Researchers at the University of Pittsburgh School of Medicine have devised a computational model that could enhance understanding, diagnosis and treatment of pressure ulcers related to spinal cord injury. In a report published online in PLOS Computational Biology, the team also described results of virtual clinical trials that showed that for effective treatment of the lesions, anti-inflammatory measures had to be applied well before the earliest clinical signs of ulcer formation.

Pressure ulcers affect more than 2.5 million Americans annually and patients who have spinal cord injuries that impair movement are more vulnerable to developing them, said senior investigator Yoram Vodovotz, Ph.D., professor of surgery and director of the Center for Inflammation and Regenerative Modeling at the Pitt School of Medicine.

"These lesions are thought to develop because immobility disrupts adequate oxygenation of tissues where the patient is lying down, followed by sudden resumption of blood flow when the patient is turned in bed to change positions," Dr. Vodovotz said. "This is accompanied by an inflammatory response that sometimes leads to further tissue damage and breakdown of the skin."

"Pressure ulcers are an unfortunately common complication after spinal cord injury and cause discomfort and functional limitations," said co-author Gwendolyn A. Sowa, M.D., Ph.D., associate professor of physical medicine and rehabilitation, Pitt School of Medicine. "Improving the individual diagnosis and treatment of pressure ulcers has the potential to reduce the cost of care and improve quality of life for persons living with spinal cord injury."

To address the complexity of the biologic pathways that create and respond to pressure sore development, the researchers designed a computational, or "in silico," model of the process based on serial photographs of developing ulcers from spinal cord-injured patients enrolled in studies at Pitt's Rehabilitation Engineering Research Center on Spinal Cord Injury. Photos were taken when the ulcer was initially diagnosed, three times per week in the acute stage and once a week as it resolved.

Then they validated the model, finding that if they started with a single small round area over a virtual bony protuberance and altered factors such as inflammatory mediators and tissue oxygenation, they could recreate a variety of irregularly shaped ulcers that mimic what is seen in reality.

They also conducted two virtual trials of potential interventions, finding that anti-inflammatory interventions could not prevent ulcers unless applied very early in their development.

In the future, perhaps a nurse or caregiver could simply send in a photo of a patient's reddened skin to a doctor using the model to find out whether it was likely to develop into a pressure sore for quick and aggressive treatment to keep it from getting far worse, Dr. Vodovotz speculated.

"Computational models like this one might one day be able to predict the clinical course of a disease or injury, as well as make it possible to do less expensive testing of experimental drugs and interventions to see whether they are worth pursuing with human trials," he said. "They hold great potential as a diagnostic and research tool."

Germinated anthrax spores (Sterne Strain)  PNNL microbiologist Josh Powell looks at anthrax spores, which have developed into bacteria over the course of 12 hours. At low doses, researchers found growth of spores is lower in human lung cells than rabbits.

New findings to help predict risk and outcomes of anthrax attacks

Cultured human lung cells infected with a benign version of anthrax spores have yielded insights into how anthrax grows and spreads in exposed people. The study, published in the Journal of Applied Microbiology, will help provide credible data for human health related to anthrax exposure and help officials better understand risks related to a potential anthrax attack.

The study also defined for the first time where the spores germinate and shows that the type of cell lines and methods of culturing affect the growth rates.

"What we're learning will help inform the National Biological Threat Risk Assessment — a computer tool being developed by the Department of Homeland Security," said Tim Straub, a chemical and biological scientist at the Department of Energy's Pacific Northwest National Laboratory. "There is little data to estimate or predict the average number of spores needed to infect someone. By better understanding exposure thresholds, the ultimate goal is to be able to predict outcomes from terrorist incidents involving Bacillus anthracis."

There are decades of data characterizing anthrax exposure in rabbits, but there is limited understanding of how this data extrapolates to humans. When researchers delved into this, working from cultured normal lung cells from each species, they found that, at low doses, the proliferation of anthrax spores is lower in human lung cells.

It's too early to say what that means for human health, but the study's methods and results may resolve a long-standing debate on the pathogen's propagation. Researchers showed that anthrax spores germinate in the lungs before making their way to the bloodstream. That has been a point of debate in the research community, with some speculating that spores, which are invisible to the naked eye, must first enter the blood stream and then grow into bacteria that can cause damage and death.

