Identification of the core regulators of the HLA I-peptide binding process

Rapid generation of functional nanovesicles from human trophectodermal cells for embryo attachment and outgrowth

Extracellular vesicles (EVs) are important mediators of embryo attachment and outgrowth critical for successful implantation. While EVs have garnered immense interest in their therapeutic potential in assisted reproductive technology by improving implantation success, their large-scale generation remains a major challenge. Here, we report a rapid and scalable production of nanovesicles (NVs) directly from human trophectoderm cells (hTSCs) via serial mechanical extrusion of cells; these NVs can be generated in approximately 6 h with a 20-fold higher yield than EVs isolated from culture medium of the same number of cells. NVs display similar biophysical traits (morphologically intact, spherical, 90-130 nm) to EVs, and are laden with hallmark players of implantation that include cell-matrix adhesion and extracellular matrix organisation proteins (ITGA2/V, ITGB1, MFGE8) and antioxidative regulators (PRDX1, SOD2). Functionally, NVs are readily taken up by low-receptive endometrial HEC1A cells and reprogram their proteome towards a receptive phenotype that support hTSC spheroid attachment. Moreover, a single dose treatment with NVs significantly enhanced adhesion and spreading of mouse embryo trophoblast on fibronectin matrix. Thus, we demonstrate the functional potential of NVs in enhancing embryo implantation and highlight their rapid and scalable generation, amenable to clinical utility.

Comparative analysis of the ex vivo IFN-gamma responses to CD8+ T cell epitopes within allelic forms of PfAMA1 in subjects with natural exposure to malaria

Antigen polymorphisms in essential malarial antigens are a key challenge to the design and development of broadly effective malaria vaccines. The effect of polymorphisms on antibody responses is fairly well studied while much fewer studies have assessed this for T cell responses. This study investigated the effect of allelic polymorphisms in the malarial antigen apical membrane antigen 1 (AMA1) on ex vivo T cell-specific IFN-γ responses in subjects with lifelong exposure to malaria. Human leukocyte antigen (HLA) class I-restricted peptides from the 3D7 clone AMA1 were bioinformatically predicted and those with variant amino acid positions used to select corresponding allelic sequences from the 7G8, FVO, FC27 and tm284 parasite strains. A total of 91 AMA1 9-10mer peptides from the five parasite strains were identified, synthesized, grouped into 42 allele sets and used to stimulate PBMCs from seven HLA class 1-typed subjects in IFN-γ ELISpot assays. PBMCs from four of the seven subjects (57%) made positive responses to 18 peptides within 12 allele sets. Fifty percent of the 18 positive peptides were from the 3D7 parasite variant. Amino acid substitutions that were associated with IFN-γ response abrogation were more frequently found at positions 1 and 6 of the tested peptides, but substitutions did not show a clear pattern of association with response abrogation. Thus, while we show some evidence of polymorphisms affecting T cell response induction, other factors including TCR recognition of HLA-peptide complexes may also be at play.

Like Wings of a Bird: Functional Divergence and Complementarity between HLA-A and HLA-B Molecules

Human leukocyte antigen (HLA) genes are among the most polymorphic of our genome, as a likely consequence of balancing selection related to their central role in adaptive immunity. HLA-A and HLA-B genes were recently suggested to evolve through a model of joint divergent asymmetric selection conferring all human populations, including those with severe loss of diversity, an equivalent immune potential. However, the mechanisms by which these two genes might undergo joint evolution while displaying very distinct allelic profiles in populations are still unknown. To address this issue, we carried out extensive data analyses (among which factorial correspondence analysis and linear modeling) on 2,909 common and rare HLA-A, HLA-B, and HLA-C alleles and 200,000 simulated pathogenic peptides by taking into account sequence variation, predicted peptide-binding affinity and HLA allele frequencies in 123 populations worldwide. Our results show that HLA-A and HLA-B (but not HLA-C) molecules maintain considerable functional divergence in almost all populations, which likely plays an instrumental role in their immune defense. We also provide robust evidence of functional complementarity between HLA-A and HLA-B molecules, which display asymmetric relationships in terms of amino acid diversity at both inter- and intraprotein levels and in terms of promiscuous or fastidious peptide-binding specificities. Like two wings of a flying bird, the functional complementarity of HLA-A and HLA-B is a perfect example, in our genome, of duplicated genes sharing their capacity of assuming common vital functions while being submitted to complex and sometimes distinct environmental pressures.

