Optimization of linear alkylbenzene sulfonate (LAS) degradation in UASB reactors by varying bioavailability of LAS, hydraulic retention time and specific organic load rate

AbstractDegradation of linear alkylbenzene sulfonate (LAS) in UASB reactors was optimized by varying the bioavailability of LAS based on the concentration of biomass in the system (1.3–16gTS/L), the hydraulic retention time (HRT), which was operated at 6, 35 or 80h, and the concentration of co-substrates as specific organic loading rates (SOLR) ranging from 0.03–0.18gCOD/gTVS.d. The highest degradation rate of LAS (76%) was related to the lowest SOLR (0.03gCOD/gTVS.d). Variation of the HRT between 6 and 80h resulted in degradation rates of LAS ranging from 18% to 55%. Variation in the bioavailability of LAS resulted in discrete changes in the degradation rates (ranging from 37–53%). According to the DGGE profiles, the archaeal communities exhibited greater changes than the bacterial communities, especially in biomass samples that were obtained from the phase separator. The parameters that exhibited more influence on LAS degradation were the SOLR followed by the HRT

Microbiome taxonomic and functional profiles of two domestic sewage treatment systems

Anaerobic systems for domestic sewage treatment, like septic tanks and anaerobic filters, are used in developing countries due to favorable economic and functional features. The anaerobic filter is used for the treatment of the septic tank effluent, to improve the COD removal efficiency of the system. The microbial composition and diversity of the microbiome from two wastewater treatment systems (factory and rural school) were compared through 16S rRNA gene sequencing using MiSeq 2 × 250 bp Illumina sequencing platform. Additionally, 16S rRNA data were used to predict the functional profile of the microbial communities using PICRUSt2. Results indicated that hydrogenotrophic methanogens, like Methanobacterium, were found in higher abundance in both systems compared to acetotrophic methanogens belonging to Methanosaeta genus. Also, important syntrophic microorganisms (Smithella, Syntrophus, Syntrophobacter) were found in the factory and rural school wastewater treatment systems. Microbial communities were also compared between stages (septic tank and anaerobic filter) of each wastewater treatment stage, revealing that, in the case of the rural school, both microbial communities were quite similar most likely due to hydraulic short-circuit issues. Meanwhile, in the factory, microbial communities from the septic tank and anaerobic filter were different. The school system showed lower COD removal rates (2–30%), which were probably related to a higher abundance of Firmicutes members in addition to the hydraulic short-circuit and low abundance of Chloroflexi members. On the other hand, the fiberglass factory presented higher COD removal rates (60–83%), harboring phyla reported as the core microbiome of anaerobic digesters (Bacteroidetes, Chloroflexi, and Proteobacteria phyla). The knowledge of the structure and composition of wastewater treatment systems may provide support for the improvement of the pollutant removal in anaerobic processCONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQ401720/2016-

Feedback on a c omparative metatranscriptomic analysis

National audienceThe progress of next generation sequencing favors the development of more comprehensive ecosystem studies thanks to metatranscriptomic approaches. These latter can indeed provide access to functional information at a good analysis depth. Through a study of anaerobic digesters treating anionic surfactant contaminated wastewater [1] (namely the linear alkylbenzene sulfonate, LAS), we developed a bioinformatics pipeline to perform the RNAseq data analysis for shotgun metatranscriptomics data.In this pipe-line, the raw data are cleaned and pre-processed. Reads corresponding to rRNA are detected and discarded from the datasets. After a normalization step based on k-mer counts, the mRNA reads from the datasets are de novo co-assembled using the Trinity software. Coding regions of the metatranscriptomic assembly are subsequently predicted and annotated. For functional annotation, sequences with matches to the eggNOG and KEGG GENES databases are retrieved to establish functional categories and reconstruct the metabolic pathways. For taxonomic classification, the sequences are assigned by comparing them to a NCBI-nr database. For each dataset individually, reads are mapped back to the co-assembled contigs. Eventually, a count table is constructed; it contains, for each predicted gene, the counts obtained by samples, as well as the associated taxonomic and functional annotations.After aggregation and statistical analysis, this study enabled detecting active genes likely involved in each step of LAS biodegradation and exploring the microbial active core related to LAS degradation.We developed a bioinformatics pipeline to perform the RNAseq data analysis for shotgun metatranscriptomics data, through a study of anaerobic digesters treating anionic surfactant contaminated wastewater.In this pipeline, the raw data are cleaned and pre-processed. Reads corresponding to rRNA are detected and discarded from the datasets. After a normalization step based on k-mer counts, the mRNA reads from the datasets are de novo co-assembled. Coding regions of the metatranscriptomic assembly are subsequently predicted and annotated. Taxonomic and functional annotations are obtained by comparison to public reference databases. The latter are used to define functional categories and reconstruct metabolic pathways. For each dataset individually, reads are mapped back to the co-assembled contigs. Finally, a count table is constructed; it contains, for each predicted gene, the counts obtained by samples, as well as the associated taxonomic and functional annotations.After aggregation and statistical analysis, this study enabled detecting active genes likely involved in each step of the anionic surfactant degradation and exploring the associated microbial activse core

