Glyphosate is an herbicide found worldwide in glyphosate-based formulations (GBFs). Although glyphosate appears to have a low toxicity profile for humans and mammals, conflicting reports exist regarding the risk for cancer in humans. US-EPA and European regulatory agencies have described glyphosate as unlikely to pose a carcinogenic hazard to humans. However, the International Agency for Research on Cancer (IARC) classified glyphosate as "probably carcinogenic to humans (Group 2A)," citing "mechanistic data provide strong evidence for genotoxicity and oxidative stress." Given these discrepancies, the Division of Translational Toxicology at NIEHS designed an experimental strategy to expand mechanistic evidence and address critical gaps within existing literature (e.g. mechanistic evaluations of glyphosate alongside GBFs, inclusion of context-defining positive controls). Cell morphology, viability, H2O2, and γH2AX formation were assayed in human keratinocytes (HaCaT), previously cited by IARC, and human hepatocytes (HepaRG) to derive benchmark concentrations and fold-change response metrics. Our findings revealed glyphosate alone was weakly and inconsistently bioactive for oxidative stress and DNA damage when compared with positive controls. In contrast, most of the 13 GBFs evaluated were more clearly bioactive with no apparent correlation to varied glyphosate concentrations. Hierarchical clustering of biological responses revealed some bioactive GBFs to cluster near well-characterized positive controls for oxidative stress, whereas 4 GBFs clustered more similarly to negative controls and glyphosate. Collectively, this study provides a robust dataset with context-defining results that advance our understanding of the hazard potential of GBFs while revealing that glyphosate is likely not a primary driver of oxidative stress from GBF exposures.

Environmental exposures significantly influence cancer risk, but their mutational impact remains unclear. We perform whole-exome sequencing of hepatocellular carcinomas (HCCs) from B6C3F1/N mice that arise spontaneously with age (2 years old) or following chronic exposure to one of ten potential human carcinogens that operate through genotoxic or non-genotoxic mechanisms. HCCs from mice exposed to drinking water disinfection byproducts, such as bromochloroacetic acid (BCA) and bromodichloroacetic acid (BDCA), show dose-dependent increases in mutational burden, distinct mutational signatures (BCA-mSBS12 and BDCA-mSBS25), and enrichment of the aTn→aCn mutational motif. In contrast, HCCs from other exposures, as well as from spontaneous tumors, show comparable mutational burdens, mutational signatures, and enrichment of the nCg→nTg mutational motif. These findings suggest that many environmental carcinogens promote tumorigenesis by amplifying endogenous mutagenic processes rather than initiating distinct mutational events. Our results highlight the utility of rodent models for investigating environmental carcinogenesis and provide insights relevant to human cancer risk assessment.

Screening chemicals using the zebrafish embryo developmental toxicity assay requires visual assessment of larval morphological changes based on images by experienced screeners. The process is time-consuming and prone to subjectivity. However, deep learning models trained using labeled image data offer a more objective and efficient alternative. As part of the Systematic Evaluation of the Application of Zebrafish in Toxicology (SEAZIT) project, which aims to provide a scientific basis for programmatic decisions on the routine use of zebrafish in toxicological evaluations, we developed deep-learning classification and segmentation models to support image-based assessments. Using labeled SEAZIT image data from embryos exposed to various chemicals for 5 days, we trained classification models to detect 20 distinct types of larval morphological changes. We also developed segmentation models to identify and measure larval regions of interest. In a baseline binary classification task distinguishing normal embryos from those with any morphological change, our multi-view convolutional neural network (MVCNN) achieved an F1 score of 0.88, demonstrating strong overall performance. Among classifiers for specific morphological changes, eight achieved F1 scores above 0.70. Grouping related abnormalities further improved performance, with five out of seven grouped classifiers reaching F1 scores near 0.80. Segmentation models for most of the regions of interest (9 out of 11) have an Intersection over Union (IoU) score of at least 0.80. Together, these models facilitate rapid, standardized and reliable evaluation of larval morphological changes in zebrafish images, reducing the need for subjective manual review and enhancing reproducibility in development toxicity screening.

