The rs1477196 SNP of the FTO Gene is Associated with Primary Knee Osteoarthritis in a Female Population from Northern Mexico.

Background: Osteoarthritis is a frequent rheumatic disease. Some single-nucleotide polymorphisms of the gene associated with fat mass and obesity are associated with increased body mass index and knee osteoarthritis. Objective: The objective of this study was to analyze the association of single nucleotide polymorphism rs1477196 of the fat mass and obesity gene with primary knee osteoarthritis. Methods: This observational and cross-sectional study included 347 Mexican participants. We performed the genotypification analysis with TaqMan® probe C_2031262_10 for rs1477196 (Thermo Fisher Scientific). Multivariate analysis included covariables such as age, type 2 diabetes, obesity, and postmenopause. Results: Type 2 diabetes, obesity, and postmenopause were associated with primary knee osteoarthritis in female participants. We did not find an association between rs1477196 and obesity. In the codominant and dominant genetic models, rs1477196 was significantly associated with primary knee osteoarthritis only in the female group, including in the model adjusted by other covariables (odds ratio = 2.517; 1.035-6.123; p = 0.042 and odds ratio = 2.387; 1.054-5.407; p = 0.037, respectively). The interaction between rs1477196 and obesity was significantly associated with primary knee osteoarthritis in female participants (p = 0.039 and p = 0.043). Conclusions: Our findings suggest that the rs1477196 variant of the fat and obesity mass gene may be associated with the risk of primary knee osteoarthritis in women.

A Proteome-Wide Immunoinformatics Tool to Accelerate T-Cell Epitope Discovery and Vaccine Design in the Context of Emerging Infectious Diseases: An Ethnicity-Oriented Approach.

Emerging infectious diseases (EIDs) caused by viruses are increasing in frequency, causing a high disease burden and mortality world-wide. The COVID-19 pandemic caused by the novel SARS-like coronavirus (SARS-CoV-2) underscores the need to innovate and accelerate the development of effective vaccination strategies against EIDs. Human leukocyte antigen (HLA) molecules play a central role in the immune system by determining the peptide repertoire displayed to the T-cell compartment. Genetic polymorphisms of the HLA system thus confer a strong variability in vaccine-induced immune responses and may complicate the selection of vaccine candidates, because the distribution and frequencies of HLA alleles are highly variable among different ethnic groups. Herein, we build on the emerging paradigm of rational epitope-based vaccine design, by describing an immunoinformatics tool (Predivac-3.0) for proteome-wide T-cell epitope discovery that accounts for ethnic-level variations in immune responsiveness. Predivac-3.0 implements both CD8+ and CD4+ T-cell epitope predictions based on HLA allele frequencies retrieved from the Allele Frequency Net Database. The tool was thoroughly assessed, proving comparable performances (AUC ~0.9) against four state-of-the-art pan-specific immunoinformatics methods capable of population-level analysis (NetMHCPan-4.0, Pickpocket, PSSMHCPan and SMM), as well as a strong accuracy on proteome-wide T-cell epitope predictions for HIV-specific immune responses in the Japanese population. The utility of the method was investigated for the COVID-19 pandemic, by performing in silico T-cell epitope mapping of the SARS-CoV-2 spike glycoprotein according to the ethnic context of the countries where the ChAdOx1 vaccine is currently initiating phase III clinical trials. Potentially immunodominant CD8+ and CD4+ T-cell epitopes and population coverages were predicted for each population (the Epitope Discovery mode), along with optimized sets of broadly recognized (promiscuous) T-cell epitopes maximizing coverage in the target populations (the Epitope Optimization mode). Population-specific epitope-rich regions (T-cell epitope clusters) were further predicted in protein antigens based on combined criteria of epitope density and population coverage. Overall, we conclude that Predivac-3.0 holds potential to contribute in the understanding of ethnic-level variations of vaccine-induced immune responsiveness and to guide the development of epitope-based next-generation vaccines against emerging pathogens, whose geographic distributions and populations in need of vaccinations are often well-defined for regional epidemics.

A snapshot of human leukocyte antigen (HLA) diversity using data from the Allele Frequency Net Database.

