SummaryUnderstanding the molecular basis of zinc (Zn) uptake and transport in staple cereal crops is critical for improving both Zn content and tolerance to low‐Zn soils. This study demonstrates the importance of group F bZIP transcription factors and ZIP transporters in responses to Zn deficiency in wheat (Triticum aestivum). Seven group F TabZIP genes and 14 ZIPs with homeologs were identified in hexaploid wheat. Promoter analysis revealed the presence of Zn‐deficiency‐response elements (ZDREs) in a number of the ZIPs. Functional complementation of the zrt1/zrt2 yeast mutant by TaZIP3, ‐6, ‐7, ‐9 and ‐13 supported an ability to transport Zn. Group F TabZIPs contain the group‐defining cysteine–histidine‐rich motifs, which are the predicted binding site of Zn2+ in the Zn‐deficiency response. Conservation of these motifs varied between the TabZIPs suggesting that individual TabZIPs may have specific roles in the wheat Zn‐homeostatic network. Increased expression in response to low Zn levels was observed for several of the wheat ZIPs and bZIPs; this varied temporally and spatially suggesting specific functions in the response mechanism. The ability of the group F TabZIPs to bind to specific ZDREs in the promoters of TaZIPs indicates a conserved mechanism in monocots and dicots in responding to Zn deficiency. In support of this, TabZIPF1‐7DL and TabZIPF4‐7AL afforded a strong level of rescue to the Arabidopsis hypersensitive bzip19 bzip23 double mutant under Zn deficiency. These results provide a greater understanding of Zn‐homeostatic mechanisms in wheat, demonstrating an expanded repertoire of group F bZIP transcription factors, adding to the complexity of Zn homeostasis.

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Accurate surveillance of the COVID-19 pandemic can be weakened by under-reporting of cases, particularly due to asymptomatic or pre-symptomatic infections, resulting in bias. Quantification of SARS-CoV-2 RNA in wastewater can be used to infer infection prevalence, but uncertainty in sensitivity and considerable variability has meant that accurate measurement remains elusive. Here, we use data from 45 sewage sites in England, covering 31% of the population, and estimate SARS-CoV-2 prevalence to within 1.1% of estimates from representative prevalence surveys (with 95% confidence). Using machine learning and phenomenological models, we show that differences between sampled sites, particularly the wastewater flow rate, influence prevalence estimation and require careful interpretation. We find that SARS-CoV-2 signals in wastewater appear 4–5 days earlier in comparison to clinical testing data but are coincident with prevalence surveys suggesting that wastewater surveillance can be a leading indicator for symptomatic viral infections. Surveillance for viruses in wastewater complements and strengthens clinical surveillance, with significant implications for public health.

Faecal shedding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its subsequent detection in wastewater turned the spotlight onto wastewater-based epidemiology (WBE) for monitoring the coronavirus-disease 2019 (COVID-19) pandemic. WBE for SARS-CoV-2 has been deployed in 70 countries, providing insights into disease prevalence, forecasting and the spatiotemporal tracking and emergence of SARS-CoV-2 variants. Wastewater, however, is a complex sample matrix containing numerous reverse transcription quantitative PCR (RT-qPCR) inhibitors whose concentration and diversity are influenced by factors including population size, surrounding industry and agriculture and climate. Such differences in the RT-qPCR inhibitor profile are likely to impact the quality of data produced by WBE and potentially produce erroneous results.To help determine the possible impact of RT-qPCR assay on data quality, two assays employed by different laboratories within the UK’s SARS-CoV-2 wastewater monitoring programme were assessed in the Cefas laboratory in Weymouth, UK. The assays were based on Fast Virus (FV) and qScript (qS) chemistries using the same primers and probes, but at different concentrations and under different cycling conditions. Bovine serum albumin and MgSO4 were also added to the FV assay reaction mixture. Two-hundred and eighty-six samples were analysed, and an external control RNA (EC RNA)-based method was used to measure RT-qPCR inhibition. Compared with qS, FV showed a 40.5% reduction in mean inhibition and a 57.0% reduction in inter-sample inhibition variability. A 4.1-fold increase in SARS-CoV-2 quantification was seen for FV relative to qS; partially due (1.5-fold) to differences in reverse transcription efficiency and the use of a dsDNA standard. Analytical variability was reduced by 51.2% using FV while qS increased the number of SARS-CoV-2 negative samples by 2.6-fold. This study indicates the importance of thorough method optimisation for RT-qPCR-based WBE which should be performed using a selection of samples which are representative of the physiochemical properties of wastewater. Furthermore, RT-qPCR inhibition, analytical variability and reverse transcription efficiency should be key considerations during assay optimisation. A standardised framework for the optimisation and validation of WBE procedures should be formed including concessions for emergency response situations that would allow flexibility in the process to address the difficult balance between the urgency of providing data and the availability of resources.