Translatome analysis of tuberous sclerosis complex 1 patient-derived neural progenitor cells reveals rapamycin-dependent and independent alterations

BACKGROUND

Tuberous sclerosis complex (TSC) is an inherited neurocutaneous disorder caused by mutations in the TSC1 or TSC2 genes, with patients often exhibiting neurodevelopmental (ND) manifestations termed TSC-associated neuropsychiatric disorders (TAND) including autism spectrum disorder (ASD) and intellectual disability. Hamartin (TSC1) and tuberin (TSC2) proteins form a complex inhibiting mechanistic target of rapamycin complex 1 (mTORC1) signaling. Loss of TSC1 or TSC2 activates mTORC1 that, among several targets, controls protein synthesis by inhibiting translational repressor eIF4E-binding proteins. Using TSC1 patient-derived neural progenitor cells (NPCs), we recently reported early ND phenotypic changes, including increased cell proliferation and altered neurite outgrowth in TSC1-null NPCs, which were unaffected by the mTORC1 inhibitor rapamycin.

METHODS

Here, we used polysome profiling, which quantifies changes in mRNA abundance and translational efficiencies at a transcriptome-wide level, to compare CRISPR-edited TSC1-null with CRISPR-corrected TSC1-WT NPCs generated from one TSC donor (one clone/genotype). To assess the relevance of identified gene expression alterations, we performed polysome profiling in postmortem brains from ASD donors and age-matched controls. We further compared effects on translation of a subset of transcripts and rescue of early ND phenotypes in NPCs following inhibition of mTORC1 using the allosteric inhibitor rapamycin versus a third-generation bi-steric, mTORC1-selective inhibitor RMC-6272.

RESULTS

Polysome profiling of NPCs revealed numerous TSC1-associated alterations in mRNA translation that were largely recapitulated in human ASD brains. Moreover, although rapamycin treatment partially reversed the TSC1-associated alterations in mRNA translation, most genes related to neural activity/synaptic regulation or ASD were rapamycin-insensitive. In contrast, treatment with RMC-6272 inhibited rapamycin-insensitive translation and reversed TSC1-associated early ND phenotypes including proliferation and neurite outgrowth that were unaffected by rapamycin.

CONCLUSIONS

Our work reveals ample mRNA translation alterations in TSC1 patient-derived NPCs that recapitulate mRNA translation in ASD brain samples. Further, suppression of TSC1-associated but rapamycin-insensitive translation and ND phenotypes by RMC-6272 unveils potential implications for more efficient targeting of mTORC1 as a superior treatment strategy for TAND.

Translatome analysis of Tuberous Sclerosis Complex-1 patient-derived neural progenitor cells reveal rapamycin-dependent and independent alterations

Tuberous sclerosis complex (TSC) is an inherited neurocutaneous disorder caused by mutations in TSC1 or TSC2 genes, with patients often exhibiting neurodevelopmental (ND) manifestations termed TSC-associated neuropsychiatric disorders (TAND) including autism spectrum disorder (ASD). The hamartin-tuberin (TSC1-TSC2) protein complex inactivates mechanistic target of rapamycin complex 1 (mTORC1) signaling, leading to increased protein synthesis via inactivation of translational repressor eIF4E-binding proteins (4E-BPs). In TSC1-null neural progenitor cells (NPCs), we previously reported early ND phenotypic changes, including increased proliferation/altered neurite outgrowth, which were unaffected by mTORC1-inhibitor rapamycin. Here, using polysome-profiling to quantify translational efficiencies at a transcriptome-wide level, we observed numerous TSC1-dependent alterations in NPCs, largely recapitulated in post-mortem brains from ASD donors. Although rapamycin partially reversed TSC1-associated alterations, most neural activity/synaptic- or ASD-related genes remained insensitive but were inhibited by third-generation bi-steric, mTORC1-selective inhibitor RMC-6272, which also reversed altered ND phenotypes. Together these data reveal potential implications for treatment of TAND.

Stress-induced perturbations in intracellular amino acids reprogram mRNA translation in osmoadaptation independently of the ISR

The integrated stress response (ISR) plays a pivotal role in adaptation of translation machinery to cellular stress. Here, we demonstrate an ISR-independent osmoadaptation mechanism involving reprogramming of translation via coordinated but independent actions of mTOR and plasma membrane amino acid transporter SNAT2. This biphasic response entails reduced global protein synthesis and mTOR signaling followed by translation of SNAT2. Induction of SNAT2 leads to accumulation of amino acids and reactivation of mTOR and global protein synthesis, paralleled by partial reversal of the early-phase, stress-induced translatome. We propose SNAT2 functions as a molecular switch between inhibition of protein synthesis and establishment of an osmoadaptive translation program involving the formation of cytoplasmic condensates of SNAT2-regulated RNA-binding proteins DDX3X and FUS. In summary, we define key roles of SNAT2 in osmotolerance.

flexiMAP: a regression-based method for discovering differential alternative polyadenylation events in standard RNA-seq data

Motivation

We present flexible Modeling of Alternative PolyAdenylation (flexiMAP), a new beta-regression-based method implemented in R, for discovering differential alternative polyadenylation events in standard RNA-seq data.

Results

We show, using both simulated and real data, that flexiMAP exhibits a good balance between specificity and sensitivity and compares favourably to existing methods, especially at low fold changes. In addition, the tests on simulated data reveal some hitherto unrecognized caveats of existing methods. Importantly, flexiMAP allows modeling of multiple known covariates that often confound the results of RNA-seq data analysis.

