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Studying blood serum compounds of different molecular weights has led scientists to a set of biomarkers that may enable development of a highly accurate screening test for early-stage ovarian cancer.

Using advanced liquid chromatography and mass spectrometry techniques coupled with machine learning computer algorithms, researchers have identified 16 metabolite compounds that provided unprecedented accuracy in distinguishing 46 women with early-stage ovarian cancer from a control group of 49 women who did not have the disease. Blood samples for the study were collected from a broad geographic area – Canada, Philadelphia and Atlanta.

While the set of biomarkers reported in this study are the most accurate reported thus far for early-stage ovarian cancer, more extensive testing across a larger population will be needed to determine if the high diagnostic accuracy will be maintained across a larger group of women representing a diversity of ethnic and racial groups.

The research was reported November 17 in the journal Scientific Reports, an open access journal from the publishers of Nature.

“This work provides a proof of concept that using an integrated approach combining analytical chemistry and learning algorithms may be a way to identify optimal diagnostic features,” said John McDonald, a professor in the School of Biology at the Georgia Institute of Technology and director of its Integrated Cancer Research Center. “We think our results show great promise and we plan to further validate our findings across much larger samples.”

Ovarian cancer has been difficult to treat because it typically is not diagnosed until after it has metastasized to other areas of the body. Researchers have been seeking a routine screening test that could diagnose the disease in stage one or stage two – when the cancer is confined to the ovaries.

Working with three cancer treatment centers in the U.S. and Canada, the Georgia Tech researchers obtained blood samples from women with stage one and stage two ovarian cancer. They separated out the serum, which contains proteins and metabolites – molecules produced by enzymatic reactions in the body.

The serum samples were analyzed by ultra-performance liquid chromatography-mass spectrometry (UPLC-MS), which is two instruments joined together to better separate samples into their individual components. Heavier molecules are separated from lighter molecules, and the molecular signatures are determined with enough accuracy to identify the specific compounds. The Georgia Tech researchers decided to look only at the metabolites in their research.

“People have been looking at proteins for diagnosis of ovarian cancer for a couple of decades, and the results have not been very impressive,” said Facundo Fernández, a professor in Georgia Tech’s School of Chemistry and Biochemistry who led the analytical chemistry part of the research. “We decided to look in a different place for molecules that could potentially provide diagnostic capabilities. It’s one of the places that people had really not studied before.”

Samples from each of the 46 cancer patients were divided so they could be analyzed in duplicate. The researchers also looked at serum samples from 49 women who did not have cancer. The work required eliminating unrelated compounds such as caffeine, and molecules that were not present in all the cancer patients.

“We used really high resolution equipment and instrumentation to be able to separate most of the components of the samples,” Fernández explained. “Otherwise, detection of early-stage ovarian cancer is very difficult because you have a lot of confounding factors.”

The chemical work identified about a thousand candidate compounds. That number was reduced to about 255 through the work of research scientist David Gaul, who removed duplicates and unrelated molecules from the collection.

These 255 compounds were then analyzed by a learning algorithm which evaluated the predictive value of each one. Molecules that did not contribute to the predictive accuracy of the screening were eliminated. Ultimately, the algorithm produced a list of 16 molecules that together differentiated cancer patients with extremely high accuracy – greater than 90 percent.

“The algorithm looks at the metabolic features and correlates them with whether the samples were from cancer or control patients,” McDonald explained. “The algorithm has no idea what these compounds are. It is simply looking for the combination of molecules that provides the optimal predictive accuracy. What is encouraging is that many of the diagnostic features identified are metabolites that have been previously implicated in ovarian cancer.”

As a next step, McDonald and Fernández would like to study samples from a larger population that includes significant numbers of different ethnic and racial groups. Those individuals may have different metabolites that could serve as biomarkers for ovarian cancer.

Though sophisticated laboratory equipment was required to identify the 16 molecules, a screening test would not require the same level of sophistication, Fernández said.

