Dynamics of transcriptional and post-transcriptional regulation

Nanodynamo quantifies subcellular RNA dynamics revealing extensive coupling between steps of the RNA life cycle

The coordinated action of transcriptional and post-transcriptional machineries shapes gene expression programs at steady state and determines their concerted response to perturbations. We have developed Nanodynamo, an experimental and computational workflow for quantifying the kinetic rates of nuclear and cytoplasmic steps of the RNA life cycle. Nanodynamo is based on mathematical modelling following sequencing of native RNA from cellular fractions and polysomes. We have applied this workflow to triple-negative breast cancer cells, revealing widespread post-transcriptional RNA processing that is mutually exclusive with its co-transcriptional counterpart. We used Nanodynamo to unravel the coupling between transcription, processing, export, decay and translation machineries. We have identified a number of coupling interactions within and between the nucleus and cytoplasm that largely contribute to coordinating how cells respond to perturbations that affect gene expression programs. Nanodynamo will be instrumental in unravelling the determinants and regulatory processes involved in the coordination of gene expression responses.

Hypoxia and inflammation induce synergistic transcriptome turnover in macrophages

Macrophages are effector immune cells that experience substantial changes to oxygenation when transiting through tissues, especially when entering tumors or infected wounds. How hypoxia alters gene expression and macrophage effector function at the post-transcriptional level remains poorly understood. Here, we use TimeLapse-seq to measure how inflammatory activation modifies the hypoxic response in primary macrophages. Nucleoside recoding sequencing allows the derivation of steady-state transcript levels, degradation rates, and transcriptional synthesis rates from the same dataset. We find that hypoxia produces distinct responses from resting and inflammatory macrophages. Hypoxia induces destabilization of mRNA transcripts, though inflammatory macrophages substantially increase mRNA degradation compared to resting macrophages. Increased RNA turnover results in the upregulation of ribosomal protein genes and downregulation of extracellular matrix components in inflammatory macrophages. Pathways regulated by mRNA decay in vitro are differentially regulated in tumor-associated macrophages implying that mixed stimuli could induce post-transcriptional regulation of macrophage function in solid tumors.

Untranslated yet indispensable-UTRs act as key regulators in the environmental control of gene expression

To survive and thrive in a dynamic environment, plants must continuously monitor their surroundings and adjust their development and physiology accordingly. Changes in gene expression underlie these developmental and physiological adjustments, and are traditionally attributed to widespread transcriptional reprogramming. Growing evidence, however, suggests that post-transcriptional mechanisms also play a vital role in tailoring gene expression to a plant's environment. Untranslated regions (UTRs) act as regulatory hubs for post-transcriptional control, harbouring cis-elements that affect an mRNA's processing, localization, translation, and stability, and thereby tune the abundance of the encoded protein. Here, we review recent advances made in understanding the critical function UTRs exert in the post-transcriptional control of gene expression in the context of a plant's abiotic environment. We summarize the molecular mechanisms at play, present examples of UTR-controlled signalling cascades, and discuss the potential that resides within UTRs to render plants more resilient to a changing climate.

Perspectives on computational modeling of biological systems and the significance of the SysMod community

Motivation

In recent years, applying computational modeling to systems biology has caused a substantial surge in both discovery and practical applications and a significant shift in our understanding of the complexity inherent in biological systems.

Results

In this perspective article, we briefly overview computational modeling in biology, highlighting recent advancements such as multi-scale modeling due to the omics revolution, single-cell technology, and integration of artificial intelligence and machine learning approaches. We also discuss the primary challenges faced: integration, standardization, model complexity, scalability, and interdisciplinary collaboration. Lastly, we highlight the contribution made by the Computational Modeling of Biological Systems (SysMod) Community of Special Interest (COSI) associated with the International Society of Computational Biology (ISCB) in driving progress within this rapidly evolving field through community engagement (via both in person and virtual meetings, social media interactions), webinars, and conferences.

Availability and implementation

Additional information about SysMod is available at https://sysmod.info.

