Modeling metastatic progression from cross-sectional cancer genomics data.

MOTIVATION: Metastasis formation is a hallmark of cancer lethality. Yet, metastases are generally unobservable during their early stages of dissemination and spread to distant organs. Genomic datasets of matched primary tumors and metastases may offer insights into the underpinnings and the dynamics of metastasis formation. RESULTS: We present metMHN, a cancer progression model designed to deduce the joint progression of primary tumors and metastases using cross-sectional cancer genomics data. The model elucidates the statistical dependencies among genomic events, the formation of metastasis, and the clinical emergence of both primary tumors and their metastatic counterparts. metMHN enables the chronological reconstruction of mutational sequences and facilitates estimation of the timing of metastatic seeding. In a study of nearly 5000 lung adenocarcinomas, metMHN pinpointed TP53 and EGFR as mediators of metastasis formation. Furthermore, the study revealed that post-seeding adaptation is predominantly influenced by frequent copy number alterations. AVAILABILITY AND IMPLEMENTATION: All datasets and code are available on GitHub at https://github.com/cbg-ethz/metMHN.

Anomaly detection in mixed high-dimensional molecular data.

MOTIVATION: Mixed molecular data combines continuous and categorical features of the same samples, such as OMICS profiles with genotypes, diagnoses, or patient sex. Like all high-dimensional molecular data, it is prone to incorrect values that can stem from various sources for example the technical limitations of the measurement devices, errors in the sample preparation, or contamination. Most anomaly detection algorithms identify complete samples as outliers or anomalies. However, in most cases, not all measurements of those samples are erroneous but only a few one-dimensional features within the samples are incorrect. These one-dimensional data errors are continuous measurements that are either located outside or inside the normal ranges of their features but in both cases show atypical values given all other continuous and categorical features in the sample. Additionally, categorical anomalies can occur for example when the genotype or diagnosis was submitted wrongly. RESULTS: We introduce ADMIRE (Anomaly Detection using MIxed gRaphical modEls), a novel approach for the detection and correction of anomalies in mixed high-dimensional data. Hereby, we focus on the detection of single (one-dimensional) data errors in the categorical and continuous features of a sample. For that the joint distribution of continuous and categorical features is learned by mixed graphical models, anomalies are detected by the difference between measured and model-based estimations and are corrected using imputation. We evaluated ADMIRE in simulation and by screening for anomalies in one of our own metabolic datasets. In simulation experiments, ADMIRE outperformed the state-of-the-art methods of Local Outlier Factor, stray, and Isolation Forest. AVAILABILITY AND IMPLEMENTATION: All data and code is available at https://github.com/spang-lab/adadmire. ADMIRE is implemented in a Python package called adadmire which can be found at https://pypi.org/project/adadmire.

Epstein-Barr virus status of sporadic Burkitt lymphoma is associated with patient age and mutational features.

Sporadic Burkitt lymphoma (BL) is the most frequent tumour of children and adolescents but a rare subtype of lymphomas in adults. To date most molecular data have been obtained from lymphomas arising in the young. Recently, Epstein-Barr virus (EBV) positive and negative BL in young patients was shown to differ in molecular features. In the present study, we present a large age-overarching cohort of sporadic BL (n = 162) analysed by immunohistochemistry, translocations of MYC proto-oncogene, basic helix-loop-helix transcription factor (MYC), B-cell leukaemia/lymphoma 2 (BCL2) and B-cell leukaemia/lymphoma 6 (BCL6) and by targeted sequencing. We illustrate an age-associated inter-tumoral molecular heterogeneity in this disease. Mutations affecting inhibitor of DNA binding 3, HLH protein (ID3), transcription factor 3 (TCF3) and cyclin D3 (CCND3), which are highly recurrent in paediatric BL, and expression of sex determining region Y-box transcription factor 11 (SOX11) declined with patient age at diagnosis (P = 0·0204 and P = 0·0197 respectively). In contrast, EBV was more frequently detected in adult patients (P = 0·0262). Irrespective of age, EBV-positive sporadic BL showed significantly less frequent mutations in ID3/TCF3/CCND3 (P = 0·0088) but more often mutations of G protein subunit alpha 13 (GNA13; P = 0·0368) and forkhead box O1 (FOXO1; P = 0·0044) compared to EBV-negative tumours. Our findings suggest that among sporadic BL an EBV-positive subgroup of lymphomas increases with patient age that shows distinct pathogenic features reminiscent of EBV-positive endemic BL.

