At the age of big data sequencing, what's new about the naughty and efficient microbes within the WWTPs

Transcription

1 At the age of big data sequencing, what's new about the naughty and efficient microbes within the WWTPs Jean-Jacques Godon INRA, UR0050, Laboratoire de Biotechnologie de l Environnement, Narbonne, F-11100

2 Biological wastewater treatment plants (WWTPs) employing activated sludge represent the most widely used biotechnological process on Earth. WWTPs are designed to remove nutrients and pollutants in sewage through the action of various microorganisms. There is increasing interest to understand microbial community compositions and functions. a major concern about some human pathogens may survive and grow rapidly under favorable conditions

3 Our objective is to provide example on big data technology sequencing and to illustrate how it can affect WWTP knowledge.

4 to answer to focused questions: what is the diversity in term of microbes and in term of genes? Do these data are generic or controlled by WWTP characteristics, operational parameters, and geographic locations? what is the microbial composition of untreated wastewaters? How human sewage microbiomes were able to survive through the WWTPs? Which pathogens and antibiotic resistance genes are present in WWTPs.

5 What big data mean! Why big data! How big data is useful or useless

6 Sequencing is a prominent example of a big data technology because of the massive amount of information it produces and its complexity, diversity, and timeliness. The DNA Data deluge

7 Cost of DNA sequencing

8 Game over.

9 The two possible strategies to face with DNA Data

10 Big data about! 16S rdna All the bacteria present = who is there Metagenome All the genes present = which potentiel activities Metatranscriptome All the transcripted genes = which possible activities Metaproteome All the proteins present = which activities Metabolome All the metabolites present = which metetolites is product

11 Big data for what? Which question? about microbiology about process health safety about process improvement Which answer? about microbiology about process health safety about process improvement

12 7 examples with questions and answers Wich question? Wich answer?

13 Example 1 Molecular diversity of a wastewater treatment plant (Sanapareddy et al.; 2009) Question. Which genetic diversity

14 Which taxonomic level is relevant? At which taxonomic level the 16S rdna sequence is already known? In red unknown fraction Sanapareddy N et al. Appl. Environ. Microbiol. 2009

15 Where the sequence (16S rdna) come from? Sanapareddy N et al. Appl. Environ. Microbiol. 2009

16 Functional categories provided for our data set by the SEED server ( Sanapareddy N et al. Appl. Environ. Microbiol. 2009;75:

17 Conclusion example 1 Results point to the extraordinary diversity of microbial communities and that the genomes of the most abundant organisms in the wastewater treatment plant are unknown. Origin of the main 16S rrna gene sequences found were from freshwater, soil, and other wastewater environments. The relatively small number of human-derived 16S rrna sequences observed show that the environment within the wastewater treatment plant exhibits strong selection pressure against the microbes that are present in human feces. Despite the great diversity of microbes in the treatment plant, analysis at the protein level is surprisingly tractable, with the sequences from the treatment plant displaying a distinct metabolic profile consistent with what we would expect based on the plant's function.

18 Example 2 Overall functional gene diversity of microbial communities in three full-scale activated sludge bioreactors (Xia et al.; 2014) Question. Similarities of microbial communities both in functional genes and 16S rdna genes Relationship between functional gene structures and treatment efficiencies 3 WWTPs Same location Differents parameters

19 Carbon-cycling genes High similarities of microbial communities of the investigated systems were detected, both in functional genes and 16S rrna genes, consistent with some previous investigations. Xia et al.; 2014

20 Relationships between community structures and environmental factors Canonical correspondence analysis Sludge retention time, DO concentrations and total nitrogen as key factors shaping the overall functional gene compositions Xia et al.; 2014

21 Conclusion example 2 High similarities of microbial communities of the investigated systems were detected, both in functional genes and 16S rdna genes Relationship between functional gene structures and treatment efficiencies were detected. Xia et al.; 2014

22 Example 3 Analysis of bacterial diversity in 14 wastewater treatment systems in china (Wang et al.; 2012) Question. Role of geographic locations on core microbial community Parameters related with core microbial community

