Impairment of the neurotrophic signaling hub B-Raf contributes to motoneuron degeneration in spinal muscular atrophy.

Spinal muscular atrophy (SMA) is a motoneuron disease caused by deletions of the Survival of Motoneuron 1 gene (SMN1) and low SMN protein levels. SMN restoration is the concept behind a number of recently approved drugs which result in impressive yet limited effects. Since SMN has already been enhanced in treated patients, complementary SMN-independent approaches are needed. Previously, a number of altered signaling pathways which regulate motoneuron degeneration have been identified as candidate targets. However, signaling pathways form networks, and their connectivity is still unknown in SMA. Here, we used presymptomatic SMA mice to elucidate the network of altered signaling in SMA. The SMA network is structured in two clusters with AKT and 14-3-3 ζ/δ in their centers. Both clusters are connected by B-Raf as a major signaling hub. The direct interaction of B-Raf with 14-3-3 ζ/δ is important for an efficient neurotrophic activation of the MEK/ERK pathway and crucial for motoneuron survival. Further analyses in SMA mice revealed that both proteins were down-regulated in motoneurons and the spinal cord with B-Raf being reduced at presymptomatic stages. Primary fibroblasts and iPSC-derived motoneurons from SMA patients both showed the same pattern of down-regulation. This mechanism is conserved across species since a Caenorhabditis elegans SMA model showed less expression of the B-Raf homolog lin-45 Accordingly, motoneuron survival was rescued by a cell autonomous lin-45 expression in a C. elegans SMA model resulting in improved motor functions. This rescue was effective even after the onset of motoneuron degeneration and mediated by the MEK/ERK pathway.

Effects of a Sertoli cell-specific knockout of Connexin43 on maturation and proliferation of postnatal Sertoli cells.

Adult male Sertoli cell-specific Connexin43 knockout mice (SCCx43KO) exhibit higher Sertoli cell (SC) numbers per seminiferous tubule compared to their wild type (WT) littermates. Thus, deletion of this testicular gap junction protein seems to affect the proliferative potential and differentiation of "younger" SC. Although SC have so far mostly been characterised as postmitotic cells that cease to divide and become an adult, terminally differentiated cell population at around puberty, there is rising evidence that there exist exceptions from this for a very long time accepted paradigm. Aim of this study was to investigate postnatal SC development and to figure out underlying causes for observed higher SC numbers in adult KO mice. Therefore, the amount of SC mitotic figures was compared, resulting in slightly more and prolonged detection of SC mitotic figures in KO mice compared to WT. SC counting per tubular cross section revealed significantly different time curves, and comparing proliferation rates using Bromodesoxyuridine and Sox9 showed higher proliferation rates in 8-day old KO mice. SC proliferation was further investigated by Ki67 immunohistochemistry. SC in KO mice displayed a delayed initiation of cell-cycle-inhibitor p27Kip1 synthesis and prolonged synthesis of the phosphorylated tumour suppressor pRb and proliferation marker Ki67. Thus, the higher SC numbers in adult male SCCx43KO mice may arise due to two different reasons: Firstly, in prepubertal KO mice, the proliferation rate of SC was higher. Secondly, there were differences in their ability to cease proliferation as shown by the delayed initiation of p27Kip1 synthesis and the prolonged production of phosphorylated pRb and Ki67. Immunohistochemical results indicating a prolonged period of SC proliferation in SCCx43KO were confirmed by detection of proliferating SC in 17-days-old KO mice. In conclusion, deletion of the testicular gap junction protein Cx43 might prevent normal SC maturation and might even alter also the proliferation potential of adult SC.

Dimension reduction and outlier detection of 3-D shapes derived from multi-organ CT images.

