Acanthamoeba and a bacterial endocytobiont isolated from recreational freshwaters.

The quality of many freshwater environments is impacted by human activities, so that many rivers may represent a vehicle for the transmission of health-related microorganisms. This work aimed to isolate and identify genetically free-living amoeba (FLA) of the genus Acanthamoeba from a recreational river in Salta, Argentina, and isolate, if possible, an endocytobiont. Sampling took place at four points (P1-P4) throughout the river in the winter and the summer seasons. Free-living amoebae and Acanthamoeba were recovered from 20-L water concentrated through an ultrafiltration system. Isolation was performed in agar plates, confirmation of Acanthamoeba genus by PCR, and fellow identification and classification based on their sequence analyses. High concentrations of indicator bacteria were found especially in P2, which is intensively used for recreation. Out of a total of 29 FLA isolations, 9 were identified as Acanthamoeba genotype T4 subtype A, the most frequent genotype found in nature and associated with causing human disease. From an axenic culture of Acanthamoeba spp. (KY751412), a bacterial endocytobiont was isolated, and identified as Stenotrophomonas maltophilia. The endocytobiont showed resistance and intermediate resistance to a wide range of widely used antibiotics. Results were in concordance with the cosmopolitan behavior of Acanthamoeba, and showed the importance of studying this group of amoebae and related microorganisms in recreational environments.

Rotavirus contamination of surface waters from the northwest of Argentina.

Fecal pollution of water is a serious concern because it is associated with the transmission of pathogens. The aim of this study was to analyze the occurrence of group A rotavirus (RVA) in surface waters from the Arias-Arenales River in Salta, a northern city in Argentina, and to define possible sources of fecal viral pollution. A total of 116 water samples were analyzed and RVA was detected in 3.4% (95% CI: 0.1-7.0%), with concentrations ranging from 1.9 × 105 to 3.8 × 106 genome copies per liter. RVA strains were characterized as G1P[8], G4P[8] and G9P[8], which are common genotypes circulating in the local population. The Arias-Arenales River presented unusual and sporadic contamination by RVA, originated from stormwater discharges and a variety of non-identified sources, and support the essential need of viral indicators for enhanced monitoring of water quality.

Isolation and characterization of indigenous Streptomyces and Lentzea strains from soils containing boron compounds in Argentina.

The Salta Province - in the northwest of Argentina - is the main worldwide producer of hydroboracite and leads in exports of boron mineral and its derivatives in Latin America. In addition to the natural presence of boron compounds in the soils, there are others contaminated due to the boron mining industry. Although some bacteria are known to require boron for their growth or to be capable of storing boron, no studies have been published about Streptomyces or Lentzea genera's capacity to tolerate high boron concentrations, or about their metabolic capacities in boron contaminated environments. The results of this research show the isolation and molecular characterization of eight strains belonging to the actinobacteria phylum collected from different soils contaminated with high boron concentration in Salta state. The boron tolerance assays, which show that three of the strains were able to tolerate up 60-80 mM boron, demonstrate the potential capability of this group of bacteria to grow and maybe to remove boron from the environment. They appear to be promising, considering that these microorganisms are infrequent pathogens, are metabolically versatile and many Streptomyces can synthesize boron containing metabolites.

Strategies to optimize monitoring schemes of recreational waters from Salta, Argentina: a multivariate approach.

