Biology subjects

Hesselberth, J. R.

Publications and source records attributed to Hesselberth, J. R..

4 recordsLinked to original sources

bioRxiv2018

Multiple decay events target HAC1 mRNA during splicing to regulate the unfolded protein response

In the unfolded protein response (UPR), protein-folding stress in the endoplasmic reticulum (ER) activates a large transcriptional program to increase ER folding capacity. During the budding yeast UPR, the trans-ER-membrane kinase-endoribonuclease Ire1 excises an intron from the HAC1 mRNA and the exon cleavage products are ligated and translated to a transcription factor that induces hundreds of stress-response genes. HAC1 cleavage by Ire1 is thought to be the rate limiting step of its processing. Using cells with mutations in RNA repair and decay enzymes, we show that phosphorylation of two different HAC1 splicing intermediates by Trl1 RNA 5'-kinase is required for their degradation by the 5'[->]3' exonuclease Xrn1 to enact opposing effects on the UPR. Kinase-mediated decay (KMD) of cleaved HAC1 3'-exon competes with its ligation to limit productive splicing and suppress the UPR, whereas KMD of the excised intron activates HAC1 translation, likely by relieving an inhibitory base-pairing interaction between the intron and 5'-untranslated region. We also found that ligated but 2'-phosphorylated HAC1 mRNA is endonucleolytically cleaved, yielding a KMD intermediate with both 5'- and 2'-phosphates at its 5'-end that inhibit 5'[->]3' decay, and suggesting that Ire1 initiates the degradation of incompletely processed HAC1s to proofread ligation or attenuate the UPR. These multiple decay events expand the scope of RNA-based regulation in the budding yeast UPR and may have implications for the control of the metazoan UPR by mRNA processing.

molecular biology↗

bioRxiv2018

Recovery and analysis of transcriptome subsets from pooled single-cell RNA-seq libraries

Single-cell RNA sequencing (scRNA-seq) methods generate sparse gene expression profiles for thousands of single cells in a single experiment. The information in these profiles is sufficient to classify cell types by distinct expression patterns but the high complexity of scRNA-seq libraries often prevents full characterization of transcriptomes from individual cells. To extract more focused gene expression information from scRNA-seq libraries, we developed a strategy to physically recover the DNA molecules comprising transcriptome subsets, enabling deeper interrogation of the isolated molecules by another round of DNA sequencing. We applied the method in cell-centric and gene-centric modes to isolate cDNA fragments from scRNA-seq libraries. First, we resampled the transcriptomes of rare, single megakaryocytes from a complex mixture of lymphocytes and analyzed them in a second round of DNA sequencing, yielding up to 20-fold greater sequencing depth per cell and increasing the number of genes detected per cell from a median of 1,313 to 2,002. We similarly isolated mRNAs from targeted T cells to improve the reconstruction of their VDJ-rearranged immune receptor mRNAs. Second, we isolated CD3D mRNA fragments expressed across cells in a scRNA-seq library prepared from a clonal T cell line, increasing the number of cells with detected CD3D expression from 59.7% to 100%. Transcriptome resampling is a general approach to recover targeted gene expression information from single-cell RNA sequencing libraries that enhances the utility of these costly experiments, and may be applicable to the targeted recovery of molecules from other single-cell assays.

genomics↗

bioRxiv2018

Dynamic temperature-sensitive A-to-I RNA editing in the brain of a heterothermic mammal during hibernation

RNA editing diversifies genomically encoded information to expand the complexity of the transcriptome. In ectothermic organisms, including Drosophila and Cephalopoda, where body temperature mirrors ambient temperature, decreases in environmental temperature lead to increases in A-to-I RNA editing and cause amino acid recoding events that are thought to be adaptive responses to temperature fluctuations. In contrast, endothermic mammals, including humans and mice, typically maintain a constant body temperature despite environmental changes. Here, A-to-I editing primarily targets repeat elements, rarely results in the recoding of amino acids and plays a critical role in innate immune tolerance. Hibernating ground squirrels provide a unique opportunity to examine RNA editing in a heterothermic mammal whose body temperature varies over 30{degrees}C and can be maintained at 5{degrees}C for many days during torpor. We profiled the transcriptome in three brain regions at six physiological states to quantify RNA editing and determine whether cold-induced RNA editing modifies the transcriptome as a potential mechanism for neuroprotection at low temperature during hibernation. We identified 5,165 A-to-I editing sites in 1,205 genes with dynamically increased editing after prolonged cold exposure. The majority (99.6%) of the cold-increased editing sites are outside of previously annotated coding regions, 82.7% lie in SINE-derived repeats, and 12 sites are predicted to recode amino acids. Additionally, A-to-I editing frequencies increase with increasing cold-exposure demonstrating that ADAR remains active during torpor. Our findings suggest that dynamic A-to-I editing at low body temperature may provide a neuroprotective mechanism to limit aberrant dsRNA accumulation during torpor in the mammalian hibernator.

genomics↗

bioRxiv2016

Semi-automated genome annotation using epigenomic data and Segway

Biochemical techniques measure many individual properties of chromatin along the genome. These properties include DNA accessibility (measured by DNase-seq) and the presence of individual transcription factors and histone modifications (measured by ChIP-seq). Segway is software that transforms multiple datasets on chromatin properties into a single annotation of the genome that a biologist can more easily interpret. This protocol describes how to use Segway to annotate the genome, starting with reads from a ChIP-seq experiment. It includes pre-processing of data, training the Segway model, annotating the genome, assigning biological meanings to labels, and visualizing the annotation in a genome browser.

bioinformatics↗