Serum proteome signatures associated with ileal and colonic ulcers in Crohn's disease.

At a clinical level, ileal and colonic Crohn's disease (CD) are considered as separate entities. These subphenotypes need to be better supported by biological data to develop personalised medicine in CD. To this end, we combined different technologies (proximity extension assay, selected reaction monitoring, and high-sensitivity turbidimetric immunoassay (hsCRP)) to measure 207 immune-related serum proteins in CD patients presenting no endoscopic lesions (endoscopic remission) (n = 23), isolated ileal ulcers (n = 17), or isolated colonic ulcers (n = 16). We showed that isolated ileal ulcers and isolated colonic ulcers were specifically associated with 6 and 18 serum proteins, respectively: (high level: JUN, CNTNAP2; low level: FCRL6, LTA, CLEC4A, NTF4); (high level: hsCRP, IL6, APCS, CFB, MBL2, IL7, IL17A, CCL19, CXCL10, CSF3, IL10, CLEC4G, MMP12, VEGFA; low level: CLEC3B, GSN, TNFSF12, TPSAB1). Isolated ileal ulcers and isolated colonic ulcers were detected by hsCRP with an area under the receiver operating characteristics curve of 0.64 (p-value = 0.07) and 0.77 (p-value = 0.001), respectively. We highlighted distinct serum proteome profiles associated with ileal and colonic ulcers in CD, this finding might support the development of therapeutics and biomarkers tailored to disease location. SIGNIFICANCE: Although ileal and colonic Crohn's disease present important clinical differences (eg, progression, response to treatment and reliability of biomarkers), these two entities are managed with the same therapeutic strategy. The biological specificities of ileal and colonic Crohn's disease need to be better characterised to develop more personalised approaches. The present study used robust technologies (selected reaction monitoring, proximity extension assays and turbidimetric immunoassay) to quantify precisely 207 serum immune-related proteins in three groups of Crohn's disease patients presenting: 1) no endoscopic lesions (endoscopic remission) (n = 23); 2) isolated ileal ulcers (n = 17); 3) isolated colonic ulcers (n = 16). We found distinct serum proteome signatures associated with ileal and colonic ulcers. Our findings could foster the development of biomarkers and treatments tailored to Crohn's disease location.

Distinct blood protein profiles associated with the risk of short-term and mid/long-term clinical relapse in patients with Crohn's disease stopping infliximab: when the remission state hides different types of residual disease activity.

OBJECTIVE: Despite being in sustained and stable remission, patients with Crohn's disease (CD) stopping anti-tumour necrosis factor α (TNFα) show a high rate of relapse (~50% within 2 years). Characterising non-invasively the biological profiles of those patients is needed to better guide the decision of anti-TNFα withdrawal. DESIGN: Ninety-two immune-related proteins were measured by proximity extension assay in serum of patients with CD (n=102) in sustained steroid-free remission and stopping anti-TNFα (infliximab). As previously shown, a stratification based on time to clinical relapse was used to characterise the distinct biological profiles of relapsers (short-term relapsers: <6 months vs mid/long-term relapsers: >6 months). Associations between protein levels and time to clinical relapse were determined by univariable Cox model. RESULTS: The risk (HR) of mid/long-term clinical relapse was specifically associated with a high serum level of proteins mainly expressed in lymphocytes (LAG3, SH2B3, SIT1; HR: 2.2-4.5; p<0.05), a low serum level of anti-inflammatory effectors (IL-10, HSD11B1; HR: 0.2-0.3; p<0.05) and cellular junction proteins (CDSN, CNTNAP2, CXADR, ITGA11; HR: 0.4; p<0.05). The risk of short-term clinical relapse was specifically associated with a high serum level of pro-inflammatory effectors (IL-6, IL12RB1; HR: 3.5-3.6; p<0.05) and a low or high serum level of proteins mainly expressed in antigen presenting cells (CLEC4A, CLEC4C, CLEC7A, LAMP3; HR: 0.4-4.1; p<0.05). CONCLUSION: We identified distinct blood protein profiles associated with the risk of short-term and mid/long-term clinical relapse in patients with CD stopping infliximab. These findings constitute an advance for the development of non-invasive biomarkers guiding the decision of anti-TNFα withdrawal.

