TCA (Krebs) Cycle: Key Steps, Products, Readouts, and Diagram Guide

The TCA cycle is a core focus in many metabolomics studies, offering insights into mitochondrial activity, metabolic rewiring, and pathway-level responses to experimental perturbations. This article outlines how TCA-related data can support research objectives, what types of readouts are commonly used, and how to interpret key patterns. You'll also find guidance on when to consider ¹³C flux analysis and links to related topics such as sample preparation, matrix considerations, data interpretation, and platform selection.

What Is The TCA (Krebs) Cycle?

The tricarboxylic acid cycle (TCA cycle)—also commonly known as the Krebs cycle or the citric acid cycle—is a central metabolic pathway that plays a key role in energy production and biosynthesis. All three terms refer to the same set of mitochondrial reactions:

• TCA cycle emphasizes its three carboxyl group-containing intermediates (e.g., citrate, isocitrate, α-ketoglutarate).
• Krebs cycle is named after Sir Hans Krebs, who first described the cycle in the 1930s.
• Citric acid cycle highlights the initial metabolite formed—citrate, a tricarboxylic acid—by the condensation of acetyl-CoA and oxaloacetate.

In eukaryotic cells, the TCA cycle takes place within the mitochondrial matrix, linking glycolysis-derived acetyl-CoA to oxidative phosphorylation via generation of reducing equivalents (NADH and FADH₂). At the same time, it provides biosynthetic precursors—such as oxaloacetate and α-ketoglutarate—that feed into amino acid, nucleotide, and lipid metabolism.

Understanding the TCA cycle and how its intermediate levels change under different conditions can provide insight into:

• How compounds affect mitochondrial function and energy metabolism
• Whether a perturbation alters pathway entry, specific enzymes, or biosynthetic outflows
• Whether a phenotype involves metabolic rewiring, stress adaptation, or nutrient rerouting

Where Does The TCA Cycle Occur?

The tricarboxylic acid (TCA) cycle occurs inside the mitochondrial matrix, a specialized compartment within eukaryotic cells. This is where all eight enzymatic steps of the cycle are carried out in a coordinated and enclosed environment, optimized for energy metabolism and cofactor exchange.

This location is not incidental—mitochondria provide the structural and biochemical setting for the TCA cycle to function as part of the broader metabolic network. The proximity of the cycle to the electron transport chain, also located in the inner mitochondrial membrane, allows for seamless transfer of electrons from NADH and FADH₂, which are generated during TCA reactions.

While the cycle itself is mitochondrial, several TCA intermediates—such as citrate, malate, and α-ketoglutarate—can cross the mitochondrial membrane via specific transporters. These metabolites often serve dual roles, participating in both energy production and biosynthetic pathways outside the mitochondria.

For researchers conducting metabolomics experiments, it's useful to keep in mind that standard extraction methods capture total intracellular pools, without distinguishing between mitochondrial and cytosolic compartments. As such, changes observed in TCA-related metabolites may reflect both mitochondrial activity and cytosolic metabolic demands.

For study designs that must separate mitochondrial from cytosolic signals—or choose the right matrix for decision-making—see Krebs (TCA) Cycle Analysis: Choosing the Right Sample Type & Localization.

Key Steps And Enzymes: A "Step–Enzyme–Readout" Map

Understanding the sequence of reactions and associated enzymes in the TCA cycle is essential for interpreting metabolomics results in research settings. Below is a concise map linking each step to its corresponding enzyme and potential metabolite readouts commonly observed in steady-state profiling.

Citrate Synthase (CS)

Reaction: Oxaloacetate (OAA) + Acetyl-CoA → Citrate

Interpretation: Increased citrate may indicate enhanced acetyl-CoA supply or entry-point activity. Low citrate could reflect upstream PDH limitations, OAA depletion, or strong downstream consumption.

Aconitase (ACO)

Reaction: Citrate ⇌ Isocitrate via cis-Aconitate

Interpretation: An elevated citrate:isocitrate ratio can suggest ACO inhibition or oxidative stress, as ACO is sensitive to redox conditions.

Isocitrate Dehydrogenase (IDH)

Reaction: Isocitrate → α-Ketoglutarate (α-KG) + CO₂ + NAD(P)H

Interpretation: Accumulation of α-KG and concurrent depletion of isocitrate may reflect increased flux through this node or altered NADPH/NADH balance depending on the isoform involved.

