Biology subjects

Johnson, C. W.

Publications and source records attributed to Johnson, C. W..

12 recordsLinked to original sources

bioRxiv2026

ExPert infers causal perturbations from cell states across biological and clinical contexts

Pooled single-cell CRISPR screens have driven substantial effort toward models predicting the response to a perturbation. We posed the inverse question: which perturbation produced an observed cell state? Here we developed ExPert (Extracting Perturbations), which learns one prototype per perturbation in a latent space shared by 12 cellular contexts, trained on 8,964 perturbations and 19.5 million cells, and ranks 19,895 candidate genes per query, over half absent from training. ExPert more than doubled held-out-context recall over the best existing method with 1,000-fold less compute, and transferred without retraining to drugs, protein degradation and combinations. In alcohol-associated liver disease it implicated mRNA export, unchanged in expression, and we experimentally confirmed nuclear poly(A) retention and isoform switching. In primary mitochondrial disease it recovered the affected complex from blood, localized the signal to monocytes, and flagged a misdiagnosed control. Perturbation atlases can thus be queried in reverse, from state to likely driver.

systems biology↗

bioRxiv2026

Overcoming protocatechuate and catechol accumulation in muconic acid production via adaptive laboratory evolution and metabolic engineering in Pseudomonas putida

Muconic acid is an industrially valuable molecule that can be biologically produced from diverse biogenic and waste-derived feedstocks, including sugars and lignin- and plastic-derived aromatic compounds. However, accumulation of protocatechuate (PCA) has been observed in multiple microbes engineered for muconate production when the PCA decarboxylase, AroY, is used. This raises the question of whether PCA decarboxylation represents a rate-limiting step and how this bottleneck might be alleviated, especially given the toxicity and reactivity of PCA and catechol intermediates. To address this, we performed adaptive laboratory evolution (ALE) on a strain of Pseudomonas putida originally engineered for muconate production from aromatic compounds, but with catBC restored, to select for improved conversion of PCA and, in separate lineages, 4-hydroxybenzoate. Contrary to our expectations, the predominant beneficial mutations localized to the catA1 cassette encoding catechol 1,2-dioxygenase, rather than aroY or its associated cofactor biosynthesis genes. Transcriptomic analysis revealed elevated catA1 expression in evolved isolates from ALE, and introduction of these mutations improved productivity in strains designed for muconate production from both aromatic and sugar substrates. Quantitative proteomics and biochemical assays demonstrated that the mutations also led to increased CatA1 protein abundance and modest enhancements in catalytic efficiency, respectively, with strain phenotypes largely driven by high CatA1 levels and potentially synergistic kinetic improvements. Additional reverse-engineering studies identified variants with modest effects on muconate accumulation, including those with potential to enhance biosynthesis of the prenylated FMN cofactor of AroY. Collectively, these results indicate that catechol, not PCA, is the principal bottleneck in muconate production via the PCA decarboxylation route originally demonstrated by Draths et al., refining our understanding of pathway limitations and offering new strategies for improving rate, yield, and strain resilience in muconate bioproduction. HighlightsO_LIAccumulation of metabolic intermediates was alleviated by adaptive laboratory evolution C_LIO_LISequencing, proteomics, and enzyme kinetics revealed mechanisms for adaptation C_LIO_LIIncreased CatA1 expression reduced bottlenecks and improved muconate production C_LI

synthetic biology↗

bioRxiv2026

Pathway selection for arabinose utilization in Pseudomonas putida reveals a rate-yield tradeoff in muconic acid production from lignocellulosic sugars

Engineering heterologous utilization of substrates requires selection of catabolic pathways that balance strain performance and product biosynthesis. Here, we compare the oxidative and isomerase arabinose utilization pathways in Pseudomonas putida strains engineered for cis,cis-muconic acid production from glucose and xylose. Based on the point of entry into central carbon metabolism, we hypothesized that the oxidative arabinose pathway would enable higher productivity while the arabinose isomerase pathway would enable higher muconate yield. In both strains, additional modifications were engineered to improve muconic acid production including sugar transporter tuning, catechol 1,2-dioxygenase overexpression, a feedback-resistant DAHP synthase, and a flux-stabilizing gltA variant. Consistent with our hypothesis, the oxidative arabinose pathway supported faster growth and higher productivity (0.58 g/L/h), whereas the arabinose isomerase pathway improved carbon efficiency, achieving muconate yields of up to 50 C-mol% in fed-batch bioreactors. Process modeling indicates that these performance metrics can reduce the minimum selling price of muconate-derived adipic acid to $2.74/kg and greenhouse gas emissions to 1.31 kg CO2e/kg, approaching cost parity and reducing emissions by 86% relative to fossil carbon-derived adipic acid. Overall, this study presents a systematic comparison of sugar catabolic pathways that enabled development of strains suited for the tradeoffs between rate and yield.

