Francesco Strino

PCGF Homologs, CBX Proteins, and RYBP Define Functionally Distinct PRC1 Family Complexes

The heterogeneous nature of mammalian PRC1 complexes has hindered our understanding of their biological functions. Here, we present a comprehensive proteomic and genomic analysis that uncovered six major groups of PRC1 complexes, each containing a distinct PCGF subunit, a RING1A/B ubiquitin ligase, and a unique set of associated polypeptides. These PRC1 complexes differ in their genomic localization, and only a small subset colocalize with H3K27me3. Further biochemical dissection revealed that the six PCGF-RING1A/B combinations form multiple complexes through association with RYBP or its homolog YAF2, which prevents the incorporation of other canonical PRC1 subunits, such as CBX, PHC, and SCM. Although both RYBP/YAF2- and CBX/PHC/SCM-containing complexes compact chromatin, only RYBP stimulates the activity of RING1B toward H2AK119ub1, suggesting a central role in PRC1 function. Knockdown of RYBP in embryonic stem cells compromised their ability to form embryoid bodies, likely because of defects in cell proliferation and maintenance of H2AK119ub1 levels.

FIREWACh: high-throughput functional detection of transcriptional regulatory modules in mammalian cells

Promoters and enhancers establish precise gene transcription patterns. The development of functional approaches for their identification in mammalian cells has been complicated by the size of these genomes. Here we report a high-throughput functional assay for directly identifying active promoter and enhancer elements called FIREWACh (Functional Identification of Regulatory Elements Within Accessible Chromatin), which we used to simultaneously assess over 80,000 DNA fragments derived from nucleosome-free regions within the chromatin of embryonic stem cells (ESCs) and identify 6,364 active regulatory elements. Many of these represent newly discovered ESC-specific enhancers, showing enriched binding-site motifs for ESC-specific transcription factors including SOX2, POU5F1 (OCT4) and KLF4. The application of FIREWACh to additional cultured cell types will facilitate functional annotation of the genome and expand our view of transcriptional network dynamics.

TrAp: a tree approach for fingerprinting subclonal tumor composition

Revealing the clonal composition of a single tumor is essential for identifying cell subpopulations with metastatic potential in primary tumors or with resistance to therapies in metastatic tumors. Sequencing technologies provide only an overview of the aggregate of numerous cells. Computational approaches to de-mix a collective signal composed of the aberrations of a mixed cell population of a tumor sample into its individual components are not available. We propose an evolutionary framework for deconvolving data from a single genome-wide experiment to infer the composition, abundance and evolutionary paths of the underlying cell subpopulations of a tumor. We have developed an algorithm (TrAp) for solving this mixture problem. In silico analyses show that TrAp correctly deconvolves mixed subpopulations when the number of subpopulations and the measurement errors are moderate. We demonstrate the applicability of the method using tumor karyotypes and somatic hypermutation data sets. We applied TrAp to Exome-Seq experiment of a renal cell carcinoma tumor sample and compared the mutational profile of the inferred subpopulations to the mutational profiles of single cells of the same tumor. Finally, we deconvolve sequencing data from eight acute myeloid leukemia patients and three distinct metastases of one melanoma patient to exhibit the evolutionary relationships of their subpopulations.

Picking ChIP-seq peak detectors for analyzing chromatin modification experiments

Numerous algorithms have been developed to analyze ChIP-Seq data. However, the complexity of analyzing diverse patterns of ChIP-Seq signals, especially for epigenetic marks, still calls for the development of new algorithms and objective comparisons of existing methods. We developed Qeseq, an algorithm to detect regions of increased ChIP read density relative to background. Qeseq employs critical novel elements, such as iterative recalibration and neighbor joining of reads to identify enriched regions of any length. To objectively assess its performance relative to other 14 ChIP-Seq peak finders, we designed a novel protocol based on Validation Discriminant Analysis (VDA) to optimally select validation sites and generated two validation datasets, which are the most comprehensive to date for algorithmic benchmarking of key epigenetic marks. In addition, we systematically explored a total of 315 diverse parameter configurations from these algorithms and found that typically optimal parameters in one dataset do not generalize to other datasets. Nevertheless, default parameters show the most stable performance, suggesting that they should be used. This study also provides a reproducible and generalizable methodology for unbiased comparative analysis of high-throughput sequencing tools that can facilitate future algorithmic development.

