Participation in preschool activities of children with autistic spectrum disorder and comparison to typically developing children.

BACKGROUND: Participation is essential to children's development and is a major focus of intervention. This study aimed to describe the participation patterns of children with ASD, in comparison to typically developing (TD) children. METHODS: 70 preschoolers participated: 33 children with ASD, attending non-inclusive-education settings; and 37 TD children, attending mainstream educational settings. Two occupational therapists assessed their participation through structured observations in self-care activities, play, learning, and social participation areas; demographic and environmental questionnaires were also completed. RESULTS: In the ASD group, frequency of participation was found to be significantly higher in ADL and learning than in other areas; level of performance was found to be significantly lower in social participation than in other areas. The TD group scored significantly higher than did the ASD group in most areas and scales. Initial findings tentatively showed that a structured educational environment for children with ASD may increase participation frequency. CONCLUSIONS: The findings are consistent with recognized disabilities in play and social participation among children with ASD, and their limitations in participation, compared to their TD peers in most areas. Further studies are needed to evaluate additional psychometric properties of the different scales, and the impact of educational environments on participation.

The Structured Preschool Participation Observation (SPO) for Children with ASD: Adaptation, Initial Psychometric Properties, and Children's Participation.

Aims: Due to the lack of tools evaluating participation of children with ASD in the educational setting, this study aimed to adapt the Structured Preschool Participation Observation (SPO), which assess the participation of preschool children attending mainstream-educational settings to children with ASD attending non-inclusive special education (content validity), to measure its initial psychometric properties (internal reliability, inter-rater reliability), and to describe children's participation characteristics, creating an effective tool to fill this gap.Methods: Content validity was evaluated by 21 experts using questionnaires. Thirty-five children with ASD were observed in their educational setting using the adapted tool (SPO-ASD).Results: Content validity was satisfactory regarding the items and their classification into occupational areas. Moderate to excellent internal consistency (α = .73-.92) and inter-rater reliability (ICC = .61-.95, p<.05) were found for all scales and most areas. Children's participation frequency was high in learning and activities of daily living (ADL), low in play and social participation. Performance level was low in social participation. Enjoyment level was low, and needed assistance in ADL was high.Conclusions: Based on our initial evaluation, the SPO-ASD may be suitable for assessing participation of children with ASD attending special education preschools. Additional studies are needed to more securely establish its psychometric properties.

Computational based design and tracking of synthetic variants of Porcine circovirus reveal relations between silent genomic information and viral fitness.

Viral genomes not only code the protein content, but also include silent, overlapping codes which are important to the regulation of the viral life cycle and affect its evolution. Due to the high density of these codes, their non-modular nature and the complex intracellular processes they encode, the ability of current approaches to decipher them is very limited. We describe the first computational-experimental pipeline for studying the effects of viral silent and non-silent information on its fitness. The pipeline was implemented to study the Porcine Circovirus type 2 (PCV2), the shortest known eukaryotic virus, and includes the following steps: (1) Based on the analyses of 2100 variants of PCV, suspected silent codes were inferred. (2) Five hundred variants of the PCV2 were designed to include various 'smart' silent mutations. (3) Using state of the art synthetic biology approaches, the genomes of these five hundred variants were generated. (4) Competition experiments between the variants were performed in Porcine kidney-15 (PK15) cell-lines. (5) The variant titers were analyzed based on novel next-generation sequencing (NGS) experiments. (6) The features related to the titer of the variants were inferred and their analyses enabled detection of various novel silent functional sequence and structural motifs. Furthermore, we demonstrate that 50 of the silent variants exhibit higher fitness than the wildtype in the analyzed conditions.

