The concept of the exposome encompasses all the factors influencing human health throughout the life course. The exposome induces epigenetic changes, such as DNA methylation, which influence gene expression and impact overall health. Several recent studies have explored how repetitive elements (REs) in the genome can be activated in response to environmental stimuli. However, most of these investigations have assumed that altered RE methylation is always detrimental to individual health. The MAMELI project proposes an alternative hypothesis: that some REs are plastic entities capable of responding physiologically to environmental stimuli without compromising genome stability. This hypothesis suggests that the ability of DNA to adapt to environmental triggers could be monitored and used as an indicator of health resilience. To test this hypothesis, the MAMELI project will enroll 6,200 participants from the city of Legnano (Italy) and will be conducted in three main phases: i) A total of 200 healthy participants will undergo DNA methylation analysis through third-generation sequencing at two time points: T0 (baseline) and T1 (6 months after T0). This phase aims to identify a set of REs (“differential REs”) whose methylation changes in response to the exposome without affecting genome stability; ii) This phase will include 2,700 subjects (the original 200 participants from the discovery phase plus 2,500 additional subjects). The goal will be to develop a predictive algorithm (the MAMELI algorithm) that links the exposome to RE methylation status, creating a “RE methylation signature” reflecting the environmental impact on DNA methylation; iii) In this phase, the MAMELI algorithm will be applied to a separate cohort of 3,500 participants to compare the measured RE methylation signature with values predicted by the algorithm. Additionally, an intervention study will be embedded within the cohort to assess the reversibility of RE methylation following lifestyle changes. The MAMELI project offers a novel perspective in the field of epigenetics and environmental health, demonstrating how the epigenome can act as a sensor for environmental changes and how this interaction can be harnessed for disease prevention. If validated, the MAMELI algorithm could become a powerful tool for identifying individuals at risk and developing personalized interventions to improve global health outcomes.

MicroRNAs (miRNAs) are small non-coding RNAs that negatively regulate gene expression at the post-transcriptional level. In plants, miRNAs are involved in environmental responses and can be transferred to other species to mediate cross-kingdom regulation of gene expression. This mechanism has recently been proposed in mammals, yet evidence remains scarce and inconsistent. Multiple studies have shown that a fraction of plant-derived miRNAs (pmiRNAs) present in food resist processing, cooking, and digestion. Evidence suggests that dietary-derived pmiRNAs might be absorbed in the gastrointestinal tract and enter circulation, predominantly packaged in extracellular vesicles, and reach different tissues, where they might exert cross-kingdom gene expression regulation. Nonetheless, several attempts to reproduce or confirm the above results have failed, raising questions about the reliability of studies supporting the hypothesis of cross-kingdom gene regulation by pmiRNAs. In this review, we recapitulate the state-of-the-art knowledge in the field, addressing both supporting and opposing evidence, as well as the main analytical challenges that need to be taken into consideration, in an effort to provide a comprehensive framework on the controversial evidence collected so far and support the use of best practices for future research.

Major depressive disorder (MDD) has been recently linked to air pollution exposure; nevertheless, the biological mechanisms underlying this association remain underinvestigated. Air pollution might modulate the microRNA (miRNA) content of neuron-derived extracellular vesicles (NdEVs), potentially mirroring brain epigenetic alterations. In the present study, we investigated the relationship between air pollution, NdEV miRNAs, and MDD severity in a population of 200 patients with depression. After signing informed consent, participants compiled questionnaires about demographics, lifestyle and clinical history, and donated a blood sample. MDD severity was assessed by five scales. Particulate matter ≤2.5 μm (PM_2.5) and nitrogen dioxide (NO₂) exposure was assigned based on participants’ residential address. Plasma NdEVs were obtained by L1CAM immunocapture. NdEV miRNAs were queried by RT-qPCR (microarray) following a two-stage approach. Associations between air pollutants, NdEV miRNAs, and MDD severity were assessed by multivariable regression models. The regulatory function of NdEV miRNAs was investigated by gene target and pathway analysis. As a result, exposure to NO₂ was associated with decreased levels of miR-191 and miR-24, while PM_2.5 was negatively associated with miR-191, miR-223, miR-24, miR-320, miR-451, miR-572, and miR-638. Decreased miR-126, miR-19b, miR-320, miR-451, miR-572, and miR-638 were associated with higher MDD severity scores. Target genes at the interface between air pollution exposure and MDD severity were mainly involved in inflammation and cell cycle regulation. These findings suggest that air pollutants might modulate MDD severity by triggering NdEV miRNA alterations. Longitudinal studies are needed to evaluate whether NdEV miRNAs might serve as novel biomarkers for MDD prognosis.

