Biology subjects

Carpenter, C.

Publications and source records attributed to Carpenter, C..

13 recordsLinked to original sources

bioRxiv2026

Modeling patient variants of Cnot1 and Cdc42bpb results in distinct forms of congenital diaphragmatic hernia in mice

Congenital diaphragmatic hernia (CDH) is a severe congenital anomaly characterized by impairment of both diaphragm and lung development in utero. CDH presents as a spectrum of forms and severities, with diaphragm defects arising in the dorsal/posterior region typically correlating with more severe pulmonary disease and higher risk of mortality than those appearing in ventral/anterior regions. The genetic etiology underlying CDH is complex, with many genes implicated showing variable expressivity and incomplete penetrance in both human patients and mouse models. Here we present in vivo validation of two genes previously unassociated with CDH: the CDC42-interacting kinase CDC42BPB; and CNOT1, a scaffolding protein of the CCR4-NOT protein complex, critical for mRNA regulation through modifications such as deadenylation. Each gene was found to have a damaging, de novo missense variant in a recent large-scale CDH patient sequencing screen. Loss of Cdc42bpb leads to ventral diaphragmatic hernias, heart septal defects and minor lung epithelial differentiation defects in mouse embryos. Installation of the orthologous patient-specific missense variant through CRISPR/Cas9 editing leads to less severe ventral diaphragm defects. Mouse embryos with either one or two copies of the orthologous Cnot1 variant, c.1867C>T (p.R623W), develop dorsal diaphragmatic hernias with low (<50%) penetrance, and mutants showed alterations in mRNA isoform expression consistent with the molecular role of Cnot1 in RNA splicing. These results underscore the power of in vivo functional modeling to validate genes and patient-specific variants uncovered by patient sequencing, reveal two previously unrecognized genetic causes of CDH, and highlight the heterogeneity of different patient anatomic presentations.

genetics↗

bioRxiv2026

Systemic hypoxia suppresses solid tumor growth

Local hypoxia is a hallmark of solid tumors and a negative prognostic factor in the progression and treatment of cancer. Here, we showed that systemic hypoxia, in contrast to localized tumor hypoxia, decreases tumor growth in vivo across multiple cancer types and preclinical models. The reduced tumor growth in systemic hypoxia was not explained by hypoglycemia, hypoinsulinemia, or HIF activation. Instead, metabolite profiling in tumors and tumor interstitial fluid revealed extensive perturbations in purine-related metabolites. Stable isotope tracing demonstrated that systemic hypoxia caused tumors to suppress de novo purine synthesis. Furthermore, tumors did not develop resistance to systemic hypoxia therapy, and when used in combination with chemotherapy or immunotherapy, systemic hypoxia dramatically suppressed tumor growth. Finally, we showed that systemic hypoxia can be achieved pharmacologically with the small molecule HypoxyStat. These findings challenge the long-held paradigm of hypoxia as a negative prognostic factor in cancer progression, and they suggest a potential therapeutic role for systemic hypoxia in suppressing solid tumor growth.

cancer biology↗

bioRxiv2026

Stack: In-Context Learning of Single-Cell Biology

Foundation models trained on single-cell transcriptomic data offer the promise of identifying and predicting the diversity of cellular phenotypes across species, diseases, and other biological conditions. However, the current models are limited to their supervised training conditions and tasks, which limits their utility for biological discovery. Here, we present SO_SCPLOWTACKC_SCPLOW, a foundation model trained on 149 million uniformly preprocessed human single cells that leverages tabular attention to generate representations for each cell informed by the cells in its context. SO_SCPLOWTACKC_SCPLOW offers substantial improvements for downstream tasks in the zero-shot setting compared to baselines, whether they are zero-shot, fine-tuned, or trained from scratch on the target dataset. SO_SCPLOWTACKC_SCPLOW can perform in-context learning from unlabeled cells representing arbitrary conditions, such as a chemical perturbation or a different donor, and predict the effect of those conditions on a target cell population without requiring data-specific fine-tuning. We apply SO_SCPLOWTACKC_SCPLOW to generate Perturb Sapiens, the first human whole-organism atlas of perturbed cells, spanning 28 tissues, 40 cell types, and 892 drug, cytokine, and genetic perturbations. We validated subsets of Perturb Sapiens using in vitro stimulation profiles. SO_SCPLOWTACKC_SCPLOW uniquely empowers prioritization of donor-specific perturbation effects, a capability we validated in our newly collected DiseasePert-3M data, comprising T cells from 40 donors across 14 diseases, stimulated with 11 cytokines. Overall, SO_SCPLOWTACKC_SCPLOW presents a new modeling framework where cells themselves act as guiding examples at inference time, unlocking general-purpose in-context learning capabilities for single-cell biology.