BACKGROUND: Unsupervised clustering and outlier detection are important in medical research to understand the distributional composition of a collective of patients. A number of clustering methods exist, also for high-dimensional data after dimension reduction. Clustering and outlier detection may, however, become less robust or contradictory if multiple high-dimensional data sets per patient exist. Such a scenario is given when the focus is on 3-D data of multiple organs per patient, and a high-dimensional feature matrix per organ is extracted. METHODS: We use principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE) and multiple co-inertia analysis (MCIA) combined with bagplots to study the distribution of multi-organ 3-D data taken by computed tomography scans. After point-set registration of multiple organs from two public data sets, multiple hundred shape features are extracted per organ. While PCA and t-SNE can only be applied to each organ individually, MCIA can project the data of all organs into the same low-dimensional space. RESULTS: MCIA is the only approach, here, with which data of all organs can be projected into the same low-dimensional space. We studied how frequently (i.e., by how many organs) a patient was classified to belong to the inner or outer 50% of the population, or as an outlier. Outliers could only be detected with MCIA and PCA. MCIA and t-SNE were more robust in judging the distributional location of a patient in contrast to PCA. CONCLUSIONS: MCIA is more appropriate and robust in judging the distributional location of a patient in the case of multiple high-dimensional data sets per patient. It is still recommendable to apply PCA or t-SNE in parallel to MCIA to study the location of individual organs.

Simultaneous Inference of Multiple Binary Endpoints in Biomedical Research: Small Sample Properties of Multiple Marginal Models and a Resampling Approach.

In biomedical research, the simultaneous inference of multiple binary endpoints may be of interest. In such cases, an appropriate multiplicity adjustment is required that controls the family-wise error rate, which represents the probability of making incorrect test decisions. In this paper, we investigate two approaches that perform single-step p $p$ -value adjustments that also take into account the possible correlation between endpoints. A rather novel and flexible approach known as multiple marginal models is considered, which is based on stacking of the parameter estimates of the marginal models and deriving their joint asymptotic distribution. We also investigate a nonparametric vector-based resampling approach, and we compare both approaches with the Bonferroni method by examining the family-wise error rate and power for different parameter settings, including low proportions and small sample sizes. The results show that the resampling-based approach consistently outperforms the other methods in terms of power, while still controlling the family-wise error rate. The multiple marginal models approach, on the other hand, shows a more conservative behavior. However, it offers more versatility in application, allowing for more complex models or straightforward computation of simultaneous confidence intervals. The practical application of the methods is demonstrated using a toxicological dataset from the National Toxicology Program.

Discordant Health Implications and Molecular Mechanisms of Vitamin D in Clinical and Preclinical Studies of Prostate Cancer: A Critical Appraisal of the Literature Data.

Clinical and preclinical studies have provided conflicting data on the postulated beneficial effects of vitamin D in patients with prostate cancer. In this opinion piece, we discuss reasons for discrepancies between preclinical and clinical vitamin D studies. Different criteria have been used as evidence for the key roles of vitamin D. Clinical studies report integrative cancer outcome criteria such as incidence and mortality in relation to vitamin D status over time. In contrast, preclinical vitamin D studies report molecular and cellular changes resulting from treatment with the biologically active vitamin D metabolite, 1,25-dihydroxyvitamin D3 (calcitriol) in tissues. However, these reported changes in preclinical in vitro studies are often the result of treatment with biologically irrelevant high calcitriol concentrations. In typical experiments, the used calcitriol concentrations exceed the calcitriol concentrations in normal and malignant prostate tissue by 100 to 1000 times. This raises reasonable concerns regarding the postulated biological effects and mechanisms of these preclinical vitamin D approaches in relation to clinical relevance. This is not restricted to prostate cancer, as detailed data regarding the tissue-specific concentrations of vitamin D metabolites are currently lacking. The application of unnaturally high concentrations of calcitriol in preclinical studies appears to be a major reason why the results of preclinical in vitro studies hardly match up with outcomes of vitamin D-related clinical studies. Regarding future studies addressing these concerns, we suggest establishing reference ranges of tissue-specific vitamin D metabolites within various cancer entities, carrying out model studies on human cancer cells and patient-derived organoids with biologically relevant calcitriol concentrations, and lastly improving the design of vitamin D clinical trials where results from preclinical studies guide the protocols and endpoints within these trials.

