Machine learning linked evolutionary biosensor array for highly sensitive and specific molecular identification.

Bacteria initiate complicated signaling cascades from the detection of intracellular metabolites or exogenous substances by hundreds of transcription factors, which have been widely investigated as genetically-encoded biosensors for molecular recognition. However, the limited number of transcription factors and their broad substrate specificity result in ambiguity in small molecule identification. This study presents a new small molecule fingerprinting technique using evolutionary biosensor arrays with a machine learning technique that can capture highly specific substrate signals. Employing multiple mutant transcription factors derived from a single transcription factor has effectively circumvented the limited availability of transcription factors induced by a small molecule of our interest. This method achieved up to 95.3% true positive rate for identifying small molecules, and the high-resolution protein engineering technique improved the limit of detection 75-fold. The signal trade-offs with background noises caused by the complex cellular biochemistry of mutant transcription factors enable the biosensor arrays to be more informative in terms of statistical variance. The machine learning technology, coupled with the single transcription factor-driven evolutionary biosensor array, will open new avenues for molecular fingerprinting technologies.

[1]  Matthew C.J. Wilce,et al.  Structural Basis of Selective Aromatic Pollutant Sensing by the Effector Binding Domain of MopR, an NtrC Family Transcriptional Regulator. , 2016, ACS chemical biology.

[2]  S. Belkin,et al.  Broad spectrum detection and "barcoding" of water pollutants by a genome-wide bacterial sensor array. , 2013, Water research.

[3]  Jinhyuk Lee,et al.  Evolution of enzymes with new specificity by high-throughput screening using DmpR-based genetic circuits and multiple flow cytometry rounds , 2018, Scientific Reports.

[4]  Francesca Volpetti,et al.  Cascaded amplifying circuits enable ultrasensitive cellular sensors for toxic metals , 2019, Nature Chemical Biology.

[5]  B. Wanner,et al.  One-step inactivation of chromosomal genes in Escherichia coli K-12 using PCR products. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[6]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[7]  Hak-Sung Kim,et al.  Toward a generalized and high-throughput enzyme screening system based on artificial genetic circuits. , 2014, ACS synthetic biology.

[8]  Fuzhong Zhang,et al.  Biosensors and their applications in microbial metabolic engineering. , 2011, Trends in microbiology.

[9]  Moonjeong Kim,et al.  A synthetic microbial biosensor for high-throughput screening of lactam biocatalysts , 2018, Nature Communications.

[10]  David C Cullen,et al.  A cell array biosensor for environmental toxicity analysis. , 2005, Biosensors & bioelectronics.

[11]  H. Clausen‐Schaumann,et al.  Mechanochemistry: the mechanical activation of covalent bonds. , 2005, Chemical reviews.

[12]  Chi Bun Ching,et al.  Development of Colorimetric-Based Whole-Cell Biosensor for Organophosphorus Compounds by Engineering Transcription Regulator DmpR. , 2016, ACS synthetic biology.

[13]  Seok-Yong Lee,et al.  Regulation of the transcriptional activator NtrC1: structural studies of the regulatory and AAA+ ATPase domains. , 2003, Genes & development.

[14]  M. Forsman,et al.  An aromatic effector specificity mutant of the transcriptional regulator DmpR overcomes the growth constraints of Pseudomonas sp. strain CF600 on para-substituted methylphenols , 1994, Journal of bacteriology.

[15]  V. Shingler,et al.  Novel Effector Control through Modulation of a Preexisting Binding Site of the Aromatic-responsive ς54-Dependent Regulator DmpR* , 1999, The Journal of Biological Chemistry.

[16]  Cheryl R. Kuske,et al.  Generation of Novel Bacterial Regulatory Proteins That Detect Priority Pollutant Phenols , 2000, Applied and Environmental Microbiology.

[17]  Fernando de la Cruz,et al.  Transcription factor-based biosensors enlightened by the analyte , 2015, Front. Microbiol..

[18]  Huan Wang,et al.  Design, construction, and characterization of a set of biosensors for aromatic compounds. , 2014, ACS synthetic biology.

[19]  Malika Charrad,et al.  NbClust: An R Package for Determining the Relevant Number of Clusters in a Data Set , 2014 .

[20]  W. Lim,et al.  Freeze-dried recombinant bacteria for on-site detection of phenolic compounds by color change. , 2005, Journal of biotechnology.

[21]  Marina Juribašić,et al.  Mechanochemical C-H bond activation: rapid and regioselective double cyclopalladation monitored by in situ Raman spectroscopy. , 2014, Chemical communications.

[22]  Jameson K. Rogers,et al.  Biosensor-based engineering of biosynthetic pathways. , 2016, Current opinion in biotechnology.

[23]  Haseong Kim,et al.  A Cell-Cell Communication-Based Screening System for Novel Microbes with Target Enzyme Activities. , 2016, ACS synthetic biology.

[24]  Douglas C. Friedman,et al.  Building a global alliance of biofoundries , 2019, Nature Communications.

[25]  Grzegorz Psuj.  Multi-Sensor Data Integration Using Deep Learning for Characterization of Defects in Steel Elements † , 2018, Sensors.

[26]  W. Wackernagel,et al.  Gene disruption in Escherichia coli: TcR and KmR cassettes with the option of Flp-catalyzed excision of the antibiotic-resistance determinant. , 1995, Gene.

[27]  C. Strauss,et al.  A microbial sensor for organophosphate hydrolysis exploiting an engineered specificity switch in a transcription factor , 2016, Nucleic acids research.

[28]  S. Mandal,et al.  A molecular tuning fork in single-molecule mechanochemical sensing. , 2015, Angewandte Chemie.

[29]  Jean-Louis Marty,et al.  Biosensors based on highly sensitive acetylcholinesterases for enhanced carbamate insecticides detection , 2006 .

[30]  Narayanan Eswar,et al.  Protein structure modeling with MODELLER. , 2008, Methods in molecular biology.

[31]  J. Maindonald.  Statistical Learning from a Regression Perspective , 2008 .

[32]  E. Woo,et al.  Structural Analysis of the Phenol-Responsive Sensory Domain of the Transcription Activator PoxR. , 2016, Structure.

[33]  Anil Kumar,et al.  An Effective Strategy for a Whole-Cell Biosensor Based on Putative Effector Interaction Site of the Regulatory DmpR Protein , 2012, PloS one.

[34]  Christopher A. Voigt,et al.  Escherichia coli “Marionette” strains with 12 highly optimized small-molecule sensors , 2018, Nature Chemical Biology.

[35]  V. Shingler,et al.  Sensing of aromatic compounds by the DmpR transcriptional activator of phenol-catabolizing Pseudomonas sp. strain CF600 , 1994, Journal of bacteriology.

[36]  F. Dickert,et al.  Detection of volatile compounds with mass-sensitive sensor arrays in the presence of variable ambient humidity. , 1999, Analytical chemistry.