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Global and even national genome surveillance approaches do not provide the resolution necessary for rapid and accurate direct response by local public health authorities. Hence, a regional network of microbiological laboratories in collaboration with the health departments of all districts of the German federal state of Mecklenburg-Western Pomerania (M-V) was formed to investigate the regional molecular epidemiology of circulating SARS-CoV-2 lineages between 11/2020 and 03/2022. More than 4750 samples from all M-V counties were sequenced using Illumina and Nanopore technologies. Overall, 3493 (73.5%) sequences fulfilled quality criteria for time-resolved and/or spatially-resolved maximum likelihood phylogenic analyses and k-mean/ median clustering (KMC). We identified 116 different Pangolin virus lineages that can be assigned to 16 Nextstrain clades. The ten most frequently detected virus lineages belonged to B.1.1.7, AY.122, AY.43, BA.1, B.1.617.2, BA.1.1, AY.9.2, AY.4, P.1 and AY.126. Time-resolved phylogenetic analyses showed the occurrence of virus clades as determined worldwide, but with a substantial delay of one to two months. Further spatio-temporal phylogenetic analyses revealed a regional outbreak of a Gamma variant limited to western M-V counties. Finally, KMC elucidated a successive introduction of the various virus lineages into M-V, possibly triggered by vacation periods with increased (inter-) national travel activities. The COVID-19 pandemic in M-V was shaped by a combination of several SARS-CoV-2 introductions, lockdown measures, restrictive quarantine of patients and the lineage specific replication rate. Complementing global and national surveillance, regional surveillance adds value by providing a higher level of surveillance resolution tailored to local health authorities.
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.
Background
Vaccine-induced immune thrombotic thrombocytopenia (VITT) is a prothrombotic, heparin-induced thrombocytopenia (HIT)-mimicking, adverse reaction caused by platelet-activating anti-platelet factor 4 (PF4) antibodies that occurs rarely after adenovirus vector-based COVID-19 vaccination. Strength of PF4-dependent enzyme immunoassay (EIA) reactivity—judged by optical density (OD) measurements—strongly predicts platelet-activating properties of HIT antibodies in a functional test. Whether a similar relationship holds for VITT antibodies is unknown.
Objectives
To evaluate probability for positive platelet activation testing for VITT antibodies based upon EIA OD reactivity; and to investigate simple approaches to minimize false-negative platelet activation testing for VITT.
Methods
All samples referred for VITT testing were systematically evaluated by semiquantitative in-house PF4/heparin-EIA (OD readings) and PF4-induced platelet activation (PIPA) testing within a cohort study. EIA-positive sera testing PIPA-negative were retested following 1/4 to 1/10 dilution. Logistic regression was performed to predict the probability of a positive PIPA per magnitude of EIA reactivity.
Results
Greater EIA ODs in sera from patients with suspected VITT correlated strongly with greater likelihood of PIPA reactivity. Of 61 sera (with OD values >1.0) testing negative in the PIPA, a high proportion (27/61, 44.3%) became PIPA positive when tested at 1/4 to 1/10 dilution.
Conclusions
VITT serology resembles HIT in that greater EIA OD reactivity predicts higher probability of positive testing for platelet-activating antibodies. Unlike the situation with HIT antibodies, however, diluting putative VITT serum increases probability of a positive platelet activation assay, suggesting that optimal complex formation depends on the stoichiometric ratio of PF4 and anti-PF4 VITT antibodies.
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.
Introduction
Heart rate variability (HRV), defined as the variability of consecutive heart beats, is an important biomarker for dysregulations of the autonomic nervous system (ANS) and is associated with the development, course, and outcome of a variety of mental and physical health problems. While guidelines recommend using 5 min electrocardiograms (ECG), recent studies showed that 10 s might be sufficient for deriving vagal-mediated HRV. However, the validity and applicability of this approach for risk prediction in epidemiological studies is currently unclear to be used.
Methods
This study evaluates vagal-mediated HRV with ultra-short HRV (usHRV) based on 10 s multichannel ECG recordings of N = 4,245 and N = 2,392 participants of the Study of Health in Pomerania (SHIP) from two waves of the SHIP-TREND cohort, additionally divided into a healthy and health-impaired subgroup. Association of usHRV with HRV derived from long-term ECG recordings (polysomnography: 5 min before falling asleep [N = 1,041]; orthostatic testing: 5 min of rest before probing an orthostatic reaction [N = 1,676]) and their validity with respect to demographic variables and depressive symptoms were investigated.
Results
High correlations (r = .52–.75) were revealed between usHRV and HRV. While controlling for covariates, usHRV was the strongest predictor for HRV. Furthermore, the associations of usHRV and HRV with age, sex, obesity, and depressive symptoms were similar.
Conclusion
This study provides evidence that usHRV derived from 10 s ECG might function as a proxy of vagal-mediated HRV with similar characteristics. This allows the investigation of ANS dysregulation with ECGs that are routinely performed in epidemiological studies to identify protective and risk factors for various mental and physical health problems.
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.
Bioinformatics Algorithms and Predictive Models: The Grand Challenge in Computational Virology
(2021)
Never in the past has the relevance of bioinformatic and predictive tools been more central
in the field of virology as today. SARS-CoV-2 has brought along a huge health burden, but also
a deeper awareness that scientific progress can no longer be effective without extensive systems
for data storage, sharing and analysis, as well as computational tools dedicated to molecular
epidemiology, NGS data analysis, prediction of drug targets, multi-OMIC data integration, and
many other applications.
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.
Influenza A Virus (IAV) infection followed by bacterial pneumonia often leads to hospitalization and death in individuals from high risk groups. Following infection, IAV triggers the process of viral RNA replication which in turn disrupts healthy gut microbial community, while the gut microbiota plays an instrumental role in protecting the host by evolving colonization resistance. Although the underlying mechanisms of IAV infection have been unraveled, the underlying complex mechanisms evolved by gut microbiota in order to induce host immune response following IAV infection remain evasive. In this work, we developed a novel Maximal-Clique based Community Detection algorithm for Weighted undirected Networks (MCCD-WN) and compared its performance with other existing algorithms using three sets of benchmark networks. Moreover, we applied our algorithm to gut microbiome data derived from fecal samples of both healthy and IAV-infected pigs over a sequence of time-points. The results we obtained from the real-life IAV dataset unveil the role of the microbial families Ruminococcaceae, Lachnospiraceae, Spirochaetaceae and Prevotellaceae in the gut microbiome of the IAV-infected cohort. Furthermore, the additional integration of metaproteomic data enabled not only the identification of microbial biomarkers, but also the elucidation of their functional roles in protecting the host following IAV infection. Our network analysis reveals a fast recovery of the infected cohort after the second IAV infection and provides insights into crucial roles of Desulfovibrionaceae and Lactobacillaceae families in combating Influenza A Virus infection. Source code of the community detection algorithm can be downloaded from https://github.com/AniBhar84/MCCD-WN.