Early dissemination seeds metastasis in breast cancer.

Accumulating data suggest that metastatic dissemination often occurs early during tumour formation, but the mechanisms of early metastatic spread have not yet been addressed. Here, by studying metastasis in a HER2-driven mouse breast cancer model, we show that progesterone-induced signalling triggers migration of cancer cells from early lesions shortly after HER2 activation, but promotes proliferation in advanced primary tumour cells. The switch from migration to proliferation was regulated by increased HER2 expression and tumour-cell density involving microRNA-mediated progesterone receptor downregulation, and was reversible. Cells from early, low-density lesions displayed more stemness features, migrated more and founded more metastases than cells from dense, advanced tumours. Notably, we found that at least 80% of metastases were derived from early disseminated cancer cells. Karyotypic and phenotypic analysis of human disseminated cancer cells and primary tumours corroborated the relevance of these findings for human metastatic dissemination.

Low-rank tensor methods for Markov chains with applications to tumor progression models.

Cancer progression can be described by continuous-time Markov chains whose state space grows exponentially in the number of somatic mutations. The age of a tumor at diagnosis is typically unknown. Therefore, the quantity of interest is the time-marginal distribution over all possible genotypes of tumors, defined as the transient distribution integrated over an exponentially distributed observation time. It can be obtained as the solution of a large linear system. However, the sheer size of this system renders classical solvers infeasible. We consider Markov chains whose transition rates are separable functions, allowing for an efficient low-rank tensor representation of the linear system's operator. Thus we can reduce the computational complexity from exponential to linear. We derive a convergent iterative method using low-rank formats whose result satisfies the normalization constraint of a distribution. We also perform numerical experiments illustrating that the marginal distribution is well approximated with low rank.

Modelling cancer progression using Mutual Hazard Networks.

MOTIVATION: Cancer progresses by accumulating genomic events, such as mutations and copy number alterations, whose chronological order is key to understanding the disease but difficult to observe. Instead, cancer progression models use co-occurrence patterns in cross-sectional data to infer epistatic interactions between events and thereby uncover their most likely order of occurrence. State-of-the-art progression models, however, are limited by mathematical tractability and only allow events to interact in directed acyclic graphs, to promote but not inhibit subsequent events, or to be mutually exclusive in distinct groups that cannot overlap. RESULTS: Here we propose Mutual Hazard Networks (MHN), a new Machine Learning algorithm to infer cyclic progression models from cross-sectional data. MHN model events by their spontaneous rate of fixation and by multiplicative effects they exert on the rates of successive events. MHN compared favourably to acyclic models in cross-validated model fit on four datasets tested. In application to the glioblastoma dataset from The Cancer Genome Atlas, MHN proposed a novel interaction in line with consecutive biopsies: IDH1 mutations are early events that promote subsequent fixation of TP53 mutations. AVAILABILITY AND IMPLEMENTATION: Implementation and data are available at https://github.com/RudiSchill/MHN. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The long non-coding RNA LINC00941 and SPRR5 are novel regulators of human epidermal homeostasis.

Several long non-coding RNAs (lncRNAs) act as regulators of cellular homeostasis; however, few of these molecules were functionally characterized in a mature human tissue environment. Here, we report that the lncRNA LINC00941 is a crucial regulator of human epidermal homeostasis. LINC00941 is enriched in progenitor keratinocytes and acts as a repressor of keratinocyte differentiation. Furthermore, LINC00941 represses SPRR5, a previously uncharacterized molecule, which functions as an essential positive regulator of keratinocyte differentiation. Interestingly, 54.8% of genes repressed in SPRR5-deficient epidermal tissue are induced in LINC00941-depleted organotypic epidermis, suggesting a common mode of action for both molecules.

