Publications
#equal contribution; *corresponding author
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Preventing escape and malfunction of recoded cells due to tRNA base changes
Chiappino-Pepe A*, Radford F#, Budnik B#, Tas H, Augustin TL, Burgess HM, Moret M, Dharani AM, Zeng Q, Fan W, Afrikawala MM, Thapa S, Kuru E, Narasimhan K, Marchand JA, Perrotta RM, Stokes JM, Lunshof JE, Aach J, Tam JM, Church GM*
(2024) bioRxiv-DOI: https://doi.org/10.1101/2024.07.18.604179
> We discovered molecular processes involved in the evolution, escape, and malfunction of an engineered genetic code using a 61-codon E. coli strain. We show that cells with an engineered genetic code can express wild-type genes that provide a fitness advantage, like antibiotic resistance genes, and we developed genomic technology to prevent it.
Tweetutorial: https://x.com/AnushChP/status/1814719793494167649
Synthetic genomes unveil the effects of synonymous recoding
Nyerges A*, Chiappino-Pepe A#, Budnik B#, Baas-Thomas M, Flynn R, Yan S, Ostrov N, Liu M, Wang M, Zheng Q, Hu F, Chen K, Rudolph A, Chen D, Ahn J, Spencer O, Ayalavarapu V, Tarver A, Harmon-Smith M, Hamilton M, Blaby I, Yoshikuni Y, Hajian B, Jin A, Kintses B, Szamel M, Seregi V, Shen Y, Li Z, Church GM*
(2024) bioRxiv-DOI: https://doi.org/10.1101/2024.06.16.599206
> We report our progress and findings on the construction of a 57-codon E. coli strain. We developed an omics-based approach for the identification and troubleshooting of cellular fitness issues arising from an E. coli 57-codon genome design.
Machine Learning and Directed Evolution of Base Editing Enzymes
Perrotta R#*, Vinke S#, Ferreira R#, Moret M, Mahas A, Chiappino-Pepe A, Riedmayr LM, Mehra A, Lehmann LS, Church GM*
(2024) bioRxiv-DOI: https://doi.org/10.1101/2024.05.17.594556
> This work shows newly designed sequence-specific guide RNA libraries that reduce bystander editing and increase editing efficiency of base editors. It involves phage assisted non-continuous evolution and machine learning to improve deaminase activity.
Single-cell imaging of compartmentalized NADPH and metabolic network analysis reveal SLC25A1 mediated NADPH shuttle for cell growth.
Moon SJ, Chiappino-Pepe A, Dong W, Kelleher JK, VanderHeiden MGV, Stephanopoulos G, Sikes HD*
(2024) bioRxiv-DOI: https://doi.org/10.1101/2024.04.27.591477
> This work measures NADPH levels in intracellular compartments of different cancer cell lines using genetically encoded reporters. It studies the influence of alternative media and NADPH levels on cancer cell growth rate.
Strategies to identify and edit improvements in synthetic genome segments episomally
Rudolph AI*, Nyerges A, Chiappino-Pepe A, Landon M, Baas-Thomas M, Church MG
(2023) Nucleic Acids Research gkad692. DOI: https://doi.org/10.1093/nar/gkad692
> This work updates a multiplex automated genome engineering (MAGE) protocol to improve recombineering frequency and multiplexability. It was applied to correct fitness deleterious mutations involved in the design of a 57-codon E. coli genome.
Deep learning-guided discovery of an antibiotic targeting Acinetobacter baumannii
Liu G#, Catacutan DB#, Rathod K#, Swanson K, Wengong J, Mohamed JC, Chiappino-Pepe A, Syed SA, Fragis M, Rachwalski K, Magolan J, Surette MG, Coombes BK, Jaakkola T, Barzilay R, Collins JJ*, Stokes JM*
(2023) Nature Chemical Biology 19:1342–1350. DOI: https://doi.org/10.1038/s41589-023-01349-8
> This work identified an antibiotic with AI that targets specifically the pathogenic bacterium A. baumannii and not E. coli. We studied the mode of action of the new antibiotic.
