Akhilesh Pandey, M.D., Ph.D. is a Professor at the McKusick-Nathans Institute of Genetic Medicine and the Departments of Biological Chemistry, Oncology and Pathology at the Johns Hopkins University School of Medicine. He is also the Founder and Director of the Institute of Bioinformatics, a non-profit research institute in Bangalore, India. He obtained his medical degree from Armed Forces Medical College in Pune, India and completed his residency in Pathology at the Brigham and Women's Hospital at Harvard Medical School in Boston. He obtained his Ph.D. in the laboratory of Vishva Dixit at the University of Michigan, Ann Arbor in 1995 and carried out his Postdoctoral work in the laboratory of Harvey Lodish at the Whitehead Institute for Biomedical Research, Massachusetts Institute of Technology. Subsequently, he was a Visiting Scientist with Matthias Mann at the University of Southern Denmark for three years before joining Johns Hopkins University School of Medicine in 2002. During his tenure in Denmark, he developed the SILAC method for quantitative proteomics, which is now a gold standard for accurate quantitation of proteins and post-translational modifications. He has pioneered methods for quantitative proteomics and for analysis of post-translational modifications by mass spectrometry. Dr. Pandey’s laboratory is taking a systems biology approach by combining many 'Omics' technologies to study diseases ranging from infectious diseases to cancer. He has published over 300 papers and currently serves as an Editorial Board member of Molecular and Cellular Proteomics, Journal of Proteome Research, Proteomics, Clinical Proteomics, Journal of Translational Medicine and DNA Research and as an Associate Editor of Journal of Proteomics.


Current methods applied in Clinical Microbiology Laboratories for microbial genus and species identification include biochemical based methods, 16S rRNA sequencing, and MALDI-TOF mass spectrometry. A major limitation of these methods is they are unable to provide information regarding associated antimicrobial resistance mechanisms. As highly drug-resistant bacteria are becoming increasingly endemic, rapid and accurate methods for identifying resistance genes are critical. Here, we demonstrate the use of whole-genome sequencing (WGS) and high-resolution Fourier transform tandem mass spectrometry for rapid microbial identification and resistance. As proof of principle, we have successfully identified carbapenem resistance and susceptible Klebsiella pneumoniae isolates. We used pressure cycling technology (Barocycler) for sample preparation to ensure maximal recovery of the material. For next generation sequencing, indexed libraries were prepared and sequenced on a MiSeq system which generated 50 million reads per sample at a depth of ~50X coverage. A rapid one minute trypsin digestion was carried out and purified peptides were analyzed by a rapid high-resolution Fourier transform mass spectrometry method that we developed on an Orbitrap Fusion Lumos Tribrid mass spectrometer for identifying and discriminating carbapenem resistance isolates with a run time of 15 minutes. We identified ~2,500 peptides from ~1,200 proteins from each sample. Importantly, we confidently identified 6 unique peptides that were derived from a carbapenem-resistant isolate producing carbapenem-hydrolyzing β-lactamase protein, KPC-3. These peptides were identified in all the blaKPC-3 positive isolates but were not detectable in the carbapenem-susceptible strains. Similarly, we detected multiple peptides derived from proteins that encode other carbapenem-hydrolyzing β-lactamases including OXA-181, OXA-48 and VIM. Finally, we established assays for targeted detection of carbapenemases by parallel reaction monitoring (PRM). We were able to easily detect multiple peptides from KPC-2, KPC-3, OXA-181, OXA-48 and VIM carbapenemases to accurately classify the isolates. Overall, our findings demonstrate the power of high-resolution mass spectrometry for rapid microbial identification and associated carbapenem hydrolyzing enzymes. We conclude that these methods are highly sensitive, accurate and have a potential to identify and differentiate the microbes that are susceptible to antimicrobial agents and polymorphisms at the strain level.