Designing Biological Circuits: Synthetic Biology Within the Operon Model and Beyond.

In 1961, Jacob and Monod proposed the operon model of gene regulation. At the model's core was the modular assembly of regulators, operators, and structural genes. To illustrate the composability of these elements, Jacob and Monod linked phenotypic diversity to the architectures of regulatory circuits. In this review, we examine how the circuit blueprints imagined by Jacob and Monod laid the foundation for the first synthetic gene networks that launched the field of synthetic biology in 2000. We discuss the influences of the operon model and its broader theoretical framework on the first generation of synthetic biological circuits, which were predominantly transcriptional and posttranscriptional circuits. We also describe how recent advances in molecular biology beyond the operon model-namely, programmable DNA- and RNA-binding molecules as well as models of epigenetic and posttranslational regulation-are expanding the synthetic biology toolkit and enabling the design of more complex biological circuits.

Single-cell protein activity analysis identifies recurrence-associated renal tumor macrophages.

Clear cell renal carcinoma (ccRCC) is a heterogeneous disease with a variable post-surgical course. To assemble a comprehensive ccRCC tumor microenvironment (TME) atlas, we performed single-cell RNA sequencing (scRNA-seq) of hematopoietic and non-hematopoietic subpopulations from tumor and tumor-adjacent tissue of treatment-naive ccRCC resections. We leveraged the VIPER algorithm to quantitate single-cell protein activity and validated this approach by comparison to flow cytometry. The analysis identified key TME subpopulations, as well as their master regulators and candidate cell-cell interactions, revealing clinically relevant populations, undetectable by gene-expression analysis. Specifically, we uncovered a tumor-specific macrophage subpopulation characterized by upregulation of TREM2/APOE/C1Q, validated by spatially resolved, quantitative multispectral immunofluorescence. In a large clinical validation cohort, these markers were significantly enriched in tumors from patients who recurred following surgery. The study thus identifies TREM2/APOE/C1Q-positive macrophage infiltration as a potential prognostic biomarker for ccRCC recurrence, as well as a candidate therapeutic target.

Co-optation of Tandem DNA Repeats for the Maintenance of Mesenchymal Identity.

Tandem repeats (TRs) are generated by DNA replication errors and retain a high level of instability, which in principle would make them unsuitable for integration into gene regulatory networks. However, the appearance of DNA sequence motifs recognized by transcription factors may turn TRs into functional cis-regulatory elements, thus favoring their stabilization in genomes. Here, we show that, in human cells, the transcriptional repressor ZEB1, which promotes the maintenance of mesenchymal features largely by suppressing epithelial genes and microRNAs, occupies TRs harboring dozens of copies of its DNA-binding motif within genomic loci relevant for maintenance of epithelial identity. The deletion of one such TR caused quasi-mesenchymal cancer cells to reacquire epithelial features, partially recapitulating the effects of ZEB1 gene deletion. These data demonstrate that the high density of identical motifs in TRs can make them suitable platforms for recruitment of transcriptional repressors, thus promoting their exaptation into pre-existing cis-regulatory networks.

Toward Minimal Residual Disease-Directed Therapy in Melanoma.

Many patients with advanced cancers achieve dramatic responses to a panoply of therapeutics yet retain minimal residual disease (MRD), which ultimately results in relapse. To gain insights into the biology of MRD, we applied single-cell RNA sequencing to malignant cells isolated from BRAF mutant patient-derived xenograft melanoma cohorts exposed to concurrent RAF/MEK-inhibition. We identified distinct drug-tolerant transcriptional states, varying combinations of which co-occurred within MRDs from PDXs and biopsies of patients on treatment. One of these exhibited a neural crest stem cell (NCSC) transcriptional program largely driven by the nuclear receptor RXRG. An RXR antagonist mitigated accumulation of NCSCs in MRD and delayed the development of resistance. These data identify NCSCs as key drivers of resistance and illustrate the therapeutic potential of MRD-directed therapy. They also highlight how gene regulatory network architecture reprogramming may be therapeutically exploited to limit cellular heterogeneity, a key driver of disease progression and therapy resistance.

A Single-Cell Transcriptome Atlas of the Aging Drosophila Brain.

