Lysophosphatidic acid-3 receptor-mediated feed-forward production of lysophosphatidic acid: an initiator of nerve injury-induced neuropathic pain

<p>Abstract</p> <p>Background</p> <p>We previously reported that intrathecal injection of lysophosphatidylcholine (LPC) induced neuropathic pain through activation of the lysophosphatidic acid (LPA)-1 receptor, possibly via conversion to LPA by autotaxin (ATX).</p> <p>Results</p> <p>We examined <it>in vivo </it>LPA-induced LPA production using a biological titration assay with B103 cells expressing LPA₁receptors. Intrathecal administration of LPC caused time-related production of LPA in the spinal dorsal horn and dorsal roots, but not in the dorsal root ganglion, spinal nerve or sciatic nerve. LPC-induced LPA production was markedly diminished in ATX heterozygotes, and was abolished in mice that were deficient in LPA₃, but not LPA₁or LPA₂receptors. Similar time-related and LPA₃receptor-mediated production of LPA was observed following intrathecal administration of LPA. In an <it>in vitro </it>study using spinal cord slices, LPA-induced LPA production was also mediated by ATX and the LPA₃receptor. Intrathecal administration of LPA, in contrast, induced neuropathic pain, which was abolished in mice deficient in LPA₁or LPA₃receptors.</p> <p>Conclusion</p> <p>These findings suggest that feed-forward LPA production is involved in LPA-induced neuropathic pain.</p

Evidence of a metabolic memory to early-life dietary restriction in male C57BL/6 mice

&lt;p&gt;Background: Dietary restriction (DR) extends lifespan and induces beneficial metabolic effects in many animals. What is far less clear is whether animals retain a metabolic memory to previous DR exposure, that is, can early-life DR preserve beneficial metabolic effects later in life even after the resumption of ad libitum (AL) feeding. We examined a range of metabolic parameters (body mass, body composition (lean and fat mass), glucose tolerance, fed blood glucose, fasting plasma insulin and insulin-like growth factor 1 (IGF-1), insulin sensitivity) in male C57BL/6 mice dietary switched from DR to AL (DR-AL) at 11 months of age (mid life). The converse switch (AL-DR) was also undertaken at this time. We then compared metabolic parameters of the switched mice to one another and to age-matched mice maintained exclusively on an AL or DR diet from early life (3 months of age) at 1 month, 6 months or 10 months post switch.&lt;/p&gt; &lt;p&gt;Results: Male mice dietary switched from AL-DR in mid life adopted the metabolic phenotype of mice exposed to DR from early life, so by the 10-month timepoint the AL-DR mice overlapped significantly with the DR mice in terms of their metabolic phenotype. Those animals switched from DR-AL in mid life showed clear evidence of a glycemic memory, with significantly improved glucose tolerance relative to mice maintained exclusively on AL feeding from early life. This difference in glucose tolerance was still apparent 10 months after the dietary switch, despite body mass, fasting insulin levels and insulin sensitivity all being similar to AL mice at this time.&lt;/p&gt; &lt;p&gt;Conclusions: Male C57BL/6 mice retain a long-term glycemic memory of early-life DR, in that glucose tolerance is enhanced in mice switched from DR-AL in mid life, relative to AL mice, even 10 months following the dietary switch. These data therefore indicate that the phenotypic benefits of DR are not completely dissipated following a return to AL feeding. The challenge now is to understand the molecular mechanisms underlying these effects, the time course of these effects and whether similar interventions can confer comparable benefits in humans.&lt;/p&gt

A comparison of three clustering methods for finding subgroups in MRI, SMS or clinical data: SPSS TwoStep Cluster analysis, Latent Gold and SNOB

Background: There are various methodological approaches to identifying clinically important subgroups and one method is to identify clusters of characteristics that differentiate people in cross-sectional and/or longitudinal data using Cluster Analysis (CA) or Latent Class Analysis (LCA). There is a scarcity of head-to-head comparisons that can inform the choice of which clustering method might be suitable for particular clinical datasets and research questions. Therefore, the aim of this study was to perform a head-to-head comparison of three commonly available methods (SPSS TwoStep CA, Latent Gold LCA and SNOB LCA). Methods. The performance of these three methods was compared: (i) quantitatively using the number of subgroups detected, the classification probability of individuals into subgroups, the reproducibility of results, and (ii) qualitatively using subjective judgments about each program's ease of use and interpretability of the presentation of results.We analysed five real datasets of varying complexity in a secondary analysis of data from other research projects. Three datasets contained only MRI findings (n = 2,060 to 20,810 vertebral disc levels), one dataset contained only pain intensity data collected for 52 weeks by text (SMS) messaging (n = 1,121 people), and the last dataset contained a range of clinical variables measured in low back pain patients (n = 543 people). Four artificial datasets (n = 1,000 each) containing subgroups of varying complexity were also analysed testing the ability of these clustering methods to detect subgroups and correctly classify individuals when subgroup membership was known. Results: The results from the real clinical datasets indicated that the number of subgroups detected varied, the certainty of classifying individuals into those subgroups varied, the findings had perfect reproducibility, some programs were easier to use and the interpretability of the presentation of their findings also varied. The results from the artificial datasets indicated that all three clustering methods showed a near-perfect ability to detect known subgroups and correctly classify individuals into those subgroups. Conclusions: Our subjective judgement was that Latent Gold offered the best balance of sensitivity to subgroups, ease of use and presentation of results with these datasets but we recognise that different clustering methods may suit other types of data and clinical research questions

