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> Genes & Molecular Sequences > Gene or Genome > Major Histocompatibility Complex

Major Histocompatibility Complex

The Major Histocompatibility Complex (MHC) is a set of cell surface proteins essential for the adaptive immue response.
MHC molecules presnet antigenic peptides to T cells, triggering activation and immune cell responses.
This complex plays a pivotal role in self/non-self discrimination, transplant rejection, and susceptibility to autoimmune diseases.
Understandig the MHC system is crucial for advancing immunology, transplantation, and personalized medicine reasearch.
PubCompare.ai leverages AI to optimize MHC protocol comparisons, streamlining discovery and improving reproducibility.

Most cited protocols related to «Major Histocompatibility Complex»

1
Conditional Analysis of T2D GWAS Loci
We used GCTA12 (link) to perform approximate conditional analyses to detect distinct association signals at each of the genome-wide significant risk loci for T2D (newly identified or confirmed, except at the major histocompatibility complex (MHC) region). GCTA performs conditional analysis using association summary statistics from GWAS meta-analysis and estimated LD from a sufficiently large reference study used in the meta-analysis. We used a reference sample of 6,000 (nearly) unrelated (pairwise relatedness <0.025) individuals of white British origin, randomly selected from the UK Biobank, to model patterns of LD between variants. The reference panel of genotypes consisted of the same 39 million variants from the HRC reference panel assessed in our GWAS, but with an additional quality control step to exclude SNPs with low imputation quality (proper-info<0.4) or deviation from Hardy-Weinberg equilibrium (p<1x10-6). For each locus, we first searched ±500kb surrounding the lead SNP (using summary statistics from BMI unadjusted or adjusted analysis, as appropriate) to ensure potential long-range genetic influences were assessed. Within a region, conditionally independent variants that reached locus-wide significance (p<10-5) were considered as index SNPs for distinct association signals. If the minimum distance between any distinct signals from two separate loci was less than 500kb, we performed additional conditional analysis taking both regions (encompassing ±500kb from both ends) and reassessed the independence of each signal.
Mahajan A., Taliun D., Thurner M., Robertson N.R., Torres J.M., Rayner N.W., Steinthorsdottir V., Scott R.A., Grarup N., Cook J.P., Schmidt E.M., Wuttke M., Sarnowski C., Mägi R., Nano J., Gieger C., Trompet S., Lecoeur C., Preuss M., Prins B.P., Guo X., Bielak L.F., Bennett A.J., Bork-Jensen J., Brummett C.M., Canouil M., Eckardt K.U., Fischer K., Kardia S.L., Kronenberg F., Läll K., Liu C.T., Locke A.E., Luan J., Ntalla I., Nylander V., Schönherr S., Schurmann C., Yengo L., Bottinger E.P., Brandslund I., Christensen C., Dedoussis G., Florez J.C., ford I., Franco O.H., Frayling T.M., Giedraitis V., Hackinger S., Hattersley A.T., Herder C., Ikram M.A., Ingelsson M., Jørgensen M.E., Jørgensen T., Kriebel J., Kuusisto J., Ligthart S., Lindgren C.M., Linneberg A., Lyssenko V., Mamakou V., Meitinger T., Mohlke K.L., Morris A.D., Nadkarni G., Pankow J.S., Peters A., Sattar N., Stančáková A., Strauch K., Taylor K.D., Thorand B., Thorleifsson G., Thorsteinsdottir U., Tuomilehto J., Witte D.R., Dupuis J., Peyser P.A., Zeggini E., Loos R.J., Froguel P., Ingelsson E., Lind L., Groop L., Laakso M., Collins F.S., Jukema J.W., Palmer C.N., Grallert H., Metspalu A., Dehghan A., Köttgen A., Abecasis G., Meigs J.B., Rotter J.I., Marchini J., Pedersen O., Hansen T., Langenberg C., Wareham N.J., Stefansson K., Gloyn A.L., Morris A.P., Boehnke M, & McCarthy M.I. (2018). Fine-mapping of an expanded set of type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps. Nature genetics, 50(11), 1505-1513.
