Biology subjects

Roberts, I.

Publications and source records attributed to Roberts, I..

At least 19 recordsLinked to original sources

bioRxiv2026

Heritable single-cell gene expression states shape functional variability in innate immune responses

Activation of innate immunity at the single-cell level is inherently heterogeneous, yet the mechanisms underlying this variability remain incompletely understood. Here, we integrate transcriptomics, high-content imaging and mathematical modelling to quantify transcriptional heritability within the evolutionarily conserved Toll-like receptor (TLR) system. RNA-seq-based fluctuation tests identified a subset of TLR4-dependent genes, including cytokines and immune effectors, that retain transcriptional heritability for more than 25 cell divisions in clonal macrophage populations. High-content microscopy confirmed gene-specific propagation of heritable states and revealed that environmental context shapes their persistence and expression. CD36, a scavenger receptor involved in bacterial recognition and lipid uptake, exhibited a stable, cell density-reinforced heritable state, whereas the inflammatory programme exemplified by IL1{beta} was transient, with heritability decaying upon clonal expansion. The interplay between heritable transcriptional states and population context generated emergent spatial organisation in high-density populations, with CD36-high cells forming discrete pockets and IL1{beta}-high cells enriched in surrounding regions. Functionally, CD36 expression determined clonal susceptibility to Listeria monocytogenes infection, linking transcriptional heritability to heterogeneous infection outcomes. Together, these findings identify transcriptional heritability as a key determinant of innate immune heterogeneity and demonstrate how heritable cellular states interact with population context to generate complex immune behaviours. Key point summaryO_LIMemorySeq and scRNA-seq identify long-term heritable gene expression states within TLR4-induced macrophage populations. C_LIO_LIHigh-content imaging across thousands of clonal populations reveals gene-specific dynamics of heritable states at the protein level. C_LIO_LIIl1{beta} and Cd36 define mutually exclusive heritable states that are differently regulated by population context and drive spatial organisation in cellular monolayers. C_LIO_LIHeritable CD36 protein expression shapes heterogeneous outcomes during Listeria monocytogenes infection. C_LI

immunology↗

bioRxiv2026

Self-supervised learning for a gene program-centric view of cell states

Single-cell omics has extended the biological interrogation of cell state from examining the expression of individual genes to unbiased profiling of tens of thousands of genes at once. However, extracting biological insights from such high-dimensional data remains challenging. To enable downstream analyses, many computational approaches compress cell state into a single latent representation. This can obscure the structure of underlying gene programs (GP), defined as coordinated sets of biologically related genes, such as signalling pathway response modules or transcription factor targets. Here, we present Tripso, a self-supervised transformer deep learning model which learns multiple GP-specific embeddings from predefined GPs, while also enabling the discovery of novel, data-driven GPs. Tripso facilitates principled comparisons across development, disease, and experimental systems. Firstly, in a dataset of human hematopoietic cells spanning prenatal development through adulthood and aging and including newly generated data, Tripso resolved age-specific GP patterns, including elevated JAK-STAT activity in pediatric hematopoietic cells and postnatal shifts in IKZF1 GP activity during B cell differentiation. Secondly, leveraging Tripso GP embeddings and comparing in vivo to in vitro data, we hypothesized and experimentally validated that inhibition of the SEC61 translocon improved maintenance of hematopoietic stem cells in culture. Finally, Tripsos capacity for data-driven GP discovery revealed a previously uncharacterized tissue-resident memory T cell GP with increased activity in atopic dermatitis. Its spatial co-localization with sebaceous gland-associated immune niches was demonstrated in spatial transcriptomic and proteomic data. Thus, by moving beyond single embeddings of cellular states, Tripso enables interpretable and actionable discoveries, demonstrating how GP-centric modelling can generate hypotheses with substantial biomedical relevance. By anchoring cellular representations in meaningful GPs, Tripso establishes a principled and biologically grounded framework towards the development of interpretable virtual cell models.

