Biology subjects

Myers, C. L.

Publications and source records attributed to Myers, C. L..

At least 19 recordsLinked to original sources

bioRxiv2026

Geroprotective drug discovery with an AI-enabled assay for heat resistance

Drugs that slow the rate of organismal aging (geroprotectors) have the potential to improve human healthspan by preventing the development of multiple chronic diseases. The nematode C. elegans is a proven system for identifying anti-aging drugs, but methods for candidate nomination that scale to high throughput remain limited and have not been widely adopted. To accelerate the discovery process, we have developed an open-source AI-enabled screening platform that provides a rapid, automated, posture-based score of C. elegans survival. We used this workflow to screen a library of 2,782 FDA-approved drugs and identified 31 compounds that reproducibly increase heat stress resistance, a known predictor of longevity. Follow-up studies confirmed that many of these compounds confer lifespan extension in worms, and several compounds also have anti-senescent activity in human cells. This simplified and adaptable AI-enabled workflow therefore has the potential to accelerate the discovery of translatable drugs that slow aging.

pharmacology and toxicology↗

bioRxiv2026

Expanding TheCellMap.org to visualize a genome-scale genetic interaction network for a human cell line

Genetic interaction networks map functional connections between genes and their corresponding pathways and complexes. We previously developed TheCellMap.org as a central repository for storing and analyzing quantitative genetic interaction data produced by genome-scale Synthetic Genetic Array (SGA) analysis in the budding yeast, Saccharomyces cerevisiae. We have expanded TheCellMap.org to include ~89,000 quantitative genetic interactions identified from genome-scale CRISPR-based analysis of ~4 million human gene pairs in the haploid cell line, HAP1. TheCellMap.org enables users to readily access, visualize and explore human HAP1 genetic interactions, as well as to extract and reorganize sub-networks, applying data-driven network layouts in an intuitive and interactive manner.

systems biology↗

bioRxiv2026

Single cell Correlation Analysis (SCA): Identifying self-renewing subpopulation of human acute myeloid leukemia stem cells using single cell RNA sequencing analysis

Leukemia stem cells (LSCs), a rare and self-renewing subpopulation, drive Acute myeloid leukemia (AML) relapse and therapy resistance. While single-cell gene expression profiling (scGEP) offers high-resolution insights into LSC biology, existing computational tools rely on relative and arbitrary similarity metrics. This limits their ability to definitively identify true biological cell identities or compare rare cell populations across independent datasets. To overcome these limitations, we developed Single cell Correlation Analysis (SCA), a novel computational method that utilizes a permutation-based false discovery rate (FDR) framework and common background datasets to establish standardized, statistically rigorous thresholds of similarity. We applied SCA to query human AML scRNA-seq datasets using an experimentally validated murine scGEP of LSC self-renewal as our reference. SCA demonstrated superior specificity and precision, maintaining low false positive rates compared to existing reference-based annotation tools. Using SCA, we successfully identified cells expressing a conserved self-renewal program (scaLSC-SR) in both adult and pediatric human AML samples. These scaLSC-SR cells share classical LSC immunophenotypic markers (e.g., CD34, CD96, CD200) and exhibit transcriptomic hallmarks of LSC biology, including evasion of apoptosis and immune responses. Furthermore, this self-renewal program is significantly enriched in poor-risk genetic subtypes, specifically TP53 and NRAS mutated AML. Finally, we derived a 28-gene signature (LSC-SR28) from human AML patients that accurately captures LSC stemness and is highly prognostic across multiple independent clinical cohorts. SCA provides a robust, statistically principled framework for identifying rare and biologically meaningful cell populations across heterogeneous single-cell datasets. By successfully identifying a functional self-renewal profile to human AML, SCA reveals conserved, clinically relevant LSC populations that drive relapse and therapy resistance across different age groups.

cancer biology↗

bioRxiv2025

Quantitative analysis of genetic interactions in human cells from genome-wide CRISPR-Cas9 screens

Genetic interaction (GI) networks in model organisms have revealed how combinations of genome variants can impact phenotypes. To advance efforts toward a reference human GI network, we developed the quantitative Genetic Interaction (qGI) score, a method for precise GI measurement from genome-wide CRISPR-Cas9 screens in different query mutants constructed in a single human cell line. We found surprising prevalent systematic variation unrelated to GIs in CRISPR screen data, including both genomically linked effects and functionally coherent covariation. Leveraging [~]40 control screens in wild-type cells and half a billion differential fitness effect measurements, we developed a pipeline for CRISPR screen data processing and normalization to correct these artifacts and measure accurate, quantitative GIs. We also comprehensively characterized GI reproducibility by characterizing 4 - 5 biological replicates for [~]125,000 unique gene pairs. The qGI framework enables systematic identification of human GIs and provides broadly applicable strategies for analyzing context-specific CRISPR screen data.