Publication 2018
Childbirth Genome Genome-Wide Association Study Genotype Major Histocompatibility Complex Single Nucleotide Polymorphism White Person
2
Comparing DEPICT and MAGENTA for Complex Trait Analysis
Due to the lack of an unbiased set of gold standard pathways for any complex trait, we compared DEPICT and MAGENTA22 (link) by counting the number of statistically significant gene sets predicted based on Crohn’s disease, height and LDL loci. Prior to the benchmark, we estimated the type-1 error rate of both methods by running them with summary statistics from 100 null GWAS constructed based on simulated Gaussian phenotypes with no genetic basis, and HapMap Project release 2 imputed DGI Consortium genotype data (Supplementary Figs 1 and 3). For the null analyses, the top 200 independent loci from each null GWAS were used as input, whereas genome-wide significant loci were used as input in the Crohn’s disease, height and LDL analyses. All MAGENTA runs were based on the complete set of summary statistics. We restricted the comparison to a list of 1,280 gene sets (gene ontology terms, Kyoto encyclopedia of genes and genomes and REACTOME pathways) with overlapping identifiers between both methods. DEPICT was run on reconstituted gene sets. MAGENTA was run with default settings and both methods excluded the major histocompatibility complex region. The non-probabilistic, binary (yes/no) version of the reconstituted gene sets used in one of the MAGENTA comparisons were constructed by applying a threshold on the gene scores for a given reconstituted gene set (all genes above a permutation-based cutoff were considered part of the given reconstituted gene sets, as reported in ref 6 (link)). Entries with ‘NA in columns ‘DEPICT with predefined gene sets P’ and ‘DEPICT with predefined gene sets FDR’ in Supplementary Data 4–6 marked predefined gene sets for which enrichment could not be computed in the DEPICT analysis based on predefined gene sets.
Pers T.H., Karjalainen J.M., Chan Y., Westra H.J., Wood A.R., Yang J., Lui J.C., Vedantam S., Gustafsson S., Esko T., Frayling T., Speliotes E.K., Boehnke M., Raychaudhuri S., Fehrmann R.S., Hirschhorn J.N, & Franke L. (2015). Biological interpretation of genome-wide association studies using predicted gene functions. Nature communications, 6, 5890.
Publication 2015
Crohn Disease Figs Gene Order Genes Genes, vif Genome Genome-Wide Association Study Genotype Gold Major Histocompatibility Complex Phenotype Polygenic Traits Reproduction rosaniline hydrochloride
3
Protocol for MeDIP with LM-PCR
MeDIP was performed using a previously published protocol20 (link), but we also included a ligation-mediated PCR (LM-PCR) step43 (link) to amplify the material (the LM-PCR step was not performed for MeDIP-seq). Hybridizations of pre- and post-LM-PCR samples on custom tile-path arrays (2kb resolution) for the human Major Histocompatibility Complex (MHC) showed that the LM-PCR did not introduce significant bias (Supplementary Fig. 1 online). A detailed protocol is provided in the Supplementary Methods online.
Down T.A., Rakyan V.K., Turner D.J., Flicek P., Li H., Kulesha E., Gräf S., Johnson N., Herrero J., Tomazou E.M., Thorne N.P., Bäckdahl L., Herberth M., Howe K.L., Jackson D.K., Miretti M.M., Marioni J.C., Birney E., Hubbard T.J., Durbin R., Tavaré S, & Beck S. (2008). A Bayesian deconvolution strategy for immunoprecipitation-based DNA methylome analysis. Nature biotechnology, 26(7), 779-785.
Publication 2008
Crossbreeding Homo sapiens Ligation Major Histocompatibility Complex
4
Identifying Associated Genetic Loci
From the set of associated SNPs at a particular threshold (such as genome-wide significance, P<5 × 10−8), we generated independent ‘lead SNPs’ by retaining the most significant SNP from each set of SNPs that are in LD (pairwise r2>0.1) and/or in proximity (physical distance of < 1 Mb). We computed pairwise LD coefficients based on the imputation panel used in the GWAS, either HapMap Project release 2 and 3 CEU genotype data27 (link) or 1000 Genomes Project Phase 1 CEU, GBR and TSI genotype data28 (link). We defined positions in the human genome according to genome build GRCh37. Next, we created lists of genes at associated loci by mapping genes to loci if they resided within, or were overlapping with, boundaries defined by the most distal SNPs in either direction with LD r2>0.5 to the given lead SNP (see Supplementary Note 1 for justification of this locus definition). If no genes were within the locus defined by r2 > 0.5, the gene nearest to the given lead SNP was included. Loci with overlapping genes were then merged. Due to the extended LD in the major histocompatibility complex region and the resulting challenges in delineating associated loci, genes within base pairs 25,000,000–35,000,000 on chromosome 6 were excluded. DEPICT takes as input a set of independent, associated SNPs and automates all other steps outlined here.
