Diurnal and circadian rhythmicity of the human blood transcriptome overlaps with organ- and tissue-specific expression of a non-human primate

Retrospective discrimination of PNES and epileptic seizure types using blood RNA signatures

OBJECTIVES

The ability to differentiate epileptic- and non-epileptic events is challenging due to a lack of reliable molecular seizure biomarker that provide a retrospective diagnosis. Here, we use next generation sequencing methods on whole blood samples to identify changes in RNA expression following seizures.

METHODS

Blood samples were obtained from 32 patients undergoing video electroencephalogram (vEEG) monitoring. Blood samples were collected in PaxGene tubes at baseline (admission) and following a seizure event (4-6 h and 24 h later or discharge). EEG and video of clinical events were reviewed by the clinical team and study epileptologist and were classified as epileptic seizure, psychogenic nonepileptic spell (PNES), or other. RNA was extracted from blood and RNA expression was determined using RNA-sequencing.

RESULTS

We show significant differences in RNA profiles between patients that did or did not experience an epileptic seizure. Compared to baseline patients with PNES show large increases in RNA expression 4-6 h and 24 h post seizure. Conversely, genes that changed following epileptic seizure showed more modest changes associated with a decrease in immune system function. Transcript usage was changed between patients with PNES and epileptic seizure at all three time points examined. Lists of genes differentially expressed following PNES or epileptic seizure vs. all baseline samples were used as classifiers for prediction. Models generated using random forest and radial support vector machine algorithms were 100% accurate at predicting both PNES and epileptic seizures.

SIGNIFICANCE

These data suggest that blood gene expression changes may have utility to retrospectively identify patients who have suffered a seizure or seizure-like event as a cause of transient loss of consciousness.

Daily rhythm in DNA methylation and the effect of total sleep deprivation

Numerous hormones and genes exhibit diurnal 24-hr rhythms that can also be affected by sleep deprivation. Here we studied diurnal rhythms in DNA methylation under a 24-hr sleep/wake cycle and a subsequent 29 hr of continual wakefulness (1 night of sleep deprivation). Fifteen healthy men (19-35 years) spent 3 days/nights in a sleep laboratory: (1) adaptation; (2) baseline; (3) total sleep deprivation day/night. DNA methylation was analysed from peripheral blood leukocytes, collected every 3 hr for 45 hr (starting at 15:00 hours) during the baseline period and the total sleep deprivation period. Epigenome-wide DNA methylation variation was assessed with the Infinium MethylationEPIC v2.0 Beadchip kit. Rhythm analysis was performed separately for the baseline and the total sleep deprivation time-series data. Pairwise analysis between diurnal samples and sleep deprivation samples at the same timepoint was also carried out to detect differentially methylated positions related to sleep deprivation. Of all DNA methylation sites, 14% exhibited a diurnal rhythm in methylation on the baseline day/night that was altered by sleep deprivation. During sleep deprivation, the number of differentially methylated positions increased towards the end of the sleep deprivation period, with a dominating pattern of hypomethylation. Among differentially methylated positions, an enrichment of genes related to the FAS immune response pathway was detected. In conclusion, DNA methylation exhibits diurnal rhythmicity, and this time-of-day variation needs to be considered when studying DNA methylation as a biomarker in biomedical studies. In addition, the observed DNA methylation changes under wakefulness might serve as a mediator of sleep deprivation-related immune response alterations.

Metabolomics and proteomics in occupational medicine: a comprehensive systematic review

BACKGROUND

Occupational biomonitoring is essential for assessing health risks linked to workplace exposures. The use of 'omics' technologies, such as metabolomics and proteomics, has become crucial in detecting subtle biological alterations induced by occupational hazards, thereby opening novel avenues for biomarker discovery.

AIMS

This systematic review aims to evaluate the application of metabolomics and proteomics in occupational health.

METHODS

Following the PRISMA guidelines, we conducted a comprehensive search on PubMed, Scopus, and Web of Science for original human studies that use metabolomics or proteomics to assess occupational exposure biomarkers. The risk of bias was assessed by adapting the Cochrane Collaboration tool and the Newcastle-Ottawa Quality Assessment Scale.

RESULTS

Of 2311 initially identified articles, 85 met the eligibility criteria. These studies were mainly conducted in China, Europe, and the United States of America, covering a wide range of occupational exposures. The findings revealed that metabolomics and proteomics approaches effectively identified biomarkers related to chemical, physical, biomechanical, and psychosocial hazards. Analytical methods varied, with mass spectrometry-based techniques emerging as the most prevalent. The risk of bias was generally low to moderate, with specific concerns about exposure measurement and confounding factors.

CONCLUSIONS

Integrating metabolomics and proteomics in occupational health biomonitoring significantly advances our understanding of exposure effects and facilitates the development of personalized preventive interventions. However, challenges remain regarding the complexity of data analysis, biomarker specificity, and the translation of findings into preventive measures. Future research should focus on longitudinal studies and biomarker validation across diverse populations to improve the reliability and applicability of occupational health interventions.

Translational potential of mouse models of human metabolic disease

Obesity causes significant morbidity and mortality globally. Research in the last three decades has delivered a step-change in our understanding of the fundamental mechanisms that regulate energy homeostasis, building on foundational discoveries in mouse models of metabolic disease. However, not all findings made in rodents have translated to humans, hampering drug discovery in this field. Here, we review how studies in mice and humans have informed our current framework for understanding energy homeostasis, discuss their challenges and limitations, and offer a perspective on how human studies may play an increasingly important role in the discovery of disease mechanisms and identification of therapeutic targets in the future.