DNA adducts from benzo(a)pyrene (BaP) and other polycyclic aromatic hydrocarbons (PAH) are related to tumor initiation in many tissues including mammary epithelia. T47D mammary cells are able metabolizers of BaP, forming DNA and cellular protein adducts but also a sizeable amount of extracellular protein adducts. GSH S-transferases (GST) help mitigate adduct formation by glutathione (GSH) conjugation. Here, we varied GSH levels using buthionine sulfoximine (BSO) and BaP pretreatments to deplete or augment GSH, respectively, to study adduct formation and metabolism at 4 µM 3 H-BaP over 24–48hr. An inverse relationship was observed between GSH levels and nuclear protein and DNA adducts. Time course experiments showed extracellular protein adducts, identified primarily as bovine serum albumin and α1-AT (α-1-antitrypsin) in culture medium, were 5–10 times greater than cellular protein adducts and comprised 8–9% of total metabolized 3 H-BaP. However, specific adduct binding (adducts/mg protein) in cells was much greater than for extracellular protein, likely from their intracellular proximity to CYP-mediated metabolism to BaP reactive metabolites. Proportions of 3 H-BaP hydroxylated and conjugated metabolites in BSO and BaP pretreated cells were not greatly altered from DMSO control after 24 hr. Bioinformatic analysis of T47D cell gene expression indicated CYP1B1 and CYP1A1 were primary enzymes for BaP bioactivation. We surmised reactive BaP metabolites that escaped conjugation reactions were sufficiently stable to migrate into the extracellular space. These results suggest BaP reactive metabolites like BPDE (BaP-diol-epoxide) can easily translocate across cell membranes despite robust conjugation systems and ready supplies of essential co-substrates for sulfate or GSH conjugations. The implications in vivo are that BaP reactive metabolites can enter adjacent epithelia and some fraction could result in DNA binding and somatic mutations in cancer susceptibility genes over time. The relationship continues to grow between PAH exposure and pollution, and many malignancies including breast cancers.

Understanding the complex interplay of genetic and environmental factors in disease etiology and the role of gene-environment interactions (GEIs) across human development stages is important. We review the state of GEI research, including challenges in measuring environmental factors and advantages of GEI analysis in understanding disease mechanisms. We discuss the evolution of GEI studies from candidate gene-environment studies to genome-wide interaction studies (GWISs) and the role of multi-omics in mediating GEI effects. We review advancements in GEI analysis methods and the importance of large-scale datasets. We also address the translation of GEI findings into precision environmental health (PEH), showcasing real-world applications in healthcare and disease prevention. Additionally, we highlight societal considerations in GEI research, including environmental justice, the return of results to participants, and data privacy. Overall, we underscore the significance of GEI for disease prediction and prevention and advocate for integrating the exposome into PEH omics studies.

Renal proximal tubule epithelial cells (RPTECs) are a primary site for kidney injury. We created two RPTEC lines from CD-1 mice immortalized with hTERT (human telomerase reverse transcriptase) or SV40 LgT antigen (Simian Virus 40 Large T antigen). Our hypothesis was that low-level, repeated exposure to subcytotoxic levels of 0.25–2.5 μM cisplatin (CisPt) or 12.5–100 μM aflatoxin B1 (AFB1) would activate distinctive genes and pathways in these two differently immortalized cell lines. RNA-seq showed only LgT cells responded to AFB1 with 1139 differentially expressed genes (DEGs) at 72 h. The data suggested that AFB1 had direct nephrotoxic properties on the LgT cells. However, both the cell lines responded to 2.5 μM CisPt from 3 to 96 h expressing 2000–5000 total DEGs. For CisPt, the findings indicated a coordinated transcriptional program of injury signals and repair from the expression of immune receptors with cytokine and chemokine secretion for leukocyte recruitment; robust expression of synaptic and substrate adhesion molecules (SAMs) facilitating the expression of neural and hormonal receptors, ion channels/transporters, and trophic factors; and the expression of nephrogenesis transcription factors. Pathway analysis supported the concept of a renal repair transcriptome. In summary, these cell lines provide in vitro models for the improved understanding of repeated renal injury and repair mechanisms. High-throughput screening against toxicant libraries should provide a wider perspective of their capabilities in nephrotoxicity.

