Estimating actual SARS-CoV-2 infections from secondary data.

Eminent in pandemic management is accurate information on infection dynamics to plan for timely installation of control measures and vaccination campaigns. Despite huge efforts in diagnostic testing of individuals, the underestimation of the actual number of SARS-CoV-2 infections remains significant due to the large number of undocumented cases. In this paper we demonstrate and compare three methods to estimate the dynamics of true infections based on secondary data i.e., (a) test positivity, (b) infection fatality and (c) wastewater monitoring. The concept is tested with Austrian data on a national basis for the period of April 2020 to December 2022. Further, we use the results of prevalence studies from the same period to generate (upper and lower bounds of) credible intervals for true infections for four data points. Model parameters are subsequently estimated by applying Approximate Bayesian Computation-rejection sampling and Genetic Algorithms. The method is then validated for the case study Vienna. We find that all three methods yield fairly similar results for estimating the true number of infections, which supports the idea that all three datasets contain similar baseline information. None of them is considered superior, as their advantages and shortcomings depend on the specific case study at hand.

Decentralised system for demand-oriented collection of food waste - Assessment of biomethane potential, pathogen development and microbial community structure.

Enormous amounts of food waste (FW) are produced worldwide, requiring efficient disposal strategies, both economically and ecologically. Anaerobic digestion to produce biomethane is among the most promising strategies, but requires proper solutions for storage and delivery of the waste material. Here, a decentralized system for demand-oriented FW storage and its practical usability was assessed. FW was stored under batch and fed-batch strategies at 5 °C, 20 °C and 30 °C for 28 days. The results showed that FW can be stored without cooling since bacterially produced lactic acid rapidly stabilized the material and inactivated pathogens. While FW storage worked well under all storage conditions and strategies, 16S analysis revealed a distinct microbiota, which was highly characteristic for each storage temperature. Moreover, FW storage had no negative impact on methane yield and stored FW contained readily degradable substances for demand-oriented biogas production.

Expanding the Pathogen Panel in Wastewater Epidemiology to Influenza and Norovirus.

Since the start of the 2019 pandemic, wastewater-based epidemiology (WBE) has proven to be a valuable tool for monitoring the prevalence of SARS-CoV-2. With methods and infrastructure being settled, it is time to expand the potential of this tool to a wider range of pathogens. We used over 500 archived RNA extracts from a WBE program for SARS-CoV-2 surveillance to monitor wastewater from 11 treatment plants for the presence of influenza and norovirus twice a week during the winter season of 2021/2022. Extracts were analyzed via digital PCR for influenza A, influenza B, norovirus GI, and norovirus GII. Resulting viral loads were normalized on the basis of NH4-N. Our results show a good applicability of ammonia-normalization to compare different wastewater treatment plants. Extracts originally prepared for SARS-CoV-2 surveillance contained sufficient genomic material to monitor influenza A, norovirus GI, and GII. Viral loads of influenza A and norovirus GII in wastewater correlated with numbers from infected inpatients. Further, SARS-CoV-2 related non-pharmaceutical interventions affected subsequent changes in viral loads of both pathogens. In conclusion, the expansion of existing WBE surveillance programs to include additional pathogens besides SARS-CoV-2 offers a valuable and cost-efficient possibility to gain public health information.

Prediction of hospitalisations based on wastewater-based SARS-CoV-2 epidemiology.

Wastewater-based epidemiology is widely applied in Austria since April 2020 to monitor the SARS-CoV-2 pandemic. With a steadily increasing number of monitored wastewater facilities, 123 plants covering roughly 70 % of the 9 million population were monitored as of August 2022. In this study, the SARS-CoV-2 viral concentrations in raw sewage were analysed to infer short-term hospitalisation occupancy. The temporal lead of wastewater-based epidemiological time series over hospitalisation occupancy levels facilitates the construction of forecast models. Data pre-processing techniques are presented, including the approach of comparing multiple decentralised wastewater signals with aggregated and centralised clinical data. Time­lead quantification was performed using cross-correlation analysis and coefficient of determination optimisation approaches. Multivariate regression models were successfully applied to infer hospitalisation bed occupancy. The results show a predictive potential of viral loads in sewage towards Covid-19 hospitalisation occupancy, with an average lead time towards ICU and non-ICU bed occupancy between 14.8-17.7 days and 8.6-11.6 days, respectively. The presented procedure provides access to the trend and tipping point behaviour of pandemic dynamics and allows the prediction of short-term demand for public health services. The results showed an increase in forecast accuracy with an increase in the number of monitored wastewater treatment plants. Trained models are sensitive to changing variant types and require recalibration of model parameters, likely caused by immunity by vaccination and/or infection. The utilised approach displays a practical and rapidly implementable application of wastewater-based epidemiology to infer hospitalisation occupancy.

