Biology subjects

Liu, A.

Publications and source records attributed to Liu, A..

At least 145 records · Page 8Linked to original sources

bioRxiv2021

CCR5 closes the temporal window for memory linking

Real world memories are formed in a particular context and are not acquired or recalled in isolation 1-5. Time is a key variable in the organization of memories, since events experienced close in time are more likely to be meaningfully associated, while those experienced with a longer interval are not1-4. How does the brain segregate events that are temporally distinct? Here, we report that a delayed (12-24h) increase in the expression of the C-C chemokine receptor type 5 (CCR5), an immune receptor well known as a co-receptor for HIV infection6,7, following the formation of a contextual memory, determines the duration of the temporal window for associating or linking that memory with subsequent memories. This delayed CCR5 expression in mouse dorsal CA1 (dCA1) neurons results in a decrease in neuronal excitability, which in turn negatively regulates neuronal memory allocation, thus reducing the overlap between dCA1 memory ensembles. Lowering this overlap affects the ability of one memory to trigger the recall of the other, thus closing the temporal window for memory linking. Remarkably, our findings also show that an age-related increase in CCL5/CCR5 expression leads to impairments in memory linking in aged mice, which could be reversed with a CCR5 knockout and an FDA approved drug that inhibits this receptor, a result with significant clinical implications. All together the findings reported here provide the first insights into the molecular and cellular mechanisms that shape the temporal window for memory linking.

neuroscience↗

bioRxiv2021

Genome sequencing of white-blotched river stingray (Potamotrygon leopoldi) provides novel clues for niche-adaptation and skeleton formation

The white-blotched river stingray (Potamotrygon leopoldi) is a cartilaginous fish native to the Xingu River, a tributary of the Amazon River system. It possesses a lot of unique biological features such as disc-like body shape, bizarre color pattern and living in freshwater habitat while most stingrays and their close relatives are sea dwellers. As a member of the Potamotrygonidae family, P. leopoldi bears evolutionary signification in fish phylogeny, niche adaptation and skeleton formation. In this study, we present its draft genome of 4.11 Gb comprised of 16,227 contigs and 13,238 scaffolds, which has contig N50 of 3,937 kilobases and scaffold N50 of 5,675 kilobases in size. Our analysis shows that P. leopoldi is a slow-evolving fish, diverged from elephant shark about 96 million years ago. We find that two gene families related to immune system, immunoglobulin heavy constant delta genes, and T-cell receptor alpha/delta variable genes, stand out expanded in P. leopoldi only, suggesting robustness in response to freshwater pathogens in adapting novel environments. We also identified the Hox gene clusters in P. leopoldi and discovered that seven Hox genes shared by five representative fishes are missing in P. leopoldi. The RNA-seq data from P. leopoldi and other three fish species demonstrate that fishes have a more diversified tissue expression spectrum as compared to the corresponding mammalian data. Our functional studies suggest that the lack of genes encoding vitamin D-binding protein in cartilaginous (both P. leopoldi and Callorhinchus milii) fishes could partly explain the absence of hard bone in their endoskeleton. Overall, this genome resource provides new insights into the niche-adaptation, body plan and skeleton formation of P. leopoldi as well as the genome evolution in cartilaginous fish.

genomics↗

bioRxiv2021

Nanocrown electrodes for robust and scalable intracellular recordings of cardiomyocytes for cardiotoxicity screening

Drug-induced cardiotoxicity arises primarily when a compound alters the electrophysiological properties of cardiomyocytes. Features of intracellular action potentials (iAPs) are powerful biomarkers that predict proarrhythmic risks. However, the conventional patch clamp techniques for measuring iAPs are either laborious and low throughput or not suitable for measuring electrically connected cardiomyocytes. In the last decade, a number of vertical nanoelectrodes have been demonstrated to achieve parallel and minimally-invasive iAP recordings. Nanoelectrodes show great promise, but the large variability in success rate, signal strength, and the low throughput of device fabrication have hindered them from being broadly adopted for proarrhythmia drug assessment. In this work, we developed vertically-aligned and semi-hollow nanocrown electrodes that are mechanically robust and made through a scalable fabrication process. Nanocrown electrodes achieve >99% success rates in obtaining intracellular access through electroporation, allowing reliable and simultaneous iAP recordings from up to 57 human pluripotent stem-cell-derived cardiomyocytes (hPSC-CMs). The accuracy of nanocrown electrode recordings is validated by simultaneous patch clamp recording from the same cell. Nanocrown electrodes enable prolonged iAP recording for continual monitoring of the same cells upon the sequential addition of four to five incremental drug doses. In this way, the dose-response data is self-referencing, which avoids the cell-to-cell variations inherent to hPSC-CMs. We are hopeful that this technology development is a step towards establishing an iAP screening assay for preclinical evaluation of drug-induced arrhythmogenicity.

