Response of Wastewater-Based Epidemiology Predictor for the Second Wave of COVID-19 in Ahmedabad, India: A Long-term Data Perspective

Wastewater-based epidemiology (WBE) monitoring can play a key role in managing future pandemics because it covers both pre-symptomatic and asymptomatic cases, especially in densely populated areas with limited community health care. In the present work, wastewater monitoring was employed in Ahmedabad, India, after the successful containment of the first wave of COVID-19 to predict resurgence of the disease in the expected second wave of the pandemic. Here we show wastewater levels of COVID-19 virus particles (i.e., SARS-CoV-2) positively correlated with the number of confirmed clinical cases during the first wave, and provided early detection of COVID-19 presence before the second wave in Ahmedabad and an WBE-based city zonation plan was developed for health protection. A eight-month data of Surveillance of Wastewater for Early Epidemic Prediction (SWEEP) was gathered, including weekly SARS-CoV-2 RNA wastewater analysis (n=287) from nine locations between September 2020 and April 2021. Across this period, 258 out of 287 samples were positive for least two out of three SARS-CoV-2 genes (N, ORF 1ab, and S). Monitoring showed a substantial decline in all three gene markers between October and September 2020, followed by an abrupt increase in November 2020. Similar changes were seen in March 2021, which preceded the second COVID-19 wave. Measured wastewater ORF-1ab gene copies ranged from 6.1 x 102 (October, 2020) to 1.4 x 104 (November, 2020) copies/mL, and wastewater gene levels typically lead confirmed cases by one to two weeks. The study highlights the value of WBE as a monitoring tool to predict waves within a pandemic, identifying local disease hotspots within a city and guiding rapid management interventions. HighlightsO_LIEight-months of SARS-CoV-2 gene variations explicitly predicts 2nd COVID-19 wave. C_LIO_LI258 out of 287 wastewater samples were positive for SARS-CoV-2 genes. C_LIO_LIWBE offers a lead time of 1-2 weeks relative to clinical cases. C_LIO_LIModel suggests that ORF 1ab gene is the most effective as a marker gene in WBE study. C_LIO_LIWBE RT-PCR screening for pathogens should be mandatory for global health monitoring. C_LI

Understanding community level influences on COVID-19 prevalence in England: New insights from comparison over time and space

The COVID-19 pandemic has impacted communities far and wide and put tremendous pressure on healthcare systems of countries across the globe. Understanding and monitoring the major influences on COVID-19 prevalence is essential to inform policy making and device appropriate packages of non-pharmaceutical interventions (NPIs). This study evaluates community level influences on COVID-19 incidence in England and their variations over time with specific focus on understanding the impact of working in so called high-risk industries such as care homes and warehouses. Analysis at community level allows accounting for interrelations between socioeconomic and demographic profile, land use, and mobility patterns including residents self-selection and spatial sorting (where residents choose their residential locations based on their travel attitudes and preferences or social structure and inequality); this also helps understand the impact of policy interventions on distinct communities and areas given potential variations in their mobility, vaccination rates, behavioural responses, and health inequalities. Moreover, community level analysis can feed into more detailed epidemiological and individual models through tailoring and directing policy questions for further investigation. We have assembled a large set of static (socioeconomic and demographic profile and land use characteristics) and dynamic (mobility indicators, COVID-19 cases and COVID-19 vaccination uptake in real time) data for small area statistical geographies (Lower Layer Super Output Areas, LSOA) in England making the dataset, arguably, the most comprehensive set assembled in the UK for community level analysis of COVID-19 infection. The data are integrated from a wider range of sources including telecommunications companies, test and trace data, national travel survey, Census and Mid-Year estimates. To tackle methodological challenges specifically accounting for highly interrelated influences, we have augmented different statistical and machine learning techniques. We have adopted a two-stage modelling framework: a) Latent Cluster Analysis (LCA) to classify the country into distinct land use and travel patterns, and b) multivariate linear regression to evaluate influences at each distinct travel cluster. We have also segmented our data into different time periods based on changes in policies and evolvement in the course of pandemic (such as the emergence of a new variant of the virus). By segmenting and comparing influences across spaces and time, we examine more homogeneous behaviour and uniform distribution of infection risks which in turn increase the potential to make causal inferences and help understand variations across communities and over time. Our findings suggest that there exist significant spatial variations in risk influences with some being more consistent and persistent over time. Specifically, the analysis of industrial sectors shows that communities of workers in care homes and warehouses and to a lesser extent textile and ready meal industries tend to carry a higher risk of infection across all spatial clusters and over the whole period we modelled in this study. This demonstrates the key role that workplace risk has to play in COVID-19 risk of outbreak after accounting for the characteristics of workers residential area (including socioeconomic and demographic profile and land use features), vaccination rate, and mobility patterns.