bioinformatics↗

bioRxiv2025

An image-based CRISPR screen reveals splicing-mediated control of HP1α condensates

Heterochromatin Protein 1 (HP1) is a fundamental component of constitutive heterochromatin, forming subnuclear condensates whose regulation and function remain poorly understood. Here, we present an image-based CRISPR screen targeting nuclear factors that identifies splicing as a pivotal pathway regulating HP1 condensates. We discovered that unspliced intronic RNA modulates HP1 condensates by interacting co-transcriptionally with HP1. By modulating the intron content, RNA processing restricts HP1-RNA interactions at chromatin, thus enabling heterochromatin organization. Disruption of HP1 condensates due to enhanced interactions with unspliced RNA leads to loss of heterochromatin and the activation of stress response protective genes. We propose that RNA is a central component of heterochromatin that modulates HP1 condensates, and that RNA processing enzymes act as a surveillance mechanism for condensates by dynamically regulating the network of multi-valent interactions between RNA and chromatin factors. This model underscores the crosstalk between chromatin organization, transcription, and RNA processing, potentially governing broader nuclear functions.

genetics↗

bioRxiv2025

Predicting cellular responses to perturbation across diverse contexts with STATE

Cellular responses to perturbations are a cornerstone for understanding biological mechanisms and selecting drug targets. While machine learning models offer tremendous potential for predicting perturbation effects, they currently struggle to generalize to unobserved cellular contexts. Here, we introduce SO_SCPLOWTATEC_SCPLOW, a transformer model that predicts perturbation effects while accounting for cellular heterogeneity within and across experiments. SO_SCPLOWTATEC_SCPLOW predicts perturbation effects across sets of cells and is trained using gene expression data from over 100 million perturbed cells. SO_SCPLOWTATEC_SCPLOW improved discrimination of effects on large datasets by more than 30% and identified differentially expressed genes across genetic, signaling and chemical perturbations with significantly improved accuracy. Using its cell embedding trained on observational data from 167 million cells, SO_SCPLOWTATEC_SCPLOW identified strong perturbations in novel cellular contexts where no perturbations were observed during training. We further introduce Cell-Eval, a comprehensive evaluation framework that highlights SO_SCPLOWTATEC_SCPLOWs ability to detect cell type-specific perturbation responses, such as cell survival. Overall, the performance and flexibility of SO_SCPLOWTATEC_SCPLOW sets the stage for scaling the development of virtual cell models.

systems biology↗

bioRxiv2025

Affinity-matured CD72-targeting Nanobody CAR T-cells Enhance Elimination of Antigen-Low B-cell Malignancies

