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.

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

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. 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).

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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).

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.

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.