Genetic Engineering and Reverse Aging
Genetic engineering could allow those living 70 years chronologically to still maintain the health and vitality of 30-year-olds – at an expense. But such intervention might come at a price.
Since Kyoto University biologist Shinya Yamanaka won a share of the Nobel Prize for developing methods to transform adult cells into stem cells, teams have employed these tools to reverse aging.
Epigenetics
Epigenetics is a layer of information that sits atop DNA that regulates how genes are activated or silenced within different cells, such as your nerve cells and muscle cells. Epigenetics also plays a key role in your health and appearance – it regulates which proteins are produced, when and where, without altering genetic code itself. Epigenetic changes begin prior to birth and help determine how your cells work as well as their appearance – epigenetic changes help determine which genes switch “on” or off which impacts how well cells work while others don’t – for instance your nerve and muscle cells both share DNA but work differently due to epigenetic controls: your body activates specific muscle genes while turning off necessary nerve cells’ essential genes for functions or appearance purposes based on epigenetic controls which genes need turning on or off for certain cells depending on epigenetic effects from within or without.
Researchers have long acknowledged the correlation between epigenetic changes and aging and their observable manifestations, such as DNA breaks or misalignments, such as DNA repairs.
Shinya Yamanaka earned him a share of the Nobel Prize in 2012 for his groundbreaking research. He discovered a cocktail of proteins called Yamanaka factors which can reprogram old cells into flexible stem cells and significantly extend life spans while alleviating age-related symptoms in mice using gene therapy delivery of these factors. His team used gene therapy delivery of these factors into mice to extend lifespan significantly as well as reverse various signs of ageing symptoms.
Stelios Andreadis of the University of Buffalo recently conducted research demonstrating how embryonic gene NANOG can reset an individual’s epigenetic clock and rejuvenate cells and tissues. His team performed temporary, fast-healing DNA breaks in mice that disrupted key epigenetic process called chromatin remodeling – this connective structure holds DNA together and its disorganization can alter gene expression, leading cells to behave abnormally and potentially leading to changes in cell behavior.
DNA methylation, in which chemical groups attached to specific areas of DNA inhibit or promote gene expression. Methylation processes are affected by environmental factors like stress and diet; decreased methylation reduces protein production in fat cells while increasing inflammation; these and other epigenetic processes can be used as indicators of biological age but they’re reversible through lifestyle modifications or drugs.
Transcriptomics
Transcriptomics is the study of RNA, the chemical cousin of DNA. By studying its transcripts, researchers can gain an insight into which genes are active within specific cells or whether or not certain treatments have any impact. With this data in hand, scientists gain a better understanding of how the body normally operates and why something might go amiss when something goes amiss.
Transcriptomics has two primary goals. One is to find ways to modify gene expression to delay or reverse aging, but the technique can also be used to gain insight into how different genes function together; studies have discovered that certain transcription factor (TFs) genes influence other gene expression patterns.
Researchers have demonstrated that transcription factors (TFs) bind to DNA and regulate the activity of other genes by controlling how much mRNA they produce. By studying transcriptomes from different cells, researchers can identify these TFs and see which ones have an up or down regulation which might make them targets for reverse aging therapies.
Transcriptomics has been transformed by new technologies. Falling costs have allowed small laboratories to conduct transcriptionomic experiments on thousands of samples, while combining transcriptomics with other “omics”, like proteomics, genomics and metabolomics provides a comprehensive picture of genome function.
Scientists employ various approaches to generate and analyze transcriptome data, such as microarray analysis, sequence assembly, gene annotation and advanced data mining and machine learning techniques. These techniques serve as powerful tools for studying how genetics influence cell functions across species ranging from plants and animals to humans.
One of the most promising applications of transcriptomics has been single-cell transcriptional profiling (scTP). This technique allows scientists to examine individual cells within bulk tissue samples, making gene expression research much simpler. Furthermore, using single-cell samples allows scTP practitioners to avoid problems associated with cell composition bias that arise when working with bulk samples.
scTP has proven particularly helpful for studying aging at a cellular level. A recently published paper used this technique to create single-cell transcriptomic aging clocks in neurogenic regions of mice, showing how exercise and heterochronic parabiosis could reverse transcriptomic aging. Another group used scTP in human peripheral blood mononuclear cells and discovered that several repressor genes can control multiple downstream genes to reverse transcriptomic aging.
Gene Expression
Gene expression, the process that transforms genetic information into functional RNA molecules and proteins, must be tailored precisely for individual cell types and conditions. Gene expression acts both as an “on/off switch” to control when and how many RNA molecules are made, as well as volume control to set production volumes. Protein products produced from many genes act as regulators that affect production or activity levels of other genes.
Scientists can manipulate gene function through either adding new sequences into cells or changing their genetic code that determines their activity. Recombination is one such technique for inserting mutant versions of genes into cells; its promoter allows these mutant genes to produce their product only under specific conditions (for instance when temperature-sensitive genes or signaling proteins are activated), producing their product only when its promoter becomes active; then their function can be examined later.
An advanced method for gene replacement involves creating conditional mutants, in which one gene is disrupted at specific moments during development in only certain tissues. This can be accomplished by inserting a fragment containing your desired mutant gene into a vector carrying gene-blocking sequences and injecting this vector into embryonic stem cells which grow into different tissues cultured on plastic dishes; rare colonies containing the insert will then be isolated and used to generate mutant mice.
Scientists created a mouse model of premature aging by inserting a gene fragment with an unfavorable mutation into CISD2, which codes for a protein that prevents correct assembly of mitochondria, thus decreasing energy production and hastening cell aging.
Biopharmaceutical company employing AAV as therapeutic delivery vehicles has reported that injection of an AAV containing three Yamanaka factors into aged mouse liver cells caused their genomic methylation patterns to return to those seen in younger animals – supporting the idea that using the same protein-boosting mechanism that rejuvenates cells in lab can also reverse aging processes in organisms such as mice and humans.
Boolean Network
Boolean networks (BN) can be an invaluable asset when modeling biological regulatory systems. These models rely on rules that describe interactions among various components, providing a good way to represent dynamics of these systems and can even help researchers understand how the interactions among various regulatory systems interact with one another and influence each other.
A BN graph represents an individual component of a system and their interactions via nodes and directed edges, with Boolean functions connecting nodes together to determine their state and value of nodes being either true or false, providing an approximation of how each component might actually behave in real time.
BNs are dynamical systems and their states fluctuate over time. A BN’s update function is determined by combining Boolean functions that control each node; these must be monotone or near monotone in nature. There are various techniques for modeling BNs such as stochastic processes and dynamical programming; another way is using random BNs to produce ensembles with specific properties. A mathematical method called the satisfiability problem checks whether given updates apply to an underlying Boolean network.
Graphical methods may also be utilized when modeling BNs. This graphical approach allows researchers to view and visualize the structure of the BN, analyze its interactions among various nodes, find its shortest route between nodes and determine any bounded oscillations that might arise within its structure.
Bounded oscillation is a dynamical pattern found within a BN that occurs when nodes are activated or deactivated at different times, simulating cell and tissue behaviors and cancer biology by leading to proliferation in response to growth factors. It plays an essential role in cancer biology by creating dynamical models for cell and tissue proliferation in response to growth factors.
