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What is Genome wide analysis and Locus specific analysis

What is Genome wide analysis and Locus specific analysis


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I am reading some articles on genetic variations and I see there are two types of analysis one is genome wide genetic variation analysis and second one is locus specific genetic variation analysis. I don't understand what these two, genome wide and locus specific analysis mean and what is the difference between these two analysis or why do we need both type of analysis?


Welcome to Biology.SE!

A locus (plur. loci) is a region of arbitrary size on a chromosome. A locus can be a single nucleotide or it can much larger (like $10^7$ sites).

A genome-wide analysis is therefore an analysis using the whole genome at once while a locus-specific analysis is the same analysis performed at the level of individual loci.

To make an analogy, if a genome-wide analysis is like a country-wide analysis, then a locus specific analysis is like a city specific analysis.

Without more context, it will not be possible to say more than that!


Biological Pathway Analysis

Ramakanth Chirravuri Venkata , Dario Ghersi , in Encyclopedia of Bioinformatics and Computational Biology , 2019

Pathway Analysis in Genome-Wide Association Studies

Genome Wide Association Studies (GWAS) use statistical models to investigate the association between variants (Single Nucleotide Polymorphisms) and phenotypes. Despite the debate regarding statistical significance vs. biological relevance in the scientific community, GWAS have been an important source for generating novel hypotheses in the field of genetics. GWAS tend to be suitable for detecting common variants associated with specific phenotypes ( Teslovich et al., 2010 Visscher et al., 2012 ). However, in the case of complex diseases associating individual variants to specific phenotype can be challenging due to their small effect sizes. Another considerable issue with GWAS is that they use stringent p-value thresholds that are corrected for multiple hypothesis testing, in order to control false positives. This threshold may occasionally get rid of moderately significant genes that may represent biologically plausible candidates for genotype-phenotype associations ( Jia et al., 2011 ).

Pathway analysis may substantially alleviate these problems by examining the combined effects of related single variants, and also by aiding in the identification of related novel variants that are associated with the phenotypes. Pathway analysis examines association with the phenotype of interest using gene sets instead of individual genes ( Kao et al., 2016 Zhong et al., 2010 ). Pathway analysis of GWAS studies generally consists of three steps: (1) determination of gene sets suitable for pathway analysis (e.g., Gene Ontology ( Ashburner et al., 2000 ) or KEGG ( Ogata et al., 1999 )) (2) mapping variants to their respective genes and (3) using statistical models to examine associations.


Molecular Basis of Venous Insufficiency

GEERT W. SCHMID-SCHÖNBEIN , in The Vein Book , 2007

GENETIC MECHANISMS

Genetic linkage analysis has brought to light that there are patients with familial risk factors. An imbalance between collagen I and collagen III has been found in proximal segments of human varicose saphenous veins in addition to fragmentation of elastin fibrils. 144 The proportion of collagen type III is significantly decreased in cultured smooth muscle cells and dermal fibroblasts derived from patients with varicose veins indicating a deficiency in collagen type III. 145 Such type of remodeling of the extracellular matrix has been proposed to be due to activation of matrix metalloproteinases (MMPs). Expression of MMP-1, MMP-2, MMP-9, and tissue inhibitor of metalloproteinases-1 has been proposed 146,147 with a possible imbalance between these enzymes and their inhibitors.


In situ Spike-in Control in Every Sample

High quality data is ensured even in non-traditional organisms

Methylation Ratio Correlation

The observed methylation levels of a spike-in control composed of 6 unique double-stranded synthetic amplicons that have specific DNA methylation levels ranging from 0 to 100% is highly correlated with expected methylation level using conventional WGBS. Analysis of spike-in control using our bioinformatic service ensures high quality data is produced in every single sample.

Bisulfite Conversion Efficiency

Bisulfite conversion efficiency from various species calculated using both non-CpG context from gDNA and in situ spike-in control shows that the spike-in control is a better measurement for bisulfite conversion efficiency when working with non-traditional organisms that have methylation in non-CpG context


Materials and Methods

Strains and Genomes

Strain K-10 (Li et al., 2005, 2019), Telford (Brauning et al., 2019) and S397 (Bannantine et al., 2012) were included in this study as references of the major lineages that have emerged during the evolution of Map. Isolates were propagated on slopes of modified Middlebrook 7H11 supplemented with 20% (vol/vol) heat inactivated newborn calf serum, 2.5% (vol/vol) glycerol, 2 mM asparagine, 10% (vol/vol) Middlebrook oleic acid-albumin- dextrose-catalase (OADC) enrichment medium (Becton Dickinson, Oxford, Oxfordshire, United Kingdom), Selectatabs (code MS 24 MAST Laboratories Ltd., Merseyside, United Kingdom), and 2 μg ml-1 mycobactin J (Allied Monitor, Fayette, MO, United States). The complete genome sequence of K-10 (C-type, NC_002944.2), Telford (S-type subtype I, NZ_CP033688.1) and S397 (S-type subtype III, NZ_CP053749.1) were downloaded from the NCBI RefSeq (O’Leary et al., 2016 Table 1). S397 was annotated using PGAP (Tatusova et al., 2016).