bioinformatics↗

bioRxiv2026

Predicting how perturbations reshape cellular trajectories with PerturbGen

A major challenge in biology is predicting how cells transition between states over time and how perturbations disrupt these transitions. Understanding such dynamics is critical for identifying interventions that reverse pathological programs or reprogram cells toward desired states. Although recent computational approaches can predict single-cell perturbation responses in silico, they cannot predict responses across dynamic cell trajectories, for example how early perturbations reconfigure later cell states. To address this gap, we introduce PerturbGen, a generative foundation model trained on over 100 million single-cell transcriptomes that predicts perturbation responses along cellular trajectories. PerturbGen predicts how genetic perturbation at source state shapes downstream states, alters gene programs and trajectories across time, for example in differentiation or disease progression. We apply PerturbGen to three newly generated multi-condition human single-cell datasets spanning immune responses, hematopoiesis and skin development. In an in vivo immune challenge, PerturbGen predicts that knocking out an IL1B signal in myeloid cells attenuates later cytokine-interferon programs, with downstream changes consistent with a reversal of IL-1{beta} stimulation signature. In hematopoiesis, anchoring perturbation-induced programs to human genetics enables simulation of monogenic blood disorders, recapitulates established disease-associated biology whilst systematically revealing lineage-specific programs, including in lineages where this was not previously possible. In skin organoids, PerturbGen predicted that Wnt activation enhances stromal differentiation recapitulating the trajectory observed in human prenatal skin, findings that were functionally validated by experimentally activating Wnt signaling. Together, PerturbGen extends modeling of gene perturbations from static to dynamic cellular systems. We envision PerturbGen enabling the creation of in silico, trajectory-aware perturbation atlases and virtual cells across diverse biological scenarios, supporting optimization of disease models and prioritization of candidate molecular interventions for therapeutic discovery.

bioinformatics↗

bioRxiv2025

Repurposing statins and phenothiazines to treat chemoresistant neuroblastoma.

Relapse and treatment resistance are common in children with high-risk neuroblastoma, and novel therapies are needed. Conventional drug discovery is slow, expensive, often fails in practice, and consequently falls short in addressing pediatric and rare conditions. In such instances, drug repurposing is a promising strategy. Here, we used two independent in silico prediction tools including machine learning to identify approved drugs for repurposing against neuroblastoma. The combination of statins and phenothiazines showed strong synergistic effects in human neuroblastoma organoids, decreased tumor growth and prolonged survival in MYCN-amplified neuroblastoma patient-derived xenografts. The drug combination altered cholesterol metabolism through a dual-hit mechanism and induced a phenotypic switch towards an adrenergic cell state accompanied by increased sensitivity to chemotherapy. Integration of the drug combination into standard-of-care chemotherapy regressed tumors and prolonged survival in chemoresistant patient-derived xenografts. Thus, a combination of safe and approved medications added to standard-of-care chemotherapy outperforms chemotherapy alone in chemoresistant neuroblastoma. TeaserUsing in silico prediction tools we repurposed statins and phenothiazines as a combination with translational potential in high-risk neuroblastoma.

cancer biology↗

bioRxiv2025

AC electro-osmosis in bacterial biofilms: a cautionary tale for electrophysiology experiments

Synthetic cationic fluorophores are used widely as probes to measure the membrane potentials of bacterial cells, eukaryotic cells and organelles, such as mitochondria. An external oscillating electric field was applied to Escherichia coli cells using microelectrodes and AC electro-osmosis was observed for the fluorophores, independent of the electrophysiology of the bacteria, giving rise to phantom action potentials. The fluorophores migrate around the microfluidic device in vortices modulating their concentration having decreases or dips in fluorescence. We show that the fluorescent dips are universally present when using cationic fluorophores, such as thioflavin-T, propidium iodide, Syto9 and Sytox Green, with or without E. coli cells in the inoculum, when stimulated with AC voltages. This is in contrast to the study of Stratford et al (PNAS, 2019) who claim the existence of action potentials. Furthermore, E. coli biofilms also demonstrated similar phenomena with dips in the fluorescence. We measured the relaxation times of the fluorophores experiencing AC electro-osmosis, which depended on the biofilm, the cells and the fluorophores used. PI had the smallest relaxation time and Syto9 the highest. Removing the cells resulted in longer relaxation times and introducing biofilm did not significantly change the relaxation times compared with the single cell experiments. Furthermore, fluorescently labelled DNA and fluorescent colloidal beads also demonstrate fluorescent dips through AC electro-osmosis, showing that these particles can be driven through biofilms. This is the first study of AC electro-osmosis in bacterial biofilms, indicating a surprisingly high mobility of charged molecules within the extracellular polymeric substance, which could be used to treat biofilms i.e. to increase the kinetics of delivery of antibiotics.