Network meta-analysis of transcriptome expression changes in different manifestations of dengue virus infection.

BACKGROUND: Several studies have been performed to study transcriptome profiles after dengue virus infections with partly different results. Due to slightly different settings of the individual studies, different genes and enriched gene sets are reported in these studies. The main aim of this network meta-analysis was to aggregate a selection of these studies to identify genes and gene sets that are more generally associated with dengue virus infection, i.e. with less dependence on the individual study settings. METHODS: We performed network meta-analysis by different approaches using publicly available gene expression data of five selected studies from the Gene Expression Omnibus database. The study network includes dengue fever (DF), hemorrhagic fever (DHF), shock syndrome (DSS) patients as well as convalescent and healthy control individuals. After data merging and missing value imputation, study-specific batch effects were removed. Pairwise differential expression analysis and subsequent gene-set enrichment analysis were performed between the five study groups. Furthermore, mutual information networks were derived from the top genes of each group comparison, and the separability between the three patient groups was studied by machine learning models. RESULTS: From the 10 possible pairwise group comparisons in the study network, six genes (IFI27, TPX2, CDT1, DTL, KCTD14 and CDCA3) occur with a noticeable frequency among the top listed genes of each comparison. Thus, there is an increased evidence that these genes play a general role in dengue virus infections. IFI27 and TPX2 have also been highlighted in the context of dengue virus infection by other studies. A few of the identified gene sets from the network meta-analysis overlap with findings from the original studies. Mutual information networks yield additional genes for which the observed pairwise correlation is different between the patient groups. Machine learning analysis shows a moderate separability of samples from the DF, DHF and DSS groups (accuracy about 80%). CONCLUSIONS: Due to an increased sample size, the network meta-analysis could reveal additional genes which are called differentially expressed between the studied groups and that may help to better understand the molecular basis of this disease.

Measuring reproducibility of virus metagenomics analyses using bootstrap samples from FASTQ-files.

MOTIVATION: High-throughput sequencing data can be affected by different technical errors, e.g. from probe preparation or false base calling. As a consequence, reproducibility of experiments can be weakened. In virus metagenomics, technical errors can result in falsely identified viruses in samples from infected hosts. We present a new resampling approach based on bootstrap sampling of sequencing reads from FASTQ-files in order to generate artificial replicates of sequencing runs which can help to judge the robustness of an analysis. In addition, we evaluate a mixture model on the distribution of read counts per virus to identify potentially false positive findings. RESULTS: The evaluation of our approach on an artificially generated dataset with known viral sequence content shows in general a high reproducibility of uncovering viruses in sequencing data, i.e. the correlation between original and mean bootstrap read count was highly correlated. However, the bootstrap read counts can also indicate reduced or increased evidence for the presence of a virus in the biological sample. We also found that the mixture-model fits well to the read counts, and furthermore, it provides a higher accuracy on the original or on the bootstrap read counts than on the difference between both. The usefulness of our methods is further demonstrated on two freely available real-world datasets from harbor seals. AVAILABILITY AND IMPLEMENTATION: We provide a Phyton tool, called RESEQ, available from https://github.com/babaksaremi/RESEQ that allows efficient generation of bootstrap reads from an original FASTQ-file. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Comparison of PHI and PHI Density for Prostate Cancer Detection in a Large Retrospective Caucasian Cohort.

