General linear mixed models estimates of the relationship between baseline CSF biomarkers and MMSE score at baseline and over follow-up.

a<p>Standardized estimates with their standard error (SE) computerized from mixed models adjusted for age, sex, CSF biomarker, and their interactions with time (one model per biomarker).</p

Baseline characteristics of the study sample.

<p>Baseline characteristics of the study sample.</p

Predicted trajectories of MMSE score according to tertiles of CSF phosphorylated PKR.

<p>Mixed model is adjusted for age, sex, tertiles of CSF pPKR and their interactions with time in months. Solid line: lower tertile of pPKR (0–65 ODU), long-dashed sline: middle tertile of pPKR (65–100 ODU), short-dashed line : upper tertile of pPKR (>100 ODU). Cognitive decline is more pronounced in patients with high levels of CSF pPKR (short-dashed line).</p

CSF biomarker levels in converters and non-converters MCI patients.

a<p>Kruskal-Wallis Test.</p

Lithium response in bipolar disorders and core clock genes expression

<p><b>Objectives:</b> We examine whether the lithium response is associated with changes in the expression of core clock genes.</p> <p><b>Methods:</b> The effect of a therapeutic concentration of lithium (1 mM) on the expression levels of 17 circadian genes was examined in lymphoblastoid cell lines (LCLs) derived from two well-characterized groups of bipolar disorder patients, defined as lithium non-responders (NR, <i>n</i> = 20) or excellent responders (ER, <i>n</i> = 16). Quantitative real-time PCR (qRT-PCR) was conducted at 2, 4 and 8 days (d2, d4 and d8) with and without lithium exposure.</p> <p><b>Results:</b> At d2, in ER only, <i>BHLHE41</i>, <i>RORA</i>, <i>PER1</i>, <i>ARNTL</i>, <i>CRY2</i>, <i>BHLHE40</i> and <i>CSNK1D</i> were upregulated, whereas <i>NR1D1</i> was downregulated. At d4, in ER only, <i>CRY1</i> was downregulated. At d8, in NR only, <i>GSK3β</i> was upregulated and <i>DBP</i>, <i>TIMELESS</i> and <i>CRY1</i> were downregulated. Significant Group × Lithium interactions existed for <i>NR1D1</i> at d2 (<i>P</i> = 0.02), and <i>CRY1</i> at d4 (<i>P</i> = 0.02). Longitudinal analyses showed differential temporal evolutions between NR and ER (significant Time × Group interaction) for <i>PER3</i>, <i>NR1D1</i>, <i>DBP</i>, <i>RORA</i>, <i>CSNK1D and TIMELESS</i>; and a significant Time × Lithium interaction for <i>NR1D1</i>. Coexpression data analyses suggested distinct groups of circadian genes concurrently modulated by lithium.</p> <p><b>Conclusions:</b> In LCLs, lithium influences expression of circadian genes with differences in amplitude and kinetics according to the patient’s lithium response status.</p

Lithium effects on serine-threonine kinases activity: High throughput kinomic profiling of lymphoblastoid cell lines from excellent-responders and non-responders bipolar patients

<p><b>Objectives:</b> Lithium is the leading mood stabiliser for maintenance treatment in bipolar disorder (BD). However, response to lithium is heterogeneous with more than 60% of patients experiencing partial or no response. <i>In vitro</i> and <i>in vivo</i> molecular studies have reported the implication of kinases in the pathophysiology of BD.</p> <p><b>Methods:</b> Since kinases are putative targets for lithium therapeutic action, we conducted the first pilot study using kinase array technology to evaluate the global serine/threonine kinases (STK) profiles in cell lines from BD I subtype patients classified as lithium excellent-responders (ER) and non-responder (NR) to lithium treatment.</p> <p><b>Results:</b> We found significant differences in the basal STK profiles between ER and NR to lithium. We also tested lithium influence on the global STK profile and found no significant difference between ER vs NR cell lines.</p> <p><b>Conclusions:</b> The results obtained in this exploratory study suggest that multiplex kinase activity profiling could provide a complementary approach in the study of biomarkers of therapeutic response in BD.</p