The extensive allelic variability observed in several genes related to the immune response and its significance in different areas including transplantation, disease association studies, diversity in human populations, among many others, has led the scientific community to analyse these variants among individuals. Serving as an electronic data warehouse, the Allele Frequency Net Database (AFND, http://www.allelefrequencies.net) contains data on the frequency of immune related genes and their corresponding alleles from more than 1700 worldwide population samples covering more than ten million unrelated individuals. The collection of population data sets available in AFND encompasses different polymorphic regions including the highly-polymorphic human leukocyte antigen (HLA) system for which more than 1200 populations are available. In this article, we provide an insight of the high diversity found in the HLA region by examining population data sets stored in AFND, as well as a description of the available data sets for further analyses.

Predictive immunogenetic markers in COVID-19.

Immunorelevant genes are among the most probable modulators of coronavirus disease 2019 (COVID-19) progression and prognosis. However, in the few months of the pandemic, data generated on host genetics has been scarce. The present study retrieved data sets of HLA-B alleles, KIR genes and functional single nucleotide polymorphisms (SNPs) in cytokines related to COVID-19 cytokine storm from two publicly available databases: Allele Frequency Net Database and Ensembl, and correlated these frequency data with Case Fatality Rate (CFR) and Daily Death Rates (DDR) across countries. Correlations of eight HLA-B alleles and polymorphisms in three cytokine genes (IL6, IL10, and IL12B) were observed and were mainly associated with DDR. Additionally, HLA-B correlations suggest that differences in allele affinities to SARS-CoV-2 peptides are also associated with DDR. These results may provide rationale for future host genetic marker surveys on COVID-19.

Analysis of Polymorphisms in the MATN3 and DOT1L Genes and CTX-II Urinary Levels in Patients with Knee Osteoarthritis in a Northeast Mexican-Mestizo Population.

Aims: To investigate the possible roles of the single nucleotide polymorphisms (SNPs) MATN3 (rs77245812) and DOT1L (rs12982744) with susceptibility to knee osteoarthritis (KOA) among mestizos from the northeast region of Mexico. In addition, we analyzed the relationship of their urinary levels of carboxy terminal telopeptide of collagen type II (CTX-II) and the radiological grade of disease. Materials and Methods: A total of 223 individuals from a Northeast Mexico Mestizo population were included in this study: 110 patients with primary KOA and 113 healthy controls. Genotyping of the MATN3 (rs77245812) and DOT1L (rs12982744) SNPs was performed by real-time polymerase chain reaction. Results: No association was found between the polymorphisms MATN3 (rs77245812), DOT1L (rs12982744), and the risk of developing KOA (odds ratio [OR] = 1.33, 95% confidence interval [CI] = 0.42-6.48, p = 0.621) (OR = 2.03, 95% CI = 0.35-11.5, p = 0.422). However, urinary CTX-II levels were considerably higher by radiographic grade. Conclusions: An increase in CTX-II per radiographic grade was observed in the case group, but no association was found between MATN3 and DOT1L genes and the risk of KOA in Mexican mestizos.

Allele frequency net database (AFND) 2020 update: gold-standard data classification, open access genotype data and new query tools.

The Allele Frequency Net Database (AFND, www.allelefrequencies.net) provides the scientific community with a freely available repository for the storage of frequency data (alleles, genes, haplotypes and genotypes) related to human leukocyte antigens (HLA), killer-cell immunoglobulin-like receptors (KIR), major histocompatibility complex Class I chain related genes (MIC) and a number of cytokine gene polymorphisms in worldwide populations. In the last five years, AFND has become more popular in terms of clinical and scientific usage, with a recent increase in genotyping data as a necessary component of Short Population Report article submissions to another scientific journal. In addition, we have developed a user-friendly desktop application for HLA and KIR genotype/population data submissions. We have also focused on classification of existing and new data into 'gold-silver-bronze' criteria, allowing users to filter and query depending on their needs. Moreover, we have also continued to expand other features, for example focussed on HLA associations with adverse drug reactions. At present, AFND contains >1600 populations from >10 million healthy individuals, making AFND a valuable resource for the analysis of some of the most polymorphic regions in the human genome.