BACKGROUND: Beyond prostate-specific antigen (PSA), other biomarkers for prostate cancer (PCa) detection are available and need to be evaluated for clinical routine. OBJECTIVE: The aim of the study was to evaluate the Prostate Health Index (PHI) density (PHID) in comparison with PHI in a large Caucasian group >1,000 men. METHODS: PHID values were used from available patient data with PSA, free PSA, and [-2]pro-PSA and prostate volume from 3 former surveys from 2002 to 2014. Those 1,446 patients from a single-center cohort included 701 men with PCa and 745 with no PCa. All patients received initial or repeat biopsies. The diagnostic accuracy was evaluated by receiver operating characteristic (ROC) curves comparing area under the ROC curves (AUCs), precision-recall approach, and decision curve analysis (DCA). RESULTS: PHID medians differed almost 2-fold between PCa (1.12) and no PCa (0.62) in comparison to PHI (48.6 vs. 33; p always <0.0001). However, PHID and PHI were equal regarding the AUC (0.737 vs. 0.749; p = 0.226), and the curves of the precision-recall analysis also overlapped in the sensitivity range between 70 and 100%. DCA had a maximum net benefit of only ∼5% for PHID versus PHI between 45 and 55% threshold probability. Contrary, in the 689 men with a prostate volume ≤40 cm3, PHI (AUC 0.732) showed a significant larger AUC than PHID (AUC 0.69, p = 0.014). CONCLUSIONS: Based on DCA, PHID had only a small advantage in comparison with PHI alone, while ROC analysis and precision-recall analysis showed similar results. In smaller prostates, PHI even outperformed PHID. The increment for PHID in this large Caucasian cohort is too small to justify a routine clinical use.

Augmentation of Transcriptomic Data for Improved Classification of Patients with Respiratory Diseases of Viral Origin.

To better understand the molecular basis of respiratory diseases of viral origin, high-throughput gene-expression data are frequently taken by means of DNA microarray or RNA-seq technology. Such data can also be useful to classify infected individuals by molecular signatures in the form of machine-learning models with genes as predictor variables. Early diagnosis of patients by molecular signatures could also contribute to better treatments. An approach that has rarely been considered for machine-learning models in the context of transcriptomics is data augmentation. For other data types it has been shown that augmentation can improve classification accuracy and prevent overfitting. Here, we compare three strategies for data augmentation of DNA microarray and RNA-seq data from two selected studies on respiratory diseases of viral origin. The first study involves samples of patients with either viral or bacterial origin of the respiratory disease, the second study involves patients with either SARS-CoV-2 or another respiratory virus as disease origin. Specifically, we reanalyze these public datasets to study whether patient classification by transcriptomic signatures can be improved when adding artificial data for training of the machine-learning models. Our comparison reveals that augmentation of transcriptomic data can improve the classification accuracy and that fewer genes are necessary as explanatory variables in the final models. We also report genes from our signatures that overlap with signatures presented in the original publications of our example data. Due to strict selection criteria, the molecular role of these genes in the context of respiratory infectious diseases is underlined.

Vector and Host C-Type Lectin Receptor (CLR)-Fc Fusion Proteins as a Cross-Species Comparative Approach to Screen for CLR-Rift Valley Fever Virus Interactions.

Rift Valley fever virus (RVFV) is a mosquito-borne bunyavirus endemic to Africa and the Arabian Peninsula, which causes diseases in humans and livestock. C-type lectin receptors (CLRs) represent a superfamily of pattern recognition receptors that were reported to interact with diverse viruses and contribute to antiviral immune responses but may also act as attachment factors or entry receptors in diverse species. Human DC-SIGN and L-SIGN are known to interact with RVFV and to facilitate viral host cell entry, but the roles of further host and vector CLRs are still unknown. In this study, we present a CLR-Fc fusion protein library to screen RVFV-CLR interaction in a cross-species approach and identified novel murine, ovine, and Aedes aegypti RVFV candidate receptors. Furthermore, cross-species CLR binding studies enabled observations of the differences and similarities in binding preferences of RVFV between mammalian CLR homologues, as well as more distant vector/host CLRs.

Sequential MAVS and MyD88/TRIF signaling triggers anti-viral responses of tick-borne encephalitis virus-infected murine astrocytes.

