Biology subjects

MacLean, A. L.

Publications and source records attributed to MacLean, A. L..

At least 19 recordsLinked to original sources

bioRxiv2026

A class act: HDAC1-Malat1 regulates MDSC apoptosis and cell cycling to decrease suppression of T cells

Myeloid derived suppressor cells (MDSCs) are key players in the immune-suppressed tumor microenvironment (TME) and significantly contribute to immune checkpoint inhibition (ICI) resistance, making them favorable targets for cancer immunotherapy. Epigenetic reprogramming of MDSCs using histone deacetylase (HDAC) inhibitors shows promise to sensitize the TME to ICIs. However, the molecular mechanism of HDAC inhibition in MDSCs has yet to be elucidated. Murine and human MDSC models treated with Entinostat revealed that the long non-coding RNA Malat1 downregulates pSTAT3 and decreases MDSC-mediated suppression of T cell proliferation. Through HDAC inhibitor screens, we identified HDAC1 as preferentially regulating Malat1 expression, STAT3 activation, and MDSC suppression. We also show that HDAC1 inhibition increases MDSC apoptosis by shifting pro-vs. anti-apoptotic signals and increases G0/G1 cell cycle arrest via decreasing G1-S transition cyclin-CDK complexes. Collectively, our findings provide a multi-pronged mechanism of HDAC inhibition in MDSCs that inform the development of future rational combination therapies. One Sentence SummaryHDAC1 inhibition in MDSCs increases Malat1, decreases pSTAT3, induces apoptosis/cell cycle arrest, and decreases suppression of T cells

immunology↗

bioRxiv2025

Beyond RECIST: mathematical modeling and Bayesian inference reveal the importance of immune parameters in metastatic breast cancer

Successful immunotherapies must overcome patient- and organ-specific tumor heterogeneities to mount an effective response. Yet tumor dynamics remain poorly characterized in organ-specific contexts and associated immune environments. To quantify heterogeneous tumor responses, we developed methods to fit mathematical models of the tumor-immune dynamics to patients undergoing combination therapy for metastatic breast cancer: checkpoint inhibition via nivolumab + ipilimumab combined with entinostat, as measured by RECIST criteria. In a subset of patients additional immune dynamics were quantified by spatial proteomics. Bayesian parameter inference revealed that only immune-modulatory parameters controlled response; parameters controlling cytotoxicity were uninformative. Through posterior parameter sampling and simulation, we created virtual tumor cohorts, enabling extrapolation beyond the data to predict probabilities of response in metastatic lesions where no data exist. We validated pre-dictions from our virtual tumor population using held-out data characterizing off-target lesions from the patient cohort. Profile-wise likelihood analysis revealed that scans in the week immediately following treatment hold particularly high value in identifying the tumor dynamics. Overall, we demonstrate how through modeling & inference cohort size limitations can be over-come through the creation of virtual tumor populations, giving insight into the site-specific mechanisms of disease progression and response.

systems biology↗

bioRxiv2025

Cancer systems immunology reveals myeloid - T cell interactions and B cell activation mediate response to checkpoint inhibition in metastatic breast cancer

Sensitization of the immune-suppressed tumor microenvironment (TME) of breast cancer by histone deacetylase inhibition shows promise, but the mechanisms of sensitization are unknown. We investigated the TME of breast-to-lung metastases by combining experimental and clinical data with theory. Knowledge-guided subclustering of single-cell RNA-sequencing data and cell circuits analysis identified 39 cell states and salient interactions, of which myeloid, T cell and B cell subpopulations were most affected by treatment. Using functional immunologic assays, we verified that inhibition of the ICAM pathway partially recapitulated treatment effects. Mathematical modeling of tumor-immune dynamics confirmed that tumor reduction required simultaneous modulation of multiple TME interactions. We found evidence that treatment affected anti-tumor antibody production. Analysis of patient biopsies via spatial proteomics corroborated preclinical findings: in responders we observed increased B cell activation, mature tertiary lymphoid structures, and increased CD8+ T cell--macrophage distances with treatment. Overall, this study provides a framework for the discovery of cell-cell interactions that govern responses in complex TMEs. Statement of significanceThis study provides a framework for the discovery of cell-cell interactions that control responses in complex TMEs. We not only identify impactful tumor immunologic interactions that facilitate sensitization of the metastatic TME but also demonstrate how interdisciplinary data integration fuels cancer systems immunology to accelerate discovery of mechanisms of successful immunotherapeutic response in breast cancers and other previously unresponsive solid tumor types.

cancer biology↗

bioRxiv2025

Inference of marker genes of subtle cell state changes via iterative logistic regression

