Integrating spatial transcriptomics and bulk RNA-seq: predicting gene expression with enhanced resolution through graph attention networks.

Spatial transcriptomics data play a crucial role in cancer research, providing a nuanced understanding of the spatial organization of gene expression within tumor tissues. Unraveling the spatial dynamics of gene expression can unveil key insights into tumor heterogeneity and aid in identifying potential therapeutic targets. However, in many large-scale cancer studies, spatial transcriptomics data are limited, with bulk RNA-seq and corresponding Whole Slide Image (WSI) data being more common (e.g. TCGA project). To address this gap, there is a critical need to develop methodologies that can estimate gene expression at near-cell (spot) level resolution from existing WSI and bulk RNA-seq data. This approach is essential for reanalyzing expansive cohort studies and uncovering novel biomarkers that have been overlooked in the initial assessments. In this study, we present STGAT (Spatial Transcriptomics Graph Attention Network), a novel approach leveraging Graph Attention Networks (GAT) to discern spatial dependencies among spots. Trained on spatial transcriptomics data, STGAT is designed to estimate gene expression profiles at spot-level resolution and predict whether each spot represents tumor or non-tumor tissue, especially in patient samples where only WSI and bulk RNA-seq data are available. Comprehensive tests on two breast cancer spatial transcriptomics datasets demonstrated that STGAT outperformed existing methods in accurately predicting gene expression. Further analyses using the TCGA breast cancer dataset revealed that gene expression estimated from tumor-only spots (predicted by STGAT) provides more accurate molecular signatures for breast cancer sub-type and tumor stage prediction, and also leading to improved patient survival and disease-free analysis. Availability: Code is available at https://github.com/compbiolabucf/STGAT.

Structure of a key intermediate of the SMN complex reveals Gemin2's crucial function in snRNP assembly.

The SMN complex mediates the assembly of heptameric Sm protein rings on small nuclear RNAs (snRNAs), which are essential for snRNP function. Specific Sm core assembly depends on Sm proteins and snRNA recognition by SMN/Gemin2- and Gemin5-containing subunits, respectively. The mechanism by which the Sm proteins are gathered while preventing illicit Sm assembly on non-snRNAs is unknown. Here, we describe the 2.5 Å crystal structure of Gemin2 bound to SmD1/D2/F/E/G pentamer and SMN's Gemin2-binding domain, a key assembly intermediate. Remarkably, through its extended conformation, Gemin2 wraps around the crescent-shaped pentamer, interacting with all five Sm proteins, and gripping its bottom and top sides and outer perimeter. Gemin2 reaches into the RNA-binding pocket, preventing RNA binding. Interestingly, SMN-Gemin2 interaction is abrogated by a spinal muscular atrophy (SMA)-causing mutation in an SMN helix that mediates Gemin2 binding. These findings provide insight into SMN complex assembly and specificity, linking snRNP biogenesis and SMA pathogenesis.

Incomplete time-series gene expression in integrative study for islet autoimmunity prediction.

Type 1 diabetes (T1D) outcome prediction plays a vital role in identifying novel risk factors, ensuring early patient care and designing cohort studies. TEDDY is a longitudinal cohort study that collects a vast amount of multi-omics and clinical data from its participants to explore the progression and markers of T1D. However, missing data in the omics profiles make the outcome prediction a difficult task. TEDDY collected time series gene expression for less than 6% of enrolled participants. Additionally, for the participants whose gene expressions are collected, 79% time steps are missing. This study introduces an advanced bioinformatics framework for gene expression imputation and islet autoimmunity (IA) prediction. The imputation model generates synthetic data for participants with partially or entirely missing gene expression. The prediction model integrates the synthetic gene expression with other risk factors to achieve better predictive performance. Comprehensive experiments on TEDDY datasets show that: (1) Our pipeline can effectively integrate synthetic gene expression with family history, HLA genotype and SNPs to better predict IA status at 2 years (sensitivity 0.622, AUC 0.715) compared with the individual datasets and state-of-the-art results in the literature (AUC 0.682). (2) The synthetic gene expression contains predictive signals as strong as the true gene expression, reducing reliance on expensive and long-term longitudinal data collection. (3) Time series gene expression is crucial to the proposed improvement and shows significantly better predictive ability than cross-sectional gene expression. (4) Our pipeline is robust to limited data availability. Availability: Code is available at https://github.com/compbiolabucf/TEDDY.

