How does one quantify the deleteriome/adductome?

#1

Gladyshev’s main points on age-related expansion of the small-molecule landscape

Where he said it What he said Why it matters
eLife 2014 – Age- and diet-associated metabolome remodeling Long-term, untargeted LC-MS on >15 000 features in fly cohorts showed that “aging is associated with increased metabolite diversity and the appearance of many low-abundance compounds,” a trend that levelled off only in extreme old age. Gladyshev interprets the ever-growing tail of rare peaks as chemical “noise” created by damage and side-reactions; counting them is a quantitative read-out of cumulative molecular damage.
Aging Cell 2016 – “rising deleteriome” review He defines the deleteriome as the totality of all age-related deleterious changes (mutations, epimutations, damaged metabolites, mis-folded proteins, etc.). Because the deleteriome is intrinsically heterogeneous, he argues the best biomarkers must capture “many diverse age-related parameters … for example, **genome-wide epigenetic changes, mutations, nontargeted metabolite profiling and gene expression.” Untargeted metabolomics is explicitly singled out as one of the few assays that can see the explosion of molecular species he predicts.
J. Biol. Chem. 2017 – “Non-enzymatic molecular damage as a prototypic driver of aging” The paper explains that metabolic substrates possess “chemical potentialities far exceeding metabolic requirements”; the unused potential is realized spontaneously through Michael additions, Amadori rearrangements, Pictet–Spengler reactions, quinone polymerisation, carbamylation, etc., continually generating new small molecules and adducts that accumulate with age. This mechanistic work links the appearance of novel metabolites/adducts directly to unavoidable side-chemistry, making increased metabolite diversity a cause rather than a mere symptom of aging.
Metabolomist podcast (“Aging Fluidity & Omics Signatures”, Jun 2024) Gladyshev reiterates that metabolomics gives a unique window on “fluid” age signatures that DNA methylation clocks cannot see, emphasising how small-molecule heterogeneity tracks rejuvenation in parabiosis, reprogramming and stress-recovery models. Shows he still sees untargeted metabolite/adduct profiling as central to next-generation aging clocks and intervention testing.

Conceptual take-aways

  • **Why metabolite/adduct diversity grows:**Metabolism is imperfect. Every enzyme’s substrate specificity leaks; non-enzymatic chemistry is relentless. Each leak or side-reaction creates a novel small molecule or a covalent adduct (the adductome). Because repair/clearance is never 100 %, the chemical state-space of an organism broadens over time.
  • **Link to the adductome idea:**Gladyshev’s “deleteriome” explicitly includes the adductome—the constellation of covalent modifications on macromolecules produced by reactive metabolites and environmental electrophiles. His 2017 paper details how catecholamine quinones, thioester-derived acylations, Schiff-base cascades, etc., feed this pool.
  • Biomarker implications:
    • Metabolite-count curves (number of LC-MS features vs. age) can serve as a bulk deleteriome read-out; in flies they correlate with mortality deceleration late in life.
    • Because many emergent species are ultra-low abundance, high-resolution, untargeted platforms (DIA-HRMS, pan-protein adductomics) are needed.
    • Combining diversity metrics with canonical clocks (DNAm, proteomics) should improve biological-age estimation.
  • **Intervention view:**Interventions that slow the creation of novel species (methionine restriction, lowered temperature, partial reprogramming) or accelerate their clearance (autophagy boosters, enhanced detox pathways) are, in Gladyshev’s framework, genuine anti-aging strategies.

In short: Gladyshev sees the ever-expanding catalogue of small molecules and adducts—not a single “dominant” lesion—as the molecular fingerprint of aging. Measuring that chemical diversification is, in his view, one of the most direct ways to gauge and eventually control the aging process.

https://academic.oup.com/exposome/article/4/1/osae001/7574628?login=false

#2

Short answer first

Untargeted multi-adductomics (trying to catch every covalent adduct on DNA + RNA + proteins + small metabolites at once) is harder than classic proteomics or metabolomics because

  1. the chemical space is orders of magnitude larger and poorly catalogued,
  2. adducts sit at attomole–femtomole abundances in very “dirty” matrices,
  3. each biopolymer class needs different extraction / digestion / ionisation conditions,
  4. there is no mature search-engine / spectral-library ecosystem, and
  5. you often need ultra-high-resolution, multi-stage, sometimes ion-mobility MS to separate isomers and confirm structures.

