Synthetic Biology
We are employing engineering principles to model, design and build synthetic gene circuits and programmable cells, in order to create novel classes of diagnostics & therapeutics. We are also using deep learning approaches to discover new genetic parts and enhance the synthetic biology design process.
Antibiotics & AI
As part of the Antibiotics-AI Project, we are harnessing the power of artificial intelligence (AI) to discover novel classes of antibiotics and rapidly understand how they work. We are also using deep learning approaches for the de novo design of new antibiotics and the development of combination treatments.
The Collins Lab is part of the Institute for Medical Engineering and Science (IMES) and the Department of Biological Engineering at MIT, the Harvard-MIT Program in Health Sciences and Technology (HST), the Broad Institute of MIT and Harvard, and the Wyss Institute for Biologically Inspired Engineering at Harvard. At MIT, our lab is part of the Synthetic Biology Center, the Computational and Systems Biology Initiative, and the Microbiology Graduate Program.
RECENT PUBLICATIONS
Customizable gene sensing and response without altering endogenous coding sequences
Fabio Caliendo, Elvira Vitu, Junmin Wang, Shuo-Hsiu Kuo, Hayden Sandt, Casper Nørskov Enghuus, Jesse Tordoff, Neslly Estrada, James J. Collins, and Ron Weiss
Nature Chemical Biology (2024)
Synthetic biology aims to modify cellular behaviors by implementing genetic circuits that respond to changes in cell state. Integrating genetic biosensors into endogenous gene coding sequences using clustered regularly interspaced short palindromic repeats and Cas9 enables interrogation of gene expression dynamics in the appropriate chromosomal context. However, embedding a biosensor into a gene coding sequence may unpredictably alter endogenous gene regulation. To address this challenge, we developed an approach to integrate genetic biosensors into endogenous genes without modifying their coding sequence by inserting into their terminator region single-guide RNAs that activate downstream circuits. Sensor dosage responses can be fine-tuned and predicted through a mathematical model. We engineered a cell stress sensor and actuator in CHO-K1 cells that conditionally activates antiapoptotic protein BCL-2 through a downstream circuit, thereby increasing cell survival under stress conditions. Our gene sensor and actuator platform has potential use for a wide range of applications that include biomanufacturing, cell fate control and cell-based therapeutics.
Rapid discovery and evolution of nanosensors containing fluorogenic amino acids
Erkin Kuru, Jonathan Rittichier, Helena de Puig, Allison Flores, Subhrajit Rout, Isaac Han, Abigail E. Reese, Thomas M. Bartlett, Fabio De Moliner, Sylvie G. Bernier, Jason D. Galpin, Jorge Marchand, William Bedell, Lindsey Robinson-McCarthy, Christopher A. Ahern, Thomas G. Bernhardt, David Z. Rudner, James J. Collins, Marc Vendrell, and George M. Church
Nature Communications (2024)
Binding-activated optical sensors are powerful tools for imaging, diagnostics, and biomolecular sensing. However, biosensor discovery is slow and requires tedious steps in rational design, screening, and characterization. Here we report on a platform that streamlines biosensor discovery and unlocks directed nanosensor evolution through genetically encodable fluorogenic amino acids (FgAAs). Building on the classical knowledge-based semisynthetic approach, we engineer ~15 kDa nanosensors that recognize specific proteins, peptides, and small molecules with up to 100-fold fluorescence increases and subsecond kinetics, allowing real-time and wash-free target sensing and live cell bioimaging. An optimized genetic code expansion chemistry with FgAAs further enables rapid (~3 h) ribosomal nanosensor discovery via the cell-free translation of hundreds of candidates in parallel and directed nanosensor evolution with improved variant-specific sensitivities (up to ~250-fold) for SARS-CoV-2 antigens. Altogether, this platform could accelerate the discovery of fluorogenic nanosensors and pave the way to modify proteins with other non-standard functionalities for diverse applications.
