Mutations causing medullary cystic kidney disease type 1 lie in a large VNTR in MUC1 missed by massively parallel sequencing

Although genetic lesions responsible for some mendelian disorders can be rapidly discovered through massively parallel sequencing of whole genomes or exomes, not all diseases readily yield to such efforts. We describe the illustrative case of the simple mendelian disorder medullary cystic kidney disease type 1 (MCKD1), mapped more than a decade ago to a 2-Mb region on chromosome 1. Ultimately, only by cloning, capillary sequencing and de novo assembly did we find that each of six families with MCKD1 harbors an equivalent but apparently independently arising mutation in sequence markedly under-represented in massively parallel sequencing data: the insertion of a single cytosine in one copy (but a different copy in each family) of the repeat unit comprising the extremely long (~1.5–5 kb), GC-rich (>80%) coding variable-number tandem repeat (VNTR) sequence in the MUC1 gene encoding mucin 1. These results provide a cautionary tale about the challenges in identifying the genes responsible for mendelian, let alone more complex, disorders through massively parallel sequencing

A High-Throughput Chromatin Immunoprecipitation Approach Reveals Principles of Dynamic Gene Regulation in Mammals

Understanding the principles governing mammalian gene regulation has been hampered by the difficulty in measuring in vivo binding dynamics of large numbers of transcription factors (TF) to DNA. Here, we develop a high-throughput Chromatin ImmunoPrecipitation (HT-ChIP) method to systematically map protein-DNA interactions. HT-ChIP was applied to define the dynamics of DNA binding by 25 TFs and 4 chromatin marks at 4 time-points following pathogen stimulus of dendritic cells. Analyzing over 180,000 TF-DNA interactions we find that TFs vary substantially in their temporal binding landscapes. This data suggests a model for transcription regulation whereby TF networks are hierarchically organized into cell differentiation factors, factors that bind targets prior to stimulus to prime them for induction, and factors that regulate specific gene programs. Overlaying HT-ChIP data on gene-expression dynamics shows that many TF-DNA interactions are established prior to the stimuli, predominantly at immediate-early genes, and identified specific TF ensembles that coordinately regulate gene-induction

Cell scientist to watch – Mitchell Guttman

Mitchell received a bachelor's degree in molecular and computational biology and a master's degree in computational biology and bioinformatics from the University of Pennsylvania. He then joined the laboratory of Eric Lander at the Broad Institute of MIT and Harvard and was awarded his PhD in 2012. The same year he was named in the Forbes ‘30 under 30: science and healthcare’ list of rising stars and received an NIH early independence award. Mitchell subsequently moved to the California Institute of Technology as an Assistant Professor to establish his own laboratory. He has received numerous awards, including being named a Robertson Investigator of the New York Stem Cell Foundation, an Investigator at the Heritage Medical Research Institute and a Pew-Stewart scholar for cancer research in 2015. Having identified and characterised a new class of functional large non-coding RNA (lncRNA) genes, his laboratory aims to understand the mechanisms by which lncRNAs act to control cellular functions through regulation of proteins, binding to genomic DNA targets and contributing to nuclear organisation

Approaches for Understanding the Mechanisms of Long Noncoding RNA Regulation of Gene Expression

Mammalian genomes encode tens of thousands of long noncoding RNAs (lncRNAs) that have been implicated in a diverse array of biological processes and human diseases. In recent years, the development of new tools for studying lncRNAs has enabled important progress in defining the mechanisms by which Xist and other lncRNAs function. This collective work provides a framework for how to define the mechanisms by which lncRNAs act. This includes defining lncRNA function, identifying and characterizing lncRNA–protein interactions, and lncRNA localization in the cell. In this review, we discuss various experimental approaches for deciphering lncRNA mechanisms and discuss issues and limitations in interpreting these results. We explore what these data can reveal about lncRNA function and mechanism as well as emerging insights into lncRNA biology that have been derived from these studies

A high-resolution map of human evolutionary constraint using 29 mammals

The comparison of related genomes has emerged as a powerful lens for genome interpretation. Here we report the sequencing and comparative analysis of 29 eutherian genomes. We confirm that at least 5.5% of the human genome has undergone purifying selection, and locate constrained elements covering ~4.2% of the genome. We use evolutionary signatures and comparisons with experimental data sets to suggest candidate functions for ~60% of constrained bases. These elements reveal a small number of new coding exons, candidate stop codon readthrough events and over 10,000 regions of overlapping synonymous constraint within protein-coding exons. We find 220 candidate RNA structural families, and nearly a million elements overlapping potential promoter, enhancer and insulator regions. We report specific amino acid residues that have undergone positive selection, 280,000 non-coding elements exapted from mobile elements and more than 1,000 primate- and human-accelerated elements. Overlap with disease-associated variants indicates that our findings will be relevant for studies of human biology, health and disease

