A survey of genome-wide association studies, polygenic scores and UK Biobank highlights resources for autoimmune disease genetics

Exploration and verification a 13-gene diagnostic framework for ulcerative colitis across multiple platforms via machine learning algorithms

Ulcerative colitis (UC) is a chronic inflammatory bowel disease with intricate pathogenesis and varied presentation. Accurate diagnostic tools are imperative to detect and manage UC. This study sought to construct a robust diagnostic model using gene expression profiles and to identify key genes that differentiate UC patients from healthy controls. Gene expression profiles from eight cohorts, encompassing a total of 335 UC patients and 129 healthy controls, were analyzed. A total of 7530 gene sets were computed using the GSEA method. Subsequent batch correction, PCA plots, and intersection analysis identified crucial pathways and genes. Machine learning, incorporating 101 algorithm combinations, was employed to develop diagnostic models. Verification was done using four external cohorts, adding depth to the sample repertoire. Evaluation of immune cell infiltration was undertaken through single-sample GSEA. All statistical analyses were conducted using R (Version: 4.2.2), with significance set at a P value below 0.05. Employing the GSEA method, 7530 gene sets were computed. From this, 19 intersecting pathways were discerned to be consistently upregulated across all cohorts, which pertained to cell adhesion, development, metabolism, immune response, and protein regulation. This corresponded to 83 unique genes. Machine learning insights culminated in the LASSO regression model, which outperformed others with an average AUC of 0.942. This model's efficacy was further ratified across four external cohorts, with AUC values ranging from 0.694 to 0.873 and significant Kappa statistics indicating its predictive accuracy. The LASSO logistic regression model highlighted 13 genes, with LCN2, ASS1, and IRAK3 emerging as pivotal. Notably, LCN2 showcased significantly heightened expression in active UC patients compared to both non-active patients and healthy controls (P < 0.05). Investigations into the correlation between these genes and immune cell infiltration in UC highlighted activated dendritic cells, with statistically significant positive correlations noted for LCN2 and IRAK3 across multiple datasets. Through comprehensive gene expression analysis and machine learning, a potent LASSO-based diagnostic model for UC was developed. Genes such as LCN2, ASS1, and IRAK3 hold potential as both diagnostic markers and therapeutic targets, offering a promising direction for future UC research and clinical application.

Utility of polygenic scores across diverse diseases in a hospital cohort for predictive modeling

Polygenic scores estimate genetic susceptibility to diseases. We systematically calculated polygenic scores across 457 phenotypes using genotyping array data from China Medical University Hospital. Logistic regression models assessed polygenic scores' ability to predict disease traits. The polygenic score model with the highest accuracy, based on maximal area under the receiver operating characteristic curve (AUC), is provided on the GeneAnaBase website of the hospital. Our findings indicate 49 phenotypes with AUC greater than 0.6, predominantly linked to endocrine and metabolic diseases. Notably, hyperplasia of the prostate exhibited the highest disease prediction ability (P value = 1.01 × 10-19, AUC = 0.874), highlighting the potential of these polygenic scores in preventive medicine and diagnosis. This study offers a comprehensive evaluation of polygenic scores performance across diverse human traits, identifying promising applications for precision medicine and personalized healthcare, thereby inspiring further research and development in this field.

Functional genomics in inborn errors of immunity

Inborn errors of immunity (IEI) comprise a diverse spectrum of 485 disorders as recognized by the International Union of Immunological Societies Committee on Inborn Error of Immunity in 2022. While IEI are monogenic by definition, they illuminate various pathways involved in the pathogenesis of polygenic immune dysregulation as in autoimmune or autoinflammatory syndromes, or in more common infectious diseases that may not have a significant genetic basis. Rapid improvement in genomic technologies has been the main driver of the accelerated rate of discovery of IEI and has led to the development of innovative treatment strategies. In this review, we will explore various facets of IEI, delving into the distinctions between PIDD and PIRD. We will examine how Mendelian inheritance patterns contribute to these disorders and discuss advancements in functional genomics that aid in characterizing new IEI. Additionally, we will explore how emerging genomic tools help to characterize new IEI as well as how they are paving the way for innovative treatment approaches for managing and potentially curing these complex immune conditions.

