Tag Archives: genomics

Caught in the act: Video system for mapping behaviors (Royal Society: Interface)

Studies of animal behavior have come a long way from the days when scientists followed their subjects around with pen and notepad. But although cameras have replaced clipboards, evaluating the resulting videos is still a cumbersome process.

To make that job easier and more comprehensive, researchers at Princeton have built a computerized system that analyzes videos to reveal what animals do, how often and for how long, and then generates an easy-to-understand map of the behaviors.

By looking at the map, the researchers can tell what sorts of movements the animal did without having to spend time watching the video. They used the system to track the behavior of fruit flies (Drosophila melanogaster) and found that it accurately detected behaviors such as grooming a leg or waggling a wing.

Map that indicates the type of movements of a fly.

Princeton researchers have developed a computerized system for evaluating animal behavior. A video system records an animal, in this case a fruit fly, for one hour. Then a computer program analyzes the video to create a map of the behaviors. The map can reveal, for example, the differences in movements between male and female fruit flies. (Image source: Joshua Shaevitz)

The researchers’ goal was a system that could create accurate records of behaviors for use in studies of the mechanisms behind those behaviors – in other words, the genes and brain circuits that govern movements. Knowing which genes and neural circuits govern behavior will help answer basic scientific questions and could shed light on the mechanisms behind conditions such as autism.

Described in the journal The Royal Society Interface, the system was built for monitoring Drosophila flies, which are commonly used in studies of genes, but the researchers say that the system can also analyze the movements of other creatures, including worms, mice and humans.

The new system is a significant advance over current approaches because it takes note of all the animal’s activities, not just behaviors that seem important to researchers, said Joshua Shaevitz, associate professor of physics and the Lewis-Sigler Institute for Integrative Genomics, who led the study. “We don’t know which behaviors are important to a fruit fly, or a mouse,” he said. “Instead, we ask the computer to find behaviors that are frequently repeated, which tend to be the ones that are worth studying.”

Researchers need reliable ways to catalog behaviors in studies of how genes and neural pathways control behavior. A common experiment involves deleting a gene in an organism such as a fruit fly or worm to look for any resulting loss of function or change in the organism’s behavior. But without reliable behavioral data, researchers cannot make firm conclusions about the role of the deleted gene.

With the new system, a high-speed camera records the animal for a given period of time, and the computer sorts the resulting video frames. For the current study, the researchers recorded individual fruit flies for a period of one hour, resulting in an enormous amount of data. The challenge was how to sort the frames and translate the images into data that could be evaluated.

“We have to figure out how the body parts of the fly are positioned in relation to each other at each point in time, and then find a way to tell how those body parts are moving, while keeping the amount of data manageable using the available computing power,” said Gordon Berman, an associate research scholar in the Lewis-Sigler Institute for Integrative Genomics. “Then you have to project the information into some form that allows you to understand the data.”

The researchers accomplished this goal by writing a computer program that converts the information about a fly’s position and movements into mathematical descriptions that are then grouped according to their similarities. The computer places these groupings on a two-dimensional map, so that different regions of the map represent different types of movements.

A computerized system for evaluating animal behavior.

Individual fruit flies were filmed for about one hour, resulting in over 300,000 video frames. Then, a computer program converts the fly’s position in each frame into mathematical descriptions that represent movements. The computer then groups the movements on a map, so that one region of the map represents leg movements while another represents wing movements, and so on. By looking at the map, the researchers can tell what sorts of movements the fly did, such as grooming its front leg, grooming its head, or waggling its wing. (Image source: Joshua Shaevitz)

To verify that the system worked, the researchers collected data from 50 male and 50 female flies. They identified hundreds of behaviors, including ones that had never been documented and others that were consistent with findings from human observers. For example, some of the findings consistent with previous observations were that male fruit flies kick out their legs when grooming, whereas females don’t, and that young females are more active than young males. “Males and females do things slightly differently, and we can pick up on that right away,” said Shaevitz.

Male fruit flies kick out with their legs when grooming:

whereas females do not:

The team is now expanding the capabilities of the method, Shaevitz said. “The technique that we have developed can also be used to study patterns of behavior in humans,” Shaevitz said. “There are several conditions that are diagnosed on the basis of behavior, such as autism. Having a more quantitative way of measuring behavior could improve the accuracy of diagnoses.”

The study co-authors included William Bialek, Princeton’s John Archibald Wheeler/Battelle Professor in Physics and the Lewis-Sigler Institute for Integrative Genomics, and Daniel Choi, a graduate student working with Shaevitz.

The work was funded through awards from the National Institutes of Health (GM098090, GM071508), the National Science Foundation (PHY 0957573, PHY 1066293), the Pew Charitable Trusts, the Swartz Foundation and the Alfred P. Sloan Foundation.

Read the abstract

Gordon J. Berman, Daniel M. Choi, William Bialek, Joshua W. Shaevitz. Mapping the stereotyped behaviour of freely moving fruit flies. The Journal of the Royal Society Interface (2014). DOI: 10.1098/rsif.2014.0672 Published online 20 August 2014 http://rsif.royalsocietypublishing.org/content/11/99/20140672

Unlocking the potential of bacterial gene clusters to discover new antibiotics (Proc. Natl. Acad. Sci.)