A high-throughput synthetic biology approach for studying combinatorial chromatin-based transcriptional regulation
Miguel A. Alcantar, Max A. English, Jacqueline A. Valeri, and James J. Collins
Molecular Cell (2024)
The construction of synthetic gene circuits requires the rational combination of multiple regulatory components, but predicting their behavior can be challenging due to poorly understood component interactions and unexpected emergent behaviors. In eukaryotes, chromatin regulators (CRs) are essential regulatory components that orchestrate gene expression. Here, we develop a screening platform to investigate the impact of CR pairs on transcriptional activity in yeast. We construct a combinatorial library consisting of over 1,900 CR pairs and use a high-throughput workflow to characterize the impact of CR co-recruitment on gene expression. We recapitulate known interactions and discover several instances of CR pairs with emergent behaviors. We also demonstrate that supervised machine learning models trained with low-dimensional amino acid embeddings accurately predict the impact of CR co-recruitment on transcriptional activity. This work introduces a scalable platform and machine learning approach that can be used to study how networks of regulatory components impact gene expression.
Discovery of antibiotics that selectively kill MEtabolically DORMANT BACTERIA
Erica J. Zheng, Jacqueline A. Valeri, Ian W. Andrews, Aarti Krishnan, Parijat Bandyopadhyay, Melis N. Anahtar, Alice Herneisen, Fabian Schulte, Brooke Linnehan, Felix Wong, Jonathan M. Stokes, Lars D. Renner, Sebastian Lourido and James J. Collins
Cell Chemical Biology (2024)
There is a need to discover and develop non-toxic antibiotics that are effective against metabolically dormant bacteria, which underlie chronic infections and promote antibiotic resistance. Traditional antibiotic discovery has historically favored compounds effective against actively metabolizing cells, a property that is not predictive of efficacy in metabolically inactive contexts. Here, we combine a stationary-phase screening method with deep learning-powered virtual screens and toxicity filtering to discover compounds with lethality against metabolically dormant bacteria and favorable toxicity profiles. The most potent and structurally distinct compound without any obvious mechanistic liability was semapimod, an anti-inflammatory drug effective against stationary-phase E. coli and A. baumannii. Integrating microbiological assays, biochemical measurements, and single-cell microscopy, we show that semapimod selectively disrupts and permeabilizes the bacterial outer membrane by binding lipopolysaccharide. This work illustrates the value of harnessing non-traditional screening methods and deep learning models to identify non-toxic antibacterial compounds that are effective in infection-relevant contexts.
Rapid, multiplexed, and enzyme-free nucleic acid detection using programmable aptamer-based RNA switches
Zhaoqing Yan, Amit Eshed, Anli A. Tang, Nery R. Arevalos, Zackary M. Ticktin, Soma Chaudhary, Duo Ma, Griffin McCutcheon, Yudan Li, Kaiyue Wu, Sanchari Saha, Jonathan Alcantar-Fernandez, Jose L. Moreno-Camacho, Abraham Campos-Romero, James J. Collins, Peng Yin and Alexander A. Green
Chem (2024)
Rapid, simple, and low-cost diagnostic technologies are crucial tools for combating infectious disease. We describe a class of aptamer-based RNA switches, or aptaswitches, that recognize target nucleic acid molecules and initiate the folding of a reporter aptamer. Aptaswitches can detect virtually any sequence and provide an intense fluorescent readout without intervening enzymes, generating signals in as little as 5 min and enabling detection by eye with minimal equipment. Aptaswitches can be used to regulate the folding of seven fluorogenic aptamers, providing a general means of controlling aptamers and an array of multiplexable reporter colors. By coupling isothermal amplification reactions with aptaswitches, we reach sensitivities down to 1 RNA copy/μL in one-pot reactions. Application of multiplexed all-in-one reactions against RNA from clinical saliva samples yields an overall accuracy of 96.67% for detection of SARS-CoV-2 in 30 min. Aptaswitches are thus versatile tools for nucleic acid detection that are readily integrated into rapid diagnostic assays.