DNA Recombination Governing the Stability of Mitochondrial DNA and Cellular Energy Supply.

The overall success of somatic cell nuclear transfer (SCNT) cloning is rather unsatisfactory, both in terms of efficacy and from an animal health and welfare point of view. Most research activities have concentrated on epigenetic reprogramming problems as one major cause of SCNT failure. The present review addresses the limited success of mammalian SCNT from yet another viewpoint, the mitochondrial perspective. Mitochondria have a broad range of critical functions in cellular energy supply, cell signalling and programmed cell death and, thus, affect embryonic and fetal development, suggesting that inadequate or perturbed mitochondrial functions may adversely affect SCNT success. A survey of perinatal clinical data from human subjects with deficient mitochondrial respiratory chain activity has revealed a plethora of phenotypes that have striking similarities with abnormalities commonly encountered in SCNT fetuses and offspring. We discuss the limited experimental data on nuclear–mitochondrial interaction effects in SCNT and explore the potential effects in the context of new findings about the biology of mitochondria. These include mitochondrial fusion/fission, mitochondrial complementation and mitochondrial DNA recombination, processes that are likely to be affected by and impact on SCNT cloning. Furthermore, we indicate pathways that could link epigenetic reprogramming and mitochondria effects in SCNT and address questions and perspectives for future research.

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Mitochondria at Work: New Insights into Regulation and Dysregulation of Cellular Energy Supply and Metabolism

Biomedicines ◽

10.3390/biomedicines8110526 ◽

2020 ◽

Vol 8 (11) ◽

pp. 526

Author(s):

Volker Schirrmacher

Keyword(s):

Chronic Diseases ◽

Cancer Progression ◽

Energy Supply ◽

Immune Cell ◽

Cell Types ◽

Immune Defense ◽

Cellular Energy ◽

Heart Muscle Cells ◽

Great Relevance ◽

New Strategies

Mitochondria are of great relevance to health, and their dysregulation is associated with major chronic diseases. Research on mitochondria—156 brand new publications from 2019 and 2020—have contributed to this review. Mitochondria have been fundamental for the evolution of complex organisms. As important and semi-autonomous organelles in cells, they can adapt their function to the needs of the respective organ. They can program their function to energy supply (e.g., to keep heart muscle cells going, life-long) or to metabolism (e.g., to support hepatocytes and liver function). The capacity of mitochondria to re-program between different options is important for all cell types that are capable of changing between a resting state and cell proliferation, such as stem cells and immune cells. Major chronic diseases are characterized by mitochondrial dysregulation. This will be exemplified by cardiovascular diseases, metabolic syndrome, neurodegenerative diseases, immune system disorders, and cancer. New strategies for intervention in chronic diseases will be presented. The tumor microenvironment can be considered a battlefield between cancer and immune defense, competing for energy supply and metabolism. Cancer cachexia is considered as a final stage of cancer progression. Nevertheless, the review will present an example of complete remission of cachexia via immune cell transfer. These findings should encourage studies along the lines of mitochondria, energy supply, and metabolism.

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RAD51C/XRCC3 Facilitates Mitochondrial DNA Replication and Maintains Integrity of the Mitochondrial Genome

Molecular and Cellular Biology ◽

10.1128/mcb.00489-17 ◽

2017 ◽

Vol 38 (3) ◽

Cited By ~ 6

Author(s):

Anup Mishra ◽

Sneha Saxena ◽

Anjali Kaushal ◽

Ganesh Nagaraju

Keyword(s):

