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. 2024 Jul 17;10(14):e34756.
doi: 10.1016/j.heliyon.2024.e34756. eCollection 2024 Jul 30.

The genomic mosaic of mitochondrial dysfunction: Decoding nuclear and mitochondrial epigenetic contributions to maternally inherited diabetes and deafness pathogenesis

Luigi Donato  1   2 , Concetta Scimone  1   2 , Simona Alibrandi  1   2 , Maria Vadalà  2   3 , Massimo Castellucci  3 , Vincenza Maria Elena Bonfiglio  3 , Sergio Zaccaria Scalinci  4 , Giorgia Abate  1 , Rosalia D'Angelo  1 , Antonina Sidoti  1
Affiliations

The genomic mosaic of mitochondrial dysfunction: Decoding nuclear and mitochondrial epigenetic contributions to maternally inherited diabetes and deafness pathogenesis

Luigi Donato et al. Heliyon. .

Abstract

Aims: Maternally inherited diabetes and deafness (MIDD) is a complex disorder characterized by multiorgan clinical manifestations, including diabetes, hearing loss, and ophthalmic complications. This pilot study aimed to elucidate the intricate interplay between nuclear and mitochondrial genetics, epigenetic modifications, and their potential implications in the pathogenesis of MIDD.

Main methods: A comprehensive genomic approach was employed to analyze a Sicilian family affected by clinically characterized MIDD, negative to the only known causative m.3243 A > G variant, integrating whole-exome sequencing and whole-genome bisulfite sequencing of both nuclear and mitochondrial analyses.

Key findings: Rare and deleterious variants were identified across multiple nuclear genes involved in retinal homeostasis, mitochondrial function, and epigenetic regulation, while complementary mitochondrial DNA analysis revealed a rich tapestry of genetic diversity across genes encoding components of the electron transport chain and ATP synthesis machinery. Epigenetic analyses uncovered significant differentially methylated regions across the genome and within the mitochondrial genome, suggesting a nuanced landscape of epigenetic modulation.

Significance: The integration of genetic and epigenetic data highlighted the potential crosstalk between nuclear and mitochondrial regulation, with specific mtDNA variants influencing methylation patterns and potentially impacting the expression and regulation of mitochondrial genes. This pilot study provides valuable insights into the complex molecular mechanisms underlying MIDD, emphasizing the interplay between nucleus and mitochondrion, tracing the way for future research into targeted therapeutic interventions and personalized approaches for disease management.

