Oxygen mediated mobilization and co-occurrence of antibiotic resistance in lab-scale bioreactor using metagenomic binning.

Sub-lethal levels of antibiotic stimulate bacteria to generate reactive oxygen species (ROS) that promotes emergence and spread of antibiotic resistance mediated by mobile genetic elements (MGEs). Nevertheless, the influence of dissolved oxygen (DO) levels on mobility of antibiotic resistance genes (ARGs) in response to ROS-induced stress remains elusive. Thus, the study employs metagenomic assembly and binning approaches to decipher mobility potential and co-occurrence frequency of ARGs and MGEs under hyperoxic (5.5-7 mgL- 1), normoxic (2.5-4 mgL- 1), and hypoxic (0.5-1 mgL- 1) conditions in lab-scale bioreactor for 6 months. Among 163 high-quality metagenome-assembled genomes (MAGs) recovered from 13 metagenomes, 42 MAGs harboured multiple ARGs and were assigned to priority pathogen group. Total ARG count increased by 4.3 and 2.5% in hyperoxic and normoxic, but decreased by 0.53% in hypoxic conditions after 150 days. On contrary, MGE count increased by 7.3-1.3% in all the DO levels, with only two ARGs showed positive correlation with MGEs in hypoxic compared to 20 ARGs under hyperoxic conditions. Opportunistic pathogens (Escherichia, Klebsiella, Clostridium, and Proteus) were detected as potential hosts of ARGs wherein co-localisation of critical ARG gene cassette (sul1, dfr1,adeF, and qacC) were identified in class 1 integron/Tn1 family transposons. Thus, enhanced co-occurrence frequency of ARGs with MGEs in pathogens suggested promotion of ARGs mobility under oxidative stress. The study offers valuable insights into ARG dissemination and hosts dynamics that is essential for controlling oxygen-related stress for mitigating MGEs and ARGs in the environment.

Tailored microbial consortium producing hydrolytic enzyme cocktail for maximum saccharification of wheat straw.

The potential of hydrolytic enzyme cocktail obtained from designed bacterial consortium WSh-1 comprising Bacillus subtilis CRN 16, Paenibacillus dendritiformis CRN 18, Niallia circulans CRN 24, Serratia marscens CRN 29, and Streptomyces sp. CRN 30, was investigated for maximum saccharification. Activity was further enhanced to 1.01 U/ml from 0.82 U/ml by supplementing growth medium with biotin and cellobiose as a cofactor and inducer. Through kinetic analysis, the enzyme cocktail showed a high wheat straw affinity with Michaelis-Menten constant (Km) of 0.68 µmol/L and a deconstruction rate (Vmax) of 4.5 U/ml/min. The statistical optimization of critical parameters increased saccharification to 89 %. The optimized process in a 5-L lab-scale bioreactor yielded 501 mg/g of reducing sugar from NaOH-pretreated wheat straw. Lastly, genomic insights revealed unique abundant oligosaccharide deconstruction enzymes with the most diverse CAZyme profile. The consortium-mediated enzyme cocktails offer broader versatility with efficiency for the economical and sustainable valorization of lignocellulosic waste.

Meta-analysis of wastewater microbiome for antibiotic resistance profiling.

The microbial composition and stress molecules are main drivers influencing the development and spread of antibiotic resistance bacteria (ARBs) and genes (ARGs) in the environment. A reliable and rapid method for identifying associations between microbiome composition and resistome remains challenging. In the present study, secondary metagenome data of sewage and hospital wastewaters were assessed for differential taxonomic and ARG profiling. Subsequently, Random Forest (RF)-based ML models were used to predict ARG profiles based on taxonomic composition and model validation on hospital wastewaters. Total ARG abundance was significantly higher in hospital wastewaters (15 ppm) than sewage (5 ppm), while the resistance towards methicillin, carbapenem, and fluoroquinolone were predominant. Although, Pseudomonas constituted major fraction, Streptomyces, Enterobacter, and Klebsiella were characteristic of hospital wastewaters. Prediction modeling showed that the relative abundance of pathogenic genera Escherichia, Vibrio, and Pseudomonas contributed most towards variations in total ARG count. Moreover, the model was able to identify host-specific patterns for contributing taxa and related ARGs with >90% accuracy in predicting the ARG subtype abundance. More than >80% accuracy was obtained for hospital wastewaters, demonstrating that the model can be validly extrapolated to different types of wastewater systems. Findings from the study showed that the ML approach could identify ARG profile based on bacterial composition including 16S rDNA amplicon data, and can serve as a viable alternative to metagenomic binning for identification of potential hosts of ARGs. Overall, this study demonstrates the promising application of ML techniques for predicting the spread of ARGs and provides guidance for early warning of ARBs emergence.

Repercussions of Prolonged Pesticide Use on Natural Soil Microbiome Dynamics Using Metagenomics Approach.

The residual pesticides in soil can affect the natural microbiome composition and genetic profile that drive nutrient cycling and soil fertility. In the present study, metagenomic approach was leveraged to determine modulations in nutrient cycling and microbial composition along with connected nexus of pesticide, antibiotic, and heavy metal resistance in selected crop and fallow soils having history of consistent pesticide applications. GC-MS analysis estimated residuals of chlorpyrifos, hexachlorbenzene, and dieldrin showing persistent nature of pesticides that pose selective pressure for microbial adaptation. Taxonomic profiling showed increased abundance of pesticide degrading Streptomyces, Xanthomonas, Cupriavidus, and Pseudomonas across the selected soils. Genes encoding for pesticide degrading cytochrome p450, organophosphorus hydrolase, aldehyde dehydrogenase, and oxidase were predominant and positively correlated with Bacillus, Sphingobium, and Burkholderia. Nitrogen-fixing genes (nifH, narB, and nir) were relatively less abundant in crop soils, correlating to the decrease in nitrogen-fixing bacteria (Anabaena, Pantoea, and Azotobacter). Microbial enzymes involved in carbon (pfkA, gap, pgi, and tpiA) and phosphorus cycle (gmbh and phnJ) were significantly higher in crop soils indicating extensive utilization of pesticide residuals as a nutrient source by the indigenous soil microbiota. Additionally, presence of antibiotic and heavy metal resistance genes suggested potential cross-resistance under pressure from pesticide residues. The results implied selective increase in pesticide degrading microbes with decrease in beneficial bacteria that resulted in reduced soil health and fertility. The assessment of agricultural soil microbial profile will provide a framework to develop sustainable agriculture practices to conserve soil health and fertility.