Characterization of a gp130 Signaling Receptor Polymorphism

We previously reported an association between human herpesvirus 8 (HHV-8) seroprevalence and increased prostate cancer risk among men on the Caribbean island of Tobago. More recently, we have found a single nucleotide polymorphism (SNP) in the IL-6 gp130 signaling receptor at position 148 associated with increased prostate cancer risk among HHV-8 seropositive men. The high risk genotype (Gly) was associated with increased prostate cancer risk among HHV-8 seropositive men (OR= 3.1) compared to the low risk genotype (Arg). This research aims to further explore the effect of this SNP on gp130 function. The gp130 genotype at position 148 was determined in lymphoblastoid B cell lines (LCLs) derived from Tobago men (representing high and low risk genotypes) and two prostate cancer cell lines (PC3 and DU 145; high risk and low risk respectively). Growth curves were performed by Dr. Jill Henning for LCLs by using 25ng/mL of Interleukin-6 (IL-6), Interleukin-11 (IL-11), or Oncostatin M (OSM). It was discovered that IL-6 had an effect on the growth of LCLs, but IL-11 and OSM did not. I repeated growth curves on LCLs using a concentration of 10ng/mL, and found that there was still a difference in growth at this lower concentration. Levels of phosphorylated STAT3 were measured on cells treated with 10 or 100ng/mL IL-6, IL-11, or OSM for various times (2-60 minutes). Comparative IL-6-mediated downstream signaling between the two genotypes was analyzed in LCLs at 10 minutes post-treatment using the JAK/STAT Pathway PCR Array Plates (SABiosciences, Valencia, CA), and the Human IL-6 Pathway PCR Array Plates (Life Technologies, Grand Island, NY). LCLs homozygous for the high-risk gp130 genotype grew significantly faster compared to LCLs homozygous for the low-risk genotype in response to IL-6 but not IL-11 or OSM. LCLs homozygous for both high-risk gp130 genotype as well as LCLs homozygous for the low-risk genotype both showed activation of STAT3 in response to IL-6 by 10 minutes post-treatment. DU 145 (low-risk genotype) cells showed STAT3 activation following IL-6 treatment while PC3 (high-risk genotype) failed to show any STAT3 activation even after 1hr of IL-6 treatment. Both cell lines showed STAT3 activation after OSM treatment while neither line showed activation following IL-11 treatment. RT-PCR analyses of JAK/STAT pathway genes and IL-6 pathway genes in LCLs following IL-6 treatment showed differential gene regulation between genotypes. For example, using the IL-6 pathway plates, the high-risk genotype showed a down-regulation of TP53BP2 (apoptosis stimulating protein of p53-2), while the low-risk genotype showed an up-regulation of this gene. This protein is known to inhibit cell growth and stimulate apoptosis, and is frequently suppressed in human cancers. This differential gene regulation of TP53BP2 is one example of a gene that is differentially regulated between the high- and low-risk genotypes. These data suggest that the G148R SNP of gp130 is involved in cell proliferation mediated by IL-6 downstream signaling. The public health relevance observed by these results suggests that the resulting genotypes in the G148R polymorphism exhibit different biological affects upon treatment with cytokines that utilize the gp130 signaling receptor. The high-risk genotype could result in an increase in inflammation, which could ultimately contribute to the development or advancement of prostate cancer

Utilization of HIV-1 envelope V3 to identify X4- and R5-specific Tat and LTR sequence signatures.

BACKGROUND: HIV-1 entry is a receptor-mediated process directed by the interaction of the viral envelope with the host cell CD4 molecule and one of two co-receptors, CCR5 or CXCR4. The amino acid sequence of the third variable (V3) loop of the HIV-1 envelope is highly predictive of co-receptor utilization preference during entry, and machine learning predictive algorithms have been developed to characterize sequences as CCR5-utilizing (R5) or CXCR4-utilizing (X4). It was hypothesized that while the V3 loop is predominantly responsible for determining co-receptor binding, additional components of the HIV-1 genome may contribute to overall viral tropism and display sequence signatures associated with co-receptor utilization. RESULTS: The accessory protein Tat and the HlV-1 long terminal repeat (LTR) were analyzed with respect to genetic diversity and compared by Jensen-Shannon divergence which resulted in a correlation with both mean genetic diversity as well as the absolute difference in genetic diversity between R5- and X4-genome specific trends. As expected, the V3 domain of the gp120 protein was enriched with statistically divergent positions. Statistically divergent positions were also identified in Tat amino acid sequences within the transactivation and TAR-binding domains, and in nucleotide positions throughout the LTR. We further analyzed LTR sequences for putative transcription factor binding sites using the JASPAR transcription factor binding profile database and found several putative differences in transcription factor binding sites between R5 and X4 HIV-1 genomes, specifically identifying the C/EBP sites I and II, and Sp site III to differ with respect to sequence configuration for R5 and X4 LTRs. CONCLUSION: These observations support the hypothesis that co-receptor utilization coincides with specific genetic signatures in HIV-1 Tat and the LTR, likely due to differing transcriptional regulatory mechanisms and selective pressures applied within specific cellular targets during the course of productive HIV-1 infection

HIV-1 Genetic Variation Resulting in the Development of New Quasispecies Continues to Be Encountered in the Peripheral Blood of Well-Suppressed Patients.

