bootGSEA: a bootstrap and rank aggregation pipeline for multi-study and multi-omics enrichment analyses.

Introduction: Gene set enrichment analysis (GSEA) subsequent to differential expression analysis is a standard step in transcriptomics and proteomics data analysis. Although many tools for this step are available, the results are often difficult to reproduce because set annotations can change in the databases, that is, new features can be added or existing features can be removed. Finally, such changes in set compositions can have an impact on biological interpretation. Methods: We present bootGSEA, a novel computational pipeline, to study the robustness of GSEA. By repeating GSEA based on bootstrap samples, the variability and robustness of results can be studied. In our pipeline, not all genes or proteins are involved in the different bootstrap replicates of the analyses. Finally, we aggregate the ranks from the bootstrap replicates to obtain a score per gene set that shows whether it gains or loses evidence compared to the ranking of the standard GSEA. Rank aggregation is also used to combine GSEA results from different omics levels or from multiple independent studies at the same omics level. Results: By applying our approach to six independent cancer transcriptomics datasets, we showed that bootstrap GSEA can aid in the selection of more robust enriched gene sets. Additionally, we applied our approach to paired transcriptomics and proteomics data obtained from a mouse model of spinal muscular atrophy (SMA), a neurodegenerative and neurodevelopmental disease associated with multi-system involvement. After obtaining a robust ranking at both omics levels, both ranking lists were combined to aggregate the findings from the transcriptomics and proteomics results. Furthermore, we constructed the new R-package "bootGSEA," which implements the proposed methods and provides graphical views of the findings. Bootstrap-based GSEA was able in the example datasets to identify gene or protein sets that were less robust when the set composition changed during bootstrap analysis. Discussion: The rank aggregation step was useful for combining bootstrap results and making them comparable to the original findings on the single-omics level or for combining findings from multiple different omics levels.

Grainyhead-like 2 Deficiency and Kidney Cyst Growth in a Mouse Model.

BACKGROUND: The transcription factor Grainyhead-like 2 (GRHL2) plays a crucial role in maintaining the epithelial barrier properties of the renal collecting duct and is essential for osmoregulation. We noticed a reduction in GRHL2 expression in cysts derived from the collecting ducts in kidneys affected by Autosomal Dominant Polycystic Kidney Disease (ADPKD). However, the specific role of GRHL2 in cystic kidney disease remains unknown. METHODS: The functional role of the transcription factor Grhl2 in the context of cystic kidney disease was examined through analysis of its expression pattern in patient samples with ADPKD and generating a transgenic cystic kidney disease (TCKD) mouse model by overexpressing the human proto-oncogene c-MYC in kidney collecting ducts. Next, TCKD mice bred with collecting duct-specific Grhl2 knockout mice (Grhl2KO). The resulting TCKD-Grhl2KO mice and their littermates were examined by various types of histological and biochemical assays and gene profiling analysis via RNA-seq. RESULTS: A comprehensive examination of kidney samples from patients with ADPKD revealed GRHL2 downregulation in collecting duct-derived cyst epithelia. Comparative analysis of TCKD and TCKD-Grhl2KO mice exhibited that the collecting duct-specific deletion of Grhl2 resulted in markedly aggravated cyst growth, worsened kidney dysfunction, and shortened life span. Furthermore, transcriptomic analyses indicated sequential downregulation of kidney epithelial cyst development regulators (Frem2, Muc1, Cdkn2c, Pkd2, and Tsc1) during cyst progression in kidneys of TCKD-Grhl2KO mice which included presumed direct Grhl2 target genes. CONCLUSIONS: These results suggest GRHL2 as a potential progression modifier, especially for cysts originating from collecting ducts.

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.

Clinical implications of AGR2 in primary prostate cancer: Results from a large-scale study.

