Real-Time Quaking-Induced Conversion (RT-QuIC) is an in vitro assay that, for the first time, specifically discriminates patients with prion disease. Here, using cerebrospinal fluid (CSF) samples from 239 patients with definite or probable prion disease and 100 patients with a definite alternative diagnosis, we compared the performance of the first (PQ-CSF) and second generation (IQ-CSF) RT-QuIC assays, and investigated the diagnostic value of IQ-CSF across the broad spectrum of human prions. Our results confirm the high sensitivity of IQ-CSF for detecting human prions with a sub-optimal sensitivity for the sporadic CJD subtypes MM2C and MM2T, and a low sensitivity limited to variant CJD, Gerstmann-Sträussler-Scheinker syndrome and fatal familial insomnia. While we found no difference in specificity between PQ-CSF and IQ-CSF, the latter showed a significant improvement in sensitivity, allowing prion detection in about 80% of PQ-CSF negative CJD samples. Our results strongly support the implementation of IQ-CSF in clinical practice. By rapidly confirming or excluding CJD with high accuracy the assay is expected to improve the outcome for patients and their enrollment in therapeutic trials.An early and accurate  C), into an abnormally folded, beta-sheet enriched, isoform (PrPSc)1. While the mechanism of initial PrPSc formation remains largely unexplained, compelling evidence indicates that disease propagation involves the templated misfolding of PrPC by PrPSc3.Human transmissible spongiform encephalopathies (TSEs) or prion diseases are neurodegenerative disorders characterized by the conversion of a constitutively expressed cellular glycoprotein, the prion protein  correlating at molecular level with the genotype at the polymorphic codon 129  in the gene encoding the prion protein (PRNP) and the type (1 or 2) of PrPSc accumulating in the brain9. This phenotypic diversity mostly relates to the biology of prions, which exist in different strains, thought to be enciphered in distinct PrPSc conformations, that are able to transmit distinctive phenotypic traits, including incubation time, clinical signs, progression rate, type and patterns of PrPSc deposition, and neuropathological lesions11. Specifically, current evidence indicate that five out of six sCJD subtypes  behave as distinct prion strains after serial transmission into animal models. As the only exception, the VV2 and MV2K variants showed the same transmission properties, indicating a host-genotype (codon 129) effect15.Human prion diseases are highly heterogeneous disorders including four major disease groups, namely Creutzfeldt-Jakob disease (CJD), fatal insomnia, Gerstmann-Sträussler-Scheinker (GSS) syndrome, and variably protease-sensitive prionopathy (VPSPr)17. However, because of the lack of full specificity of these investigations, the post-mortem demonstration of PrPSc in the CNS tissue is still required for the definitive diagnosis of prion disease18.Due to of the significant phenotypic overlap with a number of other medical conditions which present with a rapidly progressive neurological syndrome, the clinical diagnosis of prion disease is often challenging. The introduction of diagnostic investigations such as brain diffusion weighted-MRI (DW-MRI) and surrogate CSF biomarkers such as the 14-3-3 and t-tau proteins has significantly increased the clinical diagnostic confidencein vivo diagnostic test for sCJD19. By exploiting the self-replicating (seeding) power of pathogenic PrPSc, RT-QuIC ultra-sensitively detects limited amounts of abnormal protein in CSF and other tissue samples20. The RT-QuIC technology builds on aspects of earlier QuIC22 and amyloid seeding assays23, to provide high PrPSc sensitivity and specificity in a high throughput multiwell plate format with fluorescence detection. Results obtained in several laboratories with the first generation of this assay (PQ-CSF), mainly using full-length (23–231) hamster recombinant prion protein (rPrPsen) as the substrate, demonstrated a very high specificity but a suboptimal sensitivity, especially in sCJD subtypes associated with PrPSc type 227. However, Orrù et al.28 recently introduced an improved, second generation RT-QuIC for CSF (IQ-CSF) assay which uses a truncated form of hamster recombinant PrP  as a substrate and other experimental conditions. Initial evaluation of the IQ-CSF assay indicated greater analytical and diagnostic sensitivity, and markedly shorter testing times31.Recently, the Real-Time Quaking-Induced Conversion (RT-QuIC) assay has been proposed as the first specific In this study, we contribute our experience with the second generation IQ-CSF in a large patient cohort including the largest patient population analysed to date affected by atypical and rare disease subtypes. Furthermore, to explore the performance of IQ-CSF in full, we