Biodegradation of Sulfamethoxazole in Milkfish (Chanos chanos) Pond Sediments

New insights into bioaugmented removal of sulfamethoxazole in sediment microcosms: degradation efficiency, ecological risk and microbial mechanisms

BACKGROUND

Bioaugmentation has the potential to enhance the ability of ecological technology to treat sulfonamide-containing wastewater, but the low viability of the exogenous degraders limits their practical application. Understanding the mechanism is important to enhance and optimize performance of the bioaugmentation, which requires a multifaceted analysis of the microbial communities. Here, DNA-stable isotope probing (DNA-SIP) and metagenomic analysis were conducted to decipher the bioaugmentation mechanisms in stabilization pond sediment microcosms inoculated with sulfamethoxazole (SMX)-degrading bacteria (Pseudomonas sp. M2 or Paenarthrobacter sp. R1).

RESULTS

The bioaugmentation with both strains M2 and R1, especially strain R1, significantly improved the biodegradation rate of SMX, and its biodegradation capacity was sustainable within a certain cycle (subjected to three repeated SMX additions). The removal strategy using exogenous degrading bacteria also significantly abated the accumulation and transmission risk of antibiotic resistance genes (ARGs). Strain M2 inoculation significantly lowered bacterial diversity and altered the sediment bacterial community, while strain R1 inoculation had a slight effect on the bacterial community and was closely associated with indigenous microorganisms. Paenarthrobacter was identified as the primary SMX-assimilating bacteria in both bioaugmentation systems based on DNA-SIP analysis. Combining genomic information with pure culture evidence, strain R1 enhanced SMX removal by directly participating in SMX degradation, while strain M2 did it by both participating in SMX degradation and stimulating SMX-degrading activity of indigenous microorganisms (Paenarthrobacter) in the community.

CONCLUSIONS

Our findings demonstrate that bioaugmentation using SMX-degrading bacteria was a feasible strategy for SMX clean-up in terms of the degradation efficiency of SMX, the risk of ARG transmission, as well as the impact on the bacterial community, and the advantage of bioaugmentation with Paenarthrobacter sp. R1 was also highlighted. Video Abstract.

Sulfonamide-metabolizing microorganisms and mechanisms in antibiotic-contaminated wetland sediments revealed by stable isotope probing and metagenomics

Sulfonamide (SA) antibiotics are ubiquitous pollutants in livestock breeding and aquaculture wastewaters, which increases the propagation of antibiotic resistance genes. Microbes with the ability to degrade SA play important roles in SA dissipation, but their diversity and the degradation mechanism in the field remain unclear. In the present study, we employed DNA-stable isotope probing (SIP) combined with metagenomics to explore the active microorganisms and mechanisms of SA biodegradation in antibiotic-contaminated wetland sediments. DNA-SIP revealed various SA-assimilating bacteria dominated by members of Proteobacteria, such as Bradyrhizobium, Gemmatimonas, and unclassified Burkholderiaceae. Both sulfadiazine and sulfamethoxazole were dissipated mainly through the initial ipso-hydroxylation, and were driven by similar microbes. sadA gene, which encodes an NADH-dependent monooxygenase, was enriched in the ¹³C heavy DNA, confirming its catalytic capacity for the initial ipso-hydroxylation of SA in sediments. In addition, some genes encoding dioxygenases were also proposed to participate in SA hydroxylation and aromatic ring cleavage based on metagenomics analysis, which might play an important role in SA metabolism in the sediment ecosystem when Proteobacteria was the dominant active bacteria. Our work elucidates the ecological roles of uncultured microorganisms in their natural habitats and gives a deeper understanding of in-situ SA biodegradation mechanisms.

