In the face of a flu epidemic, a one-size fits all vaccine strategy won't be effective, a York University study has found. Instead, strategies need to change significantly depending on the characteristics of each region in Canada and how easily the particular flu strain spreads.

"The window of time around the onset of the epidemic is going to be vastly different between a remote population and an urban one, and this is something public health needs to pay attention to when developing vaccine strategies," says York U researcher and study lead Seyed Moghadas. "Different populations require different vaccination policies to minimize the impact of the disease."

The study, published today in the journal Scientific Reports by Nature, looked at which strategies produce the lowest number of infections and lead to the least number of hospitalizations.

"Studies such as this, that combine big data and [super]computer simulations, have the potential to inform evidence-based decision-making in public health," said Marek Laskowski, a York U researcher involved in the study.

The research found that the different age demographics of remote and urban populations have a significant impact on the outcome of vaccinations. Many remote areas of the country have a higher percentage of children, who are key transmitters of the virus, compared with urban centres, which generally have fewer children, but more young and middle-aged adults.

The research looked at how the different areas responded when the flu vaccine was given in either a single dose or two doses before, during and after the start of the epidemic.

The study found that for most strategies the attack rates of the virus in the urban population was lowest for children under five, but in the remote population, adults older than 50 had the lowest attack rates. But those attack rates varied depending on when and how the vaccinations were given.

There is a window of time before and after the onset of an epidemic when the choice of vaccination strategy could significantly affect the outcome, said Moghadas. Demographic variables could play an important role in determining which strategy to use.

"In all strategies for a highly transmissible virus, delivering the vaccine after the start of the epidemic had no or minimal effect," he said. Even with the usual seasonal flu virus, if the vaccine was given after the virus has started to spread, it has little effect on who and how many people get sick.

Early vaccination leads to the best outcomes from both a public health and socioeconomic perspective, he said. It reduces the rates of infection, hospitalization and death, along with stress on the healthcare system.

The research is significant especially in light of new technologies that promise quicker production of flu vaccines, unlike the current egg-based technology which takes four to six months.

"In the case of epidemic emergencies, that's actually a very long process. A time line of six months for vaccine production means it is basically the end of the epidemic by the time we get the vaccine," said Moghadas.

As new technology allows for faster vaccines, strategies to distribute them quickly need to be in place and those strategies will depend on the makeup of each region.

First genome-wide characterization of short genetic repeats shows that they regulate gene expression -- study broadens understanding of the genetic basis of different illnessesIn the first study to run a genome-wide analysis of Short Tandem Repeats (STRs) in gene expression, a large team of super computational geneticists led by investigators from Columbia Engineering and the New York Genome Center has shown that STRs, thought to be just neutral, or "junk," actually play an important role in regulating gene expression. The work, which uncovers a new class of genetic variants that modulate gene expression, is published on Nature Genetics's Advance Online Publication website on December 7.

"Our work expands the repertoire of functional genetic elements," says the study's leader Yaniv Erlich, who is an assistant professor of computer science at Columbia Engineering, a member of Columbia's Data Science Institute, and a core member of the New York Genome Center. "We expect our findings will lead to a better understanding of disease mechanisms and perhaps eventually help to identify new drug targets."

Genomic variants are what makes our DNA different from each other, and come, Erlich explains, "like spelling errors in different flavors." The most common variants are SNPs (single nucleotide polymorphisms). Computational geneticists have been focused mostly on SNPs that look like a single letter typo--mother vs. muther--and their effect on complex human traits.

Erlich's study looked at Short Tandem Repeats (STRs), variants that create what look like typos: stutter vs. stututututututter. Most researchers, assuming that STRs were neutral, dismissed them as not important. In addition, these variants are extremely hard to study. "They look so different to analysis algorithms," Erlich notes, "that they just usually classify them as noise and skip these positions."

Erlich used a multitude of statistical genetic and integrative genomics analyzed to reveal that STRs have a function: they act like springs or knobs that can expand and contract, and fine-tune the nearby gene expression. Different lengths correspond to different tensions of the spring and can control gene expression and disease traits. He is calling these variants eSTRs, or expression STRs, to note that they regulate gene expression. He and his team also discovered that these eSTRs can be associated with a range of conditions including Crohn's diseases, high blood pressure, and a range of metabolites. These eSTRs explain on average 10 to 15% the genetic differences of gene expression between individuals.

"We've known that STRs are known to play a role in these diseases, but no one has ever conducted a genome-wide scan to find their effect on complex traits," Erlich adds. "If we want to do personalized medicine, we really need to understand every part of the genome, including repeat elements--there's a lot of exciting biology ahead."

Erlich and his team, which included researchers from Harvard, MIT, Stanford University, and Mount Sinai, plan next to study the effect of these eSTRs on more human diseases and better understand their molecular mechanism.

Andrew Ferguson

Borrowing from several statistical science models, an interdisciplinary team of researchers from the University of Illinois at Urbana-Champaign has developed a novel supercomputational approach for massively accelerating the search for a hepatitis C vaccine.

"Hepatitis C virus infects 170 million people and kills 350,000 annually," explained Andrew L. Ferguson, an assistant professor of materials science and engineering and of chemical and biomolecular engineering at Illinois. "Effective drug treatments have recently become available, but their high cost makes them effectively unavailable in the developing world where most infections exist.

"A vaccine offers the best hope for global control of the epidemic, but despite 20 years of study, none yet exists. A challenge to vaccine design is that we do not know what parts of the virus we should target to best protect the host. In other words, we do not know how to hit the virus where it hurts. In this work, we present an approach to systematically identify vulnerable targets and computationally design hepatitis C vaccine candidates predicted to cripple the virus."