Computational discovery and ex-vivo validation study of novel antigenic vaccine candidates against tuberculosis

Tuberculosis (TB) is a complex infectious bacterial disease, which has evolved with highly successful mechanisms to interfere with host defenses and existing classes of antibiotics to resist eradication. The single obtainable TB vaccine, Bacille Calmette-Guerin (BCG) has failed to provide regular defense for respiratory TB in adults. In this study, a bioinformatics and immunoinformatics approach was applied on Mycobacterium tuberculosis (Mtb) H37Rv proteomes to discover the potential subunit vaccine candidates that elicit both tuberculosis-specific T-cells and B-cell immune response. A total of 4049 proteins of MtbH37RvMtbH37Rv were retrieved and subjected to in silico sequence-based analysis. Finally, five (P9WL69 (Rv2599), P9WIG1 (Rv0747), P9WLQ1 (Rv1987), O53608 (Rv0063), O06624 (Rv1566c)) novel putative proteins were selected. Among the five putative antigenic vaccine candidates, P9WL69 protein was selected for the ex-vivo validation study. The P9WL69 protein encoding gene was amplified and cloned on pET21b vector. The success of the recombinant clone (pET21b-RV2599) was confirmed by colony PCR, insert release test and sequencing. Furthermore, the identified epitopes of the P9WL69 protein were considered for in silico docking and molecular dynamics simulation study using Toll-like Receptors (TLRs) (TLR-2, TLR-4, TLR-9), Mannose receptor, and Myeloid differentiation 88 (MYD88) to understand their binding affinity towards the development of immunogenic vaccines against tuberculosis.

Anthem: a user customised tool for fast and accurate prediction of binding between peptides and HLA class I molecules

Neopeptide-based immunotherapy has been recognised as a promising approach for the treatment of cancers. For neopeptides to be recognised by CD8+ T cells and induce an immune response, their binding to human leukocyte antigen class I (HLA-I) molecules is a necessary first step. Most epitope prediction tools thus rely on the prediction of such binding. With the use of mass spectrometry, the scale of naturally presented HLA ligands that could be used to develop such predictors has been expanded. However, there are rarely efforts that focus on the integration of these experimental data with computational algorithms to efficiently develop up-to-date predictors. Here, we present Anthem for accurate HLA-I binding prediction. In particular, we have developed a user-friendly framework to support the development of customisable HLA-I binding prediction models to meet challenges associated with the rapidly increasing availability of large amounts of immunopeptidomic data. Our extensive evaluation, using both independent and experimental datasets shows that Anthem achieves an overall similar or higher area under curve value compared with other contemporary tools. It is anticipated that Anthem will provide a unique opportunity for the non-expert user to analyse and interpret their own in-house or publicly deposited datasets.

Identification of leukemia stem cell expression signatures through Monte Carlo feature selection strategy and support vector machine

Acute myeloid leukemia (AML) is a type of blood cancer characterized by the rapid growth of immature white blood cells from the bone marrow. Therapy resistance resulting from the persistence of leukemia stem cells (LSCs) are found in numerous patients. Comparative transcriptome studies have been previously conducted to analyze differentially expressed genes between LSC⁺ and LSC^- cells. However, these studies mainly focused on a limited number of genes with the most obvious expression differences between the two cell types. We developed a computational approach incorporating several machine learning algorithms, including Monte Carlo feature selection (MCFS), incremental feature selection (IFS), support vector machine (SVM), Repeated Incremental Pruning to Produce Error Reduction (RIPPER), to identify gene expression features specific to LSCs. One thousand 0ne hudred fifty-nine features (genes) were first identified, which can be used to build the optimal SVM classifier for distinguishing LSC⁺ and LSC^- cells. Among these 1159 genes, the top 17 genes were identified as LSC-specific biomarkers. In addition, six classification rules were produced by RIPPER algorithm. The subsequent literature review on these features/genes and the classification rules and functional enrichment analyses of the 1159 features/genes confirmed the relevance of extracted genes and rules to the characteristics of LSCs.