Imbalance of Genes Encoding Natural Killer Immunoglobulin-Like Receptors and Human Leukocyte Antigen in Patients With Biliary Cancer.

BACKGROUND & AIMS: Bile duct tumors are rare and have poor prognoses. Natural killer (NK) cells are frequent in human liver and infiltrate these tumors but do not control their progression. Responses of NK cells are regulated by NK immunoglobulin-like receptors (KIRs), which interact with HLA class I ligands. We aimed to characterize the features of the KIR gene loci and their ligands in patients with bile duct cancer (BDC). METHODS: We performed combined multidimensional characterization of genes that encode KIRs and their ligands in blood samples from patients with BDC from Sweden, followed for up to 8 years after diagnosis (n = 148), in 2 geographically matched cohorts of healthy individuals from Northern Europe (n = 204 and n = 900), and in healthy individuals from 6 geographically unrelated populations (n = 2917). We used real-time polymerase chain reaction, RNA sequencing, immunohistochemistry, and flow cytometry to evaluate NK-cell presence, as well as KIR and KIR-ligand expression in bile duct tumors and control tissues. RESULTS: Patients with bile duct tumors had multiple alterations at the KIR gene loci. KIR loci are grouped into genotypes that encode more inhibitory (group A) and more activating (group B) receptors, which can be subdivided into centromeric and telomeric fragments. Patients with BDC had a lower prevalence of KIR2DL3, which was linked to disequilibrium in centromeric A/B and B/B genotypes, compared with control individuals. The associations between KIRs and KIR ligands differed between patients with BDC and control individuals; patients had an altered balance between activating and inhibitory KIRs. KIR-positive NK cells infiltrated biliary tumors that expressed matched KIR ligands. CONCLUSIONS: In a multidimensional analysis of DNA from blood samples of patients with BDC in Europe, we found patients to have multiple alterations at the KIR and HLA gene loci compared with control individuals. These alterations might affect NK-cell tumor surveillance. NK cells from bile duct tumors expressed KIRs and were found in tumors that expressed cognate ligands. This should be considered in development of immune-based therapies for BDC.

Allele Frequency Net Database.

The allele frequency net database (AFND, http://www.allelefrequencies.net ) is an online web-based repository that contains information on the frequencies of immune-related genes and their corresponding alleles in worldwide human populations. At present, the system contains data from 1505 populations in more than ten million individuals on the frequency of genes from different polymorphic regions including data for the human leukocyte antigens (HLA) system. This resource has been widely used in a variety of contexts such as histocompatibility, immunology, epidemiology, pharmacogenetics, and population genetics, among many others. In this chapter, we present some of the more commonly used searching mechanisms and some of the most recent developments included in AFND.

A web resource for mining HLA associations with adverse drug reactions: HLA-ADR.

Human leukocyte antigens (HLA) are an important family of genes involved in the immune system. Their primary function is to allow the host immune system to be able to distinguish between self and non-self peptides-e.g. derived from invading pathogens. However, these genes have also been implicated in immune-mediated adverse drug reactions (ADRs), presenting a problem to patients, clinicians and pharmaceutical companies. We have previously developed the Allele Frequency Net Database (AFND) that captures the allelic and haplotype frequencies for these HLA genes across many healthy populations from around the world. Here, we report the development and release of the HLA-ADR database that captures data from publications where HLA alleles and haplotypes have been associated with ADRs (e.g. Stevens-Johnson Syndrome/toxic epidermal necrolysis and drug-induced liver injury). HLA-ADR was created by using data obtained through systematic review of the literature and semi-automated literature mining. The database also draws on data already present in AFND allowing users to compare and analyze allele frequencies in both ADR patients and healthy populations. The HLA-ADR database provides clinicians and researchers with a centralized resource from which to investigate immune-mediated ADRs.Database URL: http://www.allelefrequencies.net/hla-adr/.

Allele Frequencies Net Database: Improvements for storage of individual genotypes and analysis of existing data.

