How Cells Manage Chance

We asked the heads of our scientific divisions to tell us about some of the big questions in fundamental biomedical science that researchers are investigating with NIGMS support. This article is the second in an occasional series that explores these questions and explains how pursuing the answers could advance understanding of important biological processes.

Sample slide, variability of mRNA in yeast cells
The number of copies of mRNA molecules (bright green) observed here in yeast cells (dark blue) fluctuates randomly. Credit: David Ball, Virginia Tech.

For some health conditions, the cause is clear: A single altered gene is responsible. But for many others, the path to disease is more complex. Scientists are working to understand how factors like genetics, lifestyle and environmental exposures all contribute to disease. Another important, but less well-known, area of investigation is the role of chance at the molecular level.

One team working in this field is led by John Tyson Exit icon at Virginia Tech. The group focuses on how chance events affect the cell division cycle, in which a cell duplicates its contents and splits into two. This cycle is the basis for normal growth, reproduction and the replenishment of skin, blood and other cells throughout the body. Errors in the cycle are associated with a number of conditions, including birth defects and cancer. Continue reading

The Simple Rules Bacteria Follow to Survive

Left: Football stadium. Right: Colored contoured lines showing the periodic stops in the growth of a bacterial colony
Football image credit: Stock image. The colored contoured lines show the periodic stops in the growth of a bacterial colony. Credit: Süel Lab, UCSD.

What do these images of football fans and bacterial cells have in common? By following simple rules, each individual allows the group to accomplish tasks none of them could do alone—a stadium wave that ripples through the crowd or a cell colony that rebounds after antibiotic treatment.

These collective behaviors are just a few examples of what scientists call emergent phenomena. While the reasons for the emergence of such behavior in groups of birds, fish, ants and other creatures is well understood, they’ve been less clear in bacteria. Two independent research teams have now identified some of the rules bacterial cells follow to enable the colony to persist. Continue reading

Data-Mining Study Explores Health Outcomes from Common Heartburn Drugs

Results of a data-mining study suggest a link between a common heartburn drug and heart attacks. Credit: Stock image.

Scouring through anonymized health records of millions of Americans, data-mining scientists found an association between a common heartburn drug and an elevated risk for heart attacks. Their preliminary results suggest that there may be a link between the two factors.

For 60 million Americans, heartburn is a painful and common occurrence caused by stomach acid rising through the esophagus. It’s treated by drugs such as proton-pump inhibitors (PPIs) that lower acid production in the stomach. Taken by about one in every 14 Americans, PPIs, which include Nexium and Prilosec, are the most popular class of heartburn drugs.

PPIs have long been thought to be completely safe for most users. But a preliminary laboratory study published in 2013 suggested that this may not be the case. The study, led by a team of researchers at Stanford University, showed that PPIs could affect biochemical reactions outside of their regular acid suppression action that would have harmful effects on the heart. Continue reading

Digging Deeply Into Data for the Causes of Disease

Hunting for the cause of a disease can be like tracing a river back to its many sources. Myriad factors, large and small, may contribute to a condition. One approach to the search focuses on the massive amounts of genomic and other biological data that scientists are gathering in the course of their studies. To examine this data and look for meaningful patterns and other clues, scientists turn to bioinformatics, a field focused on the development of analytical methods and software tools.

Here are a few examples of how National Institutes of Health-funded scientists are using bioinformatics to dig deeply into data and learn more about the development of diseases, including Huntington’s, preeclampsia and asthma.

Huntington’s Disease

Network of proteins that interact with huntingtin

Researchers have mapped a network of 2,141 proteins that all interact either directly or through one other protein with huntingtin (red), the protein associated with Huntington’s disease. Credit: Cendrine Tourette, Buck Institute for Research on Aging, J Biol Chem 2014 Mar 7;289(10):6709-26 Exit icon.

The cause of Huntington’s disease, a degenerative neurological disorder with no known cure, may appear simple. It begins with a change in a single gene that alters the shape and functioning of the huntingtin protein. But this protein, whether in its normal or altered form, does not act alone. It interacts with other proteins, which in turn interact with others.

