Transcriptional burst kinetics are linked to short term transcriptional memory

Transcriptional bursting is thought to be a stochastic process that allows the dynamic regulation of most genes. The random telegraph model assumes the existence of two states, ON and OFF. However recent studies indicate the presence of additional ON states, suggesting that bursting kinetics and their regulation can be quite complex. We have developed a system to study transcriptional bursting in the context of p53 biology using the endogenous p21 gene tagged with MS2 in human cells. Remarkably, we find that transcriptional bursts from the p21 gene contain multiple ON and OFF states that can be regulated by elevation of p53 levels. Distinct ON states are characterized by differences in burst duration, classified as Short and Long, with long bursts associated with higher Pol II initiation rates. Importantly, the different ON states display memory effects that allow us to predict the likelihood of properties of future bursting events. Long bursting events result in faster re-activation, longer subsequent bursts and higher transcriptional output in the future compared to short bursts. Bursting memory persists up to 2 hours suggesting a stable inheritable promoter architecture. Bursting memory at the p21 gene is the strongest under basal conditions and is suppressed by UV and inhibition of H3K9me1/2, which also increase transcriptional noise. Stabilization of p53 by Nutlin-3a partially reverses suppression of bursting memory suggesting that higher p53 levels may be a key in enforcing memory under conditions of cellular stress. Overall our data uncover a new found bursting property termed Short-Term Transcriptional Memory (STTM) that has the potential to fine-tune transcriptional output at the p21 gene.

SCReadCounts: estimation of cell-level SNVs expression from scRNA-seq data

Background Recent studies have demonstrated the utility of scRNA-seq SNVs to distinguish tumor from normal cells, characterize intra-tumoral heterogeneity, and define mutation-associated expression signatures. In addition to cancer studies, SNVs from single cells have been useful in studies of transcriptional burst kinetics, allelic expression, chromosome X inactivation, ploidy estimations, and haplotype inference. Results To aid these types of studies, we have developed a tool, SCReadCounts, for cell-level tabulation of the sequencing read counts bearing SNV reference and variant alleles from barcoded scRNA-seq alignments. Provided genomic loci and expected alleles, SCReadCounts generates cell-SNV matrices with the absolute variant- and reference-harboring read counts, as well as cell-SNV matrices of expressed Variant Allele Fraction (VAFRNA) suitable for a variety of downstream applications. We demonstrate three different SCReadCounts applications on 59,884 cells from seven neuroblastoma samples: (1) estimation of cell-level expression of known somatic mutations and RNA-editing sites, (2) estimation of cell- level allele expression of biallelic SNVs, and (3) a discovery mode assessment of the reference and each of the three alternative nucleotides at genomic positions of interest that does not require prior SNV information. For the later, we applied SCReadCounts on the coding regions of KRAS, where it identified known and novel somatic mutations in a low-to-moderate proportion of cells. The SCReadCounts read counts module is benchmarked against the analogous modules of GATK and Samtools. SCReadCounts is freely available (https://github.com/HorvathLab/NGS) as 64-bit self-contained binary distributions for Linux and MacOS, in addition to Python source. Conclusions SCReadCounts supplies a fast and efficient solution for estimation of cell-level SNV expression from scRNA-seq data. SCReadCounts enables distinguishing cells with monoallelic reference expression from those with no gene expression and is applicable to assess SNVs present in only a small proportion of the cells, such as somatic mutations in cancer.

Metabolic resilience is encoded in genome plasticity

Metabolism plays a central role in evolution, as resource conservation is a selective pressure for fitness and survival. Resource-driven adaptations offer a good model to study evolutionary innovation more broadly. It remains unknown how resource-driven optimization of genome function integrates chromatin architecture with transcriptional phase transitions. Here we show that tuning of genome architecture and heterotypic transcriptional condensates mediate resilience to nutrient limitation. Network genomic integration of phenotypic, structural, and functional relationships reveals that fat tissue promotes organismal adaptations through metabolic acceleration chromatin domains and heterotypic PGC1A condensates. We find evolutionary adaptations in several dimensions; low conservation of amino acid residues within protein disorder regions, nonrandom chromatin location of metabolic acceleration domains, condensate-chromatin stability through cis-regulatory anchoring and encoding of genome plasticity in radial chromatin organization. We show that environmental tuning of these adaptations leads to fasting endurance, through efficient nuclear compartmentalization of lipid metabolic regions, and, locally, human-specific burst kinetics of lipid cycling genes. This process reduces oxidative stress, and fatty-acid mediated cellular acidification, enabling endurance of condensate chromatin conformations. Comparative genomics of genetic and diet perturbations reveal mammalian convergence of phenotype and structural relationships, along with loss of transcriptional control by diet-induced obesity. Further, we find that radial transcriptional organization is encoded in functional divergence of metabolic disease variant-hubs, heterotypic condensate composition, and protein residues sensing metabolic variation. During fuel restriction, these features license the formation of large heterotypic condensates that buffer proton excess, and shift viscoelasticity for condensate endurance. This mechanism maintains physiological pH, reduces pH-resilient inflammatory gene programs, and enables genome plasticity through transcriptionally driven cell-specific chromatin contacts. In vivo manipulation of this circuit promotes fasting-like adaptations with heterotypic nuclear compartments, metabolic and cell-specific homeostasis. In sum, we uncover here a general principle by which transcription uses environmental fluctuations for genome function, and demonstrate how resource conservation optimizes transcriptional self-organization through robust feedback integrators, highlighting obesity as an inhibitor of genome plasticity relevant for many diseases.

