Unveiling the Biochemical Mechanisms of Aging and the Implications of Oxidative Stress on Cellular Senescence through Multi-Omics Analysis of Fibroblasts

Unveiling the Biochemical Mechanisms of Aging and the Implications of Oxidative Stress on Cellular Senescence through Multi-Omics Analysis of Fibroblasts | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Unveiling the Biochemical Mechanisms of Aging and the Implications of Oxidative Stress on Cellular Senescence through Multi-Omics Analysis of Fibroblasts View ORCID Profile Rajarshi Mandal , Ning Xie , Gil Alterovitz doi: https://doi.org/10.1101/2025.05.17.654671 Rajarshi Mandal 1 Biomedical Cybernetics Laboratory , Harvard Medical School, 75 Francis Street, Boston, MA 02115 USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Rajarshi Mandal For correspondence: rajarshi.m.1000{at}gmail.com Ning Xie 1 Biomedical Cybernetics Laboratory , Harvard Medical School, 75 Francis Street, Boston, MA 02115 USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Gil Alterovitz 1 Biomedical Cybernetics Laboratory , Harvard Medical School, 75 Francis Street, Boston, MA 02115 USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract This research investigates the complex biochemical mechanisms underlying aging by analyzing primary human fibroblasts using a longitudinal multi-omics dataset. This dataset includes cytology, DNA methylation and epigenetic clocks, bioenergetics, mitochondrial DNA sequencing, RNA sequencing, and cytokine profiling. Key findings indicate that mitochondrial efficiency declines with age, while glycolysis becomes more prevalent to compensate for energy demands. Epigenetic clocks, such as Hannum and PhenoAge, showed strong correlations with biological age (ρ > 0.650, p < 1e-6), validating the experimental setup and confirming that the cultured fibroblasts were aging appropriately. Fibroblasts with SURF1 mutations exhibited accelerated aging, marked by bioenergetic deficits, increased cell volume, and reduced proliferative capacity, underscoring the pivotal role of mitochondrial dysfunction in cellular senescence. Novel insights were gained from analyzing cytokines like IL18 and PCSK9, some of which were linked to age-related diseases such as Alzheimer’s and cardiovascular disorders. Experimental treatments revealed distinct effects on cellular aging. Dexamethasone reduced inflammation but also increased DNA methylation, induced metabolic inefficiencies, and shortened cellular lifespan. Oligomycin heightened oxidative stress and RNA degradation, emphasizing how such treatments contribute to cellular stress and metabolic imbalance while shedding light on aging mechanisms. By uncovering connections between mitochondrial dysfunction, epigenetic biomarkers, and immune dysregulation, this study identifies potential therapeutic targets for age-related diseases. Future research could validate the most promising biomarkers across diverse cell types and experimental treatments to build a more comprehensive understanding of aging. Introduction Biogerontology is a field that focuses on understanding the underlying mechanisms of aging to find potential interventions to promote longevity. Currently, there is no scientific consensus on a particular aging theory, but they have been classified into two major categories: programmed aging theories and damage theories [ 1 ]. Programmed aging theories propose that aging is genetically regulated and results in evolutionary advantages such as promoting genetic adaptation and removing post-reproductive age individuals to combat overpopulation [ 2 ]. In contrast, most evolutionary biologists believe that the lack of natural selection in post-reproductive organisms leads to reduced maintenance. One promising explanation is the free radical theory of aging, which suggests that reactive oxygen species (ROS) damage biomolecules such as DNA, protein, and lipids. This causes an accumulation of structural and functional problems inside cells [ 3 ]. ROS are highly reactive chemicals created in metabolic reactions. Common ROS include hydroperoxide, superoxide, hydroxyl radical, and singlet oxygen and these can cause oxidative stress. Researchers at MRC Laboratory of Medical Sciences extended the lifespan of Drosophila melanogaster by amplifying expression of superoxide dismutase (SOD) and catalase, which are antioxidant enzymes [ 4 ]. Mitochondria are major producers of ROS in mammalian cells, which leads to mitochondria DNA (mtDNA) being highly vulnerable to oxidative stress. After realizing that the lack of mitochondrial homeostasis is a shared hallmark among numerous age-associated ailments, the free radical theory has been updated to focus on the clonal amplification of pre-existing mtDNA mutations from reduced mitochondrial polymerase γ fidelity [ 5 ]. Epigenetics is the study of heritable or stable changes that occur without changes to the DNA sequence; some