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Gene Drive Systems

Gene Drive Systems are innovative genetic engineering techniques that enable the rapid spread of specific genetic modifications through populations.
These systems harness natural biological processes to enhance or suppress targeted traits within an organism.
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Most cited protocols related to «Gene Drive Systems»

Evaluating Transcriptional Heterogeneity with PAGODA

Since some aspects of transcriptional heterogeneity can be driven by genes that are poorly represented or not at all described by the annotated pathways, PAGODA incorporates into the overall analysis de novo gene sets that group genes showing correlated patterns of expression across the cells measured in a particular dataset. By default, PAGODA, implements a straightforward clustering procedure: a hierarchical clustering is performed using Ward method (as implemented by the hclust package in R) using a Pearson correlation distance on the normalized expression matrix (that is used for the weighted PCA step described above). The resulting dendrogram is cut to obtain a pre-defined number of de novo gene clusters (the results shown use 150 clusters). As there are many alternative methods for clustering co-expressed genes, PAGODA implementation provides parameters to use alternative clustering procedures.
Since de novo gene clusters are by purposefully selected to contain genes with correlated expression profiles, the amount of variance explained by the first principal component (magnitude of λ₁) will be higher than expected from random matrices, and cannot be modeled by the same Trace-Window F₁ distribution as previously-annotated gene set. To evaluate statistical significance of overdispersion, a background distribution of λ₁ was generated by performing the same hierarchical clustering and weighted PCA procedure on randomized matrices (where cell order was randomized for each gene independently, 100 randomizations). The λ₁ values were normalized relative to Tracy-Widom F₁ expectation as

λ_{1}^{s} = [λ_{1} - (a λ_{1}^{T W} + b n)] / \sqrt{ν_{1}^{T W}}

, where

λ_{1}^{T W}

and

ν_{1}^{T W}

are the mean and variance of λ₁ predicted by the Tracy-Window F₁ distribution, and coefficients a and b are determined by the linear model

λ_{1} ~ λ_{1}^{T W} + n

. This standardized residual

λ_{1}^{s}

was modeled using Gumbel extreme value distribution, the parameters of which were fit using extRemes package in R. The overdispersion P value for each de novo gene set were determined from the tails of that distribution. The subsequent procedures treated de novo gene sets and annotated gene sets in the same way.

Fan J., Salathia N., Liu R., Kaeser G.E., Yung Y.C., Herman J.L., Kaper F., Fan J.B., Zhang K., Chun J, & Kharchenko P.V. (2016). Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis. Nature methods, 13(3), 241-244.

Publication 2016

Cells Gene Clusters Gene Drive Systems Genes Genetic Heterogeneity Tail Transcription, Genetic

Generation of Cre-driver Transgenic Mice

Pdyn-ires Cre, Avp-ires-Cre, Crh-ires-Cre, Trh-ires-Cre and Pacap-ires-Cre mice were generated using recombineering techniques as previously described^{13 (link),21 (link)}. Briefly, a selection cassette containing an internal ribosomal entry sequence linked to Cre-recombinase and an Frt-flanked kanamycin resistance gene was targeted just downstream of the stop codon of the Prodynorphin, Arginine vasopressin, Corticotropin releasing hormone, Thyrotropin releasing hormone or Adenylate cyclase activating peptide 1 gene, respectively, in a bacterial artificial chromosome, so that Cre recombinase expression was driven by the endogenous genes. A targeting plasmid containing the Cre-containing selection cassette and 4 kb genomic sequence upstream and downstream of the Prodynorphin, Arginine vasopressin, Corticotropin releasing hormone, Thyrotropin releasing hormone or Adenylate cyclase activating peptide 1 stop codon, respectively was isolated and used for embryonic stem cell targeting. Correctly targeted clones were identified by long range PCR and injected into blastocysts. Chimeric animals generated from blastocyst implantation were then bred for germline transmission of the altered Prodynorphin, Arginine vasopressin, Corticotropin releasing hormone, Thyrotropin releasing hormone or Adenylate cyclase activating peptide 1-allele, respectively. Flp-deleter mice were then used to remove the neomycin selection cassette.
Generation of an enhanced Cre-dependent GFP reporter mice (R26-loxSTOPlox-L10-GFP) were generated using recombineering techniques as previously described^{20 (link)}. A transgene containing a lox-flanked transcriptional blocking cassette followed by eGFP fused to the L10-ribosomal subunit^{31 (link)} was placed under the control of a CMV-enhancer/chicken beta-actin promoter and targeted to the Rosa26 locus using standard techniques^{32 (link)}. Correctly targeted blastocysts were identified by long range PCR and confirmed by southern blotting and injected into blastocysts.

