Reducing mutational bias in random protein libraries

The success of protein optimization through directed molecular evolution depends to a large extent on the size and quality of the displayed library. Current low-fidelity DNA polymerases that are commonly used during random mutagenesis and recombination in vitro display strong mutational preferences, favoring the substitution of certain nucleotides over others. The result is a biased and reduced functional diversity in the library under selection. In an effort to reduce mutational bias, we combined two different low-fidelity DNA polymerases, Taq and Mutazyme, which have opposite mutational spectra. As a first step, random mutants of the Bacillus thuringiensis cry9Ca1 gene were generated by separate error-prone polymerase chain reactions (PCRs) with each of the two polymerases. Subsequent shuffling by staggered extension process (StEP) of the PCR products resulted in intermediate numbers of AT and GC substitutions, compared to the Taq or Mutazyme error-prone PCR libraries. This strategy should allow generating unbiased libraries or libraries with a specific degree of mutational bias by applying optimal mutagenesis frequencies during error-prone PCR and controlling the concentration of template in the shuffling reaction while taking into account the GC content of the target gene.     

Production of shotgun libraries using random amplification

In the following report, thermal cycling coupled with random 10-mers as primers was used to construct randomly amplified shotgun libraries (RASLs). This approach allowed shotgun libraries to be constructed from nanogram quantities of input DNA. RASLs contained inserts from throughout a target genome in an unbiased fashion and did not appear to contain chimeric sequences. This protocol should be useful for shotgun sequencing the genomes of unculturable organisms and rapidly producing shotgun libraries from cosmids, fosmids, yeast artificial chromosomes (YACs), and bacterial artificial chromosomes (BACs).     

Population genetic variation in genome-wide gene expression

Evolutionary biologists seek to understand which traits display variation, are heritable, and influence differential reproduction, because such traits respond to natural selection and underlie organic evolution. Selection acts upon individual differences within a population. Whether individual differences within a natural population include variation in gene expression levels has not yet been addressed on a genome-wide scale. Here we use DNA microarray technology for measuring comparative gene expression and a refined statistical analysis for the purpose of comparing gene expression levels in natural isolates of the wine yeast Saccharomyces cerevisiae. A method for the Bayesian analysis of gene expression levels is used to compare four natural isolates of S. cerevisiae from Montalcino, Italy. Widespread variation in amino acid metabolism, sulfur assimilation and processing, and protein degradation-primarily consisting of differences in expression level smaller than a factor of 2-is demonstrated. Genetic variation in gene expression among isolates from a natural population is present on a genomic scale. It remains to be determined what role differential gene expression may play in adaptation to new or changing environments.     

Reverse engineering and analysis of genome-wide gene regulatory networks from gene expression profiles using high-performance computing

Regulation of gene expression is a carefully regulated phenomenon in the cell. “Reverse-engineering” algorithms try to reconstruct the regulatory interactions among genes from genome-scale measurements of gene expression profiles (microarrays). Mammalian cells express tens of thousands of genes; hence, hundreds of gene expression profiles are necessary in order to have acceptable statistical evidence of interactions between genes. As the number of profiles to be analyzed increases, so do computational costs and memory requirements. In this work, we designed and developed a parallel computing algorithm to reverse-engineer genome-scale gene regulatory networks from thousands of gene expression profiles. The algorithm is based on computing pairwise Mutual Information between each gene-pair. We successfully tested it to reverse engineer the Mus Musculus (mouse) gene regulatory network in liver from gene expression profiles collected from a public repository. A parallel hierarchical clustering algorithm was implemented to discover “communities” within the gene network. Network communities are enriched for genes involved in the same biological functions. The inferred network was used to identify two mitochondrial proteins.     

Identifying microbial fitness determinants by insertion sequencing using genome-wide transposon mutant libraries

Insertion sequencing (INSeq) is a method for determining the insertion site and relative abundance of large numbers of transposon mutants in a mixed population of isogenic mutants of a sequenced microbial species. INSeq is based on a modified mariner transposon containing MmeI sites at its ends, allowing cleavage at chromosomal sites 16-17 bp from the inserted transposon. Genomic regions adjacent to the transposons are amplified by linear PCR with a biotinylated primer. Products are bound to magnetic beads, digested with MmeI and barcoded with sample-specific linkers appended to each restriction fragment. After limited PCR amplification, fragments are sequenced using a high-throughput instrument. The sequence of each read can be used to map the location of a transposon in the genome. Read count measures the relative abundance of that mutant in the population. Solid-phase library preparation makes this protocol rapid (18 h), easy to scale up, amenable to automation and useful for a variety of samples. A protocol for characterizing libraries of transposon mutant strains clonally arrayed in a multiwell format is provided.     

