Identification of a modular super-enhancer in murine retinal development

Super-enhancers are expansive regions of genomic DNA comprised of multiple putative enhancers that contribute to the dynamic gene expression patterns during development. This is particularly important in neurogenesis because many essential transcription factors have complex developmental stage– and cell–type specific expression patterns across the central nervous system. In the developing retina, Vsx2 is expressed in retinal progenitor cells and is maintained in differentiated bipolar neurons and Müller glia. A single super-enhancer controls this complex and dynamic pattern of expression. Here we show that deletion of one region disrupts retinal progenitor cell proliferation but does not affect cell fate specification. The deletion of another region has no effect on retinal progenitor cell proliferation but instead leads to a complete loss of bipolar neurons. This prototypical super-enhancer may serve as a model for dissecting the complex gene expression patterns for neurogenic transcription factors during development. Moreover, it provides a unique opportunity to alter expression of individual transcription factors in particular cell types at specific stages of development. This provides a deeper understanding of function that cannot be achieved with traditional knockout mouse approaches.

The Cell Differentiation of Idioblast Myrosin Cells: Similarities With Vascular and Guard Cells

Idioblasts are defined by abnormal shapes, sizes, and contents that are different from neighboring cells. Myrosin cells are Brassicales-specific idioblasts and accumulate a large amount of thioglucoside glucohydrolases (TGGs, also known as myrosinases) in their vacuoles. Myrosinases convert their substrates, glucosinolates, into toxic compounds when herbivories and pests attack plants. In this review, we highlight the similarities and differences between myrosin cells and vascular cells/guard cells (GCs) because myrosin cells are distributed along vascular cells, especially the phloem parenchyma, and myrosin cells share the master transcription factor FAMA with GCs for their cell differentiation. In addition, we analyzed the overlap of cell type-specific genes between myrosin cells and GCs by using published single-cell transcriptomics (scRNA-seq) data, suggesting significant similarities in the gene expression patterns of these two specialized cells.

Evaluation of animal model congruence to human depression based on large-scale gene expression patterns of the CNS

Depression is a complex mental health disorder that is difficult to study. A wide range of animal models exist and for many of these data on large-scale gene expression patterns in the CNS are available. The goal of this study was to evaluate how well animal models match human depression by evaluating congruence and discordance of large-scale gene expression patterns in the CNS between almost 300 animal models and a portrait of human depression created from male and female datasets. Multiple approaches were used, including a hypergeometric based scoring system that rewards common gene expression patterns (e.g., up-up or down-down in both model and human depression), but penalizes opposing gene expression patterns. RRHO heat maps, Uniform Manifold Approximation Plot (UMAP), and machine learning were used to evaluate matching of models to depression. The top ranked model was a histone deacetylase (HDAC2) conditional knockout in forebrain neurons. Also highly ranked were various models for Alzheimer’s, including APPsa knock-in (2nd overall), APP knockout, and an APP/PS1 humanized double mutant. Other top models were the mitochondrial gene HTRA2 knockout (that is lethal in adulthood), a modified acetylcholinesterase, a Huntington’s disease model, and the CRTC1 knockout. Over 30 stress related models were evaluated and while some matched highly with depression, others did not. In most of the top models, a consistent dysregulation of MAP kinase pathway was identified and the genes NR4A1, BDNF, ARC, EGR2, and PDE7B were consistently downregulated as in humans with depression. Separate male and female portraits of depression were also evaluated to identify potential sex specific depression matches with models. Individual human depression datasets were also evaluated to allow for comparisons across the same brain regions. Heatmap, UMAP, and machine learning results supported the hypergeometric ranking findings. Together, this study provides new insights into how large-scale gene expression patterns may be similarly dysregulated in some animals models and humans with depression that may provide new avenues for understanding and treating depression.

Cardiac physiology and metabolic gene expression during late organogenesis among F. heteroclitus embryo families from crosses between pollution-sensitive and -resistant parents

