Naomi Habib

Multiplex Genome Engineering Using CRISPR/Cas Systems

Genome Editing Clustered regularly interspaced short palindromic repeats (CRISPR) function as part of an adaptive immune system in a range of prokaryotes: Invading phage and plasmid DNA is targeted for cleavage by complementary CRISPR RNAs (crRNAs) bound to a CRISPR-associated endonuclease (see the Perspective by van der Oost). Cong et al. (p. 819, published online 3 January) and Mali et al. (p. 823, published online 3 January) adapted this defense system to function as a genome editing tool in eukaryotic cells. A bacterial genome defense system is adapted to function as a genome-editing tool in mammalian cells. [Also see Perspective by van der Oost] Functional elucidation of causal genetic variants and elements requires precise genome editing technologies. The type II prokaryotic CRISPR (clustered regularly interspaced short palindromic repeats)/Cas adaptive immune system has been shown to facilitate RNA-guided site-specific DNA cleavage. We engineered two different type II CRISPR/Cas systems and demonstrate that Cas9 nucleases can be directed by short RNAs to induce precise cleavage at endogenous genomic loci in human and mouse cells. Cas9 can also be converted into a nicking enzyme to facilitate homology-directed repair with minimal mutagenic activity. Lastly, multiple guide sequences can be encoded into a single CRISPR array to enable simultaneous editing of several sites within the mammalian genome, demonstrating easy programmability and wide applicability of the RNA-guided nuclease technology.

Densely Interconnected Transcriptional Circuits Control Cell States in Human Hematopoiesis

Though many individual transcription factors are known to regulate hematopoietic differentiation, major aspects of the global architecture of hematopoiesis remain unknown. Here, we profiled gene expression in 38 distinct purified populations of human hematopoietic cells and used probabilistic models of gene expression and analysis of cis-elements in gene promoters to decipher the general organization of their regulatory circuitry. We identified modules of highly coexpressed genes, some of which are restricted to a single lineage but most of which are expressed at variable levels across multiple lineages. We found densely interconnected cis-regulatory circuits and a large number of transcription factors that are differentially expressed across hematopoietic states. These findings suggest a more complex regulatory system for hematopoiesis than previously assumed.

In vivo interrogation of gene function in the mammalian brain using CRISPR-Cas9

Probing gene function in the mammalian brain can be greatly assisted with methods to manipulate the genome of neurons in vivo. The clustered, regularly interspaced, short palindromic repeats (CRISPR)-associated endonuclease (Cas)9 from Streptococcus pyogenes (SpCas9) can be used to edit single or multiple genes in replicating eukaryotic cells, resulting in frame-shifting insertion/deletion (indel) mutations and subsequent protein depletion. Here, we delivered SpCas9 and guide RNAs using adeno-associated viral (AAV) vectors to target single (Mecp2) as well as multiple genes (Dnmt1, Dnmt3a and Dnmt3b) in the adult mouse brain in vivo. We characterized the effects of genome modifications in postmitotic neurons using biochemical, genetic, electrophysiological and behavioral readouts. Our results demonstrate that AAV-mediated SpCas9 genome editing can enable reverse genetic studies of gene function in the brain.

Comparative Functional Genomics of the Fission Yeasts

A combined analysis of genome sequence, structure, and expression gives insights into fission yeast biology. The fission yeast clade—comprising Schizosaccharomyces pombe, S. octosporus, S. cryophilus, and S. japonicus—occupies the basal branch of Ascomycete fungi and is an important model of eukaryote biology. A comparative annotation of these genomes identified a near extinction of transposons and the associated innovation of transposon-free centromeres. Expression analysis established that meiotic genes are subject to antisense transcription during vegetative growth, which suggests a mechanism for their tight regulation. In addition, trans-acting regulators control new genes within the context of expanded functional modules for meiosis and stress response. Differences in gene content and regulation also explain why, unlike the budding yeast of Saccharomycotina, fission yeasts cannot use ethanol as a primary carbon source. These analyses elucidate the genome structure and gene regulation of fission yeast and provide tools for investigation across the Schizosaccharomyces clade.

Structure and function of a transcriptional network activated by the MAPK Hog1

Cells regulate gene expression using a complex network of signaling pathways, transcription factors and promoters. To gain insight into the structure and function of these networks, we analyzed gene expression in single- and multiple-mutant strains to build a quantitative model of the Hog1 MAPK-dependent osmotic stress response in budding yeast. Our model reveals that the Hog1 and general stress (Msn2/4) pathways interact, at both the signaling and promoter level, to integrate information and create a context-dependent response. This study lays out a path to identifying and characterizing the role of signal integration and processing in other gene regulatory networks.