Human anterior gradient-2 (AGR2) has been implicated in carcinogenesis of various solid tumours, but the expression data in prostate cancer are contradictory regarding its prognostic value. The objective of this study is to evaluate the expression of AGR2 in a large prostate cancer cohort and to correlate it with clinicopathological data. AGR2 protein expression was analysed immunohistochemically in 1023 well-characterized prostate cancer samples with a validated antibody. AGR2 expression levels in carcinomas were compared with matched tissue samples of adjacent normal glands. AGR2 expression levels were dichotomized and tested for statistical significance. Increased AGR2 expression was found in 93.5% of prostate cancer cases. AGR2 levels were significantly higher in prostate cancer compared with normal prostate tissue. A gradual loss of AGR2 expression was associated with increasing tumour grade (ISUP), and AGR2 expression is inversely related to patient survival, however, multivariable significance is not achieved. AGR2 is clearly upregulated in the majority of prostate cancer cases, yet a true diagnostic value appears unlikely. In spite of the negative correlation of AGR2 expression with increasing tumour grade, no independent prognostic significance was found in this large-scale study.

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.

Secreted NS1 proteins of tick-borne encephalitis virus and West Nile virus block dendritic cell activation and effector functions.

The flavivirus non-structural protein 1 (NS1) is secreted from infected cells into the circulation and the serum levels correlate with disease severity. The effect of secreted NS1 (sNS1) on non-infected mammalian immune cells is largely unknown. Here, we expressed recombinant sNS1 proteins of tick-borne encephalitis virus (TBEV) and West Nile virus (WNV) and investigated their effects on dendritic cell (DC) effector functions. Murine bone marrow-derived DCs (BMDCs) showed reduced surface expression of co-stimulatory molecules and decreased release of pro-inflammatory cytokines when treated with sNS1 of TBEV or WNV prior to poly(I:C) stimulation. Transcriptional profiles of BMDCs that were sNS1-exposed prior to poly(I:C) stimulation showed two gene clusters that were downregulated by TBEV or WNV sNS1 and that were associated with innate and adaptive immune responses. Functionally, both sNS1 proteins modulated the capacity for BMDCs to induce specific T-cell responses as indicated by reduced IFN-γ levels in both CD4+ and CD8+ T cells after BMDC co-cultivation. In human monocyte-derived DCs, poly(I:C)-induced upregulation of co-stimulatory molecules and cytokine responses were even more strongly impaired by TBEV sNS1 or WNV sNS1 pretreatment than in the murine system. Our findings indicate that exogenous flaviviral sNS1 proteins interfere with DC-mediated stimulation of T cells, which is crucial for the initiation of cell-mediated adaptive immune responses in human flavivirus infections. Collectively, our data determine soluble flaviviral NS1 as a virulence factor responsible for a dampened immune response to flavivirus infections. IMPORTANCE The effective initiation of protective host immune responses controls the outcome of infection, and dysfunctional T-cell responses have previously been associated with symptomatic human flavivirus infections. We demonstrate that secreted flavivirus NS1 proteins modulate innate immune responses of uninfected bystander cells. In particular, sNS1 markedly reduced the capacity of dendritic cells to stimulate T-cell responses upon activation. Hence, by modulating cellular host responses that are required for effective antigen presentation and initiation of adaptive immunity, sNS1 proteins may contribute to severe outcomes of flavivirus disease.

Altered hair root gene expression profiles highlight calcium signaling and lipid metabolism pathways to be associated with curly hair initiation and maintenance in Mangalitza pigs.