BackgroundChimeric antigen receptor (CAR) T-cell therapies are highly efficacious for several different hematologic cancers. However, for most CAR T targets it is observed that low surface antigen density on tumors can significantly reduce therapeutic efficacy. Here, we explore this dynamic in the context of CD72, a surface antigen we recently found as a promising target for refractory B-cell cancers, but for which CD72 low antigen density can lead to therapeutic resistance in preclinical models. MethodsPrimary samples were accessed via institutional review board-approved protocols. Affinity-matured and humanized nanobody clones were previously described in Temple et al.1 CAR T-cells were generated via lentiviral transduction. In vitro cytotoxicity assays were performed using luciferase-labeled cell lines. In vivo studies were performed using cell line- or patient-derived xenografts implanted in NOD scid gamma (NSG) mice. ResultsWe first confirmed ubiquitous CD72 expression across a range of primary B-cell non-Hodgkin lymphomas. We further found that after resistance to CD19-directed therapies, across both B-cell acute lymphoblastic leukemia (B-ALL) models and primary tumor samples, surface CD72 expression was largely preserved while CD22 expression was significantly diminished. Affinity maturation of a nanobody targeting CD72, when incorporated into chimeric antigen receptor (CAR) T-cells, led to more effective elimination in vitro of isogenic models of CD72 low-expressing tumors. These results suggested that nanobody-based CAR T-cells (nanoCARs) may exhibit a similar relationship between binder affinity, antigen expression, and efficacy as previously demonstrated only for scFv-based CAR T-cells. Surprisingly, however, this significantly improved in vitro efficacy only translated to modest in vivo survival benefit. As a parallel strategy to enhance CAR T function, we found that the small molecule bryostatin could also significantly increase CD72 surface antigen density on B-cell malignancy models. Structural modeling and biochemical analysis identified critical residues improving CD72 antigen recognition of our lead affinity-matured nanobody. ConclusionsTogether, these findings support affinity-matured CD72 nanoCARs as a potential immunotherapy product for CD19-refractory B-cell cancers. Our results also suggest that for B-ALL in particular, CD72 may be a preferable second-line immunotherapy target over CD22. What is already known on this topicPrevious work using single chain variable fragment (scFv) based CAR Ts has suggested that improving affinity for target antigen could potentially help mitigate tumor resistance mediated by antigen downregulation, or baseline low antigen density. However, it is unknown whether this same dynamic holds for CAR T-cells that utilize different antigen recognition elements, such as nanobodies. What this study addsHere we show that affinity maturation of nanobody-based CAR T-cells (nanoCARs) targeting CD72 can improve their in vitro efficacy versus CD72-low tumors; however, in vivo efficacy differences are more modest. Furthermore, we show that for refractory B-cell malignancies, surface CD72 appears preserved after CD19 resistance even in situations where CD22 is strongly downregulated. How this study might affect research, practice or policyCD72 warrants further investigation as a preferred immunotherapy target in the context of CD19-refractory B-cell cancers, though nanobody affinity maturation is not a universal solution to the challenge of low tumor surface antigen density.

immunology↗

bioRxiv2025

Discovery of a tRNA-regulatory transcription factor that suppresses breast cancer metastasis

Transfer RNAs (tRNAs), once viewed as static adaptors in translation, are now recognized as dynamic regulators of gene expression. While recent studies have illuminated roles for tRNA stability, the upstream mechanisms governing tRNA transcription remain poorly understood. To address this gap, we generated the EXpression atlas of tRNA (EXTRNA), a high-resolution tRNA expression dataset spanning 24 cell lines across 9 human tissues. EXTRNA revealed both tissue-type-specific expression programs ("tRNAomes") and unexpected intra-tissue heterogeneity across breast cancer samples. Integrating EXTRNA with computational network analysis and data from other publicly available datasets, we identified Zinc Finger ZZ-Type And EF-Hand Domain Containing 1 (ZZEF1) as the first sequence-specific transcription factor of a particular tRNA. ZZEF1 promoted tRNA-LysUUU transcription by partnering with the ATP-dependent chromatin remodeler Chromodomain Helicase DNA Binding Protein 6 (CHD6), enhancing chromatin accessibility at tRNA-Lys-TTT-3 loci. ZZEF1 deficiency reduced tRNA-LysUUU abundance, decreased the translational efficiency of AAR codon-enriched mRNAs--including the tumor suppressor Serine/Threonine Kinase 3 (STK3)--and promoted metastatic progression in breast cancer in vivo. Together, our findings establish a previously unrecognized mechanism for RNA polymerase III-mediated tRNA transcription and define a regulatory circuit linking chromatin remodeling, codon-specific translation, and tumor suppression. More broadly, this work introduces a framework for dissecting the regulatory logic of the tRNAome and highlights tRNA expression control as a promising avenue for therapeutic intervention.

genomics↗

bioRxiv2025

scBaseCamp: An AI agent-curated, uniformly processed, and continually expanding single cell data repository

Single-cell RNA sequencing has transformed cell biology by enabling precise transcriptomic measurements of individual cells. The Sequence Read Archive (SRA) is the largest public repository of sequencing reads, yet much of it remains underutilized due to unstandardized metadata and the cost of processing reads. Here, we introduce scBaseCount, a single-cell RNA sequencing database that leverages an AI agent to automate discovery and metadata extraction, and standardize data processing. Built by directly mining all 10x Genomics datasets from SRA, scBaseCount is the largest freely accessible public repository of single-cell gene expression data, comprising over 502 million cells across 27 organisms and 75 tissues, offering an unbiased view of the composition of data within SRA. Uniform processing enables measurement of both intronic and exonic reads, non-coding gene expression and improves alignment across experiments as well as the performance of AI models trained on this phenotypically diverse data. Moreover, scBaseCount provides a blueprint for how AI can be leveraged to curate and autonomously update large biological data repositories.