A swapped genetic code prevents viral infections and gene transfer
Nyerges A*, Vinke S, Flynn R, Owen SV, Rand EA, Bogdan B, Keen E, Narasimhan K, Marchand JA, Baas-Thomas M, Liu M, Chen K, Chiappino-Pepe A, Hu F, Baym M, Church GM*
(2023) Nature 615:720–727. DOI: https://doi.org/10.1038/s41586-023-05824-z
> This work shows that cells with an engineered genetic code can be infected with viruses coding for tRNAs (tRNAs cognate to "free" codons deleted in the genome of the recoded cell). It proposes repurposing "free" codons to incorporate leucine.
NICEgame: A workflow for annotating the knowledge gaps in metabolic reconstructions, using known and hypothetical reactions
Vayena E#, Chiappino-Pepe A#, MohammadiPeyhani H, Francioli Y, Hadadi N, Ataman M, Hafner J, Pavlou S, Hatzimanikatis V*
(2022) PNAS 119(46) e2211197119. DOI: https://doi.org/10.1073/pnas.2211197119. Link to repository: NICEgame
> We present the first computational pipeline to identify unknown biochemical transformations and responsible metabolic enzymes missing in genome annotations. We use E. coli as the hardest study case for characterization of remaining metabolic gaps.
Genome reconstructions of metabolism of Plasmodium RBC and liver stages
Chiappino-Pepe A, Pandey V, Billker O*
(2021) Current Opinion in Microbiology 63:259–266. DOI: https://doi.org/10.1016/j.mib.2021.08.006
> We review the available genome-scale metabolic models and metabolic modeling approaches for malaria parasites.
NICEdrug.ch, a workflow for rational drug design and systems-level analysis of drug metabolism
MohammadiPeyhani H, Chiappino-Pepe A#, Haddadi Kiandohkt#, Hafner J, Hadadi N, Hatzimanikatis V*
(2021) eLife 10:e65543. DOI: https://doi.org/10.7554/eLife.65543. Link to database: NICEdrug.ch
> We developed the first large database and analysis of drug metabolism by identifying reactive sites in 200k small molecules and proposing inhibited enzymes and putative biotransformations in different cells.
I am specially excited with my analysis on antimalarial drugs that act as competitive inhibitors in Plasmodium without inhibiting human enzymes!
Tweetutorial: https://x.com/AnushChP/status/1267062026691579905
PhenoMapping: a protocol to map cellular phenotypes to metabolic bottlenecks, identify conditional essentiality, and curate metabolic models
Chiappino-Pepe A*, Hatzimanikatis V
(2020) STAR Protocols 2 (1), 100280. DOI: https://doi.org/10.1016/j.xpro.2020.100280. Link to repository: PhenoMapping
> We present the PhenoMapping protocol developed in our Cell publication (https://doi.org/10.1016/j.cell.2019.10.030), and also used in our Cell Host & Microbe publication (https://doi.org/10.1016/j.chom.2020.01.002).
PhenoMapping identifies what combinatorial subset of constraints are responsible (active) for the prediction of a phenotype like the essentiality of a gene for growth. This is really useful for analysis of phenotypes upon omics data integration into a metabolic model.
Tweetutorial: https://x.com/AnushChP/status/1353707882412445697
Synthetic genomes with altered genetic codes
Ostrov N*, Nyerges A, Chiappino-Pepe A, Rudolph A, Baas-Thomas M, Church GM
(2020) Current Opinion in Systems Biology 24:32–40. DOI: https://doi.org/10.1016/j.coisb.2020.09.007
> We review the synthetic genomes available and their properties.
A Deep Learning Approach to Antibiotic Discovery
Stokes JM, Yang K#, Swanson K#, Jin W, Cubillos-Ruiz A, Donghia NM, MacNair CR, French S, Carfrae LA, Bloom-Ackerman Z, Tran VM, Chiappino-Pepe A, Badran AH, Andrews IW, Chory EJ, Church GM, Brown ED, Jaakkola TS, Barzilay R*, Collins JJ*
(2020) Cell 180 (4): 688-702.e13. DOI: https://doi.org/10.1016/j.cell.2020.01.021
> This work shows a pioneering approach to identify new antibiotics with deep learning models.
Functional and computational genomics reveal unprecedented flexibility in stage-specific Toxoplasma metabolism
Krishnan A, Lunghi M#, Kloehn J#, Chiappino-Pepe A, Waldman B, Nicolas D, Varesio E, Hehl A, Lourido S, Hatzimanikatis V, Soldati-Favre D*
(2020) Cell Host & Microbe 27:1–17. DOI: https://doi.org/10.1016/j.chom.2020.01.002
> This work characterizes experimentally and computationally the metabolism of Toxoplasma gondii in a fast growing stage. We developed the most comprehensive model of T. gondii thanks to PhenoMapping and a large-scale CRISPR gene knockout screen.