Several recreational surface waters in Salta, Argentina, were selected to assess their quality. Seventy percent of the measurements exceeded at least one of the limits established by international legislation becoming unsuitable for their use. To interpret results of complex data, multivariate techniques were applied. Arenales River, due to the variability observed in the data, was divided in two: upstream and downstream representing low and high pollution sites, respectively, and cluster analysis supported that differentiation. Arenales River downstream and Campo Alegre Reservoir were the most different environments, and Vaqueros and La Caldera rivers were the most similar. Canonical correlation analysis allowed exploration of correlations between physicochemical and microbiological variables except in both parts of Arenales River, and principal component analysis allowed finding relationships among the nine measured variables in all aquatic environments. Variable's loadings showed that Arenales River downstream was impacted by industrial and domestic activities, Arenales River upstream was affected by agricultural activities, Campo Alegre Reservoir was disturbed by anthropogenic and ecological effects, and La Caldera and Vaqueros rivers were influenced by recreational activities. Discriminant analysis allowed identification of subgroup of variables responsible for seasonal and spatial variations. Enterococcus, dissolved oxygen, conductivity, E. coli, pH, and fecal coliforms are sufficient to spatially describe the quality of the aquatic environments. Regarding seasonal variations, dissolved oxygen, conductivity, fecal coliforms, and pH can be used to describe water quality during dry season, while dissolved oxygen, conductivity, total coliforms, E. coli, and Enterococcus during wet season. Thus, the use of multivariate techniques allowed optimizing monitoring tasks and minimizing costs involved.

Evaluation of concentration efficiency of the Pseudomonas aeruginosa phage PP7 in various water matrixes by different methods.

Enteric viruses monitoring in surface waters requires the concentration of viruses before detection assays. The aim of this study was to evaluate different methods in terms of recovery efficiencies of bacteriophage PP7 of Pseudomonas aeruginosa, measured by real-time PCR, using it as a viral control process in water analysis. Different nucleic acid extraction methods (silica-guanidinium thiocyanate, a commercial kit (Qiagen Viral RNA Kit) and phenol-chloroform with alcohol precipitation) exhibited very low recovery efficiencies (0.08-4.18 %), being the most efficient the commercial kit used for subsequent experiments. To evaluate the efficiency of three concentration methods, PBS (as model for clean water) and water samples from rivers were seeded to reach high (HC, 10(6) pfu ml(-1)) and low concentrations (LC, 10(4) pfu ml(-1)) of PP7. Tangential ultrafiltration proved to be more efficient (50.36 ± 12.91, 17.21 ± 9.22 and 12.58 ± 2.35 % for HC in PBS and two river samples, respectively) than adsorption-elution with negatively charged membranes (1.00 ± 1.34, 2.79 ± 2.62 and 0.05 ± 0.08 % for HC in PBS and two river samples, respectively) and polyethylene glycol precipitation (15.95 ± 7.43, 4.01 ± 1.12 and 3.91 ± 0.54 %, for HC in PBS and two river samples, respectively), being 3.2-50.4 times more efficient than the others for PBS and 2.7-252 times for river samples. Efficiencies also depended on the initial virus concentration and aqueous matrixes composition. In consequence, the incorporation of an internal standard like PP7 along the process is useful as a control of the water concentration procedure, the nucleic acid extraction, the presence of inhibitors and the variability of the recovery among replicas, and for the calculation of the sample limit of detection. Thus, the use of a process control, as presented here, is crucial for the accurate quantification of viral contamination.

Sewershed surveillance as a tool for smart management of a pandemic in threshold countries. Case study: Tracking SARS-CoV-2 during COVID-19 pandemic in a major urban metropolis in northwestern Argentina.

Wastewater-based epidemiology is an economical and effective tool for monitoring the COVID-19 pandemic. In this study we proposed sampling campaigns that addressed spatial-temporal trends within a metropolitan area. This is a local study of detection and quantification of SARS-CoV-2 in wastewater during the onset, rise, and decline of COVID-19 cases in Salta city (Argentina) over the course of a twenty-one-week period (13 Aug to 30 Dec) in 2020. Wastewater samples were gathered from 13 sewer manholes specific to each sewershed catchment, prior to convergence or mixing with other sewer lines, resulting in samples specific to individual catchments with defined areas. The 13 sewershed catchments selected comprise 118,832 connections to the network throughout the city, representing 84.7 % (534,747 individuals) of the total population. The number of COVID19-related exposure and symptoms cases in each area were registered using an application developed for smartphones by the provincial government. Geographical coordinates provided by the devices were recorded, and consequently, it was possible to geolocalise all app-cases and track them down to which of the 13 sampling catchments belonged. RNA fragments of SARS-CoV-2 were detected in every site since the beginning of the monitoring, anticipating viral circulation in the population. Over the course of the 21-week study, the concentrations of SARS-CoV-2 ranged between 1.77 × 104 and 4.35 × 107 genome copies/L. There was a correspondence with the highest viral load in wastewater and the peak number of cases reported by the app for each catchment. The associations were evaluated with correlation analysis. The viral loads of SARS-CoV-2 in wastewater were a feasible means to describe the trends of COVID-19 infections. Surveillance at sewershed scale, provided reliable and strategic information that could be used by local health stakeholders to manage the COVID-19 pandemic.