A Novel Metabolic Signature To Predict the Requirement of Dialysis or Renal Transplantation in Patients with Chronic Kidney Disease.

Identification of chronic kidney disease patients at risk of progressing to end-stage renal disease (ESRD) is essential for treatment decision-making and clinical trial design. Here, we explored whether proton nuclear magnetic resonance (NMR) spectroscopy of blood plasma improves the currently best performing kidney failure risk equation, the so-called Tangri score. Our study cohort comprised 4640 participants from the German Chronic Kidney Disease (GCKD) study, of whom 185 (3.99%) progressed over a mean observation time of 3.70 ± 0.88 years to ESRD requiring either dialysis or transplantation. The original four-variable Tangri risk equation yielded a C statistic of 0.863 (95% CI, 0.831-0.900). Upon inclusion of NMR features by state-of-the-art machine learning methods, the C statistic improved to 0.875 (95% CI, 0.850-0.911), thereby outperforming the Tangri score in 94 out of 100 subsampling rounds. Of the 24 NMR features included in the model, creatinine, high-density lipoprotein, valine, acetyl groups of glycoproteins, and Ca2+-EDTA carried the highest weights. In conclusion, proton NMR-based plasma fingerprinting improved markedly the detection of patients at risk of developing ESRD, thus enabling enhanced patient treatment.

Automated macrophage counting in DLBCL tissue samples: a ROF filter based approach.

BACKGROUND: For analysis of the tumor microenvironment in diffuse large B-cell lymphoma (DLBCL) tissue samples, it is desirable to obtain information about counts and distribution of different macrophage subtypes. Until now, macrophage counts are mostly inferred from gene expression analysis of whole tissue sections, providing only indirect information. Direct analysis of immunohistochemically (IHC) fluorescence stained tissue samples is confronted with several difficulties, e.g. high variability of shape and size of target macrophages and strongly inhomogeneous intensity of staining. Consequently, application of commercial software is largely restricted to very rough analysis modes, and most macrophage counts are still obtained by manual counting in microarrays or high power fields, thus failing to represent the heterogeneity of tumor microenvironment adequately. METHODS: We describe a Rudin-Osher-Fatemi (ROF) filter based segmentation approach for whole tissue samples, combining floating intensity thresholding and rule-based feature detection. Method is validated against manual counts and compared with two commercial software kits (Tissue Studio 64, Definiens AG, and Halo, Indica Labs) and a straightforward machine-learning approach in a set of 50 test images. Further, the novel method and both commercial packages are applied to a set of 44 whole tissue sections. Outputs are compared with gene expression data available for the same tissue samples. Finally, the ROF based method is applied to 44 expert-specified tumor subregions for testing selection and subsampling strategies. RESULTS: Among all tested methods, the novel approach is best correlated with manual count (0.9297). Automated detection of evaluation subregions proved to be fully reliable. Comparison with gene expression data obtained for the same tissue samples reveals only moderate to low correlation levels. Subsampling within tumor subregions is possible with results almost identical to full sampling. Mean macrophage size in tumor subregions is 152.5±111.3 µm2. CONCLUSIONS: ROF based approach is successfully applied to detection of IHC stained macrophages in DLBCL tissue samples. The method competes well with existing commercial software kits. In difference to them, it is fully automated, externally repeatable, independent on training data and completely documented. Comparison with gene expression data indicates that image morphometry constitutes an independent source of information about antibody-polarized macrophage occurence and distribution.

Identification of ADGRE5 as discriminating MYC target between Burkitt lymphoma and diffuse large B-cell lymphoma.

