The EN-TEx resource of multi-tissue personal epigenomes & variant-impact models.

Understanding how genetic variants impact molecular phenotypes is a key goal of functional genomics, currently hindered by reliance on a single haploid reference genome. Here, we present the EN-TEx resource of 1,635 open-access datasets from four donors (∼30 tissues × âˆ¼15 assays). The datasets are mapped to matched, diploid genomes with long-read phasing and structural variants, instantiating a catalog of >1 million allele-specific loci. These loci exhibit coordinated activity along haplotypes and are less conserved than corresponding, non-allele-specific ones. Surprisingly, a deep-learning transformer model can predict the allele-specific activity based only on local nucleotide-sequence context, highlighting the importance of transcription-factor-binding motifs particularly sensitive to variants. Furthermore, combining EN-TEx with existing genome annotations reveals strong associations between allele-specific and GWAS loci. It also enables models for transferring known eQTLs to difficult-to-profile tissues (e.g., from skin to heart). Overall, EN-TEx provides rich data and generalizable models for more accurate personal functional genomics.

Nodal modulator (NOMO) is required to sustain endoplasmic reticulum morphology.

The endoplasmic reticulum (ER) is a membrane-bound organelle responsible for protein folding, lipid synthesis, and calcium homeostasis. Maintenance of ER structural integrity is crucial for proper function, but much remains to be learned about the molecular players involved. To identify proteins that support the structure of the ER, we performed a proteomic screen and identified nodal modulator (NOMO), a widely conserved type I transmembrane protein of unknown function, with three nearly identical orthologs specified in the human genome. We found that overexpression of NOMO1 imposes a sheet morphology on the ER, whereas depletion of NOMO1 and its orthologs causes a collapse of ER morphology concomitant with the formation of membrane-delineated holes in the ER network positive for the lysosomal marker lysosomal-associated protein 1. In addition, the levels of key players of autophagy including microtubule-associated protein light chain 3 and autophagy cargo receptor p62/sequestosome 1 strongly increase upon NOMO depletion. In vitro reconstitution of NOMO1 revealed a "beads on a string" structure likely representing consecutive immunoglobulin-like domains. Extending NOMO1 by insertion of additional immunoglobulin folds results in a correlative increase in the ER intermembrane distance. Based on these observations and a genetic epistasis analysis including the known ER-shaping proteins Atlastin2 and Climp63, we propose a role for NOMO1 in the functional network of ER-shaping proteins.

2C-ChIP: measuring chromatin immunoprecipitation signal from defined genomic regions with deep sequencing.

BACKGROUND: Understanding how transcription occurs requires the integration of genome-wide and locus-specific information gleaned from robust technologies. Chromatin immunoprecipitation (ChIP) is a staple in gene expression studies, and while genome-wide methods are available, high-throughput approaches to analyze defined regions are lacking. RESULTS: Here, we present carbon copy-ChIP (2C-ChIP), a versatile, inexpensive, and high-throughput technique to quantitatively measure the abundance of DNA sequences in ChIP samples. This method combines ChIP with ligation-mediated amplification (LMA) and deep sequencing to probe large genomic regions of interest. 2C-ChIP recapitulates results from benchmark ChIP approaches. We applied 2C-ChIP to the HOXA cluster to find that a region where H3K27me3 and SUZ12 linger encodes HOXA-AS2, a long non-coding RNA that enhances gene expression during cellular differentiation. CONCLUSIONS: 2C-ChIP fills the need for a robust molecular biology tool designed to probe dedicated genomic regions in a high-throughput setting. The flexible nature of the 2C-ChIP approach allows rapid changes in experimental design at relatively low cost, making it a highly efficient method for chromatin analysis.

Posterior inference of Hi-C contact frequency through sampling.

