Biology subjects

Kim, I.

Publications and source records attributed to Kim, I..

43 records · Page 3Linked to original sources

bioRxiv2019

Performance analysis of novel toxin-antidote CRISPR gene drive systems

Gene drives can potentially fixate in a population by biasing inheritance in their favor, opening up a variety of potential applications in areas such as disease-vector control and conservation. CRISPR homing gene drives have shown much promise for providing an effective drive mechanism, but they typically suffer from the rapid formation of resistance alleles. Even if the problem of resistance can be overcome, the utility of such drives would still be limited by their tendency to spread into all areas of a population. To provide additional options for gene drive applications that are substantially less prone to the formation of resistance alleles and could potentially remain confined to a target area, we developed several designs for CRISPR-based gene drives utilizing toxin-antidote (TA) principles. These drives target and disrupt an essential gene with the drive providing rescue. Here, we assess the performance of several types of TA gene drive systems using modeling and individual-based simulations. We show that Toxin-Antidote Recessive Embryo (TARE) drive should allow for the design of robust, regionally confined, population modification strategies with high flexibility in choosing drive promoters and recessive lethal targets. Toxin-Antidote Dominant Embryo (TADE) drive requires a haplolethal target gene and a germline-restricted promoter but should enable the design of both faster regional population modification drives and even regionally-confined population suppression drives. Toxin-antidote dominant sperm (TADS) drive can be used for population modification or suppression. It spreads nearly as quickly as a homing drive and can flexibly use a variety of promoters, but unlike the other TA systems, it is not regionally confined and requires highly specific target genes. Overall, our results suggest that CRISPR-based TA gene drives provide promising candidates for further development in a variety of organisms and may allow for flexible ecological engineering strategies.

genetics↗

bioRxiv2019

Percentage of Foveal versus Total Macular Geographic Atrophy as a Predictor of Visual Acuity in Age-related Macular Degeneration

ObjectivesTo investigate the relationship between visual acuity (VA), total area of geographic atrophy (GA) and percentage of foveal GA. MethodsMulticenter, retrospective cross-sectional study of patients with GA due to age-related macular degeneration. Demographics, VA, fundus autofluorescence (FAF) and optical coherence tomography (OCT) images were collected. Using FAF images aided by OCT, foveal sparing status, GA pattern, total GA size, and percentage of GA covering the foveal area - area within a 1.5 mm diameter circle centered on the fovea centralis - were assessed. Univariable and multiple linear regression analyses were performed. Results54 eyes (mean age 78.7 {+/-}7.7 (SD), 60.0% female) were studied. Mean VA was 0.8 {+/-} 0.6 logMAR, mean total GA 8.8 {+/-} 6.7 mm2 and mean percentage of foveal GA was 71.5 {+/-} 30.9%. Of all assessed eyes, 48.2% (n = 26) presented with multifocal GA, and 18.5% (n = 10) had foveal sparing. Multiple regression analysis revealed that, controlling for age and GA pattern, the percentage of foveal GA presented a statistically significant association with VA ({beta} = 0.41, P = 0.004). No significant associations were observed with mean total GA size, while controlling for the same variables ({beta} = 0.010, P = 0.440). ConclusionPercentage of foveal GA was significantly associated with VA impairment, while the same was not verified for total GA area. These findings suggest that percentage of foveal GA may represent a more useful tool for assessing the impact of GA on VA. Further validation is needed in larger cohorts.

neuroscience↗

bioRxiv2019

Link clustering explains non-central essential genes in protein interaction networks

Essential genes (EGs) often form central nodes in protein-protein interaction (PPI) networks. However, many reports have shown that numerous EGs are non-central, suggesting that another principle governs gene essentiality. We propose link clustering as a distinct indicator of the essentiality for non-central nodes. Specifically, in various human and yeast PPI networks, we found that 29 to 47% of EGs were better characterized by link clustering than by centrality. Such non-central EGs with clustered links have significant impacts on communities at lower hierarchical levels, suggesting that their essentiality derives from functional dependency among relevant local neighbors, rather than their implication on global connectivity. Moreover, these non-central EGs exhibited several distinct characteristics: they tend to be younger and fast-evolving, and likely change their essentiality across different human cell lines and between human and mouse than central-EGs. Author summaryThe centrality in network structure was thought to be the cause of gene essentiality, as it conveys nodes importance in given networks. However, many essential genes are also found to be non-central, which leads us to ask whether a principle other than centrality may govern gene essentiality. Here we demonstrate that link clustering conveying functional dependency between nodes is the other principle characterizing gene essentiality. The link clustering explains numerous essential genes that are non-central, which are distinct from central ones regarding their molecular function and evolution. Our results clearly establish a two-dimensional principle governing gene essentiality.

systems biology↗

bioRxiv2018

A SIMPLE APPROXIMATION TO THE BIAS IN THE GENETIC EFFECT ESTIMATES WHEN MULTIPLE DISEASE STATES SHARE A CLINICAL DIAGNOSIS