synthetic biology↗

bioRxiv2026

Simultaneous optimization of lignocellulosic sugar catabolism via systematic laboratory evolution in dynamic conditions

Efficient co-utilization of hexose and pentose sugars from lignocellulose is essential for microbial production of bio-based chemicals, yet engineered non-native catabolic pathways can be suboptimal and evolutionarily unstable in complex resource environments. We used a Pseudomonas putida strain, previously engineered to catabolize xylose and arabinose to examine how resource abundance, temporal availability, and sub-culturing criteria shape evolutionary outcomes. Using an automated adaptive laboratory evolution (ALE) platform, we evolved the strain under static conditions with single selection pressures and dynamic regimes that imposed selection pressures on multiple sugars. These environments drove divergence between catabolic specialists and generalists. While selection regimes with weak or absent selection for xylose frequently resulted in loss of xylose catabolism, evolution under carbon-limited, mixed-sugar environments promoted stable retention and coordinated optimization of multiple catabolic pathways, increasing total sugar consumption in mixed-sugar conditions. Genomic, proteomic, and biochemical analyses showed that evolutionary stability was determined by pathway-specific fitness costs, leading to either pathway loss or cost-reducing refinement, depending on selection strength. An isolated generalist clone also exhibited improved indigoidine production from mixed sugars when compared to the parental strain. Together, these findings link resource dynamics to fitness landscapes that determine catabolic specialization, generalization, evolutionary trade-offs, and applicability to bioconversion.

bioengineering↗

bioRxiv2025

Cooperativity between Ras pathway mutations in colonic tumorigenesis

The Kirsten rat sarcoma (KRAS) gene is the most frequently mutated oncogene in colorectal cancer (CRC). We previously characterized two activating alleles of KRAS, A59T and A59E, that show impaired BRAF dimerization. These alleles of KRAS are enriched in CRC tumors with genetic alterations in genes that regulate MAPK signaling (e.g. EGFR, NF1). Using a new conditional mouse model of K-Ras (LSL-K-Ras A59E) we show that, despite its inability to universally activate RAF kinases, K-Ras A59E disturbs colon epithelium and decreases mouse survival in a tumor model. By combining LSL-K-Ras A59E mice with a conditional knockout of Nf1, we demonstrate cooperation between these alleles at multiple levels. Consistent with cooperation and clinical observations, we show that K-Ras A59E neither promotes EGF independence of organoid growth, nor confers intrinsic resistance to EGFR inhibition. Thus, our data provide a deeper understanding of the role of RAF isoforms in mutant KRAS driven CRC and argue for the use of anti-EGFR therapies against some Ala59 mutant alleles of KRAS. Statement of significanceOur studies on oncogenic K-RAS mutants demonstrate that the biochemical properties of a mutant oncoprotein are reflected in the somatic genetics of cancer, in particular in the cooperating mutations that occur. These mutant-specific genetic interactions influence therapeutic responses. As such, KRAS mutation may not be a univariate predictor of response to EGFR inhibition.

cancer biology↗

bioRxiv2024

How Fungi Biosynthesize 3-Nitropropanoic Acid: the Simplest yet Lethal Mycotoxin

We uncovered the biosynthetic pathway of the lethal mycotoxin 3-nitropropanoic acid (3-NPA) from koji mold Aspergillus oryzae through genome mining, genetic inactivation and biochemical characterization. The biosynthetic gene cluster (BGC) of 3-NPA, which encodes an amine oxidase and a decarboxylase, is conserved in many fungi used in food processing, although most of the strains have not been reported to produce 3-NPA. Our discovery will lead to efforts that improve the safety profiles of these indispensable microorganisms in making food, alcoholic beverages, and seasoning.

