Richa Batra

KeyPathwayMiner 4.0: condition-specific pathway analysis by combining multiple omics studies and networks with Cytoscape

BackgroundOver the last decade network enrichment analysis has become popular in computational systems biology to elucidate aberrant network modules. Traditionally, these approaches focus on combining gene expression data with protein-protein interaction (PPI) networks. Nowadays, the so-called omics technologies allow for inclusion of many more data sets, e.g. protein phosphorylation or epigenetic modifications. This creates a need for analysis methods that can combine these various sources of data to obtain a systems-level view on aberrant biological networks.ResultsWe present a new release of KeyPathwayMiner (version 4.0) that is not limited to analyses of single omics data sets, e.g. gene expression, but is able to directly combine several different omics data types. Version 4.0 can further integrate existing knowledge by adding a search bias towards sub-networks that contain (avoid) genes provided in a positive (negative) list. Finally the new release now also provides a set of novel visualization features and has been implemented as an app for the standard bioinformatics network analysis tool: Cytoscape.ConclusionWith KeyPathwayMiner 4.0, we publish a Cytoscape app for multi-omics based sub-network extraction. It is available in Cytoscape’s app store http://apps.cytoscape.org/apps/keypathwayminer or via http://keypathwayminer.mpi-inf.mpg.de.

5-azacytidine inhibits nonsense-mediated decay in a MYC-dependent fashion

Nonsense‐mediated RNA decay (NMD) is an RNA‐based quality control mechanism that eliminates transcripts bearing premature translation termination codons (PTC). Approximately, one‐third of all inherited disorders and some forms of cancer are caused by nonsense or frame shift mutations that introduce PTCs, and NMD can modulate the clinical phenotype of these diseases. 5‐azacytidine is an analogue of the naturally occurring pyrimidine nucleoside cytidine, which is approved for the treatment of myelodysplastic syndrome and myeloid leukemia. Here, we reveal that 5‐azacytidine inhibits NMD in a dose‐dependent fashion specifically upregulating the expression of both PTC‐containing mutant and cellular NMD targets. Moreover, this activity of 5‐azacytidine depends on the induction of MYC expression, thus providing a link between the effect of this drug and one of the key cellular pathways that are known to affect NMD activity. Furthermore, the effective concentration of 5‐azacytidine in cells corresponds to drug levels used in patients, qualifying 5‐azacytidine as a candidate drug that could potentially be repurposed for the treatment of Mendelian and acquired genetic diseases that are caused by PTC mutations.

Classification of Breast Cancer Subtypes by combining Gene Expression and DNA Methylation Data

Selecting the most promising treatment strategy for breast cancer crucially depends on determining the correct subtype. In recent years, gene expression profiling has been investigated as an alternative to histochemical methods. Since databases like TCGA provide easy and unrestricted access to gene expression data for hundreds of patients, the challenge is to extract a minimal optimal set of genes with good prognostic properties from a large bulk of genes making a moderate contribution to classification. Several studies have successfully applied machine learning algorithms to solve this so-called gene selection problem. However, more diverse data from other OMICS technologies are available, including methylation. We hypothesize that combining methylation and gene expression data could already lead to a largely improved classification model, since the resulting model will reflect differences not only on the transcriptomic, but also on an epigenetic level. We compared so-called random forest derived classification models based on gene expression and methylation data alone, to a model based on the combined features and to a model based on the gold standard PAM50. We obtained bootstrap errors of 10-20% and classification error of 1-50%, depending on breast cancer subtype and model. The gene expression model was clearly superior to the methylation model, which was also reflected in the combined model, which mainly selected features from gene expression data. However, the methylation model was able to identify unique features not considered as relevant by the gene expression model, which might provide deeper insights into breast cancer subtype differentiation on an epigenetic level.

On the performance of de novo pathway enrichment

De novo pathway enrichment is a powerful approach to discover previously uncharacterized molecular mechanisms in addition to already known pathways. To achieve this, condition-specific functional modules are extracted from large interaction networks. Here, we give an overview of the state of the art and present the first framework for assessing the performance of existing methods. We identified 19 tools and selected seven representative candidates for a comparative analysis with more than 12,000 runs, spanning different biological networks, molecular profiles, and parameters. Our results show that none of the methods consistently outperforms the others. To mitigate this issue for biomedical researchers, we provide guidelines to choose the appropriate tool for a given dataset. Moreover, our framework is the first attempt for a quantitative evaluation of de novo methods, which will allow the bioinformatics community to objectively compare future tools against the state of the art.Computational biology: Evaluation of network-based pathway enrichment toolsDe novo pathway enrichment methods are essential to understand disease complexity. They can uncover disease-specific functional modules by integrating molecular interaction networks with expression profiles. However, how should researchers choose one method out of several? In this article, a group of scientists from Denmark and Germany presents the first attempt to quantitatively evaluate existing methods. This framework will help the biomedical community to find the appropriate tool(s) for their data. They created synthetic gold standards and simulated expression profiles to perform a systematic assessment of various tools. They observed that the choice of interaction network, parameter settings, preprocessing of expression data and statistical properties of the expression profiles influence the results to a large extent. The results reveal strengths and limitations of the individual methods and suggest using two or more tools to obtain comprehensive disease-modules.