“Once you know what these molecules are, the next step would be to set up a clinical assay,” he said. “Mass spectrometry is a common tool in this field. We could use a clinical mass spectrometer to look at only the molecules we are interested in. Moving this to a clinical assay would take work, but I don’t see any technical barriers to doing it.”

The Fernández and McDonald groups have used a similar approach with prostate cancer and plan to explore its utility for detecting other types of cancer.

The research was supported by grants from The Laura Crandall Brown Ovarian Cancer Foundation, The Ovarian Cancer Research Fund, The Ovarian Cancer Institute, Northside Hospital (Atlanta), The Robinson Family Fund, and the Deborah Nash Endowment Fund.

CITATION: David A. Gaul, et al., “Highly-accurate metabolomics detection of early-stage ovarian cancer,” (Scientific Reports, 2015). http://www.dx.doi.org/10.1038/srep16351

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In the current issue of the journal Science, researchers at Michigan State University, the Georgia Institute of Technology and the University of Texas at Austin demonstrate how a new virus evolves, which sheds light on how easy it can be for diseases to gain dangerous mutations.

The scientists showed for the first time how the virus called “Lambda” evolved to find a new way to attack host cells, an innovation that took four mutations to accomplish. This virus infects bacteria, in particular the common E. coli bacterium. Lambda isn’t dangerous to humans, but this research demonstrated how viruses evolve complex and potentially deadly new traits, said Justin Meyer, MSU graduate student, who co-authored the paper with Richard Lenski, MSU Hannah Distinguished Professor of Microbiology and Molecular Genetics.

“We were surprised at first to see Lambda evolve this new function, this ability to attack and enter the cell through a new receptor­ – and it happened so fast,” Meyer said. “But when we re-ran the evolution experiment, we saw the same thing happen over and over.”

This paper comes on the heels of news that scientists in the U.S. and the Netherlands produced a deadly version of bird flu. Even though bird flu is a mere five mutations away from becoming transmissible between humans, it’s highly unlikely the virus could naturally obtain all of the beneficial mutations all at once. However, it might evolve sequentially, gaining benefits one-by-one, if conditions are favorable at each step, he added.

Through research conducted at BEACON, MSU’s National Science Foundation Center for the Study of Evolution in Action, Meyer and his colleagues’ ability to duplicate the results implied that adaptation by natural selection, or survival of the fittest, had an important role in the virus’ evolution.

When the genomes of the adaptable virus were sequenced, they always had four mutations in common.

“The parallelism shown in the evolutionary history of adaptable viruses was striking and was far beyond what is expected by chance,” noted paper co-author Joshua Weitz, an assistant professor in the School of Biology at Georgia Tech.

In contrast, the viruses that didn’t evolve the new way of entering cells had some of the four mutations but never all four together, said Meyer, who holds the Barnett Rosenberg Fellowship in MSU’s College of Natural Science.

“In other words, natural selection promoted the virus’ evolution because the mutations helped them use both their old and new attacks,” Meyer said. “The finding raises questions of whether the five bird flu mutations may also have multiple functions, and could they evolve naturally?”

Additional authors of the paper include Devin Dobias, former MSU undergraduate (now a graduate student at Washington University in St. Louis); Ryan Quick, MSU undergraduate; and Jeff Barrick, a former Lenski lab researcher now on the faculty at the University of Texas at Austin.

Funding for the research was provided in part by the National Science Foundation, Defense Advanced Research Projects Agency, James S. McDonnell Foundation and Burroughs Wellcome Fund.

This research was supported in part by the Defense Advanced Research Projects Agency (DARPA) (Award No. HR0011-09-1-0055) and the National Science Foundation (NSF). The content is solely the responsibility of the principal investigator and does not necessarily represent the official views of DARPA or NSF.

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Scientists from Baylor College of Medicine and the Georgia Institute of Technology have won $900,000 from the Ovarian Cancer Research Fund to investigate the early detection of ovarian cancer.

