New findings, published in PLOS Computational Biology, help demonstrate the evolutionary basis for allergy. Molecular similarities in food and environmental proteins that cause allergy (such as pollen), and multicellular parasites (such as parasitic worms), have been identified systematically for the first time. 

A study led by Dr Nicholas Furnham (London School of Hygiene & Tropical Medicine), supports the hypothesis that allergic reactions are a flawed antibody response towards harmless environmental allergens. 

It is thought that part of our immune system has evolved to combat and provide immunity against infection by parasitic worms. However, in the absence of parasitic infection, this same arm of the immune system can become hyper-responsive and mistakenly target allergenic proteins in food or the environment. This results in an unregulated allergic response, which can sometimes be lethal.

The researchers used computational techniques to predict which proteins in parasitic worms would cause an immune response similar to an allergic reaction in humans. Their experimental studies supported these predictions and, for the first time, they identified a protein in a parasitic worm that is similar to a protein that was previously thought to be encoded only in the genomes of plants. This protein is one of the most common proteins in pollen that causes allergy in humans.

The study provides tools that will make it easier for scientists to predict proteins in food and the environment that are likely to cause allergy, and to design protein molecules for treating allergy.

Dr Furnham said: "Our findings address an outstanding question: what makes an allergen an allergen? We've shown that the off-target effects of the immune system in allergy are due to the significant molecular similarities we have identified between environmental allergens and parasitic worm proteins. The findings demonstrate that allergy is the price we pay for having immunity to parasites."

They're among the most powerful tools for shedding new light on cancer growth and evolution, but mathematical models of the disease for years have faced an either/or stand off. 

Though models have been developed that capture the spatial aspects of tumors, those models typically don't study genetic changes. Non-spatial models, meanwhile, more accurately portray tumors' evolution, but not their three-dimensional structure. 

A collaboration between Harvard, Edinburgh, and Johns Hopkins Universities including Martin Nowak, Director of the Program for Evolutionary Dynamics and Professor of Mathematics and of Biology at Harvard, has now developed the first model of solid tumors that reflects both their three-dimensional shape and genetic evolution. The new model explains why cancer cells have a surprising number of genetic mutations in common, how driver mutations spread through the whole tumor and how drug resistance evolves. The study is described in an August 26 paper in Nature.

"Previously, we and others have mostly used non-spatial models to study cancer evolution," Nowak said. "But those models do not describe the spatial characteristics of solid tumors. Now, for the first time, we have a computational model that can do that." 

A key insight of the new model, Nowak said, is the ability for cells to migrate locally. 

"Cellular mobility makes cancers grow fast, and it makes cancers homogenous in the sense that cancer cells share a common set of mutations. It is responsible for the rapid evolution of drug resistance," Nowak said. "I further believe that the ability to form metastases, which is what actually kills patients, is a consequence of selection for local migration." 

Nowak and colleagues, including Bartek Waclaw of the University of Edinburgh, who is the first author of the study, Ivana Bozic of Harvard University and Bert Vogelstein of Johns Hopkins University, set out to improve on past models, because they were unable to answer critical questions about the spatial architecture of genetic evolution. 

"The majority of the mathematical models in the past counted the number of cells that have particular mutations, but not their spatial arrangement," Nowak said. Understanding that spatial structure is important, he said, because it plays a key role in how tumors grow and evolve. 

In a spatial model cells divide only if they have the space to do so. This results in slow growth unless cells can migrate locally. 

"By giving cells the ability to migrate locally," Nowak said, "individual cells can always find new space where they can divide. 

The result isn't just faster tumor growth, but a model that helps to explain why cancer cells share an unusually high number of genetic mutations, and how drug resistance can rapidly evolve in tumors. 

As they divide, all cells - both healthy and cancerous - accumulate mutations, Nowak said, and most are so called "passenger" mutations that have little effect on the cell. 

In cancer cells, however, approximately 5 percent are what scientists call "driver" mutations - changes that allow cells to divide faster or live longer. In addition to rapid tumor growth, those mutations carry some previous passenger mutations forward, and as a result cancer cells often have a surprising number of mutations in common. 

