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In this study, Sharma et al. describe a novel cytoplasmic poly(A) binding protein, neuPABP, specifically found in postnatal brain neurons undergoing synaptogenesis. Unlike other PABPs, neuPABP does not bind eIF4G to support mRNA translation; instead, neuPABP interacts with neuron-specific, noncoding RNA BC1, as well as select ribosomal and mitochondrial mRNAs, in free ribonucleoprotein complexes, suggesting a role for neuPABP in suppressing their translation.

The basal breast cancer subtype is enriched for triple-negative breast cancer (TNBC) and displays consistent large chromosomal deletions. Here, we characterize evolution and maintenance of chromosome 4p (chr4p) loss in basal breast cancer. Analysis of The Cancer Genome Atlas data shows recurrent deletion of chr4p in basal breast cancer. Phylogenetic analysis of a panel of 23 primary tumor/patient-derived xenograft basal breast cancers reveals early evolution of chr4p deletion. Mechanistically we show that chr4p loss is associated with enhanced proliferation. Gene function studies identify an unknown gene, C4orf19, within chr4p, which suppresses proliferation when overexpressed—a member of the PDCD10-GCKIII kinase module we name PGCKA1. Genome-wide pooled overexpression screens using a barcoded library of human open reading frames identify chromosomal regions, including chr4p, that suppress proliferation when overexpressed in a context-dependent manner, implicating network interactions. Together, these results shed light on the early emergence of complex aneuploid karyotypes involving chr4p and adaptive landscapes shaping breast cancer genomes.

Subclonal reconstruction from bulk tumor DNA sequencing has become a pillar of cancer evolution studies, providing insight into the clonality and relative ordering of mutations and mutational processes. We provide an outline of the complex computational approaches used for subclonal reconstruction from single and multiple tumor samples. We identify the underlying assumptions and uncertainties in each step and suggest best practices for analysis and quality assessment. This guide provides a pragmatic resource for the growing user community of subclonal reconstruction methods.

Cancer develops through a process of somatic evolution1,2. Sequencing data from a single biopsy represent a snapshot of this process that can reveal the timing of specific genomic aberrations and the changing influence of mutational processes3. Here, by whole-genome sequencing analysis of 2,658 cancers as part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA)4, we reconstruct the life history and evolution of mutational processes and driver mutation sequences of 38 types of cancer. Early oncogenesis is characterized by mutations in a constrained set of driver genes, and specific copy number gains, such as trisomy 7 in glioblastoma and isochromosome 17q in medulloblastoma. The mutational spectrum changes significantly throughout tumour evolution in 40% of samples. A nearly fourfold diversification of driver genes and increased genomic instability are features of later stages. Copy number alterations often occur in mitotic crises, and lead to simultaneous gains of chromosomal segments. Timing analyses suggest that driver mutations often precede diagnosis by many years, if not decades. Together, these results determine the evolutionary trajectories of cancer, and highlight opportunities for early cancer detection.

RNA-binding proteins (RBPs) regulate RNA metabolism at multiple levels by affecting splicing of nascent transcripts, RNA folding, base modification, transport, localization, translation, and stability. Despite their central role in RNA function, the RNA-binding specificities of most RBPs remain unknown or incompletely defined. To address this, we have assembled a genome-scale collection of RBPs and their RNA-binding domains (RBDs) and assessed their specificities using high-throughput RNA-SELEX (HTR-SELEX). Approximately 70% of RBPs for which we obtained a motif bound to short linear sequences, whereas ∼30% preferred structured motifs folding into stem–loops. We also found that many RBPs can bind to multiple distinctly different motifs. Analysis of the matches of the motifs in human genomic sequences suggested novel roles for many RBPs. We found that three cytoplasmic proteins—ZC3H12A, ZC3H12B, and ZC3H12C—bound to motifs resembling the splice donor sequence, suggesting that these proteins are involved in degradation of cytoplasmic viral and/or unspliced transcripts. Structural analysis revealed that the RNA motif was not bound by the conventional C3H1 RNA-binding domain of ZC3H12B. Instead, the RNA motif was bound by the ZC3H12B's PilT N terminus (PIN) RNase domain, revealing a potential mechanism by which unconventional RBDs containing active sites or molecule-binding pockets could interact with short, structured RNA molecules. Our collection containing 145 high-resolution binding specificity models for 86 RBPs is the largest systematic resource for the analysis of human RBPs and will greatly facilitate future analysis of the various biological roles of this important class of proteins.