provide a direct comparison between PQ- and IQ- methodologies, focusing on a largest group of definite CJD CSF samples analysed to date that were previously tested negative by PQ-CSF.We investigated a total of 339 patients, including 239 CSFs from subjects with a definite 166) or probable (73) diagnosis of prion disease and 100 from patients with a definite alternative diagnosis (non-CJD). Specific aims of the study included: (i) the direct comparison of the overall performance of IQ-CSF with respect to the first generation RT-QuIC (PQ-CSF); (ii) the evaluation of the diagnostic accuracy of IQ-CSF across the spectrum of human prion disease subtypes; and (iii) the determination of the diagnostic performance of IQ-CSF in the clinical setting. Demographic data and investigative findings of the whole tested cohort are summarized in Table 66 or proTo this aim we used 163 cases with a probable or definitive diagnosis of prion disease and 100 non-CJD cases. Samples were largely selected based on previous results by PQ-CSF. Most significantly, they included all prion CSF samples available to us (n = 93) that were either negative or only partially positive (2 out of 4 positive wells) by PQ-CSF  among sporadic disease subtypes revealed a statistically significant difference between MM1 and MM2C  and between MM1 and VPSPr  Fig. .PRNP haplotypes associated with atypical disease phenotypes, such as R208H-129V, E219G-129V, and D178N-129V . Finally, similarly to PQ-CSF, the IQ assay demonstrated that gCJD E200K-129M is the human prion disease subtype associated with the shortest lag phase.To estimate the diagnostic performance of IQ-CSF in a clinical setting, we analyzed the assay results in a group of 187 unselected cases with a definitive neuropathologic or clinical diagnosis (either CJD or non-CJD), which were submitted to our Lab between January 2011 and March 2017. The results obtained for these groups are summarized in suppl. Table 27. The results confirm that, while PQ-CSF and IQ-CSF show a comparable performance in terms of specificity, IQ-CSF is associated with a remarkable increase in sensitivity, allowing the detection of 81% of CJD samples tested negative by PQ-CSF. Since in those CSF samples which tested negative by PQ-CSF, the mean seeding activity as detected by IQ-CSF was lower than in samples which tested positive under both conditions, the collected data clearly indicate that this novel assay is associated with a lower limit of detection for seeding prions. Moreover, they confirm that the added value of IQ-CSF goes beyond the crude numbers reflecting the increased sensitivity. Indeed, besides the faster response allowing a ~40-h reduction in average detection time for positive samples in comparison to PQ-CSF, IQ-CSF is also associated with a stronger fluorescence readings and a significant reduction of unclear or inconclusive results .In this study we have applied IQ-CSF, the second generation RT-QuIC prion assay, to a patient cohort including the largest population analysed to date affected by the less common disease subtypes. Furthermore, we provide a direct comparison between the first and second generation prion RT-QuIC, taking advantage of a very large group of samples from definite CJD cases previously tested under PQ-CSF conditions27. Thus, a critical question is how well IQ-CSF detects and differentiates the less common and often atypical prion disease variants. Our results show that the high sensitivity of IQ-CSF extends to the majority of human prion strains. Most significantly, we demonstrated in the largest series of definite cases analyzed to date (n = 59), a 98% diagnostic sensitivity for the two subtypes associated with the V2 prion strain . This is significant in that it will not only raise the overall accuracy of routine diagnosis but also improve the detection and diagnosis of these atypical subtypes. Due to the relatively frequent atypical clinical onset and/or the relatively slow disease progression (MV2K subtype), in such cases the clinical diagnosis of probable sCJD, when reached, is often delayed. In a recent study32, we found that about 20% of VV2 cases manifest symptoms and signs limited to the cerebellar/visual domain at time of first hospitalization and therefore do not fulfill current clinical criteria for possible sCJD, which are required to implement a diagnosis of probable sCJD based on surrogate CSF biomarkers and/or MRI findings18. Similarly, the diagnosis of probable sCJD in subjects with the MV2K subtype is reached, on average, only seven months after clinical onset .A critical issue concerning the evaluation of any novel diagnostic assay for prion disease is related to the extent of clinical heterogeneity shown by these patients. This is a major point of difficulty in the clinical diagnosis and differentiation of prion diseases against other