Aging is characterized by the accumulation of damage and other deleterious changes, leading to the loss of functionality and fitness. Age-related changes occur at most levels of organization of a living organism (molecular, organellar, cellular, tissue and organ). However, protein synthesis is a major biological process, and thus understanding how it changes with age is of paramount importance. Here, we discuss the relationships between lifespan, aging, protein synthesis and translational control, and expand this analysis to the various aspects of proteome behavior in organisms with age. Characterizing the consequences of changes in protein synthesis and translation fidelity, and determining whether altered translation is pathological or adaptive is necessary for understanding the aging process, as well as for developing approaches to target dysfunction in translation as a strategy for extending lifespan.

The recycling of ribosomal subunits after translation termination is critical for efficient gene expression. Tma64 (eIF2D), Tma20 (MCT-1), and Tma22 (DENR) function as 40S recycling factors in vitro, but it is unknown whether they perform this function in vivo. Ribosome profiling of tma deletion strains revealed 80S ribosomes queued behind the stop codon, consistent with a block in 40S recycling. We found that unrecycled ribosomes could reinitiate translation at AUG codons in the 3' UTR, as evidenced by peaks in the footprint data and 3' UTR reporter analysis. In vitro translation experiments using reporter mRNAs containing upstream open reading frames (uORFs) further established that reinitiation increased in the absence of these proteins. In some cases, 40S ribosomes appeared to rejoin with 60S subunits and undergo an 80S reinitiation process in 3' UTRs. These results support a crucial role for Tma64, Tma20, and Tma22 in recycling 40S ribosomal subunits at stop codons and translation reinitiation.

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Highlights d Mortality from age-related diseases is U-shaped with the nadir below reproductive age d Quantitative biomarkers of aging change continuously throughout life d Mutation burden causes early-life mortality and contributes to selection d Aging is best defined by damage rather than mortality and starts very early in life

Spatial organization of protein biosynthesis in the eukaryotic cell has been studied for more than fifty years, thus many facts have already been included in textbooks. According to the classical view, mRNA transcripts encoding secreted and transmembrane proteins are translated by ribosomes associated with endoplasmic reticulum membranes, while soluble cytoplasmic proteins are synthesized on free polysomes. However, in the last few years, new data has emerged, revealing selective translation of mRNA on mitochondria and plastids, in proximity to peroxisomes and endosomes, in various granules and at the cytoskeleton (actin network, vimentin intermediate filaments, microtubules and centrosomes). There are also long-standing debates about the possibility of protein synthesis in the nucleus. Localized translation can be determined by targeting signals in the synthesized protein, nucleotide sequences in the mRNA itself, or both. With RNA-binding proteins, many transcripts can be assembled into specific RNA condensates and form RNP particles, which may be transported by molecular motors to the sites of active translation, form granules and provoke liquid-liquid phase separation in the cytoplasm, both under normal conditions and during cell stress. The translation of some mRNAs occurs in specialized “translation factories,” assemblysomes, transperons and other structures necessary for the correct folding of proteins, interaction with functional partners and formation of oligomeric complexes. Intracellular localization of mRNA has a significant impact on the efficiency of its translation and presumably determines its response to cellular stress. Compartmentalization of mRNAs and the translation machinery also plays an important role in viral infections. Many viruses provoke the formation of specific intracellular structures, virus factories, for the production of their proteins. Here we review the current concepts of the molecular mechanisms of transport, selective localization and local translation of cellular and viral mRNAs, their effects on protein targeting and topogenesis, and on the regulation of protein biosynthesis in different compartments of the eukaryotic cell. Special attention is paid to new systems biology approaches, providing new cues to the study of localized translation.

Background High-throughput sequencing often provides a foundation for experimental analyses in the life sciences. For many such methods, an intermediate layer of bioinformatics data analysis is the genomic signal track constructed by short read mapping to a particular genome assembly. There are many software tools to visualize genomic tracks in a web browser or with a stand-alone graphical user interface. However, there are only few command-line applications suitable for automated usage or production of publication-ready visualizations. Results Here we present svist4get, a command-line tool for customizable generation of publication-quality figures based on data from genomic signal tracks. Similarly to generic genome browser software, svist4get visualizes signal tracks at a given genomic location and is able to aggregate data from several tracks on a single plot along with the transcriptome annotation. The resulting plots can be saved as the vector or high-resolution bitmap images. We demonstrate practical use cases of svist4get for Ribo-Seq and RNA-Seq data. Conclusions svist4get is implemented in Python 3 and runs on Linux. The command-line interface of svist4get allows for easy integration into bioinformatics pipelines in a console environment. Extra customization is possible through configuration files and Python API. For convenience, svist4get is provided as pypi package. The source code is available at https://bitbucket.org/artegorov/svist4get/ Electronic supplementary material The online version of this article (10.1186/s12859-019-2706-8) contains supplementary material, which is available to authorized users.

