Metal Species in Biology: Bottom-Up and Top-Down LC Approaches in Applied Toxicological Research
Bibliographic record
Abstract
Since the inception of liquid chromatography (LC) more than 100 years ago this separation technique has been developed into a powerful analytical tool that is frequently applied in life science research. To this end, unique insights into the interaction of metal species (throughout this manuscript “metal species” refers to “toxic metals, metalloid compounds, and metal-based drugs” and “toxic metals” to “toxic metals and metalloid compounds”) with endogenous ligands can be obtained by using LC approaches that involve their hyphenation with inductively coupled plasma-based element specific detectors. This review aims to provide a synopsis of the different LC approaches which may be employed to advance our understanding of these interactions either in a “bottom-up” or a “top-down” manner. In the “bottom-up” LC-configuration, endogenous ligands are introduced into a physiologically relevant mobile phase buffer, and the metal species of interest is injected. Subsequent “interrogation” of the on-column formed complex(es) by employing a suitable separation mechanism (e.g., size exclusion chromatography or reversed-phase LC) while changing the ligand concentration(s), the column temperature or the pH can provide valuable insight into the formation of complexes under near physiological conditions. This approach allows to establish the relative stability and hydrophobicity of metal-ligand complexes as well as the dynamic coordination of a metal species (injected) to two ligands (dissolved in the mobile phase). Conversely, the “top-down” analysis of a biological fluid (e.g., blood plasma) by LC (e.g., using size exclusion chromatography) can be used to determine the size distribution of endogenous metalloproteins which are collectively referred to as the “metalloproteome”. This approach can provide unique insight into the metabolism and the plasma protein binding of metal species, and can simultaneously visualize the dose-dependent perturbation of the metalloproteome by a particular metal species. The concerted application of these LC approaches is destined to provide new insight into biochemical processes which represent an important starting point to advance human health in the 21st century.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".