Biology subjects

Lahav, G.

Publications and source records attributed to Lahav, G..

11 recordsLinked to original sources

bioRxiv2025

Mechanistic modeling and machine learning identifies optimum radiotherapy schedules to prevent treatment-induced metastasis

Lung cancer patients often experience increased metastasis formation after radiotherapy. However, it is incompletely understood whether radiation affects the migratory behavior of tumor cells and how altered radiotherapy schedules might mitigate this risk. To address these questions, we performed live-cell microscopy experiments to profile changes in cell migration during radiation across 12 cancer cell lines and developed a predictive computational modeling platform describing tumor volume and dissemination during radiotherapy. Using this platform, we identified optimal fractionation schedules and then performed extensive in silico clinical trials, establishing that our optimized schedules substantially reduce metastatic seeding relative to the standard of care schedule. Training transformer models on the in silico clinical trial data enabled us to recover mechanistic parameters with high accuracy, demonstrating that the features determining optimal radiotherapy can be inferred from longitudinal tumor data. Our integrative predictive approach enables the rational design of optimum clinical trials across indications.

bioinformatics↗

bioRxiv2025

DNA damage checkpoints balance a tradeoff between diploid- and polyploid-derived arrest failures

The DNA damage checkpoint system ensures genomic integrity by preventing the division of damaged cells. This system operates primarily through the G1/S and G2/M checkpoints, which are susceptible to failure; how these checkpoints coordinate quantitatively to ensure optimal cellular outcomes remains unclear. In this study, we exposed non-cancerous human cells to exogenous DNA damage and used single-cell imaging to monitor spontaneous arrest failure. We discovered that cells fail to arrest in two major paths, resulting in two types with distinct characteristics, including ploidy, nuclear morphology, and micronuclei composition. Computational simulations and experiments revealed strengthening one checkpoint reduced one mode of arrest failure but increased the other, leading to a critical tradeoff for optimizing total arrest failure rates. Our findings suggest optimal checkpoint strengths for minimizing total error are inherently suboptimal for any single failure type, elucidating the systemic cause of genomic instability and tetraploid-like cells in response to DNA damage HighlightsO_LIArrest-failed cells result from two routes, each leading to a distinct type C_LIO_LIThese types differ in nuclear morphology, size, ploidy and micronuclei composition C_LIO_LIStrengthening one checkpoint reduces one arrest-failure type but increases the other C_LIO_LITotal error is minimized when checkpoint strengths are suboptimal for each type C_LI eTOCSingle-cell quantitative analyses and computational simulations reveal that the DNA damage checkpoint system balances a tradeoff between reducing arrest failure from diploid cells and from damage-induced polyploid cells. The study suggests that to minimize total arrest failure, the checkpoint strengths need to be sub-optimal for each individual arrest-failure type.

cell biology↗

bioRxiv2025

Neural plate pre-patterning enables specification of intermediate neural progenitors in the spinal cord

Spinal cord organization begins with the specification of a series of neural progenitor domains along the dorsal-ventral axis of the neural tube. Patterning at the dorsal and ventral ends is established by canonical morphogen gradients. Intermediate progenitor domains are located distally to morphogen sources yet display similar organization. How this is achieved is unclear and the relevant positional cues have not been identified. Here we show that intermediate progenitor domains are established through a distinct mechanism that operates in the preceding neural plate stage and leverages its multiple signaling inputs and morphogenesis. An integrated spatial atlas of transcription factor expression, signaling activity, and cell movement reveals that the neural plate comprises an array of cell states, defined by combinatorial signaling and morphogenesis-driven transitions between local signaling environments. Specifically, we find that p0 and p1 intermediate progenitors, located immediately outside the morphogen-patterned region, arise from specific precursor states that are established in the posterior neural plate by tailbud-derived Wnt signaling. Convergent-extension movements subsequently drive progenitor specification by displacing precursors out of Wnt/FGF signaling fields while shaping them into stripe-like domains. Thus, by controlling signaling exposure, tissue dynamics convert positional information encoded by signaling history into downstream fate specification. This mode of patterning may operate broadly across developmental contexts, with implications for tissue engineering and patterning-related disorders.

developmental biology↗

bioRxiv2024

Functional consequences of a p53-MDM2-p21 incoherent feedforward loop

Genetically identical tumor cells can respond heterogeneously to therapy, with a subpopulation of cells often entering a temporarily arrested treatment-tolerant state before repopulating the tumor. To investigate how heterogeneity in the cell cycle arrest protein p21 arises, we imaged the dynamics of p21 transcription and protein expression along with those of p53, its transcriptional regulator, in single MCF-7 cells using live cell fluorescence microscopy. Using this approach, we found that the rate of p21 transcription depends on the change in p53 rather than its absolute level. Through combined theoretical and experimental modeling, we determined that p21 transcription is governed by an incoherent feedforward loop mediated by MDM2. This network architecture facilitates rapid induction of p21 expression and variability in p21 transcription. Abrogating the feedforward loop overcomes rapid S-phase p21 degradation, with cells transitioning into a quiescent state that transcriptionally resembles a treatment-tolerant persister state. The potential implications of our findings warrant consideration in the design of therapeutic strategies based on activating p53.

