Periodic Reporting for period 4 - CrUCCial (Novel diagnostic and therapeutic approach to inflammatory bowel disease based on functional characterization of patients: the CrUCCial index)
Reporting period: 2021-03-01 to 2022-08-31
In the primary intestinal epithelial model, we evaluated the effect of different therapies on barrier function such as butyrate which is thought to modulate intestinal barrier function positively. To assess butyrate's effect on barrier function, we created epithelial monolayers from UC patients and non-UC controls (n = 10, each). Trans-epithelial electrical resistance (TEER) was increased in epithelial monolayers treated with 8 mM butyrate without inducing apoptosis. mRNA expression of well-known epithelial barrier genes exhibited higher levels for most genes, with the highest upregulations reported for barrier-enhancing proteins CLDN1 and OCLN, while CLDN2, a pore-forming tight junction protein, was downregulated. Since inflammation is important to IBD, we incorporated TNFα and IFN as pro-inflammatory mediators in our system, reducing TEER by 14.1% compared to negative control cultures. Quantification of inflammatory proteins in apical and basolateral media of cell cultures demonstrated that butyrate does not prevent TNFα and TNF from inducing a pro-inflammatory state, but induces more inflammatory proteins than TNFα and IFN alone. The combined therapy also upregulated the apoptosis-related protein CASP8, which may explain cell morphological alterations. UC patients' TEER and barrier gene levels were compared to non-UC controls with fewer disease-associated alleles. While both had similar median TEER and barrier gene levels, there were no differences at baseline or at different treatment settings, suggesting that patients don't need higher/other butyrate concentrations or are more sensitive than healthy controls.
CruCCial index
We characterized the individual components of the CruCCial index (ECCO and UEGW 2020). The CrUCCial index was created to reflect disease pathology in each patient. The microbial dysbiosis index (MDI) was computed as the logarithm of the sum of [abundance in taxa that expanded in CD or UC] divided by [abundance in taxa that diminished in CD or UC]. Using penalised logistic regression models, we created an inflammatory proteomic score (IPS) as a weighted sum of serum levels of inflammatory proteins (OLINK proteomics). Similarly, we created a molecular score for barrier integrity, autophagy, and unfolded protein response (UPR), these were calculated as the weighted sum of relevant gene expression in the intestinal mucosa (RNA sequencing). Patients were ranked from Q1 (the least dysbiotic, inflammatory, or dysfunctional state) through Q4 for each score (the most dysbiotic, inflammatory and dysfunctional state). The CrUCCial index was calculated in a group of 312 CD patients and 148 controls. In this cohort, the MDI and IPS were both significantly associated with CRP and FC levels. None of the individual components of the score were associated with Montreal classification. In a subset of 36 patient, various microbiome components linked with the proteome. FGF-19, for example, associated positively with Faecalibacterium and negatively with Fusicatenibacterium. We found a strong positive association between MDI and IPS. We next investigated the relationship between the CrUCCial index and major clinical outcomes, such as the need for surgery or advanced biologic therapies. We furthermore showed the dynamics of the CruCCial index in patients on biological therapy, with the inflammatory proteomics score, the UPR/ER stress score and microbiome dysbiosis index being significantly decreased in responders to biologic treatment. A similar trend was observed for barrier integrity score. In 20 individuals that we had data at baseline and 6 months after initiating biological
therapy we studied the dynamics of the CrUCCial index. In this cohort the IPS and MDI decreased significantly in responders to biologic treatment (DDW 2022).
Disease heterogeneity
We investigated disease heterogeneity – a relatively understudied and therapeutically relevant aspect of IBD. In CD, disease heterogeneity includes perianal involvement, extraintestinal symptoms, disease progression, disease location and behaviour, and need for and response to diverse medications. On a molecular and cellular level, ileal, colonic, and UC differ. We integrated multi -omic datasets derived from a well-defined, heterogeneous IBD patient pool followed by systems biology approaches. By integrating gene-expression from blood derived CD4 T-cells and monocytes, we identified underlying mechanisms (eg. IL1, IL10, interferon signaling) driving critical axes of the heterogeneous disease behavior (Sudhakar et al. IBD 2021). In another study focusing on disease-location, we integrated gene expression of different blood immune cell-types from a heterogeneous IBD cohort using a wide-range of (un)supervised approaches. We identified signatures which could be relevant for location-specific interventions. For example, we identified several pathways (FOXO, MAPK) and key molecular drivers (TNFAIP3/ PTGER4/ GPR183/ NR1D2 /SIRT1/ PRKCQ) in peripheral CD4 T cells distinguishing ileal CD from UC. In addition, we identified a multi-omic biomarker panel which distinguishes patients who respond to various anti-inflammatory drugs (Verstockt et al. BMJ Open Gastro 2022). The top-drivers and key-molecular players from the heterogeneity studies were then included to furtehr enhance the clinical relevance and applicability of the CrUCCial index.
Microbiome-host interactions
The microbiome-host axis is a key fulcrum in CD, which is characterised by intestinal dysbiosis. This is reflected in IBD patients' gut microbial communities and epithelial barrier responses. Such responses cause hyperinflammation and erosion of the mucus layer, making the epithelial barrier more vulnerable to microbial influences. To get a better understanding of microbiome-host interactions (Sudhakar et al. Front. Microbiol 2021), we co-developed MicrobioLink (Andrighetti et al. Cells, 2020). To our knowledge, this integrated microbiome-host tool is one of the first to provide a mechanism-based integration of multi-omic datasets to infer inter-species cross-talk. The tool harnesses the power of protein structural features such as eukaryotic linear motifs and domains to infer the interactions between bacterial and host proteins. By using disordered regions as a quality filter, false positive predictions are minimized. Subsequently, to trace the signal flow, it uses the concept of network diffusion, which traces the path from upstream host proteins affected by the microbial proteins to the downstream host target genes/components which are differentially expressed or modulated. As a use case, we inferred mechanistic links between the microbial and host gene expression in publicly available CD patients’ datasets (Sudhakar et al. iScience, 2022). Some of the selected features were also top-ranked within the omics datasets in the CrUCCial index. These include a protease encoded within four of the eight bacterial species enriched, and was more abundant in CD patients compared to healthy controls.