biophysics↗

bioRxiv2024

Enhancer heterogeneity in acute lymphoblastic leukemia drives differential gene expression between patients

Genetic alterations alone cannot account for the diverse phenotypes of cancer cells. Even cancers with the same driver mutation show significant transcriptional heterogeneity and varied responses to therapy. However, the mechanisms underpinning this heterogeneity remain under-explored. Here, we find that novel enhancer usage is a common feature in acute lymphoblastic leukemia (ALL). In particular, KMT2A::AFF1 ALL, an aggressive leukemia with a poor prognosis and a low mutational burden, exhibits substantial transcriptional heterogeneity between individuals. Using single cell multiome analysis and extensive chromatin profiling, we reveal that much transcriptional heterogeneity in KMT2A::AFF1 ALL is driven by novel enhancer usage. Using high resolution Micro-Capture-C in primary patient samples, we also identify patient-specific enhancer activity at key oncogenes such as MEIS1 and RUNX2, driving high levels of expression of both oncogenes in a patient-specific manner. Overall, our data show that enhancer heterogeneity is highly prevalent in KMT2A::AFF1 ALL and may also be a mechanism that drives transcriptional heterogeneity in cancer more generally. Key PointsO_LILeukemia patients with the same driver mutations often display gene expression differences C_LIO_LIUsing chromatin profiling and high resolution 3C methods we show that enhancer heterogeneity drives gene expression differences C_LI

cancer biology↗

bioRxiv2024

Bayesian Inference of Pathogen Phylogeography using the Structured Coalescent Model

Over the past decade, pathogen genome sequencing has become well established as a powerful approach to study infectious disease epidemiology. In particular, when multiple genomes are available from several geographical locations, comparing them is informative about the relative size of the local pathogen populations as well as past migration rates and events between locations. The structured coalescent model has a long history of being used as the underlying process for such phylogeographic analysis. However, the computational cost of using this model does not scale well to the large number of genomes frequently analysed in pathogen genomic epidemiology studies. Several approximations of the structured coalescent model have been proposed, but their effects are difficult to predict. Here we show how the exact structured coalescent model can be used to analyse a precomputed dated phylogeny, in order to perform Bayesian inference on the past migration history, the effective population sizes in each location, and the directed migration rates from any location to another. We describe an efficient reversible jump Markov Chain Monte Carlo scheme which is implemented in a new R package. We use simulations to demonstrate the scalability and correctness of our method and to compare it with existing comparable software. We also applied our new method to several state-of-the-art datasets on the population structure of real pathogens to showcase the relevance of our method to current data scales and research questions.

evolutionary biology↗

bioRxiv2024

The fetal specific gene LIN28B is essential for human fetal B-lymphopoiesis and initiation of KMT2A::AFF1 infant leukemia

