Biology subjects

Aran, D.

Publications and source records attributed to Aran, D..

14 recordsLinked to original sources

bioRxiv2026

Paralogous lncRNAs CYTOR and MORRBID share a conserved trans acting function in MEK ERK signaling

Background: Long non-coding RNAs (lncRNAs) exhibit rapid evolutionary turnover, often driven by genomic duplication. How paralogous lncRNAs maintain, partition, or diverge in function across distinct genomic contexts remains poorly understood. The evolutionarily conserved lncRNA MORRBID and its primate-specific paralog CYTOR provide a natural framework to interrogate the functional consequences of lncRNA duplication. Results: Although CYTOR and MORRBID have acquired distinct transcript variants influenced by their divergent genomic environments, we demonstrate that they maintain a robust, shared core function encoded by near-identical dominant two-exon transcripts. Using SNP-based paralog-specific quantification, we found that CYTOR contributes more strongly to the shared transcript pool, while both transcripts localize predominantly to the cytoplasm, consistent with a shared trans-acting function. Simultaneous repression of CYTOR and MORRBID consistently impairs cell adhesion and migration across multiple cancer models. Mechanistically, the shared CYTOR/MORRBID transcript pool associates with MEK2 and sustains MEK-ERK signaling. This signaling axis promotes FOSL1 expression and AP-1-linked transcriptional output, including expression of the downstream effector EPHA4, whose role was supported by rescue experiments. Patient tumor transcriptomes and healthy single-cell datasets further supported the associated mesenchymal, adhesion, and epithelial-mesenchymal transition program. Conclusions: Our findings establish that paralogous lncRNAs can retain a conserved mechanistic core despite context-dependent transcriptional divergence. The CYTOR/MORRBID transcript pool defines a shared lncRNA signaling module that supports MAPK-ERK signaling and adhesion-migration programs across cancer and mesenchymal-like cellular contexts. This defines a unified mechanistic framework for the shared core function of these widely studied paralogous lncRNAs.

genetics↗

bioRxiv2026

Predicting Immunotherapy Response Through Chemotherapy-Induced Tumor Microenvironment Remodeling

BackgroundChemotherapy before immunotherapy improves outcomes in multiple cancers, but the benefit is heterogeneous: only some tumors undergo the immune remodeling that makes subsequent immunotherapy effective, and no biomarker identifies these patients prospectively, leading to uniform use of chemo-immunotherapy and avoidable toxicity. MethodsA random forest model of tumor-microenvironment (TME) favorability, trained on 1,936 immunotherapy-treated patients, was applied to 334 paired pre-/post-chemotherapy biopsies across 19 studies to identify TME "converters," from which a 50-gene baseline signature was derived. The signature was tested in IMvigor210 (the only cohort containing both treatment arms) and in five independent chemo-immunotherapy validation cohorts, and for specificity against 22 immunotherapy-only cohorts. ResultsOf 209 paired-biopsy patients with unfavorable baseline TME, 61 (29%) converted to a favorable state after chemotherapy; converters were indistinguishable from non-converters at baseline by cell composition, motivating a gene-level signature. In IMvigor210, the signature discriminated responders among chemotherapy-primed patients with unfavorable baseline TME (AUC = 0.80, 95% CI: 0.67-0.94) but showed no signal in the trials immunotherapy-only arm (interaction OR = 4.42, p = 0.006). Across five independent chemo-immunotherapy cohorts, responders consistently scored higher than non-responders, yielding a significant pooled effect by meta-analysis (pooled g = 0.59, 95% CI: 0.11-1.08, p = 0.017) and individual significance in two (LUD2015-005, p = 0.009; GSE165252, p = 0.02). A meta-analysis of 22 immunotherapy-only cohorts constrained any effect there to a small magnitude (Hedges g = 0.08, 95% CI: -0.08 to 0.23), and five established immune signatures did not reproduce this treatment-context specificity. Converter tumors harbored coordinated baseline programs of proliferative stress, innate and adaptive immune readiness, and functional vasculature. ConclusionsA baseline transcriptomic signature predicts immunotherapy response specifically in chemotherapy-primed patients, a treatment-context specificity not shown by established immune biomarkers. These findings support prospective validation of the signature to guide chemotherapy- immunotherapy sequencing. SignificanceChemotherapy is now added to immunotherapy across a growing list of cancers, yet every eligible patient receives it even though only some tumors need it, and current biomarkers (PD-L1, tumor mutational burden, microsatellite instability) cannot identify which. We report the first biomarker to predict benefit from the chemotherapy component itself: a baseline gene-expression signature that flags likely responders in chemotherapy-primed settings while remaining silent under immunotherapy alone. This treatment-context specificity shifts the biomarker question from "who responds to immunotherapy" to "who needs the chemotherapy," pointing toward a way to spare patients unnecessary toxicity and rationally sequence chemo-immunotherapy.

