Lysine acylation interpretation: acetylation vs other lysine acylations without crosstalk confusion

A single lysine can carry multiple, mutually exclusive acyl marks, which is why lysine acetylation claims often drift into broader lysine acylation claims in practice. When that happens, the weak point is usually not the MS signal itself, but the interpretation layer: how the study defines evidence, reports localization, controls confounding, and chooses language that matches what was actually measured.

This article is not an overview of acylation biology or a general "how to analyze acylation" guide. Instead, it gives a reviewer-ready way to (1) decide which lysine acylation family you should target, (2) avoid mixing modification names in conclusions, and (3) report results so cross-talk does not get mistaken for specificity.

Key Takeaway: If your data cannot distinguish acetylation from other lysine acylations at the site level (with localization confidence and transparent QC), your conclusions should not either.

Why lysine acylations get misinterpreted in PTM proteomics

Most crosstalk confusion starts with a true statement that gets stretched too far: "We enriched a lysine-acylated peptide population and observed changes." In many systems, several acylations move together because they share upstream drivers (metabolite availability, compartment shifts, enzyme inhibition, or global protein abundance changes). Meanwhile, at the peptide level, multi-modified sequences and imperfect site localization increase the probability that an apparently clean "acetylation effect" is actually a mixture of acylation families, localization ambiguity, or quantification artifacts.

There is also a vocabulary problem. Teams sometimes use "acetylation" as shorthand for "lysine acylation-like signal," especially when they historically ran acetylome-focused studies. Reviewers will tolerate shorthand in intros. They will not tolerate it in results.

The goal of this article is simple: give you a defensible framework for separating (a) what you measured, (b) what you localized, (c) what you quantified, and (d) what you can infer.

What counts as "acetylation" vs "other acylations" in claims

Before you argue biology, decide what level of claim your dataset supports. This is where many manuscripts lose credibility.

A practical hierarchy of claim strength

1. Class-level detection (lowest specificity)
   • "We detected lysine acylation signals consistent with acetylation/crotonylation/…"
   • Appropriate when you cannot defend site localization or family specificity.
2. Site-level change (stronger, but still association)
   • "K123 acetylation increased under condition A vs B (BH-FDR < 0.05), localization probability ≥ X."
   • Requires explicit localization confidence and transparent filtering.
3. Pathway-level pattern (synthesis across sites)
   • "Multiple sites on mitochondrial metabolic enzymes show increased succinylation, consistent with a compartment-linked shift."
   • Requires enough sites and appropriate multiple testing control.
4. Mechanistic inference (highest risk)
   • "Enzyme Y drives acetylation at site Z."
   • Requires orthogonal evidence, or conservative wording.

Databases and large-scale compilations can remind you how often lysines are competitive nodes rather than single-mark endpoints. For example, CPLM cataloged many lysine modification types and co-occurrence patterns, which is exactly why reviewers expect you to avoid name-mixing when you summarize findings (CPLM database paper, PMC3964993). That does not mean every site is cross-talked in your experiment. It means you must make your evidentiary boundaries explicit.

Claim language rules

Use this checklist while writing Results and figure captions.

• Do not swap modification names mid-paragraph. If the data are about crotonylation, do not conclude "acetylation increased" unless you measured acetylation and can distinguish it.
• Separate "identified" from "localized." A PSM can match a modified peptide without confident site localization. State the localization threshold next to key claims.
• Bind the claim to the unit of measurement. If you measured modified peptide abundance, do not describe it as "PTM stoichiometry" unless you explicitly quantified occupancy.
• Prefer "associated with" when multiple drivers exist. Reserve "driven by" or "mediated by" for direct evidence.
• Avoid writer/eraser name-dropping without support. "Consistent with" or "compatible with" is often the correct strength for proteomics-only evidence.

For localization reporting principles, the important point is probability-style, interpretable confidence. The community has long emphasized reporting localization confidence and performance tradeoffs (see "Modification site localization scoring: strategies and performance" (PMC3418845)). You do not need to mention a specific pipeline to follow the rule: give reviewers a numeric localization confidence, disclose the cutoff, and keep it consistent across figures and tables.

