A soft-sensor approach for predicting an indicator virus removal efficiency of a pilot-scale anaerobic membrane bioreactor (AnMBR).

The anaerobic membrane bioreactor (AnMBR) is a promising technology for not only water reclamation but also virus removal; however, the virus removal efficiency of AnMBR has not been fully investigated. Additionally, the removal efficiency estimation requires datasets of virus concentration in influent and effluent, but its monitoring is not easy to perform for practical operation because the virus quantification process is generally time-consuming and requires specialized equipment and trained personnel. Therefore, in this study, we aimed to identify the key, monitorable variables in AnMBR and establish the data-driven models using the selected variables to predict virus removal efficiency. We monitored operational and environmental conditions of AnMBR in Sendai, Japan and measured virus concentration once a week for six months. Spearman's rank correlation analysis revealed that the pH values of influent and mixed liquor suspended solids (MLSS) were strongly correlated with the log reduction value of pepper mild mottle virus, indicating that electrostatic interactions played a dominant role in AnMBR virus removal. Among the candidate models, the random forest model using selected variables including influent and MLSS pH outperformed the others. This study has demonstrated the potential of AnMBR as a viable option for municipal wastewater reclamation with high microbial safety.

Pepper mild mottle virus intended for use as a process indicator for drinking water treatment: Present forms and quantitative relations to norovirus and rotavirus in surface water.

Pepper mild mottle virus (PMMoV) has been proposed as a potential indicator of human enteric viruses in environmental water and for viral removal during drinking water treatment. To investigate the occurrence and present forms of PMMoV and quantitative relations to norovirus GII and rotavirus A (RVA) in surface waters, 147 source water samples were collected from 21 drinking water treatment plants (DWTPs) in Japan between January 2018 and January 2021, and the concentrations of viruses in suspended and dissolved fractions were measured using real-time RT-PCR. PMMoV was detected in 81-100 % of samples in each sample month and observed concentrations ranged from 3.0 to 7.0 log10 copies/L. The concentrations of PMMoV were higher in dissolved fraction compared to suspended fractions, while different partitioning was observed for NoV GII depending on seasons. The concentrations of PMMoV were basically higher than those of norovirus GII (1.9-5.3 log10 copies/L) and RVA (1.9-6.6 log10 copies/L), while in 18 samples, RVA presented higher concentrations than PMMoV. Partial regions of VP7, VP4, and VP6 of the RVA in the 18 samples were amplified using nested PCR, and the genotypes were determined using an amplicon-based next-generation sequencing approach. We found that these source water samples included not only human RVA but also various animal RVA and high genetic diversity due to the existence of animal RVA was associated with a higher RVA concentration than PMMoV. Our findings suggest that PMMoV can be used as an indicator of norovirus GII and human RVA in drinking water sources and that the indicator performance should be evaluated by comparing to zoonotic viruses as well as human viruses.

Correspondence of SARS-CoV-2 genomic sequences obtained from wastewater samples and COVID-19 patient at long-term care facilities.

Wastewater-based epidemiology (WBE) has been in the spotlight because of applicability of early detection of virus outbreak and new variants at the catchment area. However, there has been a notable absence of research directly confirming the association between SARS-CoV-2 in wastewater and patient specimens. In this study, we performed a quantitative and qualitative investigation with a genetic-level comparison of SARS-CoV-2 between COVID-19 patients and SARS-CoV-2 positive wastewater samples at long-term care facilities. Wastewater samples were collected via passive sampling from manholes, and SARS-CoV-2 load in wastewater was determined by qPCR. We performed correlation analysis between SARS-CoV-2 load and COVID-19 case number, which suggested that SARS-CoV-2 was detected from wastewater earlier than ascertainment of COVID-19 case. Six and six RNA samples from COVID-19 positive cases and wastewater, respectively, from two facilities were then applied for amplicon sequencing analysis. Mutation analysis revealed high sequence similarity of SARS-CoV-2 variants between wastewater and patient samples (>99 %). To the best of our knowledge, this is the first study demonstrating that WBE is also effective in predicting predominant SARS-CoV-2 variant at facility-level, which is helpful to develop early-warning system for outbreak occurrence with predominant variant.

Decay rate estimation of respiratory viruses in aerosols and on surfaces under different environmental conditions.

Majority of the viral outbreaks are super-spreading events established within 2-10 h, dependent on a critical time interval for successful transmission between humans, which is governed by the decay rates of viruses. To evaluate the decay rates of respiratory viruses over a short span, we calculated their decay rate values for various surfaces and aerosols. We applied Bayesian regression and ridge regression and determined the best estimation for respiratory viruses, including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), severe acute respiratory syndrome coronavirus (SARS-CoV), middle east respiratory syndrome coronavirus (MERS-CoV), influenza viruses, and respiratory syncytial virus (RSV); the decay rate values in aerosols for these viruses were 4.83 ± 5.70, 0.40 ± 0.24, 0.11 ± 0.04, 2.43 ± 5.94, and 1.00 ± 0.50 h-1, respectively. The highest decay rate values for each virus type differed according to the surface type. According to the model performance criteria, the Bayesian regression model was better for SARS-CoV-2 and influenza viruses, whereas ridge regression was better for SARS-CoV and MERS-CoV. A simulation using a better estimation will help us find effective non-pharmaceutical interventions to control virus transmissions.

