Extensive gene-specific translational reprogramming in a model of B cell differentiation and Abl-dependent transformation

Signal-transducing adaptor protein-1 and protein-2 in hematopoiesis and diseases

Inflammatory and immune signals are involved in stressed hematopoiesis under myeloablation, infection, chronic inflammation, and aging. These signals also affect malignant pathogenesis, and the dysregulated immune environment which causes the resistance to treatment. On activation, various types of protein tyrosine kinases in the cytoplasm mediate the cascade, leading to the transcription of target genes in the nucleus. Adaptor molecules are commonly defined as proteins that lack enzymatic activity, DNA-binding or receptor functions and possess protein-protein or protein-lipid interaction domains. By binding to specific domains of signaling molecules, adaptor proteins adjust the signaling responses after the ligation of receptors of soluble factors, including cytokines, chemokines, and growth factors, as well as pattern recognition receptors such as toll-like receptors. The signal-transducing adaptor protein (STAP) family regulates various intracellular signaling pathways. These proteins have a pleckstrin homology domain in the N-terminal region and an SRC-homology 2-like domain in the central region, representing typical binding structures as adapter proteins. Following the elucidation of the effects of STAPs on terminally differentiated immune cells, such as macrophages, T cells, mast cells, and basophils, recent findings have indicated the critical roles of STAP-2 in B-cell progenitor cells in marrow under hematopoietic stress and STAP-1 and -2 in BCR-ABL-transduced leukemogenesis. In this review, we focus on the role of STAPs in the bone marrow.

Tunable protein synthesis by transcript isoforms in human cells

Eukaryotic genes generate multiple RNA transcript isoforms though alternative transcription, splicing, and polyadenylation. However, the relationship between human transcript diversity and protein production is complex as each isoform can be translated differently. We fractionated a polysome profile and reconstructed transcript isoforms from each fraction, which we term Transcript Isoforms in Polysomes sequencing (TrIP-seq). Analysis of these data revealed regulatory features that control ribosome occupancy and translational output of each transcript isoform. We extracted a panel of 5' and 3' untranslated regions that control protein production from an unrelated gene in cells over a 100-fold range. Select 5' untranslated regions exert robust translational control between cell lines, while 3' untranslated regions can confer cell type-specific expression. These results expose the large dynamic range of transcript-isoform-specific translational control, identify isoform-specific sequences that control protein output in human cells, and demonstrate that transcript isoform diversity must be considered when relating RNA and protein levels.

Tunable protein synthesis by transcript isoforms in human cells

Eukaryotic genes generate multiple RNA transcript isoforms though alternative transcription, splicing, and polyadenylation. However, the relationship between human transcript diversity and protein production is complex as each isoform can be translated differently. We fractionated a polysome profile and reconstructed transcript isoforms from each fraction, which we term Transcript Isoforms in Polysomes sequencing (TrIP-seq). Analysis of these data revealed regulatory features that control ribosome occupancy and translational output of each transcript isoform. We extracted a panel of 5′ and 3′ untranslated regions that control protein production from an unrelated gene in cells over a 100-fold range. Select 5′ untranslated regions exert robust translational control between cell lines, while 3′ untranslated regions can confer cell type-specific expression. These results expose the large dynamic range of transcript-isoform-specific translational control, identify isoform-specific sequences that control protein output in human cells, and demonstrate that transcript isoform diversity must be considered when relating RNA and protein levels.

DOI:http://dx.doi.org/10.7554/eLife.10921.001

To produce a protein, a gene’s DNA is first copied to make molecules of messenger RNA (mRNA). The mRNAs pass through a molecular machine known as the ribosome, which translates the genetic code to make a protein. Not all of an mRNA is translated to make a protein; the “untranslated” regions play crucial roles in regulating how much of the protein is produced.

In animals, plants and other eukaryotes, many mRNAs are made up of small pieces that are “spliced” together. During this process, proteins are deposited on the mRNA to mark the splice junctions, which are then cleared when the mRNA is translated. Many different mRNAs can be produced from the same gene by splicing different combinations of RNA pieces. Each of these mRNA “isoforms” can, in principle, contain a unique set of features that control its translation. Hence each mRNA isoform can be translated differently so that different amounts of the corresponding protein product are produced. However, the relationship between the variety of isoforms and the control of translation is complex and not well understood.

