A variational autoencoder trained with priors from canonical pathways increases the interpretability of transcriptome data.

Interpreting transcriptome data is an important yet challenging aspect of bioinformatic analysis. While gene set enrichment analysis is a standard tool for interpreting regulatory changes, we utilize deep learning techniques, specifically autoencoder architectures, to learn latent variables that drive transcriptome signals. We investigate whether simple, variational autoencoder (VAE), and beta-weighted VAE are capable of learning reduced representations of transcriptomes that retain critical biological information. We propose a novel VAE that utilizes priors from biological data to direct the network to learn a representation of the transcriptome that is based on understandable biological concepts. After benchmarking five different autoencoder architectures, we found that each succeeded in reducing the transcriptomes to 50 latent dimensions, which captured enough variation for accurate reconstruction. The simple, fully connected autoencoder, performs best across the benchmarks, but lacks the characteristic of having directly interpretable latent dimensions. The beta-weighted, prior-informed VAE implementation is able to solve the benchmarking tasks, and provide semantically accurate latent features equating to biological pathways. This study opens a new direction for differential pathway analysis in transcriptomics with increased transparency and interpretability.

A multicenter study of asymmetric and symmetric dimethylarginine as predictors of mortality risk in hospitalized COVID-19 patients.

Mortality of patients hospitalized with COVID-19 has remained high during the consecutive SARS-CoV-2 pandemic waves. Early discrimination of patients at high mortality risk is crucial for optimal patient care. Symmetric (SDMA) and asymmetric dimethylarginine (ADMA) have been proposed as possible biomarkers to improve risk prediction of COVID-19 patients. We measured SDMA, ADMA, and other L-arginine-related metabolites in 180 patients admitted with COVID-19 in four German university hospitals as compared to 127 healthy controls. Patients were treated according to accepted clinical guidelines and followed-up until death or hospital discharge. Classical inflammatory markers (leukocytes, CRP, PCT), renal function (eGFR), and clinical scores (SOFA) were taken from hospital records. In a small subgroup of 23 COVID-19 patients, sequential blood samples were available and analyzed for biomarker trends over time until 14 days after admission. Patients had significantly elevated SDMA, ADMA, and L-ornithine and lower L-citrulline concentrations than controls. Within COVID-19 patients, SDMA and ADMA were significantly higher in non-survivors (n = 41, 22.8%) than in survivors. In ROC analysis, the optimal cut-off to discriminate non-survivors from survivors was 0.579 µmol/L for SDMA and 0.599 µmol/L for ADMA (both p < 0.001). High SDMA and ADMA were associated with odds ratios for death of 11.45 (3.37-38.87) and 5.95 (2.63-13.45), respectively. Analysis of SDMA and ADMA allowed discrimination of a high-risk (mortality, 43.7%), medium-risk (15.1%), and low-risk group (3.6%); risk prediction was significantly improved over classical laboratory markers. We conclude that analysis of ADMA and SDMA after hospital admission significantly improves risk prediction in COVID-19.

A precise performance-based reimbursement model for the multi-centre NAPKON cohorts - development and evaluation.

Fair allocation of funding in multi-centre clinical studies is challenging. Models commonly used in Germany - the case fees ("fixed-rate model", FRM) and up-front staffing and consumables ("up-front allocation model", UFAM) lack transparency and fail to suitably accommodate variations in centre performance. We developed a performance-based reimbursement model (PBRM) with automated calculation of conducted activities and applied it to the cohorts of the National Pandemic Cohort Network (NAPKON) within the Network of University Medicine (NUM). The study protocol activities, which were derived from data management systems, underwent validation through standardized quality checks by multiple stakeholders. The PBRM output (first funding period) was compared among centres and cohorts, and the cost-efficiency of the models was evaluated. Cases per centre varied from one to 164. The mean case reimbursement differed among the cohorts (1173.21€ [95% CI 645.68-1700.73] to 3863.43€ [95% CI 1468.89-6257.96]) and centres and mostly fell short of the expected amount. Model comparisons revealed higher cost-efficiency of the PBRM compared to FRM and UFAM, especially for low recruitment outliers. In conclusion, we have developed a reimbursement model that is transparent, accurate, and flexible. In multi-centre collaborations where heterogeneity between centres is expected, a PBRM could be used as a model to address performance discrepancies.Trial registration: https://clinicaltrials.gov/ct2/show/NCT04768998 ; https://clinicaltrials.gov/ct2/show/NCT04747366 ; https://clinicaltrials.gov/ct2/show/NCT04679584 .