Table 1. Details of the strains and genomes used and information on the number of copies of the IS900.

In vitro IS900-RFLP

Mycobacterium avium subsp. paratuberculosis strains were typed by BstEII IS900-RFLP as described previously (Thibault et al., 2007). Profiles were designated according to nomenclature previously described (Collins et al., 1997 Pavlik et al., 1999 Mobius et al., 2009). Profiles were analyzed using Bionumerics TM software version 7.6.3 (Applied Maths, Belgium).

Bioinformatic Analysis

IS900 Sequence Identification and in silico IS900 RFLP Workflow

We developed an in silico analysis pipeline for IS900 RFLP profiling using complete genome sequences as the input (Figure 1). As a first step, all BstEII restriction sites were located in the genome using in-house script (available at: https://forgemia.inra.fr/public-pgba/is900-rflp-in-silico) developed with Biopython (v1.76) (Cock et al., 2009). IS900 copies in the genome sequence were identified using a blastn version 2.9.0 (Altschul et al., 1990) search of IS900 sequence retrieved from the NCBI database (accession no. X16293) with a percent identity of 99% and an e-value of 1e-100 to exclude all false positive hits. For each hit, upstream and downstream sequences nearest the BstEII restriction sites were retrieved from the BstEII restriction map and length of the BstEII fragment was computed. A gel migration equation was previously determined using GelAnalyzer 19.1 1 and used to convert fragment length into migration distance for further visualization of the RFLP profile. Migration data and coordinates of IS900 copies were saved in .tsv and .rflp files, respectively, for visualization of the profile and further investigation of locus distribution. Visualization of RFLP profiles was performed using python library matplotlib (v3.3.0) 2 .

Figure 1. Bioinformatic analysis pipeline. Details of steps performed by the IS900 RFLP in silico pipeline. From complete genome sequences, BstEII restriction site and IS900 sequence positions were identified. Both data sets are merged together to extract the BstEII fragment sizes from the genome sequence. Previous positions IS900 and sequence orientation are stored in a.rflp file for further analysis. BstEII fragment sizes are converted into migration distance based on calculation from an in vitro gel migration (see section “Materials and Methods”) and saved in the .tsv file for further visualization of the RFLP profile.

IS900 Sequence Polymorphism

In order to confirm IS900 sequence polymorphisms previously described (Semret et al., 2006 Castellanos et al., 2009), IS900 copies from the three genomes were extracted and aligned using Multalin (Corpet, 1988) with the 𠇍NA” symbol comparison table. Shorter IS900 copies in the alignment were manually checked with Artemis software version 18.1.0 (Carver et al., 2008) to confirm blastn results.

Mauve Alignment

Synteny alignments were determined with Mauve (snapshot_2015-02-13 build 0) (Darling et al., 2004). In order to avoid false indications of inversions or other rearrangements, these genomes were first shifted to start at the dnaA gene prior to Mauve analysis. Using the complete genome sequences of each strain, we performed a 1 vs. 1 genome alignment using progressive Mauve (Darling et al., 2010) and also an alignment of the three genome sequences in order to visualize differences in genomic organization.

Orthology Analysis

To identify IS900 copies inserted at orthologous genomic sites between the genomes of K-10, Telford and S397, we performed blastn searches using as queries, 2,000 bp of upstream and downstream genomic regions flanking each IS900 copy. These orthologous flanking regions from one genome were aligned to the two other genomes and compared to identify orthologous loci. Therefore, blastn results were parsed in order to select the best match. The best match was defined with the following criteria: (1) an e-value of 0 and (2) a minimum coverage of 80%. For many flanking regions, blastn yielded only one result. But for a few others, more than one result was returned. In cases where the coverage of the best result is below 80%, we searched for other results around 4,000 bp from the first result to identify genome rearrangement/differences and merged them. Finally, orthologous loci were considered linked to an IS900 loci if the center of the BLAST hit fell within the 2,000 bp region flanking the IS900 element in the target genome.

IS900 Sites Gene Ontology Enrichment Analysis

This approach aimed to determine if the genes near insertion sites were enriched for any particular function. Protein sequences upstream and downstream of IS900 were extracted, if available, from the RefSeq annotation. Functional annotation was performed using eggNOG-mapper-2.0.1 (Huerta-Cepas et al., 2017) based on eggNOG orthology data (Huerta-Cepas et al., 2019). Sequence searches were performed using DIAMOND version 2.0.5 (Buchfink et al., 2015).