well-known examples include DNA methylation, histone modification, and non-coding RNA regulation [ 6 ]. DNA methylation is the biological process where methyl groups are added to a DNA molecule, which has been shown to cause genomic imprinting, X-chromosome inactivation, and repression of transposable elements [ 7 , 8 ]. Recently, it has been found to strongly correlate with biological age through epigenetic clocks, which are biochemical tests that quantify biological age from CpG site methylation data. The most renowned clocks include Horvath, Hannum, PhenoAge, and GrimAge; each has their own unique architecture and differentiating factor [ 9 ]. Researchers at Columbia University created a longitudinal dataset with high temporal resolution from cultured primary human fibroblasts measured across their replicative lifespans [ 10 ], which we thoroughly analyzed in this study. A fibroblast is a cell that helps connective tissue form through secreting collagen proteins that maintain structure and provide support. These were isolated from skin of six healthy donors and dermal punch biopsies of three individuals with a lethal variant of SURF1 mutation (IRB #AAAB0483). Surfeit locus protein 1 (SURF1) is part of the mitochondrial translation regulation assembly intermediate of cytochrome c oxidase (COX) complex [ 11 ]. Mutations in its encoding gene have been associated with Leigh syndrome, a neurological condition that affects the central nervous system, and Charcot–Marie–Tooth disease [ 12 ]. The dataset from the Columbia University researchers encompasses various molecular and cellular data types, including cytology, DNA methylation and epigenetic clocks, bioenergetics, mitochondrial DNA sequencing, RNA sequencing, and cytokine profiling. Some of the variables include total days grown, days after treatment, clinical condition, and treatment types. The “Clinical Condition” refers to whether the fibroblast has the SURF1 mutation while the “Treatments” refers to the name of the experimental treatment. Some treatments in the dataset include Dexamethasone, Oligomycin, Mitochondrial Nutrient Uptake Inhibitors + Dexamethasone, Mitochondrial Nutrient Uptake Inhibitors, 5-azacytidine, Contact Inhibition, 5-azacytidine + Mitochondrial Nutrient Uptake Inhibitors, Galactose, 2-Deoxy-D-glucose, and betahydroxybutyrate. Due to a low amount of data in some categories for most treatments, we analyzed the effects of only Dexamethasone and Oligomycin in this paper. Dexamethasone, a corticosteroid fluorinated at position 9, is most frequently used for treating inflammation, arthritis, severe allergies, and asthma because it is a glucocorticoid receptor agonist [ 13 , 14 ]. It is similar to a natural hormone produced by human adrenal glands and was even found to reduce mortality in patients with severe COVID-19 [ 15 ]. Oligomycin, a macrolide produced by bacteria in the genus Streptomyces , inhibits ATP synthase (Complex V) by blocking the F O subunit of the proton channel, halting oxidative phosphorylation. However, researchers are looking into its use as an antibiotic [ 16 ]. The control group in the dataset was the cells without the SURF1 mutation cultured without any experimental medium. The experimental groups included cells with the SURF1 mutation and healthy cells cultured with various treatments like Dexamethasone and Oligomycin. We tested a plethora of hypotheses in this study. For example, we hypothesized that DNA methylation and mitochondrial DNA sequencing biomarkers’ correlation with biological age would support the free radical theory of aging. We also hypothesized that fibroblasts with the SURF1 mitochondrial mutation would exhibit exacerbated oxidative stress and impaired bioenergetics, validating mitochondrial dysfunction as a critical driver of senescence. Finally, we hypothesized that experimental treatments such as Dexamethasone and Oligomycin would emulate various conditions so that we could better understand how certain biological pathways are impacted as we age. Methods Data Preprocessing The original dataset is available at the Columbia Picard Shiny App . First, the dataset was downloaded from https://columbia-picard.shinyapps.io/shinyapp-Lifespan_Study/ in the “Download Data” subpage with the checkbox selected for “Click to download the full sample-set.” After it was uploaded to Kaggle at https://www.kaggle.com/datasets/rm1000/multi-omics-aging-with-mitochondrial-perturbations , a Jupyter Notebook running Python 3.12 was created. Next, we imported the essential libraries for data preprocessing including math, NumPy, pandas, matplotlib, seaborn, SciPy, and warnings. The csv file containing the original data was read using pandas’ read_csv function and the column names were manually renamed for clarity and conciseness. Data Visualization Next, we wanted to visualize some biomarker measurement distributions from each of the six categories