SARS-CoV-2 vaccines are crucial in controlling COVID-19, but knowledge of which factors determine waning immunity is limited. We examined antibody levels and T-cell gamma-interferon release after two doses of BNT162b2 vaccine or a combination of ChAdOx1-nCoV19 and BNT162b2 vaccines for up to 230 days after the first dose. Generalized mixed models with and without natural cubic splines were used to determine immunity over time. Antibody responses were influenced by natural infection, sex, and age. IgA only became significant in naturally infected. A one-year IgG projection suggested an initial two-phase response in those given the second dose delayed (ChAdOx1/BNT162b2) followed by a more rapid decrease of antibody levels. T-cell responses correlated significantly with IgG antibody responses. Our results indicate that IgG levels will drop at different rates depending on prior infection, age, sex, T-cell response, and the interval between vaccine injections. Only natural infection mounted a significant and lasting IgA response.

BackgroundPrevious studies have indicated inferior responses to severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) vaccination in solid organ transplant (SOT) recipients. We examined the development of anti-receptor-binding domain (RBD) immunoglobulin G (IgG) after two doses of BNT162b2b in SOT recipients 6 months after vaccination and compared to that of immunocompetent controls.MethodsWe measured anti-RBD IgG after two doses of BNT162b2 in 200 SOT recipients and 200 matched healthy controls up to 6 months after first vaccination. Anti-RBD IgG concentration and neutralizing capacity of antibodies were measured at first and second doses of BNT162b2 and 2 and 6 months after the first dose. T-cell responses were measured 6 months after the first dose.ResultsIn SOT recipients, geometric mean concentration (GMC) of anti-RBD IgG increased from first to second dose (1.14 AU/ml, 95% CI 1.08–1.24 to 11.97 AU/ml, 95% CI 7.73–18.77) and from second dose to 2 months (249.29 AU/ml, 95% CI 153.70–385.19). Six months after the first vaccine, anti-RBD IgG declined (55.85 AU/ml, 95% CI 36.95–83.33). At all time points, anti-RBD IgG was lower in SOT recipients than that in controls. Fewer SOT recipients than controls had a cellular response (13.1% vs. 59.4%, p < 0.001). Risk factors associated with humoral non-response included age [relative risk (RR) 1.23 per 10-year increase, 95% CI 1.11–1.35, p < 0.001], being within 1 year from transplantation (RR 1.55, 95% CI 1.30–1.85, p < 0.001), treatment with mycophenolate (RR 1.54, 95% CI 1.09–2.18, p = 0.015), treatment with corticosteroids (RR 1.45, 95% CI 1.10–1.90, p = 0.009), kidney transplantation (RR 1.70, 95% CI 1.25–2.30, p = 0.001), lung transplantation (RR 1.63, 95% CI 1.16–2.29, p = 0.005), and de novo non-skin cancer comorbidity (RR 1.52, 95% CI, 1.26–1.82, p < 0.001).ConclusionImmune responses to BNT162b2 are inferior in SOT recipients compared to healthy controls, and studies aiming to determine the clinical impact of inferior vaccine responses are warranted.

Funding informationGPI-anchors constitute a very important post-translational modification, linking many proteins to the outer face of the plasma membrane in eukaryotic cells. Since experimental validation of GPI-anchors is slow and costly, computational approaches for predicting them from amino acid sequences are needed. However, the most recent GPI predictor is more than a decade old, and considerable progress has been made in machine learning since then. We present a new dataset and a novel method, NetGPI, for GPI prediction. The predictor is based on recurrent neural networks, incorporating an attention mechanism that simultaneously detects GPIanchors and points out the location of their ω-sites. The performance of NetGPI is superior to existing methods with regards to discrimination between GPI-anchors and other proteins and approximate (±1 position) placement of the ω-site. NetGPI is available at: https://services.healthtech.dtu.dk/service.php?NetGPI-1.0. K E Y W O R D S glycosylphosphatidylinositol, lipid anchored proteins, post-translational modification, protein sorting, prediction, neural networks

Background Patients diagnosed with ischemic heart disease (IHD) are becoming increasingly multi-morbid, and studies designed to analyze the full spectrum are few. Methods Disease trajectories, defined as time-ordered series of diagnoses, were used to study the temporality of multi-morbidity. The main data source was The Danish National Patient Register (NPR) comprising 7,179,538 individuals in the period 1994–2018. Patients with a diagnosis code for IHD were included. Relative risks were used to quantify the strength of the association between diagnostic co-occurrences comprised of two diagnoses that were overrepresented in the same patients. Multiple linear regression models were then fitted to test for temporal associations among the diagnostic co-occurrences, termed length two disease trajectories. Length two disease trajectories were then used as basis for constructing disease trajectories of three diagnoses. Results In a cohort of 570,157 IHD disease patients, we identified 1447 length two disease trajectories and 4729 significant length three disease trajectories. These included 459 distinct diagnoses. Disease trajectories were dominated by chronic diseases and not by common, acute diseases such as pneumonia. The temporal association of atrial fibrillation (AF) and IHD differed in different IHD subpopulations. We found an association between osteoarthritis (OA) and heart failure (HF) among patients diagnosed with OA, IHD, and then HF only. Conclusions The sequence of diagnoses is important in characterization of multi-morbidity in IHD patients as the disease trajectories. The study provides evidence that the timing of AF in IHD marks distinct IHD subpopulations; and secondly that the association between osteoarthritis and heart failure is dependent on IHD.