Identifying the Candidate Insertion Sites

In order to identify targeted insertion site motifs in the genomes of Map, 10 bp of upstream and downstream sequence of each IS900 were extracted. Each strand of the IS element was considered and extracted sequences were reverse complemented if needed. Insertion sequences containing deletions or duplications were excluded from the analysis. Multalin (Corpet, 1988) was used to align upstream and downstream sequences using the 𠇍NA” symbol comparison table. The alignment of the upstream sequence was performed with “gap penalty at opening” set to 1 and “gap penalty at extension” set to 0. The alignment of the downstream sequence was performed with default parameters. Upstream and downstream IS900 flanking regions from each genome were aligned to the M. avium subsp. hominissuis (Mah) 104 genome using blastn in order to find orthologous regions. Only adjacent regions have been retained. Putative target sequences, previously identified in the three genomes, were extracted manually from the Mah 104 genome using Artemis software version 18.1.0 (Carver et al., 2008) based on blastn results. MEME version 5.3.1 (Bailey and Elkan, 1994) was used to identify a putative target site motif.


Introduction

Haemorrhoids are normal anal vascular cushions filled with blood at the junction of the rectum and the anus. It is assumed that their main role in humans is to maintain continence1 but other functions such as sensing fullness, pressure and perceiving anal contents have been suggested given the sensory innervation.2 Haemorrhoidal disease (hereafter referred to as HEM) occurs when haemorrhoids enlarge and become symptomatic (sometimes associated with rectal bleeding and itching/soiling) due to the deterioration or prolapse of the anchoring connective tissue, the dilation of the haemorrhoidal plexus or the formation of blood clots. Severe forms of HEM often require surgical treatment and the removal of abnormally enlarged and/or thrombosed haemorrhoids.1 HEM prevalence increases with age and shows staggering figures worldwide (up to 86% prevalence in some reports),3 whereby a large proportion of cases remain undetected as asymptomatic or mild enough to be self-treated with over-the-counter treatment. HEM represents a considerable medical and socioeconomic burden with an estimated annual cost of US$800 million in the USA alone, mainly related to the large number of haemorrhoidectomies performed every year.4

A number of HEM risk factors have been suggested, including human erect position. The tight anal sealing provided by the elaborated haemorrhoidal plexus may have developed during human evolution co-occurring with permanent bipedalism, as shown by our histology comparison of four different mammals (human, gorilla, baboon, mouse online supplemental figure 1, online supplemental material 1). Other suggested risk factors are a sedentary lifestyle, obesity, reduced dietary fibre intake, spending excess time on the toilet, straining during defecation, strenuous lifting, constipation, diarrhoea, pelvic floor dysfunction, pregnancy and giving natural birth, with several being controversially reported. The hypothetical model shown in online supplemental figure 2 summarises the contemporary concepts regarding the pathophysiology of HEM development.5 Until today, HEM aetiopathogenesis is poorly investigated, and neither the exact molecular mechanisms nor the reason(s) why only some people develop HEM are known. Genetic susceptibility may play a role in HEM development, but no large-scale, genome-wide association study (GWAS) for HEM has ever been conducted. To evaluate the contribution of genetic variation to the genetic architecture of HEM, we carried out a GWAS meta-analysis in 218 920 affected individuals and 725 213 population controls of European ancestry.

Supplemental material


10.5: Quantitative Trait Locus (QTL) Analysis

  • Contributed by Todd Nickle and Isabelle Barrette-Ng
  • Professors (Biology) at Mount Royal University & University of Calgary

Most of the phenotypic traits commonly used in introductory genetics are qualitative, meaning that the phenotype exists in only two (or possibly a few more) discrete, alternative forms, such as either purple or white flowers, or red or white eyes. These qualitative traits are therefore said to exhibit discrete variation. On the other hand, many interesting and important traits exhibit continuous variation these exhibit a continuous range of phenotypes that are usually measured quantitatively, such as intelligence, body mass, blood pressure in animals (including humans), and yield, water use, or vitamin content in crops. Traits with continuous variation are often complex, and do not show the simple Mendelian segregation ratios (e.g. 3:1) observed with some qualitative traits. Many complex traits are also influenced heavily by the environment. Nevertheless, complex traits can often be shown to have a component that is heritable, and which must therefore involve one or more genes.

How can genes, which are inherited (in the case of a diploid) as at most two variants each, explain the wide range of continuous variation observed for many traits? The lack of an immediately obvious explanation to this question was one of the early objections to Mendel's explanation of the mechanisms of heredity. However, upon further consideration, it becomes clear that the more loci that contribute to trait, the more phenotypic classes may be observed for that trait (Figure (PageIndex<1>)).