A dvanced age, even in healthy individuals, is accompanied by progressive decline of cognitive, metabolic and physiological abilities, and can enhance susceptibility to neurodegenerative, cardiovascular and chronic inflammatory diseases 1,2. Operationally, it is often difficult to determine whether age-associated signatures reflect changes of individual cells or changes in cell-type abundances, especially when performing whole-tissue transcriptional or epigenetic characterization. And even despite a large amount of clinical and epidemiological data 3-7 , we understand very little about the nature of age-associated changes in specific primary cell populations of healthy individuals, particularly with respect to age-associated alterations of the epigenetic landscape. To address this question directly, we focused on classical CD14 + CD16 − monocytes, as they are homogeneous, easily accessible and relatively abundant in blood, which permits multiomics profiling of these cells obtained from a single blood draw. Epigenetic aging can manifest in two key aspects: via age-associated changes in chromatin modifications and in DNA methylation. Robustness of the connection between aging and DNA methylation has been well acknowledged 8-12 ; yet, despite the large number of studies, cell-specific regions of age-associated DNA methylation/demethylation have not been reported so far. Previous studies have predominantly used DNA methylation arrays that detect changes of a predefined set of distant solitary cytosines across the genome 4. This design prevents identification of differentially methylated regions (DMRs), which are expected to be more biologically relevant compared with changes in single isolated CpG sites. In this Article, we used parallel multiomics approaches to characterize intracellular states and extracellular environments of monocytes along healthy aging. To allow for simultaneous identification of continuous age-associated DNA methylation regions and corresponding chromatin context, we utilized enhanced reduced representation bisulfite sequencing (eRRBS) coupled with the ultra-low-input chromatin immunoprecipitation followed by sequencing (ULI-ChIP-seq) 13 approach to profile chromatin modifications from limited input material. Our approach led to the identification of more than 1,000 DMRs, which could not be achieved via methylation array technology. We found no evidence of large-scale remodelling of the chromatin modification landscape along healthy aging, yet revealed distinct chromatin features that were characteristic of age-associated DNA hyper-and hypomethylated regions. Integration of the obtained DMR signatures with

TMA20 (MCT-1), TMA22 (DENR) and TMA64 (eIF2D) are eukaryotic translation factors involved in ribosome recycling and re-initiation. They operate with P-site bound tRNA in post-termination or (re-)initiation translation complexes, thus participating in the removal of 40S ribosomal subunit from mRNA stop codons after termination and controlling translation re-initiation on mRNAs with upstream open reading frames (uORFs), as well as de novo initiation on some specific mRNAs. Here we report ribosomal profiling data of S.cerevisiae strains with individual deletions of TMA20 , TMA64 or both TMA20 and TMA64 genes. We provide RNA-Seq and Ribo-Seq data from yeast strains grown in the rich YPD or minimal SD medium. We illustrate our data by plotting differential distribution of ribosomal-bound mRNA fragments throughout uORFs in 5′-untranslated region (5′ UTR) of GCN4 mRNA and on mRNA transcripts encoded in MAT locus in the mutant and wild-type strains, thus providing a basis for investigation of the role of these factors in the stress response, mating and sporulation. We also document a shift of transcription start site of the APC4 gene which occurs when the neighboring TMA64 gene is replaced by the standard G418-resistance cassette used for the creation of the Yeast Deletion Library. This shift results in dramatic deregulation of the APC4 gene expression, as revealed by our Ribo-Seq data, which can be probably used to explain strong genetic interactions of TMA64 with genes involved in the cell cycle and mitotic checkpoints. Raw RNA-Seq and Ribo-Seq data as well as all gene counts are available in NCBI Gene Expression Omnibus (GEO) repository under GEO accession GSE122039 ( https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE122039 ).