Ranking and combining multiple predictors without labeled data

Significance A key challenge in a broad range of decision-making and classification problems is how to rank and combine the possibly conflicting suggestions of several advisers of unknown reliability. We provide mathematical insights of striking conceptual simplicity that explain mutual relationships between independent advisers. These insights enable the design of efficient, robust, and reliable methods to rank the advisers’ performances and construct improved predictions in the absence of ground truth. Furthermore, these methods are robust to the presence of small subgroups of malicious advisers (cartels) attempting to veer the combined decisions to their interest. In a broad range of classification and decision-making problems, one is given the advice or predictions of several classifiers, of unknown reliability, over multiple questions or queries. This scenario is different from the standard supervised setting, where each classifier’s accuracy can be assessed using available labeled data, and raises two questions: Given only the predictions of several classifiers over a large set of unlabeled test data, is it possible to (i) reliably rank them and (ii) construct a metaclassifier more accurate than most classifiers in the ensemble? Here we present a spectral approach to address these questions. First, assuming conditional independence between classifiers, we show that the off-diagonal entries of their covariance matrix correspond to a rank-one matrix. Moreover, the classifiers can be ranked using the leading eigenvector of this covariance matrix, because its entries are proportional to their balanced accuracies. Second, via a linear approximation to the maximum likelihood estimator, we derive the Spectral Meta-Learner (SML), an unsupervised ensemble classifier whose weights are equal to these eigenvector entries. On both simulated and real data, SML typically achieves a higher accuracy than most classifiers in the ensemble and can provide a better starting point than majority voting for estimating the maximum likelihood solution. Furthermore, SML is robust to the presence of small malicious groups of classifiers designed to veer the ensemble prediction away from the (unknown) ground truth.

SFMBT1 functions with LSD1 to regulate expression of canonical histone genes and chromatin-related factors

SFMBT1 (Scm [Sex comb on midleg] with four MBT [malignant brain tumor] domains 1) is a poorly characterized mammalian MBT domain-containing protein homologous to Drosophila SFMBT, a Polycomb group protein involved in epigenetic regulation of gene expression. Here, we show that SFMBT1 regulates transcription in somatic cells and during spermatogenesis through the formation of a stable complex with LSD1 and CoREST. When bound to its gene targets, SFMBT1 recruits its associated proteins and causes chromatin compaction and transcriptional repression. SFMBT1, LSD1, and CoREST share a large fraction of target genes, including those encoding replication-dependent histones. Simultaneous occupancy of histone genes by SFMBT1, LSD1, and CoREST is regulated during the cell cycle and correlates with the loss of RNA polymerase II at these promoters during G2, M, and G1. The interplay between the repressive SFMBT1-LSD1-CoREST complex and RNA polymerase II contributes to the timely transcriptional regulation of histone genes in human cells. SFMBT1, LSD1, and CoREST also form a stable complex in germ cells, and their chromatin binding activity is regulated during spermatogenesis.

IL-7 Functionally Segregates the Pro-B Cell Stage by Regulating Transcription of Recombination Mediators across Cell Cycle

Ag receptor diversity involves the introduction of DNA double-stranded breaks during lymphocyte development. To ensure fidelity, cleavage is confined to the G0-G1 phase of the cell cycle. One established mechanism of regulation is through periodic degradation of the RAG2 recombinase protein. However, there are additional levels of protection. In this paper, we show that cyclical changes in the IL-7R signaling pathway functionally segregate pro-B cells according to cell cycle status. In consequence, the level of a downstream effector of IL-7 signaling, phospho-STAT5, is inversely correlated with cell cycle expression of Rag, a key gene involved in recombination. Higher levels of phopho-STAT5 in S-G2 correlate with decreased Rag expression and Rag relocalization to pericentromeric heterochromatin. These cyclical changes in transcription and locus repositioning are ablated upon transformation with v-Abl, which renders STAT5 constitutively active across the cell cycle. We propose that this activity of the IL-7R/STAT5 pathway plays a critical protective role in development, complementing regulation of RAG2 at the protein level, to ensure that recombination does not occur during replication. Our data, suggesting that pro-B cells are not a single homogeneous population, explain inconsistencies in the role of IL-7 signaling in regulating Igh recombination.