Massively parallel single-nucleus RNA-seq with DroNc-seq

Single-nucleus RNA sequencing (sNuc-seq) profiles RNA from tissues that are preserved or cannot be dissociated, but it does not provide high throughput. Here, we develop DroNc-seq: massively parallel sNuc-seq with droplet technology. We profile 39,111 nuclei from mouse and human archived brain samples to demonstrate sensitive, efficient, and unbiased classification of cell types, paving the way for systematic charting of cell atlases.

A novel Bayesian DNA motif comparison method for clustering and retrieval.

Characterizing the DNA-binding specificities of transcription factors is a key problem in computational biology that has been addressed by multiple algorithms. These usually take as input sequences that are putatively bound by the same factor and output one or more DNA motifs. A common practice is to apply several such algorithms simultaneously to improve coverage at the price of redundancy. In interpreting such results, two tasks are crucial: clustering of redundant motifs, and attributing the motifs to transcription factors by retrieval of similar motifs from previously characterized motif libraries. Both tasks inherently involve motif comparison. Here we present a novel method for comparing and merging motifs, based on Bayesian probabilistic principles. This method takes into account both the similarity in positional nucleotide distributions of the two motifs and their dissimilarity to the background distribution. We demonstrate the use of the new comparison method as a basis for motif clustering and retrieval procedures, and compare it to several commonly used alternatives. Our results show that the new method outperforms other available methods in accuracy and sensitivity. We incorporated the resulting motif clustering and retrieval procedures in a large-scale automated pipeline for analyzing DNA motifs. This pipeline integrates the results of various DNA motif discovery algorithms and automatically merges redundant motifs from multiple training sets into a coherent annotated library of motifs. Application of this pipeline to recent genome-wide transcription factor location data in S. cerevisiae successfully identified DNA motifs in a manner that is as good as semi-automated analysis reported in the literature. Moreover, we show how this analysis elucidates the mechanisms of condition-specific preferences of transcription factors.

A functional selection model explains evolutionary robustness despite plasticity in regulatory networks

Evolutionary rewiring of regulatory networks is an important source of diversity among species. Previous evidence suggested substantial divergence of regulatory networks across species. However, systematically assessing the extent of this plasticity and its functional implications has been challenging due to limited experimental data and the noisy nature of computational predictions. Here, we introduce a novel approach to study cis‐regulatory evolution, and use it to trace the regulatory history of 88 DNA motifs of transcription factors across 23 Ascomycota fungi. While motifs are conserved, we find a pervasive gain and loss in the regulation of their target genes. Despite this turnover, the biological processes associated with a motif are generally conserved. We explain these trends using a model with a strong selection to conserve the overall function of a transcription factor, and a much weaker selection over the specific genes it targets. The model also accounts for the turnover of bound targets measured experimentally across species in yeasts and mammals. Thus, selective pressures on regulatory networks mostly tolerate local rewiring, and may allow for subtle fine‐tuning of gene regulation during evolution.

An integrative clustering and modeling algorithm for dynamical gene expression data

Motivation: The precise dynamics of gene expression is often crucial for proper response to stimuli. Time-course gene-expression profiles can provide insights about the dynamics of many cellular responses, but are often noisy and measured at arbitrary intervals, posing a major analysis challenge. Results: We developed an algorithm that interleaves clustering time-course gene-expression data with estimation of dynamic models of their response by biologically meaningful parameters. In combining these two tasks we overcome obstacles posed in each one. Moreover, our approach provides an easy way to compare between responses to different stimuli at the dynamical level. We use our approach to analyze the dynamical transcriptional responses to inflammation and anti-viral stimuli in mice primary dendritic cells, and extract a concise representation of the different dynamical response types. We analyze the similarities and differences between the two stimuli and identify potential regulators of this complex transcriptional response. Availability: The code to our method is freely available http://www.compbio.cs.huji.ac.il/DynaMiteC. Contact: nir@cs.huji.ac.il

Arboretum: Reconstruction and analysis of the evolutionary history of condition-specific transcriptional modules

Comparative functional genomics studies the evolution of biological processes by analyzing functional data, such as gene expression profiles, across species. A major challenge is to compare profiles collected in a complex phylogeny. Here, we present Arboretum, a novel scalable computational algorithm that integrates expression data from multiple species with species and gene phylogenies to infer modules of coexpressed genes in extant species and their evolutionary histories. We also develop new, generally applicable measures of conservation and divergence in gene regulatory modules to assess the impact of changes in gene content and expression on module evolution. We used Arboretum to study the evolution of the transcriptional response to heat shock in eight species of Ascomycota fungi and to reconstruct modules of the ancestral environmental stress response (ESR). We found substantial conservation in the stress response across species and in the reconstructed components of the ancestral ESR modules. The greatest divergence was in the most induced stress, primarily through module expansion. The divergence of the heat stress response exceeds that observed in the response to glucose depletion in the same species. Arboretum and its associated analyses provide a comprehensive framework to systematically study regulatory evolution of condition-specific responses.