Effect of Growth Media on the Diversity of Neocallimastigomycetes from Non-Rumen Habitats.

Anaerobic fungi (AF), belonging to the phylum Neocallimastigomycota, are a pivotal component of the digestive tract microbiome of various herbivorous animals. In the last decade, the diversity of AF has rapidly expanded due to the exploration of numerous (novel) habitats. Studies aiming at understanding the role of AF require robust and reliable isolation and cultivation techniques, many of which remained unchanged for decades. Using amplicon sequencing, we compared three different media: medium with rumen fluid (RF), depleted rumen fluid (DRF), and no rumen fluid (NRF) to enrich the AF from the feces of yak, as a rumen control; and Przewalski's horse, llama, guanaco, and elephant, as a non-rumen habitats. The results revealed the selective enrichment of Piromyces and Neocallimastix from the feces of elephant and llama, respectively, in the RF medium. Similarly, the enrichment culture in DRF medium explicitly manifested Piromyces-related sequences from elephant feces. Five new clades (MM1-5) were defined from llama, guanaco, yak, and elephant feces that could as well be enriched from llama and elephant samples using non-conventional DRF and NRF media. This study presents evidence for the selective enrichment of certain genera in medium with RF and DRF from rumen as well as from non-rumen samples. NRF medium is suggested for the isolation of AF from non-rumen environments.

No time to die: Comparative study on preservation protocols for anaerobic fungi.

Anaerobic fungi (AF, phylum Neocallimastigomycota) are best known for their ability to anaerobically degrade recalcitrant lignocellulosic biomass through mechanic and enzymatic means. While their biotechnological potential is well-recognized, applied research on AF is still hampered by the time-consuming and cost-intensive laboratory routines required to isolate, maintain, and preserve AF cultures. Reliable long-term preservation of specific AF strains would aid basic as well as applied research, but commonly used laboratory protocols for AF preservation can show erratic survival rates and usually exhibit only moderate resuscitation success for up to one or two years after preservation. To address both, the variability, and the preservation issues, we have set up a cross-laboratory, year-long study. We tested five different protocols for the preservation of AF. The experiments were performed at three different laboratories (Austria, Germany, Switzerland) with the same three morphologically distinct AF isolates (Anaeromyces mucronatus, Caeocmyces sp., and Neocallimastix cameroonii) living in stable co-culture with their naturally occurring, syntrophic methanogens. We could show that handling greatly contributes to the variability of results, especially in Anaeromyces mucronatus. Cryopreservation of (mature) biomass in liquid nitrogen had the highest overall survival rates (85-100%, depending on the strain and laboratory). Additionally, preservation on agar at 39°C had surprisingly high survival rates for up to 9 months, if pieces of agar containing mature AF thalli were resuscitated. This low-cost, low-effort method could replace consecutive batch cultivation for periods of up to 6 months, while long-term preservation is best done by cryopreservation in liquid nitrogen. Regardless of the method, however, preserving several replicates (>three) of the same strain is highly advisable.

Data modelling recipes for SARS-CoV-2 wastewater-based epidemiology.

Wastewater based epidemiology is recognized as one of the monitoring pillars, providing essential information for pandemic management. Central in the methodology are data modelling concepts for both communicating the monitoring results but also for analysis of the signal. It is due to the fast development of the field that a range of modelling concepts are used but without a coherent framework. This paper provides for such a framework, focusing on robust and simple concepts readily applicable, rather than applying latest findings from e.g., machine learning. It is demonstrated that data preprocessing, most important normalization by means of biomarkers and equal temporal spacing of the scattered data, is crucial. In terms of the latter, downsampling to a weekly spaced series is sufficient. Also, data smoothing turned out to be essential, not only for communication of the signal dynamics but likewise for regressions, nowcasting and forecasting. Correlation of the signal with epidemic indicators requires multivariate regression as the signal alone cannot explain the dynamics but - for this case study - multiple linear regression proofed to be a suitable tool when the focus is on understanding and interpretation. It was also demonstrated that short term prediction (7 days) is accurate with simple models (exponential smoothing or autoregressive models) but forecast accuracy deteriorates fast for longer periods.

Viral variant-resolved wastewater surveillance of SARS-CoV-2 at national scale.