Comparative FAIRE‐seq Analysis Reveals Distinguishing Features of the Chromatin Structure of Ground State‐ and Primed‐Pluripotent Cells

Both pluripotent embryonic stem cells (ESCs), established from preimplantation murine blastocysts, and epiblast stem cells (EpiSCs), established from postimplantation embryos, can self‐renew in culture or differentiate into each of the primary germ layers. While the core transcription factors (TFs) OCT4, SOX2, and NANOG are expressed in both cell types, the gene expression profiles and other features suggest that ESCs and EpiSCs reflect distinct developmental maturation stages of the epiblast in vivo. Accordingly, “naïve” or “ground state” ESCs resemble cells of the inner cell mass, whereas “primed” EpiSCs resemble cells of the postimplantation egg cylinder. To gain insight into the relationship between naïve and primed pluripotent cells, and of each of these pluripotent states to that of nonpluripotent cells, we have used FAIRE‐seq to generate a comparative atlas of the accessible chromatin regions within ESCs, EpiSCs, multipotent neural stem cells, and mouse embryonic fibroblasts. We find a distinction between the accessible chromatin patterns of pluripotent and somatic cells that is consistent with the highly related phenotype of ESCs and EpiSCs. However, by defining cell‐specific and shared regions of open chromatin, and integrating these data with published gene expression and ChIP analyses, we also illustrate unique features of the chromatin of naïve and primed cells. Functional studies suggest that multiple stage‐specific enhancers regulate ESC‐ or EpiSC‐specific gene expression, and implicate auxiliary TFs as important modulators for stage‐specific activation by the core TFs. Together these observations provide insights into the chromatin structure dynamics accompanying transitions between these pluripotent states. Stem Cells 2015;33:378–391

Arpeggio: harmonic compression of ChIP-seq data reveals protein-chromatin interaction signatures

Researchers generating new genome-wide data in an exploratory sequencing study can gain biological insights by comparing their data with well-annotated data sets possessing similar genomic patterns. Data compression techniques are needed for efficient comparisons of a new genomic experiment with large repositories of publicly available profiles. Furthermore, data representations that allow comparisons of genomic signals from different platforms and across species enhance our ability to leverage these large repositories. Here, we present a signal processing approach that characterizes protein–chromatin interaction patterns at length scales of several kilobases. This allows us to efficiently compare numerous chromatin-immunoprecipitation sequencing (ChIP-seq) data sets consisting of many types of DNA-binding proteins collected from a variety of cells, conditions and organisms. Importantly, these interaction patterns broadly reflect the biological properties of the binding events. To generate these profiles, termed Arpeggio profiles, we applied harmonic deconvolution techniques to the autocorrelation profiles of the ChIP-seq signals. We used 806 publicly available ChIP-seq experiments and showed that Arpeggio profiles with similar spectral densities shared biological properties. Arpeggio profiles of ChIP-seq data sets revealed characteristics that are not easily detected by standard peak finders. They also allowed us to relate sequencing data sets from different genomes, experimental platforms and protocols. Arpeggio is freely available at http://sourceforge.net/p/arpeggio/wiki/Home/.

VDA, a Method of Choosing a Better Algorithm with Fewer Validations

The multitude of bioinformatics algorithms designed for performing a particular computational task presents end-users with the problem of selecting the most appropriate computational tool for analyzing their biological data. The choice of the best available method is often based on expensive experimental validation of the results. We propose an approach to design validation sets for method comparison and performance assessment that are effective in terms of cost and discrimination power. Validation Discriminant Analysis (VDA) is a method for designing a minimal validation dataset to allow reliable comparisons between the performances of different algorithms. Implementation of our VDA approach achieves this reduction by selecting predictions that maximize the minimum Hamming distance between algorithmic predictions in the validation set. We show that VDA can be used to correctly rank algorithms according to their performances. These results are further supported by simulations and by realistic algorithmic comparisons in silico. VDA is a novel, cost-efficient method for minimizing the number of validation experiments necessary for reliable performance estimation and fair comparison between algorithms. Our VDA software is available at http://sourceforge.net/projects/klugerlab/files/VDA/