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The recent SARS-CoV-2 pandemic manifests itself as a mild respiratory tract infection in most individuals, leading to COVID-19 disease. However, in some infected individuals, this can progress to severe pneumonia and acute respiratory distress syndrome (ARDS), leading to multi-organ failure and death. This study explores the proteomic differences between mild, severe, and critical COVID-19 positive patients to further understand the disease progression, identify proteins associated with disease severity, and identify potential therapeutic targets. Blood protein profiling was performed on 59 COVID-19 mild (n = 26), severe (n = 9) or critical (n = 24) cases and 28 controls using the OLINK inflammation, autoimmune, cardiovascular and neurology panels. Differential expression analysis was performed within and between disease groups to generate nine different analyses. From the 368 proteins measured per individual, more than 75% were observed to be significantly perturbed in COVID-19 cases. Six proteins (IL6, CKAP4, Gal-9, IL-1ra, LILRB4 and PD-L1) were identified to be associated with disease severity. The results have been made readily available through an interactive web-based application for instant data exploration and visualization, and can be accessed at https://phidatalab-shiny.rosalind.kcl.ac.uk/COVID19/ . Our results demonstrate that dynamic changes in blood proteins associated with disease severity can potentially be used as early biomarkers to monitor disease severity in COVID-19 and serve as potential therapeutic targets.

To accurately interpret COVID-19 seroprevalence surveys, knowledge of serum-IgG responses to SARS-CoV-2 with a better understanding of patients who do not seroconvert, is imperative. This study aimed to describe serum-IgG responses to SARS-CoV-2 in a cohort of patients with both severe and mild COVID-19, including extended studies of patients who remained seronegative more than 90 days post symptom onset. SARS-CoV-2-specific IgG antibody levels were quantified using two clinically validated and widely used commercial serological assays (Architect, Abbott Laboratories and iFlash 1800, YHLO), detecting antibodies against the spike and nucleocapsid proteins. Forty-seven patients (mean age 49 years, 38% female) were included. All (15/15) patients with severe symptoms and 29/32 (90.6%) patients with mild symptoms of COVID-19 developed SARS-CoV-2-specific IgG antibodies in serum. Time to seroconversion was significantly shorter (median 11 vs. 22 days, P = 0.04) in patients with severe compared to mild symptoms. Of the three patients without detectable IgG-responses after >90 days, all had detectable virus-neutralizing antibodies and in two, spike-protein receptor binding domain-specific IgG was detected with an in-house assay. Antibody titers were preserved during follow-up and all patients who seroconverted, irrespective of the severity of symptoms, still had detectable IgG levels >75 days post symptom onset. Patients with severe COVID-19 both seroconvert earlier and develop higher concentrations of SARS-CoV-2-specific IgG than patients with mild symptoms. Of those patients who not develop detectable IgG antibodies, all have detectable virus-neutralizing antibodies, suggesting immunity. Our results showing that not all COVID-19 patients develop detectable IgG using two validated commercial clinical methods, even over time, are vital for the interpretation of COVID-19 seroprevalence surveys.

Background The COVID-19 pandemic, caused by the coronavirus SARS-CoV-2, is rapidly spreading worldwide. There is limited information about prognostic markers that could help clinicians to identify COVID-19 patients with a poor prognosis. Serum levels of the immune activation marker neopterin has shown to be of prognostic value in patients with SARS. The aim of this study was to investigate whether serum neopterin is associated with the severity of COVID-19. Methods We included 34 patients with confirmed COVID-19 between March 3 and March 30, 2020. Fifteen patients had mild disease and did not require hospitalization, whereas 19 patients developed severe COVID-19 requiring intensive care. Concentrations of serum neopterin, tryptophan, and kynurenine were measured at and repeatedly after inclusion. Results We found a more than two-fold higher mean concentration of neopterin in severely ill patients (mean value 42.0 nmol/L (SD 18.2)) compared to patients with mild symptoms (16.9 nmol/L (SD 11.0)). All of the severe cases had elevated neopterin concentrations (> 9.1 nmol/L) at the initial sampling with values ranging from 17.2 to 86.7 nmol/L. In comparison, 10 of 15 patients with mild disease had neopterin levels above 9.1 nmol/L, with concentrations in the range from 4.9 to 31.6 nmol/L. Neopterin levels gradually decreased during the course of COVID-19, but severe cases maintained elevated levels for a longer period. Moreover, lower levels of tryptophan and higher levels of kynurenine, indicating an increased tryptophan catabolism, were seen in the group with severe cases. Conclusions In conclusion, we found that serum neopterin levels are associated with the severity of COVID-19. Our findings suggest that neopterin could be used as a prognostic marker, but further studies are needed to elucidate how it can be used in the clinic.

