Cancer drug sensitivity estimation using modular deep Graph Neural Networks

Computational drug sensitivity models have the potential to improve therapeutic outcomes by identifying targeted drugs components that are tailored to the transcriptomic profile of a given primary tumor. The SMILES representation of molecules that is used by state-of-the-art drug-sensitivity models is not conducive for neural networks to generalize to new drugs, in part because the distance between atoms does not generally correspond to the distance between their representation in the SMILES strings. Graph-attention networks, on the other hand, are high-capacity models that require large training-data volumes which are not available for drug-sensitivity estimation. We develop a modular drug-sensitivity graph-attentional neural network. The modular architecture allows us to separately pre-train the graph encoder and graph-attentional pooling layer on related tasks for which more data are available. We observe that this model outperforms reference models for the use cases of precision oncology and drug discovery; in particular, it is better able to predict the specific interaction between drug and cell line that is not explained by the general cytotoxicity of the drug and the overall survivability of the cell line. The complete source code is available at https://zenodo.org/doi/10.5281/zenodo.8020945. All experiments are based on the publicly available GDSC data.

Depletion of TBC1D4 improves the metabolic exercise response by overcoming genetically induced peripheral insulin resistance

The RabGTPase-activating protein (RabGAP) TBC1D4 (=AS160) represents a key component in the regulation of glucose transport into skeletal muscle and white adipose tissue (WAT) and is therefore crucial during the development of insulin resistance and type-2 diabetes. Increased daily activity has been shown to be associated with improved postprandial hyperglycemia in allele carriers of a loss-of-function variant in the human TBC1D4 gene. Using conventional Tbc1d4-deficient mice (D4KO) fed a high-fat diet (HFD), we show that already a moderate endurance exercise training leads to substantially improved glucose and insulin tolerance and enhanced expression levels of markers for mitochondrial activity and browning in WAT from D4KO animals. Importantly, in vivo and ex vivo analyses of glucose uptake revealed increased glucose clearance in interscapular brown adipose tissue (iBAT) and WAT from trained D4KO mice. Thus, chronic exercise is able to overcome the genetically induced insulin resistance caused by the Tbc1d4-depletion. Gene variants in TBC1D4 may be relevant in future precision medicine as determinants of exercise response.

Neurofibromin 1 controls metabolic balance and Notch-dependent quiescence of murine juvenile myogenic progenitors

Patients affected by neurofibromatosis type 1 (NF1) frequently show muscle weakness with unknown etiology. Here we show that, in mice, Neurofibromin 1 (Nf1) is not required in muscle fibers, but specifically in early postnatal myogenic progenitors (MPs), where Nf1 loss led to cell cycle exit and differentiation blockade, depleting the MP pool resulting in reduced myonuclear accretion as well as reduced muscle stem cell numbers. This was caused by precocious induction of stem cell quiescence coupled to metabolic reprogramming of MPs impinging on glycolytic shutdown, which was conserved in muscle fibers. We show that a Mek/Erk/NOS pathway hypersensitizes Nf1-deficient MPs to Notch signaling, consequently, early postnatal Notch pathway inhibition ameliorated premature quiescence, metabolic reprogramming and muscle growth. This reveals an unexpected role of Ras/Mek/Erk signaling supporting postnatal MP quiescence in concert with Notch signaling, which is controlled by Nf1 safeguarding coordinated muscle growth and muscle stem cell pool establishment. Furthermore, our data suggest transmission of metabolic reprogramming across cellular differentiation, affecting fiber metabolism and function in NF1.