Alternative polyadenylation and dynamic 3' UTR length is associated with polysome recruitment throughout the cardiomyogenic differentiation of hESCs

Alternative polyadenylation (APA) increases transcript diversity through the generation of isoforms with varying 3' untranslated region (3' UTR) lengths. As the 3' UTR harbors regulatory element target sites, such as miRNAs or RNA-binding proteins, changes in this region can impact post-transcriptional regulation and translation. Moreover, the APA landscape can change based on the cell type, cell state, or condition. Given that APA events can impact protein expression, investigating translational control is crucial for comprehending the overall cellular regulation process. Revisiting data from polysome profiling followed by RNA sequencing, we investigated the cardiomyogenic differentiation of pluripotent stem cells by identifying the transcripts that show dynamic 3' UTR lengthening or shortening, which are being actively recruited to ribosome complexes. Our findings indicate that dynamic 3' UTR lengthening is not exclusively associated with differential expression during cardiomyogenesis but rather with recruitment to polysomes. We confirm that the differentiated state of cardiomyocytes shows a preference for shorter 3' UTR in comparison to the pluripotent stage although preferences vary during the days of the differentiation process. The most distinct regulatory changes are seen in day 4 of differentiation, which is the mesoderm commitment time point of cardiomyogenesis. After identifying the miRNAs that would target specifically the alternative 3' UTR region of the isoforms, we constructed a gene regulatory network for the cardiomyogenesis process, in which genes related to the cell cycle were identified. Altogether, our work sheds light on the regulation and dynamic 3' UTR changes of polysome-recruited transcripts that take place during the cardiomyogenic differentiation of pluripotent stem cells.

Gapless genome assembly and epigenetic profiles reveal gene regulation of whole-genome triplication in lettuce

BACKGROUND

Lettuce, an important member of the Asteraceae family, is a globally cultivated cash vegetable crop. With a highly complex genome (∼2.5 Gb; 2n = 18) rich in repeat sequences, current lettuce reference genomes exhibit thousands of gaps, impeding a comprehensive understanding of the lettuce genome.

FINDINGS

Here, we present a near-complete gapless reference genome for cutting lettuce with high transformability, using long-read PacBio HiFi and Nanopore sequencing data. In comparison to stem lettuce genome, we identify 127,681 structural variations (SVs, present in 0.41 Gb of sequence), reflecting the divergence of leafy and stem lettuce. Interestingly, these SVs are related to transposons and DNA methylation states. Furthermore, we identify 4,612 whole-genome triplication genes exhibiting high expression levels associated with low DNA methylation levels and high N6-methyladenosine RNA modifications. DNA methylation changes are also associated with activation of genes involved in callus formation.

CONCLUSIONS

Our gapless lettuce genome assembly, an unprecedented achievement in the Asteraceae family, establishes a solid foundation for functional genomics, epigenomics, and crop breeding and sheds new light on understanding the complexity of gene regulation associated with the dynamics of DNA and RNA epigenetics in genome evolution.

RNA methylations in hepatic fibrosis, a gradually emerging new treatment strategy

BACKGROUND

Hepatic fibrosis (HF) is a pathological process caused by excessive accumulation of extracellular matrix caused by a series of causes, leading to the formation of fiber scar. RNA methylation is a newly discovered epigenetic modification that exists widely in eukaryotes and prokaryotes and plays a crucial role in the pathogenesis of many diseases.

RESULTS

The occurrence and development of HF are regulated by many factors, including excessive deposition of extracellular matrix, activation of hepatic stellate cells, inflammation, and oxidative stress. RNA methylations of different species have become a crucial regulatory mode of transcript expression, And participate in the pathogenesis of tumors, nervous system diseases, autoimmune diseases, and other diseases. In addition, there are five common types of RNA methylation, but only m6A plays a crucial regulatory role in HF. The pathophysiological regulation of m6A on HF is achieved by the combination of the methylated transferase, demethylated enzyme, and methylated reading protein.