Similarly, drug resistance emerges when cells mutate to become resistant to a particular treatment. While targeted therapies wipe out nearly all other cells, the few resistant cells begin to quickly replicate, causing a relapse of the cancer. 

"This migration ability helps to explain how driver mutations are able to dominate a tumor, and also why targeted therapies fail within a few months as resistance evolves," Nowak said. "So what we have is a computer model for solid tumors, and it's this local migration that is of crucial importance." 

"Our approach does not provide a miraculous cure for cancer." said Bartek Waclaw, "However, it suggests possible ways of improving cancer therapy. One of them could be targeting cellular motility (that is local migration) and not just growth as standard therapies do."

U-M researchers have developed the first-ever 3D complete computer model to help study treatment for pelvic organ prolapse.

Researchers hope new supercomputer model will open door to studies and improved treatment for women with painful pelvic prolapse

It’s a mysterious condition often linked to childbirth that causes distress and discomfort and requires surgery for more than 200,000 women a year – but there’s no good way to study it.

Now, researchers at the University of Michigan have developed the first-ever 3D complete supercomputer model to help study treatment for pelvic organ prolapse, a weakening of muscles and ligaments that causes organs like the bladder to drop from their normal place. For many women, the condition causes urinary problems, painful intercourse and uncomfortable pressure.

The biomechanical model, created from a 3D MRI of a healthy 45-year-old woman, was featured in the Journal of Biomechanics.  The model was used to evaluate the effects of changing individual aspects of the complex system to see how it affects the other organs; something that can’t be done in women.

“What’s revolutionary about using computer models is that for the first time in the OB/GYN field, biomechanics is being used to understand not only what happens during birth but how those injuries may evolve into bigger problems later in a woman’s life,” says senior author John O.L. DeLancey, M.D. director of Pelvic Floor Research and the Norman F. Miller Professor of Obstetrics and Gynecology at the U-M Medical School.

“This specific biomechanical model allows us to better understand the intricacies inside the pelvic floor, such as changes in muscle strength and ligament stiffness that occur with pelvic prolapse. The hope is that this [super]computer model will help us understand why some operations to repair prolapse fail.”

DeLancey says about 15-20 percent of repair procedures for pelvic prolapse don’t work but the reasons are unclear. 

While animal models can be used to study organs like kidneys, the pelvic floor in humans is vastly different in four-legged animals. Authors say supercomputer stimulation will be a key piece of improving pelvic floor research and ultimately treatment for each individual patient.

While there are as many operations for pelvic floor disorders as there are for breast cancer and twice as many as there are for prostate cancer, authors note the condition is less talked about because of its sometimes embarrassing nature.

“Pelvic prolapse has been called a hidden epidemic because it’s not discussed as openly and as often as other conditions but it’s more prevalent than people realize,” says lead author Jiajia Luo, Ph.D., researcher with the Pelvic Floor Research Group and Biomechanics Research Laboratory at the U-M Department of Mechanical Engineering.

“Pelvic prolapse can cause great discomfort for women but we are limited in tailoring treatment. We hope that the insight gained from this sophisticated pelvic model and associated research opens the door to personalizing interventions and improving outcomes for women with this painful condition.”

CAPTION The new imaging technique could help with the early detection of Alzheimer's disease. CREDIT EPFL/Dimitri Van De Ville

Various types of information can be ascertained by the way blood flows through the brain. When a region of the brain has been activated, blood flow increases and oxygenation rises. By observing variations in blood flow with the help of non-invasive imaging, it is possible to determine which regions are at work at a given point in time and how they work together. 

On the basis of this principle, researchers Isik Karahanoglu and Dimitri Van De Ville have managed to visualize the different activation regions of the brain. They combined a new modeling technique and a medical imaging technique in a project bridging EPFL and the University of Geneva (UNIGE). The research, published in Nature Communications, provides new insights into how the brain organizes itself, and sets the stage for early diagnosis of neurological disorders like Alzheimer's, in which these networks break down.

In most brain-related disorders, several neural networks - rather than an isolated region - break down. Understanding how the regions interact provides insight into how these disorders work.

