Analysis of a large SARS-CoV-2 (Alpha) outbreak in a Catalan prison using conventional and genomic epidemiology.

Enforcing strict protocols that prevent transmission of airborne infections in prisons is challenging. We examine a large SARS-CoV-2 outbreak in a Catalan penitentiary center from February-March 2021, prior to vaccination deployment. The aim was to describe the evolution of the outbreak using classical and genomic epidemiology and the containment strategy applied. The outbreak was initially detected in one module but spread to four, infecting 7 staff members and 140 incarcerated individuals, 6 of whom were hospitalized (4.4%). Genomic analysis confirmed a single origin (B.1.1.7). Contact tracing identified transmission vectors between modules and prevented further viral spread. In future similar scenarios, the control strategy described here may help limiting transmission of airborne infections in correctional settings.

Usefulness of dried blood spot samples for monitoring hepatitis C treatment outcome and reinfection among people who inject drugs in a test-and-treat program.

Dried blood spots (DBS) are a reliable tool to diagnose viremic hepatitis C virus (HCV) infection. We evaluated the clinical performance of a DBS-based molecular assay for the assessment of cure and reinfection after on-site treatment at a harm reduction center (HRC). Genotyping from DBS samples was also assessed to discriminate reinfection from treatment failure. People who inject drugs (PWID) from an ongoing test-and-treat pilot at the largest HRC in Barcelona were included in the study. HCV-RNA detection from DBS collected after treatment (with follow-up at 12, 36, and 60 weeks) was compared with a molecular point-of-care test using finger-stick blood (GeneXpert). Baseline and follow-up DBS samples were genotyped by NS5B sequencing or commercial real-time PCR. Among treated patients, 193 follow-up DBS samples were tested. The DBS-based assay showed 100% specificity (129/129), and sensitivity ranged from 84.4% to 96.1% according to different viral load cut-offs (from detectable to 3000 IU/mL). Sensitivity as test of cure (follow-up 12) ranged from 85.1% to 97.4%. Among the 64 patients with recurrent viremia, 10.9% had low viral loads (≤1000 IU/mL); HCV genotyping allowed us to classify 73.5% of viremic cases either as reinfection or as treatment failure. DBS samples are useful to assess cure and differentiate reinfection from relapse after HCV antiviral treatment in the real world, facilitating decentralization of treatment and posttreatment follow-up in PWID. However, a fraction of patients presented with low viral loads, limiting viremia detection and genotyping in DBS and, therefore, repeat testing is recommended.

SARS-CoV-2 Secondary Attack Rates in Vaccinated and Unvaccinated Household Contacts during Replacement of Delta with Omicron Variant, Spain.

We performed a prospective, cross-sectional study of household contacts of symptomatic index case-patients with SARS-CoV-2 infection during the shift from Delta- to Omicron-dominant variants in Spain. We included 466 household contacts from 227 index cases. The secondary attack rate was 58.2% (95% CI 49.1%-62.6%) during the Delta-dominant period and 80.9% (95% CI 75.0%-86.9%) during the Omicron-dominant period. During the Delta-dominant period, unvaccinated contacts had higher probability of infection than vaccinated contacts (odds ratio 5.42, 95% CI 1.6-18.6), but this effect disappeared at ≈20 weeks after vaccination. Contacts showed a higher relative risk of infection (9.16, 95% CI 3.4-25.0) in the Omicron-dominant than Delta-dominant period when vaccinated within the previous 20 weeks. Our data suggest vaccine evasion might be a cause of rapid spread of the Omicron variant. We recommend a focus on developing vaccines with long-lasting protection against severe disease, rather than only against infectivity.

Mathematical modeling of SARS-CoV-2 variant substitutions in European countries: transmission dynamics and epidemiological insights.

