Data Visualization of CRISPR-Cas9 Guide RNA Design Tools.

With the advancement of genomic engineering and genetic modification techniques, the uptake of computational tools to design guide RNA increased drastically. Searching for genomic targets to design guides with maximum on-target activity (efficiency) and minimum off-target activity (specificity) is now an essential part of genome editing experiments. Today, a variety of tools exist that allow the search of genomic targets and let users customize their search parameters to better suit their experiments. Here we present an overview of different ways to visualize these searched CRISPR target sites along with specific downstream information like primer design, restriction enzyme activity and mutational outcome prediction after a double-stranded break. We discuss the importance of a good visualization summary to interpret information along with different ways to represent similar information effectively.

Unsupervised machine learning framework for discriminating major variants of concern during COVID-19.

Due to the high mutation rate of the virus, the COVID-19 pandemic evolved rapidly. Certain variants of the virus, such as Delta and Omicron emerged with altered viral properties leading to severe transmission and death rates. These variants burdened the medical systems worldwide with a major impact to travel, productivity, and the world economy. Unsupervised machine learning methods have the ability to compress, characterize, and visualize unlabelled data. This paper presents a framework that utilizes unsupervised machine learning methods to discriminate and visualize the associations between major COVID-19 variants based on their genome sequences. These methods comprise a combination of selected dimensionality reduction and clustering techniques. The framework processes the RNA sequences by performing a k-mer analysis on the data and further visualises and compares the results using selected dimensionality reduction methods that include principal component analysis (PCA), t-distributed stochastic neighbour embedding (t-SNE), and uniform manifold approximation projection (UMAP). Our framework also employs agglomerative hierarchical clustering to visualize the mutational differences among major variants of concern and country-wise mutational differences for selected variants (Delta and Omicron) using dendrograms. We also provide country-wise mutational differences for selected variants via dendrograms. We find that the proposed framework can effectively distinguish between the major variants and has the potential to identify emerging variants in the future.

Assessment of Pre-Clinical Liver Models Based on Their Ability to Predict the Liver-Tropism of Adeno-Associated Virus Vectors.

The liver is a prime target for in vivo gene therapies using recombinant adeno-associated viral vectors. Multiple clinical trials have been undertaken for this target in the past 15 years; however, we are still to see market approval of the first liver-targeted adeno-associated virus (AAV)-based gene therapy. Inefficient expression of the therapeutic transgene, vector-induced liver toxicity and capsid, and/or transgene-mediated immune responses reported at high vector doses are the main challenges to date. One of the contributing factors to the insufficient clinical outcomes, despite highly encouraging preclinical data, is the lack of robust, biologically and clinically predictive preclinical models. To this end, this study reports findings of a functional evaluation of 6 AAV vectors in 12 preclinical models of the human liver, with the aim to uncover which combination of models is the most relevant for the identification of AAV capsid variant for safe and efficient transgene delivery to primary human hepatocytes. The results, generated by studies in models ranging from immortalized cells, iPSC-derived and primary hepatocytes, and primary human hepatic organoids to in vivo models, increased our understanding of the strengths and weaknesses of each system. This should allow the development of novel gene therapies targeting the human liver.

Data-driven platform for identifying variants of interest in COVID-19 virus.

New SARS-CoV-2 variants emerge as part of the virus' adaptation to the human host. The Health Organizations are monitoring newly emerging variants with suspected impact on disease or vaccination efficacy as Variants Being Monitored (VBM), like Delta and Omicron. Genetic changes (SNVs) compared to the Wuhan variant characterize VBMs with current emphasis on the spike protein and lineage markers. However, monitoring VBMs in such a way might miss SNVs with functional effect on disease. Here we introduce a lineage-agnostic genome-wide approach to identify SNVs associated with disease. We curated a case-control dataset of 10,520 samples and identified 117 SNVs significantly associated with adverse patient outcome. While 40% (47) SNV are already monitored and 36% (43) are in the spike protein, we also identified 70 new SNVs that are associated with disease outcome. 31 of these are disease-worsening and predominantly located in the 3'-5' exonuclease (NSP14) with structural modelling revealing a concise cluster in the Zn binding domain that has known host-immune modulating function. Furthermore, we generate clade-independent VBM groupings by identifying interacting SNVs (epistasis). We find 37 sets of higher-order epistatic interactions joining 5 genomic regions (nsp3, nsp14, Spike S1, ORF3a, N). Structural modelling of these regions provides insights into potential mechanistic pathways of increased virulence as well as orthogonal methods of validation. Clade-independent monitoring of functionally interacting (epistasis, co-evolution) SNVs detected emerging VBM a week before they were flagged by Health Organizations and in conjunction with structural modelling provides faster, mechanistic insight into emerging strains to guide public health interventions.

A bioinformatic pipeline for simulating viral integration data.

