Random Barcodes Research Articles

Phage resistance mutations affecting the bacterial cell surface increase susceptibility to fungi in a model cheese community.

Diverse populations of bacteriophages infect and coevolve with their bacterial hosts. Although host recognition and infection occur within microbiomes, the molecular mechanisms underlying host-phage interactions within a community context remain poorly studied. The biofilms (rinds) of aged cheeses contain taxonomically diverse microbial communities that follow reproducible growth patterns and can be manipulated under laboratory conditions. In this study, we use cheese as a model for studying phage-microbe interactions by identifying and characterizing a tractable host-phage pair co-occurring within a model Brie-like community. We isolated a novel bacteriophage, TS33, that kills Hafnia sp. JB232, a member of the model community. TS33 is easily propagated in the lab and naturally co-occurs in the cheese community, rendering it a prime candidate for the study of host-phage interactions. We performed growth assays of the Hafnia, TS33, and the fungal community members, Geotrichum candidum and Penicillium camemberti. Employing Random Barcode Transposon Sequencing experiments, we identified candidate host factors that contribute to TS33 infectivity, many of which are homologs of bacterial O-antigen genes. Hafnia mutants in these genes exhibit decreased susceptibility to phage infection, but experience negative fitness effects in the presence of the fungi. Therefore, mutations in O-antigen biosynthesis homologs may have antagonistic pleiotropic effects in Hafnia that have major consequences for its interactions with the rest of the community. Ongoing and future studies aim to unearth the molecular mechanisms by which the O-antigen of Hafnia mediates its interactions with its viral and fungal partners.

Rhizobium determinants of rhizosphere persistence and root colonization.

Bacterial persistence in the rhizosphere and colonization of root niches are critical for the establishment of many beneficial plant-bacteria interactions including those between Rhizobium leguminosarum and its host legumes. Despite this, most studies on R. leguminosarum have focused on its symbiotic lifestyle as an endosymbiont in root nodules. Here, we use random barcode transposon sequencing to assay gene contributions of R. leguminosarum during competitive growth in the rhizosphere and colonization of various plant species. This facilitated the identification of 189 genes commonly required for growth in diverse plant rhizospheres, mutation of 111 of which also affected subsequent root colonization (rhizosphere progressive), and a further 119 genes necessary for colonization. Common determinants reveal a need to synthesize essential compounds (amino acids, ribonucleotides, and cofactors), adapt metabolic function, respond to external stimuli, and withstand various stresses (such as changes in osmolarity). Additionally, chemotaxis and flagella-mediated motility are prerequisites for root colonization. Many genes showed plant-specific dependencies highlighting significant adaptation to different plant species. This work provides a greater understanding of factors promoting rhizosphere fitness and root colonization in plant-beneficial bacteria, facilitating their exploitation for agricultural benefit.

Single-cell lineage capture across genomic modalities with CellTag-multi reveals fate-specific gene regulatory changes.

Complex gene regulatory mechanisms underlie differentiation and reprogramming. Contemporary single-cell lineage-tracing (scLT) methods use expressed, heritable DNA barcodes to combine cell lineage readout with single-cell transcriptomics. However, reliance on transcriptional profiling limits adaptation to other single-cell assays. With CellTag-multi, we present an approach that enables direct capture of heritable random barcodes expressed as polyadenylated transcripts, in both single-cell RNA sequencing and single-cell Assay for Transposase Accessible Chromatin using sequencing assays, allowing for independent clonal tracking of transcriptional and epigenomic cell states. We validate CellTag-multi to characterize progenitor cell lineage priming during mouse hematopoiesis. Additionally, in direct reprogramming of fibroblasts to endoderm progenitors, we identify core regulatory programs underlying on-target and off-target fates. Furthermore, we reveal the transcription factor Zfp281 as a regulator of reprogramming outcome, biasing cells toward an off-target mesenchymal fate. Our results establish CellTag-multi as a lineage-tracing method compatible with multiple single-cell modalities and demonstrate its utility in revealing fate-specifying gene regulatory changes across diverse paradigms of differentiation and reprogramming.

