DeepAntigen: a novel method for neoantigen prioritization via 3D genome and deep sparse learning

Investigating TCR-pMHC interactions for TCRs without identified epitopes by constructing a computational pipeline

The molecular mechanisms underlying epitope recognition by T cell receptors (TCRs) are critical for activating T cell immune responses and rationally designing TCR-based therapeutics. Single-cell sequencing techniques vastly boost the accumulation of TCR sequences, while the limitation of available TCR-pMHC structures hampers further investigations. In this study, we proposed a computational pipeline that incorporates structural information and single-cell sequencing data to investigate the epitope-recognition mechanisms for TCRs without identified epitopes. By antigen specificity clustering, we mapped the epitope sequences between epitope-known and epitope-unknown TCRs from COVID-19 patients. One reported SARS-CoV-2 epitope, NQKLIANQF (S919-927), was identified for a TCR expressed by 614 T cells (TCR-614). Epitope screening also identified a potential cross-reactive epitope, KLKTLVATA (NSP31790-1798), for a TCR expressed by 204 T cells (TCR-204). By molecular dynamics (MD) simulations, we revealed the detailed epitope-recognition mechanisms for both TCRs. The structural motifs responsible for epitope recognition revealed by the MD simulations are consistent with the sequential features recognized by the sequence-based clustering method. We hope that this strategy could facilitate the discovery and optimization of TCR-based therapeutics.

3D genome contributes to MHC-II neoantigen prediction

Reliable and ultra-fast DNA and RNA sequencing have been achieved with the emergence of high-throughput sequencing technology. When combining the results of DNA and RNA sequencing for tumor cells of cancer patients, neoantigens that potentially stimulate the immune response of either CD4+ or CD8+ T cells can be identified. However, due to the abundance of somatic mutations and the high polymorphic nature of human leukocyte antigen (HLA) it is challenging to accurately predict the neoantigens. Moreover, comparing to HLA-I presented peptides, the HLA-II presented peptides are more variable in length, making the prediction of HLA-II loaded neoantigens even harder. A number of computational approaches have been proposed to address this issue but none of them considers the DNA origin of the neoantigens from the perspective of 3D genome. Here we investigate the DNA origins of the immune-positive and non-negative HLA-II neoantigens in the context of 3D genome and discovered that the chromatin 3D architecture plays an important role in more effective HLA-II neoantigen prediction. We believe that the 3D genome information will help to increase the precision of HLA-II neoantigen discovery and eventually benefit precision and personalized medicine in cancer immunotherapy.

Unraveling schizophrenia's genetic complexity through advanced causal inference and chromatin 3D conformation

Schizophrenia is a polygenic complex disease with a heritability as high as 80 %, yet the mechanism of polygenic interaction in its pathogenesis remains unclear. Studying the interaction and regulation of schizophrenia susceptibility genes is crucial for unraveling the pathogenesis of schizophrenia and developing antipsychotic drugs. Therefore, we developed a bioinformatics method named GRACI (Gene Regulation Analysis based on Causal Inference) based on the principles of information theory, a causal inference model, and high order chromatin 3D conformation. GRACI captures the interaction and regulatory relationships between schizophrenia susceptibility genes by analyzing genotyping data. Two datasets, comprising 1459 and 2065 samples respectively, were analyzed, and the gene networks from both datasets were constructed. GRACI showcased superior accuracy when compared to widely adopted methods for detecting gene-gene interactions and intergenic regulation. This alignment was further substantiated by its correlation with chromatin high-order conformation patterns. Using GRACI, we identified three potential genes-KCNN3, KCNH1, and KCND3-that are directly associated with schizophrenia pathogenesis. Furthermore, the results of GRACI on the standalone dataset illustrated the method's applicability to other complex diseases. GRACI download: https://github.com/liuliangjie19/GRACI.

Transformers meets neoantigen detection: a systematic literature review

Cancer immunology offers a new alternative to traditional cancer treatments, such as radiotherapy and chemotherapy. One notable alternative is the development of personalized vaccines based on cancer neoantigens. Moreover, Transformers are considered a revolutionary development in artificial intelligence with a significant impact on natural language processing (NLP) tasks and have been utilized in proteomics studies in recent years. In this context, we conducted a systematic literature review to investigate how Transformers are applied in each stage of the neoantigen detection process. Additionally, we mapped current pipelines and examined the results of clinical trials involving cancer vaccines.