mentioned earlier (samples in Fig. 1 ). To achieve this, we chose a column for each category and plotted them using seaborn’s histplot function with 16 bins. In other words, the x-axis contains ranges of values for the chosen columns while the y-axis displays the percentage of datapoints that belong to the corresponding range of values. Download figure Open in new tab Figure 1: Visualizing dependent variables (biomarkers) with histograms. A. The histogram shows the distribution of cell diameter values. B. The histogram displays the amount of DNA methylation on the encoding gene of Cystatin C. C. The histogram shows the maximum measured PPR glycolysis. D. The histogram shows the minimum deletion length in mitochondrial DNA. E. The histogram displays DV200, a metric used to quantify the quality of RNA by measuring the percentage of fragments over 200 nucleotides. F. The histogram shows the amount of TNF receptor superfamily member 8, a cell membrane protein, across all measurements. Download figure Open in new tab Figure 2: Revealing correlation between dependent variables with clustermaps. A. There is a moderate amount of positive and negative correlations for cytology columns. B. There is a significant positive correlation between DNA methylation and clocks columns. C. There is a significant positive correlation between bioenergetics columns. D. There is little correlation between mitochondrial DNA sequencing columns. E. There is a strong positive correlation between some RNA sequencing columns. F. There is a weak positive correlation between some cytokines. Download figure Open in new tab Figure 3: Visualizing biomarker measurements over age. The scatterplot on the left plots doubling rate against total days grown and there is a clear negative relationship. The scatterplot on the right graphs insulin-like growth factor-binding protein 6 and total days grown; there is a weak negative relationship. Download figure Open in new tab Figure 4: Impact of SURF1 mutation on biomarkers. The graph on the left indicates that the SURF1 mutation positively impacts the amount of interleukin 8. The scatterplot on the right implies that the SURF1 mutation negatively impacts the amount of ATP oxidation in the cultured fibroblasts. Download figure Open in new tab Figure 5: Impact of experimental treatments on biomarkers. The top scatterplot shows the effect of 7 treatments on the baseline extracellular acidification rate. The middle scatterplot shows the effect of 9 treatments on population doublings. The bottom scatterplot shows the effect of 9 treatments on the Horvath2 skin and blood epigenetic clock. After that, we visualized the correlations between the columns in each of the categories. Clustermaps were perfect for this because they plotted matrix data as a hierarchically clustered heatmap. The dark red indicates a strong positive correlation, the grey indicates no correlation, and the dark blue indicates a strong negative correlation. The column names are not included because there were too many of them to display. Statistical Tests First, we identified treatments correlated with the number of days grown after treatment by running student’s t-test for paired samples on the experimental treatments and the number of days grown after treatment. For each of the biomarker categories, the following statistical tests were run. To figure out which biomarkers were predictors of total days grown, we ran Spearman’s rank correlation between the biomarkers and total days. This is because there are two quantitative variables that are nonparametric. The data corresponding to this statistical test was visualized using scatterplots with the total days grown on the x-axis and the biomarker on the y-axis. Next, we ran student’s t-test for paired samples between the biomarkers and whether or not the fibroblast has the SURF1 mutation to determine which biomarkers were impacted by the SURF1 mutation. This is because there is one quantitative and one qualitative variable with two paired groups that are parametric. The data corresponding to this statistical test was visualized using scatterplots with the total days grown on the x-axis, the biomarker on the y-axis, and whether the fibroblast had the SURF1 mutation as the color of the point. To figure out which biomarkers were impacted by each treatment, we ran student’s t-test for paired samples between the biomarkers and whether or not the specific treatment had been used. This is because there is one quantitative and one qualitative variable with two paired groups that are parametric. We also created scatterplots with the days after treatment on the x-axis, the biomarker on the y-axis, and the treatment as the color of the point. Three example scatterplots with their legends are shown below. Results In each of the data tables, the first column has the category, the second column has the biomarker measurement, the third column