similarity across frogs in the taxa that colonize community vacancies resulting from the removal of Bd. Our results indicate that the skin microbiota of mountain yellow-legged frogs has low resilience following Bd-induced disturbance and is further altered by the process of clearing Bd infection, which may have implications for the conservation of this endangered amphibian.


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