Premature and accelerated ageing: HIV or HAART?

Highly Active Anti-Retroviral Therapy (HAART) has significantly increased life expectancy of the HIV-positive population. Nevertheless, the average lifespan of HIV patients remains shorter compared to uninfected individuals. Immunosenescence, a current explanation for this difference invokes heavily on viral stimulus despite HAART efficiency in viral suppression. We propose here that the premature and accelerated ageing of HIV patients can also be caused by adverse effects of antiretroviral drugs, specifically those that affect the mitochondria. The Nucleoside Reverse Transcriptase Inhibitor (NRTI) antiretroviral drug class for instance, is known to cause depletion of mitochondrial DNA via inhibition of the mitochondrial specific DNA polymerase-ƴ. Besides NRTIs, other antiretroviral drug classes such as Protease Inhibitors also cause severe mitochondrial damage by increasing oxidative stress and diminishing mitochondrial function. We also discuss important areas for future research and argue in favour of the use of C. elegans as a novel model system for studying these effects

Genetic interference following ingestion of anti-GFP dsRNA-expressing <i>B</i>. <i>subtilis</i> by <i>C</i>. <i>elegans</i>.

<p>(A) Physical map of the pBSR vector. The DNA sequence corresponding to dsRNA of interest was cloned between flanking copies of the P<i>spac</i> promoter to replace the spacer. <i>B</i>. <i>subtilis</i> strain BG322 was used as a host. GFP-expressing <i>C</i>. <i>elegans</i> strains TJ356 (B) and SD1084 (C) were fed on BG322 strains transformed with original pBSR vector and on bacteria expressing dsRNA corresponding to the <i>gfp</i> coding region. Under these conditions, 95% of the animals showed dramatic decrease in GFP expression after 24 hours of feeding. DAF-16::GFP and <i>SUR-5</i>::GFP expression is significantly decreased in the GFP (RNAi) treated animals. RNAi was induced starting at L4 larvae stage by feeding worms <i>B</i>. <i>subtilis</i> bacteria expressing dsRNA against GFP. GFP expression was measured at day 2 of adulthood. The y-axis denotes GFP expression (arbitrary units). Average expression and Standard Error from 20 animals are shown. *-p-value < 0.001 (t-test p-values). Scale bar = 100 μm.</p

Effects of various <i>E</i>. <i>coli</i> and <i>B</i>. <i>subtilis</i> strains on longevity.

<p>(A) Worms fed on wild type <i>B</i>. <i>subtilis</i> strain (PY79) live 65% longer compared to wild type, standard laboratory food <i>E</i>. <i>coli</i> strain (OP50). (B) Worms fed on RNase III-null <i>B</i>. <i>subtilis</i> (BG322) strain live 55% longer compared to DE3, <i>E</i>. <i>coli</i> strain used for expressing stable dsRNA for RNA interference. Median survival on: OP50 = 21 days, PY79 = 35 days DE3 = 18 days, BG322 = 28 days. Age refers to days of adulthood. Three biological replicates were observed for each experiment (n = 70–80 worms per experiment); error bars indicate Standard Error. In both graphs p < 0.0001.</p

DAF-16/FOXO activated in <i>daf-2</i> (RNAi) and <i>glp-1</i>(RNAi) mutants when grown on <i>E</i>. <i>coli</i> or <i>B</i>. <i>subtilis</i>.

<p>Green fluorescent protein images of adult TJ356 (Is<i>daf-16</i>::GFP) transgenic animals shown at 40X. (A) Images of an animal raised on <i>E</i>. <i>coli</i> expressing empty vector for RNA interference. (B) Images of an animal grown on <i>E</i>. <i>coli</i> and exposed to <i>E</i>. <i>coli</i> expressing <i>daf-2</i> dsRNA for 48 hours post development. Note strong nuclear localization in most tissues, including the intestine. (C) Image of an animal grown on <i>B</i>. <i>subtilis</i> expressing the empty vector for RNA interference. Images of animals fed <i>B</i>. <i>subtilis</i> expressing either (D) <i>daf-2</i> dsRNA, (E) <i>glp-1</i> dsRNA, or (F) <i>unc-62</i> dsRNA for 48 hours post development. Note weak activation of DAF-16/FOXO in the head area upon treatment with daf-2 and glp-1 dsRNA. Scale bar = 100μm</p

16S rRNA gene sequencing and healthy reference ranges for 28 clinically relevant microbial taxa from the human gut microbiome