bioinformatics↗

bioRxiv2023

Reply to: Caution Regarding the Specificities of Pan-Cancer Microbial Structure

The cancer microbiome field tremendously accelerated following the release of our manuscript nearly three years ago1, including direct validation of our cancer type-specific conclusions in independent, international cohorts2,3 and the tumor microbiomes adoption into the hallmarks of cancer4. Disentangling contamination signals from biological signals is an important consideration for this research field. Therefore, despite numerous, high-impact, peer-reviewed research papers that either validated our conclusions or extended them using data we released2,5-13, we carefully considered criticism raised by Gihawi et al. about potential mishandling of contaminants, batch effects, and machine learning approaches--all of which were central topics in our manuscript. Nonetheless, a close examination of each concern alongside the original manuscript and re-analyses of our published data strongly demonstrates the robustness of the original findings. To remove all doubt, however, we have reproduced all key conclusions from the original manuscript using only overlapping bacterial genera identified in a highly decontaminated, multi-cancer, international cohort (Weizmann Institute of Science, WIS)2, with or without batch correction, and with multiclass machine learning analyses to mitigate class imbalances. Our published pan-cancer mycobiome manuscript3 also affirms these findings using updated, state-of-the-art methods. We also note that every analysis shown here was possible using public data and code that we had already provided.

bioinformatics↗

bioRxiv2022

Genetic architecture and evolution of color variation in American black bears

Color variation is a frequent evolutionary substrate for camouflage in small mammals but the underlying genetics and evolutionary forces that drive color variation in natural populations of large mammals are mostly unexplained. The American black bear, Ursus americanus, exhibits a range of colors including the cinnamon morph which has a similar color to the brown bear, U. arctos, and is found at high frequency in the American southwest. Reflectance and chemical melanin measurements showed little distinction between U. arctos and cinnamon U. americanus individuals. We used a genome-wide association for hair color as a quantitative trait in 151 U. americanus individuals and identified a single major locus (P < 10-13). Additional genomic and functional studies identified a missense alteration (R153C) in Tyrosinase-related protein 1 (TYRP1) that impaired protein localization and decreased pigment production. Population genetic analyses and demographic modeling indicated that the R153C variant arose 9.36kya in a southwestern population where it likely provided a selective advantage, spreading both northwards and eastwards by gene flow. A different TYRP1 allele, R114C, contributes to the characteristic brown color of U. arctos, but is not fixed across the range. HIGHLIGHTSO_LIThe cinnamon morph of American black bears and brown bears have different missense mutations in TYRP1 that account for their similar coloration C_LIO_LITYRP1 variants in American black bears and brown bears are loss-of-function alleles associated with impaired protein localization to melanosomes C_LIO_LIIn American black bears, the variant causing the cinnamon morph arose 9,360 years ago in the western lineage where it provides an adaptive advantage, and has spread northwards and eastwards by migration C_LI

evolutionary biology↗

bioRxiv2021

Phenotypic analysis of catastrophic childhood epilepsy genes: The Epilepsy Zebrafish Project

Genetic engineering techniques have contributed to the now widespread use of zebrafish to investigate gene function, but zebrafish-based human disease studies, and particularly for neurological disorders, are limited. Here we used CRISPR-Cas9 to generate 40 single-gene mutant zebrafish lines representing catastrophic childhood epilepsies. We evaluated larval phenotypes using electrophysiological, behavioral, neuro-anatomical, survival and pharmacological assays. Phenotypes with unprovoked electrographic seizure activity (i.e., epilepsy) were identified in zebrafish lines for 8 genes; ARX, EEF1A, GABRB3, GRIN1, PNPO, SCN1A, STRADA and STXBP1. A unifying epilepsy classification scheme was developed based on local field potential recordings and blinded scoring from ~3300 larvae. We also created an open-source database containing sequencing information, survival curves, behavioral profiles and representative electrophysiology data. We offer all zebrafish lines as a resource to the neuroscience community and envision them as a starting point for further functional analysis and/or identification of new therapies.

neuroscience↗

bioRxiv2020

Elucidating the viral and host factors enabling the cross-species transmission of primate lentiviruses from simians to humans

The HIV-1 epidemic originated from a cross-species transmission of a primate lentivirus from chimpanzees to humans near the turn of the 18th century. Simian immunodeficiency viruses have been jumping between old world monkeys in West/Central Africa for thousands of years. So why did HIV-1 only emerge in the past century? This study examined the replicative fitness, transmission, restriction, and cytopathogenicity of 26 primate lentiviruses. Pairwise competitions of these primate lentiviruses revealed that SIVcpz had the highest replicative fitness in human or chimpanzee peripheral blood mononuclear cells, even higher fitness than HIV-1 group M strains responsible for 37 million infections worldwide. In contrast the "HIV-2 lineage" (SIVsmm, SIVmac, SIVagm, and HIV-2) had the lowest replicative fitness. SIVcpz strains were less inhibited by human restriction factors than the "HIV-2 lineage" strains, a restriction that was inversely correlated with replicative fitness. SIVcpz from the chimpanzee subspecies Pan troglodytes troglodytes (Ptt) was slightly more fit in human cells than the strains from Pt schweinfurthii (Pts). However, unlike all other primate lentiviruses (including the HIV-2 lineage), SIVcpz was nonpathogenic in human tonsillar tissue and did not deplete CD4+ T-cells, consistent with the slow or nonpathogenic disease observed in chimpanzees. Despite the close phylogenetic relationship between SIVcpz_Ptt and HIV-1, this epidemic was either caused by cross species transmission of a rare, undiscovered SIVcpz strain of higher virulence or higher virulence differentially evolved among HIV-1 subtypes during the human epidemic. Author summaryInvasion of wild animal habitats by humans can have devastating consequences for the human population as evident by the HIV-1 and SARS-CoV-2 epidemics. With SARS-CoV-2, a recent zoonotic jump, likely from bats, will help to identify a coronavirus progenitor. In contrast, simian immunodeficiency virus (SIV) jumped into humans over 100 years ago from a possibly extinct sub-species of chimpanzees and/or extinct lineage of SIV. We examined replicative fitness and pathogenesis of 26 different primate lentiviruses in human and chimpanzee primary lymphoid cells from blood and within tonsils. SIV from a specific chimpanzee species and lowland gorillas were the most capable of infecting and replicating in human and chimp lymphoid cells but they did not result in the pathogenesis related to disease in humans. In contrast, SIV from other old world monkeys were pathogenic but could not replicate efficiently in human cells. We propose the main HIV-1 is derived from a distinct jump of a very rare SIV strain in chimps leading to AIDS pandemic.

microbiology↗

bioRxiv2019

Metabolome-informed microbiome analysis refines metadata classifications and reveals unexpected medication transfer in captive cheetahs

Structured AbstractEven high-quality collection and reporting of study metadata in microbiome studies can lead to various forms of inadvertently missing or mischaracterized information that can alter the interpretation or outcome of the studies, especially with non-model organisms. Metabolomic profiling of fecal microbiome samples can provide empirical insight into unanticipated confounding factors that are not possible to obtain even from detailed care records. We illustrate this point using data from cheetahs from the San Diego Zoo Safari Park. The metabolomic characterization indicated that one cheetah had to be moved from the non-antibiotic-exposed to the antibiotic-exposed group. The detection of the antibiotic in this second cheetah was likely due to grooming interactions with the cheetah that was administered antibiotics. Similarly, because transit time for stool is variable, early fecal samples within the first few days of antibiotic prescription do not all contain detectable antibiotics. Therefore, the microbiome is not affected by the antibiotics at those time points. These insights significantly altered the way the samples were grouped for analysis (antibiotic vs no antibiotic), and the subsequent understanding of the effect of the antibiotics on the cheetah microbiome. Metabolomics also revealed information about numerous other medications, and provided unexpected dietary insights that in turn improved our understanding of the molecular patterns on the impact on the community microbial structure. These results suggest that untargeted metabolomics data provide empirical evidence to correct records of non-model organisms in captivity, although we also expect these methods will be appropriate for experimental conditions typical in human studies.\n\nImportanceMetabolome-informed analyses can enhance omics studies by enabling the correct partitioning of samples by identifying hidden confounders inadvertently misrepresented or omitted from carefully curated metadata. We demonstrate the utility of metabolomics in a study characterizing the microbiome associated with liver disease in cheetahs. Metabolome-informed reinterpretation of metagenome and metabolome profiles factored in an unexpected transfer of antibiotics preventing misinterpretation of the data. Our work suggests that untargeted metabolomics can be used to verify, augment, and correct sample metadata to support improved grouping of sample data for microbiome analyses, here for non-model organisms in captivity. However, the techniques also suggest a path forward for correcting clinical information in human studies to enable higher-precision analyses.

systems biology↗