Exploring microbial community structures and functions of activated sludge by high-throughput sequencing
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».