23 Heat map of the 10 most abundant genera in each sample. Wang X et al. Appl. Environ. Microbiol. 2012;78:

24 Canonical correspondence analysis (CCA) of pyrosequencing data and measurable variables in the 14 samples. Wang X et al. Appl. Environ. Microbiol. 2012;78:

25 Variation partitioning analysis of microbial community explained by wastewater characteristics (W), operational parameters (O), and geographic location (G). Wang X et al. Appl. Environ. Microbiol. 2012;78:

26 Conclusion example 3 There was a core microbial community in the microbial populations of WWTPs at different geographic locations Results showed that water temperature, conductivity, ph, and DO were correlated most strongly to the variance of bacterial communities Wastewater characteristics had the greatest contribution to the bacterial community variance and explained 26% of the variance of bacterial communities, followed by operational parameters (24%) and geographic locations (15%).

27 Example 4 Comparison of the microbial community structures of untreated wastewaters from different geographic locales (Shanks et al.; 2013) Question. Role of WWTPs geographic location Fecal portion of the untreated sewage community

28 Phylum relative abundance box plot for all phyla Shanks O C et al. Appl. Environ. Microbiol. 2013;79:

29 Nonmetric multidimensional scaling plots of sewage-infrastructure classified Bacteroidetes (left) and Firmicutes (right) community composition in northern (green, 41 to 50) and southern (blue, <41) latitudinal groupings. Shanks O C et al. Appl. Environ. Microbiol. 2013;79:

30 Conclusion example 4 Bacteroidetes and Firmicutes are the primary bacterial members in human fecal samples but were much less prevalent than Proteobacteria The high abundance of non-fecal associated taxa The results presented here indicate that the fecal portion of the untreated sewage community remains relatively stable across a wide range of locations. Latitude as a predictor of untreated sewage microbial community structure.

31 Example 5 Tracking human sewage microbiome in a municipal wastewater treatment plant (Cai et al., 2014) Question. Pathogen from human sewage in WWTPs

32 Comparison between influent, activated slude and effluent Cai et al., 2014

33 Comparison between influent, activated slude and effluent

34 Pathogen abundance Cai et al., 2014

35 Stability over time Principal coordinate analysis based on abundance for the 100 most dominant genera Cai et al., 2014

36 Conclusion example 5 The majority of bacterial sequences from sewage influent represent a shared sewage microbial community new sewage microbial community structure that does not resemble the human fecal microbial community. Stable over time Cai et al., 2014

37 Example 6 Detecting human bacterial pathogens in wastewater treatment plant including influent, activated sludge, and effluent, using a high-throughput shotgun sequencing technique. (Cai et al.; 2013) Question. Detection of human pathogen in influent, activated sludge, and effluent

38 Comparison of human pathogenic bacteria detected using three different analyses (16S rrna genes, VFs, and MetaPhlAn). Cai et al., 2013

39 Conclusion example 6 Using 16S rrna genes, the pathogenic bacteria abundance in the total bacterial population was % for activated sludge samples and % for influent and effluent samples. Using 16S rrna genes, Mycobacterium tuberculosis was the most dominant pathogenic bacterium, accounting for 28 84% and 87 90% of total pathogenic bacteria in activated sludge and effluent. Cai et al., 2013

40 Example 7 Exploring Variation of Antibiotic Resistance Genes in Activated Sludge (Yang et al.; 2013) Question. Which antibiotic resistance genes in activated sludge

41 Link between source and antibiotic genes Yang et al.; 2013

42 Comparison with other microbial ecosystems Yang et al.; 2013

43 Conclusion example 7 The existence of a broad-spectrum of different Antibiotic Resistance Genes, some of which have never been reported in activated sludge before. The most abundant Antibiotic Resistance Genes were aminoglycoside and tetracycline resistance genes. The abundances of these resistance genes were generally higher in the samples collected in the winters than the samples collected in the contiguous summer Yang et al.; 2013

44 Conclusion Which answer? about microbiology about process health safety about process improvement

45 Conclusion Big data increases our knowledge on microbiology Not in process improvement Because we are unable to link microbiology data with process improvement Big data is a good tools to know where pathogen come from How pathogens or pathogenic gens survive in WWTPs

46 Thanks for your attention