An information-theoretic approach for measuring the distance of organ tissue samples using their transcriptomic signatures

Trust your gut: Establishing confidence in gastrointestinal models - An overview of the state of the science and contexts of use

The webinar series and workshop titled “Trust Your Gut: Establishing Confidence in Gastrointestinal Models – An Overview of the State of the Science and Contexts of Use” was co-organized by NICEATM, NIEHS, FDA, EPA, CPSC, DoD, and the Johns Hopkins Center for Alternatives to Animal Testing (CAAT) and hosted at the National Institutes of Health in Bethesda, MD, USA on October 11-12, 2023. New approach methods (NAMs) for assessing issues of gastrointestinal tract (GIT)- related toxicity offer promise in addressing some of the limitations associated with animal-based assessments. GIT NAMs vary in complexity, from two-dimensional monolayer cell line-based systems to sophisticated 3-dimensional organoid systems derived from human primary cells. Despite advances in GIT NAMs, challenges remain in fully replicating the complex interactions and pro­cesses occurring within the human GIT. Presentations and discussions addressed regulatory needs, challenges, and innovations in incorporating NAMs into risk assessment frameworks; explored the state of the science in using NAMs for evaluating systemic toxicity, understanding absorption and pharmacokinetics, evaluating GIT toxicity, and assessing potential allergenicity; and discussed strengths, limitations, and data gaps of GIT NAMs as well as steps needed to establish confidence in these models for use in the regulatory setting.

Identifying the effectiveness of 3D culture systems to recapitulate breast tumor tissue in situ

PURPOSE

Breast cancer heterogeneity contributes to chemotherapy resistance and decreased patient survival. To improve patient outcomes it is essential to develop a technology that is able to rapidly select the most efficacious therapy that targets the diverse phenotypes present within the tumor. Breast cancer organoid technologies are proposed as an attractive approach for evaluating drug responses prior to patient therapy. However, there remain challenges in evaluating the effectiveness of organoid cultures to recapitulate the heterogeneity present in the patient tumor in situ.

METHOD

Organoids were generated from seven normal breast and nineteen breast cancer tissues diagnosed as estrogen receptor positive or triple negative. The Jensen-Shannon divergence index, a measure of the similarity between distributions, was used to compare and evaluate heterogeneity in starting tissue and their resultant organoids. Heterogeneity was analyzed using cytokeratin 8 and cytokeratin 14, which provided an easily scored readout.

RESULTS

In the in vitro culture system HER1 and FGFR were able to drive intra-tumor heterogeneity to generate divergent phenotypes that have different sensitivities to chemotherapies.

CONCLUSION

Our methodology, which focuses on quantifiable cellular phenotypes, provides a tractable system that complements omics approaches to provide an unprecedented view of heterogeneity and will enhance the identification of novel therapies and facilitate personalized medicine.

Organoids as tools for fundamental discovery and translation-a Keystone Symposia report

Complex three-dimensional in vitro organ-like models, or organoids, offer a unique biological tool with distinct advantages over two-dimensional cell culture systems, which can be too simplistic, and animal models, which can be too complex and may fail to recapitulate human physiology and pathology. Significant progress has been made in driving stem cells to differentiate into different organoid types, though several challenges remain. For example, many organoid models suffer from high heterogeneity, and it can be difficult to fully incorporate the complexity of in vivo tissue and organ development to faithfully reproduce human biology. Successfully addressing such limitations would increase the viability of organoids as models for drug development and preclinical testing. On April 3-6, 2022, experts in organoid development and biology convened at the Keystone Symposium "Organoids as Tools for Fundamental Discovery and Translation" to discuss recent advances and insights from this relatively new model system into human development and disease.

A microengineered Brain-Chip to model neuroinflammation in humans

Species differences in brain and blood-brain barrier (BBB) biology hamper the translation of findings from animal models to humans, impeding the development of therapeutics for brain diseases. Here, we present a human organotypic microphysiological system (MPS) that includes endothelial-like cells, pericytes, glia, and cortical neurons and maintains BBB permeability at in vivo relevant levels. This human Brain-Chip engineered to recapitulate critical aspects of the complex interactions that mediate neuroinflammation and demonstrates significant improvements in clinical mimicry compared to previously reported similar MPS. In comparison to Transwell culture, the transcriptomic profiling of the Brain-Chip displayed significantly advanced similarity to the human adult cortex and enrichment in key neurobiological pathways. Exposure to TNF-α recreated the anticipated inflammatory environment shown by glia activation, increased release of proinflammatory cytokines, and compromised barrier permeability. We report the development of a robust brain MPS for mechanistic understanding of cell-cell interactions and BBB function during neuroinflammation.

Revealing potential diagnostic gene biomarkers of septic shock based on machine learning analysis

Background

Sepsis is an inflammatory response caused by infection with pathogenic microorganisms. The body shock caused by it is called septic shock. In view of this, we aimed to identify potential diagnostic gene biomarkers of the disease.

Material and methods

Firstly, mRNAs expression data sets of septic shock were retrieved and downloaded from the GEO (Gene Expression Omnibus) database for differential expression analysis. Functional enrichment analysis was then used to identify the biological function of DEmRNAs (differentially expressed mRNAs). Machine learning analysis was used to determine the diagnostic gene biomarkers for septic shock. Thirdly, RT-PCR (real-time polymerase chain reaction) verification was performed. Lastly, GSE65682 data set was utilized to further perform diagnostic and prognostic analysis of identified superlative diagnostic gene biomarkers.