SARS-CoV-2 surveillance by wastewater-based epidemiology is poised to provide a complementary approach to sequencing individual cases. However, robust quantification of variants and de novo detection of emerging variants remains challenging for existing strategies. We deep sequenced 3,413 wastewater samples representing 94 municipal catchments, covering >59% of the population of Austria, from December 2020 to February 2022. Our system of variant quantification in sewage pipeline designed for robustness (termed VaQuERo) enabled us to deduce the spatiotemporal abundance of predefined variants from complex wastewater samples. These results were validated against epidemiological records of >311,000 individual cases. Furthermore, we describe elevated viral genetic diversity during the Delta variant period, provide a framework to predict emerging variants and measure the reproductive advantage of variants of concern by calculating variant-specific reproduction numbers from wastewater. Together, this study demonstrates the power of national-scale WBE to support public health and promises particular value for countries without extensive individual monitoring.

Microbial response on the first full-scale DEMON® biomass transfer for mainstream deammonification.

Sidestream partial nitritation and deammonification (pN/A) of high-strength ammonia wastewater is a well-established technology. Its expansion to the mainstream is, however mainly impeded by poor retention of anaerobic ammonia oxidizing bacteria (AnAOB), insufficient repression of nitrite oxidizing bacteria (NOB) and difficult control of soluble chemical oxygen demand and nitrite levels. At the municipal wastewater treatment plant in Strass (Austria) the microbial consortium was exhaustively monitored at full-scale over one and a half year with regular transfer of sidestream DEMON® biomass and further retention and enrichment of granular anammox biomass via hydrocyclone operation. Routine process parameters were surveyed and the response and evolution of the microbiota was followed by molecular tools, ex-situ activity tests and further, AnAOB quantification through particle tracking and heme measurement. After eight months of operation, the first anaerobic, simultaneous depletion of ammonia and nitrite was observed ex-situ, together with a direction to higher nitrite generation (68% of total NOx-N) as compared to nitrate under aerobic conditions. Our dissolved oxygen (DO) scheme allowed for transient anoxic conditions and had a strong influence on nitrite levels and the NOB community, where Nitrobacter eventually dominated Nitrospira. The establishment of a minor but stable AnAOB biomass was accompanied by the rise of Chloroflexi and distinct emergence of Chlorobi, a trend not seen in the sidestream system. Interestingly, the most pronounced switch in the microbial community and noticeable NOB repression occurred during unfavorable conditions, i.e. the cold winter season and high organic load. Further abatement of NOB was achieved through bioaugmentation of aerobic ammonia oxidizing bacteria (AerAOB) from the sidestream-DEMON® tank. Performance of the sidestream pN/A was not impaired by this operational scheme and the average volumetric nitrogen removal rate of the mainstream even doubled in the second half of the monitoring campaign. We conclude that a combination of both, regular sidestream-DEMON® biomass transfer and granular SRT increase via hydrocyclone operation was crucial for AnAOB establishment within the mainstream.

Quest for Optimal Regression Models in SARS-CoV-2 Wastewater Based Epidemiology.

Wastewater-based epidemiology is a recognised source of information for pandemic management. In this study, we investigated the correlation between a SARS-CoV-2 signal derived from wastewater sampling and COVID-19 incidence values monitored by means of individual testing programs. The dataset used in the study is composed of timelines (duration approx. five months) of both signals at four wastewater treatment plants across Austria, two of which drain large communities and the other two drain smaller communities. Eight regression models were investigated to predict the viral incidence under varying data inputs and pre-processing methods. It was found that population-based normalisation and smoothing as a pre-processing of the viral load data significantly influence the fitness of the regression models. Moreover, the time latency lag between the wastewater data and the incidence derived from the testing program was found to vary between 2 and 7 days depending on the time period and site. It was found to be necessary to take such a time lag into account by means of multivariate modelling to boost the performance of the regression. Comparing the models, no outstanding one could be identified as all investigated models are revealing a sufficient correlation for the task. The pre-processing of data and a multivariate model formulation is more important than the model structure.

Detection and Stability of SARS-CoV-2 Fragments in Wastewater: Impact of Storage Temperature.

SARS-CoV-2 wastewater epidemiology suffers from uncertainties concerning sample storage. We show the effect of the storage of wastewater on the detectable SARS-CoV-2 load. Storage at 4 °C for up to 9 days had no significant effect, while storage at -20 °C led to a significant reduction in gene copy numbers.

Data filtering methods for SARS-CoV-2 wastewater surveillance.

