Associations with age and glomerular filtration rate in a referred population with chronic kidney disease: methods and baseline data from a UK multicentre cohort study (NURTuRE-CKD).

BACKGROUND: Chronic kidney disease (CKD) is common but heterogenous and is associated with multiple adverse outcomes. The National Unified Renal Translational Research Enterprise (NURTuRE)-CKD cohort was established to investigate risk factors for clinically important outcomes in persons with CKD referred to secondary care. METHODS: Eligible participants with CKD stages G3-4 or stages G1-2 plus albuminuria >30 mg/mmol were enrolled from 16 nephrology centres in England, Scotland and Wales from 2017 to 2019. Baseline assessment included demographic data, routine laboratory data and research samples. Clinical outcomes are being collected over 15 years by the UK Renal Registry using established data linkage. Baseline data are presented with subgroup analysis by age, sex and estimated glomerular filtration rate (eGFR). RESULTS: A total of 2996 participants was enrolled. Median (interquartile range) age was 66 (54-74) years, eGFR 33.8 (24.0-46.6) mL/min/1.73 m2 and urine albumin to creatinine ratio 209 (33-926) mg/g; 58.5% were male. Of these participants, 1883 (69.1%) were in high-risk CKD categories. Primary renal diagnosis was CKD of unknown cause in 32.3%, glomerular disease in 23.4% and diabetic kidney disease in 11.5%. Older participants and those with lower eGFR had higher systolic blood pressure and were less likely to be treated with renin-angiotensin system inhibitors (RASi) but were more likely to receive a statin. Female participants were less likely to receive a RASi or statin. CONCLUSIONS: NURTuRE-CKD is a prospective cohort of persons who are at relatively high risk of adverse outcomes. Long-term follow-up and a large biorepository create opportunities for research to improve risk prediction and to investigate underlying mechanisms to inform new treatment development.

Ror2 signaling is required for local upregulation of GDF6 and activation of BMP signaling at the neural plate border.

The receptor tyrosine kinase Ror2 is a major Wnt receptor that activates ß-catenin-independent signaling and plays a conserved role in the regulation of convergent extension movements and planar cell polarity in vertebrates. Mutations in the ROR2 gene cause recessive Robinow syndrome in humans, a short-limbed dwarfism associated with craniofacial malformations. Here, we show that Ror2 is required for local upregulation of gdf6 at the neural plate border in Xenopus embryos. Ror2 morphant embryos fail to upregulate neural plate border genes and show defects in the induction of neural crest cell fate. These embryos lack the spatially restricted activation of BMP signaling at the neural plate border at early neurula stages, which is required for neural crest induction. Ror2-dependent planar cell polarity signaling is required in the dorsolateral marginal zone during gastrulation indirectly to upregulate the BMP ligand Gdf6 at the neural plate border and Gdf6 is sufficient to rescue neural plate border specification in Ror2 morphant embryos. Thereby, Ror2 links Wnt/planar cell polarity signaling to BMP signaling in neural plate border specification and neural crest induction.

Proteomic profiling of breast cancer metabolism identifies SHMT2 and ASCT2 as prognostic factors.

BACKGROUND: Breast cancer tumors are known to be highly heterogeneous and differences in their metabolic phenotypes, especially at protein level, are less well-understood. Profiling of metabolism-related proteins harbors the potential to establish new patient stratification regimes and biomarkers promoting individualized therapy. In our study, we aimed to examine the relationship between metabolism-associated protein expression profiles and clinicopathological characteristics in a large cohort of breast cancer patients. METHODS: Breast cancer specimens from 801 consecutive patients, diagnosed between 2009 and 2011, were investigated using reverse phase protein arrays (RPPA). Patients were treated in accordance with national guidelines in five certified German breast centers. To obtain quantitative expression data, 37 antibodies detecting proteins relevant to cancer metabolism, were applied. Hierarchical cluster analysis and individual target characterization were performed. Clustering results and individual protein expression patterns were associated with clinical data. The Kaplan-Meier method was used to estimate survival functions. Univariate and multivariate Cox regression models were applied to assess the impact of protein expression and other clinicopathological features on survival. RESULTS: We identified three metabolic clusters of breast cancer, which do not reflect the receptor-defined subtypes, but are significantly correlated with overall survival (OS, p ≤ 0.03) and recurrence-free survival (RFS, p ≤ 0.01). Furthermore, univariate and multivariate analysis of individual protein expression profiles demonstrated the central role of serine hydroxymethyltransferase 2 (SHMT2) and amino acid transporter ASCT2 (SLC1A5) as independent prognostic factors in breast cancer patients. High SHMT2 protein expression was significantly correlated with poor OS (hazard ratio (HR) = 1.53, 95% confidence interval (CI) = 1.10-2.12, p ≤ 0.01) and RFS (HR = 1.54, 95% CI = 1.16-2.04, p ≤ 0.01). High protein expression of ASCT2 was significantly correlated with poor RFS (HR = 1.31, 95% CI = 1.01-1.71, p ≤ 0.05). CONCLUSIONS: Our data confirm the heterogeneity of breast tumors at a functional proteomic level and dissects the relationship between metabolism-related proteins, pathological features and patient survival. These observations highlight the importance of SHMT2 and ASCT2 as valuable individual prognostic markers and potential targets for personalized breast cancer therapy. TRIAL REGISTRATION: ClinicalTrials.gov, NCT01592825 . Registered on 3 May 2012.