Analysis of Expression Pattern of snoRNAs in Different Cancer Types with Machine Learning Algorithms

Small nucleolar RNAs (snoRNAs) are a new type of functional small RNAs involved in the chemical modifications of rRNAs, tRNAs, and small nuclear RNAs. It is reported that they play important roles in tumorigenesis via various regulatory modes. snoRNAs can both participate in the regulation of methylation and pseudouridylation and regulate the expression pattern of their host genes. This research investigated the expression pattern of snoRNAs in eight major cancer types in TCGA via several machine learning algorithms. The expression levels of snoRNAs were first analyzed by a powerful feature selection method, Monte Carlo feature selection (MCFS). A feature list and some informative features were accessed. Then, the incremental feature selection (IFS) was applied to the feature list to extract optimal features/snoRNAs, which can make the support vector machine (SVM) yield best performance. The discriminative snoRNAs included HBII-52-14, HBII-336, SNORD123, HBII-85-29, HBII-420, U3, HBI-43, SNORD116, SNORA73B, SCARNA4, HBII-85-20, etc., on which the SVM can provide a Matthew’s correlation coefficient (MCC) of 0.881 for predicting these eight cancer types. On the other hand, the informative features were fed into the Johnson reducer and repeated incremental pruning to produce error reduction (RIPPER) algorithms to generate classification rules, which can clearly show different snoRNAs expression patterns in different cancer types. The analysis results indicated that extracted discriminative snoRNAs can be important for identifying cancer samples in different types and the expression pattern of snoRNAs in different cancer types can be partly uncovered by quantitative recognition rules.

Analysis of peptide-SLA binding by establishing immortalized porcine alveolar macrophage cells with different SLA class II haplotypes

Primary porcine alveolar macrophages (PAM) are useful for studying viral infections and immune response in pigs; however, long-term use of these cells is limited by the cells’ short lifespan. We immortalized primary PAMs by transfecting them with both hTERT and SV40LT and established two immortalized cell lines (iPAMs) actively proliferating even after 35 passages. These cells possessed the characteristics of primary PAMs, including strong expression of swine leukocyte antigen (SLA) class II genes and the inability to grow anchorage-independently. We characterized their SLA genes and subsequently performed peptide-SLA binding assays using a peptide from porcine circovirus type 2 open reading frame 2 to experimentally measure the binding affinity of the peptide to SLA class II. The number of peptides bound to cells measured by fluorescence was very low for PK15 cells (7.0% ± 1.5), which are not antigen-presenting cells, unlike iPAM61 (33.7% ± 3.4; SLA-DQA*0201/0303, DQB1*0201/0901, DRB1*0201/1301) and iPAM303 (73.3% ± 5.4; SLA DQA*0106/0201, DQB1*0202/0701, DRB1*0402/0602). The difference in peptide binding between the two iPAMs was likely due to the allelic differences between the SLA class II molecules that were expressed. The development of an immortal PAM cell panel harboring diverse SLA haplotypes and the use of an established method in this study can become a valuable tool for evaluating the interaction between antigenic peptides and SLA molecules and is important for many applications in veterinary medicine including vaccine development.

Identification of the copy number variant biomarkers for breast cancer subtypes

Breast cancer is a common and threatening malignant disease with multiple biological and clinical subtypes. It can be categorized into subtypes of luminal A, luminal B, Her2 positive, and basal-like. Copy number variants (CNVs) have been reported to be a potential and even better biomarker for cancer diagnosis than mRNA biomarkers, because it is considerably more stable and robust than gene expression. Thus, it is meaningful to detect CNVs of different cancers. To identify the CNV biomarker for breast cancer subtypes, we integrated the CNV data of more than 2000 samples from two large breast cancer databases, METABRIC and The Cancer Genome Atlas (TCGA). A Monte Carlo feature selection-based and incremental feature selection-based computational method was proposed and tested to identify the distinctive core CNVs in different breast cancer subtypes. We identified the CNV genes that may contribute to breast cancer tumorigenesis as well as built a set of quantitative distinctive rules for recognition of the breast cancer subtypes. The tenfold cross-validation Matthew's correlation coefficient (MCC) on METABRIC training set and the independent test on TCGA dataset were 0.515 and 0.492, respectively. The CNVs of PGAP3, GRB7, MIR4728, PNMT, STARD3, TCAP and ERBB2 were important for the accurate diagnosis of breast cancer subtypes. The findings reported in this study may further uncover the difference between different breast cancer subtypes and improve the diagnosis accuracy.

Data mining of the cancer-related lncRNAs GO terms and KEGG pathways by using mRMR method

LncRNAs plays an important role in the regulation of gene expression. Identification of cancer-related lncRNAs GO terms and KEGG pathways is great helpful for revealing cancer-related functional biological processes. Therefore, in this study, we proposed a computational method to identify novel cancer-related lncRNAs GO terms and KEGG pathways. By using existing lncRNA database and Max-relevance Min-redundancy (mRMR) method, GO terms and KEGG pathways were evaluated based on their importance on distinguishing cancer-related and non-cancer-related lncRNAs. Finally, GO terms and KEGG pathways with high importance were presented and analyzed. Our literature reviewing showed that the top 10 ranked GO terms and pathways were really related to interpretable tumorigenesis according to recent publications.