Pers T.H., Karjalainen J.M., Chan Y., Westra H.J., Wood A.R., Yang J., Lui J.C., Vedantam S., Gustafsson S., Esko T., Frayling T., Speliotes E.K., Boehnke M., Raychaudhuri S., Fehrmann R.S., Hirschhorn J.N, & Franke L. (2015). Biological interpretation of genome-wide association studies using predicted gene functions. Nature communications, 6, 5890.
Publication 2015
Chromosomes, Human, Pair 6 Genes Genetic Loci Genome Genome, Human Genome-Wide Association Study Genotype Major Histocompatibility Complex Single Nucleotide Polymorphism
5
Fine-Mapping Genetic Associations Using SAIGE and SuSiE
The mixed-model logistic regression method SAIGE (v.0.35.8.8)54 (link) was used for association analysis. We used sex, age, genotyping batch and ten PCs as covariates (see  Supplementary Methods for details). We used SuSiE55 (link) for fine-mapping. We fine-mapped all regions with variants that had values of P < 1 × 10−6 and extended regions 1.5 Mb upstream and downstream from each lead variant. Finally, overlapping regions were merged and subjected to fine-mapping. The major histocompatibility complex region (chromosome 6: 25–36 Mb) was excluded owing to its complex LD structure. We allowed up to ten independent signals per region, and SuSiE reports a 95% credible set for each independent signal. As LD, we used in-sample dosages (that is, cases and controls used for each phenotype) computed with LDStore2. The FinnGen fine-mapping pipeline is available in GitHub (https://github.com/FINNGEN/finemapping-pipeline).
To define independent signals within a locus, we utilized fine-mapping results. For each locus, we report the credible set as an independent hit if it represents a primary strongest signal with lead P < 5 × 10−8. For secondary hits, we required genome-wide significance and log Bayes factor (BF) > 2. The BF filtering was necessary because SuSiE sometimes reports multiple credible sets for a single strong signal but this is indicated in SuSiE as a low BF (the model does not improve by adding another signal in the region that is not an independent signal).
Kurki M.I., Karjalainen J., Palta P., Sipilä T.P., Kristiansson K., Donner K.M., Reeve M.P., Laivuori H., Aavikko M., Kaunisto M.A., Loukola A., Lahtela E., Mattsson H., Laiho P., Della Briotta Parolo P., Lehisto A.A., Kanai M., Mars N., Rämö J., Kiiskinen T., Heyne H.O., Veerapen K., Rüeger S., Lemmelä S., Zhou W., Ruotsalainen S., Pärn K., Hiekkalinna T., Koskelainen S., Paajanen T., Llorens V., Gracia-Tabuenca J., Siirtola H., Reis K., Elnahas A.G., Sun B., Foley C.N., Aalto-Setälä K., Alasoo K., Arvas M., Auro K., Biswas S., Bizaki-Vallaskangas A., Carpen O., Chen C.Y., Dada O.A., Ding Z., Ehm M.G., Eklund K., Färkkilä M., Finucane H., Ganna A., Ghazal A., Graham R.R., Green E.M., Hakanen A., Hautalahti M., Hedman Å.K., Hiltunen M., Hinttala R., Hovatta I., Hu X., Huertas-Vazquez A., Huilaja L., Hunkapiller J., Jacob H., Jensen J.N., Joensuu H., John S., Julkunen V., Jung M., Junttila J., Kaarniranta K., Kähönen M., Kajanne R., Kallio L., Kälviäinen R., Kaprio J., Kerimov N., Kettunen J., Kilpeläinen E., Kilpi T., Klinger K., Kosma V.M., Kuopio T., Kurra V., Laisk T., Laukkanen J., Lawless N., Liu A., Longerich S., Mägi R., Mäkelä J., Mäkitie A., Malarstig A., Mannermaa A., Maranville J., Matakidou A., Meretoja T., Mozaffari S.V., Niemi M.E., Niemi M., Niiranen T., O´Donnell C.J., Obeidat M., Okafo G., Ollila H.M., Palomäki A., Palotie T., Partanen J., Paul D.S., Pelkonen M., Pendergrass R.K., Petrovski S., Pitkäranta A., Platt A., Pulford D., Punkka E., Pussinen P., Raghavan N., Rahimov F., Rajpal D., Renaud N.A., Riley-Gillis B., Rodosthenous R., Saarentaus E., Salminen A., Salminen E., Salomaa V., Schleutker J., Serpi R., Shen H.Y., Siegel R., Silander K., Siltanen S., Soini S., Soininen H., Sul J.H., Tachmazidou I., Tasanen K., Tienari P., Toppila-Salmi S., Tukiainen T., Tuomi T., Turunen J.A., Ulirsch J.C., Vaura F., Virolainen P., Waring J., Waterworth D., Yang R., Nelis M., Reigo A., Metspalu A., Milani L., Esko T., Fox C., Havulinna A.S., Perola M., Ripatti S., Jalanko A., Laitinen T., Mäkelä T.P., Plenge R., McCarthy M., Runz H., Daly M.J, & Palotie A. (2023). FinnGen provides genetic insights from a well-phenotyped isolated population. Nature, 613(7944), 508-518.