bioengineering↗

bioRxiv2021

Deriving time-concordant event cascades from gene expression data: A case study for Drug-Induced Liver Injury (DILI)

Adverse event pathogenesis is often a complex process which compromises multiple events ranging from the molecular to the phenotypic level. In toxicology, Adverse Outcome Pathways (AOPs) aim to formalize this as temporal sequences of events, in which event relationships should be supported by causal evidence according to the tailored Bradford-Hill criteria. One of the criteria is whether events are consistently observed in a certain temporal order and, in this work, we study this time concordance using the concept of "first activation" as data-driven means to generate hypotheses on potentially causal mechanisms. As a case study, we analysed liver data from repeat- dose studies in rats from the TG-GATEs database which comprises measurements across eight timepoints, ranging from 3 hours to 4 weeks post-treatment. We identified time concordant gene expression-derived events preceding adverse histopathology, which serves as surrogate readout for Drug-Induced Liver Injury (DILI). We find known mechanisms in DILI to be time-concordant, and show further that significance, frequency and log fold change (logFC) of differential expression are metrics which can additionally prioritize events although not necessary to be mechanistically relevant. Moreover, we used the temporal order of transcription factor (TF) expression and regulon activity to identify transcriptionally regulated TFs and subsequently combined this with prior knowledge on functional interactions to derive detailed gene-regulatory mechanisms, such as reduced Hnf4a activity leading to decreased expression and activity of Cebpa. At the same time, also potentially novel events are identified such as Sox13 which is highly significantly time- concordant and shows sustained activation over time. Overall, we demonstrate how time-resolved transcriptomics can derive and support mechanistic hypotheses by quantifying time concordance and how this can be combined with prior causal knowledge, with the aim of both understanding mechanisms of toxicity, as well as potential applications to the AOP framework. We make our results available in the form of a Shiny app (https://github.com/anikaliu/DILICascades_App), which allows users to query events of interest in more detail. Author SummaryUnderstanding mechanisms from systems-scale biological data is of great relevance in toxicology as well as drug discovery; however how to generate causal hypotheses instead of correlations is by no means clear. In this work, we study the conserved temporal order of events and present an automatable framework to quantify and characterize time concordance across a large set of time- series. We apply this concept to events derived from time-resolved gene expression and histopathology from the TG-GATEs in vivo liver data as a case study. We were able to recover known events involved in the pathogenesis of Drug-Induced Liver Injury (DILI), and identify potentially novel pathway and transcription factors (TFs) which precede adverse histopathology. As complementary sources of evidence for causality, we additionally show how time concordance and prior knowledge on plausible interactions between TFs can be combined to derive causal hypotheses on the TFs mode of regulation and interaction partners. Overall, the results derived in our case study can serve as valuable hypothesis-free starting points for the development of Adverse Outcome Pathways for DILI, and demonstrate that our approach provides a novel angle to prioritize mechanistically relevant events.

bioinformatics↗

bioRxiv2021

The gut microbial signature of gestational diabetes mellitus and the association with diet intervention

Gestational diabetes mellitus (GDM) is a high-risk pregnancy complication that is associated with metabolic disorder phenotypes, such as abnormal blood glucose and obesity. The link between microbiota and diet management contributes to metabolic homeostasis in GDM. Therefore, it is crucial to understand the structure of the gut microbiota in GDM and to explore the effect of dietary management on the microbiota structure. In this study, we analyzed the composition of the gut microbiota between 27 GDM and 30 healthy subjects at two time points using Illumina HiSeq 2500 platform. The taxonomy analyses suggested that the overall bacteria clustered by diabetes status, rather than diet intervention. Of particular interest, the phylum Acidobacteria in GDM was significantly increased, and positively correlated with blood glucose levels. Moreover, Partial least-squares discriminant analysis (PLS-DA) revealed that certain genera in the phyla Firmicutes, Bacteroidetes, Proteobacteria, and Lentisphaerae characterized the GDM gut microbiota. Correlation analysis indicated that blood glucose levels and BMI index were correlated with the relative abundance of SCFAS-producing genera. Through the comparison between the GDM and healthy samples with or without diet intervention, we discovered that the role of short-term diet management in GDM processes is associated with the change in the Firmicutes/Bacteroidetes ratio and some specific taxa, rather than an alternative gut microbial pattern. Our study have important implications for understanding the beneficial effects of diet intervention on the specific gut microbiota and thus possibly their metabolism in pregnant women with GDM. ImportanceUnderstanding the composition and dynamics of the gut microbiota in GDM women under diet intervention is important because there may be opportunities for preventive strategies. We examined the relationships between GDM gut microbiota at two times before and after the diet intervention during second trimester of pregnancy and clinical characteristics in cohort of GDM women. We found that short-term diet management in GDM processes is associated with changes in the Firmicutes/Bacteroidetes ratio and some specific taxa rather than an alternative gut microbial pattern. Our study highlights the importance of considering diet intervention as the rescue of microbial dysfunction of GDM disease and can serve as a strategy for early prevention in future study.