A simple and quick PCR based method for detection of Omicron variant of SARS-CoV-2

SARS-CoV-2 pandemic has changed the global landscape since last two years. Against many challenges posed by the COVID-19 pandemic to the humanity, the pace of solutions created by mankind is exemplary; diagnostics, vaccines, alternate therapies, to name a few. With a rapidly changing virus strain, its early identification in the community can be a quick solution to trace the individuals and thus control its spread. This paper describes PCR based quick method for differentiation of Omicron variant of SARS-CoV-2 from other variants. Timely identification of this new variant will enable better management of pandemic control in the population. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=104 SRC="FIGDIR/small/21268053v1_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@7a5e9forg.highwire.dtl.DTLVardef@1da2339org.highwire.dtl.DTLVardef@3eab87org.highwire.dtl.DTLVardef@6f3258_HPS_FORMAT_FIGEXP M_FIG C_FIG

Defective ORF8 dimerization in delta variant of SARS CoV2 leads to abrogation of ORF8 MHC-I interaction and overcome suppression of adaptive immune response

In India, the breakthrough infections during second wave of COVID-19 pandemic was due to SARS-COV-2 delta variant (B.1.617.2). It was reported that majority of the infections were caused by the delta variant and only 9.8% percent cases required hospitalization whereas, only 0.4% fatality was observed. Sudden dropdown in COVID-19 infections was observed within a short timeframe, suggesting better host adaptation with evolved delta variant. Down regulation of host immune response against SARS-CoV-2 by ORF8 induced MHC-I degradation has been reported earlier. The Delta variant carried mutations (deletion) at Asp119 and Phe120 amino acids which are critical for ORF8 dimerization. The deletions of amino acids Asp119 and Phe120 in ORF8 of delta variant results in structural instability of ORF8 dimer caused by disruption of hydrogen bonding and salt bridges as revealed by structural analysis and MD simulation studies of ORF8 dimer. Further, flexible docking of wild type and mutant ORF8 dimer revealed reduced interaction of mutant ORF8 dimer with MHC-I as compared to wild type ORF8 dimer with MHC-1, thus implicating its possible role in MHC-I expression and host immune response against SARS-CoV-2. We thus propose that mutant ORF8 may not hindering the MHC-I expression thereby resulting in better immune response against SARS-CoV-2 delta variant, which partly explains the sudden drop of SARS-CoV-2 infection rate in the second wave of SARS-CoV-2 predominated by delta variant in India Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/457457v1_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@751eeaorg.highwire.dtl.DTLVardef@140b5b5org.highwire.dtl.DTLVardef@159a3a5org.highwire.dtl.DTLVardef@6c206_HPS_FORMAT_FIGEXP M_FIG C_FIG

Genomic epidemiology of early SARS-CoV-2 transmission dynamics in Gujarat, India

Genomic surveillance of SARS-CoV-2 has played a decisive role in understanding the transmission and evolution of the virus during its emergence and continued circulation. However, limited genomic sampling in many high-incidence countries has impeded detailed studies of SARS-CoV-2 genomic epidemiology. Consequently, critical questions remain about the generation and global distribution of virus genetic diversity. To address this gap, we investigated SARS-CoV-2 transmission dynamics in Gujarat, India, during its first epidemic wave and shed light on virus spread in one of the pandemics hardest-hit regions. By integrating regional case data and 434 whole virus genome sequences sampled across 20 districts from March to July 2020, we reconstructed the epidemic dynamics and spatial spread of SARS-CoV-2 in Gujarat, India. Our findings revealed that global and regional connectivity, along with population density, were significant drivers of the Gujarat SARS-CoV-2 outbreak. The three most populous districts in Gujarat accounted [~]84% of total cases during the first wave. Moreover, we detected over 100 virus lineage introductions, which were primarily associated with international travel. Within Gujarat, virus dissemination occurred predominantly from densely populated regions to geographically proximate locations with low-population density. Our findings suggest SARS-CoV-2 transmission follows a gravity model in India, with urban centres contributing disproportionately to onward virus spread.