systems biology↗

bioRxiv2023

MSModDetector: A Tool for Detecting Mass Shifts and Post-Translational Modifications in Individual Ion Mass Spectrometry Data

MotivationPost-translational modifications (PTMs) on proteins regulate protein structures and functions. A single protein molecule can possess multiple modification sites that can accommodate various PTM types, leading to a variety of different patterns, or combinations of PTMs, on that protein. Different PTM patterns can give rise to distinct biological functions. To facilitate the study of multiple PTMs, top-down mass spectrometry (MS) has proven to be a useful tool to measure the mass of intact proteins, thereby enabling even widely separated PTMs to be assigned to the same protein molecule and allowing determination of how many PTMs are attached to a single protein. ResultsWe developed a Python module called MSModDetector that studies PTM patterns from individual ion mass spectrometry (I MS) data. I MS is an intact protein mass spectrometry approach that generates true mass spectra without the need to infer charge states. The algorithm first detects and quantifies mass shifts for a protein of interest and subsequently infers potential PTM patterns using linear programming. The algorithm is evaluated on simulated I MS data and experimental I MS data for the tumor suppressor protein p53. We show that MSModDetector is a useful tool for comparing a proteins PTM pattern landscape across different conditions. An improved analysis of PTM patterns will enable a deeper understanding of PTM-regulated cellular processes. AvailabilityThe source code is available at https://github.com/marjanfaizi/MSModDetector together with the scripts used for analyses and to generate the figures presented in this study.

bioinformatics↗

bioRxiv2022

Signal integration by a bHLH circuit enables fate choice in neural stem cells

Stem cells integrate information from multiple signals in their environment to make fate decisions. It is unclear how signal integration is linked to the coordinated activation of a target fate program and simultaneous inactivation of competing fates. Here, we investigated this question in mouse neural stem cells, which differentiate synergistically into astrocytes in response to combined treatment with Bone Morphogenetic Protein (BMP) and Leukemia Inhibitory Factor (LIF) at the expense of alternative neuronal or oligodendrocyte fates. Analysis of the expression dynamics of Glial Fibrillary Acidic Protein (GFAP), an early astrocyte marker, showed that its synergistic activation in BMP and LIF reflects early activation by LIF which is sustained by a delayed contribution to its expression from BMP. In parallel, multiplexed RNA-FISH analysis of 14 basic helix-loop-helix (bHLH) transcription factors, known to regulate alternative fates, showed that LIF and BMP individually control different subsets of bHLHs, but together suppress all bHLHs known to promote alternative fates. Ectopic expression experiments showed that these bHLHs also inhibit GFAP induction, suggesting that suppression of alternative fates by BMP + LIF simultaneously relieves GFAP inhibition. In particular, BMP primarily affected inhibitory bHLHs indirectly, through induction of Id factors, explaining why it has a delayed contribution to GFAP transcription compared to LIF. These results show that a circuit of bHLH factors enables both synergistic astrocytic differentiation and suppression of alternative fates in NSCs. Signal integration by bHLH circuits for fate choice could be broadly relevant, given the widespread utilization of these and other bHLH factors across diverse developmental contexts.

systems biology↗

bioRxiv2022

Multi range ERK responses shape the proliferative trajectory of single cells following oncogene induced senescence

Oncogene-induced senescence (OIS) is a phenomenon in which aberrant oncogene expression causes non-transformed cells to enter a non-proliferative state. Cells undergoing OIS display phenotypic heterogeneity, with some cells senescing and others remaining proliferative. The causes of the heterogeneity remain poorly understood. We studied the sources of heterogeneity in the responses of human epithelial cells to oncogenic BRAFV600E expression. We found that a narrow expression range of BRAFV600E generated a wide range of activities of its downstream effector ERK. In population-level and single cell assays, ERK activity displayed a non-monotonic relationship to proliferation, with intermediate ERK activities leading to maximal proliferation. We profiled gene expression across a range of ERK activities over time and characterized four distinct ERK response classes, which we propose act in concert to generate the unique ERK-proliferation response. Altogether, our studies mapped the input-output relationships between ERK activity and proliferation providing important insights into how heterogeneity can be generated during OIS.

systems biology↗

bioRxiv2020

Cycling cancer persister cells arise from lineages with distinct transcriptional and metabolic programs

