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Background: The COVID-19 pandemic has been striking for three years and, despite the regular arise of new variants, populations are now widely immune and protected from severe symptoms. However, immunocompromised patients still have worse clinical outcomes, higher mortality and rarely develop effective immunity through vaccination or infection. Here, we studied the temporal distribution of infections, viral loads (VL) as well as the viral genetic diversity among an immunocompromised patient cohort, between January 2021 and September 2022. Method(s): Overall, 478 immunocompromised patients (solid organ transplant, HIV positive, cancer, autoimmune disease) and 234 controls (healthcare workers) from Pitie-Salpetriere and Bichat Claude-Bernard University hospitals (Paris, FRANCE) were diagnosed with SARS-CoV-2 infection by RT-qPCR. Whole genome sequencing was performed according to ARTIC protocol on Oxford Nanopore platform. All 712 full viral genomes were used to determine lineages and mapped to Wuhan-Hu-1 reference to produce a maximum likelihood phylogenetic tree (IQTree, 1000 bootstraps). Differences in temporal distributions of infections and VL were assessed using nonparametric statistical tests. Result(s): According to phylogenetic analysis, genomes from SARS-CoV- 2 infecting immunocompromised patients and those infecting healthy individuals are distributed in a similar way. No significant genetic differences can be observed between viral genomes from patients and controls within the different lineages. Temporal distribution of COVID-19 infections were also similar between immunocompromised patients and controls, with the exception of BA.2 variant for which controls were infected earlier (p< 0.001). VL were significantly lower in immunocompromised patients infected with Omicron variants (p=0.04). No differences in VL were observed for Alpha and Delta variants. Conclusion(s): At diagnosis, no intrinsic genetic divergence was observed in virus infecting immunocompromised patients compared to those circulating in the general population. Similarities in temporal distribution of infections between controls and patients suggest that these different groups become infected concomitantly. VL appeared to be lower for Omicron variants in immunocompromised patients. An earlier VL peak of Omicron and a testing of immunocompromised patients hospitalized once severe symptoms have appeared could indicate a delayed testing in these patients, once the replicative phase over. (Figure Presented).
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Background: In 2018 we reported the emergence of the new HIV-1 recombinant CRF94-02BF2 involved in a large transmission cluster of 49 French MSM mostly infected in 2016-2017. This CRF94 raised concerns of enhanced virulence. Prevention actions were undertaken in the area and population affected. This study reported the molecular and epidemiological evolution of this CRF94 until June 2022. Method(s): In 2021-2022, French sequence databases were screened for patients infected with HIV-1 subtype CRF94 or similar strain. HIV subtyping was confirmed by phylogenetic analysis of genes encoding both protease and reverse transcriptase (1070bps), and integrase (696bps) using IQ-Tree. Five whole genomes, related but distinct from CRF94, were obtained with the DeepChek assay Whole Genome kits. Recombination breakpoints were estimated using RDP4 and SimPlot. Mann-Whitney and LogRank tests were used for statistical analyses to compare patients' characteristics. Result(s): In June 2022, 49 new HIV-1 sequences were collected: 14 clustered with the 49 previous CRF94, 32 formed a new cluster next to but distinct from CRF94, and 3 strains could not be classified. Analysis of 5 whole genomes from the new cluster revealed a new recombinant, the CRF132-94B, mainly consisting of CRF94 which recombined with subtype B in the POL and accessory genes. Vif gene changed from the F2 to the B subtype. Both CRF94 and 132 clusters involved >95% of MSM, mostly infected < 1 year before diagnosis. However, there were differences: 97% were diagnosed in 2013-2019 for CRF94 vs 90% in 2020-2022 for CRF132. At time of diagnosis, 33% of patients infected with CRF94 knew the Prep vs 95% for CRF132. In the cluster CRF94, patients were older (34 vs 30 years, p=0.02), had higher viral loads (5.42 vs 4.42 log10 copies/Ml;p< 0.001), a lower CD4 cell counts (358 vs 508 /mm3, p=0.002). On treatment, the patients with the CRF94 reached viremia < 50 copies/Ml significantly later than those infected with CRF132 (p=0.0002). The prevention activities targeting the CRF94 cluster could explained the few patients infected with this strain after 2018. The CRF132 is mainly located in another Paris region area, but no specific transmission place has been identified. Conclusion(s): After 2019, the CRF94 spread seems greatly slowed down but the very close CRF132-94B has given birth to a new highly active cluster in 2020- 2022, despite the COVID social-distancing and a strong knowledge of the Prep. CRF132 appears to be less virulent perhaps due to the Vif gene change. Identified breakpoints positions of the new HIV-1 CRF132-94B. GenBank accession numbers of the five references : ON901787 to ON901791.
