Bioinformatics Solutions (Canada)

Abstract Study question Can non-invasive aneuploidy detection using nanopore sequencing of spent embryo culture medium (SEM) replace PGT-A of trophectoderm biopsy (TEB) samples? Summary answer Concordance of 80% for good-quality SEM confirm feasibility of nanopore-sequencing, while low-quality/day-5 samples show reduced concordance. The procedure can complement but not replace other methods. What is known already PGT-A increases the chances of identifying a viable embryo, but the invasive biopsy required for PGT-A is technically challenging and may negatively impact embryo viability. The analysis of SEM holds promise for non-invasive PGT-A; however various technical challenges remain, raising concerns about its clinical utility. Study design, size, duration In this experimental comparative study, the detection of aneuploidy in SEM samples using nanopore sequencing technology is compared to routine PGT-A of TEB samples. A total of 174 SEM samples of day (D) 5, D6 or D7 blastocysts from 41 patients undergoing fertility treatment were analyzed for aneuploidy and compared to PGT-A results of corresponding TEB samples. The study was approved by the Ethic Committee of the Medical University Vienna (EK-1397/2022). Participants/materials, setting, methods For the niPGT-A analysis, SEM samples were amplified using whole genome amplification (WGA) and prepared for nanopore sequencing on the portable MinION sequencing device. A nanopore-specific data analysis pipeline was optimized for automated aneuploidy calling in SEM samples, and the results were compared to routine TEB-based PGT-A using array comparative genomic hybridization (aCGH). Concordance rates were stratified based on multiple criteria to identify conditions that maximize consistency and support potential clinical application of SEM analysis. Main results and the role of chance This is the first experimental study, systematically evaluating nanopore sequencing for aneuploidy analysis using SEM. PGT-A results of both TEB and SEM were successfully generated and compared for 143 embryos (29 D5 and 114 D6/7 samples). Sample-level concordance for D5 samples was significantly lower (51.7%), compared to D6/7 samples, showing concordance rates of 77.2%. For good-quality D6/7 samples, concordance reached 80.4% (82/102 samples concordantly euploid or aneuploid). Sex chromosomes were concordantly detected in 91.6% of all included samples. Although maternal contamination cannot be completely ruled out, no statistically significant evidence was observed, as false-negative euploid, female results were not overrepresented. Among 28 false-negative samples (SEM: euploid, TEB: aneuploid), 13 (46.4%) showed only segmental (6/28, 21%) or mosaic (7/28, 25%) aneuploidies in TEB, suggesting potential uncertainty in embryo status as previously reported. Segmental aneuploidies were accurately identified in 6 SEM samples from cases with known parental translocations. Notably, one segmental aneuploidy linked to a known translocation carrier was detected only in SEM. Limitations, reasons for caution The reference method aCGH is limited in resolution compared to NGS-based approaches. Additionally, TEB-based PGT-A results in general might not always represent the whole embryo, especially for mosaic and segmental results. Whole blastocyst screening as reference would therefore be ideal, but legally not possible in this study. Wider implications of the findings Concordance rates of 80% for good-quality D6/7 SEM samples compared to invasive TEB samples confirm feasibility of this fast and cost-effective technology. However, reduced concordance in D5/low DNA samples highlights limitations in clinical applicability. Robust validation and methodological adaptations need to be implemented before niPGT-A might replace invasive PGT-A. Trial registration number No

Transfer RNAs (tRNAs) play an essential role in protein synthesis by linking the nucleic acid sequences of gene products to the amino acid sequences of proteins. There are >400 functional tRNA genes in humans, and adding to this diversity, there are many single-nucleotide polymorphisms in tRNAs across our population, including anticodon variants that mistranslate the genetic code. In human genomes, we identified three rare alanine tRNA (tRNAAla) variants with nonsynonymous anticodon mutations: tRNAAla CGC G35T, tRNAAla UGC G35A, and tRNAAla AGC C36T. Since alanyl-tRNA synthetase (AlaRS) does not recognize the anticodon, we hypothesized that these tRNAAla variants will misincorporate Ala at glutamate (Glu), valine (Val), and threonine (Thr) codons, respectively. We found that expressing the naturally occurring tRNAAla variants in human cells led to defects in protein production without a substantial impact on cell growth. Using mass spectrometry, we confirmed and estimated Ala misincorporation levels at Glu (0.7%), Val (5%), and Thr (0.1%) codons. Although Ala misincorporation was higher at Val codons, cells misincorporating Ala at Glu codons had the most severe defect in protein production. The data demonstrate the ability of natural human tRNAAla variants to generate mistranslation, leading to defects in protein production that depend on the nature of the amino acid replacement.

