Hilal Kazan

Chemogenomics and orthology-based design of antibiotic combination therapies

Combination antibiotic therapies are being increasingly used in the clinic to enhance potency and counter drug resistance. However, the large search space of candidate drugs and dosage regimes makes the identification of effective combinations highly challenging. Here, we present a computational approach called INDIGO, which uses chemogenomics data to predict antibiotic combinations that interact synergistically or antagonistically in inhibiting bacterial growth. INDIGO quantifies the influence of individual chemical–genetic interactions on synergy and antagonism and significantly outperforms existing approaches based on experimental evaluation of novel predictions in Escherichia coli. Our analysis revealed a core set of genes and pathways (e.g. central metabolism) that are predictive of antibiotic interactions. By identifying the interactions that are associated with orthologous genes, we successfully estimated drug‐interaction outcomes in the bacterial pathogens Mycobacterium tuberculosis and Staphylococcus aureus, using the E. coli INDIGO model. INDIGO thus enables the discovery of effective combination therapies in less‐studied pathogens by leveraging chemogenomics data in model organisms.

Quaking promotes monocyte differentiation into pro-atherogenic macrophages by controlling pre-mRNA splicing and gene expression

A hallmark of inflammatory diseases is the excessive recruitment and influx of monocytes to sites of tissue damage and their ensuing differentiation into macrophages. Numerous stimuli are known to induce transcriptional changes associated with macrophage phenotype, but posttranscriptional control of human macrophage differentiation is less well understood. Here we show that expression levels of the RNA-binding protein Quaking (QKI) are low in monocytes and early human atherosclerotic lesions, but are abundant in macrophages of advanced plaques. Depletion of QKI protein impairs monocyte adhesion, migration, differentiation into macrophages and foam cell formation in vitro and in vivo. RNA-seq and microarray analysis of human monocyte and macrophage transcriptomes, including those of a unique QKI haploinsufficient patient, reveal striking changes in QKI-dependent messenger RNA levels and splicing of RNA transcripts. The biological importance of these transcripts and requirement for QKI during differentiation illustrates a central role for QKI in posttranscriptionally guiding macrophage identity and function.

Target-independent prediction of drug synergies using only drug lipophilicity.

Physicochemical properties of compounds have been instrumental in selecting lead compounds with increased drug-likeness. However, the relationship between physicochemical properties of constituent drugs and the tendency to exhibit drug interaction has not been systematically studied. We assembled physicochemical descriptors for a set of antifungal compounds (“drugs”) previously examined for interaction. Analyzing the relationship between molecular weight, lipophilicity, H-bond donor, and H-bond acceptor values for drugs and their propensity to show pairwise antifungal drug synergy, we found that combinations of two lipophilic drugs had a greater tendency to show drug synergy. We developed a more refined decision tree model that successfully predicted drug synergy in stringent cross-validation tests based on only lipophilicity of drugs. Our predictions achieved a precision of 63% and allowed successful prediction for 58% of synergistic drug pairs, suggesting that this phenomenon can extend our understanding for a substantial fraction of synergistic drug interactions. We also generated and analyzed a large-scale synergistic human toxicity network, in which we observed that combinations of lipophilic compounds show a tendency for increased toxicity. Thus, lipophilicity, a simple and easily determined molecular descriptor, is a powerful predictor of drug synergy. It is well established that lipophilic compounds (i) are promiscuous, having many targets in the cell, and (ii) often penetrate into the cell via the cellular membrane by passive diffusion. We discuss the positive relationship between drug lipophilicity and drug synergy in the context of potential drug synergy mechanisms.

Modeling the combined effect of RNA-binding proteins and microRNAs in post-transcriptional regulation

Recent studies show that RNA-binding proteins (RBPs) and microRNAs (miRNAs) function in coordination with each other to control post-transcriptional regulation (PTR). Despite this, the majority of research to date has focused on the regulatory effect of individual RBPs or miRNAs. Here, we mapped both RBP and miRNA binding sites on human 3′UTRs and utilized this collection to better understand PTR. We show that the transcripts that lack competition for HuR binding are destabilized more after HuR depletion. We also confirm this finding for PUM1(2) by measuring genome-wide expression changes following the knockdown of PUM1(2) in HEK293 cells. Next, to find potential cooperative interactions, we identified the pairs of factors whose sites co-localize more often than expected by random chance. Upon examining these results for PUM1(2), we found that transcripts where the sites of PUM1(2) and its interacting miRNA form a stem-loop are more stabilized upon PUM1(2) depletion. Finally, using dinucleotide frequency and counts of regulatory sites as features in a regression model, we achieved an AU-ROC of 0.86 in predicting mRNA half-life in BEAS-2B cells. Altogether, our results suggest that future studies of PTR must consider the combined effects of RBPs and miRNAs, as well as their interactions.