Knowing the precise location and pathway of spore germination and understanding that the bacteria begin producing toxins that damage tissue directly in the lungs may eventually impact treatment options. The finding also likely indicates added susceptibility in individuals who already have lung issues, such as smokers or those with asthma.

Making conditions real

Most of what researchers know about anthrax comes from studying cancerous lung cells of both humans and rabbits because they are easy to grow in a lab. But cancer cells are very different from normal cells, which are referred to as primary cells.

For this study, PNNL researchers wanted to see if normal cells reacted differently. So, they carefully cultured primary rabbit lung cells on special inserts in petri dishes, coaxing them to form small pieces of 3-D lung tissue about the size of a quarter.

"The cells are fed with nutrients from below and we trick the top layer of cells into thinking they are at the air/liquid interface as they would be in a living lung," said Josh Powell, a microbiologist at PNNL.

Researchers observed the top layer of cells producing sticky mucus, which traps the anthrax spores. This did not occur with cells completely submerged in the growth medium where the spores just float on top. This suggests that this mucus facilitates germination of the spores into bacteria.

"Byproducts secreted in the mucus by lung cells, in reaction to the anthrax, cause the spore to proliferate very quickly," said Powell. "We don't know what those byproducts are yet, but this is the first time it's been shown that growth rate is impacted by these byproducts secreted by the lungs."

Additional biochemical tests revealed that nutrients in the standard culture media provide an extra, unnatural fuel that makes spores germinate faster than would likely happen in the natural lung.

"These finding have implications for how we study pathogens within in vitro cell systems," said Powell. "Understanding the impacts of the methodology ensures we get the best data we can from both species on specific rates of spore intake or dose, clearance, germination and proliferation in a lab setting."

Researchers hope to reproduce this study using the more virulent strain at DHS's National Biodefense Analysis and Countermeasures Center in Frederick, Md., rather than the similar but milder Sterne strain used in this study, which is virtually unable to cause illness in people or animals.

Predicting to protect

In the next phase of the project, researchers will put this experimental data into a supercomputational model to more accurately predict outcomes of anthrax exposure. For instance, a model based on primary cell data may calculate how much time doctors have to initiate treatment, how many spores are likely needed to cause disease or mortality in humans, or be able to determine if there is a "safe" level for exposure or a required level of cleanup of a contaminated area.

Once the models are refined with data from the latest experiments, those numbers will be checked aggoverainst animal data to see if they are indeed predicting outcomes accurately. The models could also potentially speed future drug design.

Researchers hope these fundamental findings and models can be applied to other diseases related to inhaled pathogens, such as the flu or SARS coronavirus. "This is an investment that may eventually help officials triage, treat and influence drug discovery for these lung illnesses," said Powell.

A depiction of the double helical structure of DNA. Its four coding units (A, T, C, G) are color-coded in pink, orange, purple and yellow. NHGRI

Up to one-fifth of human DNA act as dimmer switches for nearby genes, but scientists have long been unable to identify precisely which mutations in these genetic control regions really matter in causing common diseases. Now, a decade of work at Johns Hopkins has yielded a supercomputer formula that predicts with far more accuracy than current methods which mutations are likely to have the largest effect on the activity of the dimmer switches, suggesting new targets for diagnosis and treatment of many diseases.

A summary of the research will bepublished online June 15 in the journal Nature Genetics.

“Our computer program can comb through the genetic information from a specific cell type and predict which ‘dimmer switch’ mutations are most likely to alter the cell’s gene activity, and therefore its function,” says Michael Beer, Ph.D., associate professor of biomedical engineering at the Johns Hopkins University School of Medicine.

“The plan is to continually improve the formula as we learn more about these regulatory regions,” says Beer, “but already it can narrow down a list of disease-associated mutations by a factor of 20, allowing researchers to focus on the ones that are most likely to matter.”

Researchers around the world have sequenced the genomes of many patients suffering from common multigene diseases, looking for shared mutations in their control regions. The trouble is, Beer says, that these studies yield hundreds of mutations, most of which are benign. So he and his team of researchers set out to design a computer program that could learn the difference between mutations that are likely to affect gene activity levels and those that likely won't.