E156/G and Arg158, Phe-157/del mutation in NTD of spike protein in B.1.167.2 lineage of SARS-CoV-2 leads to immune evasion through antibody escape

Emerging variants of SARS-CoV-2 with better immune escape mechanisms and higher transmissibility remains a persistent threat across the globe. B.1.617.2 (Delta) variant was first emerged from Maharashtra, India in December, 2020. This variant is classified to be a major cause and concern of the second wave of COVID-19 in India. In the present study, we explored the genomic and structural basis of this variant through computational analysis, protein modelling and molecular dynamics (MD) simulations approach. B.1.617.2 variant carried E156G and Arg158, Phe-157/del mutations in NTD of spike protein. These mutations in N-terminal domain (NTD) of spike protein of B.1.617.2 variant revealed more rigidity and reduced flexibility compared to spike protein of Wuhan isolate. Further, docking and MD simulation study with 4A8 monoclonal antibody which was reported to bind NTD of spike protein suggested reduced binding of B.1.617.2 spike protein compared to that of spike protein of Wuhan isolate. The results of the present study demonstrate the possible case of immune escape and thereby fitness advantage of the new variant and further warrants demonstration through experimental evidence. Our study identified the probable mechanism through which B.1.617.2 variant is more pathogenically evolved with higher transmissibility as compared to the wild-type. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=93 SRC="FIGDIR/small/447321v3_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@b92cborg.highwire.dtl.DTLVardef@1d261f7org.highwire.dtl.DTLVardef@11da73eorg.highwire.dtl.DTLVardef@1cef6ca_HPS_FORMAT_FIGEXP M_FIG C_FIG

In-Silico analysis reveals lower transcription efficiency of C241T variant of SARS-CoV-2 with host replication factors MADP1 and hnRNP-1

Novel severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) has claimed more than 1.5 million lives worldwide and counting. As per the GISAID database, the genomics of SARS-CoV2 is extensively studied with more than 500 genome submissions per day. Out of several hotspot mutations within the SARS-CoV-2 genome, researchers have focused a lot on missense variants but the least work is done on the UTRs. One of the most frequent 5 UTR variants in the SARS-CoV-2 genome is the C241T with a global frequency of more than 0.9. In the present study, the effect of the C241T mutation has been studied with respect to change in RNA structure and its interaction with the host replication factors MADP1 Zinc finger CCHC-type and RNA-binding motif 1 (hnRNP1). The results obtained from molecular docking and molecular dynamics simulation indicated weaker interaction of C241T mutant stem loops with host transcription factor MADP1 indicating reduced replication efficiency. The results are also correlated with increased recovery rates and decreased death rates of global SARS-CoV-2 cases.

Genomic variations in SARS-CoV-2 genomes from Gujarat: Underlying role of variants in disease epidemiology

Humanity has seen numerous pandemics during its course of evolution. The list includes many such as measles, Ebola, SARS, MERS, etc. Latest edition to this pandemic list is COVID-19, caused by the novel coronavirus, SARS-CoV-2. As of 4th July 2020, COVID-19 has affected over 10 million people from 170+ countries, and 5,28,364 deaths. Genomic technologies have enabled us to understand the genomic constitution of the pathogens, their virulence, evolution, rate of mutations, etc. To date, more than 60,000 virus genomes have been deposited in the public depositories like GISAID and NCBI. While we are writing this, India is the 3rd most-affected country with COVID-19 with 0.6 million cases, and >18000 deaths. Gujarat is the fourth highest affected state with 5.44 percent death rate compared to national average of 2.8 percent. Here, 361 SARS-CoV-2 genomes from across Gujarat have been sequenced and analyzed in order to understand its phylogenetic distribution and variants against global and national sequences. Further, variants were analyzed from diseased and recovered patients from Gujarat and the World to understand its role in pathogenesis. From missense mutations, found from Gujarat SARS-CoV-2 genomes, C28854T, deleterious mutation in nucleocapsid (N) gene was found to be significantly associated with mortality in patients. The other significant deleterious variant found in diseased patients from Gujarat and the world is G25563T, which is located in Orf3a and has a potential role in viral pathogenesis. SARS-CoV-2 genomes from Gujarat are forming distinct cluster under GH clade of GISAID.