If the number of phenotypic classes is sufficiently large (as with three or more loci), individual classes may become indistinguishable from each other (particularly when environmental effects are included), and the phenotype appears as a continuous variation (Figure (PageIndex<2>)). Thus, quantitative traits are sometimes called polygenic traits, because it is assumed that their phenotypes are controlled by the combined activity of many genes. Note that this does not imply that each of the individual genes has an equal influence on a polygenic trait &ndash some may have major effect, while others only minor. Furthermore, any single gene may influence more than one trait, whether these traits are quantitative or qualitative traits.

Figure (PageIndex<2>): The more loci that affect a trait, the larger the number of phenotypic classes that can be expected. For some traits, the number of contributing loci is so large that the phenotypic classes blend together in apparently continuous variation. (Original-Deyholos-CC:AN)

We can use molecular markers to identify at least some of the genes (those with a major influence) that affect a given quantitative trait. This is essentially an extension of the mapping techniques we have already considered for discrete traits. A QTL mapping experiment will ideally start with two pure-breeding lines that differ greatly from each other in respect to one or more quantitative traits (Figure (PageIndex<3>)). The parents and all of their progeny should be raised under as close to the same environmental conditions as possible, to ensure that observed variation is due to genetic rather than external environmental factors. These parental lines must also be polymorphic for a large number of molecular loci, meaning that they must have different alleles from each other at hundreds of loci. The parental lines are crossed, and then this F1 individual, in which recombination between parental chromosomes has occurred is self-fertilized (or back-crossed). Because of recombination (both crossing over and independent assortment), each of the F2 individuals will contain a different combination of molecular markers, and also a different combination of alleles for the genes that control the quantitative trait of interest (Table (PageIndex<1>)).

Figure (PageIndex<3>): Strategy for a typical QTL mapping experiment. Two parents that differ in a quantitative trait (e.g. fruit mass) are crossed, and the F1 is self-fertilized (as shown by the cross-in-circle symbol). The F2 progeny will show a range of quantitative values for the trait. The task is then to identify alleles of markers from one parent that are strongly correlated with the quantitative trait. For example, markers from the large-fruit parent that are always present in large-fruit F2 individuals (but never in small-fruit individuals) are likely linked to loci that control fruit mass.

Table (PageIndex<1>) Genotypes and quantitative data for some individuals from the crosses shown in Figure (PageIndex<8>)

Figure (PageIndex<4>): Plots of fruit mass and genotype for selected loci from Table (PageIndex<1>). For most loci (e.g. H), the genotype shows no significant correlation with fruit weight. However, for some molecular markers, the genotype will be highly correlated with fruit weight. Both D and K influence fruit weight, but the effect of genotype at locus D is larger than at locus K. (Original-Deyholos-CC:AN)

CRISPR’s Rapid Rise Shakes Up Genome-Wide Screening

A truly disruptive technology, CRISPR screening roughly displaces its predecessors then refines itself, as shown by its functional genomics applications and its ability to complement single-cell transcriptomics

Using a genome-scale, loss-of-function CRISPR screen, scientists from New York University, the New York Genome Center, and the Icahn School of Medicine at Mount Sinai identified human genes and gene regulatory networks that are required by SARS-CoV-2. The scientists also demonstrated that suppression of these genes and networks confers resistance to viral infection. For example, loss of RAB7A effectively sequesters the ACE2 receptor inside cells, preventing it from serving as an entry point for the virus. [Vince Dittmer/NYGC]

CRISPR has been grabbing headlines because of its potential as a form of gene therapy. But CRISPR also deserves attention because of its contributions to genome-wide screening. Although these contributions usually escape public notice, they are nothing less than revolutionary.

Soon after CRISPR gene editing was introduced to the world in 2012, researchers led by Feng Zhang, PhD, core institute member at the Broad Institute of MIT and Harvard, set to work building a genome-scale loss-of-function screen that relied on the CRISPR-Cas9 nuclease instead of small interfering RNA (siRNA). 1 Then CRISPR screening quickly began acquiring various refinements. (For details, see the below sidebar, “Establishing the Basics of CRISPR Screening.”)

By now, CRISPR screening has become a familiar technique. But it continues to become more powerful and sophisticated. In this article, we will see how CRISPR screening is enabling advances in drug development, functional human genomics, and basic science. Also, we will look into blue-sky developments that will allow CRISPR screening to do even more.

Characterizing coding and noncoding elements

Neville Sanjana, PhD, who worked with the Zhang laboratory on early CRISPR screens, says his group at the New York Genome Center (NYGC) and New York University (NYU) is now developing gene editing technologies for functional genomics applications, with the ambitious goal of understanding the function of all the elements of the human genome—both coding and noncoding.