Infant ALL (iALL) is initiated in utero, most often by rearrangement of the KMT2A gene (KMT2Ar). It carries a very poor prognosis despite a lack of additional oncogenic driver mutations common in childhood ALL. Here, we aimed to identify specific properties of human fetal hematopoietic stem/progenitor cells (HSPC) that promote leukemic transformation in KMT2Ar iALL using molecular, functional and in vivo assays. First, by comparing transcriptomes of human fetal HSPC to adult HSPC we derived a fetal-specific gene signature and identified the fetal oncogene LIN28B and its downstream effectors among the top hits. These genes were also expressed in iALL. Functional assays revealed that LIN28B was essential in human fetal liver (FL) CD34+ cells to maintain proliferation and stemness, and support B- and NK-lymphopoiesis. To interrogate the role of LIN28B in iALL, we utilised a human FL-derived CRISPR-Cas9 KMT2A::AFF1 model. In this model, LIN28B-expressing leukemias were more proliferative in vitro and in vivo, with this advantage being lost upon LIN28B knockdown. Mechanistic studies showed that LIN28B acts by stabilizing key early B-lymphoid genes, epigenetic regulators, and cell cycle and anti-apoptotic genes. Finally, In the absence of LIN28B, human FL CD34+ cells fail to transform upon induction of KMT2A::AFF1 translocation. Thus, LIN28B has an essential role in normal human fetal B-lymphopoiesis, and is necessary for the initiation of KMT2A::AFF1 iALL in fetal cells in the absence of co-operating mutations. It has a role in making leukemias more aggressive, suggesting it is a potential target in LIN28B-expressing leukemias.

cancer biology↗

bioRxiv2023

Electrical impedance spectroscopy with bacterial biofilms: neuronal-like behaviour

Negative capacitance at low frequencies for neurons was first demonstrated in 1941 (Kenneth S. Cole) using extracellular electrodes. The phenomenon subsequently was explained by Cole using the Hodgkin-Huxley model and is due to the activity of voltage-gated potassium ion channels. We show that E. coli biofilms exhibit significant stable negative capacitances at low frequencies when they experience a small DC bias voltage in electrical impedance spectroscopy experiments. Using a frequency domain Hodgkin-Huxley model, we characterize the conditions for the emergence of this feature and demonstrate that the negative capacitance exists only in biofilms containing living cells. Furthermore, we established the importance of the voltage-gated potassium ion channel, Kch, using knock-down mutants. The experiments provide further evidence for voltage-gated ion channels in E. coli and a new, low-cost method to probe biofilm electrophysiology e.g. to understand the efficacy of antibiotics. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=67 SRC="FIGDIR/small/568527v1_ufig1.gif" ALT="Figure 1"> View larger version (11K): org.highwire.dtl.DTLVardef@e3df05org.highwire.dtl.DTLVardef@46c5d2org.highwire.dtl.DTLVardef@6572a6org.highwire.dtl.DTLVardef@4a104a_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2023

Electrical Signalling in Three Dimensional Bacterial Biofilms Using an Agent Based Fire-Diffuse-Fire Model

Agent based models were used to describe electrical signalling in bacterial biofilms in three dimensions. Specifically, wavefronts of potassium ions in E. coli biofilms subjected to stress from blue light were modelled from experimental data. Electrical signalling only occurs when the biofilms grow beyond a threshold size, which we have shown to vary with the K+ ion diffusivity and the K+ ion threshold concentration which triggered firing in the fire-diffuse-fire model. The transport of the propagating wavefronts shows super-diffusive scaling on time. K+ ion diffusivity is the main factor that affects the wavefront velocity. The K+ ion diffusivity and the firing threshold also affect the anomalous exponent for the propagation of the wavefront determining whether the wavefront is sub-diffusive or super-diffusive. The geometry of the biofilm and its relation to the mean square displacement (MSD) of the wavefront as a function of time was investigated for spherical, cylindrical, cubical and mushroom-like structures. The MSD varied significantly with geometry; an additional regime to the kinetics occurred when the potassium wavefront leaves the biofilm. Adding cylindrical defects to the biofilm, which are known to occur in E. coli biofilms, the wavefront MSD also had an extra kinetic regime for the propagation through the defect.

biophysics↗

bioRxiv2022

Recombinant expression and characterisation of a lipase from the Antarctic zooplankton Salpa thompsoni

