This post will be reinstated shortly….
We’re excited to announce that we’ve just been awarded an Amazon Web Services (AWS) in Education Research Grant in support of our cancer biomarker project! This grant will allow us to run our proteogenomic software Peppy on the AWS Cloud, mining proteomic and genomic data for aberrations that may be at the root of breast cancer. Ultimately, this will help with the project’s larger goal of finding blood-based protein biomarkers for the disease. Rather than hunting through the protein data for a proverbial ‘needle in a haystack,’ we are focusing on the DNA mutations that give rise to cancer, then tracing a path outward to the proteins.
We’re in the discovery stage of a five-year project funded by the National Institutes of Health (NIH), as a member of the Clinical Proteomic Tumor Analysis Consortium (CPTAC). Our lab’s role in this project is in the computation: to mine the genomes of breast cancer patients for mutations that may be causing their disease. To do this, we use our unique proteogenomic mapping software, Peppy, capable of processing an entire human genome in a single run. By linking mass spectrometry data from proteins to an in silico translation and digestion of genomes, Peppy can pinpoint the genomic regions of breast cancer patients where aberrations may be causing cancer-related proteins to be over-expressed. Peppy requires a lot of computer memory and processing power to handle entire genomes, so running the program on the AWS Cloud is an obvious choice.
We’re thrilled about the possibilities now open to us by running Peppy on the Cloud, especially in accelerating the tempo of this research. Thank you, AWS!
Check out a recent article in Genome Technology (scroll to pg. 21) to learn more about our cancer biomarker project!
A failing promise-
They were called “magic bullets”- antibiotics, once held sacred as the panacea that would cure almost all kinds of infectious diseases. Their continued success is reflected by the decrease in the morbidity of bacterial infections during the past few decades. However, the increasing misuse of antibiotics for human and non-therapeutic animal treatment have led to greater and greater resistance, in a phenomenon that has become a serious worldwide concern for health care systems.
The mysteries of initial resistance –
While there are a number of known resistance mechanisms that develop in bacteria, the series of steps and molecular mechanisms that lead bacteria from being antibiotic susceptible to antibiotic resistant still remain unclear. The prevailing view has been that resistance arises from the spontaneous mutation of individual bacteria, though recent research suggests that the process is not this simplistic. Recently, we published a novel view of this development (http://aac.asm.org/cgi/content/short/AAC.00762-10v1). In our research, we chose Pseudomonas aeruginosa, which is an opportunistic pathogen, and the antibiotic “ciprofloxacin” as our study subjects. P. aeruginosa is responsible for many chronic lung infections and 10% of hospital-acquired infections; and when repeatedly treated by ciprofloxacin (a commonly used antibiotic for P. aeruginosa infection), this organism rapidly acquires high-level resistance. To shed light on P. aeruginosa’s proclivity for quickly developing new defenses, we set out to investigate the development of initial resistance using a proteomics and genomics approach. In vivo cultures were supplemented by mathematical modeling to simulate the resistance development process in P. aeruginosa.
What we have learned from our study-
We found that when continuously exposed to ciprofloxacin for up to 48 hours, P. aeruginosa acquired drug resistance in a multistage process. The drug is initially very effective at killing susceptible P. aeruginosa. However, a tolerant/ persistent subpopulation survives and soon emerges to reconstitute the population with significantly increased antibiotic resistance. Our resistance distribution assay and mathematical model suggest that this resistance is not the result of a preexisting mutant in the population. Further, our proteomics assay data indicate that preexisting cellular pathways may support the resistance development process, too. Considering these results, it appears that the development of high level resistance in P. aeruginosa is a stepwise fine-tuning process, not one simply caused by an all-at-once single gene mutation.
Please find more information at (http://aac.asm.org/cgi/content/short/AAC.00762-10v1). Feel free to contact us if you have questions or are interested in further collaboration. Thanks.
Dennis Crenshaw talks about his work and helps us get our heads around the Ultra-Structure concept.
Maarten Leerkes explains how his passions in the private sector have lead to his research interests as a post-doc.
Jainab tells us about her decision to enter bioinformatics and how our lab is working to find valuable information for gene therapy.
Serguei Simonov mesmerizes us with a Russian poem and explains his path to bioinformatics.
Introducing Mark Holmes: Lab systems administrator; coffee aficionado.
Have some bio-entities? Want to have a flexible way to store them in a database? Try Ultra-Structure! So extendable, you’ll find you’ll never have to create custom tables again.