Availability and implementation

The flexiMAP R package is available at: https://github.com/kszkop/flexiMAP. Scripts and data to reproduce the analysis in this paper are available at: https://doi.org/10.5281/zenodo.3689788.

Supplementary information

Supplementary data are available at Bioinformatics online.

Translational offsetting as a mode of estrogen receptor α-dependent regulation of gene expression

Estrogen receptor alpha (ERα) activity is associated with increased cancer cell proliferation. Studies aiming to understand the impact of ERα on cancer-associated phenotypes have largely been limited to its transcriptional activity. Herein, we demonstrate that ERα coordinates its transcriptional output with selective modulation of mRNA translation. Importantly, translational perturbations caused by depletion of ERα largely manifest as "translational offsetting" of the transcriptome, whereby amounts of translated mRNAs and corresponding protein levels are maintained constant despite changes in mRNA abundance. Transcripts whose levels, but not polysome association, are reduced following ERα depletion lack features which limit translation efficiency including structured 5'UTRs and miRNA target sites. In contrast, mRNAs induced upon ERα depletion whose polysome association remains unaltered are enriched in codons requiring U34-modified tRNAs for efficient decoding. Consistently, ERα regulates levels of U34-modifying enzymes and thereby controls levels of U34-modified tRNAs. These findings unravel a hitherto unprecedented mechanism of ERα-dependent orchestration of transcriptional and translational programs that may be a pervasive mechanism of proteome maintenance in hormone-dependent cancers.

Untranslated Parts of Genes Interpreted: Making Heads or Tails of High-Throughput Transcriptomic Data via Computational Methods: Computational methods to discover and quantify isoforms with alternative untranslated regions

In this review we highlight the importance of defining the untranslated parts of transcripts, and present a number of computational approaches for the discovery and quantification of alternative transcription start and poly-adenylation events in high-throughput transcriptomic data. The fate of eukaryotic transcripts is closely linked to their untranslated regions, which are determined by the position at which transcription starts and ends at a genomic locus. Although the extent of alternative transcription starts and alternative poly-adenylation sites has been revealed by sequencing methods focused on the ends of transcripts, the application of these methods is not yet widely adopted by the community. We suggest that computational methods applied to standard high-throughput technologies are a useful, albeit less accurate, alternative to the expertise-demanding 5' and 3' sequencing and they are the only option for analysing legacy transcriptomic data. We review these methods here, focusing on technical challenges and arguing for the need to include better normalization of the data and more appropriate statistical models of the expected variation in the signal.

Dysregulation of Alternative Poly-adenylation as a Potential Player in Autism Spectrum Disorder

We present here the hypothesis that alternative poly-adenylation (APA) is dysregulated in the brains of individuals affected by Autism Spectrum Disorder (ASD), due to disruptions in the calcium signaling networks. APA, the process of selecting different poly-adenylation sites on the same gene, yielding transcripts with different-length 3′ untranslated regions (UTRs), has been documented in different tissues, stages of development and pathologic conditions. Differential use of poly-adenylation sites has been shown to regulate the function, stability, localization and translation efficiency of target RNAs. However, the role of APA remains rather unexplored in neurodevelopmental conditions. In the human brain, where transcripts have the longest 3′ UTRs and are thus likely to be under more complex post-transcriptional regulation, erratic APA could be particularly detrimental. In the context of ASD, a condition that affects individuals in markedly different ways and whose symptoms exhibit a spectrum of severity, APA dysregulation could be amplified or dampened depending on the individual and the extent of the effect on specific genes would likely vary with genetic and environmental factors. If this hypothesis is correct, dysregulated APA events might be responsible for certain aspects of the phenotypes associated with ASD. Evidence supporting our hypothesis is derived from standard RNA-seq transcriptomic data but we suggest that future experiments should focus on techniques that probe the actual poly-adenylation site (3′ sequencing). To address issues arising from the use of post-mortem tissue and low numbers of heterogeneous samples affected by confounding factors (such as the age, gender and health of the individuals), carefully controlled in vitro systems will be required to model the effect of calcium signaling dysregulation in the ASD brain.

iGEMS: an integrated model for identification of alternative exon usage events

DNA microarrays and RNAseq are complementary methods for studying RNA molecules. Current computational methods to determine alternative exon usage (AEU) using such data require impractical visual inspection and still yield high false-positive rates. Integrated Gene and Exon Model of Splicing (iGEMS) adapts a gene-level residuals model with a gene size adjusted false discovery rate and exon-level analysis to circumvent these limitations. iGEMS was applied to two new DNA microarray datasets, including the high coverage Human Transcriptome Arrays 2.0 and performance was validated using RT-qPCR. First, AEU was studied in adipocytes treated with (n = 9) or without (n = 8) the anti-diabetes drug, rosiglitazone. iGEMS identified 555 genes with AEU, and robust verification by RT-qPCR (∼90%). Second, in a three-way human tissue comparison (muscle, adipose and blood, n = 41) iGEMS identified 4421 genes with at least one AEU event, with excellent RT-qPCR verification (95%, n = 22). Importantly, iGEMS identified a variety of AEU events, including 3′UTR extension, as well as exon inclusion/exclusion impacting on protein kinase and extracellular matrix domains. In conclusion, iGEMS is a robust method for identification of AEU while the variety of exon usage between human tissues is 5–10 times more prevalent than reported by the Genotype-Tissue Expression consortium using RNA sequencing.

Multiple sources of bias confound functional enrichment analysis of global -omics data

Serious and underappreciated sources of bias mean that extreme caution should be applied when using or interpreting functional enrichment analysis to validate findings from global RNA- or protein-expression analyses.