microbiology↗

bioRxiv2021

AngioPlate - Biofabrication of perfusable complex tissues in multi-well plates with 4D subtractive manufacturing

Organ-on-a-chip systems that recapitulate tissue-level functions have been proposed to improve in vitro-in vivo correlation in drug development. Significant progress has been made to control the cellular microenvironment with mechanical stimulation and fluid flow. However, it has been challenging to introduce complex 3D tissue structures due to the physical constraints of microfluidic channels or membranes in organ-on-a-chip systems. Although this problem could be addressed with the integration of 3D bioprinting, it is not an easy task because the two technologies have fundamentally different fabrication processes. Inspired by 4D bioprinting, we develop a 4D subtractive manufacturing technique where a flexible sacrificial material can be patterned on a 2D surface, change shape when exposed to aqueous hydrogel, and subsequently degrade to produce perfusable networks in a natural hydrogel matrix that can be populated with cells. The technique is applied to fabricate organ-specific vascular networks, vascularized kidney proximal tubules, and terminal lung alveoli in a customized 384-well plate and then further scaled to a 24-well plate format to make a large vascular network, vascularized liver tissues, and for integration with ultrasound imaging. This biofabrication method eliminates the physical constraints in organ-on-a-chip systems to incorporate complex ready-to-perfuse tissue structures in an open-well design.

bioengineering↗

bioRxiv2021

A novel small molecule that induces cytotoxicity in lung cancer cells inhibits disulfide reductases GSR and TXNRD1

High-throughput phenotype-based screening of large libraries of novel compounds without known targets can identify small molecules that elicit a desired cellular response, but additional approaches are required to find and characterize their targets and mechanisms of action. Here we show that a compound termed lung cancer screen 3 (LCS3), previously selected for its ability to impair the growth of human lung adenocarcinoma (LUAD) cell lines, but not normal lung cells, induces oxidative stress and activates the NRF2 signaling pathway by generating reactive oxygen species (ROS) in sensitive LUAD cell lines. To identify the target that mediates this effect, we applied thermal proteome profiling (TPP) and uncovered the disulfide reductases GSR and TXNRD1 as LCS3 targets. Through enzymatic assays using purified protein, we confirmed that LCS3 inhibits disulfide reductase activity through a reversible, uncompetitive mechanism. Further, we demonstrate that LCS3-sensitive LUAD cells are correspondingly sensitive to the synergistic inhibition of glutathione and thioredoxin pathways. Lastly, a genome-wide CRISPR knockout screen identified the loss of NQO1 as a mechanism of LCS3 resistance. This work highlights the ability of TPP to uncover targets of small molecules identified by high-throughput screens and demonstrates the potential utility of inhibiting disulfide reductases as a therapeutic strategy for LUAD.

cancer biology↗

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↗

bioRxiv2021

Arabidopsis stomatal polarity protein BASL mediates distinct processes before and after cell division to coordinate cell size and fate asymmetries