Viral integration is a complex biological process, and it is useful to have a reference integration dataset with known properties to compare experimental data against, or for comparing with the results from computational tools that detect integration. To generate these data, we developed a pipeline for simulating integrations of a viral or vector genome into a host genome. Our method reproduces more complex characteristics of vector and viral integration, including integration of sub-genomic fragments, structural variation of the integrated genomes, and deletions from the host genome at the integration site. Our method [1] takes the form of a snakemake [2] pipeline, consisting of a Python [3] script using the Biopython [4] module that simulates integrations of a viral reference into a host reference. This produces a reference containing integrations, from which sequencing reads are simulated using ART [5]. The IDs of the reads crossing integration junctions are then annotated using another python script to produce the final output, consisting of the simulated reads and a table of the locations of those integrations and the reads crossing each integration junction. To illustrate our method, we provide simulated reads, integration locations, as well as the code required to simulate integrations using any virus and host reference. This simulation method was used to investigate the performance of viral integration tools in our research [6].

Identification of Protein Isoforms Using Reference Databases Built from Long and Short Read RNA-Sequencing.

Alternative splicing can lead to distinct protein isoforms. These can have different functions in specific cells and tissues or in different developmental stages. In this study, we explored whether transcripts assembled from long read, nanopore-based, direct RNA-sequencing (RNA-seq) could improve the identification of protein isoforms in human K562 cells. By comparing with Illumina-based short read RNA-seq, we showed that a large proportion of Ensembl transcripts (5949/14,326) and genes expressing alternatively spliced transcripts (486/2981) identified with long direct reads were missed by short paired-end reads. By co-analyzing proteomic and transcriptomic data, we also showed that some peptides (826/35,976), proteins (262/3215), and protein isoforms arising from distinct transcript variants (574/1212) identified with isoform-specific peptides via custom long-read-based databases were missed in Illumina-derived databases. Finally, we generated unequivocal peptide evidence for a set of protein isoforms and showed that long read, direct RNA-seq allows the discovery of novel protein isoforms not already in reference databases or custom databases built from short read RNA-seq data. Our analysis highlights the benefits of long read RNA-seq data in the generation of reference databases to increase tandem mass spectrometry (MS/MS) identification of protein isoforms.

Novel human liver-tropic AAV variants define transferable domains that markedly enhance the human tropism of AAV7 and AAV8.

Recent clinical successes have intensified interest in using adeno-associated virus (AAV) vectors for therapeutic gene delivery. The liver is a key clinical target, given its critical physiological functions and involvement in a wide range of genetic diseases. Here, we report the bioengineering of a set of next-generation AAV vectors, named AAV-SYDs (where "SYD" stands for Sydney, Australia), with increased human hepato-tropism in a liver xenograft mouse model repopulated with primary human hepatocytes. We followed a two-step process that staggered directed evolution and domain-swapping approaches. Using DNA-family shuffling, we first mapped key AAV capsid regions responsible for efficient human hepatocyte transduction in vivo. Focusing on these regions, we next applied domain-swapping strategies to identify and study key capsid residues that enhance primary human hepatocyte uptake and transgene expression. Our findings underscore the potential of AAV-SYDs as liver gene therapy vectors and provide insights into the mechanism responsible for their enhanced transduction profile.

Isling: A Tool for Detecting Integration of Wild-Type Viruses and Clinical Vectors.

Detecting viral and vector integration events is a key step when investigating interactions between viral and host genomes. This is relevant in several fields, including virology, cancer research and gene therapy. For example, investigating integrations of wild-type viruses such as human papillomavirus and hepatitis B virus has proven to be crucial for understanding the role of these integrations in cancer. Furthermore, identifying the extent of vector integration is vital for determining the potential for genotoxicity in gene therapies. To address these questions, we developed isling, the first tool specifically designed for identifying viral integrations in both wild-type and vector from next-generation sequencing data. Isling addresses complexities in integration behaviour including integration of fragmented genomes and integration junctions with ambiguous locations in a host or vector genome, and can also flag possible vector recombinations. We show that isling is up to 1.6-fold faster and up to 170% more accurate than other viral integration tools, and performs well on both simulated and real datasets. Isling is therefore an efficient and application-agnostic tool that will enable a broad range of investigations into viral and vector integration. These include comparisons between integrations of wild-type viruses and gene therapy vectors, as well as assessing the genotoxicity of vectors and understanding the role of viruses in cancer.

Optimized nickase- and nuclease-based prime editing in human and mouse cells.