Stem cells regulate their self-renewal and differentiation fate outcomes through both symmetric and asymmetric divisions. m6A RNA methylation controls symmetric commitment and inflammation of hematopoietic stem cells (HSCs) through unknown mechanisms. Here, we demonstrate that the nuclear speckle protein SON is an essential m6A target required for murine HSC self-renewal, symmetric commitment, and inflammation control. Global profiling of m6A identified that m6A mRNA methylation of Sonincreases during HSC commitment. Upon m6A depletion, SonmRNA increases, but its protein is depleted. Reintroduction of SON rescues defects in HSC symmetric commitment divisions and engraftment. Conversely, Sondeletion results in a loss of HSC fitness, while overexpression of SON improves mouse and human HSC engraftment potential by increasing quiescence. Mechanistically, we found that SON rescues MYC and suppresses the METTL3-HSC inflammatory gene expression program, including CCL5, through transcriptional regulation. Thus, our findings define a m6A-SON-CCL5 axis that controls inflammation and HSC fate.

Transcription factor (TF) binding specificities (motifs) are essential for the analysis of gene regulation. Accurate prediction of TF motifs is critical, because it is infeasible to assay all TFs in all sequenced eukaryotic genomes. There is ongoing controversy regarding the degree of motif diversification among related species that is, in part, because of uncertainty in motif prediction methods. Here we describe similarity regression, a significantly improved method for predicting motifs, which we use to update and expand the Cis-BP database. Similarity regression inherently quantifies TF motif evolution, and shows that previous claims of near-complete conservation of motifs between human and Drosophilaare inflated, with nearly half of the motifs in each species absent from the other, largely due to extensive divergence in C2H2 zinc finger proteins. We conclude that diversification in DNA-binding motifs is pervasive, and present a new tool and updated resource to study TF diversity and gene regulation across eukaryotes.

Alternative polyadenylation (APA) affects most mammalian genes. The genome-wide investigation of APA has been hampered by an inability to reliably profile it using conventional RNA-seq. We describe ‘Quantification of APA’ (QAPA), a method that infers APA from conventional RNA-seq data. QAPA is faster and more sensitive than other methods. Application of QAPA reveals discrete, temporally coordinated APA programs during neurogenesis and that there is little overlap between genes regulated by alternative splicing and those by APA. Modeling of these data uncovers an APA sequence code. QAPA thus enables the discovery and characterization of programs of regulated APA using conventional RNA-seq.

Blood stem cells intricately regulate their fate, balancing self-renewal and differentiation through symmetric and asymmetric divisions. When exposed to various stresses they can rapidly undergo symmetric commitment divisions to generate differentiated progenitors for immune regeneration or driving inflammation. In previous studies, we and others found that m 6A RNA methylation controls symmetric commitment and inflammation in hematopoietic stem cells (HSCs) (Cheng and Luo et al., Cell Reports 2019). However, the precise molecular mechanisms underlying the role of m 6A and its influence on the inflammatory program in stem cells remain elusive.