α-Ketoglutarate Dehydrogenase Complex (α-KGDH)

Reaction: α-KG → Succinyl-CoA + CO₂ + NADH

Interpretation: Elevated α-KG with low downstream intermediates may indicate a bottleneck at α-KGDH, possibly linked to cofactor availability or redox state.

Succinyl-CoA Synthetase (SCS)

Reaction: Succinyl-CoA → Succinate (with GTP or ATP generation)

Interpretation: Succinate accumulation may result from efficient upstream production. Succinyl-CoA is typically inferred through succinate and related context due to analytical instability.

Succinate Dehydrogenase (SDH / Complex II)

Reaction: Succinate → Fumarate + FADH₂

Interpretation: The succinate:fumarate ratio is a useful marker for SDH activity and coupling to the electron transport chain. A high ratio can reflect impaired downstream flow or respiratory chain effects.

Fumarase (FH)

Reaction: Fumarate → Malate

Interpretation: Malate trends help differentiate whether fumarate accumulation is due to upstream input or reduced conversion downstream.

Malate Dehydrogenase (MDH)

Reaction: Malate → Oxaloacetate + NADH

Interpretation: Because oxaloacetate is labile, MDH behavior is often inferred from malate levels and overall cycle dynamics. The malate:α-KG ratio can also offer insight into the balance of forward and reverse flow.

These steps collectively form the basis for interpreting observed shifts in metabolite profiles. When analyzing experimental data, the strength of your interpretation often depends not on a single metabolite but on the overall pattern and ratios across multiple steps in the cycle.

TCA Cycle Products And Practical Readouts

In metabolomics studies, the TCA cycle is often profiled using a targeted panel of readily detectable intermediates that reflect the overall activity of central carbon metabolism. Rather than measuring every node in the pathway, focus is placed on a subset of metabolites that are analytically robust, biologically informative, and well-suited to the platform used.

Commonly Quantified Metabolites

The following intermediates are typically included in TCA-related analysis panels:

• Citrate
• Isocitrate
• α-Ketoglutarate (α-KG)
• Succinate
• Fumarate
• Malate

Each of these compounds can be consistently detected in cell, tissue, or biofluid extracts using LC–MS or GC–MS methods, depending on matrix and derivatization strategy. Their relative levels offer insight into the metabolic state of mitochondria and carbon flow through central metabolism.

Note: Some intermediates such as oxaloacetate and succinyl-CoA are unstable or challenging to detect under standard workflows. These are often interpreted indirectly through adjacent metabolite behavior.

Value of Readout Patterns

The true interpretive value of TCA measurements comes not just from absolute concentrations, but from the relative distribution of intermediates across the cycle. For example:

• A broader accumulation across multiple nodes may suggest a slowdown in downstream consumption or a shift in metabolic demand.
• A localized increase (e.g., isolated rise in succinate) can indicate a regulatory block or specific enzyme inhibition.
• A compression of the range—where most intermediates are close to baseline—might reflect a tightly regulated or homeostatic state.

These patterns are often compared between treatment and control groups, or across time points, to identify dynamic changes in metabolic balance.

Supporting Experimental Objectives

Measured TCA intermediates can support a range of research aims, such as:

• Detecting early metabolic shifts after compound treatment
• Evaluating mitochondrial engagement in genetic or pharmacological models
• Screening for indirect effects on redox balance or energy metabolism
• Providing orthogonal support for transcriptomics or proteomics findings
• Informing whether further analysis (e.g., ¹³C tracing) is warranted

Because these metabolites sit at the crossroads of energy production and biosynthesis, even subtle changes in their profiles can provide useful leads for follow-up experiments.

How To Read A TCA Cycle Diagram

This diagram shows the full set of chemical reactions and intermediates that make up the tricarboxylic acid (TCA) cycle, also known as the Krebs or citric acid cycle. Each step is annotated with:

• Metabolite names and chemical structures
• Enzyme names (e.g., citrate synthase, succinate dehydrogenase)
• Cofactors such as NAD⁺, FAD, and GDP/GTP
• Reaction types (condensation, dehydrogenation, decarboxylation, etc.)

Although the TCA cycle is cyclical by nature, measured changes often appear non-uniform. In steady-state metabolomics, not every intermediate is detected in every sample, and changes may occur only in part of the cycle.