<div><p>Changes in the relative abundances of many intestinal microorganisms, both those that naturally occur in the human gut microbiome and those that are considered pathogens, have been associated with a range of diseases. To more accurately diagnose health conditions, medical practitioners could benefit from a molecular, culture-independent assay for the quantification of these microorganisms in the context of a healthy reference range. Here we present the targeted sequencing of the microbial 16S rRNA gene of clinically relevant gut microorganisms as a method to provide a gut screening test that could assist in the clinical diagnosis of certain health conditions. We evaluated the possibility of detecting 46 clinical prokaryotic targets in the human gut, 28 of which could be identified with high precision and sensitivity by a bioinformatics pipeline that includes sequence analysis and taxonomic annotation. These targets included 20 commensal, 3 beneficial (probiotic), and 5 pathogenic intestinal microbial taxa. Using stool microbiome samples from a cohort of 897 healthy individuals, we established a reference range defining clinically relevant relative levels for each of the 28 targets. Our assay quantifies 28 targets in the context of a healthy reference range and correctly reflected 38/38 verification samples of real and synthetic stool material containing known gut pathogens. Thus, we have established a method to determine microbiome composition with a focus on clinically relevant taxa, which has the potential to contribute to patient diagnosis, treatment, and monitoring. More broadly, our method can facilitate epidemiological studies of the microbiome as it relates to overall human health and disease.</p></div

Bioinformatics target identification performance metrics.

<p>The 46 preliminary targets identified from literature and available clinical tests are comprised of 15 genera and 31 species. To optimize the bioinformatics pipeline for accurate detection of the maximum number of targets, the following performance metrics were evaluated based on the number of true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN) detected in a manually curated amplicon database (described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0176555#pone.0176555.s005" target="_blank">S1 Doc</a>): specificity = TN / (TN + FP); sensitivity = TP / (TP + FN); precision = TP / (TP + FP); and negative predictive value (NPV) = TN / (TN + FN). After optimization, 28/46 preliminary targets passed our stringent threshold of 90% (red vertical line) for each of the parameters, resulting in the accurate detection of all genera (light blue) except for <i>Pseudoflavonifractor</i>, and 14/31 species (dark blue).</p

Experimental validation of the clinical 16S rRNA gene sequencing for pathogens on the screening test panel using verification samples.

<p>Commercially available verification samples (Luminex) containing real or synthetic stool samples positive for at least one control taxon from the target panel were tested using the DNA extraction, amplification and bioinformatics pipeline described in this paper. Of the 35 samples on this panel, 33 yielded 10,000 or more reads. Together, these 33 samples contained the 5 pathogenic taxa in our target list, all of which were accurately identified at a level above the maximum value of the healthy range (red lines). All 33 control samples tested within the healthy range for the remainder of the taxa on our panel (not shown), and thus were considered negative for the pathogenic taxa shown here. Five samples positive for <i>Yersinia</i>, a genus that is not present in our target list, were included as additional negative controls. These samples are visualized for the <i>Escherichia-Shigella</i> genus as they contained DNA for this taxon within the healthy range.</p

Reference ranges from a cohort of healthy individuals for 28 clinically relevant species and genera.

<p>Healthy participant stool microbiome data were analyzed to determine the empirical reference ranges for each target. The boxplot displays the relative abundance for each of 897 self-reported healthy individuals, revealing the healthy ranges of abundance for the taxa in the test panel. The healthy distribution is used to define the 99% confidence interval (red line). Boxes indicate the 25th–75th percentile, and the median coverage is indicated by a horizontal line in each box. Even in this healthy cohort, many of the bacteria that are associated with poor health conditions are present at some level. As most taxa are absent in a significant number of individuals most boxes expand to 0%, the healthy lower limit (not shown).</p

Human health associations of the 28 targets microorganisms.

<p>All of the 28 taxa on the test have been associated with human health in the gut microbiome. Here we show the associations for 13 specific conditions. 13 of the taxa are associated with health conditions, meaning that these microorganisms have been shown to be elevated in patients suffering from these conditions. The 11 microorganisms that are inversely associated were found to be less abundant in people who have this condition in the scientific literature (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0176555#pone.0176555.s002" target="_blank">S2 Table</a>). 4 taxa are associated with some and inversely associated with other conditions. Interestingly, both elevated and reduced levels of <i>Lactobacillus</i> have been associated with obesity [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0176555#pone.0176555.ref044" target="_blank">44</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0176555#pone.0176555.ref046" target="_blank">46</a>].</p