We present iterative logistic regression (iLR) for the identification of small sets of informative marker genes. Differential expression and marker gene selection methods for single-cell RNA sequencing (scRNAseq) data can struggle to identify small sets of informative genes, especially for subtle differences between cell states, as can be induced by disease or treatment. iLR applied logistic regression iteratively with a Pareto front optimization to balance gene set size with classification performance. We benchmark iLR on in silico datasets demonstrating comparable performance to the state-of-the-art at single-cell classification using only a fraction of the genes. We test iLR on its ability to distinguish neuronal cell subtypes in healthy vs. autism spectrum disorder patients and find it achieves high accuracy with small sets of disease-relevant genes. We apply iLR to investigate immunotherapeutic effects in cell types from different tumor microenvironments and find that iLR infers informative genes that translate across organs and even species (mouse-to-human) comparison. We predicted via iLR that entinostat acts in part through the modulation of myeloid cell differentiation routes in the lung microenvironment. Overall, iLR provides means to infer interpretable transcriptional signatures from complex datasets with prognostic or therapeutic potential.

bioinformatics↗

bioRxiv2025

Longitudinal single cell RNA-sequencing reveals evolution of micro- and macro-states in chronic myeloid leukemia

Single cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cancer, yet identifying meaningful disease states from single cell data remains challenging. Here, we systematically explore the chronic myeloid leukemia (CML) specific information content encoded in single cell versus bulk transcriptomics to resolve this paradox and clarify how discrete disease-defining states emerge from inherently noisy single cell data. We demonstrate that, while CML single cell transcriptomes exist along continuous transcriptional microstates, clinically relevant leukemia phenotypes clearly manifest only at the pseudobulk (macrostate) level. By leveraging state-transition theory, we reveal how robust disease phenotype state-transitions are governed by cell type specific contributions. Our results establish a theoretical framework explaining why discrete disease phenotypes remain hidden at the single cell scale but emerge clearly at the aggregated macrostate level, enabling previously inaccessible biological insights into leukemia evolution. By resolving how single-cell variation aggregates into macroscopic disease states, our framework provides new insight into CML progression and offers a broadly applicable strategy for exploring disease dynamics across cancers and other complex conditions.

systems biology↗

bioRxiv2024

Transition paths across the EMT landscape are dictated by network logic

During development and cancer metastasis, cells transition reversibly from epithelial to mesenchymal via intermediate cell states during epithelial-mesenchymal transition (EMT). EMT is controlled by gene regulatory networks (GRNs) and can be described by a three-node GRN that permits tristable EMT landscapes. In this GRN, multiple inputs regulate the transcription factor ZEB that induces EMT. It is unknown how to choose the network logic for such regulation. Here we explore the effects of network logic on a tristable EMT network. We discover that the choice of additive vs multiplicative logic affects EMT phenotypes, leading to opposing predictions regarding the factors controlling EMT transition paths. We show that strong inhibition of miR-200 destabilizes the epithelial state and initiates EMT for multiplicative (AND) but not additive (OR) logic, suggesting that AND logic is in better agreement with experimental measurements of the effects of miR-200 regulation on EMT. Using experimental single-cell data, stochastic simulations, and perturbation analysis, we demonstrate how our results can be used to design experiments to infer the network logic of an EMT GRN in live cells. Our results explain how the manipulation of molecular interactions can stabilize or destabilize EMT hybrid states, of relevance during cancer progression and metastasis. More generally, we highlight the importance of the choice of network logic in GRN models in the presence of biological noise and multistability.

systems biology↗

bioRxiv2024

The RNA exosome maintains cellular RNA homeostasis by controlling transcript abundance in the brain

Intracellular ribonucleases (RNases) are essential in all aspects of RNA metabolism, including maintaining accurate RNA levels. Inherited mutations in genes encoding ubiquitous RNases are associated with human diseases, primarily affecting the nervous system. Recessive mutations in genes encoding an evolutionarily conserved RNase complex, the RNA exosome, lead to syndromic neurodevelopmental disorders characterized by progressive neurodegeneration, such as Pontocerebellar Hypoplasia Type 1b (PCH1b). We establish a CRISPR/Cas9-engineered Drosophila model of PCH1b to study cell-type-specific post-transcriptional regulatory functions of the nuclear RNA exosome complex within fly head tissue. Here, we report that pathogenic RNA exosome mutations alter activity of the complex, causing widespread dysregulation of brain-enriched cellular transcriptomes, including rRNA processing defects--resulting in tissue-specific, progressive neurodegenerative effects in flies. These findings provide a comprehensive understanding of RNA exosome function within a developed animal brain and underscore the critical role of post-transcriptional regulatory machinery in maintaining cellular RNA homeostasis within the brain.