The Sanjana laboratory has been developing screens that do more than just target genes. “[We have been] looking at noncoding regions like transcription factor binding sites [or] noncoding RNAs, or [at] regions deep in intergenic space that are associated with common or rare diseases,” Sanjana says. “[Some of this work] has led to amazing clinical translation.”

The principal example of that clinical translation was a noncoding CRISPR screen that identified a region of the genome involved in repressing fetal hemoglobin. The screen showed that when that noncoding region was perturbed, fetal hemoglobin could be derepressed. This finding led to a new therapeutic target for sickle-cell disease (SCD) and β-thalassemia, two diseases affecting adult hemoglobin. 2

“The exact guide RNA from our CRISPR screen is what is actually used for the first-in-human CRISPR therapy,” notes Sanjana. That breakthrough therapy uses CRISPR-Cas9 gene editing to repress the BCL11A gene specifically in blood stem cells, causing them to reactivate the fetal hemoglobin gene. The engineered cells were then successfully used to treat two patients—one with SCD (Victoria Gray), and one with β-thalassemia. (The work was led by investigators from CRISPR Therapeutics and Vertex Pharmaceuticals.)

A major challenge in gene editing therapies is getting edits to happen in the right cell types. In this case, cell-type specificity is a product of editing the noncoding genome, because this particular noncoding region is functional only in blood cells, according to Sanjana. 3

Identifying host factors for SARS-CoV-2 infection

More recently, Sanjana and colleagues have tackled COVID-19 by deploying a CRISPR screen to knock out every gene in the human genome in lung cells to find genes and pathways required for SARS-CoV-2 infection. Sanjana explains that the goal was to find multiple points of attack for blocking the SARS-CoV-2 virus.

The approach was inspired by the successful development of antiretroviral cocktails for HIV that prevent viral escape mutations. Those mutations otherwise occur when the virus is challenged with a single antiviral drug. For some of the top candidate genes involved in viral entry, a search for existing drugs that inhibit the protein products of these genes came up empty.

“We thought, let’s see if there might be some other common mechanism or convergent pathway that we can target,” Sanjana recalls. To conduct this work, Sanjana and colleagues picked up another new CRISPR screening tool. The tool is called “enhanced CRISPR-compatible indexing of transcriptomes and epitopes with sequencing,” or ECCITE-seq. It takes CRISPR screening to the single-cell level while detecting proteins and transcripts in parallel.

That analysis pointed to the cholesterol biosynthesis pathway and increasing intracellular cholesterol as a potential block on viral infection. This key discovery opened a wealth of heart disease drugs for potential repurposing. With the help of other team members, Sanjana zeroed in on the calcium channel blocker amlopidine, an FDA-approved drug, and found that it had a powerful inhibitory effect on the virus.

Commercializing CRISPR screening

Companies specializing in CRISPR screening for drug discovery and development are taking advantage of single-cell analysis capabilities. For example, single-cell CRISPR analysis is a key service of Cellecta, which offers pooled lentiviral libraries with barcoded guide RNA (gRNA) expression cassettes to enable the analyses known as Perturb-seq and CROP-seq (CRISPR droplet sequencing).

Cellecta has developed technology that combines pooled genetic screening and single-cell expression analysis. With this technology, individual cells are are labeled with unique barcodes so that subpopulations of cells with distinct phenotypes may be identified. Because the barcodes are designed to express RNA transcripts in the cells, it is possible to assess how variant cells respond to experimental conditions. The detection of expressed barcode RNA may be carried out with various expression profiling methods.

In these analyses, genetic perturbations (gene knockouts or knockdowns) are applied, and the resulting phenotypes are studied at the level of the transcriptome to infer genetic function. Barcoding of the gRNAs allows the pooled perturbations to be deconvoluted and associated with specific phenotypes.

“A typical use is an experiment in a heterogeneous cell population,” says Donato Tedesco, PhD, Cellecta’s director of research and development. “If you want to identify a subset of cells within the general population that have different responses to a knockout, the only way to do that is single-cell analysis.”

He relates that Cellecta’s customers often use CRISPR screens looking for knockouts that either antagonize or synergize with an experimental drug, in experiments aiming at elucidating the drug’s mechanism of action.

Another company offering CRISPR screening services is Synthego, which works with pharmaceutical and biotechnology firms. For Robert Deans, PhD, chief scientific officer of Synthego, riding the single-cell analysis trend in CRISPR screening comes down to automation and robotics—and lots of it.

Synthego has been applying machine learning and automation to handle the massive data output from these types of experiments. That powerful analysis feeds back into the screening. The company can boost efficiency by tweaking aspects of guide design and optimizing the ribonuclear protein complex.