Systematic assessment of long-read RNA-seq methods for transcript identification and quantification

The Long-read RNA-Seq Genome Annotation Assessment Project (LRGASP) Consortium was formed to evaluate the effectiveness of long-read approaches for transcriptome analysis. The consortium generated over 427 million long-read sequences from cDNA and direct RNA datasets, encompassing human, mouse, and manatee species, using different protocols and sequencing platforms. These data were utilized by developers to address challenges in transcript isoform detection and quantification, as well as de novo transcript isoform identification. The study revealed that libraries with longer, more accurate sequences produce more accurate transcripts than those with increased read depth, whereas greater read depth improved quantification accuracy. In well-annotated genomes, tools based on reference sequences demonstrated the best performance. When aiming to detect rare and novel transcripts or when using reference-free approaches, incorporating additional orthogonal data and replicate samples are advised. This collaborative study offers a benchmark for current practices and provides direction for future method development in transcriptome analysis.

IsoTools: a flexible workflow for long-read transcriptome sequencing analysis

MOTIVATION

Long-read transcriptome sequencing (LRTS) has the potential to enhance our understanding of alternative splicing and the complexity of this process requires the use of versatile computational tools, with the ability to accommodate various stages of the workflow with maximum flexibility.

RESULTS

We introduce IsoTools, a Python-based LRTS analysis framework that offers a wide range of functionality for transcriptome reconstruction and quantification of transcripts. Furthermore, we integrate a graph-based method for identifying alternative splicing events and a statistical approach based on the beta-binomial distribution for detecting differential events. To demonstrate the effectiveness of our methods, we applied IsoTools to PacBio LRTS data of human hepatocytes treated with the histone deacetylase inhibitor valproic acid. Our results indicate that LRTS can provide valuable insights into alternative splicing, particularly in terms of complex and differential splicing patterns, in comparison to short-read RNA-seq.

AVAILABILITY AND IMPLEMENTATION

IsoTools is available on GitHub and PyPI, and its documentation, including tutorials, CLI, and API references, can be found at https://isotools.readthedocs.io/.

Deletion of Tbc1d4/As160 abrogates cardiac glucose uptake and increases myocardial damage after ischemia/reperfusion

BACKGROUND

Type 2 Diabetes mellitus (T2DM) is a major risk factor for cardiovascular disease and associated with poor outcome after myocardial infarction (MI). In T2DM, cardiac metabolic flexibility, i.e. the switch between carbohydrates and lipids as energy source, is disturbed. The RabGTPase-activating protein TBC1D4 represents a crucial regulator of insulin-stimulated glucose uptake in skeletal muscle by controlling glucose transporter GLUT4 translocation. A human loss-of-function mutation in TBC1D4 is associated with impaired glycemic control and elevated T2DM risk. The study's aim was to investigate TBC1D4 function in cardiac substrate metabolism and adaptation to MI.

METHODS

Cardiac glucose metabolism of male Tbc1d4-deficient (D4KO) and wild type (WT) mice was characterized using in vivo [¹⁸F]-FDG PET imaging after glucose injection and ex vivo basal/insulin-stimulated [³H]-2-deoxyglucose uptake in left ventricular (LV) papillary muscle. Mice were subjected to cardiac ischemia/reperfusion (I/R). Heart structure and function were analyzed until 3 weeks post-MI using echocardiography, morphometric and ultrastructural analysis of heart sections, complemented by whole heart transcriptome and protein measurements.

RESULTS

Tbc1d4-knockout abolished insulin-stimulated glucose uptake in ex vivo LV papillary muscle and in vivo cardiac glucose uptake after glucose injection, accompanied by a marked reduction of GLUT4. Basal cardiac glucose uptake and GLUT1 abundance were not changed compared to WT controls. D4KO mice showed mild impairments in glycemia but normal cardiac function. However, after I/R D4KO mice showed progressively increased LV endsystolic volume and substantially increased infarction area compared to WT controls. Cardiac transcriptome analysis revealed upregulation of the unfolded protein response via ATF4/eIF2α in D4KO mice at baseline. Transmission electron microscopy revealed largely increased extracellular matrix (ECM) area, in line with decreased cardiac expression of matrix metalloproteinases of D4KO mice.

