A novel pathogenic mutation of MeCP2 impairs chromatin association independent of protein levels.

Loss-of-function mutations in MECP2 cause Rett syndrome (RTT), a severe neurological disorder that mainly affects girls. Mutations in MECP2 do occur in males occasionally and typically cause severe encephalopathy and premature lethality. Recently, we identified a missense mutation (c.353G>A, p.Gly118Glu [G118E]), which has never been seen before in MECP2, in a young boy who suffered from progressive motor dysfunction and developmental delay. To determine whether this variant caused the clinical symptoms and study its functional consequences, we established two disease models, including human neurons from patient-derived iPSCs and a knock-in mouse line. G118E mutation partially reduces MeCP2 abundance and its DNA binding, and G118E mice manifest RTT-like symptoms seen in the patient, affirming the pathogenicity of this mutation. Using live-cell and single-molecule imaging, we found that G118E mutation alters MeCP2's chromatin interaction properties in live neurons independently of its effect on protein levels. Here we report the generation and characterization of RTT models of a male hypomorphic variant and reveal new insight into the mechanism by which this pathological mutation affects MeCP2's chromatin dynamics. Our ability to quantify protein dynamics in disease models lays the foundation for harnessing high-resolution single-molecule imaging as the next frontier for developing innovative therapies for RTT and other diseases.

MeCP2 regulates Gdf11, a dosage-sensitive gene critical for neurological function.

Loss- and gain-of-function of MeCP2 causes Rett syndrome (RTT) and MECP2 duplication syndrome (MDS), respectively. MeCP2 binds methyl-cytosines to finely tune gene expression in the brain, but identifying genes robustly regulated by MeCP2 has been difficult. By integrating multiple transcriptomics datasets, we revealed that MeCP2 finely regulates growth differentiation factor 11 (Gdf11). Gdf11 is down-regulated in RTT mouse models and, conversely, up-regulated in MDS mouse models. Strikingly, genetically normalizing Gdf11 dosage levels improved several behavioral deficits in a mouse model of MDS. Next, we discovered that losing one copy of Gdf11 alone was sufficient to cause multiple neurobehavioral deficits in mice, most notably hyperactivity and decreased learning and memory. This decrease in learning and memory was not due to changes in proliferation or numbers of progenitor cells in the hippocampus. Lastly, loss of one copy of Gdf11 decreased survival in mice, corroborating its putative role in aging. Our data demonstrate that Gdf11 dosage is important for brain function.

Disruption of MeCP2-TCF20 complex underlies distinct neurodevelopmental disorders.

MeCP2 is associated with Rett syndrome (RTT), MECP2 duplication syndrome, and a number of conditions with isolated features of these diseases, including autism, intellectual disability, and motor dysfunction. MeCP2 is known to broadly bind methylated DNA, but the precise molecular mechanism driving disease pathogenesis remains to be determined. Using proximity-dependent biotinylation (BioID), we identified a transcription factor 20 (TCF20) complex that interacts with MeCP2 at the chromatin interface. Importantly, RTT-causing mutations in MECP2 disrupt this interaction. TCF20 and MeCP2 are highly coexpressed in neurons and coregulate the expression of key neuronal genes. Reducing Tcf20 partially rescued the behavioral deficits caused by MECP2 overexpression, demonstrating a functional relationship between MeCP2 and TCF20 in MECP2 duplication syndrome pathogenesis. We identified a patient exhibiting RTT-like neurological features with a missense mutation in the PHF14 subunit of the TCF20 complex that abolishes the MeCP2-PHF14-TCF20 interaction. Our data demonstrate the critical role of the MeCP2-TCF20 complex for brain function.

Heterozygous loss-of-function variants significantly expand the phenotypes associated with loss of GDF11.

