Long RNA-Mediated Chromatin Regulation in Fission Yeast and Mammals

As part of a complex network of genome control, long regulatory RNAs exert significant influences on chromatin dynamics. Understanding how this occurs could illuminate new avenues for disease treatment and lead to new hypotheses that would advance gene regulatory research. Recent studies using the model fission yeast Schizosaccharomyces pombe (S. pombe) and powerful parallel sequencing technologies have provided many insights in this area. This review will give an overview of key findings in S. pombe that relate long RNAs to multiple levels of chromatin regulation: histone modifications, gene neighborhood regulation in cis and higher-order chromosomal ordering. Moreover, we discuss parallels recently found in mammals to help bridge the knowledge gap between the study systems.

Gene regulatory evolution in cold-adapted fly populations neutralizes plasticity and may undermine genetic canalization

The relationships between adaptive evolution, phenotypic plasticity, and canalization remain incompletely understood. Theoretical and empirical studies have made conflicting arguments on whether adaptive evolution may enhance or oppose the plastic response. Gene regulatory traits offer excellent potential to study the relationship between plasticity and adaptation, and they can now be studied at the transcriptomic level. Here we take advantage of three closely-related pairs of natural populations of Drosophila melanogaster from contrasting thermal environments that reflect three separate instances of cold tolerance evolution. We measure the transcriptome-wide plasticity in gene expression levels and alternative splicing (intron usage) between warm and cold laboratory environments. We find that suspected adaptive changes in both gene expression and alternative splicing tend to neutralize the ancestral plastic response. Further, we investigate the hypothesis that adaptive evolution can lead to decanalization of selected gene regulatory traits. We find strong evidence that suspected adaptive gene expression (but not splicing) changes in cold-adapted populations are more vulnerable to the genetic perturbation of inbreeding than putatively neutral changes. We find some evidence that these patterns may reflect a loss of genetic canalization accompanying adaptation, although other processes including hitchhiking recessive deleterious variants may contribute as well. Our findings augment our understanding of genetic and environmental effects on gene regulation in the context of adaptive evolution.

Data-driven modeling predicts gene regulatory network dynamics during the differentiation of multipotential hematopoietic progenitors

Cellular differentiation during hematopoiesis is guided by gene regulatory networks (GRNs) comprising transcription factors (TFs) and the effectors of cytokine signaling. Based largely on analyses conducted at steady state, these GRNs are thought to be organized as a hierarchy of bistable switches, with antagonism between Gata1 and PU.1 driving red- and white-blood cell differentiation. Here, we utilize transient gene expression patterns to infer the genetic architecture—the type and strength of regulatory interconnections—and dynamics of a twelve-gene GRN including key TFs and cytokine receptors. We trained gene circuits, dynamical models that learn genetic architecture, on high temporal-resolution gene-expression data from the differentiation of an inducible cell line into erythrocytes and neutrophils. The model is able to predict the consequences of gene knockout, knockdown, and overexpression experiments and the inferred interconnections are largely consistent with prior empirical evidence. The inferred genetic architecture is densely interconnected rather than hierarchical, featuring extensive cross-antagonism between genes from alternative lineages and positive feedback from cytokine receptors. The analysis of the dynamics of gene regulation in the model reveals that PU.1 is one of the last genes to be upregulated in neutrophil conditions and that the upregulation of PU.1 and other neutrophil genes is driven by Cebpa and Gfi1 instead. This model inference is confirmed in an independent single-cell RNA-Seq dataset from mouse bone marrow in which Cebpa and Gfi1 expression precedes the neutrophil-specific upregulation of PU.1 during differentiation. These results demonstrate that full PU.1 upregulation during neutrophil development involves regulatory influences extrinsic to the Gata1-PU.1 bistable switch. Furthermore, although there is extensive cross-antagonism between erythroid and neutrophil genes, it does not have a hierarchical structure. More generally, we show that the combination of high-resolution time series data and data-driven dynamical modeling can uncover the dynamics and causality of developmental events that might otherwise be obscured.

Comprehensive bioinformatic analysis of the expression and prognostic significance of TSC22D domain family genes in acute myeloid leukemia

Background: TSC22D domain family genes, including Tsc22d1-4, have been extensively reported to be involved in tumors. However, their expression profiles and prognostic significance in acute myeloid leukemia (AML) remain unknown. Methods: The present study investigated the expression profiles and prognostic significance of TSC22D domain family genes in AML through the use of multiple online databases, including the CCLE, EMBL-EBI, HPA, Oncomine,GEPIA2, UALCAN, BloodSpot, and GSCALite databases. The cBioPortal and GSCALite databases were used to explore the genetic alteration and copy number variation (CNV) of the Tsc22d3 gene. The TRRUST (Version 2) database was used to explore the gene ontology biological process, disease ontology, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways associated with the Tsc22d3 gene. The AnimalTFDB3.0, STRING, and Harmonizome databases were used to investigate the protein–protein interaction (PPI) network of the Tsc22d3 gene. The Harmonizome database was used for Tsc22d3 gene regulatory kinase analysis. The TargetScanHuman 7.2, MiRDB, and ENCORI databases were used to execute the analysis of the Tsc22d3 gene regulatory miRNAs. Then, the GSCALite and GEPIA2021 databases were used to investigate the correlation between Tsc22d3 expression and immune infiltration. Results: The expression of the Tsc22d3 gene was upregulated markedly in AML cells relative to normal hematopoietic stem cells. The expression of the Tsc22d3 gene was increased in AML tumor samples compared with healthy bone marrow samples. And overexpression of the Tsc22d3 gene was associated with poor OS in AML patients.This study implied that the Tsc22d3 gene is a new biomarker for predicting the prognosis of AML. Furthermore, gene ontology analysis showed that Tsc22d3 was involved in leukemia. Functional enrichment analysis showed that the Tsc22d3 gene has many biological functions, including the regulation of many genes, kinases, miRNAs, signaling pathways, and immune infiltration.Therefore, this study suggests that the Tsc22d3 gene may be a potential therapeutic target for AML. Conclusions: Tsc22d3 gene expression was upregulated in AML, and overexpression was associated with poor OS in AML patients. Therefore, the Tsc22d3 gene may serve as a novel prognostic biomarker and therapeutic target for AML.