Genome-wide fitness profiling reveals molecular mechanisms that bacteria use to interact with Trichoderma atroviride exometabolites.

Trichoderma spp. are ubiquitous rhizosphere fungi capable of producing several classes of secondary metabolites that can modify the dynamics of the plant-associated microbiome. However, the bacterial-fungal mechanisms that mediate these interactions have not been fully characterized. Here, a random barcode transposon-site sequencing (RB-TnSeq) approach was employed to identify bacterial genes important for fitness in the presence of Trichoderma atroviride exudates. We selected three rhizosphere bacteria with RB-TnSeq mutant libraries that can promote plant growth: the nitrogen fixers Klebsiella michiganensis M5aI and Herbaspirillum seropedicae SmR1, and Pseudomonas simiae WCS417. As a non-rhizosphere species, Pseudomonas putida KT2440 was also included. From the RB-TnSeq data, nitrogen-fixing bacteria competed mainly for iron and required the siderophore transport system TonB/ExbB for optimal fitness in the presence of T. atroviride exudates. In contrast, P. simiae and P. putida were highly dependent on mechanisms associated with membrane lipid modification that are required for resistance to cationic antimicrobial peptides (CAMPs). A mutant in the Hog1-MAP kinase (Δtmk3) gene of T. atroviride showed altered expression patterns of many nonribosomal peptide synthetase (NRPS) biosynthetic gene clusters with potential antibiotic activity. In contrast to exudates from wild-type T. atroviride, bacterial mutants containing lesions in genes associated with resistance to antibiotics did not show fitness defects when RB-TnSeq libraries were exposed to exudates from the Δtmk3 mutant. Unexpectedly, exudates from wild-type T. atroviride and the Δtmk3 mutant rescued purine auxotrophic mutants of H. seropedicae, K. michiganensis and P. simiae. Metabolomic analysis on exudates from wild-type T. atroviride and the Δtmk3 mutant showed that both strains excrete purines and complex metabolites; functional Tmk3 is required to produce some of these metabolites. This study highlights the complex interplay between Trichoderma-metabolites and soil bacteria, revealing both beneficial and antagonistic effects, and underscoring the intricate and multifaceted nature of this relationship.

Clonal Dynamics of HDR-Edited HSPCs Targeting the CD33 Locus in Rhesus Macaques

For precise genome editing via CRISPR/homology-directed repair (HDR), effective and safe editing of long-term engrafting hematopoietic stem cells (LT-HSCs) requires both sufficient HDR efficiency and protection of LT-HSC function and number. Loss or damage of LT-HSCs with resultant oligoclonal hematopoiesis can increase the risk of malignant transformation or long-term hematopoietic and immune dysfunction. HDR has adverse impacts on LT-HSC function as revealed by ex vivo studies and murine xenograft models, likely due to p53 activation, intracellular innate immune activation, and/or other pathways. However, the impact of HDR on true LT-HSC clonal dynamics in a relevant large animal model has not previously been studied. We tracked the clonal dynamics of CRISPR/HDR-edited barcoded HSPCs following autologous transplantation in rhesus macaques (RMs). HDR-favored gRNAs targeting the CD33 gene locus and an ssODN containing a 6 bp barcode ID and 14 bp random barcode sequences and flanked by CD33 homology arms were designed. Autologous CD34+ cells were electroporated with the Cas9 RNP and HDR cassette in the presence of i53 mRNA to inhibit p53-mediated cell death and reinfused into animal 1 following myeloablation. In animal 1, CD33 KO neutrophils were prevalent early following engraftment and then rapidly decreased over the next two months. Similar to CD33 expression level, we revealed that HDR knock-ins as well as total indel frequency drastically decreased following transplantation. This was in marked contrast to macaques transplanted with CD34+ HSPCs edited at the CD33 locus only for NHEJ, without inclusion of an HDR cassette (Kim et al, Cell, 2018, and Figure 1 below). We next performed a competitive transplantation comparing clonal dynamics of edited cells to barcoded lentiviral (LV)-transduced cells in two macaques. For each animal, CD34+ cells were fractionated into 2 aliquots, and half were transduced with a barcoded GFP-expressing LV vector and half electroporated with the Cas9 RNP and HDR cassette with higher concentrations of i53 mRNA to enhance protection of CRISPR edited LT-HSCs. The two cell populations were combined and reinfused into myeloablated animals. CD33 KO cells in both animals decreased rapidly, however the higher concentration of i53 mRNA facilitated a less steep reduction in CD33 total indel frequency in vivo over time. However, HDR in these two animals also markedly decreased, similar to the pattern in animal 1. In contrast, LV-transduced GFP+ cell engraftment stabilized at 20% after 2 months. We retrieved embedded barcodes from CRISPR/HDR cells and revealed that a diversity of HDR clones contributed to early hematopoietic reconstitution, with restriction to a much smaller set of long-term clones at steady state, proportional in the 3 animals to the level of HDR overall. The diversity of HDR-edited contributing clones was far less than LV-transduced contributing clones long-term post-transplantation. In conclusion, CRISPR/HDR edited cells were found to decrease rapidly after autologous transplantation in rhesus macaques despite substantial gene editing assessed in the infusion product prior to infusion, in contrast to much more stable LV-transduced cells. Clonality of HDR-edited cells drastically shrank at early stage and then relied on several dominant clones, which was somewhat improved by use of high dose i53 mRNA during editing. It will be important to find improved approaches to retain LT-HSC functional activity during the HDR editing process, or explore targeted gene correction approaches such as base or PRIME editing that may better preserve HSC activity. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Fast trimer statistics facilitate accurate decoding of large random DNA barcode sets even at large sequencing error rates.