In the case of SARS-CoV-2 pandemic management, wastewater-based epidemiology aims to derive information on the infection dynamics by monitoring virus concentrations in the wastewater. However, due to the intrinsic random fluctuations of the viral signal in wastewater caused by several influencing factors that cannot be determined in detail (e.g. dilutions; number of people discharging; variations in virus excretion; water consumption per day; transport and fate processes in sewer system), the subsequent prevalence analysis may result in misleading conclusions. It is thus helpful to apply data filtering techniques to reduce the noise in the signal. In this paper we investigate 13 smoothing algorithms applied to the virus signals monitored in four wastewater treatment plants in Austria. The parameters of the algorithms have been defined by an optimization procedure aiming for performance metrics. The results are further investigated by means of a cluster analysis. While all algorithms are in principle applicable, SPLINE, Generalized Additive Model and Friedman's Super Smoother are recognized as superior methods in this context (with the latter two having a tendency to over-smoothing). A first analysis of the resulting datasets indicates the positive effect of filtering to the correlation of the viral signal to monitored incidence values.

Effect of biomass fly ashes from fast pyrolysis bio-oil production on soil properties and plant yield.

A globally increased demand for fuels and environmental concerns regarding fossil sources call for sustainable alternatives. Fast pyrolysis is a promising approach for converting different types of biomass to renewable Fast Pyrolysis Bio-Oil (FPBO) that can be used for heating, power generation and mobility. Side-products emerging from the process include low calorific gases and charcoal. Both are further combusted to generate energy for the process. From the charcoal, the process leaves behind fly ashes (FAs) that contain macro- and micronutrients. In this regard, FPBO-FAs might present valuable soil fertilizers, but also bear the risk of soil heavy metal (HM) contamination. In this study, the risk and potential benefit of FPBO-FAs derived from three different biomass sources (bark, forest residue and Miscanthus sp.) as soil amendments was tested. Twice, in autumn 2017 and 2018, FPBO-FAs were applied to the field (500 kg ash ha-1 y-1) in a grassland experiment. Neither physico-chemical and microbiological soil properties nor plant yield were affected following FPBO-FAs application. Seasonal differences and changes from year to year, however, were evident, both for some soil and plant properties. The lack of effects on (i) plant yield, (ii) soil microbiological and physicochemical properties, (iii) heavy metal concentrations in soil and plant suggest that the product may safely be applied. The fact that these field-trial results are in discordance with previous greenhouse trials suggest, however, that long-term trials would be needed.

The Molecular Information About Deadwood Bacteriomes Partly Depends on the Targeted Environmental DNA.

Microbiome studies mostly rely on total DNA extracts obtained directly from environmental samples. The total DNA consists of both intra- and extracellular DNA, which differ in terms of their ecological interpretation. In the present study, we have investigated for the first time the differences among the three DNA types using microbiome sequencing of Picea abies deadwood logs (Hunter decay classes I, III, and V). While the bacterial compositions of all DNA types were comparable in terms of more abundant organisms and mainly depended on the decay class, we found substantial differences between DNA types with regard to less abundant amplicon sequence variants (ASVs). The analysis of the sequentially extracted intra- and extracellular DNA fraction, respectively, increased the ecological depth of analysis compared to the directly extracted total DNA pool. Both DNA fractions were comparable in proportions and the extracellular DNA appeared to persist in the P. abies deadwood logs, thereby causing its masking effect. Indeed, the extracellular DNA masked the compositional dynamics of intact cells in the total DNA pool. Our results provide evidence that the choice of DNA type for analysis might benefit a study's answer to its respective ecological question. In the deadwood environment researched here, the differential analysis of the DNA types underlined the relevance of Burkholderiales, Rhizobiales and other taxa for P. abies deadwood decomposition and revealed that the role of Acidobacteriota under this scenario might be underestimated, especially compared to Actinobacteriota.

Impact of Processed Food (Canteen and Oil Wastes) on the Development of Black Soldier Fly (Hermetia illucens) Larvae and Their Gut Microbiome Functions.

