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Oligodendroglioma poses a biological conundrum for malignant adult human gliomas: it is a tumor type that is universally incurable for patients, and yet, only a few of the human tumors have been established as cell populations in vitro or as intracranial xenografts in vivo. Their survival, thus, may emerge only within a specific environmental context. To determine the fate of human oligodendroglioma in an experimental model, we studied the development of an anaplastic tumor after intracranial implantation into enhanced green fluorescent protein (eGFP) positive NOD/SCID mice. Remarkably after nearly nine months, the tumor not only engrafted, but it also retained classic histological and genetic features of human oligodendroglioma, in particular cells with a clear cytoplasm, showing an infiltrative growth pattern, and harboring mutations of IDH1 (R132H) and of the tumor suppressor genes, FUBP1 and CIC. The xenografts were highly invasive, exhibiting a distinct migration and growth pattern around neurons, especially in the hippocampus, and following white matter tracts of the corpus callosum with tumor cells accumulating around established vasculature. Although tumors exhibited a high growth fraction in vivo, neither cells from the original patient tumor nor the xenograft exhibited significant growth in vitro over a six-month period. This glioma xenograft is the first to display a pure oligodendroglioma histology and expression of R132H. The unexpected property, that the cells fail to grow in vitro even after passage through the mouse, allows us to uniquely investigate the relationship of this oligodendroglioma with the in vivo microenvironment. [ABSTRACT FROM AUTHOR]

Summary: Virus infection-induced global protein synthesis suppression is linked to assembly of stress granules (SGs), cytosolic aggregates of stalled translation preinitiation complexes. To study long-term stress responses, we developed an imaging approach for extended observation and analysis of SG dynamics during persistent hepatitis C virus (HCV) infection. In combination with type 1 interferon, HCV infection induces highly dynamic assembly/disassembly of cytoplasmic SGs, concomitant with phases of active and stalled translation, delayed cell division, and prolonged cell survival. Double-stranded RNA (dsRNA), independent of viral replication, is sufficient to trigger these oscillations. Translation initiation factor eIF2α phosphorylation by protein kinase R mediates SG formation and translation arrest. This is antagonized by the upregulation of GADD34, the regulatory subunit of protein phosphatase 1 dephosphorylating eIF2α. Stress response oscillation is a general mechanism to prevent long-lasting translation repression and a conserved host cell reaction to multiple RNA viruses, which HCV may exploit to establish persistence. [ABSTRACT FROM AUTHOR]

Summary: Hepatitis C virus (HCV) is a major causative agent of chronic liver disease in humans. To gain insight into host factor requirements for HCV replication, we performed a siRNA screen of the human kinome and identified 13 different kinases, including phosphatidylinositol-4 kinase III alpha (PI4KIIIα), as being required for HCV replication. Consistent with elevated levels of the PI4KIIIα product phosphatidylinositol-4-phosphate (PI4P) detected in HCV-infected cultured hepatocytes and liver tissue from chronic hepatitis C patients, the enzymatic activity of PI4KIIIα was critical for HCV replication. Viral nonstructural protein 5A (NS5A) was found to interact with PI4KIIIα and stimulate its kinase activity. The absence of PI4KIIIα activity induced a dramatic change in the ultrastructural morphology of the membranous HCV replication complex. Our analysis suggests that the direct activation of a lipid kinase by HCV NS5A contributes critically to the integrity of the membranous viral replication complex. [ABSTRACT FROM AUTHOR]

For neuroblastoma, the most common extracranial tumour of childhood, identification of new biomarkers and potential therapeutic targets is mandatory to improve risk stratification and survival rates. MicroRNAs are deregulated in most cancers, including neuroblastoma. In this study, we analysed 430 miRNAs in 69 neuroblastomas by stem-loop RT-qPCR. Prediction of event-free survival (EFS) with support vector machines (SVM) and actual survival times with Cox regression-based models (CASPAR) were highly accurate and were independently validated. SVM-accuracy for prediction of EFS was 88.7% (95% CI: 88.5-88.8%). For CASPAR-based predictions, 5y-EFS probability was 0.19% (95% CI: 0-38%) in the CASPAR-predicted short survival group compared with 0.78% (95%CI: 64-93%) in the CASPAR-predicted long survival group. Both classifiers were validated on an independent test set yielding accuracies of 94.74% (SVM) and 5y-EFS probabilities as 0.25 (95% CI: 0.0-0.55) for short versus 1 ± 0.0 for long survival (CASPAR), respectively. Amplification of the MYCN oncogene was highly correlated with deregulation of miRNA expression. In addition, 37 miRNAs correlated with TrkA expression, a marker of excellent outcome, and 6 miRNAs further analysed in vitro were regulated upon TrkA transfection, suggesting a functional relationship. Expression of the most significant TrkA-correlated miRNA, miR-542-5p, also discriminated between local and metastatic disease and was inversely correlated with MYCN amplification and event-free survival. We conclude that neuroblastoma patient outcome prediction using miRNA expression is feasible and effective. Studies testing miRNA-based predictors in comparison to and in combination with mRNA and aCGH information should be initiated. Specific miRNAs ( e.g., miR-542-5p) might be important in neuroblastoma tumour biology, and qualify as potential therapeutic targets. [ABSTRACT FROM AUTHOR]

Fibroblast growth factor 2 (FGF2) is a potent mitogen that is exported from cells by an endoplasmic reticulum (ER)/Golgi-independent mechanism. Unconventional secretion of FGF2 occurs by direct translocation across plasma membranes, a process that depends on the phosphoinositide phosphatidylinositol 4,5-biphosphate (PI(4,5)P2) at the inner leaflet as well as heparan sulfate proteoglycans at the outer leaflet of plasma membranes; however, additional core and regulatory components of the FGF2 export machinery have remained elusive. Here, using a highly effective RNAi screening approach, we discovered Tec kinase as a novel factor involved in unconventional secretion of FGF2. Tec kinase does not affect FGF2 secretion by an indirect mechanism, but rather forms a heterodimeric complex with FGF2 resulting in phosphorylation of FGF2 at tyrosine 82, a post-translational modification shown to be essential for FGF2 membrane translocation to cell surfaces. Our findings suggest a crucial role for Tec kinase in regulating FGF2 secretion under various physiological conditions and, therefore, provide a new perspective for the development of a novel class of antiangiogenic drugs targeting the formation of the FGF2/Tec complex. [ABSTRACT FROM AUTHOR]

Motivation: The reconstruction of signaling pathways from gene knockdown data is a novel research field enabled by developments in RNAi screening technology. However, while RNA interference is a powerful technique to identify genes related to a phenotype of interest, their placement in the corresponding pathways remains a challenging problem. Difficulties are aggravated if not all pathway components can be observed after each knockdown, but readouts are only available for a small subset. We are then facing the problem of reconstructing a network from incomplete data. [ABSTRACT FROM PUBLISHER]