Algorithms for ribosome traffic engineering and their potential in improving host cells' titer and growth rate.

mRNA translation is a fundamental cellular process consuming most of the intracellular energy; thus, it is under extensive evolutionary selection for optimization, and its efficiency can affect the host's growth rate. We describe a generic approach for improving the growth rate (fitness) of any organism by introducing synonymous mutations based on comprehensive computational models. The algorithms introduce silent mutations that may improve the allocation of ribosomes in the cells via the decreasing of their traffic jams during translation respectively. As a result, resources availability in the cell changes leading to improved growth-rate. We demonstrate experimentally the implementation of the method on Saccharomyces cerevisiae: we show that by introducing a few mutations in two computationally selected genes the mutant's titer increased. Our approach can be employed for improving the growth rate of any organism providing the existence of data for inferring models, and with the relevant genomic engineering tools; thus, it is expected to be extremely useful in biotechnology, medicine, and agriculture.

Predictive biophysical modeling and understanding of the dynamics of mRNA translation and its evolution.

mRNA translation is the fundamental process of decoding the information encoded in mRNA molecules by the ribosome for the synthesis of proteins. The centrality of this process in various biomedical disciplines such as cell biology, evolution and biotechnology, encouraged the development of dozens of mathematical and computational models of translation in recent years. These models aimed at capturing various biophysical aspects of the process. The objective of this review is to survey these models, focusing on those based and/or validated on real large-scale genomic data. We consider aspects such as the complexity of the models, the biophysical aspects they regard and the predictions they may provide. Furthermore, we survey the central systems biology discoveries reported on their basis. This review demonstrates the fundamental advantages of employing computational biophysical translation models in general, and discusses the relative advantages of the different approaches and the challenges in the field.

Selection for reduced translation costs at the intronic 5' end in fungi.

It is generally believed that introns are not translated; therefore, the potential intronic features that may be related to the translation step (occurring after splicing) have yet to be thoroughly studied. Here, focusing on four fungi, we performed for the first time a comprehensive study aimed at characterizing how translation efficiency is encoded in introns and affects their evolution. By analysing their intronome we provide evidence of selection for STOP codons close to the intronic 5' end, and show that the beginning of introns are selected for significantly high translation, presumably to reduce translation and metabolic costs in cases of non-spliced introns. Ribosomal profiling data analysis in Saccharomyces cerevisiae supports the conjecture that in this organism intron retention frequently occurs, introns are partially translated, and their translation efficiency affects organismal fitness. We show that the reported results are more significant in highly translated and highly spliced genes, but are not associated only with genes with a specific function. We also discuss the potential relation of the reported signals to efficient nonsense-mediated decay due to splicing errors. These new discoveries are supported by population-genetics considerations. In addition, they are contributory steps towards a broader understanding of intron evolution and the effect of silent mutations on gene expression and organismal fitness.

Complementary Post Transcriptional Regulatory Information is Detected by PUNCH-P and Ribosome Profiling.

Two novel approaches were recently suggested for genome-wide identification of protein aspects synthesized at a given time. Ribo-Seq is based on sequencing all the ribosome protected mRNA fragments in a cell, while PUNCH-P is based on mass-spectrometric analysis of only newly synthesized proteins. Here we describe the first Ribo-Seq/PUNCH-P comparison via the analysis of mammalian cells during the cell-cycle for detecting relevant differentially expressed genes between G1 and M phase. Our analyses suggest that the two approaches significantly overlap with each other. However, we demonstrate that there are biologically meaningful proteins/genes that can be detected to be post-transcriptionally regulated during the mammalian cell cycle only by each of the approaches, or their consolidation. Such gene sets are enriched with proteins known to be related to intra-cellular signalling pathways such as central cell cycle processes, central gene expression regulation processes, processes related to chromosome segregation, DNA damage, and replication, that are post-transcriptionally regulated during the mammalian cell cycle. Moreover, we show that combining the approaches better predicts steady state changes in protein abundance. The results reported here support the conjecture that for gaining a full post-transcriptional regulation picture one should integrate the two approaches.

Rationally designed, heterologous S. cerevisiae transcripts expose novel expression determinants.

