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During cervical carcinogenesis, T‐helper (Th)‐17 cells accumulate in the peripheral blood and tumor tissues of cancer patients. We previously demonstrated that Th17 cells are associated with therapy resistance as well as cervical cancer metastases and relapse; however, the underlying Th17‐driven mechanisms are not fully understood. Here, using microarrays, we found that Th17 cells induced an epithelial‐to‐mesenchymal transition (EMT) phenotype of cervical cancer cells and promoted migration and invasion of 2D cultures and 3D spheroids via induction of microRNA miR‐142‐5p. As the responsible mechanism, we identified the subunits C and D of the succinate dehydrogenase (SDH) complex as new targets of miR‐142‐5p and provided evidence that Th17–miR‐142‐5p‐dependent reduced expression of SDHC and SDHD mediated enhanced migration and invasion of cancer cells using small interfering RNAs (siRNAs) for SDHC and SDHD, and miR‐142‐5p inhibitors. Consistently, patients exhibited high levels of succinate in their serum associated with lymph node metastases and diminished expression of SDHD in patient biopsies correlated with increased numbers of Th17 cells. Correspondingly, a combination of weak or negative SDHD expression and a ratio of Th17/CD4+ T cells > 43.90% in situ was associated with reduced recurrence‐free survival. In summary, we unraveled a previously unknown molecular mechanism by which Th17 cells promote cervical cancer progression and suggest evaluation of Th17 cells as a potential target for immunotherapy in cervical cancer.

Abstract The human microbiome emerges as a promising reservoir for diagnostic markers and therapeutics. Since host-associated microbiomes at various body sites differ and diseases do not occur in isolation, a comprehensive analysis strategy highlighting the full potential of microbiomes should include diverse specimen types and various diseases. To ensure robust data quality and comparability across specimen types and diseases, we employ standardized protocols to generate sequencing data from 1931 prospectively collected specimens, including from saliva, plaque, skin, throat, eye, and stool, with an average sequencing depth of 5.3 gigabases. Collected from 515 patients, these samples yield an average of 3.7 metagenomes per patient. Our results suggest significant microbial variations across diseases and specimen types, including unexpected anatomical sites. We identify 583 unexplored species-level genome bins (SGBs) of which 189 are significantly disease-associated. Of note, the existence of microbial resistance genes in one specimen was indicative of the same resistance genes in other specimens of the same patient. Annotated and previously undescribed SGBs collectively harbor 28,315 potential biosynthetic gene clusters (BGCs), with 1050 significant correlations to diseases. Our combinatorial approach identifies distinct SGBs and BGCs, emphasizing the value of pan-body pan-disease microbiomics as a source for diagnostic and therapeutic strategies.

Non-thermal plasma, a partially ionized gas, holds significant potential for clinical applications, including wound-healing support, oral therapies, and anti-tumour treatments. While its applications showed promising outcomes, the underlying molecular mechanisms remain incompletely understood. We thus apply non-thermal plasma to mouse auricular skin and conducted non-coding RNA sequencing, as well as single-cell blood sequencing. In a time-series analysis (five timepoints spanning 2 hours), we compare the expression of microRNAs in the plasma-treated left ears to the unexposed right ears of the same mice as well as to the ears of unexposed control mice. Our findings indicate specific effects in the treated ears for a set of five miRNAs: mmu-miR-144-5p, mmu-miR-144-3p, mmu-miR-142a-5p, mmu-miR-223-3p, and mmu-miR-451a. Interestingly, mmu-miR-223-3p also exhibits an increase over time in the right non-treated ear of the exposed mice, suggesting systemic effects. Notably, this miRNA, along with mmu-miR-142a-5p and mmu-miR-144-3p, regulates genes and pathways associated with wound healing and tissue regeneration (namely ErbB, FoxO, Hippo, and PI3K-Akt signalling). This co-regulation is particularly remarkable considering the significant seed dissimilarities among the miRNAs. Finally, single-cell sequencing of PBMCs reveals the downregulation of 12 from 15 target genes in B-cells, Cd4+ and Cd8+ T-cells. Collectively, our data provide evidence for a systemic effect of non-thermal plasma.

Abstract Background Tumor cells release extracellular vesicles (EVs) that contribute to the polarization of macrophages towards tumor-associated macrophages (TAMs). High expression levels of the RNA binding protein IGF2BP2/IMP2 are correlated with increased tumor cell proliferation, invasion, and poor prognosis in the clinic. However, there is a lack of understanding of whether IMP2 affects the cargo of cancer cell-derived EVs, thereby modulating macrophage polarization. Methods EVs were isolated from IMP2-expressing HCT116 parental cells (WT) and CRISPR/Cas9 IMP2 knockout (KO) cells. EVs were characterized according to MISEV guidelines, microRNA cargo was assessed by microRNA-Seq, and the protein cargo was analyzed by proteomics. Primary human monocyte-derived macrophages (HMDMs) were polarized by EVs, and the expression of genes and surface markers was assessed using qPCR and flow cytometry, respectively. Morphological changes of macrophages, as well as the migratory potential of cancer cells, were assessed by the Incucyte® system and macrophage matrix degradation potential by zymography. Changes in the metabolic activity of macrophages were quantified using a Seahorse® analyzer. For in vivo studies, EVs were injected into the yolk sac of zebrafish larvae, and macrophages were isolated by fluorescence-activated cell sorting. Results EVs from WT and KO cells had a similar size and concentration and were positive for 25 vesicle markers. The expression of tumor-promoting genes was higher in macrophages polarized with WT EVs than KO EVs, while the expression of TNF and IL6 was reduced. A similar pattern was observed in macrophages from zebrafish larvae treated in vivo. WT EV-polarized macrophages showed a higher abundance of TAM-like surface markers, higher matrix degrading activity, as well as a higher promotion of cancer cell migration. MicroRNA-Seq revealed a significant difference in the microRNA composition of WT and KO EVs, particularly a high abundance of miR-181a-5p in WT EVs, which was absent in KO EVs. Inhibitors of macropinocytosis and phagocytosis antagonized the delivery of miR-181a-5p into macrophages and the downregulation of the miR-181a-5p target DUSP6. Proteomics data showed differences in protein cargo in KO vs. WT EVs, with the differentially abundant proteins mainly involved in metabolic pathways. WT EV-treated macrophages exhibited a higher basal oxygen consumption rate and a lower extracellular acidification rate than KO EV-treated cells. Conclusion Our results show that IMP2 determines the cargo of EVs released by cancer cells, thereby modulating the EVs' actions on macrophages. Expression of IMP2 is linked to the secretion of EVs that polarize macrophages towards a tumor-promoting phenotype.

