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Non-coding RNA (ncRNA) classes take over important housekeeping and regulatory functions and are quite heterogeneous in terms of length, sequence conservation and secondary structure. High-throughput sequencing reveals that the expressed novel ncRNAs and their classification are important to understand cell regulation and identify potential diagnostic and therapeutic biomarkers. To improve the classification of ncRNAs, we investigated different approaches of utilizing primary sequences and secondary structures as well as the late integration of both using machine learning models, including different neural network architectures. As input, we used the newest version of RNAcentral, focusing on six ncRNA classes, including lncRNA, rRNA, tRNA, miRNA, snRNA and snoRNA. The late integration of graph-encoded structural features and primary sequences in our MncR classifier achieved an overall accuracy of >97%, which could not be increased by more fine-grained subclassification. In comparison to the actual best-performing tool ncRDense, we had a minimal increase of 0.5% in all four overlapping ncRNA classes on a similar test set of sequences. In summary, MncR is not only more accurate than current ncRNA prediction tools but also allows the prediction of long ncRNA classes (lncRNAs, certain rRNAs) up to 12.000 nts and is trained on a more diverse ncRNA dataset retrieved from RNAcentral.
Background: Retrospective research on real-world data provides the ability to gain evidence on specific topics especially when running across different sites in research networks. Those research networks have become increasingly relevant in recent years; not least due to the special situation caused by the COVID-19 pandemic. An important requirement for those networks is the data harmonization by ensuring the semantic interoperability. Aims: In this paper we demonstrate (1) how to facilitate digital infrastructures to run a retrospective study in a research network spread across university and non-university hospital sites; and (2) to answer a medical question on COVID-19 related change in diagnostic counts for diabetes-related eye diseases. Materials and methods: The study is retrospective and non-interventional and runs on medical case data documented in routine care at the participating sites. The technical infrastructure consists of the OMOP CDM and other OHDSI tools that is provided in a transferable format. An ETL process to transfer and harmonize the data to the OMOP CDM has been utilized. Cohort definitions for each year in observation have been created centrally and applied locally against medical case data of all participating sites and analyzed with descriptive statistics. Results: The analyses showed an expectable drop of the total number of diagnoses and the diagnoses for diabetes in general; whereas the number of diagnoses for diabetes-related eye diseases surprisingly decreased stronger compared to non-eye diseases. Differences in relative changes of diagnoses counts between sites show an urgent need to process multi-centric studies rather than single-site studies to reduce bias in the data. Conclusions: This study has demonstrated the ability to utilize an existing portable and standardized infrastructure and ETL process from a university hospital setting and transfer it to non-university sites. From a medical perspective further activity is needed to evaluate data quality of the utilized real-world data documented in routine care and to investigate its eligibility of this data for research.
(1) Background: Global incidence of type 1 diabetes (T1D) is rising and nearly half occurred in adults. However, it is unclear if certain early-life childhood T1D risk factors were also associated with adult-onset T1D. This study aimed to assess associations between birth order, delivery mode or daycare attendance and type 1 diabetes (T1D) risk in a population-based cohort and whether these were similar for childhood- and adult-onset T1D (cut-off age 15); (2) Methods: Data were obtained from the German National Cohort (NAKO Gesundheitsstudie) baseline assessment. Self-reported diabetes was classified as T1D if: diagnosis age ≤ 40 years and has been receiving insulin treatment since less than one year after diagnosis. Cox regression was applied for T1D risk analysis; (3) Results: Analyses included 101,411 participants (100 childhood- and 271 adult-onset T1D cases). Compared to “only-children”, HRs for second- or later-born individuals were 0.70 (95% CI = 0.50–0.96) and 0.65 (95% CI = 0.45–0.94), respectively, regardless of parental diabetes, migration background, birth year and perinatal factors. In further analyses, higher birth order reduced T1D risk in children and adults born in recent decades. Caesarean section and daycare attendance showed no clear associations with T1D risk; (4) Conclusions: Birth order should be considered in both children and adults’ T1D risk assessment for early detection.
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.