neurological disorders, not only because of the broad spectrum of clinical situations where the suspicion of prion disease can be raised, but also because the accuracy of diagnostic assays often varies significantly according to the disease subtype. This has been shown, not only for surrogate biomarkers such as proteins 14-3-3 and t-tau, but also for PQ-CSFet al.29, namely showing lower values in both assay sensitivity and seeding effect in MM2C in comparison to MM1. By combining our 9 cases with the 9 analyzed by Foutz et al., the overall calculated sensitivity of IQ-CSF for sCJD MM2C is 68%. Since M2C prions have a significantly reduced transmission efficiency relative to M1 prions in the most compatible host genotype33, these results may indicate that the response detected by IQ-CSF reflects such a property of prions. Indeed, previous studies, combining brain sample dilutions and/or the use of different substrates have already demonstrated the potential of RT-QuIC for prion strain discrimination35. However, whether these preliminary data reflect differences in abnormal PrP concentrations, strain-specific PrPSc properties or both remain to be seen. Thus, additional work with each type of human prions will be required to better establish the quantitative relationships between RT-QuIC seeding activity and the levels of PrPSc in tissue samples.As far as the other sCJD subtypes are concerned, which are all very rare, our results show a lower sensitivity of the IQ-CSF for the MM2C and, possibly, MM2T. Most significantly, for MM2C our results are in line with those of Foutz 6. Furthermore, as for sCJD MM2C, we detected a significantly lower seeding effect in VPSPr in comparison to the most common sCJD prions (e.g. M1 and V2 strains), further supporting the idea that IQ-CSF may, with the limitations outlined above, differentiate between prions with relatively high or low propagation efficiency (in their most compatible host genotype).Finally, although with data obtained from a limited number of samples, we demonstrated here for the first time that the IQ-CSF may detect prion seeding activity even in VPSPr, a highly distinctive prion disease phenotype, which is notoriously associated with a low sensitivity of surrogate biomarker assays31, demonstrate that prion CSF RT-QuIC is not only fully specific, but also has even higher sensitivity than the surrogate biomarker assays making it the most powerful currently available tool for the clinical diagnosis of prion disease in humans. Given the high diagnostic performance of IQ-CSF, the role of surrogate CSF markers in the differential diagnosis of rapidly progressive neurological syndromes should be reconsidered. If a complementary role of such assays beside RT-QuIC should be envisaged, the choice should definitely go towards biomarkers such as t-tau or, perhaps, the neurofilament proteins36 , rather than 14-3-3, a biomarker whose application has been limited to the differential diagnosis of CJD. Indeed, in case where the RT-QuIC assay is negative, the result of the t-tau assay may be helpful in re-directing the diagnostic work-up.The present results, combined with those of other recent studiesIn conclusion, the results of the present study strongly support the rapid implementation of IQ-CSF testing in clinical practice. By rapidly confirming or excluding the CJD diagnoses with high accuracy the assay is expected to improve the outcome for patients as well as their enrollment in future therapeutic trials.We retrospectively analyzed CSF samples from 339 patients, including 282 samples sent for diagnostic purpose to the Laboratory of Neuropathology (NP-Lab), Institute of Neurological Sciences of Bologna (ISNB), 50 samples collected at the IRCCS Foundation Carlo Besta Neurological Institute (INCB), and 7 samples collected at the National CJD Research and Surveillance Unit of the UK. The study was conducted according to the revised Declaration of Helsinki and Good Clinical Practice guidelines and regulations. Informed consent was given by study participants or their next of kin. Data collection and CSF sample analysis of clinically suspected cases in Italy is an integral part of the National CJD surveillance study, which was approved by the Ethic Committee of the Istituto Superiore di Sanità (CE-ISS 09/266 on 29 may 2009).CSF samples were collected by lumbar puncture (LP) following a standard procedure, centrifuged at 1000 × g for 10 min and stored in polypropylene tubes at −80 °C until analysis.PRNP gene was performed in all subjects as previously described37. All definitive cases were classified based on histopathological features, PRNP genotype and PrPSc type, as described38. The 125 tested definite (sporadic or acquired) CJD included 43 MM(V)1 or MM(V)1+2 C (abbreviated in the manuscript as MM1), 33 VV2, 26 MV2K or MV2K+2 C (abbreviated