Spinal Muscle Atrophy Disease Modelling as Bayesian Network

We investigate the molecular gene expressions studies and public databases for disease modelling using Probabilistic Graphical Models and Bayesian Inference. A case study on Spinal Muscle Atrophy Genome-Wide Association Study results is modelled and analyzed. The genes up and down-regulated in two stages of the disease development are linked to prior knowledge published in the public domain and co-expressions network is created and analyzed. The Molecular Pathways triggered by these genes are identified. The Bayesian inference posteriors distributions are estimated using a variational analytical algorithm and a Markov chain Monte Carlo sampling algorithm. Assumptions, limitations and possible future work are concluded.

A functional genetic toolbox for human tissue-derived organoids

Human organoid systems recapitulate key features of organs offering platforms for modelling developmental biology and disease. Tissue-derived organoids have been widely used to study the impact of extrinsic niche factors on stem cells. However, they are rarely used to study endogenous gene function due to the lack of efficient gene manipulation tools. Previously, we established a human foetal lung organoid system (Nikolić et al., 2017). Here, using this organoid system as an example we have systematically developed and optimised a complete genetic toolbox for use in tissue-derived organoids. This includes 'Organoid Easytag' our efficient workflow for targeting all types of gene loci through CRISPR-mediated homologous recombination followed by flow cytometry for enriching correctly-targeted cells. Our toolbox also incorporates conditional gene knock-down or overexpression using tightly-inducible CRISPR interference and CRISPR activation which is the first efficient application of these techniques to tissue-derived organoids. These tools will facilitate gene perturbation studies in tissue-derived organoids facilitating human disease modelling and providing a functional counterpart to many on-going descriptive studies, such as the Human Cell Atlas Project.

Current strategies of mechanical stimulation for maturation of cardiac microtissues

The most advanced in vitro cardiac models are today based on the use of induced pluripotent stem cells (iPSCs); however, the maturation of cardiomyocytes (CMs) has not yet been fully achieved. Therefore, there is a rising need to move towards models capable of promoting an adult-like cardiomyocytes phenotype. Many strategies have been applied such as co-culture of cardiomyocytes, with fibroblasts and endothelial cells, or conditioning them through biochemical factors and physical stimulations. Here, we focus on mechanical stimulation as it aims to mimic the different mechanical forces that heart receives during its development and the post-natal period. We describe the current strategies and the mechanical properties necessary to promote a positive response in cardiac tissues from different cell sources, distinguishing between passive stimulation, which includes stiffness, topography and static stress and active stimulation, encompassing cyclic strain, compression or perfusion. We also highlight how mechanical stimulation is applied in disease modelling.

Induced Pluripotent Stem Cells (iPSCs)—Roles in Regenerative Therapies, Disease Modelling and Drug Screening

The discovery of induced pluripotent stem cells (iPSCs) has made an invaluable contribution to the field of regenerative medicine, paving way for identifying the true potential of human embryonic stem cells (ESCs). Since the controversy around ethicality of ESCs continue to be debated, iPSCs have been used to circumvent the process around destruction of the human embryo. The use of iPSCs have transformed biological research, wherein increasing number of studies are documenting nuclear reprogramming strategies to make them beneficial models for drug screening as well as disease modelling. The flexibility around the use of iPSCs include compatibility to non-invasive harvesting, and ability to source from patients with rare diseases. iPSCs have been widely used in cardiac disease modelling, studying inherited arrhythmias, neural disorders including Alzheimer’s disease, liver disease, and spinal cord injury. Extensive research around identifying factors that are involved in maintaining the identity of ESCs during induction of pluripotency in somatic cells is undertaken. The focus of the current review is to detail all the clinical translation research around iPSCs and the strength of its ever-growing potential in the clinical space.

A pre-registered short-term forecasting study of COVID-19 in Germany and Poland during the second wave

Disease modelling has had considerable policy impact during the ongoing COVID-19 pandemic, and it is increasingly acknowledged that combining multiple models can improve the reliability of outputs. Here we report insights from ten weeks of collaborative short-term forecasting of COVID-19 in Germany and Poland (12 October–19 December 2020). The study period covers the onset of the second wave in both countries, with tightening non-pharmaceutical interventions (NPIs) and subsequently a decay (Poland) or plateau and renewed increase (Germany) in reported cases. Thirteen independent teams provided probabilistic real-time forecasts of COVID-19 cases and deaths. These were reported for lead times of one to four weeks, with evaluation focused on one- and two-week horizons, which are less affected by changing NPIs. Heterogeneity between forecasts was considerable both in terms of point predictions and forecast spread. Ensemble forecasts showed good relative performance, in particular in terms of coverage, but did not clearly dominate single-model predictions. The study was preregistered and will be followed up in future phases of the pandemic.

Disease Modelling of Cognitive Outcomes and Biomarkers in the European Prevention of Alzheimer’s Dementia Longitudinal Cohort

A key challenge for the secondary prevention of Alzheimer’s dementia is the need to identify individuals early on in the disease process through sensitive cognitive tests and biomarkers. The European Prevention of Alzheimer’s Dementia (EPAD) consortium recruited participants into a longitudinal cohort study with the aim of building a readiness cohort for a proof-of-concept clinical trial and also to generate a rich longitudinal data-set for disease modelling. Data have been collected on a wide range of measurements including cognitive outcomes, neuroimaging, cerebrospinal fluid biomarkers, genetics and other clinical and environmental risk factors, and are available for 1,828 eligible participants at baseline, 1,567 at 6 months, 1,188 at one-year follow-up, 383 at 2 years, and 89 participants at three-year follow-up visit. We novelly apply state-of-the-art longitudinal modelling and risk stratification approaches to these data in order to characterise disease progression and biological heterogeneity within the cohort. Specifically, we use longitudinal class-specific mixed effects models to characterise the different clinical disease trajectories and a semi-supervised Bayesian clustering approach to explore whether participants can be stratified into homogeneous subgroups that have different patterns of cognitive functioning evolution, while also having subgroup-specific profiles in terms of baseline biomarkers and longitudinal rate of change in biomarkers.