An artificial intelligence-assisted clinical framework to facilitate diagnostics and translational discovery in hematologic neoplasia.

BACKGROUND: The increasing volume and intricacy of sequencing data, along with other clinical and diagnostic data, like drug responses and measurable residual disease, creates challenges for efficient clinical comprehension and interpretation. Using paediatric B-cell precursor acute lymphoblastic leukaemia (BCP-ALL) as a use case, we present an artificial intelligence (AI)-assisted clinical framework clinALL that integrates genomic and clinical data into a user-friendly interface to support routine diagnostics and reveal translational insights for hematologic neoplasia. METHODS: We performed targeted RNA sequencing in 1365 cases with haematological neoplasms, primarily paediatric B-cell precursor acute lymphoblastic leukaemia (BCP-ALL) from the AIEOP-BFM ALL study. We carried out fluorescence in situ hybridization (FISH), karyotyping and arrayCGH as part of the routine diagnostics. The analysis results of these assays as well as additional clinical information were integrated into an interactive web interface using Bokeh, where the main graph is based on Uniform Manifold Approximation and Projection (UMAP) analysis of the gene expression data. At the backend of the clinALL, we built both shallow machine learning models and a deep neural network using Scikit-learn and PyTorch respectively. FINDINGS: By applying clinALL, 78% of undetermined patients under the current diagnostic protocol were stratified, and ambiguous cases were investigated. Translational insights were discovered, including IKZF1plus status dependent subpopulations of BCR::ABL1 positive patients, and a subpopulation within ETV6::RUNX1 positive patients that has a high relapse frequency. Our best machine learning models, LDA and PASNET-like neural network models, achieve F1 scores above 97% in predicting patients' subgroups. INTERPRETATION: An AI-assisted clinical framework that integrates both genomic and clinical data can take full advantage of the available data, improve point-of-care decision-making and reveal clinically relevant insights promptly. Such a lightweight and easily transferable framework works for both whole transcriptome data as well as the cost-effective targeted RNA-seq, enabling efficient and equitable delivery of personalized medicine in small clinics in developing countries. FUNDING: German Ministry of Education and Research (BMBF), German Research Foundation (DFG) and Foundation for Polish Science.

Validation of the predictive value of BDNF -87 methylation for antidepressant treatment success in severely depressed patients-a randomized rater-blinded trial.

BACKGROUND: Brain-derived neurotrophic factor (BDNF) is essential for antidepressant treatment of major depressive disorder (MDD). Our repeated studies suggest that DNA methylation of a specific CpG site in the promoter region of exon IV of the BDNF gene (CpG -87) might be predictive of the efficacy of monoaminergic antidepressants such as selective serotonin reuptake inhibitors (SSRIs), serotonin-norepinephrine reuptake inhibitors (SNRIs), and others. This trial aims to evaluate whether knowing the biomarker is non-inferior to treatment-as-usual (TAU) regarding remission rates while exhibiting significantly fewer adverse events (AE). METHODS: The BDNF trial is a prospective, randomized, rater-blinded diagnostic study conducted at five university hospitals in Germany. The study's main hypothesis is that {1} knowing the methylation status of CpG -87 is non-inferior to not knowing it with respect to the remission rate while it significantly reduces the AE rate in patients experiencing at least one AE. The baseline assessment will occur upon hospitalization and a follow-up assessment on day 49 (± 3). A telephone follow-up will be conducted on day 70 (± 3). A total of 256 patients will be recruited, and methylation will be evaluated in all participants. They will be randomly assigned to either the marker or the TAU group. In the marker group, the methylation results will be shared with both the patient and their treating physician. In the TAU group, neither the patients nor their treating physicians will receive the marker status. The primary endpoints include the rate of patients achieving remission on day 49 (± 3), defined as a score of ≤ 10 on the Hamilton Depression Rating Scale (HDRS-24), and the occurrence of AE. ETHICS AND DISSEMINATION: The trial protocol has received approval from the Institutional Review Boards at the five participating universities. This trial holds significance in generating valuable data on a predictive biomarker for antidepressant treatment in patients with MDD. The findings will be shared with study participants, disseminated through professional society meetings, and published in peer-reviewed journals. TRIAL REGISTRATION: German Clinical Trial Register DRKS00032503. Registered on 17 August 2023.