To address these questions, Floor and Doudna measured the translation of over 60,000 mRNA isoforms made from almost 14,000 human genes. The experiments show that untranslated regions at the end of the mRNA (known as the 3′ end) strongly influence translation, even if the protein coding regions remain the same. Furthermore, the data showed that mRNAs with more splice junctions are translated better, implying an mRNA has some sort of memory of how many junctions it had even after the protein markers have been cleared.

Next, Floor and Doudna inserted regulatory sequences from differently translated isoforms into an unrelated “reporter” gene. This dramatically changed the amount of protein produced from the reporter gene, in a manner predicted by the earlier experiments. Untranslated regions at the beginning of the mRNAs (known as the 5′ end) controlled the amount of protein produced from the reporter consistently across different types of cells from the body. On the other hand, the 3′ regions can tune the level of protein production in particular types of cells.

Floor and Doudna’s findings demonstrate that differences between mRNA isoforms of a gene can have a big effect on the level of protein production. Changes in the types of mRNA made from a gene are often associated with human diseases, and these findings suggest one reason why. Additionally, the ability to engineer translation of an mRNA using the data is likely to aid the development of mRNA-based therapies.

DOI:http://dx.doi.org/10.7554/eLife.10921.002

Fingerprints of a message: integrating positional information on the transcriptome

The recent explosion of high-throughput sequencing methods applied to RNA molecules is allowing us to go beyond the description of sequence variants and their relative abundances, as measured by RNA-seq. We can now probe for RNA engagement in polysomes, for ribosomes, RNA binding proteins and microRNAs binding sites, for RNA secondary structure and for RNA methylation. These descriptors produce a steadily growing multidimensional array of positional information on RNA sequences, whose effective integration only would bring to decipher the regulatory interplay occurring between proteins, RNAs and their modifications on the transcriptome. This interplay ultimately dictates the degree of mRNA availability to translation, and thus the occurrence of cell phenotypes. However, several issues in data presentation are slowing down effective integration. A standardization effort for new dataset types produced should be urgently undertaken to solve these issues. Providing uniformed experimental details along with datasets processed to be directly usable and employing shared formats would greatly simplify integration efforts, strengthening hypotheses stemming from correlative observations and eventually bringing to mechanistic understanding.

Bioinformatic analysis of proteomics data

Most biochemical reactions in a cell are regulated by highly specialized proteins, which are the prime mediators of the cellular phenotype. Therefore the identification, quantitation and characterization of all proteins in a cell are of utmost importance to understand the molecular processes that mediate cellular physiology. With the advent of robust and reliable mass spectrometers that are able to analyze complex protein mixtures within a reasonable timeframe, the systematic analysis of all proteins in a cell becomes feasible. Besides the ongoing improvements of analytical hardware, standardized methods to analyze and study all proteins have to be developed that allow the generation of testable new hypothesis based on the enormous pre-existing amount of biological information. Here we discuss current strategies on how to gather, filter and analyze proteomic data sates using available software packages.

Hyper conserved elements in vertebrate mRNA 3'-UTRs reveal a translational network of RNA-binding proteins controlled by HuR

Little is known regarding the post-transcriptional networks that control gene expression in eukaryotes. Additionally, we still need to understand how these networks evolve, and the relative role played in them by their sequence-dependent regulatory factors, non-coding RNAs (ncRNAs) and RNA-binding proteins (RBPs). Here, we used an approach that relied on both phylogenetic sequence sharing and conservation in the whole mapped 3'-untranslated regions (3'-UTRs) of vertebrate species to gain knowledge on core post-transcriptional networks. The identified human hyper conserved elements (HCEs) were predicted to be preferred binding sites for RBPs and not for ncRNAs, namely microRNAs and long ncRNAs. We found that the HCE map identified a well-known network that post-transcriptionally regulates histone mRNAs. We were then able to discover and experimentally confirm a translational network composed of RNA Recognition Motif (RRM)-type RBP mRNAs that are positively controlled by HuR, another RRM-type RBP. HuR shows a preference for these RBP mRNAs bound in stem-loop motifs, confirming its role as a 'regulator of regulators'. Analysis of the transcriptome-wide HCE distribution revealed a profile of prevalently small clusters separated by unconserved intercluster RNA stretches, which predicts the formation of discrete small ribonucleoprotein complexes in the 3'-UTRs.