BACKGROUND: MYC is a heterogeneously expressed transcription factor that plays a multifunctional role in many biological processes such as cell proliferation and differentiation. It is also associated with many types of cancer including the malignant lymphomas. There are two types of aggressive B-cell lymphoma, namely Burkitt lymphoma (BL) and a subgroup of diffuse large cell lymphoma (DLBCL), which both carry MYC translocations and overexpress MYC but both differ significantly in their clinical outcome. In DLBCL, MYC translocations are associated with an aggressive behavior and poor outcome, whereas MYC-positive BL show a superior outcome. METHODS: To shed light on this phenomenon, we investigated the different modes of actions of MYC in aggressive B-cell lymphoma cell lines subdivided into three groups: (i) MYC-positive BL, (ii) DLBCL with MYC translocation (DLBCLpos) and (iii) DLBCL without MYC translocation (DLBCLneg) for control. In order to identify genome-wide MYC-DNA binding sites a chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-Seq) was performed. In addition, ChIP-Seq for H3K4me3 was used for determination of genomic regions accessible for transcriptional activity. These data were supplemented with gene expression data derived from RNA-Seq. RESULTS: Bioinformatics integration of all data sets revealed different MYC-binding patterns and transcriptional profiles in MYC-positive BL and DLBCL cell lines indicating different functional roles of MYC for gene regulation in aggressive B-cell lymphomas. Based on this multi-omics analysis we identified ADGRE5 (alias CD97) - a member of the EGF-TM7 subfamily of adhesion G protein-coupled receptors - as a MYC target gene, which is specifically expressed in BL but not in DLBCL regardless of MYC translocation. CONCLUSION: Our study describes a diverse genome-wide MYC-DNA binding pattern in BL and DLBCL cell lines with and without MYC translocations. Furthermore, we identified ADREG5 as a MYC target gene able to discriminate between BL and DLBCL irrespectively of the presence of MYC breaks in DLBCL. Since ADGRE5 plays an important role in tumor cell formation, metastasis and invasion, it might also be instrumental to better understand the different pathobiology of BL and DLBCL and help to explain discrepant clinical characteristics of BL and DLBCL.

From hype to reality: data science enabling personalized medicine.

BACKGROUND: Personalized, precision, P4, or stratified medicine is understood as a medical approach in which patients are stratified based on their disease subtype, risk, prognosis, or treatment response using specialized diagnostic tests. The key idea is to base medical decisions on individual patient characteristics, including molecular and behavioral biomarkers, rather than on population averages. Personalized medicine is deeply connected to and dependent on data science, specifically machine learning (often named Artificial Intelligence in the mainstream media). While during recent years there has been a lot of enthusiasm about the potential of 'big data' and machine learning-based solutions, there exist only few examples that impact current clinical practice. The lack of impact on clinical practice can largely be attributed to insufficient performance of predictive models, difficulties to interpret complex model predictions, and lack of validation via prospective clinical trials that demonstrate a clear benefit compared to the standard of care. In this paper, we review the potential of state-of-the-art data science approaches for personalized medicine, discuss open challenges, and highlight directions that may help to overcome them in the future. CONCLUSIONS: There is a need for an interdisciplinary effort, including data scientists, physicians, patient advocates, regulatory agencies, and health insurance organizations. Partially unrealistic expectations and concerns about data science-based solutions need to be better managed. In parallel, computational methods must advance more to provide direct benefit to clinical practice.

Gene expression profiling reveals a close relationship between follicular lymphoma grade 3A and 3B, but distinct profiles of follicular lymphoma grade 1 and 2.

A linear progression model of follicular lymphomas (FL) FL1, FL2 and FL3A has been favored, since FL3A often co-exist with an FL1/2 component. FL3B, in contrast, is thought to be more closely related to diffuse large B-cell lymphoma (DLBCL), and both are often simultaneously present in one tumor (DLBCL/FL3B). To obtain more detailed insights into follicular lymphoma progression, a comprehensive analysis of a well-defined set of FL1/2 (n=22), FL3A (n=16), FL3B (n=6), DLBCL/FL3B (n=9), and germinal center B-cell-type diffuse large B-cell lymphoma (n=45) was undertaken using gene expression profiling, immunohistochemical stainings and genetic analyses by fluorescence in situ hybridization. While immunohistochemical (CD10, IRF4/MUM1, Ki67, BCL2, BCL6) and genetic profiles (translocations of BCL2, BCL6 and MYC) delineate FL1-3A from FL3B and DLBCL/FL3B, significant differences were observed between FL1/2 and FL3A upon gene expression profiling. Interestingly, FL3B turned out to be closely related to FL3A, not categorizing within a separate gene expression cluster, and both FL3A and FL3B showed overlapping profiles in between FL1/2 and diffuse large B-cell lymphoma. Finally, based upon their gene expression pattern, DLBCL/FL3B represent a composite form of FL3B and DLBCL, with the majority of samples more closely resembling the latter. The fact that gene expression profiling clearly separated FL1/2 from both FL3A and FL3B suggests a closer biological relationship between the latter. This notion, however, is in contrast to immunohistochemical and genetic profiles of the different histological FL subtypes that point to a closer relationship between FL1/2 and FL3A, and separates them from FL3B.