Ars2 links the nuclear cap-binding complex to RNA interference and cell proliferation.

Here we identify a component of the nuclear RNA cap-binding complex (CBC), Ars2, that is important for miRNA biogenesis and critical for cell proliferation. Unlike other components of the CBC, Ars2 expression is linked to the proliferative state of the cell. Deletion of Ars2 is developmentally lethal, and deletion in adult mice led to bone marrow failure whereas parenchymal organs composed of nonproliferating cells were unaffected. Depletion of Ars2 or CBP80 from proliferating cells impaired miRNA-mediated repression and led to alterations in primary miRNA processing in the nucleus. Ars2 depletion also reduced the levels of several miRNAs, including miR-21, let-7, and miR-155, that are implicated in cellular transformation. These findings provide evidence for a role for Ars2 in RNA interference regulation during cell proliferation.

53BP1-ACLY-SLBP-coordinated activation of replication-dependent histone biogenesis maintains genomic integrity.

p53-binding protein 1 (53BP1) regulates the DNA double-strand break (DSB) repair pathway and maintains genomic integrity. Here we found that 53BP1 functions as a molecular scaffold for the nucleoside diphosphate kinase-mediated phosphorylation of ATP-citrate lyase (ACLY) which enhances the ACLY activity. This functional association is critical for promoting global histone acetylation and subsequent transcriptome-wide alterations in gene expression. Specifically, expression of a replication-dependent histone biogenesis factor, stem-loop binding protein (SLBP), is dependent upon 53BP1-ACLY-controlled acetylation at the SLBP promoter. This chain of regulation events carried out by 53BP1, ACLY, and SLBP is crucial for both quantitative and qualitative histone biogenesis as well as for the preservation of genomic integrity. Collectively, our findings reveal a previously unknown role for 53BP1 in coordinating replication-dependent histone biogenesis and highlight a DNA repair-independent function in the maintenance of genomic stability through a regulatory network that includes ACLY and SLBP.

In silico model for miRNA-mediated regulatory network in cancer.

Deregulation of gene expression is associated with the pathogenesis of numerous human diseases including cancer. Current data analyses on gene expression are mostly focused on differential gene/transcript expression in big data-driven studies. However, a poor connection to the proteome changes is a widespread problem in current data analyses. This is partly due to the complexity of gene regulatory pathways at the post-transcriptional level. In this study, we overcome these limitations and introduce a graph-based learning model, PTNet, which simulates the microRNAs (miRNAs) that regulate gene expression post-transcriptionally in silico. Our model does not require large-scale proteomics studies to measure the protein expression and can successfully predict the protein levels by considering the miRNA-mRNA interaction network, the mRNA expression, and the miRNA expression. Large-scale experiments on simulations and real cancer high-throughput datasets using PTNet validated that (i) the miRNA-mediated interaction network affects the abundance of corresponding proteins and (ii) the predicted protein expression has a higher correlation with the proteomics data (ground-truth) than the mRNA expression data. The classification performance also shows that the predicted protein expression has an improved prediction power on cancer outcomes compared to the prediction done by the mRNA expression data only or considering both mRNA and miRNA. Availability: PTNet toolbox is available at http://github.com/CompbioLabUCF/PTNet.

APA-Scan: detection and visualization of 3'-UTR alternative polyadenylation with RNA-seq and 3'-end-seq data.

