Ivan G. Costa

Discovering motifs that induce sequencing errors

BackgroundElevated sequencing error rates are the most predominant obstacle in single-nucleotide polymorphism (SNP) detection, which is a major goal in the bulk of current studies using next-generation sequencing (NGS). Beyond routinely handled generic sources of errors, certain base calling errors relate to specific sequence patterns. Statistically principled ways to associate sequence patterns with base calling errors have not been previously described. Extant approaches either incur decisive losses in power, due to relating errors with individual genomic positions rather than motifs, or do not properly distinguish between motif-induced and sequence-unspecific sources of errors.ResultsHere, for the first time, we describe a statistically rigorous framework for the discovery of motifs that induce sequencing errors. We apply our method to several datasets from Illumina GA IIx, HiSeq 2000, and MiSeq sequencers. We confirm previously known error-causing sequence contexts and report new more specific ones.ConclusionsChecking for error-inducing motifs should be included into SNP calling pipelines to avoid false positives. To facilitate filtering of sets of putative SNPs, we provide tracks of error-prone genomic positions (in BED format).Availabilityhttp://discovering-cse.googlecode.com

On the selection of appropriate distances for gene expression data clustering

BackgroundClustering is crucial for gene expression data analysis. As an unsupervised exploratory procedure its results can help researchers to gain insights and formulate new hypothesis about biological data from microarrays. Given different settings of microarray experiments, clustering proves itself as a versatile exploratory tool. It can help to unveil new cancer subtypes or to identify groups of genes that respond similarly to a specific experimental condition. In order to obtain useful clustering results, however, different parameters of the clustering procedure must be properly tuned. Besides the selection of the clustering method itself, determining which distance is going to be employed between data objects is probably one of the most difficult decisions.Results and conclusionsWe analyze how different distances and clustering methods interact regarding their ability to cluster gene expression, i.e., microarray data. We study 15 distances along with four common clustering methods from the literature on a total of 52 gene expression microarray datasets. Distances are evaluated on a number of different scenarios including clustering of cancer tissues and genes from short time-series expression data, the two main clustering applications in gene expression. Our results support that the selection of an appropriate distance depends on the scenario in hand. Moreover, in each scenario, given the very same clustering method, significant differences in quality may arise from the selection of distinct distance measures. In fact, the selection of an appropriate distance measure can make the difference between meaningful and poor clustering outcomes, even for a suitable clustering method.

Human pluripotent stem cell-derived acinar/ductal organoids generate human pancreas upon orthotopic transplantation and allow disease modelling.

Objective The generation of acinar and ductal cells from human pluripotent stem cells (PSCs) is a poorly studied process, although various diseases arise from this compartment. Design We designed a straightforward approach to direct human PSCs towards pancreatic organoids resembling acinar and ductal progeny. Results Extensive phenotyping of the organoids not only shows the appropriate marker profile but also ultrastructural, global gene expression and functional hallmarks of the human pancreas in the dish. Upon orthotopic transplantation into immunodeficient mice, these organoids form normal pancreatic ducts and acinar tissue resembling fetal human pancreas without evidence of tumour formation or transformation. Finally, we implemented this unique phenotyping tool as a model to study the pancreatic facets of cystic fibrosis (CF). For the first time, we provide evidence that in vitro, but also in our xenograft transplantation assay, pancreatic commitment occurs generally unhindered in CF. Importantly, cystic fibrosis transmembrane conductance regulator (CFTR) activation in mutated pancreatic organoids not only mirrors the CF phenotype in functional assays but also at a global expression level. We also conducted a scalable proof-of-concept screen in CF pancreatic organoids using a set of CFTR correctors and activators, and established an mRNA-mediated gene therapy approach in CF organoids. Conclusions Taken together, our platform provides novel opportunities to model pancreatic disease and development, screen for disease-rescuing agents and to test therapeutic procedures.

Replicative senescence is associated with nuclear reorganization and with DNA methylation at specific transcription factor binding sites