Discovery of biomarker candidates associated with the risk of short-term and mid/long-term relapse after infliximab withdrawal in Crohn's patients: a proteomics-based study.

OBJECTIVE: A subset of Crohn's disease (CD) patients experiences mid/long-term remission after infliximab withdrawal. Biomarkers are needed to identify those patients. DESIGN: New biomarkers of relapse were searched in the baseline serum of CD patients stopping infliximab when they were under combined therapy (antimetabolite and infliximab) and stable clinical remission (diSconTinuation in CrOhn's disease patients in stable Remission on combined therapy with Immunosuppressors cohort, n=102). From shotgun proteomics experiment (discovery step), biomarker candidates were identified and further targeted by selected reaction monitoring (verification step). The dataset was stratified to search for markers of short-term (<6 months) or mid/long-term relapse (>6 months). The risk of relapse and the predicting capacity associated with biomarker candidates were evaluated using univariate Cox model and log-rank statistic, respectively. To test their complementary predicting capacity, biomarker candidates were systematically combined in pairs. RESULTS: Distinct biomarker candidates were associated with the risk (HR) of short-term (15 proteins, 2.9

Gene Regulatory Network Inference: An Introductory Survey.

Gene regulatory networks are powerful abstractions of biological systems. Since the advent of high-throughput measurement technologies in biology in the late 1990s, reconstructing the structure of such networks has been a central computational problem in systems biology. While the problem is certainly not solved in its entirety, considerable progress has been made in the last two decades, with mature tools now available. This chapter aims to provide an introduction to the basic concepts underpinning network inference tools, attempting a categorization which highlights commonalities and relative strengths. While the chapter is meant to be self-contained, the material presented should provide a useful background to the later, more specialized chapters of this book.

Unsupervised Gene Network Inference with Decision Trees and Random Forests.

In this chapter, we introduce the reader to a popular family of machine learning algorithms, called decision trees. We then review several approaches based on decision trees that have been developed for the inference of gene regulatory networks (GRNs). Decision trees have indeed several nice properties that make them well-suited for tackling this problem: they are able to detect multivariate interacting effects between variables, are non-parametric, have good scalability, and have very few parameters. In particular, we describe in detail the GENIE3 algorithm, a state-of-the-art method for GRN inference.

Tree-Based Learning of Regulatory Network Topologies and Dynamics with Jump3.

Inference of gene regulatory networks (GRNs) from time series data is a well-established field in computational systems biology. Most approaches can be broadly divided in two families: model-based and model-free methods. These two families are highly complementary: model-based methods seek to identify a formal mathematical model of the system. They thus have transparent and interpretable semantics but rely on strong assumptions and are rather computationally intensive. On the other hand, model-free methods have typically good scalability. Since they are not based on any parametric model, they are more flexible than model-based methods, but also less interpretable.In this chapter, we describe Jump3, a hybrid approach that bridges the gap between model-free and model-based methods. Jump3 uses a formal stochastic differential equation to model each gene expression but reconstructs the GRN topology with a nonparametric method based on decision trees. We briefly review the theoretical and algorithmic foundations of Jump3, and then proceed to provide a step-by-step tutorial of the associated software usage.

dynGENIE3: dynamical GENIE3 for the inference of gene networks from time series expression data.

The elucidation of gene regulatory networks is one of the major challenges of systems biology. Measurements about genes that are exploited by network inference methods are typically available either in the form of steady-state expression vectors or time series expression data. In our previous work, we proposed the GENIE3 method that exploits variable importance scores derived from Random forests to identify the regulators of each target gene. This method provided state-of-the-art performance on several benchmark datasets, but it could however not specifically be applied to time series expression data. We propose here an adaptation of the GENIE3 method, called dynamical GENIE3 (dynGENIE3), for handling both time series and steady-state expression data. The proposed method is evaluated extensively on the artificial DREAM4 benchmarks and on three real time series expression datasets. Although dynGENIE3 does not systematically yield the best performance on each and every network, it is competitive with diverse methods from the literature, while preserving the main advantages of GENIE3 in terms of scalability.

SCENIC: single-cell regulatory network inference and clustering.

