Novel insights into pathogen behavior

A new study by a team of researchers that includes University of Notre Dame scientists Joshua Shrout and Mark Alber provides new insights into the behavior of an important bacterial pathogen.

Alber, Vincent J. Duncan Family Professor of Applied Mathematics, and Shrout, an associate professor of civil and environmental engineering and earth sciences, studied Pseudomonas aeruginosa, an opportunistic pathogen responsible for both acute and persistent infections.

"While this ubiquitous environmental bacterium rarely infects healthy people, it is a common pathogen among susceptible populations, such as individuals with cystic fibrosis, burn victims, ventilator patients, and those who have had intestinal reconstruction," Shrout said. "Pseudomonas aeruginosa is among the most common hospital-acquired infection pathogens and causes of death for intensive care unit patients."

The researchers investigated, using combination of experiments and computational modeling, how bacteria swarm in groups containing millions of cells.

"We show in this paper that appendages of this bacterium called 'pili' link together to alter group motion and give swarming groups a form of braking power," Alber said." These bacterial swarms are able to change their motion as a group to avoid toxins. We showed this by demonstrating that bacteria with pili will avoid an area containing antibiotic--but cells without these pili do not slow their motion, swarm into the antiobiotic region, and are killed."

Although the study focused on Pseudomonas aeruginosa, the results potentially offer insights into the behavior of other bacteria.

"This is a very fundamental discovery that gives us insight into the response and control of bacteria that alter their behavior as an entire group," Shrout said. This knowledge may be useful to understand how pathogens and other bacteria can evade compounds we might use to control them and advance our understanding of how some infections become so difficult to cure."

"The next steps are to more specifically determine how individual cells are behaving in these swarming groups to detail how they coordinate their motion and then apply this knowledge to understand colonization of different types of surfaces such as human cells or medical plastics," Alber said.

The study appears in the Proceedings of the National Academy of Sciences (PNAS). The research was funded by a grant from the National Institutes of Health (NIH).

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The above story is based on materials provided by University of Notre Dame . Note: Materials may be edited for content and length.

Predicting antibiotic resistance

Treating bacterial infections with antibiotics is becoming increasingly difficult as bacteria develop resistance not only to the antibiotics being used against them, but also to ones they have never encountered before. By analyzing genetic and phenotypic changes in antibiotic-resistant strains of E. coli, researchers at the RIKEN Quantitative Biology Center (QBiC) in Japan have revealed a common set of features that appear to be responsible for the development of resistance to several types of antibiotics.

The study published in Nature Communications shows that resistance emerges through mutations that converge on similar physical changes in the bacteria. Quantifying these changes by measuring the expression of a small number of genes can be useful in predicting a bacteria's response to a given antibiotic, and knowing which genes are important may contribute to the development of new ways to prevent resistance.

To perform this complex genetic and phenotypic analysis, Shingo Suzuki, Takaaki Horinouchi, and Chikara Furusawa first used a technique called laboratory evolution to create 44 strains of E. coli, each resistant to one of 11 different antibiotics. Then they examined how each of these resistant strains responded to 25 antibiotics they had never encountered. The tests showed that most strains had developed resistance to several of the 25--a phenomenon called cross-resistance--even when these antibiotics worked differently from the one used to generate the resistance. The team also found that in the case of two classes of antibiotics, cross-susceptibility developed--bacteria that become resistant to one type became more vulnerable to the other.

The researchers reasoned that similar alterations in gene expression might be one cause of cross-resistance. To test this hypothesis, they identified the changes in gene expression for each of the resistant strains using microarray analysis. Then, they combined this information with the resistance, cross-resistance, and cross-susceptibility data from some strains to make a simple linear model that did a very good job predicting the resistance and susceptibility patterns of the remaining strains. Furusawa notes that, "these high-precision predictions were possible using a small number of genes, making this a powerful way to describe a bacteria's phenotype and its expected response to antibiotics."

The researchers also looked for fixed mutations that might link the development of resistance across antibiotics. For example, they found that almost all strains had fixed mutations affecting a particular multidrug efflux pump--a pump that bacteria use to expel unwanted molecules. However, one of the main findings was that although the bacteria showed similar changes in expression patterns, these often resulted from different changes in the genome, and frequently from a combination of several different mutations. Furusawa speculates that, "this type of convergent evolution may be a key factor that drives the development of antibiotic resistance."

The cross-susceptible classes of antibiotics were found to have gene expressions and fixed mutations that did not overlap at all. Strains resistant to aminoglycoside antibiotics, for example, showed mutations and down-regulation of genes that while effective in blocking aminoglycoside antibiotics, also resulted in less effective multidrug efflux pumps. This explains why these strains became more susceptible to all the other antibiotics--the bacteria could not send them out of their cells.

Understanding the common factors that result in antibiotic resistance could help combat this growing problem. As Furusawa reflects, "by making it possible to quantitatively determine what genes contribute to the development of antibiotic resistance, this research could lead to new methods for blocking acquisition of resistance and to the development of new antibiotic compounds."

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The above story is based on materials provided by RIKEN . Note: Materials may be edited for content and length.

You Can See Our Holiday Lights All the Way From Space

SAN FRANCISCO—People love the holidays so much you can see it from space.

Data collected by a satellite with a special nighttime sensor found that the glow from our collective light displays brightens many major U.S. cities as much as 50 percent between Thanksgiving and New Year’s Day.

A team led by NASA scientist Miguel Román presented nighttime light data from the Suomi NPP satellite yesterday during a press conference here at the American Geophysical Union meeting.

The polar-orbiting satellite, jointly run by NASA and NOAA, has an instrument that gathers data in 22 different bands of visible and infrared light. It can detect all sorts of light including fires, auroras, moonlight reflecting off of ice and clouds, the nocturnal glow of the atmosphere, highway lights, and even the light from a single boat in the sea.

Román’s team used an algorithm to filter out all the lights except for lights in 70 U.S. cities for the past two years. They found that major cities such as Dallas, Washington D.C. and Phoenix were between 20 and 50 percent brighter during the holiday season. They limited their study to cities that don’t typically see snow, which is so reflective that it throws measurements off.

The maps above show the brightness difference during the holidays relative to the rest of the year. Red areas indicate a decrease in brightness, yellow areas were unchanged, and green areas were brighter. If you look closely, you can see some patterns emerge.

“Where are the green areas? They are in the suburbs and the exurbs and the periphery,” Roman said. “People are leaving work for the holidays and they’re turning on the lights.”

The U.S. isn’t the only place brightening for the holidays. The pattern was also clear in 30 major towns in Puerto Rico, which is known for its nighttime holiday celebrations.

Suomi NPP also picked up a change in the Middle East. Román and Eleanor Stokes, a graduate student at Yale University, saw a distinct nighttime brightening there during Ramadan. Because Muslims fast between dawn and dusk during the holy month, a lot of activity shifts to later in the evening, causing a measurable increase in brightness in cities such as Cairo and Riyadh. Other cities, such as Instanbul, had a much smaller increase, and some regions in places like Syria and Iraq had no change at all, possibly due to unstable electrical grids, conflict, or cultural differences.

Roman and Stokes were even able to pick out distinct differences between neighborhoods in Cairo and compared the relative brightness to socioeconomic data. They found that for much of Ramadan, lighting did not increase in some of the poorest and most devout areas of the city. But, all of Cairo lit up for the celebration marking the end of Ramadan.

“These nighttime lights really are in some ways the EKG of cities,” Stokes said.