Specialized regulatory T cell stifles antibody production centers: Discovery has potential implications for cancer, autoimmune disease

ScienceDaily (July 25, 2011) — A regulatory T cell that expresses three specific genes shuts down the mass production of antibodies launched by the immune system to attack invaders, a team led by scientists at The University of Texas MD Anderson Cancer Center reported online in the journal Nature Medicine.See Also:Health & MedicineLymphomaImmune SystemStem CellsBrain TumorCancerLung CancerReferenceNatural killer cellT cellLymph nodeHeat shock protein

"Regulatory T cells prevent unwanted or exaggerated immune system responses, but the mechanism by which they accomplish this has been unclear," said paper senior author Chen Dong, Ph.D., professor in MD Anderson's Department of Immunology and director of the Center for Inflammation and Cancer.

"We've identified a molecular pathway that creates a specialized regulatory T cell, which suppresses the reaction of structures called germinal centers. This is where immune system T cells and B cells interact to swiftly produce large quantities of antibodies," Dong said.

The discovery of the germinal center off-switch, which comes two years after Dong and colleagues identified the mechanisms underlying a helper T cell that activates the centers, has potential implications for cancer and autoimmune diseases.

"In some types of cancer, the presence of many regulatory T cells is associated with poor prognosis," Dong said. "The theory is those cells suppress an immune system response in the tumor's microenvironment that otherwise might have attacked the cancer."

However, in B cell lymphomas, overproliferation and mutation of B cells are the problems, Dong said. Hitting the regulatory T cell off-switch might help against lymphomas and autoimmune diseases, while blocking it could permit an immune response against other cancers.

Antibody production central

Germinal centers are found in the lymph nodes and the spleen. They serve as gathering points for B and T cell lymphocytes, infection-fighting white blood cells.

When the adaptive immune system detects an invading bacterium or virus, B cells present a piece of the invader, an antigen, to T cells. The antigen converts a naïve T cell to a helper T cell that secretes cytokines, which help the B cells expand and differentiate into specialized antibodies to destroy the intruder.

"Germinal centers have mostly B cells with a few helper T cells to regulate them. The B cells mutate to make high-affinity antibodies and memory B cells for long-term immunity. The cell population in the germinal center structures replicates in an average of several hours, one of the fastest rates of cell replication known in mammals," Dong said.

Tracking down specialized T cell

In the Nature Medicine paper, Dong and colleagues found that a subgroup of regulatory T cells that expresses two genes, Bcl-6 and CXCR5, moves into germinal centers in both mice and humans, where they have access to B cells.

(Bcl-6 produces a protein called a transcription factor, which moves into the cell nucleus to regulate other genes. CXCR5 is a receptor protein for a signaling molecule called CXCL13.)

They also found that the Bcl-6/CXCR5 T cells aren't produced in the thymus, with other T cells, but are generated by regulatory T cell precursor cells that express Foxp3, another transcription factor.

Knocking out the regulatory T cells that express all three proteins in mice resulted in increased germinal center production of antibodies. They named this key T cell the T follicular regulatory cell, or Tfr.

In a 2009 paper in the journal Science, the researchers found that naïve T cells that expressed Bcl-6 and CXCR5 also gathered in the B cell zone of germinal centers. Expression of Bcl6 converted the T cell into a T follicular helper (Tfh) cell that launches antibody production in the germinal centers.

With Tfr turning germinal centers off and Tfh turning them on, we could potentially regulate antibody production, Dong noted. Increasing Tfr production could be a new approach to treating autoimmune inflammatory disorders, such as lupus and rheumatoid arthritis.

The team's research was funded by grants from the National Institutes of Health, the Leukemia and Lymphoma Society, MD Anderson, the American Heart Association, Doris Duke Charitable Foundation Clinical Scientist Development Award and the China Ministry of Science and Technology Protein Science Key Research Project.

Co-authors with Dong are first author Yeonseok Chung, Ph.D., Shinya Tanaka, Ph.D., Roza Nurieva, Ph.D., Gustavo Martinez, Yi-Hong Wang and Joseph Reynolds, Ph.D., of MD Anderson's Department of Immunology and the Center for Cancer Immunology; Chung also is with The University of Texas Health Science Center at Houston Institute of Molecular Medicine; Seema Rawal and Sattva Neelapu, M.D., of MD Anderson's Department of Lymphoma and Myeloma, also of the Center for Cancer Immunology; and Ziao-hui Zhou, M.D., Hui-min Fan, M.D., and Zhong-ming Liu, M.D., of Shanghai Dong Fang Hospital, Shanghai, China.

