Biological systems are resistant to genetic changes; a property known as mutational robustness, the origin of which remains an open question. In recent years, researchers have explored emergent properties of biological systems and mechanisms of genetic redundancy to reveal how mutational robustness emerges and persists. Several mechanisms have been proposed to explain the origin of mutational robustness, including molecular chaperones and gene duplication. The latter has received much attention, but its role in robustness remains controversial. Here, I examine recent findings linking genetic redundancy through gene duplication and mutational robustness. Experimental evolution and genome resequencing have made it possible to test the role of gene duplication in tolerating mutations at both the coding and regulatory levels. This evidence as well as previous findings on regulatory reprogramming of duplicates support the role of gene duplication in the origin of robustness.
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http://dx.doi.org/10.1016/j.tig.2015.04.008 | DOI Listing |
Curr Med Chem
January 2025
Shree S K Patel College of Pharmaceutical Education and Research, Ganpat University, Mahesana, Gujarat, 384012, India.
Therapeutic hurdles persist in the fight against lung cancer, although it is a leading cause of cancer-related deaths worldwide. Results are still not up to par, even with the best efforts of conventional medicine, thus new avenues of investigation are required. Examining how immunotherapy, precision medicine, and AI are being used to manage lung cancer, this review shows how these tools can change the game for patients and increase their chances of survival.
View Article and Find Full Text PDFHeliyon
July 2024
Department of Breast Surgery, Institute of Breast Disease, Second Hospital of Dalian Medical University, Zhongshan Road, Dalian, 116023, Liaoning, China.
Identifying driver genes in cancer is a difficult task because of the heterogeneity of cancer as well as the complex interactions among genes. As sequencing data become more readily available, there is a growing need for detecting cancer driver genes based on statistical and mathematical modeling methods. Currently, plenty of driver gene identification algorithms have been published, but they fail to achieve consistent results.
View Article and Find Full Text PDFNarra J
December 2024
Animal Research Facilities, Indonesia Medical Education and Research Institute (IMERI), Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia.
Clustered regularly interspaced short palindromic repeats (CRISPR)-associated nuclease 9 (CRISPR/Cas9) offers a robust approach for genome manipulation, particularly in cancer therapy. Given its high expression in triple-negative breast cancer (TNBC), targeting with CRISPR/Cas9 holds promise as a therapeutic strategy. The aim of this study was to design specific single guide ribonucleic acid (sgRNA) for CRISPR/Cas9 to permanently knock out the gene, exploring its potential as a therapeutic approach in breast cancer while addressing potential off-target effects.
View Article and Find Full Text PDFAnal Chem
January 2025
Key Laboratory of Green Chemistry & Technology of Ministry of Education, College of Chemistry, Sichuan University, Chengdu 610064, Sichuan, China.
Isothermal nucleic acid amplification techniques are promising alternatives to polymerase chain reaction (PCR) for amplifying and detecting nucleic acids under resource-limited conditions. While many isothermal amplification strategies, such as recombinase polymerase amplification (RPA), offer comparable sensitivity to PCR, they often lack the specificity and robustness for discriminating single nucleotide variants (SNVs), mainly due to the uncontrolled production of massive amplicons. Herein, we introduce a mismatch-guided DNA assembly (MGDA) approach capable of discriminating SNVs in the presence of high concentrations of wild-type (WT) interferences.
View Article and Find Full Text PDFJ Chem Theory Comput
January 2025
Department of Chemistry and Biochemistry, University of Texas at Arlington, Arlington, Texas 76019, United States.
Integrating machine learning potentials (MLPs) with quantum mechanical/molecular mechanical (QM/MM) free energy simulations has emerged as a powerful approach for studying enzymatic catalysis. However, its practical application has been hindered by the time-consuming process of generating the necessary training, validation, and test data for MLP models through QM/MM simulations. Furthermore, the entire process needs to be repeated for each specific enzyme system and reaction.
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