Mutations of EXOSC3 have been linked to the rare neurological disorder known as Pontocerebellar Hypoplasia type 1B (PCH1B). EXOSC3 is one of three putative RNA-binding structural cap proteins that guide RNA into the RNA exosome, the cellular machinery that degrades RNA. Using RNAcompete, we identified a G-rich RNA motif binding to EXOSC3. Surface plasmon resonance (SPR) and microscale thermophoresis (MST) indicated an affinity in the low micromolar range of EXOSC3 for long and short G-rich RNA sequences. Although several PCH1B-causing mutations in EXOSC3 did not engage a specific RNA motif as shown by RNAcompete, they exhibited lower binding affinity to G-rich RNA as demonstrated by MST. To test the hypothesis that modification of the RNA–protein interface in EXOSC3 mutants may be phenocopied by small molecules, we performed an in-silicoscreen of 50 000 small molecules and used enzyme-linked immunosorbant assays (ELISAs) and MST to assess the ability of the molecules to inhibit RNA-binding by EXOSC3. We identified a small molecule, EXOSC3-RNA disrupting (ERD) compound 3 (ERD03), which (i) bound specifically to EXOSC3 in saturation transfer difference nuclear magnetic resonance (STD-NMR), (ii) disrupted the EXOSC3–RNA interaction in a concentration-dependent manner, and (iii) produced a PCH1B-like phenotype with a 50% reduction in the cerebellum and an abnormally curved spine in zebrafish embryos. This compound also induced modification of zebrafish RNA expression levels similar to that observed with a morpholino against EXOSC3. To our knowledge, this is the first example of a small molecule obtained by rational design that models the abnormal developmental effects of a neurodegenerative disease in a whole organism.

Pairtreeis a clone tree reconstruction algorithm that uses somatic point mutations to build clone trees describing the evolutionary history of individual cancers. Using the Pairtree software package, we describe steps to preprocess somatic mutation data, cluster mutations into subclones, search for clone trees, and visualize clone trees. Pairtree builds clone trees using up to 100 samples from a single cancer with at least 30 subclonal populations.

A progressive increase in MECP2 protein levels is a crucial and precisely regulated event during neurodevelopment, but the underlying mechanism is unclear. We report that MECP2is regulated post-transcriptionally during in vitro differentiation of human embryonic stem cells (hESCs) into cortical neurons. Using reporters to identify functional RNA sequences in the MECP23′ UTR and genetic manipulations to explore the role of interacting factors on endogenous MECP2, we discover combinatorial mechanisms that regulate RNA stability and translation. The RNA-binding protein PUM1 and pluripotent-specific microRNAs destabilize the long MECP23′ UTR in hESCs. Hence, the 3′ UTR appears to lengthen during differentiation as the long isoform becomes stable in neurons. Meanwhile, translation of MECP2is repressed by TIA1 in hESCs until HuC predominates in neurons, resulting in a switch to translational enhancement. Ultimately, 3′ UTR-directed translational fine-tuning differentially modulates MECP2 protein in the two cell types to levels appropriate for normal neurodevelopment.

Brain tumor (BRAT) is a Drosophilamember of the TRIM-NHL protein family. This family is conserved among metazoans and its members function as post-transcriptional regulators. BRAT was thought to be recruited to mRNAs indirectly through interaction with the RNA-binding protein Pumilio (PUM). However, it has recently been demonstrated that BRAT directly binds to RNA. The precise sequence recognized by BRAT, the extent of BRAT-mediated regulation, and the exact roles of PUM and BRAT in post-transcriptional regulation are unknown. Genome-wide identification of transcripts associated with BRAT or with PUM in Drosophilaembryos shows that they bind largely non-overlapping sets of mRNAs. BRAT binds mRNAs that encode proteins associated with a variety of functions, many of which are distinct from those implemented by PUM-associated transcripts. Computational analysis of in vitroand in vivodata identified a novel RNA motif recognized by BRAT that confers BRAT-mediated regulation in tissue culture cells. The regulatory status of BRAT-associated mRNAs suggests a prominent role for BRAT in post-transcriptional regulation, including a previously unidentified role in transcript degradation. Transcriptomic analysis of embryos lacking functional BRAT reveals an important role in mediating the decay of hundreds of maternal mRNAs during the maternal-to-zygotic transition. Our results represent the first genome-wide analysis of the mRNAs associated with a TRIM-NHL protein and the first identification of an RNA motif bound by this protein family. BRAT is a prominent post-transcriptional regulator in the early embryo through mechanisms that are largely independent of PUM.

Tumors often contain multiple subpopulations of cancerous cells defined by distinct somatic mutations. We describe a new method, PhyloWGS, which can be applied to whole-genome sequencing data from one or more tumor samples to reconstruct complete genotypes of these subpopulations based on variant allele frequencies (VAFs) of point mutations and population frequencies of structural variations. We introduce a principled phylogenic correction for VAFs in loci affected by copy number alterations and we show that this correction greatly improves subclonal reconstruction compared to existing methods. PhyloWGS is free, open-source software, available at https://github.com/morrislab/phylowgs.

TRIM-NHL proteins are conserved among metazoans and control cell fate decisions in various stem cell linages. The DrosophilaTRIM-NHL protein Brain tumor (Brat) directs differentiation of neuronal stem cells by suppressing self-renewal factors. Brat is an RNA-binding protein and functions as a translational repressor. However, it is unknown which RNAs Brat regulates and how RNA-binding specificity is achieved. Using RNA immunoprecipitation and RNAcompete, we identify Brat-bound mRNAs in Drosophilaembryos and define consensus binding motifs for Brat as well as a number of additional TRIM-NHL proteins, indicating that TRIM-NHL proteins are conserved, sequence-specific RNA-binding proteins. We demonstrate that Brat-mediated repression and direct RNA-binding depend on the identified motif and show that binding of the localization factor Miranda to the Brat-NHL domain inhibits Brat activity. Finally, to unravel the sequence specificity of the NHL domain, we crystallize the Brat-NHL domain in complex with RNA and present a high-resolution protein-RNA structure of this fold.

RNA–protein interactions differ from DNA–protein interactions because of the central role of RNAsecondary structure. Some RNA‐binding domains (RBDs) recognize their target sites mainly by their shape and geometry and others are sequence‐specific but are sensitive to secondary structure context. A number of small‐ and large‐scale experimental approaches have been developed to measure RNAsassociated in vitroand in vivowith RNA‐binding proteins (RBPs). Generalizing outside of the experimental conditions tested by these assays requires computational motif finding. Often RBPmotif finding is done by adapting DNAmotif finding methods; but modeling secondary structure context leads to better recovery of RBP‐binding preferences. Genome‐wide assessment of mRNAsecondary structure has recently become possible, but these data must be combined with computational predictions of secondary structure before they add value in predicting in vivobinding. There are two main approaches to incorporating structural information into motif models: supplementing primary sequence motif models with preferred secondary structure contexts (e.g., MEMERISand RNAcontext) and directly modeling secondary structure recognized by the RBPusing stochastic context‐free grammars (e.g., CMfinderand RNApromo). The former better reconstruct known binding preferences for sequence‐specific RBPsbut are not suitable for modeling RBPsthat recognize shape and geometry of RNAs. Future work in RBPmotif finding should incorporate interactions between multiple RBDsand multiple RBPsin binding to RNA. WIREs RNA2014, 5:111–130. doi: 10.1002/wrna.1201

RNA-binding proteins play crucial roles in directing RNA translation to neuronal synapses. Staufen2 (Stau2) has been implicated in both dendritic RNA localization and synaptic plasticity in mammalian neurons. Here, we report the identification of functionally relevant Stau2 target mRNAs in neurons. The majority of Stau2-copurifying mRNAs expressed in the hippocampus are present in neuronal processes, further implicating Stau2 in dendritic mRNA regulation. Stau2 targets are enriched for secondary structures similar to those identified in the 3′ UTRs of DrosophilaStaufen targets. Next, we show that Stau2 regulates steady-state levels of many neuronal RNAs and that its targets are predominantly downregulated in Stau2-deficient neurons. Detailed analysis confirms that Stau2 stabilizes the expression of one synaptic signaling component, the regulator of G protein signaling 4 (Rgs4) mRNA, via its 3′ UTR. This study defines the global impact of Stau2 on mRNAs in neurons, revealing a role in stabilization of the levels of synaptic targets.