Metabolic resilience is encoded in genome plasticity

Metabolism plays a central role in evolution, as resource conservation is a selective pressure for fitness and survival. Resource-driven adaptations offer a good model to study evolutionary innovation more broadly. It remains unknown how resource-driven optimization of genome function integrates chromatin architecture with transcriptional phase transitions. Here we show that tuning of genome architecture and heterotypic transcriptional condensates mediate resilience to nutrient limitation. Network genomic integration of phenotypic, structural, and functional relationships reveals that fat tissue promotes organismal adaptations through metabolic acceleration chromatin domains and heterotypic PGC1A condensates. We find evolutionary innovation in several dimensions; low conservation of amino acid residues within protein disorder regions, nonrandom chromatin location of metabolic acceleration domains, condensate-chromatin stability through cis-regulatory archoring and encoding of genome plasticity in radial chromatin organization. We show that environmental tuning of these adaptations leads to fasting endurance, through efficient nuclear compartmentalization of lipid metabolic regions, and, locally, human-specific burst kinetics of lipid cycling genes. This process reduces oxidative stress, and fatty-acid mediated cellular acidification, enabling endurance of condensate chromatin conformations. Comparative genomics of genetic and diet perturbations reveal mammalian convergence of phenotype and structural relationships, along with loss of transcriptional control by diet-induced obesity. Further, we find that radial transcriptional organization is encoded in functional divergence of metabolic disease variant-hubs, heterotypic condensate composition, and evolutionary tuned protein residues sensing metabolic variation. During fuel restriction, these features license the formation of large heterotypic condensates that buffer proton excess, and shift viscoelasticity for condensate endurance. This mechanism maintains physiological pH, reduces pH-resilient inflammatory gene programs, and enables genome plasticity through transcriptionally driven cell-specific chromatin contacts. In vivo manipulation of this circuit promotes fasting-like adaptations with heterotypic nuclear compartments, metabolic and cell-specific homeostasis. In sum, we uncover here a general principle by which transcription uses environmental fluctuations for genome function, and demonstrate how resource conservation optimizes transcriptional self-organization through robust feedback integrators, highlighting obesity as an inhibitor of genome plasticity relevant for many diseases.

Transcriptional bursting shape autosomal dynamic random monoallelic expression in pre-gastrulation embryos

Recent years, allele-specific single cell RNA-seq (scRNA-seq) analysis have demonstrated wide-spread dynamic random monoallelic expression of autosomal genes (aRME). However, the origin of dynamic aRME remains poorly understood. It is believed that dynamic aRME is originated from discrete transcriptional burst of two alleles. Here, for the first time, we have profiled genome-wide pattern of dynamic aRME and allele-specific burst kinetics in mouse pre-gastrulation embryos. We found wide-spread dynamic aRME across the different lineages of pre-gastrulation embryos and which is linked to the allelic burst kinetics. Specially, we found that expression level and burst frequency are the key determinants of dynamic aRME. Altogether, our study provides significant insight about the origin of prevalent dynamic aRME and cell to cell expression heterogeneity during the early mammalian development.

Transcriptional bursts explain autosomal random monoallelic expression and affect allelic imbalance

Transcriptional bursts render substantial biological noise in cellular transcriptomes. Here, we investigated the theoretical extent of monoallelic expression resulting from transcriptional bursting and how it compared to the amounts of monoallelic expression of autosomal genes observed in single-cell RNA-sequencing (scRNA-seq) data. We found that transcriptional bursting can explain frequent observations of autosomal monoallelic gene expression in cells. Importantly, the burst frequency largely determined the fraction of cells with monoallelic expression, whereas both burst frequency and size contributed to allelic imbalance. Allelic observations deviate from the expected when analysed across heterogeneous groups of cells, suggesting that allelic modelling can provide an unbiased assessment of heterogeneity within cells. Finally, large numbers of cells are required for analyses of allelic imbalance to avoid confounding observations from transcriptional bursting. Altogether, our results shed light on the implications of transcriptional burst kinetics on allelic expression patterns and phenotypic variation between cells.