CRISPR-Cas9 Gene Editing for Sickle Cell Disease and ß-Thalassemia.

Transfusion-dependent ß-thalassemia (TDT) and sickle cell disease (SCD) are severe monogenic diseases with severe and potentially life-threatening manifestations. BCL11A is a transcription factor that represses γ-globin expression and fetal hemoglobin in erythroid cells. We performed electroporation of CD34+ hematopoietic stem and progenitor cells obtained from healthy donors, with CRISPR-Cas9 targeting the BCL11A erythroid-specific enhancer. Approximately 80% of the alleles at this locus were modified, with no evidence of off-target editing. After undergoing myeloablation, two patients - one with TDT and the other with SCD - received autologous CD34+ cells edited with CRISPR-Cas9 targeting the same BCL11A enhancer. More than a year later, both patients had high levels of allelic editing in bone marrow and blood, increases in fetal hemoglobin that were distributed pancellularly, transfusion independence, and (in the patient with SCD) elimination of vaso-occlusive episodes. (Funded by CRISPR Therapeutics and Vertex Pharmaceuticals; ClinicalTrials.gov numbers, NCT03655678 for CLIMB THAL-111 and NCT03745287 for CLIMB SCD-121.).

IDH2 mutation-induced histone and DNA hypermethylation is progressively reversed by small-molecule inhibition.

Mutations of IDH1 and IDH2, which produce the oncometabolite 2-hydroxyglutarate (2HG), have been identified in several tumors, including acute myeloid leukemia. Recent studies have shown that expression of the IDH mutant enzymes results in high levels of 2HG and a block in cellular differentiation that can be reversed with IDH mutant-specific small-molecule inhibitors. To further understand the role of IDH mutations in cancer, we conducted mechanistic studies in the TF-1 IDH2 R140Q erythroleukemia model system and found that IDH2 mutant expression caused both histone and genomic DNA methylation changes that can be reversed when IDH2 mutant activity is inhibited. Specifically, histone hypermethylation is rapidly reversed within days, whereas reversal of DNA hypermethylation proceeds in a progressive manner over the course of weeks. We identified several gene signatures implicated in tumorigenesis of leukemia and lymphoma, indicating a selective modulation of relevant cancer genes by IDH mutations. As methylation of DNA and histones is closely linked to mRNA expression and differentiation, these results indicate that IDH2 mutant inhibition may function as a cancer therapy via histone and DNA demethylation at genes involved in differentiation and tumorigenesis.

A small molecule inhibitor of mutant IDH2 rescues cardiomyopathy in a D-2-hydroxyglutaric aciduria type II mouse model.

D-2-hydroxyglutaric aciduria (D2HGA) type II is a rare neurometabolic disorder caused by germline gain-of-function mutations in isocitrate dehydrogenase 2 (IDH2), resulting in accumulation of D-2-hydroxyglutarate (D2HG). Patients exhibit a wide spectrum of symptoms including cardiomyopathy, epilepsy, developmental delay and limited life span. Currently, there are no effective therapeutic interventions. We generated a D2HGA type II mouse model by introducing the Idh2R140Q mutation at the native chromosomal locus. Idh2R140Q mice displayed significantly elevated 2HG levels and recapitulated multiple defects seen in patients. AGI-026, a potent, selective inhibitor of the human IDH2R140Q-mutant enzyme, suppressed 2HG production, rescued cardiomyopathy, and provided a survival benefit in Idh2R140Q mice; treatment withdrawal resulted in deterioration of cardiac function. We observed differential expression of multiple genes and metabolites that are associated with cardiomyopathy, which were largely reversed by AGI-026. These findings demonstrate the potential therapeutic benefit of an IDH2R140Q inhibitor in patients with D2HGA type II.

Exome sequencing, ANGPTL3 mutations, and familial combined hypolipidemia.