In many land plants, asymmetric cell divisions (ACDs) create and pattern differentiated cell types on the leaf surface. In the Arabidopsis stomatal lineage, BREAKING OF ASYMMETRY IN THE STOMATAL LINEAGE (BASL) regulates multiple aspects of ACD including division plane placement and cell fate enforcement. Polarized subcellular localization of BASL is initiated before the ACD and persists for many hours after the division in one of the two daughters. Untangling the respective contributions of polarized BASL before and after division is essential to gain a better understanding of its roles in regulating stomatal lineage ACDs and to uncover the rules that guide leaf pattern. Here we combine quantitative imaging and lineage tracking with genetic tools that provide temporally-restricted BASL expression. We find that pre-division BASL is required for division orientation, whereas BASL polarity post-division ensures proper cell fate commitment. These genetic manipulations allowed us to uncouple daughter-cell size asymmetry from polarity crescent inheritance, revealing independent effects of these two asymmetries on subsequent cell behavior. Finally, we show that there is coordination between the division frequencies of sister cells produced by ACDs, and this coupling requires BASL as an effector of peptide signaling.

plant biology↗

bioRxiv2021

Meta-imputation of transcriptome from genotypes across multiple datasets using summary-level data.

Transcriptome wide association studies (TWAS) can be used as a powerful method to identify and interpret the underlying biological mechanisms behind GWAS by mapping gene expression levels with phenotypes. In TWAS, gene expression is often imputed from individual-level genotypes of regulatory variants identified from external resources, such as Genotype-Tissue Expression (GTEx) Project. In this setting, a straightforward approach to impute expression levels of a specific tissue is to use the model trained from the same tissue type. When multiple tissues are available for the same subjects, it has been demonstrated that training imputation models from multiple tissue types improves the accuracy because of shared eQTLs between the tissues and increase in effective sample size. However, existing joint-tissue methods require access of genotype and expression data across all tissues. Moreover, they cannot leverage the abundance of various expression datasets across various tissues for non-overlapping individuals. Here, we explore the optimal way to combine imputed levels across training models from multiple tissues and datasets in a flexible manner using summary-level data. Our proposed method (SWAM) combines arbitrary number of transcriptome imputation models to linearly optimize the imputation accuracy given a target tissue. By integrating models across tissues and/or individuals, SWAM can improve the accuracy of transcriptome imputation or to improve power to TWAS without having to access each individual-level dataset. To evaluate the accuracy of SWAM, we combined 49 tissue-specific gene expression imputation models from the GTEx Project as well as from a large eQTL study of Depression Susceptibility Genes and Networks (DGN) Project and tested imputation accuracy in GEUVADIS lymphoblast cell lines samples. We also extend our meta-imputation method to meta-TWAS to leverage multiple tissues in TWAS analysis with summary-level statistics. Our results capitalize on the importance of integrating multiple tissues to unravel regulatory impacts of genetic variants on complex traits. Author SummaryThe gene expression levels within a cell are affected by various factors, including DNA variation, cell type, cellular microenvironment, disease status, and other environmental factors surrounding the individual. The genetic component of gene expression is known to explain a substantial fraction of transcriptional variation among individuals and can be imputed from genotypes in a tissue-specific manner, by training from population-scale transcriptomic profiles designed to identify expression quantitative loci (eQTLs). Imputing gene expression levels is shown to help understand the genetic basis of human disease through Transcriptome-wide association analysis (TWAS) and Mendelian Randomization (MR). However, it has been unclear how to integrate multiple imputation models trained from individual datasets to maximize their accuracy without having to access individual genotypes and expression levels that are often protected for privacy concerns. We developed SWAM (Smartly Weighted Averaging across Multiple datasets), a meta-imputation framework which can accurately impute gene expression levels from genotypes by integrating multiple imputation models without requiring individual-level data. Our method examines the similarity or differences between resources and borrowing information most relevant to the tissue of interest. We demonstrate that SWAM outperforms existing single-tissue and multi-tissue imputation models and continue to increase accuracy when integrating additional imputation models.

genomics↗

bioRxiv2021

Scaling concepts in 'omics: nuclear lamin-B scales with tumor growth and predicts poor prognosis, whereas fibrosis can be pro-survival