The microbial etiology of community-acquired pneumonia (CAP) is often unclear in clinical practice, and previous studies have produced variable results. Population-based studies examining etiology and incidence are lacking. This study examined the incidence and etiology of CAP requiring hospitalization in a population-based cohort as well as risk factors and outcomes for specific etiologies. Consecutive admissions due to CAP in Reykjavik, Iceland were studied. Etiologic testing was performed with cultures, urine-antigen detection, and polymerase chain reaction analysis of airway samples. Outcomes were length of stay, intensive care unit admission, assisted ventilation, and mortality. The inclusion rate was 95%. The incidence of CAP requiring hospitalization was 20.6 cases per 10000 adults/year. A potential pathogen was detected in 52% (164 of 310) of admissions and in 74% (43 of 58) with complete sample sets. was the most common pathogen (61 of 310, 20%; incidence: 4.1/10000). Viruses were identified in 15% (47 of 310; incidence: 3.1/10000), were identified in 12% (36 of 310; incidence: 2.4/10000), and multiple pathogens were identified in 10% (30 of 310; incidence: 2.0/10000). Recent antimicrobial therapy was associated with increased detection of ( < .001), whereas a lack of recent antimicrobial therapy was associated with increased detection of ( = .02). Symptoms and outcomes were similar irrespective of microbial etiology. Pneumococci, , and viruses are the most common pathogens associated with CAP requiring hospital admission, and they all have a similar incidence that increases with age. Symptoms do not correlate with specific agents, and outcomes are similar irrespective of pathogens identified.

Background The transmission dynamics of influenza virus within healthcare settings are not fully understood. Capturing the interplay between host, viral and environmental factors is difficult using conventional research methods. Instead, system dynamic modelling may be used to illustrate the complex scenarios including non-linear relationships and multiple interactions which occur within hospitals during a seasonal influenza epidemic. We developed such a model intended as a support for health-care providers in identifying potentially effective control strategies to prevent influenza transmission. Methods By using computer simulation software, we constructed a system dynamic model to illustrate transmission dynamics within a large acute-care hospital. We used local real-world clinical and epidemiological data collected during the season 2016/17, as well as data from the national surveillance programs and relevant publications to form the basic structure of the model. Multiple stepwise simulations were performed to identify the relative effectiveness of various control strategies and to produce estimates of the accumulated number of healthcare-associated influenza cases per season. Results Scenarios regarding the number of patients exposed for influenza virus by shared room and the extent of antiviral prophylaxis and treatment were investigated in relation to estimations of influenza vaccine coverage, vaccine effectiveness and inflow of patients with influenza. In total, 680 simulations were performed, of which each one resulted in an estimated number per season. The most effective preventive measure identified by our model was administration of antiviral prophylaxis to exposed patients followed by reducing the number of patients receiving care in shared rooms. Conclusions This study presents an system dynamic model that can be used to capture the complex dynamics of in-hospital transmission of viral infections and identify potentially effective interventions to prevent healthcare-associated influenza infections. Our simulations identified antiviral prophylaxis as the most effective way to control in-hospital influenza transmission.