Transposable elements contribute to the spatiotemporal microRNA landscape in human brain development

Transposable elements (TEs) contribute to the evolution of gene regulatory networks and are dynamically expressed throughout human brain development and disease. One gene regulatory mechanism influenced by TEs is the miRNA system of post-transcriptional control. miRNA sequences frequently overlap TE loci and this miRNA expression landscape is crucial for control of gene expression in adult brain and different cellular contexts. Despite this, a thorough investigation of the spatiotemporal expression of TE-embedded miRNAs in human brain development is lacking. Here, we identify a spatiotemporally dynamic TE-embedded miRNA expression landscape between childhood and adolescent stages of human brain development. These miRNAs sometimes arise from two apposed TEs of the same subfamily, such as for L2 or MIR elements, but in the majority of cases stem from solo TEs. They give rise to in silico predicted high-confidence pre-miRNA hairpin structures, likely represent functional miRNAs and have predicted genic targets associated with neurogenesis. TE-embedded miRNA expression is distinct in the cerebellum when compared to other brain regions, as has previously been described for gene and TE expression. Furthermore, we detect expression of previously non-annotated TE-embedded miRNAs throughout human brain development, suggestive of a previously undetected miRNA control network. Together, as with non-TE-embedded miRNAs, TE-embedded sequences give rise to spatiotemporally dynamic miRNA expression networks, the implications of which for human brain development constitute extensive avenues of future experimental research. To facilitate interactive exploration of these spatiotemporal miRNA expression dynamics, we provide the 'Brain miRTExplorer' web application freely accessible for the community.

Approximated Gene Expression Trajectories (AGETs) for Gene Regulatory Network Inference on Cell Tracks

The study of pattern formation has benefited from reverse-engineering gene regulatory network (GRN) structure from spatio-temporal quantitative gene expression data. Traditional approaches omit tissue morphogenesis, hence focusing on systems where the timescales of pattern formation and morphogenesis can be separated. In such systems, pattern forms as an emergent property of the underlying GRN. This is not the case in many animal patterning systems, where patterning and morphogenesis are simultaneous. To address pattern formation in these systems we need to adapt our methodologies to explicitly accommodate cell movements and tissue shape changes. In this work we present a novel framework to reverse-engineer GRNs underlying pattern formation in tissues experiencing morphogenetic changes and cell rearrangements. By combination of quantitative data from live and fixed embryos we approximate gene expression trajectories (AGETs) in single cells and use a subset to reverse-engineer candidate GRNs using a Markov Chain Monte Carlo approach. GRN fit is assessed by simulating on cell tracks (live-modelling) and comparing the output to quantitative data-sets. This framework outputs candidate GRNs that recapitulate pattern formation at the level of the tissue and the single cell. To our knowledge, this inference methodology is the first to integrate cell movements and gene expression data, making it possible to reverse-engineer GRNs patterning tissues undergoing morphogenetic changes.

GR-regulated enhancers play a central role in the gene regulatory networks underlying drug addiction

Substance abuse and addiction represent a major public health problem that impacts multiple dimensions of society, including healthcare, economy, and workforce. In 2021, over 100,000 drug overdose deaths have been reported in the US with an alarming increase in fatalities related to opioids and psychostimulants. Understanding of the fundamental gene regulatory mechanisms underlying addiction and related behaviors could facilitate more effective treatments. To explore how repeated drug exposure alters gene regulatory networks in the brain, we combined capped small (cs)RNA-seq, which accurately captures nascent-like initiating transcripts from total RNA, with Hi-C and single nuclei (sn)ATAC-seq. We profiled initiating transcripts in two addiction-related brain regions, the prefrontal cortex (PFC) and the nucleus accumbens (NAc), from rats that were never exposed to drugs or were subjected to prolonged abstinence after oxycodone or cocaine intravenous self-administration (IVSA). Interrogating over 100,000 active transcription start regions (TSRs) revealed that most TSRs had hallmarks of bona-fide enhancers and highlighted the KLF/SP1, RFX and AP1 transcription factors families as central to establish brain-specific gene regulatory programs. Analysis of rats with addiction-like behaviors versus controls identified addiction-associated repression of transcription at regulatory enhancers recognized by nuclear receptor subfamily 3 group C (NR3C) factors, which include glucocorticoid receptors. Cell-type deconvolution analysis using snATAC-seq uncovered a potential role of glial cells in driving the gene regulatory programs associated with addiction-related phenotypes. These findings highlight the power of advanced transcriptomics methods to provide insight into how addiction perturbs gene regulatory programs in the brain.