Wastewater-Based Estimation of the Effective Reproductive Number of SARS-CoV-2.

BACKGROUND: The effective reproductive number, Re, is a critical indicator to monitor disease dynamics, inform regional and national policies, and estimate the effectiveness of interventions. It describes the average number of new infections caused by a single infectious person through time. To date, Re estimates are based on clinical data such as observed cases, hospitalizations, and/or deaths. These estimates are temporarily biased when clinical testing or reporting strategies change. OBJECTIVES: We show that the dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA in wastewater can be used to estimate Re in near real time, independent of clinical data and without the associated biases. METHODS: We collected longitudinal measurements of SARS-CoV-2 RNA in wastewater in Zurich, Switzerland, and San Jose, California, USA. We combined this data with information on the temporal dynamics of shedding (the shedding load distribution) to estimate a time series proportional to the daily COVID-19 infection incidence. We estimated a wastewater-based Re from this incidence. RESULTS: The method to estimate Re from wastewater worked robustly on data from two different countries and two wastewater matrices. The resulting estimates were as similar to the Re estimates from case report data as Re estimates based on observed cases, hospitalizations, and deaths are among each other. We further provide details on the effect of sampling frequency and the shedding load distribution on the ability to infer Re. DISCUSSION: To our knowledge, this is the first time Re has been estimated from wastewater. This method provides a low-cost, rapid, and independent way to inform SARS-CoV-2 monitoring during the ongoing pandemic and is applicable to future wastewater-based epidemiology targeting other pathogens. https://doi.org/10.1289/EHP10050.

High-Frequency, High-Throughput Quantification of SARS-CoV-2 RNA in Wastewater Settled Solids at Eight Publicly Owned Treatment Works in Northern California Shows Strong Association with COVID-19 Incidence.

A number of recent retrospective studies have demonstrated that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA concentrations in wastewater are associated with coronavirus disease 2019 (COVID-19) cases in the corresponding sewersheds. Implementing high-resolution, prospective efforts across multiple plants depends on sensitive measurements that are representative of COVID-19 cases, scalable for high-throughput analysis, and comparable across laboratories. We conducted a prospective study across eight publicly owned treatment works (POTWs). A focus on SARS-CoV-2 RNA in solids enabled us to scale up our measurements with a commercial lab partner. Samples were collected daily, and results were posted to a website within 24 h. SARS-CoV-2 RNA in daily samples correlated with the incidence of COVID-19 cases in the sewersheds; a 1 log10 increase in SARS-CoV-2 RNA in settled solids corresponds to a 0.58 log10 (4×) increase in sewershed incidence rate. SARS-CoV-2 RNA signals measured with the commercial laboratory partner were comparable across plants and comparable to measurements conducted in a university laboratory when normalized by pepper mild mottle virus (PMMoV) RNA. Results suggest that SARS-CoV-2 RNA should be detectable in settled solids for COVID-19 incidence rates of >1/100,000 (range, 0.8 to 2.3 cases per 100,000). These sensitive, representative, scalable, and comparable methods will be valuable for future efforts to scale up wastewater-based epidemiology. IMPORTANCE Access to reliable, rapid monitoring data is critical to guide response to an infectious disease outbreak. For pathogens that are shed in feces or urine, monitoring wastewater can provide a cost-effective snapshot of transmission in an entire community via a single sample. In order for a method to be useful for ongoing COVID-19 monitoring, it should be sensitive for detection of low concentrations of SARS-CoV-2, representative of incidence rates in the community, scalable to generate data quickly, and comparable across laboratories. This paper presents a method utilizing wastewater solids to meet these goals, producing measurements of SARS-CoV-2 RNA strongly associated with COVID-19 cases in the sewershed of a publicly owned treatment work. Results, provided within 24 h, can be used to detect incidence rates as low as approximately 1/100,000 cases and can be normalized for comparison across locations generating data using different methods.

Combinatorial Library Screening with Liposomes for Discovery of Membrane Active Peptides.