Fungal pathogens deploy a set of molecules (proteins, specialized metabolites, and sRNAs), so-called effectors, to aid the infection process. In comparison to other plant pathogens, smut fungi have small genomes and secretomes of 20 Mb and around 500 proteins, respectively. Previous comparative genomic studies have shown that many secreted effector proteins without known domains, i.e., novel, are conserved only in the Ustilaginaceae family. By analyzing the secretomes of 11 species within Ustilaginaceae, we identified 53 core homologous groups commonly present in this lineage. By collecting existing mutants and generating additional ones, we gathered 44 Ustilago maydis strains lacking single core effectors as well as 9 strains containing multiple deletions of core effector gene families. Pathogenicity assays revealed that 20 of these 53 mutant strains were affected in virulence. Among the 33 mutants that had no obvious phenotypic changes, 13 carried additional, sequence-divergent, structurally similar paralogs. We report a virulence contribution of seven previously uncharacterized single core effectors and of one effector family. Our results help to prioritize effectors for understanding U. maydis virulence and provide genetic resources for further characterization. [Graphic: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Obtaining high-quality omics data at the single-cell level from archived human tissue samples is crucial for gaining insights into cellular heterogeneity and pushing the field of personalized medicine forward. In this technical brief we present a comprehensive methodological framework for the efficient enzyme-free preparation of tissue-derived single cell suspensions and their conversion into single-cell miRNA sequencing libraries. The resulting data from this study have the potential to deepen our understanding of miRNA expression at the single-cell level and its relevance in the context of the examined tissues. The workflow encompasses tissue collection, RNALater immersion, storage, thawing, TissueGrinder-mediated dissociation, miRNA lysis, library preparation, sequencing, and data analysis. Quality control measures ensure reliable miRNA data, with specific attention to sample quality. The UMAP analysis reveals tissue-specific cell clustering, while miRNA diversity reflects tissue variations. The presented workflow effectively processes preserved tissues, extending opportunities for retrospective analysis and biobank utilization.

For cancers and other pathologies, early diagnosis remains the most promising path to survival. Profiling of longitudinal cohorts facilitates insights into trajectories of biomarkers. We measured microRNA expression in 240 serum samples from patients with colon, lung, and breast cancer and from cancer-free controls. Each patient provided at least two serum samples, one prior to diagnosis and one following diagnosis. The median time interval between the samples was 11.6 years. Using computational models, we evaluated the circulating profiles of 21 microRNAs. The analysis yielded two sets of biomarkers, static ones that show an absolute difference between certain cancer types and controls and dynamic ones where the level over time provided higher diagnostic information content. In the first group, miR-99a-5p stands out for all three cancer types. In the second group, miR-155-5p allows to predict lung cancers and colon cancers. Classification in samples from cancer and non-cancer patients using gradient boosted trees reached an average accuracy of 79.9%. The results suggest that individual change over time or an absolute value at one time point may predict a disease with high specificity and sensitivity.

Previous work on murine models and humans demonstrated global as well as tissue-specific molecular ageing trajectories of RNAs. Extracellular vesicles (EVs) are membrane vesicles mediating the horizontal transfer of genetic information between different tissues. We sequenced small regulatory RNAs (sncRNAs) in two mouse plasma fractions at five time points across the lifespan from 2–18 months: (1) sncRNAs that are free-circulating (fc-RNA) and (2) sncRNAs bound outside or inside EVs (EV-RNA). Different sncRNA classes exhibit unique ageing patterns that vary between the fcRNA and EV-RNA fractions. While tRNAs showed the highest correlation with ageing in both fractions, rRNAs exhibited inverse correlation trajectories between the EV- and fc-fractions. For miRNAs, the EV-RNA fraction was exceptionally strongly associated with ageing, especially the miR-29 family in adipose tissues. Sequencing of sncRNAs and coding genes in fat tissue of an independent cohort of aged mice up to 27 months highlighted the pivotal role of miR-29a-3p and miR-29b-3p in ageing-related gene regulation that we validated in a third cohort by RT-qPCR.

Abstract Renewable energy systems depend on the weather, and weather information, thus, plays a crucial role in forecasting time series within such renewable energy systems. However, while weather data are commonly used to improve forecast accuracy, it still has to be determined in which input shape this weather data benefits the forecasting models the most. In the present paper, we investigate how transformations for weather data inputs, i. e., station-based and grid-based weather data, influence the accuracy of energy time series forecasts. The selected weather data transformations are based on statistical features, dimensionality reduction, clustering, autoencoders, and interpolation. We evaluate the performance of these weather data transformations when forecasting three energy time series: electrical demand, solar power, and wind power. Additionally, we compare the best-performing weather data transformations for station-based and grid-based weather data. We show that transforming station-based or grid-based weather data improves the forecast accuracy compared to using the raw weather data between 3.7 and 5.2%, depending on the target energy time series, where statistical and dimensionality reduction data transformations are among the best.