Predefined sets of short DNA sequences are commonly used as barcodes to identify individual biomolecules in pooled populations. Such use requires either sufficiently small DNA error rates, or else an error-correction methodology. Most existing DNA error-correcting codes (ECCs) correct only one or two errors per barcode in sets of typically ≲104 barcodes. We here consider the use of random barcodes of sufficient length that they remain accurately decodable even with ≳6 errors and even at [Formula: see text] or 20% nucleotide error rates. We show that length ∼34nt is sufficient even with ≳106 barcodes. The obvious objection to this scheme is that it requires comparing every read to every possible barcode by a slow Levenshtein or Needleman-Wunsch comparison. We show that several orders of magnitude speedup can be achieved by (i) a fast triage method that compares only trimer (three consecutive nucleotide) occurence statistics, precomputed in linear time for both reads and barcodes, and (ii) the massive parallelism available on today's even commodity-grade Graphics Processing Units (GPUs). With 106 barcodes of length 34 and 10% DNA errors (substitutions and indels), we achieve in simulation 99.9% precision (decode accuracy) with 98.8% recall (read acceptance rate). Similarly high precision with somewhat smaller recall is achievable even with 20% DNA errors. The amortized computation cost on a commodity workstation with two GPUs (2022 capability and price) is estimated as between US$ 0.15 and US$ 0.60 per million decoded reads.

Best Practices in Designing, Sequencing and Identifying Random DNA Barcodes

Best Practices in Designing, Sequencing and Identifying Random DNA Barcodes

Best Practices in Designing, Sequencing and Identifying Random DNA Barcodes

Best Practices in Designing, Sequencing and Identifying Random DNA Barcodes

A reinforcement learning framework for pooled oligonucleotide design.

The goal of oligonucleotide (oligo) design is to select oligos that optimize a set of design criteria. Oligo design problems are combinatorial in nature and require computationally intensive models to evaluate design criteria. Even relatively small problems can be intractable for brute-force approaches that test every possible combination of oligos, so heuristic approaches must be used to find near-optimal solutions. We present a general reinforcement learning (RL) framework, called OligoRL, to solve oligo design problems with complex constraints. OligoRL allows 'black-box' design criteria and can be adapted to solve many oligo design problems. We highlight the flexibility of OligoRL by building tools to solve three distinct design problems: (i) finding pools of random DNA barcodes that lack restriction enzyme recognition sequences (CutFreeRL); (ii) compressing large, non-degenerate oligo pools into smaller degenerate ones (OligoCompressor) and (iii) finding Not-So-Random hexamer primer pools that avoid rRNA and other unwanted transcripts during RNA-seq library preparation (NSR-RL). OligoRL demonstrates how RL offers a general solution for complex oligo design problems. OligoRL and all simulation codes are available as a Julia package at http://jensenlab.net/tools and archived at https://archive.softwareheritage.org/browse/origin/directory/?origin_url=https://github.com/bmdavid2/OligoRL. Supplementary data are available at Bioinformatics online.