A research team led by Robert Hughes of the Buck Institute for Research on Aging set out to understand how this ripple effect contributes to the breakdown in normal cellular function associated with Huntington’s disease. The scientists used experimental and computational approaches to map a network of 2,141 proteins that interact with the huntingtin protein either directly or through one other protein. They found that many of these proteins were involved in cell movement and intercellular communication. Understanding how the huntingtin protein leads to mistakes in these cellular processes could help scientists pursue new approaches to developing treatments. Continue reading

Meet Karen Carlson

Karen Carlson
Credit: Karen Carlson
Karen Carlson
Fields: Systems biology, bacterial biofilms
Born and raised in: Alaska
Undergraduate student at: The University of Alaska, Anchorage
When not in the lab, she’s: Out and about with her 3-year-old son, friends and family
Secret talent: “I make some really good cookies.”

Karen Carlson got a surprise in her 10th grade biology class. Not only did she find out that she enjoyed science (thanks to an inspiring teacher), but, as she puts it, “I realized that I was really good at it.”

In particular, she says, “I was good at putting all the pieces [of a scientific question] together. And that’s what I had the most fun with—looking at systems: how things fit together and the flow between them.”

These are perfect interests for a budding systems biologist, which is what Carlson is on her way to becoming. She’s a senior in college on track to graduate this year with a bachelor’s degree in biology from the University of Alaska, Anchorage (UAA). Next, she plans to enroll in a master’s degree program at UAA, and eventually to pursue a Ph.D. in a biomedical field. Continue reading

Simulating the Potential Spread of Measles

Try out FRED Measles:

  1. Go to http://fred.publichealth.
    pitt.edu/measles
    Exit icon
  2. Select “Get Started”
  3. Pick a state and city
  4. Play both simulations

To help the public better understand how measles can spread, a team of infectious disease computer modelers at the University of Pittsburgh has launched a free, mobile-friendly tool that lets users simulate measles outbreaks in cities across the country.

The tool is part of the Pitt team’s Framework for Reconstructing Epidemiological Dynamics, or FRED, that it previously developed to simulate flu epidemics. FRED is based on anonymized U.S. census data that captures demographic and geographic distributions of different communities. It also incorporates details about the simulated disease, such as how contagious it is.

Screenshot of the FRED simulation.

A free, mobile-friendly tool lets users simulate potential measles outbreaks in cities across the country. Credit: University of Pittsburgh Graduate School of Public Health.

Continue reading

E. Coli Bacteria as Medical Sensors and Hard Drives?

E.Coli
Modified E. coli bacteria can serve as sensors and data storage devices for environmental and medical monitoring. Credit: Centers for Disease Control and Prevention. View larger image

E. coli bacteria help us digest our food, produce vitamin K and have served as a model organism in research for decades. Now, they might one day be harnessed as environmental or medical sensors and long-term data storage devices Exit icon.

MIT researchers Timothy Lu Exit icon and Fahim Farzadfard modified the DNA of E. coli cells so that the cells could be deployed to detect a signal (for example, a small molecule, a drug or the presence of light) in their surroundings. To create the modified E. coli, the scientists inserted into the bacteria a custom-designed genetic tool.

When exposed to the specified signal, the tool triggers a series of biochemical processes that work together to introduce a single mutation at a specific site in the E. coli’s DNA. This genetic change serves to record exposure to the signal, and it’s passed on to subsequent generations of bacteria, providing a continued record of exposure to the signal. In essence, the modified bacteria act like a hard drive, storing biochemical memory for long periods of time. The memory can be retrieved by sequencing the bacteria or through a number of other laboratory techniques. Continue reading

Forecasting Infectious Disease Spread with Web Data

Just as you might turn to Twitter or Facebook for a pulse on what’s happening around you, researchers involved in an infectious disease computational modeling project are turning to anonymized social media and other publicly available Web data to improve their ability to forecast emerging outbreaks and develop tools that can help health officials as they respond.

Mining Wikipedia Data

Screen shot of the Wikipedia site
Incorporating real-time, anonymized data from Wikipedia and other novel sources of information is aiding efforts to forecast and respond to emerging outbreaks. Credit: Stock image.

“When it comes to infectious disease forecasting, getting ahead of the curve is problematic because data from official public health sources is retrospective,” says Irene Eckstrand of the National Institutes of Health, which funds the project, called Models of Infectious Disease Agent Study (MIDAS). “Incorporating real-time, anonymized data from social media and other Web sources into disease modeling tools may be helpful, but it also presents challenges.”

