Computational discovery of sense-antisense transcription in the human and mouse genomes

Background: Overlapping but oppositely oriented transcripts have the potential to form senseantisense perfect double-stranded (ds) RNA duplexes. Over recent years, the number and variety of examples of mammalian gene-regulatory phenomena in which endogenous dsRNA duplexes have been proposed or demonstrated to participate has greatly increased. These include genomic imprinting, RNA interference, translational regulation, alternative splicing, X-inactivation and RNA editing. We computationally mined public mouse and human expressed sequence tag (EST) databases to search for additional examples of bidirectionally transcribed genomic regions. Results: Our bioinformatics approach identified over 217 candidate overlapping transcriptional units, almost all of which are novel. From experimental validation of a subset of our predictions by orientation-specific RT-PCR, we estimate that our methodology has a specificity of 84% or greater. In many cases, regions of sense-antisense overlap within the 5´- or 3´-untranslated regions of a given transcript correlate with genomic patterns of mouse-human conservation. Conclusions: Our results, in conjunction with the literature, bring the total number of predicted and validated examples of overlapping but oppositely oriented transcripts to over 300. Several of these cases support the hypothesis that a subset of the instances of substantial mouse-human conservation in the 5´ and 3´ UTRs of transcripts might be explained in part by functionality of an overlapping transcriptional unit

The Amino-Acid Mutational Spectrum of Human Genetic Disease

Background: Nonsynonymous mutations in the coding regions of human genes are responsible for phenotypic differences between humans and for susceptibility to genetic disease. Computational methods were recently used to predict deleterious effects of nonsynonymous human mutations and polymorphisms. Here we focus on understanding the amino-acid mutation spectrum of human genetic disease. We compare the disease spectrum to the spectra of mutual amino-acid mutation frequencies, non-disease polymorphisms in human genes, and substitutions fixed between species. Results: We find that the disease spectrum correlates well with the amino-acid mutation frequencies based on the genetic code. Normalized by the mutation frequencies, the spectrum can be rationalized in terms of chemical similarities between amino acids. The disease spectrum is almost identical for membrane and non-membrane proteins. Mutations at arginine and glycine residues are together responsible for about 30% of genetic diseases, whereas random mutations at tryptophan and cysteine have the highest probability of causing disease. Conclusions: The overall disease spectrum mainly reflects the mutability of the genetic code. We corroborate earlier results that the probability of a nonsynonymous mutation causing a genetic disease increases monotonically with an increase in the degree of evolutionary conservation of the mutation site and a decrease in the solvent-accessibility of the site; opposite trends are observed for non-disease polymorphisms. We estimate that the rate of nonsynonymous mutations with a negative impact on human health is less than one per diploid genome per generation

Hippocrates Revisited? Old Ideals and New Realities

Individual genomics has arrived, personal decisions to make use of it are a new reality. What are the implications for the patient–physician relationship? In this article we address three factors that call the traditional concept of confidentiality into question. First, the illusion of absolute data safety, as shown by medical informatics. Second, data sharing as a standard practice in genomics research. Comprehensive data sets are widely accessible. Third, genotyping has become a service that is directly available to consumers. The availability and accessibility of personal health data strongly suggest that the roles in the clinical encounter need to be remodeled. The old ideal of physicians as keepers of confidential information is outstripped by the reality of individuals who decide themselves about the way of using their data

Automated Modelling of Signal Transduction Networks

Background: Intracellular signal transduction is achieved by networks of proteins and small molecules that transmit information from the cell surface to the nucleus, where they ultimately effect transcriptional changes. Understanding the mechanisms cells use to accomplish this important process requires a detailed molecular description of the networks involved. Results: We have developed a computational approach for generating static models of signal transduction networks which utilizes protein-interaction maps generated from large-scale two-hybrid screens and expression profiles from DNA microarrays. Networks are determined entirely by integrating protein-protein interaction data with microarray expression data, without prior knowledge of any pathway intermediates. In effect, this is equivalent to extracting subnetworks of the protein interaction dataset whose members have the most correlated expression profiles. Conclusion: We show that our technique accurately reconstructs MAP Kinase signaling networks in Saccharomyces cerevisiae. This approach should enhance our ability to model signaling networks and to discover new components of known networks. More generally, it provides a method for synthesizing molecular data, either individual transcript abundance measurements or pairwise protein interactions, into higher level structures, such as pathways and networks

Manipulating Replisome Dynamics to Enhance Lambda Red-Mediated Multiplex Genome Engineering

Disrupting the interaction between primase and helicase in Escherichia coli increases Okazaki fragment (OF) length due to less frequent primer synthesis. We exploited this feature to increase the amount of ssDNA at the lagging strand of the replication fork that is available for λ Red-mediated Multiplex Automatable Genome Engineering (MAGE). Supporting this concept, we demonstrate that MAGE enhancements correlate with OF length. Compared with a standard recombineering strain (EcNR2), the strain with the longest OFs displays on average 62% more alleles converted per clone, 239% more clones with 5 or more allele conversions and 38% fewer clones with 0 allele conversions in 1 cycle of co-selection MAGE (CoS-MAGE) with 10 synthetic oligonucleotides. Additionally, we demonstrate that both synthetic oligonucleotides and accessible ssDNA targets on the lagging strand of the replication fork are limiting factors for MAGE. Given this new insight, we generated a strain with reduced oligonucleotide degradation and increased genomic ssDNA availability, which displayed 111% more alleles converted per clone, 527% more clones with 5 or more allele conversions and 71% fewer clones with 0 allele conversions in 1 cycle of 10-plex CoS-MAGE. These improvements will facilitate ambitious genome engineering projects by minimizing dependence on time-consuming clonal isolation and screening