“There are a lot of common diseases, like diabetes, that are probably the result of several different mutations in control regions. The mutations don't directly cause a change in the proteins made, but they impact their abundance,” he says, and sorting out which ones matter most in diseases is key to advancing treatments.

The task has been difficult, Beer says, because not all mutations are created equal. A single alteration, say from a cysteine (C) to a guanine (G) in the four-letter alphabet of DNA, will have drastically different effects based on where it occurs in the genome, he explains.

“If it occurs in the middle of a gene that encodes a crucial protein, it could alter the code in such a way that no protein is made and the organism dies, or it could have no effect whatsoever if the function of the protein isn’t altered by the change,” he says. The same extremes could be true if the C to G mutation occurred outside of a gene, in a control region: The mutation could cause the region to stop working altogether, or it could have no effect. And between those extremes is everything else.

To develop the new formula, Beer says his team first “trained” its computer program to recognize potential control regions using a property called DNase sensitivity. DNase is an enzyme that cuts DNA wherever it is not tightly wound. The openness of particular sequences of DNA varies among different types of cells, and only control regions in open DNA can be active. How vulnerable certain stretches of DNA are to DNAse is therefore an indication of which control regions are important in a given cell type, Beer says.

Dongwon Lee, Ph.D., then a graduate student in Beer’s laboratory, taught the computer program to recognize the features of DNase-sensitive sequences in a type of cancer cell by giving the computer a list of already known sequences. It then predicted the rest of the DNase-sensitive sequences and measured how much individual sections of a sequence contributed to that region’s overall DNase sensitivity.

The computer then simulated “mutating” every DNA letter in turn and recalculated each section’s contribution to DNase sensitivity. The larger the change in sensitivity after a given mutation, the more likely it is that that mutation will affect gene activity levels in the cell, Beer says.

To test the validity of the formula, the team compared their computer predictions to the predictions made by alternative programs. When the programs’ “rules” were set to be equally thorough in their searches, Beer’s program was 56 percent accurate — 10 times more accurate than the next best program.

To further directly test the formula, Beer worked with Andrew McCallion, Ph.D., an associate professor at the McKusick-Nathans Institute of Genetic Medicine at the Johns Hopkins University School of Medicine, to predict the impact of mutations in the control regions for two pigment-related genes in mouse melanocytes (skin pigment cells). They then selected 40 mutations with different levels of predicted impact and tested their effect in melanocytes grown in the laboratory. When they measured the activity levels of the two genes, they found that there was a strong correlation between the program’s prediction and the actual change experienced by the cells.  

“My group has been working for over a decade to shed some light on the nature of regulatory mutations in common disease,” McCallion says. “The synergy of our careers and our strategies bring the Beer group and mine to an exciting place in this effort. By training the computer program with the right cellular material, we can now predict the consequences of previously undecipherable regulatory sequence mutations.”

Beer and his team repeated this targeted testing of their formula in mouse and human liver cells and in human leukemia cells, with similar results. They also tested their formula on three control region mutations already known to affect cholesterol levels, hemoglobin levels and prostate cancer. Again they found that these mutations drew higher computer scores than other mutations in the same control regions.

Finally, the team examined the control regions for T helper cells, a type of immune cell that can contribute to autoimmune diseases when its genes become disregulated. Their calculations identified 15 different control region mutations associated with nine different immune system disorders, from allergies to multiple sclerosis and Crohn’s disease. Importantly, Beer says, previous studies had associated nine of the same control regions with immune disorders, but they had not been able to hone in on the exact mutation that mattered.

Beer says: “The next step is to collect cells from patients with these autoimmune diseases, test their gene activity levels and find out if our predictions were right. If so, it should help us determine how the activity is being perturbed and how we can fix it.” The same process can theoretically be repeated on many other diseases, providing timesaving insights for each.

Other authors of the report include David Gorkin, Maggie Baker, Benjamin Strober and Alessandro Asoni of the Johns Hopkins University School of Medicine.