Time-Lapse Imaging of Neuroblastoma Cells to Determine Cell Fate upon Gene Knockdown

Neuroblastoma is the most common extra-cranial solid tumor of early childhood. Standard therapies are not effective in case of poor prognosis and chemotherapy resistance. To improve drug therapy, it is imperative to discover new targets that play a substantial role in tumorigenesis of neuroblastoma. The mitotic machinery is an attractive target for therapeutic interventions and inhibitors can be developed to target mitotic entry, spindle apparatus, spindle activation checkpoint, and mitotic exit. We present an elaborate analysis pipeline to determine cancer specific therapeutic targets by first performing a focused gene expression analysis to select genes followed by a gene knockdown screening assay of live cells. We interrogated gene expression studies of neuroblastoma tumors and selected 240 genes relevant for tumorigenesis and cell cycle. With these genes we performed time-lapse screening of gene knockdowns in neuroblastoma cells. We classified cellular phenotypes and used the temporal context of the perturbation effect to determine the sequence of events, particularly the mitotic entry preceding cell death. Based upon this phenotype kinetics from the gene knockdown screening, we inferred dynamic gene functions in mitosis and cell proliferation. We identified six genes (DLGAP5, DSCC1, SMO, SNRPD1, SSBP1, and UBE2C) with a vital role in mitosis and these are promising therapeutic targets for neuroblastoma. Images and movies of every time point of all screened genes are available at https://ichip.bioquant.uni-heidelberg.de.

Cytokine profiling of tumor interstitial fluid of the breast and its relationship with lymphocyte infiltration and clinicopathological characteristics.

ABSTRACT The tumor microenvironment is composed of many immune cell subpopulations and is an important factor in the malignant progression of neoplasms, particularly breast cancer (BC). However, the cytokine networks that coordinate various regulatory events within the BC interstitium remain largely uncharacterized. Moreover, the data obtained regarding the origin of cytokine secretions, the levels of secretion associated with tumor development, and the possible clinical relevance of cytokines remain controversial. Therefore, we profiled 27 cytokines in 78 breast tumor interstitial fluid (TIF) samples, 43 normal interstitial fluid (NIF) samples, and 25 matched serum samples obtained from BC patients with Luminex xMAP multiplex technology. Eleven cytokines exhibited significantly higher levels in the TIF samples compared with the NIF samples: interleukin (IL)-7, IL-10, fibroblast growth factor-2, IL-13, interferon (IFN)γ-inducible protein (IP-10), IL-1 receptor antagonist (IL-1RA), platelet-derived growth factor (PDGF)-β, IL-1β, chemokine ligand 5 (RANTES), vascular endothelial growth factor, and IL-12. An immunohistochemical analysis further demonstrated that IL-1RA, IP-10, IL-10, PDGF-β, RANTES, and VEGF are widely expressed by both cancer cells and tumor-infiltrating lymphocytes (TILs), whereas IP-10 and RANTES were preferentially abundant in triple-negative breast cancers (TNBCs) compared to Luminal A subtype cancers. The latter observation corresponds with the high level of TILs in the TNBC samples. IL-1β, IL-7, IL-10, and PDGFβ also exhibited a correlation between the TIF samples and matched sera. In a survival analysis, high levels of IL-5, a hallmark TH2 cytokine, in the TIF samples were associated with a worse prognosis. These findings have important implications for BC immunotherapy research.

Large‐scale tracking and classification for automatic analysis of cell migration and proliferation, and experimental optimization of high‐throughput screens of neuroblastoma cells

Computational approaches for automatic analysis of image‐based high‐throughput and high‐content screens are gaining increased importance to cope with the large amounts of data generated by automated microscopy systems. Typically, automatic image analysis is used to extract phenotypic information once all images of a screen have been acquired. However, also in earlier stages of large‐scale experiments image analysis is important, in particular, to support and accelerate the tedious and time‐consuming optimization of the experimental conditions and technical settings. We here present a novel approach for automatic, large‐scale analysis and experimental optimization with application to a screen on neuroblastoma cell lines. Our approach consists of cell segmentation, tracking, feature extraction, classification, and model‐based error correction. The approach can be used for experimental optimization by extracting quantitative information which allows experimentalists to optimally choose and to verify the experimental parameters. This involves systematically studying the global cell movement and proliferation behavior. Moreover, we performed a comprehensive phenotypic analysis of a large‐scale neuroblastoma screen including the detection of rare division events such as multi‐polar divisions. Major challenges of the analyzed high‐throughput data are the relatively low spatio‐temporal resolution in conjunction with densely growing cells as well as the high variability of the data. To account for the data variability we optimized feature extraction and classification, and introduced a gray value normalization technique as well as a novel approach for automatic model‐based correction of classification errors. In total, we analyzed 4,400 real image sequences, covering observation periods of around 120 h each. We performed an extensive quantitative evaluation, which showed that our approach yields high accuracies of 92.2% for segmentation, 98.2% for tracking, and 86.5% for classification.