Precise genomic modification using prime editing (PE) holds enormous potential for research and clinical applications. In this study, we generated all-in-one prime editing (PEA1) constructs that carry all the components required for PE, along with a selection marker. We tested these constructs (with selection) in HEK293T, K562, HeLa and mouse embryonic stem (ES) cells. We discovered that PE efficiency in HEK293T cells was much higher than previously observed, reaching up to 95% (mean 67%). The efficiency in K562 and HeLa cells, however, remained low. To improve PE efficiency in K562 and HeLa, we generated a nuclease prime editor and tested this system in these cell lines as well as mouse ES cells. PE-nuclease greatly increased prime editing initiation, however, installation of the intended edits was often accompanied by extra insertions derived from the repair template. Finally, we show that zygotic injection of the nuclease prime editor can generate correct modifications in mouse fetuses with up to 100% efficiency.

INSIDER: alignment-free detection of foreign DNA sequences.

External DNA sequences can be inserted into an organism's genome either through natural processes such as gene transfer, or through targeted genome engineering strategies. Being able to robustly identify such foreign DNA is a crucial capability for health and biosecurity applications, such as anti-microbial resistance (AMR) detection or monitoring gene drives. This capability does not exist for poorly characterised host genomes or with limited information about the integrated sequence. To address this, we developed the INserted Sequence Information DEtectoR (INSIDER). INSIDER analyses whole genome sequencing data and identifies segments of potentially foreign origin by their significant shift in k-mer signatures. We demonstrate the power of INSIDER to separate integrated DNA sequences from normal genomic sequences on a synthetic dataset simulating the insertion of a CRISPR-Cas gene drive into wild-type yeast. As a proof-of-concept, we use INSIDER to detect the exact AMR plasmid in whole genome sequencing data from a Citrobacter freundii patient isolate. INSIDER streamlines the process of identifying integrated DNA in poorly characterised wild species or when the insert is of unknown origin, thus enhancing the monitoring of emerging biosecurity threats.

Single amino acid insertion allows functional transduction of murine hepatocytes with human liver tropic AAV capsids.

Recent successes in clinical gene therapy applications have intensified the interest in using adeno-associated viruses (AAVs) as vectors for gene delivery into human liver. An inherent intriguing characteristic of AAVs is that vector variants vary substantially in their ability to transduce hepatocytes from different species. This has historically limited the value of preclinical studies using rodent models for predicting the efficiency of AAV vectors in liver-targeted gene therapy clinical studies. In this work, we aimed to investigate the key determinants of the observed differential interspecies transduction abilities among AAV variants. We took advantage of domain swapping strategies between AAV-KP1, a newly identified variant with enhanced murine liver tropism, and AAV3b, which functions poorly in mice. The systematic in vivo comparison of AAV3b/AAV-KP1 chimeric variants allowed us to identify a threonine insertion at position 265 within variable region I (VR-I) as the key residue that confers murine hepatic transduction to human-derived clade B (AAV2-like) and clade C (AAV3b-like) variants. We propose to use this insertion to generate phylogenetically related AAV surrogates in support of toxicology and dosing studies in the murine liver model.

GOANA: A Universal High-Throughput Web Service for Assessing and Comparing the Outcome and Efficiency of Genome Editing Experiments.

The increased development of functionally diverse and highly specialized genome editors has created the need for comparative analytics tools that are able to profile the mutational outcomes, particularly rare and complex outcomes, to assess the editor's applicability to different domains. To address this need, we have developed Generalizable On-target activity ANAlyzer (GOANA), a high-throughput web-based software for determining editing efficiency and cataloguing rare outcomes from next-generation sequencing data. GOANA calculates mutation frequency and outcomes relative to a supplied control sample. It is scalable to thousands of target sites across the entire genome and is 4,000% faster than CRISPResso2. Mutations are reported on a "per-read" level rather than individually, enabling the identification of co-occurring mutations. GOANA is editor agnostic and can be applied to data generated from any targeted editing experiment, including base editors. Requiring only that control and treated reads are aligned to the same reference, GOANA can handle data from any library preparation method, including pooled amplicon and whole-genome sequencing. As a proof of principle, we analyze two large data sets of CRISPR-Cas9 and CRISPR-Cas12a editing, demonstrating the power of GOANA and highlighting several key differences between the two enzymes. GOANA is available for use at https://gt-scan.csiro.au/goana/ and as a command line tool from https://github.com/BauerLab/GOANA.

"But Mouse, You Are Not Alone": On Some Severe Acute Respiratory Syndrome Coronavirus 2 Variants Infecting Mice.

