Classification of asthma with cross-cohort clinical data and with enhancer mediated gene regulation

Inflammatory diseases such as IBD and asthma, along with metabolic disorders like obesity, are complex conditions shaped by a combination of genetic, environmental, and immunological factors. Understanding the molecular mechanisms driving these diseases is crucial for the development of targeted therapies and improving patient care. With the advent of bioinformatics and data- driven approaches, we have new opportunities to identify key biomarkers, regulatory mechanisms, and gene expression patterns associated with these conditions.

In this study, we bring together different types of data, including proteomics, transcriptomics, and epigenomics, to investigate the roles of specific proteins, gene enhancers, and molecular markers in inflammatory and metabolic diseases. One of the key challenges we face is handling unstructured data from various sources, such as clinical trials and patient registries, especially when working with different cohort studies that have diverse data types and collection methods. By using robust computational tools and statistical methods, I addressed the complexity of organizing and analyzing these datasets, ensuring they are accurately combined across different cohorts.

Paper-I investigates serum proteins encoded at known IBD risk loci. By profiling serum from Crohn's disease (CD) and ulcerative colitis (UC) patients, we identified differentially expressed proteins, including LACC1, linked to genetic variants associated with IBD. This targeted proteomic analysis provided insights into potential protein biomarkers that could distinguish between IBD subtypes, offering diagnostic and therapeutic potential.

Paper-II applies biostatistical modeling and unsupervised clustering to explore asthma phenotypes by integrating clinical and molecular data from two large asthma cohorts (BIOAIR and U-BIOPRED). This data-driven approach revealed distinct asthma subgroups and associated biomarkers, emphasizing the heterogeneity of the disease. By combining bioinformatics and biostatistics, we contribute to personalized asthma management by identifying key phenotypic traits and inflammatory markers.

Paper-III focuses on enhancer regulation in severe and mild childhood asthma. Using CAGE RNA sequencing, we identified asthma-specific enhancers and their interactions with transcription start sites (TSS) of genes linked to immune responses. Bioinformatics techniques were used to map enhancer activity and gene regulation, highlighting the role of non-coding regulatory elements in asthma severity. These findings enhance our understanding of gene regulation in asthma and provide novel targets for therapeutic interventions.

Paper-IV explores enhancer activity in human white adipose tissue (WAT) and its role in insulin response. We identified active enhancers involved in regulating insulin-responsive genes in both obese and non-obese individuals, using chromatin conformation and CAGE sequencing data. This study uncovered key regulatory elements linked to obesity-related traits and insulin sensitivity, providing insights into metabolic regulation and potential intervention points for obesity and insulin resistance.

Across all studies, bioinformatics and data-driven methodologies such as GWAS, CAGE-seq, and clustering algorithms were integral in identifying disease-relevant molecular signatures. The integration of multi-omics data allowed us to gain a comprehensive understanding of gene regulation, enhancer activity, and protein biomarkers in complex diseases, demonstrating the power of computational biology in advancing precision medicine.

List of scientific papers

I. Drobin K, Assadi G, Hong MG, Andersson E, Fredolini C, Forsström B, Reznichenko A, Akhter T, Ek WE, Bonfiglio F, Berner Hansen M, Sandberg K, Greco D, Repsilber D, Schwenk JM, D'Amato M, Halfvarson J. Targeted Analysis of Serum Proteins Encoded at Known Inflammatory Bowel Disease Risk Loci. Inflammatory Bowel Diseases. 2019 Jan 10;25(2):306-316. https://doi.org/10.1093/ibd/izy326

II. Akhter T, Kolmert J, James A, Andersson LI, Adcock IM, Wheelock CE, Dahlen SE, Kupczyk M, Daub CO. Explorative Biostatistical Modelling of Selected Clinical and Molecular Data from Two Patient Cohorts to Define Common Phenotypical Traits in Asthma. [Manuscript]

III. Akhter T, Mileti E, Kere M, Kolmert J, Konradsen JR, Hedlin G, Melén E, Daub CO. Enhancers regulate genes linked to severe and mild childhood asthma. Heliyon. 2024 Jul 9;10(14). eCollection 2024 Jul 30.
https://doi.org/10.1016/j.heliyon.2024.e34386

IV. Mileti E, Kwok KH, Salvatore M, Raman A, Dias MS, Gustafsson C, Akhter T, Bonetti A, Andersson R, Månsson R, Mejhert N, Arner P, Ryden M, Daub CO. Enhancers Facilitate Acute Insulin Response in Human White Adipose Tissue. [Manuscript]