When reviewing this type of diagram:

• Focus on which intermediates were measured in your dataset
• Use relative comparisons (e.g., succinate vs. fumarate) to identify shifts
• Keep in mind that directionality and turnover are not shown—these require isotope tracing

This type of diagram is especially useful when paired with fold-change overlays, statistical markers, or annotations showing treatment vs. control comparisons.

Glycolysis → PDH → TCA: The Critical Connection

The link between glycolysis and the TCA cycle is formed by the pyruvate dehydrogenase complex (PDH). This multi-enzyme complex converts pyruvate—the end product of glycolysis—into acetyl-CoA, which then enters the TCA cycle via citrate synthase.

This connection is critical because changes in glycolytic activity, nutrient availability, or redox state can directly impact acetyl-CoA supply, and thus influence downstream TCA intermediates like citrate and α-ketoglutarate.

In metabolomics studies, the effects of PDH regulation often show up as shifts in:

• Lactate: pyruvate ratio (reflecting upstream redox balance)
• Citrate levels (reflecting acetyl-CoA entry)
• Patterns in early-cycle intermediates

When interpreting TCA data, it's important to consider whether the observed changes are driven by upstream factors, particularly if your study involves:

• Glucose metabolism
• Mitochondrial function
• Hypoxia or nutrient stress

If timing, dosing, or treatment effects may influence this entry point, sampling design should be planned accordingly to capture early responses.

When Should You Use TCA-Focused Metabolomics?

A TCA-centered analysis is most useful when your study involves mitochondrial metabolism, carbon utilization, or pathway-level responses that may not be captured through gene or protein expression alone.

Typical research scenarios include:

• Target engagement: You expect functional effects on enzymes like CS, IDH, SDH, or α-KGDH and need supporting metabolic evidence.
• Mitochondrial activity: You're investigating respiration, redox balance, or coupling efficiency—often reflected in changes to succinate, fumarate, or malate.
• Carbon source tracing: You want to infer whether glucose, glutamine, or other substrates contribute to TCA entry via acetyl-CoA or anaplerotic routes.
• Metabolic adaptation: Conditions such as hypoxia or nutrient stress may shift flux through alternative entry or exit points; metabolite levels help characterize this reprogramming.
• Functional screening: TCA patterns can serve as early indicators of pathway impact, supporting hit selection or phenotypic stratification.

If your question centers on flux direction or carbon routing, a static metabolite panel may not be enough. In that case, we recommend ¹³C-based flux analysis.

Choosing the Right Analytical Platform for TCA Metabolomics

Selecting the most appropriate analytical platform depends on your study's goals, sample type, and required sensitivity. The table below summarizes key features of commonly used platforms for TCA metabolomics.

Platform Comparison for TCA Metabolomics

How We Match Platform to Project

We recommend or deploy platforms based on:

• Analyte priorities (TCA only vs. multi-pathway)
• Matrix type (cells, tissues, biofluids)
• Sample volume and availability
• Study phase (screening vs. flux vs. mechanistic confirmation)

In some cases, we may combine platforms (e.g., GC–MS for organic acids + LC–MS for broader coverage) to deliver the most informative and reliable results.

Common Pitfalls (And How To Avoid Them)

Confusing Pool Size With Flux

Static abundance changes do not prove net directional changes. Rising citrate might mean increased production, decreased downstream consumption, or both. If the conclusion depends on directionality, use ¹³C labeling and MID (mass isotopologue distribution) analysis.

Over-interpreting Single Nodes

Interpretation should rely on patterns and ratios—for example, succinate:fumarate or citrate:isocitrate—plus upstream context. A single metabolite rarely carries the whole story.

Underestimating Pre-analytical Effects

TCA intermediates can be labile. Delays in quenching, improper containers, or multiple freeze–thaw cycles create artifacts that exceed biological differences. Follow a matrix-appropriate SOP with time-to-quench control, internal standards, and stability checks: /blog/tca-sample-prep-checklist-quench-extract-stability.html.

Ignoring Matrix-Specific Ion Suppression

Cells, tissues, and biofluids have different matrix effects. Use matrix-matched calibration, internal standards, and robust normalization (protein, cell count, or tissue mass) to maintain comparability. See /blog/tca-matrix-specific-and-mitochondrial-localization.html.