Tracking SARS-CoV-2 in rivers as a tool for epidemiological surveillance.

The aim of this work was to evaluate if rivers could be used for SARS-CoV-2 surveillance. Five sampling points from three rivers (AR-1 and AR-2 in Arenales River, MR-1 and MR-2 in Mojotoro River, and CR in La Caldera River) from Salta (Argentina), two of them receiving discharges from wastewater plants (WWTP), were monitored from July to December 2020. Fifteen water samples from each point (75 in total) were collected and characterized physico-chemically and microbiologically and SARS-CoV-2 was quantified by RT-qPCR. Also, two targets linked to human contributions, human polyomavirus (HPyV) and RNase P, were quantified and used to normalize SARS-CoV-2 concentration, which was compared to reported COVID-19 cases. Statistical analyses allowed us to verify the correlation between SARS-CoV-2 and the concentration of fecal indicator bacteria (FIB), as well as to find similarities and differences between sampling points. La Caldera River showed the best water quality; FIBs were within acceptable limits for recreational activities. Mojotoro River's water quality was not affected by the northern WWTP of the city. Instead, Arenales River presented the poorest water quality; at AR-2 was negatively affected by the discharges of the southern WWTP, which contributed to significant increase of fecal contamination. SARS-CoV-2 was found in about half of samples in low concentrations in La Caldera and Mojotoro Rivers, while it was high and persistent in Arenales River. No human tracers were detected in CR, only HPyV was found in MR-1, MR-2 and AR-1, and both were quantified in AR-2. The experimental and normalized viral concentrations strongly correlated with reported COVID-19 cases; thus, Arenales River at AR-2 reflected the epidemiological situation of the city. This is the first study showing the dynamic of SARS-CoV-2 concentration in an urban river highly impacted by wastewater and proved that can be used for SARS-CoV-2 surveillance to support health authorities.

Virtual screening of plant-derived compounds against SARS-CoV-2 viral proteins using computational tools.

The new SARS-CoV-2, responsible for the COVID-19 pandemic, has been threatening public health worldwide for more than a year. The aim of this work was to evaluate compounds of natural origin, mainly from medicinal plants, as potential SARS-CoV-2 inhibitors through docking studies. The viral spike (S) glycoprotein and the main protease Mpro, involved in the recognition of virus by host cells and in viral replication, respectively, were the main molecular targets in this study. Molecular docking was performed using AutoDock, which allowed us to select the plant actives with the highest affinity towards the viral targets and to identify the interaction molecular sites with the SARS-CoV2 proteins. The best energy binding values for S protein were, in kcal/mol: -19.22 for glycyrrhizin, -17.84 for gitoxin, -12.05 for dicumarol, -10.75 for diosgenin, and -8.12 for delphinidin. For Mpro were, in kcal/mol: -9.36 for spirostan, -8.75 for N-(3-acetylglycyrrhetinoyl)-2-amino-propanol, -8.41 for α-amyrin, -8.35 for oleanane, -8.11 for taraxasterol, and -8.03 for glycyrrhetinic acid. In addition, the synthetic drugs umifenovir, chloroquine, and hydroxychloroquine were used as controls for S protein, while atazanavir and nelfinavir were used for Mpro. Key hydrogen bonds and hydrophobic interactions between natural compounds and the respective viral proteins were identified, allowing us to explain the great affinity obtained in those compounds with the lowest binding energies. These results suggest that these natural compounds could potentially be useful as drugs to be experimentally evaluated against COVID-19.