Abstract Wind power and other forms of renewable energy sources play an ever more important role in the energy supply of today's power grids. Forecasting renewable energy sources has therefore become essential in balancing the power grid. While a lot of focus is placed on new forecasting methods, little attention is given on how to compare, reproduce and transfer the methods to other use cases and data. One reason for this lack of attention is the limited availability of open‐source datasets, as many currently used datasets are non‐disclosed and make reproducibility of research impossible. This unavailability of open‐source datasets is especially prevalent in commercially interesting fields such as wind power forecasting. However, with this paper, we want to enable researchers to compare their methods on publicly available datasets by providing the, to our knowledge, largest up‐to‐date overview of existing open‐source wind power datasets, and a categorization into different groups of datasets that can be used for wind power forecasting. We show that there are publicly available datasets sufficient for wind power forecasting tasks and discuss the different data groups properties to enable researchers to choose appropriate open‐source datasets and compare their methods on them.

Abstract In the electricity grid, constantly balancing the supply and demand is critical for the network’s stability and any expected deviations require balancing efforts. This balancing becomes more challenging in future energy systems characterised by a high proportion of renewable generation due to the increased volatility of these renewables. In order to know when any balancing efforts are required, it is essential to predict the so-called net load, the difference between forecast energy demand and renewable supply. Although various forecasting approaches exist for both the individual components of the net load and the net load itself, it is unclear if it is more beneficial to aggregate several specialised forecasts to obtain the net load or to aggregate the input data to forecast the net load with one approach directly. Therefore, the present paper compares three net load forecasting approaches that exploit different levels of aggregation. We compare an aggregated strategy that directly forecasts the net load, a partially aggregated strategy that forecasts demand and supply separately, and a disaggregated strategy that forecasts demand and supply from each generator separately. We evaluate the forecast performance of all strategies with a simple and a complex forecasting model, both for deterministic and probabilistic forecasts, using one year of data from a simulated realistic future energy system characterised by a high share of renewable energy sources. We find that the partially aggregated strategy performs best, suggesting that a balance between specifically tailored forecasting models and aggregation is advantageous.

Abstract Capturing the uncertainty in probabilistic wind power forecasts is challenging, especially when uncertain input variables, such as the weather, play a role. Since ensemble weather predictions aim to capture the uncertainty in the weather system, they can be used to propagate this uncertainty through to subsequent wind power forecasting models. However, as weather ensemble systems are known to be biassed and underdispersed, meteorologists post‐process the ensembles. This post‐processing can successfully correct the biasses in the weather variables but has not been evaluated thoroughly in the context of subsequent forecasts, such as wind power generation forecasts. The present paper evaluates multiple strategies for applying ensemble post‐processing to probabilistic wind power forecasts. We use Ensemble Model Output Statistics (EMOS) as the post‐processing method and evaluate four possible strategies: only using the raw ensembles without post‐processing, a one‐step strategy where only the weather ensembles are post‐processed, a one‐step strategy where we only post‐process the power ensembles and a two‐step strategy where we post‐process both the weather and power ensembles. Results show that post‐processing the final wind power ensemble improves forecast performance regarding both calibration and sharpness whilst only post‐processing the weather ensembles does not necessarily lead to increased forecast performance.

The composition of the gut microbiota is linked to multiple diseases, including Parkinson’s disease (PD). Abundance of bacteria producing short-chain fatty acids (SCFAs) and fecal SCFA concentrations are reduced in PD. SCFAs exert various beneficial functions in humans. In the interventional, monocentric, open-label clinical trial “Effects of Resistant Starch on Bowel Habits, Short Chain Fatty Acids and Gut Microbiota in Parkinson’s Disease” (RESISTA-PD; ID: NCT02784145), we aimed at altering fecal SCFAs by an 8-week prebiotic intervention with resistant starch (RS). We enrolled 87 subjects in three study-arms: 32 PD patients received RS (PD + RS), 30 control subjects received RS, and 25 PD patients received solely dietary instructions. We performed paired-end 100 bp length metagenomic sequencing of fecal samples using the BGISEQ platform at an average of 9.9 GB. RS was well-tolerated. In the PD + RS group, fecal butyrate concentrations increased significantly, and fecal calprotectin concentrations dropped significantly after 8 weeks of RS intervention. Clinically, we observed a reduction in non-motor symptom load in the PD + RS group. The reference-based analysis of metagenomes highlighted stable alpha-diversity and beta-diversity across the three groups, including bacteria producing SCFAs. Reference-free analysis suggested punctual, yet pronounced differences in the metagenomic signature in the PD + RS group. RESISTA-PD highlights that a prebiotic treatment with RS is safe and well-tolerated in PD. The stable alpha-diversity and beta-diversity alongside altered fecal butyrate and calprotectin concentrations call for long-term studies, also investigating whether RS is able to modify the clinical course of PD.

High-quality DNA extraction is a crucial step in metagenomic studies. Bias by different isolation kits impairs the comparison across datasets. A trending topic is, however, the analysis of multiple metagenomes from the same patients to draw a holistic picture of microbiota associated with diseases. We thus collected bile, stool, saliva, plaque, sputum, and conjunctival swab samples and performed DNA extraction with three commercial kits. For each combination of the specimen type and DNA extraction kit, 20-gigabase (Gb) metagenomic data were generated using short-read sequencing. While profiles of the specimen types showed close proximity to each other, we observed notable differences in the alpha diversity and composition of the microbiota depending on the DNA extraction kits. No kit outperformed all selected kits on every specimen. We reached consistently good results using the Qiagen QiAamp DNA Microbiome Kit. Depending on the specimen, our data indicate that over 10 Gb of sequencing data are required to achieve sufficient resolution, but DNA-based identification is superior to identification by mass spectrometry. Finally, long-read nanopore sequencing confirmed the results (correlation coefficient > 0.98). Our results thus suggest using a strategy with only one kit for studies aiming for a direct comparison of multiple microbiotas from the same patients.