Functional large non-coding RNAs in mammals

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Biology, 2012.Cataloged from PDF version of thesis.Includes bibliographical references.It is now clear that RNA is more than a messenger and performs vast and diverse functions. These functional RNAs include the ribosomal, transfer, and splicing-associated RNAs along with a cast of tiny RNAs, including microRNAs and other families. In addition to these classic examples, there were a handful of known functional large ncRNAs that play important biological roles. To identify additional functional large ncRNAs we exploited a chromatin signature of actively transcribed genes to define discrete transcriptional units that do not overlap any known proteincoding genes. Using this approach we identified -3,500 transcriptional units in the human and mouse genomes that produce multi-exonic RNAs that lack any coding potential. We termed these large intergenic non-coding RNAs (lincRNAs). Importantly, these lincRNAs exhibit strong purifying selection across various mammalian genomes. To determine whether the lincRNA transcripts themselves have biological functions, we undertook systematic loss-of-function experiments on most lincRNAs defined in mouse embryonic stem cells (ESCs). We showed that knockdown of the vast majority of ESC-expressed lincRNAs has a strong effect on gene expression patterns in ESCs, of comparable magnitude to that seen for the well-known ESC regulatory proteins. We identify dozens of lincRNAs that upon loss-of-function cause an exit from the pluripotent state and dozens of additional lincRNAs that, while not essential for the maintenance of pluripotency, act to repress lineage-specific gene expression programs in ESCs. Despite their important functional roles, how lincRNAs exert their influence was unknown. We showed that many lincRNAs physically interact with the Polycomb Repressive Complex. We systematically analyzed chromatin-modifying proteins that have been shown to play critical roles in ESCs and identified 11 additional chromatin complexes that physically interact with the ESC lincRNAs. Altogether, we found that -30% of the ESC lincRNAs are associated with multiple chromatin complexes. These interactions are important for proper regulation of gene expression programs in ES cells. Our data suggests a model whereby a distinct set of lincRNAs is transcribed in a cell type and interacts with ubiquitous regulatory protein complexes to give rise to cell-type-specific RNA-protein complexes that coordinate cell-type specific gene expression programs.by Mitchell Guttman.Ph.D

Methods for comprehensive experimental identification of RNA-protein interactions

The importance of RNA-protein interactions in controlling mRNA regulation and non-coding RNA function is increasingly appreciated. A variety of methods exist to comprehensively define RNA-protein interactions. We describe these methods and the considerations required for designing and interpreting these experiments

A signaling pathway leading to metastasis is controlled by N-cadherin and the FGF receptor

The intracellular signaling events causing tumor cells to become metastatic are not well understood. N-cadherin and FGF-2 synergistically increase migration, invasion, and secretion of extracellular proteases in breast tumor cells. Here, we define a metastatic signaling cascade activated by N-cadherin and FGF-2. In the presence of N-cadherin, FGF-2 caused sustained activation of the MAPK-ERK pathway, leading to MMP-9 gene transcription and cellular invasion. N-cadherin prevented the FGF receptor (FGFR) from undergoing ligand-induced internalization, resulting in increased FGFR-1 stability. Association of FGFR-1 with N-cadherin was mediated by the first two Ig-like domains of FGFR-1. These results suggest that protection of the FGFR-1 from ligand-induced downregulation by N-cadherin enhances receptor signaling and provides a mechanism by which tumor cells can acquire metastatic properties

Computational methods for transcriptome annotation and quantification using RNA-seq

High-throughput RNA sequencing (RNA-seq) promises a comprehensive picture of the transcriptome, allowing for the complete annotation and quantification of all genes and their isoforms across samples. Realizing this promise requires increasingly complex computational methods. These computational challenges fall into three main categories: (i) read mapping, (ii) transcriptome reconstruction and (iii) expression quantification. Here we explain the major conceptual and practical challenges, and the general classes of solutions for each category. Finally, we highlight the interdependence between these categories and discuss the benefits for different biological applications

RNA and dynamic nuclear organization

The human genome consists of more than 2 m of linear DNA, which is packaged into a three-dimensional structure in the nucleus of each cell. To ensure proper cell-type–specific gene regulation, each cell must organize its DNA, RNA, and protein within the nucleus in ways that differ in each cell type. It had long been suspected that RNA itself might be a key organizing factor that shapes this dynamic nuclear floor plan (1), with recent research pointing to a role for nuclear-retained long noncoding RNAs (lncRNAs) in organizing nuclear architecture. Here we provide a perspective on the classical and newly emerging role of RNA in this process