Functional characterization of calliphorid cell death genes and cellularization gene promoters for controlling gene expression and cell viability in early embryos.

The New World screwworm fly, Cochliomyia hominivorax, and the Australian sheep blow fly, Lucilia cuprina, are major pests of livestock. The sterile insect technique was used to eradicate C. hominivorax from North and Central America. This involved area-wide releases of male and female flies that had been sterilized by radiation. Genetic systems have been developed for making 'male-only' strains that would improve the efficiency of genetic control of insect pests. One system involves induction of female lethality in embryos through activation of a pro-apoptotic gene by the tetracycline-dependent transactivator. Sex-specific expression is achieved using an intron from the transformer gene, which we previously isolated from several calliphorids. In the present study, we report the isolation of the promoters from the C. hominivorax slam and Lucilia sericata bnk cellularization genes and show that these promoters can drive expression of a GFP reporter gene in early embryos of transgenic L. cuprina. Additionally, we report the isolation of the L. sericata pro-apoptotic hid and rpr genes, identify conserved motifs in the encoded proteins and determine the relative expression of these genes at different stages of development. We show that widespread expression of the L. sericata pro-apoptotic genes was lethal in Drosophila melanogaster. The isolated gene promoters and pro-apoptotic genes could potentially be used to build transgenic embryonic sexing strains of calliphorid livestock pests.

A de novo transcriptome assembly of Lucilia sericata (Diptera: Calliphoridae) with predicted alternative splices, single nucleotide polymorphisms and transcript expression estimates.

The blow fly Lucilia sericata (Diptera: Calliphoridae) (Meigen) is a nonmodel organism with no reference genome that is associated with numerous areas of research spanning the ecological, evolutionary, medical, veterinary and forensic sciences. To facilitate scientific discovery in this species, the transcriptome was assembled from more than six billion bases of Illumina and twenty-one million bases of 454 sequence derived from embryonic, larval, pupal, adult and larval salivary gland libraries. The assembly was carried out in a manner that enabled identification of putative single nucleotide polymorphisms (SNPs) and alternative splices, and that provided expression estimates for various life history stages and for salivary tissue. The assembled transcriptome was also used to identify transcribed transposable elements in L. sericata. The results of this study will enable blow fly biologists, dipterists and comparative genomicists to more rapidly develop and test molecular and genetic hypotheses, especially those regarding blow fly development and salivary gland biology.

Finding weak motifs in DNA sequences.

Recognition of regulatory sites in unaligned DNA sequences is an old and well-studied problem in computational molecular biology. Recently, large-scale expression studies and comparative genomics brought this problem into a spotlight by generating a large number of samples with unknown regulatory signals. Here we develop algorithms for recognition of signals in corrupted samples (where only a fraction of sequences contain sites) with biased nucleotide composition. We further benchmark these and other algorithms on several bacterial and archaeal sites in a setting specifically designed to imitate the situations arising in comparative genomics studies.

Combinatorial approaches to finding subtle signals in DNA sequences.

Signal finding (pattern discovery in unaligned DNA sequences) is a fundamental problem in both computer science and molecular biology with important applications in locating regulatory sites and drug target identification. Despite many studies, this problem is far from being resolved: most signals in DNA sequences are so complicated that we don't yet have good models or reliable algorithms for their recognition. We complement existing statistical and machine learning approaches to this problem by a combinatorial approach that proved to be successful in identifying very subtle signals.

Algorithms and software for support of gene identification experiments.

MOTIVATION: Gene annotation is the final goal of gene prediction algorithms. However, these algorithms frequently make mistakes and therefore the use of gene predictions for sequence annotation is hardly possible. As a result, biologists are forced to conduct time-consuming gene identification experiments by designing appropriate PCR primers to test cDNA libraries or applying RT-PCR, exon trapping/amplification, or other techniques. This process frequently amounts to 'guessing' PCR primers on top of unreliable gene predictions and frequently leads to wasting of experimental efforts. RESULTS: The present paper proposes a simple and reliable algorithm for experimental gene identification which bypasses the unreliable gene prediction step. Studies of the performance of the algorithm on a sample of human genes indicate that an experimental protocol based on the algorithm's predictions achieves an accurate gene identification with relatively few PCR primers. Predictions of PCR primers may be used for exon amplification in preliminary mutation analysis during an attempt to identify a gene responsible for a disease. We propose a simple approach to find a short region from a genomic sequence that with high probability overlaps with some exon of the gene. The algorithm is enhanced to find one or more segments that are probably contained in the translated region of the gene and can be used as PCR primers to select appropriate clones in cDNA libraries by selective amplification. The algorithm is further extended to locate a set of PCR primers that uniformly cover all translated regions and can be used for RT-PCR and further sequencing of (unknown) mRNA.

Gene hunting without sequencing genomic clones: finding exon boundaries in cDNAs.

We propose a new experimental protocol, ExonPCR, which is able to identify exon boundaries in a cDNA even in the absence of any genomic clones. ExonPCR can bypass the isolation, characterization, and DNA sequencing of subclones of genomic DNA to determine exon boundaries: a major effort in the process of positional cloning. Given a cDNA sequence, ExonPCR uses a series of "adaptive" steps to analyze the PCR products from cDNA and genomic DNA thereby revealing the approximate positions of "hidden" exon boundaries in the cDNA. The nucleotide sequence of adjacent intronic regions is determined by ligation-mediated PCR. Primers adjacent to the "hidden" exon boundaries are used to amplify genomic DNA followed by limited DNA sequencing of the PCR product. The method was successfully tested on the 3-kb hMSH2 cDNA with 16 known exons and the 9-kb PRDII-BF1 cDNA with a previously unknown number of exons. We subsequently developed the ExonPCR algorithm and software to direct the experimental protocol using a strategy that is analogous to that used in the game "Twenty Questions." Through the use of ExonPCR, the search for disease-causing mutations can be initiated almost immediately after cDNA clones in a genetically mapped region become available. This approach would be most valuable in gene discovery strategies that focus initially on cDNA isolation.

Las Vegas algorithms for gene recognition: suboptimal and error-tolerant spliced alignment.

Recently, Gelfand, Mironov and Pevzner (1996) proposed a spliced alignment approach to gene recognition that provides 99% accurate recognition of human genes if a related mammalian protein is available. However, even 99% accurate gene predictions are insufficient for automated sequence annotation in large-scale sequencing projects and therefore have to be complemented by experimental gene verification. One hundred percent accurate gene predictions would lead to a substantial reduction of experimental work on gene identification. Our goal is to develop an algorithm that either predicts an exon assembly with accuracy sufficient for sequence annotation or warns a biologist that the accuracy of a prediction is insufficient and further experimental work is required. We study suboptimal and error-tolerant spliced alignment problems as the first steps towards such an algorithm, and report an algorithm which provides 100% accurate recognition of human genes in 37% of cases (if a related mammalian protein is available). In 52% of genes, the algorithm predicts at least one exon with 100% accuracy.