Investigating metabolic dysfunction and arrhythmogenesis in an early-onset atrial fibrillation patient cohort

Despite the prevalence of atrial fibrillation (AF) and the burden it places on health care systems, there remains much that is unknown regarding heritable factors influencing its development and progression. In this study, I investigated whole-exome sequencing (WES) data from a cohort of patients presenting with early-onset AF to explore the role that metabolic dysfunction might play in contributing to disease onset. I curated a metabolism-related gene panel and, following in silico prediction of variant pathogenicity, performed gene-level burden testing using reference data from the Genome Aggregation Database (gnomAD) and the human mitochondrial genome database MITOMAP. I further explored genes associating with AF in the UK Biobank data set, and discovered associations with several AF comorbidities including diabetes, hypertension, and stroke

Possible A2E Mutagenic Effects on RPE Mitochondrial DNA from Innovative RNA-Seq Bioinformatics Pipeline

Mitochondria are subject to continuous oxidative stress stimuli that, over time, can impair their genome and lead to several pathologies, like retinal degenerations. Our main purpose was the identification of mtDNA variants that might be induced by intense oxidative stress determined by N-retinylidene-N-retinylethanolamine (A2E), together with molecular pathways involving the genes carrying them, possibly linked to retinal degeneration. We performed a variant analysis comparison between transcriptome profiles of human retinal pigment epithelial (RPE) cells exposed to A2E and untreated ones, hypothesizing that it might act as a mutagenic compound towards mtDNA. To optimize analysis, we proposed an integrated approach that foresaw the complementary use of the most recent algorithms applied to mtDNA data, characterized by a mixed output coming from several tools and databases. An increased number of variants emerged following treatment. Variants mainly occurred within mtDNA coding sequences, corresponding with either the polypeptide-encoding genes or the RNA. Time-dependent impairments foresaw the involvement of all oxidative phosphorylation complexes, suggesting a serious damage to adenosine triphosphate (ATP) biosynthesis, that can result in cell death. The obtained results could be incorporated into clinical diagnostic settings, as they are hypothesized to modulate the phenotypic expression of mtDNA pathogenic variants, drastically improving the field of precision molecular medicine

Bioinformatics Tools and Databases to Assess the Pathogenicity of Mitochondrial DNA Variants in the Field of Next Generation Sequencing

The development of next generation sequencing (NGS) has greatly enhanced the diagnosis of mitochondrial disorders, with a systematic analysis of the whole mitochondrial DNA (mtDNA) sequence and better detection sensitivity. However, the exponential growth of sequencing data renders complex the interpretation of the identified variants, thereby posing new challenges for the molecular diagnosis of mitochondrial diseases. Indeed, mtDNA sequencing by NGS requires specific bioinformatics tools and the adaptation of those developed for nuclear DNA, for the detection and quantification of mtDNA variants from sequence alignment to the calling steps, in order to manage the specific features of the mitochondrial genome including heteroplasmy, i.e., coexistence of mutant and wildtype mtDNA copies. The prioritization of mtDNA variants remains difficult, relying on a limited number of specific resources: population and clinical databases, and in silico tools providing a prediction of the variant pathogenicity. An evaluation of the most prominent bioinformatics tools showed that their ability to predict the pathogenicity was highly variable indicating that special efforts should be directed at developing new bioinformatics tools dedicated to the mitochondrial genome. In addition, massive parallel sequencing raised several issues related to the interpretation of very low mtDNA mutational loads, discovery of variants of unknown significance, and mutations unrelated to patient phenotype or the co-occurrence of mtDNA variants. This review provides an overview of the current strategies and bioinformatics tools for accurate annotation, prioritization and reporting of mtDNA variations from NGS data, in order to carry out accurate genetic counseling in individuals with primary mitochondrial diseases

Mitochondrial DNA: Hotspot for potential gene modifiers regulating hypertrophic cardiomyopathy