Hair types have been under strong targeted selection in domestic animals for their impact on skin protection, thermoregulation and exterior morphology, and subsequent economic importance. In pigs, a very special hair phenotype was observed in Mangalitza, who expresses a thick coat of curly bristles and downy hair. Two breed-specific missense variants in TRPM2 and CYP4F3 were suggested to be associated with the Mangalitza pig's hair shape due to their role in hair follicle morphogenesis reported for human and mice. However, the mechanism behind this expression of a curly hair type is still unclear and needs to be explored. In our study, hair shafts were measured and investigated for the curvature of the hair in Mangalitza and crossbreeds in comparison to straight-coated pigs. For molecular studies, hair roots underwent RNA sequencing for a differential gene expression analysis using DESeq2. The output matrix of normalized counts was then used to construct weighted gene co-expression networks. The resulting hair root gene expression profiles highlighted 454 genes to be significantly differentially expressed for initiation of curly hair phenotype in newborn Mangalitza piglets versus post-initiation in later development. Furthermore, 2,554 genes showed a significant differential gene expression in curly hair in comparison to straight hair. Neither TRPM2 nor CYP4F3 were identified as differentially expressed. Incidence of the genes in weighted co-expression networks associated with TRPM2 and CYP4F3, and prominent interactions of subsequent proteins with lipids and calcium-related pathways suggested calcium signaling and/or lipid metabolism as essential players in the induction of the curly hair as well as an ionic calcium-dependency to be a prominent factor for the maintenance of this phenotype. Subsequently, our study highlights the complex interrelations and dependencies of mutant genes TRPM2 and CYP4F3 and associated gene expression patterns, allowing the initiation of curly hair type during the development of a piglet as well as the maintenance in adult individuals.

Cross-reactive antibodies against Langat virus protect mice from lethal tick-borne encephalitis virus infection.

Introduction: Naturally attenuated Langat virus (LGTV) and highly pathogenic tick-borne encephalitis virus (TBEV) share antigenically similar viral proteins and are grouped together in the same flavivirus serocomplex. In the early 1970s, this has encouraged the usage of LGTV as a potential live attenuated vaccine against tick-borne encephalitis (TBE) until cases of encephalitis were reported among vaccinees. Previously, we have shown in a mouse model that immunity induced against LGTV protects mice against lethal TBEV challenge infection. However, the immune correlates of this protection have not been studied. Methods: We used the strategy of adoptive transfer of either serum or T cells from LGTV infected mice into naïve recipient mice and challenged them with lethal dose of TBEV. Results: We show that mouse infection with LGTV induced both cross-reactive antibodies and T cells against TBEV. To identify correlates of protection, Monitoring the disease progression in these mice for 16 days post infection, showed that serum from LGTV infected mice efficiently protected from developing severe disease. On the other hand, adoptive transfer of T cells from LGTV infected mice failed to provide protection. Histopathological investigation of infected brains suggested a possible role of microglia and T cells in inflammatory processes within the brain. Discussion: Our data provide key information regarding the immune correlates of protection induced by LGTV infection of mice which may help design better vaccines against TBEV.

Assessing Outlier Probabilities in Transcriptomics Data When Evaluating a Classifier.

Outliers in the training or test set used to fit and evaluate a classifier on transcriptomics data can considerably change the estimated performance of the model. Hence, an either too weak or a too optimistic accuracy is then reported and the estimated model performance cannot be reproduced on independent data. It is then also doubtful whether a classifier qualifies for clinical usage. We estimate classifier performances in simulated gene expression data with artificial outliers and in two real-world datasets. As a new approach, we use two outlier detection methods within a bootstrap procedure to estimate the outlier probability for each sample and evaluate classifiers before and after outlier removal by means of cross-validation. We found that the removal of outliers changed the classification performance notably. For the most part, removing outliers improved the classification results. Taking into account the fact that there are various, sometimes unclear reasons for a sample to be an outlier, we strongly advocate to always report the performance of a transcriptomics classifier with and without outliers in training and test data. This provides a more diverse picture of a classifier's performance and prevents reporting models that later turn out to be not applicable for clinical diagnoses.

Identifying biomarkers deciphering sepsis from trauma-induced sterile inflammation and trauma-induced sepsis.