Perfluorooctanoic acid (PFOA) is tumorigenic in rats and mice and potentially tumorigenic in humans. Here, we studied long-term PFOA exposure with an in vitro transformation model using the rat liver epithelial cell, TRL 1215. Cells were cultured in 10 μM (T10), 50 μM (T50) and 100 μM (T100) PFOA for 38 weeks and compared to passage-matched control cells. T100 cells showed morphological changes, loss of cell contact inhibition, formation of multinucleated giant and spindle-shaped cells. T10, T50, and T100 cells showed increased LC50 values 20%, 29% to 35% above control with acute PFOA treatment, indicating a resistance to PFOA toxicity. PFOA-treated cells showed increases in Matrix metalloproteinase-9 secretion, cell migration, and developed more and larger colonies in soft agar. Microarray data showed Myc pathway activation at T50 and T100, associating Myc upregulation with PFOA-induced morphological transformation. Western blot confirmed that PFOA produced significant increases in c-MYC protein expression in a time- and concentration-related manner. Tumor invasion indicators MMP-2 and MMP-9, cell cycle regulator cyclin D1, and oxidative stress protein GST were all significantly overexpressed in T100 cells. Taken together, chronic in vitro PFOA exposure produced multiple cell characteristics of malignant progression and differential gene expression changes suggestive of rat liver cell transformation.

We recently developed a rat whole exome sequencing (WES) panel and used it to evaluate early somatic mutations in archival liver tissues from F344/N rats exposed to the hepatocarcinogen, Aflatoxin B1 (AFB1), a widely studied, potent mutagen and hepatocarcinogen associated with hepatocellular carcinoma (HCC). Rats were exposed to 1-ppm AFB1 in feed for 14, 90, and 90 days plus a recovery 60-day, non-exposure period (150-day) timepoint. Isolated liver DNA was exome sequenced. We identified 172 sequence variants across all timepoints, of which 101 were non-synonymous variants. Well-annotated genes carried a diverse set of 29 non-synonymous mutations at 14 days, increasing to 39 mutations at 90 days and then decreasing to 33 mutations following the 60-day recovery. Gene Set Enrichment Analysis conducted on previously reported, available RNA expression data of the same exome sequenced archival samples identified altered transcripts in pathways associated with malignant transformation. These included HALLMARK gene sets associated with cell proliferation (MYC Targets Version 1 and Version 2, E2F targets), cell cycle (G2M checkpoint, mitotic spindle), cell death (apoptosis), and DNA damage (DNA repair, UV response Up, Reactive oxygen species) pathways. DriverNet Impact analysis integrated exome-seq and expression data to reveal somatic mutations in Mcm8, Bdp1, and Cct6a that may drive cancer formation. Connectivity with transcript expression changes identified these genes as the top-ranked candidate driver genes associated with hepatocellular transformation. In conclusion, exome sequencing revealed early somatic mutations that may play a role in cancer cell transformation that are translatable to aflatoxin-induced HCC.

High-throughput chemical screening approaches often employ microscopy to capture photomicrographs from multi-well cell culture plates, generating thousands of images that require time-consuming human analysis. To automate this subjective and time-consuming manual process, we have developed a method that uses deep learning to automatically classify digital assay images. We have trained a convolutional neural network (CNN) to perform binary and multi-class classification. The binary classifier binned assay images into healthy (comparable to untreated controls) and altered (not comparable to untreated-control) classes with >98% accuracy; the multi-class classifier assigned "Healthy," "Intermediate" and "Altered" labels to assay images with >95% accuracy. Our dataset comprised high-resolution assay images from primary human hepatocytes and undifferentiated (proliferating) and differentiated 2D cultures of HepaRG cells. In this study we have focused on testing and fine-tuning various CNN architectures, including ResNet 34, 50 and 101. To visualize regions in the images that the CNN model used for classification, we employed Class Activation Maps (CAM). This allowed us to better understand the inner workings of the neural network and led to additional optimizations of the algorithm. The results indicate a strong correspondence between dosage and classifier-predicted scores, suggesting that these scores might be useful in further characterizing benchmark dose. Together, these results clearly demonstrate that deep-learning based automated image classification of cell morphology changes upon chemical-induced stress can yield highly accurate and reproducible assessments of cytotoxicity across a variety of cell types.

