Search

A quantitative description of the relationship between protein expression levels and open reading frame (ORF) nucleotide sequences is important for understanding natural systems, designing synthetic systems, and optimizing heterologous expression. Codon identity, mRNA secondary structure, and nucleotide composition within ORFs markedly influence expression levels. Bioinformatic analysis of ORF sequences in 816 bacterial genomes revealed that these features show distinct regional trends. To investigate their effects on protein expression, we designed 285 synthetic genes and determined corresponding expression levels in vitro using Escherichia coli extracts. We developed a mathematical function, parameterized using this synthetic gene data set, which enables computation of protein expression levels from ORF nucleotide sequences. In addition to its practical application in the design of heterologous expression systems, this equation provides mechanistic insight into the factors that control translation efficiency. We found that expression is strongly dependent on the presence of high AU content and low secondary structure in the ORF 5' region. Choice of high-frequency codons contributes to a lesser extent. The 3' terminal AU content makes modest, but detectable contributions. We present a model for the effect of these factors on the three phases of ribosomal function: initiation, elongation, and termination.

Facile “writing” of DNA fragments that encode entire gene sequences potentially has widespread applications in biological analysis and engineering. Rapid writing of open reading frames (ORFs) for expressed proteins could transform protein engineering and production for protein design, synthetic biology, and structural analysis. Here we present a process, protein fabrication automation (PFA), which facilitates the rapid de novo construction of any desired ORF from oligonucleotides with low effort, high speed, and little human interaction. PFA comprises software for sequence design, data management, and the generation of instruction sets for liquid-handling robotics, a liquid-handling robot, a robust PCR scheme for gene assembly from synthetic oligonucleotides, and a genetic selection system to enrich correctly assembled full-length synthetic ORFs. The process is robust and scalable.

Genetically engineered animal models are major tools of a drug discovery pipeline because they facilitate understanding of the molecular and biochemical basis of disease. These highly complex models of human disease often require increasingly convoluted genetic analysis. With growing needs for throughput and consistency, we find that traditional aspiration-and-dispense liquid-handling robots no longer have the required speed, quality, or reproducibility.We present an adaptation and installation of an acoustic droplet ejection (ADE) liquid-handling system for ultra-high-throughput screening of genetically engineered models. An ADE system is fully integrated with existing laboratory processes and platforms to facilitate execution of PCR and quantitative PCR (qPCR) reactions. Such a configuration permits interrogation of highly complex genetic models in a variety of backgrounds. Our findings demonstrate that a single ADE system replaces 8-10 traditional liquid-handling robots while increasing quality and reproducibility.We demonstrate significant improvements achieved by transitioning to an ADE device: extremely low detectable cross-contamination in PCR and qPCR despite extensive use, greatly increased data reproducibility (large increases in data quality and Cq consistency), lowered reaction volumes for large cost savings, and nearly a magnitude increase in speed per instrument. We show several comparisons between traditional- and ADE-based pipetting for a qPCR-based workflow.

Theoretical studies focusing on the nature of landscapes that correlate molecular sequences to molecular function have mainly been carried out in silico due to the vast amounts of data that are needed. Automated in vitro selection is capable of producing significant amounts of data in a short time, making theoretical modeling with real experimental data attainable. A Biomek 2000 Laboratory Automation Workstation has been outfitted to carry out multiple in vitro nucleic acid selections in parallel, yielding substantial amounts of data for theoretical studies. A random sequence population of nucleic acids is initially generated by a combination of chemical synthesis and enzymatic amplification. On the workstation, this population is parsed for its ability to bind a protein, lysozyme. After each round of selection, the selected nucleic acid binding species (also known as aptamers) are amplified by a combination of reverse transcription polymerase chain reaction (PCR) and in vitro transcription. All eight pools that have undergone selection have yielded different sequences.

The in vitro selection of aptamers that bind to low molecular weight targets is commonly a tedious, time-consuming project. We have expanded current automated selection protocols to include aptamer selections against small molecules including the aminoglycosides neomycin, kanamycin, and tobramycin. This modified procedure decreases both the frequency of manual handling of the selection reagents and the time required to perform the experiment, generating aptamers against the chosen target at a much greater rate. Using this process, we have selected aptamers of good affinity against all three aminoglycosides chosen. The method is suitable for integration with high-throughput technologies, greatly expanding the possibility of discovering useful aptamers against other low weight targets. (JALA 2004;9:150-4)

Optimizing the buffer conditions of the selection of nucleic acid binding species (aptamers), increases the likelihood of producing a target aptamer. Aptamers, with high target affinity and specificity, are often compared to antibodies, as aptamers emerge in the industry as diagnostic and therapeutic tools. The increased demand for aptamers encourages high-throughput aptamer generation. The selection buffer conditions may vary as widely as the selection targets, and therefore buffer optimization is helpful if not required for effective aptamer selections. Such optimization work is time consuming and repetitious, which bodes well for high-throughput applications. To accommodate this, an automated buffer testing protocol has been developed to test target-to-unselected RNA pool binding in the presence of 96 different buffer conditions. The dynamic program may vary the monovalent salt(s) identity, monovalent salt(s) concentration, divalent salt(s) identity, divalent salt concentration, buffer identity, buffer concentration, and pH. The optimized buffer conditions likely increase the probability of a successful selection and therefore promote higher ratios of successful aptamer selections against a variety of targets. Preliminary results show trends with the buffer matrix solutions and lysozyme:unselected pool binding. In general, an inverse relationship between lysozyme binding and monovalent salt concentration is observed. (JALA 2004;9:117-22)