As part of the first Industrial Methodology for Process Verification in Research Challenge, the aim of the MS Diagnostic sub-challenge was to identify a robust diagnostic signature for relapsing-remitting multiple sclerosis from gene expression data. In this regard, we built a classifier that discriminates samples into two phenotype groups, either RRMS or controls, using the transcriptome of peripheral blood mononuclear cells. For our classifier, we used logistic regression with elastic net regression as implemented in the glmnet package in R. We selected the values of the regularization hyper-parameters using cross-validation performance on the provided training data, number of non-zero parameters in our model, and based on the distribution of output values when the input vector for the test data were used with our classifier. We analyzed our classifier performance with two different strategies for feature extraction, using either only genes or including additional constructed features from gene pathways data. The two different strategies produced little differences in performance when comparing the 10-fold cross-validation of the training data and prediction on the test data. Our final submission for the sub-challenge used only genes as features, and identified a diagnostic signature consisting of 58 genes, that was ranked second out of a total of 39 submissions.

While many RNA-binding proteins (RBPs) bind RNA in a sequence-specific manner, their sequence preferences alone do not distinguish known target RNAs from other potential targets that are coexpressed and contain the same sequence motifs. Recently, the mRNA targets of dozens of RNA-binding proteins have been identified, facilitating a systematic study of the features of target transcripts. Using these data, we demonstrate that calculating the predicted structural accessibility of a putative RBP binding site allows one to significantly improve the accuracy of predicting in vivo binding for the majority of sequence-specific RBPs. In our new in silico approach, accessibility is predicted based solely on the mRNA sequence without consideration of the locations of bound trans-factors; as such, our results suggest a greater than previously anticipated role for intrinsic mRNA secondary structure in determining RBP binding target preference. Target site accessibility aids in predicting target transcripts and the binding sites for RBPs with a range of RNA-binding domains and subcellular functions. Based on this work, we introduce a new motif-finding algorithm that identifies accessible sequence-specific RBP motifs from in vivo binding data.

•Oral cancer is highly stratified by tumor as opposed to host characteristics.•Host peripheral blood data can impact prognosis in oral cancer.•Autoimmune diseases are macroscopic reflection of host immune dysregulation.•A score was created known as AI score to depict host status.•AI score was useful as a prognostic tool in oral cancer and should be more widely investigated.

Genome-wide microarray designs containing millions to hundreds of millions of probes are available for a variety of mammals, including mouse and human. These genome tiling arrays can potentially lead to significant advances in science and medicine, e.g., by indicating new genes and alternative primary and secondary transcripts. While bottom-up pattern matching techniques (e.g., hierarchical clustering) can be used to find gene structures in microarray data, we believe the many interacting hidden variables and complex noise patterns more naturally lead to an analysis based on generative models. We describe a generative model of tiling data and show how the sum-product algorithm can be used to infer hybridization noise, probe sensitivity, new transcripts, and alternative transcripts. The method, called GenRate, maximizes a global scoring function that enables multiple transcripts to compete for ownership of putative probes.We apply GenRate to a new exon tiling dataset from mouse chromosome 4 and show that it makes significantly more predictions than a previously described hierarchical clustering method at the same false positive rate. GenRate correctly predicts many known genes and also predicts new gene structures. As new problems arise, additional hidden variables can be incorporated into the model in a principled fashion, so we believe that GenRate will prove to be a useful tool in the new era of genome-wide tiling microarray analysis.

MicroRNAs (miRNAs) are short, stable, noncoding RNAs involved in post-transcriptional gene silencing via hybridization to mRNA. Few have been thoroughly characterized in any species. Here, we describe a method to detect miRNAs using micro-arrays, in which the miRNAs are directly hybridized to the array. We used this method to analyze miRNA expression across 17 mouse organs and tissues. More than half of the 78 miRNAs detected were expressed in specific adult tissues, suggesting that miRNAs have widespread regulatory roles in adults. By comparing miRNA levels to mRNA levels determined in a parallel microarray analysis of the same tissues, we found that the expression of target mRNAs predicted on the basis of sequence complementarity is unrelated to the tissues in which the corresponding miRNA is expressed.