BACKGROUND: The eukaryotic genome is capable of producing multiple isoforms from a gene by alternative polyadenylation (APA) during pre-mRNA processing. APA in the 3'-untranslated region (3'-UTR) of mRNA produces transcripts with shorter or longer 3'-UTR. Often, 3'-UTR serves as a binding platform for microRNAs and RNA-binding proteins, which affect the fate of the mRNA transcript. Thus, 3'-UTR APA is known to modulate translation and provides a mean to regulate gene expression at the post-transcriptional level. Current bioinformatics pipelines have limited capability in profiling 3'-UTR APA events due to incomplete annotations and a low-resolution analyzing power: widely available bioinformatics pipelines do not reference actionable polyadenylation (cleavage) sites but simulate 3'-UTR APA only using RNA-seq read coverage, causing false positive identifications. To overcome these limitations, we developed APA-Scan, a robust program that identifies 3'-UTR APA events and visualizes the RNA-seq short-read coverage with gene annotations. METHODS: APA-Scan utilizes either predicted or experimentally validated actionable polyadenylation signals as a reference for polyadenylation sites and calculates the quantity of long and short 3'-UTR transcripts in the RNA-seq data. APA-Scan works in three major steps: (i) calculate the read coverage of the 3'-UTR regions of genes; (ii) identify the potential APA sites and evaluate the significance of the events among two biological conditions; (iii) graphical representation of user specific event with 3'-UTR annotation and read coverage on the 3'-UTR regions. APA-Scan is implemented in Python3. Source code and a comprehensive user's manual are freely available at https://github.com/compbiolabucf/APA-Scan . RESULT: APA-Scan was applied to both simulated and real RNA-seq datasets and compared with two widely used baselines DaPars and APAtrap. In simulation APA-Scan significantly improved the accuracy of 3'-UTR APA identification compared to the other baselines. The performance of APA-Scan was also validated by 3'-end-seq data and qPCR on mouse embryonic fibroblast cells. The experiments confirm that APA-Scan can detect unannotated 3'-UTR APA events and improve genome annotation. CONCLUSION: APA-Scan is a comprehensive computational pipeline to detect transcriptome-wide 3'-UTR APA events. The pipeline integrates both RNA-seq and 3'-end-seq data information and can efficiently identify the significant events with a high-resolution short reads coverage plots.

Multi-omics data integration by generative adversarial network.

MOTIVATION: Accurate disease phenotype prediction plays an important role in the treatment of heterogeneous diseases like cancer in the era of precision medicine. With the advent of high throughput technologies, more comprehensive multi-omics data is now available that can effectively link the genotype to phenotype. However, the interactive relation of multi-omics datasets makes it particularly challenging to incorporate different biological layers to discover the coherent biological signatures and predict phenotypic outcomes. In this study, we introduce omicsGAN, a generative adversarial network model to integrate two omics data and their interaction network. The model captures information from the interaction network as well as the two omics datasets and fuse them to generate synthetic data with better predictive signals. RESULTS: Large-scale experiments on The Cancer Genome Atlas breast cancer, lung cancer and ovarian cancer datasets validate that (i) the model can effectively integrate two omics data (e.g. mRNA and microRNA expression data) and their interaction network (e.g. microRNA-mRNA interaction network). The synthetic omics data generated by the proposed model has a better performance on cancer outcome classification and patients survival prediction compared to original omics datasets. (ii) The integrity of the interaction network plays a vital role in the generation of synthetic data with higher predictive quality. Using a random interaction network does not allow the framework to learn meaningful information from the omics datasets; therefore, results in synthetic data with weaker predictive signals. AVAILABILITY AND IMPLEMENTATION: Source code is available at: https://github.com/CompbioLabUCF/omicsGAN. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Imputation of spatially-resolved transcriptomes by graph-regularized tensor completion.