Tick-borne encephalitis virus (TBEV), a member of the Flaviviridae family, is typically transmitted upon tick bite and can cause meningitis and encephalitis in humans. In TBEV-infected mice, mitochondrial antiviral-signaling protein (MAVS), the downstream adaptor of retinoic acid-inducible gene-I (RIG-I)-like receptor (RLR) signaling, is needed to induce early type I interferon (IFN) responses and to confer protection. To characterize the brain-resident cell subset that produces protective IFN-ß in TBEV-infected mice, we isolated neurons, astrocytes, and microglia from mice and exposed these cell types to TBEV in vitro. Under such conditions, neurons showed the highest percentage of infected cells, whereas astrocytes and microglia were infected to a lesser extent. In the supernatant (SN) of infected neurons, IFN-ß was not detectable, while infected astrocytes showed high and microglia low IFN-ß expression. Transcriptome analyses of astrocytes implied that MAVS signaling was needed early after TBEV infection. Accordingly, MAVS-deficient astrocytes showed enhanced TBEV infection and significantly reduced early IFN-ß responses. Nevertheless, at later time points, moderate amounts of IFN-ß were detected in the SN of infected MAVS-deficient astrocytes. Transcriptome analyses indicated that MAVS deficiency negatively affected the induction of early anti-viral responses, which resulted in significantly increased TBEV replication. Treatment with MyD88 and TRIF inhibiting peptides reduced only late IFN-ß responses of TBEV-infected WT astrocytes and blocked entirely IFN-ß responses of infected MAVS-deficient astrocytes. Thus, upon TBEV exposure of brain-resident cells, astrocytes are important IFN-ß producers showing biphasic IFN-ß induction that initially depends on MAVS and later on MyD88/TRIF signaling.

PHI density prospectively improves prostate cancer detection.

PURPOSE: To evaluate the Prostate Health Index (PHI) density (PHID) in direct comparison with PHI in a prospective large cohort. METHODS: PHID values were calculated from prostate-specific antigen (PSA), free PSA and [- 2]proPSA and prostate volume. The 1057 patients included 552 men with prostate cancer (PCa) and 505 with no evidence of malignancy (NEM). In detail, 562 patients were biopsied at the Charité Hospital Berlin and 495 patients at the Sana Hospital Offenbach. All patients received systematic or magnetic resonance imaging (MRI)/ultrasound fusion-guided biopsies. The diagnostic accuracy was evaluated by receiver operating characteristic (ROC) curves comparing areas under the ROC-curves (AUC). The decision curve analysis (DCA) was performed with the MATLAB Neural Network Toolbox. RESULTS: PHID provided a significant larger AUC than PHI (0.835 vs. 0.801; p = 0.0013) in our prospective cohort of 1057 men from 2 centers. The DCA had a maximum net benefit of ~ 5% for PHID vs. PHI between 35 and 65% threshold probability. In those 698 men within the WHO-calibrated PSA grey-zone up to 8 ng/ml, PHID was also significantly better than PHI (AUC 0.819 vs. 0.789; p = 0.0219). But PHID was not different from PHI in the detection of significant PCa. CONCLUSIONS: Based on ROC analysis and DCA, PHID had an advantage in comparison with PHI alone to detect any PCa but PHI and PHID performed equal in detecting significant PCa.

Limited utility of qPCR-based detection of tumor-specific circulating mRNAs in whole blood from clear cell renal cell carcinoma patients.

BACKGROUND: RNA sequencing data is providing abundant information about the levels of dysregulation of genes in various tumors. These data, as well as data based on older microarray technologies have enabled the identification of many genes which are upregulated in clear cell renal cell carcinoma (ccRCC) compared to matched normal tissue. Here we use RNA sequencing data in order to construct a panel of highly overexpressed genes in ccRCC so as to evaluate their RNA levels in whole blood and determine any diagnostic potential of these levels for renal cell carcinoma patients. METHODS: A bioinformatics analysis with Python was performed using TCGA, GEO and other databases to identify genes which are upregulated in ccRCC while being absent in the blood of healthy individuals. Quantitative Real Time PCR (RT-qPCR) was subsequently used to measure the levels of candidate genes in whole blood (PAX gene) of 16 ccRCC patients versus 11 healthy individuals. PCR results were processed in qBase and GraphPadPrism and statistics was done with Mann-Whitney U test. RESULTS: While most analyzed genes were either undetectable or did not show any dysregulated expression, two genes, CDK18 and CCND1, were paradoxically downregulated in the blood of ccRCC patients compared to healthy controls. Furthermore, LOX showed a tendency towards upregulation in metastatic ccRCC samples compared to non-metastatic. CONCLUSIONS: This analysis illustrates the difficulty of detecting tumor regulated genes in blood and the possible influence of interference from expression in blood cells even for genes conditionally absent in normal blood. Testing in plasma samples indicated that tumor specific mRNAs were not detectable. While CDK18, CCND1 and LOX mRNAs might carry biomarker potential, this would require validation in an independent, larger patient cohort.