Keywords: Epigenetics; MIDD; WES; WGS; mtDNA.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
Pedigree of the Sicilian family affected by a suspected orphan form of MIDD. The affected (black fill) and unaffected (no fill) members are shown. Yellow fill indicates symptomatology potentially related only to specific aspect of main pathological phenotype (e.g. diabetes). Gradient black fill denotes the variable expression of the proband's phenotype. More details through the text. Arrow: proband; circle: female; square: male.
Fig. 2
Fig. 2
Color fundus and autofluorescence of proband and her youngest daughter. Proband's color fundus showed retinal pigment epithelium atrophy at the posterior pole encircling the macular region (A. Left eye. B. Right Eye). Proband's fundus autofluorescence highlights hyperpigmented areas show increased autofluorescence, whereas hypopigmented spots exhibit decreased in autofluorescence, corresponding to chorioretinal atrophy; adjacent to the temporal vascular arcade speckled AF patterns surrounding areas of atrophy. (C. Left eye. D. Right Eye). E-H panels show the youngest daughter's color fundus (E. Left eye. F. Right Eye) and fundus autofluorescence (G. Left eye. H. Right Eye).
Fig. 3
Fig. 3
Multidimensional analysis of WGS genotypic variations and phenotypic expressions. These figures collectively contribute to a deeper understanding of the complex interplay between genetic variations and their phenotypic outcomes in the context of MIDD. Panel A delineates the Interesting Diseases and Bio-Functions, spotlighting crucial biological processes and disease states significantly associated with our dataset, underscoring their potential impact and relevance. Panel B details the Tox List, identifying specific toxicological responses and pathways activated in response to chemical exposures or environmental stressors, offering insights into the toxicogenomic profile of our dataset. Panel C illustrates Overlapping Canonical Pathways, capturing the intersection of significantly enriched pathways across different conditions or datasets, highlighting shared molecular mechanisms.
Fig. 4
Fig. 4
Analysis of Pathway Enrichment in Mitochondrial Diabetes: Prioritizing Biological Impact through Canonical Pathways. These figures collectively contribute to a deeper understanding of the complex interplay between genetic variations and their phenotypic outcomes in the context of MIDD. Panel D presents a Canonical Pathways Vertical Stacked Bar Chart, quantitatively summarizing the -log(p-value) of enriched pathways to prioritize those most significantly affected, providing a clear hierarchy of biological impact.
Fig. 5
Fig. 5
Visualizing Pathway Enrichment in Mitochondrial Diabetes: Insights from a Canonical Pathways Bubble Chart. These figures collectively contribute to a deeper understanding of the complex interplay between genetic variations and their phenotypic outcomes in the context of MIDD. Lastly, Panel E showcases a Canonical Pathways Bubble Chart, where the size and color of each bubble represent the significance and the ratio of dataset genes involved in each pathway, respectively, offering a visually intuitive understanding of pathway enrichment and relevance.
Fig. 6
Fig. 6
Integrative Analysis of Molecular Mechanisms related to mtDNA variants. Panel A delineates the spectrum of diseases and bio-functions, including hereditary, metabolic, neurological, and ophthalmic disorders, underlining the genetic and metabolic underpinnings of these conditions. Panel B showcases a vertical stacked bar chart of Canonical Pathways, with oxidative phosphorylation and mitochondrial dysfunction highlighted, emphasizing their critical roles in cellular energetics and disease pathogenesis. Panel C reveals machine-learning-derived disease pathways, focusing on mitochondrial DNA-related disorders, indicating the significant impact of mitochondrial genetics on disease phenotypes. Panel D presents a bubble chart of Canonical Pathways, emphasizing nuclear receptor signaling, cell stress and injury, and transcriptional regulation, illustrating key regulatory mechanisms implicated in disease processes. Panel E illustrates the convergence of overlapping canonical pathways, providing a holistic view of shared molecular mechanisms across diverse pathological states. This figure encapsulates the comprehensive and multifaceted analysis conducted, offering profound insights into the biological basis of disease, with a particular emphasis on mitochondrial function, genetic predispositions, and cellular homeostasis mechanisms.
Fig. 7
Fig. 7
Unveiling Key Disease Pathways and Biological Functions Through Advanced Molecular Analysis. Panels A and B highlight machine learning-derived disease pathways for ocular conditions, including Syndromic Ciliopathy and Hereditary Eye Diseases.
Fig. 8
Fig. 8
Comparative Analysis of Disease Relevance Across Datasets. This vertical bar chart displays various diseases and conditions, each represented by bars indicating changes or consistencies in data relevance. Red bars signify a downgrade in relevance or inconsistent data, green bars denote an upgrade or positive shift in relevance, and gray bars show no significant change or overlap with the dataset. The percentage scale on the x-axis quantifies these changes, offering insights into the biological impact of these conditions on cellular and metabolic processes.
Fig. 9
Fig. 9
Detailed Pattern Analysis of Disease Relevance. This horizontal bar chart further explores the pattern variability across diseases and conditions as indicated by dataset findings. Bars are colored to represent positive (orange), negative (blue), and inactive (gray) patterns, with their significance reflected through the -log(p-value) on the x-axis. This detailed visualization aids in understanding the significant levels of biological and clinical relevance of the diseases in the dataset.
Fig. 10
Fig. 10
Quantitative Assessment of Pathway Impact Using Vertical Stacked Bars. This chart visualizes the impact of various signaling pathways such as CREB, G-Protein Coupled Receptor, and Xenobiotic Metabolism, using stacked bars to indicate the level of pathway activity. Red bars highlight pathways where activity is decreased, while green bars signify pathways with increased activity. The magnitude of the effect is quantified using -log(p-value) along the x-axis, illustrating the statistical significance of the changes observed in these pathways.