Metabolomics analysis reveals novel serum metabolite alterations in cancer cachexia.

Background: Cachexia is a body wasting syndrome that significantly affects well-being and prognosis of cancer patients, without effective treatment. Serum metabolites take part in pathophysiological processes of cancer cachexia, but apart from altered levels of select serum metabolites, little is known on the global changes of the overall serum metabolome, which represents a functional readout of the whole-body metabolic state. Here, we aimed to comprehensively characterize serum metabolite alterations and analyze associated pathways in cachectic cancer patients to gain new insights that could help instruct strategies for novel interventions of greater clinical benefit. Methods: Serum was sampled from 120 metastatic cancer patients (stage UICC IV). Patients were grouped as cachectic or non-cachectic according to the criteria for cancer cachexia agreed upon international consensus (main criterium: weight loss adjusted to body mass index). Samples were pooled by cachexia phenotype and assayed using non-targeted gas chromatography-mass spectrometry (GC-MS). Normalized metabolite levels were compared using t-test (p < 0.05, adjusted for false discovery rate) and partial least squares discriminant analysis (PLS-DA). Machine-learning models were applied to identify metabolite signatures for separating cachexia states. Significant metabolites underwent MetaboAnalyst 5.0 pathway analysis. Results: Comparative analyses included 78 cachectic and 42 non-cachectic patients. Cachectic patients exhibited 19 annotable, significantly elevated (including glucose and fructose) or decreased (mostly amino acids) metabolites associating with aminoacyl-tRNA, glutathione and amino acid metabolism pathways. PLS-DA showed distinct clusters (accuracy: 85.6%), and machine-learning models identified metabolic signatures for separating cachectic states (accuracy: 83.2%; area under ROC: 88.0%). We newly identified altered blood levels of erythronic acid and glucuronic acid in human cancer cachexia, potentially linked to pentose-phosphate and detoxification pathways. Conclusion: We found both known and yet unknown serum metabolite and metabolic pathway alterations in cachectic cancer patients that collectively support a whole-body metabolic state with impaired detoxification capability, altered glucose and fructose metabolism, and substrate supply for increased and/or distinct metabolic needs of cachexia-associated tumors. These findings together imply vulnerabilities, dependencies and targets for novel interventions that have potential to make a significant impact on future research in an important field of cancer patient care.

Human Bronchial Epithelial Cell Transcriptome Changes in Response to Serum from Patients with Different Status of Inflammation.

PURPOSE: To investigate the transcriptome of human bronchial epithelial cells (HBEC) in response to serum from patients with different degrees of inflammation. METHODS: Serum from 19 COVID-19 patients obtained from the Hannover Unified Biobank was used. At the time of sampling, 5 patients had a WHO Clinical Progression Scale (WHO-CPS) score of 9 (severe illness). The remaining 14 patients had a WHO-CPS of below 9 (range 1-7), and lower illness. Multiplex immunoassay was used to assess serum inflammatory markers. The culture medium of HBEC was supplemented with 2% of the patient's serum, and the cells were cultured at 37 °C, 5% CO2 for 18 h. Subsequently, cellular RNA was used for RNA-Seq. RESULTS: Patients with scores below 9 had significantly lower albumin and serum levels of E-selectin, IL-8, and MCP-1 than patients with scores of 9. Principal component analysis based on 500 "core genes" of RNA-seq segregated cells into two subsets: exposed to serum from 4 (I) and 15 (II) patients. Cells from a subset (I) treated with serum from 4 patients with a score of 9 showed 5566 differentially expressed genes of which 2793 were up- and 2773 downregulated in comparison with cells of subset II treated with serum from 14 patients with scores between 1 and 7 and one with score = 9. In subset I cells, a higher expression of TLR4 and CXCL8 but a lower CDH1, ACE2, and HMOX1, and greater effects on genes involved in metabolic regulation, cytoskeletal organization, and kinase activity pathways were observed. CONCLUSION: This simple model could be useful to characterize patient serum and epithelial cell properties.