Deducing generic causal relations between RNA transcript features and protein expression profiles from endogenous gene expression data remains a major unsolved problem in biology. The analysis of gene expression from heterologous genes contributes significantly to solving this problem, but has been heavily biased toward the study of the effect of 5' transcript regions and to prokaryotes. Here, we employ a synthetic biology driven approach that systematically differentiates the effect of different regions of the transcript on gene expression up to 240 nucleotides into the ORF. This enabled us to discover new causal effects between features in previously unexplored regions of transcripts, and gene expression in natural regimes. We rationally designed, constructed, and analyzed 383 gene variants of the viral HRSVgp04 gene ORF, with multiple synonymous mutations at key positions along the transcript in the eukaryote S. cerevisiae. Our results show that a few silent mutations at the 5'UTR can have a dramatic effect of up to 15 fold change on protein levels, and that even synonymous mutations in positions more than 120 nucleotides downstream from the ORF 5'end can modulate protein levels up to 160%-300%. We demonstrate that the correlation between protein levels and folding energy increases with the significance of the level of selection of the latter in endogenous genes, reinforcing the notion that selection for folding strength in different parts of the ORF is related to translation regulation. Our measured protein abundance correlates notably(correlation up to r = 0.62 (p=0.0013)) with mean relative codon decoding times, based on ribosomal densities (Ribo-Seq) in endogenous genes, supporting the conjecture that translation elongation and adaptation to the tRNA pool can modify protein levels in a causal/direct manner. This report provides an improved understanding of transcript evolution, design principles of gene expression regulation, and suggests simple rules for engineering synthetic gene expression in eukaryotes.

Exploiting hidden information interleaved in the redundancy of the genetic code without prior knowledge.

MOTIVATION: Dozens of studies in recent years have demonstrated that codon usage encodes various aspects related to all stages of gene expression regulation. When relevant high-quality large-scale gene expression data are available, it is possible to statistically infer and model these signals, enabling analysing and engineering gene expression. However, when these data are not available, it is impossible to infer and validate such models. RESULTS: In this current study, we suggest Chimera-an unsupervised computationally efficient approach for exploiting hidden high-dimensional information related to the way gene expression is encoded in the open reading frame (ORF), based solely on the genome of the analysed organism. One version of the approach, named Chimera Average Repetitive Substring (ChimeraARS), estimates the adaptability of an ORF to the intracellular gene expression machinery of a genome (host), by computing its tendency to include long substrings that appear in its coding sequences; the second version, named ChimeraMap, engineers the codons of a protein such that it will include long substrings of codons that appear in the host coding sequences, improving its adaptation to a new host's gene expression machinery. We demonstrate the applicability of the new approach for analysing and engineering heterologous genes and for analysing endogenous genes. Specifically, focusing on Escherichia coli, we show that it can exploit information that cannot be detected by conventional approaches (e.g. the CAI-Codon Adaptation Index), which only consider single codon distributions; for example, we report correlations of up to 0.67 for the ChimeraARS measure with heterologous gene expression, when the CAI yielded no correlation. AVAILABILITY AND IMPLEMENTATION: For non-commercial purposes, the code of the Chimera approach can be downloaded from http://www.cs.tau.ac.il/∼tamirtul/Chimera/download.htm. CONTACT: tamirtul@post.tau.ac.il SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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.

Alternative transcription initiation and the AUG context configuration control dual-organellar targeting and functional competence of Arabidopsis Lon1 protease.