Background and aims: In this study ten mouse strains representing similar to 90% of genetic diversity in laboratory mice (B6C3F1/J, C57BL/6J, C3H/HeJ, A/J, NOD.B1oSnH2/J, NZO/HILtJ, 129S1/SvImJ, WSB/EiJ, PWK/PhJ, CAST/ EiJ) were examined to identify the mouse strain with the lowest incidence of cancer. The unique single polymorphisms (SNPs) associated with this low cancer incidence are reported. Methods: Evaluations of cancer incidence in the 10 mouse strains were based on gross and microscopic diagnosis of tumors. Single nucleotide polymorphisms (SNPs) in the coding regions of the genome were derived from the respective mouse strains located in the Sanger mouse sequencing database and the B6C3F1/N genome from the National Toxicology Program (NTP). Results: The WSB strain had an overall lower incidence of both benign and malignant tumors compared to the other mouse strains. At 2 years, the incidence of total malignant tumors (Poly-3 incidence rate) ranged from 2% (WSB) to 92% (C3H) in males, and 14% (WSB) to 93% (NZO) in females, and the total incidence of benign and malignant tumor incidence ranged from 13% (WSB) to 99% (C3H) in males and 25% (WSB) to 96% (NOD) in females. Single nucleotide polymorphism (SNP) patterns were examined in the following strains: B6C3F1/N, C57BL/6J, C3H/HeJ, 129S1/SvImJ, A/J, NZO/HILtJ, CAST/EiJ, PWK/PhJ, and WSB/EiJ. We identified 7519 SNPs (involving 5751 Ensembl transcripts of 3453 Ensembl Genes) that resulted in a unique amino acid change in the coding region of the WSB strain. Conclusions: The inherited genetic patterns in the WSB cancer-resistant mouse strain occurred in genes involved in multiple cell functions including mitochondria, metabolic, immune, and membrane-related cell functions. The unique SNP patterns in a cancer resistant mouse strain provides insights for understanding and developing strategies for cancer prevention.

Cell-free DNA circulates in plasma at low levels as a normal by-product of cellular apoptosis. Multiple clinical pathologies, as well as environmental stressors can lead to increased circulating cell-free DNA (ccfDNA) levels. Plasma DNA studies frequently employ targeted amplicon deep sequencing platforms due to limited concentrations (ng/ml) of ccfDNA in the blood. Here, we report whole genome sequencing (WGS) and read distribution across chromosomes of ccfDNA extracted from two human plasma samples from normal, healthy subjects, representative of limited clinical samples at <1 ml. Amplification was sufficiently robust with similar to 90% of the reference genome (GRCh38.p2) exhibiting 10X coverage. Chromosome read coverage was uniform and directly proportional to the number of reads for each chromosome across both samples. Almost 99% of the identified genomic sequence variants were known annotated dbSNP variants in the hg38 reference genome. A high prevalence of C>T and T>C mutations was present along with a strong concordance of variants shared between the germline genome databases; gnomAD (81.1%) and the 1000 Genome Project (93.6%). This study demonstrates isolation and amplification procedures from low input ccfDNA samples that can detect sequence variants across the whole genome from amplified human plasma ccfDNA that can translate to multiple clinical research disciplines.

Interpretation of untargeted metabolomics data from both in vivo and physiologically relevant in vitro model systems continues to be a significant challenge for toxicology research. Potency-based modeling of toxicological responses has served as a pillar of interpretive context and translation of testing data. In this study, we leverage the resolving power of concentration-response modeling through benchmark concentration (BMC) analysis to interpret untargeted metabolomics data from differentiated cultures of HepaRG cells exposed to a panel of reference compounds and integrate data in a potency-aligned framework with matched transcriptomic data. For this work, we characterized biological responses to classical human liver injury compounds and comparator compounds, known to not cause liver injury in humans, at 10 exposure concentrations in spent culture media by untargeted liquid chromatography-mass spectrometry analysis. The analyte features observed (with limited metabolites identified) were analyzed using BMC modeling to derive compound-induced points of departure. The results revealed liver injury compounds produced concentration-related increases in metabolomic response compared to those rarely associated with liver injury (ie, sucrose, potassium chloride). Moreover, the distributions of altered metabolomic features were largely comparable with those observed using high throughput transcriptomics, which were further extended to investigate the potential for in vitro observed biological responses to be observed in humans with exposures at therapeutic doses. These results demonstrate the utility of BMC modeling of untargeted metabolomics data as a sensitive and quantitative indicator of human liver injury potential.