Genome-Wide Identification of Genes Important for Growth of Dickeya dadantii and Dickeya dianthicola in Potato (Solanum tuberosum) Tubers.

Dickeya species are causal agents of soft rot diseases in many economically important crops, including soft rot disease of potato (Solanum tuberosum). Using random barcode transposon-site sequencing (RB-TnSeq), we generated genome-wide mutant fitness profiles of Dickeya dadantii 3937, Dickeya dianthicola ME23, and Dickeya dianthicola 67-19 isolates collected after passage through several in vitro and in vivo conditions. Though all three strains are pathogenic on potato, D. dadantii 3937 is a well-characterized model while D. dianthicola strains ME23 and 67-19 are recent isolates. Strain ME23 specifically was identified as a representative strain from a 2014 outbreak on potato. This study generated comparable gene fitness measurements across ecologically relevant conditions for both model and non-model strains. Tubers from the potato cultivars “Atlantic,” “Dark Red Norland,” and “Upstate Abundance” provided highly similar conditions for bacterial growth. Using the homolog detection software PyParanoid, we matched fitness values for orthologous genes in the three bacterial strains. Direct comparison of fitness among the strains highlighted shared and variable traits important for growth. Bacterial growth in minimal medium required many metabolic traits that were also essential for competitive growth in planta, such as amino acid, carbohydrate, and nucleotide biosynthesis. Growth in tubers specifically required the pectin degradation gene kduD. Disruption in three putative DNA-binding proteins had strain-specific effects on competitive fitness in tubers. Though the Soft Rot Pectobacteriaceae can cause disease with little host specificity, it remains to be seen the extent to which strain-level variation impacts virulence.

UMIc: A Preprocessing Method for UMI Deduplication and Reads Correction.

A recent refinement in high-throughput sequencing involves the incorporation of unique molecular identifiers (UMIs), which are random oligonucleotide barcodes, on the library preparation steps. A UMI adds a unique identity to different DNA/RNA input molecules through polymerase chain reaction (PCR) amplification, thus reducing bias of this step. Here, we propose an alignment free framework serving as a preprocessing step of fastq files, called UMIc, for deduplication and correction of reads building consensus sequences from each UMI. Our approach takes into account the frequency and the Phred quality of nucleotides and the distances between the UMIs and the actual sequences. We have tested the tool using different scenarios of UMI-tagged library data, having in mind the aspect of a wide application. UMIc is an open-source tool implemented in R and is freely available from https://github.com/BiodataAnalysisGroup/UMIc.

A genome-scale CRISPR interference guide library enables comprehensive phenotypic profiling in yeast

BackgroundCRISPR/Cas9-mediated transcriptional interference (CRISPRi) enables programmable gene knock-down, yielding loss-of-function phenotypes for nearly any gene. Effective, inducible CRISPRi has been demonstrated in budding yeast, and genome-scale guide libraries enable systematic, genome-wide genetic analysis.ResultsWe present a comprehensive yeast CRISPRi library, based on empirical design rules, containing 10 distinct guides for most genes. Competitive growth after pooled transformation revealed strong fitness defects for most essential genes, verifying that the library provides comprehensive genome coverage. We used the relative growth defects caused by different guides targeting essential genes to further refine yeast CRISPRi design rules. In order to obtain more accurate and robust guide abundance measurements in pooled screens, we link guides with random nucleotide barcodes and carry out linear amplification by in vitro transcription.ConclusionsTaken together, we demonstrate a broadly useful platform for comprehensive, high-precision CRISPRi screening in yeast.