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Publication 2023
Chromosomes, Human, Pair 6 Genome Major Histocompatibility Complex Phenotype

Most recents protocols related to «Major Histocompatibility Complex»

1
Immune Landscape Analysis in LUAD
We extracted the expression data of ITGA8, 150 marker genes of five types of immune pathways: chemokine (14 (link)), receptor (15 (link)), major histocompatibility complex [MHC, 21], immunoinhibitor (16 (link)), immunostimulator (17 (link)), and 60 marker genes of two types of immune checkpoint pathways: inhibitory (16 (link)) and stimulatory (18 (link)) in reference to studies in LUAD samples, and performed correlation analysis between them (19 (link), 20 (link)).
Using the R software package ESTIMATE (21 (link)), we calculated the stromal, immune, and ESTIMATE scores in each tumor for each patient based on gene expression.
Using Timer (22 (link)) of the R software package IOBR (15 (link)), we reassessed B cell, CD4+ T cell, CD8+ T cell, neutrophil, macrophage, and dendritic cell (DC) infiltration scores in each tumor for each patient based on gene expression.
We obtained the cytotoxic T lymphocyte score of a LUAD dataset (GSE13213) with Tumor Immune Dysfunction and Exclusion (TIDE, http://tide.dfci.harvard.edu) to predict the response of patients to immunotherapy. We downloaded the scatter chart of correlation analysis between TIDE score (23 (link)) and ITGA8 gene expression on the official website of TIDE.
Li X., Zhu G., Li Y., Huang H., Chen C., Wu D., Cao P., Shi R., Su L., Zhang R., Liu H, & Chen J. (2023). LINC01798/miR-17-5p axis regulates ITGA8 and causes changes in tumor microenvironment and stemness in lung adenocarcinoma. Frontiers in Immunology, 14, 1096818.
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Publication 2023
Antigen-Presenting Cells B-Lymphocytes CD4 Positive T Lymphocytes CD8-Positive T-Lymphocytes Cell Cycle Checkpoint Genes Chemokine Cytotoxic T-Lymphocytes Gene Expression Genetic Markers Immune System Diseases Immunotherapy Macrophage Major Histocompatibility Complex Neoplasms Neutrophil Patients Psychological Inhibition
2
Polygenic Risk Scores and C4 Expression
Polygenic risk scores (PRS) were calculated for EUR subjects. Genotype imputation was performed on the Michigan Imputation Server [69 (link)] using the TOPMed reference panel. Quality control included filtering for genotyping rate > 0.99, sample missingness < 0.01, Hardy-Weinberg Equilibrium P > 1 × 10−6, minor allele frequency > 1%, and imputation score > 0.8. GCTA v1.93.31 [70 (link)] was used to compute the genetic relationship matrix; a relatedness cutoff of 0.05 was applied. Genetic variants in the major histocompatibility complex (MHC) locus were excluded prior to PRS calculation. PRS were generated using summary statistics from the largest available genome-wide association study of schizophrenia [10 (link)] (40,675 cases, 64,643 controls) and adult brain surface area [71 ] (33,992 subjects) in PRS-CS [72 ], applying the LD reference panel from the 1000 Genomes Project phase 3 samples and the recommended global shrinkage parameter for highly polygenic traits (phi = 1e−2). Analyses assessing the interaction between C4 expression and PRS were restricted to youth with common C4 structural haplotypes (AL, AL-AL, AL-BL, AL-BS, BS), resulting in a final sample of 3730 EUR youth (female N = 1737; male N = 1993) and 7,715,663 genetic variants.