Seeing if a region is in "on" or "off" mode

There is already an imaging technique called "functional magnetic resonance imaging" (fMRI), which records variations in blood flow. But this process has its flaws. Thanks to a complex computational method, the researchers were able to clean up the imperfect signals obtained from fMRI and get a precise and dynamic picture of blood flow in the brain. They can see which regions of the brain are activated in an explicit "on" or "off" mode.

"Imagine taking pictures of a rainbow-coloured windmill that is turning very fast. With the old technique, the colours are fuzzy and run together," said Van De Ville. "With our method we can clearly see the border between each colour on each photo." Similarly, the dynamic map shows which regions activate simultaneously in the brain and where they are located. 

Non-stimulated brain for better data gathering

To identify the regions that work together, the tests were done on healthy, non-stimulated subjects. Even when in a state of 'rest' and not being used, a patient's brain has regions that are constantly activating and deactivating. "The patient must not do anything once in the MRI machine. The data are thus not distorted by the stress or fatigue that stimulation or a task could cause," said Karahanoglu.

Surprising results

In all, the researchers identified 13 main networks, i.e. those that send out the strongest signals. On average, four of these networks were active at the same time. "Until now, we thought the regions took turns activating, and that they did so with little coordination," added Van De Ville. 

A diagnostic tool for doctors

The next step consists in using this technique to diagnose neurological disorders. Alzheimer disease, for example shows deterioration in brain networks even when clinical symptoms are undetectable or negligible. Using fMRI to detect, as early as possible, cases that are most likely to develop into Alzheimer's would improve drug administration. Drugs currently in development could then be administered during the phase in which they would be most effective. Research along these lines is underway in collaboration with other neuroscience and clinical teams. Isik Karahanoglu, who is currently a post-doctoral fellow at Harvard Medical School, is also applying the technique to better understand alterations in Autism Spectrum Disorder.

Columbia University scientists have developed a computational method to investigate the relationship between birth month and disease risk. The researchers used this algorithm to examine New York City medical databases and found 55 diseases that correlated with the season of birth. Overall, the study indicated people born in May had the lowest disease risk, and those born in October the highest. The study was published in theJournal of American Medical Informatics Association.

"This data could help scientists uncover new disease risk factors," said study senior author Nicholas Tatonetti, PhD, an assistant professor of biomedical informatics at Columbia University Medical Center (CUMC) and Columbia's Data Science Institute. The researchers plan to replicate their study with data from several other locations in the U.S. and abroad to see how results vary with the change of seasons and environmental factors in those places. By identifying what's causing disease disparities by birth month, the researchers hope to figure out how they might close the gap.

Earlier research on individual diseases such as ADHD and asthma suggested a connection between birth season and incidence, but no large-scale studies had been undertaken. This motivated Columbia's scientists to compare 1,688 diseases against the birth dates and medical histories of 1.7 million patients treated at NewYork-Presbyterian Hospital/CUMC between 1985 and 2013.

The study ruled out more than 1,600 associations and confirmed 39 links previously reported in the medical literature. The researchers also uncovered 16 new associations, including nine types of heart disease, the leading cause of death in the United States. The researchers performed statistical tests to check that the 55 diseases for which they found associations did not arise by chance.

"It's important not to get overly nervous about these results because even though we found significant associations the overall disease risk is not that great," notes Dr. Tatonetti. "The risk related to birth month is relatively minor when compared to more influential variables like diet and exercise."

The new data are consistent with previous research on individual diseases. For example, the study authors found that asthma risk is greatest for July and October babies. An earlier Danish study on the disease found that the peak risk was in the months (May and August) when Denmark's sunlight levels are similar to New York's in the July and October period.

For ADHD, the Columbia data suggest that around one in 675 occurrences could relate to being born in New York in November. This result matches a Swedish study showing peak rates of ADHD in November babies.

The researchers also found a relationship between birth month and nine types of heart disease, with people born in March facing the highest risk for atrial fibrillation, congestive heart failure, and mitral valve disorder. One in 40 atrial fibrillation cases may relate to seasonal effects for a March birth. A previous study using Austrian and Danish patient records found that those born in months with higher heart disease rates--March through June--had shorter life spans.

"Faster computers and electronic health records are accelerating the pace of discovery," said the study's lead author, Mary Regina Boland, a graduate student at Columbia. "We are working to help doctors solve important clinical problems using this new wealth of data."

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