The Allele Frequencies Net Database (AFND) is a freely accessible database which stores population frequencies for alleles or genes of the immune system in worldwide populations. Herein we introduce two new tools. We have defined new classifications of data (gold, silver and bronze) to assist users in identifying the most suitable populations for their tasks. The gold standard datasets are defined by allele frequencies summing to 1, sample sizes >50 and high resolution genotyping, while silver standard datasets do not meet gold standard genotyping resolution and/or sample size criteria. The bronze standard datasets are those that could not be classified under the silver or gold standards. The gold standard includes >500 datasets covering over 3 million individuals from >100 countries at one or more of the following loci: HLA-A, -B, -C, -DPA1, -DPB1, -DQA1, -DQB1 and -DRB1 - with all loci except DPA1 present in more than 220 datasets. Three out of 12 geographic regions have low representation (the majority of their countries having less than five datasets) and the Central Asia region has no representation. There are 18 countries that are not represented by any gold standard datasets but are represented by at least one dataset that is either silver or bronze standard. We also briefly summarize the data held by AFND for KIR genes, alleles and their ligands. Our second new component is a data submission tool to assist users in the collection of the genotypes of the individuals (raw data), facilitating submission of short population reports to Human Immunology, as well as simplifying the submission of population demographics and frequency data.

A bioinformatics tool for epitope-based vaccine design that accounts for human ethnic diversity: application to emerging infectious diseases.

BACKGROUND: Peptide vaccination based on multiple T-cell epitopes can be used to target well-defined ethnic populations. Because the response to T-cell epitopes is restricted by HLA proteins, the HLA specificity of T-cell epitopes becomes a major consideration for epitope-based vaccine design. We have previously shown that CD4+ T-cell epitopes restricted by 95% of human MHC class II proteins can be predicted with high-specificity. METHODS: We describe here the integration of epitope prediction with population coverage and epitope selection algorithms. The population coverage assessment makes use of the Allele Frequency Net Database. We present the computational platform Predivac-2.0 for HLA class II-restricted epitope-based vaccine design, which accounts comprehensively for human genetic diversity. RESULTS: We validated the performance of the tool on the identification of promiscuous and immunodominant CD4+ T-cell epitopes from the human immunodeficiency virus (HIV) protein Gag. We further describe an application for epitope-based vaccine design in the context of emerging infectious diseases associated with Lassa, Nipah and Hendra viruses. Putative CD4+ T-cell epitopes were mapped on the surface glycoproteins of these pathogens and are good candidates to be experimentally tested, as they hold potential to provide cognate help in vaccination settings in their respective target populations. CONCLUSION: Predivac-2.0 is a novel approach in epitope-based vaccine design, particularly suited to be applied to virus-related emerging infectious diseases, because the geographic distributions of the viruses are well defined and ethnic populations in need of vaccination can be determined ("ethnicity-oriented approach"). Predivac-2.0 is accessible through the website http://predivac.biosci.uq.edu.au/.

Allele frequency net 2015 update: new features for HLA epitopes, KIR and disease and HLA adverse drug reaction associations.

It has been 12 years since the Allele Frequency Net Database (AFND; http://www.allelefrequencies.net) was first launched, providing the scientific community with an online repository for the storage of immune gene frequencies in different populations across the world. There have been a significant number of improvements from the first version, making AFND a primary resource for many clinical and scientific areas including histocompatibility, immunogenetics, pharmacogenetics and anthropology studies, among many others. The most widely used part of AFND stores population frequency data (alleles, genes or haplotypes) related to human leukocyte antigens (HLA), killer-cell immunoglobulin-like receptors (KIR), major histocompatibility complex class I chain-related genes (MIC) and a number of cytokine gene polymorphisms. AFND now contains >1400 populations from more than 10 million healthy individuals. Here, we report how the main features of AFND have been updated to include a new section on 'HLA epitope' frequencies in populations, a new section capturing the results of studies identifying HLA associations with adverse drug reactions (ADRs) and one for the examination of infectious and autoimmune diseases associated with KIR polymorphisms-thus extending AFND to serve a new user base in these growing areas of research. New criteria on data quality have also been included.