We present SCENIC, a computational method for simultaneous gene regulatory network reconstruction and cell-state identification from single-cell RNA-seq data (http://scenic.aertslab.org). On a compendium of single-cell data from tumors and brain, we demonstrate that cis-regulatory analysis can be exploited to guide the identification of transcription factors and cell states. SCENIC provides critical biological insights into the mechanisms driving cellular heterogeneity.

Combining tree-based and dynamical systems for the inference of gene regulatory networks.

MOTIVATION: Reconstructing the topology of gene regulatory networks (GRNs) from time series of gene expression data remains an important open problem in computational systems biology. Existing GRN inference algorithms face one of two limitations: model-free methods are scalable but suffer from a lack of interpretability and cannot in general be used for out of sample predictions. On the other hand, model-based methods focus on identifying a dynamical model of the system. These are clearly interpretable and can be used for predictions; however, they rely on strong assumptions and are typically very demanding computationally. RESULTS: Here, we propose a new hybrid approach for GRN inference, called Jump3, exploiting time series of expression data. Jump3 is based on a formal on/off model of gene expression but uses a non-parametric procedure based on decision trees (called 'jump trees') to reconstruct the GRN topology, allowing the inference of networks of hundreds of genes. We show the good performance of Jump3 on in silico and synthetic networks and applied the approach to identify regulatory interactions activated in the presence of interferon gamma.

Mapping gene regulatory networks in Drosophila eye development by large-scale transcriptome perturbations and motif inference.

Genome control is operated by transcription factors (TFs) controlling their target genes by binding to promoters and enhancers. Conceptually, the interactions between TFs, their binding sites, and their functional targets are represented by gene regulatory networks (GRNs). Deciphering in vivo GRNs underlying organ development in an unbiased genome-wide setting involves identifying both functional TF-gene interactions and physical TF-DNA interactions. To reverse engineer the GRNs of eye development in Drosophila, we performed RNA-seq across 72 genetic perturbations and sorted cell types and inferred a coexpression network. Next, we derived direct TF-DNA interactions using computational motif inference, ultimately connecting 241 TFs to 5,632 direct target genes through 24,926 enhancers. Using this network, we found network motifs, cis-regulatory codes, and regulators of eye development. We validate the predicted target regions of Grainyhead by ChIP-seq and identify this factor as a general cofactor in the eye network, being bound to thousands of nucleosome-free regions.

Bridging physiological and evolutionary time-scales in a gene regulatory network.

Gene regulatory networks (GRNs) govern phenotypic adaptations and reflect the trade-offs between physiological responses and evolutionary adaptation that act at different time-scales. To identify patterns of molecular function and genetic diversity in GRNs, we studied the drought response of the common sunflower, Helianthus annuus, and how the underlying GRN is related to its evolution. We examined the responses of 32,423 expressed sequences to drought and to abscisic acid (ABA) and selected 145 co-expressed transcripts. We characterized their regulatory relationships in nine kinetic studies based on different hormones. From this, we inferred a GRN by meta-analyses of a Gaussian graphical model and a random forest algorithm and studied the genetic differentiation among populations (FST ) at nodes. We identified two main hubs in the network that transport nitrate in guard cells. This suggests that nitrate transport is a critical aspect of the sunflower physiological response to drought. We observed that differentiation of the network genes in elite sunflower cultivars is correlated with their position and connectivity. This systems biology approach combined molecular data at different time-scales and identified important physiological processes. At the evolutionary level, we propose that network topology could influence responses to human selection and possibly adaptation to dry environments.

NIMEFI: gene regulatory network inference using multiple ensemble feature importance algorithms.

One of the long-standing open challenges in computational systems biology is the topology inference of gene regulatory networks from high-throughput omics data. Recently, two community-wide efforts, DREAM4 and DREAM5, have been established to benchmark network inference techniques using gene expression measurements. In these challenges the overall top performer was the GENIE3 algorithm. This method decomposes the network inference task into separate regression problems for each gene in the network in which the expression values of a particular target gene are predicted using all other genes as possible predictors. Next, using tree-based ensemble methods, an importance measure for each predictor gene is calculated with respect to the target gene and a high feature importance is considered as putative evidence of a regulatory link existing between both genes. The contribution of this work is twofold. First, we generalize the regression decomposition strategy of GENIE3 to other feature importance methods. We compare the performance of support vector regression, the elastic net, random forest regression, symbolic regression and their ensemble variants in this setting to the original GENIE3 algorithm. To create the ensemble variants, we propose a subsampling approach which allows us to cast any feature selection algorithm that produces a feature ranking into an ensemble feature importance algorithm. We demonstrate that the ensemble setting is key to the network inference task, as only ensemble variants achieve top performance. As second contribution, we explore the effect of using rankwise averaged predictions of multiple ensemble algorithms as opposed to only one. We name this approach NIMEFI (Network Inference using Multiple Ensemble Feature Importance algorithms) and show that this approach outperforms all individual methods in general, although on a specific network a single method can perform better. An implementation of NIMEFI has been made publicly available.