The integration of deep learning approaches in biomedical research has been transformative, enabling breakthroughs in various applications. Despite these strides, its application in protein inference is impeded by the scarcity of extensively labeled data sets, a challenge compounded by the high costs and complexities of accurate protein annotation. In this study, we introduce GraphPI, a novel framework that treats protein inference as a node classification problem. We treat proteins as interconnected nodes within a protein-peptide-PSM graph, utilizing a graph neural network-based architecture to elucidate their interrelations. To address label scarcity, we train the model on a set of unlabeled public protein data sets with pseudolabels derived from an existing protein inference algorithm, enhanced by self-training to iteratively refine labels based on confidence scores. Contrary to prevalent methodologies necessitating data set-specific training, our research illustrates that GraphPI, due to the well-normalized nature of Percolator features, exhibits universal applicability without data set-specific fine-tuning, a feature that not only mitigates the risk of overfitting but also enhances computational efficiency. Our empirical experiments reveal notable performance on various test data sets and deliver significantly reduced computation times compared to common protein inference algorithms.

De novo peptide sequencing is one of the most fundamental research areas in mass spectrometry–based proteomics. Many methods have often been evaluated using a couple of simple metrics that do not fully reflect their overall performance. Moreover, there has not been an established method to estimate the false discovery rate (FDR) of de novo peptide-spectrum matches. Here we propose NovoBoard, a comprehensive framework to evaluate the performance of de novo peptide-sequencing methods. The framework consists of diverse benchmark datasets (including tryptic, nontryptic, immunopeptidomics, and different species) and a standard set of accuracy metrics to evaluate the fragment ions, amino acids, and peptides of the de novo results. More importantly, a new approach is designed to evaluate de novo peptide-sequencing methods on target-decoy spectra and to estimate and validate their FDRs. Our FDR estimation provides valuable information to assess the reliability of new peptides identified by de novo sequencing tools, especially when no ground-truth information is available to evaluate their accuracy. The FDR estimation can also be used to evaluate the capability of de novo peptide sequencing tools to distinguish between de novo peptide-spectrum matches and random matches. Our results thoroughly reveal the strengths and weaknesses of different de novo peptide-sequencing methods and how their performances depend on specific applications and the types of data.

Covalent antibody recruiting molecules (cARMs) constitute a proximity-inducing chemical strategy to modulate the recognition and elimination of cancer cells by the immune system. Recognition is achieved through synthetic bifunctional molecules that use covalency to stably bridge endogenous hapten-specific antibodies like anti-dinitrophenyl (anti-DNP), with tumor antigens on cancer cell surfaces. To recruit these antibodies, cARMs are equipped with the native hapten-binding molecule. The majority of cancer-killing immune machinery, however, recognizes epitopes on protein ligands and not small molecule haptens (e.g., Fc receptors, pathogen-specific antibodies). To access this broader class of immune machinery for recruitment, we developed a covalent immune proximity-inducing strategy. This strategy uses synthetic bifunctional electrophilic peptides derived from the native protein ligand. These bifunctional peptides are engineered to contain both a tumor-targeting molecule and a sulfonyl (VI) fluoride exchange (SuFEx) electrophile. As a proof of concept, we synthesized bifunctional electrophilic peptides derived from glycoprotein D (gD) on herpes simplex virus (HSV), to recruit gD-specific serum anti-HSV antibodies to cancer cells expressing the prostate-specific membrane antigen (PSMA). We demonstrate that serum anti-HSV antibodies can be selectively and irreversibly targeted by these electrophilic peptides and that the reaction rate can be uniquely enhanced by tuning SuFEx chemistry without a loss in selectivity. In cellular assays, electrophilic peptides demonstrated enhanced anti-tumor immunotherapeutic efficacy compared to analogous peptides lacking electrophilic functionality. This enhanced efficacy was especially prominent in the context of (a) natural anti-HSV antibodies isolated from human serum and (b) harder to treat tumor cells associated with lower PSMA expression levels. Overall, we demonstrate a new covalent peptide-based approach to immune proximity induction and reveal the potential utility of anti-viral antibodies in synthetic tumor immunotherapy.

Transient expression of recombinant proteins in plants is being used as a platform for production of therapeutic proteins. Benefits of this system include a reduced cost of drug development, rapid delivery of new products to the market, and an ability to provide safe and efficacious medicines for diseases. Although plant-based production systems offer excellent potential for therapeutic protein production, barriers, such as plant host defense response, exist which negatively impact the yield of product. Here we provide a protocol using tandem mass tags and mass spectrometry-based proteomics to quickly and robustly quantify the change in abundance of host defense proteins produced during the production process. These proteins can then become candidates for genetic manipulation to create host plants with reduced plant defenses capable of producing higher therapeutic protein yields.

ABSTRACT Development of fully protective dengue virus (DV) vaccines has been problematic as infection with DV requires a broad antibody immune response that targets all 4 possible serotypes. Herein, we used an integrated systems vaccinology approach to identify prevaccination features that allow the development of fully protective DV-specific antibody responses. This approach allowed us to identify a transcription network in a subset of monocytes defined by the expression of CD68 and downstream of specific pro- and anti-inflammatory cytokines. Moreover, we identified metabolites as drivers of an immune response that induced neutralizing antibodies to the 4 DV serotypes. Specifically, PC/PE drove the production of TGF-B in CD68 low monocytes, which was a positive correlate of the protective antibody response. In contrast, primary and secondary bile acids triggered a proinflammatory response downstream of TGR5 signaling and inflammasome activation in CD68 high monocytes, which was associated to a non-protective antibody response. These features were validated in vitro in primary myeloid cells. Our results highlight the role of cell and systemic metabolism as regulators of protective immune responses to vaccination, and that systems vaccinology is a key tool to identify such mechanisms.

