Learning the sequence code for mRNA and protein abundance in human immune cells
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1005690 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR25649616_1.fastq.gz | Fastqsanger.gz | |||
| SRR25649616_2.fastq.gz | Fastqsanger.gz | |||
| SRR25649617_1.fastq.gz | Fastqsanger.gz | |||
| SRR25649617_2.fastq.gz | Fastqsanger.gz | |||
| SRR25649618_1.fastq.gz | Fastqsanger.gz |
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Project description:mRNA and protein abundance are defined by transcriptional and post-transcriptional regulatory mechanisms. Here, we develop a machine learning pipeline, termed SONAR, to decipher the endogenous sequence code that determines mRNA and protein abundance in human cells. SONAR models predict up to 62% of mRNA and 63% of protein abundance independent of promoter or enhancer information, and reveal a strong—yet dynamic—cell-type specific sequence code. We also find that the effect of sequence features is dependent on their location within the mRNA transcript. Using SONAR, we design synthetic 3’UTRs, with which protein expression levels can be manipulated and tailored to a specific cell-type. Beyond its fundamental findings, our work provides novel means to improve immunotherapies and biotechnology applications.
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Project description:The context-dependent correlation between mRNA and protein abundance has long been debated. RNA sequencing (RNA-seq), a high-throughput, commonly used method for analyzing transcriptional dynamics and identifying biomarkers, leaves questions about whether we can translate RNA-seq-identified gene signatures directly to protein changes. In this study, we utilized a set of 17 widely assessed immune and wound healing mediators in the context of canine Volumetric Muscle Loss (VML) to investigate the correlation of mRNA and protein abundance. Our data reveal an overall agreement between mRNA and protein levels on these 17 mediators when examining samples from the same experimental condition, such as the same wound biopsy. However, we observed a lack of correlation between mRNA and protein levels for individual genes under different conditions, underscoring the challenges in converting transcript level changes directly into corresponding protein level changes. As an initial attempt to address this discrepancy, we developed a machine-learning model to predict protein abundances from RNA-seq data, achieving high accuracy (Spearman's Rho: 0.78-0.99, imputed versus measured protein abundance; pooling all biopsies; 5-fold cross-validation). Our approach also effectively corrected multiple extreme outliers measured by antibody-based protein assays. Additionally, this model has the potential to detect post-translational modification events, as shown by accurately estimating activated transforming growth factor (TGF)-β1 levels. While preliminary, this study introduces a promising strategy for translating RNA-seq data into protein abundance and the associated biological relevance.
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Project description:Unraveling the Complex Relationship Between mRNA and Protein Abundance: A Machine Learning-Based Approach for Imputing Protein Levels from RNA-seq Data
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Project description:- mRNA abundance from wild type grown in LB or M9 (mRNA vs. Genomic DNA) : GSM30231 - GSM30238, GSM30338-GSM30341 - mRNA abundance grown in LB with norfloxacin treatment for 10 min or 30 min (mRNA vs. Genomic DNA): GSM30247-GSM30262 - mRNA abundance from gyrA (D82G) mutant grown in LB (mRNA vs. Genomic DNA):GSM30239-GSM30246 - Transcription profile between wild type and gyrA (D82G) mutant: GSM30330-GSM30337 Keywords: other
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Project description:- mRNA abundance from wild type grown in LB or M9 (mRNA vs. Genomic DNA) : GSM30231 - GSM30238, GSM30338-GSM30341 - mRNA abundance grown in LB with norfloxacin treatment for 10 min or 30 min (mRNA vs. Genomic DNA): GSM30247-GSM30262 - mRNA abundance from gyrA (D82G) mutant grown in LB (mRNA vs. Genomic DNA):GSM30239-GSM30246 - Transcription profile between wild type and gyrA (D82G) mutant: GSM30330-GSM30337
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Project description:Machine learning and modeling approaches have been used to classify protein sequences for a broad set of tasks including predicting protein function, structure, expression, and localization. Some recent studies have successfully predicted whether a given gene is expressed as mRNA or even translated to proteins potentially, but given that not all genes are expressed in every condition and tissue, the challenge remains to predict condition-specific expression. To address this gap, we developed a machine learning approach to predict tissue-specific gene expression across 23 different tissues in maize, solely based on DNA promoter and protein sequences. For class labels, we defined high and low expression levels for mRNA and protein abundance and optimized classifiers by systematically exploring various methods and combinations of k-mer sequences in a two-phase approach. In the first phase, we developed Markov model classifiers for each tissue and built a feature vector based on the predictions. In the second phase, the feature vector was used as an input to a Bayesian network for final classification. Our results show that these methods can achieve high classification accuracy of up to 95% for predicting gene expression for individual tissues. By relying on sequence alone, our method works in settings where costly experimental data are unavailable and reveals useful insights into the functional, evolutionary, and regulatory characteristics of genes.
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Project description:The sensitivity of malignant tissues to T cell-based cancer immunotherapies is dependent on the presence of targetable HLA class I ligands on the tumor cell surface. Peptide intrinsic factors, such as HLA class I affinity, likelihood of proteasomal processing, and transport into the ER lumen have all been established as determinants of HLA ligand presentation. However, the role of sequence features at the gene and protein level as determinants of epitope presentation has not been systematically evaluated. To address this, we performed HLA ligandome mass spectrometry on patient-derived melanoma lines and used this data-set to evaluate the contribution of 7,124 gene and protein sequence features to HLA sampling. This analysis reveals that a number of predicted modifiers of mRNA and protein abundance and turn-over, including predicted mRNA methylation and protein ubiquitination sites, inform on the presence of HLA ligands. Importantly, integration of gene and protein sequence features into a machine learning approach augments HLA ligand predictions to a comparable degree as predictive models that include experimental measures of gene expression. Our study highlights the value of gene and protein features to HLA ligand predictions.
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