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MHC-I associated peptides (MAPs) play a central role in the elimination of virus-infected and neoplastic cells by CD8 T cells. However, accurately predicting the MAP repertoire remains difficult, because only a fraction of the transcriptome generates MAPs. In this study, we investigated whether codon arrangement (usage and placement) regulates MAP biogenesis. We developed an artificial neural network called Codon Arrangement MAP Predictor (CAMAP), predicting MAP presentation solely from mRNA sequences flanking the MAP-coding codons (MCCs), while excluding the MCC per se . CAMAP predictions were significantly more accurate when using original codon sequences than shuffled codon sequences which reflect amino acid usage. Furthermore, predictions were independent of mRNA expression and MAP binding affinity to MHC-I molecules and applied to several cell types and species. Combining MAP ligand scores, transcript expression level and CAMAP scores was particularly useful to increase MAP prediction accuracy. Using an in vitro assay, we showed that varying the synonymous codons in the regions flanking the MCCs (without changing the amino acid sequence) resulted in significant modulation of MAP presentation at the cell surface. Taken together, our results demonstrate the role of codon arrangement in the regulation of MAP presentation and support integration of both translational and post-translational events in predictive algorithms to ameliorate modeling of the immunopeptidome.

MHC-I associated peptides (MAPs) play a central role in the elimination of virus-infected and neoplastic cells by CD8 T cells. However, accurately predicting the MAP repertoire remains difficult, because only a fraction of the transcriptome generates MAPs. In this study, we investigated whether codon arrangement (usage and placement) regulates MAP biogenesis. We developed an artificial neural network called Codon Arrangement MAP Predictor (CAMAP), predicting MAP presentation solely from mRNA sequences flanking the MAP-coding codons (MCCs), while excluding the MCC per se. CAMAP predictions were significantly more accurate when using original codon sequences than shuffled codon sequences which reflect amino acid usage. Furthermore, predictions were independent of mRNA expression and MAP binding affinity to MHC-I molecules and applied to several cell types and species. Combining MAP ligand scores, transcript expression level and CAMAP scores was particularly useful to increase MAP prediction accuracy. Using an in vitro assay, we showed that varying the synonymous codons in the regions flanking the MCCs (without changing the amino acid sequence) resulted in significant modulation of MAP presentation at the cell surface. Taken together, our results demonstrate the role of codon arrangement in the regulation of MAP presentation and support integration of both translational and post-translational events in predictive algorithms to ameliorate modeling of the immunopeptidome. Author summary MHC-I associated peptides (MAPs) are small fragments of intracellular proteins presented at the surface of cells and used by the immune system to detect and eliminate cancerous or virus-infected cells. While it is theoretically possible to predict which portions of the intracellular proteins will be naturally processed by the cells to ultimately reach the surface, current methodologies have prohibitively high false discovery rates. Here we introduce an artificial neural network called Codon Arrangement MAP Predictor (CAMAP) which integrates information from mRNA-to-protein translation to other factors regulating MAP biogenesis (e.g. MAP ligand score and transcript expression levels) to improve MAP prediction accuracy. While most MAP predictive approaches focus on MAP sequences per se, CAMAP’s novelty is to analyze the MAP-flanking mRNA sequences, thereby providing completely independent information for MAP prediction. We show on several datasets that the integration of CAMAP scores with other known factors involved in MAP presentation (i.e. MAP ligand score and mRNA expression) significantly improves MAP prediction accuracy, and further validate CAMAP learned features using an in-vitro assay. These findings may have major implications for the design of vaccines against cancers and viruses, and in times of pandemics could accelerate the identification of relevant MAPs of viral origins.

