Data_Sheet_1_Cerebrospinal fluid level of proNGF as potential diagnostic biomarker in patients with frontotemporal dementia.docx

IntroductionFrontotemporal dementia (FTD) is an extremely heterogeneous and complex neurodegenerative disease, exhibiting different phenotypes, genetic backgrounds, and pathological states. Due to these characteristics, and to the fact that clinical symptoms overlap with those of other neurodegenerative diseases or psychiatric disorders, the diagnosis based only on the clinical evaluation is very difficult. The currently used biomarkers help in the clinical diagnosis, but are insufficient and do not cover all the clinical needs.MethodsBy the means of a new immunoassay, we have measured and analyzed the proNGF levels in 43 cerebrospinal fluids (CSF) from FTD patients, and compared the results to those obtained in CSF from 84 Alzheimer’s disease (AD), 15 subjective memory complaints (SMC) and 13 control subjects.ResultsA statistically significant difference between proNGF levels in FTD compared to AD, SMC and controls subjects was found. The statistical models reveal that proNGF determination increases the accuracy of FTD diagnosis, if added to the clinically validated CSF biomarkers.DiscussionThese results suggest that proNGF could be included in a panel of biomarkers to improve the FTD diagnosis.</p

Length distribution of human VH nanobody library sequences.

<p>Barplot of the length distribution of human VH nanobody library sequences coloured by reading frame.</p

Chain/VDJ assortment independence of libraries.

<p>A) hscFv1. B) hscFv2. C) hVH. Top panels: barplots of forward and reverse primer distributions. Bottom panels: heatmaps of library primers distributions. Observed distribution is the primer pair proportion found after sequencing. Expected distribution is the multiplication of the two primers proportion (expected distribution given the independence between chains for the scFv libraries or given a balanced VDJ recombination for hVH). UC = unclassified. This category includes all the sequences that do not match any primer. The name of the primers is a shorter version of the original name listed in Supporting Information (Primer used for library construction).</p

Phi-X derived and Phred score derived error rate distribution.

<p>A) Phred score error rate distribution for the hscFv1 library of the merged reads. Error rate increases with sequencing cycles. B) Control Phi-X derived error rate distribution for the hscFv1 library of the merged reads. Error rate is more prominent in the early sequencing cycles (spikes), with a small increase at the end of each read. The error distribution does not match the Phred score distribution and the shape differs as well. C) Scatter plot of the correlation of Q-score and log₂(% Mismatches) in Phi-x control spike-in library. Each point represents the mean value from a single flow cell tile at a given sequencing read number, encoded by colour (red to blue: R1 cycle 1 to 350; R2 cycle 1 to 250; colour flex point is set at cycle 38). The Q score in the first 40 reads fails to be predictive of mismatch rate. Similar results were obtained for hscFv2 and hVH libraries.</p

CSB ablation induced apoptosis is mediated by increased endoplasmic reticulum stress response - Table 1

<p>CSB ablation induced apoptosis is mediated by increased endoplasmic reticulum stress response</p> - Table

Diagram of DEAL workflow.

<p>A) Diagram of the seed creation process. In the figure, the black arrows represent the combined reads of the scFv library after the trimming. The seed is created combining the two seeding regions. The seeding regions are placed in the CDR3s to maximize the number of different seeds: the higher the number, the faster the program will run. B) Binary tree of the seeds. The program uses a binary tree approach to group identical seeds. During the comparison, if one sequence does not match any other sequences seen so far, a new branch of the tree is created in the mismatching position. C) The input of the binary comparison step. While the seeding step takes only into account the diversity of the seeding regions, the binary comparison analyzes the whole length of the combined reads. D) Flagging process. If some positions of the sequence are unreliable due to being associated to a low Phred quality score (as shown in the figure) or to a poor quality cycle (from Phi-X errors, not shown in the figure), the program flag them for correction. E) The three different scenarios that can occur during binary comparison among the sequences in the same seeding group. Mismatching (top): if two compared sequences differ in even only one position (bold) where none of the alternatives are flagged, the program recognize them as different sequences and does not group them. Matching sequences with a position having one flagged nucleotide (middle): the program recognizes the two sequences as identical and groups them together. All the positions where one of the sequences has a flag is resolved, during merging, as the not flagged nucleotide on the other sequence. Matching sequences with a position having both alternative nucleotides flagged (bottom): the program recognizes the two sequences as identical and groups them together. All the positions where both sequences have a flag are resolved using the IUPAC nucleobases ambiguity codes. The resulting merged sequence is flagged in that position.</p

CSB ablation induced apoptosis is mediated by increased endoplasmic reticulum stress response

<div><p>The DNA repair protein Cockayne syndrome group B (CSB) has been recently identified as a promising anticancer target. Suppression, by antisense technology, of this protein causes devastating effects on tumor cells viability, through a massive induction of apoptosis, while being non-toxic to non-transformed cells. To gain insights into the mechanisms underlying the pro-apoptotic effects observed after CSB ablation, global gene expression patterns were determined, to identify genes that were significantly differentially regulated as a function of CSB expression. Our findings revealed that response to endoplasmic reticulum stress and response to unfolded proteins were ranked top amongst the cellular processes affected by CSB suppression. The major components of the endoplasmic reticulum stress-mediated apoptosis pathway, including pro-apoptotic factors downstream of the ATF3-CHOP cascade, were dramatically up-regulated. Altogether our findings add new pieces to the understanding of CSB mechanisms of action and to the molecular basis of CS syndrome.</p></div

hVH library protein complexity.

<p>hVH library protein complexity.</p

Rats used for microarray analysis exhibited motor behaviors similar to the behaviors of the whole rat group.

<p><i>A,</i> The quality of the reaching behavior of the 4 rats in the <i>12-day-Reach</i> group (red, mean±SE) showed much improvement over time with the percentage of successful trials with dropped pellets of Day 8 to 12 being clearly lower than that of Day 1 to 5 (*, p<0.05, ANOVA). Similarly, a decreasing trend of this measure of learning was also observed in the <i>5-day-Reach</i> group (green). In this figure we have normalized the learning curve of every rat to its maximum value to account for inter-individual variability in initial performance. <i>B,</i> The probability of successful pellet retrieval per reach for the rats in the <i>12-day-Reach</i> group over the Learned (red, mean±SE) and Not-Learned (blue) Slots. Similar to the behavioral trend of the whole rat group (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0061496#pone-0061496-g002" target="_blank">Fig. 2D</a>), the learning curve for the Learned Slots exhibited a sigmoid time course, with performance starting to increase at Day 5.1±3.2 (t_10%-max, black dotted line, mean±SE), and with the probability values of Day 1 to 5 smaller than those of Day 8 to 12 (*, p<0.05, ANOVA). The success probability values for the <i>5-day-Reach</i> group (N = 4; green; all slots) were also not statistically different from the values for the <i>12-day-Reach</i> group over the first 5 days (p>0.05, ANOVA).</p