CONCLUSIONS

TBC1D4 is essential for insulin-stimulated cardiac glucose uptake and metabolic flexibility. Tbc1d4-deficiency results in elevated cardiac endoplasmic reticulum (ER)-stress response, increased deposition of ECM and aggravated cardiac damage following MI. Hence, impaired TBC1D4 signaling contributes to poor outcome after MI.

Deregulation and epigenetic modification of BCL2-family genes cause resistance to venetoclax in hematologic malignancies

The BCL2 inhibitor venetoclax has been approved to treat different hematological malignancies. Because there is no common genetic alteration causing resistance to venetoclax in chronic lymphocytic leukemia (CLL) and B-cell lymphoma, we asked if epigenetic events might be involved in venetoclax resistance. Therefore, we employed whole-exome sequencing, methylated DNA immunoprecipitation sequencing, and genome-wide clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 screening to investigate venetoclax resistance in aggressive lymphoma and high-risk CLL patients. We identified a regulatory CpG island within the PUMA promoter that is methylated upon venetoclax treatment, mediating PUMA downregulation on transcript and protein level. PUMA expression and sensitivity toward venetoclax can be restored by inhibition of methyltransferases. We can demonstrate that loss of PUMA results in metabolic reprogramming with higher oxidative phosphorylation and adenosine triphosphate production, resembling the metabolic phenotype that is seen upon venetoclax resistance. Although PUMA loss is specific for acquired venetoclax resistance but not for acquired MCL1 resistance and is not seen in CLL patients after chemotherapy-resistance, BAX is essential for sensitivity toward both venetoclax and MCL1 inhibition. As we found loss of BAX in Richter's syndrome patients after venetoclax failure, we defined BAX-mediated apoptosis to be critical for drug resistance but not for disease progression of CLL into aggressive diffuse large B-cell lymphoma in vivo. A compound screen revealed TRAIL-mediated apoptosis as a target to overcome BAX deficiency. Furthermore, antibody or CAR T cells eliminated venetoclax resistant lymphoma cells, paving a clinically applicable way to overcome venetoclax resistance.

Pre-Training on In Vitro and Fine-Tuning on Patient-Derived Data Improves Deep Neural Networks for Anti-Cancer Drug-Sensitivity Prediction

Large-scale databases that report the inhibitory capacities of many combinations of candidate drug compounds and cultivated cancer cell lines have driven the development of preclinical drug-sensitivity models based on machine learning. However, cultivated cell lines have devolved from human cancer cells over years or even decades under selective pressure in culture conditions. Moreover, models that have been trained on in vitro data cannot account for interactions with other types of cells. Drug-response data that are based on patient-derived cell cultures, xenografts, and organoids, on the other hand, are not available in the quantities that are needed to train high-capacity machine-learning models. We found that pre-training deep neural network models of drug sensitivity on in vitro drug-sensitivity databases before fine-tuning the model parameters on patient-derived data improves the models’ accuracy and improves the biological plausibility of the features, compared to training only on patient-derived data. From our experiments, we can conclude that pre-trained models outperform models that have been trained on the target domains in the vast majority of cases.

Matching anticancer compounds and tumor cell lines by neural networks with ranking loss

Computational drug sensitivity models have the potential to improve therapeutic outcomes by identifying targeted drug components that are likely to achieve the highest efficacy for a cancer cell line at hand at a therapeutic dose. State of the art drug sensitivity models use regression techniques to predict the inhibitory concentration of a drug for a tumor cell line. This regression objective is not directly aligned with either of these principal goals of drug sensitivity models: We argue that drug sensitivity modeling should be seen as a ranking problem with an optimization criterion that quantifies a drug's inhibitory capacity for the cancer cell line at hand relative to its toxicity for healthy cells. We derive an extension to the well-established drug sensitivity regression model PaccMann that employs a ranking loss and focuses on the ratio of inhibitory concentration and therapeutic dosage range. We find that the ranking extension significantly enhances the model's capability to identify the most effective anticancer drugs for unseen tumor cell profiles based in on in-vitro data.

