32 research outputs found

    Accumulation of Endogenous LITAF in Aggresomes

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    LITAF is a 161 amino acid cellular protein which includes a proline rich N-terminus and a conserved C-terminal domain known as the simple-like domain. Mutations in LITAF have been identified in Charcot-Marie tooth disease, a disease characterized by protein aggregates. Cells transfected with cellular LITAF reveal that LITAF is localized to late endosomes/lysosomes. Here we investigated the intracellular localization of endogenous LITAF. We demonstrated that endogenous LITAF accumulates at a discrete cytoplasmic site in BGMK cells that we identify as the aggresome. To determine the domain within LITAF that is responsible for the localization of LITAF to aggresomes, we created a construct that contained the C-terminal simple-like domain of LITAF and found that this construct also localizes to aggresomes. These data suggest the simple-like domain is responsible for targeting endogenous LITAF to the aggresome

    The Mutational Spectrum in a Cohort of Charcot-Marie-Tooth Disease Type 2 among the Han Chinese in Taiwan

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    BACKGROUND: Charcot-Marie-Tooth disease type 2 (CMT2) is a clinically and genetically heterogeneous group of inherited axonal neuropathies. The aim of this study was to extensively investigate the mutational spectrum of CMT2 in a cohort of patients of Han Chinese. METHODOLOGY AND PRINCIPAL FINDINGS: Genomic DNA from 36 unrelated Taiwanese CMT2 patients of Han Chinese descent was screened for mutations in the coding regions of the MFN2, RAB7, TRPV4, GARS, NEFL, HSPB1, MPZ, GDAP1, HSPB8, DNM2, AARS and YARS genes. Ten disparate mutations were identified in 14 patients (38.9% of the cohort), including p.N71Y in AARS (2.8%), p.T164A in HSPB1 (2.8%), and p.[H256R]+[R282H] in GDAP1 (2.8%) in one patient each, three NEFL mutations in six patients (16.7%) and four MFN2 mutations in five patients (13.9%). The following six mutations were novel: the individual AARS, HSPB1 and GDAP1 mutations and c.475-1G>T, p.L233V and p.E744M mutations in MFN2. An in vitro splicing assay revealed that the MFN2 c.475-1G>T mutation causes a 4 amino acid deletion (p.T159_Q162del). Despite an extensive survey, the genetic causes of CMT2 remained elusive in the remaining 22 CMT2 patients (61.1%). CONCLUSIONS AND SIGNIFICANCE: This study illustrates the spectrum of CMT2 mutations in a Taiwanese CMT2 cohort and expands the number of CMT2-associated mutations. The relevance of the AARS and HSPB1 mutations in the pathogenesis of CMT2 is further highlighted. Moreover, the frequency of the NEFL mutations in this study cohort was unexpectedly high. Genetic testing for NEFL and MFN2 mutations should, therefore, be the first step in the molecular diagnosis of CMT2 in ethnic Chinese

    Steering Evolution with Sequential Therapy to Prevent the Emergence of Bacterial Antibiotic Resistance

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    The increasing rate of antibiotic resistance and slowing discovery of novel antibiotic treatments presents a growing threat to public health. Here, we consider a simple model of evolution in asexually reproducing populations which considers adaptation as a biased random walk on a fitness landscape. This model associates the global properties of the fitness landscape with the algebraic properties of a Markov chain transition matrix and allows us to derive general results on the non-commutativity and irreversibility of natural selection as well as antibiotic cycling strategies. Using this formalism, we analyze 15 empirical fitness landscapes of E. coli under selection by different β-lactam antibiotics and demonstrate that the emergence of resistance to a given antibiotic can be either hindered or promoted by different sequences of drug application. Specifically, we demonstrate that the majority, approximately 70%, of sequential drug treatments with 2–4 drugs promote resistance to the final antibiotic. Further, we derive optimal drug application sequences with which we can probabilistically ‘steer’ the population through genotype space to avoid the emergence of resistance. This suggests a new strategy in the war against antibiotic–resistant organisms: drug sequencing to shepherd evolution through genotype space to states from which resistance cannot emerge and by which to maximize the chance of successful therapy
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