From left to right, several of the authors of the ProDeGe paper published in The ISME Journal: Nikos Kyrpides, Scott Clingenpeel, Kristin Tennessen, Tanja Woyke, Amrita Pati, and Evan Andersen. Tennessen will talk about ProDeGe at the September 2015 Microbial Genomics & Metagenomics Workshop ( at the DOE JGI.

Single cell genomics and metagenomics are pioneering techniques that have helped researchers assess environmental microbial community structure and function. As projects applying these techniques scale up, however, researchers are hindered by the lack of a high-throughput process to review assembled genome sequences. Currently removing the contaminant sequences from the microbial genomes being uploaded to public databases is a manual and time-consuming process that requires information about the contaminant sequences in order to remove them.

To help resolve this obstacle, a team from the Prokaryotic Super Program at the U.S. Department of Energy Joint Genome Institute (DOE JGI), a DOE Office of Science User Facility, has developed the first computational protocol for quick and automated removal of contaminant sequences from draft genomes. They describe the tool called ProDeGe (Protocol for Decontamination of Genomes) in a study published online June 9, 2015 in The ISME Journal.

Though the team says ProDeGe works on any type of genome sequence, for the study, it was benchmarked using 182 manually screened single amplified genomes (SAGs) from two publicly available datasets, one of them the Microbial Dark Matter project and the other using Arabidopsis endophyte data enabled by collaborators at the University of North Carolina, who are coauthoring this paper.

Speedy Sequence Decontamination

The tool classifies sequences as either "clean," or "contaminant," and runs, the team reported, at a rate of 0.30 CPU core hours per megabase of sequence. "It takes an expert approximately six hours to manually decontaminate 1 megabase of sequence," noted the study's first author Kristin Tennessen, "so using ProDeGe results in a speedup of about 20 times." If the manual user is inexperienced, she added, the increase in the decontamination rate can be even greater.

Nikos Kyrpides, head of the Prokaryote Super Program at the DOE JGI, said that the emergence of software solutions--such as ProDeGe--to daunting computational challenges is consistent with one of the three "pillars" of the Institute's 10-Year Strategic Vision. "With an emphasis on Biological Data Interpretation," he added, "the DOE JGI has played a leadership role in developing, standardizing and providing access for users to high-quality genome assemblies, annotations and other computational genomics tools."

ProDeGe is pre-calibrated to remove at least 84 percent of contaminant sequence, and the team found it performed best when it could compare the test sequence against homologs in the database that corresponded at the Class level or deeper. If the sequences belong to novel organisms, the team reported, ProDeGe removes contaminants solely by checking the sequence composition.

Sequence Decontamination Tool for Quality Control

"Given the enormous volume of environmental sequence information generated each year and the increasing popularity of single-cell genomic sequencing," said Steven Hallam, a longtime DOE JGI collaborator at the University of British Columbia and a ProDeGe user. "ProDeGe fills a critical gap in QA/QC workflows that actually scales effectively between individual users and platform services."

The research team added that, "ProDeGe is the first step towards establishing a standard for quality control of genomes from both cultured and uncultured microorganisms. It is valuable for preventing the dissemination of contaminated sequence data into public databases, avoiding resulting misleading analyses. The fully automated nature of the pipeline relieves scientists of hours of manual screening, producing reliably clean datasets and enabling the high-throughput screening of datasets for the first time. ProDeGe therefore represents a critical component in our toolkit, during an era of next-generation DNA sequencing and cultivation-independent microbial genomics."

Speaking as one who has used the ProDeGe tool, Ramunas Stepanaukas, director of the Bigelow Laboratory Single Cell Genomics Center, and a DOE JGI collaborator added that, "Single cell genomics and metagenomics have become major sources of information about the biology of the uncultured microorganisms, which are the predominant component of most ecosytems on our planet. The risk of DNA contamination is a significant challenge to both single cell genomic sequencing and metagenome assemblies. The prevention, detection and removal of contaminants from single cell genomics and metagenomics data is of key importance for understanding the ecosytems on our planet. Novel laboratory and computational tools, such as ProDeGe, will be critical to ensure high standards of data quality in these emerging research fields."