has the p-value, and the fourth column the result of the statistical test run using the Python library SciPy rounded to 3 decimal points if the p-value is less than 0.05. This means that the particular result was statistically significant, and the null hypothesis can be rejected with 95% confidence. The first data table contains the result of Spearman’s rank correlation between the dependent variables (biomarkers) and total days grown. View this table: View inline View popup Table 1: Quantifying correlation between biomarkers and biological age. Among all of the dependent variables (biomarkers), population doublings, mitotic age, DNAmLeptin, Hannum clock, TGF alpha BR63 18 plex, IL 18 IL 1F4 BR78 35 plex, days per passage, and PCPhenoAge clock had the strongest positive correlation with biological age. PCDNAmTL, doubling rate, divisions per passage, PCSK9 BR44 18 plex, IGFBP 6 BR27 35 plex, DNAmPAI1, IL 8 CXCL8 BR18 35 plex, and live cells counted had the strongest negative correlation with biological age. In addition, all of these dependent variables (biomarkers) had p-values of less than 1e-6. The full results are thoroughly analyzed in the discussion section. The next data table contains the result of student’s t-test for paired samples between the dependent variables (biomarkers) and clinical condition (normal, SURF1 mutation). View this table: View inline View popup Table 2: Quantifying impact of SURF1 mutation on biomarkers. Among all of the dependent variables (biomarkers), PPRgly, ATPglyc, Baseline ECAR, PPRtotal, and cells plated were the most positively impacted by the SURF1 mutation. PPRresp, basal respiration, ATPox, ATP linked respiration, OCRcoupled, baseline respiration, resting metabolic rate mlO2 per kilogram per min, and population doublings were the most negatively impacted by the SURF1 mutation. In addition, all of these dependent variables (biomarkers) listed above had p-values of less than 1e-6. The full results are thoroughly analyzed in the discussion section. The final data table contains the result of student’s t-test for paired samples between the dependent variables (biomarkers) and experimental treatment (control, Dexamethasone, Oligomycin). View this table: View inline View popup Table 3: Quantifying impact of Dexamethasone and Oligomycin on biomarkers. Among all of the dependent variables (biomarkers), Oxidative spare capacity, max respiration, max metabolic rate mlO2 per kilogram per min, PPRresp max, TFPI BR64 18 plex, cells plated, and IGFBP rp1 IGFBP 7 BR77 35 plex were the most positively impacted by Dexamethasone. Cell volume, cell diameter, Lumican BR13 18 plex, coupling efficiency, PPRgly, ATPglyc, PPRtotal, and baseline ECAR were the most negatively impacted by Dexamethasone. In addition, all of these dependent variables (biomarkers) listed above had p-values of less than 1e-6. The full results are thoroughly analyzed in the discussion section. The Columbia University researchers did not measure cytokines in normal fibroblasts with Oligomycin as the experimental treatment, which is why there is a blank section in the data table above. Among all of the dependent variables (biomarkers), PPRgly, ATPglyc, PPRtotal, baseline ECAR, cells plated, resting metabolic rate watts per kilogram, ATPtotal, and resting metabolic rate picoWatts per cell were the most positively impacted by Oligomycin. OCRcoupled, ATP linked respiration, ATPox, ATPglyc spare, glycolytic spare capacity, ATPtotal spare, baseline respiration, and resting metabolic rate mlO2 per kilogram per min were the mos negatively impacted by Oligomycin. In addition, all of these dependent variables (biomarkers) listed above had p-values of less than 1e-6. The full results are thoroughly analyzed in the discussion section. Discussion In this section, we analyzed our findings across all six molecular and cellular data types we studied. For each category, we discussed correlations with biological age, the effects of the SURF1 mutation, and the influence of treatments like Dexamethasone and Oligomycin. Cytology The doubling rate, population doubling time, population doublings, divisions per passage, and days per passage all measure a specific aspect of cell growth and proliferation. A doubling is when the population of cells has doubled, and a passage refers to the process of moving the cells to a new area containing fresh growth medium. Population doublings clearly had a positive correlation with age as it only ever increased over time. Days per passage was held constant, yet divisions per passage had a negative correlation with age. This provides evidence for the Hayflick limit, which states that cells undergo mitosis less frequently as they age, eventually stopping proliferation completely [ 17 ]. This is also supported by the fact that doubling rate also had a negative correlation with age. Population doublings, divisions per passage, and doubling rate