Mitochondria are essential cellular organelles that participate in important cellular processes, including bioenergetics, metabolism, and signaling. Recent functional and proteomic studies have revealed the remarkable complexity of mitochondrial protein organization. Mitochondrial protein machineries with diverse functions such as protein translocation, respiration, metabolite transport, protein quality control and the control of membrane architecture interact with each other in dynamic networks. The goal of this study was to identify protein expression changes in a human cardiomyocyte cell line treated with several mitochondrial toxicants which inhibit mitochondrial membrane potential (MMP) and mitochondrial respiration. AC16 human cardiomyocyte cells were treated with carbonyl cyanidep-(trifluoromethoxy)phenylhydrazone (FCCP), dinoterb, picoxystrobin, pinacyanol, and triclocarban for 18 h around the IC(50)values generated from MMP assay. The samples were harvested and labeled with tandem mass tags with different mass isotopes. Peptide assignment was performed in Proteome Discoverer. Each dataset was analyzed in Ingenuity Pathway Analysis (IPA). In the proteomic profile, these compounds showed dysregulation of a group of mitochondrial proteins (e.g., NDUA, NDUB, BCS1, CYB5B, and SDHF2), as well as proteins involved in lipid metabolism (e.g., CPT, MECR, and LPGAT1), cytoskeleton protein changes (e.g., CROCC, LAMC3, FBLN1, and FMN2) and stress response (e.g., IKBKG, IKBB, SYVN1, SOD2, and CPIN1). Proteomic data from the current study provides key insights into chemical induced cellular pathway dysregulation, supporting the use of proteomic profiling as a sensitive method to further explore molecular functions and disease pathogenesis upon exposure to environmental chemicals.

A 5-day in vivo rat model was evaluated as an approach to estimate chemical exposures that may pose minimal risk by comparing benchmark dose (BMD) values for transcriptional changes in the liver and kidney to BMD values for toxicological endpoints from traditional toxicity studies. Eighteen chemicals, most having been tested by the National Toxicology Program in 2-year bioassays, were evaluated. Some of these chemicals are potent hepatotoxicants (eg, DE71, PFOA, and furan) in rodents, some exhibit toxicity but have minimal hepatic effects (eg, acrylamide and alpha,beta-thujone), and some exhibit little overt toxicity (eg, ginseng and milk thistle extract) based on traditional toxicological evaluations. Male Sprague Dawley rats were exposed once daily for 5 consecutive days by oral gavage to 8-10 dose levels for each chemical. Liver and kidney were collected 24 h after the final exposure and total RNA was assayed using high-throughput transcriptomics (HTT) with the rat S1500(+) platform. HTT data were analyzed using BMD Express 2 to determine transcriptional gene set BMD values. BMDS was used to determine BMD values for histopathological effects from chronic or subchronic toxicity studies. For many of the chemicals, the lowest transcriptional BMDs from the 5-day assays were within a factor of 5 of the lowest histopathological BMDs from the toxicity studies. These data suggest that using HTT in a 5-day in vivo rat model provides reasonable estimates of BMD values for traditional apical endpoints. This approach may be useful to prioritize chemicals for further testing while providing actionable data in a timely and cost-effective manner.

CAsE-PE cells are an arsenic-transformed, human prostate epithelial line containing oncogenic mutations in KRAS compared to immortalized, normal KRAS parent cells, RWPE-1. We previously reported increased copy number of mutated KRAS in CASE-PE cells, suggesting gene amplification. Here, KRAS flanking genomic and transcriptomic regions were sequenced in CASE-PE cells for insight into KRAS amplification. Comparison of DNA-Seq and RNA-Seq showed increased reads from background aligning to all KRAS exons in CAsE-PE cells, while a uniform DNA-Seq read distribution occurred in RWPE-1 cells with normal transcript expression. We searched for KRAS fusions in DNA and RNA sequencing data finding a portion of reads aligning to KRAS and viral sequence. After generation of cDNA from total RNA, short and long KRAS probes were generated to hybridize cDNA and KRAS enriched fragments were PacBio sequenced. More KRAS reads were captured from CAsE-PE cDNA versus RWPE-1 by each probe set. Only CASE-PE cDNA showed KRAS viral fusion transcripts, primarily mapping to LTR and endogenous retrovirus sequences on either 5'- or 3'-ends of KRAS. Most KRAS viral fusion transcripts contained 4 to 6 exons but some PacBio sequences were in unusual orientations, suggesting viral insertions within the gene body. Additionally, conditioned media was extracted for potential retroviral particles. RNA-Seq of culture media isolates identified KRAS retroviral fusion transcripts in CASE-PE media only. Truncated KRAS transcripts suggested multiple retroviral integration sites occurred within the KRAS gene producing KRAS retroviral fusions of various lengths. Findings suggest activation of endogenous retroviruses in arsenic carcinogenesis should be explored.