Altered DNA Methylation Profiles in SF3B1 Mutated CLL Patients

Mutations in splicing factor genes have a severe impact on the survival of cancer patients. Splicing factor 3b subunit 1 (SF3B1) is one of the most frequently mutated genes in chronic lymphocytic leukemia (CLL); patients carrying these mutations have a poor prognosis. Since the splicing machinery and the epigenome are closely interconnected, we investigated whether these alterations may affect the epigenomes of CLL patients. While an overall hypomethylation during CLL carcinogenesis has been observed, the interplay between the epigenetic stage of the originating B cells and SF3B1 mutations, and the subsequent effect of the mutations on methylation alterations in CLL, have not been investigated. We profiled the genome-wide DNA methylation patterns of 27 CLL patients with and without SF3B1 mutations and identified local decreases in methylation levels in SF3B1^mut CLL patients at 67 genomic regions, mostly in proximity to telomeric regions. These differentially methylated regions (DMRs) were enriched in gene bodies of cancer-related signaling genes, e.g., NOTCH1, HTRA3, and BCL9L. In our study, SF3B1 mutations exclusively emerged in two out of three epigenetic stages of the originating B cells. However, not all the DMRs could be associated with the methylation programming of B cells during development, suggesting that mutations in SF3B1 cause additional epigenetic aberrations during carcinogenesis.

Large-scale literature mining to assess the relation between anti-cancer drugs and cancer types

Background

There is a huge body of scientific literature describing the relation between tumor types and anti-cancer drugs. The vast amount of scientific literature makes it impossible for researchers and physicians to extract all relevant information manually.

Methods

In order to cope with the large amount of literature we applied an automated text mining approach to assess the relations between 30 most frequent cancer types and 270 anti-cancer drugs. We applied two different approaches, a classical text mining based on named entity recognition and an AI-based approach employing word embeddings. The consistency of literature mining results was validated with 3 independent methods: first, using data from FDA approvals, second, using experimentally measured IC-50 cell line data and third, using clinical patient survival data.

Results

We demonstrated that the automated text mining was able to successfully assess the relation between cancer types and anti-cancer drugs. All validation methods showed a good correspondence between the results from literature mining and independent confirmatory approaches. The relation between most frequent cancer types and drugs employed for their treatment were visualized in a large heatmap. All results are accessible in an interactive web-based knowledge base using the following link: https://knowledgebase.microdiscovery.de/heatmap.

Conclusions

Our approach is able to assess the relations between compounds and cancer types in an automated manner. Both, cancer types and compounds could be grouped into different clusters. Researchers can use the interactive knowledge base to inspect the presented results and follow their own research questions, for example the identification of novel indication areas for known drugs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-021-02941-z.

PWD/Ph-Encoded Genetic Variants Modulate the Cellular Wnt/β-Catenin Response to Suppress Apc Min-Triggered Intestinal Tumor Formation

Genetic predisposition affects the penetrance of tumor-initiating mutations, such as APC mutations that stabilize β-catenin and cause intestinal tumors in mice and humans. However, the mechanisms involved in genetically predisposed penetrance are not well understood. Here, we analyzed tumor multiplicity and gene expression in tumor-prone Apc ^Min/+ mice on highly variant C57BL/6J (B6) and PWD/Ph (PWD) genetic backgrounds. (B6 × PWD) F1 APC ^Min offspring mice were largely free of intestinal adenoma, and several chromosome substitution (consomic) strains carrying single PWD chromosomes on the B6 genetic background displayed reduced adenoma numbers. Multiple dosage-dependent modifier loci on PWD chromosome 5 each contributed to tumor suppression. Activation of β-catenin-driven and stem cell-specific gene expression in the presence of Apc ^Min or following APC loss remained moderate in intestines carrying PWD chromosome 5, suggesting that PWD variants restrict adenoma initiation by controlling stem cell homeostasis. Gene expression of modifier candidates and DNA methylation on chromosome 5 were predominantly cis controlled and largely reflected parental patterns, providing a genetic basis for inheritance of tumor susceptibility. Human SNP variants of several modifier candidates were depleted in colorectal cancer genomes, suggesting that similar mechanisms may also affect the penetrance of cancer driver mutations in humans. Overall, our analysis highlights the strong impact that multiple genetic variants acting in networks can exert on tumor development. SIGNIFICANCE: These findings in mice show that, in addition to accidental mutations, cancer risk is determined by networks of individual gene variants.

