Mutations in human mitochondrial DNA influence aging, induce severe neuromuscular pathologies, cause maternally inherited metabolic diseases, and suppress apoptosis. Since the genetic stability of mitochondrial DNA depends on the accuracy of DNA polymerase ␥ (pol ␥), we investigated the fidelity of DNA synthesis by human pol ␥. Comparison of the wild-type 140-kDa catalytic subunit to its exonuclease-deficient derivative indicates pol ␥ has high base substitution fidelity that results from high nucleotide selectivity and exonucleolytic proofreading. pol ␥ is also relatively accurate for single-base additions and deletions in non-iterated and short repetitive sequences. However, when copying homopolymeric sequences longer than four nucleotides, pol ␥ has low frameshift fidelity and also generates base substitutions inferred to result from a primer dislocation mechanism. The ability of pol ␥ both to make and to proofread dislocation intermediates is the first such evidence for a family A polymerase. Including the p55 accessory subunit, which confers processivity to the pol ␥ catalytic subunit, decreases frameshift and base substitution fidelity. Kinetic analyses indicate that p55 promotes extension of mismatched termini to lower the fidelity. These data suggest that homopolymeric runs in mitochondrial DNA may be particularly prone to frameshift mutation in vivo due to replication errors by pol ␥.

Recent years have witnessed a paradigm shift in the storage of Electronic Health Records (EHRs) on mobile cloud environments, where mobile devices are integrated with cloud computing to facilitate medical data exchanges among patients and healthcare providers. This advanced model enables healthcare services with low operational cost, high flexibility, and EHRs availability. However, this new paradigm also raises concerns about data privacy and network security for e-health systems. How to reliably share EHRs among mobile users while guaranteeing high-security levels in the mobile cloud is a challenging issue. In this paper, we propose a novel EHRs sharing framework that combines blockchain and the decentralized interplanetary file system (IPFS) on a mobile cloud platform. Particularly, we design a trustworthy access control mechanism using smart contracts to achieve secure EHRs sharing among different patients and medical providers. We present a prototype implementation using Ethereum blockchain in a real data sharing scenario on a mobile app with Amazon cloud computing. The empirical results show that our proposal provides an effective solution for reliable data exchanges on mobile clouds while preserving sensitive health information against potential threats. The system evaluation and security analysis also demonstrate the performance improvements in lightweight access control design, minimum network latency with high security and data privacy levels, compared to the existing data sharing models.INDEX TERMS Electronic health records (EHRs), EHRs sharing, mobile cloud computing (MCC), Internet of Medical Things (IoMT), blockchain, smart contracts, access control, privacy, security.

The very first infected novel coronavirus case (COVID-19) was found in Hubei, China in Dec. 2019. The COVID-19 pandemic has spread over 214 countries and areas in the world, and has significantly affected every aspect of our daily lives. At the time of writing this article, the numbers of infected cases and deaths still increase significantly and have no sign of a well-controlled situation, e.g., as of 13 July 2020, from a total number of around 13.1 million positive cases, 571, 527 deaths were reported in the world. Motivated by recent advances and applications of artificial intelligence (AI) and big data in various areas, this paper aims at emphasizing their importance in responding to the COVID-19 outbreak and preventing the severe effects of the COVID-19 pandemic. We firstly present an overview of AI and big data, then identify the applications aimed at fighting against COVID-19, next highlight challenges and issues associated with state-of-the-art solutions, and finally come up with recommendations for the communications to effectively control the COVID-19 situation. It is expected that this paper provides researchers and communities with new insights into the ways AI and big data improve the COVID-19 situation, and drives further studies in stopping the COVID-19 outbreak. INDEX TERMS Artificial intelligence (AI), big data, COVID-19, coronavirus, epidemic outbreak, deep learning, data analytics, machine learning.