Development of a program for in silico optimized selection of oligonucleotide-based molecular barcodes.

Short DNA oligonucleotides (~4 mer) have been used to index samples from different sources, such as in multiplex sequencing. Presently, longer oligonucleotides (8–12 mer) are being used as molecular barcodes with which to distinguish among raw DNA molecules in many high-tech sequence analyses, including low-frequent mutation detection, quantitative transcriptome analysis, and single-cell sequencing. Despite some advantages of using molecular barcodes with random sequences, such an approach, however, makes it impossible to know the exact sequences used in an experiment and can lead to inaccurate interpretation due to misclustering of barcodes arising from the occurrence of unexpected mutations in the barcodes. The present study introduces a tool developed for selecting an optimal barcode subset during molecular barcoding. The program considers five barcode factors: GC content, homopolymers, simple sequence repeats with repeated units of dinucleotides, Hamming distance, and complementarity between barcodes. To evaluate a selected barcode set, penalty scores for the factors are defined based on their distributions observed in random barcodes. The algorithm employed in the program comprises two steps: i) random generation of an initial set and ii) optimal barcode selection via iterative replacement. Users can execute the program by inputting barcode length and the number of barcodes to be generated. Furthermore, the program accepts a user’s own values for other parameters, including penalty scores, for advanced use, allowing it to be applied in various conditions. In many test runs to obtain 100000 barcodes with lengths of 12 nucleotides, the program showed fast performance, efficient enough to generate optimal barcode sequences with merely the use of a desktop PC. We also showed that VFOS has comparable performance, flexibility in program running, consideration of simple sequence repeats, and fast computation time in comparison with other two tools (DNABarcodes and FreeBarcodes). Owing to the versatility and fast performance of the program, we expect that many researchers will opt to apply it for selecting optimal barcode sets during their experiments, including next-generation sequencing.

Reducing noise and stutter in short tandem repeat loci with unique molecular identifiers

Unique molecular identifiers (UMIs) are a promising approach to contend with errors generated during PCR and massively parallel sequencing (MPS). With UMI technology, random molecular barcodes are ligated to template DNA molecules prior to PCR, allowing PCR and sequencing error to be tracked and corrected bioinformatically. UMIs have the potential to be particularly informative for the interpretation of short tandem repeats (STRs). Traditional MPS approaches may simply lead to the observation of alleles that are consistent with the hypotheses of stutter, while with UMIs stutter products bioinformatically may be re-associated with their parental alleles and subsequently removed. Herein, a bioinformatics pipeline named strumi is described that is designed for the analysis of STRs that are tagged with UMIs. Unlike other tools, strumi is an alignment-free machine learning driven algorithm that clusters individual MPS reads into UMI families, infers consensus super-reads that represent each family and provides an estimate the resulting haplotype’s accuracy. Super-reads, in turn, approximate independent measurements not of the PCR products, but of the original template molecules, both in terms of quantity and sequence identity. Provisional assessments show that naïve threshold-based approaches generate super-reads that are accurate (∼97 % haplotype accuracy, compared to ∼78 % when UMIs are not used), and the application of a more nuanced machine learning approach increases the accuracy to ∼99.5 % depending on the level of certainty desired. With these features, UMIs may greatly simplify probabilistic genotyping systems and reduce uncertainty. However, the ability to interpret alleles at trace levels also permits the interpretation, characterization and quantification of contamination as well as somatic variation (including somatic stutter), which may present newfound challenges.

Development of a massively parallel reporter assay to identify functional regulatory variants in the PICALM Alzheimer disease associated locus