随机SNP在全基因组关联研究人群分层分析中的应用

在复杂疾病的全基因组关联研究中,人群分层现象会增加结果的假阳性率,因此考虑人群遗传结构、控制人群分层是很有必要的。而在人群分层研究中,使用随机选择的SNP的效果还有待进一步探讨。文章利用HapMap Phase2人群中无关个体的Affymetrix SNP 6.0芯片分型数据,在全基因组上随机均匀选择不同数量的SNP,同时利用f值和Fisher精确检验方法筛选祖先信息标记（Ancestry Informative Markers,AIMs）。然后利用HapMap Phase3中的无关个体的数据,以F-statistics和STRUCTURE分析两种方法评估所选出的不同SNP组合对人群的区分效果。研究发现,随机均匀分布于全基因组的SNP可用于识别人群内部存在的遗传结构。文章进一步提示,在全基因组关联研究中,当没有针对特定人群的AIMs时,可在全基因组上随机选择3000以上均匀分布的SNP来控制人群分层。     

Identifying gene and protein mentions in text using conditional random fields

BACKGROUND: We present a model for tagging gene and protein mentions from text using the probabilistic sequence tagging framework of conditional random fields (CRFs). Conditional random fields model the probability P(t/o) of a tag sequence given an observation sequence directly, and have previously been employed successfully for other tagging tasks. The mechanics of CRFs and their relationship to maximum entropy are discussed in detail. RESULTS: We employ a diverse feature set containing standard orthographic features combined with expert features in the form of gene and biological term lexicons to achieve a precision of 86.4% and recall of 78.7%. An analysis of the contribution of the various features of the model is provided.     

Creating random mutagenesis libraries using megaprimer PCR of whole plasmid

The conventional method for cloning a DNA fragment is to insert it into a vector and ligate it. Although this method is commonly used, it is labor intensive because the ratio and concentrations of the DNA insert and the vector need optimizing. Even then, the resultant library is often plagued with unwanted plasmids that have no inserts or multiple inserts. These species have to be eradicated to avoid tedious screening, especially when producing a mutant gene library. To overcome these problems, we modified the QuikChange protocol so that each plasmid carries a single insert. Although the QuikChange was originally developed for site-directed mutagenesis using complementary mutagenic oligonucleotide primers in whole plasmid PCR, we found that the protocol also worked for megaprimers consisting of hundreds of nucleotides. Based on this discovery, we used insert fragments, which we wanted to clone, as the primers in the QuikChange reaction. The resultant libraries were virtually free from species with no inserts or multiple inserts. The present method, which we designated MEGAWHOP (megaprimer PCR of whole plasmid), is thus ideal for creating random mutagenesis megalibraries.     

A densely interconnected genome-wide network of microRNAs and oncogenic pathways revealed using gene expression signatures

MicroRNAs (miRNAs) are important components of cellular signaling pathways, acting either as pathway regulators or pathway targets. Currently, only a limited number of miRNAs have been functionally linked to specific signaling pathways. Here, we explored if gene expression signatures could be used to represent miRNA activities and integrated with genomic signatures of oncogenic pathway activity to identify connections between miRNAs and oncogenic pathways on a high-throughput, genome-wide scale. Mapping >300 gene expression signatures to >700 primary tumor profiles, we constructed a genome-wide miRNA-pathway network predicting the associations of 276 human miRNAs to 26 oncogenic pathways. The miRNA-pathway network confirmed a host of previously reported miRNA/pathway associations and uncovered several novel associations that were subsequently experimentally validated. Globally, the miRNA-pathway network demonstrates a small-world, but not scale-free, organization characterized by multiple distinct, tightly knit modules each exhibiting a high density of connections. However, unlike genetic or metabolic networks typified by only a few highly connected nodes ("hubs"), most nodes in the miRNA-pathway network are highly connected. Sequence-based computational analysis confirmed that highly-interconnected miRNAs are likely to be regulated by common pathways to target similar sets of downstream genes, suggesting a pervasive and high level of functional redundancy among coexpressed miRNAs. We conclude that gene expression signatures can be used as surrogates of miRNA activity. Our strategy facilitates the task of discovering novel miRNA-pathway connections, since gene expression data for multiple normal and disease conditions are abundantly available.     