Krashes M.J., Shah B.P., Madara J.C., Olson D.P., Strochlic D.E., Garfield A.S., Vong L., Pei H., Watabe-Uchida M., Uchida N., Liberles S.D, & Lowel B.B. (2014). A Novel Excitatory Paraventricular Nucleus to AgRP Neuron Circuit that Drives Hunger. Nature, 507(7491), 238-242.

Publication 2014

Adenylate Cyclase Alleles Animals Argipressin Bacterial Artificial Chromosomes beta-Actin Blastocyst Cardiac Arrest Chickens Chimera Clone Cells Codon, Terminator Corticotropin-Releasing Hormone Cre recombinase Embryonic Stem Cells Gene Drive Systems Genes Genome Germ Line Internal Ribosome Entry Sites Kanamycin Resistance Mice, Laboratory Neomycin Ovum Implantation Peptides Pituitary Adenylate Cyclase-Activating Polypeptide Plasmids prodynorphin Ribosomes Thyrotropin-Releasing Hormone Transcription, Genetic Transgenes Transmission, Communicable Disease

Neuron Data Analysis Protocols

Group d) contains functions that help users to easily analyse neuron data as both skeletons and volumes. Its biggest contributor is nat. nat.nblast allows users to deploy the NBLAST neuron similarity algorithm (Costa et al., 2016 (link)), by pairwise comparison of vector clouds describing these neurons in R. Our nabor package is a wrapper for libnabo (Elseberg et al., 2012 ), a k-nearest neighbour library which is optimised for low dimensional (e.g. 3D) spaces. The package elmr is another fly focused package that has been born out of a specific use case. Currently, ~22 laboratories and ~100 active users worldwide are engaged with reconstructing D. melanogaster neurons from EM data (Zheng et al., 2018 (link)) using CATMAID (Saalfeld et al., 2009 (link); Schneider-Mizell et al., 2016 (link)) in order to build a draft, sparse connectome. The package elmr allows users to read neurons from this environment, transform them into a template space where they can be compared with light-level neurons for which the community may have some other information (e.g. gene expression, functional characterisation, presence in genetic drive lines, etc.), then visualised and/or NBLAST-ed; all with only a few lines of code. This process enables CATMAID users to perform interim analyses as they reconstruct neurons, helping them to choose interesting targets for reconstruction and identify manually traced or automatically reconstructed neuron fragments (Dolan et al., 2019 (link)) or anatomical landmarks such as fiber tracts (Frechter et al., 2019 (link)), and so improve the efficiency of their targeted circuit reconstructions (Dolan et al., 2018a (link); Felsenberg et al., 2018 (link); Huoviala et al., 2018 (link)).

Bates A.S., Manton J.D., Jagannathan S.R., Costa M., Schlegel P., Rohlfing T, & Jefferis G.S. (2020). The natverse, a versatile toolbox for combining and analysing neuroanatomical data. eLife, 9, e53350.