We sequenced all protein-coding regions of the genome (the "exome") in two family members with combined hypolipidemia, marked by extremely low plasma levels of low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, and triglycerides. These two participants were compound heterozygotes for two distinct nonsense mutations in ANGPTL3 (encoding the angiopoietin-like 3 protein). ANGPTL3 has been reported to inhibit lipoprotein lipase and endothelial lipase, thereby increasing plasma triglyceride and HDL cholesterol levels in rodents. Our finding of ANGPTL3 mutations highlights a role for the gene in LDL cholesterol metabolism in humans and shows the usefulness of exome sequencing for identification of novel genetic causes of inherited disorders. (Funded by the National Human Genome Research Institute and others.).

The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.

Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS--the 1000 Genome pilot alone includes nearly five terabases--make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.

Evaluation of Homology-Independent CRISPR-Cas9 Off-Target Assessment Methods.

The ability to alter genomes specifically by CRISPR-Cas gene editing has revolutionized biological research, biotechnology, and medicine. Broad therapeutic application of this technology, however, will require thorough preclinical assessment of off-target editing by homology-based prediction coupled with reliable methods for detecting off-target editing. Several off-target site nomination assays exist, but careful comparison is needed to ascertain their relative strengths and weaknesses. In this study, HEK293T cells were treated with Streptococcus pyogenes Cas9 and eight guide RNAs with varying levels of predicted promiscuity in order to compare the performance of three homology-independent off-target nomination methods: the cell-based assay, GUIDE-seq, and the biochemical assays CIRCLE-seq and SITE-seq. The three methods were benchmarked by sequencing 75,000 homology-nominated sites using hybrid capture followed by high-throughput sequencing, providing the most comprehensive assessment of such methods to date. The three methods performed similarly in nominating sequence-confirmed off-target sites, but with large differences in the total number of sites nominated. When combined with homology-dependent nomination methods and confirmation by sequencing, all three off-target nomination methods provide a comprehensive assessment of off-target activity. GUIDE-seq's low false-positive rate and the high correlation of its signal with observed editing highlight its suitability for nominating off-target sites for ex vivo CRISPR-Cas therapies.

Using genetic algorithms to select most predictive protein features.

Many important characteristics of proteins such as biochemical activity and subcellular localization present a challenge to machine-learning methods: it is often difficult to encode the appropriate input features at the residue level for the purpose of making a prediction for the entire protein. The problem is usually that the biophysics of the connection between a machine-learning method's input (sequence feature) and its output (observed phenomenon to be predicted) remains unknown; in other words, we may only know that a certain protein is an enzyme (output) without knowing which region may contain the active site residues (input). The goal then becomes to dissect a protein into a vast set of sequence-derived features and to correlate those features with the desired output. We introduce a framework that begins with a set of global sequence features and then vastly expands the feature space by generically encoding the coexistence of residue-based features. It is this combination of individual features, that is the step from the fractions of serine and buried (input space 20 + 2) to the fraction of buried serine (input space 20 * 2) that implicitly shifts the search space from global feature inputs to features that can capture very local evidence such as a the individual residues of a catalytic triad. The vast feature space created is explored by a genetic algorithm (GA) paired with neural networks and support vector machines. We find that the GA is critical for selecting combinations of features that are neither too general resulting in poor performance, nor too specific, leading to overtraining. The final framework manages to effectively sample a feature space that is far too large for exhaustive enumeration. We demonstrate the power of the concept by applying it to prediction of protein enzymatic activity.

Static benchmarking of membrane helix predictions.

Prediction of trans-membrane helices continues to be a difficult task with a few prediction methods clearly taking the lead; none of these is clearly best on all accounts. Recently, we have carefully set up protocols for benchmarking the most relevant aspects of prediction accuracy and have applied it to >30 prediction methods. Here, we present the extension of that analysis to the level of an automatic web server evaluating new methods (http://cubic.bioc.columbia.edu/services/tmh_benchmark/). The most important achievements of the tool are: (i) any new method is compared to the battery of well-established tools; (ii) the battery of measures explored allows spotting strengths in methods that may not be 'best' overall. In particular, we report per-residue and per-segment scores for accuracy and the error-rates for confusing membrane helices with globular proteins or signal peptides. An additional feature is that developers can directly investigate any hydrophobicity scale for its potential in predicting membrane helices.