Artificial intelligence and neoantigens: paving the path for precision cancer immunotherapy

Cancer immunotherapy has witnessed rapid advancement in recent years, with a particular focus on neoantigens as promising targets for personalized treatments. The convergence of immunogenomics, bioinformatics, and artificial intelligence (AI) has propelled the development of innovative neoantigen discovery tools and pipelines. These tools have revolutionized our ability to identify tumor-specific antigens, providing the foundation for precision cancer immunotherapy. AI-driven algorithms can process extensive amounts of data, identify patterns, and make predictions that were once challenging to achieve. However, the integration of AI comes with its own set of challenges, leaving space for further research. With particular focus on the computational approaches, in this article we have explored the current landscape of neoantigen prediction, the fundamental concepts behind, the challenges and their potential solutions providing a comprehensive overview of this rapidly evolving field.

Machine learning based biomarker discovery for chronic kidney disease-mineral and bone disorder (CKD-MBD)

INTRODUCTION

Chronic kidney disease-mineral and bone disorder (CKD-MBD) is characterized by bone abnormalities, vascular calcification, and some other complications. Although there are diagnostic criteria for CKD-MBD, in situations when conducting target feature examining are unavailable, there is a need to investigate and discover alternative biochemical criteria that are easy to obtain. Moreover, studying the correlations between the newly discovered biomarkers and the existing ones may provide insights into the underlying molecular mechanisms of CKD-MBD.

METHODS

We collected a cohort of 116 individuals, consisting of three subtypes of CKD-MBD: calcium abnormality, phosphorus abnormality, and PTH abnormality. To identify the best biomarker panel for discrimination, we conducted six machine learning prediction methods and employed a sequential forward feature selection approach for each subtype. Additionally, we collected a separate prospective cohort of 114 samples to validate the discriminative power of the trained prediction models.

RESULTS

Using machine learning under cross validation setting, the feature selection method selected a concise biomarker panel for each CKD-MBD subtype as well as for the general one. Using the consensus of these features, best area under ROC curve reached up to 0.95 for the training dataset and 0.74 for the perspective dataset, respectively.

DISCUSSION/CONCLUSION

For the first time, we utilized machine learning methods to analyze biochemical criteria associated with CKD-MBD. Our aim was to identify alternative biomarkers that could serve not only as early detection indicators for CKD-MBD, but also as potential candidates for studying the underlying molecular mechanisms of the condition.

The clinical progress and challenges of mRNA vaccines

Owing to the breakthroughs in the prevention and control of the COVID-19 pandemic, messenger RNA (mRNA)-based vaccines have emerged as promising alternatives to conventional vaccine approaches for infectious disease prevention and anticancer treatments. Advantages of mRNA vaccines include flexibility in designing and manipulating antigens of interest, scalability in rapid response to new variants, ability to induce both humoral and cell-mediated immune responses, and ease of industrialization. This review article presents the latest advances and innovations in mRNA-based vaccines and their clinical translations in the prevention and treatment of infectious diseases or cancers. We also highlight various nanoparticle delivery platforms that contribute to their success in clinical translation. Current challenges related to mRNA immunogenicity, stability, and in vivo delivery and the strategies for addressing them are also discussed. Finally, we provide our perspectives on future considerations and opportunities for applying mRNA vaccines to fight against major infectious diseases and cancers. This article is categorized under: Therapeutic Approaches and Drug Discovery > Emerging Technologies Therapeutic Approaches and Drug Discovery > Nanomedicine for Infectious Disease Biology-Inspired Nanomaterials > Lipid-Based Structures.

Computational Methods in Immunology and Vaccinology: Design and Development of Antibodies and Immunogens

The design of new biomolecules able to harness immune mechanisms for the treatment of diseases is a prime challenge for computational and simulative approaches. For instance, in recent years, antibodies have emerged as an important class of therapeutics against a spectrum of pathologies. In cancer, immune-inspired approaches are witnessing a surge thanks to a better understanding of tumor-associated antigens and the mechanisms of their engagement or evasion from the human immune system. Here, we provide a summary of the main state-of-the-art computational approaches that are used to design antibodies and antigens, and in parallel, we review key methodologies for epitope identification for both B- and T-cell mediated responses. A special focus is devoted to the description of structure- and physics-based models, privileged over purely sequence-based approaches. We discuss the implications of novel methods in engineering biomolecules with tailored immunological properties for possible therapeutic uses. Finally, we highlight the extraordinary challenges and opportunities presented by the possible integration of structure- and physics-based methods with emerging Artificial Intelligence technologies for the prediction and design of novel antigens, epitopes, and antibodies.

