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
Catalase activity deficiency sensitizes multidrug-resistant Mycobacterium tuberculosis to the ATP synthase inhibitor bedaquiline
Boatema Ofori-Anyinam, Meagan Hamblin, Miranda L. Coldren, Barry Li, Gautam Mereddy, Mustafa Shaikh, Avi Shah, Courtney Grady, Navpreet Ranu, Sean Lu, Paul C. Blainey, Shuyi Ma, James J. Collins, and Jason H. Yang
Nature Communications (2024)
Multidrug-resistant tuberculosis (MDR-TB), defined as resistance to the firstline drugs isoniazid and rifampin, is a growing source of global mortality and threatens global control of tuberculosis disease. The diarylquinoline bedaquiline has recently emerged as a highly efficacious drug against MDR-TB and kills Mycobacterium tuberculosis by inhibiting mycobacterial ATP synthase. However, the mechanisms underlying bedaquiline’s efficacy against MDR-TB remain unknown. Here we investigate bedaquiline hyper-susceptibility in drugresistant Mycobacterium tuberculosis using systems biology approaches. We discovered that MDR clinical isolates are commonly sensitized to bedaquiline. This hypersensitization is caused by several physiological changes induced by deficient catalase activity. These include enhanced accumulation of reactive oxygen species, increased susceptibility to DNA damage, induction of sensitizing transcriptional programs, and metabolic repression of several biosynthetic pathways. In this work we demonstrate how resistance-associated changes in bacterial physiology can mechanistically induce collateral antimicrobial drug sensitivity and reveal druggable vulnerabilities in antimicrobial resistant pathogens.
Deep generative design of RNA aptamers using structural predictions
Felix Wong, Dongchen He, Aarti Krishnan, Liang Hong, Alexander Z. Wang, Jiuming Wang, Zhihang Hu, Satotaka Omori, Alicia Li, Jiahua Rao, Qinze Yu, Wengong Jin, Tianqing Zhang, Katherine Ilia, Jack X. Chen, Shuangjia Zheng, Irwin King, Yu Li, and James J. Collins
Nature Computational Science (2024)
RNAs represent a class of programmable biomolecules capable of performing diverse biological functions. Recent studies have developed accurate RNA three-dimensional structure prediction methods, which may enable new RNAs to be designed in a structure-guided manner. Here, we develop a structure-to-sequence deep learning platform for the de novo generative design of RNA aptamers. We show that our approach can design RNA aptamers that are predicted to be structurally similar, yet sequence dissimilar, to known light-up aptamers that fuoresce in the presence of small molecules. We experimentally validate several generated RNA aptamers to have fuorescent activity, show that these aptamers can be optimized for activity in silico, and fnd that they exhibit a mechanism of fuorescence similar to that of known light-up aptamers. Our results demonstrate how structural predictions can guide the targeted and resource-efcient design of new RNA sequences.
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.