MTAP Deletions in Cancer Create Vulnerability to Targeting of the MAT2A/PRMT5/RIOK1 Axis.

Homozygous deletions of p16/CDKN2A are prevalent in cancer, and these mutations commonly involve co-deletion of adjacent genes, including methylthioadenosine phosphorylase (MTAP). Here, we used shRNA screening and identified the metabolic enzyme, methionine adenosyltransferase II alpha (MAT2A), and the arginine methyltransferase, PRMT5, as vulnerable enzymes in cells with MTAP deletion. Metabolomic and biochemical studies revealed a mechanistic basis for this synthetic lethality. The MTAP substrate methylthioadenosine (MTA) accumulates upon MTAP loss. Biochemical profiling of a methyltransferase enzyme panel revealed that MTA is a potent and selective inhibitor of PRMT5. MTAP-deleted cells have reduced PRMT5 methylation activity and increased sensitivity to PRMT5 depletion. MAT2A produces the PRMT5 substrate S-adenosylmethionine (SAM), and MAT2A depletion reduces growth and PRMT5 methylation activity selectively in MTAP-deleted cells. Furthermore, this vulnerability extends to PRMT5 co-complex proteins such as RIOK1. Thus, the unique biochemical features of PRMT5 create an axis of targets vulnerable in CDKN2A/MTAP-deleted cancers.

Transmembrane helix predictions revisited.

Methods that predict membrane helices have become increasingly useful in the context of analyzing entire proteomes, as well as in everyday sequence analysis. Here, we analyzed 27 advanced and simple methods in detail. To resolve contradictions in previous works and to reevaluate transmembrane helix prediction algorithms, we introduced an analysis that distinguished between performance on redundancy-reduced high- and low-resolution data sets, established thresholds for significant differences in performance, and implemented both per-segment and per-residue analysis of membrane helix predictions. Although some of the advanced methods performed better than others, we showed in a thorough bootstrapping experiment based on various measures of accuracy that no method performed consistently best. In contrast, most simple hydrophobicity scale-based methods were significantly less accurate than any advanced method as they overpredicted membrane helices and confused membrane helices with hydrophobic regions outside of membranes. In contrast, the advanced methods usually distinguished correctly between membrane-helical and other proteins. Nonetheless, few methods reliably distinguished between signal peptides and membrane helices. We could not verify a significant difference in performance between eukaryotic and prokaryotic proteins. Surprisingly, we found that proteins with more than five helices were predicted at a significantly lower accuracy than proteins with five or fewer. The important implication is that structurally unsolved multispanning membrane proteins, which are often important drug targets, will remain problematic for transmembrane helix prediction algorithms. Overall, by establishing a standardized methodology for transmembrane helix prediction evaluation, we have resolved differences among previous works and presented novel trends that may impact the analysis of entire proteomes.

Using multiple structure alignments, fast model building, and energetic analysis in fold recognition and homology modeling.

We participated in the fold recognition and homology sections of CASP5 using primarily in-house software. The central feature of our structure prediction strategy involved the ability to generate good sequence-to-structure alignments and to quickly transform them into models that could be evaluated both with energy-based methods and manually. The in-house tools we used include: a) HMAP (Hybrid Multidimensional Alignment Profile)-a profile-to-profile alignment method that is derived from sequence-enhanced multiple structure alignments in core regions, and sequence motifs in non-structurally conserved regions. b) NEST-a fast model building program that applies an "artificial evolution" algorithm to construct a model from a given template and alignment. c) GRASP2-a new structure and alignment visualization program incorporating multiple structure superposition and domain database scanning modules. These methods were combined with model evaluation based on all atom and simplified physical-chemical energy functions. All of these methods were under development during CASP5 and consequently a great deal of manual analysis was carried out at each stage of the prediction process. This interactive model building procedure has several advantages and suggests important ways in which our and other methods can be improved, examples of which are provided.

An inhibitor of mutant IDH1 delays growth and promotes differentiation of glioma cells.

