Computational Genomics and Systems Biology Symposium 2020

3rd – 4th December 2020, Human Biology Building at NUIG

2019 Keynotes

The making of N-glycoproteins

Markus Aebi
Department of Biology, Institute of Microbiology, ETH Zurich, Switzerland

Abstract: Two dedicated organelles, the Endoplasmic Reticulum (ER) and the Golgi contain the synthetic machinery that generates N-linked glycans, the most divers modification of proteins. The ER pathway generates a structurally highly conserved oligosaccharide that is transferred to selected asparagine residues of nascent polypeptide chains by oligosaccharyltransferase. These oligosaccharides are essential for the folding and the quality control process of glycoproteins. The subsequent trimming and remodeling of N-linked glycans in the Golgi compartment follows a completely different regime and generates a heterogeneous assembly of N-glycans in a species-, protein- and site-specific manner. 

We have established MS-based analytical systems for the site-specific quantification of N-linked glycans and combined it with expression systems of model glycoproteins (such as IgG) in cell lines. We quantified the site-specific glyco-structures and followed the time-course of intracellular N-glycan processing in vivo using time-resolved SILAC experiments in CHO cells. Based on the experimental data, we developed a mathematical model for glycoprotein processing in the ER and the Golgi. The model allowed for a detailed description of both ER- and Golgi localized processing pathways.   

Using functional genomics to understand patient heterogeneity within disease and in response to treatment

Emma Davenport
Wellcome Sanger Institute

Abstract: Our groups research interest lies in integrating functional genomics and clinical data to understand how genetics contributes to patient heterogeneity. We focus on how environmental perturbations modulate the regulation of gene expression in the immune cells of an individual. This can be determined by mapping in vivo expression quantitative trait locus (eQTL) interactions, where an eQTL effect is modulated by the environmental perturbation. Identifying such interactions can give insights into condition dependent regulators of gene expression and therefore insights into disease and drug mechanisms of action.

To explore heterogeneity in treatment response, we developed a statistical framework to investigate how the administration of a drug alters the relationship between genomic variation and gene expression. Through an industry collaboration with Pfizer, we have analysed data from an anti-IL-6 clinical trial in patients with systemic lupus erythematosus. We have identified in vivo eQTL interaction events with IL-6 and IFN, two clinically important cytokines with dramatic variation in this cohort due to therapy and disease status respectively. We find transcription factor binding motifs interrupted by eQTL interaction SNPs, which point to key regulatory mediators of these environmental perturbations.In collaboration with the Genomics Advances in Sepsis (GAinS) Investigators we have profiled gene expression to determine disease heterogeneity within a cohort of sepsis patients. A key finding was a gene expression signature stratifying patients into two distinct sepsis response signature (SRS) groups. The SRS1 group identifies individuals with an immunosuppressed phenotype and was associated with higher mortality. We identify in vivo eQTL interactions with SRS group including for a number of transcription factors such as TAF1C, IRF5 and EPAS1.

Whole-genome sequencing and complex trait association studies

Arthur Gilly
Helmholtz Center, Munich

Abstract: Common and low-frequency variants are now routinely and successfully assayed in populations, and have provided insight into complex disease mechanisms through genetic association studies (GWAS). In this talk, I will explore variations to the established GWAS study design, with a particular focus on whole-genome sequencing (WGS) methods. First, sequencing-based  population reference panels can improve detection of variants of interest in special populations such as isolates. Second, very-low depth sequencing (1x) can provide access to low-frequency variants not detected using a hybrid genotyping and imputation method. Third, high-depth WGS allows to accurately genotype both rare and copy-number variants, which are understudied contributors to the aetiology of complex traits and require a different testing framework than the one used in GWAS. Finally, studying intermediate traits, such as circulating protein levels, can provide further insight into the genetic risk of complex diseases.

Tightening the Loop with Theory: Precision Experiments

Quincey Justman
Cell Systems, Editor-in-chief

Abstract: The ability to make precise measurements at scale, in vivo and under challenging conditions, is forcing a reckoning in biology.  These data challenge our mental models of how the cell works.  They demand new theories that will help us discover and understand new biology, but the theories can only be as good as the data themselves.  In this talk, Quincey Justman, the Editor-in-Chief of Cell Systems, will talk about the relationships between models, theories, and experiments, with the goal of a more productive conversation between the three.