Temporal variations of human and animal Rotavirus A genotypes in surface water used for drinking water production.

Rotavirus is a major cause of gastroenteritis among infants and children. In this study, nested PCR assays were developed to amplify partial regions of the VP7, VP4, and VP6 genes of Rotavirus A (RVA) for amplicon-based Illumina MiSeq sequencing to investigate RVA genotypes in environmental water samples. Eight sets of inner primers were first designed and screened for use in the nested PCR assays, and four sets of them could produce amplicons. Six sets of outer primers were then designed and combined with the four sets of inner primers that worked. The assays were evaluated for sensitivity using raw water samples collected from one drinking water treatment plant between April 2019 and March 2020 (Sample Set 1; N = 12) and seven DWTPs between 2018 and 2020 (Sample Set 2; N = 18). In total, 43 amplicons from Set 1 were sequenced and diverse sequences from human, porcine, bovine, equine, and feline RVA were observed. Human G8, G3, and G2 genotypes were obtained, with G8 predominant (relative abundance, 36-87%) in samples taken during the rotavirus epidemic season between April and June. Porcine G5, G11, and G4, and bovine G10 and G6 genotypes were also detected. VP4 sequence analysis revealed that the human P[8] genotype was present throughout the year, whereas P[4] and P[9] were present only in the epidemic season. The vaccine strains P[5] and P[8] (RotaTeq®) were also detected. Our approach enables the identification of prevalent human and animal RVA genotypes and their host species that potentially caused fecal contamination in water sources.

The Intrapopulation Genetic Diversity of RNA Virus May Influence the Sensitivity of Chlorine Disinfection.

RNA virus populations are not clonal; rather, they comprise a mutant swarm in which sequences are slightly different from the master sequence. Genetic diversity within a population (intrapopulation genetic diversity) is critical for RNA viruses to survive under environmental stresses. Disinfection has become an important practice in the control of pathogenic viruses; however, the impact of intrapopulation genetic diversity on the sensitivity of disinfection, defined as -log10 (postdisinfected infectious titer/predisinfected titer), has not been elucidated. In this study, we serially passaged populations of rhesus rotavirus. We demonstrated that populations with reduced chlorine sensitivity emerged at random and independently of chlorine exposure. Sequencing analysis revealed that compared with sensitive populations, less-sensitive ones had higher non-synonymous genetic diversity of the outer capsid protein gene, suggesting that changes in the amino acid sequences of the outer capsid protein were the main factors influencing chlorine sensitivity. No common mutations were found among less-sensitive populations, indicating that rather than specific mutations, the diversity of the outer capsid protein itself was associated with the disinfection sensitivity and that the disinfection sensitivity changed stochastically. Simulation results suggest that the disinfection sensitivity of a genetically diverse population is destabilized if cooperative viral clusters including multiple sequences are formed. These results advocate that any prevention measures leading to low intrapopulation genetic diversity are important to prevent the spread and evolution of pathogenic RNA viruses in society.

Predictive water virology using regularized regression analyses for projecting virus inactivation efficiency in ozone disinfection.

Wastewater reclamation and reuse have been practically applied to water-stressed regions, but waterborne pathogens remaining in insufficiently treated wastewater are of concern. Sanitation Safety Planning adopts the hazard analysis and critical control point (HACCP) approach to manage human health risks upon exposure to reclaimed wastewater. HACCP requires a predetermined reference value (critical limit: CL) at critical control points (CCPs), in which specific parameters are monitored and recorded in real time. A disinfection reactor of a wastewater treatment plant (WWTP) is regarded as a CCP, and one of the CCP parameters is the disinfection intensity (e.g., initial disinfectant concentration and contact time), which is proportional to the log reduction value (LRV) of waterborne pathogens. However, the achievable LRVs are not always stable because the disinfection intensity is affected by water quality parameters, which vary among WWTPs. In this study, we established models for projecting virus LRVs using ozone, in which water quality and operational parameters were used as explanatory variables. For the model construction, we used five machine learning algorithms and found that automatic relevance determination with interaction terms resulted in better prediction performances for norovirus and rotavirus LRVs. Poliovirus and coxsackievirus LRVs were predicted well by a Bayesian ridge with interaction terms and lasso with quadratic terms, respectively. The established models were relatively robust to predict LRV using new datasets that were out of the range of the training data used here, but it is important to collect LRV datasets further to make the models more predictable and flexible for newly obtained datasets. The modeling framework proposed here can help WWTP operators and risk assessors determine the appropriate CL to protect human health in wastewater reclamation and reuse.

Hierarchical Bayesian modeling for predictive environmental microbiology toward a safe use of human excreta: Systematic review and meta-analysis.

