Networking and co-occurrence of virulent and multidrug resistant environmental bacteria in different aquatic systems: A gap in MDR-virulence transfer?

Co-occurrence of resistance and virulence is often overlooked in aquatic bacteria as environmental reservoirs, while transmission of these characteristics to clinically significant strains present unforeseen problems in future. In this investigation, environmental bacteria identified concurrently from multiple aquatic habitats viz., groundwater, canal, river and coastal waters were profiled for antibiotic resistance, metal tolerance, virulence factors and genes coding for these determinants. Strains from polluted river and canal exhibited higher resistance and virulence, especially Pseudomonas gessardii and P. fluorescens displayed high antibiotic resistance index (ARI > 0.6-0.8) with Alkaline Protease and Phospholipase production. Opportunistic pathogens including Vibrio parahaemolyticus, V. alginolyticus, V. vulnificus, Corynebacterium and Comamonas testosteroni expressed all three virulence factors with relatively low resistance. However, V. vulnificus and V. alginolyticus exhibited multiclass antibiotic resistance (5/6 classes). Metagenomic analysis revealed that genes corresponding to beta-lactam resistance were significantly higher (p < 0.05) in freshwater than seawater, while multidrug resistance gene were higher (p < 0.05) in seawater. In all aquatic bodies, abundant virulence genes belonged to secretion system proteins followed by motility related genes. Culturable bacteria revealed differential distribution of positive and negative correlation between 31 targeted genes with expressed resistance and virulence. Among Acinetobacter, significant positive correlation was found between Phospholipase production, other virulence genes (OVGs) and resistance to DNA Synthesis Inhibitors (DSI). In Pseudomonas, positive correlation was detected between toxin genes (toxA, eta, hlyA and stx) and resistance to cell wall synthesis inhibitors (CSI) as well as with OVGs and adhesion genes (eae, afa, papC and papA). Network analysis displayed unique clustering of genes ncc, arsB, strA, merA and intI dominated by non-pathogens and distinct clustering of genes pho, erm, nfsA, trh, lasB, tdh and invA by Vibrio. This investigation extends insight on co-occurring resistance and virulence in aquatic reservoir bacteria that could pose serious threats to public health in future.

Arsenic resistance and horizontal gene transfer are associated with carbon and nitrogen enrichment in bacteria

Coastal waters are confluences receiving large amounts of point and non-point sources of pollution. An attempt was made to explore microbial community interactions in response to carbon, nitrogen and metal pollution. Additionally, experiments were designed to analyze the influence of these factors on horizontal gene transfer (HGT). Shift in bacterial diversity dynamics by arsenic stress and nutrient addition in coastal waters was explored by metagenomics of microcosm setups. Phylogenetic analysis revealed equal distribution of Gammaproteobacteria (29%) and Betaproteobacteria (28%) in control microcosm. This proportional diversity from control switched to unique distribution of Gammaproteobacteria (44.5%)> Flavobacteria (17.7%)> Bacteriodia (11.92%)> Betaproteobacteria (11.52%) in microcosm supplemented with carbon, nitrogen and metal (C + N + M). Among metal-stressed systems, alpha diversity analysis indicated highest diversity of genera in C + N + M followed by N + M > C+M> metal alone. Arsenic and ampicillin sensitive E. coli XL1 blue and environmental strains (Vibrio tubiashii W85 and E. coli W101) were tested for efficiency of uptake of plasmid (P) pUCminusMCS (arsB^Ramp^R) under varying stress conditions. Transformation experiments revealed that combined effect of carbon, nitrogen and metal on horizontal gene transfer (HGT) was significantly higher (p < 0.01) than individual factors. The effect of carbon on HGT was proved to be superior to nitrogen under metal stressed conditions. Presence of arsenic in experimental setups (P + M, P + N + M and P + C + M) enhanced the HGT compared to non-metal counterparts supplemented with carbon or nitrogen. Arsenic resistant bacterial isolates (n = 200) were tested for the ability to utilize various carbon and nitrogen substrates and distinct positive correlation (p < 0.001) was found between arsenic resistance and utilization of urea and nitrate. However, evident positive correlation was not found between carbon sources and arsenic resistance. Our findings suggest that carbon and nitrogen pollution in aquatic habitats under arsenic stress determine the microbial community dynamics and critically influence uptake of genetic material from the surrounding environment.