Circular RNAs and Their Linear Transcripts as Diagnostic and Prognostic Tissue Biomarkers in Prostate Cancer after Prostatectomy in Combination with Clinicopathological Factors.

As new biomarkers, circular RNAs (circRNAs) have been largely unexplored in prostate cancer (PCa). Using an integrative approach, we aimed to evaluate the potential of circRNAs and their linear transcripts (linRNAs) to act as (i) diagnostic biomarkers for differentiation between normal and tumor tissue and (ii) prognostic biomarkers for the prediction of biochemical recurrence (BCR) after radical prostatectomy. In a first step, eight circRNAs (circATXN10, circCRIM1, circCSNK1G3, circGUCY1A2, circLPP, circNEAT1, circRHOBTB3, and circSTIL) were identified as differentially expressed via a genome-wide circRNA-based microarray analysis of six PCa samples. Additional bioinformatics and literature data were applied for this selection process. In total, 115 malignant PCa and 79 adjacent normal tissue samples were examined using robust RT-qPCR assays specifically established for the circRNAs and their linear counterparts. Their diagnostic and prognostic potential was evaluated using receiver operating characteristic curves, Cox regressions, decision curve analyses, and C-statistic calculations of prognostic indices. The combination of circATXN10 and linSTIL showed a high discriminative ability between malignant and adjacent normal tissue PCa. The combination of linGUCY1A2, linNEAT1, and linSTIL proved to be the best predictive RNA-signature for BCR. The combination of this RNA signature with five established reference models based on only clinicopathological factors resulted in an improved predictive accuracy for BCR in these models. This is an encouraging study for PCa to evaluate circRNAs and their linRNAs in an integrative approach, and the results showed their clinical potential in combination with standard clinicopathological variables.

Prognostic Role of Survivin and Macrophage Infiltration Quantified on Protein and mRNA Level in Molecular Subtypes Determined by RT-qPCR of KRT5, KRT20, and ERBB2 in Muscle-Invasive Bladder Cancer Treated by Adjuvant Chemotherapy.