The research, which comprises three separate projects, includes work with a new mouse model of ovarian cancer to identify early detection biomarkers; an effort to characterize proteins and protein variants secreted from ovarian tumors that could serve as serum biomarkers; and work to identify metabolic changes that could help diagnose the disease.

"This grant is a program project development grant, and the idea is to bring together a number of individuals around a common theme," Martin Matzuk, a BCM professor of pathology and immunology and one of the leaders of the project, told ProteoMonitor. "We were previously funded by OCRF along with a number of investigators to focus on the role of microRNAs in ovarian cancer. That work has gone very well, so we put together another proposal in which we decided to focus on biomarkers, whether they're protein or small molecule."

Matzuk is collaborating on the work with his BCM colleague Laising Yen as well as John McDonald, a professor, the associate dean for biology program development in the school of Biology at Georgia Tech, and a chief research scientist at Atlanta's Ovarian Cancer Institute.
McDonald, who will head up the search for metabolomic biomarkers, leads a research team that published a paper in August 2010 detailing a metabolomic ovarian cancer diagnostic that identified women with ovarian cancer with 100 percent accuracy in a 94-subject trial (PM 8/20/2010).

That test used direct-analysis-in-real-time mass spectrometry to measure thousands of metabolites in subjects' blood samples, classifying them with a functional support vector machine-based machine-learning algorithm. McDonald's team is still validating their findings, McDonald told ProteoMonitor this week, but thus far "everything is looking good," and, he said, the researchers hope to finish validating the results sometime within the year.

Under the OCRF grant, the Georgia Tech team plans to use LC-MS/MS to identify specific metabolites detected by their DART-MS work in hopes of combining them with protein biomarkers identified by Matzuk's lab to build an early detection panel for ovarian cancer.
The DART analysis "gives us thousands of features, and for most of them we don't know what they are," McDonald said. "From a diagnostic point of view we don't really care as long as it's a reliable diagnostic. But at the same time we're now running LC-MS/MS to try to whittle it down to identify … the specific metabolites involved."

"The idea is that we'll put it together [with Matzuk's markers] to see what an optimal diagnostic might consist of," he said.

Matzuk and the BCM researchers will be looking for protein biomarkers using a recently developed mouse model of high-grade serous ovarian cancer in which the cancer actually begins in the fallopian tube as opposed to the ovary itself. The model reflects an alternate view of ovarian cancer development "that is gaining a lot of support," Matzuk said.

Because ovarian cancer is difficult to detect early, often by the time patient samples are collected it's "too late to be trying to figure out what are the changes with regard to proteins or metabolic changes," he said. "The nice thing about having a mouse model is that these animals get cancers universally, and so you can open the animals up at a certain period and say, 'OK, at this time point what are the expression changes in these cancers? What are the earliest time points [they are visible]?"

"The goal of all three projects is to [identify] the various transcripts that are out there in these cancers," Matzuk said. "The idea is, once we catalog all of them, to go back in and then screen or develop antibodies to new variants of proteins or new secreted proteins and see whether or not those could be better markers."

The ultimate goal of the work, he said, "is to generate enough data so that we could actually go into the National Institutes of Health for a bigger project that we could start not only between our groups, but also with other groups and centers to look at various biomarkers."

Price will be a major consideration for any early detection test, Matzuk said, noting that he thinks even existing triage tests like Vermillion's OVA1 don't offer enough to justify their cost. Given the low prevalence of ovarian cancer in the general population, he said, any broad screening test for the disease would need to cost under $50 for it to be covered widely by insurers.

"I run a clinical chemistry laboratory in the county hospital, and for us to be doing this kind of screening of healthy women you need to have the cost low," he said.
However, Matzuk suggested, declining instrumentation prices could help bring costs down in the future – particularly in the case of mass spec-based tests, where multiplexing could significantly lower the price of multi-analyte assays.

"Maybe everyone will have [mass spec] analysis of their serum at some point," he said. "I think right now the instrumentation is too expensive and the testing is too expensive to go ahead and say this is for general [screening] tests, but if it turns out that these tests are extremely valuable, people are going to find a way to make them cheaper."