Sediments quality must be considered when evaluating freshwater aquatic environments used for recreational activities.

Although water quality from freshwater recreational aquatic environments (RAEs) has been long analyzed worldwide, little information is available about their sediments. The aim of this work was to study the physicochemical and bacteriological quality of water and sediment under different seasonal events. For that, Wierna River (WR) and General Belgrano reservoir (GB) were used as freshwater RAEs models. A total of 33 water and 33 sediment samples (15 from WR and 18 from GB from each phase) were collected and analyzed. Physicochemical variables in water (pH, turbidity, dissolved oxygen, temperature, conductivity, alkalinity, hardness) and sediments (organic matter, humidity, ash, and conductivity) were measured. For the bacteriological characterization, total aerobic mesophiles, total and thermotolerant coliforms, E. coli, enteroccocci, Salmonella spp., and Pseudomonas aeruginosa were evaluated using culture-based methods. Universal and human Bacteroides were also quantified by real-time PCR. Univariate (Kruskall-Wallis), bivariate (Spearman correlation), and multivariate (cluster analysis, principal component analysis) statistical techniques were applied for data analysis. All bacterial indicators were almost two-logs higher in sediments than in water, for both RAEs. Also, due to rainfall events and recreational activities, sediments were resuspended in surface water exceeding in most cases the limit values established by international regulation for bacteria. Significant correlation was observed between culturable bacteria and turbidity (p < 0.05) supporting this. We found that while physicochemical variables clustered samples by geographical location in water and sediments, microbiological aggrupation in water was mostly driven by seasonal events. No aggrupation was observed when using microbiological variables in sediments. Thus, geographical location, type of water and sediments, and seasonal events influenced on RAEs quality. Including sediment analysis during RAEs monitoring campaigns is essential as it will allow knowing the real health risk to which bathers are exposed and proposing solutions to mitigate it.

Data fitting approach more critical than exposure scenarios and treatment of censored data for quantitative microbial risk assessment.

Recreational waters are a source of many diseases caused by human viral pathogens, including norovirus genogroup II (NoV GII) and enterovirus (EV). Water samples from the Arenales river in Salta, Argentina, were concentrated by ultrafiltration and analyzed for the concentrations of NoV GII and EV by quantitative PCR. Out of 65 samples, 61 and 59 were non-detects (below the Sample Limit of Detection limit, SLOD) for EV and NoV GII, respectively. We hypothesized that a finite number of environmental samples would lead to different conclusions regarding human health risks based on how data were treated and fitted to existing distribution functions. A quantitative microbial risk assessment (QMRA) was performed and the risk of infection was calculated using: (a) two methodological approaches to find the distributions that best fit the data sets (methods H and R), (b) four different exposure scenarios (primary contact for children and adults and secondary contact by spray inhalation/ingestion and hand-to-mouth contact), and (c) five alternatives for treating censored data. The risk of infection for NoV GII was much higher (and exceeded in most cases the acceptable value established by the USEPA) than for EV (in almost all the scenarios within the recommended limit), mainly due to the low infectious dose of NoV. The type of methodology used to fit the monitoring data was critical for these datasets with numerous non-detects, leading to very different estimates of risk. Method R resulted in higher projected risks than Method H. Regarding the alternatives for treating censored data, replacing non-detects by a unique value like the average or median SLOD to simplify the calculations led to the loss of information about the particular characteristics of each sample. In addition, the average SLOD was highly impacted by extreme values (due to events such as precipitations or point source contamination). Instead, using the SLOD or half- SLOD captured the uniqueness of each sample since they account for the history of the sample including the concentration procedure and the detection method used. Finally, substitution of non-detects by Zero is not realistic since a negative result would be associated with a SLOD that can change by developing more efficient and sensitive methodology; hence this approach would lead to an underestimation of the health risk. Our findings suggest that in most cases the use of the half-SLOD approach is appropriate for QMRA modeling.