Fig. 11
Fig. 11
Detailed Visualization of Pathway Activity Patterns in Horizontal Bars. This chart expands on the analysis of pathway significance by detailing the positive, negative, and inactive activity patterns across various biological pathways like Insulin Secretion, Cardiac Hypertrophy, and Hepatic Fibrosis. The orange bars indicate pathways with positive deviations from the norm, blue bars represent negative deviations, and gray bars indicate no significant activity pattern. The length of each bar is proportional to the -log(p-value), providing a clear metric of how significant the deviation is in each case. This detailed visualization helps in identifying the most critical pathways affected in the dataset.
Fig. 12
Fig. 12
Unveiling Key Disease Pathways and Biological Functions Through Advanced Molecular Analysis. Panel A presents a Canonical Pathways Bubble Chart, revealing central biosynthetic and signaling pathways. Panel B depicts Overlapping Canonical Pathways, indicating common molecular mechanisms. Panel C illustrates the predicted influence of AGT on cell dynamics and neuropathic pain, suggesting new therapeutic avenues.
Fig. 13
Fig. 13
Dissecting the Genetic and Epigenetic Landscape of mtDNA: a Comprehensive View of Mitochondrial Dysfunction and Its Potential Role in MIDD. This figure encapsulates the integrative findings from our latest research, providing a panoramic view of the molecular intricacies underlying a spectrum of hereditary and metabolic disorders. Panel A delineates Diseases and Bio-Functions, spotlighting critical areas such as Hereditary Disorder, Metabolic Disorder, and Ophthalmic Disease, underscoring the genetic and metabolic complexities driving these conditions. Panel B focuses on Machine Learning Disease Pathways, with an exclusive look at Mitochondrial DNA-related Disorder, highlighting the cutting-edge approach to uncovering the genetic roots of mitochondrial dysfunctions. Panel C and D, both dedicated to Canonical Pathways, converge on Mitochondrial Dysfunction, emphasizing its pivotal role across various disease states and its impact on cellular metabolism and energy production. Panel E, Overlapping Canonical Pathways, reinforces the centrality of Mitochondrial Dysfunction, illustrating its ubiquitous influence across the analyzed conditions and suggesting a universal target for therapeutic intervention. Together, these panels offer a comprehensive snapshot of the current state of knowledge on the molecular mechanisms of disease, pointing to mitochondrial health as a key factor in the pathogenesis of MIDD.
Fig. 14
Fig. 14
Differential Methylation Landscape across Epigenetic and Mitochondrial Homeostasis-related Nuclear Genes. The figure showcases a heatmap that elucidates the differential methylation patterns of nuclear genes associated with epigenetic regulation and mitochondrial function. The Log2 Fold Change (FC) values, calculated by comparing individual II3 against II4, illuminate the methylation dynamics at CpG-rich regions of the genes, either at the 5′ or 3′ ends. The intensity of the colors in the heatmap reflects the degree of change in methylation levels between the two individuals. Genes with increased methylation at the 5′ end might indicate regulatory regions where hypermethylation could lead to gene silencing. Conversely, genes with decreased methylation at the 3′ end could suggest regions where hypomethylation might enhance gene expression.
Fig. 15
Fig. 15
Circular Visualization of the Human Mitochondrial Genome and Associated Variant Information. The circular plot depicts the human mitochondrial genome, with various features such as protein-coding genes (e.g., MT-ND1, MT-CO1), tRNAs, and non-coding regions (e.g., D-loop) annotated along the perimeter. The inner tracks display variant-specific information, including methylation levels in cases and controls, variant frequencies, variant heteroplasmy, and conservation scores across species. Several variants, highlighted in the figure, exhibit notable differences in methylation levels between cases and controls, suggesting potential associations between specific mtDNA variants and altered epigenetic patterns. The observed differences in methylation may influence the expression and regulation of mitochondrial genes, contributing to the phenotypic manifestations observed in the study population.
Fig. 16
Fig. 16
Nuclear-mitochondrial cross-talk hypothesis in MIDD pathogenesis. The figure summarizes the central hypothesis of the research article, which proposes that mutations and methylation alterations in both nuclear and mitochondrial genes could impair related molecular mechanisms, ultimately leading to the MIDD phenotype observed in the family under study. The nucleus, represented by the blue oval, contains the nuclear DNA (nDNA), which is depicted as a double helix. Several genes of interest, including ALDH3A2, ALMS1, CAPN5, COQ4, EYS, MKKS, MTOR, POTEJ, RP1L1, SPHK2, USH2A, and WDPCP, are labeled in the nucleus. These genes are implicated in various cellular processes, such as retinal homeostasis, mitochondrial function, and epigenetic regulation. The mitochondrion, illustrated as a green structure, contains the mtDNA, shown as a circular molecule. Several mitochondrial genes, including MT-ND1, MT-ND2, MT-ND4, MT-ND5, MT-CO2, MT-CO3, MT-ATP6, and MT-ATP8, are labeled within the mitochondrion. These genes encode components of the electron transport chain and ATP synthesis machinery, essential for mitochondrial energy production. The figure emphasizes the cross-talk between the nucleus and the mitochondria through several interconnecting arrows. These arrows represent the potential influence of nuclear gene variants and epigenetic modifications on mitochondrial function, as well as the impact of mtDNA variants and epigenetic changes on nuclear gene expression and regulation. The cross-talk between the nucleus and mitochondria, mediated by genetic and epigenetic factors, plays a crucial role in the pathogenesis of MIDD, as evidenced by the complex interplay of nuclear and mitochondrial gene variants, epigenetic modifications, and their potential impact on cellular processes and mitochondrial function.
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