This research, published in the May issue of the journal GENETICS, helps shed light on times when the human population moved close to extinction and helps scientists close in on gene mutations that make some demographic groups more likely to develop diseases such as cancer, heart disease, diabetes, among others.

"We know that many diseases are caused by a combination of genetic and environmental factors," said Kirk E. Lohmueller, one of the researchers involved in the work from Cornell University. "To find the genes that contribute to disease, it's very helpful to know the demographic history of the population being studied. Accurate estimates of population events help inform the search for mutations that might have been helpful and necessary for survival at the time, but no longer necessary and potentially harmful today."

In their work, Lohmueller and colleagues confirmed the existence of a major decline in European populations (called a "bottleneck") 32,500-47,500 years ago. They used computer simulations to model the expected correlation among segments of DNA containing very small genetic mutations that only involve a single letter of the genetic code (called "single nucleotide polymorphisms" or SNPs). Prior to this development, methods used to identify major population events relied on the frequency patterns of individual SNPs, while ignoring the patterns of specific groups of SNPs. This work shows that looking at groups of SNPs helps us better understand what happened long before there was a human historical record.

"When we think of the past, we often think in terms of the historical or geological records," said Mark Johnston, Editor-in-Chief of the journal GENETICS . "What makes this development so amazing is that it helps align these records with an emerging biological record based on our DNA. This technique can be applied to any species, making it possible for us to learn and compare the biological histories of all living creatures."

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The method of treating yeast particles to remove components that would cause an immune response and generate oral delivery vehicles composed of "beta1,3-D glucan" was developed by UMMS research professor and paper co-author Gary R. Ostroff, PhD. The method of using glucan particles as a drug delivery system has been tested in a number of animal models. In December 2008, the Massachusetts Life Sciences Center awarded a three-year, $750,000 cooperative research grant to UMMS and biotech startup RXi Pharmaceuticals to investigate the development of a range of orally delivered RNAi therapeutics using the glucan particle model. (RXi was co-founded by Nobel Laureate Mello, who serves on its Scientific Advisory Board, and Czech.)

In the series of experiments, the researchers were able to silence gene expression both in vitro and in vivo, in a mouse model, at a range of doses and concentrations; oral delivery of as little as 20 micrograms per kilogram of body weight of siRNA silenced a signaling protein called MAP4K4, a key player in the inflammatory response in disease processes like arthritis. (By contrast, research studies evaluating intravenous injections of siRNAs often used concentrations from 12 to 500 times higher.)

"In the future, this paper will be viewed as a landmark in the process of translating RNAi into effective new therapies for human diseases," said Terence R. Flotte, MD, dean of the school of medicine at UMMS. "It addresses one of the most fundamental problems in the field, that of delivery of the RNAi molecule to the cells affected by the disease process."

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