Identification of Differentially Expressed Genes between Original Breast Cancer and Xenograft Using Machine Learning Algorithms

Breast cancer is one of the most common malignancies in women. Patient-derived tumor xenograft (PDX) model is a cutting-edge approach for drug research on breast cancer. However, PDX still exhibits differences from original human tumors, thereby challenging the molecular understanding of tumorigenesis. In particular, gene expression changes after tissues are transplanted from human to mouse model. In this study, we propose a novel computational method by incorporating several machine learning algorithms, including Monte Carlo feature selection (MCFS), random forest (RF), and rough set-based rule learning, to identify genes with significant expression differences between PDX and original human tumors. First, 831 breast tumors, including 657 PDX and 174 human tumors, were collected. Based on MCFS and RF, 32 genes were then identified to be informative for the prediction of PDX and human tumors and can be used to construct a prediction model. The prediction model exhibits a Matthews coefficient correlation value of 0.777. Seven interpretable interactions within the informative gene were detected based on the rough set-based rule learning. Furthermore, the seven interpretable interactions can be well supported by previous experimental studies. Our study not only presents a method for identifying informative genes with differential expression but also provides insights into the mechanism through which gene expression changes after being transplanted from human tumor into mouse model. This work would be helpful for research and drug development for breast cancer.

Distinguishing three subtypes of hematopoietic cells based on gene expression profiles using a support vector machine

Hematopoiesis is a complicated process involving a series of biological sub-processes that lead to the formation of various blood components. A widely accepted model of early hematopoiesis proceeds from long-term hematopoietic stem cells (LT-HSCs) to multipotent progenitors (MPPs) and then to lineage-committed progenitors. However, the molecular mechanisms of early hematopoiesis have not been fully characterized. In this study, we applied a computational strategy to identify the gene expression signatures distinguishing three types of closely related hematopoietic cells collected in recent studies: (1) hematopoietic stem cell/multipotent progenitor cells; (2) LT-HSCs; and (3) hematopoietic progenitor cells. Each cell in these cell types was represented by its gene expression profile among a total number of 20,475 genes. The expression features were analyzed by a Monte-Carlo Feature Selection (MCFS) method, resulting in a feature list. Then, the incremental feature selection (IFS) and a support vector machine (SVM) optimized with a sequential minimum optimization (SMO) algorithm were employed to access the optimal classifier with the highest Matthews correlation coefficient (MCC) value of 0.889, in which 6698 features were used to represent cells. In addition, through an updated program of MCFS method, seventeen decision rules can be obtained, which can classify the three cell types with an overall accuracy of 0.812. Using a literature review, both the rules and the top features used for building the optimal classifier were confirmed to be commonly used or potential biological markers for distinguishing the three cell types of HSPCs. This article is part of a Special Issue entitled: Accelerating Precision Medicine through Genetic and Genomic Big Data Analysis edited by Yudong Cai & Tao Huang.

Identifying and analyzing different cancer subtypes using RNA-seq data of blood platelets

Detection and diagnosis of cancer are especially important for early prevention and effective treatments. Traditional methods of cancer detection are usually time-consuming and expensive. Liquid biopsy, a newly proposed noninvasive detection approach, can promote the accuracy and decrease the cost of detection according to a personalized expression profile. However, few studies have been performed to analyze this type of data, which can promote more effective methods for detection of different cancer subtypes. In this study, we applied some reliable machine learning algorithms to analyze data retrieved from patients who had one of six cancer subtypes (breast cancer, colorectal cancer, glioblastoma, hepatobiliary cancer, lung cancer and pancreatic cancer) as well as healthy persons. Quantitative gene expression profiles were used to encode each sample. Then, they were analyzed by the maximum relevance minimum redundancy method. Two feature lists were obtained in which genes were ranked rigorously. The incremental feature selection method was applied to the mRMR feature list to extract the optimal feature subset, which can be used in the support vector machine algorithm to determine the best performance for the detection of cancer subtypes and healthy controls. The ten-fold cross-validation for the constructed optimal classification model yielded an overall accuracy of 0.751. On the other hand, we extracted the top eighteen features (genes), including TTN, RHOH, RPS20, TRBC2, in another feature list, the MaxRel feature list, and performed a detailed analysis of them. The results indicated that these genes could be important biomarkers for discriminating different cancer subtypes and healthy controls.