Genome engineering in Saccharomyces cerevisiae using CRISPR-Cas systems

Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) and CRISPR-associated (Cas) systems in bacteria and archaea use RNA-guided nuclease activity to provide adaptive immunity against invading foreign nucleic acids. Here, we report the use of type II bacterial CRISPR-Cas system in Saccharomyces cerevisiae for genome engineering. The CRISPR-Cas components, Cas9 gene and a designer genome targeting CRISPR guide RNA (gRNA), show robust and specific RNA-guided endonuclease activity at targeted endogenous genomic loci in yeast. Using constitutive Cas9 expression and a transient gRNA cassette, we show that targeted double-strand breaks can increase homologous recombination rates of single- and double-stranded oligonucleotide donors by 5-fold and 130-fold, respectively. In addition, co-transformation of a gRNA plasmid and a donor DNA in cells constitutively expressing Cas9 resulted in near 100% donor DNA recombination frequency. Our approach provides foundations for a simple and powerful genome engineering tool for site-specific mutagenesis and allelic replacement in yeast

Iterative capped assembly: rapid and scalable synthesis of repeat-module DNA such as TAL effectors from individual monomers

DNA built from modular repeats presents a challenge for gene synthesis. We present a solid surface-based sequential ligation approach, which we refer to as iterative capped assembly (ICA), that adds DNA repeat monomers individually to a growing chain while using hairpin ‘capping’ oligonucleotides to block incompletely extended chains, greatly increasing the frequency of full-length final products. Applying ICA to a model problem, construction of custom transcription activator-like effector nucleases (TALENs) for genome engineering, we demonstrate efficient synthesis of TALE DNA-binding domains up to 21 monomers long and their ligation into a nuclease-carrying backbone vector all within 3 h. We used ICA to synthesize 20 TALENs of varying DNA target site length and tested their ability to stimulate gene editing by a donor oligonucleotide in human cells. All the TALENS show activity, with the ones >15 monomers long tending to work best. Since ICA builds full-length constructs from individual monomers rather than large exhaustive libraries of pre-fabricated oligomers, it will be trivial to incorporate future modified TALE monomers with improved or expanded function or to synthesize other types of repeat-modular DNA where the diversity of possible monomers makes exhaustive oligomer libraries impractical

Genome-Scale Promoter Engineering by Coselection MAGE

Multiplex Automated Genome Engineering (MAGE) employs short oligonucleotides to scarlessly modify genomes. However, insertions of >10 bases are still inefficient, but can be improved substantially by selection of highly modified chromosomes. Here, we describe Co-Selection MAGE (CoS-MAGE) to optimize biosynthesis of aromatic amino acid derivatives by combinatorially inserting multiple T7 promoters simultaneously into 12 genomic operons. Promoter libraries can be quickly generated to study gain-of-function epistatic interactions in gene networks

Using Bacteria to Determine Protein Kinase Specificity and Predict Target Substrates

The identification of protein kinase targets remains a significant bottleneck for our understanding of signal transduction in normal and diseased cellular states. Kinases recognize their substrates in part through sequence motifs on substrate proteins, which, to date, have most effectively been elucidated using combinatorial peptide library approaches. Here, we present and demonstrate the ProPeL method for easy and accurate discovery of kinase specificity motifs through the use of native bacterial proteomes that serve as in vivo libraries for thousands of simultaneous phosphorylation reactions. Using recombinant kinases expressed in E. coli followed by mass spectrometry, the approach accurately recapitulated the well-established motif preferences of human basophilic (Protein Kinase A) and acidophilic (Casein Kinase II) kinases. These motifs, derived for PKA and CK II using only bacterial sequence data, were then further validated by utilizing them in conjunction with the scan-x software program to computationally predict known human phosphorylation sites with high confidence

Stable Gene Targeting in Human Cells Using Single-Strand Oligonucleotides with Modified Bases

Recent advances allow multiplexed genome engineering in E. coli, employing easily designed oligonucleotides to edit multiple loci simultaneously. A similar technology in human cells would greatly expedite functional genomics, both by enhancing our ability to test how individual variants such as single nucleotide polymorphisms (SNPs) are related to specific phenotypes, and potentially allowing simultaneous mutation of multiple loci. However, oligo-mediated targeting of human cells is currently limited by low targeting efficiencies and low survival of modified cells. Using a HeLa-based EGFP-rescue reporter system we show that use of modified base analogs can increase targeting efficiency, in part by avoiding the mismatch repair machinery. We investigate the effects of oligonucleotide toxicity and find a strong correlation between the number of phosphorothioate bonds and toxicity. Stably EGFP-corrected cells were generated at a frequency of $\sim$0.05% with an optimized oligonucleotide design combining modified bases and reduced number of phosphorothioate bonds. We provide evidence from comparative RNA-seq analysis suggesting cellular immunity induced by the oligonucleotides might contribute to the low viability of oligo-corrected cells. Further optimization of this method should allow rapid and scalable genome engineering in human cells