Cancer Biology↗

bioRxiv2025

CellMentor: Cell-Type Aware Dimensionality Reduction for Single-cell RNA-Sequencing Data

Single-cell RNA sequencing (scRNA-seq) enables high-resolution profiling of individual cells, yet transforming this high-dimensional data into biologically meaningful representations remains a critical challenge. Current dimensionality reduction methods often fail to effectively balance technical noise reduction with preservation of cell-type-specific biological signals, particularly when integrating data across multiple experiments. Here, we present CellMentor, a novel supervised non-negative matrix factorization (NMF) framework that leverages labeled reference datasets to learn biologically meaningful latent spaces that can be transferred across related datasets. CellMentor employs a loss function that preserves cell type identity by simultaneously minimizing variation within known cell populations while maximizing distinctions between different cell types. We evaluated CellMentor against state-of-the-art dimensionality reduction and integration methods using controlled simulations of increasing difficulty and diverse real tissue types, each consisting of a labeled reference dataset and an unlabeled query dataset. In simulations, CellMentor maintained near-perfect clustering performance even under challenging conditions where other methods failed. In real datasets from PBMC, pancreas, and melanoma tissues, CellMentor demonstrated superior cell type separation while effectively mitigating batch effects. CellMentor also excelled at detecting rare cell populations and maintained reasonable performance when encountering novel cell types absent from reference data. With its robust batch correction capabilities and ability to preserve biologically meaningful cell type distinctions, CellMentor is particularly valuable for integrative analyses across multiple experiments.

bioinformatics↗

bioRxiv2024

xCell 2.0: Robust Algorithm for cell type Proportion Estimation Predicts Response to Immune Checkpoint Blockade

BackgroundAccurate estimation of cell type proportions from bulk gene expression data is essential for understanding the cellular heterogeneity underlying complex tissues and diseases. Here, we introduce xCell 2.0, an advanced version of the xCell algorithm, featuring a training function that permits the utilization of any reference dataset. xCell 2.0 generates cell type gene signatures using an improved methodology, including automated handling of cell type dependencies and more robust signature generation. MethodsWe benchmarked xCell 2.0 against ten popular deconvolution methods using nine human and mouse reference sets and 26 validation datasets, encompassing 1,749 samples and 67 cell types. Additionally, we validated xCell 2.0 using the independent Deconvolution DREAM Challenge dataset. As an applicative test case, we curated pan-cancer data of 2,007 patients pre-treated with immune checkpoint blockade (ICB). Features of the tumor microenvironment (TME) were generated using xCell 2.0 and other methods and fed into a LightGBM model using nested cross-validation to obtain robust predictions of ICB response. ResultsBenchmarking results showed that xCell 2.0 outperformed all other tested methods across distinct reference datasets, demonstrating superior accuracy and consistency across diverse biological contexts. xCell 2.0 also showed the best performance in minimizing spillover effects between related cell types. In the ICB response prediction task, xCell 2.0-derived TME features significantly improved prediction accuracy compared to models using only cancer type and treatment information, and outperformed other deconvolution methods and established ICB prediction scores. ConclusionsxCell 2.0 is a versatile and robust tool for cell type deconvolution that maintains high performance across various reference types and biological contexts. It is available both via a web application and as a Bioconductor-compatible package, equipped with a large collection of pre-trained cell type signatures for human and mouse research. The improved prediction of ICB responses highlights the potential of xCell 2.0 to advance precision medicine in cancer and other diseases.

