Biology subjects

Hormoz, S.

Publications and source records attributed to Hormoz, S..

15 recordsLinked to original sources

bioRxiv2025

Cooperative short- and long-range interactions enable robust symmetry breaking and axis formation

Establishing the anterior-posterior (A-P) axis is a key symmetry-breaking event in mammalian development. Gastruloids, aggregates of embryonic stem cells, undergo a similar process, forming a single posterior pole from an initially spherical aggregate. This requires the aggregate to convert an inside-outside difference in cell state into one A-P axis. We asked what cell-cell interactions can make this transition robust. We built an agent-based coarse-grained model with an outer cell population and an inner cell population. Cells interacted through short-range adhesion and, in some simulations, through an effective long-range attraction. Adhesion alone rarely produced a single stable axis, often leaving weakly separated or unstable clusters. Adding long-range attraction between outer cells changed this behavior. The outer cells collected into one pole, the inner cells remained in the opposite domain, and most cells stayed in one aggregate. This occurred across a broad range of adhesion strengths. The model also made a simple prediction. If two polarized gastruloids merge in opposite orientations, the two posterior poles should move around the fused aggregate and join into one pole. Human gastruloid merging experiments showed this behavior. Same-orientation pairs fused rapidly. Opposite-orientation pairs fused more slowly as their posterior poles moved around the aggregate and converged. Finally, we built a minimal gene-regulatory model that generated inner and outer states and turned on the same mechanical interactions. In this model, an initially uniform cell population formed one polarized axis. Together, these results suggest that short-range adhesion and long-range attraction can work together to convert radial patterning into one body axis. They also suggest a design principle for synthetic developmental biology: adhesion can sort cells locally, but building one reproducible axis may require interactions that act beyond direct cell-cell contact.

systems biology↗

bioRxiv2025

Discovery of A Small Molecule non-IMiD Degrader of ZBTB7A for the Treatment of β-hemoglobinopathies

Sickle cell disease and {beta}-thalassemia, two major {beta}-hemoglobinopathies, pose significant clinical challenges globally. Current treatments often face limitations in efficacy and tolerability. The transcription factor ZBTB7A has emerged as a promising therapeutic target for reactivating fetal hemoglobin expression. Here, we report the discovery and characterization of SH6, a small molecule non-IMiD degrader of ZBTB7A. SH6 induces fetal hemoglobin in erythroid cell lines in a CRBN and ZBTB7A-dependent manner, and it is capable of inducing fetal hemoglobin expression in healthy donor, SCD and {beta}-thalassemia patient CD34+ cell derived erythroid cells. The efficacy of SH6 is confirmed in a xenotransplantation humanized mouse model. SH6 outperforms currently available therapeutic agents in vitro, and shows synergy with hypomethylating agents. SH6 exhibits a favorable in vivo toxicity profile. Our findings establish SH6 as a promising therapeutic lead candidate for further optimization towards clinical development for treatment of sickle cell disease and {beta}-thalassemia.

cell biology↗

bioRxiv2025

Robustness of Selection and Timing Inference under Model Variation in Population Genetics

1In population genetics, accurately inferring the selection coefficient and the time of onset of advantageous mutations from genetic data is fundamental for understanding evolutionary processes. Here, we investigate how mismatches between the true evolutionary process and the inference model--specifically in the reproductive variance ({sigma}2) and the number of generations (L)--affect the posterior distributions of the selection coefficient and the time of onset. Using the Kolmogorov forward and backward equations, we model the stochastic dynamics of gene frequencies under selection and drift. We show that while the posterior distribution of the selection coefficient remains unaffected by changes in{sigma} 2 and L, this invariance does not apply to the time of onset. By framing the problem as a first passage time issue, we derive explicit expressions for the offsets in the posterior mean and variance of the time of onset that result from incorrect assumptions about{sigma} 2 and L. Our analysis reveals that these offsets are related to the mean and variance of the first passage time required for the allele frequency to reach a certain threshold, starting from an initial frequency determined by the model parameters. Under the assumption of a uniform prior for the time of onset, we find that the offset in the inferred mean is given by the difference in the effective generation duration ({Delta} = 1/{sigma}2) between the true process and the inference model. We validate our theoretical findings through simulations, demonstrating that the empirical offsets closely match our predictions. Furthermore, we generalize our results to accommodate non-uniform prior distributions, such as exponential priors, and provide numerical methods for calculating offsets under arbitrary priors. Stochastic fluctuations due to genetic drift, which are influenced by the reproductive variance and generational structure, can introduce significant biases in the posterior distribution of time of onset of advantageous mutations. By quantifying these biases, our framework enables more accurate adjustments to inferences drawn from genetic data, thereby enhancing our understanding of evolutionary dynamics and improving the reliability of population genetic analyses.