The pathogen concentration in human excreta needs to be managed appropriately, but a predictive approach has yet to be implemented due to a lack of kinetics models for pathogen inactivation that are available under varied environmental conditions. Our goals were to develop inactivation kinetics models of microorganisms applicable under varied environmental conditions of excreta matrices and to identify the appropriate indicators that can be monitored during disinfection processes. We conducted a systematic review targeting previous studies that presented time-course decay of a microorganism and environmental conditions of matrices. Defined as a function of measurable factors including treatment time, pH, temperature, ammonia concentration and moisture content, the kinetic model parameters were statistically estimated using hierarchical Bayesian modeling. The inactivation kinetics models were constructed for Escherichia coli, Salmonella, Enterococcus, Ascaris eggs, bacteriophage MS2, enterobacteria phage phiX174 and adenovirus. The inactivation rates of a microorganism were predicted using the established model. Ascaris eggs were identified as the most tolerant microorganisms, followed by bacteriophage MS2 and Enterococcus. Ammonia concentration, temperature and moisture content were the critical factors for the Ascaris inactivation. Our model predictions coincided with the current WHO guidelines. The developed inactivation kinetics models enable us to predict microbial concentration in excreta matrices under varied environmental conditions, which is essential for microbiological risk management in emerging resource recovery practices from human excreta.

Bottleneck Size-Dependent Changes in the Genetic Diversity and Specific Growth Rate of a Rotavirus A Strain.

RNA viruses form a dynamic distribution of mutant swarms (termed "quasispecies") due to the accumulation of mutations in the viral genome. The genetic diversity of a viral population is affected by several factors, including a bottleneck effect. Human-to-human transmission exemplifies a bottleneck effect, in that only part of a viral population can reach the next susceptible hosts. In the present study, two lineages of the rhesus rotavirus (RRV) strain of rotavirus A were serially passaged five times at a multiplicity of infection (MOI) of 0.1 or 0.001, and three phenotypes (infectious titer, cell binding ability, and specific growth rate) were used to evaluate the impact of a bottleneck effect on the RRV population. The specific growth rate values of lineages passaged under the stronger bottleneck (MOI of 0.001) were higher after five passages. The nucleotide diversity also increased, which indicated that the mutant swarms of the lineages under the stronger bottleneck effect were expanded through the serial passages. The random distribution of synonymous and nonsynonymous substitutions on rotavirus genome segments indicated that almost all mutations were selectively neutral. Simple simulations revealed that the presence of minor mutants could influence the specific growth rate of a population in a mutant frequency-dependent manner. These results indicate a stronger bottleneck effect can create more sequence spaces for minor sequences.IMPORTANCE In this study, we investigated a bottleneck effect on an RRV population that may drastically affect the viral population structure. RRV populations were serially passaged under two levels of a bottleneck effect, which exemplified human-to-human transmission. As a result, the genetic diversity and specific growth rate of RRV populations increased under the stronger bottleneck effect, which implied that a bottleneck created a new space in a population for minor mutants originally existing in a hidden layer, which includes minor mutations that cannot be distinguished from a sequencing error. The results of this study suggest that the genetic drift caused by a bottleneck in human-to-human transmission explains the random appearance of new genetic lineages causing viral outbreaks, which can be expected according to molecular epidemiology using next-generation sequencing in which the viral genetic diversity within a viral population is investigated.

Sign-constrained linear regression for prediction of microbe concentration based on water quality datasets.

This study presents a novel methodology for estimating the concentration of environmental pollutants in water, such as pathogens, based on environmental parameters. The scientific uniqueness of this study is the prevention of excess conformity in the model fitting by applying domain knowledge, which is the accumulated scientific knowledge regarding the correlations between response and explanatory variables. Sign constraints were used to express domain knowledge, and the effect of the sign constraints on the prediction performance using censored datasets was investigated. As a result, we confirmed that sign constraints made prediction more accurate compared to conventional sign-free approaches. The most remarkable technical contribution of this study is the finding that the sign constraints can be incorporated in the estimation of the correlation coefficient in Tobit analysis. We developed effective and numerically stable algorithms for fitting a model to datasets under the sign constraints. This novel algorithm is applicable to a wide variety of the prediction of pollutant contamination level, including the pathogen concentrations in water.

Assays for the Specific Growth Rate and Cell-binding Ability of Rotavirus.

Rotavirus is the main etiological factor for infantile diarrhea. It is a double-stranded (ds) RNA virus and forms a genetically diverse population, known as quasispecies, owing to their high mutation rate. Here, we describe how to measure the specific growth rate and the cell-binding ability of rotavirus as its phenotypes. Rotavirus is treated with trypsin to recognize the cell receptor and then inoculated into MA104 cell culture. The supernatant, including viral progenies, is collected intermittently. The plaque assay is used to confirm the virus titer (plaque-forming unit: pfu) of each collected supernatant. The specific growth rate is estimated by fitting time-course data of pfu/mL to the modified Gompertz model. In the assay of cell-binding, MA104 cells in a 24-well plate are infected with rotavirus and incubated for 90 min at 4 °C for rotavirus adsorption to cell receptors. A low temperature restrains rotavirus from invading the host cell. After washing to remove unbound virions, RNA is extracted from virions attached to cell receptors followed by cDNA synthesis and reverse-transcription quantitative PCR (RT-qPCR). These protocols can be applied for investigating the phenotypic differences among viral strains.