Cellular homeostasis relies on components of protein quality control including chaperones and proteases. In bacteria and eukaryotic organelles, Lon proteases play a critical role in removing irreparably damaged proteins and thereby preventing the accumulation of deleterious degradation-resistant aggregates. Gene expression, live-cell imaging, immunobiochemical, and functional complementation approaches provide conclusive evidence for Lon1 dual-targeting to chloroplasts and mitochondria. Dual-organellar deposition of Lon1 isoforms depends on both transcriptional regulation and alternative translation initiation via leaky ribosome scanning from the first AUG sequence context that deviates extensively from the optimum Kozak consensus. Organelle-specific Lon1 targeting results in partial complementation of Arabidopsis lon1-1 mutants, whereas full complementation is solely accomplished by dual-organellar targeting. Both the optimal and non-optimal AUG sequence contexts are functional in yeast and facilitate leaky ribosome scanning complementing the pim1 phenotype when the mitochondrial presequence is used. Bioinformatic search identified a limited number of Arabidopsis genes with Lon1-type dual-targeting sequence organization. Lon4, the paralog of Lon1, has an ambiguous presequence likely evolved from the twin presequences of an ancestral Lon1-like gene, generating a single dual-targeted protein isoform. We postulate that Lon1 and its subfunctional paralog Lon4 evolved complementary subsets of transcriptional and posttranscriptional regulatory components responsive to environmental cues for dual-organellar targeting.

New universal rules of eukaryotic translation initiation fidelity.

The accepted model of eukaryotic translation initiation begins with the scanning of the transcript by the pre-initiation complex from the 5'end until an ATG codon with a specific nucleotide (nt) context surrounding it is recognized (Kozak rule). According to this model, ATG codons upstream to the beginning of the ORF should affect translation. We perform for the first time, a genome-wide statistical analysis, uncovering a new, more comprehensive and quantitative, set of initiation rules for improving the cost of translation and its efficiency. Analyzing dozens of eukaryotic genomes, we find that in all frames there is a universal trend of selection for low numbers of ATG codons; specifically, 16-27 codons upstream, but also 5-11 codons downstream of the START ATG, include less ATG codons than expected. We further suggest that there is selection for anti optimal ATG contexts in the vicinity of the START ATG. Thus, the efficiency and fidelity of translation initiation is encoded in the 5'UTR as required by the scanning model, but also at the beginning of the ORF. The observed nt patterns suggest that in all the analyzed organisms the pre-initiation complex often misses the START ATG of the ORF, and may start translation from an alternative initiation start-site. Thus, to prevent the translation of undesired proteins, there is selection for nucleotide sequences with low affinity to the pre-initiation complex near the beginning of the ORF. With the new suggested rules we were able to obtain a twice higher correlation with ribosomal density and protein levels in comparison to the Kozak rule alone (e.g. for protein levels r=0.7 vs. r=0.31; p<10(-12)).

Mapping the translation initiation landscape of an S. cerevisiae gene using fluorescent proteins.

Accurate and efficient gene expression requires that protein translation initiates from mRNA transcripts with high fidelity. At the same time, indiscriminate initiation of translation from multiple ATG start-sites per transcript has been demonstrated, raising fundamental questions regarding the rate and rationale governing alternative translation initiation. We devised a sensitive fluorescent reporter assay for monitoring alternative translation initiation. To demonstrate it, we map the translation initiation landscape of a Saccharomyces cerevisiae gene (RMD1) with a typical ATG sequence context profile. We found that up to 3%-5% of translation initiation events occur from alternative out-of-frame start codons downstream of the main ATG. Initiation from these codons follows the ribosome scanning model: initiation rates from different start sites are determined by ATG order, rather than their context strength. Genomic analysis of S. cerevisiae further supports the scanning model: ATG codons downstream rather than upstream of the main ATG tend to have higher context scores.

Transcript features alone enable accurate prediction and understanding of gene expression in S. cerevisiae.