Abstract: High-throughput techniques allow measurement of hundreds of cell components simultaneously. The inference of interactions between cell components from these experimental data facilitates the understanding of complex regulatory processes. Differential equations have been established to model the dynamic behavior of these regulatory networks quantitatively. Usually traditional regression methods for estimating model parameters fail in this setting, since they overfit the data. This is even the case, if the focus is on modeling subnetworks of, at most, a few tens of components. In a Bayesian learning approach, this problem is avoided by a restriction of the search space with prior probability distributions over model parameters. This paper combines both differential equation models and a Bayesian approach. We model the periodic behavior of proteins involved in the cell cycle of the budding yeast Saccharomyces cerevisiae, with differential equations, which are based on chemical reaction kinetics. One property of these systems is that they usually converge to a steady state, and lots of efforts have been made to explain the observed periodic behavior. We introduce an approach to infer an oscillating network from experimental data. First, an oscillating core network is learned. This is extended by further components by using a Bayesian approach in a second step. A specifically designed hierarchical prior distribution over interaction strengths prevents overfitting, and drives the solutions to sparse networks with only a few significant interactions. We apply our method to a simulated and a real world dataset and reveal main regulatory interactions. Moreover, we are able to reconstruct the dynamic behavior of the network. [Copyright &y& Elsevier]

Summary: With the widespread availability of high-throughput experimental technologies it has become possible to study hundreds to thousands of cellular factors simultaneously, such as coding- or non-coding mRNA or protein concentrations. Still, extracting information about the underlying regulatory or signaling interactions from these data remains a difficult challenge. We present a flexible approach towards network inference based on linear programming. Our method reconstructs the interactions of factors from a combination of perturbation/non-perturbation and steady-state/time-series data. We show both on simulated and real data that our methods are able to reconstruct the underlying networks fast and efficiently, thus shedding new light on biological processes and, in particular, into disease's mechanisms of action. We have implemented the approach as an R package available through bioconductor. Availability and implementation: This R package is freely available under the Gnu Public License (GPL-3) from bioconductor.org (http://bioconductor.org/packages/release/bioc/html/lpNet.html) and is compatible with most operating systems (Windows, Linux, Mac OS) and hardware architectures. [ABSTRACT FROM AUTHOR]

Background: The diagnosis of rare uterine leiomyosarcoma (uLMS) remains a challenge given the high incidence rates of benign uterine tumors such as leiomyoma (LM). In the last decade, several clinical scores and blood serum markers have been proposed. The aim of this study is to validate and update the pLMS clinical scoring system, evaluating the accuracy of the scoring system by Zhang et al. and examining the discriminatory ability of blood markers such as serum lactate dehydrogenase (LDH), neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR). Methods: In a case-control study, 90 new uLMS from the DKSM consultation registry and 659 prospectively recruited LM cases from the Saarland University Hospital were used for validation. Welch's t-test and Hedges' g were used to evaluate blood markers and optimal thresholds and diagnostic odds ratios were calculated. Scoring systems were compared using receiver operating characteristics and proposed diagnostic cut-offs were reviewed. Missing values were imputed by random forest imputation to create the updated scoring system 'pLMS2' using penalized logistic regression based on the pooled data sets of 384 uLMS and 1485 LM. Results: pLMS achieved an AUC of 0.97 on the validation data, but sensitivity and specificity varied at the proposed thresholds due to a shift in the score distributions. 43 uLMS and 578 LM were included in the comparison of pLMS with Zhang's scoring system, with pLMS being superior (AUC 0.960 vs 0.845). LDH, NLR, and PLR achieved a diagnostic odds ratios of 18.03, 8.64 and 4.81, respectively. pLMS2 is based on subscores for menopausal status interacting with age, tumor diameter, intermenstrual bleeding, hypermenorrhea, dysmenorrhea, postmenstrual bleeding, rapid tumor growth, and suspicious sonography. Conclusions: Validation of the pLMS shows stable discriminatory ability as expressed by AUC, although caution should be taken with cut-off values, as sensitivity and specificity may vary. Data collection of the updated clinical score pLMS2 remains simple and convenient, with no additional cost. The proposed thresholds of 1.5 and 5.5 can be used as a guide to avoid unnecessary or inappropriate surgery and to make the use of further diagnostic measures cost-effective. LDH, NLR and PLR provide further evidence to differentiate uLMS from LM in conjunction with clinical data. [ABSTRACT FROM AUTHOR]

Hepatitis C virus (HCV) infection is a leading cause of liver cirrhosis and cancer1. Cell entry of HCV2 and other pathogens3-5 is mediated by tight junction (TJ) proteins, but successful therapeutic targeting of TJ proteins has not been reported yet. Using a human liver-chimeric mouse model6 we show that a monoclonal antibody specific for TJ protein claudin-17 eliminates chronic HCV infection without detectable toxicity. This antibody inhibits HCV entry, cell-cell transmission and virus-induced signaling events. Antibody treatment reduces the number of HCV-infected hepatocytes in vivo, highlighting the need for de novo infection via host entry factors to maintain chronic infection. In summary, we demonstrate that an antibody targeting a virus receptor can cure chronic viral infection and uncover TJ proteins as targets for antiviral therapy., Version of Record

Many recent studies highlight the fundamental importance of viruses. Besides their important role as human and animal pathogens, their beneficial, commensal or harmful functions are poorly understood. By developing and applying tailored bioinformatical tools in important virological models, the Marie Skłodowska-Curie Initiative International Training Network VIROINF will provide a better understanding of viruses and the interaction with their hosts. This will open the door to validate methods of improving viral growth, morphogenesis and development, as well as to control strategies against unwanted microorganisms. The key feature of VIROINF is its interdisciplinary nature, which brings together virologists and bioinformaticians to achieve common goals. [ABSTRACT FROM AUTHOR]