CONCLUSIONS

RNA methylated methyltransferase, demethylase, and reading protein extensively affect the pathological mechanism of HF, which may be a new therapeutic and diagnostic target, representing a new class of therapeutic strategies.

bakR: uncovering differential RNA synthesis and degradation kinetics transcriptome-wide with Bayesian hierarchical modeling

Differential expression analysis of RNA sequencing (RNA-seq) data can identify changes in cellular RNA levels, but provides limited information about the kinetic mechanisms underlying such changes. Nucleotide recoding RNA-seq methods (NR-seq; e.g., TimeLapse-seq, SLAM-seq, etc.) address this shortcoming and are widely used approaches to identify changes in RNA synthesis and degradation kinetics. While advanced statistical models implemented in user-friendly software (e.g., DESeq2) have ensured the statistical rigor of differential expression analyses, no such tools that facilitate differential kinetic analysis with NR-seq exist. Here, we report the development of Bayesian analysis of the kinetics of RNA (bakR; https:// github.com/simonlabcode/bakR), an R package to address this need. bakR relies on Bayesian hierarchical modeling of NR-seq data to increase statistical power by sharing information across transcripts. Analyses of simulated data confirmed that bakR implementations of the hierarchical model outperform attempts to analyze differential kinetics with existing models. bakR also uncovers biological signals in real NR-seq data sets and provides improved analyses of existing data sets. This work establishes bakR as an important tool for identifying differential RNA synthesis and degradation kinetics.

RPLP1 Is Up-Regulated in Human Adenomyosis and Endometrial Adenocarcinoma Epithelial Cells and Is Essential for Cell Survival and Migration In Vitro

Adenomyosis is defined as the development of endometrial epithelial glands and stroma within the myometrial layer of the uterus. These "ectopic" lesions share many cellular characteristics with endometriotic epithelial cells as well as endometrial adenocarcinoma cells, including enhanced proliferation, migration, invasion and progesterone resistance. We recently reported that the 60S acidic ribosomal protein P1, RPLP1, is up-regulated in endometriotic epithelial cells and lesion tissue where it plays a role in cell survival. To evaluate if a similar pattern of expression and function for RPLP1 exists in adenomyosis and endometrial cancer, we examined RPLP1 expression in adenomyosis and endometrial cancer tissue specimens and assessed its function in vitro using well-characterized cell lines. A total of 12 control endometrial biopsies and 20 eutopic endometrial and matched adenomyosis biopsies as well as 103 endometrial adenocarcinoma biopsies were evaluated for RPLP1 localization by immunohistochemistry. Endometrial adenocarcinoma cell lines, Ishikawa, HEC1A, HEC1B and AN3 were evaluated for RPLP1 protein and transcript expression, while in vitro function was evaluated by knocking down RPLP1 expression and assessing cell survival and migration. RPLP1 protein was up-regulated in eutopic epithelia as well as in adenomyosis lesions compared to eutopic endometria from control subjects. RPLP1 was also significantly up-regulated in endometrial adenocarcinoma tissue. Knockdown of RPLP1 in endometrial adenocarcinoma cell lines was associated with reduced cell survival and migration. RPLP1 expression is up-regulated in eutopic and ectopic adenomyotic epithelia as well as in the epithelia of endometrial cancer specimens. In vitro studies support an essential role for RPLP1 in mediating cell survival and migration, processes which are all involved in pathophysiology associated with both diseases.

Combined modelling of mRNA decay dynamics and single-molecule imaging in the Drosophila embryo uncovers a role for P-bodies in 5' to 3' degradation

Regulation of mRNA degradation is critical for a diverse array of cellular processes and developmental cell fate decisions. Many methods for determining mRNA half-lives rely on transcriptional inhibition or metabolic labelling. Here, we use a non-invasive method for estimating half-lives for hundreds of mRNAs in the early Drosophila embryo. This approach uses the intronic and exonic reads from a total RNA-seq time series and Gaussian process regression to model the dynamics of premature and mature mRNAs. We show how regulation of mRNA stability is used to establish a range of mature mRNA dynamics during embryogenesis, despite shared transcription profiles. Using single-molecule imaging, we provide evidence that, for the mRNAs tested, there is a correlation between short half-life and mRNA association with P-bodies. Moreover, we detect an enrichment of mRNA 3' ends in P-bodies in the early embryo, consistent with 5' to 3' degradation occurring in P-bodies for at least a subset of mRNAs. We discuss our findings in relation to recently published data suggesting that the primary function of P-bodies in other biological contexts is mRNA storage.