Cumulate causes for the low contents of sulfide-loving elements in the continental crust

Despite the economic importance of chalcophile (sulfide-loving) and siderophile (metal-loving) elements (CSEs), it is unclear how they become enriched or depleted in the continental crust, compared with the oceanic crust. This is due in part to our limited understanding of the partitioning behaviour of the CSEs. Here I compile compositional data for mid-ocean ridge basalts and subduction-related volcanic rocks. I show that the mantle-derived melts that contribute to oceanic and continental crust formation rarely avoid sulfide saturation during cooling in the crust and, on average, subduction-zone magmas fractionate sulfide at the base of the continental crust prior to ascent. Differentiation of mantle-derived melts enriches lower crustal sulfide- and silicate-bearing cumulates in some CSEs compared with the upper crust. This storage predisposes the cumulate-hosted compatible CSEs (such as Cu and Au) to be recycled back into the mantle during subduction and delamination, resulting in their low contents in the bulk continental crust and potentially contributing to the scarcity of ore deposits in the upper continental crust. By contrast, differentiation causes the upper oceanic and continental crust to become enriched in incompatible CSEs (such as W) compared with the lower oceanic and continental crust. Consequently, incompatible CSEs are predisposed to become enriched in subduction-zone magmas that contribute to continental crust formation and are less susceptible to removal from the continental crust via delamination compared with the compatible CSEs

Stromal Cell-Derived Factor 1 Polymorphism in Retinal Vein Occlusion

BACKGROUND: Stromal cell-derived factor 1 (SDF1) has crucial role in the regulation of angiogenesis and ocular neovascularisation (NV). The purpose of this study was to evaluate the association between SDF1-3'G(801)A polymorphism and NV complications of retinal vein occlusion (RVO). METHODS: 130 patients with RVO (median age: 69.0, range 35-93 years; male/female- 58/72; 55 patients had central RVO, 75 patients had branch RVO) were enrolled in this study. In the RVO group, 40 (30.8%) patients were diagnosed with NV complications of RVO and 90 (69.2%) patients without NVs. The median follow up period was 40.3 months (range: 18-57 months). The SDF1-3'G(801)A polymorphism was detected by PCR-RFLP. Allelic prevalence was related to reference values obtained in the control group consisted of 125 randomly selected, age and gender matched, unrelated volunteers (median age: 68.0, range 36-95 years; male/female- 53/72). Statistical analysis of the allele and genotype differences between groups (RVO patients vs controls; RVO patients with NV vs RVO patients without NV) was determined by chi-squared test. P value of <0.05 was considered statistically significant. RESULTS: Hardy-Weinberg criteria was fulfilled in all groups. The SDF1-3'G(801)A allele and genotype frequencies of RVO patients were similar to controls (SDF1-3'A allele: 22.3% vs 20.8%; SDF1-3'(801)AA: 5.4% vs 4.8%, SDF1-3'(801)GG: 60.8% vs 63.2%). The frequency of SDF1-3'(801)AA and SDF1-3'(801)GA genotypes, as well as the SDF1-3'(801)A allele frequency were higher in RVO patients with NV versus in patients without NV complication (SDF1-3'(801)AA+AG genotypes: 57.5% vs 31.1%, p = 0.008; SDF1-3'(801)A allele: 35.0% vs 16.7%, p = 0.002) or versus controls (SDF1-3'(801)AA+AG genotypes 57.5% vs 36.8%, p = 0.021; SDF1-3'(801)A allele: 35.0% vs 20.8% p = 0.01). Carrying of SDF1-3'(801)A allele increased the risk of neovascularisation complications of RVO by 2.69 (OR, 95% CI = 1.47-4.93). CONCLUSION: These findings suggest that carrying SDF1-3'(801)A allele plays a role in the development of neovascular complications in retinal vein occlusion

A genome-wide assessment of the role of untagged copy number variants in type 1 diabetes.

Genome-wide association studies (GWAS) for type 1 diabetes (T1D) have successfully identified more than 40 independent T1D associated tagging single nucleotide polymorphisms (SNPs). However, owing to technical limitations of copy number variants (CNVs) genotyping assays, the assessment of the role of CNVs has been limited to the subset of these in high linkage disequilibrium with tag SNPs. The contribution of untagged CNVs, often multi-allelic and difficult to genotype using existing assays, to the heritability of T1D remains an open question. To investigate this issue, we designed a custom comparative genetic hybridization array (aCGH) specifically designed to assay untagged CNV loci identified from a variety of sources. To overcome the technical limitations of the case control design for this class of CNVs, we genotyped the Type 1 Diabetes Genetics Consortium (T1DGC) family resource (representing 3,903 transmissions from parents to affected offspring) and used an association testing strategy that does not necessitate obtaining discrete genotypes. Our design targeted 4,309 CNVs, of which 3,410 passed stringent quality control filters. As a positive control, the scan confirmed the known T1D association at the INS locus by direct typing of the 5' variable number of tandem repeat (VNTR) locus. Our results clarify the fact that the disease association is indistinguishable from the two main polymorphic allele classes of the INS VNTR, class I-and class III. We also identified novel technical artifacts resulting into spurious associations at the somatically rearranging loci, T cell receptor, TCRA/TCRD and TCRB, and Immunoglobulin heavy chain, IGH, loci on chromosomes 14q11.2, 7q34 and 14q32.33, respectively. However, our data did not identify novel T1D loci. Our results do not support a major role of untagged CNVs in T1D heritability