Cervical cancer therapy is still a major clinical challenge, as patients substantially differ in their response to standard treatments, including chemoradiotherapy (CRT). During cervical carcinogenesis, T‐helper (Th)‐17 cells accumulate in the peripheral blood and tumor tissues of cancer patients and are associated with poor prognosis. In this prospective study, we find increased Th17 frequencies in the blood of patients after chemoradiotherapy and a post‐therapeutic ratio of Th17/CD4+ T cells > 8% was associated with early recurrence. Furthermore, Th17 cells promote resistance of cervical cancer cells toward CRT, which was dependent on the AKT signaling pathway. Consistently, patients with high Th17 frequencies in pretherapeutic biopsies exhibit lower response to primary CRT. This work reveals a key role of Th17 cells in CRT resistance and elevated Th17 frequencies in the blood after CRT correspond with early recurrence. Our results may help to explain individual treatment responses of cervical cancer patients and suggest evaluation of Th17 cells as a novel predictive biomarker for chemoradiotherapy responses and as a potential target for immunotherapy in cervical cancer.

MicroRNAs (miRNAs) expressed in endothelial cells (ECs) are powerful regulators of angiogenesis, which is essential for tumor growth and metastasis. Here, we demonstrated that miR-22 is preferentially and highly expressed in ECs, while its endothelial level is significantly downregulated in human non-small cell lung cancer (NSCLC) tissues when compared to matched nontumor lung tissues. This reduction of endothelial miR-22 is possibly induced by NSCLC cell-secreted interleukin-1β and subsequently activated transcription factor nuclear factor-κB. Endothelial miR-22 functions as a potent angiogenesis inhibitor that inhibits all of the key angiogenic activities of ECs and consequently NSCLC growth through directly targeting sirtuin 1 and fibroblast growth factor receptor 1 in ECs, leading to inactivation of AKT/mammalian target of rapamycin signaling. These findings provide insight into the molecular mechanisms of NSCLC angiogenesis and indicate that endothelial miR-22 represents a potential target for the future antiangiogenic treatment of NSCLC.

Wind energy represents one of the leading renewable energy sectors and is considered instrumental in the ongoing decarbonization process. Accurate forecasts are essential for a reliable large-scale wind power integration, allowing efficient operation and maintenance, planning of unit commitment, and scheduling by system operators. However, due to non-stationarity, randomness, and intermittency, forecasting wind power is challenging. This work investigates a multi-modal approach for wind power forecasting by considering turbine-level time series collected from SCADA systems and high-resolution Numerical Weather Prediction maps. A neural architecture based on stacked Recurrent Neural Networks is proposed to process and combine different data sources containing spatio-temporal patterns. This architecture allows combining the local information from the turbine’s internal operating conditions with future meteorological data from the surrounding area. Specifically, this work focuses on multi-horizon turbine-level hourly forecasts for an entire wind farm with a lead time of 90 h. This work explores the impact of meteorological variables on different spatial scales, from full grids to cardinal point features, on wind power forecasts. Results show that a subset of features associated with all wind directions, even when spatially distant, can produce more accurate forecasts with respect to full grids and reduce computation times. The proposed model outperforms the linear regression baseline and the XGBoost regressor achieving an average skill score of 25%. Finally, the integration of SCADA data in the training process improved the predictions allowing the multi-modal neural network to model not only the meteorological patterns but also the turbine’s internal behavior.

Aging is a key risk factor for chronic diseases of the elderly. Here the authors perform large-scale miRNA profiling of blood from individuals of a range of ages and show that common diseases alter the physiological age-related blood microRNA profile.

Penile squamous cell cancer (PSCC) is the most frequent penile malignant disease. Infections with human papillomaviruses (HPV) are a major etiologic driver of PSCC. However, the molecular details of the underlying carcinogenesis are understudied because of rare clinical specimens and missing cell lines. Here, we investigated if the expression of high-risk HPV16 oncogenes causes an augmentation of the Wnt pathway using unique HPV-positive penile cancer (PeCa) cell lines in monolayer and organotypic 3D raft cultures as well as tissue micro arrays containing clinical tissue specimens. The HPV oncoproteins enhanced the expression of Leucine-rich repeat-containing G-protein coupled receptor 6 (LGR6) and the HPV-positive PeCa cells expressed a signature of Wnt target and stemness-associated genes. However, the notable lack of nuclear β-catenin in vitro and in situ raised the question if the enhanced expression of Wnt pathway factors is tantamount to an active Wnt signaling. Subsequent TOP-flash reporter assays revealed Wnt signaling as absent and not inducible by respective Wnt ligands in PeCa cell lines. The HPV-positive PeCa cells and especially HPV-positive PeCa specimens of the tumor core expressed the Wnt antagonist and negative feedback-regulator Dickkopf1 (DKK1). Subsequent neutralization experiments using PeCa cell line-conditioned media demonstrated that DKK1 is capable to impair ligand-induced Wnt signaling. While gene expression analyses suggested an augmented and active canonical Wnt pathway, the respective signaling was inhibited due to the endogenous expression of the antagonist DKK1. Subsequent TMA stainings indicated Dkk1 as linked with HPV-positivity and metastatic disease progression in PeCa suggesting potential as a prognostic marker.

To forecast wind power generation in the scale of years to decades, outputs from climate models are often used. However, one major limitation of the data projected by these models is their coarse temporal resolution—usually not finer than three hours and sometimes as coarse as one month. Due to the non-linear relationship between wind speed and wind power, and the long forecast horizon considered, small changes in wind speed can result in big changes in projected wind power generation. Our study indicates that the distribution of observed 10 min wind speed data is relatively well preserved using three- or six-hourly instantaneous values. In contrast, daily or monthly values, as well as any averages, including three-hourly averages, are almost never capable of preserving the distribution of the underlying higher resolution data. Assuming that climate models behave in a similar manner to observations, our results indicate that output at three-hourly or six-hourly temporal resolution is high enough for multi-decadal wind power generation forecasting. In contrast, wind speed projections of lower temporal resolution, or averages over any time range, should be handled with care.