Liver diseases are important causes of morbidity and mortality worldwide. The aim of this study was to identify differentially expressed microRNAs (miRNAs), target genes, and key pathways as innovative diagnostic biomarkers in liver patients with different pathology and functional state. We determined, using RT-qPCR, the expression of 472 miRNAs in 125 explanted livers from subjects with six different liver pathologies and from control livers. ANOVA was employed to obtain differentially expressed miRNAs (DEMs), and miRDB (MicroRNA target prediction database) was used to predict target genes. A miRNA–gene differential regulatory (MGDR) network was constructed for each condition. Key miRNAs were detected using topological analysis. Enrichment analysis for DEMs was performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID). We identified important DEMs common and specific to the different patient groups and disease progression stages. hsa-miR-1275 was universally downregulated regardless the disease etiology and stage, while hsa-let-7a*, hsa-miR-195, hsa-miR-374, and hsa-miR-378 were deregulated. The most significantly enriched pathways of target genes controlled by these miRNAs comprise p53 tumor suppressor protein (TP53)-regulated metabolic genes, and those involved in regulation of methyl-CpG-binding protein 2 (MECP2) expression, phosphatase and tensin homolog (PTEN) messenger RNA (mRNA) translation and copper homeostasis. Our findings show a novel panel of deregulated miRNAs in the liver tissue from patients with different liver pathologies. These miRNAs hold potential as biomarkers for diagnosis and staging of liver diseases. [ABSTRACT FROM AUTHOR]

While ionizing radiation (IR) is a powerful tool in medical diagnostics, nuclear medicine, and radiology, it also is a serious threat to the integrity of genetic material. Mutagenic effects of IR to the human genome have long been the subject of research, yet still comparatively little is known about the genome-wide effects of IR exposure on the DNA-sequence level. In this study, we employed high throughput sequencing technologies to investigate IR-induced DNA alterations in human gingiva fibroblasts (HGF) that were acutely exposed to 0.5, 2, and 10 Gy of 240 kV X-radiation followed by repair times of 16 h or 7 days before whole-genome sequencing (WGS). Our analysis of the obtained WGS datasets revealed patterns of IR-induced variant (SNV and InDel) accumulation across the genome, within chromosomes as well as around the borders of topologically associating domains (TADs). Chromosome 19 consistently accumulated the highest SNVs and InDels events. Translocations showed variable patterns but with recurrent chromosomes of origin (e.g., Chr7 and Chr16). IR-induced InDels showed a relative increase in number relative to SNVs and a characteristic signature with respect to the frequency of triplet deletions in areas without repetitive or microhomology features. Overall experimental conditions and datasets the majority of SNVs per genome had no or little predicted functional impact with a maximum of 62, showing damaging potential. A dose-dependent effect of IR was surprisingly not apparent. We also observed a significant reduction in transition/transversion (Ti/Tv) ratios for IR-dependent SNVs, which could point to a contribution of the mismatch repair (MMR) system that strongly favors the repair of transitions over transversions, to the IR-induced DNA-damage response in human cells. Taken together, our results show the presence of distinguishable characteristic patterns of IR-induced DNA-alterations on a genome-wide level and implicate DNA-repair mechanisms in the formation of these signatures. [ABSTRACT FROM AUTHOR]

The Explainable Modular Neural Network (XModNN) enables the identification of biomarkers, facilitating the classification of diseases and clinical parameters in transcriptomic datasets. The modules within XModNN represent specific pathways or genes of a functional hierarchy. The incorporation of biological insights into the architectural design reduced the number of parameters. This is further reinforced by the weighted multi-loss progressive training, which enables successful classification with a reduced number of replicates. The combination of this workflow with layer-wise relevance propagation ensures a robust post hoc explanation of the individual module contribution. Two use cases were employed to predict sex and neuroblastoma cell states, demonstrating that XModNN, in contrast to standard statistical approaches, results in a reduced number of candidate biomarkers. Moreover, the architecture enables the training on a limited number of examples, attaining the same performance and robustness as support vector machine and random forests. The integrated pathway relevance analysis improves a standard gene set overrepresentation analysis, which relies solely on gene assignment. Two crucial genes and three pathways were identified for sex classification, while 26 genes and six pathways are highly important to discriminate adrenergic–mesenchymal cell states in neuroblastoma cancer. [ABSTRACT FROM AUTHOR]

Purpose: Discrimination of uterine leiomyosarcoma (LMS) and leiomyoma (LM) prior to surgery by basic preoperative characteristics and development of a preoperative leiomyosarcoma score.Methods: A predominantly prospective cohort of 826 patients with LM from a clinical institution and an outpatient center was included in the study. Further a predominantly retrospective cohort of 293 patients with LMS was included from the counseling database of the German Clinical Center of Excellence for Genital Sarcoma and Mixed Tumors (DKSM, University Medicine Greifswald, Germany). We analyzed and compared anamnestic, epidemiological and clinical findings between both cohorts. Tenfold cross-validated logistic regression and random forest was performed on the 80% training set. The preoperative LMS score (pLMS) was developed based on logistic regression and independently evaluated by analyzing the area under the receiver operating characteristic curve (AUC) with the 20% test set.Results: In the LMS cohort, 63.1% had initially surgery for presumed LM and only 39.6% of endometrial biopsies revealed LMS. Key features for LMS discrimination were found to be bleeding symptoms: intermenstrual bleeding [RRc = 2.71, CI = (1.90-3.49), p < 0.001], hypermenorrhea [RRc = 0.28, CI = (0.15-0.50), p < 0.001], dysmenorrhea [RRc = 0.22, CI = (0.10-0.51), p < 0.001], postmenstrual bleeding [RRc = 2.08, CI = (1.30-2.75), p < 0.001], suspicious sonography [RRc = 1.21, CI = (1.19-1.22), p < 0.001] and the tumor diameter (each centimeter difference: β = 0.24, SD = 0.04, p < 0.001). pLMS achieved a mean cross-validated AUC of 0.969 (SD = 0.019) in the training set and an AUC of 0.968 in the test set.Conclusions: The presented score is based on basic clinical characteristics and allows the prediction of LMS prior to a planned surgery of a uterine mass. In case pLMS is between - 3 and + 1, we suggest subsequent diagnostics, such as endometrial biopsy, color Doppler sonography, LDH measurement, MRI and transcervical biopsy. [ABSTRACT FROM AUTHOR]

Hypomethylating agents decitabine and azacytidine are regarded as interchangeable in the treatment of acute myeloid leukemia (AML). However, their mechanisms of action remain incompletely understood, and predictive biomarkers for HMA efficacy are lacking. Here, we show that the bioactive metabolite decitabine triphosphate, but not azacytidine triphosphate, functions as activator and substrate of the triphosphohydrolase SAMHD1 and is subject to SAMHD1-mediated inactivation. Retrospective immunohistochemical analysis of bone marrow specimens from AML patients at diagnosis revealed that SAMHD1 expression in leukemic cells inversely correlates with clinical response to decitabine, but not to azacytidine. SAMHD1 ablation increases the antileukemic activity of decitabine in AML cell lines, primary leukemic blasts, and xenograft models. AML cells acquire resistance to decitabine partly by SAMHD1 up-regulation. Together, our data suggest that SAMHD1 is a biomarker for the stratified use of hypomethylating agents in AML patients and a potential target for the treatment of decitabine-resistant leukemia. In acute myeloid leukemia, hypomethylating agents decitabine and azacytidine are used interchangeably. Here, the authors show that the major metabolite of decitabine, but not azacytidine, is subject to SAMHD1 inactivation, highlighting SAMHD1 as a potential biomarker and therapeutic target [ABSTRACT FROM AUTHOR]

