The most important genes/proteins/enzymes for aging

The two most important classes of proteins are the long-lived structural proteins and the repair ones (DNA repair, breakdown ones [autophagy and proteostasis - , still trying to look up membrane repair]).

ERCC1, proteasome genes (esp beta7 subunit), chaperone-mediated autophagy genes (eg LC3)

20S proteasome catalytic subunit β5

19S proteasome subunit PSMD11/Rpn6 [25, 28, 103], which is an essential subunit for the activity of the 26S/30S proteasome that stabilizes the otherwise weak interaction between the 20S core and the 19S cap

Rpn11 overexpression suppresses the age-dependent reduction of 26S/30S proteasome activity and extends lifespan of flies [118]. In addition, increased levels of Rpn11 suppress expanded PolyQ-induced progressive neurodegeneration [118]. In C. elegans, overexpression of Rpn6 is sufficient to extend longevity under proteotoxic stress conditions and reduces toxic aggregates in PolyQ-disease models [17]. Increased assembly of active proteasomes induced by overexpression of the 20S proteasome subunit β5 confers resistance to oxidative stress and delays senescence in human fibroblasts

glyoxalases (GLOs), which eliminate reactive glucose metabolites (e.g., methylglyoxal), saving cells from glycation. See Glyoxalase system - Wikipedia

For example, catalase, superoxide dismutases, peroxiredoxins, thioredoxins, glutaredoxins, glutathione, glutathione peroxidases, and methionine sulfoxide reductases all represent families of enzymes that have evolved to directly or indirectly cope with harmful ROS and thereby mitigate potentially damaging nonenzymatic modification to macromolecules (Meyer et al., 2009, Muller et al., 2007).

(B and C) In an analogous manner, oxidative stress and nitrosative stress cause the oxidation (B) and nitrosylation (C) of protein residues, respectively. These damaging nonenzymatic protein modifications are removed by methionine sulfoxide reductases (MsrA,B) and thioredoxins (Trx) or the S-nitrosoglutathione reductase (GSNOR) system, which preserve protein function and support cellular health.

glyoxylase I and II enzyme system, which serves to detoxify intracellular glycation-inducing dicarbonyl compounds such as methylglyoxal

basically “repair” genes many upregulated by NRF2
SIRT6 (more than the other sirtuins - vera says it’s the ONLY one that increases longevity)

In Drosophila, flies with mutations that augment JNK signaling accumulate less oxidative damage and live dramatically longer than wild-type flies.[13][14]

telomerase genes: (ALT, WRN helicase, Rad52, ku67, POT, Shelterin, TRF1) [important but not central]

list of bowhead whale and naked mole rat genes with highest dN/dS values

dopamine genes (DAT, D2/D4)

MAYBE on FGF21 - High FGF21, Low Insulin And Glucose: A Pro-Longevity Strategy? - YouTube

thioredoxin

Slightly important but not critical for longevity

REST protein

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An intriguing observation is that the autophagic vacuoles identified by LC3-mCherry were virtually all positive for LAMP1, a marker of late endosomes and lysosomes, indicating that dendrites mainly contain autolysosomes and no or very few autophagosomes (LC3-positive and LAMP1-negative) and late endosomes/lysosomes (LC3-negative and LAMP1-positive). One is left wondering if it results from LC3 overexpression and overflooding to interconnected organelles

Knowledge of the Wallerian degeneration mechanism has been transformed by the identification of genetic mutations that delay it by ~10-fold. The identification of Wallerian degeneration slow (Wlds), a mutant fusion protein, revealed a key role for NAD-related metabolism (438). Based on this, a wild-type NAD synthesizing enzyme, NMNAT2, was shown to be lost rapidly after axotomy and essential for axon survival (247). When NMNAT2 expression is constitutively blocked, axons fail to grow beyond a short distance, and if knocked down after axons have grown, these axons degenerate through the Wallerian pathway (246). In essence, removing NMNAT2 spontaneously activates the Wallerian pathway even without injury. NMNAT2 matches many of the properties of the ‟neuronal trophic substanceˮ postulated by Lubinska

From Horvath 2021 paper, the strongest hypermethylation sites with age:

LHFPL4/LHFPL3 genes (strongly hypermethylated with aging and we still don’t know WHY)

Also hypermethylated: TLX3, EVX2, NEUROD2, ZIC2 (all TFs), CELF6, PAX2, BDNF, PAX5, NRN1, PRDM13, OTP, NEUROD1