Feedback on a c omparative metatranscriptomic analysis

National audienceThe progress of next generation sequencing favors the development of more comprehensive ecosystem studies thanks to metatranscriptomic approaches. These latter can indeed provide access to functional information at a good analysis depth. Through a study of anaerobic digesters treating anionic surfactant contaminated wastewater [1] (namely the linear alkylbenzene sulfonate, LAS), we developed a bioinformatics pipeline to perform the RNAseq data analysis for shotgun metatranscriptomics data.In this pipe-line, the raw data are cleaned and pre-processed. Reads corresponding to rRNA are detected and discarded from the datasets. After a normalization step based on k-mer counts, the mRNA reads from the datasets are de novo co-assembled using the Trinity software. Coding regions of the metatranscriptomic assembly are subsequently predicted and annotated. For functional annotation, sequences with matches to the eggNOG and KEGG GENES databases are retrieved to establish functional categories and reconstruct the metabolic pathways. For taxonomic classification, the sequences are assigned by comparing them to a NCBI-nr database. For each dataset individually, reads are mapped back to the co-assembled contigs. Eventually, a count table is constructed; it contains, for each predicted gene, the counts obtained by samples, as well as the associated taxonomic and functional annotations.After aggregation and statistical analysis, this study enabled detecting active genes likely involved in each step of LAS biodegradation and exploring the microbial active core related to LAS degradation.We developed a bioinformatics pipeline to perform the RNAseq data analysis for shotgun metatranscriptomics data, through a study of anaerobic digesters treating anionic surfactant contaminated wastewater.In this pipeline, the raw data are cleaned and pre-processed. Reads corresponding to rRNA are detected and discarded from the datasets. After a normalization step based on k-mer counts, the mRNA reads from the datasets are de novo co-assembled. Coding regions of the metatranscriptomic assembly are subsequently predicted and annotated. Taxonomic and functional annotations are obtained by comparison to public reference databases. The latter are used to define functional categories and reconstruct metabolic pathways. For each dataset individually, reads are mapped back to the co-assembled contigs. Finally, a count table is constructed; it contains, for each predicted gene, the counts obtained by samples, as well as the associated taxonomic and functional annotations.After aggregation and statistical analysis, this study enabled detecting active genes likely involved in each step of the anionic surfactant degradation and exploring the associated microbial activse core

Feedback on a c omparative metatranscriptomic analysis

National audienceThe progress of next generation sequencing favors the development of more comprehensive ecosystem studies thanks to metatranscriptomic approaches. These latter can indeed provide access to functional information at a good analysis depth. Through a study of anaerobic digesters treating anionic surfactant contaminated wastewater [1] (namely the linear alkylbenzene sulfonate, LAS), we developed a bioinformatics pipeline to perform the RNAseq data analysis for shotgun metatranscriptomics data.In this pipe-line, the raw data are cleaned and pre-processed. Reads corresponding to rRNA are detected and discarded from the datasets. After a normalization step based on k-mer counts, the mRNA reads from the datasets are de novo co-assembled using the Trinity software. Coding regions of the metatranscriptomic assembly are subsequently predicted and annotated. For functional annotation, sequences with matches to the eggNOG and KEGG GENES databases are retrieved to establish functional categories and reconstruct the metabolic pathways. For taxonomic classification, the sequences are assigned by comparing them to a NCBI-nr database. For each dataset individually, reads are mapped back to the co-assembled contigs. Eventually, a count table is constructed; it contains, for each predicted gene, the counts obtained by samples, as well as the associated taxonomic and functional annotations.After aggregation and statistical analysis, this study enabled detecting active genes likely involved in each step of LAS biodegradation and exploring the microbial active core related to LAS degradation.We developed a bioinformatics pipeline to perform the RNAseq data analysis for shotgun metatranscriptomics data, through a study of anaerobic digesters treating anionic surfactant contaminated wastewater.In this pipeline, the raw data are cleaned and pre-processed. Reads corresponding to rRNA are detected and discarded from the datasets. After a normalization step based on k-mer counts, the mRNA reads from the datasets are de novo co-assembled. Coding regions of the metatranscriptomic assembly are subsequently predicted and annotated. Taxonomic and functional annotations are obtained by comparison to public reference databases. The latter are used to define functional categories and reconstruct metabolic pathways. For each dataset individually, reads are mapped back to the co-assembled contigs. Finally, a count table is constructed; it contains, for each predicted gene, the counts obtained by samples, as well as the associated taxonomic and functional annotations.After aggregation and statistical analysis, this study enabled detecting active genes likely involved in each step of the anionic surfactant degradation and exploring the associated microbial activse core

Comparative metatranscriptomic analysis of anaerobic digesters treating anionic surfactant contaminated wastewater

Three distinct biological reactors fed with synthetic medium (UASB_Control), synthetic medium and linear alkylbenzene sulfonate (LAS; UASB_SL), and real laundry wastewater (UASB_LW) were compared using a metatranscriptomic approach to determine putative bioindicator genes and taxonomies associated to all steps of anaerobic LAS biodegradation pathway. A homemade bioinformatics pipeline combined with an R workflow was developed to perform the RNAseq data analysis. UASB_SL and UASB_LW showed similar values of LAS biological degradation (-47%) and removal (53-55%). Rarefaction analysis revealed that 1-2 million reads were sufficient to access the whole functional capacity. In the first step of LAS biodegradation pathway, fumarate reductase subunit C was detected and taxonomically assigned to the genus Syntrophobacter (0.002% - UASB_SL; 0.0015% - UASB_LW; not detected - UASB_Control). In the second step, many enzymes related to beta-oxidation were observed and most of them with low relative abundance in UASB Control and taxonomically related with Smithella. Acinetobacter and Syntrophorhabdus. For the ring cleavage step, the abundance of 6 OCI-I CoA hydrolase putative gene was ten times higher in UASB_SL and UASB_LW when compared to UASB_Control, and assigned to Desulfomonile and Syntrophorhabdus. Finally, the adenylylsulfate reductase, taxonomically related with Desulfovibrio and Desulfomonile, was observed in the desulfonation step with the highest relative abundance in UASB_LW649482494FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESP2014/16426-0; 2017/00817-