High-throughput DNA sequencing technologies are leading to a revolution in how clinicians diagnose and treat cancer. The molecular profiles of individual tumors are beginning to be used in the design of chemotherapeutic programs optimized for the treatment of individual patients. The real revolution, however, is coming with the emerging capability to inexpensively and accurately sequence the entire genome of cancers, allowing for the identification of specific mutations responsible for the disease in individual patients.

There is only one downside. Those sequencing technologies provide massive amounts of data that are not easily processed and translated by scientists. That’s why Georgia Tech has created a new data analysis algorithm that quickly transforms complex RNA sequence data into usable content for biologists and clinicians. The RNA-Seq analysis pipeline (R-SAP) was developed by School of Biology Professor John McDonald and Ph.D. Bioinformatics candidate Vinay Mittal. Details of the pipeline are published in the journal Nucleic Acids Research.

“A major bottleneck in the realization of the dream of personalized medicine is no longer technological. It’s computational,” said McDonald, director of Georgia Tech’s newly created Integrated Cancer Research Center. “R-SAP follows a hierarchical decision-making procedure to accurately characterize various classes of gene transcripts in cancer samples.”

There are at least 23,000 pieces of RNA in the human genome that encode the sequence of proteins. Millions of other pieces help regulate the production of proteins. R-SAP is able to quickly determine every gene’s level of RNA expression and provide information about splice variants, biomarkers and chimeric RNAs. Biologists and clinicians will be able to more readily use this data to compare the RNA profiles or “transcriptomes” of normal cells with those of individual cancers and thereby be in a better position to develop optimized personal therapies.

Personalized approaches to cancer medicine are already in widespread use for a few “cancer biomarkers” including variants of the BRAC 1 gene that can be used to identify women with a high risk of developing breast and ovarian cancer.

“Our goal was to design a pipeline that is easily installable with parallel processing capabilities,” said Mittal. “R-SAP can make 100 million reads in just 90 minutes. Running the program simultaneously on multiple CPUs can further decrease that time.”

R-SAP is open source software, freely accessible at the McDonald Lab website.

“This is another example of Georgia Tech’s ability to merge computer technology with science to create an essential feature of next-generation bioinformatics tools,” said McDonald. “We hope that R-SAP will be a useful and user-friendly instrument for scientists and clinicians in the field of cancer biology.”

If we were able to resurrect a dinosaur in the laboratory today how could we be certain that the particular dinosaur actually existed in the distant past and does not simply represent some mutant frankensaurus?

Ongoing research at Georgia Tech aims to answer this question in an experimental approach by adding rigor to the methods and protocols used to resurrect components of ancient life.

Dr. Eric Gaucher, Associate Professor in the School of Biology, was recently awarded $700K from the National Science Foundation (NSF) to, for the first time, benchmark ancestral sequence reconstruction methods. Prof. Gaucher’s approach involves generating a known experimental phylogeny in the lab using fluorescent proteins cloned into bacteria. Generating such a “known” phylogeny with evolved sequences will, in turn, allow the group to test resurrection predictions since the true ancestral proteins are generated in the laboratory and are thus known.

An important component of the funding involves integrating evolutionary and molecular biology research into the greater Atlanta community. In collaboration with Dunwoody High school, Dr. Gaucher and Ryan Randall have developed a new Biotechnology curriculum whereby students are introduced to the connections between genotype and phenotype by evolving fluorescent proteins at the high school. In addition, The Gaucher Group annually hosts a team of Dekalb county high school students competing in the National Siemens Competition in Math, Science and Technology, that involves bioengineering of fluorescent proteins.

For his efforts, Prof. Gaucher is also a recent recipient of Georgia Tech’s Class of 1934 Teaching Award. This award is based on student evaluations and presented to faculty with the highest ratings in overall effectiveness in teaching.