molecular biology↗

bioRxiv2024

Intratumoral CXCL12 Gradients Contextualize Tumor Cell Invasion, Migration and Immune Suppression in Breast Cancer

Although the CXCL12/CXCR4 pathway has been prior investigated for its prometastatic and immuno- suppressive roles in the tumor microenvironment, evidence on the spatiotemporal regulation of these hallmarks has been lacking. Here, we demonstrate that CXCL12 forms a gradient specifically around cancer cell intravasation doorways, also known as Tumor Microenvironment of Metastasis (TMEM) doorways, thus facilitating the chemotactic translocation of prometastatic tumor cells expressing CXCR4 toward the perivascular TMEM doorways for subsequent entry into peripheral circulation. Fur- thermore, we demonstrate that the CXCL12-rich micro-environment around TMEM doorways may cre- ate immunosuppressive niches, whereby CD8+ T cells, despite being attracted to these regions, often exhibit reduced effector functions, limiting their efficacy. While the CXCL12/CXCR4 pathway can mini- mally influence the overall composition of immune cell populations, it biases the distribution of CD8+ T cells away from TMEM doorways, justifying its prior-established role as immunosuppressive factor for CD8+ T cells. Our research suggests that the complex interactions between CXCL12 and the various tumor and immune cell types contributes not only to the completion of the initial steps of the metastatic cascade, but also offers an immunological "sanctuary" to prometastatic tumor cells homed around TMEM doorways. Overall, our study enhances our current understanding on the mechanisms, via which CXCL12 orchestrates tumor cell behavior and immune dynamics, potentially guiding future thera- peutic strategies to combat breast cancer metastasis and improve anti-tumor immunity.

cancer biology↗

bioRxiv2024

Modeling the dynamics of EMT reveals genes associated with pan-cancer intermediate states and plasticity

Epithelial-mesenchymal transition (EMT) is a developmental cell state transition co-opted by cancer to drive metastasis and resistance. Stable EMT intermediate states play a particularly important role in cell state plasticity and confer metastatic potential. To explore the dynamics of EMT and identify marker genes of highly metastatic intermediate cells, we analyzed EMT across multiple tumor types and stimuli via mathematical modeling with single-cell RNA-sequencing (scRNA-seq) data. We identified pan-cancer genes consistently expressed or upregulated in EMT intermediate states, most of which were not previously annotated as markers of EMT. Using Bayesian parameter inference, we fit a simple mathematical model to scRNA-seq data, revealing tumor-specific transition rates. This mathematical model offers a framework to quantify EMT progression. A consensus analysis of differential intermediate expression, regulation, and model-derived dynamics identified marker genes associated with persistence of the intermediate EMT state. SFN and NRG1 emerged as genes with the strongest evidence for their role influencing intermediate EMT dynamics. Through analysis of an independent cell line, we verified the role of SFN as a marker intermediate EMT transition. Modeling and inference of genes associated with EMT dynamics offer means to find biomarkers and to identify therapeutic approaches to harness or reverse tumor-promoting cell state transitions driven by EMT.

systems biology↗

bioRxiv2024

Data-driven model discovery and model selection for noisy biological systems

Biological systems exhibit complex dynamics that differential equations can often adeptly represent. Ordinary differential equation models are widespread; until recently their construction has required extensive prior knowledge of the system. Machine learning methods offer alternative means of model construction: differential equation models can be learnt from data via model discovery using sparse identification of nonlinear dynamics (SINDy). However, SINDy struggles with realistic levels of biological noise and does not incorporate prior knowledge of the system. We propose a data-driven framework for model discovery and model selection using hybrid dynamical systems: partial models containing missing terms. Neural networks are used to approximate the unknown dynamics of a system, enabling the denoising the data while simultaneously learning the latent dynamics. Simulations from the fitted neural network are then used to infer models using SINDy. We show, via model selection, that model discovery in SINDy with hybrid dynamical systems outperforms alternative approaches. We find it possible to infer models correctly up to high levels of biological noise of different types. We demonstrate the potential to learn models from sparse, noisy data in application to a canonical cell state transition using data derived from single-cell transcriptomics. Overall, this approach provides a practical framework for model discovery in biology in cases where data are noisy and sparse, of particular utility when the underlying biological mechanisms are partially but incompletely known.

systems biology↗

bioRxiv2024

Developmental hematopoietic stem cell variation explains clonal hematopoiesis later in life