Spatiotemporal relationships between genes expressed in tissues likely reflect physicochemical principles that range from stoichiometric interactions to co-organized fractals with characteristic scaling. For key structural factors within the nucleus and extracellular matrix (ECM), gene-gene power laws are found to be characteristic across several tumor types in The Cancer Genome Atlas (TCGA) and across single-cell RNA-seq data. The nuclear filament LMNB1 scales with many tumor-elevated proliferation genes that predict poor survival in liver cancer, and cell line experiments show LMNB1 regulates cancer cell cycle. Also high in the liver, lung, and breast tumors studied here are the main fibrosis-associated collagens, COL1A1 and COL1A2, that scale stoichiometrically with each other and super-stoichiometrically with a pan-cancer fibrosis gene set. However, high fibrosis predicts prolonged survival of patients undergoing therapy and does not correlate with LMNB1. Single-cell RNA-seq data also reveal scaling consistent with the pan-cancer power laws obtained from bulk tissue, allowing new power law relations to be predicted. Lastly, although noisy data frustrate weak scaling, concepts such as stoichiometric scaling highlight a simple, internal consistency check to qualify expression data. ClassificationApplied Physical Sciences (major) and Cell Biology (minor) Significance StatementNon-linear scaling analyses pervade polymer physics and chemistry and conceivably provide new insight into polymeric assemblies of genes expressed in tissues as well as co-regulated gene sets. Fractal scaling and stoichiometric scaling are among the gene-gene power law results identified here for key structural polymers in nuclei or extracellular matrix in human cancer data. Among nuclear envelope factors that might scale with DNA mass, only one nuclear filament scales with tumor proliferation and predicts poor survival in some cancer types. Collagen-1 scales with fibrosis and also tends to increase in multiple tumor types, but patients in therapy surprisingly survive longest with the highest levels of fibrosis, consistent with a therapeutic response.

cancer biology↗

bioRxiv2021

ClusterMap: multi-scale clustering analysis of spatial gene expression

Quantifying RNAs in their spatial context is crucial to understanding gene expression and regulation in complex tissues. In situ transcriptomic methods generate spatially resolved RNA profiles in intact tissues. However, there is a lack of a unified computational framework for integrative analysis of in situ transcriptomic data. Here, we present an unsupervised and annotation-free framework, termed ClusterMap, which incorporates physical proximity and gene identity of RNAs, formulates the task as a point pattern analysis problem, and thus defines biologically meaningful structures and groups. Specifically, ClusterMap precisely clusters RNAs into subcellular structures, cell bodies, and tissue regions in both two- and three-dimensional space, and consistently performs on diverse tissue types, including mouse brain, placenta, gut, and human cardiac organoids. We demonstrate ClusterMap to be broadly applicable to various in situ transcriptomic measurements to uncover gene expression patterns, cell-cell interactions, and tissue organization principles from high-dimensional transcriptomic images.

genomics↗

bioRxiv2021

Trade-offs between antiseptic cytotoxicity and efficacy in a human ex vivo wound contamination model

Wound cleansing agents are routine in wound care, even in the absence of signs of infection. Antiseptic activity prevents contaminating microbes from establishing an infection while also raising concerns of cytotoxicity and delayed wound healing. Here, we used an ex vivo human skin excisional wound model to evaluate the cytotoxicity of five clinically-used wound cleaning agents (saline, povidone iodine, Dove(R) soap, Dial(R) soap, and chlorhexidine gluconate). We established a wound contamination model using [~]100 cells of Pseudomonas aeruginosa per wound to evaluate antiseptic efficacy and microbial biofilm spatial organization. We found that Dial(R) soap and chlorhexidine gluconate significantly reduced metabolic activity of the biopsies, while all treatments except saline affected local cellular viability. Within the contamination model, only chlorhexidine gluconate treatment resulted in significantly lower P. aeruginosa counts at 24 hours post-treatment, driven by sub-limit-of-detection counts immediately post-treatment. Later applications of chlorhexidine gluconate had no effects on microbial growth, with microscopy showing extensive surface colonization of the wound bed. We present a clinically-relevant model for evaluating antiseptic cytotoxicity and efficacy, with the ability to resolve spatial localization and temporal dynamics of tissue viability and microbial growth.

microbiology↗

bioRxiv2021

Thermal laser ablation with tunable lesion size reveals multiple origins of seizure-like convulsions in Caenorhabditis elegans

Laser microsurgery has long been an important means of assessing the functions of specific cells and tissues. Most laser ablation systems use short, highly focused laser pulses to create plasma-mediated lesions with dimensions on the order of the wavelength of light. While the small size of the lesion enables ablation with high spatial resolution, it also makes it difficult to ablate larger structures. We developed an infrared laser ablation system capable of thermally lesioning tissues with spot sizes tunable by the duration and amplitude of laser pulses. We used our laser system in the roundworm C. elegans to kill single neurons and to sever the dorsal and ventral nerve cords, structures that are difficult to lesion using a plasma-based ablation system. We used these ablations to investigate the source of convulsions in a gain-of-function mutant for the acetylcholine receptor ACR-2. Severing the ventral nerve cord caused convulsions to occur independently anterior and posterior to the lesion, suggesting that convulsions can arise independently from distinct subsets of the motor circuit.

