Pathogenic characteristics of persistent feline enteric coronavirus infection in cats.

Feline coronaviruses (FCoV) comprise two biotypes: feline enteric coronaviruses (FECV) and feline infectious peritonitis viruses (FIPV). FECV is associated with asymptomatic persistent enteric infections, while FIPV causes feline infectious peritonitis (FIP), a usually fatal systemic disease in domestic cats and some wild Felidae. FIPV arises from FECV by mutation. FCoV also occur in two serotypes, I and II, of which the serotype I viruses are by far the most prevalent in the field. Yet, most of our knowledge about FCoV infections relates to serotype II viruses, particularly about the FIPV, mainly because type I viruses grow poorly in cell culture. Hence, the aim of the present work was the detailed study of the epidemiologically most relevant viruses, the avirulent serotype I viruses. Kittens were inoculated oronasally with different doses of two independent FECV field strains, UCD and RM. Persistent infection could be reproducibly established. The patterns of clinical symptoms, faecal virus shedding and seroconversion were monitored for up to 10 weeks revealing subtle but reproducible differences between the two viruses. Faecal virus, i.e. genomic RNA, was detected during persistent FECV infection only in the large intestine, downstream of the appendix, and could occasionally be observed also in the blood. The implications of our results, particularly our insights into the persistently infected state, are discussed.

Response prediction to a multitargeted kinase inhibitor in cancer cell lines and xenograft tumors using high-content tyrosine peptide arrays with a kinetic readout.

Multitargeted kinase inhibitors have shown clinical efficacy in a range of cancer types. However, two major problems associated with these drugs are the low fraction of patients for which these treatments provide initial clinical benefit and the occurrence of resistance during prolonged therapy. Several types of predictive biomarkers have been suggested, such as expression level and phosphorylation status of the major targeted kinase(s), mutational status of the kinases involved and of key components of the downstream signaling cascades, and gene expression signatures. In this work, we describe the development of a response prediction platform that does not require prior knowledge of the relevant kinases targeted by the inhibitor; instead, a phosphotyrosine peptide profile using peptide arrays with a kinetic readout is derived in lysates in the presence and absence of a kinase inhibitor. We show in a range of cell lines and in xenograft tumors that this approach allows for the stratification of responders and nonresponders to a multitargeted kinase inhibitor.

Testing for trends in dose-response microarray experiments: a comparison of several testing procedures, multiplicity and resampling-based inference.

Dose-response studies are commonly used in experiments in pharmaceutical research in order to investigate the dependence of the response on dose, i.e., a trend of the response level toxicity with respect to dose. In this paper we focus on dose-response experiments within a microarray setting in which several microarrays are available for a sequence of increasing dose levels. A gene is called differentially expressed if there is a monotonic trend (with respect to dose) in the gene expression. We review several testing procedures which can be used in order to test equality among the gene expression means against ordered alternatives with respect to dose, namely Williams' (Williams 1971 and 1972), Marcus' (Marcus 1976), global likelihood ratio test (Bartholomew 1961, Barlow et al. 1972, and Robertson et al. 1988), and M (Hu et al. 2005) statistics. Additionally we introduce a modification to the standard error of the M statistic. We compare the performance of these five test statistics. Moreover, we discuss the issue of one-sided versus two-sided testing procedures. False Discovery Rate (Benjamni and Hochberg 1995, Ge et al. 2003), and resampling-based Familywise Error Rate (Westfall and Young 1993) are used to handle the multiple testing issue. The methods above are applied to a data set with 4 doses (3 arrays per dose) and 16,998 genes. Results on the number of significant genes from each statistic are discussed. A simulation study is conducted to investigate the power of each statistic. A R library IsoGene implementing the methods is available from the first author.