A comprehensive database of high-throughput sequencing-based RNA secondary structure probing data (Structure Surfer)

BackgroundRNA molecules fold into complex three-dimensional shapes, guided by the pattern of hydrogen bonding between nucleotides. This pattern of base pairing, known as RNA secondary structure, is critical to their cellular function. Recently several diverse methods have been developed to assay RNA secondary structure on a transcriptome-wide scale using high-throughput sequencing. Each approach has its own strengths and caveats, however there is no widely available tool for visualizing and comparing the results from these varied methods.MethodsTo address this, we have developed Structure Surfer, a database and visualization tool for inspecting RNA secondary structure in six transcriptome-wide data sets from human and mouse (http://tesla.pcbi.upenn.edu/strucuturesurfer/). The data sets were generated using four different high-throughput sequencing based methods. Each one was analyzed with a scoring pipeline specific to its experimental design. Users of Structure Surfer have the ability to query individual loci as well as detect trends across multiple sites.ResultsHere, we describe the included data sets and their differences. We illustrate the database’s function by examining known structural elements and we explore example use cases in which combined data is used to detect structural trends.ConclusionsIn total, Structure Surfer provides an easy-to-use database and visualization interface for allowing users to interrogate the currently available transcriptome-wide RNA secondary structure information for mammals.

JBASE: Joint Bayesian Analysis of Subphenotypes and Epistasis

Motivation: Rapid advances in genotyping and genome-wide association studies have enabled the discovery of many new genotype–phenotype associations at the resolution of individual markers. However, these associations explain only a small proportion of theoretically estimated heritability of most diseases. In this work, we propose an integrative mixture model called JBASE: joint Bayesian analysis of subphenotypes and epistasis. JBASE explores two major reasons of missing heritability: interactions between genetic variants, a phenomenon known as epistasis and phenotypic heterogeneity, addressed via subphenotyping. Results: Our extensive simulations in a wide range of scenarios repeatedly demonstrate that JBASE can identify true underlying subphenotypes, including their associated variants and their interactions, with high precision. In the presence of phenotypic heterogeneity, JBASE has higher Power and lower Type 1 Error than five state-of-the-art approaches. We applied our method to a sample of individuals from Mexico with Type 2 diabetes and discovered two novel epistatic modules, including two loci each, that define two subphenotypes characterized by differences in body mass index and waist-to-hip ratio. We successfully replicated these subphenotypes and epistatic modules in an independent dataset from Mexico genotyped with a different platform. Availability and implementation: JBASE is implemented in C++, supported on Linux and is available at http://www.cs.toronto.edu/∼goldenberg/JBASE/jbase.tar.gz. The genotype data underlying this study are available upon approval by the ethics review board of the Medical Centre Siglo XXI. Please contact Dr Miguel Cruz at mcruzl@yahoo.com for assistance with the application. Contact: anna.goldenberg@utoronto.ca Supplementary information: Supplementary data are available at Bioinformatics online.

Predicting clinical outcomes in neuroblastoma with genomic data integration

BackgroundNeuroblastoma is a heterogeneous disease with diverse clinical outcomes. Current risk group models require improvement as patients within the same risk group can still show variable prognosis. Recently collected genome-wide datasets provide opportunities to infer neuroblastoma subtypes in a more unified way. Within this context, data integration is critical as different molecular characteristics can contain complementary signals. To this end, we utilized the genomic datasets available for the SEQC cohort patients to develop supervised and unsupervised models that can predict disease prognosis.ResultsOur supervised model trained on the SEQC cohort can accurately predict overall survival and event-free survival profiles of patients in two independent cohorts. We also performed extensive experiments to assess the prediction accuracy of high risk patients and patients without MYCN amplification. Our results from this part suggest that clinical endpoints can be predicted accurately across multiple cohorts. To explore the data in an unsupervised manner, we used an integrative clustering strategy named multi-view kernel k-means (MVKKM) that can effectively integrate multiple high-dimensional datasets with varying weights. We observed that integrating different gene expression datasets results in a better patient stratification compared to using these datasets individually. Also, our identified subgroups provide a better Cox regression model fit compared to the existing risk group definitions.ConclusionAltogether, our results indicate that integration of multiple genomic characterizations enables the discovery of subtypes that improve over existing definitions of risk groups. Effective prediction of survival times will have a direct impact on choosing the right therapies for patients.ReviewersThis article was reviewed by Susmita Datta, Wenzhong Xiao and Ziv Shkedy.