Restriction enzyme-free construction of random gene mutagenesis libraries in Escherichia coli

Directed evolution relies on both random and site-directed mutagenesis of individual genes and regulatory elements to create variants with altered activity profiles for engineering applications. Central to these experiments is the construction of large libraries of related variants. However, a number of technical hurdles continue to limit routine construction of random mutagenesis libraries in Escherichia coli, in particular, inefficiencies during digestion and ligation steps. Here, we report a restriction enzyme-free approach to library generation using megaprimers termed MegAnneal. Target DNA is first exponentially amplified using error-prone polymerase chain reaction (PCR) and then linearly amplified with a single 3′ primer to generate long, randomly mutated, single-stranded megaprimers. These are annealed to single-stranded dUTP-containing template plasmid and extended with T7 polymerase to create a complementary strand, and the resulting termini are ligated with T4 DNA ligase. Using this approach, we are able to reliably generate libraries of approximately 10⁷ colony-forming units (cfu)/μg DNA/transformation in a single day. We have created MegAnneal libraries based on three different single-chain antibodies and identified variants with enhanced expression and ligand-binding affinity. The key advantages of this approach include facile amplification, restriction enzyme-free library generation, and a significantly reduced risk of mutations outside the targeted region and wild-type contamination as compared with current methods.     

Mapping epitopes of neutralizing monoclonal antibodies using phage random peptide libraries

Identification of protective determinants from microbial proteins is a necessary step in the rational design of subunit vaccines. We have previously used a synthetic peptide scan (Pepscan) assay to map a panel of eight neutralizing monoclonal antibodies (mAb; designated as C1.1 to C1.8) to a common motif sequence from Chlamydia trachomatis. In the present study, five of the eight mAbs were used to screen phage random peptide libraries. mAbs C1.1 and C1.3 selected a motif sequence of G-L-X-N-D from a pIII-based phage random peptide library and a motif sequence of G-X-X-N-D from a pVIII-based random peptide library while mAbs C1.6 to C1.8 failed to select recognizable motifs from either of the phage libraries. However, C1.6 to C1.8 bound to the same motif sequence displayed on phage when the appropriate conformational constraints were imposed onto the motif sequence. Thus the specificity of the mAbs identified on Pepscan assays correlates with the mAbs’ dependence on local epitope constraints displayed on the phage surface. Received 12 August 1996/ Accepted in revised form 03 November 1996     

Designing gene libraries from protein profiles for combinatorial protein experiments

Protein combinatorial libraries provide new ways to probe the determinants of folding and to discover novel proteins. Such libraries are often constructed by expressing an ensemble of partially random gene sequences. Given the intractably large number of possible sequences, some limitation on diversity must be imposed. A non-uniform distribution of nucleotides can be used to reduce the number of possible sequences and encode peptide sequences having a predetermined set of amino acid probabilities at each residue position, i.e., the amino acid sequence profile. Such profiles can be determined by inspection, multiple sequence alignment or physically-based computational methods. Here we present a computational method that takes as input a desired sequence profile and calculates the individual nucleotide probabilities among partially random genes. The calculated gene library can be readily used in the context of standard DNA synthesis to generate a protein library with essentially the desired profile. The fidelity between the desired profile and the calculated one coded by these partially random genes is quantitatively evaluated using the linear correlation coefficient and a relative entropy, each of which provides a measure of profile agreement at each position of the sequence. On average, this method of identifying such codon frequencies performs as well or better than other methods with regard to fidelity to the original profile. Importantly, the method presented here provides much better yields of complete sequences that do not contain stop codons, a feature that is particularly important when all or large fractions of a gene are subject to combinatorial mutation.     

Advances in plant cell type-specific genome-wide studies of gene expression

Cell is the functional unit of life.To study the complex interactions of systems of biological molecules,it is crucial to dissect these molecules at the cell level.In recent years,major progresses have been made by plant biologists to profile gene expression in specific cell types at the genome-wide level.Approaches based on the isolation of cells,polysomes or nuclei have been developed and successfully used for studying the cell types from distinct organs of several plant species.These cell-level data sets revealed previously unrecognized cellular properties,such as cell-specific gene expression modules and hormone response centers,and should serve as essential resources for functional genomic analyses.Newly developed technologies are more affordable to many laboratories and should help to provide new insights at the cellular resolution in the near future.     

Common gene expression strategies revealed by genome-wide analysis in yeast

Background  

Gene expression is a two-step synthesis process that ends with the necessary amount of each protein required to perform its function. Since the protein is the final product, the main focus of gene regulation should be centered on it. However, because mRNA is an intermediate step and the amounts of both mRNA and protein are controlled by their synthesis and degradation rates, the desired amount of protein can be achieved following different strategies.