Comparative study on gene set and pathway topology-based enrichment methods.

BACKGROUND: Enrichment analysis is a popular approach to identify pathways or sets of genes which are significantly enriched in the context of differentially expressed genes. The traditional gene set enrichment approach considers a pathway as a simple gene list disregarding any knowledge of gene or protein interactions. In contrast, the new group of so called pathway topology-based methods integrates the topological structure of a pathway into the analysis. METHODS: We comparatively investigated gene set and pathway topology-based enrichment approaches, considering three gene set and four topological methods. These methods were compared in two extensive simulation studies and on a benchmark of 36 real datasets, providing the same pathway input data for all methods. RESULTS: In the benchmark data analysis both types of methods showed a comparable ability to detect enriched pathways. The first simulation study was conducted with KEGG pathways, which showed considerable gene overlaps between each other. In this study with original KEGG pathways, none of the topology-based methods outperformed the gene set approach. Therefore, a second simulation study was performed on non-overlapping pathways created by unique gene IDs. Here, methods accounting for pathway topology reached higher accuracy than the gene set methods, however their sensitivity was lower. CONCLUSIONS: We conducted one of the first comprehensive comparative works on evaluating gene set against pathway topology-based enrichment methods. The topological methods showed better performance in the simulation scenarios with non-overlapping pathways, however, they were not conclusively better in the other scenarios. This suggests that simple gene set approach might be sufficient to detect an enriched pathway under realistic circumstances. Nevertheless, more extensive studies and further benchmark data are needed to systematically evaluate these methods and to assess what gain and cost pathway topology information introduces into enrichment analysis. Both types of methods for enrichment analysis require further improvements in order to deal with the problem of pathway overlaps.

A global microRNA screen identifies regulators of the ErbB receptor signaling network.

BACKGROUND: The growth factor heregulin (HRG) potently stimulates epithelial cell survival and proliferation through the binding of its cognate receptor ErbB3 (also known as HER3). ErbB3-dependent signal transmission relies on the dimerization partner ErbB2, a receptor tyrosine kinase that is frequently overexpressed and/or amplified in breast cancer cells. Substantial evidence suggests that deregulated ErbB3 expression also contributes to the transformed phenotype of breast cancer cells. RESULTS: By genome-wide screening, we identify 43 microRNAs (miRNAs) that specifically impact HRG-induced activation of the PI3K-Akt pathway. Bioinformatic analysis combined with experimental validation reveals a highly connected molecular miRNA-gene interaction network particularly for the negative screen hits. For selected miRNAs, namely miR-149, miR-148b, miR-326, and miR-520a-3p, we demonstrate the simultaneous downregulation of the ErbB3 receptor and multiple downstream signaling molecules, explaining their efficient dampening of HRG responses and ascribing to these miRNAs potential context-dependent tumor suppressive functions. CONCLUSIONS: Given the contribution of HRG signaling and the PI3K-Akt pathway in particular to tumorigenesis, this study not only provides mechanistic insight into the function of miRNAs but also has implications for future clinical applications.

rBiopaxParser--an R package to parse, modify and visualize BioPAX data.