Since the discovery of nitrogen mustard, the first anticancer agent, chemotherapy has palyed an important role in the treatment of several types of cancer. Despite the progress and the advencement of anti-cancer drugs, chemotherapy is often limited by resistance mechanisms developed by cancer cells. Among the most common mechanisms is the expulsion of anticancer drugs out of the cells, resulting in a decreased intracellular concentration and lowered antitumor activity. The expulsion of anticancer drugs is mediated by the over-expression of the transmembrane transport protein where the best characterized is P glycoprotein (Pgp). This protein utilize the energy of ATP to actively carry out structurally and functionally unrelated anticancer agents across the plasma membrane. Encoded by the MDR1 gene, the predominant role of Pgp is detected in more than 50 % of breast cancer patient. Unlike chemical inhibitors, small interfering RNAs (MDR1-siRNA) provide a more specific downregulation of Pgp and reversion of the drug resistance. These small double-stranded RNA (21 nucleotides) are able to bind to a specific mRNA sequence of the MDR1 gene, causing its degradation and thereby disabling translation of Pgp. However, the efficiency of MDR1-siRNA is limited by their vulnerability in the physiological medium and their inability to cross cell barriers. For this purpose, a delivery system is needed to potentiate the effect of siRNA. In this study, the delivery system used is chitosan, a natural, biodegradable and non-toxic polymer. Chitosan is characterized by its degree of deacetylation (DDA), molecular weight (MM) and amine to phosphate (N: P) ratio. For simplicity, these parameters are described as follows [DDA-MM- N:P]. Chitosan is able to interact electrostatically with the MDR1-siRNA due to its cationic charge, leading to chitosan/MDR1-siRNA nanoparticles. The nanoparticles size and morphology was evaluated by dynamic light diffusions (DLS) and scanning electron microscopy (ESEM). Nanoparticles surface charge was characterized by measuring the zeta potential. Furthermore, the formation of chitosan nanoparticles and their stability under variable pH was evaluated by RiboGreen and polyacrylamide gel electrophoresis. The nanoparticles ability to bypass cellular membrane was analyzed by confocal microscopy and flow cytometry (FACS). The efficacy of nanoparticles in gene silencing was assessed by quantitative RT-PCR, and confirmed at the phenotypic level by western blot analysis. Whereas, the toxicity profile of chitosan nanoparticles was evaluated using Alamar Blue proliferation assay. The obtained results showed that the size, charge and efficacy of nanoparticles depend on physicochemical parameters of chitosan. The size of the nanoparticles prepared with the following formulations 92-10-5, 92-150-5, 82-200-5 and 80-10-5 increased from 80 to 230 nm, depending on chitosan molecular weight. Whereas the positive charge of the nanoparticles is enhanced with DDA and the N: P ratio. The chitosan nanoparticles are stable at pH 6.5 independently of incubation time. Furthermore, the nanoparticles are able to bypass the cell barriers and efficiently downregulate the MDR1 gene expression. Nanoparticles could induce a higher gene silencing (90% reduction in MDR1) depending on chitosan molecular weight and transfection method. The gene silencing efficiency of the nanoparticles was confirmed by reduced Pgp expression to the same level of sensitive cells. These results highlight the efficacy of chitosan-siRNA nanoparticles to mediate a specific inhibition of MDR1 gene, responsible for chemoresistance. The absence of toxicity, specificity and the phenotypic silencing of the Pgp, paves the way for further studies to evince the ability of nanoparticles to reverse multidrug resistance in vitro and in vivo . Due to its effective gene delivery and its safety profile, chitosan could also be marketed as a non-toxic transfection reagent competing with lipid delivery systems.

Abstract MHC-I associated peptides (MAPs) play a central role in the elimination of virus-infected and neoplastic cells by CD8 T cells. However, accurately predicting the MAP repertoire remains difficult, because only a fraction of the transcriptome generates MAPs. In this study, we investigated whether codon arrangement (usage and placement) regulates MAP biogenesis. We developed an artificial neural network called Codon Arrangement MAP Predictor (CAMAP), predicting MAP presentation solely from mRNA sequences flanking the MAP-coding codons (MCCs), while excluding the MCC per se. CAMAP predictions were significantly more accurate when using original codon sequences than shuffled codon sequences which reflect amino acid usage. Furthermore, predictions were independent of mRNA expression and MAP binding affinity to MHC-I molecules and applied to several cell types and species. Combining MAP ligand scores, transcript expression level and CAMAP scores was particularly useful to increaser MAP prediction accuracy. Using an in vitro assay, we showed that varying the synonymous codons in the regions flanking the MCCs (without changing the amino acid sequence) resulted in significant modulation of MAP presentation at the cell surface. Taken together, our results demonstrate the role of codon arrangement in the regulation of MAP presentation and support integration of both translational and post-translational events in predictive algorithms to ameliorate