PAOs in phosphorus removal reactors and ARGs in environmental microbiomes : new insights from bioinformatics

Abstract

This thesis demonstrated the application of bioinformatics analysis, big data mining and data integration to reveal new insights to interpret and control the conventional and emerging pollutants in wastewater treatment plants (WWTPs). Phosphorus (P) is one of the conventional pollutants that have been intensively studied for more than forty years. The first part of this thesis focused on the functional microbes as the primary contributor to the P removal in WWTPs, i.e. the Candidatus Accumulibacter (referred to as Accumulibacter). Combining the in silicon analysis and in vitro experiments, this part achieved a comprehensive understanding into the identity, function and niche adaptation of Accumulibacter to better optimize and control the P removal process. At first, new qPCR primers were designed to identify and quantify different Candidatus Accumulibacter (referred to as Accumulibacter) lineages in WWTPs. To mine the knowledge of the genomic and metatranscriptomic datasets, a bioinformatic tool of Pan-genome and Pan-pathway Pipeline (PAPP) was developed to define the fundamental and flexible functions within the Accumulibacter family and to link the genotypes to phenotypes, and finally to optimize the experimental conditions for the enrichment of Accumulibacter. The PAPP and the novel metabolic framework developed in this study could be also applied to enrich other Accumulibacter-like not-yet-cultured microbes in the environments. In the last decade, emerging pollutants have been frequently detected in various environments and have been raised as a global-wide issue. However, the knowledge of their distribution, behavior and effect in the environments is still unclear. The second part of this thesis focused on one of the global emerging pollutants, the antibiotic resistant genes (ARGs), especially to understand their acquisition, dissemination, distribution and risk in the natural and anthropogenic environments. A framework to study the acquisition and dissemination of ARGs by whole genome analysis was established in this thesis, demonstrated by a mobile genetic element, the class 1 integrons, which is proposed as one of the major contributors to the spreading of ARGs. Firstly, the contribution of class 1 integrons to ARG spreading was evaluated by developing a bioinformatic tool of Integron Identification and Visualization Pipeline (I-VIP) and applying the I-VIP to all currently available bacterial genomes. The results reveal that the contribution of class 1 integrons to spreading ARGs in the environments could be limited by both the phylogenetic and ecological barriers. Besides, two new databases of integrons and class 1 integrases (intI1) were constructed by I-VIP and whole genome analysis, which are used as abundant resources for primer evaluation and design. Based on it, the previous qPCR primers were evaluated to cover only 30%-50% of the new intI1 database and new qPCR primers were designed to improve the coverage to 90%. Finally, this thesis comprehensively studied the distribution and risk of ARGs. Two big global datasets of 55,000 bacterial whole genomes and 860 metagenomes were investigated by big data mining for the phylogenetic distribution and ecological distribution of ARGs. The results are published as a ARGs online searching platform (ARGs-OSP, http://args-osp.herokuapp.com/), to share the ARGs profiles in different environmental samples with other researchers, meanwhile avoiding repeatedly downloading and analyzing the big datasets. The priority and risk ranking of ARGs is a good demonstration of a comprehensive downstream analysis using the ARGs-OSP. The first practical ranking system was constructed in this thesis to evaluate and assign the priority and risk of ARGs and the associated environmental habitats. All ARGs in the reference database were assessed and classified into Rank I-V by the host pathogenicity, mobility, host range and prevalence in anthropogenic environments. Then the contribution of ARGs in each rank to the total ARGs in an environment sample was quantified to assess the priority and risk of a sample. This ranking system can pick out the ARGs and habitats of top risk to human health and top priority to monitor and control. This thesis proposes it as a standard method to interpret antibiotic resistant profiles in the environments and to provide guidance for global actions to monitor and control the antibiotic resistance. In conclusion, this thesis proposes that one of the next breakthroughs in environmental microbiology is big data analysis and data integration by developing bioinformatics tools, and demonstrates this idea by the works on one conventional pollutant (P) and one group of emerging pollutants, i.e. ARGs.