Identification of a microRNA landscape targeting the PI3K/Akt signaling pathway in inflammation-induced colorectal carcinogenesis.

Inflammation can contribute to tumor formation; however, markers that predict progression are still lacking. In the present study, the well-established azoxymethane (AOM)/dextran sulfate sodium (DSS)-induced mouse model of colitis-associated cancer was used to analyze microRNA (miRNA) modulation accompanying inflammation-induced tumor development and to determine whether inflammation-triggered miRNA alterations affect the expression of genes or pathways involved in cancer. A miRNA microarray experiment was performed to establish miRNA expression profiles in mouse colon at early and late time points during inflammation and/or tumor growth. Chronic inflammation and carcinogenesis were associated with distinct changes in miRNA expression. Nevertheless, prediction algorithms of miRNA-mRNA interactions and computational analyses based on ranked miRNA lists consistently identified putative target genes that play essential roles in tumor growth or that belong to key carcinogenesis-related signaling pathways. We identified PI3K/Akt and the insulin growth factor-1 (IGF-1) as major pathways being affected in the AOM/DSS model. DSS-induced chronic inflammation downregulates miR-133a and miR-143/145, which is reportedly associated with human colorectal cancer and PI3K/Akt activation. Accordingly, conditioned medium from inflammatory cells decreases the expression of these miRNA in colorectal adenocarcinoma Caco-2 cells. Overexpression of miR-223, one of the main miRNA showing strong upregulation during AOM/DSS tumor growth, inhibited Akt phosphorylation and IGF-1R expression in these cells. Cell sorting from mouse colons delineated distinct miRNA expression patterns in epithelial and myeloid cells during the periods preceding and spanning tumor growth. Hence, cell-type-specific miRNA dysregulation and subsequent PI3K/Akt activation may be involved in the transition from intestinal inflammation to cancer.

Myelin-derived lipids modulate macrophage activity by liver X receptor activation.

Multiple sclerosis is a chronic, inflammatory, demyelinating disease of the central nervous system in which macrophages and microglia play a central role. Foamy macrophages and microglia, containing degenerated myelin, are abundantly found in active multiple sclerosis lesions. Recent studies have described an altered macrophage phenotype after myelin internalization. However, it is unclear by which mechanisms myelin affects the phenotype of macrophages and how this phenotype can influence lesion progression. Here we demonstrate, by using genome wide gene expression analysis, that myelin-phagocytosing macrophages have an enhanced expression of genes involved in migration, phagocytosis and inflammation. Interestingly, myelin internalization also induced the expression of genes involved in liver-X-receptor signaling and cholesterol efflux. In vitro validation shows that myelin-phagocytosing macrophages indeed have an increased capacity to dispose intracellular cholesterol. In addition, myelin suppresses the secretion of the pro-inflammatory mediator IL-6 by macrophages, which was mediated by activation of liver-X-receptor ß. Our data show that myelin modulates the phenotype of macrophages by nuclear receptor activation, which may subsequently affect lesion progression in demyelinating diseases such as multiple sclerosis.

Statistical interpretation of machine learning-based feature importance scores for biomarker discovery.