as MV2K), and 23 cases of rarest subtypes . The 73 probable sCJD cases were classified based on codon 129 genotype and included 39 MM, 24 MV and 10 VV subjects. A single case was classified as probable MM2T based on typical clinical, polysomnographic and PET findings39 and added to the MM2T group (n = 4) (Tables PRNP haplotypes/mutations: E200K-129M (n = 20), V210I-129M (n = 10), D178N-129M, D178N-129V, R208H-129M, P102L-129M, E219G-129V, D202N, A117V, ins-8 repeats . Finally, the non-CJD group included 100 cases with clinical features compatible with CJD, in which post-mortem findings, including histopathological and PrP western blot analysis, or the results of laboratory investigation at follow-up excluded the diagnosis of prion disease  and negative (non-CJD) controls. The RT-QuIC reaction mix contained 10 mM phosphate buffer at pH 7.4, 300 mM NaCl, 1 mM ethylenediaminetetraacetic acid tetrasodium salt dihydrate (EDTA) at pH 8.0, 10 μM thioflavin-T (ThT), 0.002% of Sodium dodecyl sulfate (SDS) and 0.1 mg/ml of Syrian hamster recombinant truncated form of prion protein (Ha rPrP 90–231)28. All the reaction solutions were freshly prepared and filtered before use with 0.22 µm sterile filters. After sealing, the plate was incubated in a FLUOstar OMEGA reader  at 55 °C, over a period of 60 hours with intermittent cycles of shaking  and rest (60 s). The fluorescence intensity of ThT-PrPSc aggregates, expressed as relative fluorescence units (RFU), was taken every 45 minutes using 450 ± 10 nm (excitation) and 480 ± 10 nm (emission) wave-lengths, with a bottom read and a gain of 1000. A CSF sample was considered prion positive if at least two out four sample replicates gave a fluorescence signal higher than the threshold cut-off value. This threshold represents the mean RFU values of negative samples plus at least 10 standard deviations. Samples were considered negative if none of the replicates surpassed the chosen cut-off. In two cases, only one of four replicate went over the threshold, thus the test was considered ambiguous/unclear and repeated.CSF samples were analyzed by the RT-QuIC assay with two different protocols. The PQ-CSF protocol was previously described27. Briefly, protein 14-3-3 was evaluated semi-quantitatively by comparing the Western Blot signals of the tested sample in comparison to those of control samples . T-tau protein was quantitatively analyzed using commercially available kits based on a sandwich ELISA method, according to the manufacturer’s instructions . Based on previous analyses27, the cut-off value chosen for t-tau was 1250 pg/ml.These assays were performed as describedRT-QuIC relative fluorescence responses were analysed and plotted using the Sigma Plot software . Depending on the data distribution, the Mann-Whitney test were used to test differences between two groups, while the one-way ANOVA (followed by Tukey’s or Bonferroni’s post hoc test) were applied for multiple group comparisons. P values < 0.05 is considered statistically significant. Unless otherwise stated, data are expressed as mean with standard error of the mean (SEM) and number of sample tested (n).The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.Supplementary information


C. difficile colitis. Although fecal microbiota transplantation helps to re-establish a normal gut function in patients, the extent of the repopulation of the recipient microbial community varies. To further understand this variation, it is important to determine the fate of donor microbes in the patients following fecal microbiota transplantation. We have developed a new method that utilizes the unique single nucleotide variants of gut microbes to accurately identify microbes in paired fecal samples from the same individual taken at different times. Using this method, we identified transplant donor microbes in seven recipients 3–6 months after fecal microbiota transplantation; in two of these fecal microbiota transplantation, we were able to identify donor microbes that persist in recipients up to 2 years post-fecal microbiota transplantation. Our study provides new insights into the dynamics of the reconstitution of the gastrointestinal microbe community structure following fecal microbiota transplantation.Fecal microbiota transplantation has been shown to be an effective treatment for patients with recurrent  Clostridium difficile (C. difficile) is the major causative agent for infective antibiotic associated diarrhea.2 Infections acquired in healthcare settings have estimated health care costs in the billions.4 Standard treatments for C. difficile infection consist of metronidazole, vancomycin, or fidaxomicin, which result in a 20% rate of recurrence; after a third recurrence, the risk of further episodes is even higher.56 Numerous studies have characterized the microbial composition of the recipient