MOTIVATION: Biological pathway data, stored in structured databases, is a useful source of knowledge for a wide range of bioinformatics algorithms and tools. The Biological Pathway Exchange (BioPAX) language has been established as a standard to store and annotate pathway information. However, use of these data within statistical analyses can be tedious. On the other hand, the statistical computing environment R has become the standard for bioinformatics analysis of large-scale genomics data. With this package, we hope to enable R users to work with BioPAX data and make use of the always increasing amount of biological pathway knowledge within data analysis methods. RESULTS: rBiopaxParser is a software package that provides a comprehensive set of functions for parsing, viewing and modifying BioPAX pathway data within R. These functions enable the user to access and modify specific parts of the BioPAX model. Furthermore, it allows to generate and layout regulatory graphs of controlling interactions and to visualize BioPAX pathways. AVAILABILITY: rBiopaxParser is an open-source R package and has been submitted to Bioconductor.

Carcinoma cells misuse the host tissue damage response to invade the brain.

The metastatic colonization of the brain by carcinoma cells is still barely understood, in particular when considering interactions with the host tissue. The colonization comes with a substantial destruction of the surrounding host tissue. This leads to activation of damage responses by resident innate immune cells to protect, repair, and organize the wound healing, but may distract from tumoricidal actions. We recently demonstrated that microglia, innate immune cells of the CNS, assist carcinoma cell invasion. Here we report that this is a fatal side effect of a physiological damage response of the brain tissue. In a brain slice coculture model, contact with both benign and malignant epithelial cells induced a response by microglia and astrocytes comparable to that seen at the interface of human cerebral metastases. While the glial damage response intended to protect the brain from intrusion of benign epithelial cells by inducing apoptosis, it proved ineffective against various malignant cell types. They did not undergo apoptosis and actually exploited the local tissue reaction to invade instead. Gene expression and functional analyses revealed that the C-X-C chemokine receptor type 4 (CXCR4) and WNT signaling were involved in this process. Furthermore, CXCR4-regulated microglia were recruited to sites of brain injury in a zebrafish model and CXCR4 was expressed in human stroke patients, suggesting a conserved role in damage responses to various types of brain injuries. Together, our findings point to a detrimental misuse of the glial damage response program by carcinoma cells resistant to glia-induced apoptosis.

Complex patterns of host switching in New World arenaviruses.

We empirically tested the long-standing hypothesis of codivergence of New World arenaviruses (NWA) with their hosts. We constructed phylogenies for NWA and all known hosts and used them in reconciliation analyses. We also constructed a phylogenetic tree of all Sigmodontinae and Neotominae rodents and tested whether viral-host associations were phylogenetically clustered. We determined host geographical overlap to determine to what extent opportunity to switch hosts was limited by host relatedness or physical proximity. With the exception of viruses from North America, no phylogenetically codivergent pattern between NWA and their hosts was found. We found that different virus clades were clustered differently and that Clade B with members pathogenic to humans was randomly distributed across the rodent phylogeny. Furthermore, viral relatedness within Clade B was significantly explained by the geographic overlap of their hosts' ranges rather than host relatedness, indicating that they are capable of host switching opportunistically. This has important bearings on their potential to become panzootic. Together, these analyses suggest that NWA have not codiverged with their hosts and instead have evolved predominantly via host switching.

Correction: A comparative study of RNA-Seq and microarray data analysis on the two examples of rectal-cancer patients and Burkitt Lymphoma cells.

[This corrects the article DOI: 10.1371/journal.pone.0197162.].

A comparative study of RNA-Seq and microarray data analysis on the two examples of rectal-cancer patients and Burkitt Lymphoma cells.