==
(it’s suspected that these hypermethylated sites don’t matter as much as the hypomethylated sites, even though the strongest age associations happen for these hypermethylated sites). Methylation accelerates the most during early development, and sure while SOME aging happens then, it’s not the critical kind of aging that reduces performance.

membrane/lipid repair maybe? https://www.nature.com/articles/s41467-019-12483-0

except this are to acute injury rather than, say, oxidized lipids in the membrane

[SIRT6 Positively Affects The Hallmarks Of Aging And Extends Lifespan]

(SIRT6 Positively Affects The Hallmarks Of Aging And Extends Lifespan - YouTube)

modifications that lack protein quality control:

Other nonenzymatic reactions involving metabolites are implicated in disease but lack known quality-control or detoxifying mechanisms. Similar to the mechanism of protein glycation in diabetes, prolonged elevation of cyanate in the blood of smokers or during renal failure can cause harmful nonenzymatic protein lysine carbamylation (Figure 2B) (Flückiger et al., 1981, Stark, 1965). Excessive protein carbamylation may be a common mechanism underlying the pathophysiology of inflammation and coronary artery disease (Wang et al., 2007).

Separately, recent work has demonstrated that the glycolytic metabolite and acyl-phosphate 1,3-bisphosphoglycerate (Figure 2) can nonenzymatically modify protein lysine residues by virtue of its highly reactive electrophilic carbonyl carbon (Moellering and Cravatt, 2013). This novel modification, 3-phosphoglyceryl-lysine, was further shown to impair the activity of select glycolytic enzymes but is not yet associated with any disease states.

More commonly, drug toxicity is often caused by electrophilic drug metabolites nonenzymatically reacting with cellular proteins, and minimizing this problem is one of the foremost challenges during the development and optimization of small-molecule pharmaceuticals (Evans et al., 2004, Horng and Benet, 2013). Interestingly, the reactivity and toxicity of many drug metabolites is dependent on their bioactivation to acyl-CoA thioesters (Sallustio et al., 2000). Collectively, these studies demonstrate that nonenzymatic reactions of endogenously generated metabolites with cellular proteins contributes to the pathophysiology of diabetes, neurodegeneration, kidney failure, inflammation, coronary artery disease, and drug toxicity.

favorite summary of the supercentenarian genes paper

Latest News

Living Beyond 100 Years

Mayka Sanchez, PhD
UIC Barcelona

It is not my case, but some people have grandparents that lived beyond 105 and even beyond 110 years! These people are named semi-supercentenarians and supercentenarians, respectively. Healthy aging and exceptional longevity (people who live more than 100 years) are deeply related, as clinical and biochemical data on centenarians showed that they can be considered as a paradigm of healthy aging as they avoid or largely postpone all major age-related diseases.

You (and other scientists) may ask yourself: How do they manage to live for so many years? Is it the good weather, enough exercise, good wine… is it in their GENES?

Genetic variability on human extreme longevity is supported by the fact that individuals surviving to the age of 105 have a chance to have a sibling surviving to that age of 35 times greater than the control population. Therefore, some genetic factors should contribute to this trait. However, longevity is a complex trait for which gene-environment interactions (that are population-specific) as well as the complex interplay of multiple genes and pathways play a major role. Therefore, well controlled studies should incorporate individuals with well-advanced age (more than 105 years old) and controls in a homogenous population all matched for geographical origin.

This is exactly what has been done by Italian scientists, including some belonging to our society, to have some answers to the genetic contribution on extreme longevity. They shared what they found in an e-LIFE article.

Whole-genome sequencing analysis of semi-supercentenarians. Elife. 2021 May 4;10:e57849. doi: 10.7554/eLife.57849. PMID: 33941312; PMCID: PMC8096429.

In this work, the authors generated and analyzed the first Whole Genome sequencing (WGS) data with high coverage (90X) in a cohort of 81 semi-supercentenarians and supercentenarians [105+/110+] recruited across the entire Italian peninsula together with a control cohort of 36 healthy geographically matched individuals. In addition, the use recently published data (Giuliani et al., 2018b) as a second independent cohort of 333 centenarians (>100 years) and 358 geographically matched controls to replicate their results. The aim of this study was to identify the genetic determinants of extreme longevity in humans.

The results showed that 105+/110+ are characterized by a peculiar genetic background associated with efficient DNA repair mechanisms, as evidenced by both germline data (common and rare variants) and somatic mutations patterns (lower mutation load in semi-supercentenarians if compared to younger healthy controls).