Objective: The purpose of this study was to identify a panel of biomarkers for distinguishing early stage sepsis patients from non-infected trauma patients. Background: Accurate differentiation between trauma-induced sterile inflammation and real infective sepsis poses a complex life-threatening medical challenge because of their common symptoms albeit diverging clinical implications, namely different therapies. The timely and accurate identification of sepsis in trauma patients is therefore vital to ensure prompt and tailored medical interventions (provision of adequate antimicrobial agents and if possible eradication of infective foci) that can ultimately lead to improved therapeutic management and patient outcome. The adequate withholding of antimicrobials in trauma patients without sepsis is also important in aspects of both patient and environmental perspective. Methods: In this proof-of-concept study, we employed advanced technologies, including Matrix-Assisted Laser Desorption/Ionization (MALDI) and multiplex antibody arrays (MAA) to identify a panel of biomarkers distinguishing actual sepsis from trauma-induced sterile inflammation. Results: By comparing patient groups (controls, infected and non-infected trauma and septic shock patients under mechanical ventilation) at different time points, we uncovered distinct protein patterns associated with early trauma-induced sterile inflammation on the one hand and sepsis on the other hand. SYT13 and IL1F10 emerged as potential early sepsis biomarkers, while reduced levels of A2M were indicative of both trauma-induced inflammation and sepsis conditions. Additionally, higher levels of TREM1 were associated at a later stage in trauma patients. Furthermore, enrichment analyses revealed differences in the inflammatory response between trauma-induced inflammation and sepsis, with proteins related to complement and coagulation cascades being elevated whereas proteins relevant to focal adhesion were diminished in sepsis. Conclusions: Our findings, therefore, suggest that a combination of biomarkers is needed for the development of novel diagnostic approaches deciphering trauma-induced sterile inflammation from actual infective sepsis.

Interactive, Browser-Based Graphics to Visualize Complex Data in Education of Biomedical Sciences for Veterinary Students.

In veterinary education, data from biomedical or natural sciences are mostly presented in the form of static or animated graphics with no or little amount of interactivity. These kinds of presentations are, however, often not sufficient to depict the complexity of the data or the presented topic. Interactive graphics, which allow to dynamically change data and related graphics, have rarely been considered as teaching tool in higher education of biomedical disciplines for veterinary education so far. In order to study the applicability and the usefulness of interactive graphics in biomedical disciplines for lecturers and students in veterinary education, three different courses from biomedical disciplines were exemplarily implemented as interactive graphics and evaluated in a pilot study by a survey amongst lecturers and students of our university. The interactive graphics were built using the Shiny environment, a web-based application framework for the statistic software R. The survey amongst lecturers and students was based on questionnaires covering questions on the handling and usefulness of the digital teaching tools. In total, n = 327 students and n = 5 lecturers participated in the evaluation study which revealed that the interactive graphics are easy to handle for lecturers and students, and that they can increase the motivation for either teaching or learning. In total, 71% of the students affirmed that interactive graphics led to an increased interest for the presented contents and 76% expressed the wish to get taught more topics with interactive graphics. We also provide a workflow that can be used as a guideline to develop interactive graphics. Supplementary Information: The online version contains supplementary material available at 10.1007/s40670-022-01613-x.

Microbiota of vaccinated and non-vaccinated clinically inconspicuous and conspicuous piglets under natural Lawsonia intracellularis infection.

Lawsonia (L.) intracellularis is a widespread, economically important bacterium causing the porcine proliferative enteropathy (PPE). In this study, we evaluated intestinal microbiota of naturally exposed L. intracellularis-positive pigs under standardized conditions. To obtain three independent repetitions, 27 L. intracellularis-infected pigs (19.0 ± 1.50 kg body weight) from one farm were divided into three groups at an age of 7 to 8 weeks (nine pigs/group). Pigs were either vaccinated against L. intracellularis via oral drenching on their 21st day of life (attenuated live vaccine) or non-vaccinated and selected according to clinical findings (pigs without deviating fecal consistency or with moderate to soft fecal consistency). Comparison of the clinically inconspicuous piglets that differed regarding their vaccination status showed fewer significant differences in fecal microbiota composition. The vaccination led to an overall enrichment of bacterial species belonging to the order Clostridiales, while species of the genus Collinsella and Prevotella were decreased. Several bacterial species belonging to the order Bacteroidales, mainly of the family Prevotellacecae, often closely matching Prevotella copri differed significantly between non-vaccinated clinically inconspicuous and conspicuous piglets. Whether those bacterial species play a role in mitigating the severity of an L. intracellularis infection remains to be defined.