neuroscience↗

bioRxiv2020

Grid cell firing fields in a volumetric space

We investigated how entorhinal grid cells represent volumetric (three-dimensional) space. On a flat surface, grid cell firing fields are circular and arranged in a close-packed hexagonal array. In three dimensions, theoretical and computational work suggests that the most efficient configuration would be a regular close packing of spherical fields. We report that in rats exploring a cubic lattice, grid cells were spatially stable and maintained normal directional modulation, theta modulation and spike dynamics. However, while the majority of grid fields were spherical, they were irregularly arranged, even when only fields abutting the lower surface (equivalent to the floor) were considered. Thus, grid organization is shaped by the environments movement affordances, and may not need to be regular to support spatial computations. One Sentence SummaryIn rats exploring a volumetric space, grid cells are spatially modulated but their firing fields are irregularly arranged.

neuroscience↗

bioRxiv2020

The SC-SNc pathway boosts appetitive locomotion in predatory hunting

Appetitive locomotion is essential for organisms to approach rewards, such as food and prey. How the brain controls appetitive locomotion is poorly understood. In a naturalistic goal-directed behavior--predatory hunting, we demonstrate an excitatory brain circuit from the superior colliculus (SC) to the substantia nigra pars compacta (SNc) to boost appetitive locomotion. The SC-SNc pathway transmitted locomotion-speed signals to dopamine neurons and triggered dopamine release in the dorsal striatum. Activation of this pathway increased the speed and frequency of approach during predatory hunting, an effect that depended on the activities of SNc dopamine neurons. Conversely, synaptic inactivation of this pathway impaired appetitive locomotion but not defensive or exploratory locomotion. Together, these data revealed the SC as an important source to provide locomotion-related signals to SNc dopamine neurons to boost appetitive locomotion.

neuroscience↗

bioRxiv2020

Extracellular signal-regulated kinase mediates chromatin rewiring and lineage transformation in lung cancer

SummaryLineage transformation between lung cancer subtypes is a poorly understood phenomenon associated with resistance to treatment and poor patient outcomes. Here, we aimed to model this transition to define underlying biological mechanisms and identify potential avenues for therapeutic intervention. Small cell lung cancer (SCLC) is neuroendocrine in origin and, in contrast to non-SCLC (NSCLC), rarely contains mutations that drive the MAPK pathway. Likewise, NSCLCs that transform to SCLC concomitantly with development of therapy resistance downregulate MAPK signaling, suggesting an inverse relationship between pathway activation and lineage state. To test this, we activated MAPK in SCLC through conditional expression of mutant KRAS or EGFR, which revealed suppression of the neuroendocrine differentiation program via ERK. We found that ERK induces the expression of ETS factors that mediate transformation into a NSCLC-like state. ATAC-seq demonstrated ERK-driven changes in chromatin accessibility at putative regulatory regions and global chromatin rewiring at neuroendocrine and ETS transcriptional targets. Further, ERK-mediated induction of ETS factors as well as suppression of neuroendocrine differentiation were dependent on histone acetyltransferase activities of CBP/p300. Overall, we describe how the ERK-CBP/p300-ETS axis promotes a lineage shift between neuroendocrine and non-neuroendocrine lung cancer phenotypes and provide rationale for the disruption of this program during transformation-driven resistance to targeted therapy.

cancer biology↗

bioRxiv2020

Candidate Explorer: a tool for discovery, evaluation, and display of mutations causing significant immune phenotypes

When applied to immunity, forward genetic studies use meiotic mapping to provide strong statistical evidence that a particular mutation is causative of a particular immune phenotype. Notwithstanding this, co-segregation of multiple mutations, occasional unawareness of mutations, and paucity of homozygotes may lead to erroneous declarations of cause and effect. We sought to improve the selection of authentic causative mutations using a machine learning software tool, Candidate Explorer (CE), which integrates 65 data features into a single numeric score, mathematically convertible to the likelihood of verification of any putative mutation-phenotype association. CE has identified most genes within which mutations can be causative of flow cytometric phenovariation in Mus musculus. The majority of these genes were not previously known to support immune function or homeostasis. Mouse geneticists will find CE data informative in identifying causative mutations within quantitative trait loci, while clinical geneticists may use CE to help connect causative variants with rare heritable diseases of immunity, even in the absence of linkage information. CE displays integrated mutation, phenotype, and linkage data, and is freely available for query online.

genetics↗