Epigenomic profiling of non-small cell lung cancer xenografts uncover LRP12 DNA methylation as predictive biomarker for carboplatin resistance

Background

Non-small cell lung cancer (NSCLC) is the most common cause of cancer-related deaths worldwide and is primarily treated with radiation, surgery, and platinum-based drugs like cisplatin and carboplatin. The major challenge in the treatment of NSCLC patients is intrinsic or acquired resistance to chemotherapy. Molecular markers predicting the outcome of the patients are urgently needed.

Methods

Here, we employed patient-derived xenografts (PDXs) to detect predictive methylation biomarkers for platin-based therapies. We used MeDIP-Seq to generate genome-wide DNA methylation profiles of 22 PDXs, their parental primary NSCLC, and their corresponding normal tissues and complemented the data with gene expression analyses of the same tissues. Candidate biomarkers were validated with quantitative methylation-specific PCRs (qMSP) in an independent cohort.

Results

Comprehensive analyses revealed that differential methylation patterns are highly similar, enriched in PDXs and lung tumor-specific when comparing differences in methylation between PDXs versus primary NSCLC. We identified a set of 40 candidate regions with methylation correlated to carboplatin response and corresponding inverse gene expression pattern even before therapy. This analysis led to the identification of a promoter CpG island methylation of LDL receptor-related protein 12 (LRP12) associated with increased resistance to carboplatin. Validation in an independent patient cohort (n = 35) confirmed that LRP12 methylation status is predictive for therapeutic response of NSCLC patients to platin therapy with a sensitivity of 80% and a specificity of 84% (p < 0.01). Similarly, we find a shorter survival time for patients with LRP12 hypermethylation in the TCGA data set for NSCLC (lung adenocarcinoma).

Conclusions

Using an epigenome-wide sequencing approach, we find differential methylation patterns from primary lung cancer and PDX-derived cancers to be very similar, albeit with a lower degree of differential methylation in primary tumors. We identify LRP12 DNA methylation as a powerful predictive marker for carboplatin resistance. These findings outline a platform for the identification of epigenetic therapy resistance biomarkers based on PDX NSCLC models.

Electronic supplementary material

The online version of this article (10.1186/s13073-018-0562-1) contains supplementary material, which is available to authorized users.

Therapeutic targeting of ependymoma as informed by oncogenic enhancer profiling

Genomic sequencing has driven precision-based oncology therapy; however, genetic drivers remain unknown or non-targetable for many malignancies, demanding alternative approaches to identify therapeutic leads. Ependymomas are chemotherapy-resistant brain tumours, which, despite genomic sequencing, lack effective molecular targets. Intracranial ependymomas are segregated based on anatomical location – supratentorial region (ST) or posterior fossa (PF) – and further divided into distinct molecular subgroups that reflect differences in age of onset, gender predominance, and response to therapy^1–3. The most common and aggressive subgroup, Posterior Fossa Ependymoma Group A (PF-EPN-A), occurs in young children and appears to lack recurrent somatic mutations². Conversely, Posterior Fossa Ependymoma Group B (PF-EPN-B) tumours display frequent large-scale copy number gains and losses yet favourable clinical outcomes^1,3. Greater than 70% of supratentorial ependymomas are defined by highly recurrent gene fusions in the NFκB subunit RELA (ST-EPN-RELA), and less frequently involve fusion of the gene encoding the transcriptional activator YAP1 (ST-EPN-YAP1).^1,3,4 Subependymomas, a distinct histologic variant, can also be found within the ST and PF compartments accounting for the majority of tumours in the molecular subgroups ST-EPN-SE and PF-EPN-SE, respectively¹. Here, we mapped active chromatin landscapes in 42 primary ependymomas in two non-overlapping primary ependymoma cohorts with the goal of identifying essential super enhancer associated genes on which tumour cells were dependent. Enhancer regions revealed putative oncogenes, molecular targets, and pathways, which when subjected to small molecule inhibitor or shRNA treatment, diminished proliferation of patient-derived neurospheres and increased survival in mouse models of ependymomas. Through profiling of transcriptional enhancers, our study provides a framework for target and drug discovery in other cancers recalcitrant to therapeutic development because of their lack of known genetic drivers.