We have created an Amino Acid-Nucleotide Interaction Database (AANT; http://aant.icmb.utexas. edu/) that categorizes all amino acid-nucleotide interactions from experimentally determined protein-nucleic acid structures, and provides users with a graphic interface for visualizing these interactions in aggregate. AANT accomplishes this by extracting individual amino acid-nucleotide interactions from structures in the Protein Data Bank, combining and superimposing these interactions into multiple structure files (e.g. 20 amino acids x 5 nucleotides) and grouping structurally similar interactions into more readily identifiable clusters. Using the Chime web browser plug-in, users can view 3D representations of the superimpositions and clusters. The unique collection and representation of data on amino acid-nucleotide interactions facilitates understanding the specificity of protein-nucleic acid interactions at a more fundamental level, and allows comparison of otherwise extremely disparate sets of structures. Moreover, by modularly representing the fundamental interactions that govern binding specificity it may prove possible to better engineer nucleic acid binding proteins.

While the in vitro selection of nucleic acid binding species (aptamers) requires numerous liquid-handling steps, these steps are relatively straightforward and the overall process is therefore amenable to automation. Here we demonstrate that automated selection techniques are capable of generating aptamers against a number of diverse protein targets. Automated selection techniques can be integrated with automated analytical methods, including sequencing, determination of binding constants, and structural analysis. The methods that have so far been developed can be further multiplexed, and it should soon be possible to attempt the selection of aptamers against organismal proteomes or metabolomes.

Cre/LoxP technology is widely used in the field of mouse genetics for spatial and/or temporal regulation of gene function. For Cre lines generated via pronuclear microinjection of a Cre transgene construct, the integration site is random and in most cases not known. Integration of a transgene can disrupt an endogenous gene, potentially interfering with interpretation of the phenotype. In addition, knowledge of where the transgene is integrated is important for planning of crosses between animals carrying a conditional allele and a given Cre allele in case the alleles are on the same chromosome. We have used targeted locus amplification (TLA) to efficiently map the transgene location in seven previously published Cre and CreERT2 transgenic lines. In all lines, transgene insertion was associated with structural changes of variable complexity, illustrating the importance of testing for rearrangements around the integration site. In all seven lines the exact integration site and breakpoint sequences were identified. Our methods, data and genotyping assays can be used as a resource for the mouse community and our results illustrate the power of the TLA method to not only efficiently map the integration site of any transgene, but also provide additional information regarding the transgene integration events.

Over the past few years, a handful of insightful researchers have bridged the gap between biological computing theory and actual DNA-based computation. By using ingenious encoding techniques and clever molecular-biological manipulations, simple versions of computationally complex problems have been experimentally approached or resolved. However, the technical problems revealed during the execution of these scientific set pieces make it unlikely that DNA will ever rival silicon for the solution of any real-world problem.

The first evidence for the emergence of land plants (embryophytes) consists of mid-Ordovician spore tetrads (∼476 Myr old)1,2. The identity of the early plants that produced these spores is unclear; they are sometimes claimed to be liverworts3,4, but there are no associated megafossils, and similar spores can be produced by a diversity of plants2. Indeed, the earliest unequivocal megafossils of land plants consist of early vascular plants and various plants of uncertain affinity1. Different phylogenetic analyses have identified liverworts, hornworts and bryophytes as each being the first lineage of land plants1,2,5,6,7,8,9,10,11,12,13; the consensus of these conflicting topologies yields an unresolved polychotomy at the base of land plants. Here we survey 352 diverse land plants and find that three mitochondrial group II introns are present, with occasional losses, in mosses, hornworts and all major lineages of vascular plants, but are entirely absent from liverworts, green algae and all other eukaryotes. These results indicate that liverworts are the earliest land plants, with the three introns having been acquired in a common ancestor of all other land plants, and have important implications concerning the early stages of plant evolution.

In vitro selection can be used to generate nucleic acid ligands (aptamers) to target molecules ranging in size and structure from cations to cells. However, the selection process is repetitive and time-consuming. We have automated a protocol for in vitro selection using an augmented Beckman Biomek 2000 pipetting robot. The automated selection procedure requires the integration of four devices and the optimization of four molecular biology methods, and is one of the most complex automated protocols attempted to date. Initial attempts at selection yielded robust replication parasites, but optimization of the automated selection procedure suppressed the emergence of these parasites and led to the selection of true nucleic acid ligands. Automated selection can now be used to generate nucleic acid aptamers in days rather than weeks or months.

Reagents for proteome research must of necessity be generated by high throughput methods. Apta mers are potentially useful as reagents to identify and quantitate individual proteins, yet are currently produced for the most part by manual selection procedures. We have developed automated selection methods, but must still individually purify protein targets. Therefore, we have attempted to select aptamers against protein targets generated by in vitro transcription and translation of individual genes. In order to specifically immobilize the protein targets for selection, they are also biotinylated in vitro. As a proof of this method, we have selected aptamers against translated human U1A, a component of the nuclear spliceosome. Selected sequences demonstrated exquisite mimicry of natural binding sequences and structures. These results not only reveal a potential path to the high throughput generation of aptamers, but also yield insights into the incredible specificity of the U1A protein for its natural RNA ligands.

Some folks certainly think so. But at some level a cellular computer is really no different than metabolism and gene regulation.Where can I find out more, especially from someone with opinions different from yours?