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Modeling plant metabolism to optimize oil production

ScienceDaily (July 26, 2011) — Scientists at the U.S. Department of Energy's (DOE) Brookhaven National Laboratory have developed a computational model for analyzing the metabolic processes in rapeseed plants -- particularly those related to the production of oils in their seeds. Their goal is to find ways to optimize the production of plant oils that have widespread potential as renewable resources for fuel and industrial chemicals.See Also:Plants & AnimalsSeedsEndangered PlantsMatter & EnergyOrganic ChemistryPetroleumEarth & ClimateEnergy and the EnvironmentOil SpillsReferenceCanolaAromatherapyBiomassLegume

The model, described in two featured articles in the August 1, 2011, issue of the Plant Journal, may help to identify ways to maximize the conversion of carbon to biomass to improve the production of plant-derived biofuels.

"To make efficient use of all that plants have to offer in terms of alternative energy, replacing petrochemicals in industrial processes, and even nutrition, it's essential that we understand their metabolic processes and the factors that influence their composition," said Brookhaven biologist Jorg Schwender, who led the development of the model with postdoctoral research associate Jordan Hay.

In the case of plant oils, the scientists' attention is focused on seeds, where oils are formed and accumulated during development. "This oil represents the most energy-dense form of biologically stored sunlight, and its production is controlled, in part, by the metabolic processes within developing seeds," Schwender said.

One way to study these metabolic pathways is to track the uptake and allotment of a form of carbon known as carbon-13 as it is incorporated into plant oil precursors and the oils themselves. But this method has limits in the analysis of large-scale metabolic networks such as those involved in apportioning nutrients under variable physiological conditions.

"It's like trying to assess traffic flow on roads in the United States by measuring traffic flow only on the major highways," Schwender said.

To address these more complex situations, the Brookhaven team constructed a computational model of a large-scale metabolic network of developing rapeseed (Brassica napus) embryos, based on information mined from biochemical literature, databases, and prior experimental results that set limits on certain variables. The model includes 572 biochemical reactions that play a role in the seed's central metabolism and/or seed oil production, and incorporates information on how those reactions are grouped together and interact.

The scientists first tested the validity of the model by comparing it to experimental results from carbon-tracing studies for a relatively simple reaction network -- the big-picture view of the metabolic pathways analogous to the traffic on U.S. highways. At that big-picture level, results from the two methods were largely consistent, providing validation for both the computer model and the experimental technique, while identifying a few exceptions that merit further exploration.

The scientists then used the model to simulate more complicated metabolic processes under varying conditions -- for example, changes in oil production or the formation of oil precursors in response to changes in available nutrients (such as different sources of carbon and nitrogen), light conditions, and other variables.

"This large-scale model is a much more realistic network, like a map that represents almost every street," Schwender said, "with computational simulations to predict what's going on." Continuing the traffic analogy, he said, "We can now try to simulate the effect of 'road blocks' or where to add new roads to most effectively eliminate traffic congestion."

The model also allows the researchers to assess the potential effects of genetic modifications (for example, inactivating particular genes that play a role in plant metabolism) in a simulated environment. These simulated "knock-out" experiments gave detailed insights into the potential function of alternative metabolic pathways -- for example, those leading to the formation of precursors to plant oils, and those related to how plants respond to different sources of nitrogen.

"The model has helped us construct a fairly comprehensive overview of the many possible alternative routes involved in oil formation in rapeseed, and categorize particular reactions and pathways according to the efficiency by which the organism converts sugars into oils. So at this stage, we can enumerate, better than before, which genes and reactions are necessary for oil formation, and which make oil production most effective," Schwender said.

The researchers emphasize that experimentation will still be essential to further elucidating the factors that can improve plant oil production. "Any kind of model is a largely simplified representation of processes that occur in a living plant," Schwender said. "But it provides a way to rapidly assess the relative importance of multiple variables and further refine experimental studies. In fact, we see our model and experimental methods such as carbon tracing as complementary ways to improve our understanding of plants' metabolic pathways."

The scientists are already incorporating information from this study that will further refine the model to increase its predictive power, as well as ways to extend and adapt it for use in studying other plant systems.

This work was supported by the DOE Office of Science.

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