Habitat selection of black grouse in an isolated population in northern Germany-the importance of mixing dry and wet habitats.

Wildlife habitats in general must provide foraging, hiding and resting places as well as sites for reproduction. Little is known about habitat selection of black grouse in the lowlands of Central Europe. We investigated habitat selection of seven radio tagged birds in an open heath and grassland area surrounded by dense pine forests in the northern German Lüneburg Heath Nature Reserve. This site carries one of the last remaining populations in the Central European lowlands. Using resource selection functions based on presence/background data, we estimated the probability of black grouse occurrence by availability of, or distance to habitat types as well as vegetation diversity indices. Black grouse preferred undisturbed and heterogeneous habitats far from dense forests with wide sand heaths, natural grasslands and intermixed bogs, diverse vegetation and food sources, low density of (loose) shrub formations and solitary trees. Wetlands were extremely important in a landscape that is dominated by dry heaths and grasslands. About 4% (9 km2) of the nature reserve was a suitable habitat for black grouse, mostly due to lack of open areas due to the amount of dense forest, and because smaller, open heaths are only partly suitable. We suggest that to improve habitat quality and quantity for the grouse, habitat patch size and connectivity must be increased, along with a mosaic of heterogeneous landscape structures in these habitat islands. Our results may be used to inform and improve black grouse habitat management in the region and elsewhere.

A comparison of strategies for generating artificial replicates in RNA-seq experiments.

Due to the overall high costs, technical replicates are usually omitted in RNA-seq experiments, but several methods exist to generate them artificially. Bootstrapping reads from FASTQ-files has recently been used in the context of other NGS analyses and can be used to generate artificial technical replicates. Bootstrapping samples from the columns of the expression matrix has already been used for DNA microarray data and generates a new artificial replicate of the whole experiment. Mixing data of individual samples has been used for data augmentation in machine learning. The aim of this comparison is to evaluate which of these strategies are best suited to study the reproducibility of differential expression and gene-set enrichment analysis in an RNA-seq experiment. To study the approaches under controlled conditions, we performed a new RNA-seq experiment on gene expression changes upon virus infection compared to untreated control samples. In order to compare the approaches for artificial replicates, each of the samples was sequenced twice, i.e. as true technical replicates, and differential expression analysis and GO term enrichment analysis was conducted separately for the two resulting data sets. Although we observed a high correlation between the results from the two replicates, there are still many genes and GO terms that would be selected from one replicate but not from the other. Cluster analyses showed that artificial replicates generated by bootstrapping reads produce it p values and fold changes that are close to those obtained from the true data sets. Results generated from artificial replicates with the approaches of column bootstrap or mixing observations were less similar to the results from the true replicates. Furthermore, the overlap of results among replicates generated by column bootstrap or mixing observations was much stronger than among the true replicates. Artificial technical replicates generated by bootstrapping sequencing reads from FASTQ-files are better suited to study the reproducibility of results from differential expression and GO term enrichment analysis in RNA-seq experiments than column bootstrap or mixing observations. However, FASTQ-bootstrapping is computationally more expensive than the other two approaches. The FASTQ-bootstrapping may be applicable to other applications of high-throughput sequencing.

Comparison between a Calving Predictive System and a Routine Prepartal Examination in German Holstein Heifers and Cows.