Background: RNA-binding proteins (RBPs) function as master regulators of gene expression. Alterations in RBP expression and function are often observed in cancer and influence critical pathways implicated in tumor initiation and growth. Identification and characterization of oncogenic RBPs and their regulatory networks provide new opportunities for targeted therapy. Results: We identify the RNA-binding protein SERBP1 as a novel regulator of glioblastoma (GBM) development. High SERBP1 expression is prevalent in GBMs and correlates with poor patient survival and poor response to chemo- and radiotherapy. SERBP1 knockdown causes delay in tumor growth and impacts cancer-relevant phenotypes in GBM and glioma stem cell lines. RNAcompete identifies a GC-rich region as SERBP1-binding motif; subsequent genomic and functional analyses establish SERBP1 regulation role in metabolic routes preferentially used by cancer cells. An important consequence of these functions is SERBP1 impact on methionine production. SERBP1 knockdown decreases methionine levels causing a subsequent reduction in histone methylation as shown for H3K27me3 and upregulation of genes associated with neurogenesis, neuronal differentiation, and function. Further analysis demonstrates that several of these genes are downregulated in GBM, potentially through epigenetic silencing as indicated by the presence of H3K27me3 sites. Conclusions: SERBP1 is the first example of an RNA-binding protein functioning as a central regulator of cancer metabolism and indirect modulator of epigenetic regulation in GBM. By bridging these two processes, SERBP1 enhances glioma stem cell phenotypes and contributes to GBM poorly differentiated state.

The age-associated accumulation of somatic mutations and large-scale structural variants (SVs) in the early hematopoietic hierarchy have been linked to premalignant stages for cancer and cardiovascular disease (CVD). However, only a small proportion of individuals harboring these mutations progress to disease, and mechanisms driving the transformation to malignancy remains unclear. Hematopoietic evolution, and cancer evolution more broadly, has largely been studied through a lens of adaptive evolution and the contribution of functionally neutral or mildly damaging mutations to early disease-associated clonal expansions has not been well characterised despite comprising the majority of the mutational burden in healthy or tumoural tissues. Through combining deep learning with population genetics, we interrogate the hematopoietic system to capture signatures of selection acting in healthy and pre-cancerous blood populations. Here, we leverage high-coverage sequencing data from healthy and pre-cancerous individuals from the European Prospective Investigation into Cancer and Nutrition Study (n=477) and dense genotyping from the Canadian Partnership for Tomorrow's Health (n=5,000) to show that blood rejects the paradigm of strictly adaptive or neutral evolution and is subject to pervasive negative selection. We observe clear age associations across hematopoietic populations and the dominant class of selection driving evolutionary dynamics acting at an individual level. We find that both the location and ratio of passenger to driver mutations are critical in determining if positive selection acting on driver mutations is able to overwhelm regulated hematopoiesis and allow clones harbouring disease-predisposing mutations to rise to dominance. Certain genes are enriched for passenger mutations in healthy individuals fitting purifying models of evolution, suggesting that the presence of passenger mutations in a subset of genes might confer a protective role against disease-predisposing clonal expansions. Finally, we find that the density of gene disruption events with known pathogenic associations in somatic SVs impacts the frequency at which the SV segregates in the population with variants displaying higher gene disruption density segregating at lower frequencies. Understanding how blood evolves towards malignancy will allow us to capture cancer in its earliest stages and identify events initiating departures from healthy blood evolution. Further, as the majority of mutations are passengers, studying their contribution to tumorigenesis, will unveil novel therapeutic targets thus enabling us to better understand patterns of clonal evolution in order to diagnose and treat disease in its infancy.

Regulation of translation during human development is poorly understood, and its dysregulation is associated with Rett syndrome (RTT). To discover shifts in mRNA ribosomal engagement (RE) during human neurodevelopment, we use parallel translating ribosome affinity purification sequencing (TRAP-seq) and RNA sequencing (RNA-seq) on control and RTT human induced pluripotent stem cells, neural progenitor cells, and cortical neurons. We find that 30% of transcribed genes are translationally regulated, including key gene sets (neurodevelopment, transcription and translation factors, and glycolysis). Approximately 35% of abundant intergenic long noncoding RNAs (lncRNAs) are ribosome engaged. Neurons translate mRNAs more efficiently and have longer 3ʹ UTRs, and RE correlates with elements for RNA-binding proteins. RTT neurons have reduced global translation and compromised mTOR signaling, and >2,100 genes are translationally dysregulated. NEDD4L E3-ubiquitin ligase is translationally impaired, ubiquitinated protein levels are reduced, and protein targets accumulate in RTT neurons. Overall, the dynamic translatome in neurodevelopment is disturbed in RTT and provides insight into altered ubiquitination that may have therapeutic implications.