T cells become licensed in the lung to enter the central nervous system.

The blood­brain barrier (BBB) and the environment of the central nervous system (CNS) guard the nervous tissue from peripheral immune cells. In the autoimmune disease multiple sclerosis, myelin-reactive T-cell blasts are thought to transgress the BBB and create a pro-inflammatory environment in the CNS, thereby making possible a second autoimmune attack that starts from the leptomeningeal vessels and progresses into the parenchyma. Using a Lewis rat model of experimental autoimmune encephalomyelitis, we show here that contrary to the expectations of this concept, T-cell blasts do not efficiently enter the CNS and are not required to prepare the BBB for immune-cell recruitment. Instead, intravenously transferred T-cell blasts gain the capacity to enter the CNS after residing transiently within the lung tissues. Inside the lung tissues, they move along and within the airways to bronchus-associated lymphoid tissues and lung-draining mediastinal lymph nodes before they enter the blood circulation from where they reach the CNS. Effector T cells transferred directly into the airways showed a similar migratory pattern and retained their full pathogenicity. On their way the T cells fundamentally reprogrammed their gene-expression profile, characterized by downregulation of their activation program and upregulation of cellular locomotion molecules together with chemokine and adhesion receptors. The adhesion receptors include ninjurin 1, which participates in T-cell intravascular crawling on cerebral blood vessels. We detected that the lung constitutes a niche not only for activated T cells but also for resting myelin-reactive memory T cells. After local stimulation in the lung, these cells strongly proliferate and, after assuming migratory properties, enter the CNS and induce paralytic disease. The lung could therefore contribute to the activation of potentially autoaggressive T cells and their transition to a migratory mode as a prerequisite to entering their target tissues and inducing autoimmune disease.

Scale-Invariant Biomarker Discovery in Urine and Plasma Metabolite Fingerprints.

Metabolomics data is typically scaled to a common reference like a constant volume of body fluid, a constant creatinine level, or a constant area under the spectrum. Such scaling of the data, however, may affect the selection of biomarkers and the biological interpretation of results in unforeseen ways. Here, we studied how both the outcome of hypothesis tests for differential metabolite concentration and the screening for multivariate metabolite signatures are affected by the choice of scale. To overcome this problem for metabolite signatures and to establish a scale-invariant biomarker discovery algorithm, we extended linear zero-sum regression to the logistic regression framework and showed in two applications to 1H NMR-based metabolomics data how this approach overcomes the scaling problem. Logistic zero-sum regression is available as an R package as well as a high-performance computing implementation that can be downloaded at https://github.com/rehbergT/zeroSum .

Comprehensive Metaboproteomics of Burkitt's and Diffuse Large B-Cell Lymphoma Cell Lines and Primary Tumor Tissues Reveals Distinct Differences in Pyruvate Content and Metabolism.

Burkitt's lymphoma (BL) and diffuse large B-cell lymphoma (DLBCL) are pathologically and clinically distinct subtypes of aggressive non-Hodgkin B-cell lymphoma. To learn more about their biology, we employed metabolomic and proteomic methods to study both established cell lines as well as cryopreserved and formalin-fixed paraffin-embedded (FFPE) tissue sections of BL and DLBCL. Strikingly, NMR analyses revealed DLBCL cell lines to produce and secrete significantly (padj = 1.72 × 10-22) more pyruvic acid than BL cell lines. This finding could be reproduced by targeted GC/MS analyses of cryopreserved tissue sections of BL and DLBCL cases. Enrichment analysis of an overlapping set of N = 2315 proteins, that had been quantified by nanoLC-SWATH-MS in BL and DLBCL cultured cells and cryosections, supported the observed difference in pyruvic acid content, as glycolysis and pyruvate metabolism were downregulated, while one-carbon metabolism was upregulated in BL compared to DLBCL. Furthermore, 92.1% of the overlapping significant proteins showed the same direction of regulation in cryopreserved and FFPE material. Proteome data are available via ProteomeXchange with identifier PXD004936.