Oral Morphine Pharmacokinetic in Obesity: The Role of P‑Glycoprotein, MRP2, MRP3, UGT2B7, and CYP3A4 Jejunal Contents and Obesity-Associated Biomarkers

The objective of our work was to study the association between the jejunal expression levels of P-gp, MRP2, MRP3, UGT2B7, CYP3A4, the <i>ABCB1</i> c.3435C > T polymorphism, and several obesity-associated biomarkers, as well as oral morphine and glucuronides pharmacokinetics in a population of morbidly obese subjects. The pharmacokinetics of oral morphine (30 mg) and its glucuronides was performed in obese patients candidate to bariatric surgery. A fragment of jejunal mucosa was preserved during surgery. Subjects were genotyped for the <i>ABCB1</i> single nucleotide polymorphism (SNP) c.3435C > T. The subjects were 6 males and 23 females, with a mean body mass index of 44.8 (35.4–61.9) kg/m². The metabolic ratios AUC_0‑inf M3G/morphine and AUC_0‑inf M6G/morphine were highly correlated (rs = 0.8, <i>p</i> < 0.0001) and were 73.2 ± 24.6 (34.7–137.7) and 10.9 ± 4.1 (3.8–20.6). The pharmacokinetic parameters of morphine and its glucuronides were not associated with the jejunal contents of P-gp, CYP3A4, MRP2, and MRP3. The jejunal content of UGT2B7 was positively associated with morphine AUC_0‑inf (rs = 0.4, <i>p</i> = 0.03). Adiponectin was inversely correlated with morphine <i>C</i>_max (rs = −0.44, <i>p</i> = 0.03). None of the factors studied was associated with morphine metabolic ratios. The interindividual variability in the jejunal content of drug transporters and metabolizing enzymes, the <i>ABCB1</i> gene polymorphism, and the low-grade inflammation did not explain the variability in morphine and glucuronide exposure. High morphine metabolic ratio argued for an increased morphine glucuronidation in morbidly obese patients

Additional file 1 of Fasting upregulates the monocarboxylate transporter MCT1 at the rat blood-brain barrier through PPAR δ activation

Supplementary Figure 1 Metabolic effect of fasting. Rats were fed ad libitum (AL)or fasted for one day (F1), two days (F2) or three days (F3). (a) Plasma Total proteins and Urea. (b) Relative weights (% of body weight) of the heart, gastrocnemius, extensor digitorum longus (EDL) and soleus muscles. Tukey boxplot (n = 12 rats per group). Kruskal-Wallis test with Dunn’s multiple comparisons test for Total Proteins. Brown-Forsythe and Welch ANOVA followed by Dunnett’s multiple comparison tests for Gastrocnemius. One-way ANOVA followed by Holm-Sidak’s multiple comparisons test for all other variables

Detection of PrP^res in fCJD^V180I and VPSPr with 1E4 and 3F4.

<p>(<i>A</i>–<i>D</i>) Brain homogenates from six fCJD^V180I cases (four 129MM and two 129MV) were treated with different amounts of PK from 0 to 100 µg/ml prior to Western blotting with 3F4 (<i>A</i> and <i>C</i>) or 1E4 (<i>B</i> and <i>D</i>). (<i>E</i>) Comparison of PrP^res from fCJD^V180I, VPSPr, and sCJD probing with 1E4.</p

Detection of two individual monoglycosylated PrP either at N181 or N197.

<p>(<i>A</i>) and (<i>B</i>) sCJDMM2 (lanes 1, 2), two VPSPr (129MM: lanes 3, 4; 129MV: lanes 5, 6), fCJD^T183A (lanes 7, 8) and fCJD^V180I (lanes 9, 10) treated with PK or PK plus PNGase F were probed with Bar209 (<i>A</i>) or V14 (<i>B</i>). (<i>C</i>) PrP from VPSPr, fCJD^V180I, and sCJD was examined with V14 (lanes 1–3) or Bar209 (lanes 4–6). (<i>D</i>) Ratio of mono- (either mono181 or mono197) to unglycosylated PrP by densitometric analysis based on three independent experiments, one of which is presented in (<i>C</i>). The black bar represents mono181:unglyc PrP, while the grey bar represents mono197:unglyc PrP from sCJD, fCJD^V180I or VPSPr. *** <i>p</i> <0.005.</p