G3BP RNA-binding proteins are important components of stress granules (SGs). Here, we analyze the role of the DrosophilaG3BP Rasputin (RIN) in unstressed cells, where RIN is not SG associated. Immunoprecipitation followed by microarray analysis identifies over 550 mRNAs that copurify with RIN. The mRNAs found in SGs are long and translationally silent. In contrast, we find that RIN-bound mRNAs, which encode core components of the transcription, splicing, and translation machinery, are short, stable, and highly translated. We show that RIN is associated with polysomes and provide evidence for a direct role for RIN and its human homologs in stabilizing and upregulating the translation of their target mRNAs. We propose that when cells are stressed, the resulting incorporation of RIN/G3BPs into SGs sequesters them away from their short target mRNAs. This would downregulate the expression of these transcripts, even though they are not incorporated into stress granules.

Mullighan: Amgen: Honoraria, Speakers Bureau; Cancer Prevention and Research Institute of Texas: Consultancy; Abbvie: Research Funding; Pfizer: Honoraria, Research Funding, Speakers Bureau; Loxo Oncology: Research Funding.

Disease recurrence remains a significant cause of mortality in B-cell acute lymphoblastic leukemia (B-ALL). Genomic analysis of matched diagnosis and relapse samples have demonstrated that relapse arises from a minor subclone already present at diagnosis and not the dominant clone in the majority of patients. However, the reasons why only some clones survive therapy and generate relapse are obscure and elucidation of the mechanisms that underlie these differing fates may be revealed by functional analysis of isolated subclones. Previous work has shown that the subclonal diversity in B-ALL exists at the level of the leukemia-initiating cells capable of generating patient derived xenografts (Notta et al., Nature, 2011). In order to investigate the functional consequences of genetic clonal evolution during disease progression, we performed in-depth genomic and functional analysis of 14 paired diagnosis/relapse samples from adult and pediatric B-ALL patients with varying cytogenetic abnormalities. Diagnosis-specific, relapse-specific, and shared clonal and subclonal variants were identified by whole exome sequencing of the patient samples. Targeted sequencing of these variants in 372 xenografts generated by transplantation of CD19+ cells in a limiting cell dilution assay uncovered clonal variation. This analysis provided for the unequivocal identification of minor subclones ancestral to the relapse, termed diagnosis Relapse-Initiating (dRI) clones, in the diagnostic sample. Our xenografting approach enabled the physical isolation of dRI clones providing a unique opportunity to interrogate their epigenetic and transcriptional landscapes in order to unravel their relapse initiating capacity. To this end, representative diagnosis, dRI and relapse clones from 5 of the 14 patients were subjected to RNAseq and ATACseq (assay for transposase-accessible chromatin using sequencing) analysis. Despite the differences in transcriptional and chromatin openness between patients, principal component analysis of subclones from individual patients positioned the dRI clones as evolutionary intermediates between the diagnosis and relapse clones. Hierarchical clustering of the most significantly differentially expressed genes and open chromatin regions demonstrated that dRI clones shared gene expression and chromatin accessibility signatures with both the dominant diagnosis clone as well as the dominant relapse clone. To gain mechanistic insight into the data we used gene set enrichment analysis (GSEA) and identified common molecular pathways present in all patients that were enriched in dRI clones and persisted at the time of relapse in comparison to the dominant diagnosis clone. dRI and relapse clones converged in the activation of genes involved in cellular functions such as endocytosis, autophagy and innate immune response. In addition, cell surface proteins like ABC transporters and ephrins were also upregulated in dRI and relapse clones. Remarkably, functional interrogation of dRI clones in secondary xenografts, in comparison to more representative diagnosis clones, displayed increased tolerance to standard chemotherapeutic agents (dexamethasone, L-asparaginase and vincristine). Investigation of the molecular pathways and cellular receptors/transporters identified by gene expression analysis are being assessed in vitroand in vivoas potential targets for novel therapeutic approaches and disease monitoring.

