Anchoring of proteins to lactic acid bacteria

The anchoring of proteins to the cell surface of lactic acid bacteria (LAB) using genetic techniques is an exciting and emerging research area that holds great promise for a wide variety of biotechnological applications. This paper reviews five different types of anchoring domains that have been explored for their efficiency in attaching hybrid proteins to the cell membrane or cell wall of LAB. The most exploited anchoring regions are those with the LPXTG box that bind the proteins in a covalent way to the cell wall. In recent years, two new modes of cell wall protein anchoring have been studied and these may provide new approaches in surface display. The important progress that is being made with cell surface display of chimaeric proteins in the areas of vaccine development and enzyme- or whole-cell immobilisation is highlighted.

CRISPR Recognition Tool (CRT): a tool for automatic detection of clustered regularly interspaced palindromic repeats

<p>Abstract</p> <p>Background</p> <p>Clustered Regularly Interspaced Palindromic Repeats (CRISPRs) are a novel type of direct repeat found in a wide range of bacteria and archaea. CRISPRs are beginning to attract attention because of their proposed mechanism; that is, defending their hosts against invading extrachromosomal elements such as viruses. Existing repeat detection tools do a poor job of identifying CRISPRs due to the presence of unique spacer sequences separating the repeats. In this study, a new tool, CRT, is introduced that rapidly and accurately identifies CRISPRs in large DNA strings, such as genomes and metagenomes.</p> <p>Results</p> <p>CRT was compared to CRISPR detection tools, Patscan and Pilercr. In terms of correctness, CRT was shown to be very reliable, demonstrating significant improvements over Patscan for measures precision, recall and quality. When compared to Pilercr, CRT showed improved performance for recall and quality. In terms of speed, CRT proved to be a huge improvement over Patscan. Both CRT and Pilercr were comparable in speed, however CRT was faster for genomes containing large numbers of repeats.</p> <p>Conclusion</p> <p>In this paper a new tool was introduced for the automatic detection of CRISPR elements. This tool, CRT, showed some important improvements over current techniques for CRISPR identification. CRT's approach to detecting repetitive sequences is straightforward. It uses a simple sequential scan of a DNA sequence and detects repeats directly without any major conversion or preprocessing of the input. This leads to a program that is easy to describe and understand; yet it is very accurate, fast and memory efficient, being O(<it>n</it>) in space and O(<it>nm</it>/<it>l</it>) in time.</p

How repetitive are genomes?

BACKGROUND: Genome sequences vary strongly in their repetitiveness and the causes for this are still debated. Here we propose a novel measure of genome repetitiveness, the index of repetitiveness, I(r), which can be computed in time proportional to the length of the sequences analyzed. We apply it to 336 genomes from all three domains of life. RESULTS: The expected value of I(r )is zero for random sequences of any G/C content and greater than zero for sequences with excess repeats. We find that the I(r )of archaea is significantly smaller than that of eubacteria, which in turn is smaller than that of eukaryotes. Mouse chromosomes have a significantly higher I(r )than human chromosomes and within each genome the Y chromosome is most repetitive. A sliding window analysis reveals that the human HOXA cluster and two surrounding genes are characterized by local minima in I(r). A program for calculating the I(r )is freely available at . CONCLUSION: The general measure of DNA repetitiveness proposed in this paper can be efficiently computed on a genomic scale. This reveals a broad spectrum of repetitiveness among diverse genomes which agrees qualitatively with previous studies of repeat content. A sliding window analysis helps to analyze the intragenomic distribution of repeats

Biological and biomedical implications of the co-evolution of pathogens and their hosts

Co-evolution between host and pathogen is, in principle, a powerful determinant of the biology and genetics of infection and disease. Yet co-evolution has proven difficult to demonstrate rigorously in practice, and co-evolutionary thinking is only just beginning to inform medical or veterinary research in any meaningful way, even though it can have a major influence on how genetic variation in biomedically important traits is interpreted. Improving our understanding of the biomedical significance of co-evolution will require changing the way in which we look for it, complementing the phenomenological approach traditionally favored by evolutionary biologists with the exploitation of the extensive data becoming available on the molecular biology and molecular genetics of host–pathogen interactions