BACKGROUND:In the screening phase of systematic review, researchers use detailed inclusion/exclusion criteria to decide whether each article in a set of candidate articles is relevant to the research question under consideration. A typical review may require screening thousands or tens of thousands of articles in and can utilize hundreds of person-hours of labor.METHODS:Here we introduce SWIFT-Active Screener, a web-based, collaborative systematic review software application, designed to reduce the overall screening burden required during this resource-intensive phase of the review process. To prioritize articles for review, SWIFT-Active Screener uses active learning, a type of machine learning that incorporates user feedback during screening. Meanwhile, a negative binomial model is employed to estimate the number of relevant articles remaining in the unscreened document list. Using a simulation involving 26 diverse systematic review datasets that were previously screened by reviewers, we evaluated both the document prioritization and recall estimation methods.RESULTS:On average, 95% of the relevant articles were identified after screening only 40% of the total reference list. In the 5 document sets with 5,000 or more references, 95% recall was achieved after screening only 34% of the available references, on average. Furthermore, the recall estimator we have proposed provides a useful, conservative estimate of the percentage of relevant documents identified during the screening process.CONCLUSION:SWIFT-Active Screener can result in significant time savings compared to traditional screening and the savings are increased for larger project sizes. Moreover, the integration of explicit recall estimation during screening solves an important challenge faced by all machine learning systems for document screening: when to stop screening a prioritized reference list. The software is currently available in the form of a multi-user, collaborative, online web application.

The TempO-Seq S1500+ platform(s), now available for human, mouse, rat, and zebrafish, measures a discrete number of genes that are representative of biological and pathway co-regulation across the entire genome in a given species. While measurement of these genes alone provides a direct assessment of gene expression activity, extrapolating expression values to the whole transcriptome (~26 000 genes in humans) can estimate measurements of non-measured genes of interest and increases the power of pathway analysis algorithms by using a larger background gene expression space. Here, we use data from primary hepatocytes of 54 donors that were treated with the endoplasmic reticulum (ER) stress inducer tunicamycin and then measured on the human S1500+ platform containing ~3000 representative genes. Measurements for the S1500+ genes were then used to extrapolate expression values for the remaining human transcriptome. As a case study of the improved downstream analysis achieved by extrapolation, the "measured only" and "whole transcriptome" (measured + extrapolated) gene sets were compared. Extrapolation increased the number of significant genes by 49%, bringing to the forefront many that are known to be associated with tunicamycin exposure. The extrapolation procedure also correctly identified established tunicamycin-related functional pathways reflected by coordinated changes in interrelated genes while maintaining the sample variability observed from the "measured only" genes. Extrapolation improved the gene- and pathway-level biological interpretations for a variety of downstream applications, including differential expression analysis, gene set enrichment pathway analysis, DAVID keyword analysis, Ingenuity Pathway Analysis, and NextBio correlated compound analysis. The extrapolated data highlight the role of metabolism/metabolic pathways, the ER, immune response, and the unfolded protein response, each of which are key activities associated with tunicamycin exposure that were unrepresented or underrepresented in one or more of the analyses of the original "measured only" dataset. Furthermore, the inclusion of the extrapolated genes raised "tunicamycin" from third to first upstream regulator in Ingenuity Pathway Analysis and from sixth to second most correlated compound in NextBio analysis. Therefore, our case study suggests an approach to extend and enhance data from the S1500+ platform for improved insight into biological mechanisms and functional outcomes of diseases, drugs, and other perturbations.

Toll-like receptors (TLRs) are pathogen-recognition receptors that trigger the innate immune response. Recent reports have identified accessory proteins that provide essential support to TLR function through ligand delivery and receptor trafficking. Herein, we introduce leucine-rich repeats (LRRs) and calponin homology containing 4 (Lrch4) as a novel TLR accessory protein. Lrch4 is a membrane protein with nine LRRs in its predicted ectodomain. It is widely expressed across murine tissues and has two expression variants that are both regulated by lipopolysaccharide (LPS). Predictive modeling indicates that Lrch4 LRRs conform to the horseshoe-shaped structure typical of LRRs in pathogen-recognition receptors and that the best structural match in the protein database is to the variable lymphocyte receptor of the jawless vertebrate hagfish. Silencing Lrch4 attenuates cytokine induction by LPS and multiple other TLR ligands and dampens the in vivo innate immune response. Lrch4 promotes proper docking of LPS in lipid raft membrane microdomains. We provide evidence that this is through regulation of lipid rafts as Lrch4 silencing reduces cell surface gangliosides, a metric of raft abundance, as well as expression and surface display of CD14, a raft-resident LPS co-receptor. Taken together, we identify Lrch4 as a broad-spanning regulator of the innate immune response and a potential molecular target in inflammatory disease.