Publication 2020

Anatomic Landmarks cDNA Library Childbirth Cloning Vectors Connectome Fibrosis Gene Drive Systems Gene Expression Light Neurons Reconstructive Surgical Procedures Ribs Skeleton

Robust Cell-Type Identification via scRNA-seq Alignment

Initial data exploration revealed that clustering was driven by individual of origin in addition to cell type identity, which makes it difficult to analyze changes in the relative abundance or gene expression of a given cell type across disease progression or brain regions. To recover clusters defined by mainly by cell type identity, data was aligned across samples from each brain region using with scAlign^{65 (link)} (version 1.0.0), which leverages a neural network to learn a low-dimensional alignment space in which cells from different datasets group by biological function independent of technical and experimental factors. As noted by Johansen & Quon^{65 (link)}, scAlign converges faster with little loss of performance when the input data is represented by principal components or canonical correlation vectors. Therefore, prior to running scAlign, the top 2000 genes with the highest combined biological variance were used as the feature set for canonical correlation analysis (CCA), which was implemented using Seurat::RunMultiCCA with parameter num.cc = 15. The number of canonical coordinates to use for scAlign was determined by the elbow method using Seurat::MetageneBicorPlot. scAlign was then run on the cell loadings along the top 10 canonical correlation vectors with the parameters options = scAlignOptions(steps = 10000, log.every = 5000, architecture = ‘large’, num.dim = 64), encoder.data = ‘cca’, supervised = ‘none’, run.encoder = TRUE, run.decoder = FALSE, log.results = TRUE, and device = ‘CPU’. Clustering was then performed on the full dimensionality of the ouptut from scAlign using Seurat::FindClusters with parameter resolution = 0.8 for the SFG and resolution = 0.6 for the EC. Clusters were visualized with tSNE using Seurat::RunTSNE on the full dimensinality of the output from scAlign with parameter do.fast = TRUE. Alignment using scAlign followed by clustering was also performed for all samples from both brain regions jointly.
To assign clusters identified in the aligned subspace generated by scAlign to major brain cell types, the following marker genes were used: SLC17A7 and CAMK2A for excitatory neurons, GAD1 and GAD2 for inhibitory neurons, SLC1A2 and AQP4 for astrocytes, MBP and MOG for oligodendrocytes, PDGFRA and SOX10 for oligodendrocyte precursor cells (OPCs), CD74 and CX3CR1 for microglia/myeloid cells, and CLDN5 and FLT1 for endothelial cells. Clusters expressing markers for more than one cell type, most likely reflecting doublets, were removed from downstream analyses.

Leng K., Li E., Eser R., Piergies A., Sit R., Tan M., Neff N., Li S.H., Rodriguez R.D., Suemoto C.K., Leite R.E., Ehrenberg A.J., Pasqualucci C.A., Seeley W.W., Spina S., Heinsen H., Grinberg L.T, & Kampmann M. (2021). Molecular characterization of selectively vulnerable neurons in Alzheimer’s Disease. Nature neuroscience, 24(2), 276-287.

Publication 2020

Astrocytes Biological Processes Biopharmaceuticals Brain Cells Cloning Vectors Disease Progression Elbow Endothelial Cells FLT1 protein, human Gene Drive Systems Gene Expression Genes Genetic Diversity glutamate decarboxylase 1 (brain, 67kDa), human Medical Devices Microglia Myeloid Cells Neurons Oligodendrocyte Precursor Cells Oligodendroglia Psychological Inhibition SLC1A2 protein, human SOX10 Transcription Factor