No abstract

No abstract

Progressive external ophthalmoplegia (PEO) is a heritable mitochondrial disorder characterized by the accumulation of multiple point mutations and large deletions in mtDNA. Autosomal dominant PEO was recently shown to co-segregate with a heterozygous Y955C mutation in the human gene encoding the sole mitochondrial DNA polymerase, DNA polymerase ␥ (pol ␥). Since Tyr-955 is a highly conserved residue critical for nucleotide recognition among family A DNA polymerases, we analyzed the effects of the Y955C mutation on the kinetics and fidelity of DNA synthesis by the purified human mutant polymerase in complex with its accessory subunit. The Y955C enzyme retains a wild-type catalytic rate (k cat ) but suffers a 45-fold decrease in apparent binding affinity for the incoming nucleoside triphosphate (K m ). The Y955C derivative is 2-fold less accurate for base pair substitutions than wild-type pol ␥ despite the action of intrinsic exonucleolytic proofreading. The full mutator effect of the Y955C substitution was revealed by genetic inactivation of the exonuclease, and error rates for certain mismatches were elevated by 10 -100-fold. The error-prone DNA synthesis observed for the Y955C pol ␥ is consistent with the accumulation of mtDNA mutations in patients with PEO.Disruption of mitochondrial energy metabolism causes mitochondrial disorders that play a central role in many degenerative diseases, aging, and cancer. Hundreds of mitochondrial and nuclear gene products are required for the proper functioning of the mitochondria. Accordingly heritable mitochondrial diseases exhibit both maternal and Mendelian modes of inheritance with considerable genetic heterogeneity (1-3).Progressive external ophthalmoplegia (PEO) 1 and mitochondrial neurogastrointestinal encephalomyopathy belong to a subclass of autosomal mitochondrial disorders associated with depletion of the mitochondrial genome and/or the accumulation of mutations and deletions within mtDNA (1, 4 -6). Within the last two years, several nuclear genes controlling maintenance of mtDNA have been identified at disease loci, including the genes for adenine nucleotide translocator 1 (ANT1) at locus 4q34 -35 (7), thymidine phosphorylase at locus 22q13.32-qter (8), a putative mitochondrial helicase (Twinkle) at locus 10q24 (9), an unidentified gene at locus 3p14 -21 (10), and the sole mitochondrial DNA polymerase (pol ␥) at locus 15q22-26 (11). Sequence analysis through the pol ␥ gene (12) in a Belgian pedigree with dominant PEO identified a heterozygous A to G mutation at codon 955 (Y955C) (11). Located in the active site of pol ␥, Tyr-955 is a highly conserved residue among a wide variety of DNA polymerases. As a family A DNA polymerase, pol ␥ is related to Escherichia coli DNA polymerase I and bacteriophage T7 DNA polymerase, and amino acid sequence alignments reveal that Tyr-955 in pol ␥ is equivalent to Tyr-766 in E. coli pol I and Tyr-530 in T7 DNA polymerase (see Fig. 1A). The three-dimensional structure of T7 DNA polymerase (13) in a ternary complex with DNA and a nuc...

No abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly pathogenic virus that has caused the global COVID-19 pandemic. Tracing the evolution and transmission of the virus is crucial to respond to and control the pandemic through appropriate intervention strategies. This paper reports and analyses genomic mutations in the coding regions of SARS-CoV-2 and their probable protein secondary structure and solvent accessibility changes, which are predicted using deep learning models. Prediction results suggest that mutation D614G in the virus spike protein, which has attracted much attention from researchers, is unlikely to make changes in protein secondary structure and relative solvent accessibility. Based on 6324 viral genome sequences, we create a spreadsheet dataset of point mutations that can facilitate the investigation of SARS-CoV-2 in many perspectives, especially in tracing the evolution and worldwide spread of the virus. Our analysis results also show that coding genes E, M, ORF6, ORF7a, ORF7b and ORF10 are most stable, potentially suitable to be targeted for vaccine and drug development.