Dengue virus (DENV) is a human arboviral pathogen accounting for 390 million infections every year. The available vaccine has limited efficacy, and DENV-specific drugs have not been generated. To better understand DENV-host cell interaction, we employed RNA interference-based screening of the human kinome and identified fibroblast growth factor receptor 4 (FGFR4) to control the DENV replication cycle. Pharmacological inhibition of FGFR exerts a reciprocal effect by reducing DENV RNA replication and promoting the production of infectious virus particles. Addressing the latter effect, we found that the FGFR signaling pathway modulates intracellular distribution of DENV particles in a PI3K-dependent manner. Upon FGFR inhibition, virions accumulate in the trans -Golgi network compartment, where they undergo enhanced maturation cleavage of the envelope protein precursor membrane (prM), rendering virus particles more infectious. This study reveals an unexpected reciprocal role of a cellular receptor tyrosine kinase regulating DENV RNA replication and the production of infectious virions. • DENV host dependency and restriction kinases are identified through a kinome screen • FGFR4 reciprocally affects viral replication and infectivity • Inhibition of FGFR4 decreases replication and increases specific infectivity of virions • Increase in specific infectivity is linked to enhanced proteolytic cleavage of prM Cortese et al. conduct a human kinome RNAi-based screen and identify fibroblast growth factor receptor 4 (FGFR4) as a kinase that has a reciprocal effect on the DENV life cycle. Inhibition of the FGFR pathway reduces RNA replication while increasing production of infectious virus particles through enhanced proteolytic cleavage of prM. [ABSTRACT FROM AUTHOR]

Aortic valve stenosis (AVS) is one of the most common valve diseases in the world. However, detailed biological understanding of the myocardial changes in AVS hearts on the proteome level is still lacking. Proteomic studies using high-resolution mass spectrometry of formalin-fixed and paraffin-embedded (FFPE) human myocardial tissue of AVS-patients are very rare due to methodical issues. To overcome these issues this study used high resolution mass spectrometry in combination with a stem cell-derived cardiac specific protein quantification-standard to profile the proteomes of 17 atrial and 29 left ventricular myocardial FFPE human myocardial tissue samples from AVS-patients. In our proteomic analysis we quantified a median of 1980 (range 1495–2281) proteins in every single sample and identified significant upregulation of 239 proteins in atrial and 54 proteins in ventricular myocardium. We compared the proteins with published data. Well studied proteins reflect disease-related changes in AVS, such as cardiac hypertrophy, development of fibrosis, impairment of mitochondria and downregulated blood supply. In summary, we provide both a workflow for quantitative proteomics of human FFPE heart tissue and a comprehensive proteomic resource for AVS induced changes in the human myocardium. [ABSTRACT FROM AUTHOR]

Summary Hepatitis B viruses (HBVs), which are enveloped viruses with reverse-transcribed DNA genomes, constitute the family Hepadnaviridae . An outstanding feature of HBVs is their streamlined genome organization with extensive gene overlap. Remarkably, the ∼1,100 bp open reading frame (ORF) encoding the envelope proteins is fully nested within the ORF of the viral replicase P. Here, we report the discovery of a diversified family of fish viruses, designated nackednaviruses, which lack the envelope protein gene, but otherwise exhibit key characteristics of HBVs including genome replication via protein-primed reverse-transcription and utilization of structurally related capsids. Phylogenetic reconstruction indicates that these two virus families separated more than 400 million years ago before the rise of tetrapods. We show that HBVs are of ancient origin, descending from non-enveloped progenitors in fishes. Their envelope protein gene emerged de novo , leading to a major transition in viral lifestyle, followed by co-evolution with their hosts over geologic eras. [ABSTRACT FROM AUTHOR]

Since late 2021, the variant landscape of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been dominated by the variant of concern (VoC) Omicron and its sublineages. We and others have shown that the detection of Omicron-BA.1 and -BA.2-positive respiratory specimens by rapid antigen tests (RATs) is impaired compared to Delta VoC-containing samples. Here, in a single-center retrospective laboratory study, we evaluated the performance of ten most commonly used RATs for the detection of Omicron-BA.4 and -BA.5 infections. We used 171 respiratory swab specimens from SARS-CoV-2 RNA-positive patients, of which 71 were classified as BA.4 and 100 as BA.5. All swabs were collected between July and September 2022. 50 SARS-CoV-2 PCR-negative samples from healthy individuals, collected in October 2022, showed high specificity in 9 out of 10 RATs. When assessing analytical sensitivity using clinical specimens, the 50% limit of detection (LoD50) ranged from 7.6 × 104 to 3.3 × 106 RNA copies subjected to the RATs for BA.4 compared to 6.8 × 104 to 3.0 × 106 for BA.5. Overall, intra-assay differences for the detection of these two Omicron subvariants were not significant for both respiratory swabs and tissue culture-expanded virus isolates. In contrast, marked heterogeneity was observed among the ten RATs: to be positive in these point-of-care tests, up to 443-fold (BA.4) and up to 56-fold (BA.5) higher viral loads were required for the worst performing RAT compared to the best performing RAT. True-positive rates for Omicron-BA.4- or -BA.5-containing specimens in the highest viral load category (Ct values < 25) ranged from 94.3 to 34.3%, dropping to 25.6 to 0% for samples with intermediate Ct values (25–30). We conclude that the high heterogeneity in the performance of commonly used RATs remains a challenge for the general public to obtain reliable results in the evolving Omicron subvariant-driven pandemic. [ABSTRACT FROM AUTHOR]