Allele frequencies database.

This review describes a database for the collection, archiving, sorting, searching and display of gene and allele frequencies for immunogenetic genes.

Numerical compression schemes for proteomics mass spectrometry data.

The open XML format mzML, used for representation of MS data, is pivotal for the development of platform-independent MS analysis software. Although conversion from vendor formats to mzML must take place on a platform on which the vendor libraries are available (i.e. Windows), once mzML files have been generated, they can be used on any platform. However, the mzML format has turned out to be less efficient than vendor formats. In many cases, the naïve mzML representation is fourfold or even up to 18-fold larger compared with the original vendor file. In disk I/O limited setups, a larger data file also leads to longer processing times, which is a problem given the data production rates of modern mass spectrometers. In an attempt to reduce this problem, we here present a family of numerical compression algorithms called MS-Numpress, intended for efficient compression of MS data. To facilitate ease of adoption, the algorithms target the binary data in the mzML standard, and support in main proteomics tools is already available. Using a test set of 10 representative MS data files we demonstrate typical file size decreases of 90% when combined with traditional compression, as well as read time decreases of up to 50%. It is envisaged that these improvements will be beneficial for data handling within the MS community.

A tutorial for software development in quantitative proteomics using PSI standard formats.

The Human Proteome Organisation - Proteomics Standards Initiative (HUPO-PSI) has been working for ten years on the development of standardised formats that facilitate data sharing and public database deposition. In this article, we review three HUPO-PSI data standards - mzML, mzIdentML and mzQuantML, which can be used to design a complete quantitative analysis pipeline in mass spectrometry (MS)-based proteomics. In this tutorial, we briefly describe the content of each data model, sufficient for bioinformaticians to devise proteomics software. We also provide guidance on the use of recently released application programming interfaces (APIs) developed in Java for each of these standards, which makes it straightforward to read and write files of any size. We have produced a set of example Java classes and a basic graphical user interface to demonstrate how to use the most important parts of the PSI standards, available from http://code.google.com/p/psi-standard-formats-tutorial. This article is part of a Special Issue entitled: Computational Proteomics in the Post-Identification Era. Guest Editors: Martin Eisenacher and Christian Stephan.

The mzQuantML data standard for mass spectrometry-based quantitative studies in proteomics.

The range of heterogeneous approaches available for quantifying protein abundance via mass spectrometry (MS)(1) leads to considerable challenges in modeling, archiving, exchanging, or submitting experimental data sets as supplemental material to journals. To date, there has been no widely accepted format for capturing the evidence trail of how quantitative analysis has been performed by software, for transferring data between software packages, or for submitting to public databases. In the context of the Proteomics Standards Initiative, we have developed the mzQuantML data standard. The standard can represent quantitative data about regions in two-dimensional retention time versus mass/charge space (called features), peptides, and proteins and protein groups (where there is ambiguity regarding peptide-to-protein inference), and it offers limited support for small molecule (metabolomic) data. The format has structures for representing replicate MS runs, grouping of replicates (for example, as study variables), and capturing the parameters used by software packages to arrive at these values. The format has the capability to reference other standards such as mzML and mzIdentML, and thus the evidence trail for the MS workflow as a whole can now be described. Several software implementations are available, and we encourage other bioinformatics groups to use mzQuantML as an input, internal, or output format for quantitative software and for structuring local repositories. All project resources are available in the public domain from the HUPO Proteomics Standards Initiative http://www.psidev.info/mzquantml.

A database for curating the associations between killer cell immunoglobulin-like receptors and diseases in worldwide populations.