Results

A total of 843 DEmRNAs, including 458 up-regulated and 385 down-regulated DEmRNAs were obtained in septic shock. 15 superlative diagnostic gene biomarkers (such as RAB13, KIF1B, CLEC5A, FCER1A, CACNA2D3, DUSP3, HMGN3, MGST1 and ARHGEF18) for septic shock were identified by machine learning analysis. RF (random forests), SVM (support vector machine) and DT (decision tree) models were used to construct classification models. The accuracy of the DT, SVM and RF models were very high. Interestingly, the RF model had the highest accuracy. It is worth mentioning that ARHGEF18 and FCER1A were related to survival. CACNA2D3 and DUSP3 participated in MAPK signaling pathway to regulate septic shock.

Conclusion

Identified diagnostic gene biomarkers may be helpful in the diagnosis and therapy of patients with septic shock.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-022-07056-4.

Modeling alpha-synuclein pathology in a human brain-chip to assess blood-brain barrier disruption

Parkinson's disease and related synucleinopathies are characterized by the abnormal accumulation of alpha-synuclein aggregates, loss of dopaminergic neurons, and gliosis of the substantia nigra. Although clinical evidence and in vitro studies indicate disruption of the Blood-Brain Barrier in Parkinson's disease, the mechanisms mediating the endothelial dysfunction is not well understood. Here we leveraged the Organs-on-Chips technology to develop a human Brain-Chip representative of the substantia nigra area of the brain containing dopaminergic neurons, astrocytes, microglia, pericytes, and microvascular brain endothelial cells, cultured under fluid flow. Our αSyn fibril-induced model was capable of reproducing several key aspects of Parkinson's disease, including accumulation of phosphorylated αSyn (pSer129-αSyn), mitochondrial impairment, neuroinflammation, and compromised barrier function. This model may enable research into the dynamics of cell-cell interactions in human synucleinopathies and serve as a testing platform for target identification and validation of novel therapeutics.

Analysis of mRNA‑lncRNA and mRNA‑lncRNA-pathway co‑expression networks based on WGCNA in developing pediatric sepsis

Pediatric sepsis is a great threat to death worldwide. However, the pathogenesis has not been clearly understood until now in sepsis. This study identified differentially expressed mRNAs and lncRNAs based on Gene Expression Omnibus (GEO) database. And the weighted gene co-expression network analysis (WGCNA) was performed to explore co-expression modules associated with pediatric sepsis. Then, Gene Ontology (GO), KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway, mRNA‑lncRNA and mRNA‑lncRNA-pathway co-expression network analysis was conducted in selected significant module. A total of 1941 mRNAs and 225 lncRNAs were used to conduct WGCNA. And turquoise module was selected as a significant module that was associated with particular traits. The mRNAs functions associated with many vital processes were also shown by GO and KEGG pathway analysis in the turquoise module. Finally, 15 mRNAs (MAPK14, ITGAM, HK3, ALOX5, CR1, HCK, NCF4, PYGL, FLOT1, CARD6, NLRC4, SH3GLB1, PGS1, RAB31, LTB4R) and 4 lncRNAs (GSEC, NONHSAT160878.1, XR_926068.1 and RARA-AS1) were selected as hub genes in mRNA‑lncRNA-Pathway co-expression network. We identified 15 mRNAs and 4 lncRNAs as diagnostic markers, which have potential functions in pediatric sepsis. Our study provides more directions to study the molecular mechanism of pediatric sepsis.Abbreviations: mRNA: messenger RNA; lncRNA: long noncoding RNAs; GEO: Gene Expression Omnibus; WGCNA: weighted gene co-expression network analysis; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; SIRS: systemic inflammatory response syndrome; TOM: topological overlap measure; BP: biological process; MF: molecular function; CC: cellular component; ROC: receiver operating characteristic curve; AUC: area under curve; MAPK14: Mitogen-activated protein kinase 14; ALI: acute lung injury; ITGAM: Integrin subunit alpha M; HK3: Hexokinase 3; LPS: lipopolysaccharide; 5-LO: 5-lipoxygenase; LTs: leukotrienes; LTB4R: leukotriene B4 receptor.

Screening of Key Genes of Sepsis and Septic Shock Using Bioinformatics Analysis

Objective

Sepsis is a disease associated with high mortality. We performed bioinformatic analysis to identify key biomarkers associated with sepsis and septic shock.

Methods

The top 20% of genes showing the greatest variance between sepsis and controls in the GSE13904 dataset (children) were screened by co-expression network analysis. The differentially expressed genes (DEGs) were identified through analyzing differential gene expression between sepsis patients and control in the GSE13904 (children) and GSE154918 (adult) data sets. Intersection analysis of module genes and DEGs was performed to identify common DEGs for enrichment analysis, protein-protein interaction network (PPI network) analysis, and Short Time-series Expression Miner (STEM) analysis. The PPI network genes were ranked by degree of connectivity, and the top 100 sepsis-associated genes were identified based on the area under the receiver operating characteristic curve (AUC). In addition, we evaluated differences in immune cell infiltration between sepsis patients and controls in children (GSE13904, GSE25504) and adults (GSE9960, GSE154918). Finally, we analyzed differences in DNA methylation levels between sepsis patients and controls in GSE138074 (adults).

Results

The common genes were associated mainly with up-regulated inflammatory and metabolic responses, as well as down-regulated immune responses. Sepsis patients showed lower infiltration by most types of immune cells. Genes in the PPI network with AUC values greater than 0.9 in both GSE13904 (children) and GSE154918 (adults) were screened as key genes for diagnosis. These key genes (MAPK14, FGR, RHOG, LAT, PRKACB, UBE2Q2, ITK, IL2RB, and CD247) were also identified in STEM analysis to be progressively dysregulated across controls, sepsis patients and patients with septic shock. In addition, the expression of MAPK14, FGR, and CD247 was modified by methylation.

Conclusion

This study identified several potential diagnostic genes and inflammatory and metabolic responses mechanisms associated with the development of sepsis.