Membrane active peptides (MAPs) represent a class of short biomolecules that have shown great promise in facilitating intracellular delivery without disrupting cellular plasma membranes. Yet their clinical application has been stalled by numerous factors: off-target delivery, a requirement for high local concentration near cells of interest, degradation en route to the target site, and in the case of cell-penetrating peptides, eventual entrapment in endolysosomal compartments. The current method of deriving MAPs from naturally occurring proteins has restricted the discovery of new peptides that may overcome these limitations. Here, we describe a new branch of assays featuring high-throughput functional screening capable of discovering new peptides with tailored cell uptake and endosomal escape capabilities. The one-bead-one-compound (OBOC) combinatorial method is used to screen libraries containing millions of potential MAPs for binding to synthetic liposomes, which can be adapted to mimic various aspects of limiting membranes. By incorporating unnatural and d-amino acids in the library, in addition to varying buffer conditions and liposome compositions, we have identified several new highly potent MAPs that improve on current standards and introduce motifs that were previously unknown or considered unsuitable. Since small variations in pH and lipid composition can be controlled during screening, peptides discovered using this methodology could aid researchers building drug delivery platforms with unique requirements, such as targeted intracellular localization.

Targeting Tumor-Associated Exosomes with Integrin-Binding Peptides.

All cells expel a variety of nano-sized extracellular vesicles (EVs), including exosomes, with composition reflecting the cells' biological state. Cancer pathology is dramatically mediated by EV trafficking via key proteins, lipids, metabolites, and microRNAs. Recent proteomics evidence suggests that tumor-associated exosomes exhibit distinct expression of certain membrane proteins, rendering those proteins as attractive targets for diagnostic or therapeutic application. Yet, it is not currently feasible to distinguish circulating EVs in complex biofluids according to their tissue of origin or state of disease. Here we demonstrate peptide binding to tumor-associated EVs via overexpressed membrane protein. We find that SKOV-3 ovarian tumor cells and their released EVs express α3ß1 integrin, which can be targeted by our in-house cyclic nonapeptide, LXY30. After measuring bulk SKOV-3 EV association with LXY30 by flow cytometry, Raman spectral analysis of laser-trapped single exosomes with LXY30-dialkyne conjugate enabled us to differentiate cancer-associated exosomes from non-cancer exosomes. Furthermore, we introduce the foundation for a highly specific detection platform for tumor-EVs in solution with biosensor surface-immobilized LXY30. LXY30 not only exhibits high specificity and affinity to α3ß1 integrin-expressing EVs, but also reduces EV uptake into SKOV-3 parent cells, demonstrating the possibility for therapeutic application.

Single exosome study reveals subpopulations distributed among cell lines with variability related to membrane content.

Current analysis of exosomes focuses primarily on bulk analysis, where exosome-to-exosome variability cannot be assessed. In this study, we used Raman spectroscopy to study the chemical composition of single exosomes. We measured spectra of individual exosomes from 8 cell lines. Cell-line-averaged spectra varied considerably, reflecting the variation in total exosomal protein, lipid, genetic, and cytosolic content. Unexpectedly, single exosomes isolated from the same cell type also exhibited high spectral variability. Subsequent spectral analysis revealed clustering of single exosomes into 4 distinct groups that were not cell-line specific. Each group contained exosomes from multiple cell lines, and most cell lines had exosomes in multiple groups. The differences between these groups are related to chemical differences primarily due to differing membrane composition. Through a principal components analysis, we identified that the major sources of spectral variation among the exosomes were in cholesterol content, relative expression of phospholipids to cholesterol, and surface protein expression. For example, exosomes derived from cancerous versus non-cancerous cell lines can be largely separated based on their relative expression of cholesterol and phospholipids. We are the first to indicate that exosome subpopulations are shared among cell types, suggesting distributed exosome functionality. The origins of these differences are likely related to the specific role of extracellular vesicle subpopulations in both normal cell function and carcinogenesis, and they may provide diagnostic potential at the single exosome level.

3D plasmonic nanobowl platform for the study of exosomes in solution.

Thin silver film coated nanobowl Surface Enhanced Raman Spectroscopy (SERS) substrates are used to capture exosomes in solution for SERS measurements that can provide biochemical analysis of intact and ruptured exosomes. Exosomes derived via Total Exosome Isolation Reagent (TEIR) as well as ultracentrifugation (UC) from the SKOV3 cell line were analyzed. Spectra of exosomes derived via TEIR are dominated by a signal characteristic for the TEIR kit that needs to be subtracted for all measurements. Differences in SERS spectra recorded at different times during the drying of the exosome solution are statistically analyzed with Principal Component Analysis (PCA). At the beginning of the drying process, SERS spectra of exosomes exhibit peaks characteristic for both lipids and proteins. Later on during the drying process, new SERS peaks develop, suggesting that the initially intact exosome ruptures over time. This time-dependent evolution of SERS peaks enables analysis of exosomal membrane contents and the contents inside the exosomes.