The killer cell immunoglobulin-like receptors (KIR) play a fundamental role in the innate immune system, through their interactions with human leucocyte antigen (HLA) molecules, leading to the modulation of activity in natural killer (NK) cells, mainly related to killing pathogen-infected cells. KIR genes are hugely polymorphic both in the number of genes an individual carries and in the number of alleles identified. We have previously developed the Allele Frequency Net Database (AFND, http://www.allelefrequencies.net), which captures worldwide frequencies of alleles, genes and haplotypes for several immune genes, including KIR genes, in healthy populations, covering >4 million individuals. Here, we report the creation of a new database within AFND, named KIR and Diseases Database (KDDB), capturing a large quantity of data derived from publications in which KIR genes, alleles, genotypes and/or haplotypes have been associated with infectious diseases (e.g. hepatitis C, HIV, malaria), autoimmune disorders (e.g. type I diabetes, rheumatoid arthritis), cancer and pregnancy-related complications. KDDB has been created through an extensive manual curation effort, extracting data on more than a thousand KIR-disease records, comprising >50 000 individuals. KDDB thus provides a new community resource for understanding not only how KIR genes are associated with disease, but also, by working in tandem with the large data sets already present in AFND, where particular genes, genotypes or haplotypes are present in worldwide populations or different ethnic groups. We anticipate that KDDB will be an important resource for researchers working in immunogenetics. Database URL: http://www.allelefrequencies.net/diseases/.

Library of Apicomplexan Metabolic Pathways: a manually curated database for metabolic pathways of apicomplexan parasites.

The Library of Apicomplexan Metabolic Pathways (LAMP, http://www.llamp.net) is a web database that provides near complete mapping from genes to the central metabolic functions for some of the prominent intracellular parasites of the phylum Apicomplexa. This phylum includes the causative agents of malaria, toxoplasmosis and theileriosis-diseases with a huge economic and social impact. A number of apicomplexan genomes have been sequenced, but the accurate annotation of gene function remains challenging. We have adopted an approach called metabolic reconstruction, in which genes are systematically assigned to functions within pathways/networks for Toxoplasma gondii, Neospora caninum, Cryptosporidium and Theileria species, and Babesia bovis. Several functions missing from pathways have been identified, where the corresponding gene for an essential process appears to be absent from the current genome annotation. For each species, LAMP contains interactive diagrams of each pathway, hyperlinked to external resources and annotated with detailed information, including the sources of evidence used. We have also developed a section to highlight the overall metabolic capabilities of each species, such as the ability to synthesize or the dependence on the host for a particular metabolite. We expect this new database will become a valuable resource for fundamental and applied research on the Apicomplexa.

A software toolkit and interface for performing stable isotope labeling and top3 quantification using Progenesis LC-MS.

Numerous software packages exist to provide support for quantifying peptides and proteins from mass spectrometry (MS) data. However, many support only a subset of experimental methods or instrument types, meaning that laboratories often have to use multiple software packages. The Progenesis LC-MS software package from Nonlinear Dynamics is a software solution for label-free quantitation. However, many laboratories using Progenesis also wish to employ stable isotope-based methods that are not natively supported in Progenesis. We have developed a Java programming interface that can use the output files produced by Progenesis, allowing the basic MS features quantified across replicates to be used in a range of different experimental methods. We have developed post-processing software (the Progenesis Post-Processor) to embed Progenesis in the analysis of stable isotope labeling data and top3 pseudo-absolute quantitation. We have also created export ability to the new data standard, mzQuantML, produced by the Proteomics Standards Initiative to facilitate the development and standardization process. The software is provided to users with a simple graphical user interface for accessing the different features. The underlying programming interface may also be used by Java developers to develop other routines for analyzing data produced by Progenesis.

A critical appraisal of techniques, software packages, and standards for quantitative proteomic analysis.

New methods for performing quantitative proteome analyses based on differential labeling protocols or label-free techniques are reported in the literature on an almost monthly basis. In parallel, a correspondingly vast number of software tools for the analysis of quantitative proteomics data has also been described in the literature and produced by private companies. In this article we focus on the review of some of the most popular techniques in the field and present a critical appraisal of several software packages available to process and analyze the data produced. We also describe the importance of community standards to support the wide range of software, which may assist researchers in the analysis of data using different platforms and protocols. It is intended that this review will serve bench scientists both as a useful reference and a guide to the selection and use of different pipelines to perform quantitative proteomics data analysis. We have produced a web-based tool ( http://www.proteosuite.org/?q=other_resources ) to help researchers find appropriate software for their local instrumentation, available file formats, and quantitative methodology.