The recent discovery of mutations in metabolic enzymes has rekindled interest in harnessing the altered metabolism of cancer cells for cancer therapy. One potential drug target is isocitrate dehydrogenase 1 (IDH1), which is mutated in multiple human cancers. Here, we examine the role of mutant IDH1 in fully transformed cells with endogenous IDH1 mutations. A selective R132H-IDH1 inhibitor (AGI-5198) identified through a high-throughput screen blocked, in a dose-dependent manner, the ability of the mutant enzyme (mIDH1) to produce R-2-hydroxyglutarate (R-2HG). Under conditions of near-complete R-2HG inhibition, the mIDH1 inhibitor induced demethylation of histone H3K9me3 and expression of genes associated with gliogenic differentiation. Blockade of mIDH1 impaired the growth of IDH1-mutant--but not IDH1-wild-type--glioma cells without appreciable changes in genome-wide DNA methylation. These data suggest that mIDH1 may promote glioma growth through mechanisms beyond its well-characterized epigenetic effects.

Targeted inhibition of mutant IDH2 in leukemia cells induces cellular differentiation.

A number of human cancers harbor somatic point mutations in the genes encoding isocitrate dehydrogenases 1 and 2 (IDH1 and IDH2). These mutations alter residues in the enzyme active sites and confer a gain-of-function in cancer cells, resulting in the accumulation and secretion of the oncometabolite (R)-2-hydroxyglutarate (2HG). We developed a small molecule, AGI-6780, that potently and selectively inhibits the tumor-associated mutant IDH2/R140Q. A crystal structure of AGI-6780 complexed with IDH2/R140Q revealed that the inhibitor binds in an allosteric manner at the dimer interface. The results of steady-state enzymology analysis were consistent with allostery and slow-tight binding by AGI-6780. Treatment with AGI-6780 induced differentiation of TF-1 erythroleukemia and primary human acute myelogenous leukemia cells in vitro. These data provide proof-of-concept that inhibitors targeting mutant IDH2/R140Q could have potential applications as a differentiation therapy for cancer.

A framework for variation discovery and genotyping using next-generation DNA sequencing data.

Recent advances in sequencing technology make it possible to comprehensively catalog genetic variation in population samples, creating a foundation for understanding human disease, ancestry and evolution. The amounts of raw data produced are prodigious, and many computational steps are required to translate this output into high-quality variant calls. We present a unified analytic framework to discover and genotype variation among multiple samples simultaneously that achieves sensitive and specific results across five sequencing technologies and three distinct, canonical experimental designs. Our process includes (i) initial read mapping; (ii) local realignment around indels; (iii) base quality score recalibration; (iv) SNP discovery and genotyping to find all potential variants; and (v) machine learning to separate true segregating variation from machine artifacts common to next-generation sequencing technologies. We here discuss the application of these tools, instantiated in the Genome Analysis Toolkit, to deep whole-genome, whole-exome capture and multi-sample low-pass (∼4×) 1000 Genomes Project datasets.

Targeted exon sequencing by in-solution hybrid selection.

This unit describes a protocol for the targeted enrichment of exons from randomly sheared genomic DNA libraries using an in-solution hybrid selection approach for sequencing on an Illumina Genome Analyzer II. The steps for designing and ordering a hybrid selection oligo pool are reviewed, as are critical steps for performing the preparation and hybrid selection of an Illumina paired-end library. Critical parameters, performance metrics, and analysis workflow are discussed.

Membrane protein prediction methods.

We survey computational approaches that tackle membrane protein structure and function prediction. While describing the main ideas that have led to the development of the most relevant and novel methods, we also discuss pitfalls, provide practical hints and highlight the challenges that remain. The methods covered include: sequence alignment, motif search, functional residue identification, transmembrane segment and protein topology predictions, homology and ab initio modeling. In general, predictions of functional and structural features of membrane proteins are improving, although progress is hampered by the limited amount of high-resolution experimental information available. While predictions of transmembrane segments and protein topology rank among the most accurate methods in computational biology, more attention and effort will be required in the future to ameliorate database search, homology and ab initio modeling.