Mechanistic insights into bacterial metabolic reprogramming from omics-integrated genome-scale models
Hadadi N*, Pandey V, Chiappino-Pepe A, Morales M, Gallart-Ayala H, Florence M, Ivanisevic J, Sentchilo V, van der Meer JR
(2020) npj Systems Biology and Applications 6 (1). DOI: https://doi.org/10.1038/s41540-019-0121-4
> This work analyzes the metabolic reprogramming that Pseudomonas veronii undergoes upon changes in medium (soil, sand). We constructed the genome-scale model for this bacterium and integrated alternative omics data sets.
Genome scale identification of essential metabolic processes for targeting the Plasmodium liver stage
Stanway RR#, Bushell E#, Chiappino-Pepe A#, Roques M#, Sanderson T, Franke-Fayard B, Caldelari R, Golomingi M, Nyonda M, Pandey V, Schwach F, Chevalley S, Ramesar J, Metcalf T, Herd C, Burda PC, Rayner JC, Soldati-Favre D, Janse CJ, Hatzimanikatis V, Billker O*, Heussler VT*
(2019) Cell 179 (5): 1112-1128.e26. DOI: https://doi.org/10.1016/j.cell.2019.10.030
> We present the first large-scale screening of gene knockouts in the liver stage development of a malaria parasite and comprehensive analysis of the liver-stage Plasmodium metabolic function through genome-scale metabolic modeling. We identify seven metabolic pathways to target liver-stage malaria parasites.
>> Press coverage by the Cambridge Network, ChemEurope, SciTechDaily, Englemed, myScience, News Medical Life Sciences, Wiley Analytical Science (among others)
TEX-FBA: A constraint-based method for integrating gene expression, thermodynamics, and metabolomics data into genome-scale metabolic models
Pandey V, Hernandez Gardiol D, Chiappino-Pepe A, Hatzimanikatis V*
(2019) bioRxiv-DOI: https://doi.org/10.1101/536235. Link to repository: TEX-FBA
> We developed an optimization algorithm for the integration of transcriptomics data. This algorithm defines a consistency score when high/low fluxes can be imposed for reactions linked to high/low expression genes and does not knockout reactions.
Algorithms for Autonomous Formation of Multicellular Shapes from Single Cells
Appleton E#*, Mehdipour N#, Daifuku T#, Briers D#, Haghighi I#, Moret M, Chao G, Wannier T, Chiappino-Pepe A, Belta C*, Church GM*
(2024) ACS Synthetic Biology: https://pubs.acs.org/doi/10.1021/acssynbio.4c00037
Preprint version: Genetic design automation for autonomous formation of multicellular shapes from a single cell progenitor (2019) bioRxiv-DOI: https://doi.org/10.1101/807107
> This work proposes a computer-aided design approach to develop genetic circuits. These circuits use recombinases to guide multi-cellular formations into arbitrary shapes in human cells.
Integration of metabolic, regulatory and signaling networks towards analysis of perturbation and dynamic responses
Chiappino-Pepe A, Pandey V, Ataman M, Hatzimanikatis V*
(2017) Current Opinion in Systems Biology 2:58–65. DOI: https://doi.org/10.1016/j.coisb.2017.01.007
> We review approaches to analyze cellular networks of metabolism, regulation, and signaling in static and dynamic fashion.
Bioenergetics-based modeling of Plasmodium falciparum metabolism reveals its essential genes, nutritional requirements, and thermodynamic bottlenecks
Chiappino-Pepe A, Tymoshenko S, Ataman M, Soldati-Favre D, Hatzimanikatis V*
(2017) PLoS Computational Biology 13 (3): e1005397. DOI: https://doi.org/10.1371/journal.pcbi.1005397
> We developed a genome-scale metabolic model of the deadliest malaria parasite and performed the first thermodynamic analysis of its metabolism. We studied essentiality for growth, enzymes' substrate channeling, nutritional requirements (in terms of molecular backbones), and thermodynamic bottlenecks from metabolomics data.
>> Press coverage by the EPFL, EurekAlert, HealthCanal, myScience, newspapers (among others)