Diagnostic tests for direct pathogen detection have been instrumental to contain the severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) pandemic. Automated, quantitative, laboratory-based nucleocapsid antigen (Ag) tests for SARS-CoV-2 have been launched alongside nucleic acid-based test systems and point-of-care (POC) lateral-flow Ag tests. Here, we evaluated four commercial Ag tests on automated platforms for the detection of different sublineages of the SARS-CoV-2 Omicron variant of concern (VoC) (B.1.1.529) in comparison with "non-Omicron" VoCs. A total of 203 Omicron PCR-positive respiratory swabs (53 BA.1, 48 BA.2, 23 BQ.1, 39 XBB.1.5 and 40 other subvariants) from the period February to March 2022 and from March 2023 were examined. In addition, tissue culture-expanded clinical isolates of Delta (B.1.617.2), Omicron-BA.1, -BF.7, -BN.1 and -BQ.1 were studied. These results were compared to previously reported data from 107 clinical "non-Omicron" samples from the end of the second pandemic wave (February to March 2021) as well as cell culture-derived samples of wildtype (wt) EU-1 (B.1.177), Alpha VoC (B.1.1.7) and Beta VoC (B.1.351)). All four commercial Ag tests were able to detect at least 90.9% of Omicron-containing samples with high viral loads (Ct < 25). The rates of true-positive test results for BA.1/BA.2-positive samples with intermediate viral loads (Ct 25–30) ranged between 6.7% and 100.0%, while they dropped to 0 to 15.4% for samples with low Ct values (> 30). This heterogeneity was reflected also by the tests' 50%-limit of detection (LoD50) values ranging from 44,444 to 1,866,900 Geq/ml. Respiratory samples containing Omicron-BQ.1/XBB.1.5 or other Omicron subvariants that emerged in 2023 were detected with enormous heterogeneity (0 to 100%) for the intermediate and low viral load ranges with LoD50 values between 23,019 and 1,152,048 Geq/ml. In contrast, detection of "non-Omicron" samples was more sensitive, scoring positive in 35 to 100% for the intermediate and 1.3 to 32.9% of cases for the low viral loads, respectively, corresponding to LoD50 values ranging from 6181 to 749,792 Geq/ml. All four assays detected cell culture-expanded VoCs Alpha, Beta, Delta and Omicron subvariants carrying up to six amino acid mutations in the nucleocapsid protein with sensitivities comparable to the non-VoC EU-1. Overall, automated quantitative SARS-CoV-2 Ag assays are not more sensitive than standard rapid antigen tests used in POC settings and show a high heterogeneity in performance for VoC recognition. The best of these automated Ag tests may have the potential to complement nucleic acid-based assays for SARS-CoV-2 diagnostics in settings not primarily focused on the protection of vulnerable groups. In light of the constant emergence of new Omicron subvariants and recombinants, most recently the XBB lineage, these tests' performance must be regularly re-evaluated, especially when new VoCs carry mutations in the nucleocapsid protein or immunological and clinical parameters change. [ABSTRACT FROM AUTHOR]

Oligodendroglial tumors form a distinct subgroup of gliomas, characterized by a better response to treatment and prolonged overall survival. Most oligodendrogliomas and also some oligoastrocytomas are characterized by a unique and typical unbalanced translocation, der(1,19), resulting in a 1p/19q co-deletion. Candidate tumor suppressor genes targeted by these losses, CIC on 19q13.2 and FUBP1 on 1p31.1, were only recently discovered. We analyzed 17 oligodendrogliomas and oligoastrocytomas by applying a comprehensive approach consisting of RNA expression analysis, DNA sequencing of CIC, FUBP1, IDH1/2, and array CGH. We confirmed three different genetic subtypes in our samples: i) the “oligodendroglial” subtype with 1p/19q co-deletion in twelve out of 17 tumors; ii) the “astrocytic” subtype in three tumors; iii) the “other” subtype in two tumors. All twelve tumors with the 1p/19q co-deletion carried the most common IDH1 R132H mutation. In seven of these tumors, we found protein-disrupting point mutations in the remaining allele of CIC, four of which are novel. One of these tumors also had a deleterious mutation in FUBP1. Only by integrating RNA expression and array CGH data, were we able to discover an exon-spanning homozygous microdeletion within the remaining allele of CIC in an additional tumor with 1p/19q co-deletion. Therefore we propose that the mutation rate might be underestimated when looking at sequence variants alone. In conclusion, the high frequency and the spectrum of CIC mutations in our 1p/19q-codeleted tumor cohort support the hypothesis that CIC acts as a tumor suppressor in these tumors, whereas FUBP1 might play only a minor role. [ABSTRACT FROM AUTHOR]

Given the substantial correlation between early diagnosis and prolonged patient survival in HCV patients, it is vital to identify a reliable and accessible biomarker. The purpose of this research was to identify accurate miRNA biomarkers to aid in the early diagnosis of HCV and to identify key target genes for anti-hepatic fibrosis therapeutics. The expression of 188 miRNAs in 42 HCV liver patients with different functional states and 23 normal livers were determined using RT-qPCR. After screening out differentially expressed miRNA (DEmiRNAs), the target genes were predicted. To validate target genes, an HCV microarray dataset was subjected to five machine learning algorithms (Random Forest, Adaboost, Bagging, Boosting, XGBoost) and then, based on the best model, importance features were selected. After identification of hub target genes, to evaluate the potency of compounds that might hit key hub target genes, molecular docking was performed. According to our data, eight DEmiRNAs are associated with early stage and eight DEmiRNAs are linked to a deterioration in liver function and an increase in HCV severity. In the validation phase of target genes, model evaluation revealed that XGBoost (AUC = 0.978) outperformed the other machine learning algorithms. The results of the maximal clique centrality algorithm determined that CDK1 is a hub target gene, which can be hinted at by hsa-miR-335, hsa-miR-140, hsa-miR-152, and hsa-miR-195. Because viral proteins boost CDK1 activation for cell mitosis, pharmacological inhibition may have anti-HCV therapeutic promise. The strong affinity binding of paeoniflorin (−6.32 kcal/mol) and diosmin (−6.01 kcal/mol) with CDK1 was demonstrated by molecular docking, which may result in attractive anti-HCV compounds. The findings of this study may provide significant evidence, in the context of the miRNA biomarkers, for early-stage HCV diagnosis. In addition, recognized hub target genes and small molecules with high binding affinity may constitute a novel set of therapeutic targets for HCV. [ABSTRACT FROM AUTHOR]