BACKGROUND: Pipeline comparisons for gene expression data are highly valuable for applied real data analyses, as they enable the selection of suitable analysis strategies for the dataset at hand. Such pipelines for RNA-Seq data should include mapping of reads, counting and differential gene expression analysis or preprocessing, normalization and differential gene expression in case of microarray analysis, in order to give a global insight into pipeline performances. METHODS: Four commonly used RNA-Seq pipelines (STAR/HTSeq-Count/edgeR, STAR/RSEM/edgeR, Sailfish/edgeR, TopHat2/Cufflinks/CuffDiff)) were investigated on multiple levels (alignment and counting) and cross-compared with the microarray counterpart on the level of gene expression and gene ontology enrichment. For these comparisons we generated two matched microarray and RNA-Seq datasets: Burkitt Lymphoma cell line data and rectal cancer patient data. RESULTS: The overall mapping rate of STAR was 98.98% for the cell line dataset and 98.49% for the patient dataset. Tophat's overall mapping rate was 97.02% and 96.73%, respectively, while Sailfish had only an overall mapping rate of 84.81% and 54.44%. The correlation of gene expression in microarray and RNA-Seq data was moderately worse for the patient dataset (ρ = 0.67-0.69) than for the cell line dataset (ρ = 0.87-0.88). An exception were the correlation results of Cufflinks, which were substantially lower (ρ = 0.21-0.29 and 0.34-0.53). For both datasets we identified very low numbers of differentially expressed genes using the microarray platform. For RNA-Seq we checked the agreement of differentially expressed genes identified in the different pipelines and of GO-term enrichment results. CONCLUSION: In conclusion the combination of STAR aligner with HTSeq-Count followed by STAR aligner with RSEM and Sailfish generated differentially expressed genes best suited for the dataset at hand and in agreement with most of the other transcriptomics pipelines.

Isolated metastasis of an EGFR-L858R-mutated NSCLC of the meninges: the potential impact of CXCL12/CXCR4 axis in EGFRmut NSCLC in diagnosis, follow-up and treatment.

Brain and leptomeningeal metastasis (LMM) of non-small cell lung cancer is still associated with poor prognosis. Moreover, the current diagnostic standard for LMM often yields false negative results and the scientific progress in this field is still unsatisfying. We present a case of a 71-year old patient with an isolated LMM. While standard diagnostics could only diagnose a cancer of unknown primary, the use of [68Ga]-Pentixafor-PET/CT (CXCR4-PET/CT, a radiotracer targeting CXCR4) and a liquid biopsy of the cerebrospinal fluid revealed the primary NSCLC. The detection of L858R-EGFR, a common driver mutation in NSCLC, enabled us to treat the patient with Afatinib and monitor treatment using [68Ga]-Pentixafor PET/CT. To estimate the impact of CXCR4 signaling and its ligands in NSCLC brain metastasis we looked at their expression and correlation with EGFR mutations in a primary and brain metastasis data set and investigated the previously described binding of extracellular ubiquitin to CXCR4. In conclusion, we describe a novel approach to improve diagnostics towards LMM and underline the impact of the CXCL12/CXCR4 axis in brain metastasis in a subset of NSCLC patients. We cannot confirm a correlation of CXCR4 expression with EGFR mutations or the binding of extracellular ubiquitin as previously reported.

Ror2 Signaling and Its Relevance in Breast Cancer Progression.

Breast cancer is a heterogeneous disease and has been classified into five molecular subtypes based on gene expression profiles. Signaling processes linked to different breast cancer molecular subtypes and different clinical outcomes are still poorly understood. Aberrant regulation of Wnt signaling has been implicated in breast cancer progression. In particular Ror1/2 receptors and several other members of the non-canonical Wnt signaling pathway were associated with aggressive breast cancer behavior. However, Wnt signals are mediated via multiple complex pathways, and it is clinically important to determine which particular Wnt cascades, including their domains and targets, are deregulated in poor prognosis breast cancer. To investigate activation and outcome of the Ror2-dependent non-canonical Wnt signaling pathway, we overexpressed the Ror2 receptor in MCF-7 and MDA-MB231 breast cancer cells, stimulated the cells with its ligand Wnt5a, and we knocked-down Ror1 in MDA-MB231 cells. We measured the invasive capacity of perturbed cells to assess phenotypic changes, and mRNA was profiled to quantify gene expression changes. Differentially expressed genes were integrated into a literature-based non-canonical Wnt signaling network. The results were further used in the analysis of an independent dataset of breast cancer patients with metastasis-free survival annotation. Overexpression of the Ror2 receptor, stimulation with Wnt5a, as well as the combination of both perturbations enhanced invasiveness of MCF-7 cells. The expression-responsive targets of Ror2 overexpression in MCF-7 induced a Ror2/Wnt module of the non-canonical Wnt signaling pathway. These targets alter regulation of other pathways involved in cell remodeling processing and cell metabolism. Furthermore, the genes of the Ror2/Wnt module were assessed as a gene signature in patient gene expression data and showed an association with clinical outcome. In summary, results of this study indicate a role of a newly defined Ror2/Wnt module in breast cancer progression and present a link between Ror2 expression and increased cell invasiveness.