Comparison framework: when acetylation is the right target vs when it isn't

When your team debates "acetylome vs crotonylome vs succinylome," you're choosing an interpretation envelope. The wrong choice is not just wasted runs; it creates a manuscript where the title says acetylation and the data say "mixed acylation signal."

Below is a decision framework you can apply before sample prep, and again before writing.

Decision points (table-ready)

Use these decision points as a pre-study checklist. The intent is not to teach methods, but to align the target modification with the most plausible biology and the evidence level you need.

For background on the expanding set of lysine acylations and their links to metabolism, a broad, citable entry point is the histone acylation review (PMC5320945). For crotonylation in particular, the original large-scale identification work is open access (Tan et al., Cell 2011, PMC3176443). You do not need to cite "all acylations." Cite only what supports the interpretation boundary you are setting.

Common "wrong target" scenarios

These are the recurring manuscript failure modes.

Two extra decision checks that prevent name-mixing

If you want a simple guardrail that works across projects, add these two checks to your pre-study memo and to your rebuttal notes.

• Does your quant readout support occupancy language? If you only have enriched modified peptide intensities, you can usually defend "modified-peptide abundance changed." You often cannot defend "acetylation stoichiometry increased," and you definitely cannot defend "acetylation activity increased" without orthogonal evidence. Write this boundary into your claim plan before you see the volcano plot.
• Can your dataset distinguish modification families at the site level? If your search space or downstream reporting collapses multiple acylation families into a single label, then the paper must stick to "lysine acylation" framing. If you do report family-specific calls (acetylation vs crotonylation vs propionylation, etc.), reviewers will expect: (i) explicit localization confidence, (ii) transparent filters, and (iii) an explanation of how you prevented family name drift in the narrative.

1. Metabolic perturbation, single-mod conclusion
   • If you perturb metabolism and see widespread "acylation up," it is rarely defensible to label the effect as acetylation-specific without showing you measured acetylation and excluded other acylations.
2. Single enrichment, multiple names in the discussion
   • A dataset may support "lysine acylation signal changes." It does not support "crotonylation increased" and "acetylation decreased" unless you measured both, localized both, and controlled for missingness and multiple testing.
3. Compartment shorthand becomes mechanism
   • "Mitochondrial succinylation increased" can be a reasonable summary if site-level evidence supports it. "Succinyl-CoA drove site X succinylation" is a mechanistic leap.

If your primary question is acetylome-centered, keep the wording aligned and avoid drifting into "acylation in general." For study planning, see lysine acetylation (acetylome) proteomics services.

Pitfall 1 — Protein abundance confounding across acylation families

A frequent crosstalk illusion is simple: the protein changed.

If protein abundance increases, then every peptide signal tied to that protein is more likely to increase, including modified peptides after enrichment. If you quantify only enriched PTM peptides without reporting protein-level context, you can easily conclude "acetylation increased," when the more conservative interpretation is "the modified peptide signal increased, consistent with higher protein abundance and/or higher PTM stoichiometry."

This is not semantics. Reviewers treat PTM stoichiometry claims as stronger than "modified peptide abundance changed." The safest posture is to report what you measured, and explicitly describe what you did (and did not) do to separate PTM occupancy from protein-level change.

A useful mental model is a three-layer readout:

• Protein abundance layer: did the underlying protein change?
• Modified peptide abundance layer: did the modified peptide signal change?
• Site occupancy layer: did the fraction of molecules modified at that site plausibly change?

Many acetylome/acylome datasets directly support the middle layer, sometimes support the first, and rarely support the third without additional design and reporting.

What to report to keep interpretation honest

Example sentences reviewers tend to accept

When protein abundance might be part of the story, phrasing matters. These sentence patterns are conservative, but they keep your acetylome interpretation defensible.

• Acceptable: "Acetylated peptide abundance at K123 increased (log2FC = …; BH-FDR = …; localization prob ≥ …). Protein abundance for the parent protein was unchanged/changed in the same direction, so we interpret this as consistent with a PTM change and/or abundance contribution."
• Risky without extra evidence: "K123 acetylation increased because the enzyme was activated."
• Acceptable when multiple acylations are plausible: "Multiple lysine acylations increased on the same protein group; we therefore avoid attributing the effect to acetylation alone."