Background: Countries across Europe have faced similar evolutions of SARS-CoV-2 variants of concern, including the Alpha, Delta, and Omicron variants. Materials and methods: We used data from GISAID and applied a robust, automated mathematical substitution model to study the dynamics of COVID-19 variants in Europe over a period of more than 2 years, from late 2020 to early 2023. This model identifies variant substitution patterns and distinguishes between residual and dominant behavior. We used weekly sequencing data from 19 European countries to estimate the increase in transmissibility ( Δ ß ) between consecutive SARS-CoV-2 variants. In addition, we focused on large countries with separate regional outbreaks and complex scenarios of multiple competing variants. Results: Our model accurately reproduced the observed substitution patterns between the Alpha, Delta, and Omicron major variants. We estimated the daily variant prevalence and calculated Δ ß between variants, revealing that: ( i ) Δ ß increased progressively from the Alpha to the Omicron variant; ( i i ) Δ ß showed a high degree of variability within Omicron variants; ( i i i ) a higher Δ ß was associated with a later emergence of the variant within a country; ( i v ) a higher degree of immunization of the population against previous variants was associated with a higher Δ ß for the Delta variant; ( v ) larger countries exhibited smaller Δ ß , suggesting regionally diverse outbreaks within the same country; and finally ( v i ) the model reliably captures the dynamics of competing variants, even in complex scenarios. Conclusion: The use of mathematical models allows for precise and reliable estimation of daily cases of each variant. By quantifying Δ ß , we have tracked the spread of the different variants across Europe, highlighting a robust increase in transmissibility trend from Alpha to Omicron. Additionally, we have shown that the geographical characteristics of a country, as well as the timing of new variant entrances, can explain some of the observed differences in variant substitution dynamics across countries.

Incidence and molecular epidemiology of hepatitis C virus reinfection in prisons in Catalonia, Spain (Re-HCV study).

Hepatitis C virus (HCV) reinfection may hamper HCV elimination in prisons. We aimed to (i) determine the reinfection rate in people treated for HCV in Catalan prisons, (ii) measure reinfection in people entering prisons, and (iii) characterize the molecular epidemiology of HCV in prisons and people who inject drugs (PWID) in the community. Re-HCV was a prospective study in eight prisons (2019-2020) including two groups: (1) people cured with treatment in prison and followed-up every 6 months, and (2) people testing HCV-RNA positive at incarceration. Bio-behavioral data were collected. HCV isolates were sequenced and phylogenetically analyzed with those of PWID in the community. Reinfection follow-up after treatment was achieved in 97 individuals (103.05 person-years). Two reinfections were detected, resulting in an incidence ≤ 10/100 person-years. Among people entering prison, 2% (359/17,732) were viremic, of which 334 (93.0%) were included, and 44 (13.5%) presented with reinfection (84.7% being PWID). Frequently, HCV isolates in prisons and PWID in the community were phylogenetically related. Although HCV reinfection is low after treatment, it is common in people entering Catalan prisons. To maintain a low HCV prevalence in prisons, harm-reduction services and test-and-treat programs for PWID should be strengthened both inside and outside prisons.

Transmissibility, hospitalization, and intensive care admissions due to omicron compared to delta variants of SARS-CoV-2 in Catalonia: A cohort study and ecological analysis.