Background The teleost fish Fundulus heteroclitus inhabit estuaries heavily polluted with persistent and bioaccumulative chemicals. While embryos of parents from polluted sites are remarkably resistant to toxic sediment and develop normally, embryos of parents from relatively clean estuaries, when treated with polluted sediment extracts, are developmentally delayed, displaying deformities characteristic of pollution-induced embryotoxicity. To gain insight into parental effects on sensitive and resistant phenotypes during late organogenesis, we established sensitive, resistant, and crossed embryo families using five female and five male parents from relatively clean and predominantly PAH-polluted estuaries each, measured heart rates, and quantified individual embryo expression of 179 metabolic genes. Results Pollution-induced embryotoxicity manifested as morphological deformities, significant developmental delays, and altered cardiac physiology was evident among sensitive embryos resulting from crosses between females and males from relatively clean estuaries. Significantly different heart rates among several geographically unrelated populations of sensitive, resistant, and crossed embryo families during late organogenesis and pre-hatching suggest site-specific adaptive cardiac physiology phenotypes relative to pollution exposure. Metabolic gene expression patterns (32 genes, 17.9%, at p < 0.05; 11 genes, 6.1%, at p < 0.01) among the embryo families indicate maternal pollutant deposition in the eggs and parental effects on gene expression and metabolic alterations. Conclusion Heart rate differences among sensitive, resistant, and crossed embryos is a reliable phenotype for further explorations of adaptive mechanisms. While metabolic gene expression patterns among embryo families are suggestive of parental effects on several differentially expressed genes, a definitive adaptive signature and metabolic cost of resistant phenotypes is unclear and shows unexpected sensitive-resistant crossed embryo expression profiles. Our study highlights physiological and metabolic gene expression differences during a critical embryonic stage among pollution sensitive, resistant, and crossed embryo families, which may contribute to underlying resistance mechanisms observed in natural F. heteroclitus populations living in heavily contaminated estuaries.

Network-guided supervised learning on gene expression using a graph convolutional neural network

Background: Transcriptomic profiles have become crucial information in understanding diseases and improving treatments. While dysregulated gene sets are identified via pathway analysis, various machine learning models have been proposed for predicting phenotypes such as disease type and drug response based on gene expression patterns. However, these models still lack interpretability, as well as the ability to integrate prior knowledge from a protein-protein interaction network. Results: We propose Grandline, a graph convolutional neural network that can integrate gene expression data and structure of the protein interaction network to predict a specific phenotype. Transforming the interaction network into a spectral domain enables convolution of neighbouring genes and pinpointing high-impact subnetworks, which allow better interpretability of deep learning models. Grandline achieves high phenotype prediction accuracy (67-85% in 8 use cases), comparable to state-of-the-art machine learning models while requiring a smaller number of parameters, allowing it to learn complex but interpretable gene expression patterns from biological datasets. Conclusion: To improve the interpretability of phenotype prediction based on gene expression patterns, we developed Grandline using graph convolutional neural network technique to integrate protein interaction information. We focus on improving the ability to learn nonlinear relationships between gene expression patterns and a given phenotype and incorporation of prior knowledge, which are the main challenges of machine learning models for biological datasets. The graph convolution allows us to aggregate information from relevant genes and reduces the number of trainable parameters, facilitating model training for a small-sized biological dataset.

Evaluation of antimicrobial effects of photo-sonodynamic antimicrobial chemotherapy based on nano-micelle curcumin on virulence gene expression patterns in Acinetobacter baumannii

Background: Abaumannii baumannii rapidly resistance to a wide range of antimicrobial agents. The combination of antimicrobial photodynamic therapy (aPDT) and sonodynamic antimicrobial chemotherapy (SACT) known as photo-sonodynamic antimicrobial chemotherapy (PSACT) has received considerable attention as one of the emerging and promising strategies against microbial infections. Objective: This study aimed to investigate the antimicrobial effects of PSACT based on nano-micelle curcumin (N-MCur) on the virulence gene expression patterns in A. baumannii. Materials and methods: N-MCur as a photo-sonosensitizer was synthesized and confirmed. To determine sub-significant reduction dose of PSACT, sub-significant reduction dose of N-MCur and blue laser light during aPDT, and ultrasound power output during SACT were assessed. Finally, changes in the expression of genes involved in treated A. baumannii by minimum sub-significant reduction dose of PSACT were determined using quantitative real-time-PCR (qRT-PCR). Results: PSACT using 12.5 mM N-MCur at the ultrasound power outputs of 28.7, 36.9, and 45.2 mW/cm2 with 4 min irradiation time of blue laser, as well as, 6.2 mM N-MCur at an ultrasound power output of 45.2 mW/cm2 plus 3 min blue laser irradiation time exhibited the significant dose-dependent reduction against A. baumannii cell viability compared to the control group (P<0.05). After treatment of A. baumannii using 3.1 mM N-MCur + 2 min blue laser irradiation time + 28.7 mW/cm2 ultrasound as the minimum sub-significant reduction doses of PSACT, mRNA expression was significantly upregulated to 6.0-, 11.2-, and 13.7-folds in recA, blsA, and dnaK and downregulated to 8.6-, 10.1-, and 14.5-folds in csuE, espA, and abaI, respectively. Conclusions: N-MCur-mediated PSACT could regulate the expression of genes involved in A. baumannii pathogenesis. Therefore, PSACT can be proposed as a promising application to treat infections caused by A. baumannii.