PURPOSE: Growth differentiation factor 11 (GDF11) is a key signaling protein required for proper development of many organ systems. Only one prior study has associated an inherited GDF11 variant with a dominant human disease in a family with variable craniofacial and vertebral abnormalities. Here, we expand the phenotypic spectrum associated with GDF11 variants and document the nature of the variants. METHODS: We present a cohort of six probands with de novo and inherited nonsense/frameshift (4/6 patients) and missense (2/6) variants in GDF11. We generated gdf11 mutant zebrafish to model loss of gdf11 phenotypes and used an overexpression screen in Drosophila to test variant functionality. RESULTS: Patients with variants in GDF11 presented with craniofacial (5/6), vertebral (5/6), neurological (6/6), visual (4/6), cardiac (3/6), auditory (3/6), and connective tissue abnormalities (3/6). gdf11 mutant zebrafish show craniofacial abnormalities and body segmentation defects that match some patient phenotypes. Expression of the patients' variants in the fly showed that one nonsense variant in GDF11 is a severe loss-of-function (LOF) allele whereas the missense variants in our cohort are partial LOF variants. CONCLUSION: GDF11 is needed for human development, particularly neuronal development, and LOF GDF11 alleles can affect the development of numerous organs and tissues.

Antisense oligonucleotide therapy in a humanized mouse model of MECP2 duplication syndrome.

Many intellectual disability disorders are due to copy number variations, and, to date, there have been no treatment options tested for this class of diseases. MECP2 duplication syndrome (MDS) is one of the most common genomic rearrangements in males and results from duplications spanning the methyl-CpG binding protein 2 (MECP2) gene locus. We previously showed that antisense oligonucleotide (ASO) therapy can reduce MeCP2 protein amount in an MDS mouse model and reverse its disease features. This MDS mouse model, however, carried one transgenic human allele and one mouse allele, with the latter being protected from human-specific MECP2-ASO targeting. Because MeCP2 is a dosage-sensitive protein, the ASO must be titrated such that the amount of MeCP2 is not reduced too far, which would cause Rett syndrome. Therefore, we generated an "MECP2 humanized" MDS model that carries two human MECP2 alleles and no mouse endogenous allele. Intracerebroventricular injection of the MECP2-ASO efficiently down-regulated MeCP2 expression throughout the brain in these mice. Moreover, MECP2-ASO mitigated several behavioral deficits and restored expression of selected MeCP2-regulated genes in a dose-dependent manner without any toxicity. Central nervous system administration of MECP2-ASO is therefore well tolerated and beneficial in this mouse model and provides a translatable approach that could be feasible for treating MDS.

Identification and characterization of conserved noncoding cis-regulatory elements that impact Mecp2 expression and neurological functions.

While changes in MeCP2 dosage cause Rett syndrome (RTT) and MECP2 duplication syndrome (MDS), its transcriptional regulation is poorly understood. Here, we identified six putative noncoding regulatory elements of Mecp2, two of which are conserved in humans. Upon deletion in mice and human iPSC-derived neurons, these elements altered RNA and protein levels in opposite directions and resulted in a subset of RTT- and MDS-like behavioral deficits in mice. Our discovery provides insight into transcriptional regulation of Mecp2/MECP2 and highlights genomic sites that could serve as diagnostic and therapeutic targets in RTT or MDS.

Automated brightfield morphometry of 3D organoid populations by OrganoSeg.

Spheroid and organoid cultures are powerful in vitro models for biology, but size and shape diversity within the culture is largely ignored. To streamline morphometric profiling, we developed OrganoSeg, an open-source software that integrates segmentation, filtering, and analysis for archived brightfield images of 3D culture. OrganoSeg is more accurate and flexible than existing platforms, and we illustrate its potential by stratifying 5167 breast-cancer spheroid and 5743 colon and colorectal-cancer organoid morphologies. Organoid transcripts grouped by morphometric signature heterogeneity were enriched for biological processes not prominent in the original RNA sequencing data. OrganoSeg enables complete, objective quantification of brightfield phenotypes, which may give insight into the molecular and multicellular mechanisms of organoid regulation.

Tumor-Suppressor Inactivation of GDF11 Occurs by Precursor Sequestration in Triple-Negative Breast Cancer.