bioinformatics↗

bioRxiv2024

A new class of penicillin-binding protein inhibitors to address drug-resistant Neisseria gonorrhoeae

{beta}-Lactams are the most widely used antibiotics for the treatment of bacterial infections because of their proven track record of safety and efficacy. However, susceptibility to {beta}-lactam antibiotics is continually eroded by resistance mechanisms. Emerging multidrug-resistant (MDR) Neisseria gonorrhoeae strains possessing altered penA alleles (encoding PBP2) pose a global health emergency as they threaten the utility of ceftriaxone, the last remaining outpatient antibiotic. Here we disclose a novel benzoxaborinine-based penicillin-binding protein inhibitor series (boro-PBPi) that is envisioned to address penA-mediated resistance while offering protection against evolution and expansion of {beta}-lactamases. Optimization of boro-PBPi led to the identification of compound 21 (VNRX-14079) that exhibits potent antibacterial activity against MDR N. gonorrhoeae achieved by high affinity binding to the PBP2 target. Boro-PBPi/PBP2 complex structures confirmed covalent interaction of the boron atom with Ser310 and the importance of the {beta}3-{beta}4 loop for improved affinity. 21 elicits bactericidal activity, a low frequency of resistance, a good safety profile, suitable pharmacokinetic properties, and in vivo efficacy in a murine infection model against ceftriaxone-resistant N. gonorrhoeae. 21 is a promising anti-gonorrhea agent poised for further advancement.

microbiology↗

bioRxiv2024

Cefepime-Taniborbactam and Ceftibuten-Ledaborbactam Maintain Activity Against KPC Variants that Lead to Ceftazidime-Avibactam Resistance

Klebsiella pneumoniae carbapenemases (KPCs) are widespread {beta}-lactamases that are a major cause of clinical non-susceptibility of Gram-negative bacteria to carbapenems and other {beta}-lactam antibiotics. Ceftazidime combined with the {beta}-lactamase inhibitor avibactam (CAZ-AVI) has been effective for treating infections by KPC-producing bacteria, but emergent KPC variants confer resistance to the combination. Taniborbactam and ledaborbactam are bicyclic boronate {beta}-lactamase inhibitors under development with cefepime and ceftibuten, respectively, to treat carbapenem-resistant bacterial infections. Here, we assessed the effects of clinically important KPC-2 and KPC-3 variants (V240G, D179Y, D179Y T243M) on the antibacterial activity of cefepime-taniborbactam (FEP-TAN) and ceftibuten-ledaborbactam (CTB-LED) and examined catalytic activity and inhibition of these variants. FEP-TAN and CTB-LED were highly active against CAZ-AVI-resistant engineered E. coli strains expressing these variants. Purified KPC variants catalyzed more efficient CAZ hydrolysis than wild-type enzymes, and D179Y-containing KPC-3 variants additionally catalyzed more efficient FEP hydrolysis than wild-type KPC-3. All KPC variants poorly hydrolyzed CTB, and D179Y-containing variants demonstrated significantly higher affinity for CAZ than FEP or CTB. Second-order rate constants (k2/K) for inhibition of D179Y-containing KPC-2 variants were significantly reduced relative to wild-type KPC-2, with AVI most impacted. K2/K was less affected for D179Y-containing KPC-3 variants, and reflected robust inhibition by TAN, LED and AVI. Together, the findings illustrate a biochemical basis for greater FEP-TAN and CTB-LED antibacterial activity in KPC variant expression backgrounds relative to CAZ-AVI, whereby the boronate inhibitors have sufficient inhibitory activity, whilst FEP and CTB are poorer substrates and bind to the variant enzymes with reduced affinity compared to CAZ.

biochemistry↗

bioRxiv2023

A scalable platform for efficient CRISPR-Cas9 chemical-genetic screens of DNA damage-inducing compounds