In silico predictions combined with in vitro, in vivo, and in situ observations collectively suggest that mouse adaptation of the severe acute respiratory syndrome 2 virus requires an aromatic substitution in position 501 or position 498 (but not both) of the spike protein's receptor binding domain. This effect could be enhanced by mutations in positions 417, 484, and 493 (especially K417N, E484K, Q493K, and Q493R), and to a lesser extent by mutations in positions 486 and 499 (such as F486L and P499T). Such enhancements, due to more favorable binding interactions with residues on the complementary angiotensin-converting enzyme 2 interface, are, however, unlikely to sustain mouse infectivity on their own based on theoretical and experimental evidence to date. Our current understanding thus points to the Alpha, Beta, Gamma, and Omicron variants of concern infecting mice, whereas Delta and "Delta Plus" lack a similar biomolecular basis to do so. This paper identifies 11 countries (Brazil, Chile, Djibouti, Haiti, Malawi, Mozambique, Reunion, Suriname, Trinidad and Tobago, Uruguay, and Venezuela) where targeted local field surveillance of mice is encouraged because they may have come in contact with humans who had the virus with adaptive mutation(s). It also provides a systematic methodology to analyze the potential for other animal reservoirs and their likely locations.

Interoperable medical data: The missing link for understanding COVID-19.

Being able to link clinical outcomes to SARS-CoV-2 virus strains is a critical component of understanding COVID-19. Here, we discuss how current processes hamper sustainable data collection to enable meaningful analysis and insights. Following the 'Fast Healthcare Interoperable Resource' (FHIR) implementation guide, we introduce an ontology-based standard questionnaire to overcome these shortcomings and describe patient 'journeys' in coordination with the World Health Organization's recommendations. We identify steps in the clinical health data acquisition cycle and workflows that likely have the biggest impact in the data-driven understanding of this virus. Specifically, we recommend detailed symptoms and medical history using the FHIR standards. We have taken the first steps towards this by making patient status mandatory in GISAID ('Global Initiative on Sharing All Influenza Data'), immediately resulting in a measurable increase in the fraction of cases with useful patient information. The main remaining limitation is the lack of controlled vocabulary or a medical ontology.

Supporting pandemic response using genomics and bioinformatics: A case study on the emergent SARS-CoV-2 outbreak.

Pre-clinical responses to fast-moving infectious disease outbreaks heavily depend on choosing the best isolates for animal models that inform diagnostics, vaccines and treatments. Current approaches are driven by practical considerations (e.g. first available virus isolate) rather than a detailed analysis of the characteristics of the virus strain chosen, which can lead to animal models that are not representative of the circulating or emerging clusters. Here, we suggest a combination of epidemiological, experimental and bioinformatic considerations when choosing virus strains for animal model generation. We discuss the currently chosen SARS-CoV-2 strains for international coronavirus disease (COVID-19) models in the context of their phylogeny as well as in a novel alignment-free bioinformatic approach. Unlike phylogenetic trees, which focus on individual shared mutations, this new approach assesses genome-wide co-developing functionalities and hence offers a more fluid view of the 'cloud of variances' that RNA viruses are prone to accumulate. This joint approach concludes that while the current animal models cover the existing viral strains adequately, there is substantial evolutionary activity that is likely not considered by the current models. Based on insights from the non-discrete alignment-free approach and experimental observations, we suggest isolates for future animal models.

VARSCOT: variant-aware detection and scoring enables sensitive and personalized off-target detection for CRISPR-Cas9.

BACKGROUND: Natural variations in a genome can drastically alter the CRISPR-Cas9 off-target landscape by creating or removing sites. Despite the resulting potential side-effects from such unaccounted for sites, current off-target detection pipelines are not equipped to include variant information. To address this, we developed VARiant-aware detection and SCoring of Off-Targets (VARSCOT). RESULTS: VARSCOT identifies only 0.6% of off-targets to be common between 4 individual genomes and the reference, with an average of 82% of off-targets unique to an individual. VARSCOT is the most sensitive detection method for off-targets, finding 40 to 70% more experimentally verified off-targets compared to other popular software tools and its machine learning model allows for CRISPR-Cas9 concentration aware off-target activity scoring. CONCLUSIONS: VARSCOT allows researchers to take genomic variation into account when designing individual or population-wide targeting strategies. VARSCOT is available from https://github.com/BauerLab/VARSCOT .

Unlocking HDR-mediated nucleotide editing by identifying high-efficiency target sites using machine learning.

Editing individual nucleotides is a crucial component for validating genomic disease association. It is currently hampered by CRISPR-Cas-mediated "base editing" being limited to certain nucleotide changes, and only achievable within a small window around CRISPR-Cas target sites. The more versatile alternative, HDR (homology directed repair), has a 3-fold lower efficiency with known optimization factors being largely immutable in experiments. Here, we investigated the variable efficiency-governing factors on a novel mouse dataset using machine learning. We found the sequence composition of the single-stranded oligodeoxynucleotide (ssODN), i.e. the repair template, to be a governing factor. Furthermore, different regions of the ssODN have variable influence, which reflects the underlying mechanism of the repair process. Our model improves HDR efficiency by 83% compared to traditionally chosen targets. Using our findings, we developed CUNE (Computational Universal Nucleotide Editor), which enables users to identify and design the optimal targeting strategy using traditional base editing or - for-the-first-time - HDR-mediated nucleotide changes.