Mitochondrial Dna ◽

Mitochondrial Genome ◽

Hot Spot ◽

Neurological Diseases ◽

Replication Stress ◽

Genome Maintenance ◽

Mtdna Mutation ◽

Direct Role ◽

D Loop ◽

The Stability

ABSTRACT Mechanisms underlying mitochondrial genome maintenance have recently gained wide attention, as mutations in mitochondrial DNA (mtDNA) lead to inherited muscular and neurological diseases, which are linked to aging and cancer. It was previously reported that human RAD51, RAD51C, and XRCC3 localize to mitochondria upon oxidative stress and are required for the maintenance of mtDNA stability. Since RAD51 and RAD51 paralogs are spontaneously imported into mitochondria, their precise role in mtDNA maintenance under unperturbed conditions remains elusive. Here, we show that RAD51C/XRCC3 is an additional component of the mitochondrial nucleoid having nucleus-independent roles in mtDNA maintenance. RAD51C/XRCC3 localizes to the mtDNA regulatory regions in the D-loop along with the mitochondrial polymerase POLG, and this recruitment is dependent upon Twinkle helicase. Moreover, upon replication stress, RAD51C and XRCC3 are further enriched at the mtDNA mutation hot spot region D310. Notably, the absence of RAD51C/XRCC3 affects the stability of POLG on mtDNA. As a consequence, RAD51C/XRCC3-deficient cells exhibit reduced mtDNA synthesis and increased lesions in the mitochondrial genome, leading to overall unhealthy mitochondria. Together, these findings lead to the proposal of a mechanism for a direct role of RAD51C/XRCC3 in maintaining mtDNA integrity under replication stress conditions.

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Characterization of the cell death modes and the associated changes in cellular energy supply in response to AlPcS4-PDT

Photochemical & Photobiological Sciences ◽

10.1039/b108816e ◽

2002 ◽

Vol 1 (3) ◽

pp. 172-177 ◽

Cited By ~ 60

Author(s):

Kristjan Plaetzer ◽

Tobias Kiesslich ◽

Barbara Krammer ◽

Peter Hammerl

Keyword(s):

Cell Death ◽

Energy Supply ◽

Cellular Energy

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Heterologous mitochondrial DNA recombination in human cells

Human Molecular Genetics ◽

10.1093/hmg/ddh326 ◽

2004 ◽

Vol 13 (24) ◽

pp. 3171-3179 ◽

Cited By ~ 74

Author(s):

Marilena D'Aurelio ◽

Carl D. Gajewski ◽

Michael T. Lin ◽

William M. Mauck ◽

Leon Z. Shao ◽

...

Keyword(s):

Mitochondrial Dna ◽

Dna Recombination ◽

Human Cells ◽

Mitochondrial Dna Recombination

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Predictive Analytics for Maintaining Power System Stability in Smart Energy Communities

10.31265/usps.83 ◽

2021 ◽

Author(s):

Aida Mehdipour Pirbazari

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Energy Supply ◽

Predictive Analytics ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

System Stability ◽

Energy Forecasting ◽

The Stability ◽

Energy Prosumers

Digitalization and decentralization of energy supply have introduced several challenges to emerging power grids known as smart grids. One of the significant challenges, on the demand side, is preserving the stability of the power systems due to locally distributed energy sources such as micro-power generation and storage units among energy prosumers at the household and community levels. In this context, energy prosumers are defined as energy consumers who also generate, store and trade energy. Accurate predictions of energy supply and electric demand of prosuemrs can address the stability issues at local levels. This study aims to develop appropriate forecasting frameworks for such environments to preserve power stability. Building on existing work on energy forecasting at low-aggregated levels, it asks: What factors influence most on consumption and generation patterns of residential customers as energy prosumers. It also investigates how the accuracy of forecasting models at the household and community levels can be improved. Based on a review of the literature on energy forecasting and per- forming empirical study on real datasets, the forecasting frameworks were developed focusing on short-term prediction horizons. These frameworks are built upon predictive analytics including data col- lection, data analysis, data preprocessing, and predictive machine learning algorithms based on statistical learning, artificial neural networks and deep learning. Analysis of experimental results demonstrated that load observa- tions from previous hours (lagged loads) along with air temperature and time variables highly affects the households’ consumption and generation behaviour. The results also indicate that the prediction accuracy of adopted machine learning techniques can be improved by feeding them with highly influential variables and appliance-level data as well as by combining multiple learning algorithms ranging from conventional to deep neural networks. Further research is needed to investigate online approaches that could strengthen the effectiveness of forecasting in time-sensitive energy environments.

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