genetics↗

bioRxiv2024

The role of LEDGF in transcription is exploited by HIV-1 to position integration

HIV-1 integration occurs across actively transcribed genes due to the interaction of integrase with host chromatin factor LEDGF. Although LEDGF was originally isolated as a co-activator that stimulates promoter activity in purified systems, this role is inconsistent with LEDGF-mediated integration across gene bodies and with data indicating LEDGF is a histone chaperone that promotes transcriptional elongation. We found LEDGF is enriched in pronounced peaks that match the enrichments of H3K4me3 and RNA Pol II at transcription start sites (TSSs) of active promoters. Our genome-wide chromatin mapping revealed that MLL1 had a dominant role in recruiting LEDGF to promoters and the presence of LEDGF recruits RNA Pol II. Enrichment of LEDGF at TSSs correlates strongly with levels of integration across the transcribed sequences, indicating that LEDGF at TSSs contributed to integration across gene bodies. Although the N-terminal Pro-Trp-Trp-Pro (PWWP) domain of LEDGF interacts with nucleosomes containing H3K36me3, a modification thought to recruit LEDGF to chromatin, we found H3K36me3 does not contribute to gene specificity of integration. These data support a dual role model of LEDGF where it is tethered to promoters by MLL1 and recruits RNA Pol II. Subsequently, LEDGF travels across genes to effect HIV-1 integration. Our data also provides a mechanistic context for the contribution made by LEDGF to MLL1-based infant acute leukemia and acute myeloid leukemia in adults.

genomics↗

bioRxiv2024

scAI-SNP: a method for inferring ancestry from single-cell data

Collaborative efforts, such as the Human Cell Atlas, are rapidly accumulating large amounts of single-cell data. To ensure that single-cell atlases are representative of human genetic diversity, we need to determine the ancestry of the donors from whom single-cell data are generated. Self-reporting of race and ethnicity, although important, can be biased and is not always available for the datasets already collected. Here, we introduce scAI-SNP, a tool to infer ancestry directly from single-cell genomics data. To train scAI-SNP, we identified 4.5 million ancestry-informative single-nucleotide polymorphisms (SNPs) in the 1000 Genomes Project dataset across 3201 individuals from 26 population groups. For a query single-cell data set, scAI-SNP uses these ancestry-informative SNPs to compute the contribution of each of the 26 population groups to the ancestry of the donor from whom the cells were obtained. Using diverse single-cell data sets with matched whole-genome sequencing data, we show that scAI-SNP is robust to the sparsity of single-cell data, can accurately and consistently infer ancestry from samples derived from diverse types of tissues and cancer cells, and can be applied to different modalities of single-cell profiling assays, such as single-cell RNA-seq and single-cell ATAC-seq. Finally, we argue that ensuring that single-cell atlases represent diverse ancestry, ideally alongside race and ethnicity, is ultimately important for improved and equitable health outcomes by accounting for human diversity.

bioinformatics↗

bioRxiv2023

An improved rhythmicity analysis method using Gaussian Processes detects cell-density dependent circadian oscillations in stem cells