Cold marine environments are abundant on earth and represent a rich resource for low temperature enzymes. Here we apply in silico bioprospecting methods followed by in vitro expression and biochemical analyses to characterise a novel low temperature lipase from the Antarctic tunicate Salpa thompsoni. A 586 amino acid pancreatic lipase-like gene was identified from S. thompsoni transcriptomic data, expressed as a hexahistadine fusion protein in Escherichia coli at 10{degrees}C and purified by affinity chromatography. Hydrolysis of the synthetic substrate {rho}-nitrophenyl butyrate (PNPB) showed that this recombinant protein has optimal activity at 20 {degrees}C and pH 7, and a specific activity of 3.16 U/mg under this condition. Over 60% of enzyme activity was maintained between 15 to 25 {degrees}C, with a sharp decrease outside this range. These results are indicative of cold active psychrophilic enzyme activity. A meta-analysis of lipase activities towards PNPB showed that the novel S. thompsoni lipase displays a higher activity at lower temperatures relative to previously characterised enzymes. The work demonstrates a methodology for conversion of transcriptomic to in vitro expression data for the discovery of new cold-active biocatalysts from marine organisms.

synthetic biology↗

bioRxiv2022

Single-cell imaging reveals non-cooperative and cooperative infection strategies of Listeria monocytogenes in macrophages

Pathogens have developed intricate strategies to overcome the hosts innate immune responses. In this paper we use live-cell microscopy with a single bacterium resolution to follow in real time interactions between the food-borne pathogen L. monocytogenes and host macrophages, a key event controlling the infection in vivo. We demonstrate that infection results in heterogeneous outcomes, with only a subset of bacteria able to establish a replicative invasion of macrophages. The fate of individual bacteria in the same host cell was independent from each other and non-cooperative, but a higher multiplicity of infection resulted in a reduced probability of replication. Using internalisation assays and conditional probabilities to mathematically describe the multi-stage invasion process, we demonstrate that the secreted Listeriolysin toxin (LLO) of the PrfA regulon regulates replication probability by compromising the ability to phagocytose bacteria. Using strains expressing fluorescent reporters to follow transcription of either the LLO-encoding hly or actA genes, we show that replicative bacteria exhibited higher PrfA regulon expression in comparison to those bacteria that did not replicate, however elevated PrfA expression per se was not sufficient to increase the probability of replication. Overall, this demonstrates a new role for the population-level, but not single cell PrfA-mediated cooperativity to regulate outcomes of host pathogen interactions. Key pointsO_LIL. monocytogenes invasion of innate immune macrophages results in heterogeneous infection outcomes at the single cell level C_LIO_LIFate of individual bacteria in the same host cell is independent from each other and non-cooperative C_LIO_LIBacterial populations coordinate host cell uptake via the rate of phagocytosis to reduce internalization at high MOI C_LIO_LIThe PrfA regulon system is necessary but not sufficient for L. monocytogenes replication, but population-level PrfA virulence regulates single cell outcome probability C_LI

microbiology↗

bioRxiv2022

PAF1 and FACT cooperate with MLL-AF4 to drive enhancer activity in leukemia

Aberrant enhancer activation has been identified as a key mechanism driving oncogene expression in many cancers. Here we use TOPmentation (Transcription factor-OPtimized ChIPmentation) to probe enhancer usage in primary MLL-rearranged acute lymphoblastic leukemia. We find that MLL-AF4, commonly held to promote transcription by binding to gene promoters, is also present at many active enhancers, where it assembles a complex of transcriptional co-activators normally found in the gene body. This includes DOT1L, ENL, PAF1, and a newly identified interaction with the histone chaperone FACT. By chemical degradation, we demonstrate that PAF1 and FACT are required for enhancer activity, including maintaining histone H3K27 acetylation, enhancer RNA transcription and enhancer-promoter interactions. This work identifies novel roles for PAF1 and FACT in enhancer function, and reveals an enhancer-targeting mechanism by which MLL-AF4 upregulates transcription, recruiting transcription machinery through a network of multivalent interactions to control enhancer activity and gene expression in acute leukemias.

molecular biology↗

bioRxiv2021

MassGenie: a transformer-based deep learning method for identifying small molecules from their mass spectra