Blood-borne small non-coding (sncRNAs) are among the prominent candidates for blood-based diagnostic tests. Often, high-throughput approaches are applied to discover biomarker signatures. These have to be validated in larger cohorts and evaluated by adequate statistical learning approaches. Previously, we published high-throughput sequencing based microRNA (miRNA) signatures in Alzheimer’s disease (AD) patients in the United States (US) and Germany. Here, we determined abundance levels of 21 known circulating miRNAs in 465 individuals encompassing AD patients and controls by RT-qPCR. We computed models to assess the relation between miRNA expression and phenotypes, gender, age, or disease severity (Mini-Mental State Examination; MMSE). Of the 21 miRNAs, expression levels of 20 miRNAs were consistently de-regulated in the US and German cohorts. 18 miRNAs were significantly correlated with neurodegeneration (Benjamini-Hochberg adjusted P

Abstract Background Vascular shear stress promotes endothelial cell sprouting in vitro. The impact of hemodynamic forces on microRNA (miRNA) and gene expression within growing vascular networks in vivo, however, remain poorly investigated. Arteriovenous (AV) shunts are an established model for induction of neoangiogenesis in vivo and can serve as a tool for analysis of hemodynamic effects on miRNA and gene expression profiles over time. Methods AV shunts were microsurgically created in rats and explanted on postoperative days 5, 10 and 15. Neoangiogenesis was confirmed by histologic analysis and micro-computed tomography. MiRNA and gene expression profiles were determined in tissue specimens from AV shunts by microarray analysis and quantitative real-time polymerase chain reaction and compared with sham-operated veins by bioinformatics analysis. Changes in protein expression within AV shunt endothelial cells were determined by immunohistochemistry. Results Samples from AV shunts exhibited a strong overexpression of proangiogenic cytokines, oxygenation-associated genes (HIF1A, HMOX1), and angiopoetic growth factors. Significant inverse correlations of the expressions of miR-223-3p, miR-130b-3p, miR-19b-3p, miR-449a-5p, and miR-511-3p which were up-regulated in AV shunts, and miR-27b-3p, miR-10b-5p, let-7b-5p, and let-7c-5p, which were down-regulated in AV shunts, with their predicted interacting targets C–X–C chemokine receptor 2 (CXCR2), interleukin-1 alpha (IL1A), ephrin receptor kinase 2 (EPHA2), synaptojanin-2 binding protein (SYNJ2BP), forkhead box C1 (FOXC1) were present. CXCL2 and IL1A overexpression in AV shunt endothelium was confirmed at the protein level by immunohistochemistry. Conclusions Our data indicate that flow-stimulated angiogenesis is determined by an upregulation of cytokines, oxygenation associated genes and miRNA-dependent regulation of FOXC1, EPHA2 and SYNJ2BP.

Gene amplifications in amphibians and flies are known to occur during development and have been well characterized, unlike in mammalian cells, where they are predominantly investigated as an attribute of tumors. Recently, we first described gene amplifications in human and mouse neural stem cells, myoblasts, and mesenchymal stem cells during differentiation. The mechanism leading to gene amplifications in amphibians and flies depends on endocycles and multiple origin-firings. So far, there is no knowledge about a comparable mechanism in normal human cells. Here, we describe rereplication during the early myotube differentiation of human skeletal myoblast cells, using fiber combing and pulse-treatment with EdU (5′-Ethynyl-2′-deoxyuridine)/CldU (5-Chlor-2′-deoxyuridine) and IdU (5-Iodo-2′-deoxyuridine)/CldU. We found rereplication during a restricted time window between 2 h and 8 h after differentiation induction. Rereplication was detected in cells simultaneously with the amplification of the MDM2 gene. Our findings support rereplication as a mechanism enabling gene amplification in normal human cells.

The rapid transformation of the electricity sector increases both the opportunities and the need for Data Analytics. In recent years, various new methods and fields of application have been emerging. As research is growing and becoming more diverse and specialized, it is essential to integrate and structure the fragmented body of scientific work. We therefore conduct a systematic review of studies concerned with developing and applying Data Analytics methods in the context of the electricity value chain. First, we provide a quantitative high-level overview of the status quo of Data Analytics research, and show historical literature growth, leading countries in the field and the most intensive international collaborations. Then, we qualitatively review over 200 high-impact studies to present an in-depth analysis of the most prominent applications of Data Analytics in each of the electricity sector’s areas: generation, trading, transmission, distribution, and consumption. For each area, we review the state-of-the-art Data Analytics applications and methods. In addition, we discuss used data sets, feature selection methods, benchmark methods, evaluation metrics, and model complexity and run time. Summarizing the findings from the different areas, we identify best practices and what researchers in one area can learn from other areas. Finally, we highlight potential for future research.

Chronic obstructive pulmonary disease (COPD) significantly increases the risk of developing cancer. Biomarker studies frequently follow a case-control set-up in which patients diagnosed with a disease are compared to controls. Longitudinal cohort studies such as the COPD-centered German COPD and SYstemic consequences-COmorbidities NETwork (COSYCONET) study provide the patient and biomaterial base for discovering predictive molecular markers. We asked whether microRNA (miRNA) profiles in blood collected from COPD patients prior to a tumor diagnosis could support an early diagnosis of tumor development independent of the tumor type. From 2741 participants of COSYCONET diagnosed with COPD, we selected 534 individuals including 33 patients who developed cancer during the follow-up period of 54 months and 501 patients who did not develop cancer, but had similar age, gender and smoking history. Genome-wide miRNA profiles were generated and evaluated using machine learning techniques. For patients developing cancer we identified nine miRNAs with significantly decreased abundance (two-tailed unpaired t-test adjusted for multiple testing P

Abstract The present article describes a concept for the creation and application of energy forecasting models in a distributed environment. Additionally, a benchmark comparing the time required for the training and application of data-driven forecasting models on a single computer and a computing cluster is presented. This comparison is based on a simulated dataset and both R and Apache Spark are used. Furthermore, the obtained results show certain points in which the utilization of distributed computing based on Spark may be advantageous.