Population-Based Biobanking.

Population-based biobanking is an essential element of medical research that has grown substantially over the last two decades, and many countries are currently pursuing large national biobanking initiatives. The rise of individual biobanks is paralleled by various networking activities in the field at both the national and international level, such as BBMRI-ERIC in the EU. A significant contribution to population-based biobanking comes from large cohort studies and national repositories, including the United Kingdom Biobank (UKBB), the CONSTANCES project in France, the German National Cohort (NAKO), LifeLines in the Netherlands, FinnGen in Finland, and the All of Us project in the U.S. At the same time, hospital-based biobanking has also gained importance in medical research. We describe some of the scientific questions that can be addressed particularly well by the use of population-based biobanks, including the discovery and calibration of biomarkers and the identification of molecular correlates of health parameters and disease states. Despite the tremendous progress made so far, some major challenges to population-based biobanking still remain, including the need to develop strategies for the long-term sustainability of biobanks, the handling of incidental findings, and the linkage of sample-related and sample-derived data to other relevant resources.

Effects of Repeated Freeze and Thaw Cycles on the Genome-Wide DNA Methylation Profile of Isolated Genomic DNA.

The characterization of DNA methylation patterns to identify epigenetic markers for complex human diseases is an important and rapidly evolving part in biomedical research. DNA samples collected and stored in clinical biobanks over the past years are an important source for future epigenetic studies. Isolated gDNA is considered stable when stored at low temperatures for several years. However, the effect of multiple use and the associated repeated thawing of long-term stored DNA samples on DNA methylation patterns has not yet been investigated. In this study, we examined the influence of up to 10 freeze and thaw cycles on global DNA methylation by comparing genome-wide methylation profiles. DNA samples from 19 healthy volunteers were either frozen at -80°C or subjected to up to 10 freeze and thaw cycles. Genome-wide DNA methylation was analyzed after 0, 1, 3, 5, or 10 thaw cycles using the Illumina Infinium MethylationEPIC BeadChip. Evaluation of the global DNA methylation profile by beta-value density plots and multidimensional scaling plots revealed an expected clear participant-dependent variability, but a very low variability depending on the freeze and thaw cycles. In accordance, no significant difference in any of the methylated cytosine/guanine sites studied could be detected in the performed statistical analyses. Our results suggest that long-term frozen DNA samples are still suitable for epigenetic studies after multiple thaw cycles.

Ethical and coordinative challenges in setting up a national cohort study during the COVID-19 pandemic in Germany.

With the outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), global researchers were confronted with major challenges. The German National Pandemic Cohort Network (NAPKON) was launched in fall 2020 to effectively leverage resources and bundle research activities in the fight against the coronavirus disease 2019 (COVID-19) pandemic. We analyzed the setup phase of NAPKON as an example for multicenter studies in Germany, highlighting challenges and optimization potential in connecting 59 university and nonuniversity study sites. We examined the ethics application process of 121 ethics submissions considering durations, annotations, and outcomes. Study site activation and recruitment processes were investigated and related to the incidence of SARS-CoV-2 infections. For all initial ethics applications, the median time to a positive ethics vote was less than two weeks and 30 of these study sites (65%) joined NAPKON within less than three weeks each. Electronic instead of postal ethics submission (9.5 days (Q1: 5.75, Q3: 17) vs. 14 days (Q1: 11, Q3: 26), p value = 0.01) and adoption of the primary ethics vote significantly accelerated the ethics application process. Each study center enrolled a median of 37 patients during the 14-month observation period, with large differences depending on the health sector. We found a positive correlation between recruitment performance and COVID-19 incidence as well as hospitalization incidence. Our analysis highlighted the challenges and opportunities of the federated system in Germany. Digital ethics application tools, adoption of a primary ethics vote and standardized formal requirements lead to harmonized and thus faster study initiation processes during a pandemic.