Clonal hematopoiesis becomes increasingly common with age, but its cause is enigmatic because driver mutations are often absent. Serial observations infer weak selection indicating variants are acquired much earlier in life with unexplained initial growth spurts. Here we use fluctuating CpG methylation as a lineage marker to track stem cell clonal dynamics of hematopoiesis. We show, via the shared prenatal circulation of monozygotic twins, that weak selection conferred by stem cell variation created before birth can reliably yield clonal hematopoiesis later in life. Theory indicates weak selection will lead to dominance given enough time and large enough population sizes. Human hematopoiesis satisfies both these conditions. Stochastic loss of weakly selected variants is naturally prevented by the expansion of stem cell lineages during development. The dominance of stem cell clones created before birth is supported by blood fluctuating CpG methylation patterns that exhibit low correlation between unrelated individuals but are highly correlated between many elderly monozygotic twins. Therefore, clonal hematopoiesis driven by weak selection in later life appears to reflect variation created before birth.

systems biology↗

bioRxiv2023

Gastrulation-stage gene expression in Nipbl+/- mouse embryos foreshadows the development of syndromic birth defects

In animal models, Nipbl-deficiency phenocopies gene expression changes and birth defects seen in Cornelia de Lange Syndrome (CdLS), the most common cause of which is Nipbl-haploinsufficiency. Previous studies in Nipbl+/- mice suggested that heart development is abnormal as soon as cardiogenic tissue is formed. To investigate this, we performed single-cell RNA-sequencing on wildtype (WT) and Nipbl+/- mouse embryos at gastrulation and early cardiac crescent stages. Nipbl+/-embryos had fewer mesoderm cells than WT and altered proportions of mesodermal cell subpopulations. These findings were associated with underexpression of genes implicated in driving specific mesodermal lineages. In addition, Nanog was found to be overexpressed in all germ layers, and many gene expression changes observed in Nipbl+/- embryos could be attributed to Nanog overexpression. These findings establish a link between Nipbl-deficiency, Nanog overexpression, and gene expression dysregulation/lineage misallocation, which ultimately manifest as birth defects in Nipbl+/- animals and CdLS. TeaserGene expression changes during gastrulation of Nipbl-deficient mice shed light on early origins of structural birth defects.

developmental biology↗

bioRxiv2023

Direct androgen receptor regulation of sexually dimorphic gene expression in the mammalian kidney

Mammalian organs exhibit distinct physiology, disease susceptibility and injury responses between the sexes. In the mouse kidney, sexually dimorphic gene activity maps predominantly to proximal tubule (PT) segments. Bulk RNA-seq data demonstrated sex differences were established from 4 and 8 weeks after birth under gonadal control. Hormone injection studies and genetic removal of androgen and estrogen receptors demonstrated androgen receptor (AR) mediated regulation of gene activity in PT cells as the regulatory mechanism. Interestingly, caloric restriction feminizes the male kidney. Single-nuclear multiomic analysis identified putative cis-regulatory regions and cooperating factors mediating PT responses to AR activity in the mouse kidney. In the human kidney, a limited set of genes showed conserved sex-linked regulation while analysis of the mouse liver underscored organ-specific differences in the regulation of sexually dimorphic gene expression. These findings raise interesting questions on the evolution, physiological significance, and disease and metabolic linkage, of sexually dimorphic gene activity.

developmental biology↗

bioRxiv2023

Gene regulatory network inference with popInfer reveals dynamic regulation of hematopoietic stem cell quiescence upon diet restriction and aging

Inference of gene regulatory networks (GRNs) can reveal cell state transitions from single-cell genomics data. However, obstacles to temporal inference from snapshot data are difficult to overcome. Single-nuclei multiomics data offer means to bridge this gap and derive temporal information from snapshot data using joint measurements of gene expression and chromatin accessibility in the same single cells. We developed popInfer to infer networks that characterize lineage-specific dynamic cell state transitions from joint gene expression and chromatin accessibility data. Benchmarking against alternative methods for GRN inference, we showed that popInfer achieves higher accuracy in the GRNs inferred. popInfer was applied to study single-cell multiomics data characterizing hematopoietic stem cells (HSCs) and the transition from HSC to a multipotent progenitor cell state during murine hematopoiesis across age and dietary conditions. From networks predicted by popInfer, we discovered gene interactions controlling entry to/exit from HSC quiescence that are perturbed in response to diet or aging.

systems biology↗

bioRxiv2022

Myeloid-derived suppressor cell dynamics control outcomes in the metastatic niche