Background The durability of SARS‐CoV‐2 antibody response and the resulting immunity to COVID‐19 is unclear. Objectives To investigate long‐term humoral immunity to SARS‐CoV‐2. Methods In this nationwide, longitudinal study, we determined antibody response in 411 patients aged 0–93 years from two waves of infections (March to December 2020) contributing 1063 blood samples. Each individual had blood drawn on 4–5 occasions 1–15 months after disease onset. We measured total anti‐SARS‐CoV‐2 receptor‐binding domain (RBD) antibody using a qualitative RBD sandwich ELISA, IgM, IgG and IgA levels using an quantitative in‐house ELISA‐based assay  and neutralizing antibodies (NAbs) using an in‐house ELISA‐based pseudoneutralizing assay. IgG subclasses were analyzed in a subset of samples by ELISA‐based assay. We used nonlinear models to study the durability of SARS‐CoV‐2 antibody responses and its influence over time. Results After 15 months, 94% still had detectable circulating antibodies, mainly the IgG isotype, and 92% had detectable NAbs. The distribution of IgG antibodies varied significantly over time, characterized by a biphasic pattern with an initial decline followed by a plateau after approximately 7 months. However, the NAbs remained relatively stable throughout the period. The strength of the antibody response was influenced by smoking and hospitalization, with lower IgG levels in smokers and higher levels in hospitalized individuals. Antibody stability over time was mainly associated with male sex and older age with higher initial levels but more marked decrease. Conclusions The humoral immune response to SARS‐CoV‐2 infection varies depending on behavioral factors and disease severity, and antibody stability over 15 months was associated with sex and age.

A crucial process in the production of industrial enzymes is recombinant gene expression, which aims to induce enzyme overexpression of the genes in a host microbe. Current approaches for securing overexpression rely on molecular tools such as adjusting the recombinant expression vector, adjusting cultivation conditions, or performing codon optimizations. However, such strategies are time-consuming, and an alternative strategy would be to select genes for better compatibility with the recombinant host. Several methods for predicting expressibility and solubility are available; however, they are all optimized for the expression host Escherichia coli. We show that these tools are not suited for predicting expression potential in the industrially important host Bacillus subtilis. Instead, we build a B. subtilis-specific machine learning model for expressibility prediction. Given millions of unlabelled proteins, and a small labelled dataset, we can successfully train such a predictive model. The unlabelled proteins provide a performance boost relative to using amino acid frequencies of the labelled proteins as input. On average, we obtain a modest performance of 0.64 area-under-the-curve (AUC) and 0.2 Matthews correlation coeffcient (MCC). However, we find that this is sufficient to be useful for prioritization of expression candidates. Moreover, the predicted class probabilities are correlated with expression levels. A number of features related to protein expression, including base frequencies and solubility, are captured by the model.

IntroductionWe investigated humoral and T-cell responses within 12 months after first BNT162b2 vaccine in solid organ transplant (SOT) recipients and controls who had received at least three vaccine doses. Furthermore, we compared the immune response in participants with and without previous SARS-CoV-2 infection.MethodsWe included adult liver, lung, and kidney transplant recipients, and controls were selected from a parallel cohort of healthcare workers.ResultsAt 12th-month, the IgG geometric mean concentrations (GMCs) (P<0.001), IgA GMCs (P=0.003), and median IFN-γ (P<0.001) were lower in SOT recipients than in controls. However, in SOT recipients and controls with previous infection, the neutralizing index was 99%, and the IgG, and IgA responses were comparable. After adjustment, female-sex (aOR: 3.6, P<0.009), kidney (aOR: 7.0, P= 0.008) or lung transplantation (aOR: 7.5, P= 0.014), and use of mycophenolate (aOR: 5.2, P=0.03) were associated with low IgG non response. Age (OR:1.4, P=0.038), time from transplantation to first vaccine (OR: 0.45, P<0.035), and previous SARS-CoV-2 infection (OR: 0.14, P<0.001), were associated with low IgA non response. Diabetes (OR:2.4, P=0.044) was associated with T-cell non response.ConclusionIn conclusion, humoral and T-cell responses were inferior in SOT recipients without previous SARS-CoV-2 infection but comparable to controls in SOT recipients with previous infection.

Solubility and expression levels of proteins can be a limiting factor for large-scale studies and industrial production. By determining the solubility and expression directly from the protein sequence, the success rate of wet-lab experiments can be increased. In this study, we focus on predicting the solubility and usability for purification of proteins expressed in Escherichia coli directly from the sequence. Our model NetSolP is based on deep-learning protein language models called transformers and we show that it achieves state-of-the-art performance and improves extrapolation across datasets. As we find current methods are built on biased datasets, we curate existing datasets by using strict sequence-identity partitioning and ensure that there is minimal bias in the sequences.