At minimum, your results table and methods text should make these interpretation boundaries visible:

• Site identity and modification type (do not bury the mod family in free text)
• Localization confidence (probability or equivalent score, plus threshold)
• Effect size (log2 fold change, and how it was computed)
• Multiple testing control (BH-FDR at the site level; state what was tested)
• Filtering rules (exact gates for inclusion/exclusion)
• Protein-level context (whether protein abundance was assessed and how you used it in interpretation)

If you present "lysine acetylation up" as a biological conclusion, include a sentence that binds it to evidence: "acetylated peptide abundance increased at localized site(s) with BH-FDR control; protein abundance trends were/weren't concordant." That sentence often determines whether reviewers accept your acetylome interpretation.

Pitfall 2 — Batch effects and missingness masquerading as crosstalk

Batch effects and missing values can create patterns that look like modification competition or pathway rewiring.

• A batch shift in signal intensity can make one condition appear globally higher in one modification family.
• Missingness can be structured (batch-associated missing values), not random. When missingness correlates with batch or condition, it can inflate false discoveries and amplify spurious cross-family "differences."

For a reviewer-ready discussion of batch effect assessment and correction workflows in proteomics, see the step-by-step protocol article (PMC8447595). For a direct analysis of how batch-associated missing values can affect imputation and downstream false discovery, see the BEAMs paper (PMC12066825).

Cohort-like QC gates

You do not need to write a software tutorial to be transparent. You do need to disclose gates and diagnostics that demonstrate the results are not batch artifacts.

Consider reporting (in Methods or Supplement):

• ID trends across batches: number of localized sites per run/batch, and whether trends track batch rather than biology
• Intensity distribution checks: per-sample distributions and outlier handling logic
• Missingness overview: site-level missingness rate by condition and batch; flag condition-skewed missingness explicitly
• Batch drift check: correlation summaries or clustering showing whether batch dominates the structure

If your paper includes a "PTM crosstalk" claim, reviewers will ask whether the observed anticorrelation could be a missingness artifact. A simple missingness flag column in the results table often defuses that critique.

Rework triggers

If any of the following are true, interpret crosstalk cautiously and consider reworking analysis/reporting before writing strong conclusions:

• One batch contains most of one condition (confounded design).
• "Differential" sites are enriched for high missingness or near-threshold localization.
• A small subset of samples drives most of the effect.
• The direction of change flips after batch correction or after excluding high-missingness sites.

How to structure results tables for multi-acylation interpretation

Most crosstalk confusion becomes inevitable when the results table is built like a single-modification paper.

If your study touches multiple lysine acylations, your tables should make it hard for a reader to accidentally mix them. That is not cosmetic. It is a guardrail.

Below is a schema that supports reviewer questions and prevents over-claiming.

Minimum fields for each site

Include these fields per site, even if some are empty for certain analyses. The goal is interpretability.

• Modification type (lysine acetylation, crotonylation, propionylation, butyrylation, succinylation)
• Site ID (protein accession + residue position; keep consistent across families)
• Localization confidence (probability/score) + the threshold you used
• Effect size (log2) (condition A vs B)
• BH-FDR (state what level this FDR corresponds to)
• Missingness flag (high missingness; condition-skewed missingness)
• Batch flag (batch-associated shift detected)
• Filter reason (if removed: low localization, failed missingness gate, failed batch gate)

Now add two fields that improve interpretability specifically for multi-acylation manuscripts:

• Evidence tier (class-level / site-level / pathway-level). This keeps captions and conclusions aligned with the dataset.
• Claim label (acetylation-specific / acylation-family / multi-acylation). This is editorial, but it prevents "acetylation" from leaking into every summary sentence.

This is where reporting transparency becomes a design choice, not an afterthought. If you include missingness flags, batch flags, and filter reasons in the same table as your effect sizes, you have already answered many reviewer objections before they are raised.

How to write figure captions that avoid over-claiming

Cross-family site harmonization (small detail, big impact)

If you run multiple acylation enrichments (or reanalyze the same data with multiple acylation definitions), reviewers often encounter a subtle but damaging ambiguity: the same residue appears as several near-duplicate rows with inconsistent identifiers.