Myeloid-derived suppressor cells (MDSCs) play a prominent and rising role in the tumor microenvironment. An understanding of the tumor-MDSC interactions that influence disease progression is critical, and currently lacking. To address this, we developed a mathematical model of metastatic growth and progression in immune-rich tumor microenvironments. We model the tumor-immune dynamics with stochastic delay differential equations, and study the impact of delays in MDSC activation/recruitment on tumor growth outcomes. We find when the circulating level of MDSCs is low, the MDSC delay has a pronounced impact on the probability of new metastatic establishment: blocking MDSC recruitment can reduce the probability of metastasis by as much as 50%. We also quantify the extent to which decreasing the immuno-suppressive capability of the MDSCs impacts the probability that a new metastasis will persist or grow. In order to quantify patient-specific MDSC dynamics under different conditions we fit individual tumors treated with immune checkpoint inhibitors to the tumor-MDSC model via Bayesian parameter inference. We reveal that control of the inhibition rate of natural killer cells by MDSCs has a larger influence on tumor outcomes than controlling the tumor growth rate directly. Posterior classification of tumor outcomes demonstrates that incorporating knowledge of the MDSC responses improves predictive accuracy from 63% to 82%. Our results illustrate the importance of MDSC dynamics in the tumor microenvironment and predict interventions that may shift environments towards a less immune-suppressed state. We argue that there is a pressing need to more often consider MDSCs in analyses of tumor microenvironments.

systems biology↗

bioRxiv2022

Single-cell Ca2+ parameter inference reveals how transcriptional states inform dynamic cell fates

Single-cell genomic technologies offer vast new resources with which to study cells, but their potential to inform parameter inference of cell dynamics has yet to be fully realized. Here we develop methods for Bayesian parameter inference with data that jointly measure gene expression and Ca2+ dynamics in single cells. We propose to share information between cells via transfer learning: for a sequence of cells, the posterior distribution of one cell is used to inform the prior distribution of the next. In application to intracellular Ca2+ signaling dynamics, we fit the parameters of a dynamical model for thousands of cells with variable single-cell responses. We show that transfer learning accelerates inference with sequences of cells regardless of how the cells are ordered. However, only by ordering cells based on their transcriptional similarity can we distinguish Ca2+ dynamic profiles and associated marker genes from the posterior distributions. Inference results reveal complex and competing sources of cell heterogeneity: parameter covariation can diverge between the intracellular and intercellular contexts. Overall, we discuss the extent to which single-cell parameter inference informed by transcriptional similarity can quantify relationships between gene expression states and signaling dynamics in single cells.

systems biology↗

bioRxiv2021

A single-cell resolved cell-cell communication model explains lineage commitment in hematopoiesis

Cells do not function in isolation. Arguably, every cell fate decision occurs in response to environmental signals. In many cases cell-cell communication alters the dynamics of a cells internal gene regulatory network to initiate cell fate transitions, yet models rarely take this into account. Here we develop a multiscale perspective to study the granulocyte-monocyte vs. megakaryocyte-erythrocyte fate decisions. This transition is dictated by the GATA1-PU.1 network, a classical example of a bistable cell fate system. We show that, for a wide range of cell communication topologies, even subtle changes in signaling can have pronounced effects on cell fate decisions. We go on to show how cell-cell coupling through signaling can spontaneously break the symmetry of a homogenous cell population. Noise, both intrinsic and extrinsic, shapes the decision landscape profoundly, and affects the transcriptional dynamics underlying this important hematopoietic cell fate decision-making system.

systems biology↗

bioRxiv2020

RVAgene: Generative modeling of gene expression time series data

Methods to model dynamic changes in gene expression at a genome-wide level are not currently sufficient for large (temporally rich or single-cell) datasets. Variational autoencoders offer means to characterize large datasets and have been used effectively to characterize features of single-cell datasets. Here we extend these methods for use with gene expression time series data. We present RVAgene: a recurrent variational autoencoder to model gene expression dynamics. RVAgene learns to accurately and efficiently reconstruct temporal gene profiles. It also learns a low dimensional representation of the data via a recurrent encoder network that can be used for biological feature discovery, and can generate new gene expression data by sampling from the latent space. We test RVAgene on simulated and real biological datasets, including embryonic stem cell differentiation and kidney injury response dynamics. In all cases, RVAgene accurately reconstructed complex gene expression temporal profiles. Via cross validation, we show that a low-error latent space representation can be learnt using only a fraction of the data. Through clustering and gene ontology term enrichment analysis on the latent space, we demonstrate the potential of RVAgene for unsupervised discovery. In particular, RVAgene identifies new programs of shared gene regulation of Lox family genes in response to kidney injury.

systems biology↗