Background: Essential genes are critical for the development of all organisms and are associated with many human diseases. These genes have been a difficult category to study prior to the availability of balanced lethal strains. Despite the power of targeted mutagenesis, there are limitations in identifying mutations in essential genes. In this paper, we describe the identification of coding regions for essential genes mutated using forward genetic screens in Caenorhabditis elegans. The lethal mutations described here were isolated and maintained by a wild-type allele on a rescuing duplication.
Results: We applied whole genome sequencing to identify the causative molecular lesion resulting in lethality in existing C. elegans mutant strains. These strains are balanced and can be easily maintained for subsequent characterization. Our method can be effectively used to analyze mutations in a large number of essential genes. We describe here the identification of 64 essential genes in a region of chromosome I covered by the duplication sDp2. Of these, 42 are nonsense mutations, six are splice signal mutations, one deletion, and 15 are non-synonymous mutations. Many of the essential genes in this region function in cell cycle, transcriptional regulation, and RNA processing.
Conclusions: The essential genes identified here are represented by mutant strains, many of which have more than one mutant allele. The genetic resource can be utilized to further our understanding of essential gene function and will be applicable to the study of C. elegans development, conserved cellular function, and ultimately lead to improved human health.
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http://dx.doi.org/10.1186/1471-2164-15-361 | DOI Listing |
Alzheimers Dement
December 2024
The University of Texas Health Science Center at San Antonio, San Antonio, TX, USA.
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View Article and Find Full Text PDFAlzheimers Dement
December 2024
The Jackson Laboratory, Bar Harbor, ME, USA.
Background: Late-onset Alzheimer's disease (LOAD) is the leading cause of dementia and a major contributor to increased mortality. Recent human datasets have revealed many LOAD genetic risk factors that are correlated with the degree of AD burden. Further, the complexity and heterogeneity of LOAD appears to be promoted by interactions between genetics and environmental factors such as diet, sedentary behavior, and exposure to toxicants, like lead (Pb), cadmium (Cd), and arsenic (As).
View Article and Find Full Text PDFAlzheimers Dement
December 2024
University of Missouri, Columbia, MO, USA.
Background: Preclinical animal models are essential for the development of effective treatments. For instance, the 5xFAD mouse model successfully represents the pathophysiology of Alzheimer's disease (AD). Expression of humanized APP (K670N/M671L - Swedish, I716V - Florida, V717I - London) and PSEN1 (M146L and L286V), found in early onset AD patients, induces the production of amyloid-β 42 (Aβ42) and amyloid deposition, gliosis, and progressive neuronal loss.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Columbia University, New York, NY, USA.
Background: The connection between inflammasomes and Alzheimer's disease (AD) has garnered significant interest, with emerging evidence suggesting genetic associations and functional implications. Notably, studies have reported the upregulation of inflammasome components like NLRP1, NLRP3, and Caspase-1 in AD patients. Moreover, genetic polymorphisms in inflammasome-related genes are linked to increased AD risk.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Department of Population and Quantitative Health Sciences, Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA.
Background: Recent advances in Alzheimer's Disease (AD) research have emphasized the importance of recruiting from diverse populations. Notably, African-descent individuals have an almost doubled risk of developing AD compared to European-descent individuals. Transcriptome-wide association studies (TWAS) have advanced the analysis of non-coding variants by integrating gene expression with GWAS data.
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