Kaposi's sarcoma-associated herpesvirus OX2 glycoprotein activates myeloid-lineage cells to induce inflammatory cytokine production

Kaposi's sarcoma is an inflammatory cytokine-mediated angioproliferative disease which is triggered by infection by Kaposi's sarcoma-associated herpesvirus (KSHV). KSHV contains an open reading frame, K14, that has significant homology with cellular OX2, designated viral OX2 (vOX2). In this report, we demonstrate that vOX2 encodes a glycosylated cell surface protein with an apparent molecular mass of 55 kDa. Purified glycosylated vOX2 protein dramatically stimulated primary monocytes, macrophages, and dendritic cells to produce the inflammatory cytokines interleukin 1beta (IL-1beta), IL-6, monocyte chemoattractant protein 1, and TNF-alpha. Furthermore, expression of vOX2 on B lymphocytes stimulated monocytes to produce inflammatory cytokines in mixed culture. These results demonstrate that like its cellular counterpart, vOX2 targets myeloid-lineage cells, but unlike cellular OX2, which delivers a restrictive signal, KSHV vOX2 provides an activating signal, resulting in the production of inflammatory cytokines. Thus, this is a novel viral strategy where KSHV has acquired the cellular OX2 gene to induce inflammatory cytokine production, which potentially promotes the cytokine-mediated angiogenic proliferation of KSHV-infected cells.     

Gramicidin A channel as a test ground for molecular dynamics force fields

We use the well-known structural and functional properties of the gramicidin A channel to test the appropriateness of force fields commonly used in molecular dynamics (MD) simulations of ion channels. For this purpose, the high-resolution structure of the gramicidin A dimer is embedded in a dimyristoylphosphatidylcholine bilayer, and the potential of mean force of a K(+) ion is calculated along the channel axis using the umbrella sampling method. Calculations are performed using two of the most common force fields in MD simulations: CHARMM and GROMACS. Both force fields lead to large central barriers for K(+) ion permeation, that are substantially higher than those deduced from the physiological data by inverse methods. In long MD simulations lasting over 60 ns, several ions are observed to enter the binding site but none of them crossed the channel despite the presence of a large driving field. The present results, taken together with many earlier studies, highlights the shortcomings of the standard force fields used in MD simulations of ion channels and calls for construction of more appropriate force fields for this purpose.     

Iron metabolism in diabetes-induced Alzheimer’s disease: a focus on insulin resistance in the brain

Alzheimer’s disease (AD) is characterized by an excessive accumulation of toxic amyloid beta (Aβ) plaques and memory dysfunction. The onset of AD is influenced by age, genetic background, and impaired glucose metabolism in the brain. Several studies have demonstrated that diabetes involving insulin resistance and glucose tolerance could lead to AD, ultimately resulting in cognitive dysfunction. Even though the relationship between diabetes and AD was indicated by significant evidences, the critical mechanisms and metabolic alterations in diabetes induced AD are not clear until now. Recently, iron metabolism has been shown to play multiple roles in the central nervous system (CNS). Iron deficiency and overload are associated with neurodegenerative diseases. Iron binds to Aβ and subsequently regulates Aβ toxicity in the CNS. In addition, previous studies have shown that iron is involved in the aggravation of insulin resistance. Considering these effects of iron metabolism in CNS, we expect that iron metabolism may play crucial roles in diabetic AD brain. Thus, we review the recent evidence regarding the relationship between diabetes-induced AD and iron metabolism.     

Heterogeneous genetic invasions of three insecticide resistance mutations in Indo‐Pacific populations of Aedes aegypti (L.)

Nations throughout the Indo‐Pacific region use pyrethroid insecticides to control Aedes aegypti, the mosquito vector of dengue, often without knowledge of pyrethroid resistance status of the pest or origin of resistance. Two mutations (V1016G + F1534C) in the sodium channel gene (Vssc) of Ae. aegypti modify ion channel function and cause target‐site resistance to pyrethroid insecticides, with a third mutation (S989P) having a potential additive effect. Of 27 possible genotypes involving these mutations, some allelic combinations are never seen whereas others predominate. Here, five allelic combinations common in Ae. aegypti from the Indo‐Pacific region are described and their geographical distributions investigated using genome‐wide SNP markers. We tested the hypothesis that resistance allele combinations evolved de novo in populations versus the alternative that dispersal of Ae. aegypti between populations facilitated genetic invasions of allele combinations. We used latent factor mixed‐models to detect SNPs throughout the genome that showed structuring in line with resistance allele combinations and compared variation at SNPs within the Vssc gene with genome‐wide variation. Mixed‐models detected an array of SNPs linked to resistance allele combinations, all located within or in close proximity to the Vssc gene. Variation at SNPs within the Vssc gene was structured by resistance profile, whereas genome‐wide SNPs were structured by population. These results demonstrate that alleles near to resistance mutations have been transferred between populations via linked selection. This indicates that genetic invasions have contributed to the widespread occurrence of Vssc allele combinations in Ae. aegypti in the Indo‐Pacific region, pointing to undocumented mosquito invasions between countries.     

Generation of “Virtual” Control Groups for Single Arm Prostate Cancer Adjuvant Trials

It is difficult to construct a control group for trials of adjuvant therapy (Rx) of prostate cancer after radical prostatectomy (RP) due to ethical issues and patient acceptance. We utilized 8 curve-fitting models to estimate the time to 60%, 65%, … 95% chance of progression free survival (PFS) based on the data derived from Kattan post-RP nomogram. The 8 models were systematically applied to a training set of 153 post-RP cases without adjuvant Rx to develop 8 subsets of cases (reference case sets) whose observed PFS times were most accurately predicted by each model. To prepare a virtual control group for a single-arm adjuvant Rx trial, we first select the optimal model for the trial cases based on the minimum weighted Euclidean distance between the trial case set and the reference case set in terms of clinical features, and then compare the virtual PFS times calculated by the optimum model with the observed PFSs of the trial cases by the logrank test. The method was validated using an independent dataset of 155 post-RP patients without adjuvant Rx. We then applied the method to patients on a Phase II trial of adjuvant chemo-hormonal Rx post RP, which indicated that the adjuvant Rx is highly effective in prolonging PFS after RP in patients at high risk for prostate cancer recurrence. The method can accurately generate control groups for single-arm, post-RP adjuvant Rx trials for prostate cancer, facilitating development of new therapeutic strategies.     

Comprehensive Analysis to Improve the Validation Rate for Single Nucleotide Variants Detected by Next-Generation Sequencing

Next-generation sequencing (NGS) has enabled the high-throughput discovery of germline and somatic mutations. However, NGS-based variant detection is still prone to errors, resulting in inaccurate variant calls. Here, we categorized the variants detected by NGS according to total read depth (TD) and SNP quality (SNPQ), and performed Sanger sequencing with 348 selected non-synonymous single nucleotide variants (SNVs) for validation. Using the SAMtools and GATK algorithms, the validation rate was positively correlated with SNPQ but showed no correlation with TD. In addition, common variants called by both programs had a higher validation rate than caller-specific variants. We further examined several parameters to improve the validation rate, and found that strand bias (SB) was a key parameter. SB in NGS data showed a strong difference between the variants passing validation and those that failed validation, showing a validation rate of more than 92% (filtering cutoff value: alternate allele forward [AF]≥20 and AF<80 in SAMtools, SB<–10 in GATK). Moreover, the validation rate increased significantly (up to 97–99%) when the variant was filtered together with the suggested values of mapping quality (MQ), SNPQ and SB. This detailed and systematic study provides comprehensive recommendations for improving validation rates, saving time and lowering cost in NGS analyses.