Inferring Regulatory Programs Governing Region Specificity of Neuroepithelial Stem Cells during Early Hindbrain and Spinal Cord Development

Nanofiber Composite Microchannel-Containing Injectable Hydrogels for Cartilage Tissue Regeneration

Articular cartilage tissue is incapable of self-repair and therapies for cartilage defects are still lacking. Injectable hydrogels have drawn much attention in the field of cartilage regeneration. Herein, the novel design of nanofiber composite microchannel-containing hydrogels inspired by the tunnel-piled structure of subway tunnels is proposed. Based on the aldehydized polyethylene glycol/carboxymethyl chitosan (APA/CMCS) hydrogels, thermosensitive gelatin microrods (GMs) are used as a pore-forming agent, and coaxial electrospinning polylactic acid/gelatin fibers (PGFs) loaded with kartogenin (KGN) are used as a reinforcing agent and a drug delivery system to construct the nanofiber composite microchannel-containing injectable hydrogels (APA/CMCS/KGN@PGF/GM hydrogels). The in situ formation, micromorphology and porosity, swelling and degradation, mechanical properties, self-healing behavior, as well as drug release of the nanofiber composite microchannel-containing hydrogels are investigated. The hydrogel exhibits good self-healing ability, and the introduction of PGF nanofibers can significantly improve the mechanical properties. The drug delivery system can realize sustained release of KGN to match the process of cartilage repair. The microchannel structure effectively promotes bone marrow mesenchymal stem cell (BMSC) proliferation and ingrowth within the hydrogels. In vitro and animal experiments indicate that the APA/CMCS/KGN@PGF/GM hydrogels can enhance the chondrogenesis of BMSCs and promote neocartilage formation in the rabbit cartilage defect model.

Network-based approaches for modeling disease regulation and progression

Molecular interaction networks lay the foundation for studying how biological functions are controlled by the complex interplay of genes and proteins. Investigating perturbed processes using biological networks has been instrumental in uncovering mechanisms that underlie complex disease phenotypes. Rapid advances in omics technologies have prompted the generation of high-throughput datasets, enabling large-scale, network-based analyses. Consequently, various modeling techniques, including network enrichment, differential network extraction, and network inference, have proven to be useful for gaining new mechanistic insights. We provide an overview of recent network-based methods and their core ideas to facilitate the discovery of disease modules or candidate mechanisms. Knowledge generated from these computational efforts will benefit biomedical research, especially drug development and precision medicine. We further discuss current challenges and provide perspectives in the field, highlighting the need for more integrative and dynamic network approaches to model disease development and progression.

Temporal change in chromatin accessibility predicts regulators of nodulation in Medicago truncatula

Background

Symbiotic associations between bacteria and leguminous plants lead to the formation of root nodules that fix nitrogen needed for sustainable agricultural systems. Symbiosis triggers extensive genome and transcriptome remodeling in the plant, yet an integrated understanding of the extent of chromatin changes and transcriptional networks that functionally regulate gene expression associated with symbiosis remains poorly understood. In particular, analyses of early temporal events driving this symbiosis have only captured correlative relationships between regulators and targets at mRNA level. Here, we characterize changes in transcriptome and chromatin accessibility in the model legume Medicago truncatula, in response to rhizobial signals that trigger the formation of root nodules.

Results

We profiled the temporal chromatin accessibility (ATAC-seq) and transcriptome (RNA-seq) dynamics of M. truncatula roots treated with bacterial small molecules called lipo-chitooligosaccharides that trigger host symbiotic pathways of nodule development. Using a novel approach, dynamic regulatory module networks, we integrated ATAC-seq and RNA-seq time courses to predict cis-regulatory elements and transcription factors that most significantly contribute to transcriptomic changes associated with symbiosis. Regulators involved in auxin (IAA4-5, SHY2), ethylene (EIN3, ERF1), and abscisic acid (ABI5) hormone response, as well as histone and DNA methylation (IBM1), emerged among those most predictive of transcriptome dynamics. RNAi-based knockdown of EIN3 and ERF1 reduced nodule number in M. truncatula validating the role of these predicted regulators in symbiosis between legumes and rhizobia.

Conclusions

Our transcriptomic and chromatin accessibility datasets provide a valuable resource to understand the gene regulatory programs controlling the early stages of the dynamic process of symbiosis. The regulators identified provide potential targets for future experimental validation, and the engineering of nodulation in species is unable to establish that symbiosis naturally.

