Stratifying patients with peripheral neuropathic pain based on sensory profiles : algorithm and sample size recommendations

In a recent cluster analysis, it has been shown that patients with peripheral neuropathic pain can be grouped into 3 sensory phenotypes based on quantitative sensory testing profiles, which are mainly characterized by either sensory loss, intact sensory function and mild thermal hyperalgesia and/or allodynia, or loss of thermal detection and mild mechanical hyperalgesia and/or allodynia. Here, we present an algorithm for allocation of individual patients to these subgroups. The algorithm is nondeterministic-ie, a patient can be sorted to more than one phenotype-and can separate patients with neuropathic pain from healthy subjects (sensitivity: 78%, specificity: 94%). We evaluated the frequency of each phenotype in a population of patients with painful diabetic polyneuropathy (n = 151), painful peripheral nerve injury (n = 335), and postherpetic neuralgia (n = 97) and propose sample sizes of study populations that need to be screened to reach a subpopulation large enough to conduct a phenotype-stratified study. The most common phenotype in diabetic polyneuropathy was sensory loss (83%), followed by mechanical hyperalgesia (75%) and thermal hyperalgesia (34%, note that percentages are overlapping and not additive). In peripheral nerve injury, frequencies were 37%, 59%, and 50%, and in postherpetic neuralgia, frequencies were 31%, 63%, and 46%. For parallel study design, either the estimated effect size of the treatment needs to be high (> 0.7) or only phenotypes that are frequent in the clinical entity under study can realistically be performed. For crossover design, populations under 200 patients screened are sufficient for all phenotypes and clinical entities with a minimum estimated treatment effect size of 0.5.Peer reviewe

Insights into Cleavage Specificity from the Crystal Structure of Foot-and-Mouth Disease Virus 3C Protease Complexed with a Peptide Substrate.

Accepted versio

Transcriptional Regulation of N-Acetylglutamate Synthase

The urea cycle converts toxic ammonia to urea within the liver of mammals. At least 6 enzymes are required for ureagenesis, which correlates with dietary protein intake. The transcription of urea cycle genes is, at least in part, regulated by glucocorticoid and glucagon hormone signaling pathways. N-acetylglutamate synthase (NAGS) produces a unique cofactor, N-acetylglutamate (NAG), that is essential for the catalytic function of the first and rate-limiting enzyme of ureagenesis, carbamyl phosphate synthetase 1 (CPS1). However, despite the important role of NAGS in ammonia removal, little is known about the mechanisms of its regulation. We identified two regions of high conservation upstream of the translation start of the NAGS gene. Reporter assays confirmed that these regions represent promoter and enhancer and that the enhancer is tissue specific. Within the promoter, we identified multiple transcription start sites that differed between liver and small intestine. Several transcription factor binding motifs were conserved within the promoter and enhancer regions while a TATA-box motif was absent. DNA-protein pull-down assays and chromatin immunoprecipitation confirmed binding of Sp1 and CREB, but not C/EBP in the promoter and HNF-1 and NF-Y, but not SMAD3 or AP-2 in the enhancer. The functional importance of these motifs was demonstrated by decreased transcription of reporter constructs following mutagenesis of each motif. The presented data strongly suggest that Sp1, CREB, HNF-1, and NF-Y, that are known to be responsive to hormones and diet, regulate NAGS transcription. This provides molecular mechanism of regulation of ureagenesis in response to hormonal and dietary changes

Second generation tyrosine kinase inhibitors prevent disease progression in high-risk (high CIP2A) chronic myeloid leukaemia patients.

High cancerous inhibitor of PP2A (CIP2A) protein levels at diagnosis of chronic myeloid leukaemia (CML) are predictive of disease progression in imatinib-treated patients. It is not known whether this is true in patients treated with second generation tyrosine kinase inhibitors (2G TKI) from diagnosis, and whether 2G TKIs modulate the CIP2A pathway. Here, we show that patients with high diagnostic CIP2A levels who receive a 2G TKI do not progress, unlike those treated with imatinib (P=<0.0001). 2G TKIs induce more potent suppression of CIP2A and c-Myc than imatinib. The transcription factor E2F1 is elevated in high CIP2A patients and following 1 month of in vivo treatment 2G TKIs suppress E2F1 and reduce CIP2A; these effects are not seen with imatinib. Silencing of CIP2A, c-Myc or E2F1 in K562 cells or CML CD34+ cells reactivates PP2A leading to BCR-ABL suppression. CIP2A increases proliferation and this is only reduced by 2G TKIs. Patients with high CIP2A levels should be offered 2G TKI treatment in preference to imatinib. 2G TKIs disrupt the CIP2A/c-Myc/E2F1 positive feedback loop, leading to lower disease progression risk. The data supports the view that CIP2A inhibits PP2Ac, stabilising E2F1, creating a CIP2A/c-Myc/E2F1 positive feedback loop, which imatinib cannot overcome