Smaug is an RNA-binding protein that induces the degradation and represses the translation of mRNAs in the early Drosophilaembryo. Smaug has two identified direct target mRNAs that it differentially regulates: nanosand Hsp83. Smaug represses the translation of nanosmRNA but has only a modest effect on its stability, whereas it destabilizes Hsp83mRNA but has no detectable effect on Hsp83translation. Smaug is required to destabilize more than one thousand mRNAs in the early embryo, but whether these transcripts represent direct targets of Smaug is unclear and the extent of Smaug-mediated translational repression is unknown.

During the maternal-to-zygotic transition (MZT) vast changes in the embryonic transcriptome are produced by a combination of two processes: elimination of maternally provided mRNAs and synthesis of new transcripts from the zygotic genome. Previous genome-wide analyses of the MZT have been restricted to whole embryos. Here we report the first such analysis for primordial germ cells (PGCs), the progenitors of the germ-line stem cells.

Motivation: Alternative splicing (AS) is a frequent step in metozoan gene expression whereby the exons of genes are spliced in different combinations to generate multiple isoforms of mature mRNA. AS functions to enrich an organism's proteomic complexity and regulates gene expression. Despite its importance, the mechanisms underlying AS and its regulation are not well understood, especially in the context of global gene expression patterns. We present here an algorithm referred to as the Generative model for the Alternative Splicing Array Platform (GenASAP) that can predict the levels of AS for thousands of exon skipping events using data generated from custom microarrays. GenASAP uses Bayesian learning in an unsupervised probability model to accurately predict AS levels from the microarray data. GenASAP is capable of learning the hybridization profiles of microarray data, while modeling noise processes and missing or aberrant data. GenASAP has been successfully applied to the global discovery and analysis of AS in mammalian cells and tissues. Results: GenASAP was applied to data obtained from a custom microarray designed for the monitoring of 3126 AS events in mouse cells and tissues. The microarray design included probes specific for exon body and junction sequences formed by the splicing of exons. Our results show that GenASAP provides accurate predictions for over one-third of the total events, as verified by independent RT–PCR assays. Contact: ofer@psi.toronto.edu Supplementary information: http://www.psi.toronto.edu/GenASAP

Motivation: We address the problem of multi-way clustering of microarray data using a generative model. Our algorithm, probabilistic sparse matrix factorization (PSMF), is a probabilistic extension of a previous hard-decision algorithm for this problem. PSMF allows for varying levels of sensor noise in the data, uncertainty in the hidden prototypes used to explain the data and uncertainty as to the prototypes selected to explain each data vector. Results: We present experimental results demonstrating that our method can better recover functionally-relevant clusterings in mRNA expression data than standard clustering techniques, including hierarchical agglomerative clustering, and we show that by computing probabilities instead of point estimates, our method avoids converging to poor solutions. Contact: delbert@psi.toronto.edu

Motivation: Microarray designs containing millions to hundreds of millions of probes that tile entire genomes are currently being released. Within the next 2 months, our group will release a microarray data set containing over 12 000 000 microarray measurements taken from 37 mouse tissues. A problem that will become increasingly significant in the upcoming era of genome-wide exon-tiling microarray experiments is the removal of cross-hybridization noise. We present a probabilistic generative model for cross-hybridization in microarray data and a corresponding variational learning method for cross-hybridization compensation, GenXHC, that reduces cross-hybridization noise by taking into account multiple sources for each mRNA expression level measurement, as well as prior knowledge of hybridization similarities between the nucleotide sequences of microarray probes and their target cDNAs. Results: The algorithm is applied to a subset of an exon-resolution genome-wide Agilent microarray data set for chromosome 16 of Mus musculus and is found to produce statistically significant reductions in cross-hybridization noise. The denoised data is found to produce enrichment in multiple gene ontology–biological process (GO–BP) functional groups. The algorithm is found to outperform robust multi-array analysis, another method for cross-hybridization compensation. Contact: jim@psi.toronto.edu