Dynamic regulatory module networks for inference of cell type-specific transcriptional networks

Changes in transcriptional regulatory networks can significantly alter cell fate. To gain insight into transcriptional dynamics, several studies have profiled bulk multi-omic data sets with parallel transcriptomic and epigenomic measurements at different stages of a developmental process. However, integrating these data to infer cell type–specific regulatory networks is a major challenge. We present dynamic regulatory module networks (DRMNs), a novel approach to infer cell type–specific cis-regulatory networks and their dynamics. DRMN integrates expression, chromatin state, and accessibility to predict cis-regulators of context-specific expression, where context can be cell type, developmental stage, or time point, and uses multitask learning to capture network dynamics across linearly and hierarchically related contexts. We applied DRMNs to study regulatory network dynamics in three developmental processes, each showing different temporal relationships and measuring a different combination of regulatory genomic data sets: cellular reprogramming, liver dedifferentiation, and forward differentiation. DRMN identified known and novel regulators driving cell type–specific expression patterns, showing its broad applicability to examine dynamics of gene regulatory networks from linearly and hierarchically related multi-omic data sets.

Transcriptome-wide transmission disequilibrium analysis identifies novel risk genes for autism spectrum disorder

Recent advances in consortium-scale genome-wide association studies (GWAS) have highlighted the involvement of common genetic variants in autism spectrum disorder (ASD), but our understanding of their etiologic roles, especially the interplay with rare variants, is incomplete. In this work, we introduce an analytical framework to quantify the transmission disequilibrium of genetically regulated gene expression from parents to offspring. We applied this framework to conduct a transcriptome-wide association study (TWAS) on 7,805 ASD proband-parent trios, and replicated our findings using 35,740 independent samples. We identified 31 associations at the transcriptome-wide significance level. In particular, we identified POU3F2 (p = 2.1E-7), a transcription factor mainly expressed in developmental brain. Gene targets regulated by POU3F2 showed a 2.7-fold enrichment for known ASD genes (p = 2.0E-5) and a 2.7-fold enrichment for loss-of-function de novo mutations in ASD probands (p = 7.1E-5). These results provide a novel connection between rare and common variants, whereby ASD genes affected by very rare mutations are regulated by an unlinked transcription factor affected by common genetic variations.

Post-transcriptional control of cellular differentiation by the RNA exosome complex

Given the complexity of intracellular RNA ensembles and vast phenotypic remodeling intrinsic to cellular differentiation, it is instructive to consider the role of RNA regulatory machinery in controlling differentiation. Dynamic post-transcriptional regulation of protein-coding and non-coding transcripts is vital for establishing and maintaining proteomes that enable or oppose differentiation. By contrast to extensively studied transcriptional mechanisms governing differentiation, many questions remain unanswered regarding the involvement of post-transcriptional mechanisms. Through its catalytic activity to selectively process or degrade RNAs, the RNA exosome complex dictates the levels of RNAs comprising multiple RNA classes, thereby regulating chromatin structure, gene expression and differentiation. Although the RNA exosome would be expected to control diverse biological processes, studies to elucidate its biological functions and how it integrates into, or functions in parallel with, cell type-specific transcriptional mechanisms are in their infancy. Mechanistic analyses have demonstrated that the RNA exosome confers expression of a differentiation regulatory receptor tyrosine kinase, downregulates the telomerase RNA component TERC, confers genomic stability and promotes DNA repair, which have considerable physiological and pathological implications. In this review, we address how a broadly operational RNA regulatory complex interfaces with cell type-specific machinery to control cellular differentiation.

Data integration for inferring context-specific gene regulatory networks

Transcriptional regulatory networks control context-specific gene expression patterns and play important roles in normal and disease processes. Advances in genomics are rapidly increasing our ability to measure different components of the regulation machinery at the single-cell and bulk population level. An important challenge is to combine different types of regulatory genomic measurements to construct a more complete picture of gene regulatory networks across different disease, environmental, and developmental contexts. In this review, we focus on recent computational methods that integrate regulatory genomic data sets to infer context specificity and dynamics in regulatory networks.

Identification of FMR1-regulated molecular networks in human neurodevelopment

RNA-binding proteins (RNA-BPs) play critical roles in development and disease to regulate gene expression. However, genome-wide identification of their targets in primary human cells has been challenging. Here, we applied a modified CLIP-seq strategy to identify genome-wide targets of the FMRP translational regulator 1 (FMR1), a brain-enriched RNA-BP, whose deficiency leads to Fragile X Syndrome (FXS), the most prevalent inherited intellectual disability. We identified FMR1 targets in human dorsal and ventral forebrain neural progenitors and excitatory and inhibitory neurons differentiated from human pluripotent stem cells. In parallel, we measured the transcriptomes of the same four cell types upon FMR1 gene deletion. We discovered that FMR1 preferentially binds long transcripts in human neural cells. FMR1 targets include genes unique to human neural cells and associated with clinical phenotypes of FXS and autism. Integrative network analysis using graph diffusion and multitask clustering of FMR1 CLIP-seq and transcriptional targets reveals critical pathways regulated by FMR1 in human neural development. Our results demonstrate that FMR1 regulates a common set of targets among different neural cell types but also operates in a cell type-specific manner targeting distinct sets of genes in human excitatory and inhibitory neural progenitors and neurons. By defining molecular subnetworks and validating specific high-priority genes, we identify novel components of the FMR1 regulation program. Our results provide new insights into gene regulation by a critical neuronal RNA-BP in human neurodevelopment.