That typically pushes you up to Orbitrap Tribrid, ≥12 T FT-ICR, or timsTOF-type platforms—which are indeed the pricier end of the mass-spec spectrum—although a well-tuned high-end proteomics Orbitrap can get you surprisingly far.

Where the extra complexity comes from

Factor Proteomics Metabolomics Untargeted multi-adductomics
Analyte universe ~ 19 K human proteins, well-defined tryptic peptides ~ 10 K catalogued endogenous metabolites 10^5 – 10^7 conceivable electrophile × nucleophile adducts + cross-links
Typical abundance pmol–nmol nmol–µmol amol–fmol (∼1 adduct per 10^7–10^10 building blocks)
Sample prep One enzyme digest Organic extraction Parallel DNA/RNA enzymolysis + protein digestion + SPE enrichment; often isotope-dilution standards
Separation µLC-MS UHPLC-MS UHPLC-nanoLC + 2 D or ion-mobility to resolve isomers/cross-links
Databases / search engines Uniprot, Mascot, MaxQuant HMDB, GNPS, METLIN Fragment libraries are nascent; tools like wSIM-City, adductomicsR; many features remain “unknown unknowns”
Spectrometer spec 70 k–120 k @ m/z 200 Orbitrap/Q-TOF similar ≥240 k Orbitrap or 12–15 T FT-ICR, MS^3 capability, sub-ppm mass error, often with ion-mobility; see comparison ID-X vs 12 T solariX

Why those specs matter

  • Resolving isomeric adduct families (e.g., positional DNA adduct isomers or hybrid DNA-protein cross-links) demands <2 ppm mass error + isotopic fine-structure. HRMS platforms such as Orbitrap Eclipse (240 k) or 12 T FT-ICR deliver that; mid-range Q-TOFs usually cannot.
  • MS^3 or MS^n is often required to strip the sugar/base or peptide backbone and see the diagnostic aglycone/side-chain fragments (the wSIM-City wide-SIM/MS^2 workflow is a good example).
  • Ion-mobility (e.g., timsTOF, cyclic-TWIMS) helps disentangle adduct isobars that even 1 M resolving power can’t fully separate.

Cost reality check

Instrument Rough US street price*
Triple-quad LC-QQQ (targeted metabolomics) $350–500 k
Q-Exactive HF-X (workhorse proteomics) $700–900 k
Orbitrap Eclipse Tribrid (MS^3, ETD, UVPD) $1.1–1.4 M
12–15 T FT-ICR (Bruker solariX XR) $2–2.8 M

*Purchase price only; service contracts, nano-LCs, SPE robots and isotopically labelled standards add noticeably.

So, yes—the “all-in exposome” vision generally drifts into the same capital-equipment bracket as top-end phospho-proteomics or native top-down MS.

Practical take-aways

  • Start with what you have. A modern Orbitrap used for proteomics can do discovery adductomics if you (i) optimise nano-flow, (ii) run both polarity modes, and (iii) build custom neutral-loss/precursor lists.
  • Invest first in front-end and software. Solid-phase enrichment, enzymatic cleanup, and good open-source pipelines (e.g., adductomicsR, MS-Dial 5) often move the needle before a new instrument.
  • Collaborate. Many exposome consortia (NIEHS NEXUS ring trial surveys are active right now) are sharing reference materials and spectral data so each lab doesn’t have to own a 15 T magnet.

Bottom line: Untargeted multi-adductomics is essentially proteomics plus metabolomics plus covalent chemistry, all at trace levels. That forces you into higher-resolution, multi-stage MS territory and creates huge informatics overhead—but if you already run high-end proteomics, you mostly need method tweaks and clever software, not necessarily a brand-new $2 M box.