Case-control genome-wide association (CC-GWAS) studies might provide valuable clues to the underlying pathophysiologic mechanisms of complex diseases, such as neurodegenerative disease, cancer. A commonly overlooked complication is that multiple distinct disease states might present with the same set of symptoms and hence share a clinical diagnosis. These disease states can only be distinguished in a biomarker evaluation that might not be feasible on the whole set of cases in the large number of samples that are typically needed for CC-GWAS. Instead, the biomarkers are measured on a subset of cases. Or an external reliability study estimates frequencies of the disease states of interest within the clinically diagnosed set of cases. These frequencies often vary by the genetic and/or non-genetic variables. We derive a simple approximation that relates the genetic effect estimates obtained in a logistic regression model with the clinical diagnosis as an outcome variable to the estimates in the relationship to the true disease state of interest. We performed simulation studies to assess accuracy of the approximation that weve derived. We next applied the derived approximation to the analysis of the genetic basis of innate immune system of Alzheimers disease.

genetics↗

bioRxiv2018

Single-nuclei RNA-seq on human retinal tissue provides improved transcriptome profiling.

Gene expression profiling is an effective way to provide insights into cell function. However, for heterogeneous tissues, bulk RNA-Seq can only provide the average gene expression profile for all cells from the tissue, making the interpretation of the sequencing result challenging. Single-cell RNA-seq, on the other hand, generates transcriptomic profiles of individual cell and cell types, making it a powerful method to decode the heterogeneity in complex tissues. The retina is a heterogeneous tissue composed of multiple cell types with distinct functions. Here we report the first single-nuclei RNA-seq transcriptomic study on human neural retinal tissue to identify transcriptome profile for individual cell types. Six retina samples from three healthy donors were profiled and RNA-seq data with high quality was obtained for 4730 single nuclei. All seven major cell types were observed from the dataset and signature genes for each cell type were identified by differential gene express analysis. The gene expression of the macular and peripheral retina was compared at the cell type level, showing significant improvement from previous bulk RNA-seq studies. Furthermore, our dataset showed improved power in prioritizing genes associated with human retinal diseases compared to both mouse single-cell RNA-seq and human bulk RNA-seq results. In conclusion, we demonstrated that feasibility of obtaining single cell transcriptome from human frozen tissues to provide additional insights that is missed by either the human bulk RNA-seq or the animal models.

genomics↗

bioRxiv2018

A simple approximation to bias in gene-environment interaction estimates when a case might not be the case

Case-control genetic association studies are often used to examine the role of the genetic basis in complex diseases, such as cancer and neurodegenerative diseases. The role of the genetic basis might vary by non-genetic (environmental) measures, what is traditionally defined as gene-environment interactions (GxE). A commonly overlooked complication is that the set of clinically diagnosed cases might be contaminated by a subset with a nuisance pathologic state that presents with the same symptoms as the pathologic state of interest. The genetic basis of the pathologic state of interest might differ from that of the nuisance pathologic state. Often frequencies of the pathologically defined states within the clinically diagnosed set of cases vary by the environment. We derive a simple and general approximation to bias in GxE parameter estimates when presence of the nuisance pathologic state is ignored. We then perform extensive simulation studies to show that ignoring presence of the nuisance pathologic state can result in substantial bias in GxE estimates and that the approximation we derived is reasonably accurate in finite samples. We demonstrate the applicability of the proposed approximation in a study of Alzheimers disease.

genetics↗

bioRxiv2017

A Strategy for Large-Scale Systematic Pan-Cancer Germline Rare Variation Analysis

Traditionally, genetic studies in cancer are focused on somatic mutations found in tumors and absent from the normal tissue. Identification of shared attributes in germline variation could aid discrimination of high-risk from likely benign mutations and narrow the search space for new cancer predisposing genes. Extraordinary progress made in analysis of common variation with GWAS methodology does not provide sufficient resolution to understand rare variation. To fulfil missing classification for rare germline variation we assembled datasets of whole exome sequences from >2,000 patients with different types of cancers: breast cancer, colon cancer and cutaneous and ocular melanomas matched to more than 7,000 non-cancer controls and analyzed germline variation in known cancer predisposing genes to identify common properties of disease associated mutations and new candidate cancer susceptibility genes. Lists of all cancer predisposing genes were divided into subclasses according to the mode of inheritance of the related cancer syndrome or contribution to known major cancer pathways. Out of all subclasses only genes linked to dominant syndromes presented significant rare germline variants enrichment in cases. Separate analysis of protein-truncating and missense variation in this subclass of genes confirmed significant prevalence of protein-truncating variants in cases only in loss-of-function tolerant genes (pLI<0.1), while ultra-rare missense mutations were significantly overrepresented in cases only in constrained genes (pLI>0.9). Taken together, our findings provide insights into the distribution and types of mutations underlying inherited cancer predisposition.

genetics↗