Non-genetic mechanisms have recently emerged as important drivers of therapy failure in cancer (Salgia and Kulkarni, 2018), where some cancer cells can enter a reversible drug-tolerant persister state in response to treatment (Vallette et al., 2019). While most cancer persisters, like their bacterial counterparts, remain arrested in the presence of drug, a rare subset of cancer persisters can re-enter the cell cycle under constitutive drug treatment (Sharma et al., 2010). Little is known about the non-genetic mechanisms that enable cancer persisters to maintain proliferative capacity in the presence of drug. Here, using time-lapse imaging, we found that cycling persisters emerge early in the course of treatment of EGFR-mutant lung cancer cells with the EGFR inhibitor osimertinib. To study this rare, transiently-resistant, proliferative persister population we developed Watermelon, a new high-complexity expressed barcode lentiviral library for simultaneous tracing each cells clonal origin, proliferative state, and transcriptional state. Analysis of Watermelon-transduced PC9 cells demonstrated that cycling and non-cycling persisters arise from different pre-existing cell lineages with distinct transcriptional and metabolic programs. The proliferative capacity of persisters is associated with an upregulation of antioxidant gene programs and a metabolic shift to fatty acid oxidation in specific subpopulations of tumor cells. Mitigating oxidative stress or blocking metabolic reprograming significantly alters the fraction of cycling persister cells. In human tumors, programs associated with cycling persisters were induced in malignant cells in response to multiple tyrosine kinase inhibitors. The Watermelon system enabled the identification of rare persister lineages, that are preferentially poised through specific gene programs to proliferate under drug pressure, thus exposing new vulnerabilities that can be targeted to delay or even prevent disease recurrence.

cancer biology↗

bioRxiv2018

Analysing protein post-translational modform regions by linear programming

Post-translational modifications (PTMs) at multiple sites can collectively influence protein function but the scope of such PTM coding has been challenging to determine. The number of potential combinatorial patterns of PTMs on a single molecule increases exponentially with the number of modification sites and a population of molecules exhibits a distribution of such \"modforms\". Estimating these \"modform distributions\" is central to understanding how PTMs influence protein function. Although mass-spectrometry (MS) has made modforms more accessible, we have previously shown that current MS technology cannot recover the modform distribution of heavily modified proteins. However, MS data yield linear equations for modform amounts, which constrain the distribution within a high-dimensional, polyhedral \"modform region\". Here, we show that linear programming (LP) can efficiently determine a range within which each modform value must lie, thereby approximating the modform region. We use this method on simulated data for mitogen-activated protein kinase 1 with the 7 phosphorylations reported on UniProt, giving a modform region in a 128 dimensional space. The exact dimension of the region is determined by the number of linearly independent equations but its size and shape depend on the data. The average modform range, which is a measure of size, reduces when data from bottom-up (BU) MS, in which proteins are first digested into peptides, is combined with data from top-down (TD) MS, in which whole proteins are analysed. Furthermore, when the modform distribution is structured, as might be expected of real distributions, the modform region for BU and TD combined has a more intricate polyhedral shape and is substantially more constrained than that of a random distribution. These results give the first insights into high-dimensional modform regions and confirm that fast LP methods can be used to analyse them. We discuss the problems of using modform regions with real data, when the actual modform distribution will not be known.

systems biology↗

bioRxiv2018

A multi-modal proteomics strategy for characterizing posttranslational modifications of tumor suppressor p53 reveals many sites but few modified forms

Post-translational modifications (PTMs) are found on most proteins, particularly on \"hub\" proteins like the tumor suppressor p53, which has over 100 possible PTM sites. Substantial crosstalk between PTM sites underlies the ability of such proteins to integrate diverse signals and coordinate downstream responses. However, disentangling the combinatorial explosion in global PTM patterns across an entire protein (\"modforms\") has been challenging, as conventional peptide-based mass spectrometry strategies (so-called \"bottom-up\" MS) destroy such global correlations. Alternatively, direct analysis of intact and modified proteins using \"top-down\" MS retains global information. Here, we applied both strategies to recombinant p53 phosphorylated in vitro with Chk1 kinase, which exhibited 41 modified sites by bottom-up MS, but no more than 8 modified sites per molecule detected by top-down MS. This observation that many low-abundance modifications comprise relatively few modforms above a 1% threshold indicates that endogenous p53 PTM complexity may be more definable than previously thought.

systems biology↗

bioRxiv2017

Stereotyped p53 binding tuned by chromatin accessibility

The tumor suppressor p53 is a major regulator of the DNA damage response and has been suggested to selectively bind and activate cell type specific gene expression programs, however recent studies and meta-analyses of genomic data propose largely uniform, and condition independent, p53 binding. To systematically assess the cell type specificity of p53, we measured its association with DNA in 12 p53 wild-type cell lines, from a range of epithelial linages, in response to ionizing radiation. We found that the majority of bound sites were occupied across all cell lines, however we also identified a subset of binding sites that were specific to one or a few cell lines. Unlike the shared p53-bound genome, which was not dependent on chromatin accessibility, the association of p53 with these atypical binding sites was well explained by chromatin accessibility and could be modulated by forcing cell state changes such as the epithelial-to-mesenchymal transition. These results position p53 as having both universal and cell type specific regulatory programs that have different regulators and dependence on chromatin state.

genomics↗