modeling of the immunopeptidome. Author summary MHC-I associated peptides (MAPs) are small fragments of intracellular proteins presented at the surface of cells and used by the immune system to detect and eliminate cancerous or virus-infected cells. While it is theoretically possible to predict which portions of the intracellular proteins will be naturally processed by the cells to ultimately reach the surface, current methodologies have prohibitively high false discovery rates. Here we introduce an artificial neural network called Codon Arrangement MAP Predictor (CAMAP) which integrates information from mRNA-to-protein translation to other factors regulating MAP biogenesis (e.g. MAP ligand score and transcript expression levels) to improve MAP prediction accuracy. While most MAP predictive approaches focus on MAP sequences per se, CAMAP’s novelty is to analyze the MAP-flanking mRNA sequences, thereby providing completely independent information for MAP prediction. We show on several datasets that the integration of CAMAP scores with other known factors involved in MAP presentation (i.e. MAP ligand score and mRNA expression) significantly improves MAP prediction accuracy, and further validate CAMAP learned features using an in-vitro assay. These findings may have major implications for the design of vaccines against cancers and viruses, and in times of pandemics could accelerate the identification of relevant MAPs of viral origins. Competing Interest Statement The authors have declared no competing interest. Footnotes * Tariq Daouda is now affiliated to: (1) Broad Institute of MIT and Harvard, Cambridge, United States; (2) Center for Cancer Research, Massachusetts General Hospital, Charlestown, United States; (3) Department of Medicine, Harvard Medical School, Boston, United States; and (4) Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital, Charlestown, United States. * Addressed potential biases: We show that the higher prediction accuracy of CAMAPs trained with original codon sequences compared to shuffled sequences are not due to potential biases in our datasets (i.e. MAP binding affinity, transcript expression levels or sequence homology). New evaluation of the False Positive Rate reduction when CAMAP is combined with other predictive measure: We applied the validation method of Croft S. et al (PLoS Computational Biology, 2020) and show that CAMAP significantly increases the prediction accuracy of Mass-Spectrometry identified epitopes. Combining CAMAP score to the ligand score and expression levels reduces the False Positive Rate for the identification of the top 1% epitopes from 32.84% to 1.11%. * Abbreviations MHC-I major histocompatibility complex class-I MAP MHC-I associated peptides CAMAP Codon arrangement MAP predictor DRiP defective ribosomal product ANN artificial neural network MCC MAP-coding codons B-LCL B-lymphoblastoid cell line KL Kullback-Leibler BS binding score OVA ovalbumin protein WT wildtype EP enhanced presentation RP reduced presentation

The transcription factors signal transducer and activator of transcription 1 and 3 (STAT1 and STAT3) play essential roles in immune and non-immune cell function. The clinical characterization of patients carrying germline gain or loss-of-function (GOF or LOF) mutations in these genes has significantly improved our understanding of their physiological and pathological roles. Although patients with LOF, GOF, and GOF mutations are classified into distinct inborn errors of immunity (IEI) categories, namely Hyper-IgE Syndrome, Regulatory T cell defects, and predisposition to Mucocutaneous Candidiasis, respectively, there is notable clinical overlap among these disorders. We describe a 17-year-old girl with recurrent lung infection leading to bronchiectasis, chronic onychomycosis, recurrent vulvovaginal candidiasis, and oral thrush. Additional findings included short stature, delayed puberty, and retained primary teeth. Laboratory results revealed eosinophilia and elevated IgE serum levels, with a NIH HIES score of 53. A rare heterozygous deletion within the 3'UTR of the gene (c.*351_*353del) was identified through a candidate gene approach. Although the variant is in a non-coding region, increased phosphorylation and elevated suppressor of cytokine signaling 3 (SOCS3) expressions suggested a potential GOF effect. analysis further predicted that the deletion disrupts microRNA (miRNA) binding sites and RNA binding proteins (RBP), potentially impairing post-transcriptional regulation and contributing to overexpression. Given the complexity of the phenotype and the atypical location of the variation, whole-exome sequencing (WES) was performed, revealing a heterozygous missense mutation in the DNA-binding domain ( , ), previously reported in autosomal dominant chronic mucocutaneous candidiasis (AD-CMC). Functional assays on lymphocytes confirmed an increased phosphorylation compared to both LOF patient and healthy controls. This case highlights the diagnostic complexity of overlapping IEI phenotypes and the value of combining targeted and WES strategies. This dual molecular diagnosis, comprising a regulatory variant in and a pathogenic coding mutation in , emphasizes the need to include non-coding regions in genetic analyses. It also underscores the value of using techniques that offer a broader genomic view and capture all coding exons, enabling a more comprehensive correlation with the clinical and immunological phenotype.