Biology subjects

Elowitz, M.

Publications and source records attributed to Elowitz, M..

10 recordsLinked to original sources

bioRxiv2026

In vivo spatial coordination with synthetic paracrine signaling

The immune system uses paracrine signaling to spatially confine potent responses such as inflammation. A bio-orthogonal synthetic paracrine system could enable engineering of analogous multicellular circuits in which different cell types coordinate their functions in a spatially organized fashion. Here, using the plant hormone auxin as a bio-orthogonal chemical signal, we introduce programmable paracrine circuits that distribute sensing and effector functions to different cell types to spatially restrict responses in mouse xenografts. Cells engineered to express auxin biosynthetic genes generated auxin-dense regions with tunable length scales in vivo. This localized signaling ability enabled design of a multicellular sentinel-effector system, in which THP-1 sentinel cells conditionally produce auxin in regions expressing the tumor-specific antigen EGFRvIII, and Jurkat effector cells respond by locally modulating the activity of a chimeric antigen receptor (CAR). This two-cell type system was able to achieve localized activation of engineered effector cells in vivo. These results establish a foundation for engineering multicellular therapeutic systems that focus responses in specific tissue contexts or disease sites.

Synthetic Biology↗

bioRxiv2023

Principles of Computation by Competitive Protein Dimerization Networks

Many biological signaling pathways employ proteins that competitively dimerize in diverse combinations. These dimerization networks can perform biochemical computations, in which the concentrations of monomers (inputs) determine the concentrations of dimers (outputs). Despite their prevalence, little is known about the range of input-output computations that dimerization networks can perform (their "expressivity") and how it depends on network size and connectivity. Using a systematic computational approach, we demonstrate that even small dimerization networks (3-6 monomers) are expressive, performing diverse multi-input computations. Further, dimerization networks are versatile, performing different computations when their protein components are expressed at different levels, such as in different cell types. Remarkably, individual networks with random interaction affinities, when large enough ([≥]8 proteins), can perform nearly all ([~]90%) potential one-input network computations merely by tuning their monomer expression levels. Thus, even the simple process of competitive dimerization provides a powerful architecture for multi-input, cell-type-specific signal processing. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/564854v2_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@1917f39org.highwire.dtl.DTLVardef@13795bcorg.highwire.dtl.DTLVardef@47913eorg.highwire.dtl.DTLVardef@90bd30_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2023

Lineage motifs: developmental modules for control of cell type proportions

In multicellular organisms, cell types must be produced and maintained in appropriate proportions. One way this is achieved is through committed progenitor cells that produce specific sets of descendant cell types. However, cell fate commitment is probabilistic in most contexts, making it difficult to infer progenitor states and understand how they establish overall cell type proportions. Here, we introduce Lineage Motif Analysis (LMA), a method that recursively identifies statistically overrepresented patterns of cell fates on lineage trees as potential signatures of committed progenitor states. Applying LMA to published datasets reveals spatial and temporal organization of cell fate commitment in zebrafish and rat retina and early mouse embryo development. Comparative analysis of vertebrate species suggests that lineage motifs facilitate adaptive evolutionary variation of retinal cell type proportions. LMA thus provides insight into complex developmental processes by decomposing them into simpler underlying modules.

developmental biology↗

bioRxiv2023

Tuning methylation-dependent silencing dynamics by synthetic modulation of CpG density

Methylation of cytosines in CG dinucleotides (CpGs) within promoters has been shown to lead to gene silencing in mammals in natural contexts. Recently, engineered recruitment of methyltransferases (DNMTs) at specific loci was shown to be sufficient to silence synthetic and endogenous gene expression through this mechanism. A critical parameter for DNA methylation-based silencing is the distribution of CpGs within the target promoter. However, how the number or density of CpGs in the target promoter affects the dynamics of silencing by DNMT recruitment has remained unclear. Here we constructed a library of promoters with systematically varying CpG content, and analyzed the rate of silencing in response to recruitment of DNMT. We observed a tight correlation between silencing rate and CpG content. Further, methylation-specific analysis revealed a constant accumulation rate of methylation at the promoter after DNMT recruitment. We identified a single CpG site between TATA box and transcription start site (TSS) that accounted for a substantial part of the difference in silencing rates between promoters with differing CpG content, indicating that certain residues play disproportionate roles in controlling silencing. Together, these results provide a library of promoters for synthetic epigenetic and gene regulation applications, as well as insights into the regulatory link between CpG content and silencing rate.