Detecting oscillations in time series remains a challenging problem even after decades of research. In chronobiology, rhythms in time series (for instance gene expression, eclosion, egg-laying and feeding) datasets tend to be low amplitude, display large variations amongst replicates, and often exhibit varying peak-to-peak distances (non-stationarity). Most currently available rhythm detection methods are not specifically designed to handle such datasets. Here we introduce a new method, ODeGP (Oscillation Detection using Gaussian Processes), which combines Gaussian Process (GP) regression with Bayesian inference to provide a flexible approach to the problem. Besides naturally incorporating measurement errors and non-uniformly sampled data, ODeGP uses a recently developed kernel to improve detection of non-stationary waveforms. An additional advantage is that by using Bayes factors instead of p-values, ODeGP models both the null (non-rhythmic) and the alternative (rhythmic) hypotheses. Using a variety of synthetic datasets we first demonstrate that ODeGP almost always outperforms eight commonly used methods in detecting stationary as well as non-stationary oscillations. Next, on analyzing existing qPCR datasets that exhibit low amplitude and noisy oscillations, we demonstrate that our method is more sensitive compared to the existing methods at detecting weak oscillations. Finally, we generate new qPCR time-series datasets on pluripotent mouse embryonic stem cells, which are expected to exhibit no oscillations of the core circadian clock genes. Surprisingly, we discover using ODeGP that increasing cell density can result in the rapid generation of oscillations in the Bmal1 gene, thus highlighting our methods ability to discover unexpected patterns. In its current implementation, ODeGP (available as an R package) is meant only for analyzing single or a few time-trajectories, not genome-wide datasets.

systems biology↗

bioRxiv2022

Accurate de novo detection of somatic mutations in high-throughput single-cell profiling data sets

Characterization of somatic mutations at single-cell resolution is essential to study cancer evolution, clonal mosaicism, and cell plasticity. However, detection of mutations in single cells remains technically challenging. Here, we describe SComatic, an algorithm designed for the detection of somatic mutations in single-cell transcriptomic and ATAC-seq data sets without requiring matched bulk or single-cell DNA sequencing data. Using >1.5M single cells from 383 single-cell RNAseq and single-cell ATAC-seq data sets spanning cancer and non-neoplastic samples, we show that SComatic detects mutations in single cells, even in differentiated cells from polyclonal tissues not amenable to mutation detection using existing methods. In addition, SComatic permits the estimation of mutational burdens and de novo mutational signature analysis at single-cell and cell-type resolution. Notably, using matched exome and single-cell RNAseq data, we show that SComatic achieves a 20 to 40-fold increase in precision as compared to existing algorithms for somatic SNV calling without compromising sensitivity. Overall, SComatic opens the possibility to study somatic mutagenesis at unprecedented scale and resolution using high-throughput single-cell profiling data sets.

genomics↗

bioRxiv2022

Fitness of a clonal population can be inferred from lineage trees without knowledge of the biological details

Inferring the rate at which a clonal population grows, or its fitness, is important for many biomedical applications. For example, measuring the fitness of mutated cells in a patient with cancer may provide important information about prognosis and treatment. Similarly, measuring the fitness of new viral strains that emerge during a pandemic can inform how to plan an effective response. In previous work, the lineage trees constructed from individuals randomly sampled from the population at the final time-point have been used to infer the fitness and the times at which the mutation providing the fitness advantage arose in a diverse set of systems, such as blood cancers [1], [2] and the influenza virus [3]. However, it is not clear to what extent the inferred values depend on the exact biological details assumed in the models used for the inference. In this paper we show that coalescent statistics of lineage trees are invariant to changes in key parameters underlying the expansion, such as the distribution of the number of progenies produced by each individual and heterogeneity in the expansion rate. In addition, we show that competition between drift and selection imply that the fitness of the mutated population and when the mutation occurred can be inferred without knowledge of the mutation rate per generation even though the population size itself cannot be inferred. Lastly, we show that our results also generalize to cases where multiple competing mutations result in multiple distinct subclones with different values of fitness. Taken together, our results show that inferring fitness from lineage trees is robust to most model assumptions.

evolutionary biology↗

bioRxiv2021

Collective polymerase dynamics emerge from DNA Supercoiling Mediated Transcription

All biological processes ultimately come from physical interactions. The mechanical properties of DNA play a critical role in transcription. RNA polymerase can over or under twist DNA (referred to as DNA supercoiling) when it moves along a gene resulting in mechanical stresses in DNA that impact its own motion and that of other polymerases. For example, when enough supercoiling accumulates, an isolated polymerase halts and transcription stops. DNA supercoiling can also mediate non-local interactions between polymerases that shape gene expression fluctuations. Here, we construct a comprehensive model of transcription that captures how RNA polymerase motion changes the degree of DNA supercoiling which in turn feeds back into the rate at which polymerases are recruited and move along the DNA. Surprisingly, our model predicts that a group of three or more polymerases move together at a constant velocity and sustain their motion (forming what we call a polymeton) whereas one or two polymerases would have halted. We further show that accounting for the impact of DNA supercoiling on both RNA polymerase recruitment and velocity recapitulates empirical observations of gene expression fluctuations. Finally, we propose a mechanical toggle switch whereby interactions between genes are mediated by DNA twisting as opposed to proteins. Understanding the mechanical regulation of gene expression provides new insights into how endogenous genes can interact and informs the design of new forms of engineered interactions. PACS numbers:

