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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
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.
Chronic kidney disease (CKD) is a major public health burden affecting more than 500 million people worldwide. Podocytopathies are the main cause for the majority of CKD cases due to pathogenic morphological as well as molecular biological alterations of postmitotic podocytes. Podocyte de-differentiation is associated with foot process effacement subsequently leading to proteinuria. Since currently no curative drugs are available, high throughput screening methods using a small number of animals are a promising and essential tool to identify potential drugs against CKD in the near future. Our study presents the implementation of the already established mouse GlomAssay as a semi-automated high-throughput screening method—shGlomAssay—allowing the analysis of several hundreds of FDA-verified compounds in combination with downstream pathway analysis like transcriptomic and proteomic analyses from the same samples, using a small number of animals. In an initial prescreening we have identified vitamin D3 and its analog calcipotriol to be protective on podocytes. Furthermore, by using RT-qPCR, Western blot, and RNA sequencing, we found that mRNA and protein expression of nephrin, the vitamin D receptor and specific podocyte markers were significantly up-regulated due to vitamin D3- and calcipotriol-treatment. In contrast, kidney injury markers were significantly down-regulated. Additionally, we found that vitamin D3 and calcipotriol have had neither influence on the expression of the miR-21 and miR-30a nor on miR-125a/b, a miRNA described to regulate the vitamin D receptor. In summary, we advanced the established mouse GlomAssay to a semi-automated high-throughput assay and combined it with downstream analysis techniques by using only a minimum number of animals. Hereby, we identified the vitamin D signaling pathway as podocyte protective and to be counteracting their de-differentiation.
Objective: In the rat, the pancreatic islet transplantation model is an established method to induce hepatocellular carcinomas (HCC), due to insulin-mediated metabolic and molecular alterations like increased glycolysis and de novo lipogenesis and the oncogenic AKT/mTOR pathway including upregulation of the transcription factor Carbohydrate-response element-binding protein (ChREBP). ChREBP could therefore represent an essential oncogenic co-factor during hormonally induced hepatocarcinogenesis. Methods: Pancreatic islet transplantation was implemented in diabetic C57Bl/6J (wild type, WT) and ChREBP-knockout (KO) mice for 6 and 12 months. Liver tissue was examined using histology, immunohistochemistry, electron microscopy and Western blot analysis. Finally, we performed NGS-based transcriptome analysis between WT and KO liver tumor tissues. Results: Three hepatocellular carcinomas were detectable after 6 and 12 months in diabetic transplanted WT mice, but only one in a KO mouse after 12 months. Pre-neoplastic clear cell foci (CCF) were also present in liver acini downstream of the islets in WT and KO mice. In KO tumors, glycolysis, de novo lipogenesis and AKT/mTOR signalling were strongly downregulated compared to WT lesions. Extrafocal liver tissue of diabetic, transplanted KO mice revealed less glycogen storage and proliferative activity than WT mice. From transcriptome analysis, we identified a set of transcripts pertaining to metabolic, oncogenic and immunogenic pathways that are differentially expressed between tumors of WT and KO mice. Of 315 metabolism-associated genes, we observed 199 genes that displayed upregulation in the tumor of WT mice, whereas 116 transcripts showed their downregulated expression in KO mice tumor. Conclusions: The pancreatic islet transplantation model is a suitable method to study hormonally induced hepatocarcinogenesis also in mice, allowing combination with gene knockout models. Our data indicate that deletion of ChREBP delays insulin-induced hepatocarcinogenesis, suggesting a combined oncogenic and lipogenic function of ChREBP along AKT/mTOR-mediated proliferation of hepatocytes and induction of hepatocellular carcinoma.
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 ofIR to the human genome have long been the subject of research, yet still comparatively little isknown 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 alterationsin human gingiva fibroblasts (HGF) that were acutely exposed to 0.5, 2, and 10 Gy of 240 kVX-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 topologicallyassociating domains (TADs). Chromosome 19 consistently accumulated the highest SNVs andInDels events. Translocations showed variable patterns but with recurrent chromosomes of origin(e.g., Chr7 andChr16). IR-induced InDels showed a relative increase in number relative to SNVs anda characteristic signature with respect to the frequency of triplet deletions in areas without repetitiveor microhomology features. Overall experimental conditions and datasets the majority of SNVs pergenome had no or little predicted functional impact with a maximum of 62, showing damagingpotential. A dose-dependent effect of IR was surprisingly not apparent. We also observed a significantreduction in transition/transversion (Ti/Tv) ratios for IR-dependent SNVs, which could point to acontribution of the mismatch repair (MMR) system that strongly favors the repair of transitions overtransversions, to the IR-induced DNA-damage response in human cells. Taken together, our resultsshow the presence of distinguishable characteristic patterns of IR-induced DNA-alterations on agenome-wide level and implicate DNA-repair mechanisms in the formation of these signatures