Abstract Infection with Yellow Fever (YF) can lead to multiple outcomes ranging from death from total organ failure to clearance of viremia and survival. The mechanisms underlying these differences in clinical outcomes have yet to be defined. We had access to a cohort of YF infected subjects that showed these range of outcomes. An unbiased integrated OMICs approach was used to identify pathways and effector molecules that drive the severe disease and death as compared to resolution of infection. We used the MELD and SIC score as objective markers of disease severity. We show that a specific signature of upregulated innate pro-inflammatory cytokines significantly demarcates subjects with severe disease leading to death from subjects who clear virus. Pathogen sequencing showed heightened levels of proteolytic bacteria at the i.e Actinobacteria and these were correlated to lower levels of tryptophan and tyrosine amino acids measured by untargeted metabolomics. These two features were significantly associated to MELD scores synonymous of milder disease. Propionate a bacterial metabolite that triggers Treg differentiation that can as well limit the hyperimmune activation associated to severe outcome was also associated to improved outcome. Our results suggest a model whereby proteolytic bacteria limit the availability of the aromatic amino acid pool available for cytokine production thereby preventing the induction of the cytokine storm that is associated to severe disease and death.

Abstract Rocaglates are under active development for cancer therapy, with the first-in-class member zotatifin recently entering Phase I/II clinical trial for advanced solid tumor malignancies (NCT04092673). Initiation of this trial was based purely on rocaglates' inhibitory activity against translation initiation factor and RNA helicase eIF4A. Rapid development of these compounds towards clinical use urgently requires a complete understanding of their mechanisms. Critically, however, the full effects of rocaglates on protein synthesis are unknown, especially with regards to up-regulated proteins and rocaglate-activated translation factors that mediate their potent anticancer activity. Past rocaglate studies focused exclusively on mechanisms and targets of translational inhibition, relying exclusively on indirect RNA-based approaches that do not reliably capture proteome-level changes, a caveat proven in cancer cells responding to drugs. Formal proteomic investigations of rocaglate effects are non-existent in the literature. We addressed these vital knowledge gaps in this study. An original proteome-level analysis using metabolic pulse-labeling and TMT-mass spectrometry revealed extensive rocaglate-induced changes in protein output (translatome), including myriad rocaglate-up-regulated proteins that drive cytotoxicity through currently uncharacterized mechanisms. Specifically, we find in vitro and in vivo that rocaglates, including zotatifin, induce GEF-H1, activating anti-survival RHOA/JNK signaling. Intriguingly, these responses occur regardless of eIF4A expression. Beyond translatome remodeling, mass spectrometry-based MATRIX interrogation of the protein synthesis machinery revealed widespread rocaglate-dependent changes. As a prime example, rocaglate-augmented eEF1ϵ1 activity mediates initiator methionine-tRNAiMet delivery to drive synthesis of rocaglate-inducible proteins and phenotypes. Overall, this study transforms the current definition of rocaglates from one-dimensional eIF4A inhibitors to translation remodelers that globally reprogram protein output and the translation machinery. As human patients begin treatment with these compounds, our discovery of rocaglate-inducible proteins and rocaglate-activated translation factors that drive drug-specific responses highlights an entirely uncharacterized yet critical aspect of rocaglate potency. These timely findings emphasize systemic translational modeling as a newer concept for cancer therapy, and advocate for a systematic overhaul in our understanding of drugs traditionally defined as translation inhibitors. Citation Format: Jyun David Ho, Jr, Tyler A. Cunningham, Caroline A. Coughlin, Artavazd Arumov, Evan R. Roberts, Daniel Bilbao, Jonathan R. Krieger, Stephen Lee, Jonathan H. Schatz. Translatome and protein synthesis machinery remodeling by rocaglates drives antitumor activity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1031.

Abstract Identifying antigens displayed specifically on tumour cell surfaces by human leukocyte antigen (HLA) proteins is important for the development of immunotherapies and cancer vaccines. The difficulty in capturing an HLA ligandome stems from the fact that many HLA ligands are derived from splicing events or contain mutations, hindering their identification in a standard database search. To address this challenge, we developed an immunopeptidomics workflow with PEAKS XPro that uses de novo sequencing to uncover such peptides and identifies mutations for neoantigen discovery. We demonstrate the utility of this workflow by re-analyzing HLA-I ligandome datasets and reveal a vast diversity in peptide sequences among clones derived from a colorectal cancer tumour. Over 8000 peptides predicted to bind HLA-I molecules were identified by de novo sequencing only (not found in the UniProt database) and make up over 50% of identified peptides from each sample. Lastly, tumour-specific mutations and consensus sequence motif characteristics are defined. This workflow is widely applicable to any immunopeptidomic mass spectrometry dataset and does not require custom database generation for neoantigen discovery.