Using CRISPR-Cas9 technology to create Danio rerio dnah7 mutants

CRISPR-Cas9 is a recent discovered genetic editing mechanism, that shows a lot of versatility. This allows scientists to do genetic manipulation with relative ease when compared with others current genetic tools available. One possible application of the CRISPR-Cas9 system is to mimic human disease mutations by targeting orthologous genes in animal models, which allows a better characterization of the mechanisms behind a particular disease. Cilia are hair-like structures that protrude from the cell surface in organisms and can be classified as motile or non-motile. They are responsible for several important functions throughout the human body. Such functions include, generating fluid flow and sensing mechanical or chemical cues from the surrounding environment. If these are compromised it can lead to ciliopathies. Ciliopathies are a group of diseases and syndromic diseases characterized by malfunctioning of cilia. Motile cilia can lead to a disease known as primary ciliary dyskinesia (PCD). More than 35 genes have been linked with cilia motility in PCD patients. Some of these genes are associated with the inner dynein arms present in the axoneme. A better understanding of mutations in these genes would help the characterization of PCD. Using CRISPR-Cas9 we tried to cause a mutation in dnah7, a gene that encodes a protein present in inner dynein arms. Two SgRNAs were selected to disrupt dnah7 and injected into zebrafish embryos. These F0 embryos were screened for mutations outcrossed and left to sexually mature. When matured, the progeny was screened again to find any heritable mutations. Meanwhile, analyses of cilia beat frequency and pattern, the readouts of cilia function, were made in a set of wild type and ccdc40 MO injected zebrafish. Additionally, two SgRNAs were designed for targeting another PCD commonly mutated gene named rsph4a, a gene coding for a protein present in the radial spokes of the axonemes

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost