Engineering of increased L-Threonine production in bacteria by combinatorial cloning and machine learning

Hanke, Paul; Parrello, Bruce; Vasieva, Olga; Akins, Chase; Chlenski, Philippe; Babnigg, Gyorgy; Henry, Chris; Foflonker, Fatima; Brettin, Thomas; Antonopoulos, Dionysios; Stevens, Rick; Fonstein, Michael

doi:10.1016/j.mec.2023.e00225

Title: Engineering of increased L-Threonine production in bacteria by combinatorial cloning and machine learning

Journal Article · 16 June 2023 · Metabolic Engineering Communications

DOI: https://doi.org/10.1016/j.mec.2023.e00225 · OSTI ID:1988321

Hanke, Paul ^[1]; Parrello, Bruce ^[2]; Vasieva, Olga ^[3]; Akins, Chase ^[1]; Chlenski, Philippe ^[4]; Babnigg, Gyorgy ^[1]; Henry, Chris ^[1]; Foflonker, Fatima ^[1]; Brettin, Thomas ^[1]; Antonopoulos, Dionysios ^[1]; Stevens, Rick ^[5]; Fonstein, Michael ^[1]

Argonne National Laboratory (ANL), Argonne, IL (United States)
University of Chicago, IL (United States)
BSMI, Northbrook, IL (United States)
Columbia University, New York, NY (United States)
Argonne National Laboratory (ANL), Argonne, IL (United States); University of Chicago, IL (United States)

The goal of this study is to develop a general strategy for bacterial engineering using an integrated synthetic biology and machine learning (ML) approach. This strategy was developed in the context of increasing L-threonine production in Escherichia coli ATCC 21277. A set of 16 genes was initially selected based on metabolic pathway relevance to threonine biosynthesis and used for combinatorial cloning to construct a set of 385 strains to generate training data (i.e., a range of L-threonine titers linked to each of the specific gene combinations). Hybrid (regression/classification) deep learning (DL) models were developed and used to predict additional gene combinations in subsequent rounds of combinatorial cloning for increased L-threonine production based on the training data. As a result, E. coli strains built after just three rounds of iterative combinatorial cloning and model prediction generated higher L-threonine titers (from 2.7 g/L to 8.4 g/L) than those of patented L-threonine strains being used as controls (4-5 g/L). Interesting combinations of genes in L-threonine production included deletions of the tdh, metL, dapA, and dhaM genes as well as overexpression of the pntAB, ppc, and aspC genes. Mechanistic analysis of the metabolic system constraints for the best performing constructs offers ways to improve the models by adjusting weights for specific gene combinations. Graph theory analysis of pairwise gene modifications and corresponding levels of L-threonine production also suggests additional rules that can be incorporated into future ML models.

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Research Organization:: Argonne National Laboratory (ANL), Argonne, IL (United States)

Sponsoring Organization:: USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE Office of Science (SC), Biological and Environmental Research (BER)

Grant/Contract Number:: AC02-06CH11357

OSTI ID:: 1988321

Alternate ID(s):: OSTI ID: 2475507

Journal Information:: Metabolic Engineering Communications, Vol. 17; ISSN 2214-0301

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Title: Engineering of increased L-Threonine production in bacteria by combinatorial cloning and machine learning

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