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Applications of the Green Fluorescent Protein and Its Variants in Tumor Angiogenesis and Physiology Studies
Published in Mary-Ann Mycek, Brian W. Pogue, Handbook of Biomedical Fluorescence, 2003
Chuan-Yuan Li, Yiting Cao, Mark W. Dewhirst
Gene therapy is a relatively new field in cancer research. With the completion of the human genome and rapid advances in genomics, there are a large number of candidate genes suitable for testing in cancer gene therapy studies. One of the biggest hurdles in cancer gene therapy is effective delivery of therapeutic genes to tumor cells. This is an important issue in cancer therapy because virtually all of the gene therapy vectors are bigger than traditional chemotherapeutic drugs. Various strategies have been taken to make gene therapy tumor-cell specific. One of the tools that should be very useful in facilitating the creation of more effective tumor cell targeting gene therapy vectors is an approach that allows the noninvasive, dynamic monitoring of gene transduction in vivo by various gene therapy vectors. Again, GFP is an ideal tool in this case. By use of a dorsal skinfold window chamber, we were able to monitor the activity of adenovirus in infecting a mammary cell carcinoma noninvasively over the course of days. In addition, long-term, noninvasive monitoring of gene expression is achieved in the retina [46] and in the brochial epithelium [47].
Roles of Nucleotide Sequence Analysis in Human Genetics and Genomics
Published in Hajiya Mairo Inuwa, Ifeoma Maureen Ezeonu, Charles Oluwaseun Adetunji, Emmanuel Olufemi Ekundayo, Abubakar Gidado, Abdulrazak B. Ibrahim, Benjamin Ewa Ubi, Medical Biotechnology, Biopharmaceutics, Forensic Science and Bioinformatics, 2022
S. S. D. Mohammed, I. Abraham, D. Enoma, L. E. Okoror
Human disease is not left out of this myriad of applications as diseases often have a molecular genetic basis. Genomics is a relatively new scientific discipline, having DNA sequencing as its core technology. This in turn has enabled enquiry into diseases at a genome-wide scale (Koboldt et al., 2013). Genotyping by Sequencing Techniques and standardized ontological annotation both plays functional roles in methodically analyzing the effects of human genomic variation and phenotypes of model organisms (Belizário and Hilliker, 2013). These can play key roles in finding candidate genes and new clues for disease’s etiology and treatment (Drmanac et al., 2010). Sanger sequencing, genome-wide association analysis, and Next-Generation Sequencing (NGS) analysis, respectively, is the order in which sequencing has evolved at the whole genome and exome sequencing levels (Hitomi and Tokunaga, 2017; Petersen et al., 2017). Sequence analysis, whole genome phylogenetic analysis, and genetic phylogenetic analysis were employed in the current covid-19 disease outbreak (Suzuki et al., 2020). They aimed to understand the current evolutionary dynamics of bovine coronaviruses (BCoVs) through whole genome analysis of multiple Japanese BCoV isolates collected in recent years. This is significant as coronavirus can cross the species barrier and infect humans with a severe respiratory syndrome (Qiang et al., 2020). Genomic analysis also enabled scientists identify SARS-COv-2 to have greatest similarity with the bat coronavirus (Li et al., 2020; Zhou et al., 2020). Sequence analysis is also used in the study of cancerous cells’ DNA. This is to give an insight into the molecular features of the disease (Li et al., 2020). As the cancerous phenotype is complex, it is only fitting that we turn to advances in sequences to address this problem. GWAS is widely used in understanding complex non-communicable diseases. They are useful for detecting variants at genomic loci which are associated with complex traits in the population (Visscher et al., 2012). These traits include complex diseases such as heart disease, diabetes, auto-immune diseases, and psychiatric disorders. Specifically, NGS has been very useful in the fight against cancer as it has made patient selection for targeted drugs possible, some of which have shown remarkable efficacy in cancers that have the cognate molecular signatures (Adashek et al., 2020). In general, analysis of sequences (coding and non-coding) is playing a major role in current clinical practice. They will continue to grow as the cost of sequencing continuously reduces and eventually will become a key component of healthcare systems.
Discovery of genetic risk factors for disease
Published in Journal of the Royal Society of New Zealand, 2018
Lack of replication in candidate gene studies demonstrated the need for much larger studies and more comprehensive maker coverage. A landmark paper published by the Wellcome Trust Case Control Consortium (WTCCC) in 2007 used high-throughput genotyping chip technology and compared the frequencies of many thousands of SNPs between a common set of 3000 controls and 2000 cases each of bipolar disorder, coronary heart disease, Crohn’s disease, high blood pressure, rheumatoid arthritis and diabetes types 1 and 2. This study established large scale genome-wide association studies (GWAS) as a powerful approach to identify genetic variants associated with common complex diseases (Wellcome Trust Case Control 2007). The approach is agnostic to assumptions about underlying disease biology in the selection of candidate genes.
Application of an in silico approach identifies a genetic locus within ITGB2, and its interactions with HSPG2 and FGF9, to be associated with anterior cruciate ligament rupture risk
Published in European Journal of Sport Science, 2023
Senanile B. Dlamini, Colleen J. Saunders, Mary-Jessica N. Laguette, Andrea Gibbon, Junaid Gamieldien, Malcolm Collins, Alison V. September
To date, candidate gene association studies based on our understanding of connective tissue biology have successfully identified more than 80 genetic loci, highlighting at least 46 genes, to be implicated in the predisposition to connective tissue injuries including those affecting ligaments and tendons (Rahim et al., 2016). Many more genes require identification and validation as each effective risk allele is most likely making a small contribution to the overall genetic risk. The associated genes implicated thus far encode structural components, as well as regulators of biological processes within these connective tissues (Rahim et al., 2016). A recognisable limitation of the candidate gene approach is that it disregards potentially important genes that are not obvious candidates. It is therefore important that researchers use a more comprehensive approach to improve our understanding of the genetic complexity contributing to musculoskeletal soft tissue injury risks. There is a vast array of research information stored in public domains and, as researchers, we are not always able to keep up with the speed of these publications and how they link and integrate with clinical conditions. It is therefore not surprising that we see an increased creation of knowledge graphs for many multifactorial phenotypes (Hassani-Pak & Rawlings, 2017; Mohamed et al., 2021; Nicholson & Greene, 2020). These knowledge graphs are created by integrating and extracting knowledge from publicly available biomedical literature and linking it with the relevant information from curated biological databases (Hassani-Pak & Rawlings, 2017; Mohamed et al., 2021; Nicholson & Greene, 2020). Biomedical knowledge graphs can be used to effectively gain more comprehensive insight into identifying biological relationships between genes, proteins and molecular networks linked to a clinical condition. Thereby, it can assist in exploring the pathobiology of complex phenotypes such as ligament and tendon injuries towards improved diagnoses, treatment design and drug therapy.