bioinformatics↗

bioRxiv2024

Cellular Dynamics Upon Immune Checkpoint Inhibition

Immune checkpoint inhibitors (ICI) have transformed cancer therapy, yet the basis of variable patient responses remains unclear. We assembled a longitudinal single-cell RNA sequencing atlas of 441 samples from 241 patients across ten cancers to map treatment-associated remodeling of the tumor immune microenvironment (TIME). Using a hierarchical reference-guided deep-phenotyping framework, we defined 77 immune and stromal subtypes and resolved four conserved TIME subtypes. Approximately 40% of tumors shifted between states during therapy, and the transition was more predictive of outcome than the baseline state. Across 1,988 bulk transcriptomic tumors, favorable transitions toward inflamed or B-cell-enriched subtype tracked with improved response and survival, while persistence in or shifts towards myeloid dominance indicated resistance. We derived a transition score that predicted outcomes for baseline tumors across independent cohorts. These findings establish immunotype transitions as a central determinant of ICI, offering new avenues for response prediction and rational immunotherapy design. HighlightsO_LIA pan-cancer meta-analysis maps treatment-associated remodeling of the tumor immune microenvironment. C_LIO_LIFour conserved TIME states emerge across cancer subtypes. C_LIO_LITIME transition patterns during treatment are associated with clinical outcome. C_LIO_LIBaseline immune programs derived transition scores predict treatment response and survival. C_LI

cancer biology↗

bioRxiv2024

Defective interferon signaling in the circulating monocytes of type 2 diabetic mice

Type 2 diabetes mellitus (T2DM) is associated with poor outcome after stroke. Peripheral monocytes play a critical role in the secondary injury and recovery of damaged brain tissue after stroke, but the underlying mechanisms are largely unclear. To investigate transcriptome changes and molecular networks across monocyte subsets in response to T2DM and stroke, we performed single-cell RNA-sequencing (scRNAseq) from peripheral blood mononuclear cells and bulk RNA-sequencing from blood monocytes from four groups of adult mice, consisting of T2DM model db/db and normoglycemic control db/+ mice with or without ischemic stroke. Via scRNAseq we found that T2DM expands the monocyte population at the expense of lymphocytes, which was validated by flow cytometry. Among the monocytes, T2DM also disproportionally increased the inflammatory subsets with Ly6C+ and negative MHC class II expression (MO.6C+II-). Conversely, monocytes from control mice without stroke are enriched with steady-state classical monocyte subset of MO.6C+II+ but with the least percentage of MO.6C+II- subtype. Apart from enhancing inflammation and coagulation, enrichment analysis from both scRNAseq and bulk RNAseq revealed that T2DM specifically suppressed type-1 and type-2 interferon signaling pathways crucial for antigen presentation and the induction of ischemia tolerance. Preconditioning by lipopolysaccharide conferred neuroprotection against ischemic brain injury in db/+ but not in db/db mice and coincided with a lesser induction of brain Interferon-regulatory-factor-3 in the brains of the latter mice. Our results suggest that the increased diversity and altered transcriptome in the monocytes of T2DM mice underlie the worse stroke outcome by exacerbating secondary injury and potentiating stroke-induced immunosuppression. Significance StatementThe mechanisms involved in the detrimental diabetic effect on stroke are largely unclear. We show here, for the first time, that peripheral monocytes have disproportionally altered the subsets and changed transcriptome under diabetes and/or stroke conditions. Moreover, genes in the IFN-related signaling pathways are suppressed in the diabetic monocytes, which underscores the immunosuppression and impaired ischemic tolerance under the T2DM condition. Our data raise a possibility that malfunctioned monocytes may systemically and focally affect the host, leading to the poor outcome of diabetes in the setting of stroke. The results yield important clues to molecular mechanisms involved in the detrimental diabetic effect on stroke outcome.

neuroscience↗

bioRxiv2024

SLAYER: Synthetic Lethality Analysis for Enhanced Targeted Therapy Implicates AhR inhibitor as a Target in RB1-Mutant Bladder Tumors

Synthetic lethality represents a promising therapeutic approach in precision oncology, yet systematic identification of clinically relevant synthetic lethal interactions remains challenging. Here we present SLAYER (Synthetic Lethality AnalYsis for Enhanced taRgeted therapy), a computational framework that integrates cancer genomic data and genome-wide CRISPR knockout screens to identify potential synthetic lethal interactions. SLAYER employs parallel analytical approaches examining both direct mutation-dependency associations and pathway-mediated relationships across 808 cancer cell lines. Our integrative method identified 4,332 statistically significant interactions, which were refined to 142 high-confidence candidates through stringent filtering for effect size, druggability, and clinical prevalence. Systematic validation against protein interaction databases revealed a 15-fold enrichment of known associations among SLAYER predictions compared to random gene pairs. Through pathway-level analysis, we identified inhibition of the aryl hydrocarbon receptor (AhR) as potentially synthetically lethal with RB1 mutations in bladder cancer. Experimental studies demonstrated selective sensitivity to AhR inhibition in RB1-mutant versus wild-type bladder cancer cells, which probably operates through indirect pathway-mediated mechanisms rather than direct genetic interaction. In summary, by integrating mutation profiles, gene dependencies, and pathway relationships, our approach provides a resource for investigating genetic vulnerabilities across cancer types.

bioinformatics↗

bioRxiv2021

Molecular programs of fibrotic change in aging human lung

Aging is associated with both overt and subclinical lung fibrosis, which increases risk for mortality from viruses and other respiratory pathogens. The molecular programs that induce fibrosis in the aging lung are not well understood. To overcome this knowledge gap, we undertook multimodal profiling of distal lung samples from healthy human donors across the lifespan. Telomere shortening, a cause of cell senescence and fibrosis, was progressive with age in a sample of 86 lungs and was associated with foci of DNA damage. Bulk RNA sequencing confirmed activation of cellular senescence and pro-fibrotic pathways as well as genes necessary for collagen processing with increasing age. These findings were validated in independent datasets for lung and sun-exposed skin, but not other organs including heart, liver and kidney. Cell type deconvolution analysis revealed a progressive loss of lung epithelial cells and an increasing proportion of fibroblasts. Consistent with the observed pro-fibrotic transcriptional profile, second harmonic imaging demonstrated increased density of interstitial collagen in aged human lungs. Furthermore, regions of parenchymal fibrosis were associated with decreased alveolar expansion and surfactant secretion. These findings reveal the transcriptional and structural features of fibrosis and associated physiologic impairments in normal lung aging.

genomics↗

bioRxiv2019

EpiSort: Enumeration of cell types using targeted bisulfite sequencing

The cellular composition of tumors is now recognized as an essential phenotype, with implications to diagnostic, progression and therapy efficacy. A tool for accurate profiling of the tumor microenvironment is lacking, as single-cell methods and computational approaches are not applicable or suffer from low accuracy. Here we present EpiSort, a novel strategy based on targeted bisulfite sequencing, which allows the accurate enumeration of 23 cell types and may be applicable to cancer studies.

immunology↗

bioRxiv2018

Comprehensive transcriptomic analysis of cell lines as models of primary tumor samples across 22 tumor types

Cancer cell lines are commonly used as models for cancer biology. While they are limited in their ability to capture complex interactions between tumors and their surrounding environment, they are a cornerstone of cancer research and many important findings have been discovered utilizing cell line models. Not all cell lines are appropriate models of primary tumors, however, which may contribute to the difficulty in translating in vitro findings to patients. Previous studies have leveraged public datasets to evaluate cell lines as models of primary tumors, but they have been limited in scope to specific tumor types and typically ignore the presence of tumor infiltrating cells in the primary tumor samples. We present here a comprehensive pan-cancer analysis utilizing approximately 9,000 transcriptomic profiles from The Cancer Genome Atlas and the Cancer Cell Line Encyclopedia to evaluate cell lines as models of primary tumors across 22 different tumor types. After adjusting for tumor purity in the primary tumor samples, we performed correlation analysis and differential gene expression analysis between the primary tumor samples and cell lines. We found that cell-cycle pathways are consistently upregulated in cell lines, while no pathways are consistently upregulated across the primary tumor samples. In a case study, we compared colorectal cancer cell lines with primary tumor samples across the colorectal subtypes and identified three colorectal cell lines that were derived from fibroblasts rather than tumor epithelial cells. Lastly, we propose a new set of cell lines panel, the TCGA-110, which contains the most representative cell lines from 22 different tumor types as a more comprehensive and informative alternative to the NCI-60 panel. Our analysis of the other tumor types are available in our web app (http://comphealth.ucsf.edu/TCGA110) as a resource to the cancer research community, and we hope it will allow researchers to select more appropriate cell line models and increase the translatability of in vitro findings.

bioinformatics↗

bioRxiv2018

CD161 mediates prenatal immune suppression of IFNγ-producing PLZF+ T cells

While the fetal immune system defaults to a program of tolerance, there is concurrent need for protective immunity to meet the antigenic challenges after birth. Activation of fetal T cells is associated with fetal inflammation and the termination of pregnancy, yet which fetal T cells contribute to this process is poorly understood. Here we show a transcriptionally distinct population of pro-inflammatory T cells that predominates in the human fetal intestine. Activation of PLZF+ T cells results in rapid production of Th1 cytokines and is inhibited upon ligation of surface CD161. This mechanism of fetal immune suppression may inform how immune dysregulation could result in fetal and neonatal inflammatory pathologies such as preterm birth. Our data support that human development of protective adaptive immunity originates in utero within the specialized microenvironment of the fetal intestine.

immunology↗

bioRxiv2018

Single-cell RNA-seq reveals profibrotic macrophages in lung fibrosis

Myeloid cells localize to peripheral tissues in a wide range of pathologic contexts. However, appreciation of distinct myeloid subtypes has been limited by the signal averaging inherent to bulk sequencing approaches. Here we applied single-cell RNA sequencing (scRNA-seq) to map cellular heterogeneity in lung fibrosis induced by bleomycin injury in mice. We first developed a computational framework that enables unbiased, granular cell-type annotation of scRNA-seq. This approach identified a macrophage subpopulation that was specific to injured lung and notable for high expression of Cx3cr1+ and MHCII genes. We found that these macrophages, which bear a gene expression profile consistent with monocytic origin, progressively acquire alveolar macrophage identity and localize to sites of fibroblast accumulation. Probing their functional role, in vitro studies showed a trophic effect of these cells on fibroblast activation, and ablation of Cx3cr1-expressing cells suppressed fibrosis in vivo. We also found by gene set analysis and immunofluorescence that markers of these macrophages were upregulated in samples from patients with lung fibrosis compared with healthy controls. Taken together, our results uncover a specific pathologic subgroup of macrophages with markers that could enable their therapeutic targeting for fibrosis.

immunology↗

bioRxiv2018

Tumor cell-adipocyte gap junctions activate lipolysis and are essential for breast tumorigenesis

A pro-tumorigenic role for adipocytes has been identified in breast cancer, and reliance on fatty acid catabolism found in aggressive tumors. The molecular mechanisms by which tumor cells coopt neighboring adipocytes, however, remain elusive. Here, we describe a direct interaction linking tumorigenesis to adjacent adipocytes. We examine breast tumors and their normal adjacent tissue from several patient cohorts, patient-derived xenografts and mouse models, and find that lipolysis and lipolytic signaling are activated in neighboring adipose tissue. We find that functional gap junctions form between breast cancer cells and adipocytes. As a result, cAMP is transferred from breast cancer cells to adipocytes and activates lipolysis in a gap junction-dependent manner. We identify connexin 31 (GJB3), which promotes receptor triple negative breast cancer growth and activation of lipolysis in vivo. Thus, direct tumor cell-adipocyte interaction contributes to tumorigenesis and may serve as a new therapeutic target in breast cancer. One sentence summaryGap junctions between breast cancer cells and adipocytes transfer cAMP and activate lipolysis in the breast tumor microenvironment to support growth.

cancer biology↗

bioRxiv2017

xCell: Digitally portraying the tissue cellular heterogeneity landscape

Tissues are complex milieu consisting of numerous cell-types. Numerous recent methods attempt to enumerate cell subsets from transcriptomes. However, available method used limited source for training and displayed only partial portrayal of the full cellular landscape. Here we present xCell, a novel gene-signature based method for inferring 64 immune and stroma cell-types. We harmonized 1,822 pure human cell-types transcriptomes from various sources, employed curve fitting approach for linear comparison of cell-types, and introduced a novel spillover compensation technique for separating between cell-types. Using extensive in silico analyses and comparison to cytometry immunophenotyping we show that xCell outperforms other methods: http://xCell.ucsf.edu/.

bioinformatics↗