The diversity of cell types and regulatory states in the brain, and how these change during aging, remains largely unknown. We present a single-cell transcriptome atlas of the entire adult Drosophila melanogaster brain sampled across its lifespan. Cell clustering identified 87 initial cell clusters that are further subclustered and validated by targeted cell-sorting. Our data show high granularity and identify a wide range of cell types. Gene network analyses using SCENIC revealed regulatory heterogeneity linked to energy consumption. During aging, RNA content declines exponentially without affecting neuronal identity in old brains. This single-cell brain atlas covers nearly all cells in the normal brain and provides the tools to study cellular diversity alongside other Drosophila and mammalian single-cell datasets in our unique single-cell analysis platform: SCope (http://scope.aertslab.org). These results, together with SCope, allow comprehensive exploration of all transcriptional states of an entire aging brain.

An Integrated Epigenomic and Transcriptomic Map of Mouse and Human αß T Cell Development.

αß lineage T cells, most of which are CD4+ or CD8+ and recognize MHC I- or MHC II-presented antigens, are essential for immune responses and develop from CD4+CD8+ thymocytes. The absence of in vitro models and the heterogeneity of αß thymocytes have hampered analyses of their intrathymic differentiation. Here, combining single-cell RNA and ATAC (chromatin accessibility) sequencing, we identified mouse and human αß thymocyte developmental trajectories. We demonstrated asymmetric emergence of CD4+ and CD8+ lineages, matched differentiation programs of agonist-signaled cells to their MHC specificity, and identified correspondences between mouse and human transcriptomic and epigenomic patterns. Through computational analysis of single-cell data and binding sites for the CD4+-lineage transcription factor Thpok, we inferred transcriptional networks associated with CD4+- or CD8+-lineage differentiation, and with expression of Thpok or of the CD8+-lineage factor Runx3. Our findings provide insight into the mechanisms of CD4+ and CD8+ T cell differentiation and a foundation for mechanistic investigations of αß T cell development.

Studying lineage decision-making in vitro: emerging concepts and novel tools.

Correct and timely lineage decisions are critical for normal embryonic development and homeostasis of adult tissues. Therefore, the search for fundamental principles that underlie lineage decision-making lies at the heart of developmental biology. Here, we review attempts to understand lineage decision-making as the interplay of single-cell heterogeneity and gene regulation. Fluctuations at the single-cell level are an important driving force behind cell-state transitions and the creation of cell-type diversity. Gene regulatory networks amplify such fluctuations and define stable cell types. They also mediate the influence of signaling inputs on the lineage decision. In this review, we focus on insights gleaned from in vitro differentiation of embryonic stem cells. We discuss emerging concepts, with an emphasis on transcriptional regulation, dynamical aspects of differentiation, and functional single-cell heterogeneity. We also highlight some novel tools to study lineage decision-making in vitro.

Comparative analysis of gene regulatory networks: from network reconstruction to evolution.

Regulation of gene expression is central to many biological processes. Although reconstruction of regulatory circuits from genomic data alone is therefore desirable, this remains a major computational challenge. Comparative approaches that examine the conservation and divergence of circuits and their components across strains and species can help reconstruct circuits as well as provide insights into the evolution of gene regulatory processes and their adaptive contribution. In recent years, advances in genomic and computational tools have led to a wealth of methods for such analysis at the sequence, expression, pathway, module, and entire network level. Here, we review computational methods developed to study transcriptional regulatory networks using comparative genomics, from sequence to functional data. We highlight how these methods use evolutionary conservation and divergence to reliably detect regulatory components as well as estimate the extent and rate of divergence. Finally, we discuss the promise and open challenges in linking regulatory divergence to phenotypic divergence and adaptation.

Genes Involved in the Development and Physiology of Both the Peripheral and Central Auditory Systems.

The genetic approach, based on the study of inherited forms of deafness, has proven to be particularly effective for deciphering the molecular mechanisms underlying the development of the peripheral auditory system, the cochlea and its afferent auditory neurons, and how this system extracts the physical parameters of sound. Although this genetic dissection has provided little information about the central auditory system, scattered data suggest that some genes may have a critical role in both the peripheral and central auditory systems. Here, we review the genes controlling the development and function of the peripheral and central auditory systems, focusing on those with demonstrated intrinsic roles in both systems and highlighting the current underappreciation of these genes. Their encoded products are diverse, from transcription factors to ion channels, as are their roles in the central auditory system, mostly evaluated in brainstem nuclei. We examine the ontogenetic and evolutionary mechanisms that may underlie their expression at different sites.

Integrative multi-omics increase resolution of the sea urchin posterior gut gene regulatory network at single-cell level.

