Mansoor Saqi

Very preterm birth (VPT; ≤ 32 weeks’ gestation) is associated with altered brain development and cognitive and behavioral difficulties across the lifespan. However, heterogeneity in outcomes among individuals born VPT makes it challenging to identify those most vulnerable to neurodevelopmental sequelae. Here, we aimed to stratify VPT children into distinct behavioral subgroups and explore between-subgroup differences in neonatal brain structure and function. 198 VPT children (98 females) previously enrolled in the Evaluation of Preterm Imaging study (EudraCT 2009-011602-42) underwent Magnetic Resonance Imaging at term-equivalent age and neuropsychological assessments at 4-7 years. Using an integrative clustering approach, we combined neonatal socio-demographic, clinical factors and childhood socio-emotional and executive function outcomes, to identify distinct subgroups of children based on their similarity profiles in a multidimensional space. We characterized resultant subgroups using domain-specific outcomes (temperament, psychopathology, IQ and cognitively stimulating home environment) and explored between-subgroup differences in neonatal brain volumes (voxel-wise Tensor-Based-Morphometry), functional connectivity (voxel-wise degree centrality) and structural connectivity (Tract-Based-Spatial-Statistics). Results showed two-and three-cluster data-driven solutions. The two-cluster solution comprised a ‘resilient’ subgroup (lower psychopathology and higher IQ, executive function and socio-emotional outcomes) and an ‘at-risk’ subgroup (poorer behavioral and cognitive outcomes). The three-cluster solution showed an additional third ‘intermediate’ subgroup displaying behavioral and cognitive outcomes intermediate between the resilient and at-risk subgroups. The resilient subgroup had the most cognitively stimulating home environment and the at-risk subgroup showed the highest neonatal clinical risk, while the intermediate subgroup showed the lowest clinical but the highest socio-demographic risk. Compared to the intermediate subgroup, the resilient subgroup displayed larger neonatal insular and orbitofrontal volumes and stronger orbitofrontal functional connectivity, while the at-risk group showed widespread white matter microstructural alterations. These findings suggest that risk stratification following VPT birth is feasible and could be used translationally to guide personalized interventions aimed at promoting children’s resilience.

Introduction Interleukin (IL) 22 is a key cytokine involved in regulation of epithelial function and its role in IBD remains highly controversial. Some studies indicate a protective role in epithelial regeneration, whereas other studies imply a pro-inflammatory role. Insights are now needed to improve of understanding of the functional role of this important cytokine and how its expression might impact patients with ulcerative colitis (UC). In this study, we have probed the clinical and functional significance of IL-22 responsive transcriptional modules and causal networks in diseased tissue of over 500 UC patients and in preclinical models of colitis. Methods We mapped the transcriptional landscape of human colonic epithelial 3D mini-gut organoids in response to treatment with IL22, and other pro-inflammatory cytokines. We tested the clinical significance of the IL22-regulated transcriptome by probing whole colonic biopsies from 550 ulcerative colitis (UC) patients from UNIFI clinical trial programme (patients with UC treated with ustekinumab, an anti-IL12p40 antibody). The functional role of IL22 regulated biological pathways were evaluated in pre-clinical models of UC. Results IL-22 regulated pro-inflammatory biological pathways involved in microbial recognition, cancer and immune cell chemotaxis. IL-22 was an especially potent regulator of the CXC family chemokines CXCL1, CXCL2, CXCL5, CXCL6 and CXCL8, which are all powerful neutrophil-selective chemokines. There was a positive correlation between the IL-22 transcriptional programme and the histological severity of inflammation (r=0.49, p+ neutrophils to the colon. Conclusions The interleukin 22/neutrophil axis is functionally important in colitis and is associated with treatment resistance in ulcerative colitis

Sepsis is a common illness. Immune responses are considered major drivers of sepsis illness and outcomes. However, there are no proven immunomodulator therapies in sepsis. We hypothesised that in-depth characterisation of sepsis-specific immune trajectory may inform immunomodulation in sepsis-related critical illness. We describe the protocol of the IMMERSE study to address this hypothesis. We include critically ill sepsis patients without documented immune comorbidity and age–sex matched cardiac surgical patients as controls. We plan to perform an in-depth biological characterisation of innate and adaptive immune systems, platelet function, humoral components and transcriptional determinants of the immune system responses in sepsis. This will be done at pre-specified time points during their critical illness to generate an illness trajectory. The sample size for each biological assessment is different and is described in detail. In summary, the overall aim of the IMMERSE study is to increase the granularity of longitudinal immunology model of sepsis to inform future immunomodulation trials.