Gene Drive Systems: Mechanisms and Applications

In our model, we consider five types of homing gene drive systems:
1) Standard drive. The standard homing drive is a population modification system. Its primary drive mechanism occurs in germline cells during early meiosis. When it operates successfully, the drive allele replaces wild-type alleles in the germline. However, resistance alleles can also form, preventing the spread of the drive.
2) Population suppression drive. This drive increases in frequency in the same manner as the standard homing drive, and resistance alleles develop under the same circumstances. However, the drive targets a recessive female fertility gene and disrupts the function of the gene with its presence. Resistance alleles can also disrupt the function of the target gene. Females with two disrupted copies of the gene are rendered sterile, while males are unaffected. Notably, unlike the standard homing drive, this drive does not carry any payload, as, it accomplishes its goal simply with its presence. Such a drive was successful in laboratory populations of the mosquito A. gambiae (21 (link)).
3) Haplolethal drive. This drive system is a modification of the standard homing drive system. It targets a gene that is critical to the viability of the individual. However, the drive contains a recoded portion of the gene that is immune to Cas9 cleavage, so the presence of the drive does not disrupt the function of the target. If an individual receives a resistance allele that disrupts the haplolethal target, then that individual will not be viable, preventing these resistance alleles from entering the population. A haplolethal homing drive was successful in a laboratory population of the fruit fly D. melanogaster (16 ).
4) Recessive lethal drive. This drive is similar to the haplolethal drive, but the target is recessive lethal. Only individuals carrying two resistance alleles that disrupt the target gene function are nonviable. Thus, resistance alleles are removed from the population more slowly. However, this drive may be easier to engineer because the drive can provide rescue even in the presence of a resistance allele. It is also more tolerant of a high rate of embryo resistance allele formation because this allows it to operate better as a toxin-antidote system (25 , 26 ).
5) Gene disruption drive. The gene disruption homing drive is a population modification system that is similar to the suppression drive in that its presence disrupts the target gene, as do resistance alleles. However, individuals with two disrupted copies of this gene remain viable and fertile, although they suffer from a small additional fitness cost. The purpose of this drive is to remove the functionality of a particular gene from the population, which can provide benefits such as reduction in disease transmission (27 (link), 28 (link)). An advantage of this drive is that there is no need for a recoded sequence. However, finding suitable targets for particular applications could potentially be difficult.

Champer S.E., Oh S.Y., Liu C., Wen Z., Clark A.G., Messer P.W, & Champer J. (2020). Computational and experimental performance of CRISPR homing gene drive strategies with multiplexed gRNAs. Science Advances, 6(10), eaaz0525.

Publication 2020

Alleles Antidote Culicidae Cytokinesis Drosophila Embryo Females Fertility Gene Drive Systems Genes Genes, Recessive Genes, vif Germ Cells Germ Line Males Meiosis Operator, Genetic Sterility, Reproductive Toxins, Biological Transmission, Communicable Disease

Most recents protocols related to «Gene Drive Systems»

CRISPR-based Cucumber Genome Editing

CRISPR/Cas9 genome editing construct for CsMS editing was generated using the pHEE401E vector tagged with GFP. Two sgRNAs were driven by the U6 promoter, and the Cas9 protein was driven by the egg cell-specific promoter [34 (link)]. The empty pHEE401E vector was used as a control. Two sgRNAs were targeted against cucumber malate synthase gene (CsaV3_1G009520) designed using CRISPR-GE tool (http://skl.scau.edu.cn/) [35 (link)]. For analysis the activity of CsLBD16(LATERAL ORGAN BOUNDARIESDOMAIN, Csa3G398920) which homolog of the Arabidopsis LATERAL ORGAN BOUNDARIES-DOMAIN 16 (AtLBD16, At2g42430) under nematode parasitism, the vector pCAMBIA1391 carrying GUS gene was driven by the 2000 bp promoter region of the CsLBD16 gene, generating a pCsLBD16::GUS recombinant construct. The primer's sequences for construction of two vectors are listed in Table 1.Table 1

List of primers used in this study

Primer name	Application	Primer (5'–3')
qCsMS-F	Relative expression	GCCTTGTTGTTTGTCGCTGA
qCsMS-R		TTAGTCGCCGGATCAAACCC
qCsLBD16-F		CAGAAACCCTAATGGATTCAGGAAG
qCsLBD16-R		GTGGGCTTGGGTTGTTCGTAATTTG
qCsTUB-F		CATTCTCTCTTGGAACACACTGA
qCsTUB-R		TCAAACTGGCAGTTAAAGATGAAA
pCsLBD16-F	pCAMBIA-1391	GCGCGCCAAGCTTGGCTGCAGACCTAAGTCCGAAGCCATAAGTGAC
pCsLBD16-R	pCAMBIA-1391	TCTTAGAATTCCCGGGGATCCGGGAAAATAGAAGAAATGGCCGTGC
CsMSDT1-BsF	CsMS-pHEE401E	ATATATGGTCTCGATTGAGAGGCTACGACGTTCCAGGTT
CsMSDT1-F0		TGAGAGGCTACGACGTTCCAGGTTTTAGAGCTAGAAATAGC
CsMSDT2-R0		AACTGCTAATTTTCGACGCTCTCAATCTCTTAGTCGACTCTAC
CsMSDT2-BsR		ATTATTGGTCTCGAAACTGCTAATTTTCGACGCTCTCAA
GFP-F	GFP characterization	CAAGGGCGAGGAGCTGTTCACCG
GFP-R	GFP characterization	CAGCTCGTCCATGCCGTGAGTGA
CsMSCsa9-F	Mutant characterization	GCTTGGGATGTATTCCGAATCA
CsMSCsa9-R	Mutant characterization	GGATGAAGATTTACCTGGAGTG