Current approaches to define chemical-genetic interactions (CGIs) in human cell lines are resource-intensive. We designed a scalable chemical-genetic screen platform by generating a DNA damage response (DDR)-focused custom sgRNA library. We performed five proof-of-principle compound screens and found that the compounds known modes-of-action (MoA) were enriched among the compounds CGIs. These scalable screens recapitulated expected CGIs at a comparable signal-to-noise ratio (SNR) relative to genome-wide screens. Furthermore, time-resolved CGIs, captured by sequencing screens at various time points, suggested an unexpected, late time point interstrand-crosslinking (ICL) repair pathway response to camptothecin-induced DNA damage. Our approach can facilitate screening compounds at scale and produce biologically informative CGI profiles.

systems biology↗

bioRxiv2023

Machine Learning Investigation Of Gene Expression Datasets Reveals TP53 Mutant-like AML With Wild Type TP53 And Poor Prognosis

Acute myeloid leukemia (AML) with TP53 mutations (TP53Mut) has poor clinical outcomes with 1-year survival rates of less than 10%. We investigated whether this AML subtype harbors a distinct gene expression profiling (GEP), what this GEP reveals about TP53Mut AML pathophysiology, and whether this GEP is prognostic in TP53 wild type (TP53WT) AML. We applied a supervised machine-learning approach to assess whether a unique TP53Mut GEP could be detected. Using the BEAT-AML dataset, we randomly divided the samples into training and testing datasets, while the TCGA dataset was reserved as a validation dataset. We trained a ridge regression machine learning model to classify TP53Mut and TP53WT cases. This model was highly accurate in distinguishing TP53Mut versus TP53WT cases in both the test and validation data sets. Additionally, we noted a cohort of TP53WT samples with high ridge regression scores and poor overall survival, suggesting share clinical and GEP features with TP53Mut AML. We defined these TP53WT samples as TP53 mutant-like (TP53Mut-like) AMLs. We trained a second ridge regression model to specifically detect TP53Mut-like samples in the BEAT AML dataset and found that TCGA data also harbors TP53Mut-like samples. The TP53Mut-like samples in the TCGA also have a worse OS rate than TP53WT cases. Using drug sensitivity data from 122 small molecules in the BEAT AML dataset, we found TP53Mut-like AMLs have distinct drug sensitivity patterns compared to TP53WT. Finally, we identified a 25 gene signature that can identify TP53Mut-like cases. This signature could be used clinically to identify this novel subset of poor-prognosis AML.

cancer biology↗

bioRxiv2023

Dimensionality reduction methods for extracting functional networks from large-scale CRISPR screens

CRISPR-Cas9 screens facilitate the discovery of gene functional relationships and phenotype-specific dependencies. The Cancer Dependency Map (DepMap) is the largest compendium of whole-genome CRISPR screens aimed at identifying cancer-specific genetic dependencies across human cell lines. A mitochondria-associated bias has been previously reported to mask signals for genes involved in other functions, and thus, methods for normalizing this dominant signal to improve co-essentiality networks are of interest. In this study, we explore three unsupervised dimensionality reduction methods - autoencoders, robust, and classical principal component analyses (PCA) - for normalizing the DepMap to improve functional networks extracted from these data. We propose a novel "onion" normalization technique to combine several normalized data layers into a single network. Benchmarking analyses reveal that robust PCA combined with onion normalization outperforms existing methods for normalizing the DepMap. Our work demonstrates the value of removing low-dimensional signals from the DepMap before constructing functional gene networks and provides generalizable dimensionality reduction-based normalization tools.

bioinformatics↗

bioRxiv2022

Dynamic decomposition of transcriptome responses during plant effector-triggered immunity revealed conserved responses in two distinct cell populations

O_LIRapid plant immune responses in the appropriate cells are needed for effective defense against pathogens. Although transcriptome analysis is often used to describe overall immune responses, collecting transcriptome data with sufficient resolution in both space and time is challenging. C_LIO_LIWe reanalyzed public Arabidopsis time-course transcriptome data obtained after a low-dose inoculation of a Pseudomonas syringae strain expressing the effector AvrRpt2, which induces Effector-Triggered Immunity (ETI) in Arabidopsis. Double-peak time-course patterns were prevalent among thousands of upregulated genes. We implemented a multi-compartment modeling approach to decompose the double-peak pattern into two single-peak patterns for each gene. C_LIO_LIThe decomposed peaks revealed an "echoing" pattern: the peak times of the first and second peaks correlated well across most upregulated genes. We demonstrated that two peaks likely represent responses of two distinct cell populations, which respond either cell-autonomously or indirectly to AvrRpt2. Thus, the peak decomposition extracted spatial information from the time-course data. C_LIO_LIThe echoing pattern also indicated a conserved transcriptome response between two cell populations despite different elicitor types. WRKY transcription factors appeared to underlie the conserved transcriptome response. Activation of a WRKY network via different entry-point WRKYs could explain the conserved transcriptome response elicited by different elicitor types. C_LI

