Enhanced detection of pathogenic enteric viruses in coastal marine environment by concentration using methacrylate monolithic chromatographic supports paired with quantitative PCR

AbstractCurrently, around 50% of the world's population lives in towns and cities within 100 km of the coast. Monitoring of viruses that are frequently present in contaminated coastal environments, such as rotavirus (RoV) and norovirus (NoV), which are also the major cause of human viral gastroenteritis, is essential to ensure the safe use of these water bodies. Since exposure to as few as 10–100 particles of RoV or NoV may induce gastrointestinal disease, there is a need to develop a rapid and sensitive diagnostic method for their detection in coastal water samples. In this study, we evaluate the application of methacrylate monolithic chromatographic columns, commercially available as convective interaction media (CIM®), to concentrate pathogenic enteric viruses from saline water samples prior to virus quantification by one-step reverse transcription quantitative PCR (RT-qPCR). Using RoV and NoV as model enteric viruses, we present our results on the most effective viral concentration conditions from saline water matrices using butyl (C4) hydrophobic interaction monolithic support (CIM® C4). C4 monolithic columns exhibit a good capacity to bind both RoV and NoV and both viruses can be eluted in a single step. Our protocol using a 1 ml C4 column enables processing of 400 ml saline water samples in less than 60 min and increases the sensitivity of RoV and NoV detection by approximately 50-fold and 10-fold respectively. The protocol was also scaled up using larger capacity 8 ml C4 columns to process 4000 ml of seawater samples with concentration factors of 300-fold for RoV and 40-fold for NoV, without any significant increase in processing time. Furthermore, C4 monolithic columns were adapted for field use in an on-site application of RoV concentration from seawater samples with performance equivalent to that of the reference laboratory setup. Overall, the results from successful deployment of CIM C4 columns for concentration of rotavirus and norovirus in seawater samples reiterate the utility of monolithic supports as efficient, scalable and modular preparative tools for processing environmental water samples to enhance viral detection using molecular methods

The body weight (BW) of the trout at the start of the V-challenge and body composition of the fish at the start and at the end of the V-challenge.

<p>Data represent treatment means according to their early nutritional history (M or V) and family (C1, C2, C3). P-values (2-way ANOVA) show the significance of the effects of nutritional history (N Hist), family (Fam) and their interaction (FxNH).</p

Formulation, approximate crude protein (CP) levels of ingredients and analysed composition of the experimental diets M (fishmeal and fish oil-based) and V (all fishmeal and fish oil replaced by plant protein and plant oil sources).

<p>Consisting of (% blend): rapeseed oil (50), palm oil (30), linseed oil (20).</p><p>INRA UPAE, 78352 Jouy en Josas, France.</p

Data on growth, feed intake and nutrient utilization of the trout during the 25-day V-challenge.

<p>Data represent treatment means according to their early nutritional history (M or V) and family (C1, C2, C3). P-values (2-way ANOVA) show the significance of the effects of nutritional history (N Hist), family (Fam) and their interaction (FxNH).</p

Specific growth rate (SGR) of the trout during the 25-day V-challenge according to the early nutritional history (M or V) and family (C1, C2, C3).

<p>Values are means ± SEM (n = 4, except for C3M and C1V with n = 3). Dotted bars represent the effect of nutritional history (M or V) during the V-challenge, averaged over all three families (ALL, means ± SEM, n = 11). The significance of the effects of nutritional history, family (C2>C1>C3) and their interaction (2-way ANOVA) is added in the figure, * indicates a significant effect of nutritional history (V>M, p<0.05).</p

Feed efficiency (FE) during the restricted V-challenge.

<p>Two families of rainbow trout with nutritional history (M or V) received for 4 weeks diet V at 0.75% of their body weight (restricted feeding). Values are means ± SEM (n = 2). The significance of the effects of nutritional history, family (C2>C1) and interaction (P values, 2-way ANOVA) is provided in the <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0083162#s3" target="_blank">results</a> section, * shows a significant effect of nutritional history (V>M, p<0.05).</p

Feed efficiency (FE) of the trout during the 25-day V-challenge according to the early nutritional history (M or V) and family (C1, C2, C3).

<p>Values are means ± SEM (n = 4, except for C3M and C1V with n = 3). Dotted bars represent the effect of nutritional history (M or V) during the V-challenge, averaged over all three families (ALL, means ± SEM, n = 11). The significance of the effects of nutritional history, family (C2 = C1>C3) and their interaction (2-way ANOVA) is provided in the figure. * indicates a significant effect of nutritional history (V>M, p<0.05).</p

Using multi-tracer inference to move beyond single-catchment ecohydrology

Protecting or restoring aquatic ecosystems in the face of growing anthropogenic pressures requires an understanding of hydrological and biogeochemical functioning across multiple spatial and temporal scales. Recent technological and methodological advances have vastly increased the number and diversity of hydrological, biogeochemical, and ecological tracers available, providing potentially powerful tools to improve understanding of fundamental problems in ecohydrology, notably: 1. Identifying spatially explicit flowpaths, 2. Quantifying water residence time, and 3. Quantifying and localizing biogeochemical transformation. In this review, we synthesize the history of hydrological and biogeochemical theory, summarize modem tracer methods, and discuss how improved understanding of flowpath, residence time, and biogeochemical transformation can help ecohydrology move beyond description of site-specific heterogeneity. We focus on using multiple tracers with contrasting characteristics (crossing proxies) to infer ecosystem functioning across multiple scales. Specifically, we present how crossed proxies could test recent ecohydrological theory, combining the concepts of hotspots and hot moments with the Damkohler number in what we call the HotDam framework