OBJECTIVES: Bladder cancer is a heterogeneous malignancy. Therefore, it is difficult to find single predictive markers. Moreover, most studies focus on either the immunohistochemical or molecular assessment of tumor tissues by next-generation sequencing (NGS) or PCR, while a combination of immunohistochemistry (IHC) and PCR for tumor marker assessment might have the strongest impact to predict outcome and select optimal therapies in real-world application. We investigated the role of proliferation survivin/BIRC5 and macrophage infiltration (CD68, MAC387, CLEVER-1) on the basis of molecular subtypes of bladder cancer (KRT5, KRT20, ERBB2) to predict outcomes of adjuvant treated muscle-invasive bladder cancer patients with regard to progression-free survival (PFS) and disease-specific survival (DSS). MATERIALS AND METHODS: We used tissue microarrays (TMA) from n = 50 patients (38 males, 12 female) with muscle-invasive bladder cancer. All patients had been treated with radical cystectomy followed by adjuvant triple chemotherapy. Median follow-up time was 60.5 months. CD68, CLEVER-1, MAC387, and survivin protein were detected by immunostaining and subsequent visual inspection. BIRC5, KRT5, KRT20, ERBB2, and CD68 mRNAs were detected by standardized RT-qPCR after tissue dot RNA extraction using a novel stamp technology. All these markers were evaluated in three different centers of excellence. RESULTS: Nuclear staining rather than cytoplasmic staining of survivin predicted DSS as a single marker with high levels of survivin being associated with better PFS and DSS upon adjuvant chemotherapy (p = 0.0138 and p = 0.001, respectively). These results were validated by the quantitation of BIRC5 mRNA by PCR (p = 0.0004 and p = 0.0508, respectively). Interestingly, nuclear staining of survivin protein was positively associated with BIRC5 mRNA, while cytoplasmic staining was inversely related, indicating that the translocation of survivin protein into the nucleus occurred at a discrete, higher level of its mRNA. Combining survivin/BIRC5 levels based on molecular subtype being assessed by KRT20 expression improved the predictive value, with tumors having low survivin/BIRC5 and KRT20 mRNA levels having the best survival (75% vs. 20% vs. 10% 5-year DSS, p = 0.0005), and these values were independent of grading, node status, and tumor stage in multivariate analysis (p = 0.0167). Macrophage infiltration dominated in basal tumors and was inversely related with the luminal subtype marker gene expression. The presence of macrophages in survivin-positive or ERBB2-positive tumors was associated with worse DSS. CONCLUSIONS: For muscle-invasive bladder cancer patients, the proliferative activity as determined by the nuclear staining of survivin or RT-qPCR on the basis of molecular subtype characteristics outperforms single marker detections and single technology approaches. Infiltration by macrophages detected by IHC or PCR is associated with worse outcome in defined subsets of tumors. The limitations of this study are the retrospective nature and the limited number of patients. However, the number of molecular markers has been restricted and based on predefined assumptions, which resulted in the dissection of muscle-invasive disease into tumor-biological axes of high prognostic relevance, which warrant further investigation and validation.

Network meta-analysis correlates with analysis of merged independent transcriptome expression data.

BACKGROUND: Using meta-analysis, high-dimensional transcriptome expression data from public repositories can be merged to make group comparisons that have not been considered in the original studies. Merging of high-dimensional expression data can, however, implicate batch effects that are sometimes difficult to be removed. Removing batch effects becomes even more difficult when expression data was taken using different technologies in the individual studies (e.g. merging of microarray and RNA-seq data). Network meta-analysis has so far not been considered to make indirect comparisons in transcriptome expression data, when data merging appears to yield biased results. RESULTS: We demonstrate in a simulation study that the results from analyzing merged data sets and the results from network meta-analysis are highly correlated in simple study networks. In the case that an edge in the network is supported by multiple independent studies, network meta-analysis produces fold changes that are closer to the simulated ones than those obtained from analyzing merged data sets. Finally, we also demonstrate the practicability of network meta-analysis on a real-world data example from neuroinfection research. CONCLUSIONS: Network meta-analysis is a useful means to make new inferences when combining multiple independent studies of molecular, high-throughput expression data. This method is especially advantageous when batch effects between studies are hard to get removed.

Apelin and apelin receptor expression in renal cell carcinoma.

BACKGROUND: The APLNR (apelin receptor) has been shown to be an essential gene for cancer immunotherapy, with deficiency in APLNR leading to immunotherapy failure. The aim of this study is to investigate the expression of APLN (apelin) and APLNR in patients with renal cell carcinoma (RCC), and its association with clinicopathological parameters and survival. METHODS: Three well-characterised patient cohorts with RCC were used: Study cohort 1 (clear-cell RCC; APLN/APLNR mRNA expression; n = 166); TCGA validation cohort (clear-cell RCC; APLN/APLNR mRNA expression; n = 481); Study cohort 2 (all RCC subtypes; APLNR protein expression/immunohistochemistry; n = 300). Associations between mRNA/protein expression and clinicopathological variables/patients' survival were tested statistically. RESULTS: While APLN showed only very weak association with tumour histological grade (TCGA cohort), APLNR/mRNA protein expression correlate significantly with ccRCC aggressiveness. APLNR is expressed in tumour vasculature and tumour cells at different levels, and these expression levels associate with tumour aggressiveness in opposing directions. APLNR expression was negatively correlated with PD-L1 expression by tumour cells in a subset of patients with ccRCC. APLNR expression in either compartment is an independent prognostic factor for survival of patients with ccRCC. CONCLUSION: The APLNR/APLN-system appears to play an important role in ccRCC, warranting further clinical investigation.