QSEA-modelling of genome-wide DNA methylation from sequencing enrichment experiments

Genome-wide enrichment of methylated DNA followed by sequencing (MeDIP-seq) offers a reasonable compromise between experimental costs and genomic coverage. However, the computational analysis of these experiments is complex, and quantification of the enrichment signals in terms of absolute levels of methylation requires specific transformation. In this work, we present QSEA, Quantitative Sequence Enrichment Analysis, a comprehensive workflow for the modelling and subsequent quantification of MeDIP-seq data. As the central part of the workflow we have developed a Bayesian statistical model that transforms the enrichment read counts to absolute levels of methylation and, thus, enhances interpretability and facilitates comparison with other methylation assays. We suggest several calibration strategies for the critical parameters of the model, either using additional data or fairly general assumptions. By comparing the results with bisulfite sequencing (BS) validation data, we show the improvement of QSEA over existing methods. Additionally, we generated a clinically relevant benchmark data set consisting of methylation enrichment experiments (MeDIP-seq), BS-based validation experiments (Methyl-seq) as well as gene expression experiments (RNA-seq) derived from non-small cell lung cancer patients, and show that the workflow retrieves well-known lung tumour methylation markers that are causative for gene expression changes, demonstrating the applicability of QSEA for clinical studies. QSEA is implemented in R and available from the Bioconductor repository 3.4 (www.bioconductor.org/packages/qsea).

Quantitative Comparison of Large-Scale DNA Enrichment Sequencing Data

DNA enrichment followed by sequencing (DNA-IP seq) is a versatile tool in molecular biology with a wide variety of applications. Computational analysis of differential DNA enrichment between conditions is important for identifying epigenetic alterations in disease compared to healthy controls and for revealing dynamic epigenetic modifications throughout normal and distorted cell differentiation and development. We present a protocol for genome-wide comparative analysis of DNA-IP sequencing data to identify statistically significant differential sequencing coverage between two conditions by considering variation across replicates. The protocol provides a detailed description for the comparative analysis of DNA-IP sequencing data including basic data processing, quality controls, and identification of differential enrichment using the Bioconductor package "MEDIPS".

The histone deacetylase SIRT6 controls embryonic stem cell fate via TET-mediated production of 5-hydroxymethylcytosine

How embryonic stem cells (ESCs) commit to specific cell lineages and yield all cell types of a fully formed organism remains a major question. ESC differentiation is accompanied by large-scale histone and DNA modifications, but the relations between these epigenetic categories are not understood. Here we demonstrate the interplay between the histone deacetylase sirtuin 6 (SIRT6) and the ten-eleven translocation enzymes (TETs). SIRT6 targets acetylated histone H3 at Lys 9 and 56 (H3K9ac and H3K56ac), while TETs convert 5-methylcytosine into 5-hydroxymethylcytosine (5hmC). ESCs derived from Sirt6 knockout (S6KO) mice are skewed towards neuroectoderm development. This phenotype involves derepression of OCT4, SOX2 and NANOG, which causes an upregulation of TET-dependent production of 5hmC. Genome-wide analysis revealed neural genes marked with 5hmC in S6KO ESCs, thereby implicating TET enzymes in the neuroectoderm-skewed differentiation phenotype. We demonstrate that SIRT6 functions as a chromatin regulator safeguarding the balance between pluripotency and differentiation through Tet-mediated production of 5hmC.