High-throughput spatial-transcriptomics RNA sequencing (sptRNA-seq) based on in-situ capturing technologies has recently been developed to spatially resolve transcriptome-wide mRNA expressions mapped to the captured locations in a tissue sample. Due to the low RNA capture efficiency by in-situ capturing and the complication of tissue section preparation, sptRNA-seq data often only provides an incomplete profiling of the gene expressions over the spatial regions of the tissue. In this paper, we introduce a graph-regularized tensor completion model for imputing the missing mRNA expressions in sptRNA-seq data, namely FIST, Fast Imputation of Spatially-resolved transcriptomes by graph-regularized Tensor completion. We first model sptRNA-seq data as a 3-way sparse tensor in genes (p-mode) and the (x, y) spatial coordinates (x-mode and y-mode) of the observed gene expressions, and then consider the imputation of the unobserved entries or fibers as a tensor completion problem in Canonical Polyadic Decomposition (CPD) form. To improve the imputation of highly sparse sptRNA-seq data, we also introduce a protein-protein interaction network to add prior knowledge of gene functions, and a spatial graph to capture the the spatial relations among the capture spots. The tensor completion model is then regularized by a Cartesian product graph of protein-protein interaction network and the spatial graph to capture the high-order relations in the tensor. In the experiments, FIST was tested on ten 10x Genomics Visium spatial transcriptomic datasets of different tissue sections with cross-validation among the known entries in the imputation. FIST significantly outperformed the state-of-the-art methods for single-cell RNAseq data imputation. We also demonstrate that both the spatial graph and PPI network play an important role in improving the imputation. In a case study, we further analyzed the gene clusters obtained from the imputed gene expressions to show that the imputations by FIST indeed capture the spatial characteristics in the gene expressions and reveal functions that are highly relevant to three different kinds of tissues in mouse kidney.

mTOR Contributes to the Proteome Diversity through Transcriptome-Wide Alternative Splicing.

The mammalian target of rapamycin (mTOR) pathway is crucial in energy metabolism and cell proliferation. Previously, we reported transcriptome-wide 3'-untranslated region (UTR) shortening by alternative polyadenylation upon mTOR activation and its impact on the proteome. Here, we further interrogated the mTOR-activated transcriptome and found that hyperactivation of mTOR promotes transcriptome-wide exon skipping/exclusion, producing short isoform transcripts from genes. This widespread exon skipping confers multifarious regulations in the mTOR-controlled functional proteomics: AS in coding regions widely affects the protein length and functional domains. They also alter the half-life of proteins and affect the regulatory post-translational modifications. Among the RNA processing factors differentially regulated by mTOR signaling, we found that SRSF3 mechanistically facilitates exon skipping in the mTOR-activated transcriptome. This study reveals a role of mTOR in AS regulation and demonstrates that widespread AS is a multifaceted modulator of the mTOR-regulated functional proteome.

Platform-integrated mRNA isoform quantification.

MOTIVATION: Accurate estimation of transcript isoform abundance is critical for downstream transcriptome analyses and can lead to precise molecular mechanisms for understanding complex human diseases, like cancer. Simplex mRNA Sequencing (RNA-Seq) based isoform quantification approaches are facing the challenges of inherent sampling bias and unidentifiable read origins. A large-scale experiment shows that the consistency between RNA-Seq and other mRNA quantification platforms is relatively low at the isoform level compared to the gene level. In this project, we developed a platform-integrated model for transcript quantification (IntMTQ) to improve the performance of RNA-Seq on isoform expression estimation. IntMTQ, which benefits from the mRNA expressions reported by the other platforms, provides more precise RNA-Seq-based isoform quantification and leads to more accurate molecular signatures for disease phenotype prediction. RESULTS: In the experiments to assess the quality of isoform expression estimated by IntMTQ, we designed three tasks for clustering and classification of 46 cancer cell lines with four different mRNA quantification platforms, including newly developed NanoString's nCounter technology. The results demonstrate that the isoform expressions learned by IntMTQ consistently provide more and better molecular features for downstream analyses compared with five baseline algorithms which consider RNA-Seq data only. An independent RT-qPCR experiment on seven genes in twelve cancer cell lines showed that the IntMTQ improved overall transcript quantification. The platform-integrated algorithms could be applied to large-scale cancer studies, such as The Cancer Genome Atlas (TCGA), with both RNA-Seq and array-based platforms available. AVAILABILITY AND IMPLEMENTATION: Source code is available at: https://github.com/CompbioLabUcf/IntMTQ. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

mTOR-regulated U2af1 tandem exon splicing specifies transcriptome features for translational control.

