RESUMO
Various risk factors of Alzheimer's disease (AD) are known, such as advanced age, possession of certain genetic variants, accumulation of toxic amyloid-ß (Aß) peptides, and unhealthy lifestyle. An estimate of heritability of AD ranges from 0.13 to 0.25, indicating that its phenotypic variation is accounted for mostly by non-genetic factors. DNA methylation is regarded as an epigenetic mechanism that interfaces the genome with non-genetic factors. The Tg2576 mouse model has been insightful in AD research. These transgenic mice express a mutant form of human amyloid precursor protein linked to familial AD. At 9-13 months of age, these mice show elevated levels of Aß peptides and cognitive impairment. The current literature lacks integrative multiomics of the animal model. We applied transcriptomics and DNA methylomics to the same brain samples from ~ 11-month-old transgenic mice. We found that genes involved in extracellular matrix structures and functions are transcriptionally upregulated, and genes involved in extracellular protein secretion and localization are differentially methylated in the transgenic mice. Integrative analysis found enrichment of GO terms related to memory and synaptic functionability. Our results indicate a possibility of transcriptional modulation by DNA methylation underlying AD neuropathology.
Assuntos
Doença de Alzheimer , Camundongos , Humanos , Animais , Lactente , Doença de Alzheimer/metabolismo , Regulação para Cima , Peptídeos beta-Amiloides/metabolismo , Precursor de Proteína beta-Amiloide/genética , Precursor de Proteína beta-Amiloide/metabolismo , Camundongos Transgênicos , Encéfalo/metabolismo , Modelos Animais de DoençasRESUMO
Biological age captures some of the variance in life expectancy for which chronological age is not accountable, and it quantifies the heterogeneity in the presentation of the aging phenotype in various individuals. Among the many quantitative measures of biological age, the mathematically uncomplicated frailty/deficit index is simply the proportion of the total health deficits in various health items surveyed in different individuals. We used 3 different statistical methods that are popular in machine learning to select 17-28 health items that together are highly predictive of survival/mortality, from independent study cohorts. From the selected sets, we calculated frailty indexes and Klemera-Doubal's biological age estimates, and then compared their mortality prediction performance using Cox proportional hazards regression models. Our results indicate that the frailty index outperforms age and Klemera-Doubal's biological age estimates, especially among the oldest old who are most prone to biological aging-caused mortality. We also showed that a DNA methylation index, which was generated by applying the frailty/deficit index calculation method to 38 CpG sites that were selected using the same machine learning algorithms, can predict mortality even better than the best performing frailty index constructed from health, function, and blood chemistry.
Assuntos
Envelhecimento/fisiologia , Algoritmos , Fragilidade , Expectativa de Vida , Idoso , Idoso de 80 Anos ou mais , Metilação de DNA/genética , Fragilidade/diagnóstico , Fragilidade/genética , Fragilidade/mortalidade , Heterogeneidade Genética , Indicadores Básicos de Saúde , Humanos , Aprendizado de Máquina , Inquéritos Nutricionais/estatística & dados numéricos , Prognóstico , Reprodutibilidade dos Testes , Estados UnidosRESUMO
Many Gram-negative bacteria have two cytoplasmic thioredoxins, thioredoxin-1 and -2, encoded by the trxA and trxC genes, respectively. Both thioredoxins have the highly conserved WCGPC motif and function as disulfide-bond reductases. However, thioredoxin-2 has unique features: it has an N-terminal motif that binds a zinc ion, and its transcription is under the control of OxyR, which allows it to be up-regulated under oxidative stress. Here, we report the crystal structure of thioredoxin-2 from Rhodobacter capsulatus. The C-terminal region of thioredoxin-2 forms a canonical thioredoxin fold with a central beta-sheet consisting of five strands and four flanking alpha-helices on either side. The N-terminal zinc finger is composed of four short beta-strands (S1-S4) connected by three short loops (L1-L3). The four cysteines are at loops L1 and L3 and form a tetragonal binding site for a zinc ion. The zinc finger is close to the first beta-strand and first alpha-helix of the thioredoxin fold. Nevertheless, the zinc finger may not directly affect the oxidoreductase activity of thioredoxin-2 because the zinc finger is not near the active site of a protomer and because thioredoxin-2 is a monomer in solution. On the basis of structural similarity to the zinc fingers in Npl4 and Vps36, we propose that the N-terminal zinc finger of thioredoxin-2 mediates protein-protein interactions, possibly with its substrates or chaperones.