following transplant.11 Although the microbe composition of the recipient post transplant was different after FMT, the similarity of the reconstituted microbe community to that of the donor varied between different patients.11 The significance of these differences as they relate to the long-term stability of the FMT re-constructed gut microbe community is unknown. As a first step to understanding the microbial ecology of the reconstituted community, it is necessary to determine the fate of the donor microbes after FMT. A recent study by Li et al. has shown coexistence of donor microbes in recipient post FMT in metabolic syndrome patients.12 Their method uses a comparison of single nucleotide variants (SNV) of donor, pre-FMT and post FMT samples to show the presence of donor microbes in post FMT samples. However, the pre-FMT samples of patients with C. difficile infection have been exposed to several rounds of antibiotics resulting in a near depletion of commensal gut microbes.11 Thus, it was not practical to use the method by Li et al. to determine the presence of donor microbes in patients post-FMT. Recently, Schloissnig et al., used metagenomic sequence analysis of microbe species in the human microbiome to demonstrate that individuals have their own distinct SNV that were stable for up to 1 year.13 In this study, we have exploited the unique SNVs of gut microbes of individuals and developed a method to establish whether or not paired donor and recipient post-FMT samples share the same unique SNVs across the genome. Our findings demonstrate the colonization and persistence of certain donor microbial species in the recipient post-FMT in C. difficile patients.When antibiotic therapy fails for the patients, fecal microbiota transplantation (FMT) has had remarkable success rates of greater than 90% for alleviation of symptoms and restoration of health.13 To calculate the SNV, the metagenome is first mapped on to the reference sequences. Raw data obtained either from the NIH Human Microbiome Project or our study was aligned to the reference sequence using Burrows–Wheeler Aligner, and multi-sample SNV calling was performed using Genome Analysis Toolkit (version 3.6).15 Then, for a given species, a pairwise comparison was performed between two samples to measure their genome-wide SNV similarity. Regions of low read coverage, sequence repeats, indels, and structural variants cause alignment difficulties that may also produce increased numbers of  SNVs. Therefore, to minimize the effect of these clustered SNVs on overall genome wide similarity, we developed a window-based SNV comparison approach, window-based SNV similarity (WSS), which measures SNV variation between two samples . We applied a non-parametric test because even after applying a log transformation, the data still did not pass the Shapiro-Wilk test for normal distribution  from the NIH Human Microbiome Project (Supplementary Table C. difficile infection with the FMT samples obtained early post-FMT (1–6 months following transplant) when colonization of the gut with donor microbes should have occurred . Samples at 2 years post-FMT were available for two of the seven transplants and the recipient pre-FMT was available for four transplants. Five of the seven transplants were with un-related donors while two of the transplants were done with donors from spouses  which may be explained by the finding that Bacteroides have evolved receptors for intestinal cells that facilitate retention in the gastrointestinal tract.17B. ovatus, B. stercoris, B. massiliensis, B. celluloslyticus, and B. vulgatus were identified as having been transferred from the donor in multiple transplants while B. eggerthii and B. uniformis were each only found in one different transplant. Analysis of individual FMT highlighted the complexity of the microbial ecology of the gastrointestinal tract environment. For example, even though B. stercoris was transferred from the donor to recipient in FMT-E, the B. ovatus detected in the same donor and recipient post-FMT comparison was not similar. In the FMT-D transplant, the B. vulgatus of the donor and recipient was not similar; however, in this transplant B. ergerthii, B. sp2116, B. stercoris, and B. celluloslyticus were transferred from donor to recipient. Interestingly, in the study where the same donor was transplanted into separate recipients (FMT-FG), four Bacteroides spp.  in FMT-F were identified as similar to the donor (DF-T1F) while no Bacteroides spp. had sufficient sequence coverage for WSS score calculation in the FMT-G recipient (DG-T1G). This result could not be explained by a mechanical failure of the FMT-G transplant since P. merdae, Acidaminococcus spD21, and A. putredinis from the donor were identified in both FMT-F and in FMT-G.For all pairwise comparisons of FMT-related samples that were