ProDeGe's web interface for uploading and analyzing datasets can be found at Standalone software for ProDeGe can be downloaded from can be run on a system with Perl, R, and NCBI Blast.

"The impact of human mobility on disease dynamics has been the focus of mathematical epidemiology for many years, especially since the 2002-03 SARS outbreak, which showed that an infectious agent can spread across the globe very rapidly via transportation networks," says mathematician Gergely Röst. Röst is co-author of a paper to be published this week in the SIAM Journal on Applied Dynamical Systems that presents a mathematical model to study the effects of individual movement on infectious disease spread. 

"More recently, the risk of polio in Europe has been elevated by human migration, and many countries were concerned about the possibility of Ebola getting out of West-Africa," continues Röst, who co-authored the paper with Diana Knipl and Pawel Pilarczyk. "There are several mathematical tools that can help in assessing how mobility facilitates disease spread, including multipatch compartmental models which are suitable to describe local disease dynamics as well as travel patterns between distinct locations, such as major cities."

The basic reproduction number--referred to as R nought or R0--the central quantity in epidemiology, determines the average number of secondary infections caused by a typical infected individual in a susceptible population. In most cases, when the reproduction number is less than 1, the system has only the "disease-free" equilibrium, and the disease is expected to die out. As the number increases through 1, a stable "endemic" equilibrium emerges, that is, the disease is maintained in the population without the need for external inputs. 

"Usually, to control an epidemic it is sufficient to decrease the basic reproduction number, R0, to below one," explains Knipl. "However, some models exhibit an interesting phenomenon called backward bifurcation, in which case the disease can sustain itself even if the reproduction number is less than one, thereby making disease control more difficult. In recent years, there has been a stream of papers in the literature investigating when such a bifurcation situation can occur in the study of various diseases."

When a backward bifurcation occurs, stable endemic equilibria can co-exist with a stable disease-free equilibrium even when the reproduction number is less than one. This makes it insufficient to simply reduce R0 to below 1 in order to eliminate disease. R0 must be further reduced to avoid endemic states and ensure eradication. Hence, a backward bifurcation has important public health implications. 

The impact of individual mobility on disease dynamics is investigated by the authors by studying the spread of disease when the population is distributed over two geographically discrete locations, connected by travel. The model uses a three-dimensional system as an epidemic building block, and then adds linear terms to equations to account for inflow and outflow of individuals during travel. 

"In our research, we investigated the interplay of backward bifurcation and spatial dispersal. As no such study has been done before, we started with a minimal system that includes only two patches and three compartments in each location such that the local dynamics shows backward bifurcation," explains Knipl. "It was fascinating to see how rich dynamics were generated by our model: instead of the usual disease free and single endemic equilibria, we found that up to nine equilibria can exist." 

The authors calculate the basic reproduction number for the two-patch model, allowing corresponding parameters in the two regions to differ. The analysis then focuses on the effect of spatial dispersal between two identical patches. Disease eradication in one patch is impossible as long as infection is present in the other region and R0 is greater than 1. The authors also show that spatial dispersal of individuals won't decrease the epidemic size when R0 is larger than 1. But for R0 less than 1, they show that a large number of steady states can exist in the model and increasing travel volumes can create or destroy these equilibria. The authors then define a critical value for the reproduction number, decreasing the value below which ensures disease eradication. 

The rich dynamics resulting from spatial dispersal is not normally seen in simple epidemic models. The stability of steady states and their bifurcations and dynamics are investigated with analytical tools and numerical simulations. 

"One of the innovative aspects of our research is the application of three different approaches to the analysis of the dynamics. Classical analytical methods are complemented with numerical simulations, and also with rigorous set-oriented numerical methods," says Pilarczyk. "Set-oriented numerical methods are special numerical tools in which sets of solutions are analyzed collectively, as opposed to following individual trajectories. They require substantial computational resources, but provide mathematically reliable results." 

While the model was not constructed to study a specific epidemic, the authors say that it can be considered as a prototype system that unites two phenomena that have been studied only separately before. They believe that similar bifurcation diagrams will be found in the future when studying more realistic models concerning a wide variety of diseases. They hope their paper will motivate such future work whenever spatial mobility and backward bifurcation are intertwined.

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