Differential immunopeptidome analysis revealed cancer specific amino acid usage of HLA class-I antigens and novel neoantigens of colorectal cancer

Knowing the nature of human leukocyte antigen (HLA) peptides, also called as immunopeptides, is indispensable to realize the cancer precision medicine such as cancer vaccination and the better prediction of efficacy for immunocheckpoint inhibitor (ICI) treatment. Direct interrogation of immunopeptides by mass spectrometry (MS) is of great use for that purpose but the reality in analyses of scarce tissue samples still confronts technical challenges. To elucidate characteristics of HLA class-I immunopeptides specifically presented on colorectal cancer (CRC), the optimized immunopeptide isolation method and differential ion mobility mass spectrometry (DIM-MS) with whole exome sequencing (WES)-based personalized database were established and subjected to differential analysis of tumor or normal tissues of CRC patients. From pilot experiments using 108 cells of colon cancer cell line HCT116, total 9,249 unique immunopeptides, including total 11 neoantigens, were identified. Next, approximately 40 mg of tumor or normal regions of CRC tissues were collected from 17 patients and analyzed by our personalized immunopeptidomic technology. As the result, 44,785 unique immunopeptides were profiled, in which 2 neoantigens carrying the mutation KRAS-G12V or CPPED1-R228Q were identified. Interestingly, specific amino acid usage of C-terminus trimming of immunopeptides was found in tumor-exclusive immunopeptides. Thus, our personalized immunopeptidome analysis significantly expands presented antigen knowledgebase and allows direct determination of neoantigens from scarce tissue specimens. This advanced immunopeptidomics holds promise to new outlook of research in immunopeptides both from basic and clinical aspects.

Convergent Usage of Amino Acids in Human Cancers as a Reversed Process of Tissue Development

Genome and transcriptome-wide amino acid usage preference across different species is a well-studied phenomenon in molecular evolution, but its characteristics and implication in cancer evolution and therapy remain largely unexplored. Here, we analyzed large-scale transcriptome/proteome profiles such as TCGA, GTEx, and CPTAC and found that compared to normal tissues, different cancer types showed a convergent pattern towards using biosynthetically low-cost amino acids. Such a pattern can be accurately captured by a single index based on the average biosynthetic energy cost of amino acids, termed Energy Cost Per Amino Acid (ECPA). With this index, we further compared the trends of amino acid usage and the contributing genes in cancer and tissue development and revealed their reversed patterns. Finally, focusing on the liver, a tissue with a dramatic increase in ECPA during development, we found that EPCA represented a powerful biomarker that could distinguish liver tumors from normal liver samples consistently across 11 independent patient cohorts (AUROC = ~0.99) and outperformed any index based on single genes. Our study reveals an important principle underlying cancer evolution and suggests the global amino acid usage as a system-level biomarker for cancer diagnosis.

Ribosome Evolution and Structural Capacitance

In addition to the canonical loss-of-function mutations, mutations in proteins may additionally result in gain-of-function through the binary activation of cryptic ‘structural capacitance elements’. Our previous bioinformatic analysis allowed us to propose a new mechanism of protein evolution - structural capacitance – that arises via the generation of new elements of microstructure upon mutations that cause a disorder-to-order (DO) transition in previously disordered regions of proteins. Here we propose that the DO transition is a necessary follow-on from expected early codon-anticodon and tRNA acceptor stem-amino acid usage, via the accumulation of structural capacitance elements - reservoirs of disorder in proteins. We develop this argument further to posit that structural capacitance is an inherent consequence of the evolution of the genetic code.

Synonymous Codon Usages as an Evolutionary Dynamic for Chlamydiaceae

The family of Chlamydiaceae contains a group of obligate intracellular bacteria that can infect a wide range of hosts. The evolutionary trend of members in this family is a hot topic, which benefits our understanding of the cross-infection of these pathogens. In this study, 14 whole genomes of 12 Chlamydia species were used to investigate the nucleotide, codon, and amino acid usage bias by synonymous codon usage value and information entropy method. The results showed that all the studied Chlamydia spp. had A/T rich genes with over-represented A or T at the third positions and G or C under-represented at these positions, suggesting that nucleotide usages influenced synonymous codon usages. The overall codon usage trend from synonymous codon usage variations divides the Chlamydia spp. into four separate clusters, while amino acid usage divides the Chlamydia spp. into two clusters with some exceptions, which reflected the genetic diversity of the Chlamydiaceae family members. The overall codon usage pattern represented by the effective number of codons (ENC) was significantly positively correlated to gene GC3 content. A negative correlation exists between ENC and the codon adaptation index for some Chlamydia species. These results suggested that mutation pressure caused by nucleotide composition constraint played an important role in shaping synonymous codon usage patterns. Furthermore, codon usage of T3ss and Pmps gene families adapted to that of the corresponding genome. Taken together, analyses help our understanding of evolutionary interactions between nucleotide, synonymous codon, and amino acid usages in genes of Chlamydiaceae family members.

Quantifying Codon Usage in Signal Peptides: Gene Expression and Amino Acid Usage Explain Apparent Selection for Inefficient Codons

The Sec secretion pathway is found across all domains of life. A critical feature of Sec secreted proteins is the signal peptide, a short peptide with distinct physicochemical properties located at the N-terminus of the protein. Previous work indicates signal peptides are biased towards translationally inefficient codons, which is hypothesized to be an adaptation driven by selection to improve the efficacy and efficiency of the protein secretion mechanisms. We investigate codon usage in the signal peptides of E. coli using the Codon Adaptation Index (CAI), the tRNA Adaptation Index (tAI), and the ribosomal overhead cost formulation of the stochastic evolutionary model of protein production rates (ROC-SEMPPR). Comparisons between signal peptides and 5’-end of cytoplasmic proteins using CAI and tAI are consistent with a preference for inefficient codons in signal peptides. Simulations reveal these differences are due to amino acid usage and gene expression - we find these differences disappear when accounting for both factors. In contrast, ROC-SEMPPR, a mechanistic population genetics model capable of separating the effects of selection and mutation bias, shows codon usage bias (CUB) of the signal peptides is indistinguishable from the 5’-ends of cytoplasmic proteins. Additionally, we find CUB at the 5’-ends is weaker than later segments of the gene. Results illustrate the value in using models grounded in population genetics to interpret genetic data. We show failure to account for mutation bias and the effects of gene expression on the efficacy of selection against translation inefficiency can lead to a misinterpretation of codon usage patterns.