Hypertrophic cardiomyopathy (HCM) is a prevalent and untreatable cardiovascular disease with a highly complex clinical and genetic causation. HCM patients bearing similar sarcomeric mutations display variable clinical outcomes, implying the involvement of gene modifiers that regulate disease progression. As individuals exhibiting mutations in mitochondrial DNA (mtDNA) present cardiac phenotypes, the mitochondrial genome is a promising candidate to harbor gene modifiers of HCM. Herein, we sequenced the mtDNA of isogenic pluripotent stem cell-cardiomyocyte models of HCM focusing on two sarcomeric mutations. This approach was extended to unrelated patient families totaling 52 cell lines. By correlating cellular and clinical phenotypes with mtDNA sequencing, potentially HCM-protective or -aggravator mtDNA variants were identified. These novel mutations were mostly located in the non-coding control region of the mtDNA and did not overlap with those of other mitochondrial diseases. Analysis of unrelated patients highlighted family-specific mtDNA variants, while others were common in particular population haplogroups. Further validation of mtDNA variants as gene modifiers is warranted but limited by the technically challenging methods of editing the mitochondrial genome. Future molecular characterization of these mtDNA variants in the context of HCM may identify novel treatments and facilitate genetic screening in cardiomyopathy patients towards more efficient treatment options

Enhanced mitochondrial genome analysis: bioinformatic and long-read sequencing advances and their diagnostic implications

Introduction: Primary mitochondrial diseases (PMDs) comprise a large and heterogeneous group of genetic diseases that result from pathogenic variants in either nuclear DNA (nDNA) or mitochondrial DNA (mtDNA). Widespread adoption of next-generation sequencing (NGS) has improved the efficiency and accuracy of mtDNA diagnoses; however, several challenges remain. Areas covered: In this review, we briefly summarize the current state of the art in molecular diagnostics for mtDNA and consider the implications of improved whole genome sequencing (WGS), bioinformatic techniques, and the adoption of long-read sequencing, for PMD diagnostics. Expert opinion: We anticipate that the application of PCR-free WGS from blood DNA will increase in diagnostic laboratories, while for adults with myopathic presentations, WGS from muscle DNA may become more widespread. Improved bioinformatic strategies will enhance WGS data interrogation, with more accurate delineation of mtDNA and NUMTs (nuclear mitochondrial DNA segments) in WGS data, superior coverage uniformity, indirect measurement of mtDNA copy number, and more accurate interpretation of heteroplasmic large-scale rearrangements (LSRs). Separately, the adoption of diagnostic long-read sequencing could offer greater resolution of complex LSRs and the opportunity to phase heteroplasmic variants

Metabolic therapy and bioenergetic analysis: The missing piece of the puzzle.

Background Aberrant metabolism is recognized as a hallmark of cancer, a pillar necessary for cellular proliferation. Regarding bioenergetics (ATP generation), most cancers display a preference not only toward aerobic glycolysis (“Warburg effect”) and glutaminolysis (mitochondrial substrate level-phosphorylation) but also toward other metabolites such as lactate, pyruvate, and fat-derived sources. These secondary metabolites can assist in proliferation but cannot fully cover ATP demands. Scope of review The concept of a static metabolic profile is challenged by instances of heterogeneity and flexibility to meet fuel/anaplerotic demands. Although metabolic therapies are a promising tool to improve therapeutic outcomes, either via pharmacological targets or press-pulse interventions, metabolic plasticity is rarely considered. Lack of bioenergetic analysis in vitro and patient-derived models is hindering translational potential. Here, we review the bioenergetics of cancer and propose a simple analysis of major metabolic pathways, encompassing both affordable and advanced techniques. A comprehensive compendium of Seahorse XF bioenergetic measurements is presented for the first time. Major conclusions Standardization of principal readouts might help researchers to collect a complete metabolic picture of cancer using the most appropriate methods depending on the sample of interest.post-print3250 K

The unresolved role of mitochondrial DNA in Parkinson's disease: an overview of published studies, their limitations, and future prospects