systems biology↗

bioRxiv2022

Systems-level transcriptional regulation of Caenorhabditis elegans metabolism

Metabolism is precisely controlled to ensure organismal development and homeostasis. Several mechanisms regulate metabolism, including allosteric control and transcriptional regulation of metabolic enzymes and transporters. So far, metabolism regulation has mostly been described for individual genes and pathways, and the extent of transcriptional regulation of the entire metabolic network remains largely unknown. Here, we find that more than three-quarters of all metabolic genes are transcriptionally regulated in the nematode Caenorhabditis elegans. We find that many annotated metabolic pathways are coexpressed, and we use gene expression data and the iCEL1314 metabolic network model to define coregulated sub-pathways in an unbiased manner. Using a large gene expression compendium, we determine the conditions where sub-pathways exhibit strong coexpression. Finally, we develop WormClust, a web application that enables a gene-by-gene query of genes to view their association with metabolic (sub)-pathways. Overall, this study sheds light on the ubiquity of transcriptional regulation of metabolism and provides a blueprint for similar studies in other organisms, including humans.

systems biology↗

bioRxiv2022

N-terminal acetylation shields proteins from degradation and promotes age-dependent motility and longevity

Most eukaryotic proteins are N-terminally acetylated, but the functional impact on a global scale has remained obscure. Using genome-wide CRISPR knockout screens in human cells, we reveal a strong genetic dependency between a major N-terminal acetyltransferase and specific ubiquitin ligases. Biochemical analyses uncover that both the ubiquitin ligase complex UBR4-KCMF1 and the acetyltransferase NatC recognize proteins bearing an unacetylated N-terminal methionine followed by a hydrophobic residue. NatC KO-induced protein degradation and phenotypes are reversed by UBR knockdown, demonstrating the central cellular role of this interplay. We reveal that loss of Drosophila NatC is associated with male sterility, reduced longevity, and age-dependent loss of motility due to developmental muscle defects. Remarkably, muscle-specific overexpression of UbcE2M, one of the proteins targeted for NatC KO mediated degradation, suppresses defects of NatC deletion. In conclusion, NatC-mediated N-terminal acetylation acts as a protective mechanism against protein degradation, which is relevant for increased longevity and motility. In BriefVarland, Silva et al. define that a major cellular role of N-terminal acetylation is shielding proteins from proteasomal degradation by specific ubiquitin ligases. The human N-terminal acetyltransferase NatC protects the neddylation regulator UBE2M from degradation, while overexpression of Drosophila UBE2M/UbcE2M rescues the longevity and motility defects of NatC deletion. HighlightsO_LIN-terminal acetylation by NatC protects proteins from degradation, including UBE2M C_LIO_LIUBR4-KCMF1 targets unacetylated N-terminal Met followed by a hydrophobic residue C_LIO_LIDrosophila NatC is required for adult longevity and motility in elderly C_LIO_LIOverexpression of UBE2M/UbcE2M suppresses Drosophila NatC deletion phenotypes C_LI

molecular biology↗

bioRxiv2022

Reproducibility metrics for CRISPR screens

CRISPR screens are used extensively to systematically interrogate the phenotype-to-genotype problem. In contrast to early CRISPR screens, which defined core cell fitness genes, most current efforts now aim to identify context-specific phenotypes that differentiate a cell line, genetic background or condition of interest, such as a drug treatment. While CRISPR-related technologies have shown great promise and a fast pace of innovation, a better understanding of standards and methods for quality assessment of CRISPR screen results is crucial to guide technology development and application. Specifically, many commonly used metrics for quantifying screen quality do not accurately measure the reproducibility of context-specific hits. We highlight the importance of reporting reproducibility statistics that directly relate to the purpose of the screen and suggest the use of metrics that are sensitive to context-specific signal.

genomics↗

bioRxiv2021

Comparing host module activation patterns and temporal dynamics in infection by influenza H1N1 viruses