The Changing Epidemiology of Viral Hepatitis in a Post-Soviet Country-The Case of Kyrgyzstan.

Historically, viral hepatitis has been a considerable public health problem in Central Asian countries, which may have worsened after the dissolution of the Soviet Union. However, up-to-date seroepidemiological studies are lacking. The aim of the present study was, therefore, to provide current estimates of the seroprevalence of viral hepatitis in Kyrgyzstan, one of the economically least developed countries in the region. We conducted a population-based cross-sectional study in 2018 in the capital of Kyrgyzstan, Bishkek (n = 1075). Participants, children and adults, were recruited from an outpatient clinic. The data were collected during face-to-face interviews. A blood sample (6 mL) was collected from each participant and tested with ELISA for the presence of serological markers for five viral hepatitides (A, B, C, D, and E). Post-stratification weighing was performed to obtain nationally representative findings. The overwhelming majority of the study participants were positive for anti-HAV (estimated seroprevalence, 75.3%; 95% confidence interval, 72.5-77.9%). The weighted seroprevalence estimates of HBsAg, anti-HCV, and anti-HDV were 2.2% (1.5-3.3%), 3.8% (2.8-5.1%), and 0.40% (0.15-1.01%), respectively. Anti-HEV seropositivity was 3.3% (2.4-4.5%). Of the 33 HBsAg-positive participants, five (15%) were anti-HDV-positive. Our study confirms that Kyrgyzstan remains a highly endemic country for hepatitis virus A and C infections. However, seroprevalences of HBV and HDV were lower than previously reported, and based on these data, the country could potentially be reclassified from high to (lower) intermediate endemicity. The observed anti-HEV seroprevalence resembles the low endemicity pattern characteristic of high-income countries.

The Importance of Being FAIR and FAST - The Clinical Epidemiology and Study Platform of the German Network University Medicine (NUKLEUS).

The COVID-19 pandemic has urged the need to set up, conduct and analyze high-quality epidemiological studies within a very short time-scale to provide timely evidence on influential factors on the pandemic, e.g. COVID-19 severity and disease course. The comprehensive research infrastructure developed to run the German National Pandemic Cohort Network within the Network University Medicine is now maintained within a generic clinical epidemiology and study platform NUKLEUS. It is operated and subsequently extended to allow efficient joint planning, execution and evaluation of clinical and clinical-epidemiological studies. We aim to provide high-quality biomedical data and biospecimens and make its results widely available to the scientific community by implementing findability, accessibility, interoperability and reusability - i.e. following the FAIR guiding principles. Thus, NUKLEUS might serve as role model for FAIR and fast implementation of clinical epidemiological studies within the setting of University Medical Centers and beyond.

Functional characterization of novel or yet uncharacterized ATP7B missense variants detected in patients with clinical Wilson's disease.

Wilson's disease (WD, MIM#277900) is an autosomal recessive disorder resulting in copper excess caused by biallelic variants in the ATP7B gene (MIM#606882) encoding a copper transporting P-type ATPase. ATP7B variants of unknown significance (VUS) are detected frequently, sometimes impeding a clear diagnosis. Functional analyses can help to classify these variants as benign or pathogenic. Additionally, variants already classified as (likely) pathogenic benefit from functional analyses to understand their pathomechanism, thus contribute to the development of personalized treatment approaches in the future. We described clinical features of six WD patients and functionally characterized five ATP7B missense variants (two VUS, three yet uncharacterized likely pathogenic variants), detected in these patients. We determined the protein level, copper export capacity, and cellular localization in an in vitro model and potential structural consequences using an ATP7B protein model based on AlphaFold. Our analyses give insight into the pathomechanism and allowed reclassification for the two VUS to likely pathogenic and for two of the three likely pathogenic variants to pathogenic.

Pathogen spectra in hospitalised and nonhospitalised children with community-acquired pneumonia.