Using multi-layered networks and multi-omics analysis for precision medicine studies in the gut

Tamás Korcsmáros
Earlham Institute and Quadram Institute, Norwich, UK

Abstract: Networks describe the relationships of the elements of biological systems using edges and nodes. However, the resulting representation of the system can sometimes be too simplistic to usefully model reality and provide new mechanistic insight. By combining several different interaction types within one larger multi-layered biological network, we developed resources such as SignaLink, OmniPath, NRF2ome, Autophagy Regulatory Network and SalmoNet to provide a more nuanced view than those relying on single-layer networks. Multi-layered networks display connections between multiple networks (i.e.: protein-protein interactions and their transcriptional and post-transcriptional regulators), each one of them describing a specific set of connections. We used these resources in the last couple of years to investigate cancer signaling networks, and recently inflammatory bowel disease and host-microbe interactions in the gut.

Using integrated networks, we proposed new therapeutic approaches to improve success rates and to identify suitable proteins as novel, alternative drug targets for solid tumors. We designed a computational approach, combining mutation and differential expression data with network information, to analyse the interactions of cancer-related proteins in colon, breast, liver and lung cancer. We found that first (direct) neighbours, not linked previously to the given cancer type, are similarly important as mutated proteins known to be involved in cancer development. We also found 223 drugs already in the clinic targeting these proteins but not yet used against cancer as their oncology relevance was hidden so far.

In a similar study, we investigated ulcerative colitis, a type of inflammatory bowel disease. This time we did a precision medicine approach, using patient-specific SNP-profiles for integration with multi-layered networks. We demonstrated that while most of the SNP affected proteins are not related to ulcerative colitis associated functions, their interactor partners (not mutated in patients) are annotated to central pathways with biomedically relevant regulatory roles. Moreover, we were able to cluster ulcerative colitis patients having nearly the same symptoms but possessing cluster-specific mutation sets effecting different pathways and cell types.

Finally, we also combined these multi-layered network resources to investigate host-microbe interactions. We predicted and prioritised a list of potential connections between the host autophagy machinery and a gut pathogen, Salmonella. These connections could serve as mechanisms how Salmonella is modulating the host upon infection. We established an intestinal organoid system to test and validate these predicted interactions using multi-omics readouts, including small RNA profiling and single cell sequencing.

DNA-based molecular tools for monitoring cell signaling

Ola Soderberg
Uppsala University, Department of Pharmaceutical biosciences, Biomedical center

Abstract: Methods to determine levels of protein-protein interactions are essential in analysis of cellular signaling activity. High-resolution analysis of proteins or protein complexes requires either advanced microscopy, or molecular methods that combine distance dependence with signal amplification. By using DNA molecules as a tool we have over the years developed several molecular methods utilizing a variety of enzymes to modify and amplify the DNA molecules used in the method. One such method is Proximity Ligation Assay (PLA) where pairs of antibodies equipped with DNA oligonucleotides (so-called proximity probes) are used to target proteins. Proximal binding of such probes template the creation of a circular DNA molecule, which can be amplified using rolling circle amplification (RCA). The single-stranded RCA product from a single recognition event will contain several hundreds of repetitive motifs that can be visualized by hybridization of fluorophore-conjugated oligonucleotides. The distance requirement for the formation of the DNA circles will be dependent on the size of the affinity reagents and the length and polarity of the oligonucleotides used, ranging from a few nm up to several tens of nm. An alternative to PLA would be the proximity-dependent initiation of hybridization chain reactions (proxHCR) where the proximity probes consists of DNA hairpins. The addition of an activator oligonucleotide that can hybridize to one of the hairpins will liberate a sequence motif that will invade the other hairpin, if they are in close proximity. The now bridged proximity probes will reveal an initiator motif that will prime a hybridization chain reaction of fluorophore-labeled hairpins, creating a long fluorophore-labeled double-stranded DNA molecule. 
However, more complex measurements may be required in order to model signaling pathway activity and information flow in single cells. Information on levels of both free and interacting proteins is important to determine if the interactions monitored are a result of a post-translational protein modification, e.g. phosphorylations, that changes the conformation of the proteins and subsequently results in a different dissociation constant (KD), or if the amount of interactions reflects mere expression levels of the proteins.