biophysics↗

bioRxiv2021

Paraxial mesoderm organoids model development of human somites

During the development of the vertebrate embryo, segmented structures called somites are periodically formed from the presomitic mesoderm (PSM), and give rise to the vertebral column. While somite formation has been studied in several animal models, it is less clear how well this process is conserved in humans. Recent progress has made it possible to study aspects of human paraxial mesoderm development such as the human segmentation clock in vitro using human pluripotent stem cells (hPSCs), however, somite formation has not been observed in these monolayer cultures. Here, we describe the generation of human paraxial mesoderm (PM) organoids from hPSCs (termed Somitoids), which recapitulate the molecular, morphological and functional features of paraxial mesoderm development, including formation of somite-like structures in vitro. Using a quantitative image-based screen, we identify critical parameters such as initial cell number and signaling modulations that reproducibly yielded somite formation in our organoid system. In addition, using single-cell RNA sequencing and 3D imaging, we show that PM organoids both transcriptionally and morphologically resemble their in vivo counterparts and can be differentiated into somite derivatives. Our organoid system is reproducible and scalable, allowing for the systematic and quantitative analysis of human spinal cord development and disease in vitro.

developmental biology↗

bioRxiv2021

Droplet-based single cell RNA sequencing of bacteria identifies known and previously unseen cellular states

Clonal bacterial populations rely on transcriptional variation to differentiate into specialized cell states that increase the communitys fitness. Such heterogeneous gene expression is implicated in many fundamental microbial processes including sporulation, cell communication, detoxification, substrate utilization, competence, biofilm formation, motility, pathogenicity, and antibiotic resistance1. To identify these specialized cell states and determine the processes by which they develop, we need to study isogenic bacterial populations at the single cell level2,3. Here, we develop a method that uses DNA probes and leverages an existing commercial microfluidic platform (10X Chromium) to conduct bacterial single cell RNA sequencing. We sequenced the transcriptome of over 15,000 individual bacterial cells, detecting on average 365 transcripts mapping to 265 genes per cell in B. subtilis and 329 transcripts mapping to 149 genes per cell in E. coli. Our findings correctly identify known cell states and uncover previously unreported cell states. Interestingly, we find that some metabolic pathways segregate into distinct subpopulations across different bacteria and growth conditions, suggesting that some cellular processes may be more prone to differentiation than others. Our high throughput, highly resolved single cell transcriptomic platform can be broadly used for understanding heterogeneity in microbial populations.

microbiology↗

bioRxiv2021

Computing the Riemannian curvature of image patch and single-cell RNA sequencing data manifolds using extrinsic differential geometry

Most high-dimensional datasets are thought to be inherently low-dimensional, that is, datapoints are constrained to lie on a low-dimensional manifold embedded in a high-dimensional ambient space. Here we study the viability of two approaches from differential geometry to estimate the Riemannian curvature of these low-dimensional manifolds. The intrinsic approach relates curvature to the Laplace-Beltrami operator using the heat-trace expansion, and is agnostic to how a manifold is embedded in a high-dimensional space. The extrinsic approach relates the ambient coordinates of a manifolds embedding to its curvature using the Second Fundamental Form and the Gauss-Codazzi equation. Keeping in mind practical constraints of real-world datasets, like small sample sizes and measurement noise, we found that estimating curvature is only feasible for even simple, low-dimensional toy manifolds, when the extrinsic approach is used. To test the applicability of the extrinsic approach to real-world data, we computed the curvature of a well-studied manifold of image patches, and recapitulated its topological classification as a Klein bottle. Lastly, we applied the approach to study single-cell transcriptomic sequencing (scRNAseq) datasets of blood, gastrulation, and brain cells, revealing for the first time the intrinsic curvature of scRNAseq manifolds.

