Editorial: Metabolomics in Infectious Diseases
Bibliographic record
Abstract
Metabolomics is an important emerging field of omics technology.While the metabolic pathways in prokaryotic or disease-causing agents are relatively simple compared to those of mammalian hosts, metabolite pools reflect the instant (snapshot) status of cells under healthy or diseased conditions or when infecting the host (Tan et al., 2007) (Lee et al., 2015).In humans, immune cells play a major role in the defense against microbial infection.The metabolic pathways of immune cells are under the stringent control of metabolites and small molecules under a quiescent or active state.Any subtle change in the gene expression during the diseased condition can affect the downstream pathways that consist of proteins and metabolites.Metabolites belong to different chemical groups such as amino acids, organic acid, lipids, or amines.Due to their close association with the cellular system, the detection of metabolites can provide the accurate status of the cell and can also be used as biomarkers of disease or drug targets (Rahman and Hasan 2014).Considering the importance of the field of metabolomics, the current research topic aimed to collect articles where metabolites and metabolite detection tools are used for different purposes in infectious disease.Articles published in this issue show how metabolomics can be used as biomarkers of disease during pandemics, use of metabolomics in clinical management of infectious disease, during metabolomics coinfection of virus and bacteria, metabolites associated with inflammatory disease, and specific proteins associated with transport molecules during communicable and noncommunicable disease.In this issue, an original research article by Taleb et al. (2021) shows that metabolic changes can be used to predict the recovery pattern in critically ill patients caused by the pandemic strain of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2).Using targeted metabolomics of serum samples, patients that were admitted within 48 h and were under invasive mechanical ventilation (IMV) in the intensive care unit (ICU) had different levels of hypoxanthine and betaine at the first time point of admission.These levels can predict whether the patient will require a short or long stay in the ICU.Another group of metabolites including kynurenine, 3-methylhistidine, ornithine, p-cresol sulfate, C24, and sphingomyelin was measured 1 week later and these could accurately predict the duration of IMV.Another original research conducted by Elrayees et al. (2022) shows that COVID-19 severity is high in patients with type 2 diabetes mellitus and hypertension.Using tandem mass spectrometry as the analytical platform, targeted metabolomics were conducted in serum samples from patients with different disease severity, diabetes status, and hypertension status.Using multivariate and univariate models for data analysis, the authors showed that patients with diabetes and hypertension had increased severity of COVID-19 and reduced levels of specific triacylglycerols.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.013 | 0.016 |
| Insufficient payload (model declined to judge) | 0.019 | 0.016 |
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".