Habitat requirements of the European brown hare (Lepus europaeus PALLAS 1778) in an intensively used agriculture region (Lower Saxony, Germany).

BACKGROUND: The European brown hare (Lepus europaeus) typically resides in open habitats in agriculturally dominated landscapes in Europe. Over recent decades, a widely observed population decline occurred, which was attributed to agricultural intensification. However, with political incentives for specific crops, especially maize for energy production, the habitat went through massive changes. Thus, there is the need to identify parameters that characterize a suitable habitat for the brown hare in today's agricultural lands. RESULTS: We modelled European brown hare densities spatially and temporally explicit over 10 years (2005-2014) across an entire federal state. The generalized additive mixed model confirms a constant decline of the European brown hare population in Lower Saxony. Municipalities with a high proportion of grassland and precipitation totaling up to 900 mm are more favored. Woodland showed an approximately linear negative effect. The most important agricultural crop groups such as winter grains and winter oilseed rape showed overall positive effects on hare densities. However, the effect of maize was unimodal, with a positive effect of medium proportions, but a negative effect of very high proportions. The effect of sugar beet was relatively weak but negative. Brown hares were also more abundant in municipalities with a higher density of vixen with litter and municipalities with a high proportion of wildflower strips showed higher brown hare abundance. CONCLUSION: Lower Saxony is a diverse federal state with grassland dominated areas in the northwest, more woodland in the east, but intensive arable land in most remaining areas. The European brown hare-a species with a wide ecological potency-shows preferences to both grassland and the most typical arable crop groups such as winter grains and winter oilseed rape. The substantial increase in maize production within the time frame was likely unfavourable and may be one reason for the decline. Nonetheless, political tools such as the agri-environmental scheme "wildflower strips" were beneficial for the brown hare abundance and may be an option to reverse the decline seen over the 10 years.

Gene expression profiles in neurological tissues during West Nile virus infection: a critical meta-analysis.

BACKGROUND: Infections with the West Nile virus (WNV) can attack neurological tissues in the host and alter gene expression levels therein. Several individual studies have analyzed these changes in the transcriptome based on measurements with DNA microarrays. Individual microarray studies produce a high-dimensional data structure with the number of studied genes exceeding the available sample size by far. Therefore, the level of scientific evidence of these studies is rather low and results can remain uncertain. Furthermore, the individual studies concentrate on different types of tissues or different time points after infection. A general statement regarding the transcriptional changes through WNV infection in neurological tissues is therefore hard to make. We screened public databases for transcriptome expression studies related to WNV infections and used different analysis pipelines to perform meta-analyses of these data with the goal of obtaining more stable results and increasing the level of evidence. RESULTS: We generated new lists of genes differentially expressed between WNV infected neurological tissues and control samples. A comparison with these genes to findings of a meta-analysis of immunological tissues is performed to figure out tissue-specific differences. While 5.879 genes were identified exclusively in the neurological tissues, 15 genes were found exclusively in the immunological tissues, and 44 genes were commonly detected in both tissues. Most findings of the original studies could be confirmed by the meta-analysis with a higher statistical power, but some genes and GO terms related to WNV were newly detected, too. In addition, we identified gene ontology terms related to certain infection processes, which are significantly enriched among the differentially expressed genes. In the neurological tissues, 17 gene ontology terms were found significantly different, and 2 terms in the immunological tissues. CONCLUSIONS: A critical discussion of our findings shows benefits but also limitations of the meta-analytic approach. In summary, the produced gene lists, identified gene ontology terms and network reconstructions appear to be more reliable than the results from the individual studies. Our meta-analysis provides a basis for further research on the transcriptional mechanisms by WNV infections in neurological tissues.