ARH-seq: identification of differential splicing in RNA-seq data

The computational prediction of alternative splicing from high-throughput sequencing data is inherently difficult and necessitates robust statistical measures because the differential splicing signal is overlaid by influencing factors such as gene expression differences and simultaneous expression of multiple isoforms amongst others. In this work we describe ARH-seq, a discovery tool for differential splicing in case–control studies that is based on the information-theoretic concept of entropy. ARH-seq works on high-throughput sequencing data and is an extension of the ARH method that was originally developed for exon microarrays. We show that the method has inherent features, such as independence of transcript exon number and independence of differential expression, what makes it particularly suited for detecting alternative splicing events from sequencing data. In order to test and validate our workflow we challenged it with publicly available sequencing data derived from human tissues and conducted a comparison with eight alternative computational methods. In order to judge the performance of the different methods we constructed a benchmark data set of true positive splicing events across different tissues agglomerated from public databases and show that ARH-seq is an accurate, computationally fast and high-performing method for detecting differential splicing events.

MEDIPS: genome-wide differential coverage analysis of sequencing data derived from DNA enrichment experiments

Motivation: DNA enrichment followed by sequencing is a versatile tool in molecular biology, with a wide variety of applications including genome-wide analysis of epigenetic marks and mechanisms. A common requirement of these diverse applications is a comparison of read coverage between experimental conditions. The amount of samples generated for such comparisons ranges from few replicates to hundreds of samples per condition for epigenome-wide association studies. Consequently, there is an urgent need for software that allows for fast and simple processing and comparison of sequencing data derived from enriched DNA.

Results: Here, we present a major update of the R/Bioconductor package MEDIPS, which allows for an arbitrary number of replicates per group and integrates sophisticated statistical methods for the detection of differential coverage between experimental conditions. Our approach can be applied to a diversity of quantitative sequencing data. In addition, our update adds novel functionality to MEDIPS, including correlation analysis between samples, and takes advantage of Bioconductor’s annotation databases to facilitate annotation of specific genomic regions.

Availability and implementation: The latest version of MEDIPS is available as version 1.12.0 and part of Bioconductor 2.13. The package comes with a manual containing detailed description of its functionality and is available at http://www.bioconductor.org.

Contact:lienhard@molgen.mpg.de

Supplementary information:Supplementary data are available at Bioinformatics online.

DNA-methylome analysis of mouse intestinal adenoma identifies a tumour-specific signature that is partly conserved in human colon cancer

Aberrant CpG methylation is a universal epigenetic trait of cancer cell genomes. However, human cancer samples or cell lines preclude the investigation of epigenetic changes occurring early during tumour development. Here, we have used MeDIP-seq to analyse the DNA methylome of APC^Min adenoma as a model for intestinal cancer initiation, and we present a list of more than 13,000 recurring differentially methylated regions (DMRs) characterizing intestinal adenoma of the mouse. We show that Polycomb Repressive Complex (PRC) targets are strongly enriched among hypermethylated DMRs, and several PRC2 components and DNA methyltransferases were up-regulated in adenoma. We further demonstrate by bisulfite pyrosequencing of purified cell populations that the DMR signature arises de novo in adenoma cells rather than by expansion of a pre-existing pattern in intestinal stem cells or undifferentiated crypt cells. We found that epigenetic silencing of tumour suppressors, which occurs frequently in colon cancer, was rare in adenoma. Quite strikingly, we identified a core set of DMRs, which is conserved between mouse adenoma and human colon cancer, thus possibly revealing a global panel of epigenetically modified genes for intestinal tumours. Our data allow a distinction between early conserved epigenetic alterations occurring in intestinal adenoma and late stochastic events promoting colon cancer progression, and may facilitate the selection of more specific clinical epigenetic biomarkers.