3D genome-selected microRNAs to improve Alzheimer's disease prediction

Introduction

Alzheimer's disease (AD) is a type of neurodegenerative disease that has no effective treatment in its late stage, making the early prediction of AD critical. There have been an increase in the number of studies indicating that miRNAs play an important role in neurodegenerative diseases including Alzheimer's disease via epigenetic modifications including DNA methylation. Therefore, miRNAs may serve as excellent biomarkers in early AD prediction.

Methods

Considering that the non-coding RNAs' activity may be linked to their corresponding DNA loci in the 3D genome, we collected the existing AD-related miRNAs combined with 3D genomic data in this study. We investigated three machine learning models in this work under leave-one-out cross-validation (LOOCV): support vector classification (SVC), support vector regression (SVR), and knearest neighbors (KNNs).

Results

The prediction results of different models demonstrated the effectiveness of incorporating 3D genome information into the AD prediction models.

Discussion

With the assistance of the 3D genome, we were able to train more accurate models by selecting fewer but more discriminatory miRNAs, as witnessed by several ML models. These interesting findings indicate that the 3D genome has great potential to play an important role in future AD research.

The host genetics affects gut microbiome diversity in Chinese depressed patients

The gut microbiome and host genetics are both associated with major depressive disorder (MDD); however, the molecular mechanisms among the associations are poorly understood, especially in the Asian, Chinese group. Our study applied linear discriminant analysis (LDA) effect size (LEfSe) and genome-wide association analysis in the cohort with both gut sequencing data and genomics data. We reported the different gut microbiota characteristics between MDD and control groups in the Chinese group and further constructed the association between host genetics and the gut microbiome. Actinobacteria and Pseudomonades were found more in the MDD group. We found significant differences in the ACE and Chao indexes of alpha diversity while no discrepancy in beta diversity. We found three associations between host genetics with microbiome features: beta diversity and rs6108 (p = 8.65 × 10-9), Actinobacteria and rs77379751 (p = 8.56 × 10-9), and PWY-5913 and rs1775633082 (p = 4.54 × 10-8). A species of the Romboutsia genus was co-associated with the species of Ruminococcus gnavus in an internetwork through four genes: METTL8, ITGB2, OTULIN, and PROSER3, with a strict threshold (p < 5 × 10-4). Furthermore, our findings suggested that the gut microbiome diversity might affect microRNA expression in the brain and influenced SERPINA5 and other spatially close genes afterward. These findings suggest new linkages between depression and gut microbiome in Asian, Chinese people, which might be mediated by genes and microRNA regulation in space distance.

Artificial intelligence applied in neoantigen identification facilitates personalized cancer immunotherapy

The field of cancer neoantigen investigation has developed swiftly in the past decade. Predicting novel and true neoantigens derived from large multi-omics data became difficult but critical challenges. The rise of Artificial Intelligence (AI) or Machine Learning (ML) in biomedicine application has brought benefits to strengthen the current computational pipeline for neoantigen prediction. ML algorithms offer powerful tools to recognize the multidimensional nature of the omics data and therefore extract the key neoantigen features enabling a successful discovery of new neoantigens. The present review aims to outline the significant technology progress of machine learning approaches, especially the newly deep learning tools and pipelines, that were recently applied in neoantigen prediction. In this review article, we summarize the current state-of-the-art tools developed to predict neoantigens. The standard workflow includes calling genetic variants in paired tumor and blood samples, and rating the binding affinity between mutated peptide, MHC (I and II) and T cell receptor (TCR), followed by characterizing the immunogenicity of tumor epitopes. More specifically, we highlight the outstanding feature extraction tools and multi-layer neural network architectures in typical ML models. It is noted that more integrated neoantigen-predicting pipelines are constructed with hybrid or combined ML algorithms instead of conventional machine learning models. In addition, the trends and challenges in further optimizing and integrating the existing pipelines are discussed.

Identification of Neoantigens in Cancer Cells as Targets for Immunotherapy

The clinical benefits of immune checkpoint blockage (ICB) therapy have been widely reported. In patients with cancer, researchers have demonstrated the clinical potential of antitumor cytotoxic T cells that can be reinvigorated or enhanced by ICB. Compared to self-antigens, neoantigens derived from tumor somatic mutations are believed to be ideal immune targets in tumors. Candidate tumor neoantigens can be identified through immunogenomic or immunopeptidomic approaches. Identification of neoantigens has revealed several points of the clinical relevance. For instance, tumor mutation burden (TMB) may be an indicator of immunotherapy. In various cancers, mutation rates accompanying neoantigen loads may be indicative of immunotherapy. Furthermore, mismatch repair-deficient tumors can be eradicated by T cells in ICB treatment. Hence, immunotherapies using vaccines or adoptive T-cell transfer targeting neoantigens are potential innovative strategies. However, significant efforts are required to identify the optimal epitopes. In this review, we summarize the recent progress in the identification of neoantigens and discussed preclinical and clinical studies based on neoantigens. We also discuss the issues remaining to be addressed before clinical applications of these new therapeutic strategies can be materialized.