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Background: Innate immunity is the first line of defense in response to pathogens, which acts locally and also leads the stimulation of adaptive immunity through at least with IL-1beta secretion. It has been shown that SARSCoV- 2 infection triggered the NLRP-3 inflammasome activation and the IL-1beta secretion. The aim of this study was to analyze and compare the level of IL-1beta secretion that is one of the most important innate immunity cytokines, in monocyte-like cells infected with 6 different variants of the SARS-CoV-2. Method(s): Six SARS-CoV-2 variants (historical (B.1, D614G), Alpha, Beta, Gamma, Delta and Omicron BA.1) were isolated from COVID-19 hospitalized patients. Viral stocks were obtained by inoculation in Vero and Vero-TRMPSS2 cells. THP-1 monocyte-like cells were cultured with RPMI-hepes 10% FBS-0.05 mM 2-mercaptoethanol. A total of 5 x 104 of THP-1 cells was plated per well in 96-wells plate and differentiated with 10nM of PMA for 24h. Differenciated- THP-1 were first primed with LPS 1mug/ml for 2h and infected with different SARS-CoV-2 variants with a MOI 0.1. IL-1beta was measured by luminescence in the supernatant after 24 h of infection. Result(s): We analyzed and compared IL-1beta secretion between SARS-CoV-2 virus 6 sublineages after infection of monocytes like THP-1. We observed that THP-1 cells infected with SARS-CoV-2 variants presented a significantly higher IL-1beta secretion than non-infected cells. Moreover, some SARS-CoV-2 variants led to a stronger IL-1beta secretion, and particularly we observed a significantly higher level of IL-1beta cells infected with Omicron BA.1 sublineage compared to other variants. Indeed, Omicron BA.1 infected cells presented the higher IL-1beta secretion (median 385.7 pg/ml IQR[302.6-426.3]) follows by the Delta variants and the historical variants (median 303.6 [266.3-391.9] and 281.9 [207.2-410], respectively). Alpha, Beta and Gamma variants presented the lowest IL-1beta secretion (median 228.1 [192.5-276.4], 219.1 [185.1-354.2] and 211 [149.8- 228.8]). Conclusion(s): We observed the inflammasome activation for the 6 SARS-CoV-2 sublineages with a variation in level of IL-1beta secretion. Indeed, our results suggested that Omicron BA.1 was more recognized by the innate immune cells than other SARS-CoV-2, which could in part, with its upper respiratory tract tropism, possibly explain its less clinical virulence. Taking together, these results suggest that the innate immunity response and precisely, IL-1beta secretion pathways were activated in a SARS-CoV-2 variants-dependent manner.
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Background: Immunocompromised hosts with prolonged SARS-CoV-2 infections have been associated with the emergence of novel mutations, especially in the Spike protein, a key target for vaccines and therapeutics. Here, we conducted a case-control study to measure the genetic diversity of SARSCoV- 2 and to search for immunocompromised-specific minority variants. Method(s): SARS-CoV-2-positive patients with lung/cardiac/kidney transplant, HIV-positive, or treated with high doses of corticosteroids for auto-immune diseases were considered as immunocompromised hosts. SARS-CoV-2-positive healthcare workers with no auto-immune disease were used as controls. Samples were analyzed by RT-qPCR at Pitie-Salpetriere and Bichat Claude-Bernard university hospitals (Paris, France). Samples with Cycle threshold < 30 were selected for SARSCoV- 2 whole-genome sequencing using Oxford Nanopore protocol. Raw sequence data were mapped onto the Wuhan-Hu-1 reference genome, and consensus sequences were produced to determine the lineage. Only sequences covering at least 95% at >=50X depth of the Spike gene were investigated. In-house algorithms were developed to identify all majority and minority mutations in Spike. We defined a minority variant when it was present in >=6% and < 50% of the reads;and a majority variant when it was present in >50%. Result(s): We sequenced SARS-CoV-2 genome from 478 COVID-19- positive immunocompromised patients and 234 controls. More minority non-synonymous mutations in Spike were detected in viruses from immunocompromised hosts, compared to viral genomes from controls, in both Delta (p=0.001) and Omicron (p< 0.001) lineages, but not in Alpha (p=0.66) (Figure 1). Interestingly, among the 52 patients infected with the Delta variant, we concomitantly detected at low frequencies the mutations H655Y, N764K, D796Y, in three patients (associated with different auto-immune disease), that are part of Omicron variants signature mutations. Similarly, some patients (n=7) infected by Omicron BA.1 lineage had R346T at low-frequency, later fixed in Omicron BA.4.6 and BQ.1.1 lineages. None of these mutations were observed in the viral genomes from controls. Conclusion(s): Here, we report a higher genetic diversity in Spike gene among SARS-CoV-2 sequences from immunocompromised hosts for Delta and Omicron lineages. These results suggest that immunocompromised patients are more likely to allow viral genetic diversification and are associated with a risk of emergence of novel SARS-CoV-2 variants. (Figure Presented).