Assuming Mitochondrial Effects From Cytosolic Data Alone

Transporters and cytosolic pools can blur subcellular interpretations. When localization is central to the hypothesis, combine metabolomics with fractionation or other orthogonal evidence.

For step-by-step limits on time-to-quench, container choice, extraction chemistry, internal standards, and stability checks, see TCA Cycle Sample Preparation: Collection, Quench, Extraction, and Stability Control.

Example Interpretation Patterns (Scenario-Based)

PDH Entry Constraint

Pattern: Elevated lactate:pyruvate ratio, reduced citrate, little change in α-KG

Implication: Suggests limited pyruvate conversion to acetyl-CoA

Next Step: Adjust sampling timepoints to capture early effects; assess glycolysis–TCA connectivity; consider adding upstream markers if not already included.

Aconitase Sensitivity

Pattern: Citrate accumulation with lower isocitrate; elevated citrate:isocitrate ratio

Implication: Potential ACO inhibition or redox-sensitive disruption (iron–sulfur cluster)

Next Step: Investigate oxidative context; validate redox status or ACO-specific effects in follow-up.

α-KGDH Bottleneck

Pattern: α-KG increase with low downstream intermediates (e.g., succinate)

Implication: Possible block at α-KGDH or cofactor limitation

Next Step: Examine NAD⁺/NADH balance; review any upstream anaplerotic input contributing to α-KG accumulation.

SDH / Complex II Limitation

Pattern: Succinate:fumarate ratio increases

Implication: Impaired SDH activity or reduced electron transport chain coupling

Next Step: Review mitochondrial respiration data (if available); consider respiratory complex profiling.

Anaplerotic Rewiring

Pattern: Elevated α-KG and glutamine/glutamate (if included)

Implication: Suggests increased anaplerosis via glutaminolysis

Next Step: Use ^13C-glutamine tracing to quantify label incorporation into α-KG and downstream nodes; select early sampling points to catch dynamic labeling.

Putting It All Together: From Research Question to Decision

1. Define the biological question.
   Example: "Does treatment A primarily impair complex II-coupled respiration, or does it act at pathway entry?" Being explicit about the hypothesis clarifies which nodes and ratios matter most.
2. Select the appropriate scope.
   Start with a TCA core panel and add a minimal bridging set (e.g., pyruvate, lactate) if PDH behavior is relevant. Include amino acids when you expect anaplerosis.
3. Lock sample prep.
   Use a matrix-matched SOP: container choice, time-to-quench limits, extraction chemistry, internal standards, and stability controls. This minimizes pre-analytical variance.
4. Choose a platform.
   LC–MS vs GC–MS depends on matrix, targeted compounds, and throughput. Document any cross-platform handoffs. See: /blog/lcms-vs-gcms-for-tca-method-selection.html.
5. Plan timing.
   If you suspect rapid effects, schedule early sampling (e.g., minutes to a few hours) to catch primary responses. For adaptive responses, plan longer windows.
6. Execute and review with acceptance criteria.
   Pre-define QC gates, missing-value handling, and statistical thresholds. Visualize both single-node changes and ratios. Confirm that key effects exceed batch variability.
7. Iterate based on outcomes.
   • If patterns remain ambiguous due to pooling, escalate to ¹³C flux.
   • If a single node changes without ratio support, check for pre-analytical issues or matrix effects.
   • If TCA patterns implicate broader rewiring, consider multi-omics to connect mechanisms to expression or protein activity (see our integration overview on the site).

Frequently Asked Questions

References

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3. Rathod, Ramji, Bharat Gajera, Kenneth Nazir, Janne Wallenius, and Vidya Velagapudi. "Simultaneous Measurement of Tricarboxylic Acid Cycle Intermediates in Different Biological Matrices Using LC–MS/MS; Quantitation and Comparison of TCA Cycle Intermediates in Human Serum, Plasma, Kasumi-1 Cell and Murine Liver Tissue." Metabolites 10.3 (2020): 103.
4. Yamamoto, Tetsushi, Kanta Sato, Masafumi Yamaguchi, Kuniko Mitamura, and Atsushi Taga. "Development of Simultaneous Quantitative Analysis of Tricarboxylic Acid Cycle Metabolites in Human Biological Samples by LC–MS/MS." Biochemical and Biophysical Research Communications (2021).
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