Enhanced Ribonucleoprotein Immunoprecipitation (RIP) Technique for the Identification of mRNA Species in Ribonucleoprotein Complexes

RNA binding proteins (RBPs) are critical regulators of cellular phenotypes, and dysregulated RBP expression is implicated in various diseases including cancer. A single RBP can bind to and regulate the expression of many RNA molecules via a variety of mechanisms, including translational suppression, prevention of RNA degradation, and alteration in subcellular localization. To elucidate the role of a specific RBP within a given cellular context, it is essential to first identify the group of RNA molecules to which it binds. This has traditionally been achieved using cross-linking-based assays in which cells are first exposed to agents that cross-link RBPs to nucleic acids and then lysed to extract and purify the RBP-nucleic acid complexes. The nucleic acids within the mixture are then released and analyzed via conventional means (e.g., microarray analysis, qRT-PCR, RNA sequencing, or Northern blot). While cross-linking-based ribonucleoprotein immunoprecipitation (RIP) has proven its utility within some contexts, it is technically challenging, inefficient, and suboptimal given the amount of time and resources (e.g., cells and antibodies) required. Additionally, these types of studies often require the use of over-expressed versions of proteins, which can introduce artifacts. Here, we describe a streamlined version of RIP that utilizes exclusion-based purification technologies. This approach requires significantly less starting material and resources compared to traditional RIP approaches, takes less time, which is tantamount given the labile nature of RNA, and can be used with endogenously expressed proteins. The method described here can be used to study RNA-protein interactions in a variety of cellular contexts. Graphical abstract.

Estimating RNA dynamics using one time point for one sample in a single-pulse metabolic labeling experiment

Background

Over the past decade, experimental procedures such as metabolic labeling for determining RNA turnover rates at the transcriptome-wide scale have been widely adopted and are now turning to single cell measurements. Several computational methods to estimate RNA synthesis, processing and degradation rates from such experiments have been suggested, but they all require several RNA sequencing samples. Here we present a method that can estimate those three rates from a single sample.

Methods

Our method relies on the analytical solution to the Zeisel model of RNA dynamics. It was validated on metabolic labeling experiments performed on mouse embryonic stem cells. Resulting degradation rates were compared both to previously published rates on the same system and to a state-of-the-art method applied to the same data.

Results

Our method is computationally efficient and outputs rates that correlate well with previously published data sets. Using it on a single sample, we were able to reproduce the observation that dynamic biological processes tend to involve genes with higher metabolic rates, while stable processes involve genes with lower rates. This supports the hypothesis that cells control not only the mRNA steady-state abundance, but also its responsiveness, i.e., how fast steady state is reached. Moreover, degradation rates obtained with our method compare favourably with the other tested method.

Conclusions

In addition to saving experimental work and computational time, estimating rates for a single sample has several advantages. It does not require an error-prone normalization across samples and enables the use of replicates to estimate uncertainty and assess sample quality. Finally the method and theoretical results described here are general enough to be useful in other contexts such as nucleotide conversion methods and single cell metabolic labeling experiments.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04672-4.

Genetic variants associated mRNA stability in lung

Background

Expression quantitative trait loci (eQTLs) analyses have been widely used to identify genetic variants associated with gene expression levels to understand what molecular mechanisms underlie genetic traits. The resultant eQTLs might affect the expression of associated genes through transcriptional or post-transcriptional regulation. In this study, we attempt to distinguish these two types of regulation by identifying genetic variants associated with mRNA stability of genes (stQTLs).

