There is a long history of research into body fluid biomarkers in neurodegenerative and neuroinflammatory diseases. However, only a few biomarkers in CSF are being used in clinical practice. One of the most critical factors in CSF biomarker research is the inadequate powering of studies because of the lack of sufficient samples that can be obtained in single-center studies. Therefore, collaboration between investigators is needed to establish large biobanks of well-defined samples. Standardized protocols for biobanking are a prerequisite to ensure that the statistical power gained by increasing the numbers of CSF samples is not compromised by preanalytical factors. Here, a consensus report on recommendations for CSF collection and biobanking is presented, formed by the BioMS-eu network for CSF biomarker research in multiple sclerosis. We focus on CSF collection procedures, preanalytical factors, and high-quality clinical and paraclinical information. The biobanking protocols are applicable for CSF biobanks for research targeting any neurologic disease.

NM A22834 (Albrecht) 3 Across a spectrum of living organisms, ranging from cyanobacteria to humans, it has been observed that biological functions follow a pattern of circadian rhythmicity.These endogenous rhythms display a periodicity close to 24 hours in the absence of environmental cues, thus reflecting the existence of an intrinsic biological clock. In mammals, circadian rhythms in different tissues are coordinated by a master clock located in the suprachiasmatic nucleus (SCN) of the anterior hypothalamus 1 . This circadian clock is thought to be advantageous in synchronising physiological and biochemical pathways, allowing the organism to anticipate daily changes, thus ensuring better adaptation to the environment 2 .The oscillatory mechanism of the circadian clock has been unraveled by means of genetic analysis in Drosophila and mammals [3][4][5] . In the latter, the heterodimeric complex of two transcriptional activators, CLOCK and BMAL1 (MOP3), induce the expression of several genes by interacting with the enhancer elements, termed E-boxes, of their promoters. Amongst these genes are Per1, Per2, Cry1 and Cry2, whose protein products, upon entering the nucleus, inhibit the activity of the CLOCK/BMAL1 complex, and thereby generating an inhibitory feedback loop driving recurrent rhythms in mRNA and protein levels of their own genes. This molecular mechanism seems to be present in the local clocks of most tissues and brain regions. Furthermore, these different clocks may then be synchronized by the SCN via neural and endocrine outputs 6 . Transporter 1, also known as Glast) is found to be reduced in these mice. Excess glutamate is cleared from the synaptic cleft by glutamate transporters 10 , located on astroglial cells and transported back to the neuron via the glutamine-glutamate cycle.A deficit in the removal of glutamate from the synaptic cleft, results in a hyperglutamatergic state and is suggested to produce alterations at the behavioral level 10,11 .Importantly, a hyper-glutamatergic state has been implicated in the aetiology of alcohol dependence [12][13][14] . We observe that in Per2 Brdm1 mutant mice voluntary alcohol consumption is enhanced. In humans we find an association between alcoholic patients and genetic variations in the human PER2 gene. Acamprosate, a medication thought to dampen a hyper-glutamatergic state in the alcohol dependent human brain 15-17 reduces augmented glutamate levels and normalizes enhanced alcohol consumption in Per2 Brdm1 mutant mice. These findings support the view that a hyperglutamatergic state can be involved in several aspects of alcohol dependence [12][13][14][18][19][20] . RESULTS Glutamate transporters in Per2 Brdm1 miceWild type and Per2 Brdm1 mutant mice differ in their behavioral response to a light pulse administered at zeitgeber time (ZT) 14 9 (ZT0 corresponds to lights on and ZT12 to lights off). Therefore, we set out to search for a difference in gene expression between wild type and Per2 Brdm1 mutant mice at ZT15. This time point has been chosen beca...

Chronic kidney disease (CKD) affects 8 to 16% people worldwide, with an increasing incidence and prevalence of end-stage kidney disease (ESKD). The effective management of CKD is confounded by the inability to identify patients at high risk of progression while in early stages of CKD. To address this challenge, a renal biopsy transcriptome-driven approach was applied to develop noninvasive prognostic biomarkers for CKD progression. Expression of intrarenal transcripts was correlated with the baseline estimated glomerular filtration rate (eGFR) in 261 patients. Proteins encoded by eGFR-associated transcripts were tested in urine for association with renal tissue injury and baseline eGFR. The ability to predict CKD progression, defined as the composite of ESKD or 40% reduction of baseline eGFR, was then determined in three independent CKD cohorts. A panel of intrarenal transcripts, including epidermal growth factor (EGF), a tubule-specific protein critical for cell differentiation and regeneration, predicted eGFR. The amount of EGF protein in urine (uEGF) showed significant correlation (P < 0.001) with intrarenal EGF mRNA, interstitial fibrosis/tubular atrophy, eGFR, and rate of eGFR loss. Prediction of the composite renal end point by age, gender, eGFR, and albuminuria was significantly (P < 0.001) improved by addition of uEGF, with an increase of the C-statistic from 0.75 to 0.87. Outcome predictions were replicated in two independent CKD cohorts. Our approach identified uEGF as an independent risk predictor of CKD progression. Addition of uEGF to standard clinical parameters improved the prediction of disease events in diverse CKD populations with a wide spectrum of causes and stages.

Predicting time of food availability is key for survival in most animals. Under restricted feeding conditions, this prediction is manifested in anticipatory bouts of locomotor activity and body temperature. This process seems to be driven by a food-entrainable oscillator independent of the main, light-entrainable clock located in the suprachiasmatic nucleus (SCN) of the hypothalamus . Although the SCN clockwork involves self-sustaining transcriptional and translational feedback loops based on rhythmic expression of mRNA and proteins of clock genes , the molecular mechanisms responsible for food anticipation are not well understood. Period genes Per1 and Per2 are crucial for the SCN's resetting to light . Here, we investigated the role of these genes in circadian anticipatory behavior by studying rest-activity and body-temperature rhythms of Per1 and Per2 mutant mice under restricted feeding conditions. We also monitored expression of clock genes in the SCN and peripheral tissues. Whereas wild-type and Per1 mutant mice expressed regular food-anticipatory activity, Per2 mutant mice did not show food anticipation. In peripheral tissues, however, phase shifts of clock-gene expression in response to timed food restriction were comparable in all genotypes. In conclusion, a mutation in Per2 abolishes anticipation of mealtime, without interfering with peripheral synchronization by feeding cycles.

Diabetic cardiomyopathy is a complication of type 2 diabetes, with known contributions of lifestyle and genetics. We develop environmentally and genetically driven in vitro models of the condition using human-induced-pluripotent-stem-cell-derived cardiomyocytes. First, we mimic diabetic clinical chemistry to induce a phenotypic surrogate of diabetic cardiomyopathy, observing structural and functional disarray. Next, we consider genetic effects by deriving cardiomyocytes from two diabetic patients with variable disease progression. The cardiomyopathic phenotype is recapitulated in the patient-specific cells basally, with a severity dependent on their original clinical status. These models are incorporated into successive levels of a screening platform, identifying drugs that preserve cardiomyocyte phenotype in vitro during diabetic stress. In this work, we present a patient-specific induced pluripotent stem cell (iPSC) model of a complex metabolic condition, showing the power of this technique for discovery and testing of therapeutic strategies for a disease with ever-increasing clinical significance.