Parkinson's disease (PD), a progressive neurodegenerative disorder, has long been associated with mitochondrial dysfunction in both sporadic and familial forms of the disease. Mitochondria are crucial for maintaining cellular homeostasis, and their dysfunction is detrimental to dopaminergic neurons. These neurons are highly dependent on mitochondrial adenosine triphosphate (ATP) and degenerate in PD. Mitochondria contain their own genomes (mtDNA). The role of mtDNA has been investigated in PD on the premise that it encodes vital components of the ATP-generating oxidative phosphorylation (OXPHOS) complexes and accumulates somatic variation with age. However, the association between mtDNA variation and PD remains controversial. Herein, we provide an overview of previously published studies on the role of inherited as well as somatic (acquired) mtDNA changes in PD including point mutations, deletions and depletion. We outline limitations of previous investigations and the difficulties associated with studying mtDNA, which have left its role unresolved in the context of PD. Lastly, we highlight the potential for further research in this field and provide suggestions for future studies. Overall, the mitochondrial genome is indispensable for proper cellular function and its contribution to PD requires further, more extensive investigation

The giant diploid faba genome unlocks variation in a global protein crop

Publisher Copyright: © 2023, The Author(s).Increasing the proportion of locally produced plant protein in currently meat-rich diets could substantially reduce greenhouse gas emissions and loss of biodiversity1. However, plant protein production is hampered by the lack of a cool-season legume equivalent to soybean in agronomic value2. Faba bean (Vicia faba L.) has a high yield potential and is well suited for cultivation in temperate regions, but genomic resources are scarce. Here, we report a high-quality chromosome-scale assembly of the faba bean genome and show that it has expanded to a massive 13 Gb in size through an imbalance between the rates of amplification and elimination of retrotransposons and satellite repeats. Genes and recombination events are evenly dispersed across chromosomes and the gene space is remarkably compact considering the genome size, although with substantial copy number variation driven by tandem duplication. Demonstrating practical application of the genome sequence, we develop a targeted genotyping assay and use high-resolution genome-wide association analysis to dissect the genetic basis of seed size and hilum colour. The resources presented constitute a genomics-based breeding platform for faba bean, enabling breeders and geneticists to accelerate the improvement of sustainable protein production across the Mediterranean, subtropical and northern temperate agroecological zones.Peer reviewe

Using machine learning to predict pathogenicity of genomic variants throughout the human genome