BACKGROUND: Gene expression is a central process in all living organisms. Central questions in the field are related to the way the expression levels of genes are encoded in the transcripts and affect their evolution, and the potential to predict expression levels solely by transcript features. In this study we analyze S. cerevisiae, a model organism with the most abundant relevant cellular and genomic measurements, to evaluate the accuracy in which expression levels can be predicted by different parts of the transcript. To this end, we perform various types of regression analyses based on a total of 5323 features of the transcript. The main advantage of the proposed predictors over previous ones is related to the accurate and comprehensive definitions of the relevant transcript features, which are based on biophysical knowledge of the gene transcription and translation processes, their modeling and evolution. RESULTS: Cross validation analyses of our predictors demonstrate that they achieve a correlation of 0.68/0.68/0.70/0.61/0.81 with mRNA levels, ribosomal density, protein levels, proteins per mRNA molecule (PPR), and ribosomal load (RL) respectively (all p-values <10(-140)). When we consider predictors that are based exclusively on the features related to different parts of the transcript (5'UTR, ORF, 3'UTR), the correlations with protein levels were 0.27/0.71/0.25 (all p-values <10(-5)), suggesting that the information in the UTRs is redundant, and features of the ORF alone yield similar predictions to the ones obtained based on the entire transcript. CONCLUSIONS: The reported results demonstrate that in the analyzed model organism the expression levels of a gene are encoded in the transcript. Specifically, the prediction of a large fraction of the variance of the different gene expression steps based on transcript features alone is feasible in S. cerevisiae. We report dozens of novel transcript features related to expression levels predictions, demonstrating how such analyses can aid in understanding the gene expression process and its evolution, and how such predictors can be designed for other organisms in the future.

RFMapp: ribosome flow model application.

UNLABELLED: The RFMapp is a graphical user interface application based on the RFM (ribosome flow model), enabling the estimation of the translation elongation rates of messenger ribonucleic acids (mRNAs) and the profile of ribosomal densities along the mRNAs, in a computationally efficient way. The RFMapp is based on the approach previously described by Reuveni et al., and unlike other traditional approaches in the field, which are mainly related to the genes' mean codon translation efficiency, the RFM additionally considers the codon order, the ribosomes' size and their order. Thus, it has been shown that RFM outperforms traditional predictors when analyzing both heterologous and endogenous genes. AVAILABILITY AND IMPLEMENTATION: Distributable cross-platform application and guideline are available for download at: http://www.cs.tau.ac.il/~tamirtul/RFM_Installers/install.htm.

Strong association between mRNA folding strength and protein abundance in S. cerevisiae.

One of the open questions in regulatory genomics is how the efficiency of gene translation is encoded in the coding sequence. Here we analyse recently generated measurements of folding energy in Saccharomyces cerevisiae, showing that genes with high protein abundance tend to have strong mRNA folding (mF; R=0.68). mF strength also strongly correlates with ribosomal density and mRNA levels, suggesting that this relation at least partially pertains to the efficiency of translation elongation, presumably by preventing aggregation of mRNA molecules.

ANAT: a tool for constructing and analyzing functional protein networks.

Genome-scale screening studies are gradually accumulating a wealth of data on the putative involvement of hundreds of genes in various cellular responses or functions. A fundamental challenge is to chart the molecular pathways that underlie these systems. ANAT is an interactive software tool, implemented as a Cytoscape plug-in, for elucidating functional networks of proteins. It encompasses a number of network inference algorithms and provides access to networks of physical associations in several organisms. In contrast to existing software tools, ANAT can be used to infer subnetworks that connect hundreds of proteins to each other or to a given set of "anchor" proteins, a fundamental step in reconstructing cellular subnetworks. The interactive component of ANAT provides an array of tools for evaluating and exploring the resulting subnetwork models and for iteratively refining them. We demonstrate the utility of ANAT by studying the crosstalk between the autophagic and apoptotic cell death modules in humans, using a network of physical interactions. Relative to published software tools, ANAT is more accurate and provides more features for comprehensive network analysis. The latest version of the software is available at http://www.cs.tau.ac.il/~bnet/ANAT_SI.

iMAT: an integrative metabolic analysis tool.

SUMMARY: iMAT is an Integrative Metabolic Analysis Tool, enabling the integration of transcriptomic and proteomic data with genome-scale metabolic network models to predict enzymes' metabolic flux, based on the method previously described by Shlomi et al. The prediction of metabolic fluxes based on high-throughput molecular data sources could help to advance our understanding of cellular metabolism, since current experimental approaches are limited to measuring fluxes through merely a few dozen enzymes. AVAILABILITY AND IMPLEMENTATION: http://imat.cs.tau.ac.il/.