One of the major challenges currently faced by global health systems is the prolonged COVID-19 syndrome (also known as “long COVID”) which has emerged as a consequence of the SARS-CoV-2 epidemic. It is estimated that at least 30% of patients who have had COVID-19 will develop long COVID. In this study, our goal was to assess the plasma metabolome in a total of 100 samples collected from healthy controls, COVID-19 patients, and long COVID patients recruited in Mexico between 2020 and 2022. A targeted metabolomics approach using a combination of LC–MS/MS and FIA MS/MS was performed to quantify 108 metabolites. IL-17 and leptin were measured in long COVID patients by immunoenzymatic assay. The comparison of paired COVID-19/long COVID-19 samples revealed 53 metabolites that were statistically different. Compared to controls, 27 metabolites remained dysregulated even after two years. Post-COVID-19 patients displayed a heterogeneous metabolic profile. Lactic acid, lactate/pyruvate ratio, ornithine/citrulline ratio, and arginine were identified as the most relevant metabolites for distinguishing patients with more complicated long COVID evolution. Additionally, IL-17 levels were significantly increased in these patients. Mitochondrial dysfunction, redox state imbalance, impaired energy metabolism, and chronic immune dysregulation are likely to be the main hallmarks of long COVID even two years after acute COVID-19 infection.

Research exploring the development and outcome of COVID-19 infections has led to the need to find better diagnostic and prognostic biomarkers. This cross-sectional study used targeted metabolomics to identify potential COVID-19 biomarkers that predicted the course of the illness by assessing 110 endogenous plasma metabolites from individuals admitted to a local hospital for diagnosis/treatment. Patients were classified into four groups (≈ 40 each) according to standard polymerase chain reaction (PCR) COVID-19 testing and disease course: PCR−/controls (i.e., non-COVID controls), PCR+/not-hospitalized, PCR+/hospitalized, and PCR+/intubated. Blood samples were collected within 2 days of admission/PCR testing. Metabolite concentration data, demographic data and clinical data were used to propose biomarkers and develop optimal regression models for the diagnosis and prognosis of COVID-19. The area under the receiver operating characteristic curve (AUC; 95% CI) was used to assess each models’ predictive value. A panel that included the kynurenine: tryptophan ratio, lysoPC a C26:0, and pyruvic acid discriminated non-COVID controls from PCR+/not-hospitalized (AUC = 0.947; 95% CI 0.931–0.962). A second panel consisting of C10:2, butyric acid, and pyruvic acid distinguished PCR+/not-hospitalized from PCR+/hospitalized and PCR+/intubated (AUC = 0.975; 95% CI 0.968–0.983). Only lysoPC a C28:0 differentiated PCR+/hospitalized from PCR+/intubated patients (AUC = 0.770; 95% CI 0.736–0.803). If additional studies with targeted metabolomics confirm the diagnostic value of these plasma biomarkers, such panels could eventually be of clinical use in medical practice.

Placenta‑derived exosomes play an important role in cellular communication both in the mother and the fetus. Their concentration and composition are altered in several pregnancy disorders, such as gestational diabetes mellitus (GDM). The isolation and characterization of placental exosomes from serum, plasma and tissues from patients with GDM have been previously described; however, to the best of our knowledge, to date, there is no study available on placental exosomes isolated from urine of patients with GDM. In the present study, placental exosomes were purified from urine the 1st, 2nd and 3rd trimester of gestation. Placental exosomes were characterized by transmission electron microscopy in cryogenic mode and by western blot analysis, confirming the presence of exosomal vesicles. The expression profile of five microRNAs (miR‑516‑5p, miR‑517‑3p, miR‑518‑5p, miR‑222‑3p and miR‑16‑5p) was determined by RT‑qPCR. In healthy pregnant women, the expression of the miRNAs increased across gestation, apart from miR‑516‑5p, which was not expressed at the 2nd trimester. All the miRNAs examined were downregulated in patients with GDM at the 3rd trimester of gestation. The downregulated miRNAs affected several metabolic pathways closely associated with the pathophysiology of GDM. This provides further evidence of the regulatory role of miRNAs in the GDM. This also suggests that the of urinary exosomes may be an excellent source of biomarkers and therapeutic targets.