BackgroundPrimary cells enter replicative senescence after a limited number of cell divisions. This process needs to be considered in cell culture experiments, and it is particularly important for regenerative medicine. Replicative senescence is associated with reproducible changes in DNA methylation (DNAm) at specific sites in the genome. The mechanism that drives senescence-associated DNAm changes remains unknown - it may involve stochastic DNAm drift due to imperfect maintenance of epigenetic marks or it is directly regulated at specific sites in the genome.ResultsIn this study, we analyzed the reorganization of nuclear architecture and DNAm changes during long-term culture of human fibroblasts and mesenchymal stromal cells (MSCs). We demonstrate that telomeres shorten and shift towards the nuclear center at later passages. In addition, DNAm profiles, either analyzed by MethylCap-seq or by 450k IlluminaBeadChip technology, revealed consistent senescence-associated hypermethylation in regions associated with H3K27me3, H3K4me3, and H3K4me1 histone marks, whereas hypomethylation was associated with chromatin containing H3K9me3 and lamina-associated domains (LADs). DNA hypermethylation was significantly enriched in the vicinity of genes that are either up- or downregulated at later passages. Furthermore, specific transcription factor binding motifs (e.g. EGR1, TFAP2A, and ETS1) were significantly enriched in differentially methylated regions and in the promoters of differentially expressed genes.ConclusionsSenescence-associated DNA hypermethylation occurs at specific sites in the genome and reflects functional changes in the course of replicative senescence. These results indicate that tightly regulated epigenetic modifications during long-term culture contribute to changes in nuclear organization and gene expression.

A multiple kernel learning algorithm for drug-target interaction prediction

BackgroundDrug-target networks are receiving a lot of attention in late years, given its relevance for pharmaceutical innovation and drug lead discovery. Different in silico approaches have been proposed for the identification of new drug-target interactions, many of which are based on kernel methods. Despite technical advances in the latest years, these methods are not able to cope with large drug-target interaction spaces and to integrate multiple sources of biological information.ResultsWe propose KronRLS-MKL, which models the drug-target interaction problem as a link prediction task on bipartite networks. This method allows the integration of multiple heterogeneous information sources for the identification of new interactions, and can also work with networks of arbitrary size. Moreover, it automatically selects the more relevant kernels by returning weights indicating their importance in the drug-target prediction at hand. Empirical analysis on four data sets using twenty distinct kernels indicates that our method has higher or comparable predictive performance than 18 competing methods in all prediction tasks. Moreover, the predicted weights reflect the predictive quality of each kernel on exhaustive pairwise experiments, which indicates the success of the method to automatically reveal relevant biological sources.ConclusionsOur analysis show that the proposed data integration strategy is able to improve the quality of the predicted interactions, and can speed up the identification of new drug-target interactions as well as identify relevant information for the task.AvailabilityThe source code and data sets are available at www.cin.ufpe.br/~acan/kronrlsmkl/.

Epigenetic Classification of Human Mesenchymal Stromal Cells

Summary Standardization of mesenchymal stromal cells (MSCs) is hampered by the lack of a precise definition for these cell preparations; for example, there are no molecular markers to discern MSCs and fibroblasts. In this study, we followed the hypothesis that specific DNA methylation (DNAm) patterns can assist classification of MSCs. We utilized 190 DNAm profiles to address the impact of tissue of origin, donor age, replicative senescence, and serum supplements on the epigenetic makeup. Based on this, we elaborated a simple epigenetic signature based on two CpG sites to classify MSCs and fibroblasts, referred to as the Epi-MSC-Score. Another two-CpG signature can distinguish between MSCs from bone marrow and adipose tissue, referred to as the Epi-Tissue-Score. These assays were validated by site-specific pyrosequencing analysis in 34 primary cell preparations. Furthermore, even individual subclones of MSCs were correctly classified by our epigenetic signatures. In summary, we propose an alternative concept to use DNAm patterns for molecular definition of cell preparations, and our epigenetic scores facilitate robust and cost-effective quality control of MSC cultures.

Therapeutic inhibition of inflammatory monocyte recruitment reduces steatohepatitis and liver fibrosis

Macrophages are key regulators of liver fibrosis progression and regression in nonalcoholic steatohepatitis (NASH). Liver macrophages comprise resident phagocytes, Kupffer cells, and monocyte‐derived cells, which are recruited through the chemokine receptor C‐C motif chemokine receptor 2 (CCR2). We aimed at elucidating the therapeutic effects of inhibiting monocyte infiltration in NASH models by using cenicriviroc (CVC), an oral dual chemokine receptor CCR2/CCR5 antagonist that is under clinical evaluation. Human liver tissues from NASH patients were analyzed for CCR2+ macrophages, and administration of CVC was tested in mouse models of steatohepatitis, liver fibrosis progression, and fibrosis regression. In human livers from 17 patients and 4 controls, CCR2+ macrophages increased parallel to NASH severity and fibrosis stage, with a concomitant inflammatory polarization of these cluster of differentiation 68+, portal monocyte‐derived macrophages (MoMF). Similar to human disease, we observed a massive increase of hepatic MoMF in experimental models of steatohepatitis and liver fibrosis. Therapeutic treatment with CVC significantly reduced the recruitment of hepatic Ly‐6C+ MoMF in all models. In experimental steatohepatitis with obesity, therapeutic CVC application significantly improved insulin resistance and hepatic triglyceride levels. In fibrotic steatohepatitis, CVC treatment ameliorated histological NASH activity and hepatic fibrosis. CVC inhibited the infiltration of Ly‐6C+ monocytes, without direct effects on macrophage polarization, hepatocyte fatty acid metabolism, or stellate cell activation. Importantly, CVC did not delay fibrosis resolution after injury cessation. RNA sequencing analysis revealed that MoMF, but not Kupffer cells, specifically up‐regulate multiple growth factors and cytokines associated with fibrosis progression, while Kupffer cells activated pathways related to inflammation initiation and lipid metabolism. Conclusion: Pharmacological inhibition of CCR2+ monocyte recruitment efficiently ameliorates insulin resistance, hepatic inflammation, and fibrosis, corroborating the therapeutic potential of CVC in patients with NASH. (Hepatology 2018;67:1270‐1283)

