Universal microbial diagnostics using random DNA probes

A new diagnostic platform based on randomized DNA probes can screen for common human pathogens. Early identification of pathogens is essential for limiting development of therapy-resistant pathogens and mitigating infectious disease outbreaks. Most bacterial detection schemes use target-specific probes to differentiate pathogen species, creating time and cost inefficiencies in identifying newly discovered organisms. We present a novel universal microbial diagnostics (UMD) platform to screen for microbial organisms in an infectious sample, using a small number of random DNA probes that are agnostic to the target DNA sequences. Our platform leverages the theory of sparse signal recovery (compressive sensing) to identify the composition of a microbial sample that potentially contains novel or mutant species. We validated the UMD platform in vitro using five random probes to recover 11 pathogenic bacteria. We further demonstrated in silico that UMD can be generalized to screen for common human pathogens in different taxonomy levels. UMD’s unorthodox sensing approach opens the door to more efficient and universal molecular diagnostics.

[1]  S. Frick,et al.  Compressed Sensing , 2014, Computer Vision, A Reference Guide.

[2]  Massimo Fornasier,et al.  Compressive Sensing , 2015, Handbook of Mathematical Methods in Imaging.

[3]  Hakho Lee,et al.  A magneto-DNA nanoparticle system for rapid detection and phenotyping of bacteria. , 2013, Nature nanotechnology.

[4]  Violet N. Pinto Bioterrorism: Health sector alertness , 2013, Journal of natural science, biology, and medicine.

[5]  Jiaquan Xu,et al.  Deaths: preliminary data for 2011. , 2012 .

[6]  C. Sibley,et al.  Molecular methods for pathogen and microbial community detection and characterization: Current and potential application in diagnostic microbiology , 2012, Infection, Genetics and Evolution.

[7]  Dl Hoyert,et al.  National Vital Statistics Reports NCHS.pdf , 2012 .

[8]  Lie Wang,et al.  Orthogonal Matching Pursuit for Sparse Signal Recovery With Noise , 2011, IEEE Transactions on Information Theory.

[9]  Mojdeh Mohtashemi,et al.  Open-target sparse sensing of biological agents using DNA microarray , 2011, BMC Bioinformatics.

[10]  Richard G Baraniuk,et al.  More Is Less: Signal Processing and the Data Deluge , 2011, Science.

[11]  Peter Dubruel,et al.  Recent advances in recognition elements of food and environmental biosensors: a review. , 2010, Biosensors & bioelectronics.

[12]  M. Landini,et al.  Conventional and molecular techniques for the early diagnosis of bacteraemia. , 2010, International journal of antimicrobial agents.

[13]  M. Bauer,et al.  Molecular diagnostics of sepsis--where are we today? , 2010, International journal of medical microbiology : IJMM.

[14]  K. Carroll,et al.  Blood cultures: key elements for best practices and future directions , 2010, Journal of infection and chemotherapy : official journal of the Japan Society of Chemotherapy.

[15]  Richard G. Baraniuk,et al.  Signal Processing With Compressive Measurements , 2010, IEEE Journal of Selected Topics in Signal Processing.

[16]  F. Kramer,et al.  Rapid Universal Identification of Bacterial Pathogens from Clinical Cultures by Using a Novel Sloppy Molecular Beacon Melting Temperature Signature Technique , 2009, Journal of Clinical Microbiology.

[17]  Y. Nitzan,et al.  Identification of pathogenic bacteria in blood cultures: comparison between conventional and PCR methods. , 2009, Journal of microbiological methods.

[18]  G. Warhurst,et al.  Bench-to-bedside review: The promise of rapid infection diagnosis during sepsis using polymerase chain reaction-based pathogen detection , 2009, Critical care.

[19]  Michael Klompas,et al.  Automated surveillance of health care-associated infections. , 2009, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[20]  E. Breitschwerdt,et al.  Use of broad range16S rDNA PCR in clinical microbiology. , 2009, Journal of microbiological methods.

[21]  Richard G. Baraniuk,et al.  Compressive Sensing DNA Microarrays , 2008, EURASIP J. Bioinform. Syst. Biol..

[22]  Michael Elad,et al.  On the Uniqueness of Nonnegative Sparse Solutions to Underdetermined Systems of Equations , 2008, IEEE Transactions on Information Theory.

[23]  R.G. Baraniuk,et al.  Compressive Sensing [Lecture Notes] , 2007, IEEE Signal Processing Magazine.

[24]  Joel A. Tropp,et al.  Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit , 2007, IEEE Transactions on Information Theory.

[25]  Z. Jericevic,et al.  Non-linear optimization of parameters in Michaelis-Menten kinetics , 2005 .

[26]  D. Donoho,et al.  Sparse nonnegative solution of underdetermined linear equations by linear programming. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[27]  P. Savelkoul,et al.  New developments in the diagnosis of bloodstream infections. , 2004, The Lancet. Infectious diseases.

[28]  Joel A. Tropp,et al.  Greed is good: algorithmic results for sparse approximation , 2004, IEEE Transactions on Information Theory.

[29]  J. SantaLucia,et al.  The thermodynamics of DNA structural motifs. , 2004, Annual review of biophysics and biomolecular structure.

[30]  D. Mannino,et al.  The epidemiology of sepsis in the United States from 1979 through 2000. , 2003, The New England journal of medicine.

[31]  Xiaoming Huo,et al.  Uncertainty principles and ideal atomic decomposition , 2001, IEEE Trans. Inf. Theory.

[32]  J. Mylotte,et al.  Blood Cultures: Clinical Aspects and Controversies , 2000, European Journal of Clinical Microbiology and Infectious Diseases.

[33]  Sanjay Tyagi,et al.  Molecular Beacons: Probes that Fluoresce upon Hybridization , 1996, Nature Biotechnology.

[34]  C. A. Thomas,et al.  Molecular cloning. , 1977, Advances in pathobiology.

[35]  G. Richards,et al.  The epidemiology of sepsis. , 1973, Clinical orthopaedics and related research.