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PreprintCited by 1SARS-CoV-2 and COVID-19 ResearchBioinformatics and Genomic NetworksBiotin and Related Studies

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Pooled PPIseq: screening the SARS-CoV-2 and human interface with a scalable multiplexed protein-protein interaction assay platform

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Miller, Darach, Dziulko, Adam, Levy, Sasha·2024·PLoS ONE
DOI 10.1101/2024.02.13.580123PMC PMC11741623

Preprint — published as 10.1371/journal.pone.0299440

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Abstract

P rotein- P rotein I nteractions ( PPI s) are a key interface between virus and host, and these interactions are important to both viral reprogramming of the host and to host restriction of viral infection. In particular, viral-host PPI networks can be used to further our understanding of the molecular mechanisms of tissue specificity, host range, and virulence. At higher scales, viral-host PPI screening could also be used to screen for small-molecule antivirals that interfere with essential viral-host interactions, or to explore how the PPI networks between interacting viral and host genomes co-evolve. Current high-throughput PPI assays have screened entire viral-host PPI networks. However, these studies are time consuming, often require specialized equipment, and are difficult to further scale. Here, we develop methods that make larger-scale viral-host PPI screening more accessible. This approach combines the mDHFR split-tag reporter with the iSeq2 interaction-barcoding system to permit massively-multiplexed PPI quantification by simple pooled engineering of barcoded constructs, integration of these constructs into budding yeast, and fitness measurements by pooled cell competitions and barcode-sequencing. We applied this method to screen for PPIs between SARS-CoV-2 proteins and human proteins, screening in triplicate >180,000 ORF-ORF combinations represented by >1,000,000 barcoded lineages. Our results complement previous screens by identifying 74 putative PPIs, including interactions between ORF7A with the taste receptors TAS2R41 and TAS2R7, and between NSP4 with the transmembrane KDELR2 and KDELR3. We show that this PPI screening method is highly scalable, enabling larger studies aimed at generating a broad understanding of how viral effector proteins converge on cellular targets to effect replication.