all decreased in fibroblasts with the SURF1 mutation. It can be inferred that the energy deficiency slowed down the cell cycle. This explains why individuals with Leigh syndrome experience weight faltering [ 18 ]. As expected, both Dexamethasone and Oligomycin negatively impacted these measurements as mitochondrial dysfunction and oxidative stress do not help with cell proliferation. Live cells counted and percent dead cells refer to the amount of viable and nonviable cells in the sample. Live cells counted had a negative correlation with age while percent dead cells had a positive correlation with age. This is simply due to the Hayflick limit. The SURF1 mutation decreased live cells counted while Dexamethasone and Oligomycin increased the percent dead cells. This shows the direct impact of mitochondrial dysfunction and oxidative stress on the cell cycle [ 19 ]. Finally, cell diameter and volume had a negative correlation with age. This could have been caused by most cells being in the S, G2, or M phase at the start of the experiment while spending more time in G0 or G1 towards the end of the experiment. Finally, cell diameter and volume were increased by the SURF1 mutation. This was most likely caused by impaired mitochondria being unable to maintain ion homeostasis with the environment, causing osmotic swelling [ 20 ]. DNA Methylation and Clocks All of the dependent variables ending with the word “clock” are epigenetic clocks that measure the biological age of the fibroblasts. When there is a “PC” before the name of the epigenetic clock, it refers to the principal component of the clock. The Hannum and PhenoAge clocks along with their principal components had a strong positive correlation with total days grown. This validates the experimental setup as the fibroblasts were indeed aging properly in the cell culture. GrimAge and PhenoAge along with their principal components were lower in cells with the SURF1 mutation. This is easily explained by the fact that fibroblasts with this mutation died after less than 150 days while some healthy ones survived over 500 days. Dexamethasone negatively impacted Hannum clock and its principal component. This was probably caused by a suppressed immune system and modified glucose metabolism, causing more DNA methylation [ 21 ]. The fact that Horvath1 PanTissue clock and PCHorvath2 SkinBlood clock also were negatively impacted by Dexamethasone supports the emerging idea that NDMI could be used as a biomarker for evaluation glucocorticoid treatments [ 22 ]. DunedinPACE and DunedinPoAm both had a negative correlation with biological age and Dexamethasone. This supports the Reliability Theory of Aging and Longevity which states that as cells age, the rate of aging slows [ 23 ]. DNAmLeptin quantifies the amount of methylation of the LEP gene promoter. It had a positive correlation with age, SURF1 mutation, and Dexamethasone. It follows that as humans age, leptin levels are reduced, impacting energy expenditure and appetite regulation [ 24 ]. Mitotic age estimates the total number of cell divisions that occurred. This trivially explains its strong positive correlation with age. DNAmPAI1 quantifies the amount of methylation on the SERPINE1 gene that encodes plasminogen activator inhibitor-1 (PAI1). Its negative correlation with age provides a biological explanation of why cancer and blot clots are more prevalent in older populations. This is because PAI1 has been shown to increase thrombosis and found in extreme amounts in biopsies of tumors [ 25 ]. SURF1 negatively impacting DNAmPAI1 also hints at a correlation between mitochondrial dysfunction and risk of cancer. DNAmB2M refers to amount of methylation on the B2M gene, which encodes β 2 microglobulin. It also had a negative correlation with age. Elevated amounts of this MHC class I molecule have been linked to kidney problems; this provides an explanation for why kidney diseases are more prevalent in the elderly [ 26 ]. β 2 microglobulin being negatively impacted by SURF1 mutation is due to its correlation with oxidative stress [ 27 ]. DNAmADM quantifies the amount of methylation on the ADM gene, which codes for Adrenomedullin, which is a vasodilator peptide hormone. It had a negative correlation with age, meaning that more Adrenomedullin is produced in elderly, which is a risk factor for Alzheimer’s disease [ 28 ]. Bioenergetics First, we will investigate variables related to glycolysis and oxidative phosphorylation. Most of these such as PPRgly, ATPglyc, Baseline ECAR, PPRresp max, etc. had a positive correlation with age. This indicates that as fibroblasts age, they require more energy to maintain homeostasis through stress or damage repair. Plus, a strong correlation with the glycolysis variables indicates a heightened reliance on glycolysis as cells age. This is due to an increased demand for rapid ATP production. Oxidative phosphorylation was also increased