Transcriptional profiling of TLR-4/7/8-stimulated guinea pig splenocytes and whole blood by bDNA assay.

Toll-like receptor (TLR) agonists are currently being examined as adjuvants for vaccines, with several lead candidates now in licensed products or in late-stage clinical development. Guinea pigs are widely used for preclinical testing of drugs and vaccines; however, evaluation of TLR agonists in this model is hindered by the limited availability of immunological tools and reagents. In this study, we validated the use of a branched-chain DNA (bDNA) assay known as the QuantiGene Plex 2.0 Reagent System for measuring innate cytokine and chemokine mRNA levels following TLR stimulation of guinea pig cells. Gene expression for T-helper-1 (Th1) polarizing cytokines (TNF-α, IL-1ß, IL-12) and chemokines (CXCL1, CCL2) was upregulated following ex vivo stimulation of guinea pig splenocytes and whole blood with TLR-4 or TLR-7/8 agonists. These data confirm the utility of the QuantiGene system both as an alternative to RT-PCR for measuring transcript levels and as a high-throughput screening tool for dissecting the immunological response to TLR stimulation in guinea pigs. Overall, the QuantiGene platform is reliable, reproducible, and sensitive. These agonists have the potential to be used as adjuvant components in vaccines against various pathogens.

Applying TLR synergy in immunotherapy: implications in cutaneous leishmaniasis.

Therapy of intracellular pathogens can be complicated by drug toxicity, drug resistance, and the need for prolonged treatment regimens. One approach that has shown promise is immunotherapy. Leishmaniasis, a vector-borne disease ranked among the six most important tropical infectious diseases by the World Health Organization, has been treated clinically with crude or defined vaccine preparations or cytokines, such as IFN-gamma and GM-CSF, in combination with chemotherapy. We have attempted to develop an improved and defined immunotherapeutic using a mouse model of cutaneous leishmaniasis. We hypothesized that immunotherapy may be improved by using TLR synergy to enhance the parasite-specific immune response. We formulated L110f, a well-established Leishmania poly-protein vaccine candidate, in conjunction with either monophosphoryl lipid A, a TLR4 agonist, or CpG, a TLR9 agonist, or a combination of these, and evaluated anti-Leishmania immune responses in absence or presence of active disease. Only mice treated with L110f plus monophosphoryl lipid A-CpG were able to induce a strong effective T cell response during disease and subsequently cured lesions and reduced parasite burden when compared with mice treated with L110f and either single adjuvant. Our data help to define a correlate of protection during active infection and indicate TLR synergy to be a potentially valuable tool in treating intracellular infections.

Standard mixtures for proteome studies.

Mixtures of moderate complexity were formed from 23 peptides and 12 proteins digested with trypsin, all individually characterized. These mixtures were analyzed with replicates in full and windowed m/z ranges using online high-performance reverse phase liquid chromatography coupled via electrospray ionization to an ion trap mass spectrometer. The resulting spectra were searched using SEQUEST against databases of different sizes and contents and confidences of the observed identifications were evaluated by our earlier statistical model. These data were then combined with biologically derived spectral data, searched, and further evaluated. All peptides but one and all proteins were identified with high confidence. Additionally, the presence and behavior of quadruply charged peptides was analyzed. The properties of the proposed peptide and protein mixtures as well as the performance of the statistical model were carefully investigated. These mixtures mimic the complexity seen in large-scale proteomics experiments, and are proposed to serve as quality assessment standards for future proteome studies.

LIP index for peptide classification using MS/MS and SEQUEST search via logistic regression.

This study addresses the issue of peptide identification resulting from tandem mass spectrometry proteomics analysis followed by database search. This work shows that the Logistic Identification of Peptides (LIP) Index achieves high sensitivity and specificity for peptide classification relative to a manually verified "gold" standard and also accurately estimates the probability of a correct peptide match. The LIP Index is a weighted average of SEQUEST output variables based on logistic regression models and is a transparent, easy to use, inclusive, extendable, and statistically sound approach to classify correct peptide identifications. Modifications, such as normalizing cross-correlations (Xcorr) for peptide length, adjusting for charge state, and the number of tryptic termini, significantly improve the fit the logistic regression models, as well as increase sensitivity and specificity. The LIP Index also incorporates earlier developed statistical models on spectral quality assessment and peptide identification, which further improves sensitivity and specificity.