“The name of the game, both in target identification and in editing for GMP, is precision,” Deans says. “You’re reducing the complexity of the screen. With greater precision and accuracy, your downstream interrogation simplifies.”

He reports that last year, the company used powerful data crunching and an efficient solid-phase synthesis platform to help a research collaboration led by the University of California, San Francisco, accelerate the study of potential treatment targets for coronaviruses. Synthego synthesized more than 1,000 gRNAs in just two days, and it used these gRNAs to engineer cells that incorporated more than 300 knockouts to screen host–coronavirus protein interactions. Just one person was needed to scribe in the automation and workflow.

The collaboration assisted by Synthego expanded on an earlier study, one in which 332 high-confidence SARS-CoV-2 protein–human protein interactions were mapped to identify drug repurposing opportunities. 4 In the earlier study, several drugs were mentioned, including plitidepsin, a cancer drug that is marketed by PharmaMar.

To expand on the earlier study, the collaboration in which Synthego participated sought to determine which coronavirus protein–human protein interactions (not just those involving SARS-CoV-2, but other coronaviruses) might be good targets for drugs meant to block viral replication. 5 The effort involved a combination of in vitro virus infectivity assays and in silico modeling.

In a press release about the pan-coronaviral study, Synthego included a quote attributed to Nevan J. Krogan, PhD, a UCSF researcher and one of the study’s leaders: “The precision and reproducibility of CRISPR were key to helping us study how SARS-CoV-2 affects cellular pathways and ultimately causes disease, enhancing our validation of promising therapeutic targets that may offer broad protection against infection from coronaviruses.”

Both the SARS-CoV-2 and pan-coronaviral interactome studies informed a subsequent study that focused on plitidepsin. This study, which included Krogan among its corresponding authors, indicated that the drug is effective against SARS-CoV-2 because it targets the host protein eEF1A. 6 Plitidepsin is now the subject of a Phase III study that is set to enroll patients requiring hospitalization for the management of moderate COVID-19 infection.

Improving the analysis of multimodal perturbation screens

A new computational approach to analyze data from CRISPR screening has been developed by NYU and NYGC researchers. The approach, which is called mixscape, can improve the signal-to-noise ratio in studies that combine the use of a pooled CRISPR screen to perturb genes, and the use of multimodal single-cell sequencing technologies to amass mRNA and surface protein profiles. Essentially, mixscape identifies and removes confounding sources of variation.

According to a recent study, the researchers developed mixscape to help them understand how cancer cells alter the regulation of key genes, such as the gene that encodes the immune checkpoint molecule PD-L1, to avoid detection and evade the body’s immune system. 7 Before mixscape, the researchers had difficulty interpreting data from a multimodal ECCITE-seq screen, mainly because a subset of cells “escaped” perturbation by the screen’s gRNAs, creating a lot of noise in the data.

The study’s lead author, Efthymia Papalexi, says the idea for mixscape was borrowed from the field of image recognition. Mixscape models each perturbation as a mixture of different cell responses. By doing so, it can identify and remove cells that have escaped CRISPR perturbation, allowing researchers to focus on the true biological signals.

“With this approach, we can knock out a gene, discover the molecular pathways it controls, and then associate these pathways to cellular behaviors,” Papalexi explains. For example, when applying this method in cancer cell lines, the researchers discovered that two genes frequently mutated in lung cancers—the gene for the kelch-like protein KEAP1, and the gene for the transcriptional activator NRF2—regulate the expression levels of PD-L1. This finding suggests that the genes are important in tumor development and progression.

Looking into the future of CRISPR screening

The horizon beyond these foundational CRISPR-Cas9 screening components and conditions is already being explored. The Broad Institute’s John Doench, PhD, is blazing a trail in the area of alternate Cas enzymes. The standard CRISPR-Cas9 system originates from Streptococcus pyogenes. But CRISPR systems are abundant in nature, and there are plenty of other Cas nucleases that can be exploited, says Doench, who is focusing on a protein called Cas12a from Acidaminococcus.

“It has a particularly useful property: if you want to express multiple gRNAs, you can do so from just one small transcript,” Doench points out. “Because of that, the Cas12a enzyme is particularly useful for studying combinations.”

Combinations are key to exploring synthetic lethality, where a combination of genes contribute to cell death, rather than one alone. In cancer drug discovery, synthetic lethality can link two or more genes that, individually, might not be effective targets, but when combined are lethal to cancer cells. BRCA1 and PARP are the most famous synthetic lethal pair. Doench asserts that with Cas12a, it’s now possible to screen for synthetic lethal pairs to potentially identify new cancer targets.