Purpose: We aim to compare the severity of infections between omicron and delta variants in 609,352 SARS-CoV-2 positive cases using local hospitalization, vaccination, and variants data from the Catalan Health Care System (which covers around 7. 8 million people). Methods: We performed a substitution model to establish the increase in transmissibility of omicron using variant screening data from primary care practices (PCP) and hospital admissions. In addition, we used this data from PCP to establish the two periods when delta and omicron were, respectively, dominant (above 95% of cases). After that, we performed a population-based cohort analysis to calculate the rates of hospital and intensive care unit (ICU) admissions for both periods and to estimate reduction in severity. Rate ratios (RR) and 95% confidence intervals (95% CI) were calculated and stratified by age and vaccination status. In a second analysis, the differential substitution model in primary care vs. hospitals allowed us to obtain a population-level average change in severity. Results: We have included 48,874 cases during the delta period and 560,658 during the omicron period. During the delta period, on average, 3.8% of the detected cases required hospitalization for COVID-19. This percentage dropped to 0.9% with omicron [RR of 0.46 (95% CI: 0.43 to 0.49)]. For ICU admissions, it dropped from 0.8 to 0.1% [RR 0.25 (95% CI: 0.21 to 0.28)]. The proportion of cases hospitalized or admitted to ICU was lower in the vaccinated groups, independently of the variant. Omicron was associated with a reduction in risk of admission to hospital and ICU in all age and vaccination status strata. The differential substitution models showed an average RR between 0.19 and 0.50. Conclusion: Both independent methods consistently show an important decrease in severity for omicron relative to delta. The systematic reduction happens regardless of age. The severity is also reduced for non-vaccinated and vaccinated groups, but it remains always higher in the non-vaccinated population. This suggests an overall reduction in severity, which could be intrinsic to the omicron variant. The fact is that the RR in ICU admission is systematically smaller than in hospitalization points in the same direction.

Evaluation of Fourier Transform Infrared Spectroscopy as a First-Line Typing Tool for the Identification of Extended-Spectrum ß-Lactamase-Producing Klebsiella pneumoniae Outbreaks in the Hospital Setting.

Early detection of pathogen cross-transmission events and environmental reservoirs is needed to control derived nosocomial outbreaks. Whole-genome sequencing (WGS) is considered the gold standard for outbreak confirmation, but, in most cases, it is time-consuming and has elevated costs. Consequently, the timely incorporation of WGS results to conventional epidemiology (CE) investigations for rapid outbreak detection is scarce. Fourier transform infrared spectroscopy (FTIR) is a rapid technique that establishes similarity among bacteria based on the comparison of infrared light absorption patterns of bacterial polysaccharides and has been used as a typing tool in recent studies. The aim of the present study was to evaluate the performance of the FTIR as a first-line typing tool for the identification of extended-spectrum ß-lactamase-producing Klebsiella pneumoniae (ESBL-Kp) outbreaks in the hospital setting in comparison with CE investigations using WGS as the gold standard method. Sixty-three isolates of ESBL-Kp collected from 2018 to 2021 and classified according to CE were typed by both FTIR and WGS. Concordance was measured using the Adjusted Rand index (AR) and the Adjusted Wallace coefficient (AW) for both CE and FTIR clustering considering WGS as the reference method. Both AR and AW were significantly higher for FTIR clustering than CE clustering (0.475 vs. 0.134, p = 0.01, and 0.521 vs. 0.134, p = 0.009, respectively). Accordingly, FTIR inferred more true clustering relationships than CE (38/42 vs. 24/42, p = 0.001). However, a similar proportion of genomic singletons was detected by both FTIR and CE (13/21 vs. 12/21, p = 1). This study demonstrates the utility of the FTIR method as a quick, low-cost, first-line tool for the detection of ESBL-Kp outbreaks, while WGS analyses are being performed for outbreak confirmation and isolate characterization. Thus, clinical microbiology laboratories would benefit from integrating the FTIR method into CE investigations for infection control measures in the hospital setting.

Corrigendum: Transmissibility, hospitalization, and intensive care admissions due to omicron compared to delta variants of SARS-CoV-2 in Catalonia: A cohort study and ecological analysis.

[This corrects the article DOI: 10.3389/fpubh.2022.961030.].

Monitoring SARS-CoV-2 variant transitions using differences in diagnostic cycle threshold values of target genes.