The inverse problem of mass spectrometric molecular identification ( given a mass spectrum, calculate the molecule whence it came) is largely unsolved, and is especially acute in metabolomics where many small molecules remain unidentified. This is largely because the number of experimentally available electrospray mass spectra of small molecules is quite limited. However, the forward problem ( calculate a small molecules likely fragmentation and hence at least some of its mass spectrum from its structure alone) is much more tractable, because the strengths of different chemical bonds are roughly known. This kind of molecular identification problem may be cast as a language translation problem in which the source language is a list of high-resolution mass spectral peaks and the translation a representation (for instance in SMILES) of the molecule. It is thus suitable for attack using the deep neural networks known as transformers. We here present MassGenie, a method that uses a transformer-based deep neural network, trained on ~6 million chemical structures with augmented SMILES encoding and their paired molecular fragments as generated in silico, explicitly including the protonated molecular ion. This architecture (containing some 400 million elements) is used to predict the structure of a molecule from the various fragments that may be expected to be observed when some of its bonds are broken. Despite being given essentially no detailed nor explicit rules about molecular fragmentation methods, isotope patterns, rearrangements, neutral losses, and the like, MassGenie learns the effective properties of the mass spectral fragment and valency space, and can generate candidate molecular structures that are very close or identical to those of the true molecules. We also use VAE-Sim, a previously published variational autoencoder, to generate candidate molecules that are similar to the top hit. In addition to using the top hits directly, we can produce a rank order of these by round-tripping candidate molecules and comparing them with the true molecules, where known. As a proof of principle, we confine ourselves to positive electrospray mass spectra from molecules with a molecular mass of 500Da or lower. The transformer method, applied here for the first time to mass spectral interpretation, works extremely effectively both for mass spectra generated in silico and on experimentally obtained mass spectra from pure compounds. The ability to create and to learn millions of fragmentation patterns in silico, and therefrom generate candidate structures (that do not have to be in existing libraries) directly, thus opens up entirely the field of de novo small molecule structure prediction from experimental mass spectra.

bioinformatics↗

bioRxiv2021

Spatial transcriptomic characterization of COVID-19 pneumonitis identifies immune pathways related to tissue injury

Severe lung damage in COVID-19 involves complex interactions between diverse populations of immune and stromal cells. In this study, we used a spatial transcriptomics approach to delineate the cells, pathways and genes present across the spectrum of histopathological damage in COVID-19 lung tissue. We applied correlation network-based approaches to deconvolve gene expression data from areas of interest within well preserved post-mortem lung samples from three patients. Despite substantial inter-patient heterogeneity we discovered evidence for a common immune cell signaling circuit in areas of severe tissue that involves crosstalk between cytotoxic lymphocytes and pro-inflammatory macrophages. Expression of IFNG by cytotoxic lymphocytes was associated with induction of chemokines including CXCL9, CXCL10 and CXCL11 which are known to promote the recruitment of CXCR3+ immune cells. The tumour necrosis factor (TNF) superfamily members BAFF (TNFSF13B) and TRAIL (TNFSF10) were found to be consistently upregulated in the areas with severe tissue damage. We used published spatial and single cell SARS-CoV-2 datasets to confirm our findings in the lung tissue from additional cohorts of COVID-19 patients. The resulting model of severe COVID-19 immune-mediated tissue pathology may inform future therapeutic strategies. One Sentence SummarySpatial analysis identifies IFN{gamma} response signatures as focal to severe alveolar damage in COVID-19 pneumonitis.

immunology↗

bioRxiv2021

Developmental stage- and site-specific transitions in lineage specification and gene regulatory networks in human hematopoietic stem and progenitor cells