Plus-strand RNA viruses are the largest group of viruses. Many are human pathogens that inflict a socio-economic burden. Interestingly, plus-strand RNA viruses share remarkable similarities in their replication. A hallmark of plus-strand RNA viruses is the remodeling of intracellular membranes to establish replication organelles (so-called "replication factories"), which provide a protected environment for the replicase complex, consisting of the viral genome and proteins necessary for viral RNA synthesis. In the current study, we investigate pan-viral similarities and virus-specific differences in the life cycle of this highly relevant group of viruses. We first measured the kinetics of viral RNA, viral protein, and infectious virus particle production of hepatitis C virus (HCV), dengue virus (DENV), and coxsackievirus B3 (CVB3) in the immuno-compromised Huh7 cell line and thus without perturbations by an intrinsic immune response. Based on these measurements, we developed a detailed mathematical model of the replication of HCV, DENV, and CVB3 and showed that only small virus-specific changes in the model were necessary to describe the in vitro dynamics of the different viruses. Our model correctly predicted virus-specific mechanisms such as host cell translation shut off and different kinetics of replication organelles. Further, our model suggests that the ability to suppress or shut down host cell mRNA translation may be a key factor for in vitro replication efficiency, which may determine acute self-limited or chronic infection. We further analyzed potential broad-spectrum antiviral treatment options in silico and found that targeting viral RNA translation, such as polyprotein cleavage and viral RNA synthesis, may be the most promising drug targets for all plus-strand RNA viruses. Moreover, we found that targeting only the formation of replicase complexes did not stop the in vitro viral replication early in infection, while inhibiting intracellular trafficking processes may even lead to amplified viral growth. Author summary: Plus-strand RNA viruses comprise a large group of related and medically relevant viruses. The current global pandemic of COVID-19 caused by the SARS-coronavirus-2 as well as the constant spread of diseases such as dengue and chikungunya fever show the necessity of a comprehensive and precise analysis of plus-strand RNA virus infections. Plus-strand RNA viruses share similarities in their life cycle. To understand their within-host replication strategies, we developed a mathematical model that studies pan-viral similarities and virus-specific differences of three plus-strand RNA viruses, namely hepatitis C, dengue, and coxsackievirus. By fitting our model to in vitro data, we found that only small virus-specific variations in the model were required to describe the dynamics of all three viruses. Furthermore, our model predicted that ribosomes involved in viral RNA translation seem to be a key player in plus-strand RNA replication efficiency, which may determine acute or chronic infection outcomes. Furthermore, our in-silico drug treatment analysis suggested that targeting viral proteases involved in polyprotein cleavage, in combination with viral RNA replication may represent promising drug targets with broad-spectrum antiviral activity. [ABSTRACT FROM AUTHOR]

Hepatitis C virus (HCV) infections are a global health problem, and extensive research over the last decades has been targeted at understanding its molecular biology and developing effective antiviral treatments. Recently, a number of potent direct acting antiviral drugs have been developed targeting specific processes in the viral life cycle. Here, we developed a mathematical multi-scale model of the within-host dynamics of HCV infection by integrating a standard model for viral infection with a detailed model of the viral replication cycle inside infected cells. We use this model to study patient time courses of viral load under treatment with daclatasvir, an inhibitor of the viral non-structural protein NS5A. Model analysis predicts that treatment efficacy can be increased by combining daclatasvir with dedicated viral polymerase inhibitors, corresponding to promising current strategies in drug development. Hence, our model presents a predictive tool for in silico simulations, which can be used to study and optimize direct acting antiviral drug treatment. [ABSTRACT FROM AUTHOR]

During 2022, the COVID-19 pandemic has been dominated by the variant of concern (VoC) Omicron (B.1.1.529) and its rapidly emerging subvariants, including Omicron-BA.1 and -BA.2. Rapid antigen tests (RATs) are part of national testing strategies to identify SARS-CoV-2 infections on site in a community setting or to support layman's diagnostics at home. We and others have recently demonstrated an impaired RAT detection of infections caused by Omicron-BA.1 compared to Delta. Here, we evaluated the performance of five SARS-CoV-2 RATs in a single-centre laboratory study examining a total of 140 SARS-CoV-2 PCR-positive respiratory swab samples, 70 Omicron-BA.1 and 70 Omicron-BA.2, as well as 52 SARS-CoV-2 PCR-negative swabs collected from March 8th until April 10th, 2022. One test did not meet minimal criteria for specificity. In an assessment of the analytical sensitivity in clinical specimen, the 50% limit of detection (LoD50) ranged from 4.2 × 104 to 9.2 × 105 RNA copies subjected to the RAT for Omicron-BA.1 compared to 1.3 × 105 to 1.5 × 106 for Omicron-BA.2. Overall, intra-assay differences for the detection of Omicron-BA.1-containing and Omicron-BA.2-containing samples were non-significant, while a marked overall heterogeneity among the five RATs was observed. To score positive in these point-of-care tests, up to 22-fold (LoD50) or 68-fold (LoD95) higher viral loads were required for the worst performing compared to the best performing RAT. The rates of true-positive test results for these Omicron subvariant-containing samples in the highest viral load category (Ct values < 25) ranged between 44.7 and 91.1%, while they dropped to 8.7 to 22.7% for samples with intermediate Ct values (25–30). In light of recent reports on the emergence of two novel Omicron-BA.2 subvariants, Omicron-BA.2.75 and BJ.1, awareness must be increased for the overall reduced detection rate and marked differences in RAT performance for these Omicron subvariants. [ABSTRACT FROM AUTHOR]

Background and Purpose: Development and progression of heart failure involve endothelial and myocardial dysfunction as well as a dysregulation of the NO-sGC-cGMP signalling pathway. Recently, we reported that the sGC stimulator riociguat has beneficial effects on cardiac remodelling and progression of heart failure in response to chronic pressure overload. Here, we examined if these beneficial effects of riociguat were also reflected in alterations of the myocardial proteome and microRNA profiles.Experimental Approach: Male C57BL/6N mice underwent transverse aortic constriction (TAC) and sham-operated mice served as controls. TAC and sham animals were randomised and treated with either riociguat or vehicle for 5 weeks, starting 3 weeks after surgery, when cardiac hypertrophy was established. Afterwards, we performed mass spectrometric proteome analyses and microRNA sequencing of proteins and RNAs, respectively, isolated from left ventricles (LVs).Key Results: TAC-induced changes of the LV proteome were significantly reduced by treatment with riociguat. Bioinformatics analyses revealed that riociguat improved TAC-induced cardiovascular disease-related pathways, metabolism and energy production, for example, reversed alterations in the levels of myosin heavy chain 7, cardiac phospholamban and ankyrin repeat domain-containing protein 1. Riociguat also attenuated TAC-induced changes of microRNA levels in the LV.Conclusion and Implications: The sGC stimulator riociguat exerted beneficial effects on cardiac structure and function during pressure overload, which was accompanied by a reversal of TAC-induced changes of the cardiac proteome and microRNA profile. Our data support the potential of riociguat as a novel therapeutic agent for heart failure. [ABSTRACT FROM AUTHOR]

