Activated sludge microbiome in a membrane bioreactor for treating Ramen noodle-soup wastewater

Genomic Sequencing of Clinical Cupriavidus gilardii Isolates Revealed Their Diverse Antimicrobial Resistance Mechanisms

Purpose

Cupriavidus gilardii is an emerging multidrug-resistant pathogen found in many environments and few clinical samples. The clinical infectiousness, pathogenicity, and resistance mechanisms of C. gilardii are still unclear due to the lack of clinical and sequencing data. We need to obtain insight into the clinical characteristics, virulence, and resistance mechanisms of C. gilardii.

Patients and Methods

We isolated five C. gilardii isolates from hospitalized patients and carried out assay, culture and genome sequencing. We analyzed the genomic features of clinical C. gilardii isolates and took insight into their clinical characteristics, virulence, and resistance mechanisms.

Results

These isolates were resistant to meropenem, gentamicin, and other antimicrobials due to intrinsic resistance genes. Furthermore, the sequencing results revealed the widespread presence of the MCR-5.1 gene in C. gilardii. The virulence magnitude of C. gilardii is closely correlated with the number of virulence factors they carry. Some C. gilardii strains can acquire resistance to levofloxacin through gyrA gene mutation during treatment. The diverse antimicrobial resistance mechanisms challenge the treatment of C. gilardii infections.

Conclusion

We present the genomic characteristics of clinically isolated C. gilardii to improve (i) our understanding of this pathogen and (ii) treatment options.

In-situ sludge reduction based on Mn2+-catalytic ozonation conditioning: Feasibility study and microbial mechanisms

To improve the sludge conditioning efficiency without increasing the ozone dose, an in-situ sludge reduction process based on Mn2+-catalytic ozonation conditioning was proposed. Using ozone conditioning alone as a control, a lab-scale sequencing batch reactor coupled with ozonated sludge recycle was evaluated for its operating performance at an ozone dose of 75 mg O3/g VSS and 1.5 mmol/L Mn2+ addition. The results showed a 39.4% reduction in MLSS and an observed sludge yield of 0.236 kg MLSS/kg COD for the O3+Mn2+ group compared to the O3 group (15.3% and 0.292 kg MLSS/kg COD), accompanied by better COD, NH4+-N, TN and TP removal, improved effluent SS and limited impact on excess sludge properties. Subsequently, activity tests, BIOLOG ECO microplates and 16S rRNA sequencing were applied to elucidate the changing mechanisms of Mn2+-catalytic ozonation related to microbial action: (1) Dehydrogenase activity reached a higher peak. (2) Microbial utilization of total carbon sources had an elevated effect, up to approximately 18%, and metabolic levels of six carbon sources were also increased, especially for sugars and amino acids most pronounced. (3) The abundance of Defluviicoccus under the phylum Proteobacteria was enhanced to 12.0% and dominated in the sludge, they had strong hydrolytic activity and metabolic capacity. Denitrifying bacteria of the genus Ferruginibacter also showed an abundance of 7.6%, they contributed to the solubilization and reduction of sludge biomass. These results could guide researchers to further reduce ozonation conditioning costs, improve sludge management and provide theoretical support.

Machine learning reveals the complex ecological interplay of microbiome in a full-scale membrane bioreactor wastewater treatment plant

Membrane bioreactor (MBR) systems are one of the most widely used wastewater treatment processes for various municipal and industrial waste streams. The present study aimed to advance the understanding of ecologically important keystone taxa that play an important role in full-scale MBR systems. A machine-learning (ML) modeling framework based on microbiome data was developed to successfully predict, with an average accuracy of >91.6%, the operational characteristics of three representative full-scale wastewater systems: an MBR, a conventional activated sludge system, and a sequencing batch reactor. ML-based feature-importance analysis identified Ferruginibacter as a keystone organism in the MBR system. The phylogeny and known ecophysiology of members of Ferruginibacter supported their role in metabolizing complex organic polymers (e.g., extracellular polymeric substances) in MBR systems characterized by high concentrations of mixed liquor suspended solids and a high solid retention time. ML regression modeling also revealed temporal patterns of Ferruginibacter in response to water temperature. ML modeling was thus successfully employed in the present study to investigate complex/non-linear relationships between keystone taxa and environmental conditions that cannot be detected using conventional approaches. Overall, our microbiome-data-enabled ML modeling approach represents a methodological advance for identifying keystone taxa and their complex ecological interactions, which has implications for the sustainable and predictive management of MBR systems.

Evaluating the Optimal Oil Concentrations in the Startup Performance of a Membrane Bioreactor Treating Oily Noodle-soup Wastewater

The discharge of high-strength oily wastewater adversely affects the environment; therefore, the treatment of wastewater containing fats, oils, and grease from the food industry is of importance. In this study, we used a membrane bioreactor (MBR) to treat Ramen noodle-soup wastewater, and we evaluated the optimal oil concentration in the wastewater for the startup of the MBR treatment in winter and summer. The MBR system had a sufficient startup in both seasons when fed with a 20-fold dilution of the original oily wastewater, containing approximately 950 to 1,200 mg/L oil and approximately 3,000 to 4,400 mg/L biological oxygen demand (BOD; BOD-SS load of 0.1 to 0.2 kg/kg/d). The reactor performance in winter were relatively stable during the operation. While, activated sludge microbes in summer were not highly active with a 40-fold dilution of wastewater, because of the decreased mixed liquor suspended solid concentration during the operation period. Population shifts in the sludge microbiome with increasing oil concentrations were analyzed using high-throughput sequencing, and the relative abundance of operational taxonomic units belonging to the phylum Bacteroidetes were highest in both winter and summer when fed with 20-fold dilution of the wastewater. In particular, the family Chitinophagaceae was dominant, with relative abundances of 13.5% in winter and 5.1% in summer, suggesting that this family may play important roles in the startup of a MBR treating the wastewater.

Development of a post-treatment system using a downflow hanging sponge reactor - an upflow anaerobic reactor for natural rubber processing wastewater treatment

This study aimed to evaluate the nitrogen removal of a post-treatment system for natural rubber processing wastewater (NRPW) under low chemical oxygen demand to total nitrogen (COD/TN) ratios without any supplemental external carbon source. The system including a downflow hanging sponge (DHS) reactor and an upflow anaerobic reactor (UAR) was operated in two phases. In phase 1 (day 0-102), under a nitrogen loading rate (NLR) of 0.23 ± 0.06 kgN m^-3 d^-1 and COD/TN ratio of 0.63 ± 0.47, the DHS-UAR system removed 82.5 ± 11.8% and 83.9 ± 7.6% of TN and ammonium concentrations, respectively. In phase 2 (day 103-229), higher COD/TN ratio of 1.96 ± 0.28 was applied to remove increasing NLRs. At the highest NLR of 0.51 kgN m^-3 d^-1, the system achieved TN and ammonium removal efficiencies of 93.2% and 93.7%, respectively. Nitrogen profiles and the 16S rRNA high-throughput sequencing data suggested that ammonium, a major nitrogen compound in NRPW, was utilized by nitrifying and ammonium assimilation bacteria in DHS, then removed by heterotrophic denitrifying and anammox bacteria in the UAR. The predominance of Acinetobacter detected in both reactors suggested its essential role for the nitrogen conversion.