Abstract Although microRNAs are supposed to be stable in-vivo, degradation processes potentially blur our knowledge on the small oligonucleotides. We set to quantify the effect of degradation on microRNAs in mouse to identify causes for distorted microRNAs patterns. In liver, we found 298, 99 and 8 microRNAs whose expression significantly correlated to RNA integrity, storage time at room temperature and storage time at 4 °C, respectively. Expression levels of 226 microRNAs significantly differed between liver samples with high RNA integrity compared to liver samples with low RNA integrity by more than two-fold. Especially the 157 microRNAs with increased expression in tissue samples with low RNA integrity were most recently added to miRBase. Testing potentially confounding sources, e.g. in-vitro degraded RNA depleted of small RNAs, we detected signals for 350 microRNAs, suggesting cross-hybridization of fragmented RNAs. Therefore, we conclude that especially microRNAs added in the latest miRBase versions might be artefacts due to RNA degradation. The results facilitate differentiation between degradation-resilient microRNAs, degradation-sensitive microRNAs, and likely erroneously annotated microRNAs. The latter were largely identified by NGS but not experimentally validated and can severely bias microRNA biomarker research and impact the value of microRNAs as diagnostic, prognostic or therapeutic tools.

Abstract Background Children with congenital heart defects (CHDs) are at high risk for myocardial failure after operative procedures with cardiopulmonary bypass (CPB). Recent studies suggest that microRNAs (miRNA) are involved in the development of CHDs and myocardial failure. Therefore, the aim of this study was to determine alterations in the miRNA profile in heart tissue after cardiac surgery using CPB. Methods In total, 14 tissue samples from right atrium were collected from patients before and after connection of the CPB. SurePrint™ 8 × 60K Human v21 miRNA array and quantitative reverse transcription-polymerase chain reaction (RT-qPCR) were employed to determine the miRNA expression profile from three patients before and after connection of the CPB. Enrichment analyses of altered miRNA expression were predicted using bioinformatic tools. Results According to miRNA array, a total of 90 miRNAs were significantly altered including 29 miRNAs with increased and 61 miRNAs with decreased expression after de-connection of CPB (n = 3) compared to before CPB (n = 3). Seven miRNAs had been validated using RT-qPCR in an independent cohort of 11 patients. Enrichment analyses applying the KEGG database displayed the highest correlation for signaling pathways, cellular community, cardiovascular disease and circulatory system. Conclusion Our result identified the overall changes of the miRNome in right atrium tissue of patients with CHDs after CPB. The differentially altered miRNAs lay a good foundation for further understanding of the molecular function of changed miRNAs in regulating CHDs and after CPB in particular.

Although cellular and molecular mechanisms during the course of bone healing have been thoroughly investigated, the regulation of gene expression by microRNA during bone regeneration is still poorly understood. We hypothesized that nonunion formation is associated with different microRNA expression patterns and that target proteins of these microRNAs are differently expressed in callus tissue of nonunions compared to physiologically healing bones. In a well-established femoral osteotomy model in CD-1 mice osteotomies were induced which result either in healing or in nonunion formation. MicroRNA and target protein expression was evaluated by microarray, quantitative real-time polymerase chain reaction (qrt-PCR) and Western blot. Microarray analyses demonstrated 44 microRNAs to be relevant for nonunion formation compared to physiological bone healing. In nonunions qrt-PCR could validate a higher expression of microRNA-140-3p and microRNA-140-5p. This was associated with a reduced expression of Dnpep and stromal cell-derived factor (SDF)-1α, which are both known to be target proteins of microRNA-140 and also to be involved in the process of bone healing. These data suggest that an increased expression of microRNA-140-3p and microRNA-140-5p markedly contributes to the development of nonunions, most probably by affecting bone morphogenetic protein (BMP)-2 function during the early stage of healing due to a reduced SDF-1α expression.

Chronic obstructive pulmonary disease (COPD) is associated with an increased risk of death, reducing life expectancy on average between 5 and 7 years. The survival time after diagnosis, however, varies considerably as a result of the heterogeneity of COPD. Therefore, markers that predict individual survival of COPD patients are of great value. We analyzed baseline molecular profiles and collected 54 months of follow-up data of the cohort study “COPD and SYstemic consequences-COmorbidities NETwork” (COSYCONET). Genome-wide microRNA signatures from whole blood collected at time of the inclusion in the study were generated for 533 COPD patients including patients that deceased during the 54-month follow-up period (n = 53) and patients that survived this period (n = 480). We identified two blood-born microRNAs (miR-150-5p and miR-320b) that were highly predictive for survival of COPD patients. The expression change was then confirmed by RT-qPCR in 245 individuals. Ninety percent of patients with highest expression of miR-150-5p survived the 54-month period in contrast to only 50% of patients with lowest expression intensity. Moreover, the abundance of the oncogenic miR-150-5p in blood of COPD patients was predictive for the development of cancer. Thus, molecular profiles measured at the time of a COPD diagnosis have a high predictive power for the survival of patients.