Anaerobic sulfamethoxazole-degrading bacterial consortia in antibiotic-contaminated wetland sediments identified by DNA-stable isotope probing and metagenomics analysis

Anaerobic degradation has been demonstrated as an important pathway for the removal of sulfonamide (SA) in contaminated environments, and identifying the microorganisms responsible for the degradation of SA is a key step in developing bioaugmentation approaches. In this study, we investigated the anaerobic degradation activity of three SA [sulfadiazine (SDZ), sulfamethazine (SMZ) and sulfamethoxazole (SMX)] and the associated bacterial community in wetland sediments contaminated by aquaculture (in Fujian Province, coded with FJ), livestock farming (in Sichuan Province, coded with SC), or rural wastewaters (in Guangdong Province, coded with GD). Additionally, the combination of DNA-stable isotope probing (SIP) with metagenomics was further applied to assess the active SA-degrading microbes using SMX as a model SA. Among SDZ, SMZ and SMX, only SMX could be effectively dissipated, and the degradation of SMX was relatively fast in the microcosms of sediments with higher levels of SA contamination (FJ and SC). The anaerobic biotransformation pathway of SMX was initiated by hydrogenation with the cleavage of the N-O bond on the isoxazole ring. DNA-SIP revealed that the in situ active anaerobic SMX-degraders (5, 18 and 3 genera in sediments FJ, SC and GD respectively) were dominated by Proteobacteria in sediments FJ and SC, but by Firmicutes (two Family XVIII members) in sediment GD. Mycobacterium, unclassified Burkholderiaceae and Rhodocyclaceae were identified as the dominant active SMX-degrading bacteria in both sediments FJ and SC. Higher proportions of antibiotic resistance gene and genes involved in various functional categories were observed in sediments FJ and SC.

Considering the Prospect of Utilizing Anaerobic Membrane Biofouling Layers Advantageously for the Removal of Emerging Contaminants

Membrane biofilm formation has traditionally been perceived as a wholly negative occurrence in membrane filtration-based wastewater treatment systems due to its resultant effect on transmembrane pressure and energy expenditure. This is the case for both membrane bioreactor (MBR) systems, generally, and anaerobic membrane bioreactors (AnMBRs), specifically. Insight gained through recent research, however, has revealed a potentially positive aspect to biofouling in AnMBR systems—namely, the improved removal of certain emerging contaminants (both microbial and chemical) from wastewater that would not otherwise be retained by the microfiltration/ultrafiltration membranes that are commonly used. Although the exact reasons behind this are not yet understood, the biofilm-specific anaerobic microbial communities that develop on membrane surfaces may play a key role in the phenomenon. Mechanisms of biofouling development in AnMBRs have recently been proven distinctly different from those that govern fouling in aerobic MBR systems. Based on these differences, it may be possible to devise operational strategies that promote the development of anaerobic biofilms on membranes while also minimizing transmembrane pressure increases. If achievable, this would serve as a sustainable basis for reducing the release of emerging contaminants such as organic micropollutants (OMPs) and antibiotic resistance genes (ARGs) with treated wastewater effluents.

Antifungal strains and gene mapping of secondary metabolites in mangrove sediments from Semarang city and Karimunjawa islands, Indonesia

Infection caused by pathogenic fungal species is one of the most challenging disease to be tackled today. The antifungal bacteria candidate can be found in terrestrial as well as aquatic ecosystems, with mangrove forests being one of them. The purpose of this study is to obtain candidate isolates of antifungal strains with a detection approach and gene mapping simulation of bioactive compounds producers and screening to determine qualitative antifungal activity. The research will be carried out by collecting sediment samples from the mangrove ecosystems of Karimunjawa and Mangkang sub-district of Semarang city, isolating and purifying bacteria with Humic Acid Vitamin Agar (HVA), International Streptomyces Project 1 (ISP 1) and Zobell (Marine Agar). added with antibiotics, qualitative antifungal ability screening of each isolate obtained, detection of the presence of PKS gene and NRPS using special primers using the Polymerase Chain Reaction (PCR) method, and molecular identification of each isolate by 16s rRNA sequencing method. Of the total 59 isolates produced from the sample isolation process, 31 isolates from Karimunjawa sediments and 8 isolates from Semarang sediments showed activity against test pathogenic bacteria, namely Candida albicans, Trichoderma sp., and Aspergillus niger. Detection of Biosynthethic Gene Cluster (BGC) showed that the genes encoding secondary metabolites (NRPS, PKS 1 and PKS 2) were detected in KI 2-2 isolates from Karimunjawa. NRPS were detected only in isolates SP 3-9, SH 3-4, KI 1-6, KI 2-2, KI 2-4. The secondary metabolite-encoding gene, PKS1, was detected in isolates SP 3-5, SP 3-8, KI 2-2. PKS II genes were detected only on isolates SP 2-4, SH 3-8, KI 1-6, KI 2-2, and KI 2-4. Isolate SP 3-5 was revealed as Pseudomonas aeruginosa (93.14%), isolate SP 2-4 was Zhouia amylolytica strain HN-181 (100%) and isolate SP 3-8 was P. aeruginosa strain QK -2 (100%).