De novo pathway-based biomarker identification

Abstract Gene expression profiles have been extensively discussed as an aid to guide the therapy by predicting disease outcome for the patients suffering from complex diseases, such as cancer. However, prediction models built upon single-gene (SG) features show poor stability and performance on independent datasets. Attempts to mitigate these drawbacks have led to the development of network-based approaches that integrate pathway information to produce meta-gene (MG) features. Also, MG approaches have only dealt with the two-class problem of good versus poor outcome prediction. Stratifying patients based on their molecular subtypes can provide a detailed view of the disease and lead to more personalized therapies. We propose and discuss a novel MG approach based on de novo pathways, which for the first time have been used as features in a multi-class setting to predict cancer subtypes. Comprehensive evaluation in a large cohort of breast cancer samples from The Cancer Genome Atlas (TCGA) revealed that MGs are considerably more stable than SG models, while also providing valuable insight into the cancer hallmarks that drive them. In addition, when tested on an independent benchmark non-TCGA dataset, MG features consistently outperformed SG models. We provide an easy-to-use web service at http://pathclass.compbio.sdu.dk where users can upload their own gene expression datasets from breast cancer studies and obtain the subtype predictions from all the classifiers.

MYCN-mediated overexpression of mitotic spindle regulatory genes and loss of p53-p21 function jointly support the survival of tetraploid neuroblastoma cells

High-risk neuroblastomas often harbor structural chromosomal alterations, including amplified MYCN, and usually have a near-di/tetraploid DNA index, but the mechanisms creating tetraploidy remain unclear. Gene-expression analyses revealed that certain MYCN/MYC and p53/pRB-E2F target genes, especially regulating mitotic processes, are strongly expressed in near-di/tetraploid neuroblastomas. Using a functional RNAi screening approach and live-cell imaging, we identified a group of genes, including MAD2L1, which after knockdown induced mitotic-linked cell death in MYCN-amplified and TP53-mutated neuroblastoma cells. We found that MYCN/MYC-mediated overactivation of the metaphase-anaphase checkpoint synergizes with loss of p53-p21 function to prevent arrest or apoptosis of tetraploid neuroblastoma cells.

Toll-like receptor 7/8 agonists stimulate plasmacytoid dendritic cells to initiate TH17-deviated acute contact dermatitis in human subjects

Background: A standardized human model to study early pathogenic events in patients with psoriasis is missing. Activation of Toll‐like receptor 7/8 by means of topical application of imiquimod is the most commonly used mouse model of psoriasis. Objective: We sought to investigate the potential of a human imiquimod patch test model to resemble human psoriasis. Methods: Imiquimod (Aldara 5% cream; 3M Pharmaceuticals, St Paul, Minn) was applied twice a week to the backs of volunteers (n = 18), and development of skin lesions was monitored over a period of 4 weeks. Consecutive biopsy specimens were taken for whole‐genome expression analysis, histology, and T‐cell isolation. Plasmacytoid dendritic cells (pDCs) were isolated from whole blood, stimulated with Toll‐like receptor 7 agonist, and analyzed by means of extracellular flux analysis and real‐time PCR. Results: We demonstrate that imiquimod induces a monomorphic and self‐limited inflammatory response in healthy subjects, as well as patients with psoriasis or eczema. The clinical and histologic phenotype, as well as the transcriptome, of imiquimod‐induced inflammation in human skin resembles acute contact dermatitis rather than psoriasis. Nevertheless, the imiquimod model mimics the hallmarks of psoriasis. In contrast to classical contact dermatitis, in which myeloid dendritic cells sense haptens, pDCs are primary sensors of imiquimod. They respond with production of proinflammatory and TH17‐skewing cytokines, resulting in a TH17 immune response with IL‐23 as a key driver. In a proof‐of‐concept setting systemic treatment with ustekinumab diminished imiquimod‐induced inflammation. Conclusion: In human subjects imiquimod induces contact dermatitis with the distinctive feature that pDCs are the primary sensors, leading to an IL‐23/TH17 deviation. Despite these shortcomings, the human imiquimod model might be useful to investigate early pathogenic events and prove molecular concepts in patients with psoriasis. GRAPHICAL ABSTRACT Figure. No caption available.