SummaryDrosophila melanogaster has emerged as an important model system for the study of both stem cell biology and aging. Much is known about how molecular signals from the somatic niche regulate adult stem cells in the germline, and a variety of environmental factors as well as single point mutations have been shown to affect lifespan. Relatively little is known, however, about how aging affects specific populations of cells, particularly adult stem cells that may be susceptible to aging-related damage. Here we show that male germline stem cells (GSCs) are lost from the stem cell niche during aging, but are efficiently replaced to maintain overall stem cell number. We also find that the division rate of GSCs slows significantly during aging, and that this slowing correlates with a reduction in the number of somatic hub cells that contribute to the stem cell niche. Interestingly, slowing of stem cell division rate was not observed in long-lived methuselah mutant flies. We finally investigated whether two mechanisms that are thought to be used in other adult stem cell types to minimize the effects of aging were operative in this system. First, in many adult tissues stem cells exhibit markedly fewer cell cycles relative to transit-amplifying cells, presumably protecting the stem cell pool from replication-associated damage. Second, at any given time not all stem cells actively cycle, leading to 'clonal succession' from the reserve pool of initially quiescent stem cells. We find that neither of these mechanisms is used in Drosophila male GSCs.

Using genetic and molecular analyses, we identified over 1,000 polymorphic regulators that regulate expression levels of human genes.

Genes interact in networks to orchestrate cellular processes. Analysis of these networks provides insights into gene interactions and functions. Here, we took advantage of normal variation in human gene expression to infer gene networks, which we constructed using correlations in expression levels of more than 8.5 million gene pairs in immortalized B cells from three independent samples. The resulting networks allowed us to identify biological processes and gene functions. Among the biological pathways, we found processes such as translation and glycolysis that co-occur in the same subnetworks. We predicted the functions of poorly characterized genes, including CHCHD2 and TMEM111, and provided experimental evidence that TMEM111 is part of the endoplasmic reticulum-associated secretory pathway. We also found that IFIH1, a susceptibility gene of type 1 diabetes, interacts with YES1, which plays a role in glucose transport. Furthermore, genes that predispose to the same diseases are clustered nonrandomly in the coexpression network, suggesting that networks can provide candidate genes that influence disease susceptibility. Therefore, our analysis of gene coexpression networks offers information on the role of human genes in normal and disease processes.