By applying so-called "spin glass" models from statistical physics commonly used to describe the behavior of magnets and fluids, the researchers translated clinical databases of hepatitis C virus sequences into "fitness landscapes" quantifying the replicative capacity of the virus as a function of its amino acid sequence. Charting the peaks and valleys of viral fitness, the fitness landscape reveals how best to attack the virus to force it from the high-fitness peaks down into the low-fitness valleys where it is least able to replicate and harm the host.

"We have computed the fitness landscape for the hepatitis C virus protein responsible for viral replication to identify parts of the virus most susceptible to immune attack," said Gregory R. Hart, a graduate researcher in physics and first author of the paper, "Empirical fitness models for hepatitis C virus immunogen design," appearing in the journal, Physical Biology. The research team used its model to computationally test 16.8 million candidate vaccines to identify 86 optimal formulations targeting viral vulnerabilities highly susceptible to vaccine-induced immune attack by the T-cells (white blood cells) of the host immune system.

"By identifying a small number of promising vaccine candidates within the vast search space of possible designs, our computational approach can guide experimental vaccine development and massively accelerate the search for a hepatitis C vaccine," Ferguson added. "We anticipate that with increasing computational power and reducing sequencing costs, it will soon become feasible within the coming years to apply our technology to the complete HCV proteome and perform rational in silico design of a complete anti-HCV immunogen."

Researchers at the University of Iowa College of Dentistry are partnering with a private company to develop supercomputer simulations that can help personalize cancer care by predicting how a patient will respond to a drug treatment.

The key is the creation of "virtual tumors" which are based on a patient's cancer cells and specific cancer genes. 

"Virtual tumors can be used to test the ability of drug treatments to treat cancer cell-induced immunosuppression on the host," says Kim Alan Brogden, Director of the Dows Institute for Dental Research at the UI College of Dentistry. "Thus, we are better able to zero in on what type of treatment would work best for that individual's cancer." This is Kim Alan Brogden, University of Iowa.

Brogden then tries to replicate the process in a lab by growing live cancer cells with the same genetic makeup and testing their response to the identical immunotherapy. If the response is the same, then they have identified a treatment that will work for that individual cancer patient.

"In our current studies," Brogden says, "we are seeing a 85 percent to 86 percent correlation of matches."

Brogden presented the research Dec. 5 during the 57th American Hematological Society Annual Meeting and Exposition in Orlando, Fla.

Here's how the test works: First, researchers take the genetic information from a cancer cell, import it to a computer simulation, and predict the response that cell would have to a particular treatment.

Next, they take live cancer cells, grow them in the laboratory, and determine the actual response that cell would have to the identical treatment.

If researchers get the same results from both experiments, they have a match. The cells growing in the laboratory have verified that the computer model works. If they give different results, then researchers have a mismatch, meaning the simulated model and lab tests are not in agreement and need to be aligned.

"Our goal is to develop a very patient-specific workflow that could be used early after [the] cancer diagnosis to aid in the identification of effective cancer treatments," says Brogden, who has research projects in microbiology, inflammation, and oral cancer.

The UI has been collaborating with Cellworks Group, Inc., a private company that works to personalize cancer treatment by developing virtual tumors based on a person's genetic profile.

Researchers say the technology is timely, particularly to pharmaceutical partners who want to test their cancer drugs using these simulated models.

Brogden explains that many cancers protect themselves from the immune system by overriding a patient's immune checkpoints. These checkpoints have become important targets for treating cancers through the use of drugs called "checkpoint inhibitors," which are often made of antibodies and unleash an immune system attack on cancer cells. The problem is some of these drugs only have a response rate of less than 20.5 percent in patients.

"Therefore, the success of current therapy depends upon a precision medicine approach: finding the right treatment for the right patient within a reasonable time," Brogden says.

The simulation and laboratory models also allow for the screening of combination treatments, which could involve more than one immunotherapeutic agent or a combination of immunotherapeutic and chemotherapeutic agents.

Ultimately, researchers say they hope their work leads to a personalized medicine approach that will save treatment time, cut costs, and improve long-term prognoses for cancer patients.

Investing more than 900,000 euros, the German Federal Ministry of Education and Research (BMBF) and the US-American National Institute of Health are funding a close research cooperation between Prof Dr Sen Cheng from the Mercator Research Group “Structure of Memory” at the Ruhr-Universität Bochum and Prof Dr Kamran Diba from the University of Wisconsin-Milwaukee. Together, both researchers and their three collaborators will investigate “Neural network mechanisms of sequence generation in the hippocampus”. The three-year project begins in December 2015.

Objective: shed light upon the neuronal foundations of memory

The hippocampus, a brain structure in the shape of a seahorse, plays a vital role for memory, especially for episodic memory. It is particularly relevant for remembering personal experiences. In order to understand the underlying neuronal mechanisms in the hippocampus, the activity patterns of individual neurons have to be understood. In the hippocampus of rodents, neurons are only activated if the animal is located in certain positions. These place cells are reactivated in different temporal sequences, even during sleep. It is thought that those temporal sequences are necessary for consolidating memory contents. In the course of their joint project, Sen Cheng and Kamran Diba want to investigate which mechanisms generate these neuronal sequences.

Interlocking theoretical and experimental approach

The collaboration between the two researchers is interdisciplinary. In his capacity as theoretical neuroscientists, Prof Sen Cheng develops computer models of the hippocampus. Prof Kamran Diba, on the other hand, conducts experimental work, thus gathering information on brain activity. A close collaboration between theory and experiment was a critical requirement for the funding. Researchers from Milwaukee and Bochum will maintain an ongoing exchange. This will especially benefit the junior researchers involved in the project who will thus gain international research experience.

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