Biomedical informatics has traditionally adopted a linear view of the informatics process (collect, store and analyse) in translational medicine (TM) studies; focusing primarily on the challenges in data integration and analysis. However, a data management challenge presents itself with the new lifecycle view of data emphasized by the recent calls for data re-use, long term data preservation, and data sharing. There is currently a lack of dedicated infrastructure focused on the ‘manageability’ of the data lifecycle in TM research between data collection and analysis. Current community efforts towards establishing a culture for open science prompt the creation of a data custodianship environment for management of TM data assets to support data reuse and reproducibility of research results. Here we present the development of a lifecycle-based methodology to create a metadata management framework based on community driven standards for standardisation, consolidation and integration of TM research data. Based on this framework, we also present the development of a new platform (PlatformTM) focused on managing the lifecycle for translational research data assets.

The development of computational approaches in systems biology has reached a state of maturity that allows their transition to systems medicine. Despite this progress, intuitive visualisation and context-dependent knowledge representation still present a major bottleneck. In this paper, we describe the Disease Maps Project, an effort towards a community-driven computationally readable comprehensive representation of disease mechanisms. We outline the key principles and the framework required for the success of this initiative, including use of best practices, standards and protocols. We apply a modular approach to ensure efficient sharing and reuse of resources for projects dedicated to specific diseases. Community-wide use of disease maps will accelerate the conduct of biomedical research and lead to new disease ontologies defined from mechanism-based disease endotypes rather than phenotypes.

Multilevel data integration is becoming a major area of research in systems biology. Within this area, multi-‘omics datasets on complex diseases are becoming more readily available and there is a need to set standards and good practices for integrated analysis of biological, clinical and environmental data. We present a framework to plan and generate single and multi-‘omics signatures of disease states. The framework is divided into four major steps: dataset subsetting, feature filtering, ‘omics-based clustering and biomarker identification. We illustrate the usefulness of this framework by identifying potential patient clusters based on integrated multi-‘omics signatures in a publicly available ovarian cystadenocarcinoma dataset. The analysis generated a higher number of stable and clinically relevant clusters than previously reported, and enabled the generation of predictive models of patient outcomes. This framework will help health researchers plan and perform multi-‘omics big data analyses to generate hypotheses and make sense of their rich, diverse and ever growing datasets, to enable implementation of translational P4 medicine.

Large amounts of data emerging from experiments in molecular medicine are leading to the identification of molecular signatures associated with disease subtypes. The contextualization of these patterns is important for obtaining mechanistic insight into the aberrant processes associated with a disease, and this typically involves the integration of multiple heterogeneous types of data. In this review, we discuss knowledge representations that can be useful to explore the biological context of molecular signatures, in particular three main approaches, namely, pathway mapping approaches, molecular network centric approaches and approaches that represent biological statements as knowledge graphs. We discuss the utility of each of these paradigms, illustrate how they can be leveraged with selected practical examples and identify ongoing challenges for this field of research.

The development of colorectal cancer (CRC)—the third most common cancer type—has been associated with deregulations of cellular mechanisms stimulated by both genetic and epigenetic events. StatEpigen is a manually curated and annotated database, containing information on interdependencies between genetic and epigenetic signals, and specialized currently for CRC research. Although StatEpigen provides a well-developed graphical user interface for information retrieval, advanced queries involving associations between multiple concepts can benefit from more detailed graph representation of the integrated data. This can be achieved by using a graph database (NoSQL) approach. Data were extracted from StatEpigen and imported to our newly developed EpiGeNet, a graph database for storage and querying of conditional relationships between molecular (genetic and epigenetic) events observed at different stages of colorectal oncogenesis. We illustrate the enhanced capability of EpiGeNet for exploration of different queries related to colorectal tumor progression; specifically, we demonstrate the query process for (i) stage-specific molecular events, (ii) most frequently observed genetic and epigenetic interdependencies in colon adenoma, and (iii) paths connecting key genes reported in CRC and associated events. The EpiGeNet framework offers improved capability for management and visualization of data on molecular events specific to CRC initiation and progression.