Background: Approaching epidemiological data with flexible machine learning algorithms is of great value for understanding disease-specific association patterns. However, it can be difficult to correctly extract and understand those patterns due to the lack of model interpretability. Method: We here propose a machine learning workflow that combines random forests with Bayesian network surrogate models to allow for a deeper level of interpretation of complex association patterns. We first evaluate the proposed workflow on synthetic data. We then apply it to data from the large population-based Study of Health in Pomerania (SHIP). Based on this combination, we discover and interpret broad patterns of individual serum TSH concentrations, an important marker of thyroid functionality. Results: Evaluations using simulated data show that feature associations can be correctly recovered by combining random forests and Bayesian networks. The presented model achieves predictive accuracy that is similar to state-of-the-art models (root mean square error of 0.66, mean absolute error of 0.55, coefficient of determination of R2 = 0.15). We identify 62 relevant features from the final random forest model, ranging from general health variables over dietary and genetic factors to physiological, hematological and hemostasis parameters. The Bayesian network model is used to put these features into context and make the black-box random forest model more understandable. Conclusion: We demonstrate that the combination of random forest and Bayesian network analysis is helpful to reveal and interpret broad association patterns of individual TSH concentrations. The discovered patterns are in line with state-of-the-art literature. They may be useful for future thyroid research and improved dosing of therapeutics. [ABSTRACT FROM AUTHOR]

Abstract: Infection with the hepatitis C virus represents a global public health threat given that an estimated 170 million individuals are chronically infected and thus at risk for cirrhosis and hepatocellular carcinoma. A number of direct antiviral molecules are in clinical development. However, side effects, drug resistance and viral genotype-specific differences in efficacy may limit these novel therapeutics. Therefore, a combination of well tolerated drugs with distinct mechanisms of action targeting different steps of the viral replication cycle will likely improve viral response rates and therapy success. To identify small molecules that interfere with different steps of the HCV replication cycle, we developed a novel dual reporter gene assay of the complete HCV life cycle and adapted it to 384-well high-throughput format. The system is based on a highly permissive Huh-7 cell line stably expressing a secreted luciferase. Using these cells and an efficient HCV luciferase reporter virus, perturbations of each step of the viral replication cycle as well as cell viability can be easily and quantitatively determined. The system was validated with a selected set of known HCV entry, replication and assembly inhibitors and then utilized to screen a library of small molecules derived from myxobacteria. Using this approach we identified a number of molecules that specifically inhibit HCV cell entry, or primarily virus assembly and release. Moreover, we also identified molecules that increase viral propagation. These compounds may be useful leads for development of novel HCV inhibitors and could be instrumental for the identification of as yet unknown host-derived viral resistance and dependency factors. [ABSTRACT FROM AUTHOR]

Abstract: Neuroblastoma is the most common extracranial childhood tumor, comprising 15% of all childhood cancer deaths. In an initial study, we used Affymetrix oligonucleotide microarrays to analyse gene expression in 68 primary neuroblastomas and compared different data mining approaches for prediction of early relapse. Here, we performed re-analyses of the data including prolonged follow-up and applied support vector machine (SVM) algorithms and outer cross-validation strategies to improve reliability of expression profiling based predictors. Accuracy of outcome prediction was significantly improved by the use of innovative SVM algorithms on the updated data. In addition, CASPAR, a hierarchical Bayesian approach, was used to predict survival times for the individual patient based on expression profiling data. CASPAR reliably predicted event-free survival, given a cut-off time of three years. Differential expression of genes used by CASPAR to predict patient outcome was validated in an independent cohort of 117 neuroblastomas. In conclusion, we show here for the first time that reanalysis of microarray data using improved methodology, state-of-the-art performance tests and updated follow-up data improves prognosis prediction, and may further improve risk stratification of individual patients. [Copyright &y& Elsevier]

Since autumn 2020, rapid antigen tests (RATs) have been implemented in several countries as an important pillar of the national testing strategy to rapidly screen for infections on site during the SARS-CoV-2 pandemic. The current surge in infection rates around the globe is driven by the variant of concern (VoC) omicron (B.1.1.529). Here, we evaluated the performance of nine SARS-CoV-2 RATs in a single-centre laboratory study. We examined a total of 115 SARS-CoV-2 PCR-negative and 166 SARS-CoV-2 PCR-positive respiratory swab samples (101 omicron, 65 delta (B.1.617.2)) collected from October 2021 until January 2022 as well as cell culture-expanded clinical isolates of both VoCs. In an assessment of the analytical sensitivity in clinical specimen, the 50% limit of detection (LoD50) ranged from 1.77 × 106 to 7.03 × 107 RNA copies subjected to the RAT for omicron compared to 1.32 × 105 to 2.05 × 106 for delta. To score positive in these point-of-care tests, up to 10-fold (LoD50) or 101-fold (LoD95) higher virus loads were required for omicron- compared to delta-containing samples. The rates of true positive test results for omicron samples in the highest virus load category (Ct values < 25) ranged between 31.4 and 77.8%, while they dropped to 0–8.3% for samples with intermediate Ct values (25–30). Of note, testing of expanded virus stocks suggested a comparable RAT sensitivity of both VoCs, questioning the predictive value of this type of in vitro-studies for clinical performance. Given their importance for national test strategies in the current omicron wave, awareness must be increased for the reduced detection rate of omicron infections by RATs and a short list of suitable RATs that fulfill the minimal requirements of performance should be rapidly disclosed. [ABSTRACT FROM AUTHOR]

Background and Purpose: Heart failure is associated with an impaired NO–soluble guanylyl cyclase (sGC)–cGMP pathway and its augmentation is thought to be beneficial for its therapy. We hypothesized that stimulation of sGC by the sGC stimulator riociguat prevents pathological cardiac remodelling and heart failure in response to chronic pressure overload. Experimental Approach Transverse aortic constriction or sham surgery was performed in C57BL/6N mice. After 3 weeks of transverse aortic constriction when heart failure was established, animals receive either riociguat or its vehicle for 5 additional weeks. Cardiac function was evaluated weekly by echocardiography. Eight weeks after surgery, histological analyses were performed to evaluate remodelling and the transcriptome of the left ventricles (LVs) was analysed by RNA sequencing. Cell culture experiments were used for mechanistically studies. Key Results: Transverse aortic constriction resulted in a continuous decrease of LV ejection fraction and an increase in LV mass until week 3. Five weeks of riociguat treatment resulted in an improved LV ejection fraction and a decrease in the ratio of left ventricular mass to total body weight (LVM/BW), myocardial fibrosis and myocyte cross‐sectional area. RNA sequencing revealed that riociguat reduced the expression of myocardial stress and remodelling genes (e.g. Nppa, Nppb, Myh7 and collagen) and attenuated the activation of biological pathways associated with cardiac hypertrophy and heart failure. Riociguat reversed pathological stress response in cultivated myocytes and fibroblasts. Conclusion and Implications: Stimulation of the sGC reverses transverse aortic constriction‐induced heart failure and remodelling, which is associated with improved myocardial gene expression. LINKED ARTICLES: This article is part of a themed issue on cGMP Signalling in Cell Growth and Survival. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v179.11/issuetoc [ABSTRACT FROM AUTHOR]