Monitoring the emergence of new SARS-CoV-2 variants is important to detect potential risks of increased transmission or disease severity. We investigated the identification of SARS-CoV-2 variants from real-time reverse transcriptase polymerase chain reaction (RT-PCR) routine diagnostics data. Cycle threshold (Ct) values of positive samples were collected from April 2021 to January 2022 in the Northern Metropolitan Area of Barcelona (n = 15,254). Viral lineage identification from whole genome sequencing (WGS) was available for 4618 (30.3%) of these samples. Pairwise differences in the Ct values between gene targets (ΔCt) were analyzed for variants of concern or interest circulating in our area. A specific delay in the Ct of the N-gene compared to the RdRp-gene (ΔCtNR) was observed for Alpha, Delta, Eta and Omicron. Temporal differences in ΔCtNR correlated with the dynamics of viral replacement of Alpha by Delta and of Delta by Omicron according to WGS results. Using ΔCtNR, prediction of new variants of concern at early stages of circulation was achieved with high sensitivity and specificity (91.1% and 97.8% for Delta; 98.5% and 90.8% for Omicron). Thus, tracking population-wide trends in ΔCt values obtained from routine diagnostics testing in combination with WGS could be useful for real-time management and response to local epidemics.

Epidemiological and Genomic Analysis of a Large SARS-CoV-2 Outbreak in a Long-Term Care Facility in Catalonia, Spain.

Limiting outbreaks in long-term care facilities (LTCFs) is a cornerstone strategy to avoid an excess of COVID-19-related morbidity and mortality and to reduce its burden on the health system. We studied a large outbreak that occurred at an LTCF, combining methods of classical and genomic epidemiology analysis. The outbreak lasted for 31 days among residents, with an attack rate of 98% and 57% among residents and staff, respectively. The case fatality rate among residents was 16% (n = 15). Phylogenetic analysis of 59 SARS-CoV-2 isolates revealed the presence of two closely related viral variants in all cases (B.1.177 lineage), revealing a far more complex outbreak than initially thought and suggesting an initial spread driven by staff members. In turn, our results suggest that resident relocations to mitigate viral spread might have increased the risk of infection for staff members, creating secondary chains of transmission that were responsible for prolonging the outbreak. Our results highlight the importance of considering unnoticed chains of transmission early during an outbreak and making an adequate use and interpretation of diagnostic tests. Outbreak containment measures should be carefully tailored to each LTCF. IMPORTANCE The impact of COVID-19 on long-term care facilities (LTCFs) has been disproportionately large due to the high frailty of the residents. Here, we report epidemiological and genomic findings of a large outbreak that occurred at an LTCF, which ultimately affected almost all residents and nearly half of staff members. We found that the outbreak was initially driven by staff members; however, later resident relocation to limit the outbreak resulted in transmission from residents to staff members, evidencing the complexity and different phases of the outbreak. The phylogenetic analysis of SARS-CoV-2 isolates indicated that two closely related variants were responsible for the large outbreak. Our study highlights the importance of combining methods of classical and genomic epidemiology to take appropriate outbreak containment measures in LTCFs.

Engineering Transcriptional Interference through RNA Polymerase Processivity Control.

Antisense transcription is widespread in all kingdoms of life and has been shown to influence gene expression through transcriptional interference (TI), a phenomenon in which one transcriptional process negatively influences another in cis. The processivity, or uninterrupted transcription, of an RNA polymerase (RNAP) is closely tied to levels of antisense transcription in bacterial genomes, but its influence on TI, while likely important, is not well-characterized. Here, we show that TI can be tuned through processivity control via three distinct antitermination strategies: the antibiotic bicyclomycin, phage protein Psu, and ribosome-RNAP coupling. We apply these methods toward TI and tune ribosome-RNAP coupling to produce 38-fold transcription-level gene repression due to both RNAP collisions and antisense RNA interference. We then couple protein roadblock and TI to design minimal genetic NAND and NOR logic gates. Together, these results show the importance of processivity control for strong TI and demonstrate TI's potential for synthetic biology.

Analytical Performance of Quantitative DiaSorin Liaison SARS-COV-2 Antigen Test for the Asymptomatic Population.