Human hematopoiesis is a dynamic process that starts in utero 4 weeks post-conception. Understanding the site- and stage-specific variation in hematopoiesis is important if we are to understand the origin of hematological disorders, many of which occur at specific points in the human lifespan. To unravel how the hematopoietic stem/progenitor cell (HSPC) compartments change during human ontogeny and the underlying gene regulatory mechanisms, we compared 57,489 HSPCs from 5 different tissues spanning 4 developmental stages through the human lifetime. Single-cell transcriptomic analysis identified significant site- and developmental stage-specific transitions in cellular architecture and gene regulatory networks. Uncommitted stem cells showed progression from cycling to quiescence and increased inflammatory signalling during ontogeny. We demonstrate the utility of this dataset for understanding aberrant hematopoiesis through comparison to two cancers that present at distinct timepoints in postnatal life - juvenile myelomonocytic leukemia, a childhood cancer, and myelofibrosis, which classically presents in older adults.

developmental biology↗

bioRxiv2020

A novel human fetal liver-derived model reveals that MLL-AF4 drives a distinct fetal gene expression program in infant ALL

Although 90% of children with acute lymphoblastic leukemia (ALL) are now cured1, the prognosis of infant-ALL (diagnosis within the first year of life) remains dismal2. Infant-ALL is usually caused by a single genetic hit that arises in utero: rearrangement of the MLL/KMT2A gene (MLL-r). This is sufficient to give rise to a uniquely aggressive and treatment-refractory leukemia compared to older children with the same MLL-r3-5. The reasons for disparate outcomes in patients of different ages with identical driver mutations are unknown. This paper addresses the hypothesis that fetal-specific gene expression programs co-operate with MLL-AF4 to initiate and maintain infant-ALL. Using direct comparison of fetal and adult HSC and progenitor transcriptomes we identify fetal-specific gene expression programs in primary human cells. We show that MLL-AF4-driven infant-ALL, but not MLL-AF4 childhood-ALL, displays expression of fetal-specific genes. In a direct test of this observation, we find that CRISPR-Cas9 gene editing of primary human fetal liver cells to produce a t(4;11)/MLL-AF4 translocation replicates the clinical features of infant-ALL and drives infant-ALL-specific and fetal-specific gene expression programs. These data strongly support the hypothesis that fetal-specific gene expression programs co-operate with MLL-AF4 to initiate and maintain the distinct biology of infant-ALL.

cancer biology↗

bioRxiv2020

Phenotypic analysis of an MLL-AF4 gene regulatory network reveals indirect CASP9 repression as a mode of inducing apoptosis resistance.

Regulatory interactions mediated by transcription factors (TFs) make up complex networks that control cellular behavior. Fully understanding these gene regulatory networks (GRNs) offers greater insight into the consequences of disease-causing perturbations than studying single TF binding events in isolation. Chromosomal translocations of the Mixed Lineage Leukemia gene (MLL) produce MLL fusion proteins such as MLL-AF4, causing poor prognosis acute lymphoblastic leukemias (ALLs). MLL-AF4 is thought to drive leukemogenesis by directly binding to genes and inducing aberrant overexpression of key gene targets, including anti-apoptotic factors such as BCL-2. However, this model minimizes the potential for circuit generated regulatory outputs, including gene repression. To better understand the MLL-AF4 driven regulatory landscape, we integrated ChIP-seq, patient RNA-seq and CRISPR essentiality screens to generate a model GRN. This GRN identified several key transcription factors, including RUNX1, that regulate target genes using feed-forward loop and cascade motifs. We used CRISPR screening in the presence of the BCL-2 inhibitor venetoclax to identify functional impacts on apoptosis. This identified an MLL-AF4:RUNX1 cascade that represses CASP9, perturbation of which disrupts venetoclax induced apoptosis. This illustrates how our GRN can be used to better understand potential mechanisms of drug resistance acquisition. Graphical abstract caption O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=149 SRC="FIGDIR/small/179796v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@cca8f2org.highwire.dtl.DTLVardef@67b289org.highwire.dtl.DTLVardef@1fa44aeorg.highwire.dtl.DTLVardef@1b32487_HPS_FORMAT_FIGEXP M_FIG A network model of the MLL-AF4 regulatory landscape identifies feed-forward loop and cascade motifs. Functional screening using CRISPR and venetoclax identified an MLL-AF4:RUNX1:CASP9 repressive cascade that impairs drug-induced cell death. C_FIG

genomics↗