biochemistry↗

bioRxiv2023

Allosteric site variants affect GTP hydrolysis on RAS

AbstractRAS GTPases are proto-oncoproteins that regulate cell growth, proliferation, and differentiation in response to extracellular signals. The signaling functions of RAS, and other small GTPases, are dependent on their ability to cycle between GDP-bound and GTP-bound states. Structural analyses suggest that GTP hydrolysis catalyzed by HRAS can be regulated by an allosteric site located between helices 3, 4 and loop 7. Here we explore the relationship between intrinsic GTP hydrolysis on HRAS and the position of helix 3 and loop 7 through manipulation of the allosteric site, showing that the two sites are functionally connected. We generated several hydrophobic mutations in the allosteric site of HRAS to promote shifts in helix 3 relative to helix 4. By combining crystallography and enzymology to study these mutants, we show that closure of the allosteric site correlates with increased hydrolysis of GTP on HRAS in solution. Interestingly, binding to the RAS binding domain of RAF kinase (RAF-RBD) inhibits GTP hydrolysis in the mutants. This behavior may be representative of a cluster of poorly understood mutations that occur in human tumors, which potentially cooperate with RAF complex formation to stabilize the GTP-bound state of RAS.

biochemistry↗

bioRxiv2022

Uncovering Cross-Cohort Molecular Features with Multi-Omics Integration Analysis

Integrative approaches that simultaneously model multi-omics data have gained increasing popularity because they provide holistic system biology views of multiple or all components in a biological system of interest. Canonical correlation analysis (CCA) is a correlation-based integrative method. It was initially designed to extract latent features shared between two assays by finding the linear combinations of features - referred to as canonical vectors (CVs) - within each assay that achieve maximal across-assay correlation. Sparse multiple CCA (SMCCA), a widely-used derivative of CCA, allows more than two assays but can result in non-orthogonal CVs when applied to high-dimensional data. Here, we incorporated a variation of the Gram-Schmidt (GS) algorithm with SMCCA to improve orthogonality among CVs. Applying our SMCCA-GS method to proteomics and methylomics data from the Multi-Ethnic Study of Atherosclerosis (MESA) and Jackson Heart Study (JHS), we identified strong associations between blood cell counts and protein abundance. This finding suggests that adjustment of blood cell composition should be considered in protein-based association studies. Importantly, CVs obtained from two independent cohorts demonstrate transferability across the cohorts. For example, proteomic CVs learned from JHS explain similar amounts of blood cell count phenotypic variance in MESA, explaining 39.0% ~ 50.0% variation in JHS and 38.9% ~ 49.1% in MESA, similar transferability was observed for other omics-CV-trait pairs. This suggests that biologically meaningful and cohort-agnostic variation is captured by CVs. We further developed Sparse Supervised Multiple CCA (SSMCCA) to allow supervised integration analysis for more than two assays. We anticipate that applying our SMCCA-GS and SSMCCA on various cohorts would help identify cohort-agnostic biologically meaningful relationships between multi-omics data and phenotypic traits. Author SummaryComprehensive understanding of human complex traits may benefit from incorporation of molecular features from multiple biological layers such as genome, epigenome, transcriptome, proteome, and metabolome. CCA is a correlation-based method for multi-omics data which reduces the dimension of each omic assay to several orthogonal components - commonly referred to as canonical vectors (CVs). The widely-used SMCCA method allows effective dimension reduction and integration of multi-omics data, but suffers from potentially highly correlated CVs when applied to high-dimensional omics data. Here, we improve the statistical independence among the CVs by adopting a variation of the GS algorithm. We applied our SMCCA-GS method to proteomic and methylomic data from two cohort studies, MESA and JHS. Our results reveal a pronounced effect of blood cell counts on protein abundance, strongly suggesting blood cell composition adjustment in protein-based association studies may be necessary. Finally, we present SSMCCA which allows supervised CCA analysis for the association between one phenotype of interest and more than two assays. We anticipate that SMCCA-GS would help reveal meaningful system-level factors from biological processes involving features from multiple assays; and SSMCCA would further empower interrogation of these factors for phenotypic traits related to health and diseases.

