Biology subjects

Bresser, K.

Publications and source records attributed to Bresser, K..

3 recordsLinked to original sources

bioRxiv2023

Gene and protein sequence features augment HLA class I ligand predictions

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.

immunology↗

bioRxiv2023

Learning the sequence code for mRNA and protein abundance in human immune cells

Accurate protein expression in human immune cells is essential for appropriate cellular function. The mechanisms that define protein abundance are complex and executed on transcriptional, post-transcriptional and post-translational level. Here, we present SONAR, a machine learning pipeline that learns the endogenous sequence code and that defines protein abundance in human cells. SONAR uses thousands of sequence features (SFs) to predict up to 63% of the protein abundance independently of promoter or enhancer information. SONAR uncovered the cell type-specific and activation-dependent usage of SFs. The deep knowledge of SONAR provides a map of biologically active SFs, which can be leveraged to manipulate the amplitude, timing, and cell type-specificity of protein expression. SONAR informed on the design of enhancer sequences to boost T cell receptor expression and to potentiate T cell function. Beyond providing fundamental insights in the regulation of protein expression, our study thus offers novel means to improve therapeutic and biotechnology applications. One Sentence SummarySONAR informs the design of cell type-specific protein expression in human cells

cell biology↗

bioRxiv2023

A fluorescence-based sensor screen identifies MED12 as a potential microsatellite instability regulator in colon cancer

Inactivation of the DNA mismatch repair (MMR) system, due to (epi)genetic alterations of MMR genes, increases the frequency of mutations across the genome, creating a phenotype known as microsatellite instability (MSI). Cancers with this phenotype have been associated with a better prognosis for some time, but only since recently it has been recognised as a predictive biomarker of response to immunotherapy. Because MSI tumours accumulate more insertions and/or deletions in coding regions of the genome containing microsatellites, there is an increase in neoantigens resulting from reading frame shifts, which promotes immunogenicity. To investigate if additional genes exist that can cause an MSI phenotype, we developed a fluorescence-based sensor to identify genes whose inactivation increases the rate of frameshift mutations on microsatellite sequences in cancer cells. Using genome-scale CRISPR/Cas9 screens, we identified MED12 as a potential new regulator of microsatellite instability. Consistent with this, we found that MED12 mutant colon cancers that lack mutations in the known MMR genes are more likely to be of the MSI phenotype.

cancer biology↗