The authors found that in their WGS association study five SNP common variants were the top association signals. Those SNPs are located in the same large block of linkage disequilibrium at chromosome 7 containing STK17A, COA1 and BLVRA genes. One of these five variants, rs10279856, may play a regulatory role in the region. The SNP rs10279856 seems to play a pleiotropic role as it is an eQTL for STK17A gene and for two other genes (COA1 and BLVRA).

The most frequent alleles in 105+/110+ people (rs10279856-G reference allele and rs3779059-A, rs849166-A, rs849175-A alternative alleles) were associated with an increase in SKT17A gene expression in heart and other tissues, reduced expression of COA1 gene in several tissues and increased expression of BLVRA in whole blood, while reduced expression in artery and esophagus.

STK17A gene, Serine/Threonine Kinase 17a (Apoptosis-Inducing), is involved in DNA damage response and positive regulation of apoptotic process (Sanjo et al., 1998) and regulation of reactive oxygen species (ROS) metabolic process. Overall, the authors’ findings indicate that aging and longevity might be due to an increase in SKT17A expression due to the presence of favorable genetic variants in supercentenarians, by a mechanism which maintains DNA damage responses, favoring healthy aging.

COA1 gene is a component of the MITRAC complex (mitochondrial translation regulation assembly intermediate of cytochrome c oxidase complex) that regulates cytochrome c oxidase assembly. MITRAC complexes regulate both the translation of mitochondrial-encoded components and the assembly of nuclear-translation of mitochondrial-encoded components and the assembly of nuclear-encoded components imported in mitochondrion and in particular the respiratory chain complexes I and IV. Authors do not address why the reduced expression of COA1 gene due to the most frequent alleles present in supercentenarians influences healthy aging. However, it is known that apoptosis is regulated by the redox state of cytosolic cytochrome c (a heme containing protein), that is oxidized by mitochondrial cytochrome c oxidase (COX, also referred to as complex IV).

Therefore, new exploratory roles are open to study how oxidative stress is prevented or less deleterious in supercentenarian people. Now we know that mitochondria are not only necessary for ATP generation, but it seems that mitochondrial dynamics have a crucial role for human longevity and health.

More to say about redox status and longevity…

The protein encoded by the BLVRA (Biliverdin reductase A) gene belongs to the biliverdin reductase family, members of which catalyze the conversion of biliverdin to bilirubin. Recently, it has been established that a redox cycle based on BLVRA activity provides physiologic cytoprotection as BLVRA depletion exacerbates the formation of reactive oxygen species (ROS) and increase cell death. Interestingly, BLVRA contributes significantly to modulation of the aging process by adjusting the cellular oxidative status (Kim et al., 2011). Moreover, Biliverdin reductase A was previously shown to regulate the inflammatory response to endotoxin, by inhibiting Toll-like receptor 4 (TLR4) gene expression (Wegiel et al., 2011). How low levels of BLVRA in whole blood of supercentenarians exactly contribute to longevity is a question that remains to be explored.

Finally, in this article it was also shown that supercentenarians individuals are characterized by a lower prevalence of somatic mutations in six out of seven genes involved in hematopoietic malignancies. Therefore, it seems that not only a good genetic signature in DNA repair mechanisms is needed for longevity but also clonal haematopoiesis is a crucial player for healthy aging.

Overall, living beyond 100 years seems to be a game in which our cells should escape from mutations impairing the function of genes involved in stress response and DNA repair, to avoid accumulation of DNA damage and to delay age-related decline. But if your cells cannot do that, do not worry too much as one can say in Italian: “Non c’é male che duri cent’anni” and you would be dead by that time (I mean in 100 years from now).

Take care. :slightly_smiling_face:

Mayka Sanchez
BioIron Secretary

posted: May 25, 2021

Tomer Ze’ev

1tgSpcotnsorehdd ·

DNA repair mechanisms play a vital role in healthy aging:

"Five particular genetic changes were commonly detected in the supercentenarian cohort, concentrated around two genes called STK17A and COA1.

STK17A is known to be involved in DNA damage response processes. As we age, the body’s DNA repair mechanisms become less effective. Accumulated DNA damage is known to be responsible for some signs of aging, so increased expression of STK17A can favor healthy aging by preserving DNA repair processes in old age.