High-throughput screening for the discovery of small molecules that activate silent bacterial gene clusters

High-throughput screening for the discovery of small molecules that activate silent bacterial gene clusters. Image courtesy of Mohammad Seyedsayamdost.

by Tien Nguyen, Department of Chemistry

Resistance to antibiotics has been steadily rising and poses a serious threat to the stronghold of existing treatments. Now, a method from Mohammad Seyedsayamdost, an assistant professor of chemistry at Princeton University, may open the door to the discovery of a host of potential drug candidates.

The vast majority of anti-infectives on the market today are bacterial natural products, made by biosynthetic gene clusters. Genome sequencing of bacteria has revealed that these active gene clusters are outnumbered approximately ten times by so-called silent gene clusters.

“Turning these clusters on would really expand our available chemical space to search for new antibiotic or otherwise therapeutically useful molecules,” Seyedsayamdost said.

In an article published last week in the journal Proceedings of the National Academy of Sciences, Seyedsayamdost reported a strategy to quickly screen whole libraries of compounds to find elicitors, small molecules that can turn on a specific gene cluster. He used a genetic reporter that fluoresces or generates a color when the gene cluster is activated to easily identify positive hits. Using this method, two silent gene clusters were successfully activated and a new metabolite was discovered.

Application of this work promises to uncover new bacterial natural products and provide insights into the regulatory networks that control silent gene clusters.

Read the abstract.

Seyedsayamdost, M. R. “High-throughput platform for the discovery of elicitors of silent bacterial gene clusters.” Proc. Natl. Acad. Sci. 2014, Early edition.

Nano-dissection identifies genes involved in kidney disease (Genome Research)

Scanning electron microscope (SEM) micrograph of podocytes

Researchers at Princeton and the University of Michigan have created a computer-based method for separating and identifying genes from diseased kidney cells known as podocytes, pictured above. (Image courtesy of Matthias Kretzler)

By Catherine Zandonella, Office of the Dean for Research

Understanding how genes act in specific tissues is critical to our ability to combat many human diseases, from heart disease to kidney failure to cancer.  Yet isolating individual cell types for study is impossible for most human tissues.

A new method developed by researchers at Princeton University and the University of Michigan called “in silico nano-dissection” uses computers rather than scalpels to separate and identify genes from specific cell types, enabling the systematic study of genes involved in diseases.

The team used the new method to successfully identify genes expressed in cells known as podocytes — the “work-horses” of the kidney — that malfunction in kidney disease. The investigators showed that certain patterns of activity of these genes were correlated with the severity of kidney impairment in patients, and that the computer-based approach was significantly more accurate than existing experimental methods in mice at identifying cell-lineage-specific genes. The study was published in the journal Genome Research.

Using this technique, researchers can now examine the genes from a section of whole tissue, such as a biopsied section of the kidney, for specific signatures associated with certain cell types. By evaluating patterns of gene expression under different conditions in these cells, a computer can use machine-learning techniques to deduce which types of cells are present. The system can then identify which genes are expressed in the cell type in which they are interested.  This information is critical both in defining novel disease biomarkers and in selecting potential new drug targets.

By applying the new method to kidney biopsy samples, the researchers identified at least 136 genes as expressed specifically in podocytes. Two of these genes were experimentally shown to be able to cause kidney disease. The authors also demonstrated that in silico nano-dissection can be used for cells other than those found in the kidney, suggesting that the method is useful for the study of a range of diseases.

The computational method was significantly more accurate than another commonly used technique that involves isolating specific cell types in mice. The nano-dissection method’s accuracy was 65% versus 23% for the mouse method, as evaluated by a time-consuming process known as immunohistochemistry which involves staining each gene of interest to study its expression pattern.

The research was co-led by Olga Troyanskaya, a professor of computer science and the Lewis-Sigler Institute for Integrative Genomics at Princeton, and Matthias Kretzler, a professor of computational medicine and biology at the University of Michigan. The first authors on the study were Wenjun Ju, a research assistant professor at the University of Michigan, and Casey Greene, now at the Geisel School of Medicine at Dartmouth and a former postdoctoral fellow at Princeton.

The research was supported in part by National Institutes of Health (NIH) R01 grant GM071966 to OGT and MK, by NIH grants RO1 HG005998 and DBI0546275 to OGT, by NIH center grant P50 GM071508, and by NIH R01 grant DK079912 and P30 DK081943 to MK. OGT also receives support from the Canadian Institute for Advanced Research.

Read the abstract.

Wenjun Ju, Casey S Greene, Felix Eichinger, Viji Nair, Jeffery B Hodgin, Markus Bitzer, Young-suk Lee, Qian Zhu, Masami Kehata, Min Li, Song Jiang, Maria Pia Rastaldi, Clemens D Cohen, Olga G Troyanskaya and Matthias Kretzler. 2013. Defining cell-type specificity at the transcriptional level in human disease. Genome Research. Published in Advance August 15, 2013, doi: 10.1101/gr.155697.113.

A global post-transcriptional map of the elements that modulate RNA behavior (Nature)

Read the abstract here:
Systematic discovery of structural elements governing stability of mammalian messenger RNAs.
Goodarzi H, Najafabadi HS, Oikonomou P, Greco TM, Fish L, Salavati R, Cristea IM, Tavazoie S. Nature. 2012 Apr 8. doi: 10.1038/nature11013. [Epub ahead of print]