synthetic biology↗

bioRxiv2022

A synthetic protein-level neural network in mammalian cells

Artificial neural networks provide a powerful paradigm for information processing that has transformed diverse fields. Within living cells, genetically encoded synthetic molecular networks could, in principle, harness principles of neural computation to classify molecular signals. Here, we combine de novo designed protein heterodimers and engineered viral proteases to implement a synthetic protein circuit that performs winner-take-all neural network computation. This "perceptein" circuit includes modules that compute weighted sums of input protein concentrations through reversible binding interactions, and allow for self-activation and mutual inhibition of protein components using irreversible proteolytic cleavage reactions. Altogether, these interactions comprise a network of 310 chemical reactions stemming from 8 expressed protein species. The complete system achieves signal classification with tunable decision boundaries in mammalian cells. These results demonstrate how engineered protein-based networks can enable programmable signal classification in living cells. One-Sentence SummaryA synthetic protein circuit that performs winner-take-all neural network computation in mammalian cells

synthetic biology↗

bioRxiv2021

Synthetic multistability in mammalian cells

In multicellular organisms, gene regulatory circuits generate thousands of molecularly distinct, mitotically heritable states, through the property of multistability. Designing synthetic multistable circuits would provide insight into natural cell fate control circuit architectures and allow engineering of multicellular programs that require interactions among cells in distinct states. Here we introduce MultiFate, a naturally-inspired, synthetic circuit that supports long-term, controllable, and expandable multistability in mammalian cells. MultiFate uses engineered zinc finger transcription factors that transcriptionally self-activate as homodimers and mutually inhibit one another through heterodimerization. Using model-based design, we engineered MultiFate circuits that generate up to seven states, each stable for at least 18 days. MultiFate permits controlled state-switching and modulation of state stability through external inputs, and can be easily expanded with additional transcription factors. Together, these results provide a foundation for engineering multicellular behaviors in mammalian cells.

synthetic biology↗

bioRxiv2020

Dynamics and functional roles of splicing factor autoregulation

Non-spliceosomal splicing factors are essential, conserved regulators of alternative splicing. They provide concentration-dependent control of diverse pre-mRNAs. Many splicing factors direct unproductive splicing of their own pre-mRNAs through negative autoregulation. However, the impact of such feedback loops on splicing dynamics at the single cell level remains unclear. We developed a system to dynamically, quantitatively analyze negative autoregulatory splicing by the SF2 splicing factor in response to perturbations in single HEK293 cells. Here, we show that negative autoregulatory splicing provides critical functions for gene regulation, establishing a ceiling of SF2 protein concentration, reducing cell-cell heterogeneity in SF2 levels, and buffering variation in SF2 transcription. Most importantly, it adapts SF2 splicing activity to variations in demand from other pre-mRNA substrates. A minimal mathematical model of autoregulatory splicing explains these experimentally observed features, and provides values for effective biochemical parameters. These results reveal the unique functional roles that splicing negative autoregulation plays in homeostatically regulating transcriptional programs.

systems biology↗

bioRxiv2018

Quantitative single-cell splicing analysis reveals an ‘economy of scale’ filter for gene expression

In eukaryotic cells, splicing affects the fate of each pre-mRNA transcript, helping to determine whether it is ultimately processed into an mRNA, or degraded. The efficiency of splicing plays a key role in gene expression. However, because it depends on the levels of multiple isoforms at the same transcriptional active site (TAS) in the same cell, splicing efficiency has been challenging to measure. Here, we introduce a quantitative single-molecule FISH-based method that enables determination of the absolute abundances of distinct RNA isoforms at individual TASs. Using this method, we discovered that splicing efficiency behaves in an unexpected economy of scale manner, increasing, rather than decreasing, with gene expression levels, opposite to a standard enzymatic process. This behavior could result from an observed correlation between splicing efficiency and spatial proximity to nuclear speckles. Economy of scale splicing represents a non-linear filter that amplifies the expression of genes when they are more strongly transcribed. This method will help to reveal the roles of splicing in the quantitative control of gene expression.

cell 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↗

bioRxiv2017

Challenges And Emerging Directions In Single-Cell Analysis

Single-cell analysis is a rapidly evolving approach to characterize genome-scale molecular information at the individual cell level. Development of single-cell technologies and computational methods has enabled systematic investigation of cellular heterogeneity in a wide range of tissues and cell populations, yielding fresh insights into the composition, dynamics, and regulatory mechanisms of cell states in development and disease. Despite substantial advances, significant challenges remain in the analysis, integration, and interpretation of single-cell omics data. Here, we discuss the state of the field and recent advances, and look to future opportunities.

genomics↗