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Coronavirus disease 2019 (COVID-19) has caused more than 6 million deaths. Higher values of the FIB-4 index have been shown to be associated with disease severity. Although vaccination has helped to improve clinical outcomes and overall mortality, it remains important to identify clinical parameters that can predict a likely worse prognosis. Artificial intelligence and big data processing were used to retrieve data from patients with Covid-19 admitted during the period March 2020-January 2022 at the Fondazione Policlinico Gemelli IRCCS. Patients and clinical characteristics of patients with available FIB 4 data derived from the Gemelli Generator Real World Data (G2 RWD) were used to develop predictive models of mortality during the 4 waves of the Covid-19 pandemic. A logistic regression model was applied to the training and test set. The performance of the model was assessed by means of the ROC curve. After the pre-processing steps, 1143 patients and 35 variables were included in the final dataset. The FIB-4 discretization algorithm identified a cut-off of 2.54. After fitting the model for multiple mortality regression analysis: FIB-4>= 2.53 (OR=4.53, [CI: 2.83 - 7.25]), wave 3 (OR=0.34, [CI: 0.15 - 0.75], wave 4 (OR=0.40 [CI: 0.24 - 0.66]) and LDH (OR=1.001, [CI: 1.000 - 1.002]) were considered. The machine learning approach identified a cut-off of 2.53 for FIB-4 above which the risk of death increases significantly. These data may be useful in the clinical management of patients with Covid-19, as they can be calculated from the blood test after hospital admission.
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Background: The emergence of SARS-CoV-2 variants is a major concern. As the Delta variant became dominant worldwide, obtaining specific data on the humoral and cellular responses after BNT162b2 vaccination against this variant of concern in PLWHIV is crucial. Methods: Multicenter cohort study of PLWHIV, with a CD4 cell count <500/mm3 and a viral load <50 copies/ml on stable antiretroviral therapy for at least 3 months, to explore humoral and cellular responses to BNT162b2 vaccination. IgG antibodies (Ab) to the Receptor Binding Domain (RBD) of the spike protein and their neutralization capacity, assessed by an ELISA (Genscript) and a virus neutralization test (VNT), against historical strain, Beta and Delta variants were performed before vaccination (day 0) and one month after a complete vaccination schedule (M1). Results: 97 patients were enrolled in the study (table 1. baseline characteristics). Among them, 85 patients received 2 shots (11 previous COVID-19 and 1 premature exit). The median time between the 2 shots was 28 [IQR 28-29] days. 90 patients could be evaluated at M1. The seroconversion rate in anti-RBD IgG was 97% CI95%[90%;100%] at M1. Median (IQR) anti-RBD Ab titer was 0.97 (0.97-5.3) BAU/ml at D0 and 1219 (602-1929) at M1. Neutralizing Ab capacity improved between D0 (15% CI95%[8%;23%]) and M1 (94% CI95%[87%;98%]) with the Genscript assay. Neutralizing Ab with the VNT were present at M1 for historical strain, Beta and Delta variants in 82%, 77% and 84% patients respectively. Planned subgroups analysis at M1 showed that seroconversion rate and median anti-RBD Ab titer were 91% and 852 BAU/ml in patients with CD4<250/mm3 (n=13) and 98% and 1270 BAU/ml in patients with CD4>250/mm3 (n=64) (difference of change between D0 and M1 between subgroups p=0.8224). 73% of patients with CD4<250/mm3 had neutralizing Ab and 97% of those with CD4>250/mm3 (p=0.0130). The neutralization capacity of beta variant was 50% in CD4<250/mm3 and 81% in CD4>250/mm3 (p=0.0292). No change in CD4+ or CD8+ T cells count was observed while a decrease of CD19+ B cells count was observed (208 ±124/mm3 at D0 vs 188 ±112/mm3 at M1, p<0.01). Tolerance was very good and no COVID-19 was reported until M1. Conclusion: These results show a high seroconversion rate with a Delta neutralization in PLWHIV patients after a complete BNT162b2 vaccination schedule. However, patients with CD4<250/mm3 had a decrease neutralizing Ab capacity mainly against Beta than Delta variant.