Synergy of interleukin 10 and toll-like receptor 9 signalling in B cell proliferation: Implications for lymphoma pathogenesis.

A network of autocrine and paracrine signals defines B cell homeostasis and is thought to be involved in transformation processes. Investigating interactions of these microenvironmental factors and their relation to proto-oncogenes as c-Myc (MYC) is fundamental to understand the biology of B cell lymphoma. Therefore, B cells with conditional MYC expression were stimulated with CD40L, insulin-like growth factor 1, α-IgM, Interleukin-10 (IL10) and CpG alone or in combination. The impact of forty different interventions on cell proliferation was investigated in MYC deprived cells and calculated by linear regression. Combination of CpG and IL10 led to a strong synergistic activation of cell proliferation (S-phase/doubling of total cell number) comparable to cells with high MYC expression. A synergistic up-regulation of CDK4, CDK6 and CCND3 expression by IL10 and CpG treatment was causal for this proliferative effect as shown by qRT-PCR analysis and inhibition of the CDK4/6 complex by PD0332991. Furthermore, treatment of stimulated MYC deprived cells with MLN120b, ACHP, Pyridone 6 or Ruxolitinib showed that IL10/CpG induced proliferation and CDK4 expression were JAK/STAT3 and IKK/NF-κB dependent. This was further supported by STAT3 and p65/RELA knockdown experiments, showing strongest effects on cell proliferation and CDK4 expression after double knockdown. Additionally, chromatin immunoprecipitation revealed a dual binding of STAT3 and p65 to the proximal promotor of CDK4 after IL10/CpG treatment. Therefore, the observed synergism of IL10R and TLR9 signalling was able to induce proliferation in a comparable way as aberrant MYC and might play a role in B cell homeostasis or transformation.

Cell-of-origin classification by gene expression and MYC-rearrangements in diffuse large B-cell lymphoma of children and adolescents.

We present the largest series of diffuse large B-cell lymphoma (DLBCL) in patients younger than 18 years analysed to date by gene expression profiling using Nanostring technology to identify molecular subtypes and fluorescent in situ hybridization for translocations of MYC. We show that the activated B cell-like subtype of DLBCL is exceedingly rare in children and - in contrast to adults- not associated with outcome. Furthermore, we review the current literature and demonstrate that MYC translocations are not more frequent in paediatric compared to adult DLBCL. A prognostic role of MYC in the paediatric age groups seems unlikely.

Comparison of Gene Expression Patterns Between Mouse Models of Nonalcoholic Fatty Liver Disease and Liver Tissues From Patients.