Newly Constructed Network Models of Different WNT Signaling Cascades Applied to Breast Cancer Expression Data.

INTRODUCTION: WNT signaling is a complex process comprising multiple pathways: the canonical ß-catenin-dependent pathway and several alternative non-canonical pathways that act in a ß-catenin-independent manner. Representing these intricate signaling mechanisms through bioinformatic approaches is challenging. Nevertheless, a simplified but reliable bioinformatic WNT pathway model is needed, which can be further utilized to decipher specific WNT activation states within e.g. high-throughput data. RESULTS: In order to build such a model, we collected, parsed, and curated available WNT signaling knowledge from different pathway databases. The data were assembled to construct computationally suitable models of different WNT signaling cascades in the form of directed signaling graphs. This resulted in four networks representing canonical WNT signaling, non-canonical WNT signaling, the inhibition of canonical WNT signaling and the regulation of WNT signaling pathways, respectively. Furthermore, these networks were integrated with microarray and RNA sequencing data to gain deeper insight into the underlying biology of gene expression differences between MCF-7 and MDA-MB-231 breast cancer cell lines, representing weakly and highly invasive breast carcinomas, respectively. Differential genes up-regulated in the MDA-MB-231 compared to the MCF-7 cell line were found to display enrichment in the gene set originating from the non-canonical network. Moreover, we identified and validated differentially regulated modules representing canonical and non-canonical WNT pathway components specific for the aggressive basal-like breast cancer subtype. CONCLUSIONS: In conclusion, we demonstrated that these newly constructed WNT networks reliably reflect distinct WNT signaling processes. Using transcriptomic data, we shaped these networks into comprehensive modules of the genes implicated in the aggressive basal-like breast cancer subtype and demonstrated that non-canonical WNT signaling is important in this context. The topology of these networks can be further refined in the future by integration with complementary data such as protein-protein interactions, in order to gain greater insight into signaling processes.

Anti-CSF-1 treatment is effective to prevent carcinoma invasion induced by monocyte-derived cells but scarcely by microglia.

The mononuclear phagocytic system is categorized in three major groups: monocyte-derived cells (MCs), dendritic cells and resident macrophages. During breast cancer progression the colony stimulating factor 1 (CSF-1) can reprogram MCs into tumor-promoting macrophages in the primary tumor. However, the effect of CSF-1 during colonization of the brain parenchyma is largely unknown. Thus, we analyzed the outcome of anti-CSF-1 treatment on the resident macrophage population of the brain, the microglia, in comparison to MCs, alone and in different in vitro co-culture models. Our results underline the addiction of MCs to CSF-1 while surprisingly, microglia were not affected. Furthermore, in contrast to the brain, the bone marrow did not express the alternative ligand, IL-34. Yet treatment with IL-34 and co-culture with carcinoma cells partially rescued the anti-CSF-1 effects on MCs. Further, MC-induced invasion was significantly reduced by anti-CSF-1 treatment while microglia-induced invasion was reduced to a lower extend. Moreover, analysis of lung and breast cancer brain metastasis revealed significant differences of CSF-1 and CSF-1R expression. Taken together, our findings demonstrate not only differences of anti-CSF-1 treatment on MCs and microglia but also in the CSF-1 receptor and ligand expression in brain and bone marrow as well as in brain metastasis.

R-based software for the integration of pathway data into bioinformatic algorithms.

Putting new findings into the context of available literature knowledge is one approach to deal with the surge of high-throughput data results. Furthermore, prior knowledge can increase the performance and stability of bioinformatic algorithms, for example, methods for network reconstruction. In this review, we examine software packages for the statistical computing framework R, which enable the integration of pathway data for further bioinformatic analyses. Different approaches to integrate and visualize pathway data are identified and packages are stratified concerning their features according to a number of different aspects: data import strategies, the extent of available data, dependencies on external tools, integration with further analysis steps and visualization options are considered. A total of 12 packages integrating pathway data are reviewed in this manuscript. These are supplemented by five R-specific packages for visualization and six connector packages, which provide access to external tools.