The lncRNA HOTAIR impacts on mesenchymal stem cells via triple helix formation

There is a growing perception that long non-coding RNAs (lncRNAs) modulate cellular function. In this study, we analyzed the role of the lncRNA HOTAIR in mesenchymal stem cells (MSCs) with particular focus on senescence-associated changes in gene expression and DNA-methylation (DNAm). HOTAIR binding sites were enriched at genomic regions that become hypermethylated with increasing cell culture passage. Overexpression and knockdown of HOTAIR inhibited or stimulated adipogenic differentiation of MSCs, respectively. Modification of HOTAIR expression evoked only very moderate effects on gene expression, particularly of polycomb group target genes. Furthermore, overexpression and knockdown of HOTAIR resulted in DNAm changes at HOTAIR binding sites. Five potential triple helix forming domains were predicted within the HOTAIR sequence based on reverse Hoogsteen hydrogen bonds. Notably, the predicted triple helix target sites for these HOTAIR domains were also enriched in differentially expressed genes and close to DNAm changes upon modulation of HOTAIR. Electrophoretic mobility shift assays provided further evidence that HOTAIR domains form RNA–DNA–DNA triplexes with predicted target sites. Our results demonstrate that HOTAIR impacts on differentiation of MSCs and that it is associated with senescence-associated DNAm. Targeting of epigenetic modifiers to relevant loci in the genome may involve triple helix formation with HOTAIR.

The interaction of MYC with the trithorax protein ASH2L promotes gene transcription by regulating H3K27 modification

The appropriate expression of the roughly 30,000 human genes requires multiple layers of control. The oncoprotein MYC, a transcriptional regulator, contributes to many of the identified control mechanisms, including the regulation of chromatin, RNA polymerases, and RNA processing. Moreover, MYC recruits core histone-modifying enzymes to DNA. We identified an additional transcriptional cofactor complex that interacts with MYC and that is important for gene transcription. We found that the trithorax protein ASH2L and MYC interact directly in vitro and co-localize in cells and on chromatin. ASH2L is a core subunit of KMT2 methyltransferase complexes that target histone H3 lysine 4 (H3K4), a mark associated with open chromatin. Indeed, MYC associates with H3K4 methyltransferase activity, dependent on the presence of ASH2L. MYC does not regulate this methyltransferase activity but stimulates demethylation and subsequently acetylation of H3K27. KMT2 complexes have been reported to associate with histone H3K27-specific demethylases, while CBP/p300, which interact with MYC, acetylate H3K27. Finally WDR5, another core subunit of KMT2 complexes, also binds directly to MYC and in genome-wide analyses MYC and WDR5 are associated with transcribed promoters. Thus, our findings suggest that MYC and ASH2L–KMT2 complexes cooperate in gene transcription by controlling H3K27 modifications and thereby regulate bivalent chromatin.

Impact of missing data imputation methods on gene expression clustering and classification

BackgroundSeveral missing value imputation methods for gene expression data have been proposed in the literature. In the past few years, researchers have been putting a great deal of effort into presenting systematic evaluations of the different imputation algorithms. Initially, most algorithms were assessed with an emphasis on the accuracy of the imputation, using metrics such as the root mean squared error. However, it has become clear that the success of the estimation of the expression value should be evaluated in more practical terms as well. One can consider, for example, the ability of the method to preserve the significant genes in the dataset, or its discriminative/predictive power for classification/clustering purposes.Results and conclusionsWe performed a broad analysis of the impact of five well-known missing value imputation methods on three clustering and four classification methods, in the context of 12 cancer gene expression datasets. We employed a statistical framework, for the first time in this field, to assess whether different imputation methods improve the performance of the clustering/classification methods. Our results suggest that the imputation methods evaluated have a minor impact on the classification and downstream clustering analyses. Simple methods such as replacing the missing values by mean or the median values performed as well as more complex strategies. The datasets analyzed in this study are available at http://costalab.org/Imputation/.