In addition, blue-sky variations on CRISPR screening are emerging to build upon or even replace current technologies. Those include techniques like CRISPR activation, where an enzymatically inactive Cas9 is tailored to activate gene expression, and base editor screening, where individual mutations or variants are targeted instead of genes. With that kind of fine-grained targeting, researchers could pin down the effect of a single amino acid change on a drug’s activity.

Tanaz Abid, a research associate at the Broad Institute of MIT and Harvard, readies cells for a large-scale lentiviral production run to generate a library of guide RNAs. Her work helps to maintain and enhance the Broad’s Genetic Perturbation Platform, which supports functional investigations of the mammalian genome that can reveal how genetic alterations lead to changes in phenotype.

Base editor screens could finally crack the so-called V-to-F (variants to function) challenge for the human genome, enabling a map of every possible variant of every human gene with a function. A database containing all variants with their functions could unlock the vast potential for personalized medicine.

Realizing all of these possibilities with CRISPR screening will require upgrades in other technologies. “We still need lots of good systems, and lots of good assays to read out,” Doench states. “[CRISPR screening] is one of the tools in the toolbox, but it’s by no means the only one.”

References
1. Shalem O, Sanjana NE, Hartenian E, et al. Genome-scale CRISPR-Cas9 knockout screening in human cells. Science 2014 343(6166): 84–87. DOI: 10.1126/science.1247005.
2. Canver MC, Smith EC, Sher F, et al. BCL11A enhancer dissection by Cas9-mediated in situ saturating mutagenesis.Nature 2015 527(7577): 192–197. DOI: 10.1038/nature15521.
3. Frangoul H, Altshuler D, Cappellini MD, et al. CRISPR-Cas9 Gene Editing for Sickle Cell Disease and β-Thalassemia.N. Engl. J. Med. 2021 384(3): 252–260. DOI: 10.1056/NEJMoa2031054.
4. Gordon DE, Jang GM, Bouhaddou M, et al. A SARS-CoV-2 protein interaction map reveals targets for drug repurposing.Nature 2020 583: 459–468. DOI: 10.1038/s41586-020-2286-9.
5. Gordon DE, Hiatt J, Bouhaddou M, et al. Comparative host-coronavirus protein interaction networks reveal pan-viral disease mechanisms.Science 2020 370(6521): eabe9403. DOI: 10.1126/science.abe9403.
6. White KM, Rosales R, Yildiz S, et al. Plitidepsin has potent preclinical efficacy against SARS-CoV-2 by targeting the host protein eEF1A.Science 2021 371: 926–931. DOI: 10.1126/science.abf4058.
7. Papalexi E, Mimitou EP, Butler AW, et al. Characterizing the molecular regulation of inhibitory immune checkpoints with multimodal single-cell screens.Nat. Genet. 2021 53(3): 322–331. DOI: 10.1038/s41588-021-00778-2.

Establishing the Basics of CRISPR Screening

Originally, CRISPR screening gained popularity as a way of avoiding some of the difficulties associated with small interfering RNA screening, such as off-target effects and incomplete protein depletion. In CRISPR screening, highly specific, permanent genetic modifications can be made that are effective at precluding the function of targeted genes.

The CRISPR-Cas9 nuclease characteristically engineers a double-strand break, which occurs at a specific target locus dictated by a single-guide RNA (sgRNA). The deployment of sgRNAs leads to frameshift mutations and, ultimately, loss-of-function mutations at targeted genes.

In many CRISPR screening experiments, lentiviral vectors are used to deliver plasmids encoding Cas9 and sgRNA, with different plasmids encoding different sgRNAs from a library of sgRNAs. Some libraries consist of sgRNAs that correspond to defined sets of targeted genes other libraries are more comprehensive.

Besides a choice of libraries, CRISPR screening presents a choice of formats: pooled or arrayed. In the pooled format, cells are transduced in bulk, and the entire sgRNA library is deployed. In the arrayed format, cells deposited in the wells of a multiwell plate are transduced individually, and individual sgRNAs are deployed.

The pooled format may be preferred when large libraries are deployed. However, care must be taken to ensure a low multiplicity of infection, or a low ratio of lentiviruses to cells, to minimize coinfection by multiple lentiviruses. Then, after transduced cells are selected, whether by viability-based positive or negative selection, next-generation sequencing is used to assess sgRNA representation, that is, whether DNA sequences encoding for different sgRNAs are depleted or enriched.

With a negative selection screen, that is, a traditional knockout screen, the point is to identify genes that are essential for survival or proliferation under certain conditions or selection pressures. If survival genes sustain a lethal mutation because they were targeted by an sgRNA, that sgRNA will not be represented in sequencing results, simply because cells with lethal mutations in sgRNA-targeted survival genes will have been unable to survive and reproduce. Typically, the degree to which an sgRNA has been “depleted” will be detected by comparing the sgRNA’s representation in a cell population spared the selection pressure, and that in a cell population subjected to the selection pressure.

