bims-curels Biomed News
on Leigh syndrome
Issue of 2023‒04‒16
six papers selected by
Cure Mito Foundation

  1. Eur J Neurol. 2023 Apr 10.
      BACKGROUND: Mitochondrial diseases (MDs) are heterogeneous disorders caused by mutations in nuclear DNA (nDNA) or mitochondrial DNA (mtDNA) associated with specific syndromes. However, especially in childhood, patients often display heterogeneity. Several reports about the biochemical and molecular profiles in children have been published, but studies tend to not differentiate between mtDNA and nDNA associated diseases and focus is often on a specific phenotype. Thus, large cohort studies specifically focusing on mtDNA defects in the pediatric population are lacking.METHODS: We reviewed the clinical, metabolic, biochemical, and neuroimaging data of 150 patients with MDs due to mtDNA alterations collected at our Neurological Institute over the past 20 years.
    RESULTS: MtDNA impairment is less frequent than nDNA in pediatric MDs. Ocular involvement is extremely frequent in our cohort, as is classical Leber Hereditary Optic Neuropathy, especially with onset before 12 years of age. Extra neurological manifestations and isolated myopathy appear to be rare, unlike adult phenotypes. Deep gray matter involvement, early disease onset and specific phenotypes, such as Pearson syndrome and Leigh syndrome, represent unfavorable prognostic factors. Phenotypes related to single large scale mtDNA deletions appear to be very frequent in the pediatric population. Furthermore, we report for the first time a mtDNA pathogenic variant associated with cavitating leukodystrophy.
    CONCLUSIONS: We report on a large cohort of pediatric patients with mtDNA defects, adding new data on the phenotypical characterization of mtDNA defects and possible suggestions for the diagnostic workup and therapeutic approach.
    Keywords:  mitochondrial DNA; mitochondrial disorder; pediatric; phenotypes
  2. Methods Mol Biol. 2023 ;2647 83-104
      Mitochondria are indispensable power plants of eukaryotic cells that also act as a major biochemical hub. As such, mitochondrial dysfunction, which can originate from mutations in the mitochondrial genome (mtDNA), may impair organism fitness and lead to severe diseases in humans. MtDNA is a multi-copy, highly polymorphic genome that is uniparentally transmitted through the maternal line. Several mechanisms act in the germline to counteract heteroplasmy (i.e., coexistence of two or more mtDNA variants) and prevent expansion of mtDNA mutations. However, reproductive biotechnologies such as cloning by nuclear transfer can disrupt mtDNA inheritance, resulting in new genetic combinations that may be unstable and have physiological consequences. Here, we review the current understanding of mitochondrial inheritance, with emphasis on its pattern in animals and human embryos generated by nuclear transfer.
    Keywords:  Cloning; Embryo; Heteroplasmy; MRT; Mitochondria; Nuclear transplantation; Oocyte; SCNT; mtDNA
  3. Orphanet J Rare Dis. 2023 Apr 11. 18(1): 79
      BACKGROUND: Traditional clinical trials require tests and procedures that are administered in centralized clinical research sites, which are beyond the standard of care that patients receive for their rare and chronic diseases. The limited number of rare disease patients scattered around the world makes it particularly challenging to recruit participants and conduct these traditional clinical trials.MAIN BODY: Participating in clinical research can be burdensome, especially for children, the elderly, physically and cognitively impaired individuals who require transportation and caregiver assistance, or patients who live in remote locations or cannot afford transportation. In recent years, there is an increasing need to consider Decentralized Clinical Trials (DCT) as a participant-centric approach that uses new technologies and innovative procedures for interaction with participants in the comfort of their home.
    CONCLUSION: This paper discusses the planning and conduct of DCTs, which can increase the quality of trials with a specific focus on rare diseases.