As a result of antiretroviral therapeutic strategies, human immunodeficiency virus type 1 (HIV-1) infection has become a long-term clinically manageable chronic disease for many infected individuals. However, despite this progress in therapeutic control, including undetectable viral loads and CD4+ T-cell counts in the normal range, viral mutations continue to accumulate in the peripheral blood compartment over time, indicating either low level reactivation and/or replication. Using patients from the Drexel Medicine CNS AIDS Research and Eradication Study (CARES) Cohort, whom have been sampled longitudinally for more than 7 years, genetic change was modeled against to the dominant integrated proviral quasispecies with respect to selection pressures such as therapeutic interventions, AIDS defining illnesses, and other factors. Phylogenetic methods based on the sequences of the LTR and tat exon 1 of the HIV-1 proviral DNA quasispecies were used to obtain an estimate of an average mutation rate of 5.3 nucleotides (nt)/kilobasepair (kb)/year (yr) prior to initiation of antiretroviral therapy (ART). Following ART the baseline mutation rate was reduced to an average of 1.02 nt/kb/yr. The post-ART baseline rate of genetic change, however, appears to be unique for each patient. These studies represent our initial steps in quantifying rates of genetic change among HIV-1 quasispecies using longitudinally sampled sequences from patients at different stages of disease both before and after initiation of combination ART. Notably, while long-term ART reduced the estimated mutation rates in the vast majority of patients studied, there was still measurable HIV-1 mutation even in patients with no detectable virus by standard quantitative assays. Determining the factors that affect HIV-1 mutation rates in the peripheral blood may lead to elucidation of the mechanisms associated with changes in HIV-1 disease severity

Mitochondrial Haplogroup Influences Motor Function in Long-Term HIV-1-Infected Individuals

<div><p>Evolutionary divergence of the mitochondrial genome has given rise to distinct haplogroups. These haplogroups have arisen in specific geographical locations and are responsible for subtle functional changes in the mitochondria that may provide an evolutionary advantage in a given environment. Based on these functional differences, haplogroups could define disease susceptibility in chronic settings. In this study, we undertook a detailed neuropsychological analysis of a cohort of long-term HIV-1-infected individuals in conjunction with sequencing of their mitochondrial genomes. Stepwise regression analysis showed that the best model for predicting both working memory and declarative memory were age and years since diagnosis. In contrast, years since diagnosis and sub-haplogroup were significantly predictive of psychomotor speed. Consistent with this, patients with haplogroup L3e obtained better scores on psychomotor speed and dexterity tasks when compared to the remainder of the cohort, suggesting that this haplogroup provides a protective advantage when faced with the combined stress of HIV-1 infection and long-term antiretroviral therapies. Differential performance on declarative memory tasks was noted for individuals with other sub-L haplogroups, but these differences were not as robust as the association between L3e and psychomotor speed and dexterity tasks. This work provides evidence that mitochondrial haplogroup is related to neuropsychological test performance among patients in chronic disease settings such as HIV-1 infection.</p></div

The calculated effect sizes of fixed effects for the patients shown in Fig 3.

<p>The calculated effect sizes of fixed effects for the patients shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0155382#pone.0155382.g003" target="_blank">Fig 3</a>.</p

Demographics of the entire CARES Cohort as well as the subset of patients that have been included in the CARES longitudinal arm and those that were used for confirmation by subcloning and sequencing.

<p>The values indicate the median and 95% confidence intervals. *Due to the longitudinal and archival nature of this dataset, the limit of detection with respect to viral load has changed but has ranged from <100 to <20.</p

Direct sequencing of PCR products generated directly from genomic DNA very often returns the predominant genotype.

<p><b>(A)</b> (Left) A histogram of the number of QS observed in each of the 31 samples with at least 10 Clone Reads. <b>(B)</b> A bar plot showing the fraction of the total observed QS for each unique Clone Read. Each horizontal bar represents one of the 31 samples with the width of the bar indicating the QS fraction. Each color in the bar indicates unique QS. Darker bars denote the QS that match the PCR Read.</p

The mutation rate of HIV varies over time as determined by BEAST analysis.

<p>BEAST trees from three representative patients (A0001, A0041 and A0095) are shown for both the LTR <b>(Top)</b> and <i>tat</i> exon 1 <b>(Middle)</b> regions The width of the branch indicates the rate of mutation across the branch and the branch-length represents the time since divergence. The purple nodes indicate PCR Reads, the red nodes 4.4 Kb Fragment Reads, and the green nodes indicate Clone Reads. <b>(Bottom)</b> The time-series of each patient was synchronized such that the estimated time of infection for all samples have been shifted to 0 years. The red line shows the average mutation rate of <i>tat</i> exon 1 and the red shadow shows the 95% confidence interval of the estimate. The blue line shows the average mutation rate of the LTR and the blue shadow shows the 95% confidence interval of the estimate.</p