Identification of unique mutations in SARS-CoV-2 strains isolated from India suggests its attenuated pathotype

Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), which was first reported in Wuhan, China in November 2019 has developed into a pandemic since March 2020, causing substantial human casualties and economic losses. Studies on SARS-CoV-2 are being carried out at an unprecedented rate to tackle this threat. Genomics studies, in particular, are indispensable to elucidate the dynamic nature of the RNA genome of SARS-CoV-2. RNA viruses are marked by their unique ability to undergo high rates of mutation in their genome, much more frequently than their hosts, which diversifies their strengths qualifying them to elude host immune response and amplify drug resistance. In this study, we sequenced and analyzed the genomic information of the SARS-CoV-2 isolates from two infected Indian patients and explored the possible implications of point mutations in its biology. In addition to multiple point mutations, we found a remarkable similarity between relatively common mutations of 36-nucleotide deletion in ORF8 of SARS-CoV-2. Our results corroborate with the earlier reported 29-nucleotide deletion in SARS, which was frequent during the early stage of human-to-human transmission. The results will be useful to understand the biology of SARS-CoV-2 and itsattenuation for vaccine development.

Saliva as a non-invasive sample for the detection of SARS-CoV-2: a systematic review

The accepted gold standard for diagnosing coronavirus disease (COVID-19) is the detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA from nasopharyngeal swabs (NPS). However, shortage of reagents has made NPS collection challenging, and alternative samples need to be explored. Due to its non-invasive nature, saliva has considerable diagnostic potential. Therefore, to guide diagnostic laboratories globally, we conducted a systematic review to determine the utility of saliva for the detection of SARS-CoV-2. A systematic search of major databases (PubMed, ISI Web of Science, Scopus, and Google Scholar) was performed to identify published studies in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. There was a total of 10 publications that fit the criteria for review. Most studies collected drooled whole saliva from hospitalized patients or pipetted saliva from intubated patients. Saliva was positive in 31-92% of patients depending on the cohort and length of hospitalization. Viral loads in saliva are comparable to those in NPS and ranged from 9.9 x 102 to 1.2 x 108 copies/mL during the first week of symptoms and decrease over time. Saliva can be positive up to 20 days post-symptom onset with viral loads correlating with symptom severity and degree of tissue damage. Based on these findings, we made suggestions to guide the clinical laboratory and suggest the need for diagnostic accuracy studies for the detection of SARS-CoV-2 from saliva.

Insight into the transcriptome of Arthrobotrys conoides using high throughput sequencing.

Arthrobotrys conoides is a nematode-trapping fungus belonging to Orbiliales, Ascomycota group, and traps prey nematodes by means of adhesive network. Fungus has a potential to be used as a biocontrol agent against plant parasitic nematodes. In the present study, we characterized the transcriptome of A. conoides using high-throughput sequencing technology and characterized its virulence unigenes. Total 7,255 cDNA contigs with an average length of 425 bp were generated and 6184 (61.81%) transcripts were functionally annotated and characterized. Majority of unigenes were found analogous to the genes of plant pathogenic fungi. A total of 1749 transcripts were found to be orthologous with eukaryotic proteins of KOG database. Several carbohydrate active enzymes and peptidases were identified. We also analyzed classically and nonclassically secreted proteins and confirmed by BLASTP against fungal secretome database. A total of 916 contigs were analogous to 556 unique proteins of Pathogen Host Interaction (PHI) database. Further, we identified 91 unigenes homologous to the database of fungal virulence factor (DFVF). A total of 104 putative protein kinases coding transcripts were identified by BLASTP against KinBase database, which are major players in signaling pathways. This study provides a comprehensive look at the transcriptome of A. conoides and the identified unigenes might have a role in catching and killing prey nematodes by A. conoides.