Reduced expression of COA1 in the supercentenarians was also detected. This gene plays a role in communications between a cell’s nucleus and mitochondria."

https://www.nature.com/articles/s41598-021-84640-9

also EVERYTHING in base-excision-repair (however, aging often happens where there’s an imbalance between early and late BER enzymes => enzymes can cause further damage if you have too much early BER and not enough late BER enzymes to finish what is completed)

PIMT is a repair enzyme that partially restores L-isoAsp to L-Asp. PIMT was detected in our dataset, and quantitative analysis revealed lower amounts of PIMT in hippocampal AD samples

Five particular genetic changes were commonly detected in the supercentenarian cohort, concentrated around two genes called STK17A and COA1.

STK17A is known to be involved in DNA damage response processes. As we age, the body’s DNA repair mechanisms become less effective. Accumulated DNA damage is known to be responsible for some signs of aging, so increased expression of STK17A can favor healthy aging by preserving DNA repair processes in old age.

Reduced expression of COA1 in the supercentenarians was also detected. This gene plays a role in communications between a cell’s nucleus and mitochondria.

“Previous studies showed that DNA repair is one of the mechanisms allowing an extended lifespan across species," explains senior author on the new study, Cristina Giuliani. “We showed that this is true also within humans, and data suggest that the natural diversity in people reaching the last decades of life are, in part, linked to genetic variability that gives semi-supercentenarians the peculiar capability of efficiently managing cellular damage during their life course.”

The researchers also found the supercentenarians displayed an unexpectedly low level of somatic gene mutations, which are the mutations we all generally accumulate as we grow older. It is unclear why these older subjects have avoided the age-related exponential increase usually seen with these kinds of mutations.

2.2 BioAgeAccel GWAS

In the GWAS for BioAgeAccel, 996 genetic variants were identified (p < 5 × 10−8) (Manhattan plot in Figure 1 including the top 10 mapped genes of lead SNPs detailed in Table 2). The observed p-value distribution was significantly deviant from the expected under the null (Figure 2). However, there was no evidence to suggest population stratification or cryptic relatedness (LD score regression intercept = 1.02, SE = 0.01; genomic control factor 1.11), and the proportion of inflation not explained by polygenic heritability was small, 6.58% (SE = 3.78%).

TABLE 2. Genetic loci associated with BioAgeAccel (p < 5 × 10−8) that can be mapped to genes

SNP Chr bp refA freq bJ bJ_se pJ Genes
rs17367504 1 11862778 A 0.84 0.07 0.012 9.03E−10 MTHFR
rs11591147 1 55505647 G 0.98 0.23 0.033 7.91E−13 PCSK9
rs541041 2 21294975 G 0.18 −0.09 0.011 2.33E−14 APOB - AC010872.2
rs560887 2 169763148 T 0.3 −0.06 0.009 9.83E−11 SPC25, G6PC2
rs16998073 4 81184341 A 0.71 −0.05 0.009 2.46E−08 PRDM8 - FGF5
rs1173771 5 32815028 A 0.4 −0.05 0.009 6.19E−09 NPR3 - AC025459.1
rs17477177 7 106411858 T 0.8 −0.09 0.011 4.62E−17 AC004917.1 - LINC02577
rs17321515 8 126486409 A 0.53 0.06 0.009 2.20E−12 AC091114.1
rs16926246 10 71093392 C 0.87 0.09 0.013 7.77E−13 HK1
rs2274224 10 96039597 G 0.57 0.05 0.009 2.41E−10 PLCE1, PLCE1-AS1
rs17249754 12 90060586 G 0.83 0.07 0.011 9.41E−09 ATP2B1
rs7497304 15 91429176 G 0.67 −0.05 0.009 1.89E−08 FES
rs55791371 19 11188153 A 0.88 0.14 0.013 4.95E−26 SMARCA4
rs58542926 19 19379549 C 0.92 0.11 0.016 1.78E−11 AC138430.1, TM6SF2
rs7412 19 45412079 C 0.92 0.26 0.016 3.16E−60 APOE
rs1327235 20 10969030 A 0.52 −0.05 0.009 1.02E−08 AL050403.2
  • Abbreviations: Chr: chromosome, bp: base pairs (Genome Reference Consortium Human Build 37), refA: reference/effect allele, freq: reference allele frequency, bJ, bJ_se, pJ: regression coefficient and the associated standard error and p-value, adjusted for other lead SNPs, SNPs overlapped between PhenoAgeAccel and BioAgeAccel in gray.

