Circumsporozoite protein of Plasmodium berghei: gene cloning and identification of the immunodominant epitopes.

The gene encoding the circumsporozoite (CS) protein of the rodent malaria parasite Plasmodium berghei was cloned and characterized. A cDNA library made from P. berghei sporozoite RNA was screened with a monoclonal antibody for expression of CS protein epitopes. The resulting cDNA clone was used to isolate the CS protein gene from a lambda library containing parasite blood-stage DNA. The CS protein gene contains a central region encoding two types of tandemly repeated amino acid units, flanked by nonrepeated regions encoding amino- and carboxy-terminal signal and anchorlike sequences, respectively. One of the central repeated amino acid unit types contains the immunodominant epitopes.     

Production of pro-opiomelanocortin (POMC) by a vaccinia virus transient expression system and in vitro processing of the expressed prohormone by POMC-converting enzyme

Pro-opiomelanocortin (POMC) was expressed in CV-1 (green monkey kidney) cells using a vaccinia virus transient expression system [(1986) Proc. Natl. Acad. Sci. USA 83, 8122]. The system involved infection of cells with a recombinant vaccinia virus carrying the T7 RNA polymerase gene and transfection with a plasmid containing the mouse POMC sequence flanked by the T7 RNA polymerase promoter at its 5'-end and the T7 RNA polymerase terminator at its 3'-end. Assay of the medium from transfected cells showed that 1-2 micrograms of immunoreactive ACTH was produced/10(6) cells. Analysis of the same medium by SDS-PAGE/Western blots revealed a band of 30-36 kDa, which was immunostained with both ACTH and beta-endorphin antisera. Labeling the transfected cells with [3H]Arg, followed by immunoprecipitation and SDS-PAGE showed the synthesis of a major peak of POMC, 33 kDa. Purified [3H]POMC expressed by CV-1 cells was cleaved in vitro by bovine intermediate lobe secretory vesicle pro-opiomelanocortin-converting enzyme to ACTH intermediates (19-25 kDa), beta-lipotropin and beta-endorphin. Thus, this work has demonstrated a technique for expressing microgram quantities of prohormones in mammalian cells, suitable for use as substrates for prohormone-converting enzymes in vitro.     

A single mutation affects L-serine deaminase, L-leucyl-, L-phenylalanyl-tRNA protein transferase, and proline oxidase activity in Escherichia coli K-12.

A mutation at a single locus, wyb, results in several phenotypic changes in Escherichia coli K-12. The Wyb- phenotype includes: (i) an increase in L-serine deaminase activity, together with a loss of inducibility by L-leucine; (ii) an absence of L-leucyl-, L-phenylalanyl-tRNA protein transferase activity; (iii) inducibility of proline oxidase by proline; and (iv) a loss of ability to use maltose as a carbon and energy source.     

MaxHiC: A robust background correction model to identify biologically relevant chromatin interactions in Hi-C and capture Hi-C experiments

Hi-C is a genome-wide chromosome conformation capture technology that detects interactions between pairs of genomic regions and exploits higher order chromatin structures. Conceptually Hi-C data counts interaction frequencies between every position in the genome and every other position. Biologically functional interactions are expected to occur more frequently than transient background and artefactual interactions. To identify biologically relevant interactions, several background models that take biases such as distance, GC content and mappability into account have been proposed. Here we introduce MaxHiC, a background correction tool that deals with these complex biases and robustly identifies statistically significant interactions in both Hi-C and capture Hi-C experiments. MaxHiC uses a negative binomial distribution model and a maximum likelihood technique to correct biases in both Hi-C and capture Hi-C libraries. We systematically benchmark MaxHiC against major Hi-C background correction tools including Hi-C significant interaction callers (SIC) and Hi-C loop callers using published Hi-C, capture Hi-C, and Micro-C datasets. Our results demonstrate that 1) Interacting regions identified by MaxHiC have significantly greater levels of overlap with known regulatory features (e.g. active chromatin histone marks, CTCF binding sites, DNase sensitivity) and also disease-associated genome-wide association SNPs than those identified by currently existing models, 2) the pairs of interacting regions are more likely to be linked by eQTL pairs and 3) more likely to link known regulatory features including known functional enhancer-promoter pairs validated by CRISPRi than any of the existing methods. We also demonstrate that interactions between different genomic region types have distinct distance distributions only revealed by MaxHiC. MaxHiC is publicly available as a python package for the analysis of Hi-C, capture Hi-C and Micro-C data.     

Development of a 16S rRNA gene-based prototype microarray for the detection of selected actinomycetes genera

Actinomycetes are known for their secondary metabolites, which have been successfully used as drugs in human and veterinary medicines. However, information on the distribution of this group of Gram-positive bacteria in diverse ecosystems and a comprehension of their activities in ecosystem processes are still scarce. We have developed a 16S rRNA-based taxonomic microarray that targets key actinomycetes at the genus level. In total, 113 actinomycete 16S rRNA probes, corresponding to 55 of the 202 described genera, were designed. The microarray accuracy was evaluated by comparing signal intensities with probe/target-weighted mismatch values and the Gibbs energy of the probe/target duplex formation by hybridizing 17 non-actinomycete and 29 actinomycete strains/clones with the probe set. The validation proved that the probe set was specific, with only 1.3% of false results. The incomplete coverage of actinomycetes by a genus-specific probe was caused by the limited number of 16S rRNA gene sequences in databases or insufficient 16S rRNA gene polymorphism. The microarray enabled discrimination between actinomycete communities from three forest soil samples collected at one site. Cloning and sequencing of 16S rRNA genes from one of the soil samples confirmed the microarray results. We propose that this newly constructed microarray will be a valuable tool for genus-level comparisons of actinomycete communities in various ecological conditions.