EpiMC: Detecting Epistatic Interactions Using Multiple Clusterings

Detecting single nucleotide polymorphisms (SNPs) interactions is crucial to identify susceptibility genes associated with complex human diseases in genome-wide association studies. Clustering-based approaches are widely used in reducing search space and exploring potential relationships between SNPs in epistasis analysis. However, these approaches all only use a single measure to filter out nonsignificant SNP combinations, which may be significant ones from another perspective. In this paper, we propose a two-stage approach named EpiMC (Epistatic Interactions detection based on Multiple Clusterings) that employs multiple clusterings to obtain more precise candidate sets and more comprehensively detect high-order interactions based on these sets. In the first stage, EpiMC proposes a matrix factorization based multiple clusterings algorithm to generate multiple diverse clusterings, each of which divide all SNPs into different clusters. This stage aims to reduce the chance of filtering out potential candidates overlooked by a single clustering and groups associated SNPs together from different clustering perspectives. In the next stage, EpiMC considers both the single-locus effects and interaction effects to select high-quality disease associated SNPs, and then uses Jaccard similarity to get candidate sets. Finally, EpiMC uses exhaustive search on the obtained small candidate sets to precisely detect epsitatic interactions. Extensive simulation experiments show that EpiMC has a better performance in detecting high-order interactions than state-of-the-art solutions. On the Wellcome Trust Case Control Consortium (WTCCC) dataset, EpiMC detects several significant epistatic interactions associated with breast cancer (BC) and age-related macular degeneration (AMD), which again corroborate the effectiveness of EpiMC.

Effect of exercise and diet intervention in NAFLD and NASH via GAB2 methylation

BACKGROUND

Nonalcoholic fatty liver disease (NAFLD) is a disorder that extends from simple hepatic steatosis to nonalcoholic steatohepatitis (NASH), which is effectively alleviated by lifestyle intervention. Nevertheless, DNA methylation mechanism underling the effect of environmental factors on NAFLD and NASH is still obscure. The aim of this study was to investigate the effect of exercise and diet intervention in NAFLD and NASH via DNA methylation of GAB2.

METHODS

Methylation of genomic DNA in human NAFLD was quantified using Infinium Methylation EPIC BeadChip assay after exercise (Ex), low carbohydrate diet (LCD) and exercise plus low carbohydrate diet (ELCD) intervention. The output Idat files were processed using ChAMP package. False discovery rate on genome-wide analysis of DNA methylation (q < 0.05), and cytosine-guanine dinucleotides (CpGs) which are located in promoters were used for subsequent analysis (|Δβ|≥ 0.1). K-means clustering was used to cluster differentially methylated genes according to 3D genome information from Human embryonic stem cell. To quantify DNA methylation and mRNA expression of GRB2 associated binding protein 2 (GAB2) in NASH mice after Ex, low fat diet (LFD) and exercise plus low fat diet (ELFD), MassARRAY EpiTYPER and quantitative reverse transcription polymerase chain reaction were used.

RESULTS

Both LCD and ELCD intervention on human NAFLD can induce same DNA methylation alterations at critical genes in blood, e.g., GAB2, which was also validated in liver and adipose of NASH mice after LFD and ELFD intervention. Moreover, methylation of CpG units (i.e., CpG_10.11.12) inversely correlated with mRNA expression GAB2 in adipose tissue of NASH mice after ELFD intervention.

CONCLUSIONS

We highlighted the susceptibility of DNA methylation in GAB2 to ELFD intervention, through which exercise and diet can protect against the progression of NAFLD and NASH on the genome level, and demonstrated that the DNA methylation variation in blood could mirror epigenetic signatures in target tissues of important biological function, i.e., liver and adipose tissue. Trial registration International Standard Randomized Controlled Trial Number Register (ISRCTN 42622771).

T Cell Epitope Prediction and Its Application to Immunotherapy

T cells play a crucial role in controlling and driving the immune response with their ability to discriminate peptides derived from healthy as well as pathogenic proteins. In this review, we focus on the currently available computational tools for epitope prediction, with a particular focus on tools aimed at identifying neoepitopes, i.e. cancer-specific peptides and their potential for use in immunotherapy for cancer treatment. This review will cover how these tools work, what kind of data they use, as well as pros and cons in their respective applications.