Zhang X., Li S., Li X., Song M., Ma S., Tian Y, & Gao L. (2023). Peat-based hairy root transformation using Rhizobium rhizogenes as a rapid and efficient tool for easily exploring potential genes related to root-knot nematode parasitism and host response. Plant Methods, 19, 22.

Publication 2023

Arabidopsis Cloning Vectors Clustered Regularly Interspaced Short Palindromic Repeats CRISPR-Associated Protein 9 Cucumis Gene Drive Systems Genes Malate Synthase Nematoda Oligonucleotide Primers Ovum

Genetically encoded pH sensors in Arabidopsis

For this study, we selected the genetically encoded ratiometric pH-sensor pHGFP [37 (link)]. Constructs for the stable expression in Arabidopsis thaliana of pHGFP-VTI11 and pHGFP-LTI6b translational fusions, both driven by the UBQ10 gene promoter, have been described elsewhere [38 (link),39 (link)]. Localization of the chimeric proteins pHGFP-VTI11 in the tonoplast and of pHGP-LTI6b in the plasma membrane have also been demonstrated prior to this study [38 (link),39 (link)] and were confirmed via confocal fluorescence microscopy of roots of transgenic seedlings.

Rombolá-Caldentey B., Andrés Z., Waadt R., Quintero F.J., Schumacher K, & Pardo J.M. (2023). Salinity-Induced Cytosolic Alkaline Shifts in Arabidopsis Roots Require the SOS Pathway. International Journal of Molecular Sciences, 24(4), 3549.

Publication 2023

Animals, Transgenic Arabidopsis thalianas Chimera Fluorescence Gene Drive Systems Microscopy, Confocal Plant Roots Plasma Membrane Protein Biosynthesis Proteins Seedlings

Evaluating Mendelian Randomization Assumptions

As the second (i.e., genetic variants are not associated with any potentially confounding variable) and the third (i.e., genetic variants are only associated with the outcome through the risk factor) MR assumptions are challenging to test [19 (link)], further analyses were performed to assess any potential violation of both assumptions.
First, we used PhenoScanner (available at http://www.phenoscanner.medschl.cam.ac.uk, accessed on 12 June 2021) and the GWAS catalog (available at https://www.ebi.ac.uk/gwas/, accessed on 15 June 2021) to investigate whether our selected genetic variants were associated with other traits in previous GWAS [19 (link),20 (link),21 (link)].
Second, we used Cochran’s Q test to evaluate the heterogeneity of causal effects for each variant, with a p-value < 0.05 indicating statistical significance [22 (link)]. In the case of heterogeneity, likely indicating pleiotropy [23 (link)], a random-effects IVW MR analysis was used [24 (link)]. This method relaxes the third assumption as the total pleiotropic effect of a single genetic variant no longer needs to be null but assumes a zero mean between all the genetic variants (i.e., balanced pleiotropy) [24 (link)]. In addition, we implemented MR-Egger regression [25 (link)] and the weighted median approach [26 (link)]. The MR-Egger methodology provides an unbiased causal effect estimate even if the third MR assumption is violated and all the variants are invalid instruments. However, the Instrument Strength Independent of Direct Effect (InSIDE) assumption needs to be held. This additional assumption is based on the independency between the horizontal pleiotropic effects and the variants-exposure effects. MR-Egger also provides a statistical test for overall directional pleiotropy based on whether the intercept term is different from zero, as well as the I²_GX statistic, which indicates the potential violation of the NO Measurement Error (NOME) assumption and suggests the unreliability of MR-Egger inferences at values below 90% [25 (link),27 (link)]. The weighted median estimator allows for violations of the second and the third assumptions when up to 50% of the genetic variants are invalid (i.e., violation of one or more of the three basic MR assumptions) [23 (link),26 (link)]. Both tests provide valid MR estimates in the presence of overall directional pleiotropy but suffer from reduced power [25 (link),26 (link)]. We also used the MR Pleiotropy RESidual Sum and Outlier (MR-PRESSO) method to identify and remove any outlying variants, applying a random-effects IVW model [28 (link)]. This method regresses genetic variant-outcome on genetic variant-exposure and uses the square of residuals to identify outliers. Finally, leave-one-genetic-variant-out analyses were used to assess whether any association was driven by specific genetic variants.
A two-sided p-value < 0.05 was considered statistically significant in all analyses.
We used scatter plots to present the genetic associations between body shape phenotypes and breast cancer risk, in combination with funnel plots, to visually examine the consistency of MR estimates and the potential associated bias (Supplementary Figure S1).
All analyses were conducted with the statistical software R 4.0.4 and RStudio 1.4.1106 using the MendelianRandomization 0.6.0 and MRPRESSO 1.0 R packages [28 (link),29 (link)].