Drafting gene regulatory networks (GRNs) requires embryological knowledge pertaining to the cell type families, information on the regulatory genes, causal data from gene knockdown experiments and validations of the identified interactions by cis-regulatory analysis. We use multi-omics involving next-generation sequencing to obtain the necessary information for drafting the Strongylocentrotus purpuratus (Sp) posterior gut GRN. Here, we present an update to the GRN using: (1) a single-cell RNA-sequencing-derived cell atlas highlighting the 2 day-post-fertilization (dpf) sea urchin gastrula cell type families, as well as the genes expressed at the single-cell level; (2) a set of putative cis-regulatory modules and transcription factor-binding sites obtained from chromatin accessibility ATAC-seq data; and (3) interactions directionality obtained from differential bulk RNA sequencing following knockdown of the transcription factor Sp-Pdx1, a key regulator of gut patterning in sea urchins. Combining these datasets, we draft the GRN for the hindgut Sp-Pdx1-positive cells in the 2 dpf gastrula embryo. Overall, our data suggest the complex connectivity of the posterior gut GRN and increase the resolution of gene regulatory cascades operating within it.

Bioenergetic costs and the evolution of noise regulation by microRNAs.

Noise control, together with other regulatory functions facilitated by microRNAs (miRNAs), is believed to have played important roles in the evolution of multicellular eukaryotic organisms. miRNAs can dampen protein fluctuations via enhanced degradation of messenger RNA (mRNA), but this requires compensation by increased mRNA transcription to maintain the same expression levels. The overall mechanism is metabolically expensive, leading to questions about how it might have evolved in the first place. We develop a stochastic model of miRNA noise regulation, coupled with a detailed analysis of the associated metabolic costs. Additionally, we calculate binding free energies for a range of miRNA seeds, the short sequences which govern target recognition. We argue that natural selection may have fine-tuned the Michaelis-Menten constant [Formula: see text] describing miRNA-mRNA affinity and show supporting evidence from analysis of experimental data. [Formula: see text] is constrained by seed length, and optimal noise control (minimum protein variance at a given energy cost) is achievable for seeds of 6 to 7 nucleotides in length, the most commonly observed types. Moreover, at optimality, the degree of noise reduction approaches the theoretical bound set by the Wiener-Kolmogorov linear filter. The results illustrate how selective pressure toward energy efficiency has potentially shaped a crucial regulatory pathway in eukaryotes.

A time-resolved single-cell roadmap of the logic driving anterior neural crest diversification from neural border to migration stages.

Neural crest cells exemplify cellular diversification from a multipotent progenitor population. However, the full sequence of early molecular choices orchestrating the emergence of neural crest heterogeneity from the embryonic ectoderm remains elusive. Gene-regulatory-networks (GRN) govern early development and cell specification toward definitive neural crest. Here, we combine ultradense single-cell transcriptomes with machine-learning and large-scale transcriptomic and epigenomic experimental validation of selected trajectories, to provide the general principles and highlight specific features of the GRN underlying neural crest fate diversification from induction to early migration stages using Xenopus frog embryos as a model. During gastrulation, a transient neural border zone state precedes the choice between neural crest and placodes which includes multiple converging gene programs. During neurulation, transcription factor connectome, and bifurcation analyses demonstrate the early emergence of neural crest fates at the neural plate stage, alongside an unbiased multipotent-like lineage persisting until epithelial-mesenchymal transition stage. We also decipher circuits driving cranial and vagal neural crest formation and provide a broadly applicable high-throughput validation strategy for investigating single-cell transcriptomes in vertebrate GRNs in development, evolution, and disease.

Enhancement of LIN28B-induced hematopoietic reprogramming by IGF2BP3.

Fetal hematopoietic stem and progenitor cells (HSPCs) hold promise to cure a wide array of hematological diseases, and we previously found a role for the RNA-binding protein (RBP) Lin28b in respecifying adult HSPCs to resemble their fetal counterparts. Here we show by single-cell RNA sequencing that Lin28b alone was insufficient for complete reprogramming of gene expression from the adult toward the fetal pattern. Using proteomics and in situ analyses, we found that Lin28b (and its closely related paralog, Lin28a) directly interacted with Igf2bp3, another RBP, and their enforced co-expression in adult HSPCs reactivated fetal-like B-cell development in vivo more efficiently than either factor alone. In B-cell progenitors, Lin28b and Igf2bp3 jointly stabilized thousands of mRNAs by binding at the same sites, including those of the B-cell regulators Pax5 and Arid3a as well as Igf2bp3 mRNA itself, forming an autoregulatory loop. Our results suggest that Lin28b and Igf2bp3 are at the center of a gene regulatory network that mediates the fetal-adult hematopoietic switch. A method to efficiently generate induced fetal-like hematopoietic stem cells (ifHSCs) will facilitate basic studies of their biology and possibly pave a path toward their clinical application.