in older cells. This suggests an alternative theory that there is metabolic inefficiency as cells age or mitochondrial dysregulation [ 29 ]. Coupling efficiency had a negative correlation with aging, which implies that mitochondria in the elderly have higher proton leak or ROS, which supports the free radical theory of aging. The SURF1 mutation led to higher values for glycolysis statistics and lower values for oxidative phosphorylation. This is a trivial result of COX deficiency due to a mutation in the SURF1 gene. Interestingly, resting metabolic rate was decreased due to Oligomycin. This is most likely due to a reduction in respiration rate to maintain a proton gradient and a shift towards glycolysis [ 30 ]. Mitochondrial DNA Sequencing All of the deletion columns refer to deletions in the mtDNA sequence, where one or more base pairs are absent when compared to the reference genome. None of them were correlated with biological age or impacted by Oligomycin, but some deletion columns were impacted by SURF1 mutation and Dexamethasone. Deletion count is the total number of deletions and was positively impacted by the SURF1 mutation. A high number of deletions causes genomic instability, leading to mitochondrial dysfunction such as Kearns-Sayre syndrome [ 31 ]. Mean deletion length refers to the average size of all the deletions. The mean deletion length increased due to Dexamethasone, which confirms that this corticosteroid can disrupt mtDNA’s function in patients [ 32 ]. Max deletion length had a positive correlation with SURF1 while min deletion length had no correlation. This also supports the theory that the SURF1 mutation indirectly leads to genomic instability. The fact that both max and min deletion length increased due to Dexamethasone points to the fact that this glucocorticoid agonist disrupts mtDNA repair through stress-mediated pathways. All of the repeat columns refer to repeat sequences in the mtDNA which are caused by a variety of biological processes such as polymerase slippage [ 33 ]. Max repeat length had a negative correlation with age. This is probably because long repeat sequences lead to secondary structures such as hairpins, which are more susceptible to deletions [ 34 ]. These short repeat sequences could lead to mitochondrial dysfunction; this provides a biological explanation of why diseases such as Alzheimer’s, Parkinson’s, and Amyotrophic Lateral Sclerosis are correlated with age [ 35 ]. The positive impact of the SURF1 mutation on max repeat length is because of impaired COX, leading to lower ROS. This slows the rate of oxidative damage to mtDNA, leading to a higher max repeat length [ 36 ]. This supports the free radical theory of aging. Finally, no repeat columns were impacted by Dexamethasone or Oligomycin. All of the heteroplasmy columns refer to the proportion of mtDNA molecules that have a mutation. In humans, each mutation found in about 1 to 2% of all mtDNA is referred to as microheteroplasmy. Max heteroplasmy was higher in fibroblasts with the SURF1 mutation. This suggests that mtDNA with mutations may persist longer in mitochondria undergoing oxidative stress. Both mean and min heteroplasmy were increased in fibroblasts cultured with Dexamethasone. This implies that Dexamethasone may reduce selection in mutated mtDNA through increased ROS and fewer fusion/fission events, leading to impaired mitophagy causing aerobic glycolysis to be the primary ATP source [ 37 ]. Mitochondrial dysfunction due to aging could create a positive feedback loop of mtDNA mutations accumulating, eventually causing cancer due to the Warburg effect [ 38 ]. Oligomycin positively impacting mean heteroplasmy further supports this argument and the free radical theory of aging. RNA Sequencing DV200 is the percent of RNA fragments that were greater than 200 nucleotides long. Although its primary purpose is to quantify RNA fragmentation, it also provides some insight into the original RNA condition in cells. DV200’s negative correlation with age implies that oxidative stress along with RNA turnover due to changes in ribonuclease pathways are hallmarks of aging [ 39 , 40 ]. DV200 being higher in fibroblasts treated with Oligomycin further supports the theory that oxidative stress contributes to RNA degradation in fibroblasts [ 41 ]. RNA total ng and DNA ng refer to the amount of RNA and DNA contamination in the extracted sample in nanograms. DNA ng was negatively impacted by the SURF1 mutation because the stress inhibited proliferation and disrupts the cell cycle [ 41 ]. Dexamethasone upregulates some genes for anti-inflammatory responses, which explains why RNA total ng increased in fibroblasts treated with it [ 42 ]. In contrast, DNA ng decreased because Dexamethasone inhibited the cell cycle, disrupting the synthesis of new DNA strands. Similarly, Oligomycin increased RNA total ng as the cells upregulated RNA transcription as a