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Fig 1 Complex PPIseq libraries were generated from plasmid pools using a scalable high-throughput workflow. A) Diagram of the workflow. 1) An in vitro recombinase integrates ORFs from an Entry vector pool into Gateway Destination plasmids, 2) the plasmids are digested at a I-SceI restriction site, and 3) in vitro recombination and cloning repairs plasmid with a lox site, barcode, and priming site, 4) Linearized plasmid pools are sequenced by long-read sequencing to determine the identity of the barcode and ORF integrated in each plasmid, 5) plasmids are "landed" into the yeast iSeq 2.0 genomic "landing pads" using transformation and Cre recombinase, 6) haploid pools of yeast are mated and Cre recombinase recombines the loci to form a double-barcode locus, and 7) the reconstituted split-URA3 marker is selected for to select the final double-barcoded diploid pool, ready to assay. B) A scanned image of double-barcoded diploid strains grown on media selective for the mDHFR reporter (SC-URA+100ug/mL MTX, raw image S1 Fig). ORF combinations are indicated on the margins, with F[1,2]-tagged ORF named along columns and F[3]-tagged ORF named along rows. TRIM15, AKT1, Bim, Bad, Noxa, UBE2S, and Mcl-1 are from the human ORFeome v8.1 collection, LEU2 from the yeast ORFeome collection, and the mDHFR F[1,2] and mDHFR F[3] tagged ORFs were generated here (Methods). C) A complex library of barcoded and mDHFR-tagged ORFs can be generated by transformation. The distribution of the number of unique barcoded plasmids detected per ORF, for human and viral ORF plasmid libraries, is plotted as lines and points, with each distribution’s mean and median denoted by vertical lines. D) A complex library of ORF combinations can be generated by mating. A Sankey diagram illustrates that most (~60%) of the possible (~350k) diploid double-barcode PPI-reporter ORF-ORF combinations that can be generated are observed to be represented by at least three barcode-replicate lineages in the initial library sample.
Fig 1 Complex PPIseq libraries were generated from plasmid pools using a scalable high-throughput workflow. A) Diagram of the workflow. 1) An in vitro recombinase integrates ORFs from an Entry vector pool into Gateway Destination plasmids, 2) the plasmids are digested at a I-SceI restriction site, and 3) in vitro recombination and cloning repairs plasmid with a lox site, barcode, and priming site, 4) Linearized plasmid pools are sequenced by long-read sequencing to determine the identity of the barcode and ORF integrated in each plasmid, 5) plasmids are "landed" into the yeast iSeq 2.0 genomic "landing pads" using transformation and Cre recombinase, 6) haploid pools of yeast are mated and Cre recombinase recombines the loci to form a double-barcode locus, and 7) the reconstituted split-URA3 marker is selected for to select the final double-barcoded diploid pool, ready to assay. B) A scanned image of double-barcoded diploid strains grown on media selective for the mDHFR reporter (SC-URA+100ug/mL MTX, raw image S1 Fig). ORF combinations are indicated on the margins, with F[1,2]-tagged ORF named along columns and F[3]-tagged ORF named along rows. TRIM15, AKT1, Bim, Bad, Noxa, UBE2S, and Mcl-1 are from the human ORFeome v8.1 collection, LEU2 from the yeast ORFeome collection, and the mDHFR F[1,2] and mDHFR F[3] tagged ORFs were generated here (Methods). C) A complex library of barcoded and mDHFR-tagged ORFs can be generated by transformation. The distribution of the number of unique barcoded plasmids detected per ORF, for human and viral ORF plasmid libraries, is plotted as lines and points, with each distribution’s mean and median denoted by vertical lines. D) A complex library of ORF combinations can be generated by mating. A Sankey diagram illustrates that most (~60%) of the possible (~350k) diploid double-barcode PPI-reporter ORF-ORF combinations that can be generated are observed to be represented by at least three barcode-replicate lineages in the initial library sample.CC BY · PubMed Central
Fig 2 PPIseq enables reproducible screening of a complex PPI reporter library. A) Schematic of the assay workflow. B) Examples of growth trajectories of each type of control strain generated in this pool, where points and lines denote the relative abundance of a lineage. For each control strain, a randomly selected lineage is shown from each of the three replicate cultures (line style). X-axis denotes the time point when the sample was taken. C) Violin plots of the relative fitness for experimental (vORF + hORF) and control (all other) lineages, separated by lineage type. Dot plots of the underlying data are shown for some lineage types. Lineages with low counts and outliers are excluded (Results, Methods). Horizontal line is the median of each lineage type’s fitnesses, with linetype corresponding to the lineage type. D) The mean fitness of the population as estimated by FitSeq. E) Heatmap of the fitness of each PPI assayed, averaged across all three replicates. Note that NSP2 is adjusted to mitigate bias, see Results. F,G,H) Fitness of lineages across assay replicates. High fitness lineages (putative PPIs) are correlated, but not low fitness lineages (no PPI). I) The Pearson R2 (y-axis) of the fitness of a PPIseq lineage across growth replicates, calculated for all lineages with fitnesses above a particular threshold (x-axis). Low fitness lineages (no PPI) appear uncorrelated, while high fitness lineages (putative PPIs) are well correlated. J) A Sankey diagram showing how the lineage fitness results were filtered to detect PPIs. We required a fitness call in at least three lineages representing the same ORF combination to call a PPI.
Fig 2 PPIseq enables reproducible screening of a complex PPI reporter library. A) Schematic of the assay workflow. B) Examples of growth trajectories of each type of control strain generated in this pool, where points and lines denote the relative abundance of a lineage. For each control strain, a randomly selected lineage is shown from each of the three replicate cultures (line style). X-axis denotes the time point when the sample was taken. C) Violin plots of the relative fitness for experimental (vORF + hORF) and control (all other) lineages, separated by lineage type. Dot plots of the underlying data are shown for some lineage types. Lineages with low counts and outliers are excluded (Results, Methods). Horizontal line is the median of each lineage type’s fitnesses, with linetype corresponding to the lineage type. D) The mean fitness of the population as estimated by FitSeq. E) Heatmap of the fitness of each PPI assayed, averaged across all three replicates. Note that NSP2 is adjusted to mitigate bias, see Results. F,G,H) Fitness of lineages across assay replicates. High fitness lineages (putative PPIs) are correlated, but not low fitness lineages (no PPI). I) The Pearson R2 (y-axis) of the fitness of a PPIseq lineage across growth replicates, calculated for all lineages with fitnesses above a particular threshold (x-axis). Low fitness lineages (no PPI) appear uncorrelated, while high fitness lineages (putative PPIs) are well correlated. J) A Sankey diagram showing how the lineage fitness results were filtered to detect PPIs. We required a fitness call in at least three lineages representing the same ORF combination to call a PPI.CC BY · PubMed Central
Fig 3 PPIseq confirms previously detected human-SARS-CoV-2 PPIs and finds new interactions involving taste receptors and ER-retention. A) The fraction of protein pairs screened by PPIseq that have been previously reported as PPIs in the IntAct Coronavirus dataset. Protein pairs are partitioned into those that have not (left) and have (right) been called as PPIs by PPIseq. B) The fraction of protein pairs that were validated as a PPI in a clonal growth assay when 0, 1, 2, or 3 of the PPiSeq growth replicates detected a PPI. C) GO term enrichment analysis (using clusterProfiler [82]) of human proteins that interact with the products of viral genes NSP4, ORF7B, and ORF7A. The color of each tile is the negative log10 of the adjusted p-value for the GO term enrichment, all shown are adjusted p-value < 0.05. The number in each tile is the number of human ORFs with that annotation in the set.
Fig 3 PPIseq confirms previously detected human-SARS-CoV-2 PPIs and finds new interactions involving taste receptors and ER-retention. A) The fraction of protein pairs screened by PPIseq that have been previously reported as PPIs in the IntAct Coronavirus dataset. Protein pairs are partitioned into those that have not (left) and have (right) been called as PPIs by PPIseq. B) The fraction of protein pairs that were validated as a PPI in a clonal growth assay when 0, 1, 2, or 3 of the PPiSeq growth replicates detected a PPI. C) GO term enrichment analysis (using clusterProfiler [82]) of human proteins that interact with the products of viral genes NSP4, ORF7B, and ORF7A. The color of each tile is the negative log10 of the adjusted p-value for the GO term enrichment, all shown are adjusted p-value < 0.05. The number in each tile is the number of human ORFs with that annotation in the set.CC BY · PubMed Central