Characterization of an eye field-like state during optic vesicle organoid development.

Specification of the eye field (EF) within the neural plate marks the earliest detectable stage of eye development. Experimental evidence, primarily from non-mammalian model systems, indicates that the stable formation of this group of cells requires the activation of a set of key transcription factors. This crucial event is challenging to probe in mammals and, quantitatively, little is known regarding the regulation of the transition of cells to this ocular fate. Using optic vesicle organoids to model the onset of the EF, we generate time-course transcriptomic data allowing us to identify dynamic gene expression programmes that characterize this cellular-state transition. Integrating this with chromatin accessibility data suggests a direct role of canonical EF transcription factors in regulating these gene expression changes, and highlights candidate cis-regulatory elements through which these transcription factors act. Finally, we begin to test a subset of these candidate enhancer elements, within the organoid system, by perturbing the underlying DNA sequence and measuring transcriptomic changes during EF activation.

SWI/SNF complexes are required for retinal pigmented epithelium differentiation and for the inhibition of cell proliferation and neural differentiation programs.

During embryonic development, tissue-specific transcription factors and chromatin remodelers function together to ensure gradual, coordinated differentiation of multiple lineages. Here, we define this regulatory interplay in the developing retinal pigmented epithelium (RPE), a neuroectodermal lineage essential for the development, function and maintenance of the adjacent retina. We present a high-resolution spatial transcriptomic atlas of the developing mouse RPE and the adjacent ocular mesenchyme obtained by geographical position sequencing (Geo-seq) of a single developmental stage of the eye that encompasses young and more mature ocular progenitors. These transcriptomic data, available online, reveal the key transcription factors and their gene regulatory networks during RPE and ocular mesenchyme differentiation. Moreover, conditional inactivation followed by Geo-seq revealed that this differentiation program is dependent on the activity of SWI/SNF complexes, shown here to control the expression and activity of RPE transcription factors and, at the same time, inhibit neural progenitor and cell proliferation genes. The findings reveal the roles of the SWI/SNF complexes in controlling the intersection between RPE and neural cell fates and the coupling of cell-cycle exit and differentiation.

Optic cup morphogenesis across species and related inborn human eye defects.

The vertebrate eye is shaped as a cup, a conformation that optimizes vision and is acquired early in development through a process known as optic cup morphogenesis. Imaging living, transparent teleost embryos and mammalian stem cell-derived organoids has provided insights into the rearrangements that eye progenitors undergo to adopt such a shape. Molecular and pharmacological interference with these rearrangements has further identified the underlying molecular machineries and the physical forces involved in this morphogenetic process. In this Review, we summarize the resulting scenarios and proposed models that include common and species-specific events. We further discuss how these studies and those in environmentally adapted blind species may shed light on human inborn eye malformations that result from failures in optic cup morphogenesis, including microphthalmia, anophthalmia and coloboma.

COFFEE: consensus single cell-type specific inference for gene regulatory networks.

The inference of gene regulatory networks (GRNs) is crucial to understanding the regulatory mechanisms that govern biological processes. GRNs may be represented as edges in a graph, and hence, it have been inferred computationally for scRNA-seq data. A wisdom of crowds approach to integrate edges from several GRNs to create one composite GRN has demonstrated improved performance when compared with individual algorithm implementations on bulk RNA-seq and microarray data. In an effort to extend this approach to scRNA-seq data, we present COFFEE (COnsensus single cell-type speciFic inFerence for gEnE regulatory networks), a Borda voting-based consensus algorithm that integrates information from 10 established GRN inference methods. We conclude that COFFEE has improved performance across synthetic, curated, and experimental datasets when compared with baseline methods. Additionally, we show that a modified version of COFFEE can be leveraged to improve performance on newer cell-type specific GRN inference methods. Overall, our results demonstrate that consensus-based methods with pertinent modifications continue to be valuable for GRN inference at the single cell level. While COFFEE is benchmarked on 10 algorithms, it is a flexible strategy that can incorporate any set of GRN inference algorithms according to user preference. A Python implementation of COFFEE may be found on GitHub: https://github.com/lodimk2/coffee.