The objective was to validate the efficacy of Moocall® comparing it to a routine clinical examination. Altogether 38 Holstein cows were enrolled in this study (Moocall® group: 16 heifers and 8 cows; control group: 9 heifers and 5 cows). Clinical examinations were performed every 6 h over the 7 days period before the predicted calving date. The examined traits were changes in pelvic ligament relaxation, edema of the vulva, teat filling, vaginal secretion, tail tip flexibility, tail raising and behavior. There were no significant differences in Moocall® alerts between heifers and cows. The time lag between the first warning of Moocall® and the onset of labor was 21.2 ± 20.2 h (max: 95.4 h; min: 0.1 h; p = 0.87) for heifers and 29.6 ± 29.6 h (max: 177.8 h; min: 0 h; p = 0.97) for cows. Linear models including Moocall® alerts showed a significantly better fit to the time until calving than models without Moocall® information (without variable selection: p = 0.030, with variable selection: p < 0.01). In the best-fitting model, class 2 alerts (enhanced tail activity over 2 h) contributed with a higher significance (p < 0.01). Vice versa, models including additional traits were outperformed the use of Moocall® alerts alone. In the best fitting model, class 2 alerts (enhanced tail activity during 2 h) contributed with a higher significance (p < 0.01) than any of the best clinical predictive parameters, such as pelvic ligament relaxation (p = 0.01), tail tip flexibility (p = 0.01) or behavior (p = 0.01).

CD103+ Tissue Resident T-Lymphocytes Accumulate in Lung Metastases and Are Correlated with Poor Prognosis in ccRCC.

Clear cell renal cell carcinoma (ccRCC) is a highly immunogenic tumor with variable responses to immune checkpoint therapy. The significance of the immune cell infiltrate in distant metastases, their association with the immune infiltrate in the primary tumors and their impact on prognosis are poorly described. We hypothesized that specific subtypes of immune cells may be involved in the control of metastases and may have an impact on the prognosis of ccRCC. We analyzed the immune microenvironment in ccRCC primary tumors with distant metastases, paired distant metastases and non-metastasized ccRCC (n = 25 each group) by immunohistochemistry. Confirmatory analyses for CD8+ and CD103+ cells were performed in a large ccRCC cohort (n = 241) using a TCGA-KIRC data set (ITGAE/CD103). High immune cell infiltration in primary ccRCC tumors was significantly correlated with the development of distant tumor metastasis (p < 0.05). A high density of CD103+ cells in ccRCC was more frequent in poorly differentiated tumors (p < 0.001). ccRCCs showed high levels of ITGAE/CD103 compared with adjacent non-neoplastic tissue. A higher density of CD103+ cells and a higher ITGAE/CD103 expression were significantly correlated with poor overall survival in ccRCC (log rank p < 0.05). Our results show a major prognostic value of the immune pattern, in particular CD103+ cell infiltration in ccRCC, and highlight the importance of the tumor immune microenvironment.

Vitamin D Metabolites in Nonmetastatic High-Risk Prostate Cancer Patients with and without Zoledronic Acid Treatment after Prostatectomy.

There are limited and discrepant data on prostate cancer (PCa) and vitamin D. We investigated changes in three vitamin D3 metabolites in PCa patients after prostatectomy with zoledronic acid (ZA) treatment regarding their metastasis statuses over four years. In 32 patients from the ZEUS trial, 25(OH)D3, 24,25(OH)2D3, and 1,25(OH)2D3 were measured with liquid chromatography coupled with tandem mass spectrometry at four time points. All the patients received daily calcium and vitamin D3. Bone metastases were detected in 7 of the 17 ZA-treated patients and in 5 of the 15 controls (without ZA), without differences between the groups (p = 0.725). While 25(OH)D3 and 24,25(OH)2D3 increased significantly after the study's start, with following constant values, the 1,25(OH)2D3 concentrations remained unchanged. ZA treatment did not change the levels of the three metabolites. 25(OH)D3 and 24,25(OH)2D3 were not associated with the development of bone metastases. In contrast, 1,25(OH)2D3 was also higher in patients with bone metastasis before the study's start. Thus, in high-risk PCa patients after prostatectomy, 25(OH)D3, 24,25(OH)2D3, and 1,25(OH)2D3 were not affected by supportive ZA treatment or by the development of metastasis over four years, with the exception of 1,25(OH)2D3, which was constantly higher in metastatic patients. There might be potential prognostic value if the results can be confirmed.

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.

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.