IL-1beta, IL-6, and RANTES as biomarkers of Chikungunya severity.

BACKGROUND: Little is known about the immunopathogenesis of Chikungunya virus. Circulating levels of immune mediators and growth factors were analyzed from patients infected during the first Singaporean Chikungunya fever outbreak in early 2008 to establish biomarkers associated with infection and/or disease severity. METHODS AND FINDINGS: Adult patients with laboratory-confirmed Chikungunya fever infection, who were referred to the Communicable Disease Centre/Tan Tock Seng Hospital during the period from January to February 2008, were included in this retrospective study. Plasma fractions were analyzed using a multiplex-microbead immunoassay. Among the patients, the most common clinical features were fever (100%), arthralgia (90%), rash (50%) and conjunctivitis (40%). Profiles of 30 cytokines, chemokines, and growth factors were able to discriminate the clinical forms of Chikungunya from healthy controls, with patients classified as non-severe and severe disease. Levels of 8 plasma cytokines and 4 growth factors were significantly elevated. Statistical analysis showed that an increase in IL-1beta, IL-6 and a decrease in RANTES were associated with disease severity. CONCLUSIONS: This is the first comprehensive report on the production of cytokines, chemokines, and growth factors during acute Chikungunya virus infection. Using these biomarkers, we were able to distinguish between mild disease and more severe forms of Chikungunya fever, thus enabling the identification of patients with poor prognosis and monitoring of the disease.

Flanking signal and mature peptide residues influence signal peptide cleavage.

BACKGROUND: Signal peptides (SPs) mediate the targeting of secretory precursor proteins to the correct subcellular compartments in prokaryotes and eukaryotes. Identifying these transient peptides is crucial to the medical, food and beverage and biotechnology industries yet our understanding of these peptides remains limited. This paper examines the most common type of signal peptides cleavable by the endoprotease signal peptidase I (SPase I), and the residues flanking the cleavage sites of three groups of signal peptide sequences, namely (i) eukaryotes (Euk) (ii) Gram-positive (Gram+) bacteria, and (iii) Gram-negative (Gram-) bacteria. RESULTS: In this study, 2352 secretory peptide sequences from a variety of organisms with amino-terminal SPs are extracted from the manually curated SPdb database for analysis based on physicochemical properties such as pI, aliphatic index, GRAVY score, hydrophobicity, net charge and position-specific residue preferences. Our findings show that the three groups share several similarities in general, but they display distinctive features upon examination in terms of their amino acid compositions and frequencies, and various physico-chemical properties. Thus, analysis or prediction of their sequences should be separated and treated as distinct groups. CONCLUSION: We conclude that the peptide segment recognized by SPase I extends to the start of the mature protein to a limited extent, upon our survey of the amino acid residues surrounding the cleavage processing site. These flanking residues possibly influence the cleavage processing and contribute to non-canonical cleavage sites. Our findings are applicable in defining more accurate prediction tools for recognition and identification of cleavage site of SPs.

SPdb--a signal peptide database.

BACKGROUND: The signal peptide plays an important role in protein targeting and protein translocation in both prokaryotic and eukaryotic cells. This transient, short peptide sequence functions like a postal address on an envelope by targeting proteins for secretion or for transfer to specific organelles for further processing. Understanding how signal peptides function is crucial in predicting where proteins are translocated. To support this understanding, we present SPdb signal peptide database http://proline.bic.nus.edu.sg/spdb, a repository of experimentally determined and computationally predicted signal peptides. RESULTS: SPdb integrates information from two sources (a) Swiss-Prot protein sequence database which is now part of UniProt and (b) EMBL nucleotide sequence database. The database update is semi-automated with human checking and verification of the data to ensure the correctness of the data stored. The latest release SPdb release 3.2 contains 18,146 entries of which 2,584 entries are experimentally verified signal sequences; the remaining 15,562 entries are either signal sequences that fail to meet our filtering criteria or entries that contain unverified signal sequences. CONCLUSION: SPdb is a manually curated database constructed to support the understanding and analysis of signal peptides. SPdb tracks the major updates of the two underlying primary databases thereby ensuring that its information remains up-to-date.