Hernandez L.M., Kim M., Zhang P., Bethlehem R.A., Hoftman G., Loughnan R., Smith D., Bookheimer S.Y., Fan C.C., Bearden C.E., Thompson W.K, & Gandal M.J. (2023). Multi-ancestry phenome-wide association of complement component 4 variation with psychiatric and brain phenotypes in youth. Genome Biology, 24, 42.
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Publication 2023
Adult Brain Females Genetic Diversity Genome Genome-Wide Association Study Haplotypes Major Histocompatibility Complex Males Polygenic Traits Reproduction Schizophrenia Strains Youth
3
Immunostaining Protocol for Spinal Cord and Neuromuscular Junctions
C5–C7 spinal segments or musculocutaneous nerves were cut into 15-µm-thick frozen sections for immunostaining. The blocking buffer was composed of 5% goat serum and 3% bovine serum albumin diluted in 0.1 M phosphate buffer saline (PBS). Signal was detected with Alexa fluor 546 or 488 coupled secondary antibodies (1:1000, Invitrogen). Primary antibodies were: goat anti- choline acetyltransferase (ChAT, 1:500, ab144p, Millipore), chicken anti-β-gal (1:500, ab9361, Abcam), rabbit anti-Calretinin (1:300, ab702, Abcam), mouse anti-Parvalbumin (1:1000, Mab1572, Millipore), rabbit anti-CAMKII (1:500, ab104224, Abcam), rabbit anti-vesicular GABA transporter (VGAT; 1:800, NO131013, Synaptic Systems), mouse anti-vesicular glutamate transporter 1 (vGlut1; 1:1000, Mab5502, Millipore), rat anti-major histocompatibility complex 1 (MHC1; 1:300, sc-59199, Santa Cruz), rabbit anti-glial fibrillary acidic protein (GFAP; 1:1000, AB7260, Abcam), rabbit anti-Iba1(1:1000, 019–19,741, Wako), and rabbit anti-Oligo2 (1:500, ab9610, Merck Millipore).
On day 50 after BPA, the biceps were collected and 7-µm horizontal sections were prepared with a sliding microtome (Leica, Germany) and double stained with rabbit anti-NF200 (1:500, n4142, Sigma) and α-BT (1:1000, Molecular probes, USA) to visualize neuromuscular junctions (NMJs).
Yu L., Liu M., Li F., Wang Q., Wang M., So K.F., Qu Y, & Zhou L. (2023). Celsr2 Knockout Alleviates Inhibitory Synaptic Stripping and Benefits Motoneuron Survival and Axon Regeneration After Branchial Plexus Avulsion. Molecular Neurobiology, 60(4), 1884-1900.
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Publication 2023
Alexa fluor 546 Antibodies Buffers Calmodulin-Dependent Protein Kinase II Calretinin Chickens Choline O-Acetyltransferase Frozen Sections Glial Fibrillary Acidic Protein Goat Major Histocompatibility Complex Mice, House Microtomy Molecular Probes Nerves, Musculocutaneous Neuromuscular Junction OLIG2 protein, human Parvalbumins Phosphates Rabbits Saline Solution Serum Serum Albumin, Bovine vesicular GABA transporter Vesicular Glutamate Transport Protein 1
4
Genetic Predictors of Treatment Response in Major Depressive Disorder
Polygenic risk scores (PRS). Before calculating the PRS, we further excluded the following SNPs from GWAS summary statistics: 1) SNPs with MAF < 0.1 or INFO score < 0.9; 2) duplicate SNPs; 3) strand-ambiguous SNPs; 4) SNPs in major histocompatibility complex regions (chr6:28–34 Mb). SNPs overlapping with Hapmap3 were extracted and the summary statistics were rescaled to account for linkage disequilibrium (LD) in SBayesR, which is a state-of-the-art method with high prediction accuracy in psychiatric disorders37 (link), 38 (link). Finally, PRS was calculated in imputed data for each individual as the sum of the number of risk alleles weighted by allelic effects in PLINK (version 2.0)39 (link); and standardised within the whole sample. We also calculated the PRS of MDD and bipolar disorder in the same way for sensitivity analyses.