bioinformatics↗

bioRxiv2020

Reconstructing the lineage histories and differentiation trajectories of individual cancer cells in JAK2-mutant myeloproliferative neoplasms

Some cancers originate from a single mutation event in a single cell. For example, blood cancers known as myeloproliferative neoplasms (MPN) are thought to originate through the acquisition of a driver mutation (most commonly JAK2-V617F) in a hematopoietic stem cell (HSC). However, when the mutation first occurs in individual patients and how it impacts the behavior of HSCs in their native context is not known. Here we quantified the impact of the JAK2-V617F mutation on the proliferation dynamics of HSCs and the differentiation trajectories of their progenies in individual MPN patients. We reconstructed the lineage history of individual HSCs obtained from MPN patients using the patterns of spontaneous somatic mutations accrued in their genomes over time. Strikingly, we found that the JAK2-V617F mutation occurred in a single HSC several decades before MPN diagnosis -- at age 9{+/-}2 years in a 34-year-old patient, and at age 19{+/-}3 years in a 63-year-old patient. For each patient, we inferred the number of mutated HSCs over time and computed their fitness. The population of JAK2-mutated HSCs grew exponentially by 63{+/-}15% and 44{+/-}13% every year in the two patients, respectively. To contrast the differentiation trajectories of the JAK2-mutated HSCs with those of healthy HSCs, we simultaneously measured the full transcriptome and somatic mutations in single hematopoietic stem and progenitor cells (HSPCs). We found that the fraction of JAK2-mutant HSPCs varied significantly across different myeloid cell types within the same patient. The erythroid progenitor cells were often entirely JAK2-mutant, even when the peripheral blood JAK2-V617F allele burden was low. The novel biological insights uncovered by this work have implications for the prevention and treatment of MPN, as well as the accurate assessment of disease burden in patients. The technology platforms and computational frameworks developed here are broadly applicable to other types of hematological malignancies and cancers.

cancer biology↗

bioRxiv2019

An engineered CRISPR/Cas9 mouse line for simultaneous readout of lineage histories and gene expression profiles in single cells

Tracing the lineage history of cells is key to answering diverse and fundamental questions in biology. Particularly in the context of stem cell biology, analysis of single cell lineages in their native state has elucidated novel fates and highlighted heterogeneity of function. Coupling of such ancestry information with other molecular readouts represents an important goal in the field. Here, we describe the CARLIN (for CRISPR Array Repair LINeage tracing) mouse line and corresponding analysis tools that can be used to simultaneously interrogate the lineage and transcriptomic information of single cells in vivo. This model exploits CRISPR technology to generate up to 44,000 transcribed barcodes in an inducible fashion at any point during development or adulthood, is compatible with sequential barcoding, and is fully genetically defined. We have used CARLIN to identify intrinsic biases in the activity of fetal liver hematopoietic stem cell (HSC) clones and to uncover a previously unappreciated clonal bottleneck in the response of HSCs to injury. CARLIN also allows the unbiased identification of transcriptional signatures based on in vivo stem cell function without a need for markers or cell sorting.

developmental biology↗

bioRxiv2017

Metabolic Interactions Between Dynamic Bacterial Subpopulations

Within multi-species microbial communities, individual species are known to occupy distinct metabolic niches. By contrast, it has remained largely unclear whether and how metabolic specialization occurs within clonal bacterial populations. The possibility of such metabolic specialization in clonal populations raises several questions: Does specialization occur, and if it does, which metabolic processes are involved? How is specialization coordinated? How rapidly do cells switch between states? And finally, what functions might metabolic specialization provide? One potential function of metabolic specialization could be to manage overflow metabolites such as acetate, which presents a toxic challenge due to low pH, and protective pH-neutral overflow metabolites. Here we show that exponentially dividing Bacillus subtilis cultures divide into distinct interacting metabolic subpopulations including one population that produces acetate, and another population that differentially expresses metabolic genes for the production of acetoin, a pH-neutral storage molecule. These subpopulations grew at distinct rates, and cells switched dynamically between states, with acetate influencing the relative sizes of the different subpopulations. These results show that clonal populations can use metabolic specialization to control the environment through a process of dynamic, environmentally-sensitive state-switching.

microbiology↗