Exploring microbial community structures and functions of activated sludge by high-throughput sequencing
Bibliographic record
Abstract
To investigate the diversities and abundances of nitrifiers and to apply the highthroughput\n\nsequencing technologies to analyze the overall microbial community\n\nstructures and functions in the wastewater treatment bioreactors were the major\n\nobjectives of this study. Specifically, this study was conducted: (1) to investigate the\n\ndiversities and abundances of AOA, AOB and NOB in bioreactors, (2) to explore the\n\nbacterial communities in bioreactors using 454 pyrosequencing, and (3) to analyze the\n\nmetagenomes of activated sludge using Illumina sequencing.\n\nA lab-scale nitrification bioreactor was operated for 342 days under low DO (0.15~0.5\n\nmg/L) and high nitrogen loading (0.26~0.52 kg-N/(m3d)). T-RFLP and cloning analysis\n\nshowed there were only one dominant AOA, AOB and NOB species in the bioreactor,\n\nrespectively. The amoA gene of the dominant AOA had a similarity of 89.3% with the\n\nisolated AOA species Nitrosopumilus maritimus SCM1. The AOB species detected in the\n\nbioreactor belonged to Nitrosomonas genus. The abundance of AOB was more than 40\n\ntimes larger than that of AOA. The percentage of NOB in total bacteria increased from\n\nnot detectable to 30% when DO changed from 0.15 to 0.5 mg/L. Compared with\n\ntraditional methods, pyrosequencing analysis of the bacteria in this bioreactor provided\n\nunprecedented information. 494 bacterial OTUs was obtained at 3% distance cutoff.\n\nFurthermore, 454 pyrosequencing was applied to investigate the bacterial communities of\n\nactivated sludge samples from 14 WWTPs of Asia (mainland China, Hong Kong, and\n\nSingapore) and North America (Canada and the United States). The results revealed huge\n\namounts of OTUs in activated sludge, i.e. 1183~3567 OTUs in one sludge sample at 3%\n\ndistance cutoff. Clear geographical differences among these samples were observed. The\n\nAOB amoA genes in different WWTPs were found quite diverse while the 16S rRNA\n\ngenes were relatively conserved.\n\nTo explore microbial community structures and functions in the abovementioned labscale\n\nbioreactor and a full-scale bioreactor, over six gigabases of metagenomic sequence\n\ndata and 150,000 paired-end reads of PCR amplicons were generated from the activated\n\nsludge in the two bioreactors on Illumina HiSeq2000 platform. Three kinds of sequences\n\n(16S rRNA amplicons, 16S rRNA gene tags and predicted genes) were used to conduct\n\ntaxonomic assignment and their applicabilities and reliabilities were compared. Specially,\n\nbased on 16S rRNA and amoA gene sequences, AOB were found more abundant than\n\nAOA in the two bioreactors. Furthermore, the analysis of the metabolic profiles and\n\npathways indicated that the overall pathways in the two bioreactors were quite similar.\n\nHowever, the abundances of some specific genes in the two bioreactors were different.\n\nIn addition, 454 pyrosequencing was also used to detect potentially pathogenic bacteria in\n\nenvironmental samples. It was found most abundant potentially pathogenic bacteria in the\n\nWWTPs were affiliated with Aeromonas and Clostridium. Aeromonas veronii,\n\nAeromonas hydrophila and Clostridium perfringens were species most similar to the\n\npotentially pathogenic bacteria found in this study. Overall, the percentage of the\n\nsequences closely related to known pathogenic bacteria sequences was about 0.16% of\n\nthe total sequences. Additionally, a Java application (BAND) was developed for\n\ngraphical visualization of microbial abundance data.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".