Peruchet-Noray L., Dimou N., Sedlmeier A.M., Fervers B., Romieu I., Viallon V., Ferrari P., Gunter M.J., Carreras-Torres R, & Freisling H. (2023). Body Shape Phenotypes and Breast Cancer Risk: A Mendelian Randomization Analysis. Cancers, 15(4), 1296.

Publication 2023

ARID1A protein, human Body Shape Gene Drive Systems Genetic Diversity Genetic Heterogeneity Genome-Wide Association Study Malignant Neoplasm of Breast Phenotype

Identifying Genomic Signatures of Selection

We detected candidate divergent regions (CDRs) by searching the genome for regions having high fixation index (Fst, top 1%) values and high differences in genetic diversity (Pi ratio). First, we calculated the Fst and Pi ratio along the autosomes in sliding 40-kb windows with 10-kb steps using VCF tools and in-house scripts, comparing values between generation 9 and the non-selection breeds. We restricted our CDR descriptions to the top 1% most significant windows in both Fst and ln Pi ratio, as these windows represented the extreme ends of the distributions.
The differences in allele frequencies between the two populations observed here could be driven by genetic drift and selection. To unravel these two processes, we developed a statistical test based on the assumption that genetic drift affects the whole genome, while selection affects only SNPs that are in LD with causal genes. Allele frequencies were used as test statistics. For each SNP, we tested the null hypothesis that Fst was driven purely by genetic drift against the alternative hypothesis that it was driven by both genetic drift and selection. In this process, we simulated the effect of genetic drift stochastically, which we were able to do because, as described above, the breeding history of each line from their common base population is known. In the first ten rounds of selection, 30 males and 90 females were selected for each line, which resulted in an effective population size (N_e) of 90. To verify this number, we calculated N_e using the genomic data of the 9th generation and the NS population, which yielded an effective population size for the base population of approximately 200 (Supplementary Data 3). To be cautious, we used 90 as the N_e value in subsequent analysis.
SNPs in the significantly selected signal window were extracted and filtered based on LD and individual SNP Fst values, and the MAF values of related SNPs in the G9 and NS populations were calculated. Based on the N_e and MAF values of the NS population, genetic drift was simulated over the course of nine generations, and the allele frequencies at the end of the simulation were used as indicators; specifically, the means of the top 5% and the bottom 5% were calculated and compared with the allele frequencies of the G9 and NS populations.

Wang J., Zhang J., Wang Q., Zhang Q., Thiam M., Zhu B., Ying F., Elsharkawy M.S., Zheng M., Wen J., Li Q, & Zhao G. (2023). A heterophil/lymphocyte-selected population reveals the phosphatase PTPRJ is associated with immune defense in chickens. Communications Biology, 6, 196.

