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

Droplet Digital PCR for Absolute Quantification of Extracellular MicroRNAs in Plasma and Serum: Quantification of the Cancer Biomarker hsa-miR-141.

Droplet-based digital PCR provides high-precision, absolute quantification of nucleic acid target sequences with wide-ranging applications for both research and clinical diagnostic applications. Droplet-based digital PCR enables absolute quantification by counting nucleic acid molecules encapsulated in discrete, volumetrically defined water-in-oil droplet partitions. The current available systems overcome the previous lack of scalable and practical technologies for digital PCR implementation. Extracellular microRNAs in biofluids (plasma, serum, urine, cerebrospinal fluid, etc.) are promising noninvasive biomarkers in multiple diseases and different clinical settings (e.g., diagnosis, early diagnosis, prediction of recurrence, and prognosis). Here we describe a protocol that enables highly precise and reproducible absolute quantification of extracellular microRNAs using droplet digital PCR

Applying mechanistic models in bioprocess development.

The available knowledge on the mechanisms of a bioprocess system is central to process analytical technology. In this respect, mechanistic modeling has gained renewed attention, since a mechanistic model can provide an excellent summary of available process knowledge. Such a model therefore incorporates process-relevant input (critical process variables)-output (product concentration and product quality attributes) relations. The model therefore has great value in planning experiments, or in determining which critical process variables need to be monitored and controlled tightly. Mechanistic models should be combined with proper model analysis tools, such as uncertainty and sensitivity analysis. When assuming distributed inputs, the resulting uncertainty in the model outputs can be decomposed using sensitivity analysis to determine which input parameters are responsible for the major part of the output uncertainty. Such information can be used as guidance for experimental work; i.e., only parameters with a significant influence on model outputs need to be determined experimentally. The use of mechanistic models and model analysis tools is demonstrated in this chapter. As a practical case study, experimental data from Saccharomyces cerevisiae fermentations are used. The data are described with the well-known model of Sonnleitner and Kappeli (Biotechnol Bioeng 28: 927-937, 1986) and the model is analyzed further. The methods used are generic, and can be transferred easily to other, more complex case studies as well