Background: Paediatric community-acquired pneumonia (CAP) is a leading cause of paediatric morbidity. However, particularly for outpatients with paediatric CAP, data on aetiology and management are scarce. Methods: The prospective pedCAPNETZ study multicentrically enrols children and adolescents with outpatient-treated or hospitalised paediatric CAP in Germany. Blood and respiratory specimens were collected systematically, and comprehensive analyses of pathogen spectra were conducted. Follow-up evaluations were performed until day 90 after enrolment. Results: Between December 2014 and August 2020, we enrolled 486 children with paediatric CAP at eight study sites, 437 (89.9%) of whom had radiographic evidence of paediatric CAP. Median (interquartile range) age was 4.5 (1.6-6.6) years, and 345 (78.9%) children were hospitalised. The most prevalent symptoms at enrolment were cough (91.8%), fever (89.2%) and tachypnoea (62.0%). Outpatients were significantly older, displayed significantly lower C-reactive protein levels and were significantly more likely to be symptom-free at follow-up days 14 and 90. Pathogens were detected in 90.3% of all patients (one or more viral pathogens in 68.1%; one or more bacterial strains in 18.7%; combined bacterial/viral pathogens in 4.1%). Parainfluenza virus and Mycoplasma pneumoniae were significantly more frequent in outpatients. The proportion of patients with antibiotic therapy was comparably high in both groups (92.4% of outpatients versus 86.2% of hospitalised patients). Conclusion: We present first data on paediatric CAP with comprehensive analyses in outpatients and hospitalised cases and demonstrate high detection rates of viral pathogens in both groups. Particularly in young paediatric CAP patients with outpatient care, antibiotic therapy needs to be critically debated.

Long-term human spaceflight and inflammaging: Does it promote aging?

Spaceflight and its associated stressors, such as microgravity, radiation exposure, confinement, circadian derailment and disruptive workloads represent an unprecedented type of exposome that is entirely novel from an evolutionary stand point. Within this perspective, we aimed to review the effects of prolonged spaceflight on immune-neuroendocrine systems, brain and brain-gut axis, cardiovascular system and musculoskeletal apparatus, highlighting in particular the similarities with an accelerated aging process. In particular, spaceflight-induced muscle atrophy/sarcopenia and bone loss, vascular and metabolic changes, hyper and hypo reaction of innate and adaptive immune system appear to be modifications shared with the aging process. Most of these modifications are mediated by molecular events that include oxidative and mitochondrial stress, autophagy, DNA damage repair and telomere length alteration, among others, which directly or indirectly converge on the activation of an inflammatory response. According to the inflammaging theory of aging, such an inflammatory response could be a driver of an acceleration of the normal, physiological rate of aging and it is likely that all the systemic modifications in turn lead to an increase of inflammaging in a sort of vicious cycle. The most updated countermeasures to fight these modifications will be also discussed in the light of their possible application not only for astronauts' benefit, but also for older adults on the ground.

GBA/GBN-position on the feedback of incidental findings in biobank-based research: consensus-based workflow for hospital-based biobanks.

Incidental research findings pose a considerable challenge to hospital-based research biobanks since they are acting as intermediaries between healthcare and research. In a joint action the centralized biobank ibdw (Interdisciplinary Bank of Biomaterials and Data Wuerzburg) together with local authorities drafted a coherent concept to manage incidental research findings in full compliance with relevant ethical and data privacy regulations. The concept was developed and elaborated in close collaboration with the German Biobank Alliance (GBA). Comprehensive documentation of all steps guarantees the traceability of the process. By a mandatory assessment of the findings prior to re-identification of the individual concerned, unnecessary measures can be avoided. The individual's "right not to know" is respected according to the stipulations of the informed consent. As a general principle any communication with the individual occurs exclusively through the hospital and by competent physicians with appropriate knowledge and communication skills. We propose this scheme as a blueprint for reporting workflows for incidental research findings at hospital-based biobanks.

Genetic Predisposition to Neurological Complications in Patients with COVID-19.

Several studies have identified rare and common genetic variants associated with severe COVID-19, but no study has reported genetic determinants as predisposition factors for neurological complications. In this report, we identified rare/unique structural variants (SVs) implicated in neurological functions in two individuals with neurological manifestations of COVID-19. This report highlights the possible genetic link to the neurological symptoms with COVID-19 and calls for a collective effort to study these cohorts for a possible genetic linkage.