Hi-C is one of the most widely used approaches to study three-dimensional genome conformations. Contacts captured by a Hi-C experiment are represented in a contact frequency matrix. Due to the limited sequencing depth and other factors, Hi-C contact frequency matrices are only approximations of the true interaction frequencies and are further reported without any quantification of uncertainty. Hence, downstream analyses based on Hi-C contact maps (e.g., TAD and loop annotation) are themselves point estimations. Here, we present the Hi-C interaction frequency sampler (HiCSampler) that reliably infers the posterior distribution of the interaction frequency for a given Hi-C contact map by exploiting dependencies between neighboring loci. Posterior predictive checks demonstrate that HiCSampler can infer highly predictive chromosomal interaction frequency. Summary statistics calculated by HiCSampler provide a measurement of the uncertainty for Hi-C experiments, and samples inferred by HiCSampler are ready for use by most downstream analysis tools off the shelf and permit uncertainty measurements in these analyses without modifications.

Dynamic quality control machinery that operates across compartmental borders mediates the degradation of mammalian nuclear membrane proteins.

Many human diseases are caused by mutations in nuclear envelope (NE) proteins. How protein homeostasis and disease etiology are interconnected at the NE is poorly understood. Specifically, the identity of local ubiquitin ligases that facilitate ubiquitin-proteasome-dependent NE protein turnover is presently unknown. Here, we employ a short-lived, Lamin B receptor disease variant as a model substrate in a genetic screen to uncover key elements of NE protein turnover. We identify the ubiquitin-conjugating enzymes (E2s) Ube2G2 and Ube2D3, the membrane-resident ubiquitin ligases (E3s) RNF5 and HRD1, and the poorly understood protein TMEM33. RNF5, but not HRD1, requires TMEM33 both for efficient biosynthesis and function. Once synthesized, RNF5 responds dynamically to increased substrate levels at the NE by departing from the endoplasmic reticulum, where HRD1 remains confined. Thus, mammalian protein quality control machinery partitions between distinct cellular compartments to address locally changing substrate loads, establishing a robust cellular quality control system.

Profiling Chromatin Landscape at High Resolution and Throughput with 2C-ChIP.

Chromatin immunoprecipitation (ChIP) is used to probe the presence of proteins and/or their posttranslational modifications on genomic DNA. This method is often used alongside chromosome conformation capture approaches to obtain a better-rounded view of the functional relationship between chromatin architecture and its landscape. Since the inception of ChIP, its protocol has been modified to improve speed, sensitivity, and specificity. Combining ChIP with deep sequencing has recently improved its throughput and made genome-wide profiling possible. However, genome-wide analysis is not always the best option, particularly when many samples are required to study a given genomic region or when quantitative data is desired. We recently developed carbon copy-ChIP (2C-ChIP), a new form of the high-throughput ChIP analysis method ideally suited for these types of studies. 2C-ChIP applies ligation-mediated amplification (LMA) followed by deep sequencing to quantitatively detect specified genomic regions in ChIP samples. Here, we describe the generation of 2C-ChIP libraries and computational processing of the resulting sequencing data.

Classifying and scoring of molecules with the NGN: new datasets, significance tests, and generalization.

UNLABELLED: This paper demonstrates how a Neural Grammar Network learns to classify and score molecules for a variety of tasks in chemistry and toxicology. In addition to a more detailed analysis on datasets previously studied, we introduce three new datasets (BBB, FXa, and toxicology) to show the generality of the approach. A new experimental methodology is developed and applied to both the new datasets as well as previously studied datasets. This methodology is rigorous and statistically grounded, and ultimately culminates in a Wilcoxon significance test that proves the effectiveness of the system. We further include a complete generalization of the specific technique to arbitrary grammars and datasets using a mathematical abstraction that allows researchers in different domains to apply the method to their own work. BACKGROUND: Our work can be viewed as an alternative to existing methods to solve the quantitative structure-activity relationship (QSAR) problem. To this end, we review a number approaches both from a methodological and also a performance perspective. In addition to these approaches, we also examined a number of chemical properties that can be used by generic classifier systems, such as feed-forward artificial neural networks. In studying these approaches, we identified a set of interesting benchmark problem sets to which many of the above approaches had been applied. These included: ACE, AChE, AR, BBB, BZR, Cox2, DHFR, ER, FXa, GPB, Therm, and Thr. Finally, we developed our own benchmark set by collecting data on toxicology. RESULTS: Our results show that our system performs better than, or comparatively to, the existing methods over a broad range of problem types. Our method does not require the expert knowledge that is necessary to apply the other methods to novel problems. CONCLUSIONS: We conclude that our success is due to the ability of our system to: 1) encode molecules losslessly before presentation to the learning system, and 2) leverage the design of molecular description languages to facilitate the identification of relevant structural attributes of the molecules over different problem domains.