genomics↗

bioRxiv2022

Allosteric regulation of switch-II controls K-Ras oncogenicity

Ras proteins are GTPases that regulate a wide range of cellular processes. The activity of Ras is dependent on its nucleotide-binding status, which is modulated by guanine nucleotide exchange factors (GEFs) and GTPase-activating proteins (GAPs). Previously, we demonstrated that mutation of lysine 104 to glutamine (K104Q) attenuates the transforming capacity of oncogenic K-Ras by interrupting GEF induced nucleotide exchange. To assess the effect of this mutation in vivo, we used CRISPR/Cas9 to generate mouse models carrying the K104Q point mutation in wild-type and conditional K-RasLSL-G12D alleles. Consistent with our previous findings from in vitro studies, the oncogenic activity of K-RasG12D was significantly attenuated by mutation at K104 in vivo. These data demonstrate that lysine at position 104 is critical for the full oncogenic activity of mutant K-Ras and suggest that modification at K104, for example acetylation, may also regulate its activity. In addition, animals homozygous for K104Q were viable, fertile, and arose at Mendelian frequency, indicating that K104Q is not a complete loss of function mutation. Using biochemical and structural analysis, we found that the G12D and K104Q mutations cooperate to suppress GEF-mediated nucleotide exchange, explaining the preferential effect of K104Q on oncogenic K-Ras. Finally, we discovered an allosteric regulatory network consisting of K104 and residues including G75 on switch II (SWII) that is the key for regulating the stability of the helix on SWII. In this allosteric network, K104-G75 interaction might be primary for keeping stabilization of SWII. Given the high frequency of KRAS mutations in human cancers, modulation of this network may provide a unique therapeutic approach.

cancer biology↗

bioRxiv2021

Effect sizes of causal variants for gene expression and complex traits differ between populations

Despite the growing number of genome-wide association studies (GWAS), it remains unclear to what extent gene-by-gene and gene-by-environment interactions influence complex traits in humans. The magnitude of genetic interactions in complex traits has been difficult to quantify because GWAS are generally underpowered to detect individual interactions of small effect. Here, we develop a method to test for genetic interactions that aggregates information across all trait-associated loci. Specifically, we test whether SNPs in regions of European ancestry shared between European American and admixed African American individuals have the same causal effect sizes. We hypothesize that in African Americans, the presence of genetic interactions will drive the causal effect sizes of SNPs in regions of European ancestry to be more similar to those of SNPs in regions of African ancestry. We apply our method to two traits: gene expression in 296 African Americans and 482 European Americans in the Multi-Ethnic Study of Atherosclerosis (MESA) and low-density lipoprotein cholesterol (LDL-C) in 74K African Americans and 296K European Americans in the Million Veteran Program (MVP). We find significant evidence for genetic interactions in our analysis of gene expression; for LDL-C, we observe a similar point estimate although this is not significant, likely due to lower statistical power. These results suggest that gene-by-gene or gene-by-environment interactions modify the effect sizes of causal variants in human complex traits.

genetics↗

bioRxiv2021

Regulation of GTPase function by autophosphorylation

A unifying feature of the RAS superfamily is a functionally conserved GTPase cycle that proteins use to transition between active and inactive states. Here, we demonstrate that active site autophosphorylation of some small GTPases is an intrinsic regulatory mechanism that reduces nucleotide hydrolysis and enhances nucleotide exchange, thus altering the on/off switch that forms the basis for their signaling functions. Using x-ray crystallography, nuclear magnetic resonance spectroscopy, biolayer interferometry binding assays, and molecular dynamics on autophosphorylated mutants of H-RAS and K-RAS, we show that phosphoryl transfer from GTP requires dynamic movement of the switch II domain and that autophosphorylation promotes nucleotide exchange by opening of the active site and extraction of the stabilizing Mg. Finally, we demonstrate that autophosphorylated K-RAS exhibits altered effector interactions, including a reduced affinity for RAF proteins. Thus, autophosphorylation leads to altered active site dynamics and effector interaction properties, creating a pool of GTPases that are functionally distinct from the non-phosphorylated counterpart.

biochemistry↗

bioRxiv2019

Membrane composition influences the conformation and function of the dopamine transporter in vivo

The biophysical and biochemical properties of membrane lipids can alter the conformation and function of membrane-spanning proteins, yet the specific, physiological consequence in vivo of changing the membrane milieu for a specific protein has been rarely investigated. Using various genetic approaches to eliminate expression of the membrane-associated protein Flotillin-1, we have found that the lipid environment of the dopamine transporter (DAT) is necessary for mice to respond to amphetamine but not cocaine, because the localization of DAT to cholesterol-rich membranes is required for a DAT conformation that is essential for reverse transport of dopamine. Furthermore, a conditional rather than constitutive loss-of-function approach was necessary to reveal this phenotype, indicating a broader role for membrane-protein interactions that are modulated by Flotillin-1. Taken together, these findings demonstrate how interaction of a transmembrane protein with its membrane environment can regulate distinct events in the vertebrate brain that give rise to specific behavioral outcomes.

neuroscience↗