AbstractBackgroundMost significant genome‐wide association markers for Alzheimer disease (AD) are in noncoding portions of the genome making assigning a corresponding gene involved in risk challenging. Coding variants identified in some these GWAS loci thus far do not explain the proportion of risk attributable to the associated markers, indicating a contribution of noncoding variants to AD. We set out to develop a high throughput method investigating the functional role of noncoding variants associated with AD using the PICALM locus on chromosome 14:85,600,000‐85,860,000, which has been associated with AD across multiple ancestries, as a proof of concept region.MethodWe used the gnomAD database of over 15,000 whole genomes to identify all variants located in the PICALM locus across a wide variety of ancestral populations (non‐Hispanic whites, Hispanics, African Americans, and Asians). We developed a pipeline for epigenomic annotation from available databases and the subsequent design of oligos carrying wild‐type and variant alleles for each variant. For unbiased analyses of functional potential to regulate enhancer activity of each of the alleles we established a large vector library for massively parallel reporter assays (MPRA) in AD relevant cell lines.ResultWe identified >23,000 variants for analysis in the 260 kb PICALM locus. More than 4,000 of those had prior evidence for location in transcription factor binding sites, putative promotors/enhancers and eQTLs. 180 bp oligos centered around the variant alleles were synthesized by TWIST Biosciences. 20 bp random barcodes were added to each of the oligos through PCR amplification with a barcoded primer library. Sequencing of the resulting vector library indicated highly efficient incorporation with 95% unique barcodes present in the library. Reporter gene GFP and a minimal promoter were subsequently cloned in between the oligo and the barcode to establish the final MPRA library for transformation in cell lines.ConclusionHere we established a computational framework for in‐silico epigenomic annotation and oligo design for subsequent high‐throughput analyses and developed an efficient protocol to create a final MPRA library with highly unique barcodes at fragment incorporation. Cell line transformations and transcriptome analyses of the PICALM library to identify regulatory variants are ongoing.

Bacterial-fungal interactions revealed by genome-wide analysis of bacterial mutant fitness.

Microbial interactions are expected to be major determinants of microbiome structure and function. Although fungi are found in diverse microbiomes, their interactions with bacteria remain largely uncharacterized. In this work, we characterize interactions in 16 different bacterial-fungal pairs, examining the impacts of 8 different fungi isolated from cheese rind microbiomes on 2 bacteria (Escherichia coli and a cheese-isolated Pseudomonas psychrophila). Using random barcode transposon site sequencing (RB-TnSeq) with an analysis pipeline that allows statistical comparisons between different conditions, we observed that fungal partners caused widespread changes in the fitness of bacterial mutants compared to growth alone. We found that all fungal species modulated the availability of iron and biotin to bacterial species, suggesting that these may be conserved drivers of bacterial-fungal interactions. Species-specific interactions were also uncovered, a subset of which suggest fungal antibiotic production. Changes in both conserved and species-specific interactions resulted from deletion of a global regulator of fungal specialized metabolite production. This work highlights the potential for broad impacts of fungi on bacterial species within microbiomes.

Genetic Barcodes Enable Quantitative Mapping of Operator Mutants to Gene Expression

Every organism has intricate regulatory networks that enable them to sense, move, and interact with complex environments. In E. coli, transcription factors (TFs) bind to short sequences upstream of genes, called operators, to repress or activate gene expression. Our lab has previously derived and validated a biophysical model of transcription regulation -the repression of a gene by LacI - demonstrating that repressorcopy number, the energy of TF:operator binding, and genome size all play crucial roles in determining the quantitative features of gene expression. But LacI is a small fish in the cellular pond; we must develop experimental methods that enable us to map how any TF:operator pair imparts predictable gene expression, and do so in a high-throughput manner without sacrificing quantitation. We report a high-throughput method to randomly mutagenize large libraries of operator sequences and quantify their resulting gene expression. Briefly, mutagenized binding sites for a TF are “mapped” to a random DNA barcode using next-generation sequencing. The native gene regulated by that TF is then inserted between the mutated operators and barcodes, and these libraries are genomically-integrated into E. coli. The relative abundance of cells carrying each operator mutant can be determined via DNA-sequencing of the barcodes, while their gene expression can be measured by quantitative RNA-sequencing of barcodes using a mixture of Unique Molecular Identifiers and other methods to minimize bias. These data will inform mathematical models for the de novo prediction of gene expression from regulatory sequences.