Association analysis. To examine the genetic association of treatment response with TRD status, we first tested the mean differences in the PRS of antidepressant or lithium response among TRD cases compared to non-TRD cases (t-test). Logistic regression was used to estimate the odds ratios (OR) corresponding to per standard deviation (SD) increase in the PRS of antidepressant or lithium response, adjusting for the first four principal components (PCs). We ran these models in all phenotype comparisons. The proportion of variance in MDD treatment resistance explained by PRS (Nagelkerke’s R2) was calculated by comparing the full model including both PRS and covariates to the baseline model only including covariates. We converted Nagelkerke’s R2 to the liability scale by assuming 10% MDD cases meeting our stringent definition of TRD. We tested the trend of association in PRS quartiles with treatment resistance using Chi-squared test.
We employed a Bonferroni-corrected p-value threshold for statistical significance, controlling for multiple testing across six comparisons (two PRS of treatment response in three sets of TRD/non-TRD comparisons; P ≤ 0.05/6 = 0.008). All analyses were conducted in R (Version 4.0.0)40 .
Sensitivity analysis. To account for potential genetic overlap between treatment response and corresponding psychiatric disorder risk (MDD and bipolar disorder)12 (link), 41 (link), we additionally adjusted for the PRS of MDD and bipolar disorder in the main models.
To examine whether the results were driven by lithium use in TRD patients, we further excluded patients with lithium use, and estimated the association between PRS of lithium response and treatment resistance in MDD.
Lu Y., Xiong Y., Karlsson R., Song J., Kowalec K., Rück C., Sigstrom R., Jonsson L., Clements C., Andersson E., Boberg J., Lewis C., Sullivan P, & Landén M. (2023). Investigating genetic overlap between antidepressant and lithium response and treatment resistance in major depressive disorder. Research Square.
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Publication Preprint 2023
Alleles Antidepressive Agents Bipolar Disorder Genome-Wide Association Study Hypersensitivity Lithium Major Histocompatibility Complex Mental Disorders Patients Phenotype Reproduction Single Nucleotide Polymorphism
5
Prediction of T Cell Epitopes

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Publication 2023
Alleles Allergens Antigens Epitopes Epitopes, T-Lymphocyte GPER protein, human Histocompatibility Antigens Class I Histocompatibility Antigens Class II Major Histocompatibility Complex Surface Antigens

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More about "Major Histocompatibility Complex"

The Major Histocompatibility Complex (MHC), also known as the Human Leukocyte Antigen (HLA) system, is a crucial component of the adaptive immune response.
This complex of cell surface proteins plays a pivotal role in self/non-self discrimination, transplant rejection, and susceptibility to autoimmune diseases.
Understanding the MHC system is essential for advancing immunology, transplantation, and personalized medicine research.
MHC molecules present antigenic peptides to T cells, triggering activation and immune cell responses.
This process is vital for the body's ability to recognize and respond to foreign invaders, such as pathogens, while also maintaining tolerance to self-antigens.
Optimizing MHC research is crucial for developing effective treatments and therapies, as well as improving the accuracy and reproducibility of scientific discoveries.
PubCompare.ai, a cutting-edge AI-powered platform, leverages advanced algorithms to streamline the MHC research process.
By comparing protocols from literature, preprints, and patents, the tool helps researchers identify the best approaches, products, and techniques to enhance their studies.
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The RNeasy Mini Kit, a powerful tool for RNA extraction, can also be integrated into MHC research workflows to ensure high-quality data and improved reproducibility.
Additionally, software like FACSDiva can be leveraged to analyze and interpret the results of MHC-related experiments, further optimizing the research process.
By harnessing the power of AI and leveraging the latest technological advancements, PubCompare.ai empowers researchers to streamline their MHC studies, leading to faster discoveries and more accurate, reproducible findings.
This, in turn, drives progress in the fields of immunology, transplantation, and personalized medicine, ultimately benefiting patients and advancing the frontiers of scientific knowledge.