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Perinatal transmission of human immunodeficiency virus (HIV)-1 represents a major problem in many regions of the world, especially Southern Africa. With the exception of viral and proviral load, the role for maternal cofactors in perinatal transmission outcome is largely unknown. In this study, an assessment was made of peripheral blood mononuclear cells (PBMC) gene-expression profiles to better understand transcriptional changes associated with HIV-1 infection and perinatal transmission among young adult mothers with infants in Botswana. Peripheral blood mononuclear cells specimens were used from 25 HIV+ drug naive and 20 HIV− healthy mothers, similar in age and location, collected in 1999–2000 and 2003, and processed with the exact same methods, as previously described. Expression profiling of 22 277 microarray gene probes implicated a broad initiation of innate response gene-sets, including toll-like receptor, interferon-stimulated and antiviral RNA response pathways in association with maternal HIV-1 infection. Maternal transmission status was further associated with host genes that influence RNA processing and splicing patterns. In addition to real-time polymerase chain reaction validation of specific genes, enriched category validation of PBMC profiles was conducted using two independent data sets for either HIV-1 infection or an unrelated RNA virus, severe acute respiratory virus infection. HIV-1 pathogen-specific host profiles should prove a useful tool in infection and transmission intervention efforts worldwide. Supplementary information The online version of this article (doi:10.1038/sj.gene.6364297) contains supplementary material, which is available to authorized users.

ObjectiveWith the progressive aging of the human population, there is an inexorable decline in muscle mass, strength and function. Anabolic supplementation with testosterone has been shown to effectively restore muscle mass in both young and elderly men. In this study, we were interested in identifying serum factors that change with age in two distinct age groups of healthy men, and whether these factors were affected by testosterone supplementation.MethodsWe measured the protein levels of a number of serum biomarkers using a combination of banked serum samples from older men (60 to 75 years) and younger men (ages 18 to 35), as well as new serum specimens obtained through collaboration. We compared baseline levels of all biomarkers between young and older men. In addition, we evaluated potential changes in these biomarker levels in association with testosterone dose (low dose defined as 125 mg per week or below compared to high dose defined as 300 mg per week or above) in our banked specimens.ResultsWe identified nine serum biomarkers that differed between the young and older subjects. These age-associated biomarkers included: insulin-like growth factor (IGF1), N-terminal propeptide of type III collagen (PIIINP), monokine induced by gamma interferon (MIG), epithelial-derived neutrophil-activating peptide 78 (ENA78), interleukin 7 (IL-7), p40 subunit of interleukin 12 (IL-12p40), macrophage inflammatory protein 1β (MIP-1β), platelet derived growth factor β (PDGFβ) and interferon-inducible protein 10 (IP-10). We further observed testosterone dose-associated changes in some but not all age related markers: IGF1, PIIINP, leptin, MIG and ENA78. Gains in lean mass were confirmed by dual energy X-ray absorptiometry (DEXA).ConclusionsResults from this study suggest that there are potential phenotypic biomarkers in serum that can be associated with healthy aging and that some but not all of these biomarkers reflect gains in muscle mass upon testosterone administration.

[Background] One of the challenges of the analysis of pooling-based genome wide association studies is to identify authentic associations among potentially thousands of false positive associations. [Results] We present a hierarchical and modular approach to the analysis of genome wide genotype data that incorporates quality control, linkage disequilibrium, physical distance and gene ontology to identify authentic associations among those found by statistical association tests. The method is developed for the allelic association analysis of pooled DNA samples, but it can be easily generalized to the analysis of individually genotyped samples. We evaluate the approach using data sets from diverse genome wide association studies including fetal hemoglobin levels in sickle cell anemia and a sample of centenarians and show that the approach is highly reproducible and allows for discovery at different levels of synthesis. [Conclusion] Results from the integration of Bayesian tests and other machine learning techniques with linkage disequilibrium data suggest that we do not need to use too stringent thresholds to reduce the number of false positive associations. This method yields increased power even with relatively small samples. In fact, our evaluation shows that the method can reach almost 70% sensitivity with samples of only 100 subjects., Supported by NHLBI grants R21 HL080463 (PS); R01 HL68970 (MHS); K-24, AG025727 (TP); K23 AG026754 (D.T.).