U2 auxiliary factor 1 (U2AF1) functions in 3'-splice site selection during pre-mRNA processing. Alternative usage of duplicated tandem exons in U2AF1 produces two isoforms, U2AF1a and U2AF1b, but their functional differences are unappreciated due to their homology. Through integrative approaches of genome editing, customized-transcriptome profiling and crosslinking-mediated interactome analyses, we discovered that the expression of U2AF1 isoforms is controlled by mTOR and they exhibit a distinctive molecular profile for the splice site and protein interactomes. Mechanistic dissection of mutually exclusive alternative splicing events revealed that U2AF1 isoforms' inherent differential preferences of nucleotide sequences and their stoichiometry determine the 3'-splice site. Importantly, U2AF1a-driven transcriptomes feature alternative splicing events in the 5'-untranslated region (5'-UTR) that are favorable for translation. These findings unveil distinct roles of duplicated tandem exon-derived U2AF1 isoforms in the regulation of the transcriptome and suggest U2AF1a-driven 5'-UTR alternative splicing as a molecular mechanism of mTOR-regulated translational control.

Computational Methods to Study Human Transcript Variants in COVID-19 Infected Lung Cancer Cells.

Microbes and viruses are known to alter host transcriptomes by means of infection. In light of recent challenges posed by the COVID-19 pandemic, a deeper understanding of the disease at the transcriptome level is needed. However, research about transcriptome reprogramming by post-transcriptional regulation is very limited. In this study, computational methods developed by our lab were applied to RNA-seq data to detect transcript variants (i.e., alternative splicing (AS) and alternative polyadenylation (APA) events). The RNA-seq data were obtained from a publicly available source, and they consist of mock-treated and SARS-CoV-2 infected (COVID-19) lung alveolar (A549) cells. Data analysis results show that more AS events are found in SARS-CoV-2 infected cells than in mock-treated cells, whereas fewer APA events are detected in SARS-CoV-2 infected cells. A combination of conventional differential gene expression analysis and transcript variants analysis revealed that most of the genes with transcript variants are not differentially expressed. This indicates that no strong correlation exists between differential gene expression and the AS/APA events in the mock-treated or SARS-CoV-2 infected samples. These genes with transcript variants can be applied as another layer of molecular signatures for COVID-19 studies. In addition, the transcript variants are enriched in important biological pathways that were not detected in the studies that only focused on differential gene expression analysis. Therefore, the pathways may lead to new molecular mechanisms of SARS-CoV-2 pathogenesis.

AS-Quant: Detection and Visualization of Alternative Splicing Events with RNA-seq Data.

(1) Background: A simplistic understanding of the central dogma falls short in correlating the number of genes in the genome to the number of proteins in the proteome. Post-transcriptional alternative splicing contributes to the complexity of the proteome and is critical in understanding gene expression. mRNA-sequencing (RNA-seq) has been widely used to study the transcriptome and provides opportunity to detect alternative splicing events among different biological conditions. Despite the popularity of studying transcriptome variants with RNA-seq, few efficient and user-friendly bioinformatics tools have been developed for the genome-wide detection and visualization of alternative splicing events. (2) Results: We propose AS-Quant, (Alternative Splicing Quantitation), a robust program to identify alternative splicing events from RNA-seq data. We then extended AS-Quant to visualize the splicing events with short-read coverage plots along with complete gene annotation. The tool works in three major steps: (i) calculate the read coverage of the potential spliced exons and the corresponding gene; (ii) categorize the events into five different categories according to the annotation, and assess the significance of the events between two biological conditions; (iii) generate the short reads coverage plot for user specified splicing events. Our extensive experiments on simulated and real datasets demonstrate that AS-Quant outperforms the other three widely used baselines, SUPPA2, rMATS, and diffSplice for detecting alternative splicing events. Moreover, the significant alternative splicing events identified by AS-Quant between two biological contexts were validated by RT-PCR experiment. (3) Availability: AS-Quant is implemented in Python 3.0. Source code and a comprehensive user's manual are freely available online.