Canteens represent an essential food supply hub for educational institutions, companies, and business parks. Many people in these locations rely on a guaranteed service with consistent quality. It is an ongoing challenge to satisfy the demand for sufficient serving numbers, portion sizes, and menu variations to cover food intolerances and different palates of customers. However, overestimating this demand or fluctuating quality of dishes leads to an inevitable loss of unconsumed food due to leftovers. In this study, the food waste fraction of canteen leftovers was identified as an optimal diet for black soldier fly (Hermetia illucens) larvae based on 50% higher consumption and 15% higher waste reduction indices compared with control chicken feed diet. Although the digestibility of food waste was nearly twice as high, the conversion efficiency of ingested and digested chicken feed remains unparalleled (17.9 ± 0.6 and 37.5 ± 0.9 in CFD and 7.9 ± 0.9 and 9.6 ± 1.0 in FWD, respectively). The oil separator waste fraction, however, inhibited biomass gain by at least 85% and ultimately led to a larval mortality of up to 96%. In addition to monitoring larval development, we characterized physicochemical properties of pre- and post-process food waste substrates. High-throughput amplicon sequencing identified Firmicutes, Proteobacteria, and Bacteroidota as the most abundant phyla, and Morganella, Acinetobacter, and certain Lactobacillales species were identified as indicator species. By using metagenome imputation, we additionally gained insights into the functional spectrum of gut microbial communities. We anticipate that the results will contribute to the development of decentralized waste-management sites that make use of larvae to process food waste as it has become common practice for biogas plants.

The masking effect of extracellular DNA and robustness of intracellular DNA in anaerobic digester NGS studies: A discriminatory study of the total DNA pool.

Most commonly, next generation sequencing-based microbiome studies are performed on the total DNA (totDNA) pool; however, this consists of extracellular- (exDNA) and intracellular (iDNA) DNA fractions. By investigating the microbiomes of different anaerobic digesters over time, we found that totDNA suggested lower species richness considering all and/or only common species and yielded fewer unique reads as compared to iDNA. Additionally, exDNA-derived sequences were more similar to those from totDNA than from iDNA and, finally, iDNA showed the best performance in tracking temporal changes in microbial communities. We postulate that abundant sequences present within the exDNA fraction mask the overall results of totDNA and provide evidence that exDNA has the potential to qualitatively bias microbiome studies at least in the anaerobic digester environment as it contains information about cells that were lysed hours or days ago. iDNA, however, was found to be more appropriate in providing reliable genetic information about potentially alive as well as rare microbes within the target habitat.

CoMA - an intuitive and user-friendly pipeline for amplicon-sequencing data analysis.

In recent years, there has been a veritable boost in next-generation sequencing (NGS) of gene amplicons in biological and medical studies. Huge amounts of data are produced and need to be analyzed adequately. Various online and offline analysis tools are available; however, most of them require extensive expertise in computer science or bioinformatics, and often a Linux-based operating system. Here, we introduce "CoMA-Comparative Microbiome Analysis" as a free and intuitive analysis pipeline for amplicon-sequencing data, compatible with any common operating system. Moreover, the tool offers various useful services including data pre-processing, quality checking, clustering to operational taxonomic units (OTUs), taxonomic assignment, data post-processing, data visualization, and statistical appraisal. The workflow results in highly esthetic and publication-ready graphics, as well as output files in standardized formats (e.g. tab-delimited OTU-table, BIOM, NEWICK tree) that can be used for more sophisticated analyses. The CoMA output was validated by a benchmark test, using three mock communities with different sample characteristics (primer set, amplicon length, diversity). The performance was compared with that of Mothur, QIIME and QIIME2-DADA2, popular packages for NGS data analysis. Furthermore, the functionality of CoMA is demonstrated on a practical example, investigating microbial communities from three different soils (grassland, forest, swamp). All tools performed well in the benchmark test and were able to reveal the majority of all genera in the mock communities. Also for the soil samples, the results of CoMA were congruent to those of the other pipelines, in particular when looking at the key microbial players.

Quantities of Intra- and Extracellular DNA Reveal Information About Activity and Physiological State of Methanogenic Archaea.

Although being a common aim of many microbial ecology studies, measuring individual physiological conditions of a microbial group or species within a complex consortium is still a challenge. Here, we propose a novel approach that is based on the quantification of sequentially extracted extracellular (exDNA) and intracellular DNA (iDNA) and reveals information about cell lysis and activity of methanogenic archaea within a biogas-producing microbial community. We monitored the methane production rates of differently treated batch anaerobic cultures and compared the concentrations of the alpha subunit of the methyl coenzyme M reductase gene of methanogenic archaea in extracellular and intracellular DNA fractions and in the classically extracted total DNA pool. Our results showed that this fine-tuned DNA approach coupled with the interpretation of the ratio between free exDNA and iDNA considerably improved microbial activity tracking compared to the classical extraction/quantification of total DNA. Additionally, it allowed to identify and quantify methanogenic populations that are inactive and those that are strongly influenced by cell lysis. We argue that despite the need of further studies, this method represents a novel approach to gain specific physiological information from a complex environmental sample and holds the potential to be applied to other microbes of interest.