Geschätzt mehr als 6.000 Erkrankungen werden durch Veränderungen im Genom verursacht. Ursachen gibt es viele: Eine genomische Variante kann die Translation eines Proteins stoppen, die Genregulation stören oder das Spleißen der mRNA in eine andere Isoform begünstigen. All diese Prozesse müssen überprüft werden, um die zum beschriebenen Phänotyp passende Variante zu ermitteln. Eine Automatisierung dieses Prozesses sind Varianteneffektmodelle. Mittels maschinellem Lernen und Annotationen aus verschiedenen Quellen bewerten diese Modelle genomische Varianten hinsichtlich ihrer Pathogenität. Die Entwicklung eines Varianteneffektmodells erfordert eine Reihe von Schritten: Annotation der Trainingsdaten, Auswahl von Features, Training verschiedener Modelle und Selektion eines Modells. Hier präsentiere ich ein allgemeines Workflow dieses Prozesses. Dieses ermöglicht es den Prozess zu konfigurieren, Modellmerkmale zu bearbeiten, und verschiedene Annotationen zu testen. Der Workflow umfasst außerdem die Optimierung von Hyperparametern, Validierung und letztlich die Anwendung des Modells durch genomweites Berechnen von Varianten-Scores. Der Workflow wird in der Entwicklung von Combined Annotation Dependent Depletion (CADD), einem Varianteneffektmodell zur genomweiten Bewertung von SNVs und InDels, verwendet. Durch Etablierung des ersten Varianteneffektmodells für das humane Referenzgenome GRCh38 demonstriere ich die gewonnenen Möglichkeiten Annotationen aufzugreifen und neue Modelle zu trainieren. Außerdem zeige ich, wie Deep-Learning-Scores als Feature in einem CADD-Modell die Vorhersage von RNA-Spleißing verbessern. Außerdem werden Varianteneffektmodelle aufgrund eines neuen, auf Allelhäufigkeit basierten, Trainingsdatensatz entwickelt. Diese Ergebnisse zeigen, dass der entwickelte Workflow eine skalierbare und flexible Möglichkeit ist, um Varianteneffektmodelle zu entwickeln. Alle entstandenen Scores sind unter cadd.gs.washington.edu und cadd.bihealth.org frei verfügbar.More than 6,000 diseases are estimated to be caused by genomic variants. This can happen in many possible ways: a variant may stop the translation of a protein, interfere with gene regulation, or alter splicing of the transcribed mRNA into an unwanted isoform. It is necessary to investigate all of these processes in order to evaluate which variant may be causal for the deleterious phenotype. A great help in this regard are variant effect scores. Implemented as machine learning classifiers, they integrate annotations from different resources to rank genomic variants in terms of pathogenicity. Developing a variant effect score requires multiple steps: annotation of the training data, feature selection, model training, benchmarking, and finally deployment for the model's application. Here, I present a generalized workflow of this process. It makes it simple to configure how information is converted into model features, enabling the rapid exploration of different annotations. The workflow further implements hyperparameter optimization, model validation and ultimately deployment of a selected model via genome-wide scoring of genomic variants. The workflow is applied to train Combined Annotation Dependent Depletion (CADD), a variant effect model that is scoring SNVs and InDels genome-wide. I show that the workflow can be quickly adapted to novel annotations by porting CADD to the genome reference GRCh38. Further, I demonstrate the integration of deep-neural network scores as features into a new CADD model, improving the annotation of RNA splicing events. Finally, I apply the workflow to train multiple variant effect models from training data that is based on variants selected by allele frequency. In conclusion, the developed workflow presents a flexible and scalable method to train variant effect scores. All software and developed scores are freely available from cadd.gs.washington.edu and cadd.bihealth.org

The Influence of Spectral Quality on Primary and Secondary Metabolism of Hydroponically Grown Basil

This dissertation explores the influence of spectral quality from supplemental lighting and seasonal changes on primary and secondary metabolism in hydroponically grown greenhouse basil. It aims to enhance understanding of plant/light interactions and provide practical insights for light emitting diode (LED) manufacturers and commercial growers. The research is premised on the hypothesis that altering spectral quality can significantly impact primary and secondary metabolism, potentially improving flavor and increasing phytonutrients with health benefits. This project involved four phases, each building on the results of the previous ones. In Phase 1, different basil varieties were evaluated to determine aroma volatile profiles and concentrations of key secondary metabolites. In Phase 2, discrete narrow-band blue/red (B/R) wavelengths were used to investigate their impact on aroma volatile concentrations and secondary metabolic resource partitioning in basil, revealing the influence of both seasonal and supplemental lighting effects on plant metabolism. Phase 3 explored the impacts of full spectrum white LEDs and high pressure sodium (HPS) on yield and nutrient accumulation, comparing these to the optimal narrowband B/R identified in Phase 2. The final phase connected all phases, comparing the best narrowband and full spectrum treatments to a traditional HPS treatment and natural light control. These treatments were tested across various parameters, with photosynthesis and primary metabolic data recorded, yields and biometric data taken, aroma compound concentrations, and other secondary metabolic data collected. A sensory panel was conducted, and mRNA sequencing performed to determine differences in metabolic pathway expression based on lighting treatment. Analytical data from the different light treatments, sensory panel, and mRNA data were evaluated to determine which lighting regime had the most positive impact on plant physiology and biochemistry. Variation in spectral quality across seasons influences primary and secondary metabolism, in addition to the spectral qualities of different types of supplemental lighting treatments. This holistic, interdisciplinary approach revealed a light treatment that balances yield, nutrient content, and flavor preference, providing a superior product highly preferred by consumers. The research presented in this document significantly expands our understanding of the complex interplay between light conditions and plant physiology, with implications for improving crop yield and quality in controlled environment agriculture