Triple-negative breast cancer (TNBC) is an aggressive and heterogeneous carcinoma in which various tumor-suppressor genes are lost by mutation, deletion, or silencing. Here we report a tumor-suppressive mode of action for growth-differentiation factor 11 (GDF11) and an unusual mechanism of its inactivation in TNBC. GDF11 promotes an epithelial, anti-invasive phenotype in 3D triple-negative cultures and intraductal xenografts by sustaining expression of E-cadherin and inhibitor of differentiation 2 (ID2). Surprisingly, clinical TNBCs retain the GDF11 locus and expression of the protein itself. GDF11 bioactivity is instead lost because of deficiencies in its convertase, proprotein convertase subtilisin/kexin type 5 (PCSK5), causing inactive GDF11 precursor to accumulate intracellularly. PCSK5 reconstitution mobilizes the latent TNBC reservoir of GDF11 in vitro and suppresses triple-negative mammary cancer metastasis to the lung of syngeneic hosts. Intracellular GDF11 retention adds to the concept of tumor-suppressor inactivation and reveals a cell-biological vulnerability for TNBCs lacking therapeutically actionable mutations.

A time- and matrix-dependent TGFBR3-JUND-KRT5 regulatory circuit in single breast epithelial cells and basal-like premalignancies.

Basal-like breast carcinoma is characterized by poor prognosis and high intratumour heterogeneity. In an immortalized basal-like breast epithelial cell line, we identified two anticorrelated gene-expression programs that arise among single extracellular matrix (ECM)-attached cells during organotypic three-dimensional culture. The first contains multiple TGF-ß-related genes including TGFBR3, whereas the second contains JUND and the basal-like marker KRT5. TGFBR3 and JUND interconnect through four negative-feedback loops to form a circuit that exhibits spontaneous damped oscillations in three-dimensional culture. The TGFBR3-JUND circuit is conserved in some premalignant lesions that heterogeneously express KRT5. The circuit depends on ECM engagement, as detachment causes a rewiring that is triggered by RPS6 dephosphorylation and maintained by juxtacrine tenascin C, which is critical for intraductal colonization of basal-like breast cancer cells in vivo. Intratumour heterogeneity need not stem from partial differentiation and could instead reflect dynamic toggling of cells between expression states that are not cell autonomous.

Parameterizing cell-to-cell regulatory heterogeneities via stochastic transcriptional profiles.

Regulated changes in gene expression underlie many biological processes, but globally profiling cell-to-cell variations in transcriptional regulation is problematic when measuring single cells. Transcriptome-wide identification of regulatory heterogeneities can be robustly achieved by randomly collecting small numbers of cells followed by statistical analysis. However, this stochastic-profiling approach blurs out the expression states of the individual cells in each pooled sample. Here, we show that the underlying distribution of single-cell regulatory states can be deconvolved from stochastic-profiling data through maximum-likelihood inference. Guided by the mechanisms of transcriptional regulation, we formulated plausible mixture models for cell-to-cell regulatory heterogeneity and maximized the resulting likelihood functions to infer model parameters. Inferences were validated both computationally and experimentally for different mixture models, which included regulatory states for multicellular function that were occupied by as few as 1 in 40 cells of the population. Importantly, when the method was extended to programs of heterogeneously coexpressed transcripts, we found that population-level inferences were much more accurate with pooled samples than with one-cell samples when the extent of sampling was limited. Our deconvolution method provides a means to quantify the heterogeneous regulation of molecular states efficiently and gain a deeper understanding of the heterogeneous execution of cell decisions.

Multiscale models of cell signaling.

Computational models of signal transduction face challenges of scale below the resolution of a single cell. Here, we organize these challenges around three key interfaces for multiscale models of cell signaling: molecules to pathways, pathways to networks, and networks to outcomes. Each interface requires its own set of computational approaches and systems-level data, and no single approach or dataset can effectively bridge all three interfaces. This suggests that realistic "whole-cell" models of signaling will need to agglomerate different model types that span critical intracellular scales. Future multiscale models will be valuable for understanding the impact of signaling mutations or population variants that lead to cellular diseases such as cancer.