The SNP-heritability for BioAgeAccel was estimated to be 12.39% (SE = 0.95%). Twenty lead SNPs were identified (p < 5 × 10−8) and 16 were nearby genes (Table 2). The strongest signal appeared in the APOE gene, tagged by the APOE isoform coding SNP rs7412. Several lead SNPs were associated with blood pressures. Other lead SNPs were associated with HbA1c, cardiovascular disease, and/or lipid biomarkers (Table S4).

The MAGMA gene set analysis identified 10 lipid-related gene sets at the Bonferroni-corrected level of 5%, including lipid homeostasis, lipid protein particle clearance, and triglyceride-rich plasma lipoprotein particle (Figure 2). None of the 53 tissues showed significant specificity in gene expression for the genes associated with BioAgeAccel (Figure 3).

2.3 PhenoAgeAccel GWAS vs BioAgeAccel GWAS

PhenoAgeAccel and BioAgeAccel shared the lead SNPs, rs560887 (near SPC25, G6PC2), rs17321515 (near AC091114.1), rs16926246 (near HK1), and rs7412 (near APOE). Interestingly, three out of four common lead SNPs were oppositely associated with PhenoAgeAccel and BioAgeAccel.

  • The rs560887T allele, associated with decreased HbA1c (An et al., 2014; Wheeler et al., 2017), is associated with decreased BioAgeAccel and PhenoAgeAccel.
  • The rs16926246 C allele, associated with increased HbA1c (Soranzo et al., 2010), is associated with increased BioAgeAccel, but decreased PhenoAgeAccel.
  • The rs17321515 A allele, associated with increased triglycerides (Kathiresan et al., 2008), is associated with increased BioAgeAccel, but decreased PhenoAgeAccel.
  • The rs7412T allele, or APOE e2 determined allele, associated with increased longevity (Deelen et al., 2019), is associated with decreased BioAgeAccel, but increased PhenoAgeAccel.

To investigate whether each cell type-specific clock select similar or unique genes, we compared intersections of chronological aging clock gene sets (Fig. 2b, for genes contributing to biological aging clocks, see Extended Data Fig. 2b). Interestingly, AC149090.1, was selected by chronological aging clocks from all 6 different cell types (Fig. 2b) and another gene, Ifi27, was selected by chronological aging clocks from 5 out of 6 cell types (Fig. 2b). In contrast, most genes selected by the cell-type-specific clocks were cell type-specific (Fig. 2b). The specificity of the clocks exceeded what would be expected from transcriptome cell type specificity alone (Extended Data Fig. 3a, b). However, shared genes carry a disproportionate weight within the clocks, with coefficients approximately 40% larger in magnitude (Fig. 2c, Extended Data Fig. 2c). Cell type-specific genes (Fig. 2d) and even shared genes (Fig. 2e) exhibited differences in trajectory shapes (Fcrls and Crlf2 in microglia) and expression magnitudes (e.g. Ifi27 in different cell types from the NSC lineage) during aging in different cell types. Thus, cell type-specific clocks capture useful cell-type specific expression differences and dynamics that would be missed by bulk methods.

While genes selected by the aging clocks are mostly cell-type specific, the pathways to which they belong could still be widely shared across cell types. To test this possibility, we examined the pathways enriched in the specific or shared genes selected by the chronological aging clocks. Interestingly, gene set enrichment analysis (GSEA) on the specific genes from each clock revealed enrichment for different biological processes in each cell type (Fig. 2f, Extended Data Fig. 2d for biological aging clocks), e.g. stress response for oligodendrocytes and chemotaxis for microglia. Thus, pathways for specific genes selected by the clocks are also largely cell-type specific and may reflect age-dependent changes in function in each cell type. Even though there are some common genes across all clocks and these are more heavily weighted, cell type-specific clocks did not perform as well on other cell types (Fig. 2g). There were a few biological

Brain aging

SARM1




Neurometabolism
@Neurometabolis1

·

Jul 27, 2021

Replying to

@ScienceofPD

and

@Lab_Coleman

What about crosstalk between STMN2 and SARM1? Upregulating STMN2 should inhibit SARM1 and prevent axon degeneration as well as reduce the inflammatory response to traumatic axonal injuries.

@WUSTLmed

@WashUNeuro

Dr. Jeffrey Milbrandt and Dr. Aaron DiAntonio

@Pthompsn1018