Speed Efficient Fast Fourier Transform for Signal Processing of Nucleotides to Detect Diabetic Retinopathy Using Machine Learning

Diabetic Retinopathy (DR) is a complicated disease of diabetes, which specifically affects the retina. The human-intensive analysis mechanism of DR infected retina are likely to diagnose wrongly compared to computer-intensive diagnosis systems. In this paper, in order to aid the computer based approach for the diagnosis of DR, a model based on machine learning algorithm is proposed. The nucleotides of the human retina are processed with the help of signal processing methodologies. A speed efficient Fast Fourier transform is proposed to work out the FFT of huge amount of samples with higher pace. The improvement in speed is achieved in 98% of the samples. The prediction parameters, derived from these samples are utilized to classify the healthy retina sequence and an infected retina. In this study, Fine Tree, KNN Fine, Weighted KNN, Ensemble Bagged Trees and Ensemble Subspace KNN classifiers are employed to build the models. The simulated results using MATLAB software show that the accuracy is 98% which is better than image processing based methods which were used earlier. The performance parameters such as sensitivity and specificity are determined for each model. The faithfulness of the model is studied by deriving the ROC Curve.

COVID-19 restrictions and their influences on ambient air, surface water and plastic waste in a coastal megacity, Chennai, India

Anthropogenic activities experienced a pause due to the nationwide lockdown, imposed to contain the rapid spread of COVID-19 in the third week of March 2020. The impacts of suspension of industrial activities, vehicular transport and other businesses for three months (25 March-30 June) on the environmental settings of Chennai, a coastal megacity was assessed. A significant reduction in the key urban air pollutants [PM_2.5 (66.5%), PM₁₀ (39.5%), NO₂ (94.1%), CO (29%), O₃ (45.3%)] was recorded as an immediate consequence of the reduced anthropogenic activities. Comparison of water quality of an urban river Adyar, between pre-lockdown and lockdown, showed a substantial drop in the dissolved inorganic N (47%) and suspended particulate matter (41%) during the latter period. During the pandemic, biomedical wastes in India showed an overall surge of 17%, which were predominantly plastic. FTIR-ATR analysis confirmed the polymers such as polypropylene (25.4%) and polyester (15.4%) in the personal protective equipment.

Tangential flow ultrafiltration for detection of white spot syndrome virus (WSSV) in shrimp pond water

Water represents the most important component in the white spot syndrome virus (WSSV) transmission pathway in aquaculture, yet there is very little information. Detection of viruses in water is a challenge, since their counts will often be too low to be detected by available methods such as polymerase chain reaction (PCR). In order to overcome this difficulty, viruses in water have to be concentrated from large volumes of water prior to detection. In this study, a total of 19 water samples from aquaculture ecosystem comprising 3 creeks, 10 shrimp culture ponds, 3 shrimp broodstock tanks and 2 larval rearing tanks of shrimp hatcheries and a sample from a hatchery effluent treatment tank were subjected to concentration of viruses by ultrafiltration (UF) using tangential flow filtration (TFF). Twenty to 100l of water from these sources was concentrated to a final volume of 100mL (200-1000 fold). The efficiency of recovery of WSSV by TFF ranged from 7.5 to 89.61%. WSSV could be successfully detected by PCR in the viral concentrates obtained from water samples of three shrimp culture ponds, one each of the shrimp broodstock tank, larval rearing tank, and the shrimp hatchery effluent treatment tank with WSSV copy numbers ranging from 6 to 157mL(-1) by quantitative real time PCR. The ultrafiltration virus concentration technique enables efficient detection of shrimp viral pathogens in water from aquaculture facilities. It could be used as an important tool to understand the efficacy of biosecurity protocols adopted in the aquaculture facility and to carry out epidemiological investigations of aquatic viral pathogens.