With a positive selection screen, the point is to identify genes whose mutation or silencing gives cells a survival advantage over a selection pressure. For example, an sgRNA-targeted gene may be found to confer resistance to a cancer drug. In this case, the sgRNA will be represented, or “enriched,” among the cancer cells that survive drug treatment.


Acknowledgements

We thank Dr. Peter Gregersen for generously sharing with us GWAS data from the NARAC study, providing us with meta-analysis results and comments on our findings and manuscript. AVA, CFA, and AS were supported in part by the grant 1UL1RR029893 from the National Center for Research Resources, National Institutes of Health. CFA was also supported in part by the grant R56 LM007948-04A1 from the National Library of Medicine, National Institute of Health. LP was supported by The Swedish Research Council and by The Swedish State agency VINNOVA.


Methods

The resources of peanut AhRLKs

All RLK full-length amino acid sequences in Arabidopsis were downloaded from UniProt (https://www.uniprot.org/) and these sequences were used as queries to perform a BLASTP search against A. duranensis RLKs by NCBI (https://www.ncbi.nlm.nih.gov/). These resulting sequences were then used as new queries to conduct a BLASTP search again in PEANUT GENOME RESOURSE (http://peanutgr.fafu.edu.cn/), to avoid missing potential members. The redundant entries were removed manually. Then the resulting unique sequences were analysed with both SMART (http://smart.embl-heidelberg.de) [65] and NCBI’s Conserved Domains Database (CDD http://www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi) to ensure the presence of the RLK domains in newly identified members. Only proteins containing at least one kinase domain were considered putative AhRLKs, and 1311 AhRLKs were finally obtained. The amino acid residue base, and molecular weight were predicted with ExPaSy ProtParam tool (https://web.expasy.org/protparam/). The genome sequence, protein sequences and genome annotation of the peanut were performed according to PEANUT GENOME RESOURSE (http://peanutgr.fafu.edu.cn/).

Multiple sequence alignments and phylogenetic tree construction of AhRLKs

The full-length amino acid sequences of LRR-AhRLKs, LecRLKs and 90 Al-responsive AhRLKs defined in the previous section were aligned using ClustalX in MEGA 7 with default parameters [66]. The phylogenetic tree based on the multiple sequence alignments of peanut LRR-RLKs (Fig. 1), LecRLKs (Fig. 2) and 90 AhRLKs in response to Al stress (Fig. 6) was generated by MEGA 7. A Poisson correction model was used to account for multiple substitutions, while alignment gaps were removed with partial deletion. The statistical strength was estimated by bootstrap resampling using 1000 replicates. Based on the multiple sequence alignment and the previously reported classification of Arabidopsis thaliana, the peanut RLKs were assigned to different subfamilies and subgroups [24, 67].

Chromosomal locations and duplication analysis for peanut RLKs

The physical location of AhRLKs on the chromosomes was obtained from the PEANUT GENOME RESOURSE database (http://peanutgr.fafu.edu.cn/). All members of AhRLKs were mapped onto peanut chromosomes based on their physical positions, and chromosomal location images were produced with the online software Map Gene 2 Chromosome v2 (MG2C:http://mg2c.iask.in/mg2c_v2.0/). The chromosome location information of the peanut was extracted from GFF files that contain the information of peanut genome annotation. BLASTP was performed to search for potential homologous gene pairs (E-value < 1e −5 ) across genomes. Information on homologous pairs was used as input to identify syntenic chains by MCScanX [68]. In addition, MCScanX was also used to identify tandem and segmental duplications in the AhRLK gene family. RLKs clustered together within 100 kb were regarded as tandem duplicated genes based on the criteria of other plants. The diagram was generated by TBtools [69]. The nonsynonymous (Ka) and synonymous (Ks) substitution ratios were calculated by Simple Ka/Ks Caculator in TBtools. The divergence time was calculated with the formula T = Ks/2r, and the r of dicotyledonous plants was 1.5*10^-8 synonymous substitutions per site per year [70]. We used the Geneconv program with default parameters to search evidence for tandem duplication cluster gene conversion (http://www.math.wustl.edu/

sawyer/geneconv/) [71]. Since GENECONV required at least three sequences for detecting gene conversion events, tandem duplication clusters that contained at least 3 genes were detected. For this program, the clustalW (CDS) alignment was used as the input. Geneconv can detect candidate fragments of directed gene conversion between gene pairs (allowing mismatch). Gene conversion events were considered as statistically significant when P < 0.05.


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