predicted as related, the WSS were above the WSS classification boundary cutoff  for B. vugatus and B. ovatus in the FMT-A transplant, and several Bacteroiodes spp, A. onderkondii, P. merdae, and B. intestinihominis for the FMT-B transplant. The demonstration that certain transplanted microbes can persist for up to 2 years demonstrates the potential of using FMT for long-term changes in the composition of the gastrointestinal tract microbe communities.An effective long-term stable transfer would require the microbes in the FMT to access, occupy, and possibly out-compete the resident recipient microbes for niche space in the gastrointestinal tract following transplantation.F. prausnitzii that was present at early and 2 years in FMT-A shared identity with each other but did not share identity with the transplant donor, even though the abundance of F. prausnitzii in the donor was high , possibly as a result of the extensive antibiotics used in the C. difficile recipients.19 Alternatively, it is also possible that the new strain in the recipient was a minority strain in the donor sample and thus not detected in the donor, but was at a competitive advantage following in the recipient post FMT. Thus, as suggested by Li et al. where FMT was used in patients with metabolic syndrome, as yet undefined recipient differences, such as niche competition or availability, could impact the microbe composition of the transplant recipient.12Finally, analysis of early and late samples from FMT-A revealed that the igh Fig. . It is pC. difficile has prompted the consideration of FMT to be used for treatment of other gastrointestinal diseases.20 The results of our analysis provides new insights into the ecology of the human gastrointestinal tract niches that are essential for development of new approaches to improve health via manipulation of this complex microbial community.21In summary, using a method that exploits the unique SNV patterns of gut microbes to distinguish individual strain SNV patterns, we demonstrate the presence and persistence of certain donor microbes in FMT recipients. The clinical success of FMT in treating https://aws.amazon.com/datasets/human-microbiome-project/). All gut samples were downloaded from amazon S3 (s3://human-microbiome-project/HHS/HMASM/WGS/stool/). The sequence data for the FMT are deposited in the sequence read archive under accession number SRP082182. The code repository used for analysis and processing of metagenomics is available at https://github.com/ranjit58/mgSNP.All relevant data are available from the authors upon request. The HMP metagenomics data is obtained from HMP repository hosted at Amazon (Supplementary MaterialSupplementary Tables


Microglia are dependent on signaling through the colony stimulating factor-1 receptor (CSF-1R/CD115) for growth and survival. Activation of CSF-1R can lead to cell division, while blocking CSF-1R can lead to rapid microglia cell death. CSF-1R has two ligands, the growth factors colony stimulating factor-1 (CSF-1) and the more recently identified interleukin-34 (IL-34). Studies of IL-34 activation of rodent microglia and human macrophages have suggested it has different properties to CSF-1, resulting in an anti-inflammatory reparative phenotype. The goal of this study was to identify if the responses of human postmortem brain microglia to IL-34 differed from their responses to CSF-1 with the aim of identifying different phenotypes of microglia as a result of their responses. To approach this question, we also sought to identify differences between IL-34, CSF-1, and CSF-1R expression in human brain samples to establish whether there was an imbalance in Alzheimer's disease (AD). Using human brain samples [inferior temporal gyrus (ITG) and middle temporal gyrus (MTG)] from distinct cohorts of AD, control and high pathology, or mild cognitive impairment cases, we showed that there was increased expression of CSF-1R and CSF-1 mRNAs in both series of AD cases, and reduced expression of IL-34 mRNA in AD ITG samples. There was no change in expression of these genes in RNA from cerebellum of AD, Parkinson's disease (PD), or control cases. The results suggested an imbalance in CSF-1R signaling in AD. Using RNA sequencing to compare gene expression responses of CSF-1 and IL-34 stimulated human microglia, a profile of responses to CSF-1 and IL-34 was identified. Contrary to earlier work with rodent microglia, IL-34 induced primarily a classical activation response similar to that of CSF-1. It was not possible to identify any genes expressed significantly different by IL-34-stimulated microglia compared to CSF-1-stimulated microglia, but both cytokines did induce certain alternative activation-associated genes. These profiles also showed that a number of genes associated with lysosomal function and Aβ removal were downregulated