MOTIVATION: Univariate statistical tests are widely used for biomarker discovery in bioinformatics. These procedures are simple, fast and their output is easily interpretable by biologists but they can only identify variables that provide a significant amount of information in isolation from the other variables. As biological processes are expected to involve complex interactions between variables, univariate methods thus potentially miss some informative biomarkers. Variable relevance scores provided by machine learning techniques, however, are potentially able to highlight multivariate interacting effects, but unlike the p-values returned by univariate tests, these relevance scores are usually not statistically interpretable. This lack of interpretability hampers the determination of a relevance threshold for extracting a feature subset from the rankings and also prevents the wide adoption of these methods by practicians. RESULTS: We evaluated several, existing and novel, procedures that extract relevant features from rankings derived from machine learning approaches. These procedures replace the relevance scores with measures that can be interpreted in a statistical way, such as p-values, false discovery rates, or family wise error rates, for which it is easier to determine a significance level. Experiments were performed on several artificial problems as well as on real microarray datasets. Although the methods differ in terms of computing times and the tradeoff, they achieve in terms of false positives and false negatives, some of them greatly help in the extraction of truly relevant biomarkers and should thus be of great practical interest for biologists and physicians. As a side conclusion, our experiments also clearly highlight that using model performance as a criterion for feature selection is often counter-productive. AVAILABILITY AND IMPLEMENTATION: Python source codes of all tested methods, as well as the MATLAB scripts used for data simulation, can be found in the Supplementary Material.

MicroRNAs profiling in murine models of acute and chronic asthma: a relationship with mRNAs targets.

BACKGROUND: miRNAs are now recognized as key regulator elements in gene expression. Although they have been associated with a number of human diseases, their implication in acute and chronic asthma and their association with lung remodelling have never been thoroughly investigated. METHODOLOGY/PRINCIPAL FINDINGS: In order to establish a miRNAs expression profile in lung tissue, mice were sensitized and challenged with ovalbumin mimicking acute, intermediate and chronic human asthma. Levels of lung miRNAs were profiled by microarray and in silico analyses were performed to identify potential mRNA targets and to point out signalling pathways and biological processes regulated by miRNA-dependent mechanisms. Fifty-eight, 66 and 75 miRNAs were found to be significantly modulated at short-, intermediate- and long-term challenge, respectively. Inverse correlation with the expression of potential mRNA targets identified mmu-miR-146b, -223, -29b, -29c, -483, -574-5p, -672 and -690 as the best candidates for an active implication in asthma pathogenesis. A functional validation assay was performed by cotransfecting in human lung fibroblasts (WI26) synthetic miRNAs and engineered expression constructs containing the coding sequence of luciferase upstream of the 3'UTR of various potential mRNA targets. The bioinformatics analysis identified miRNA-linked regulation of several signalling pathways, as matrix metalloproteinases, inflammatory response and TGF-ß signalling, and biological processes, including apoptosis and inflammation. CONCLUSIONS/SIGNIFICANCE: This study highlights that specific miRNAs are likely to be involved in asthma disease and could represent a valuable resource both for biological makers identification and for unveiling mechanisms underlying the pathogenesis of asthma.

Inferring regulatory networks from expression data using tree-based methods.

One of the pressing open problems of computational systems biology is the elucidation of the topology of genetic regulatory networks (GRNs) using high throughput genomic data, in particular microarray gene expression data. The Dialogue for Reverse Engineering Assessments and Methods (DREAM) challenge aims to evaluate the success of GRN inference algorithms on benchmarks of simulated data. In this article, we present GENIE3, a new algorithm for the inference of GRNs that was best performer in the DREAM4 In Silico Multifactorial challenge. GENIE3 decomposes the prediction of a regulatory network between p genes into p different regression problems. In each of the regression problems, the expression pattern of one of the genes (target gene) is predicted from the expression patterns of all the other genes (input genes), using tree-based ensemble methods Random Forests or Extra-Trees. The importance of an input gene in the prediction of the target gene expression pattern is taken as an indication of a putative regulatory link. Putative regulatory links are then aggregated over all genes to provide a ranking of interactions from which the whole network is reconstructed. In addition to performing well on the DREAM4 In Silico Multifactorial challenge simulated data, we show that GENIE3 compares favorably with existing algorithms to decipher the genetic regulatory network of Escherichia coli. It doesn't make any assumption about the nature of gene regulation, can deal with combinatorial and non-linear interactions, produces directed GRNs, and is fast and scalable. In conclusion, we propose a new algorithm for GRN inference that performs well on both synthetic and real gene expression data. The algorithm, based on feature selection with tree-based ensemble methods, is simple and generic, making it adaptable to other types of genomic data and interactions.