LAMPS: an analysis pipeline for sequence-specific ligation-mediated amplification reads.

OBJECTIVE: Ligation-Mediated Amplification (LMA) is a versatile biochemical tool for amplifying selected DNA sequences. LMA has increased in popularity due to its integration within chromosome conformation capture (5C) and chromatin immunoprecipitation (2C-ChIP) methodologies. The output of either 5C or 2C-ChIP protocols is a single-read sequencing library of ligated primer pairs that may or may not be multiplexed. While many computational tools currently exist for read mapping and analysis, these tools neither fully support multiplexed libraries nor provide qualitative reporting on the LMA primers involved. Typically, the task of library demultiplexing or primer analysis is offloaded on to the user. Our aim was to develop an easy-to-use pipeline for processing (multiplexed) single-read sequencing data produced by sequence-specific LMA. RESULTS: Here, we describe the Ligation-mediated Amplified, Multiplexed Primer-pair Sequence (LAMPS) analysis pipeline. LAMPS facilitates the analysis of multiplexed LMA sequencing data and provides a thorough assessment of a library's reads for a variety of experimental parameters (e.g., primer-pair efficiency). The standardized output of LAMPS allows for easy integration with downstream analyses, such as data track visualization on a genome browser. LAMPS is made publicly available on GitHub: https://github.com/BlanchetteLab/LAMPS.

RADICL-seq identifies general and cell type-specific principles of genome-wide RNA-chromatin interactions.

Mammalian genomes encode tens of thousands of noncoding RNAs. Most noncoding transcripts exhibit nuclear localization and several have been shown to play a role in the regulation of gene expression and chromatin remodeling. To investigate the function of such RNAs, methods to massively map the genomic interacting sites of multiple transcripts have been developed; however, these methods have some limitations. Here, we introduce RNA And DNA Interacting Complexes Ligated and sequenced (RADICL-seq), a technology that maps genome-wide RNA-chromatin interactions in intact nuclei. RADICL-seq is a proximity ligation-based methodology that reduces the bias for nascent transcription, while increasing genomic coverage and unique mapping rate efficiency compared with existing methods. RADICL-seq identifies distinct patterns of genome occupancy for different classes of transcripts as well as cell type-specific RNA-chromatin interactions, and highlights the role of transcription in the establishment of chromatin structure.

Double-Stranded Biotinylated Donor Enhances Homology-Directed Repair in Combination with Cas9 Monoavidin in Mammalian Cells.

Homology-directed repair (HDR) induced by site specific DNA double-strand breaks with CRISPR-Cas9 is a precision gene editing approach that occurs at low frequency in comparison to indel forming non-homologous end joining (NHEJ). In order to obtain high HDR percentages in mammalian cells, we engineered a Cas9 protein fused to a monoavidin domain to bind biotinylated donor DNA. In addition, we used the cationic polymer, polyethylenimine, to deliver Cas9-donor DNA complexes into cells. Improved HDR percentages of up to 90% in three loci tested (CXCR4, EMX1, and TLR) in standard HEK293T cells were observed. Our results suggest that donor DNA biotinylation and Cas9-donor conjugation in addition to delivery influence HDR efficiency.

PAM multiplicity marks genomic target sites as inhibitory to CRISPR-Cas9 editing.

In CRISPR-Cas9 genome editing, the underlying principles for selecting guide RNA (gRNA) sequences that would ensure for efficient target site modification remain poorly understood. Here we show that target sites harbouring multiple protospacer adjacent motifs (PAMs) are refractory to Cas9-mediated repair in situ. Thus we refine which substrates should be avoided in gRNA design, implicating PAM density as a novel sequence-specific feature that inhibits in vivo Cas9-driven DNA modification.