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Background: In 2020, France reported 2.7 million cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), making it the second most affected European country by the COVID-19 pandemic after the United Kingdom. However, dynamics of SARS-CoV-2 transmissions within France or between France and other countries remains partially characterized. We propose an analysis of these dynamics on multiple scales, from the continents to the French administrative regions. Methods: We produced 736 SARS-CoV-2 sequences from Ile-de-France (Paris area, France) and analyzed them concomitantly with GISAID deposited sequences to elucidate the origins and spread of the virus from January 2020 to December 2020. A total of 4,571 worldwide sequences, including 1,652 French sequences, constituted the final dataset. All sequences were selected to be representative of each country temporal distribution of SARS-CoV-2 to the week resolution. We used a maximum likelihood phylogenetic framework to estimate the most probable temporal and geographic spread of SARS-CoV-2 within France and worldwide. Depending on the geographical focus (France, Europe or worldwide), we pruned the tree accordingly in 1,000 independent replicates. Results: Phylogenetic analysis revealed that, during the 1st French epidemic wave (from March to May), the majority of viruses introduced to France came from North America (USA) and Europe (Spain, Italy, ?). France regularly transmitted to neighboring European countries: Belgium, Germany, Italy and United Kingdom. Contrary to the 1st wave, inter-country transmission events were limited to neighboring countries and intercontinental transmission were almost absent during the French 2nd wave (from September to November). At the French regions-scale, we observed that Ile-de-France (IDF) was the main source of infections for all other French regions during the 1st epidemic wave, with a minor participation of Provence-Alpes-Côte d'Azur (PACA). For the 2nd epidemic wave, PACA was the main source of infections for all other French regions, with a lower participation of IDF and other regions. Conclusion: Overall, our findings allow a more comprehensive representation of SARS-CoV-2 transmission chains related to and within France and the global temporal distribution of those events, in link with control measures applied during the whole 2020 period. IDF and PACA were the main hubs of transmissions in France for the 1st and the 2nd epidemic waves, respectively.
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Background: Since 1920, a decrease in serum cholesterol has been identified as a marker of severe pneumonia. We have assessed the performance of serum apolipoprotein-A1 (A1), the main transporter of HDL-cholesterol, to identify the early spread of coronavirus disease 2019 (Covid-19) in the general population and its diagnostic performance for the Covid-19 Methods: We compared the daily mean A1 during the first 26 weeks of 2020 in a population that is routinely followed for a risk of liver fibrosis risk in France (18,239 sera) and in the USA (161,655 sera) in relation to a local increase in confirmed cases, and in comparison to the same period in 2019 (respectively 26,513 and 211,419 sera) Linear regression curves with 95% confidence intervals between the mean daily serum levels of A1 were compared by the F-test, and a significant difference was defined as P <0.001. We prospectively assessed the sensitivity of this marker in an observational study of 136 consecutive hospitalized cases and retrospectively evaluated its specificity in 7,481 controls representing the general population Results: The mean A1 levels in these populations began decreasing in January 2020, compared to the same 26 weeks in 2019 This decrease was highly correlated to and in parallel with the daily increase in confirmed Covid-19 cases in the following 26 weeks, in both France (national and hospital cohort APHP-PSL) and USA, including the June recovery period in France Figure 1 shows the proportion of low A1 (<1 24 g/L) during the 26 weeks Same differences were observed after stratifications on the following confounding factors: previous years 2017-2018, age, gender, hepatitis C or non-alcoholic fatty liver disease, BMI, fasting glucose and triglycerides No similar changes were observed for ALT, AST, GGT, and haptoglobin A1 at the 1 25 g/L cutoff had a sensitivity of 90 6% (95%CI84 2-95 1) and a specificity of 96.1% (95.7-96.6%) for the diagnosis of Covid-19 The area under the characteristics curve was 0 978 (0 957-0 988), and outperformed haptoglobin and liver function tests For a prevalence of 1 8% (136/7617;95%CI 1 5-2 1) of Covid-19 cases, the positive predictive value (PPV) was 30 0% (95%CI 25 6-34 7) and the negative predictive value (NPV) was 99 8% (95%CI 99 7-99 9) When adjusted on the range of Covid-19 prevalence predicted in the French population, the PPV was 40 5% and NPV was 99 7% for the 2 8% lower limit, and PPV was 56 0% and NPV was 99 3% for the 7 2% higher limit In the prospective recovery subset repeated measurements showed an increase of A1 from 0 89 to 1 04 g/L in a median of 11 days (n=305;ANOVA P<0 01) Conclusion: Apolipoprotein-A1 could be both a sentinel of the pandemic in existing routine surveillance of the general population, as well as a candidate predictor of suspected Covid-19 in multivariate analysis in cases with a negative virological test.