Influenza is a serious global health threat that shows varying pathogenicity among different virus strains. Understanding similarities and differences among activated functional pathways in the host responses can help elucidate therapeutic targets responsible for pathogenesis. To compare the types and timing of functional modules activated in host cells by four influenza viruses of varying pathogenicity, we developed a new DYNAmic MOdule (DYNAMO) method that addresses the need to compare functional module utilization over time. This integrative approach overlays whole genome time series expression data onto an immune-specific functional network, and extracts conserved modules exhibiting either different temporal patterns or overall transcriptional activity. We identified a common core response to influenza virus infection that is temporally shifted for different viruses. We also identified differentially regulated functional modules that reveal unique elements of responses to different virus strains. Our work highlights the usefulness of combining time series gene expression data with a functional interaction map to capture temporal dynamics of the same cellular pathways under different conditions. Our results help elucidate conservation of the immune response both globally and at a granular level, and provide mechanistic insight into the differences in the host response to infection by influenza strains of varying pathogenicity.

bioinformatics↗

bioRxiv2021

Identification of candidate susceptibility genes to Puccinia graminis f. sp. tritici in wheat

Wheat stem rust disease caused by Puccinia graminis f. sp. tritici (Pgt) is a global threat to wheat production. Fast evolving populations of Pgt limit the efficacy of plant genetic resistance and constrain disease management strategies. Understanding molecular mechanisms that lead to rust infection and disease susceptibility could deliver novel strategies to deploy crop resistance through genetic loss of disease susceptibility. We used comparative transcriptome-based and orthology-guided approaches to characterize gene expression changes associated with Pgt infection in susceptible and resistant Triticum aestivum genotypes as well as the non-host Brachypodium distachyon. We targeted our analysis to genes with differential expression in T. aestivum and genes suppressed or not affected in B. distachyon and report several processes potentially linked to susceptibility to Pgt, such as cell death suppression and impairment of photosynthesis. We complemented our approach with a gene co-expression network analysis to identify wheat targets to deliver resistance to Pgt through removal or modification of putative susceptibility genes.

plant biology↗

bioRxiv2020

The biophysical basis of protein domain compatibility in ion channels

Understanding the biophysical mechanisms that govern the combination of protein domains into viable proteins is essential for advancing synthetic biology and biomedical engineering. Here, we use massively parallel genotype/phenotype assays to determine cell surface expression of over 300,000 variants of the inward rectifier K+ channel Kir2.1 recombined with hundreds of protein motifs. We use machine learning to derive a quantitative biophysical model and practical rules for domain recombination. Insertional fitness depends on nonlinear interactions between the biophysical properties of inserted motifs and the recipient protein, which adds a new dimension to the rational design of fusion proteins. Insertion maps reveal a generalizable hierarchical organization of Kir2.1 and several other ion channels that balances stability needed for folding and dynamics required for function. SummaryMassively parallel assays reveal interactions between donor domains and recipient proteins govern domain compatibility

biophysics↗

bioRxiv2020

Trans-acting genetic variation affects the expression of adjacent genes

Gene expression differences among individuals are shaped by trans-acting expression quantitative trait loci (eQTLs). Most trans-eQTLs map to hotspot locations that influence many genes. The molecular mechanisms perturbed by hotspots are often assumed to involve "vertical" cascades of effects in pathways that can ultimately affect the expression of thousands of genes. Here, we report that trans-eQTLs can affect the expression of adjacent genes via "horizontal" mechanisms that extend along a chromosome. Genes affected by trans-eQTL hotspots in the yeast Saccharomyces cerevisiae were more likely to be located next to each other than expected by chance. These paired hotspot effects tended to occur at adjacent genes that show coexpression in response to genetic and environmental perturbations. Physical proximity and shared chromatin state, in addition to regulation of adjacent genes by similar transcription factors, were independently associated with paired hotspot effects. The effects of trans-eQTLs can spread among neighboring genes even when these genes do not share a common function. This phenomenon could result in unexpected connections between regulatory genetic variation and phenotypes.

genetics↗

bioRxiv2020

Major functional bias for mitochondrial complexes in genome-wide CRISPR screens

We present FLEX (Functional evaluation of experimental perturbations), a pipeline that leverages several functional annotation resources to establish reference standards for benchmarking human genome-wide CRISPR screen data and methods for analyzing them. We apply FLEX to analyze data from the diverse cell line screens generated by the DepMap project. We identify a dominant mitochondria-associated signal, which our time-resolved CRISPR screens and analysis suggests may reflect screen dynamics and protein stability effects rather than genetic dependencies.

systems biology↗