BACKGROUND & AIMS: Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disorder in industrialized countries. Mouse models of NAFLD have been used in studies of pathogenesis and treatment, and have certain features of the human disease. We performed a systematic transcriptome-wide analysis of liver tissues from patients at different stages of NAFLD progression (ranging from healthy obese individuals to those with steatosis), as well as rodent models of NAFLD, to identify those that most closely resemble human disease progression in terms of gene expression patterns. METHODS: We performed a systematic evaluation of genome-wide messenger RNA expression using liver tissues collected from mice fed a standard chow diet (controls) and 9 mouse models of NAFLD: mice on a high-fat diet (with or without fructose), mice on a Western-type diet, mice on a methionine- and choline-deficient diet, mice on a high-fat diet given streptozotocin, and mice with disruption of Pten in hepatocytes. We compared gene expression patterns with those of liver tissues from 25 patients with nonalcoholic steatohepatitis (NASH), 27 patients with NAFLD, 15 healthy obese individuals, and 39 healthy nonobese individuals (controls). Liver samples were obtained from patients undergoing liver biopsy for suspected NAFLD or NASH, or during liver or bariatric surgeries. Data sets were analyzed using the limma R-package. Overlap of functional profiles was analyzed by gene set enrichment analysis profiles. RESULTS: We found differences between human and mouse transcriptomes to be significantly larger than differences between disease stages or models. Of the 65 genes with significantly altered expression in patients with NASH and 177 genes with significantly altered expression in patients with NAFLD, compared with controls, only 1-18 of these genes also differed significantly in expression between mouse models of NAFLD and control mice. However, expression of genes that regulate pathways associated with the development of NAFLD were altered in some mouse models (such as pathways associated with lipid metabolism). On a pathway level, gene expression patterns in livers of mice on the high-fat diet were associated more closely with human fatty liver disease than other models. CONCLUSIONS: In comparing gene expression profiles between liver tissues from different mouse models of NAFLD and patients with different stages of NAFLD, we found very little overlap. Our data set is available for studies of pathways that contribute to the development of NASH and NAFLD and selection of the most applicable mouse models (http://www.nash-profiler.com).

Analyzing synergistic and non-synergistic interactions in signalling pathways using Boolean Nested Effect Models.

MOTIVATION: Understanding the structure and interplay of cellular signalling pathways is one of the great challenges in molecular biology. Boolean Networks can infer signalling networks from observations of protein activation. In situations where it is difficult to assess protein activation directly, Nested Effect Models are an alternative. They derive the network structure indirectly from downstream effects of pathway perturbations. To date, Nested Effect Models cannot resolve signalling details like the formation of signalling complexes or the activation of proteins by multiple alternative input signals. Here we introduce Boolean Nested Effect Models (B-NEM). B-NEMs combine the use of downstream effects with the higher resolution of signalling pathway structures in Boolean Networks. RESULTS: We show that B-NEMs accurately reconstruct signal flows in simulated data. Using B-NEM we then resolve BCR signalling via PI3K and TAK1 kinases in BL2 lymphoma cell lines. AVAILABILITY AND IMPLEMENTATION: R code is available at https://github.com/MartinFXP/B-NEM (github). The BCR signalling dataset is available at the GEO database (http://www.ncbi.nlm.nih.gov/geo/) through accession number GSE68761. CONTACT: martin-franz-xaver.pirkl@ukr.de, Rainer.Spang@ukr.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Low urinary indoxyl sulfate levels early after transplantation reflect a disrupted microbiome and are associated with poor outcome.

Indole, which is produced from l-tryptophan by commensal bacteria expressing tryptophanase, not only is an important intercellular signal in microbial communities, but also modulates mucosal barrier function and expression of pro- and anti-inflammatory genes by intestinal epithelial cells. Here, we hypothesized that decreased urinary excretion of 3-indoxyl sulfate (3-IS), the major conjugate of indole found in humans, may be a marker of gut microbiota disruption and increased risk of developing gastrointestinal (GI) graft-versus-host-disease. Using liquid chromatography/tandem mass spectrometry, 3-IS was determined in urine specimens collected weekly within the first 28 days after allogeneic stem cell transplantation (ASCT) in 131 patients. Low 3-IS levels within the first 10 days after ASCT were associated with significantly higher transplant-related mortality (P = .017) and worse overall survival (P = .05) 1 year after ASCT. Least absolute shrinkage and selection operator regression models trained on log-normalized counts of 763 operational taxonomic units derived from next-generation sequencing of the hypervariable V3 region of the 16S ribosomal RNA gene showed members of the families of Lachnospiraceae and Ruminococcaceae of the class of Clostridia to be associated with high urinary 3-IS levels, whereas members of the class of Bacilli were associated with low 3-IS levels. Risk factors of early suppression of 3-IS levels were the type of GI decontamination (P = .01), early onset of antibiotic treatment (P = .001), and recipient NOD2/CARD15 genotype (P = .04). In conclusion, our findings underscore the relevance of microbiota-derived indole and metabolites thereof in mucosal integrity and protection from inflammation.