MITF-MIR211 axis is a novel autophagy amplifier system during cellular stress

ABSTRACT Macroautophagy (autophagy) is an evolutionarily conserved recycling and stress response mechanism. Active at basal levels in eukaryotes, autophagy is upregulated under stress providing cells with building blocks such as amino acids. A lysosome-integrated sensor system composed of RRAG GTPases and MTOR complex 1 (MTORC1) regulates lysosome biogenesis and autophagy in response to amino acid availability. Stress-mediated inhibition of MTORC1 results in the dephosphorylation and nuclear translocation of the TFE/MITF family of transcriptional factors, and triggers an autophagy- and lysosomal-related gene transcription program. The role of family members TFEB and TFE3 have been studied in detail, but the importance of MITF proteins in autophagy regulation is not clear so far. Here we introduce for the first time a specific role for MITF in autophagy control that involves upregulation of MIR211. We show that, under stress conditions including starvation and MTOR inhibition, a MITF-MIR211 axis constitutes a novel feed-forward loop that controls autophagic activity in cells. Direct targeting of the MTORC2 component RICTOR by MIR211 led to the inhibition of the MTORC1 pathway, further stimulating MITF translocation to the nucleus and completing an autophagy amplification loop. In line with a ubiquitous function, MITF and MIR211 were co-expressed in all tested cell lines and human tissues, and the effects on autophagy were observed in a cell-type independent manner. Thus, our study provides direct evidence that MITF has rate-limiting and specific functions in autophagy regulation. Collectively, the MITF-MIR211 axis constitutes a novel and universal autophagy amplification system that sustains autophagic activity under stress conditions. Abbreviations: ACTB: actin beta; AKT: AKT serine/threonine kinase; AKT1S1/PRAS40: AKT1 substrate 1; AMPK: AMP-activated protein kinase; ATG: autophagy-related; BECN1: beclin 1; DEPTOR: DEP domain containing MTOR interacting protein; GABARAP: GABA type A receptor-associated protein; HIF1A: hypoxia inducible factor 1 subunit alpha; LAMP1: lysosomal associated membrane protein 1; MAP1LC3B/LC3B: microtubule associated protein 1 light chain 3 beta; MAPKAP1/SIN1: mitogen-activated protein kinase associated protein 1; MITF: melanogenesis associated transcription factor; MLST8: MTOR associated protein, LST8 homolog; MRE: miRNA response element; MTOR: mechanistic target of rapamycin kinase; MTORC1: MTOR complex 1; MTORC2: MTOR complex 2; PRR5/Protor 1: proline rich 5; PRR5L/Protor 2: proline rich 5 like; RACK1: receptor for activated C kinase 1; RPTOR: regulatory associated protein of MTOR complex 1; RICTOR: RPTOR independent companion of MTOR complex 2; RPS6KB/p70S6K: ribosomal protein S6 kinase; RT-qPCR: quantitative reverse transcription-polymerase chain reaction; SQSTM1: sequestosome 1; STK11/LKB1: serine/threonine kinase 11; TFE3: transcription factor binding to IGHM enhancer 3; TFEB: transcription factor EB; TSC1/2: TSC complex subunit 1/2; ULK1: unc-51 like autophagy activating kinase 1; UVRAG: UV radiation resistance associated; VIM: vimentin; VPS11: VPS11, CORVET/HOPS core subunit; VPS18: VPS18, CORVET/HOPS core subunit; WIPI1: WD repeat domain, phosphoinositide interacting 1

Inferring RBP-Mediated Regulation in Lung Squamous Cell Carcinoma.

RNA-binding proteins (RBPs) play key roles in post-transcriptional regulation of mRNAs. Dysregulations in RBP-mediated mechanisms have been found to be associated with many steps of cancer initiation and progression. Despite this, previous studies of gene expression in cancer have ignored the effect of RBPs. To this end, we developed a lasso regression model that predicts gene expression in cancer by incorporating RBP-mediated regulation as well as the effects of other well-studied factors such as copy-number variation, DNA methylation, TFs and miRNAs. As a case study, we applied our model to Lung squamous cell carcinoma (LUSC) data as we found that there are several RBPs differentially expressed in LUSC. Including RBP-mediated regulatory effects in addition to the other features significantly increased the Spearman rank correlation between predicted and measured expression of held-out genes. Using a feature selection procedure that accounts for the adaptive search employed by lasso regularization, we identified the candidate regulators in LUSC. Remarkably, several of these candidate regulators are RBPs. Furthermore, majority of the candidate regulators have been previously found to be associated with lung cancer. To investigate the mechanisms that are controlled by these regulators, we predicted their target gene sets based on our model. We validated the target gene sets by comparing against experimentally verified targets. Our results suggest that the future studies of gene expression in cancer must consider the effect of RBP-mediated regulation.