Where Microbiology and Chemistry Intersect

a "wordcloud" representation of recent activity in our lab. . .

As this tagcloud representing a summary of several recent publications from my lab indicates, my research resides at intersections of microbiology and chemistry.  I am a broadly trained microbiologist with expertise applying state-of-the-art approaches, particularly in proteomics and functional genomics to: 1) leverage  beneficial aspects of microorganisms (e.g., biodegradation/ bioremediation of harmful chemical pollutants) and 2) develop approaches to mitigate negative effects of microorganisms (e.g., disease, microbial contamination of water).  Most recently, my research group has placed considerable focus on the development and optimization of rapid, mass spectrometry-enabled approaches to characterize microorganisms at the strain level.

Of Microbes and Metals. . .

Microorganisms exhibit remarkable metabolic diversity.  This diversity can be exploited in the design of strategies to clean up (bioremediate) hazardous wastes in the environment.  Unfortunately, many hazardous waste sites that contain pollutants that microorganisms can degrade also contain toxic metals that impinge upon the ability of microorganisms to clean up (biodegrade) pollutants.  For this reason, my research group has explored strategies to increase the efficacy of bioremediation of metal-contaminated environments.  We have explored through functional genomics-based approaches basic mechanisms by which metal toxicity is mediated by environmental pH.  As shown in the dendrogram at right, environmental pH affects microbial responses to metals at the level of the genome.

As shown in the three figures below, we've applied knowledge we've learned about metal toxicity to mitigate metal toxicity during hydrocarbon (naphthalene) biodegradation.  Figures below show that a modified cyclodextrin (carboxymethly beta-cyclodextrin) reduces metal toxicity during naphthalene biodegradation in the presence of toxic cadmium (A), cobalt (B), and copper (C).

Even Microbes Have Fingerprints: Microbial Profiling using Molecular- and Rapid, Proteomics-based Approaches

In other projects, we have been utilizing genetic fingerprinting (rep-PCR) to characterize environmental isolates of E. coli found contaminating recreational waters.  The top left panel below shows DNA fingerprints of about 38 E. coli isolates, while the MDS plot below shows the similarity in 3-d space of many such DNA fingerprints.

Given the time-intensive nature of PCR-based fingerprinting methods, we have developed very rapid, mass spectrometry-based methods to characterize microorganisms including E. coli and Enterococcus.  The top image below shows a simplified, overview of this approach, while the dendrogram below shows the similarity of fingerprints of several E. coli isolates from different environmental sources.  

While recently applying this rapid approach to a variety of bacteria, we noticed that the commonly employed practice of automating data acquisition reduced spectrum quality and reproducibility.  The panel below shows the reproducibility of replicate spectra of 8 different bacteria (each shown in a different color) in 3-dimensional space (via a multidimensional scaling, MDS, representation of the data) for spectra acquired by automation (panel A), an experienced human MALDI operator (panel B), and a less experienced human MALDI operator (panel C).  As is clear from these panels, both human operators obtained spectra that exhibited higher reproducibility than spectra obtained by automation.

Given this discovery, we have employed a designed experiments approach to optimize automated data acquisition of MALDI spectra. The animation shown at right highlights one of our first successes with P. aeruginosa. Green spheres represent replicate spectra obtained with optimization, while red spheres represent replicate spectra obtained without optimization.

We describe further our success optimizing automated data acquisition in one of our most recent publications (Zhang L, Borror CM, Sandrin TR. 2014.  A designed experiments approach to optimization of automated data acquisition during characterization of bacteria with MALDI-TOF mass spectrometry.  PLoS ONE. DOI: 10.1371/journal.pone.0092720).  Spectra of P. aeruginosa acquired without optimization (top spectrum) and with optimization (bottom spectrum) are shown below.  Our approach was successful not only with P. aeruginosa (A), but also with two other Gram-negative bacteria, K. pneumoniae (B) and S. marcescens (C).  Spectra obtained using optimized settings are represented by green spheres in the MDS plots below.


Antimicrobial resistant microorganisms pose a serious threat to human health.  Recently, we optimized spectrum processing parameters to enhance the ability of rapid, MS-based microbial fingerprinting to detect antibiotic-resistance in 172 strains of the food- and waterborne pathogen, Campylobacter, obtained from across four continents.  As shown in the panels below, beta-lactam resistant strains (red spheres) were more readily discriminated from susceptible strains (green spheres) when default processing parameters (A) were optimized (B).

Publications, many of which resulted from the work described above, include the following. (Undergraduate student collaborators are designated with an *, while graduate student collaborators are designated with a **. Post-doctoral collaborators are designated with a §). 

Journal Articles:


News/Other Press


 Demirev P, Sandrin TR, eds. 2016. Applications of Mass Spectrometry in Microbiology: From Strain Characterization to Rapid Screening for Antibiotic Resistance. Springer.  

Other news items:

Book Chapters:

Scholarship of Teaching and Learning

Some Recent and Forthcoming Presentations (*denotes undergraduate student author; ** denotes graduate student author)

If you are interested in getting involved in any of these projects or are interested in any kind of research in microbiology and/or environmental science, contact me via email or stop by my office (FAB N106C or FAB N303B) or lab (CLCC 311).

Support for some work shown here has been supported, in part, by the following: