Translation efficiency in humans: tissue specificity, global optimization and differences between developmental stages

Deciphering conundrums of adeno-associated virus liver-directed gene therapy: focus on hemophilia

Adeno-associated virus gene therapy has been the subject of intensive investigation for monogenic disease gene addition therapy for more than 25 years, yet few therapies have been approved by regulatory agencies. Most have not progressed beyond phase 1/2 due to toxicity, lack of efficacy, or both. The liver is a natural target for adeno-associated virus since most serotypes have a high degree of tropism for hepatocytes due to cell surface receptors for the virus and the unique liver sinusoidal geometry facilitating high volumes of blood contact with hepatocyte cell surfaces. Recessive monogenic diseases such as hemophilia represent promising targets since the defective proteins are often synthesized in the liver and secreted into the circulation, making them easy to measure, and many do not require precise regulation. Yet, despite initiation of many disease-specific clinical trials, therapeutic windows are often nonexistent, resulting in excess toxicity and insufficient efficacy. Iterative progress built on these attempts is best illustrated by hemophilia, with the first regulatory approvals for factor IX and factor VIII gene therapies eventually achieved 25 years after the first gene therapy studies in humans. Although successful gene transfer may result in the production of sufficient transgenic protein to modify the disease, many emerging questions on durability, predictability, reliability, and variability of response have not been answered. The underlying biology accounting for these heterogeneous responses and the interplay between host and virus is the subject of intense investigation and the subject of this review.

Dynamic Landscapes of tRNA Transcriptomes and Translatomes in Diverse Mouse Tissues

Although the function of tRNAs in the translational process is well established, it remains controversial whether tRNA abundance is tightly associated with translational efficiency (TE) in mammals. Moreover, how critically the expression of tRNAs contributes to the establishment of tissue-specific proteomes in mammals has not been well addressed. Here, we measured both tRNA expression using demethylase-tRNA sequencing (DM-tRNA-seq) and TE of mRNAs using ribosome-tagging sequencing (RiboTag-seq) in the brain, heart, and testis of mice. Remarkable variation in the expression of tRNA isodecoders was observed among different tissues. When the statistical effect of isodecoder-grouping on reducing variations is considered through permutating the anticodons, we observed an expected reduction in the variation of anticodon expression across all samples, an unexpected smaller variation of anticodon usage bias, and an unexpected larger variation of tRNA isotype expression at amino acid level. Regardless of whether or not they share the same anticodons, the isodecoders encoding the same amino acids are co-expressed across different tissues. Based on the expression of tRNAs and the TE of mRNAs, we find that the tRNA adaptation index (tAI) and TE are significantly correlated in the same tissues but not between tissues; and tRNA expression and the amino acid composition of translating peptides are positively correlated in the same tissues but not between tissues. We therefore hypothesize that the tissue-specific expression of tRNAs might be due to post-transcriptional mechanisms. This study provides a resource for tRNA and translation studies, as well as novel insights into the dynamics of tRNAs and their roles in translational regulation.

Human Tissues Exhibit Diverse Composition of Translation Machinery

While protein synthesis is vital for the majority of cell types of the human body, diversely differentiated cells require specific translation regulation. This suggests the specialization of translation machinery across tissues and organs. Using transcriptomic data from GTEx, FANTOM, and Gene Atlas, we systematically explored the abundance of transcripts encoding translation factors and aminoacyl-tRNA synthetases (ARSases) in human tissues. We revised a few known and identified several novel translation-related genes exhibiting strict tissue-specific expression. The proteins they encode include eEF1A1, eEF1A2, PABPC1L, PABPC3, eIF1B, eIF4E1B, eIF4ENIF1, and eIF5AL1. Furthermore, our analysis revealed a pervasive tissue-specific relative abundance of translation machinery components (e.g., PABP and eRF3 paralogs, eIF2B and eIF3 subunits, eIF5MPs, and some ARSases), suggesting presumptive variance in the composition of translation initiation, elongation, and termination complexes. These conclusions were largely confirmed by the analysis of proteomic data. Finally, we paid attention to sexual dimorphism in the repertoire of translation factors encoded in sex chromosomes (eIF1A, eIF2γ, and DDX3), and identified the testis and brain as organs with the most diverged expression of translation-associated genes.

Ribosome profiling reveals the translational landscape and allele-specific translational efficiency in rice

Translational regulation is a critical step in the process of gene expression and governs the synthesis of proteins from mRNAs. Many studies have revealed translational regulation in plants in response to various environmental stimuli. However, there have been no studies documenting the comprehensive landscape of translational regulation and allele-specific translational efficiency in multiple plant tissues, especially those of rice, a main staple crop that feeds nearly half of the world's population. Here we used RNA sequencing and ribosome profiling data to analyze the transcriptome and translatome of an elite hybrid rice, Shanyou 63 (SY63), and its parental varieties Zhenshan 97 and Minghui 63. The results revealed that gene expression patterns varied more among tissues than among varieties at the transcriptional and translational levels. We identified 3392 upstream open reading frames (uORFs), and the uORF-containing genes were enriched in transcription factors. Only 668 of 13 492 long non-coding RNAs could be translated into peptides. Finally, we discovered numerous genes with allele-specific translational efficiency in SY63 and demonstrated that some cis-regulatory elements may contribute to allelic divergence in translational efficiency. Overall, these findings may improve our understanding of translational regulation in rice and provide information for molecular breeding research.

Synonymous mutations in the phosphoglycerate kinase 1 gene induce an altered response to protein misfolding in Schizosaccharomyces pombe

Background

Proteostasis refers to the processes that regulate the biogenesis, folding, trafficking, and degradation of proteins. Any alteration in these processes can lead to cell malfunction. Protein synthesis, a key proteostatic process, is highly-regulated at multiple levels to ensure adequate adaptation to environmental and physiological challenges such as different stressors, proteotoxic conditions and aging, among other factors. Because alterations in protein translation can lead to protein misfolding, examining how protein translation is regulated may also help to elucidate in part how proteostasis is controlled. Codon usage bias has been implicated in the fine-tuning of translation rate, as more-frequent codons might be read faster than their less-frequent counterparts. Thus, alterations in codon usage due to synonymous mutations may alter translation kinetics and thereby affect the folding of the nascent polypeptide, without altering its primary structure. To date, it has been difficult to predict the effect of synonymous mutations on protein folding and cellular fitness due to a scarcity of relevant data. Thus, the purpose of this work was to assess the effect of synonymous mutations in discrete regions of the gene that encodes the highly-expressed enzyme 3-phosphoglycerate kinase 1 (pgk1) in the fission yeast Schizosaccharomyces pombe.

Results

By means of systematic replacement of synonymous codons along pgk1, we found slightly-altered protein folding and activity in a region-specific manner. However, alterations in protein aggregation, heat stress as well as changes in proteasome activity occurred independently of the mutated region. Concomitantly, reduced mRNA levels of the chaperones Hsp9 and Hsp16 were observed.

Conclusion

Taken together, these data suggest that codon usage bias of the gene encoding this highly-expressed protein is an important regulator of protein function and proteostasis.

AAV vectors: The Rubik's cube of human gene therapy

Defective genes account for ∼80% of the total of more than 7,000 diseases known to date. Gene therapy brings the promise of a one-time treatment option that will fix the errors in patient genetic coding. Recombinant viruses are highly efficient vehicles for in vivo gene delivery. Adeno-associated virus (AAV) vectors offer unique advantages, such as tissue tropism, specificity in transduction, eliciting of a relatively low immune responses, no incorporation into the host chromosome, and long-lasting delivered gene expression, making them the most popular viral gene delivery system in clinical trials, with three AAV-based gene therapy drugs already approved by the US Food and Drug Administration (FDA) or European Medicines Agency (EMA). Despite the success of AAV vectors, their usage in particular scenarios is still limited due to remaining challenges, such as poor transduction efficiency in certain tissues, low organ specificity, pre-existing humoral immunity to AAV capsids, and vector dose-dependent toxicity in patients. In the present review, we address the different approaches to improve AAV vectors for gene therapy with a focus on AAV capsid selection and engineering, strategies to overcome anti-AAV immune response, and vector genome design, ending with a glimpse at vector production methods and the current state of recombinant AAV (rAAV) at the clinical level.

The Ramp Atlas: facilitating tissue and cell-specific ramp sequence analyses through an intuitive web interface

Ramp sequences occur when the average translational efficiency of codons near the 5′ end of highly expressed genes is significantly lower than the rest of the gene sequence, which counterintuitively increases translational efficiency by decreasing downstream ribosomal collisions. Here, we show that the relative codon adaptiveness within different tissues changes the existence of a ramp sequence without altering the underlying genetic code. We present the first comprehensive analysis of tissue and cell type-specific ramp sequences and report 3108 genes with ramp sequences that change between tissues and cell types, which corresponds with increased gene expression within those tissues and cells. The Ramp Atlas (https://ramps.byu.edu/) allows researchers to query precomputed ramp sequences in 18 388 genes across 62 tissues and 66 cell types and calculate tissue-specific ramp sequences from user-uploaded FASTA files through an intuitive web interface. We used The Ramp Atlas to identify seven SARS-CoV-2 genes and seven human SARS-CoV-2 entry factor genes with tissue-specific ramp sequences that may help explain viral proliferation within those tissues. We anticipate that The Ramp Atlas will facilitate personalized and creative tissue-specific ramp sequence analyses for both human and viral genes that will increase our ability to utilize this often-overlooked regulatory region.

CHARMING: Harmonizing synonymous codon usage to replicate a desired codon usage pattern

There is a growing appreciation that synonymous codon usage, although historically regarded as phenotypically silent, can instead alter a wide range of mechanisms related to functional protein production, a term we use here to describe the net effect of transcription (mRNA synthesis), mRNA half-life, translation (protein synthesis) and the probability of a protein folding correctly to its active, functional structure. In particular, recent discoveries have highlighted the important role that sub-optimal codons can play in modifying co-translational protein folding. These results have drawn increased attention to the patterns of synonymous codon usage within coding sequences, particularly in light of the discovery that these patterns can be conserved across evolution for homologous proteins. Because synonymous codon usage differs between organisms, for heterologous gene expression it can be desirable to make synonymous codon substitutions to match the codon usage pattern from the original organism in the heterologous expression host. Here we present CHARMING (for Codon HARMonizING), a robust and versatile algorithm to design mRNA sequences for heterologous gene expression and other related codon harmonization tasks. CHARMING can be run as a downloadable Python script or via a web portal at http://www.codons.org.

Codon optimality in cancer

A key characteristic of cancer cells is their increased proliferative capacity, which requires elevated levels of protein synthesis. The process of protein synthesis involves the translation of codons within the mRNA coding sequence into a string of amino acids to form a polypeptide chain. As most amino acids are encoded by multiple codons, the nucleotide sequence of a coding region can vary dramatically without altering the polypeptide sequence of the encoded protein. Although mutations that do not alter the final amino acid sequence are often thought of as silent/synonymous, these can still have dramatic effects on protein output. Because each codon has a distinct translation elongation rate and can differentially impact mRNA stability, each codon has a different degree of 'optimality' for protein synthesis. Recent data demonstrates that the codon preference of a transcriptome matches the abundance of tRNAs within the cell and that this supply and demand between tRNAs and mRNAs varies between different cell types. The largest observed distinction is between mRNAs encoding proteins associated with proliferation or differentiation. Nevertheless, precisely how codon optimality and tRNA expression levels regulate cell fate decisions and their role in malignancy is not fully understood. This review describes the current mechanistic understanding on codon optimality, its role in malignancy and discusses the potential to target codon optimality therapeutically in the context of cancer.

CAMAP: Artificial neural networks unveil the role of codon arrangement in modulating MHC-I peptides presentation

MHC-I associated peptides (MAPs) play a central role in the elimination of virus-infected and neoplastic cells by CD8 T cells. However, accurately predicting the MAP repertoire remains difficult, because only a fraction of the transcriptome generates MAPs. In this study, we investigated whether codon arrangement (usage and placement) regulates MAP biogenesis. We developed an artificial neural network called Codon Arrangement MAP Predictor (CAMAP), predicting MAP presentation solely from mRNA sequences flanking the MAP-coding codons (MCCs), while excluding the MCC per se. CAMAP predictions were significantly more accurate when using original codon sequences than shuffled codon sequences which reflect amino acid usage. Furthermore, predictions were independent of mRNA expression and MAP binding affinity to MHC-I molecules and applied to several cell types and species. Combining MAP ligand scores, transcript expression level and CAMAP scores was particularly useful to increase MAP prediction accuracy. Using an in vitro assay, we showed that varying the synonymous codons in the regions flanking the MCCs (without changing the amino acid sequence) resulted in significant modulation of MAP presentation at the cell surface. Taken together, our results demonstrate the role of codon arrangement in the regulation of MAP presentation and support integration of both translational and post-translational events in predictive algorithms to ameliorate modeling of the immunopeptidome.

MHC-I associated peptides (MAPs) are small fragments of intracellular proteins presented at the surface of cells and used by the immune system to detect and eliminate cancerous or virus-infected cells. While it is theoretically possible to predict which portions of the intracellular proteins will be naturally processed by the cells to ultimately reach the surface, current methodologies have prohibitively high false discovery rates. Here we introduce an artificial neural network called Codon Arrangement MAP Predictor (CAMAP) which integrates information from mRNA-to-protein translation to other factors regulating MAP biogenesis (e.g. MAP ligand score and transcript expression levels) to improve MAP prediction accuracy. While most MAP predictive approaches focus on MAP sequences per se, CAMAP’s novelty is to analyze the MAP-flanking mRNA sequences, thereby providing completely independent information for MAP prediction. We show on several datasets that the integration of CAMAP scores with other known factors involved in MAP presentation (i.e. MAP ligand score and mRNA expression) significantly improves MAP prediction accuracy, and further validate CAMAP learned features using an in-vitro assay. These findings may have major implications for the design of vaccines against cancers and viruses, and in times of pandemics could accelerate the identification of relevant MAPs of viral origins.

High-throughput interrogation of programmed ribosomal frameshifting in human cells

Programmed ribosomal frameshifting (PRF) is the controlled slippage of the translating ribosome to an alternative frame. This process is widely employed by human viruses such as HIV and SARS coronavirus and is critical for their replication. Here, we developed a high-throughput approach to assess the frameshifting potential of a sequence. We designed and tested >12,000 sequences based on 15 viral and human PRF events, allowing us to systematically dissect the rules governing ribosomal frameshifting and discover novel regulatory inputs based on amino acid properties and tRNA availability. We assessed the natural variation in HIV gag-pol frameshifting rates by testing >500 clinical isolates and identified subtype-specific differences and associations between viral load in patients and the optimality of PRF rates. We devised computational models that accurately predict frameshifting potential and frameshifting rates, including subtle differences between HIV isolates. This approach can contribute to the development of antiviral agents targeting PRF.

Translational efficiency across healthy and tumor tissues is proliferation-related

Different tissues express genes with particular codon usage and anticodon tRNA repertoires. However, the codon-anticodon co-adaptation in humans is not completely understood, nor is its effect on tissue-specific protein levels. Here, we first validated the accuracy of small RNA-seq for tRNA quantification across five human cell lines. We then analyzed the tRNA abundance of more than 8,000 tumor samples from TCGA, together with their paired mRNA-seq and proteomics data, to determine the Supply-to-Demand Adaptation. We thereby elucidate that the dynamic adaptation of the tRNA pool is largely related to the proliferative state across tissues. The distribution of such tRNA pools over the whole cellular translatome affects the subsequent translational efficiency, which functionally determines a condition-specific expression program both in healthy and tumor states. Furthermore, the aberrant translational efficiency of some codons in cancer, exemplified by ProCCA and GlyGGT, is associated with poor patient survival. The regulation of these tRNA profiles is partly explained by the tRNA gene copy numbers and their promoter DNA methylation.

Challenges of the Human Proteome Project: 10-Year Experience of the Russian Consortium

This manuscript collects all the efforts of the Russian Consortium, bottlenecks revealed in the course of the C-HPP realization, and ways of their overcoming. One of the main bottlenecks in the C-HPP is the insufficient sensitivity of proteomic technologies, hampering the detection of low- and ultralow-copy number proteins forming the "dark part" of the human proteome. In the frame of MP-Challenge, to increase proteome coverage we suggest an experimental workflow based on a combination of shotgun technology and selected reaction monitoring with two-dimensional alkaline fractionation. Further, to detect proteins that cannot be identified by such technologies, nanotechnologies such as combined atomic force microscopy with molecular fishing and/or nanowire detection may be useful. These technologies provide a powerful tool for single molecule analysis, by analogy with nanopore sequencing during genome analysis. To systematically analyze the functional features of some proteins (CP50 Challenge), we created a mathematical model that predicts the number of proteins differing in amino acid sequence: proteoforms. According to our data, we should expect about 100 000 different proteoforms in the liver tissue and a little more in the HepG2 cell line. The variety of proteins forming the whole human proteome significantly exceeds these results due to post-translational modifications (PTMs). As PTMs determine the functional specificity of the protein, we propose using a combination of gene-centric transcriptome-proteomic analysis with preliminary fractionation by two-dimensional electrophoresis to identify chemically modified proteoforms. Despite the complexity of the proposed solutions, such integrative approaches could be fruitful for MP50 and CP50 Challenges in the framework of the C-HPP.

The codon sequences predict protein lifetimes and other parameters of the protein life cycle in the mouse brain

The homeostasis of the proteome depends on the tight regulation of the mRNA and protein abundances, of the translation rates, and of the protein lifetimes. Results from several studies on prokaryotes or eukaryotic cell cultures have suggested that protein homeostasis is connected to, and perhaps regulated by, the protein and the codon sequences. However, this has been little investigated for mammals in vivo. Moreover, the link between the coding sequences and one critical parameter, the protein lifetime, has remained largely unexplored, both in vivo and in vitro. We tested this in the mouse brain, and found that the percentages of amino acids and codons in the sequences could predict all of the homeostasis parameters with a precision approaching experimental measurements. A key predictive element was the wobble nucleotide. G-/C-ending codons correlated with higher protein lifetimes, protein abundances, mRNA abundances and translation rates than A-/U-ending codons. Modifying the proportions of G-/C-ending codons could tune these parameters in cell cultures, in a proof-of-principle experiment. We suggest that the coding sequences are strongly linked to protein homeostasis in vivo, albeit it still remains to be determined whether this relation is causal in nature.

Exonic splice regulation imposes strong selection at synonymous sites

What proportion of coding sequence nucleotides have roles in splicing, and how strong is the selection that maintains them? Despite a large body of research into exonic splice regulatory signals, these questions have not been answered. This is because, to our knowledge, previous investigations have not explicitly disentangled the frequency of splice regulatory elements from the strength of the evolutionary constraint under which they evolve. Current data are consistent both with a scenario of weak and diffuse constraint, enveloping large swaths of sequence, as well as with well-defined pockets of strong purifying selection. In the former case, natural selection on exonic splice enhancers (ESEs) might primarily act as a slight modifier of codon usage bias. In the latter, mutations that disrupt ESEs are likely to have large fitness and, potentially, clinical effects. To distinguish between these scenarios, we used several different methods to determine the distribution of selection coefficients for new mutations within ESEs. The analyses converged to suggest that ∼15%-20% of fourfold degenerate sites are part of functional ESEs. Most of these sites are under strong evolutionary constraint. Therefore, exonic splice regulation does not simply impose a weak bias that gently nudges coding sequence evolution in a particular direction. Rather, the selection to preserve these motifs is a strong force that severely constrains the evolution of a substantial proportion of coding nucleotides. Thus synonymous mutations that disrupt ESEs should be considered as a potentially common cause of single-locus genetic disorders.

Small ncRNA-Seq Results of Human Tissues: Variations Depending on Sample Integrity

BACKGROUND

Although mature miRNAs are relatively stable in vivo, RNA degradation can have a substantial influence on small noncoding RNA (sncRNA) profiles.

METHODS

Using different tissue storage conditions and RNA isolation procedures, we analyzed the integrity and quality of RNA isolates from human lung and heart tissues. We sequenced a total of 64 RNA samples and quantified the effect of RNA degradation, DNA contamination, and other confounding factors on the sncRNA-seq data. Besides microRNAs, other noncoding RNA species (piRNAs, tRNAs, snoRNAs, rRNAs) were investigated.

RESULTS

Consistent with previous results, we found that the tissue specificity of microRNAs is generally well preserved. The distribution of microRNA isoforms was similar to the distribution of canonical forms. New miRNAs were more frequently discovered in degraded samples. sncRNA Reads generated from degraded samples mapped frequently to piRNAs, tRNAs, snoRNAs, or scaRNAs. Sequencing reads that were depleted of sncRNAs showed an increased mapping frequency to bacterial species.

CONCLUSIONS

Our data emphasize the importance of sample integrity, especially for next-generation sequencing (NGS)-based high-throughput sncRNA profiles. For the prediction of novel miRNAs in particular, only samples with the highest RNA integrity should be used in order to avoid identification of false "miRNAs."

Why Are the Correlations between mRNA and Protein Levels so Low among the 275 Predicted Protein-Coding Genes on Human Chromosome 18?

In this work targeted (selected reaction monitoring, SRM, PASSEL: PASS00697) and panoramic (shotgun LC-MS/MS, PRIDE: PXD00244) mass-spectrometric methods as well as transcriptomic analysis of the same samples using RNA-Seq and PCR methods (SRA experiment IDs: SRX341198, SRX267708, SRX395473, SRX390071) were applied for quantification of chromosome 18 encoded transcripts and proteins in human liver and HepG2 cells. The obtained data was used for the estimation of quantitative mRNA-protein ratios for the 275 genes of the selected chromosome in the selected tissues. The impact of methodological limitations of existing analytical proteomic methods on gene-specific mRNA-protein ratios and possible ways of overcoming these limitations for detection of missing proteins are also discussed.

Ribosome reinitiation can explain length-dependent translation of messenger RNA

Models of mRNA translation usually presume that transcripts are linear; upon reaching the end of a transcript each terminating ribosome returns to the cytoplasmic pool before initiating anew on a different transcript. A consequence of linear models is that faster translation of a given mRNA is unlikely to generate more of the encoded protein, particularly at low ribosome availability. Recent evidence indicates that eukaryotic mRNAs are circularized, potentially allowing terminating ribosomes to preferentially reinitiate on the same transcript. Here we model the effect of ribosome reinitiation on translation and show that, at high levels of reinitiation, protein synthesis rates are dominated by the time required to translate a given transcript. Our model provides a simple mechanistic explanation for many previously enigmatic features of eukaryotic translation, including the negative correlation of both ribosome densities and protein abundance on transcript length, the importance of codon usage in determining protein synthesis rates, and the negative correlation between transcript length and both codon adaptation and 5' mRNA folding energies. In contrast to linear models where translation is largely limited by initiation rates, our model reveals that all three stages of translation-initiation, elongation, and termination/reinitiation-determine protein synthesis rates even at low ribosome availability.

Gene expression, nucleotide composition and codon usage bias of genes associated with human Y chromosome

Analysis of codon usage pattern is important to understand the genetic and evolutionary characteristics of genomes. We have used bioinformatic approaches to analyze the codon usage bias (CUB) of the genes located in human Y chromosome. Codon bias index (CBI) indicated that the overall extent of codon usage bias was low. The relative synonymous codon usage (RSCU) analysis suggested that approximately half of the codons out of 59 synonymous codons were most frequently used, and possessed a T or G at the third codon position. The codon usage pattern was different in different genes as revealed from correspondence analysis (COA). A significant correlation between effective number of codons (ENC) and various GC contents suggests that both mutation pressure and natural selection affect the codon usage pattern of genes located in human Y chromosome. In addition, Y-linked genes have significant difference in GC contents at the second and third codon positions, expression level, and codon usage pattern of some codons like the SPANX genes in X chromosome.

Bicodon bias can determine the role of synonymous SNPs in human diseases

Background

For a long time synonymous single nucleotide polymorphisms were considered as silent mutations. However, nowadays it is well known that they can affect protein conformation and function, leading to altered disease susceptibilities, differential prognosis and/or drug responses, among other clinically relevant genetic traits. This occurs through different mechanisms: by disrupting the splicing signals of precursor mRNAs, affecting regulatory binding-sites of transcription factors and miRNAs, or by modifying the secondary structure of mRNAs.

Results

In this paper we considered 22 human genetic diseases or traits, linked to 35 synonymous single nucleotide polymorphisms in 27 different genes. We performed a local sequence context analysis in terms of the ribosomal pause propensity affected by synonymous single nucleotide polymorphisms. We found that synonymous mutations related to the above mentioned mechanisms presented small pause propensity changes, whereas synonymous mutations that were not related to those mechanisms presented large pause propensity changes. On the other hand, we did not observe large variations in the codon usage of codons associated with these mutations. Furthermore, we showed that the changes in the pause propensity associated with benign sSNPs are significantly lower than the pause propensity changes related to sSNPs associated to diseases.

Conclusions

These results suggest that the genetic diseases or traits related to synonymous mutations with large pause propensity changes, could be the consequence of another mechanism underlying non-silent synonymous mutations. Namely, alternative protein configuration related, in turn, to alterations in the ribosome-mediated translational attenuation program encoded by pairs of consecutive codons, not codons. These findings shed light on the latter mechanism based on the perturbation of the co-translational folding process.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-3609-6) contains supplementary material, which is available to authorized users.

Differential bicodon usage in lowly and highly abundant proteins

Degeneracy in the genetic code implies that different codons can encode the same amino acid. Usage preference of synonymous codons has been observed in all domains of life. There is much evidence suggesting that this bias has a major role on protein elongation rate, contributing to differential expression and to co-translational folding. In addition to codon usage bias, other preference variations have been observed such as codon pairs. In this paper, I report that codon pairs have significant different frequency usage for coding either lowly or highly abundant proteins. These usage preferences cannot be explained by the frequency usage of the single codons. The statistical analysis of coding sequences of nine organisms reveals that in many cases bicodon preferences are shared between related organisms. Furthermore, it is observed that misfolding in the drug-transport protein, encoded by MDR1 gene, is better explained by a big change in the pause propensity due to the synonymous bicodon variant, rather than by a relatively small change in codon usage. These findings suggest that codon pair usage can be a more powerful framework to understand translation elongation rate, protein folding efficiency, and to improve protocols to optimize heterologous gene expression.

Essential Genes Embody Increased Mutational Robustness to Compensate for the Lack of Backup Genetic Redundancy

Genetic robustness is a hallmark of cells, occurring through many mechanisms and at many levels. Essential genes lack the common robustness mechanism of genetic redundancy (i.e., existing alongside other genes with the same function), and thus appear at first glance to leave cells highly vulnerable to genetic or environmental perturbations. Here we explore a hypothesis that cells might protect against essential gene loss through mechanisms that occur at various cellular levels aside from the level of the gene. Using Escherichia coli and Saccharomyces cerevisiae as models, we find that essential genes are enriched over non-essential genes for properties we call "coding efficiency" and "coding robustness", denoting respectively a gene's efficiency of translation and robustness to non-synonymous mutations. The coding efficiency levels of essential genes are highly positively correlated with their evolutionary conservation levels, suggesting that this feature plays a key role in protecting conserved, evolutionarily important genes. We then extend our hypothesis into the realm of metabolic networks, showing that essential metabolic reactions are encoded by more "robust" genes than non-essential reactions, and that essential metabolites are produced by more reactions than non-essential metabolites. Taken together, these results testify that robustness at the gene-loss level and at the mutation level (and more generally, at two cellular levels that are usually treated separately) are not decoupled, but rather, that cellular vulnerability exposed due to complete gene loss is compensated by increased mutational robustness. Why some genes are backed up primarily against loss and others against mutations still remains an open question.

Widespread signatures of local mRNA folding structure selection in four Dengue virus serotypes

BACKGROUND

It is known that mRNA folding can affect and regulate various gene expression steps both in living organisms and in viruses. Previous studies have recognized functional RNA structures in the genome of the Dengue virus. However, these studies usually focused either on the viral untranslated regions or on very specific and limited regions at the beginning of the coding sequences, in a limited number of strains, and without considering evolutionary selection.

RESULTS

Here we performed the first large scale comprehensive genomics analysis of selection for local mRNA folding strength in the Dengue virus coding sequences, based on a total of 1,670 genomes and 4 serotypes. Our analysis identified clusters of positions along the coding regions that may undergo a conserved evolutionary selection for strong or weak local folding maintained across different viral variants. Specifically, 53-66 clusters for strong folding and 49-73 clusters for weak folding (depending on serotype) aggregated of positions with a significant conservation of folding energy signals (related to partially overlapping local genomic regions) were recognized. In addition, up to 7% of these positions were found to be conserved in more than 90% of the viral genomes. Although some of the identified positions undergo frequent synonymous / non-synonymous substitutions, the selection for folding strength therein is preserved, and thus cannot be trivially explained based on sequence conservation alone.

CONCLUSIONS

The fact that many of the positions with significant folding related signals are conserved among different Dengue variants suggests that a better understanding of the mRNA structures in the corresponding regions may promote the development of prospective anti- Dengue vaccination strategies. The comparative genomics approach described here can be employed in the future for detecting functional regions in other pathogens with very high mutations rates.

Association of ADORA1 rs2228079 and ADORA2A rs5751876 Polymorphisms with Gilles de la Tourette Syndrome in the Polish Population

BACKGROUND

Gilles de la Tourette syndrome (GTS) is a neurodevelopmental disorder characterized by motor and vocal tics. Hyperactivity of dopaminergic transmission is considered a prime abnormality in the pathophysiology of tics. There are reciprocal antagonistic interactions between adenosine and dopamine transmission. The aim of the study was to analyze the association of two polymorphisms, rs2228079 in ADORA1 and rs5751876 in ADORA2A, with the risk of GTS and co-morbid disorders.

MATERIAL AND METHODS

A total of 162 Polish GTS patients and 270 healthy persons were enrolled in the study. Two polymorphisms were selected on the basis of knowledge of SNPs frequencies in ADORA1 and ADORA2A. Chi-square test was used for allelic and genotypic association studies. Association of genotypes with age of tic onset was analyzed with Mann-Whitney test. Multivariate logistic regression was used to find independent predictors of GTS risk.

RESULTS

We found that the risk of GTS was associated with rs2228079 and rs5751876 polymorphisms. The GG+GT genotypes of rs2228079 in ADORA1 were underrepresented in GTS patients (p = 0.011), whereas T allele of rs5751876 in ADORA2A was overrepresented (p = 0.017). The GG genotype of rs2228079 was associated with earlier age of tic onset (p = 0.046). We found also that the minor allele G of rs2228079 was more frequent in GTS patients with depression as compared to the patients without depression (p = 0.015). Also the genotype GG was significantly more frequent in patients with obsessive compulsive disorder/behavior (OCD/OCB, p = 0.021) and depression (p = 0.032), as compared to the patients without these co-morbidities. The minor allele T frequency of rs5751876 was lower in GTS patients with co-morbid attention deficit hyperactivity disorder (p = 0.022), and TT+TC genotypes were less frequent in the non-OCD anxiety disorder group (p = 0.045).

CONCLUSION

ADORA1 and ADORA2A variants are associated with the risk of GTS, co-morbid disorders, and may affect the age of tic onset.

Human protein interaction networks across tissues and diseases

Protein interaction networks are an important framework for studying protein function, cellular processes, and genotype-to-phenotype relationships. While our view of the human interaction network is constantly expanding, less is known about networks that form in biologically important contexts such as within distinct tissues or in disease conditions. Here we review efforts to characterize these networks and to harness them to gain insights into the molecular mechanisms underlying human disease.

Systems approach for the selection of micro-RNAs as therapeutic biomarkers of anti-EGFR monoclonal antibody treatment in colorectal cancer

miRNA plays an important role in tumourgenesis by regulating expression of oncogenes and tumour suppressors. Thus affects cell proliferation and differentiation, apoptosis, invasion and angiogenesis. miRNAs are potential biomarkers for diagnosis, prognosis and therapies of different forms of cancer. However, relationship between response of cancer patients towards targeted therapy and the resulting modifications of the miRNA transcriptome in the context of pathway regulation is poorly understood. With ever-increasing pathways and miRNA-mRNA interaction databases, freely available mRNA and miRNA expression data in multiple cancer therapy have produced an unprecedented opportunity to decipher the role of miRNAs in early prediction of therapeutic efficacy in diseases. Efficient translation of -omics data and accumulated knowledge to clinical decision-making are of paramount scientific and public health interest. Well-structured translational algorithms are needed to bridge the gap from databases to decisions. Herein, we present a novel SMARTmiR algorithm to prospectively predict the role of miRNA as therapeutic biomarker for an anti-EGFR monoclonal antibody i.e. cetuximab treatment in colorectal cancer.

Multiple roles of the coding sequence 5' end in gene expression regulation

The codon composition of the coding sequence's (ORF) 5′ end first few dozen codons is known to be distinct to that of the rest of the ORF. Various explanations for the unusual codon distribution in this region have been proposed in recent years, and include, among others, novel regulatory mechanisms of translation initiation and elongation. However, due to the fact that many overlapping regulatory signals are suggested to be associated with this relatively short region, its research is challenging. Here, we review the currently known signals that appear in this region, the theories related to the way they regulate translation and affect the organismal fitness, and the debates they provoke.

Prediction of disease-related genes based on weighted tissue-specific networks by using DNA methylation

BACKGROUND

Predicting disease-related genes is one of the most important tasks in bioinformatics and systems biology. With the advances in high-throughput techniques, a large number of protein-protein interactions are available, which make it possible to identify disease-related genes at the network level. However, network-based identification of disease-related genes is still a challenge as the considerable false-positives are still existed in the current available protein interaction networks (PIN).

RESULTS

Considering the fact that the majority of genetic disorders tend to manifest only in a single or a few tissues, we constructed tissue-specific networks (TSN) by integrating PIN and tissue-specific data. We further weighed the constructed tissue-specific network (WTSN) by using DNA methylation as it plays an irreplaceable role in the development of complex diseases. A PageRank-based method was developed to identify disease-related genes from the constructed networks. To validate the effectiveness of the proposed method, we constructed PIN, weighted PIN (WPIN), TSN, WTSN for colon cancer and leukemia, respectively. The experimental results on colon cancer and leukemia show that the combination of tissue-specific data and DNA methylation can help to identify disease-related genes more accurately. Moreover, the PageRank-based method was effective to predict disease-related genes on the case studies of colon cancer and leukemia.

CONCLUSIONS

Tissue-specific data and DNA methylation are two important factors to the study of human diseases. The same method implemented on the WTSN can achieve better results compared to those being implemented on original PIN, WPIN, or TSN. The PageRank-based method outperforms degree centrality-based method for identifying disease-related genes from WTSN.

Hsp90 promotes kinase evolution

Heat-shock protein 90 (Hsp90) promotes the maturation and stability of its client proteins, including many kinases. In doing so, Hsp90 may allow its clients to accumulate mutations as previously proposed by the capacitor hypothesis. If true, Hsp90 clients should show increased evolutionary rate compared with nonclients; however, other factors, such as gene expression and protein connectivity, may confound or obscure the chaperone’s putative contribution. Here, we compared the evolutionary rates of many Hsp90 clients and nonclients in the human protein kinase superfamily. We show that Hsp90 client status promotes evolutionary rate independently of, but in a small magnitude similar to that of gene expression and protein connectivity. Hsp90’s effect on kinase evolutionary rate was detected across mammals, specifically relaxing purifying selection. Hsp90 clients also showed increased nucleotide diversity and harbored more damaging variation than nonclient kinases across humans. These results are consistent with the central argument of the capacitor hypothesis that interaction with the chaperone allows its clients to harbor genetic variation. Hsp90 client status is thought to be highly dynamic with as few as one amino acid change rendering a protein dependent on the chaperone. Contrary to this expectation, we found that across protein kinase phylogeny Hsp90 client status tends to be gained, maintained, and shared among closely related kinases. We also infer that the ancestral protein kinase was not an Hsp90 client. Taken together, our results suggest that Hsp90 played an important role in shaping the kinase superfamily.

Sensory rewiring in an echolocator: genome-wide modification of retinogenic and auditory genes in the bat Myotis davidii

Bats comprise 20% of all mammalian species and display a number of characteristics, including true flight, echolocation, and a heightened ability to resist viral load that uniquely position this group for comparative genomic studies. Here we searched for evidence of genomic variation consistent with sensory rewiring through bat evolution. We focused on two species with divergent sensory preferences. Myotis davidii is a bat species that echolocates and possesses dim- but not daylight-adapted vision whereas the black flying fox (Pteropus alecto) has highly developed day vision but does not echolocate. Using the naked mole rat as a reference, we found five functional genes (CYP1A2, RBP3, GUCY2F, CRYBB1, and GRK7) encoding visual proteins that have degenerated into pseudogenes in M. davidii but not P. alecto. In a second approach genome-wide codon usage bias (CUB) was compared between the two bat species. This CUB ranking systematically enriched for vision-related (CLN8, RD3, IKZF1, LAMC3, CRX, SOX8, VAX2, HPS1, RHO, PRPH2, and SOX9) and hearing-related (TPRN, TMIE, SLC52A3, OTOF, WFS1, SOD1, TBX18, MAP1A, OTOS, GPX1, and USH1G) machinery in M. davidii but not P. alecto. All vision and hearing genes selectively enriched in M. davidii for which orthologs could be identified also were more biased in the echolocating M. lucifugus than the nonecholocating P. vampyrus. We suggest that the existence of codon bias in vision- and hearing-related genes in a species that has evolved echolocation implies CUB is part of evolution’s toolkit to rewire sensory systems. We propose that the two genetic changes (pseudogene formation and CUB) collectively paint a picture of that incorporates a combination of destruction and gain-of-function. Together, they help explain how natural selection has reduced physiological costs associated with the development of a smaller eye poorly adapted to day vision but that also contribute to enhanced dim light vision and the hearing adaptations consonant with echolocation.

Multiple correlations of mRNA expression and protein abundance in human cytokine profile

With the development of genomic study, researchers found that it is insufficient to predict protein expression from quantitative mRNA data in large scale, which is contrary to the traditional opinion that mRNA expression correlates with protein abundance at the single gene level. To try to solve the apparent conflicting views, here we set up a series of research models and chose soluble cytokines as targets. First, human peripheral blood mononuclear cell (PBMC) from one health donor was treated with 16 continuously changing conditions, the protein and mRNA profile were analyzed by multiplex Luminex and genomic microarray, respectively. Among the tested genes, around half mRNA correlated well with their corresponding proteins (ρ > 0.8), however if we put all the genes together, the correlation coefficient for the 16 conditions varied from 0.29 to 0.71. Second, PBMC from 14 healthy donors were stimulated with the same condition and it was found that the correlation coefficient went down (ρ < 0.6). Third, 28 rheumatoid arthritis (RA) patients were tested for their response to the same external stimuli and it turned out different individual displayed different protein expression pattern as expect. Lastly, autoimmune disease cohorts (8 diseases including RA, 103 patients in total) were assayed on the whole view. It was observed that there was still some similarity in the protein profile among patients from the single disease type although completely different patterns were displayed across different disease categories. This study built a good bridge between single gene analysis and the whole genome study and may give a reasonable explanation for the two conflicting views in current biological science.

Impairment of translation in neurons as a putative causative factor for autism

BACKGROUND

A dramatic increase in the prevalence of autism and Autistic Spectrum Disorders (ASD) has been observed over the last two decades in USA, Europe and Asia. Given the accumulating data on the possible role of translation in the etiology of ASD, we analyzed potential effects of rare synonymous substitutions associated with ASD on mRNA stability, splicing enhancers and silencers, and codon usage.

PRESENTATION OF THE HYPOTHESIS

We hypothesize that subtle impairment of translation, resulting in dosage imbalance of neuron-specific proteins, contributes to the etiology of ASD synergistically with environmental neurotoxins.

TESTING THE HYPOTHESIS

A statistically significant shift from optimal to suboptimal codons caused by rare synonymous substitutions associated with ASD was detected whereas no effect on other analyzed characteristics of transcripts was identified. This result suggests that the impact of rare codons on the translation of genes involved in neuron development, even if slight in magnitude, could contribute to the pathogenesis of ASD in the presence of an aggressive chemical background. This hypothesis could be tested by further analysis of ASD-associated mutations, direct biochemical characterization of their effects, and assessment of in vivo effects on animal models.

IMPLICATIONS OF THE HYPOTHESIS

It seems likely that the synergistic action of environmental hazards with genetic variations that in themselves have limited or no deleterious effects but are potentiated by the environmental factors is a general principle that underlies the alarming increase in the ASD prevalence.

REVIEWERS

This article was reviewed by Andrey Rzhetsky, Neil R. Smalheiser, and Shamil R. Sunyaev.

Selection on synonymous codons in mammalian rhodopsins: a possible role in optimizing translational processes

Background

Synonymous codon usage can affect many cellular processes, particularly those associated with translation such as polypeptide elongation and folding, mRNA degradation/stability, and splicing. Highly expressed genes are thought to experience stronger selection pressures on synonymous codons. This should result in codon usage bias even in species with relatively low effective population sizes, like mammals, where synonymous site selection is thought to be weak. Here we use phylogenetic codon-based likelihood models to explore patterns of codon usage bias in a dataset of 18 mammalian rhodopsin sequences, the protein mediating the first step in vision in the eye, and one of the most highly expressed genes in vertebrates. We use these patterns to infer selection pressures on key translational mechanisms including polypeptide elongation, protein folding, mRNA stability, and splicing.

Results

Overall, patterns of selection in mammalian rhodopsin appear to be correlated with post-transcriptional and translational processes. We found significant evidence for selection at synonymous sites using phylogenetic mutation-selection likelihood models, with C-ending codons found to have the highest relative fitness, and to be significantly more abundant at conserved sites. In general, these codons corresponded with the most abundant tRNAs in mammals. We found significant differences in codon usage bias between rhodopsin loops versus helices, though there was no significant difference in mean synonymous substitution rate between these motifs. We also found a significantly higher proportion of GC-ending codons at paired sites in rhodopsin mRNA secondary structure, and significantly lower synonymous mutation rates in putative exonic splicing enhancer (ESE) regions than in non-ESE regions.

Conclusions

By focusing on a single highly expressed gene we both distinguish synonymous codon selection from mutational effects and analytically explore underlying functional mechanisms. Our results suggest that codon bias in mammalian rhodopsin arises from selection to optimally balance high overall translational speed, accuracy, and proper protein folding, especially in structurally complicated regions. Selection at synonymous sites may also be contributing to mRNA stability and splicing efficiency at exonic-splicing-enhancer (ESE) regions. Our results highlight the importance of investigating highly expressed genes in a broader phylogenetic context in order to better understand the evolution of synonymous substitutions.

Auxiliary tRNAs: large-scale analysis of tRNA genes reveals patterns of tRNA repertoire dynamics

Decoding of all codons can be achieved by a subset of tRNAs. In bacteria, certain tRNA species are mandatory, while others are auxiliary and are variably used. It is currently unknown how this variability has evolved and whether it provides an adaptive advantage. Here we shed light on the subset of auxiliary tRNAs, using genomic data from 319 bacteria. By reconstructing the evolution of tRNAs we show that the auxiliary tRNAs are highly dynamic, being frequently gained and lost along the phylogenetic tree, with a clear dominance of loss events for most auxiliary tRNA species. We reveal distinct co-gain and co-loss patterns for subsets of the auxiliary tRNAs, suggesting that they are subjected to the same selection forces. Controlling for phylogenetic dependencies, we find that the usage of these tRNA species is positively correlated with GC content and may derive directly from nucleotide bias or from preference of Watson-Crick codon-anticodon interactions. Our results highlight the highly dynamic nature of these tRNAs and their complicated balance with codon usage.

A genome-wide systematic analysis reveals different and predictive proliferation expression signatures of cancerous vs. non-cancerous cells

Understanding cell proliferation mechanisms has been a long-lasting goal of the scientific community and specifically of cancer researchers. Previous genome-scale studies of cancer proliferation determinants have mainly relied on knockdown screens aimed to gauge their effects on cancer growth. This powerful approach has several limitations such as off-target effects, partial knockdown, and masking effects due to functional backups. Here we employ a complementary approach and assign each gene a cancer Proliferation Index (cPI) that quantifies the association between its expression levels and growth rate measurements across 60 cancer cell lines. Reassuringly, genes found essential in cancer gene knockdown screens exhibit significant positive cPI values, while tumor suppressors exhibit significant negative cPI values. Cell cycle, DNA replication, splicing and protein production related processes are positively associated with cancer proliferation, while cellular migration is negatively associated with it – in accordance with the well known “go or grow” dichotomy. A parallel analysis of genes' non-cancerous proliferation indices (nPI) across 224 lymphoblastoid cell lines reveals surprisingly marked differences between cancerous and non-cancerous proliferation. These differences highlight genes in the translation and spliceosome machineries as selective cancer proliferation-associated proteins. A cross species comparison reveals that cancer proliferation resembles that of microorganisms while non-cancerous proliferation does not. Furthermore, combining cancerous and non-cancerous proliferation signatures leads to enhanced prediction of patient outcome and gene essentiality in cancer. Overall, these results point to an inherent difference between cancerous and non-cancerous proliferation determinants, whose understanding may contribute to the future development of novel cancer-specific anti-proliferative drugs.

One of the hallmarks of cancer is uncontrolled cellular proliferation, and therefore many anti-cancer drugs aim to disrupt cancer proliferation. However, some of these drugs (e.g., chemotherapeutic agents) affect normal proliferating cells as well, resulting in undesirable side effects. Understanding the differences between cancerous and non-cancerous proliferation can help us design new selective drugs that kill cancer cells without harming normal cells. In this work, we use genome scale gene expression and growth rate measurements across 60 cancer cell lines (NCI-60) to uncover genetic determinants of cancerous proliferation. In parallel, gene expression and growth rate measurements of non-cancerous cell lines allow us to uncover determinants of non-cancerous proliferation. Notably, we find marked differences between the cancerous and non-cancerous proliferation. The two proliferation signatures can be used jointly to enhance the prediction of patient outcome in cancer. Notably, we find that certain genes in the translation and spliceosome machineries are involved in cancerous proliferation but not in non-cancerous proliferation, highlighting them as putative selective anti-cancer drug targets.

Bridging the gap between transcriptome and proteome measurements identifies post-translationally regulated genes

MOTIVATION

Despite much dynamical cellular behaviour being achieved by accurate regulation of protein concentrations, messenger RNA abundances, measured by microarray technology, and more recently by deep sequencing techniques, are widely used as proxies for protein measurements. Although for some species and under some conditions, there is good correlation between transcriptome and proteome level measurements, such correlation is by no means universal due to post-transcriptional and post-translational regulation, both of which are highly prevalent in cells. Here, we seek to develop a data-driven machine learning approach to bridging the gap between these two levels of high-throughput omic measurements on Saccharomyces cerevisiae and deploy the model in a novel way to uncover mRNA-protein pairs that are candidates for post-translational regulation.

RESULTS

The application of feature selection by sparsity inducing regression (l₁ norm regularization) leads to a stable set of features: i.e. mRNA, ribosomal occupancy, ribosome density, tRNA adaptation index and codon bias while achieving a feature reduction from 37 to 5. A linear predictor used with these features is capable of predicting protein concentrations fairly accurately (R² = 0.86). Proteins whose concentration cannot be predicted accurately, taken as outliers with respect to the predictor, are shown to have annotation evidence of post-translational modification, significantly more than random subsets of similar size P < 0.02. In a data mining sense, this work also shows a wider point that outliers with respect to a learning method can carry meaningful information about a problem domain.

Selection on codon bias in yeast: a transcriptional hypothesis

Codons that code for the same amino acid are often used with unequal frequencies. This phenomenon is termed codon bias. Here, we report a computational analysis of codon bias in yeast using experimental and theoretical genome-wide data. We show that the most used codons in highly expressed genes can be predicted by mRNA structural data and that the codon choice at each synonymous site within an mRNA is not random with respect to the local secondary structure. Because we also found that the folding stability of intron sequences is strongly correlated with codon bias and mRNA level, our results suggest that codon bias is linked to mRNA folding structure through a mechanism that, at least partially, operates before pre-mRNA splicing. Consistent with this, we report evidence supporting the adaptation of the tRNA pool to the codon profile of the most expressed genes rather than vice versa. We show that the correlation of codon usage with the gene expression level also includes the stop codons that are normally not decoded by aminoacyl-tRNAs. The results reported here are consistent with a role for transcriptional forces in driving codon usage bias via a mechanism that improves gene expression by optimizing mRNA folding structures.

Biological basis of miRNA action when their targets are located in human protein coding region

Recent analyses have revealed many functional microRNA (miRNA) targets in mammalian protein coding regions. But, the mechanisms that ensure miRNA function when their target sites are located in protein coding regions of mammalian mRNA transcripts are largely unknown. In this paper, we investigate some potential biological factors, such as target site accessibility and local translation efficiency. We computationally analyze these two factors using experimentally identified miRNA targets in human protein coding region. We find site accessibility is significantly increased in miRNA target region to facilitate miRNA binding. At the mean time, local translation efficiency is also selectively decreased near miRNA target region. GC-poor codons are preferred in the flank region of miRNA target sites to ease the access of miRNA targets. Within-genome analysis shows substantial variations of site accessibility and local translation efficiency among different miRNA targets in the genome. Further analyses suggest target gene’s GC content and conservation level could explain some of the differences in site accessibility. On the other hand, target gene’s functional importance and conservation level can affect local translation efficiency near miRNA target region. We hence propose both site accessibility and local translation efficiency are important in miRNA action when miRNA target sites are located in mammalian protein coding regions.

Translational signatures and mRNA levels are highly correlated in human stably expressed genes

Background

Gene expression is one of the most relevant biological processes of living cells. Due to the relative small population sizes, it is predicted that human gene sequences are not strongly influenced by selection towards expression efficiency. One of the major problems in estimating to what extent gene characteristics can be selected to maximize expression efficiency is the wide variation that exists in RNA and protein levels among physiological states and different tissues. Analyses of datasets of stably expressed genes (i.e. with consistent expression between physiological states and tissues) would provide more accurate and reliable measurements of associations between variations of a specific gene characteristic and expression, and how distinct gene features work to optimize gene expression.

Results

Using a dataset of human genes with consistent expression between physiological states we selected gene sequence signatures related to translation that can predict about 42% of mRNA variation. The prediction can be increased to 51% when selecting genes that are stably expressed in more than 1 tissue. These genes are enriched for translation and ribosome biosynthesis processes and have higher translation efficiency scores, smaller coding sequences and 3^′ UTR sizes and lower folding energies when compared to other datasets. Additionally, the amino acid frequencies weighted by expression showed higher correlations with isoacceptor tRNA gene copy number, and smaller absolute correlation values with biosynthetic costs.

Conclusion

Our results indicate that human gene sequence characteristics related to transcription and translation processes can co-evolve in an integrated manner in order to optimize gene expression.

In silico estimation of translation efficiency in human cell lines: potential evidence for widespread translational control

Recently large scale transcriptome and proteome datasets for human cells have become available. A striking finding from these studies is that the level of an mRNA typically predicts no more than 40% of the abundance of protein. This correlation represents the overall figure for all genes. We present here a bioinformatic analysis of translation efficiency – the rate at which mRNA is translated into protein. We have analysed those human datasets that include genome wide mRNA and protein levels determined in the same study. The analysis comprises five distinct human cell lines that together provide comparable data for 8,170 genes. For each gene we have used levels of mRNA and protein combined with protein stability data from the HeLa cell line to estimate translation efficiency. This was possible for 3,990 genes in one or more cell lines and 1,807 genes in all five cell lines. Interestingly, our analysis and modelling shows that for many genes this estimated translation efficiency has considerable consistency between cell lines. Some deviations from this consistency likely result from the regulation of protein degradation. Others are likely due to known translational control mechanisms. These findings suggest it will be possible to build improved models for the interpretation of mRNA expression data. The results we present here provide a view of translation efficiency for many genes. We provide an online resource allowing the exploration of translation efficiency in genes of interest within different cell lines (http://bioanalysis.otago.ac.nz/TranslationEfficiency).

Enhancing the prioritization of disease-causing genes through tissue specific protein interaction networks

The prioritization of candidate disease-causing genes is a fundamental challenge in the post-genomic era. Current state of the art methods exploit a protein-protein interaction (PPI) network for this task. They are based on the observation that genes causing phenotypically-similar diseases tend to lie close to one another in a PPI network. However, to date, these methods have used a static picture of human PPIs, while diseases impact specific tissues in which the PPI networks may be dramatically different. Here, for the first time, we perform a large-scale assessment of the contribution of tissue-specific information to gene prioritization. By integrating tissue-specific gene expression data with PPI information, we construct tissue-specific PPI networks for 60 tissues and investigate their prioritization power. We find that tissue-specific PPI networks considerably improve the prioritization results compared to those obtained using a generic PPI network. Furthermore, they allow predicting novel disease-tissue associations, pointing to sub-clinical tissue effects that may escape early detection.

Genetic characterization and codon usage bias of full-length Hepatitis E virus sequences shed new lights on genotypic distribution, host restriction and genome evolution

Hepatitis E virus (HEV) is present in different species and ecological niches. It has been divided into 4 major mammalian genotypes. In this study, 3 new full-length genomes of swine HEV were sequenced and the results did not reveal any particular host determinant in comparison with human isolates belonging to the same genotype. Nucleotide composition and codon usage bias were determined to characterize HEV host restriction and genome evolution. Peculiar nucleotide bias was observed for A and C nucleotides in all HEV genotypes. Apart from the ORF1 hypervariable region and the ORF2/3 overlapping region, no nucleotide bias was observed between the 3 codon positions. CpG dinucleotides were also shown to be under-represented in HEV as in most RNA viruses. The effective number of codon used in HEV genome was high, indicating a lack of codon bias. Correspondence analysis of the relative synonymous codon usage was performed and demonstrated that evolution of HEV is not driven by geographical or host factors, but is representative of HEV phylogeny. These results confirm that HEV genome evolution is mainly based on mutational pressure. Natural selection, for instance involving fine-tuning translation kinetics and escape from the host immune system, may also play a role in shaping the HEV genome, particularly in the ORF1 hypervariable region and the ORF2/3 overlapping region. These regions might be involved in host restriction. Finally this study revealed the need to re-evaluate the possible subtyping classification.

The relation of codon bias to tissue-specific gene expression in Arabidopsis thaliana

The codon composition of coding sequences plays an important role in the regulation of gene expression. Herein, we report systematic differences in the usage of synonymous codons among Arabidopsis thaliana genes that are expressed specifically in distinct tissues. Although we observed that both regionally and transcriptionally associated mutational biases were associated significantly with codon bias, they could not explain the observed differences fully. Similarly, given that transcript abundances did not account for the differences in codon usage, it is unlikely that selection for translational efficiency can account exclusively for the observed codon bias. Thus, we considered the possible evolution of codon bias as an adaptive response to the different abundances of tRNAs in different tissues. Our analysis demonstrated that in some cases, codon usage in genes that were expressed in a broad range of tissues was influenced primarily by the tissue in which the gene was expressed maximally. On the basis of this finding we propose that genes that are expressed in certain tissues might show a tissue-specific compositional signature in relation to codon usage. These findings might have implications for the design of transgenes in relation to optimizing their expression.

Evolution and functional impact of rare coding variation from deep sequencing of human exomes

As a first step toward understanding how rare variants contribute to risk for complex diseases, we sequenced 15,585 human protein-coding genes to an average median depth of 111× in 2440 individuals of European (n = 1351) and African (n = 1088) ancestry. We identified over 500,000 single-nucleotide variants (SNVs), the majority of which were rare (86% with a minor allele frequency less than 0.5%), previously unknown (82%), and population-specific (82%). On average, 2.3% of the 13,595 SNVs each person carried were predicted to affect protein function of ~313 genes per genome, and ~95.7% of SNVs predicted to be functionally important were rare. This excess of rare functional variants is due to the combined effects of explosive, recent accelerated population growth and weak purifying selection. Furthermore, we show that large sample sizes will be required to associate rare variants with complex traits.

Hippocampus neuronal metabolic gene expression outperforms whole tissue data in accurately predicting Alzheimer's disease progression

Numerous metabolic alterations are associated with the impairment of brain cells in Alzheimer's disease (AD). Here we use gene expression microarrays of both whole hippocampus tissue and hippocampal neurons of AD patients to investigate the ability of metabolic gene expression to predict AD progression and its cognitive decline. We find that the prediction accuracy of different AD stages is markedly higher when using neuronal expression data (0.9) than when using whole tissue expression (0.76). Furthermore, the metabolic genes' expression is shown to be as effective in predicting AD severity as the entire gene list. Remarkably, a regression model from hippocampal metabolic gene expression leads to a marked correlation of 0.57 with the Mini-Mental State Examination cognitive score. Notably, the expression of top predictive neuronal genes in AD is significantly higher than that of other metabolic genes in the brains of healthy subjects. All together, the analyses point to a subset of metabolic genes that is strongly associated with normal brain functioning and whose disruption plays a major role in AD.

Regulation of human formyl peptide receptor 1 synthesis: role of single nucleotide polymorphisms, transcription factors, and inflammatory mediators

The gene encoding the human formyl peptide receptor 1 (FPR1) is heterogeneous, containing numerous single nucleotide polymorphisms (SNPs). Here, we examine the effect of these SNPs on gene transcription and protein translation. We also identify gene promoter sequences and putative FPR1 transcription factors. To test the effect of codon bias and codon pair bias on FPR1 expression, four FPR1 genetic variants were expressed in human myeloid U937 cells fused to a reporter gene encoding firefly luciferase. No significant differences in luciferase activity were detected, suggesting that the translational regulation and protein stability of FPR1 are modulated by factors other than the SNP codon bias and the variant amino acid properties. Deletion and mutagenesis analysis of the FPR1 promoter showed that a CCAAT box is not required for gene transcription. A −88/41 promoter construct resulted in the strongest transcriptional activity, whereas a −72/41 construct showed large reduction in activity. The region between −88 and −72 contains a consensus binding site for the transcription factor PU.1. Mutagenesis of this site caused significant reduction in reporter gene expression. The PU.1 binding was confirmed in vivo by chromatin immunoprecipitation, and the binding to nucleotides −84 to −76 (TTCCTATTT) was confirmed in vitro by an electrophoretic mobility shift assay. Thus, similar to many other myeloid genes, FPR1 promoter activity requires PU.1. Two single nucleotide polymorphisms at −56 and −54 did not significantly affect FPR1 gene expression, despite differences in binding of transcription factor IRF1 in vitro. Inflammatory mediators such as interferon-γ, tumor necrosis factor-α, and lipopolysaccharide did not increase FPR1 promoter activity in myeloid cells, whereas differentiation induced by DMSO and retinoic acid enhanced the activity. This implies that the expression of FPR1 in myeloid cells is developmentally regulated, and that the differentiated cells are equipped for immediate response to microbial infections.

dbNSFP: a lightweight database of human nonsynonymous SNPs and their functional predictions

With the advance of sequencing technologies, whole exome sequencing has increasingly been used to identify mutations that cause human diseases, especially rare Mendelian diseases. Among the analysis steps, functional prediction (of being deleterious) plays an important role in filtering or prioritizing nonsynonymous SNP (NS) for further analysis. Unfortunately, different prediction algorithms use different information and each has its own strength and weakness. It has been suggested that investigators should use predictions from multiple algorithms instead of relying on a single one. However, querying predictions from different databases/Web-servers for different algorithms is both tedious and time consuming, especially when dealing with a huge number of NSs identified by exome sequencing. To facilitate the process, we developed dbNSFP (database for nonsynonymous SNPs' functional predictions). It compiles prediction scores from four new and popular algorithms (SIFT, Polyphen2, LRT, and MutationTaster), along with a conservation score (PhyloP) and other related information, for every potential NS in the human genome (a total of 75,931,005). It is the first integrated database of functional predictions from multiple algorithms for the comprehensive collection of human NSs. dbNSFP is freely available for download at http://sites.google.com/site/jpopgen/dbNSFP.

Composite effects of gene determinants on the translation speed and density of ribosomes

Background

Translation is a central process of life, and its regulation is crucial for cell growth. In this article, focusing on two model organisms, Escherichia coli and Saccharomyces cerevisiae, we study how three major local features of a gene's coding sequence (its adaptation to the tRNA pool, its amino acid charge, and its mRNA folding energy) affect its translation elongation.

Results

We find that each of these three different features has a non-negligible distinct correlation with the speed of translation elongation. In addition, each of these features might contribute independently to slowing down ribosomal speed at the beginning of genes, which was suggested in previous studies to improve ribosomal allocation and the cost of translation, and to decrease ribosomal jamming. Remarkably, a model of ribosomal translation based on these three basic features highly correlated with the genomic profile of ribosomal density. The robustness to transcription errors in terms of the values of these features is higher at the beginnings of genes, suggesting that this region is important for translation.

Conclusions

The reported results support the conjecture that translation elongation speed is affected by the three coding sequence determinants mentioned above, and not only by adaptation to the tRNA pool; thus, evolution shapes all these determinants along the coding sequences and across genes to improve the organism's translation efficiency.

Genome-scale analysis of translation elongation with a ribosome flow model

We describe the first large scale analysis of gene translation that is based on a model that takes into account the physical and dynamical nature of this process. The Ribosomal Flow Model (RFM) predicts fundamental features of the translation process, including translation rates, protein abundance levels, ribosomal densities and the relation between all these variables, better than alternative ('non-physical') approaches. In addition, we show that the RFM can be used for accurate inference of various other quantities including genes' initiation rates and translation costs. These quantities could not be inferred by previous predictors. We find that increasing the number of available ribosomes (or equivalently the initiation rate) increases the genomic translation rate and the mean ribosome density only up to a certain point, beyond which both saturate. Strikingly, assuming that the translation system is tuned to work at the pre-saturation point maximizes the predictive power of the model with respect to experimental data. This result suggests that in all organisms that were analyzed (from bacteria to Human), the global initiation rate is optimized to attain the pre-saturation point. The fact that similar results were not observed for heterologous genes indicates that this feature is under selection. Remarkably, the gap between the performance of the RFM and alternative predictors is strikingly large in the case of heterologous genes, testifying to the model's promising biotechnological value in predicting the abundance of heterologous proteins before expressing them in the desired host.