Results

Here, we presented a computational framework that takes advantage of recently developed methods to infer the mRNA stability of genes based on RNA-seq data and performed association analysis to identify stQTLs. Using the Genotype-Tissue Expression (GTEx) lung RNA-Seq data, we identified a total of 142,801 stQTLs for 3942 genes and 186,132 eQTLs for 4751 genes from 15,122,700 genetic variants for 13,476 genes on the autosomes, respectively. Interestingly, our results indicated that stQTLs were enriched in the CDS and 3’UTR regions, while eQTLs are enriched in the CDS, 3’UTR, 5’UTR, and upstream regions. We also found that stQTLs are more likely than eQTLs to overlap with RNA binding protein (RBP) and microRNA (miRNA) binding sites. Our analyses demonstrate that simultaneous identification of stQTLs and eQTLs can provide more mechanistic insight on the association between genetic variants and gene expression levels.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08405-y.

New horizons in the stormy sea of multimodal single-cell data integration

While measurements of RNA expression have dominated the world of single-cell analyses, new single-cell techniques increasingly allow collection of different data modalities, measuring different molecules, structural connections, and intermolecular interactions. Integrating the resulting multimodal single-cell datasets is a new bioinformatics challenge. Equally important, it is a new experimental design challenge for the bench scientist, who is not only choosing from a myriad of techniques for each data modality but also faces new challenges in experimental design. The ultimate goal is to design, execute, and analyze multimodal single-cell experiments that are more than just descriptive but enable the learning of new causal and mechanistic biology. This objective requires strict consideration of the goals behind the analysis, which might range from mapping the heterogeneity of a cellular population to assembling system-wide causal networks that can further our understanding of cellular functions and eventually lead to models of tissues and organs. We review steps and challenges toward this goal. Single-cell transcriptomics is now a mature technology, and methods to measure proteins, lipids, small-molecule metabolites, and other molecular phenotypes at the single-cell level are rapidly developing. Integrating these single-cell readouts so that each cell has measurements of multiple types of data, e.g., transcriptomes, proteomes, and metabolomes, is expected to allow identification of highly specific cellular subpopulations and to provide the basis for inferring causal biological mechanisms.

Computational methods for RNA modification detection from nanopore direct RNA sequencing data

The covalent modification of RNA molecules is a pervasive feature of all classes of RNAs and has fundamental roles in the regulation of several cellular processes. Mapping the location of RNA modifications transcriptome-wide is key to unveiling their role and dynamic behaviour, but technical limitations have often hampered these efforts. Nanopore direct RNA sequencing is a third-generation sequencing technology that allows the sequencing of native RNA molecules, thus providing a direct way to detect modifications at single-molecule resolution. Despite recent advances, the analysis of nanopore sequencing data for RNA modification detection is still a complex task that presents many challenges. Many works have addressed this task using different approaches, resulting in a large number of tools with different features and performances. Here we review the diverse approaches proposed so far and outline the principles underlying currently available algorithms.

A Trans-Omics Comparison Reveals Common Gene Expression Strategies in Four Model Organisms and Exposes Similarities and Differences between Them

The ultimate goal of gene expression regulation is on the protein level. However, because the amounts of mRNAs and proteins are controlled by their synthesis and degradation rates, the cellular amount of a given protein can be attained by following different strategies. By studying omics data for six expression variables (mRNA and protein amounts, plus their synthesis and decay rates), we previously demonstrated the existence of common expression strategies (CESs) for functionally related genes in the yeast Saccharomyces cerevisiae. Here we extend that study to two other eukaryotes: the yeast Schizosaccharomyces pombe and cultured human HeLa cells. We also use genomic data from the model prokaryote Escherichia coli as an external reference. We show that six-variable profiles (6VPs) can be constructed for every gene and that these 6VPs are similar for genes with similar functions in all the studied organisms. The differences in 6VPs between organisms can be used to establish their phylogenetic relationships. The analysis of the correlations among the six variables supports the hypothesis that most gene expression control occurs in actively growing organisms at the transcription rate level, and that translation plays a minor role. We propose that living organisms use CESs for the genes acting on the same physiological pathways, especially for those belonging to stable macromolecular complexes, but CESs have been modeled by evolution to adapt to the specific life circumstances of each organism.