DiNeR: a Differential graphical model for analysis of co-regulation Network Rewiring

Causal gene regulatory analysis with RNA velocity reveals an interplay between slow and fast transcription factors

Single-cell expression dynamics, from differentiation trajectories or RNA velocity, have the potential to reveal causal links between transcription factors (TFs) and their target genes in gene regulatory networks (GRNs). However, existing methods either overlook these expression dynamics or necessitate that cells be ordered along a linear pseudotemporal axis, which is incompatible with branching trajectories. We introduce Velorama, an approach to causal GRN inference that represents single-cell differentiation dynamics as a directed acyclic graph of cells, constructed from pseudotime or RNA velocity measurements. Additionally, Velorama enables the estimation of the speed at which TFs influence target genes. Applying Velorama, we uncover evidence that the speed of a TF's interactions is tied to its regulatory function. For human corticogenesis, we find that slow TFs are linked to gliomas, while fast TFs are associated with neuropsychiatric diseases. We expect Velorama to become a critical part of the RNA velocity toolkit for investigating the causal drivers of differentiation and disease.

Interplay Between the Histone Variant H2A.Z and the Epigenome in Pancreatic Cancer

BACKGROUND

The oncogenic process is orchestrated by a complex network of chromatin remodeling elements that shape the cancer epigenome. Histone variant H2A.Z regulates DNA control elements such as promoters and enhancers in different types of cancer; however, the interplay between H2A.Z and the pancreatic cancer epigenome is unknown.

OBJECTIVE

This study analyzed the role of H2A.Z in different DNA regulatory elements.

METHODS

We performed Chromatin Immunoprecipitation Sequencing assays (ChiP-seq) with total H2A.Z and acetylated H2A.Z (acH2A.Z) antibodies and analyzed published data from ChIP-seq, RNA-seq, bromouridine labeling-UV and sequencing (BruUV-seq), Hi-C and ATAC-seq (Assay for Transposase-Accessible Chromatin using sequencing) in the pancreatic cancer cell line PANC-1.

RESULTS

The results indicate that total H2A.Z facilitates the recruitment of RNA polymerase II and transcription factors at promoters and enhancers allowing the expression of pro-oncogenic genes. Interestingly, we demonstrated that H2A.Z is enriched in super-enhancers (SEs) contributing to the transcriptional activation of key genes implicated in tumor development. Importantly, we established that H2A.Z contributes to the three-dimensional (3D) genome organization of pancreatic cancer and that it is a component of the Topological Associated Domains (TADs) boundaries in PANC-1 and that total H2A.Z and acH2A.Z are associated with A and B compartments, respectively.

CONCLUSIONS

H2A.Z participates in the biology and development of pancreatic cancer by generating a pro-oncogenic transcriptome through its posttranslational modifications, interactions with different partners, and regulatory elements, contributing to the oncogenic 3D genome organization. These data allow us to understand the molecular mechanisms that promote an oncogenic transcriptome in pancreatic cancer mediated by H2A.Z.

proChIPdb: a chromatin immunoprecipitation database for prokaryotic organisms

The transcriptional regulatory network in prokaryotes controls global gene expression mostly through transcription factors (TFs), which are DNA-binding proteins. Chromatin immunoprecipitation (ChIP) with DNA sequencing methods can identify TF binding sites across the genome, providing a bottom-up, mechanistic understanding of how gene expression is regulated. ChIP provides indispensable evidence toward the goal of acquiring a comprehensive understanding of cellular adaptation and regulation, including condition-specificity. ChIP-derived data's importance and labor-intensiveness motivate its broad dissemination and reuse, which is currently an unmet need in the prokaryotic domain. To fill this gap, we present proChIPdb (prochipdb.org), an information-rich, interactive web database. This website collects public ChIP-seq/-exo data across several prokaryotes and presents them in dashboards that include curated binding sites, nucleotide-resolution genome viewers, and summary plots such as motif enrichment sequence logos. Users can search for TFs of interest or their target genes, download all data, dashboards, and visuals, and follow external links to understand regulons through biological databases and the literature. This initial release of proChIPdb covers diverse organisms, including most major TFs of Escherichia coli, and can be expanded to support regulon discovery across the prokaryotic domain.

Estimation of Gene Regulatory Networks from Cancer Transcriptomics Data

Cancer is a genetic disease in which multiple genes are perturbed. Thus, information about the regulatory relationships between genes is necessary for the identification of biomarkers and therapeutic targets. In this review, methods for inference of gene regulatory networks (GRNs) from transcriptomics data that are used in cancer research are introduced. The methods are classified into three categories according to the analysis model. The first category includes methods that use pair-wise measures between genes, including correlation coefficient and mutual information. The second category includes methods that determine the genetic regulatory relationship using multivariate measures, which consider the expression profiles of all genes concurrently. The third category includes methods using supervised and integrative approaches. The supervised approach estimates the regulatory relationship using a supervised learning method that constructs a regression or classification model for predicting whether there is a regulatory relationship between genes with input data of gene expression profiles and class labels of prior biological knowledge. The integrative method is an expansion of the supervised method and uses more data and biological knowledge for predicting the regulatory relationship. Furthermore, simulation and experimental validation of the estimated GRNs are also discussed in this review. This review identified that most GRN inference methods are not specific for cancer transcriptome data, and such methods are required for better understanding of cancer pathophysiology. In addition, more systematic methods for validation of the estimated GRNs need to be developed in the context of cancer biology.

Aging and pathological aging signatures of the brain: through the focusing lens of SIRT6

Brain-specific SIRT6-KO mice present increased DNA damage, learning impairments, and neurodegenerative phenotypes, placing SIRT6 as a key protein in preventing neurodegeneration. In the aging brain, SIRT6 levels/activity decline, which is accentuated in Alzheimer's patients. To understand SIRT6 roles in transcript pattern changes, we analyzed transcriptomes of young WT, old WT and young SIRT6-KO mice brains, and found changes in gene expression related to healthy and pathological aging. In addition, we traced these differences in human and mouse samples of Alzheimer's and Parkinson's diseases, healthy aging and calorie restriction (CR). Our results define four gene expression categories that change with age in a pathological or non-pathological manner, which are either reversed or not by CR. We found that each of these gene expression categories is associated with specific transcription factors, thus serving as potential candidates for their category-specific regulation. One of these candidates is YY1, which we found to act together with SIRT6 regulating specific processes. We thus argue that SIRT6 has a pivotal role in preventing age-related transcriptional changes in brains. Therefore, reduced SIRT6 activity may drive pathological age-related gene expression signatures in the brain.