CRISPR-assisted detection of RNA–protein interactions in living cells

Human Mast Cell Proteome Reveals Unique Lineage, Putative Functions, and Structural Basis for Cell Ablation

Resolving Fates and Single-Cell Transcriptomes of Hematopoietic Stem Cell Clones by PolyloxExpress Barcoding

Single-Cell RNA Sequencing of Tumor-Infiltrating NK Cells Reveals that Inhibition of Transcription Factor HIF-1α Unleashes NK Cell Activity

The dynamic proteome of influenza A virus infection identifies M segment splicing as a host range determinant

AbstractPandemic influenza A virus (IAV) outbreaks occur when strains from animal reservoirs acquire the ability to infect and spread among humans. The molecular basis of this species barrier is incompletely understood. Here we combine metabolic pulse labeling and quantitative proteomics to monitor protein synthesis upon infection of human cells with a human- and a bird-adapted IAV strain and observe striking differences in viral protein synthesis. Most importantly, the matrix protein M1 is inefficiently produced by the bird-adapted strain. We show that impaired production of M1 from bird-adapted strains is caused by increased splicing of the M segment RNA to alternative isoforms. Strain-specific M segment splicing is controlled by the 3′ splice site and functionally important for permissive infection.In silicoand biochemical evidence shows that avian-adapted M segments have evolved different conserved RNA structure features than human-adapted sequences. Thus, we identify M segment RNA splicing as a viral host range determinant.

Full-length transcriptome reconstruction reveals a large diversity of RNA and protein isoforms in rat hippocampus

Abstract Gene annotation is a critical resource in genomics research. Many computational approaches have been developed to assemble transcriptomes based on high-throughput short-read sequencing, however, only with limited accuracy. Here, we combine next-generation and third-generation sequencing to reconstruct a full-length transcriptome in the rat hippocampus, which is further validated using independent 5´ and 3´-end profiling approaches. In total, we detect 28,268 full-length transcripts (FLTs), covering 6,380 RefSeq genes and 849 unannotated loci. Based on these FLTs, we discover co-occurring alternative RNA processing events. Integrating with polysome profiling and ribosome footprinting data, we predict isoform-specific translational status and reconstruct an open reading frame (ORF)-eome. Notably, a high proportion of the predicted ORFs are validated by mass spectrometry-based proteomics. Moreover, we identify isoforms with subcellular localization pattern in neurons. Collectively, our data advance our knowledge of RNA and protein isoform diversity in the rat brain and provide a rich resource for functional studies.

Super-enhancers in transcriptional regulation and genome organization

AbstractGene expression is precisely controlled in a stage and cell-type-specific manner, largely through the interaction between cis-regulatory elements and their associated trans-acting factors. Where these components aggregate in promoters and enhancers, they are able to cooperate to modulate chromatin structure and support the engagement in long-range 3D superstructures that shape the dynamics of a cell's genomic architecture. Recently, the term ‘super-enhancer’ has been introduced to describe a hyper-active regulatory domain comprising a complex array of sequence elements that work together to control the key gene networks involved in cell identity. Here, we survey the unique characteristics of super-enhancers compared to other enhancer types and summarize the recent advances in our understanding of their biological role in gene regulation. In particular, we discuss their capacity to attract the formation of phase-separated condensates, and capacity to generate three-dimensional genome structures that precisely activate their target genes. We also propose a multi-stage transition model to explain the evolutionary pressure driving the development of super-enhancers in complex organisms, and highlight the potential for involvement in tumorigenesis. Finally, we discuss more broadly the role of super-enhancers in human health disorders and related potential in therapeutic interventions.

Using Cre-recombinase-driven Polylox barcoding for in vivo fate mapping in mice

A Balance of Yki/Sd Activator and E2F1/Sd Repressor Complexes Controls Cell Survival and Affects Organ Size

Polylox barcoding reveals haematopoietic stem cell fates realized in vivo

Kinetic Analysis of Protein Stability Reveals Age-Dependent Degradation

Pervasive isoform‐specific translational regulation via alternative transcription start sites in mammals

Extensive allele‐specific translational regulation in hybrid mice

Optimal consistency in microRNA expression analysis using reference-gene-based normalization

Reference gene-based normalization of expression profiles secures consistent differential expression analysis between samples of different phenotypes or biological conditions, and facilitates comparison between experimental batches.

SeqGSEA: a Bioconductor package for gene set enrichment analysis of RNA-Seq data integrating differential expression and splicing

Gene set enrichment analysis of RNA-Seq data: integrating differential expression and splicing

Pinpointing transcription factor binding sites from ChIP-seq data with SeqSite

ISOFORM ABUNDANCE INFERENCE PROVIDES A MORE ACCURATE ESTIMATION OF GENE EXPRESSION LEVELS IN RNA-SEQ

Due to its unprecedented high-resolution and detailed information, RNA-seq technology based on next-generation high-throughput sequencing significantly boosts the ability to study transcriptomes. The estimation of genes' transcript abundance levels or gene expression levels has always been an important question in research on the transcriptional regulation and gene functions. On the basis of the concept of Reads Per Kilo-base per Million reads (RPKM), taking the union-intersection genes (UI-based) and summing up inferred isoform abundance (isoform-based) are the two current strategies to estimate gene expression levels, but produce different estimations. In this paper, we made the first attempt to compare the two strategies' performances through a series of simulation studies. Our results showed that the isoform-based method gives not only more accurate estimation but also has less uncertainty than the UI-based strategy. If taking into account the non-uniformity of read distribution, the isoform-based method can further reduce estimation errors. We applied both strategies to real RNA-seq datasets of technical replicates, and found that the isoform-based strategy also displays a better performance. For a more accurate estimation of gene expression levels from RNA-seq data, even if the abundance levels of isoforms are not of interest, it is still better to first infer the isoform abundance and sum them up to get the expression level of a gene as a whole.

Using non-uniform read distribution models to improve isoform expression inference in RNA-Seq

DEGseq: an R package for identifying differentially expressed genes from RNA-seq data