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Metadata

Authors
Miller, D., Dziulko, A., & Levy, S.
Affiliations
  • SLAC National Accelerator Laboratory (US)
  • University of Colorado Boulder (US)
Corresponding author
  • Miller, Darach
  • Dziulko, Adam
  • Levy, Sasha
ORCID
Venue
PLoS ONE
Publisher
openRxiv
Published
2024-02-14
Volume
20
Issue
1
Pages
e0299440-e0299440
ISSN
1932-6203 (ISSN-L)
DOI
10.1101/2024.02.13.580123
PMC
PMC11741623
OpenAlex
W4406501025
Cited by
1
References
2
Citations by year
2026: 1
FWCI
0.77
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • PLoS ONE (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Division of Intramural Research, National Institute of Allergy and Infectious Diseases (R01AI164530)
  • National Institutes of Health
  • National Institute of Allergy and Infectious Diseases (R01AI164530)
  • SLAC National Accelerator Laboratory
Topics
  • Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 and COVID-19 Research
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cell Biology › Biotin and Related Studies
Keywords
SARS-CoV-2, protein-protein interaction, host-virus interaction, ORF7a, NSP4, antiviral drug targets
MeSH terms
COVID-19, SARS-CoV-2, Humans, Viral Proteins, Protein Interaction Mapping, Host-Pathogen Interactions, High-Throughput Screening Assays, HEK293 Cells, Protein Interaction Maps

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Miller, D., Dziulko, A., & Levy, S. (2024). Pooled PPIseq: screening the SARS-CoV-2 and human interface with a scalable multiplexed protein-protein interaction assay platform. PLoS ONE. https://doi.org/10.1101/2024.02.13.580123