Pathways of Amyloid‑β Aggregation Depend on Oligomer Shape

One of the main research topics related to Alzheimer’s disease is the aggregation of the amyloid-β peptide, which was shown to follow different pathways for the two major alloforms of the peptide, Aβ40 and the more toxic Aβ42. Experimental studies emphasized that oligomers of specific sizes appear in the early aggregation process in different quantities and might be the key toxic agents for each of the two alloforms. We use transition networks derived from all-atom molecular dynamics simulations to show that the oligomers leading to the type of oligomer distributions observed in experiments originate from compact conformations. Extended oligomers, on the other hand, contribute more to the production of larger aggregates thus driving the aggregation process. We further demonstrate that differences in the aggregation pathways of the two Aβ alloforms occur as early as during the dimer stage. The higher solvent-exposure of hydrophobic residues in Aβ42 oligomers contributes to the different aggregation pathways of both alloforms and also to the increased cytotoxicity of Aβ42

Association between Regulated upon Activation, Normal T Cells Expressed and Secreted (RANTES) -28C/G Polymorphism and Susceptibility to HIV-1 Infection: A Meta-Analysis

<div><p>Background</p><p>Many studies have investigated the distributions of RANTES genotypes between HIV-1 infected patients and uninfected individuals. However, no definite results have been put forward about whether the RANTES −28C/G polymorphism can affect HIV-1 susceptibility.</p> <p>Methods</p><p>We performed a meta-analysis of 12 studies including 7473 subjects for whom the RANTES −28C/G polymorphism was genotyped. Odds ratios (ORs) with 95% confidence intervals (CIs) were employed to assess the association of the polymorphism with HIV-1 susceptibility. By dividing the controls into healthy controls and HIV-1 exposed but seronegative (HESN) controls, we explored the both allelic and dominant genetic models.</p> <p>Results</p><p>By using the healthy controls, we found a marginally significant association between the −28C/G polymorphism and susceptibility to HIV-1 infection in the allelic model (OR = 0.82, 95%CI = 0.70–0.97). But sensitivity analysis suggested that the association was driven by one study. We further performed stratified analysis according to ethnicity. The −28G allele decreased susceptibility to HIV-1 infection in the allelic model among Asians (OR = 0.79, 95%CI = 0.66–0.94). By using the HESN controls, no association between the polymorphism −28C/G and the susceptibility to HIV-1 infection was revealed in either the allelic model (OR = 0.84, 95%CI = 0.60–1.17) or the dominant model (OR = 0.77, 95%CI = 0.54–1.10).</p> <p>Conclusions</p><p>Our findings suggested that the RANTES −28G allele might play a role in resistance to HIV-1 infection among Asians. Additional well-designed studies were required for the validation of this association.</p> </div

Funnel plots to detect publication bias in this meta-analysis.

<p>(A) Healthy controls considered; (B) HIV-1 exposed but seronegative controls considered. The horizontal line indicates the pooled log odds ratio (OR) and guidelines to assist in visualizing the funnel are pooled at 95% pseudo confidence limits for this estimate.</p

Forest plot of the association between RANTES −28C/G polymorphism and HIV-1 infection under the allelic model.

<p>(A)By using the healthy individuals as controls, the comparison which was stratified by ethnicity was carried out between the HIV-1 patients and healthy controls; (B) By using the HIV-1 exposed but seronegative (HESN) individuals as controls, the comparison was carried out between the HIV-1 patients and the HESN controls.</p

Characteristics of the included studies in this meta-analysis.

a<p>The ref was referred to the reference numbers in this study.</p>b<p>HESN: HIV-1 exposed but seronegative.</p>c<p>NA: Not available.</p

Differentially methylated genes between the streptomycin-resistant and normal groups.

Differentially methylated genes between the streptomycin-resistant and normal groups.</p

KEGG analysis of differentially methylated genes between the streptomycin-resistant and normal groups.

KEGG analysis of differentially methylated genes between the streptomycin-resistant and normal groups.</p

Transcriptome atlas and KEGG analysis of differentially expressed genes between the streptomycin-resistant and normal groups.

A) The heatmap was used to show the differentially expressed genes between the streptomycin-resistant and normal groups. B) KEGG analysis was used to compare the differentially expressed genes in the streptomycin-resistant and normal groups. Rich factor indicated the ratio of the number of differentially expressed genes enriched in each KEGG term to the number of all annotated genes in the KEGG term. Negative binomial distribution model was used to calculate P-value.</p

Informants’ Characteristics.

<p>Informants’ Characteristics.</p

Table_1_mbtD and celA1 association with ethambutol resistance in Mycobacterium tuberculosis: A multiomics analysis.xlsx

Ethambutol (EMB) is a first-line antituberculosis drug currently being used clinically to treat tuberculosis. Mutations in the embCAB operon are responsible for EMB resistance. However, the discrepancies between genotypic and phenotypic EMB resistance have attracted much attention. We induced EMB resistance in Mycobacterium tuberculosis in vitro and used an integrated genome–methylome–transcriptome–proteome approach to study the microevolutionary mechanism of EMB resistance. We identified 509 aberrantly methylated genes (313 hypermethylated genes and 196 hypomethylated genes). Moreover, some hypermethylated and hypomethylated genes were identified using RNA-seq profiling. Correlation analysis revealed that the differential methylation of genes was negatively correlated with transcription levels in EMB-resistant strains. Additionally, two hypermethylated candidate genes (mbtD and celA1) were screened by iTRAQ-based quantitative proteomics analysis, verified by qPCR, and corresponded with DNA methylation differences. This is the first report that identifies EMB resistance-related genes in laboratory-induced mono-EMB-resistant M. tuberculosis using multi-omics profiling. Understanding the epigenetic features associated with EMB resistance may provide new insights into the underlying molecular mechanisms.</p