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Clostridioides difficile produces toxins that damage the colonic epithelium, causing colitis. Variation in disease severity is poorly understood and has been attributed to host factors and virulence differences between C. difficile strains. We test 23 epidemic ST1 C. difficile clinical isolates for their virulence in mice. All isolates encode a complete Tcd pathogenicity locus and achieve similar colonization densities. However, disease severity varies from lethal to avirulent infections. Genomic analysis of avirulent isolates reveals a 69-bp deletion in the cdtR gene, which encodes a response regulator for binary toxin expression. Deleting the 69-bp sequence in virulent R20291 strain renders it avirulent in mice with reduced toxin gene transcription. Our study demonstrates that a natural deletion within cdtR attenuates virulence in the epidemic ST1 C. difficile isolates without reducing colonization and persistence. Distinguishing strains on the basis of cdtR may enhance the specificity of diagnostic tests for C. difficile colitis. [Display omitted] • Clinical ST1 C. difficile isolates with identical PaLoc can differ remarkably in virulence • Two avirulent C. difficile isolates remain avirulent in immune-deficient and germ-free mice • Avirulent C. difficile isolates contain a deletion in the regulatory gene for binary toxin • CdtR regulates Tcd and CDT toxins, thus broadly impacting the virulence of C. difficile Dong et al. show that clinical ST1 C. difficile isolates with identical pathogenicity loci have variable virulence in mice. A natural deletion within the regulatory gene (cdtR) for binary toxin attenuates virulence in epidemic ST1 C. difficile isolates without reducing colonization and persistence. [ABSTRACT FROM AUTHOR]

Abstract: Successful prediction of protein domain boundaries provides valuable information not only for the computational structure prediction of multi-domain proteins but also for the experimental structure determination. In this work, a novel index at the profile level is presented, namely, the profile domain linker propensity index (PDLI), which uses the evolutionary information of profiles for domain linker prediction. The frequency profiles are directly calculated from the multiple sequence alignments outputted by PSI-BLAST and converted into binary profiles with a probability threshold. PDLI is then obtained by the frequencies of binary profiles in domain linkers as compared to those in domains. A smooth and normalized numeric profile is generated for any amino acid sequences from which the domain linkers can be predicted. Testing on the Structural Classification of Proteins (SCOP) database and CASP6 targets shows that PDLI outperforms other indexes at the amino acid level. [Copyright &y& Elsevier]

Abstract: Protein remote homology detection is a critical step toward annotating its structure and function. Supervised learning algorithms such as support vector machine are currently the most accurate methods. The position-specific score matrices (PSSMs) contain wealthy information about the evolutionary relationship of proteins. However, the PSSMs often have different lengths, which are difficult to be used by machine-learning methods. In this study, a simple, fast and powerful method is presented for protein remote homology detection, which combines support vector machine with auto-cross covariance transformation. The PSSMs are converted into a series of fixed-length vectors by auto-cross covariance transformation and these vectors are then input to a support vector machine classifier for remote homology detection. The sequence-order effects can be effectively captured by this scheme. Experiments are performed on well-established datasets, and the remote homology is simulated at the superfamily and the fold level, respectively. The results show that the proposed method, referred to as ACCRe, is comparable or even better than the state-of-the-art methods in terms of detection performance, and its time complexity is superior to those of other profile-based SVM methods. The auto-cross covariance transformation provides a novel way for the usage of evolutionary information, which can be widely used for protein-level studies. [Copyright &y& Elsevier]

Abstract: Predicting the binding sites between two interacting proteins provides important clues to the function of a protein. In this study, we present a building block of proteins called order profiles to use the evolutionary information of the protein sequence frequency profiles and apply this building block to produce a class of propensities called order profile interface propensities. For comparisons, we revisit the usage of residue interface propensities and binary profile interface propensities for protein binding site prediction. Each kind of propensities combined with sequence profiles and accessible surface areas are inputted into SVM. When tested on four types of complexes (hetero-permanent complexes, hetero-transient complexes, homo-permanent complexes and homo-transient complexes), experimental results show that the order profile interface propensities are better than residue interface propensities and binary profile interface propensities. Therefore, order profile is a suitable profile-level building block of the protein sequences and can be widely used in many tasks of computational biology, such as the sequence alignment, the prediction of domain boundary, the designation of knowledge-based potentials and the protein remote homology detection. [Copyright &y& Elsevier]