Publication 2023

Females Gene Drive Systems Genes Genetic Diversity Genetic Drift Genome Males Population Group Single Nucleotide Polymorphism

Genetic Divergence of Populus angustifolia in Sky Islands

Populus angustifolia James (Salicaceae) is a native high-elevation, foundation tree species dominant in riparian areas (900–3500 m) across the intermountain western U.S.⁶⁸. Throughout its natural range, P. angustifolia forms distinct genetic populations, largely driven by reduced gene flow resulting from variation in the intermountain landscape^{69 (link)}. The fragmented contemporary range of P. angustifolia reflects historical bottlenecks driven by Pleistocene glacial cycles, as well as contemporary climate change^{18 ,69 (link)}. Populus angustifolia extends northwards from northern Mexico into southern Alberta, Canada; a geographic region also characterized by the presence of SI^{12 ,69 (link)}. Sky islands are isolated mountain habitats surrounded by desert lowlands or habitats outside of the range of species’ thermal tolerance, serving as a barrier to species dispersal and migration^{12 ,13 (link)}. Conversely, populations of trees from the continuous MC throughout the natural range of P. angustifolia are not dispersal-limited, as the riparian habitat between the high-elevation zones on MC is climatically suitable for within-population windborne seed dispersal and migration. Populus angustifolia exhibits both asexual and sexual reproductive strategies, mostly by cloning and pollen/seed dispersal by wind and water^{70 ,71 (link)}. Due to the obligate riparian nature of P. angustifolia, populations on SI used in this study constitute distinct populations, devoid of the potential for any significant gene flow^{36 (link)}. Isolated SI across the landscape, paired with multiple adjacent MC, serve as natural replication for our study^{72 (link)}. In May and June of 2012, 8 watershed populations (3 SI: Indian Creek “IC”, UT; Lexington Creek “LEX”, NV; Great Basin “GBS”, NV. 5 MC: Dolores River “DOL”, CO; Logan River “LOG”, UT; Ogden Canyon “OGC”, UT; San Miguel River “SMIG”, CO; Weber River “WR”, UT) of P. angustifolia were surveyed (Fig. 1a). Georeferenced climate data for mean annual temperature and mean annual precipitation were collected for each tree from WorldClim^{73 (link)}. In the field, 1350 genotype replicates (n = 193 genotypes from MC; n = 57 genotypes from SI) were georeferenced and sampled and 10 terminal branch cuttings (~ 20 cm long) were collected from each tree. The cuttings were initially established in general potting soil (equal parts vermiculite, perlite, and peat) for 4 months^{1 (link)}. Surviving cuttings were replanted in 6.4 × 36 cm plastic pots and grown under identical ambient common garden conditions at the University of Tennessee Knoxville greenhouse.

Love S.J., Schweitzer J.A, & Bailey J.K. (2023). Climate-driven convergent evolution in riparian ecosystems on sky islands. Scientific Reports, 13, 2817.

Publication 2023

Climate DNA Replication Gene Drive Systems Genes Genotype Immune Tolerance Marijuana Abuse Perlite Pollen Population Group Populus Reproduction Rivers Salicaceae Seed Dispersal Trees vermiculite Wind

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More about "Gene Drive Systems"

Gene Drive Systems are revolutionary genetic engineering techniques that rapidly spread specific genetic modifications through populations.
These innovative systems harness natural biological processes to enhance or suppress targeted traits within an organism.
Researchers can leverage PubCompare.ai's powerful AI tools to identify the most reliable and effective Gene Drive protocols from scientific literature, preprints, and patents, optimizing their research and advancing the field of genetic engineering.
By locating the best reproducible methods, scientists can accelerate the development of Gene Drive technologies with confidence.
Experience the future of scientific reproducibility today with PubCompare.ai.
Discover how PubCompare.ai's AI-driven reproducibility insights can optimize your Gene Drive Systems research.
Locate the most effective protocols from literature, pre-prints, and patents using our powerful comparison tools.
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