Multiple studies endorsed the positive effect of regular exercise on mental and physical health. However, the molecular mechanisms underlying training-induced fitness in combination with personal life-style remain largely unexplored. Circulating biomarkers such as microRNAs (miRNAs) offer themselves for studying systemic and cellular changes since they can be collected from the bloodstream in a low-invasive manner. In Homo sapiens miRNAs are known to regulate a substantial number of protein-coding genes in a post-transcriptional manner and hence are of great interest to understand differential gene expression profiles, offering a cost-effective mechanism to study molecular training adaption, and connecting the dots from genomics to observed phenotypes. Here, we investigated molecular expression patterns of 2549 miRNAs in whole-blood samples from 23 healthy and untrained adult participants of a cross-over study, consisting of eight weeks of endurance training, with several sessions per week, followed by 8 weeks of washout and another 8 weeks of running, using microarrays. Participants were randomly assigned to one of the two study groups, one of which administered carbohydrates before each session in the first training period, and switching the treatment group for the second training period. During running sessions clinical parameters as heartbeat frequency were recorded. This information was extended with four measurements of maximum oxygen uptake (VO 2 max) for each participant. We observed that multiple circulating miRNAs show expression changes after endurance training, leveraging the capability to separate the blood samples by training status. To this end, we demonstrate that most of the variance in miRNA expression can be explained by both common and known biological and technical factors. Our findings highlight six distinct clusters of miRNAs, each exhibiting an oscillating expression profile across the four study timepoints, that can effectively be utilized to predict phenotypic VO 2 max levels. In addition, we identified miR-532-5p as a candidate marker to determine personal alterations in physical training performance on a case-by-case analysis taking the influence of a carbohydrate-rich nutrition into account. In literature, miR-532-5p is known as a common down-regulated miRNA in diabetes and obesity, possibly providing a molecular link between cellular homeostasis, personal fitness levels, and health in aging. We conclude that circulating miRNA expression can be altered due to regular endurance training, independent of the carbohydrate (CHO) availability in the training timeframe. Further validation studies are required to confirm the role of exercise-affected miRNAs and the extraordinary function of miR-532-5p in modulating the metabolic response to a high availability of glucose.

Seroreactivity profiling emerges as valuable technique for minimal invasive cancer detection. Recently, we provided first evidence for the applicability of serum profiling of glioma using a limited number of immunogenic antigens. Here, we screened 57 glioma and 60 healthy sera for autoantibodies against 1827 Escherichia coli expressed clones, including 509 in-frame peptide sequences. By a linear support vector machine approach, we calculated mean specificity, sensitivity, and accuracy of 100 repetitive classifications. We were able to differentiate glioma sera from sera of the healthy controls with a specificity of 90.28%, a sensitivity of 87.31% and an accuracy of 88.84%. We were also able to differentiate World Health Organization grade IV glioma sera from healthy sera with a specificity of 98.45%, a sensitivity of 80.93%, and an accuracy of 92.88%. To rank the antigens according to their information content, we computed the area under the receiver operator characteristic curve value for each clone. Altogether, we found 46 immunogenic clones including 16 in-frame clones that were informative for the classification of glioma sera versus healthy sera. For the separation of glioblastoma versus healthy sera, we found 91 informative clones including 26 in-frame clones. The best-suited in-frame clone for the classification glioma sera versus healthy sera corresponded to the vimentin gene (VIM) that was previously associated with glioma. In the future, autoantibody signatures in glioma not only may prove useful for diagnosis but also offer the prospect for a personalized immune-based therapy.

Although an increased level of the prostate-specific antigen can be an indication for prostate cancer, other reasons often lead to a high rate of false positive results. Therefore, an additional serological screening of autoantibodies in patients' sera could improve the detection of prostate cancer. We performed protein macroarray screening with sera from 49 prostate cancer patients, 70 patients with benign prostatic hyperplasia and 28 healthy controls and compared the autoimmune response in those groups. We were able to distinguish prostate cancer patients from normal controls with an accuracy of 83.2%, patients with benign prostatic hyperplasia from normal controls with an accuracy of 86.0% and prostate cancer patients from patients with benign prostatic hyperplasia with an accuracy of 70.3%. Combining seroreactivity pattern with a PSA level of higher than 4.0 ng/ml this classification could be improved to an accuracy of 84.1%. For selected proteins we were able to confirm the differential expression by using luminex on 84 samples. We provide a minimally invasive serological method to reduce false positive results in detection of prostate cancer and according to PSA screening to distinguish men with prostate cancer from men with benign prostatic hyperplasia.

Essentially, all cells release extracellular vesicles (EVs) that end up in biofluids, including blood, and the contents of these EVs can provide a window into the status of the cells from which they are released. This is particularly interesting in cancer, since these EVs allow for ‘ex-vivo’ analysis of the properties of the tumours without the need for biopsy. Gene mutations, rearrangements, amplifications, and epigenetic changes in the transcriptome can be monitored in circulating EVs. In this study, we used two human tumour cell lines derived from an epidermoid carcinoma and a medulloblastoma, which had amplification for the epidermal growth factor receptor (EGFR) and c-Myc genes, respectively. Cells were implanted subcutaneously into immunocompromised mice, and levels of gene amplification in both groups of subcutaneous tumours were quantified. We then determined if elevated levels of transcripts for the human EGFR and c-Myc were represented in circulating EVs in tumour-bearing mice. The expression levels of both human EGFR (h-EGFR) and human c-Myc (h-c-Myc) mRNAs in circulating EVs correlated well with their amplified status in the tumours. This data provides further support to the idea that circulating EVs are a potential platform for tumour biomarkers.