To prevent that:

• Use one canonical protein accession + residue position key as the site ID, regardless of acylation family.
• Keep a separate modification type column, rather than encoding the modification in the site ID string.
• If your workflow produces multiple PSM-level observations for the same site, disclose the rollup rule (best localization, highest intensity, or statistical aggregation).

This is a reporting choice, not a software choice. It makes multi-acylation interpretation readable, and it prevents accidental double-counting in downstream pathway summaries.

Treat the caption as a micro-contract with the reviewer. A strong caption states what was measured and which gates were applied.

Instead of:

• "Acetylation increases in condition A."

Prefer:

• "Localized acetylated sites (localization prob ≥ X; BH-FDR < 0.05) show increased modified-peptide abundance in condition A vs B; sites with high missingness were flagged."

If you show multiple acylations in one figure, name them explicitly and avoid umbrella terms that imply equivalence.

A practical TOF → MOF interpretation flow

To keep cross-family conclusions stable from draft to rebuttal, run your interpretation through the same progression every time.

• TOF (Table-Only Facts): what does the table support with no prose added?
  • Example: "K123 crotonylation log2FC = 0.7; BH-FDR = 0.02; localization prob = 0.92; missingness not elevated."
• MOF (Manuscript-Only Framing): what can you say in the manuscript that is consistent with those facts, without adding new assumptions?
  • Example: "This site shows increased crotonylation in condition A, with high localization confidence."

When a manuscript drifts into confusion, it is usually because MOF statements introduced "acetylation" language that does not exist at the TOF level.

If you need a single mid-article hub for related guidance on pitfalls, QC, and deliverables, see the PTM proteomics resource library.

Reviewer-ready wording: mechanism vs association

If your data are proteomics-only, your safest and most credible writing strategy is to separate association, consistency, and mechanism.

Phrases that usually pass review

Use these when you have strong quantitative evidence but limited mechanistic validation:

• "associated with"
  • Use when the modification change tracks a condition or phenotype.
• "consistent with"
  • Use when the pattern matches an established model, but you did not test the mechanism.
• "supports the hypothesis that…"
  • Use when multiple lines of evidence in your dataset point in the same direction, but causality is not proven.

Phrases that trigger reviewer pushback

• "X drives acetylation"
• "X mediates crotonylation"
• "This demonstrates enzyme causality"

Unless you have orthogonal evidence, downgrade these.

A wording ladder for PTM crosstalk

When you observe multiple acylations at the same lysine or within the same pathway:

1. Directly supported
   • "Multiple lysine acylations changed across conditions."
2. Site-aware
   • "The same lysine position was observed with different acyl modifications across conditions, with localization confidence reported."
3. Interpretive, but cautious
   • "This pattern is compatible with competitive occupancy and/or shared upstream drivers; missingness and batch flags are disclosed to reduce misinterpretation."

If you want one compact external anchor for why acetylation participates in broader PTM crosstalk frameworks (and why language discipline matters), a classic review is "Lysine acetylation: codified crosstalk with other post-translational modifications" (PMC2551738).

Common reviewer questions (and how to pre-answer them)

If your paper touches PTM crosstalk, reviewers often ask the same three questions. You can answer them before they are asked if you structure tables and captions the right way.

1. "How confident are you about the site?"
   • Answer with a numeric localization confidence, a cutoff, and how many sites pass.
2. "Could this be missingness or batch?"
   • Answer with missingness and batch flags in the same results table as the effect sizes, plus one short QC summary in Methods/Supplement.
3. "Are you actually measuring acetylation, or ‘acylation in general'?"
   • Answer by keeping the modification family explicit in every key figure/table, and by avoiding family name substitution in Results.

Next steps (consultation-only)

If you are unsure whether your study should be acetylation-focused or broader across lysine acylations, we can help you set a claim framework and reporting schema before you run the full study. Share your intended claims, sample matrix, perturbation type, and batch constraints, and we will recommend an interpretation-ready design and deliverable structure.

Explore PTMs proteomics services.

Author: CAIMEI LI — Senior Scientist at Creative Proteomics (LinkedIn)

RUO: For research use only. Not for clinical diagnosis, treatment, or individual health assessments.