To help evaluate the Web’s potential for improving infectious disease forecasting efforts, MIDAS researcher Sara Del Valle of Los Alamos National Laboratory conducted proof-of-concept experiments involving data that Wikipedia releases hourly to any interested party. Del Valle’s research group built models based on the page view histories of disease-related Wikipedia pages in seven languages. The scientists tested the new models against their other models, which rely on official health data reported from countries using those languages. By comparing the outcomes of the different modeling approaches, the Los Alamos team concluded that the Wikipedia-based modeling results for flu and dengue fever performed better than those for other diseases. Continue reading

Asking Our Expert About Modeling Ebola

Irene Eckstrand
NIGMS’ Irene Eckstrand answers questions about modeling Ebola. Credit: National Institute of General Medical Sciences.

Ebola is the focus of many NIH-supported research efforts, from analyzing the genetics of virus samples to evaluating the safety and effectiveness of treatments and vaccines. Researchers involved in our Models of Infectious Disease Agent Study, or MIDAS, have been using computational methods to forecast the potential course of the outbreak and the impact of various intervention strategies.

Wondering how their work is going, I recently asked our modeling expert Irene Eckstrand a few questions.

How useful are the forecasts?

Forecasts give us a range of possible outcomes. In addition to being a useful public health tool to prepare for an outbreak, they’re an important research tool to test assumptions about how a disease may spread. When we compare the predicted and actual outcomes, we can confirm assumptions, such as the groups of individuals who are more likely to spread the infection to others. Continually doing this helps refine the models and ensure that their forecasts are as accurate as possible.

What are some of the challenges the modelers face?

Ebola virus
Ebola virus particles (green) attached to and budding from a cell (blue). Credit: National Institute of Allergy and Infectious Diseases.

We need data to build and test models. The data available from this outbreak have been more limited than in most previous outbreaks of Ebola simply because the public health systems are overwhelmed with sick people, and recording information is a secondary priority.

Another issue with forecasting future trends is incorporating information about the deployment of resources and the implementation of interventions that actually slowed the outbreak. We also need to incorporate changes in people’s behavior. If people think an outbreak is leveling off, they may relax the precautions they’ve been taking—and that could lead to another spike in the disease.

What other Ebola-related projects are the MIDAS modelers working on?

The MIDAS researchers are:

  • Modeling logistical factors such as the number and placement of treatment beds and staffing needs.
  • Tracking potential transmission within and between communities and at hospitals and funerals.
  • Developing a method to estimate the amount of underreporting of case data.
  • Applying models of “tipping points” to look for evidence that the disease curve is slowing.
  • Estimating the number of people who are infected but not symptomatic.
  • Creating new resources for Ebola modelers, including standards for using infectious disease data.
  • Calculating the risk of importation of cases for a wide variety of countries based on travel networks.

How are the modelers working together?

The MIDAS modelers conference call 1-2 times a week to discuss results, modeling strategies, data sources and questions amenable to modeling. They also participate in discussions with government and other academic groups, so there’s a sizable number of modelers working on a wide variety of public health, logistical and basic research questions.

If you’re interested in learning more about Ebola, Irene recommends a video overview of the 2014 outbreak from Penn State University Exit icon and a slide presentation on the myths and realities of the disease from Nigeria’s Kaduna State University Exit icon.

Intercepting Amyloid-Forming Proteins

Structure of a protein involved in disease-associated amyloid fibrils.
A molecule targets the intermediary structure of a protein involved in disease-associated amyloid fibrils. Credit: University of Washington.

Alzheimer’s disease, type 2 diabetes and many other illnesses are linked to the buildup of proteins whose structures have changed into shapes that enable the formation of cell-entangling threads called amyloid fibrils. About 10 years ago, researchers led by Valerie Daggett of the University of Washington used computer simulations to suggest that such proteins, on their way to creating fibrils, form an intermediary structure called an alpha sheet that’s even more toxic to cells than fibrils. Now Daggett’s team has experimentally investigated this possibility. The scientists made alpha sheet molecules expected to bind to amyloid-forming proteins in the computationally predicted intermediate state. When they tested the molecules on two amyloid disease-related proteins, they observed a substantial reduction in fibril formation. The work is still very preliminary, but it highlights a potential new avenue for treating a range of amyloid-related diseases.

This work also was funded by NIH’s National Institute of Allergy and Infectious Diseases.

Learn more:
University of Washington News Release Exit icon
Daggett Lab Exit icon
Monster Mash: Protein Folding Gone Wrong Article from Inside Life Science