High throughput barcoding method for genome-scale phasing

The future of human genomics is one that seeks to resolve the entirety of genetic variation through sequencing. The prospect of utilizing genomics for medical purposes require cost-efficient and accurate base calling, long-range haplotyping capability, and reliable calling of structural variants. Short-read sequencing has lead the development towards such a future but has struggled to meet the latter two of these needs. To address this limitation, we developed a technology that preserves the molecular origin of short sequencing reads, with an insignificant increase to sequencing costs. We demonstrate a novel library preparation method for high throughput barcoding of short reads where millions of random barcodes can be used to reconstruct megabase-scale phase blocks.

Omics-driven identification and elimination of valerolactam catabolism in Pseudomonas putida KT2440 for increased product titer

Pseudomonas putida is a promising bacterial chassis for metabolic engineering given its ability to metabolize a wide array of carbon sources, especially aromatic compounds derived from lignin. However, this omnivorous metabolism can also be a hindrance when it can naturally metabolize products produced from engineered pathways. Herein we show that P. putida is able to use valerolactam as a sole carbon source, as well as degrade caprolactam. Lactams represent important nylon precursors, and are produced in quantities exceeding one million tons per year (Zhang et al., 2017). To better understand this metabolism we use a combination of Random Barcode Transposon Sequencing (RB-TnSeq) and shotgun proteomics to identify the oplBA locus as the likely responsible amide hydrolase that initiates valerolactam catabolism. Deletion of the oplBA genes prevented P. putida from growing on valerolactam, prevented the degradation of valerolactam in rich media, and dramatically reduced caprolactam degradation under the same conditions. Deletion of oplBA, as well as pathways that compete for precursors L-lysine or 5-aminovalerate, increased the titer of valerolactam from undetectable after 48 h of production to ~90 mg/L. This work may serve as a template to rapidly eliminate undesirable metabolism in non-model hosts in future metabolic engineering efforts.

Massively Parallel Fitness Profiling Reveals Multiple Novel Enzymes in Pseudomonas putida Lysine Metabolism.

Despite intensive study for 50 years, the biochemical and genetic links between lysine metabolism and central metabolism in Pseudomonas putida remain unresolved. To establish these biochemical links, we leveraged random barcode transposon sequencing (RB-TnSeq), a genome-wide assay measuring the fitness of thousands of genes in parallel, to identify multiple novel enzymes in both l- and d-lysine metabolism. We first describe three pathway enzymes that catabolize l-2-aminoadipate (l-2AA) to 2-ketoglutarate (2KG), connecting d-lysine to the TCA cycle. One of these enzymes, P. putida 5260 (PP_5260), contains a DUF1338 domain, representing a family with no previously described biological function. Our work also identified the recently described coenzyme A (CoA)-independent route of l-lysine degradation that results in metabolization to succinate. We expanded on previous findings by demonstrating that glutarate hydroxylase CsiD is promiscuous in its 2-oxoacid selectivity. Proteomics of selected pathway enzymes revealed that expression of catabolic genes is highly sensitive to the presence of particular pathway metabolites, implying intensive local and global regulation. This work demonstrated the utility of RB-TnSeq for discovering novel metabolic pathways in even well-studied bacteria, as well as its utility a powerful tool for validating previous research.IMPORTANCEP. putida lysine metabolism can produce multiple commodity chemicals, conferring great biotechnological value. Despite much research, the connection of lysine catabolism to central metabolism in P. putida remained undefined. Here, we used random barcode transposon sequencing to fill the gaps of lysine metabolism in P. putida We describe a route of 2-oxoadipate (2OA) catabolism, which utilizes DUF1338-containing protein P. putida 5260 (PP_5260) in bacteria. Despite its prevalence in many domains of life, DUF1338-containing proteins have had no known biochemical function. We demonstrate that PP_5260 is a metalloenzyme which catalyzes an unusual route of decarboxylation of 2OA to d-2-hydroxyglutarate (d-2HG). Our screen also identified a recently described novel glutarate metabolic pathway. We validate previous results and expand the understanding of glutarate hydroxylase CsiD by showing that can it use either 2OA or 2KG as a cosubstrate. Our work demonstrated that biological novelty can be rapidly identified using unbiased experimental genetics and that RB-TnSeq can be used to rapidly validate previous results.