The micro RNA (miR)-34 family is composed of 5p and 3p strands of miR-34a, miR-34b, and miR-34c. The 5p strand’s expression and function is studied in cervical cancer. The 3p strand’s function and regulation remain to be elucidated. To study the function of the passenger strands of miR-34 family members, we overexpressed 5p and 3p strands using a synthetic miRNA in cervical cell lines. Cell proliferation was evaluated using crystal violet. Migration and invasion were tested using transwell assays, Western blot, and zymography. Possible specific targets and cell signaling were investigated for each strand. We found that miR-34a-5p inhibited proliferation, migration, and cell invasion accompanied by matrix metalloproteinase 9 (MMP9) activity and microtubule-associated protein 2 (MAP2) protein reduction. We also found that miR-34b-5p and miR-34c-5p inhibit proliferation and migration, but not invasion. In contrast, miR-34c-5p inhibits MMP9 activity and MAP2 protein, while miR-34b-5p has no effect on these genes. Furthermore, miR-34a-3p and miR-34b-3p inhibit proliferation and migration, but not invasion, despite the later reducing MMP2 activity, while miR-34c-3p inhibit proliferation, migration, and cell invasion accompanied by MMP9 activity and MAP2 protein inhibition. The difference in cellular processes, MMP2 and MMP9 activity, and MAP2 protein inhibition by miR-34 family members suggests the participation of other regulated genes. This study provides insights into the roles of passenger strands (strand*) of the miR-34 family in cervical cancer.

Viral sepsis has been proposed as an accurate term to describe all multisystemic dysregulations and clinical findings in severe and critically ill COVID-19 patients. The adoption of this term may help the implementation of more accurate strategies of early diagnosis, prognosis, and in-hospital treatment. We accurately quantified 110 metabolites using targeted metabolomics, and 13 cytokines/chemokines in plasma samples of 121 COVID-19 patients with different levels of severity, and 37 non-COVID-19 individuals. Analyses revealed an integrated host-dependent dysregulation of inflammatory cytokines, neutrophil activation chemokines, glycolysis, mitochondrial metabolism, amino acid metabolism, polyamine synthesis, and lipid metabolism typical of sepsis processes distinctive of a mild disease. Dysregulated metabolites and cytokines/chemokines showed differential correlation patterns in mild and critically ill patients, indicating a crosstalk between metabolism and hyperinflammation. Using multivariate analysis, powerful models for diagnosis and prognosis of COVID-19 induced sepsis were generated, as well as for mortality prediction among septic patients. A metabolite panel made of kynurenine/tryptophan ratio, IL-6, LysoPC a C18:2, and phenylalanine discriminated non-COVID-19 from sepsis patients with an area under the curve (AUC (95%CI)) of 0.991 (0.986–0.995), with sensitivity of 0.978 (0.963–0.992) and specificity of 0.920 (0.890–0.949). The panel that included C10:2, IL-6, NLR, and C5 discriminated mild patients from sepsis patients with an AUC (95%CI) of 0.965 (0.952–0.977), with sensitivity of 0.993(0.984–1.000) and specificity of 0.851 (0.815–0.887). The panel with citric acid, LysoPC a C28:1, neutrophil-lymphocyte ratio (NLR) and kynurenine/tryptophan ratio discriminated severe patients from sepsis patients with an AUC (95%CI) of 0.829 (0.800–0.858), with sensitivity of 0.738 (0.695–0.781) and specificity of 0.781 (0.735–0.827). Septic patients who survived were different from those that did not survive with a model consisting of hippuric acid, along with the presence of Type II diabetes, with an AUC (95%CI) of 0.831 (0.788–0.874), with sensitivity of 0.765 (0.697–0.832) and specificity of 0.817 (0.770–0.865).