BackgroundMyasthenia gravis is a disorder of neuromuscular transmission associated with autoantibodies against the nicotinic acetylcholine receptor. We have previously developed a customized protein macroarray comprising 1827 potential human autoantigens, which permitted to discriminate sera of patients with different cancers from sera of healthy controls, but has not yet been evaluated in antibody-mediated autoimmune diseases.ObjectiveTo determine whether autoantibody signatures obtained by protein macroarray separate sera of patients with myasthenia gravis from healthy controls.MethodsSera of patients with acetylcholine receptor antibody-positive myasthenia gravis (n = 25) and healthy controls (n = 32) were analyzed by protein macroarrays comprising 1827 peptide clones.ResultsAutoantibody signatures did not separate patients with myasthenia gravis from controls with sufficient sensitivity, specificity, and accuracy. Intensity values of one antigen (poly A binding protein cytoplasmic 1, p = 0.0045) were higher in patients with myasthenia gravis, but the relevance of this and two further antigens, 40S ribosomal protein S13 (20.8% vs. 0%, p = 0.011) and proteasome subunit alpha type 1 (25% vs. 3.1%, p = 0.035), which were detected more frequently by myasthenia gravis than by control sera, currently remains uncertain.ConclusionSeroreactivity profiles of patients with myasthenia gravis detected by a customized protein macroarray did not allow discrimination from healthy controls, compatible with the notion that the autoantibody response in myasthenia gravis is highly focussed against the acetylcholine receptor.

Wilms tumor (WT) is the most common childhood renal cancer. Recent findings of mutations in microRNA (miRNA) processing proteins suggest a pivotal role of miRNAs in WT genesis. We performed miRNA expression profiling of 36 WTs of different subtypes and four normal kidney tissues using microarrays. Additionally, we determined the gene expression profile of 28 of these tumors to identify potentially correlated target genes and affected pathways. We identified 85 miRNAs and 2107 messenger RNAs (mRNA) differentially expressed in blastemal WT, and 266 miRNAs and 1267 mRNAs differentially expressed in regressive subtype. The hierarchical clustering of the samples, using either the miRNA or mRNA profile, showed the clear separation of WT from normal kidney samples, but the miRNA pattern yielded better separation of WT subtypes. A correlation analysis of the deregulated miRNA and mRNAs identified 13,026 miRNA/mRNA pairs with inversely correlated expression, of which 2844 are potential interactions of miRNA and their predicted mRNA targets. We found significant upregulation of miRNAs-183, -301a/b and -335 for the blastemal subtype, and miRNAs-181b, -223 and -630 for the regressive subtype. We found marked deregulation of miRNAs regulating epithelial to mesenchymal transition, especially in the blastemal subtype, and miRNAs influencing chemosensitivity, especially in regressive subtypes. Further research is needed to assess the influence of preoperative chemotherapy and tumor infiltrating lymphocytes on the miRNA and mRNA patterns in WT.

High-throughput sequencing opens avenues to find genetic variations that may be indicative of an increased risk for certain diseases. Linking these genomic data to other "omics" approaches bears the potential to deepen our understanding of pathogenic processes at the molecular level. To detect novel single nucleotide polymorphisms (SNPs) for glioblastoma multiforme (GBM), we used a combination of specific target selection and next generation sequencing (NGS). We generated a microarray covering the exonic regions of 132 GBM associated genes to enrich target sequences in two GBM tissues and corresponding leukocytes of the patients. Enriched target genes were sequenced with Illumina and the resulting reads were mapped to the human genome. With this approach we identified over 6000 SNPs, including over 1300 SNPs located in the targeted genes. Integrating the genome-wide association study (GWAS) catalog and known disease associated SNPs, we found that several of the detected SNPs were previously associated with smoking behavior, body mass index, breast cancer and high-grade glioma. Particularly, the breast cancer associated allele of rs660118 SNP in the gene SART1 showed a near doubled frequency in glioblastoma patients, as verified in an independent control cohort by Sanger sequencing. In addition, we identified SNPs in 20 of 21 GBM associated antigens providing further evidence that genetic variations are significantly associated with the immunogenicity of antigens.

Several studies report autoantibody signatures in cancer. The majority of these studies analyzed adult tumors and compared the seroreactivity pattern of tumor patients with the pattern in healthy controls. Here, we compared the autoimmune response in patients with neuroblastoma and patients with Wilms tumor representing two different childhood tumors. We were able to differentiate untreated neuroblastoma patients from untreated Wilms tumor patients with an accuracy of 86.8%, a sensitivity of 87.0% and a specificity of 86.7%. The separation of treated neuroblastoma patients from treated Wilms tumor patients' yielded comparable results with an accuracy of 83.8%. We furthermore identified the antigens that contribute most to the differentiation between both tumor types. The analysis of these antigens revealed that neuroblastoma was considerably more immunogenic than Wilms tumor. The reported antigens have not been found to be relevant for comparative analyses between other tumors and controls. In summary, neuroblastoma appears as a highly immunogenic tumor as demonstrated by the extended number of antigens that separate this tumor from Wilms tumor.

Essentially, all cells release extracellular vesicles (EVs) that end up in biofluids, including blood, and the contents of these EVs can provide a window into the status of the cells from which they are released. This is particularly interesting in cancer, since these EVs allow for ‘ex-vivo’ analysis of the properties of the tumours without the need for biopsy. Gene mutations, rearrangements, amplifications, and epigenetic changes in the transcriptome can be monitored in circulating EVs. In this study, we used two human tumour cell lines derived from an epidermoid carcinoma and a medulloblastoma, which had amplification for the epidermal growth factor receptor (EGFR) and c-Myc genes, respectively. Cells were implanted subcutaneously into immunocompromised mice, and levels of gene amplification in both groups of subcutaneous tumours were quantified. We then determined if elevated levels of transcripts for the human EGFR and c-Myc were represented in circulating EVs in tumour-bearing mice. The expression levels of both human EGFR (h-EGFR) and human c-Myc (h-c-Myc) mRNAs in circulating EVs correlated well with their amplified status in the tumours. This data provides further support to the idea that circulating EVs are a potential platform for tumour biomarkers.