response to its metabolic stress. Well position refers to the specific index on a well plate and its negative impact from SURF1 and Dexamethasone are merely due to the researchers’ well location choice. RIN refers to the RNA integrity number that quantifies the quality of RNA through electrophoresis. RIN had a negative correlation with age, and this was probably the result of RNA degradation over time [ 43 ]. Finally, the negative correlation Base 30 and quality had with age was due to a few outliers. Cytokines First, we will start with the interleukins and other miscellaneous cytokines. IL18, also known as IL1F4 or interferon-gamma inducing factor, is a proinflammatory protein that in excessive amounts has been linked to Alzheimer’s and heart failure [ 44 , 45 ]. Therefore, its positive correlation with age hints that a weaker blood-brain barrier coupled with Amyloid-β accumulation could cause Alzheimer’s. Heart failure is most likely caused by increased production of interferon-γ by T cells, leading to plaque formation in blood vessels. IL1 alpha, also known as hematopoietin 1, is activated by the immune system in humans to induce fever and inflammatory responses. PCSK9, also known as proprotein convertase subtilisin/kexin type 9, is an enzyme highly expressed in the skin for cholesterol and free fatty acid synthesis. It had a negative correlation with age, indicating that older people are at higher risk of coronary artery disease due to LDL receptor degradation [ 46 ]. TFPI, also known as tissue factor pathway inhibitor, is a single-chain protein that prevents blood coagulation and regulates clotting. Its negative correlation with age explains why older people are more susceptible to ischemic stroke; a lack of TFPI enhances coagulation pathways which directly causes vascular complications [ 47 ]. CXCL8, also known as IL8 or C-X-C motif chemokine ligand 8, is both a chemokine and interleukin produced by epithelial cells to attract neutrophils to infection sites. Its negative correlation with age means that older people would have reduced inflammation at the cost of less effective immune responses. This provides a biological explanation for why the elderly have a harder time fighting pathogens [ 48 ]. CCL7, also known as monocyte-chemotactic protein 3, is a chemoattractant for leukocytes in the immune system. It has a positive correlation with aging, increasing risk of multiple sclerosis due to leukocyte migration across the blood-brain barrier [ 49 ]. The amount of CCL7 was also increased by the SURF1 mutation, hinting at link between mitochondrial dysfunction and inflammatory immune response most likely caused by a high amount of ROS. TGF alpha is a ligand that can start the EGFR pathway and is involved in tissue repair. Its positive correlation with age is most likely due to cellular senescence. GDF15 stands for growth/differentiation factor 15 and its positive correlation with age helps explain that skin thins in elderly people as mitochondrial dysfunction upregulates this protein [ 50 ]. Insulin-like growth factor-binding protein 6 (IGFBP6) is a transport protein for IGF-1, which activates the Akt signaling pathway. There was a negative correlation between IGFBP6 and age. This explains the mechanism behind why cells undergo mitosis less often as they age, which was analyzed in the cytology section. Tumor necrosis factor receptor superfamily (TNFRSF) refers to cytokine receptors that bind to TNFs and are usually found in the plasma membrane of cells. TNFRSF6 (Fas), TNFRSF8 (CD30), and TNFRSF9 (4-1BB) all had a strong negative correlation with age. Fas directly leads to programmed cell death, so lower levels of it could lead to cancer if there is not enough of it to kill cancerous cells. CD30 activates B and T cells and has been shown to protect the human body against autoimmunity. Therefore, it follows that autoimmunity is more common in older people, which precisely matches clinical studies [ 51 ]. Finally, 4-1BB stimulates T-cell proliferation so a decrease in this type 1 transmembrane protein impairs the ability of the elderly to have an effective adaptive immune response. TNF alpha and beta were negatively impacted by Dexamethasone due to its anti-inflammatory effects and the inhibition of NF-κB activation [ 52 ]. Conclusion Our research shed light on the underlying biochemical mechanisms of aging, shaped by many epigenetic and environmental factors. Analysis of the high temporal resolution multi-omics dataset revealed several aging biomarkers across various molecular and cellular data types. It also provided insights into how various biological pathways are affected. For example, increased population doublings and percent dead cells reflected cellular aging and degeneration in cytology. In mitochondrial DNA sequencing, positive aging effects included higher deletion counts and heteroplasmy, whereas negative aspects like shortened repeat