Altered and allele-specific open chromatin landscape reveals epigenetic and genetic regulators of innate immunity in COVID-19.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection causes severe COVID-19 in some patients and mild COVID-19 in others. Dysfunctional innate immune responses have been identified to contribute to COVID-19 severity, but the key regulators are still unknown. Here, we present an integrative single-cell multi-omics analysis of peripheral blood mononuclear cells from hospitalized and convalescent COVID-19 patients. In classical monocytes, we identified genes that were potentially regulated by differential chromatin accessibility. Then, sub-clustering and motif-enrichment analyses revealed disease condition-specific regulation by transcription factors and their targets, including an interaction between C/EBPs and a long-noncoding RNA LUCAT1, which we validated through loss-of-function experiments. Finally, we investigated genetic risk variants that exhibit allele-specific open chromatin (ASoC) in COVID-19 patients and identified a SNP rs6800484-C, which is associated with lower expression of CCR2 and may contribute to higher viral loads and higher risk of COVID-19 hospitalization. Altogether, our study highlights the diverse genetic and epigenetic regulators that contribute to COVID-19.

Plasma markers of COVID-19 severity: a pilot study.

BACKGROUND: SARS-CoV-2 infected patients show heterogeneous clinical presentations ranging from mild symptoms to severe respiratory failure and death. Consequently, various markers reflect this wide spectrum of disease presentations. METHODS: Our pilot cohort included moderate (n = 10) and severe (n = 10) COVID-19 patients, and 10 healthy controls. We determined plasma levels of nine acute phase proteins (APPs) by nephelometry, and full-length (M65), caspase-cleaved (M30) cytokeratin 18, and ADAMTS13 (a disintegrin-like and metalloprotease with thrombospondin type-1 motif 13) by ELISA. In addition, we examined whole plasma N-glycosylation by capillary gel electrophoresis coupled to laser-induced fluorescence detection (CGE-LIF). RESULTS: When compared to controls, COVID-19 patients had significantly lower concentrations of ADAMTS13 and albumin (ALB) but higher M30, M65, α1-acid glycoprotein (AGP), α1-antitrypsin (AAT), ceruloplasmin (CP), haptoglobin (HP), and high-sensitivity C-reactive protein (hs-CRP). The concentrations of α1-antichymotrypsin (ACT), α2-macroglobulin (A2MG) and serum amyloid A (SAA) proteins did not differ. We found significantly higher levels of AAT and M65 but lower ALB in severe compared to moderate COVID-19 patients. N-glycan analysis of the serum proteome revealed increased levels of oligomannose- and sialylated di-antennary glycans and decreased non-sialylated di-antennary glycan A2G2 in COVID-19 patients compared to controls. CONCLUSIONS: COVID-19-associated changes in levels and N-glycosylation of specific plasma proteins highlight complexity of inflammatory process and grant further investigations.

Candidate genes and sequence variants for susceptibility to mycobacterial infection identified by whole-exome sequencing.

Inborn errors of immunity are known to influence susceptibility to mycobacterial infections. The aim of this study was to characterize the genetic profile of nine patients with mycobacterial infections (eight with BCGitis and one with disseminated tuberculosis) from the Republic of Moldova using whole-exome sequencing. In total, 12 variants in eight genes known to be associated with Mendelian Susceptibility to Mycobacterial Disease (MSMD) were detected in six out of nine patients examined. In particular, a novel splice site mutation c.373-2A>C in STAT1 gene was found and functionally confirmed in a patient with disseminated tuberculosis. Trio analysis was possible for seven out of nine patients, and resulted in 23 candidate variants in 15 novel genes. Four of these genes - GBP2, HEATR3, PPP1R9B and KDM6A were further prioritized, considering their elevated expression in immune-related tissues. Compound heterozygosity was found in GBP2 in a single patient, comprising a maternally inherited missense variant c.412G>A/p.(Ala138Thr) predicted to be deleterious and a paternally inherited intronic mutation c.1149+14T>C. Functional studies demonstrated that the intronic mutation affects splicing and the level of transcript. Finally, we analyzed pathogenicity of variant combinations in gene pairs and identified five patients with putative oligogenic inheritance. In summary, our study expands the spectrum of genetic variation contributing to susceptibility to mycobacterial infections in children and provides insight into the complex/oligogenic disease-causing mode.