Translational immunology: Applying fundamental discoveries to human health and autoimmune diseases

Studying the human immune system is challenging. These challenges stem from the complexity of the immune system itself, the heterogeneity of the immune system between individuals, and the many factors that lead to this heterogeneity including the influence of genetics, environment, and immune experience. Studies of the human immune system in the context of disease are increased in complexity as multiple combinations and variations in immune pathways can lead to a single disease. Thus, although individuals with a disease may share clinical features, the underlying disease mechanisms and resulting pathophysiology can be diverse among individuals with the same disease diagnosis. This has consequences for the treatment of diseases, as no single therapy will work for everyone, therapeutic efficacy varies among patients, and targeting a single immune pathway is rarely 100% effective. This review discusses how to address these challenges by identifying and managing the sources of variation, improving access to high-quality, well-curated biological samples by building cohorts, applying new technologies such as single-cell omics and imaging technologies to interrogate samples, and bringing to bear computational expertise in conjunction with immunologists and clinicians to interpret those results. The review has a focus on autoimmune diseases, including rheumatoid arthritis, MS, systemic lupus erythematosus, and type 1 diabetes, but its recommendations are also applicable to studies of other immune-mediated diseases.

Transcriptome-wide association study identifies new susceptibility genes and pathways for spondyloarthritis

BACKGROUND

Spondyloarthritis (SpA) is a group of multifactorial bone diseases influenced by genetic factors, the environment and lifestyle. However, current studies have found a limited number of SpA-related genes, and the genetic and pathogenic mechanisms of SpA are still unclear.

METHODS

A tissue-specific transcriptome-wide association study (TWAS) of SpA was performed using GWAS (including 3966 SpA patients and 448,298 controls) summary data and gene expression weights of whole blood and skeletal muscle. The SpA-associated genes identified by TWAS were further compared with the differentially expressed genes (DEGs) identified in the SpA gene expression profile acquired from the Gene Expression Omnibus database (GEO, GSE58667). Finally, functional enrichment and annotation analyses of the identified genes were performed.

RESULTS

The TWAS detected 499 suggestive genes associated with SpA in whole blood and skeletal muscle, such as CTNNAL1 (PSM = 3.04 × 10-2, PWB = 9.58 × 10-3). The gene expression profile of SpA identified 20 candidate genes that overlapped in the TWAS data, such as MCM4 (PTWAS = 1.32 × 10-2, PDEG = 2.75 × 10-2) and KIAA1109 (PTWAS = 3.71 × 10-2, PDEG = 4.67 × 10-2). Enrichment analysis of the genes identified by TWAS identified 93 significant GO terms and 33 KEGG pathways, such as mitochondrion organization (GO: 0007005) and axon guidance (hsa04360).

CONCLUSION

We identified multiple candidate genes that were genetically related to SpA. Our study may provide novel clues regarding the genetic mechanism, diagnosis, and treatment of SpA.

Always stressed but never exhausted: how stem cells in myeloid neoplasms avoid extinction in inflammatory conditions

Chronic or recurrent episodes of acute inflammation cause attrition of normal hematopoietic stem cells (HSCs) that can lead to hematopoietic failure but they drive progression in myeloid malignancies and their precursor clonal hematopoiesis. Mechanistic parallels exist between hematopoiesis in chronic inflammation and the continuously increased proliferation of myeloid malignancies, particularly myeloproliferative neoplasms (MPNs). The ability to enter dormancy, a state of deep quiescence characterized by low oxidative phosphorylation, low glycolysis, reduced protein synthesis, and increased autophagy is central to the preservation of long-term HSCs and likely MPN SCs. The metabolic features of dormancy resemble those of diapause, a state of arrested embryonic development triggered by adverse environmental conditions. To outcompete their normal counterparts in the inflammatory MPN environment, MPN SCs co-opt mechanisms used by HSCs to avoid exhaustion, including signal attenuation by negative regulators, insulation from activating cytokine signals, anti-inflammatory signaling, and epigenetic reprogramming. We propose that new therapeutic strategies may be derived from conceptualizing myeloid malignancies as an ecosystem out of balance, in which residual normal and malignant hematopoietic cells interact in multiple ways, only few of which have been characterized in detail. Disrupting MPN SC insulation to overcome dormancy, interfering with aberrant cytokine circuits that favor MPN cells, and directly boosting residual normal HSCs are potential strategies to tip the balance in favor of normal hematopoiesis. Although eradicating the malignant cell clones remains the goal of therapy, rebalancing the ecosystem may be a more attainable objective in the short term.