The formation and progression of tumours to metastatic disease is driven by two major mechanisms, i.e. genetic alterations that activate oncogenes or inactivate tumour suppressor genes, and changes in the epigenome that cause variations in the expression of the genetic information. A deeper understanding of the interaction between the genetic and epigenetic mechanisms is critical for the selection of tumour biomarkers and for the future development of therapies. Human tumour specimens and cell lines contain a plethora of genetic and epigenetic changes, which complicate data analysis. In contrast, mouse tumour models such as the APC^Min mouse used in this study arise by a single initiating genetic mutation, yet share key traits with human cancer. Here we show that mouse adenomas acquire a multitude of epigenetic alterations, which are recurring in mouse adenoma and in human colon cancer, representing early and advanced tumours, respectively. The use of a mouse model thus allowed us to uncover a sequence of epigenetic changes occurring in tumours, which may facilitate the identification of novel clinical colon cancer biomarkers.

RNA-Seq provides new insights in the transcriptome responses induced by the carcinogen benzo[a]pyrene

Whole-genome transcriptome measurements are pivotal for characterizing molecular mechanisms of chemicals and predicting toxic classes, such as genotoxicity and carcinogenicity, from in vitro and in vivo assays. In recent years, deep sequencing technologies have been developed that hold the promise of measuring the transcriptome in a more complete and unbiased manner than DNA microarrays. Here, we applied this RNA-seq technology for the characterization of the transcriptomic responses in HepG2 cells upon exposure to benzo[a]pyrene (BaP), a well-known DNA damaging human carcinogen. Based on EnsEMBL genes, we demonstrate that RNA-seq detects ca 20% more genes than microarray-based technology but almost threefold more significantly differentially expressed genes. Functional enrichment analyses show that RNA-seq yields more insight into the biology and mechanisms related to the toxic effects caused by BaP, i.e., two- to fivefold more affected pathways and biological processes. Additionally, we demonstrate that RNA-seq allows detecting alternative isoform expression in many genes, including regulators of cell death and DNA repair such as TP53, BCL2 and XPA, which are relevant for genotoxic responses. Moreover, potentially novel isoforms were found, such as fragments of known transcripts, transcripts with additional exons, intron retention or exon-skipping events. The biological function(s) of these isoforms remain for the time being unknown. Finally, we demonstrate that RNA-seq enables the investigation of allele-specific gene expression, although no changes could be observed. Our results provide evidence that RNA-seq is a powerful tool for toxicology, which, compared with microarrays, is capable of generating novel and valuable information at the transcriptome level for characterizing deleterious effects caused by chemicals.

The Digital Bee Brain: Integrating and Managing Neurons in a Common 3D Reference System

The honeybee standard brain (HSB) serves as an interactive tool for relating morphologies of bee brain neurons and provides a reference system for functional and bibliographical properties (http://www.neurobiologie.fu-berlin.de/beebrain/). The ultimate goal is to document not only the morphological network properties of neurons collected from separate brains, but also to establish a graphical user interface for a neuron-related data base. Here, we review the current methods and protocols used to incorporate neuronal reconstructions into the HSB. Our registration protocol consists of two separate steps applied to imaging data from two-channel confocal microscopy scans: (1) The reconstruction of the neuron, facilitated by an automatic extraction of the neuron's skeleton based on threshold segmentation, and (2) the semi-automatic 3D segmentation of the neuropils and their registration with the HSB. The integration of neurons in the HSB is performed by applying the transformation computed in step (2) to the reconstructed neurons of step (1). The most critical issue of this protocol in terms of user interaction time - the segmentation process - is drastically improved by the use of a model-based segmentation process. Furthermore, the underlying statistical shape models (SSM) allow the visualization and analysis of characteristic variations in large sets of bee brain data. The anatomy of neural networks composed of multiple neurons that are registered into the HSB are visualized by depicting the 3D reconstructions together with semantic information with the objective to integrate data from multiple sources (electrophysiology, imaging, immunocytochemistry, molecular biology). Ultimately, this will allow the user to specify cell types and retrieve their morphologies along with physiological characterizations.