A large-scale comparative study of isoform expressions measured on four platforms.

BACKGROUND: Most eukaryotic genes produce different transcripts of multiple isoforms by inclusion or exclusion of particular exons. The isoforms of a gene often play diverse functional roles, and thus it is necessary to accurately measure isoform expressions as well as gene expressions. While previous studies have demonstrated the strong agreement between mRNA sequencing (RNA-seq) and array-based gene and/or isoform quantification platforms (Microarray gene expression and Exon-array), the more recently developed NanoString platform has not been systematically evaluated and compared, especially in large-scale studies across different cancer domains. RESULTS: In this paper, we present a large-scale comparative study among RNA-seq, NanoString, array-based, and RT-qPCR platforms using 46 cancer cell lines across different cancer types. The goal is to understand and evaluate the calibers of the platforms for measuring gene and isoform expressions in cancer studies. We first performed NanoString experiments on 59 cancer cell lines with 404 custom-designed probes for measuring the expressions of 478 isoforms in 155 genes, and additional RT-qPCR experiments for a subset of the measured isoforms in 13 cell lines. We then combined the data with the matched RNA-seq, Exon-array, and Microarray data of 46 of the 59 cell lines for the comparative analysis. CONCLUSION: In the comparisons of the platforms for measuring the expressions at both isoform and gene levels, we found that (1) the agreement on isoform expressions is lower than the agreement on gene expressions across the four platforms; (2) NanoString and Exon-array are not consistent on isoform quantification even though both techniques are based on hybridization reactions; (3) RT-qPCR experiments are more consistent with RNA-seq and Exon-array than NanoString in isoform quantification; (4) different RNA-seq isoform quantification methods show varying estimation results, and among the methods, Net-RSTQ and eXpress are more consistent across the platforms; and (5) RNA-seq has the best overall consistency with the other platforms on gene expression quantification.

Ars2 promotes proper replication-dependent histone mRNA 3' end formation.

Ars2 is a component of the nuclear cap-binding complex that contributes to microRNA biogenesis and is required for cellular proliferation. Here, we expand on the repertoire of Ars2-dependent microRNAs and determine that Ars2 regulates a number of mRNAs, the largest defined subset of which code for histones. Histone mRNAs are unique among mammalian mRNAs because they are not normally polyadenylated but, rather, are cleaved following a 3' stem loop. A significant reduction in correctly processed histone mRNAs was observed following Ars2 depletion, concurrent with an increase in polyadenylated histone transcripts. Furthermore, Ars2 physically associated with histone mRNAs and the noncoding RNA 7SK. Knockdown of 7SK led to an enhanced ratio of cleaved to polyadenylated histone transcripts, an effect dependent on Ars2. Together, the data demonstrate that Ars2 contributes to histone mRNA 3' end formation and expression and these functional properties of Ars2 are negatively regulated by interaction with 7SK RNA.

An integrative model for alternative polyadenylation, IntMAP, delineates mTOR-modulated endoplasmic reticulum stress response.