Dr. Frank Stewart, an assistant professor in the School of Biology, has received a Faculty Early Career Development (CAREER) Award from the National Science Foundation (NSF).  This award provides $1.2 million over five years in support of research and educational activities in Dr. Stewart’s field of marine microbiology.  According to NSF, the CAREER Program “offers the National Science Foundation's most prestigious awards in support of junior faculty who exemplify the role of teacher-scholars through outstanding research, excellent education and the integration of education and research within the context of the mission of their organizations.”

Dr. Stewart’s CAREER research will investigate the microorganisms responsible for key steps of the biological sulfur cycle in marine oxygen minimum zones (OMZs).  OMZs and other low-oxygen regions (e.g., dead zones) are likely to expand in response to future climate change.  The microbial communities that dominate these unique environments are ecologically diverse and are known to be critical mediators of global cycles, notably the nitrogen cycle.  New evidence, including work from Dr. Stewart and his collaborators, has indicated that OMZ microbes (mostly bacteria) are also actively involved in moving sulfur through the marine ecosystem, with potentially links to both the nitrogen and carbon cycles.  However, the biogeography, genomic diversity, and metabolic activity of the organisms responsible for these processes remain largely uncharacterized.  Dr. Stewart’s research will use a combination of high throughput molecular methods, microbial culturing, and shipboard experiments to shed new light on this important group of marine microorganisms.  This research will involve four oceanographic research cruises in both the Pacific Ocean and the Gulf of Mexico.

Dr. Stewart’s CAREER project is also devoted to enhancing science education across multiple academic levels.  Through a partnership with local K-12 educators and teacher-development experts at Georgia Tech, Dr. Stewart and his lab will implement A Summer Workshop in Marine Science (SWIMS), designed to help train local teachers to merge key topics in marine science with new national standards in science education.  Additional science education activities will involve internship opportunities through partnerships with other Atlanta area colleges.

Chong Shin (Assistant professor, School of Biology) has received a pilot grant from the Georgia Tech & Emory Center for Regenerative Medicine (GTEC). The objective of the project is to unveil mechanisms to program/reprogram hepatocytes/insulin secreting beta cells in zebrafish. Zebrafish not only has homologous liver and pancreas structure with mammals but also has significant capacity for regeneration. The knowledge from this study can be applied to stimulate endogenous generation/regeneration mechanisms in the human body.

Two PhD students of the School of Biology, Mustafa Burak Boz and Jin Xu, received $1,500 travel grants for their posters at the recent Georgia Tech Research and Innovation Conference (GTRIC 2012).

The title of Burak's poster is "Assembly of an icosahedral single stranded RNA virus" advised by Dr. Steve Harvey (Professor and Georgia Research Alliance Eminent Scholar in Structural Biology, School of Biology).
The title of Jin's poster is "Unraveling a new regulator in liver and endocrine pancreas fate decision and regeneration " advised by Dr. Chong Shin (Assistant Professor, School of Biology).
A total of five 5K fellowships and 30 travel grants were awarded of the approximate 300 posters in the competition.

The Georgia Tech School of Biology is pleased to announce that three of our faculty won campus-wide teaching awards this year.  Dr. Jennifer Leavey is the recipient of the 2012 Class of 1940 W. Roane Beard Outstanding Teacher award.  This award recognizes extraordinary efforts in teaching, inspiration transmitted to students, direct impact and involvement with students, intellectual integrity and scholarship, and impact on post-graduate success of students. Dr. Linda Green is the recipient of the CETL Undergraduate Educator Award. This award recognizes teaching excellence in large classes, impact on multiple diverse student populations, educational innovations, educational outreach beyond the classroom and a passion for teaching and learning. Dr. Cara Gormally will be receiving the 2012 Innovation in Co-Curricular Education Award.  This award is given to faculty who increase student learning outside the traditional curriculum and help Georgia Tech achieve its strategic goal of graduating global citizens who can contribute to all sectors of society.  Dr. Cara Gormally was nominated in particular to recognize her collaborations with the Atlanta Botanical Gardens and the Piedmont Park Conservancy in her Honors Biological Principles and Honors Organismal Biology laboratory courses.  Congratulations!