lengths increased vulnerability to age-related disorders. In RNA sequencing, increased RNA total indicated stress adaptation. On the other hand, RNA degradation and lower RIN scores affected protein synthesis. Our analysis demonstrated the importance of cytokines in aging-related diseases. Among the inflammatory cytokines, the increased levels of IL18, IL1 alpha, and CCL7 can lead to Alzheimer’s disease, chronic inflammatory disorders, multiple sclerosis, and chronic tissue damage. When it comes to tissue degeneration, increased levels of TGF alpha cause cellular proliferation and abnormal tissue repair that can lead to fibrosis and cancer; elevated levels of GDF15 correlate with reduced tissue integrity in elderly populations; whereas the reduced level of IGFBP6 impairs cell division, contributing to poor wound healing and tissue aging. Additionally, as cells age, imbalances in certain cytokines affect the immune system. For example, reduced levels of TNFRSF6 increase cancer risks and failure to control autoimmune diseases; low levels of TNFRSF8 cause autoimmune disorders in the elderly; and aging-associated decreases in TNFRSF9 cause weakened adaptive immunity. Finally, coagulation and cardiovascular risks increase with decreased PCSK9 and TFPI activity in aging. These findings demonstrate the complex cytokine networks in aging and present potential targets for precision medicine in age-related health issues. By comparing cells with the SURF1 mitochondrial mutation, associated with Leigh syndrome and Charcot–Marie–Tooth disease, to healthy ones, we had many insights into mitochondrial dysfunction and oxidative stress as accelerants of cellular senescence, supporting the free radical theory of aging while providing novel molecular analyses. The SURF1 mutation decreased doubling rates, population doublings, and divisions per passage. This resulted in impaired tissue regeneration and increased cellular senescence. SURF1 mutation decreased the live cell count highlighting accelerated cell death due to mitochondrial dysfunction. In cells with SURF1 mutation, an increase in cell diameter and volume due to osmotic swelling led to structural instability. Finally, the mutation lowered epigenetic clocks, GrimAge and PhenoAge, suggesting premature cellular aging. We analyzed fibroblasts cultured in media containing Dexamethasone and Oligomycin. Dexamethasone reduced inflammation indicating its potential use in treating autoimmune diseases and allergies. However, it increased susceptibility to infections and slower tissue recovery. Additionally, it increased DNA methylation, reduced mitochondrial efficiency, lowered Hannum clock values, and reduced cell cycle activity that could lead to increased risk of chronic conditions, mitochondrial dysfunction, reduced cellular lifespan, and delayed healing across tissues. Oligomycin increased RNA response to stress enhancing transcription to rapidly address metabolic stress. This helped in short-term cellular survival. On the other hand, Oligomycin hampered mitochondrial ATP production causing energy deficits in high-demand tissues; increased glycolysis leading to cellular stress and metabolic imbalance; reduced basal metabolic respiration; and higher RNA degradation indicating oxidative stress-driven damage that contributed to reduced protein synthesis capabilities. Overall, Dexamethasone and Oligomycin treatments on fibroblasts showed negative impacts on their bioenergetics, mitochondrial function, and biological age in comparison with the control group without these treatments. Our study provided insights into cellular aging that could be effective in prevention and treatment strategies for age-associated diseases. However, our study was limited to a specific cell type – fibroblasts. In the future, we plan to expand our research by including other cell types, a broader range of genetic mutations, and additional treatments. This approach will help develop a more comprehensive understanding of the aging process. Acknowledgements Rajarshi Mandal thanks MIT PRIMES research program for giving him the opportunity to work with Professor Alterovitz on this project. Footnotes https://columbia-picard.shinyapps.io/shinyapp-Lifespan_Study/ References [1]. ↵ da Costa , J. P. , Vitorino , R. , Silva , G. M. , Vogel , C. , Duarte , A. C. , & Rocha-Santos , T. ( 2016 ). A synopsis on aging—Theories, mechanisms and future prospects . Ageing Research Reviews , 29 , 90 – 112 . doi: 10.1016/j.arr.2016.06.005 OpenUrl CrossRef PubMed [2]. ↵ Kirkwood , Thomas B. L. , & Melov , S. ( 2011 ). On the Programmed/Non-Programmed Nature of Ageing within the Life History . Current Biology , 21 ( 18 ), R701 – R707 . doi: 10.1016/j.cub.2011.07.020 OpenUrl CrossRef PubMed [3]. ↵ Pomatto , L. C. D. , & Davies , K. J. A. ( 2018 ). Adaptive homeostasis and the free radical theory of ageing . 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