3'-untranslated regions (UTRs) can vary through the use of alternative polyadenylation sites during pre-mRNA processing. Multiple publically available pipelines combining high profiling technologies and bioinformatics tools have been developed to catalog changes in 3'-UTR lengths. In our recent RNA-seq experiments using cells with hyper-activated mammalian target of rapamycin (mTOR), we found that cellular mTOR activation leads to transcriptome-wide alternative polyadenylation (APA), resulting in the activation of multiple cellular pathways. Here, we developed a novel bioinformatics algorithm, IntMAP, which integrates RNA-Seq and PolyA Site (PAS)-Seq data for a comprehensive characterization of APA events. By applying IntMAP to the datasets from cells with hyper-activated mTOR, we identified novel APA events that could otherwise not be identified by either profiling method alone. Several transcription factors including Cebpg (CCAAT/enhancer binding protein gamma) were among the newly discovered APA transcripts, indicating that diverse transcriptional networks may be regulated by mTOR-coordinated APA. The prevention of APA in Cebpg using the CRISPR/cas9-mediated genome editing tool showed that mTOR-driven 3'-UTR shortening in Cebpg is critical in protecting cells from endoplasmic reticulum (ER) stress. Taken together, we present IntMAP as a new bioinformatics algorithm for APA analysis by which we expand our understanding of the physiological role of mTOR-coordinated APA events to ER stress response. IntMAP toolbox is available at http://compbio.cs.umn.edu/IntMAP/.

Gemin5 delivers snRNA precursors to the SMN complex for snRNP biogenesis.

The SMN complex assembles Sm cores on snRNAs, a key step in the biogenesis of snRNPs, the spliceosome's major components. Here, using SMN complex inhibitors identified by high-throughput screening and a ribo-proteomic strategy on formaldehyde crosslinked RNPs, we dissected this pathway in cells. We show that protein synthesis inhibition impairs the SMN complex, revealing discrete SMN and Gemin subunits and accumulating an snRNA precursor (pre-snRNA)-Gemin5 intermediate. By high-throughput sequencing of this transient intermediate's RNAs, we discovered the previously undetectable precursors of all the snRNAs and identified their Gemin5-binding sites. We demonstrate that pre-snRNA 3' sequences function to enhance snRNP biogenesis. The SMN complex is also inhibited by oxidation, and we show that it stalls an inventory-complete SMN complex containing pre-snRNAs. We propose a stepwise pathway of SMN complex formation and snRNP biogenesis, highlighting Gemin5's function in delivering pre-snRNAs as substrates for Sm core assembly and processing.

tRNA binds to cytochrome c and inhibits caspase activation.

The specific molecular events that characterize the intrinsic apoptosis pathway have been the subject of intense research due to the pathway's fundamental role in development, homeostasis, and cancer. This pathway is defined by the release of cytochrome c from mitochondria into the cytosol and subsequent binding of cytochrome c to the caspase activator Apaf-1. Here, we report that both mitochondrial and cytosolic transfer RNA (tRNA) bind to cytochrome c. This binding prevents cytochrome c interaction with Apaf-1, blocking Apaf-1 oligomerization and caspase activation. tRNA hydrolysis in living cells and cell lysates enhances apoptosis and caspase activation, whereas microinjection of tRNA into living cells blocks apoptosis. These findings suggest that tRNA, in addition to its well-established role in gene expression, may determine cellular responsiveness to apoptotic stimuli.

Molecular Basis and Consequences of the Cytochrome c-tRNA Interaction.

The intrinsic apoptosis pathway occurs through the release of mitochondrial cytochrome c to the cytosol, where it promotes activation of the caspase family of proteases. The observation that tRNA binds to cytochrome c revealed a previously unexpected mode of apoptotic regulation. However, the molecular characteristics of this interaction, and its impact on each interaction partner, are not well understood. Using a novel fluorescence assay, we show here that cytochrome c binds to tRNA with an affinity comparable with other tRNA-protein binding interactions and with a molecular ratio of ∼3:1. Cytochrome c recognizes the tertiary structural features of tRNA, particularly in the core region. This binding is independent of the charging state of tRNA but is regulated by the redox state of cytochrome c. Compared with reduced cytochrome c, oxidized cytochrome c binds to tRNA with a weaker affinity, which correlates with its stronger pro-apoptotic activity. tRNA binding both facilitates cytochrome c reduction and inhibits the peroxidase activity of cytochrome c, which is involved in its release from mitochondria. Together, these findings provide new insights into the cytochrome c-tRNA interaction and apoptotic regulation.