Background: Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) antigen (Ag) tests have been widely employed to identify patients for a rapid diagnosis and pandemic control. Rapid lateral-flow techniques are currently the most used, but automated technologies have emerged as another viable alternative to molecular methods. We aimed to evaluate the analytical performance of the DiaSorin Liaison SARS-CoV-2 Ag test in asymptomatic population and close contacts, for its use as a tool in pandemic control efforts. Material and Methods: A retrospective study was conducted. A total of 861 samples were included, 291 (34%) were positive for SARS-CoV-2 with cycle threshold (Ct) <40, and 570 (66%) were negative. Results: A strong correlation was observed between reverse transcriptase-PCR (RT-PCR) Ct and Ag 50% Tissue Culture Infectious Dose per milliliter (TCID50/ml; r = 0.6486; p < 0.0001) and all RT-PCR negative samples tested negative for the 200 TCID50/ml SARS-Cov-2 Ag cutoff, i.e., a specificity of 100% was reached (95% CI: 99.4-100.0%). Samples with <25 Ct and/or >106 extrapolated copies/ml were reached a sensitivity of 100% (95% IC 97.0-100.0%). For intermediate viral loads (>105 extrapolated copies/ml or <30 Ct), the sensitivity value still exceeded 80%. As with other Ag methods, samples between 30 and 40 Ct could not be detected with a reliable sensitivity. Conclusions: The LIAISON® SARS-CoV-2 Ag assay displays an acceptable sensitivity and a very high specificity that is useful for detecting the presence of SARS-CoV-2 in nasal swabs (NPS) of asymptomatic population or to regular monitoring of risk groups in controlled settings. Additionally, the flexibility in processing different samples and in the sampling preparation process makes this test an option for its use in high throughput laboratories. Automated tests may facilitate result reporting and yield consistent data, while avoiding some of the pitfalls of rapid lateral-flow techniques, such as observer variability.

Performance characteristics of five antigen-detecting rapid diagnostic test (Ag-RDT) for SARS-CoV-2 asymptomatic infection: a head-to-head benchmark comparison.

BACKGROUND: Mass testing for early identification and isolation of infectious COVID-19 individuals is efficacious for reducing disease spread. Antigen-detecting rapid diagnostic tests (Ag-RDT) may be suitable for testing strategies; however, benchmark comparisons are scarce. METHODS: We used 286 nasopharyngeal specimens from unexposed asymptomatic individuals collected between December 2020 and January 2021 to assess five Ag-RDTs marketed by Abbott, Siemens, Roche Diagnostics, Lepu Medical, and Surescreen. RESULTS: For the overall sample, the performance parameters of Ag-RDTs were as follows: Abbott assay, sensitivity 38.6% (95%CI 29.1-48.8) and specificity 99.5% (97-100%); Siemens, sensitivity 51.5% (41.3-61.6) and specificity 98.4% (95.3-99.6); Roche, sensitivity 43.6% (33.7-53.8) and specificity 96.2% (92.4-98.5); Lepu, sensitivity 45.5% (35.6-55.8) and specificity 89.2% (83.8-93.3%); Surescreen, sensitivity 28.8% (20.2-38.6) and specificity 97.8% (94.5-99.4%). For specimens with cycle threshold (Ct) <30 in RT-qPCR, all Ag-RDT achieved a sensitivity ≥70%. The modelled negative- and positive-predictive value for 1% prevalence were >99% and <50%, respectively. CONCLUSIONS: When screening unexposed asymptomatic individuals, two Ag-RDTs achieved sensitivity ≥80% for specimens with Ct<30 and specificity ≥96%. The estimated negative predictive value suggests the suitability of Ag-RDTs for mass screenings of SARS-CoV-2 infection in the general population.

The first wave of the COVID-19 epidemic in Spain was associated with early introductions and fast spread of a dominating genetic variant.