Mass loading, distribution, and removal of antibiotics and antiretroviral drugs in selected wastewater treatment plants in Kenya

The discharge of active pharmaceutical ingredients (APIs) into the aquatic environment from wastewater effluents is a concern in many countries. Although many studies have been conducted to evaluate the APIs removal efficiencies and emissions to the environment in wastewater treatment plants (WWTPs), most of these studies considered the aqueous and sludge phases, disregarding the suspended particulate matter (SPM) phase. To try to understand the role of the SPM, the occurrence of five most common antibiotics and three antiretroviral drugs (ARVDs) commonly used in Kenya were investigated in this study. APIs partitioning and mass loading in influents and effluents of three different WWTPs: trickling filters, stabilization ponds, and decentralized fecal sludge system, were evaluated. API concentration levels ranging from ˂LOQ (limit of quantification) to 92 μgL^-1 and ˂LOQ to 82.2 mgkg^-1 were observed in aqueous samples and solid samples respectively, with SPM accounting for most of the higher concentrations. The use of the aqueous phase alone for determination of removal efficiencies showed underestimations of API removal as compared to when solid phases are also considered. Negative removal efficiencies were observed, depending on the compound and the type of WWTP. The negative removals were associated with deconjugation of metabolites, aggregated accumulation of APIs in the WWTPs, as well as unaccounted hydraulic retention time during sampling. Compound characteristics, environmental factors, and WWTPs operation influenced WWTPs removal efficiencies. Wastewater stabilization ponds had the poorest removals efficiencies with an average of -322%. High total mass loads into the WWTPs influent and effluent of 22,729 and 22,385 mg day^-1 1000 PE^-1 were observed respectively. The results aims at aiding scientists and engineers in planning and designing of WWTPs. Findings also aim at aiding policy-making on pharmaceutical drug use and recommend proper wastewater management practices to manage the high mass loading observed in the WWTPs.

Machine Learning Modeling of Aerobic Biodegradation for Azo Dyes and Hexavalent Chromium

The present study emphasizes the efficacy of a biosurfactant-producing bacterial strain Klebsiella sp. KOD36 in biodegradation of azo dyes and hexavalent chromium individually and in a simultaneous system. The bacterial strain has exhibited a considerable potential for biodegradation of chromium and azo dyes in single and combination systems (maximum 97%, 94% in an individual and combined system, respectively). Simultaneous aerobic biodegradation of azo dyes and hexavalent chromium (SBAHC) was modeled using machine learning programming, which includes gene expression programming, random forest, support vector regression, and support vector regression-fruit fly optimization algorithm. The correlation coefficient includes the dispersion index, and the Willmott agreement index was employed as statistical metrics to assess the performance of each model separately. In addition, the Taylor diagram was used to further investigate the methods used. The findings of the present study were that the support vector regression-fruitfly optimization algorithm (SVR-FOA) with correlation coefficient (CC) of 0.644, (scattered index) SI of 0.374, and (Willmott’s index of agreement) WI of 0.607 performed better than the autonomous support vector regression (SVR), gene expression programming (GEP), and random forest (RF) methods. In addition, the standalone SVR model with CC of 0.146, SI of 0.473, and WI of 0.408 ranked the second best. In summary, the SBAHC can be accurately estimated using the hybrid SVR-FOA method. In other words, FOA has proven to be a powerful optimization algorithm for increasing the accuracy of the SVR method.