A versatile portfolio of diagnostic tests is essential for the containment of the severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) pandemic. Besides nucleic acid-based test systems and point-of-care (POCT) antigen (Ag) tests, quantitative, laboratory-based nucleocapsid Ag tests for SARS-CoV-2 have recently been launched. Here, we evaluated four commercial Ag tests on automated platforms and one POCT to detect SARS-CoV-2. We evaluated PCR-positive (n = 107) and PCR-negative (n = 303) respiratory swabs from asymptomatic and symptomatic patients at the end of the second pandemic wave in Germany (February–March 2021) as well as clinical isolates EU1 (B.1.117), variant of concern (VOC) Alpha (B.1.1.7) or Beta (B.1.351), which had been expanded in a biosafety level 3 laboratory. The specificities of automated SARS-CoV-2 Ag tests ranged between 97.0 and 99.7% (Lumipulse G SARS-CoV-2 Ag (Fujirebio): 97.03%, Elecsys SARS-CoV-2 Ag (Roche Diagnostics): 97.69%; LIAISON® SARS-CoV-2 Ag (Diasorin) and SARS-CoV-2 Ag ELISA (Euroimmun): 99.67%). In this study cohort of hospitalized patients, the clinical sensitivities of tests were low, ranging from 17.76 to 52.34%, and analytical sensitivities ranged from 420,000 to 25,000,000 Geq/ml. In comparison, the detection limit of the Roche Rapid Ag Test (RAT) was 9,300,000 Geq/ml, detecting 23.58% of respiratory samples. Receiver-operating-characteristics (ROCs) and Youden's index analyses were performed to further characterize the assays' overall performance and determine optimal assay cutoffs for sensitivity and specificity. VOCs carrying up to four amino acid mutations in nucleocapsid were detected by all five assays with characteristics comparable to non-VOCs. In summary, automated, quantitative SARS-CoV-2 Ag tests show variable performance and are not necessarily superior to a standard POCT. The efficacy of any alternative testing strategies to complement nucleic acid-based assays must be carefully evaluated by independent laboratories prior to widespread implementation. [ABSTRACT FROM AUTHOR]

In this work, we introduce an entirely data-driven and automated approach to reveal disease-associated biomarker and risk factor networks from heterogeneous and high-dimensional healthcare data. Our workflow is based on Bayesian networks, which are a popular tool for analyzing the interplay of biomarkers. Usually, data require extensive manual preprocessing and dimension reduction to allow for effective learning of Bayesian networks. For heterogeneous data, this preprocessing is hard to automatize and typically requires domain-specific prior knowledge. We here combine Bayesian network learning with hierarchical variable clustering in order to detect groups of similar features and learn interactions between them entirely automated. We present an optimization algorithm for the adaptive refinement of such group Bayesian networks to account for a specific target variable, like a disease. The combination of Bayesian networks, clustering, and refinement yields low-dimensional but disease-specific interaction networks. These networks provide easily interpretable, yet accurate models of biomarker interdependencies. We test our method extensively on simulated data, as well as on data from the Study of Health in Pomerania (SHIP-TREND), and demonstrate its effectiveness using non-alcoholic fatty liver disease and hypertension as examples. We show that the group network models outperform available biomarker scores, while at the same time, they provide an easily interpretable interaction network. Author summary: High-dimensional and heterogeneous healthcare data, such as electronic health records or epidemiological study data, contain much information on yet unknown risk factors that are associated with disease development. The identification of these risk factors may help to improve prevention, diagnosis, and therapy. Bayesian networks are powerful statistical models that can decipher these complex relationships. However, high dimensionality and heterogeneity of data, together with missing values and high feature correlation, make it difficult to automatically learn a good model from data. To facilitate the use of network models, we present a novel, fully automated workflow that combines network learning with hierarchical clustering. The algorithm reveals groups of strongly related features and models the interactions among those groups. It results in simpler network models that are easier to analyze. We introduce a method of adaptive refinement of such models to ensure that disease-relevant parts of the network are modeled in great detail. Our approach makes it easy to learn compact, accurate, and easily interpretable biomarker interaction networks. We test our method extensively on simulated data as well as data from the Study of Health in Pomerania (SHIP-Trend) by learning models of hypertension and non-alcoholic fatty liver disease. [ABSTRACT FROM AUTHOR]

Successful containment strategies for the SARS-CoV-2 pandemic will depend on reliable diagnostic assays. Point-of-care antigen tests (POCT) may provide an alternative to time-consuming PCR tests to rapidly screen for acute infections on site. Here, we evaluated two SARS-CoV-2 antigen tests: the STANDARD™ F COVID-19 Ag FIA (FIA) and the SARS-CoV-2 Rapid Antigen Test (RAT). For diagnostic assessment, we used a large set of PCR-positive and PCR-negative respiratory swabs from asymptomatic and symptomatic patients and health care workers in the setting of two University Hospitals in Munich, Germany, i.e. emergency rooms, patient care units or employee test centers. For FIA, overall clinical sensitivity and specificity were 45.4% (n = 381) and 97.8% (n = 360), respectively, and for RAT, 50.3% (n = 445) and 97.7% (n = 386), respectively. For primary diagnosis of asymptomatic and symptomatic individuals, diagnostic sensitivities were 60.9% (FIA) (n = 189) and 64.5% (RAT) (n = 256). This questions these tests' utility for the reliable detection of acute SARS-CoV-2-infected individuals, in particular in high-risk settings. We support the proposal that convincing high-quality outcome data on the impact of false-negative and false-positive antigen test results need to be obtained in a POCT setting. Moreover, the efficacy of alternative testing strategies to complement PCR assays must be evaluated by independent laboratories, prior to widespread implementation in national and international test strategies. [ABSTRACT FROM AUTHOR]

The simultaneous analysis of different regulatory levels of biological phenomena by means of multi-omics data integration has proven an invaluable tool in modern precision medicine, yet many processes ultimately paving the way towards disease manifestation remain elusive and have not been studied in this regard. Here we investigated the early molecular events following repetitive UV irradiation of in vivo healthy human skin in depth on transcriptomic and epigenetic level. Our results provide first hints towards an immediate acquisition of epigenetic memories related to aging and cancer and demonstrate significantly correlated epigenetic and transcriptomic responses to irradiation stress. The data allowed the precise prediction of inter-individual UV sensitivity, and molecular subtyping on the integrated post-irradiation multi-omics data established the existence of three latent molecular phototypes. Importantly, further analysis suggested a form of melanin-independent DNA damage protection in subjects with higher innate UV resilience. This work establishes a high-resolution molecular landscape of the acute epidermal UV response and demonstrates the potential of integrative analyses to untangle complex and heterogeneous biological responses. [ABSTRACT FROM AUTHOR]