The coronavirus disease 2019 (COVID-19) pandemic has affected the world radically since 2020. Spain was one of the European countries with the highest incidence during the first wave. As a part of a consortium to monitor and study the evolution of the epidemic, we sequenced 2,170 samples, diagnosed mostly before lockdown measures. Here, we identified at least 500 introductions from multiple international sources and documented the early rise of two dominant Spanish epidemic clades (SECs), probably amplified by superspreading events. Both SECs were related closely to the initial Asian variants of SARS-CoV-2 and spread widely across Spain. We inferred a substantial reduction in the effective reproductive number of both SECs due to public-health interventions (Re < 1), also reflected in the replacement of SECs by a new variant over the summer of 2020. In summary, we reveal a notable difference in the initial genetic makeup of SARS-CoV-2 in Spain compared with other European countries and show evidence to support the effectiveness of lockdown measures in controlling virus spread, even for the most successful genetic variants.

Construction of Two-Input Logic Gates Using Transcriptional Interference.

Transcriptional interference (TI) has been shown to regulate gene expression at the DNA level via different molecular mechanisms. The obstacles present on the DNA that a transcribing RNA polymerase might encounter, for example, a DNA-bound protein or another RNA polymerase, can result in TI causing termination of transcription, thus reducing gene expression. However, the potential of TI as a new strategy to engineer complex gene expression modules has not been fully explored yet. Here we created a series of two-input genetic devices that use the presence of a roadblocking protein to control gene expression and analyzed their behaviors using both experimental and mathematical modeling approaches. We explored how multiple characteristics affect the response of genetic devices engineered to act like either AND, OR, or single input logic gates. We show that the dissociation constant of the roadblocking protein, inducer activation of promoter and operator sites, and distance between tandem promoters tune gate behavior. This work highlights the potential of rationally creating different types of genetic responses using the same transcription factors in subtly different genetic architectures.

Transcriptional Interference in Convergent Promoters as a Means for Tunable Gene Expression.

An important goal of synthetic biology involves the extension and standardization of novel biological elements for applications in medicine and biotechnology. Transcriptional interference, occurring in sets of convergent promoters, offers a promising mechanism for building elements for the design of tunable gene regulation. Here, we investigate the transcriptional interference mechanisms of antisense roadblock and RNA polymerase traffic in a set of convergent promoters as novel modules for synthetic biology. We show examples of elements, including antisense roadblock, relative promoter strengths, interpromoter distance, and sequence content that can be tuned to give rise to repressive as well as cooperative behaviors, therefore resulting in distinct gene expression patterns. Our approach will be useful toward engineering new biological devices and will bring new insights to naturally occurring cis-antisense systems. Therefore, we are reporting a new biological tool that can be used for synthetic biology.

Cis-Antisense Transcription Gives Rise to Tunable Genetic Switch Behavior: A Mathematical Modeling Approach.

Antisense transcription has been extensively recognized as a regulatory mechanism for gene expression across all kingdoms of life. Despite the broad importance and extensive experimental determination of cis-antisense transcription, relatively little is known about its role in controlling cellular switching responses. Growing evidence suggests the presence of non-coding cis-antisense RNAs that regulate gene expression via antisense interaction. Recent studies also indicate the role of transcriptional interference in regulating expression of neighboring genes due to traffic of RNA polymerases from adjacent promoter regions. Previous models investigate these mechanisms independently, however, little is understood about how cells utilize coupling of these mechanisms in advantageous ways that could also be used to design novel synthetic genetic devices. Here, we present a mathematical modeling framework for antisense transcription that combines the effects of both transcriptional interference and cis-antisense regulation. We demonstrate the tunability of transcriptional interference through various parameters, and that coupling of transcriptional interference with cis-antisense RNA interaction gives rise to hypersensitive switches in expression of both antisense genes. When implementing additional positive and negative feed-back loops from proteins encoded by these genes, the system response acquires a bistable behavior. Our model shows that combining these multiple-levels of regulation allows fine-tuning of system parameters to give rise to a highly tunable output, ranging from a simple-first order response to biologically complex higher-order response such as tunable bistable switch. We identify important parameters affecting the cellular switch response in order to provide the design principles for tunable gene expression using antisense transcription. This presents an important insight into functional role of antisense transcription and its importance towards design of synthetic biological switches.