When battling an epidemic of a deadly parasite, less resistance can sometimes be better than more, a new study suggests. 

A freshwater zooplankton species known as Daphnia dentifera endures periodic epidemics of a virulent yeast parasite that can infect more than 60 percent of the Daphnia population. During these epidemics, the Daphnia population evolves quickly, balancing infection resistance and reproduction.

A new study led by Georgia Institute of Technology researchers reveals that the number of vertebrate predators in the water and the amount of food available for Daphnia to eat influence the size of the epidemics and how these “water fleas” evolve during epidemics to survive.

The study shows that lakes with high nutrient concentrations and lower predation levels exhibit large epidemics and Daphnia that become more resistant to infection by the yeast Metschnikowia bicuspidata. However, in lakes with fewer resources and high predation, epidemics remain small and Daphnia evolve increased susceptibility to the parasite.

“It’s counterintuitive to think that hosts would ever evolve greater susceptibility to virulent parasites during an epidemic, but we found that ecological factors determine whether it is better for them to evolve enhanced resistance or susceptibility to infection,” said the study’s lead author Meghan Duffy, an assistant professor in the School of Biology at Georgia Tech. “There is a trade-off between resistance and reproduction because any resources an animal devotes to defense are not available for reproduction. When ecological factors favor small epidemics, it is better for hosts to invest in reproduction rather than defense.”

This study was published in the March 30, 2012 issue of the journal Science. The research was supported by the National Science Foundation and the James S. McDonnell Foundation.

In addition to Duffy, also contributing to this study were Indiana University Department of Biology associate professor Spencer Hall and graduate student David Civitello; Christopher Klausmeier, an associate professor in the Department of Plant Biology and W.K. Kellogg Biological Station at Michigan State University; and Georgia Tech research technician Jessica Housley Ochs and graduate student Rachel Penczykowski.

For the study, the researchers monitored the levels of nutritional resources, predation and parasitic infection in seven Indiana lakes on a weekly basis for a period of four months. They calculated infection prevalence visually on live hosts using established survey methods, estimated resources by measuring the levels of phosphorus and nitrogen in the water, and assessed predation by measuring the size of uninfected adult Daphnia.

The researchers also conducted infection assays in the laboratory on Daphnia collected from each of the seven lake populations at two time points: in late July before epidemics began and in mid-November as epidemics waned. The assays measured the zooplankton’s uptake of Metschnikowia bicuspidata and infectivity of the yeast once consumed.

The infection assays showed a significant evolutionary response of Daphnia to epidemics in six of the seven lake populations. The Daphnia population became significantly more resistant to infection in three lakes and significantly more susceptible to infection in three other lakes. The hosts in the seventh lake did not show a significant change in susceptibility, but trended toward increased resistance. In the six lake populations that showed a significant evolutionary response, epidemics were larger when lakes had lower predation and higher levels of total nitrogen.

“Daphnia became more susceptible to the yeast in lakes with fewer resources and higher vertebrate predation, but evolved toward increased resistance in lakes with increased resources and lower predation,” noted Duffy.

The study’s combination of observations, experiments and mathematical modeling support the researchers’ theoretical prediction that when hosts face a resistance-reproduction tradeoff, they evolve increased resistance to infection during larger epidemics and increased susceptibility during smaller ones. Ultimately, ecological gradients, through their effects on epidemic size, influence evolutionary outcomes of hosts during epidemics.

“While the occurrence and magnitude of disease outbreaks can strongly influence host evolution, this study suggests that altering predation pressure on hosts and productivity of ecosystems may also influence this evolution,” added Duffy.

The team plans to repeat the study this summer in the same Indiana lakes to examine whether the relationships between ecological factors, epidemic size and host evolution they found in this study can be corroborated.

This work was supported in part by the National Science Foundation (NSF) (Award Nos. DEB-0841679, DEB-0841817, DEB-0845825 and OCE-171 0928819). The content is solely the responsibility of the principal investigators and does not necessarily represent the official views of the NSF.

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