DeepGRNCS: deep learning-based framework for jointly inferring gene regulatory networks across cell subpopulations.

Inferring gene regulatory networks (GRNs) allows us to obtain a deeper understanding of cellular function and disease pathogenesis. Recent advances in single-cell RNA sequencing (scRNA-seq) technology have improved the accuracy of GRN inference. However, many methods for inferring individual GRNs from scRNA-seq data are limited because they overlook intercellular heterogeneity and similarities between different cell subpopulations, which are often present in the data. Here, we propose a deep learning-based framework, DeepGRNCS, for jointly inferring GRNs across cell subpopulations. We follow the commonly accepted hypothesis that the expression of a target gene can be predicted based on the expression of transcription factors (TFs) due to underlying regulatory relationships. We initially processed scRNA-seq data by discretizing data scattering using the equal-width method. Then, we trained deep learning models to predict target gene expression from TFs. By individually removing each TF from the expression matrix, we used pre-trained deep model predictions to infer regulatory relationships between TFs and genes, thereby constructing the GRN. Our method outperforms existing GRN inference methods for various simulated and real scRNA-seq datasets. Finally, we applied DeepGRNCS to non-small cell lung cancer scRNA-seq data to identify key genes in each cell subpopulation and analyzed their biological relevance. In conclusion, DeepGRNCS effectively predicts cell subpopulation-specific GRNs. The source code is available at https://github.com/Nastume777/DeepGRNCS.

A single-cell multimodal view on gene regulatory network inference from transcriptomics and chromatin accessibility data.

Eukaryotic gene regulation is a combinatorial, dynamic, and quantitative process that plays a vital role in development and disease and can be modeled at a systems level in gene regulatory networks (GRNs). The wealth of multi-omics data measured on the same samples and even on the same cells has lifted the field of GRN inference to the next stage. Combinations of (single-cell) transcriptomics and chromatin accessibility allow the prediction of fine-grained regulatory programs that go beyond mere correlation of transcription factor and target gene expression, with enhancer GRNs (eGRNs) modeling molecular interactions between transcription factors, regulatory elements, and target genes. In this review, we highlight the key components for successful (e)GRN inference from (sc)RNA-seq and (sc)ATAC-seq data exemplified by state-of-the-art methods as well as open challenges and future developments. Moreover, we address preprocessing strategies, metacell generation and computational omics pairing, transcription factor binding site detection, and linear and three-dimensional approaches to identify chromatin interactions as well as dynamic and causal eGRN inference. We believe that the integration of transcriptomics together with epigenomics data at a single-cell level is the new standard for mechanistic network inference, and that it can be further advanced with integrating additional omics layers and spatiotemporal data, as well as with shifting the focus towards more quantitative and causal modeling strategies.

Biologically meaningful regulatory logic enhances the convergence rate in Boolean networks and bushiness of their state transition graph.

Boolean models of gene regulatory networks (GRNs) have gained widespread traction as they can easily recapitulate cellular phenotypes via their attractor states. Their overall dynamics are embodied in a state transition graph (STG). Indeed, two Boolean networks (BNs) with the same network structure and attractors can have drastically different STGs depending on the type of Boolean functions (BFs) employed. Our objective here is to systematically delineate the effects of different classes of BFs on the structural features of the STG of reconstructed Boolean GRNs while keeping network structure and biological attractors fixed, and explore the characteristics of BFs that drive those features. Using $10$ reconstructed Boolean GRNs, we generate ensembles that differ in BFs and compute from their STGs the dynamics' rate of contraction or 'bushiness' and rate of 'convergence', quantified with measures inspired from cellular automata (CA) that are based on the garden-of-Eden (GoE) states. We find that biologically meaningful BFs lead to higher STG 'bushiness' and 'convergence' than random ones. Obtaining such 'global' measures gets computationally expensive with larger network sizes, stressing the need for feasible proxies. So we adapt Wuensche's $Z$-parameter in CA to BFs in BNs and provide four natural variants, which, along with the average sensitivity of BFs computed at the network level, comprise our descriptors of local dynamics and we find some of them to be good proxies for bushiness. Finally, we provide an excellent proxy for the 'convergence' based on computing transient lengths originating at random states rather than GoE states.