Major Depressive Disorder (MDD) is a psychiatric condition whose onset and progression may be influenced by environmental factors. Our study investigates the association between greenness exposure and MDD severity, while also exploring the role of air pollutants and temperature. Three hundred ninety-six MDD patients were included. MDD severity was assessed using five rating scales. Two- and three-dimensional indicators of urban space were estimated around participants' houses: Normalized Difference Vegetation Index (NDVI), Green Volume, Grey Volume, and Normalized Difference Green-Grey Volume Index (NDGG). Annual exposure estimates of air pollutants and temperature were assigned to the participants' residential addresses. Multivariate linear and ordinal regression models were applied to assess associations between exposure variables and MDD severity, adjusting for and stratifying by air pollutants and temperature. Increasing levels of all urban space indicators (within a 100 m radius), except for Grey Volume, were associated with decreased MDD severity. The strongest effect was observed for Green Volume (e.g., Hamilton Depression Rating Scale [HAMD]: β = -2.67, 95 % confidence interval [CI]: -4.57; -0.76, for an interquartile range [IQR] increase in the exposure variable). The inverse associations between greenness and MDD severity were confirmed only when values of particulate matter ≤2.5 μm (PM2.5) were below the median (e.g. β = -5.32, 95 %CI: -8.28; -2.36 for an IQR increase in Green Volume). The same occurred with temperature levels below the median. Green spaces are associated with lower severity of depression, especially when PM2.5 and temperature levels are low.

Background The concept of the exposome encompasses all the factors influencing human health throughout the life course. The exposome induces epigenetic changes, such as DNA methylation, which influence gene expression and impact overall health. Several recent studies have explored how repetitive elements (REs) in the genome can be activated in response to environmental stimuli. However, most of these investigations have assumed that altered RE methylation is always detrimental to individual health. The MAMELI project proposes an alternative hypothesis: that some REs are plastic entities capable of responding physiologically to environmental stimuli without compromising genome stability. This hypothesis suggests that the ability of DNA to adapt to environmental triggers could be monitored and used as an indicator of health resilience. Methods To test this hypothesis, the MAMELI project will enroll 6,200 participants from the city of Legnano (Italy) and will be conducted in three main phases: i) A total of 200 healthy participants will undergo DNA methylation analysis through third-generation sequencing at two time points: T0 (baseline) and T1 (6 months after T0). This phase aims to identify a set of REs (“differential REs”) whose methylation changes in response to the exposome without affecting genome stability; ii) This phase will include 2,700 subjects (the original 200 participants from the discovery phase plus 2,500 additional subjects). The goal will be to develop a predictive algorithm (the MAMELI algorithm) that links the exposome to RE methylation status, creating a “RE methylation signature” reflecting the environmental impact on DNA methylation; iii) In this phase, the MAMELI algorithm will be applied to a separate cohort of 3,500 participants to compare the measured RE methylation signature with values predicted by the algorithm. Additionally, an intervention study will be embedded within the cohort to assess the reversibility of RE methylation following lifestyle changes. Discussion The MAMELI project offers a novel perspective in the field of epigenetics and environmental health, demonstrating how the epigenome can act as a sensor for environmental changes and how this interaction can be harnessed for disease prevention. If validated, the MAMELI algorithm could become a powerful tool for identifying individuals at risk and developing personalized interventions to improve global health outcomes.