CRISPR, ZFNS, and TALENS for Hemoglobinopathies an Analysis

Sickle Cell Disease is one of the most common genetic disorders in the United States and is incredibly prevalent throughout Africa and the Middle East. By 2050, the annual number of newborns with Sickle Cell Disease is projected to increase by 33%. A similar story can be told about Beta-Thalassemia: another hemoglobinopathy that has no standard treatment. The future of treating hemoglobinopathies looks bleak and more research must be done to prevent fatalities and the lifelong problems associated with it now. Sickle Cell Disease and Beta Thalassemia have one defining similarity: they are both monogenic disorders. This unique characteristic of having a single gene variation allows them to be the ideal candidate for one of the newest breakthroughs in biotechnology: target genome editing. As of now, there are three major competitors in the field, Zinc Finger Nuclease (ZFN), Transcription Activator-like Effector Nucleases (TALENs), and Clustered Regularly Interspaced Short Palindromic Repeat (CRISPR) target genome editing. Here I summarize the possibilities target genome editing provides in terms of research and a potential treatment for both Sickle Cell disease and Beta Thalassemia with a focus on comparing the three target genome editing technologies.

The order of macrophage 4D genome coordinates gene transcription during differentiation and infection

The highly organized three-dimensional genome is crucial for gene transcription. However, it remains elusive how the order of the genome architecture related to its function. Here, we developed a single-cell Hi-C method and proposed TAD “degree of disorder” as a measure of genome organizational patterns, which is correlated with the chromatin epigenetic states, gene expression and co-regulation, and chromatin structure variability in individual cells. Upon Mycobacterium tuberculosis infection, NF-κB enters into the nucleus, binds to the target genome regions and initiates systematic chromatin conformation reorganization. Furthermore, we identified a remote NF-κB enriched enhancer promotes the expression of PD-L1 through chromatin loop, which could be a potential anti-tuberculosis and even anti-tumor therapeutic target. The integrated Hi-C, eQTL, and GWAS analysis depicted the atlas of the long-range target genes of tuberculosis susceptible loci. Among which SNP rs1873613 is located in the anchor of a dynamic chromatin loop with LRRK2, whose inhibitor AdoCbl could be an anti-tuberculosis drug candidate. Our study provides comprehensive resources for the 4D genome of immunocytes and sheds insights into the genome organization order and the coordinated gene transcription.

Scaffolding Contigs Using Multiple Reference Genomes

Scaffolding is an important step of the genome assembly and its function is to order and orient the contigs in the assembly of a draft genome into larger scaffolds. Several single reference-based scaffolders have currently been proposed. However, a single reference genome may not be sufficient alone for a scaffolder to correctly scaffold a target draft genome, especially when the target genome and the reference genome have distant evolutionary relationship or some rearrangements. This motivates researchers to develop the so-called multiple reference-based scaffolders that can utilize multiple reference genomes, which may provide different but complementary types of scaffolding information, to scaffold the target draft genome. In this chapter, we will review some of the state-of-the-art multiple reference-based scaffolders, such as Ragout, MeDuSa and Multi-CAR, and give a complete introduction to Multi-CSAR, an improved extension of Multi-CAR.

CRISPRLearner: A Deep Learning-Based System to Predict CRISPR/Cas9 sgRNA On-Target Cleavage Efficiency

CRISPRLearner, the system presented in this paper, makes it possible to predict the on-target cleavage efficiency (also called on-target knockout efficiency) of a given sgRNA sequence, specifying the target genome that this sequence is designed for. After efficiency prediction, the researcher can evaluate its sequence and design a new one if the predicted efficiency is low. CRISPRLearner uses a deep convolutional neural network to automatically learn sequence determinants and predict the efficiency, using pre-trained models or using a model trained on a custom dataset. The convolutional neural network uses linear regression to predict efficiency based on efficiencies used to train the model. Ten different models were trained using ten different gene datasets. The efficiency prediction task attained an average Spearman correlation higher than 0.40. This result was obtained using a data augmentation technique that generates mutations of a sgRNA sequence, maintaining the efficiency value. CRISPRLearner supports researchers in sgRNA design task, predicting a sgRNA on-target knockout efficiency.

Pipeline for the Rapid Development of Cytogenetic Markers Using Genomic Data of Related Species

Repetitive DNA including tandem repeats (TRs) is a significant part of most eukaryotic genomes. TRs include rapidly evolving satellite DNA (satDNA) that can be shared by closely related species, their abundance may be associated with evolutionary divergence, and they have been widely used for chromosome karyotyping using fluorescence in situ hybridization (FISH). The recent progress in the development of whole-genome sequencing and bioinformatics tools enables rapid and cost-effective searches for TRs including satDNA that can be converted into molecular cytogenetic markers. In the case of closely related taxa, the genome sequence of one species (donor) can be used as a base for the development of chromosome markers for related species or genomes (target). Here, we present a pipeline for rapid and high-throughput screening for new satDNA TRs in whole-genome sequencing of the donor genome and the development of chromosome markers based on them that can be applied in the target genome. One of the main peculiarities of the developed pipeline is that preliminary estimation of TR abundance using qPCR and ranking found TRs according to their copy number in the target genome; it facilitates the selection of the most prospective (most abundant) TRs that can be converted into cytogenetic markers. Another feature of our pipeline is the probe preparation for FISH using PCR with primers designed on the aligned TR unit sequences and the genomic DNA of a target species as a template that enables amplification of a whole pool of monomers inherent in the chromosomes of the target species. We demonstrate the efficiency of the developed pipeline by the example of FISH probes developed for A, B, and R subgenome chromosomes of hexaploid triticale (BBAARR) based on a bioinformatics analysis of the D genome of Aegilops tauschii (DD) whole-genome sequence. Our pipeline can be used to develop chromosome markers in closely related species for comparative cytogenetics in evolutionary and breeding studies.

A non-cationic nucleic acid nanogel for the delivery of the CRISPR/Cas9 gene editing tool

A non-cationic nucleic acid nanogel bearing Cas9/sgRNA complex is developed, which facilitates cellular uptake of the gene editing tool and protects it from nuclease digestion, making the nanogel a promising delivery system for target genome editing.

BlobTools: Interrogation of genome assemblies

The goal of many genome sequencing projects is to provide a complete representation of a target genome (or genomes) as underpinning data for further analyses. However, it can be problematic to identify which sequences in an assembly truly derive from the target genome(s) and which are derived from associated microbiome or contaminant organisms. We present BlobTools, a modular command-line solution for visualisation, quality control and taxonomic partitioning of genome datasets. Using guanine+cytosine content of sequences, read coverage in sequencing libraries and taxonomy of sequence similarity matches, BlobTools can assist in primary partitioning of data, leading to improved assemblies, and screening of final assemblies for potential contaminants. Through simulated paired-end read dataset,s containing a mixture of metazoan and bacterial taxa, we illustrate the main BlobTools workflow and suggest useful parameters for taxonomic partitioning of low-complexity metagenome assemblies.