Multiorgan microphysiological systems as tools to interrogate interorgan crosstalk and complex diseases

InTiCAR: Network-based identification of significant inter-tissue communicators for autoimmune diseases

Inter-tissue communicators (ITCs) are intricate and essential aspects of our body, as they are the keepers of homeostatic equilibrium. It is no surprise that the dysregulation of the exchange between tissues are at the core of various disorders. Among such conditions, autoimmune diseases (AIDs) refer to a collection of pathological conditions where the miscommunication drives the immune system to mistakenly attack one's own body. Due to their myriad and diverse pathophysiologies, AIDs cannot be easily diagnosed or treated, and continuous efforts are required to seek for potential diagnostic markers or therapeutic targets. The identification of ITCs with significant involvement in the disease states is therefore crucial. Here, we present InTiCAR, Inter-Tissue Communicators for Autoimmune diseases by Random walk with restart, which is a network exploration-based analysis method that suggests disease-specific ITCs based on prior knowledge of disease genes, without the need for the external expression data. We first show that distinct ITC profile s can be acquired for various diseases by InTiCAR. We further illustrate that, for autoimmune diseases (AIDs) specifically, the disease-specific ITCs outperform disease genes in diagnosing patients using the UK Biobank plasma proteome dataset. Also, through CMap LINCS dataset, we find that high perturbation on the AIDs genes can be observed by the disease-specific ITCs. Our results provide and highlight unique perspectives on biological network analysis by focusing on the entities of extracellular communications.

Revolutionizing immune research with organoid-based co-culture and chip systems

The intertwined interactions various immune cells have with epithelial cells in our body require sophisticated experimental approaches to be studied. Due to the limitations of immortalized cell lines and animal models, there is an increasing demand for human in vitro model systems to investigate the microenvironment of immune cells in normal and in pathological conditions. Organoids, which are self-renewing, 3D cellular structures that are derived from stem cells, have started to provide gap-filling tissue modelling solutions. In this review, we first demonstrate with some of the available examples how organoid-based immune cell co-culture experiments can advance disease modelling of cancer, inflammatory bowel disease, and tissue regeneration. Then, we argue that to achieve both complexity and scale, organ-on-chip models combined with cutting-edge microfluidics-based technologies can provide more precise manipulation and readouts. Finally, we discuss how genome editing techniques and the use of patient-derived organoids and immune cells can improve disease modelling and facilitate precision medicine. To achieve maximum impact and efficiency, these efforts should be supported by novel infrastructures such as organoid biobanks, organoid facilities, as well as drug screening and host-microbe interaction testing platforms. All these together or in combination can allow researchers to shed more detailed, and often patient-specific, light on the crosstalk between immune cells and epithelial cells in health and disease.

Dynamic microphysiological system chip platform for high-throughput, customizable, and multi-dimensional drug screening

Spheroids and organoids have attracted significant attention as innovative models for disease modeling and drug screening. By employing diverse types of spheroids or organoids, it is feasible to establish microphysiological systems that enhance the precision of disease modeling and offer more dependable and comprehensive drug screening. High-throughput microphysiological systems that support optional, parallel testing of multiple drugs have promising applications in personalized medical treatment and drug research. However, establishing such a system is highly challenging and requires a multidisciplinary approach. This study introduces a dynamic Microphysiological System Chip Platform (MSCP) with multiple functional microstructures that encompass the mentioned advantages. We developed a high-throughput lung cancer spheroids model and an intestine-liver-heart-lung cancer microphysiological system for conducting parallel testing on four anti-lung cancer drugs, demonstrating the feasibility of the MSCP. This microphysiological system combines microscale and macroscale biomimetics to enable a comprehensive assessment of drug efficacy and side effects. Moreover, the microphysiological system enables evaluation of the real pharmacological effect of drug molecules reaching the target lesion after absorption by normal organs through fluid-based physiological communication. The MSCP could serves as a valuable platform for microphysiological system research, making significant contributions to disease modeling, drug development, and personalized medical treatment.

Gut-on-a-chip for disease models

The intestinal tract is a vital organ responsible for digestion and absorption in the human body and plays an essential role in pathogen invasion. Compared with other traditional models, gut-on-a-chip has many unique advantages, and thereby, it can be considered as a novel model for studying intestinal functions and diseases. Based on the chip design, we can replicate the in vivo microenvironment of the intestine and study the effects of individual variables on the experiment. In recent years, it has been used to study several diseases. To better mimic the intestinal microenvironment, the structure and function of gut-on-a-chip are constantly optimised and improved. Owing to the complexity of the disease mechanism, gut-on-a-chip can be used in conjunction with other organ chips. In this review, we summarise the human intestinal structure and function as well as the development and improvement of gut-on-a-chip. Finally, we present and discuss gut-on-a-chip applications in inflammatory bowel disease (IBD), viral infections and phenylketonuria. Further improvement of the simulation and high throughput of gut-on-a-chip and realisation of personalised treatments are the problems that should be solved for gut-on-a-chip as a disease model.

Adverse Outcome Pathways and Linkages to Transcriptomic Effects Relevant to Ionizing Radiation Injury

BACKGROUND

In the past three decades, a large body of data on the effects of exposure to ionizing radiation and the ensuing changes in gene expression has been generated. These data have allowed for an understanding of molecular-level events and shown a level of consistency in response despite the vast formats and experimental procedures being used across institutions. However, clarity on how this information may inform strategies for health risk assessment needs to be explored. An approach to bridge this gap is the adverse outcome pathway (AOP) framework. AOPs represent an illustrative framework characterizing a stressor associated with a sequential set of causally linked key events (KEs) at different levels of biological organization, beginning with a molecular initiating event (MIE) and culminating in an adverse outcome (AO). Here, we demonstrate the interpretation of transcriptomic datasets in the context of the AOP framework within the field of ionizing radiation by using a lung cancer AOP (AOP 272: https://www.aopwiki.org/aops/272) as a case example.

METHODS

Through the mining of the literature, radiation exposure-related transcriptomic studies in line with AOP 272 related to lung cancer, DNA damage response, and repair were identified. The differentially expressed genes within relevant studies were collated and subjected to the pathway and network analysis using Reactome and GeneMANIA platforms. Identified pathways were filtered (p < 0.001, ≥ 3 genes) and categorized based on relevance to KEs in the AOP. Gene connectivities were identified and further grouped by gene expression-informed associated events (AEs). Relevant quantitative dose-response data were used to inform the directionality in the expression of the genes in the network across AEs.

RESULTS

Reactome analyses identified 7 high-level biological processes with multiple pathways and associated genes that mapped to potential KEs in AOP 272. The gene connectivities were further represented as a network of AEs with associated expression profiles that highlighted patterns of gene expression levels.

CONCLUSIONS

This study demonstrates the application of transcriptomics data in AOP development and provides information on potential data gaps. Although the approach is new and anticipated to evolve, it shows promise for improving the understanding of underlying mechanisms of disease progression with a long-term vision to be predictive of adverse outcomes.