The risk of SARS-CoV-2 infection among health care workers (HCWs) is a concern, but studies that conclusively determine whether HCWs are over-represented remain limited. Furthermore, methods used to confirm past infection vary and the immunological response after mild COVID-19 is still not well defined. 314 HCWs were recruited from a Swedish Infectious Diseases clinic caring for COVID-19 patients. IgG antibodies were measured using two commercial assays (Abbot Architect nucleocapsid (N)-assay and YHLO iFlash-1800 N and spike (S)-assays) at five time-points, from March 2020 to January 2021, covering two pandemic waves. Seroprevalence was assessed in matched blood donors at three time-points. More extensive analyses were performed in 190 HCWs in September/October 2020, including two additional IgG-assays (DiaSorin LiaisonXL S1/S2 and Abbot Architect receptor-binding domain (RBD)-assays), neutralizing antibodies (NAbs), and CD4 T-cell reactivity using an in-house developed whole-blood assay based on flow cytometric detection of activated cells after stimulation with Spike S1-subunit or Spike, Membrane and Nucleocapsid (SMN) overlapping peptide pools. Seroprevalence was higher among HCWs compared to sex and age-matched blood donors at all time-points. Seropositivity increased from 6.4% to 16.3% among HCWs between May 2020 and January 2021, compared to 3.6% to 11.9% among blood donors. We found significant correlations and high levels of agreement between NAbs and all four commercial IgG-assays. At 200-300 days post PCR-verified infection, there was a wide variation in sensitivity between the commercial IgG-assays, ranging from <30% in the N-assay to >90% in the RBD-assay. There was only moderate agreement between NAbs and CD4 T-cell reactivity to S1 or SMN. Pre-existing CD4 T-cell reactivity was present in similar proportions among HCW who subsequently became infected and those that did not. HCWs in COVID-19 patient care in Sweden have been infected with SARS-CoV-2 at a higher rate compared to blood donors. We demonstrate substantial variation between different IgG-assays and propose that multiple serological targets should be used to verify past infection. Our data suggest that CD4 T-cell reactivity is not a suitable measure of past infection and does not reliably indicate protection from infection in naive individuals.

The proportion of elderly people living with HIV-1 (PLHIV) is rising. In older patients, comorbidities and concomitant medications are more frequent, increasing the risk of potential drug-drug interactions (PDDIs). Data on the pharmacokinetics of ART in individuals aged ≥ 65 years of age are scarce. We compared plasma drug levels of ART, PDDIs, and side-effects in PLHIV aged ≥ 65 years of age, with controls ≤ 49 years of age. Patients ≥ 65 years of age and controls ≤ 49 years of age, all of whom were on stable treatment with atazanavir (ATV), darunavir (DRV), or efavirenz (EFV) were included cross-sectionally. Plasma drug levels of ART were analyzed, comorbidities, concomitant medication, adherence, and side-effects recorded, and PDDIs analyzed using drug interactions databases. Between 2013 and 2015, we included 100 individuals ≥ 65 years of age (study group) and 99 controls (≤ 49 years of age). Steady-state DRV concentrations were significantly higher in the study group than in the control group (p = 0.047). In the ATV group there was a trend towards a significant difference (p = 0.056). No significant differences were found in the EFV arm. The DRV arm had a higher frequency of reported side-effects than the ATV and EFV arms in the study group (36.7% vs. 0% and 23.8% respectively (p = 0.014), with significant differences between DRV vs. ATV, and EFV vs. ATV). Higher steady-state plasma levels of DRV and ATV (but not EFV) were found in PLHIV aged ≥ 65 years of age, compared to controls ≤ 49 years of age.

The COVID-19 pandemic illustrates the need for serology diagnostics with improved accuracy. While conventional serology based on recognition of entire proteins or subunits thereof has made significant contribution to the antibody assessment space, it often suffers from sub-optimal specificity. Epitope-based, high-precision, serology assays hold potential to capture the high specificity and diversity of the immune system, hence circumventing the cross-reactivity with closely related microbial antigens. We herein report mapping of linear IgG and IgA antibody epitopes of the SARS-CoV-2 Spike (S) protein in samples from SARS-CoV-2 exposed individuals along with certified SARS-CoV-2 verification plasma samples using peptide arrays. We identified 21 distinct linear epitopes. Importantly, we showed that pre-pandemic serum samples contain IgG antibodies reacting to the majority of protein S epitopes, most likely as a result of prior infection with seasonal coronaviruses. Only 4 of the identified SARS-CoV-2 protein S linear epitopes were specific for SARS-CoV-2 infection. These epitopes are located at positions 278-298 and 550-586, just proximal and distal to the RBD, as well as at position 1134-1156 in the HR2 subdomain and at 1248-1271 in the C-terminal subdomain of protein S. To substantiate the applicability of our findings, we tested three of the high-accuracy protein S epitopes in a Luminex assay, using a certified validation plasma sample set from SARS-CoV-2 infected individuals. The Luminex results were well aligned with the peptide array results, and correlated very well with in-house and commercial immune assays for RBD, S1 and S1/S2 domains of protein S. We present a comprehensive mapping of linear B-cell epitopes of SARS-CoV-2 protein S, that identifies peptides suitable for a precision serology assay devoid of cross-reactivity. These results have implications for development of highly specific serology test for exposure to SARS-CoV-2 and other members of the family, as well as for rapid development of serology tests for future emerging pandemic threats.