    Keywords:  Advanced Analytics; Clinical Trial; Decentralized Clinical Trials; Digital Health Technologies; Pilot Study; Rare Disease; Real World Data
  4. Orphanet J Rare Dis. 2023 Apr 11. 18(1): 75
      BACKGROUND: There are approximately 10,000 rare diseases that affect around 30,000,000 individuals in the U.S.A., most of which do not have an FDA-approved treatment. This fact highlights the failure of traditional research approaches to overcome the unique challenges of developing rare disease treatments. The Castleman Disease Collaborative Network was founded in 2012 to advance research and treatments for Castleman disease, a rare and deadly disease that involves the immune system attacking the body's vital organs for an unknown cause. It has spearheaded a novel strategy for advancing biomedical research, the Collaborative Network Approach. This approach consists of eight steps, one of which is to identify and prioritize high-impact research questions through crowdsourcing ideas from the entire community of stakeholders: patients, loved ones, physicians, and researchers. Rather than hoping that the right researcher will apply for the right research project at the right time, crowdsourcing high-priority research projects into a research strategy ensures that the most high-impact, patient-centered studies are prioritized. The Castleman Disease Collaborative Network launched an initiative in 2021 to systematically generate this list of community-directed studies to focus Castleman disease research efforts.RESULTS: The Castleman Disease Collaborative Network was able to successfully create a patient-centered research agenda through engaging the entire community of stakeholders. The community contributed important questions about Castleman disease, which were prioritized and reviewed by our Scientific Advisory Board, and the result was a finalized list of studies that address these prioritized questions. We were also able to generate a best practices list which can serve as a model that can be utilized for other rare diseases.
    CONCLUSION: Creating a patient-centered research agenda through crowdsourcing research ideas from the community is one of the most important ways that the Castleman Disease Collaborative Network operationalizes its commitment to keeping patients at the center of research and we hope that by sharing these insights we can assist other rare disease organizations to pursue a patient-centric approach.
    Keywords:  Castleman disease; Collaborative network; Crowdsourcing; Patient-centered research agenda; Rare disease
  5. Orphanet J Rare Dis. 2023 Apr 10. 18(1): 73
    Undiagnosed Diseases Network
      INTRODUCTION: The Undiagnosed Diseases Network (UDN), a clinical research study funded by the National Institutes of Health, aims to provide answers for patients with undiagnosed conditions and generate knowledge about underlying disease mechanisms. UDN evaluations involve collaboration between clinicians and researchers and go beyond what is possible in clinical settings. While medical and research outcomes of UDN evaluations have been explored, this is the first formal assessment of the patient and caregiver experience.METHODS: We invited UDN participants and caregivers to participate in focus groups via email, newsletter, and a private participant Facebook group. We developed focus group questions based on research team expertise, literature focused on patients with rare and undiagnosed conditions, and UDN participant and family member feedback. In March 2021, we conducted, recorded, and transcribed four 60-min focus groups via Zoom. Transcripts were evaluated using a thematic analysis approach.
    RESULTS: The adult undiagnosed focus group described the UDN evaluation as validating and an avenue for access to medical providers. They also noted that the experience impacted professional choices and helped them rely on others for support. The adult diagnosed focus group described the healthcare system as not set up for rare disease. In the pediatric undiagnosed focus group, caregivers discussed a continued desire for information and gratitude for the UDN evaluation. They also described an ability to rule out information and coming to terms with not having answers. The pediatric diagnosed focus group discussed how the experience helped them focus on management and improved communication. Across focus groups, adults (undiagnosed/diagnosed) noted the comprehensiveness of the evaluation. Undiagnosed focus groups (adult/pediatric) discussed a desire for ongoing communication and care with the UDN. Diagnosed focus groups (adult/pediatric) highlighted the importance of the diagnosis they received in the UDN. The majority of the focus groups noted a positive future orientation after participation.
    CONCLUSION: Our findings are consistent with prior literature focused on the patient experience of rare and undiagnosed conditions and highlight benefits from comprehensive evaluations, regardless of whether a diagnosis is obtained. Focus group themes also suggest areas for improvement and future research related to the diagnostic odyssey.
    Keywords:  Clinical evaluation; Multidisciplinary research; Patient experience; Patient perspective; Qualitative methods; Rare disease; Undiagnosed disease
  6. Trials. 2023 Apr 10. 24(1): 263
      There are many reasons why the majority of clinical trials fail or have limited applicability to patient care. These include restrictive entry criteria, short duration studies, unrecognized adverse drug effects, and reporting of therapy assignment preferential to actual use. Frequently, experimental animal models are used sparingly and do not accurately simulate human disease. We suggest two approaches to improve the conduct, increase the success, and applicability of clinical trials. Studies can apply dosing of the investigational therapeutics and outcomes, determined from animal models that more closely simulate human disease. More extensive identification of known and potential risk factors and confounding issues, gleaned from recently organized "big data," should be utilized to create models for trials. The risk factors in each model are then accounted for and managed during each study.