Background The COVID-19 pandemic, which has a prominent social and economic impact worldwide, shows a largely unexplained male bias for the severity and mortality of the disease. Loss of chromosome Y (LOY) is a risk factor candidate in COVID-19 due to its prior association with many chronic age-related diseases, and its impact on immune gene transcription. Methods Publicly available scRNA-seq data of PBMC samples derived from male patients critically ill with COVID-19 were reanalyzed, and LOY status was added to the annotated cells. We further studied LOY in whole blood for 211 COVID-19 patients treated at intensive care units (ICU) from the first and second waves of the pandemic. Of these, 139 patients were subject to cell sorting for LOY analysis in granulocytes, low-density neutrophils (LDNs), monocytes, and PBMCs. Results Reanalysis of available scRNA-seq data revealed LDNs and monocytes as the cell types most affected by LOY. Subsequently, DNA analysis indicated that 46%, 32%, and 29% of critically ill patients showed LOY above 5% cut-off in LDNs, granulocytes, and monocytes, respectively. Hence, the myeloid lineage that is crucial for the development of severe COVID-19 phenotype is affected by LOY. Moreover, LOY correlated with increasing WHO score (median difference 1.59%, 95% HDI 0.46% to 2.71%, p =0.025), death during ICU treatment (median difference 1.46%, 95% HDI 0.47% to 2.43%, p =0.0036), and history of vessel disease (median difference 2.16%, 95% HDI 0.74% to 3.7%, p =0.004), among other variables. In 16 recovered patients, sampled during ICU stay and 93–143 days later, LOY decreased significantly in whole blood and PBMCs. Furthermore, the number of LDNs at the recovery stage decreased dramatically (median difference 76.4 per 10,000 cell sorting events, 95% HDI 55.5 to 104, p =6e−11). Conclusions We present a link between LOY and an acute, life-threatening infectious disease. Furthermore, this study highlights LOY as the most prominent clonal mutation affecting the myeloid cell lineage during emergency myelopoiesis. The correlation between LOY level and COVID-19 severity might suggest that this mutation affects the functions of monocytes and neutrophils, which could have consequences for male innate immunity.

To investigate the role of homocysteine in neuronal injury in HIV infection. Using a cross-sectional design and archived samples, we compared concentrations of plasma homocysteine and cerebrospinal fluid (CSF) neurofilament light protein (NFL), a sensitive marker of neuronal injury, in 83 HIV-1-infected subjects without antiretroviral treatment. We also analyzed plasma vitamin B12, serum folate, CSF, and plasma HIV RNA, the immune activation marker neopterin in CSF and serum, and albumin ratio as a marker of blood-brain barrier integrity. Twenty-two subjects provided a second sample median of 12.5 months after antiretroviral treatment initiation. A significant correlation was found between plasma homocysteine and CSF NFL concentrations in untreated individuals (r = 0.52, p < 0.0001). As expected, there was a significant inverse correlation between homocysteine and B12 (r = -0.41, p < 0.001) and folate (r = -0.40, p = < 0.001) levels. In a multiple linear regression analysis homocysteine stood out as an independent predictor of CSF NFL in HIV-1-infected individuals. The correlation of plasma homocysteine and CSF NFL was also present in the group receiving antiretroviral therapy (r = 0.51, p = 0.016). A correlation between plasma homocysteine and axonal injury, as measured by CSF NFL, was found in both untreated and treated HIV. While this study is not able to prove a causal link, homocysteine and functional B12/folate deficiency appear to play a role in neural injury in HIV-infected individuals.