by IL-34 and CSF-1 stimulation. Compared to earlier results our data indicate that CSF-1R stimulation by IL-34 or CSF-1 produced similar types of responses by elderly postmortem brain-derived microglia. In the search for causes and treatments for neurodegenerative diseases such as Alzheimer's disease (AD), inflammation has been a major target. Although the identification of increased microglial activation associated with AD disease pathology was made more than 25 years ago, there are many aspects of neuroinflammation that still require investigations. The initial hypotheses that activated microglia in AD brains will be causing neurotoxicity by producing increased levels of damaging cytokines, reactive oxygen species, complement factors, and other potentially neurotoxic factors has not translated to effective therapies  signaling in microglial maintenance and proliferation in normal and pathological conditions. CSF-1 was shown to be upregulated in AD and AD-like transgenic mice and was considered essential for the proliferation of microglia that occurs as a result of pathological activation in disease  as CSF-1  of the Banner Sun Health Research Institute, Sun City, Arizona. Details of this program can be found in these reviews   for each of five brain regions  for a potential summary score (0–15) and is based on the histological assessment of frequency of plaques and tangles in Thioflavin S-stained tissue sections  according to the manufacturer's directions with integrity assessed with an Agilent Bioanalyzer and RNA 6000 Nano kits . Samples used for qPCR had RIN > 7.0, and those used for RNA-seq had RIN > 8.0. Samples which did not meet this criteria were excluded from the study. RNA from brain samples (0.5 μg) and cultured cell samples 0.2 μg) were reverse transcribed using the Quantitect reverse transcription kit (Qiagen). Appropriate numbers of no reverse transcriptase controls were prepared in parallel for each batch of samples. For qPCR, cDNA samples were amplified using Perfecta Fast Mix 2x reaction mixture  supplemented with 1.25 μM of EvaGreen. The primers used to detect CSF-1R (CD115), CSF-1, IL-34, Transcription factor EB (TFEB), CD68, IL-1β, and β actin are listed in Table  μg were http://www.genecopoeia.com/product/search/detail.php?prt=1&cid=&key=H9354) . This figure illustrates results for the rabbit polyclonal to IL-34 (Abcam), but similar results were obtained for each IL-34 antibody.Protein extracts from temporal cortex or microglia were analyzed by western blot methodology for levels of IL-34 protein using our published protocols , or stimulated with IL-34  or CSF-1  for 24 h. All of these microglia cases were isolated from subjects that had clinical and neuropathological diagnoses of AD or probable AD. For these experiments, 2 × 10Ulex Europaeus (UEA)-conjugated magnetic beads (Life Technologies).Human autopsy brain microglia were isolated from frontal cortex according to our standard protocols . One-way ANOVA followed by the Fisher LSD-test for Two series of human brain tissue samples were used to assess the relative changes in expression of mRNA of CSF-1R and its ligands CSF-1 and IL-34 with progression of disease. The first series from ITG Table  containeP = 0.064) (Table Non-parametric (Spearman) correlation analyses were carried out with both series of data from cortical samples to determine if there was association between expression levels and degree of plaque or tangle pathology Table . Data sh4) Table . Overalln = 25) and AD cases (n = 16), western blot analyses using the rabbit polyclonal antibody to IL-34 were carried out  markers to assess the number of genes that were upregulated, unchanged or downregulated  for 4 days. In preliminary experiments, we showed that this dose was effective at inducing cell division provided there was 0.5–1% fetal bovine serum (FBS) in the medium. In the absence of FBS, cell division was not apparent with IL-34 treatments. Significant cell division could be visible after 4 days of treatment Figure . This waThis study contained two separate components with the overall goal of assessing the expression and function of IL-34 in AD brains, and its effects on human aged brain-derived microglia. The studies using human brain samples reported in this paper showed a different pattern of expression of IL-34 compared to CSF-1 in AD. In one series of samples, there were decreased IL-34 mRNA expression and protein levels of IL-34 in AD brains, while there were increased expression of CSF-1 and CSF-1R mRNA in AD. In the second series of samples, there was not a significant change in IL-34 mRNA in the AD samples, while increased expression of CSF-1 and CSF-1R mRNA was confirmed. Using histological data that describes the severity of AD plaque and tangle pathology in each case, we showed significant correlations in expression of CSF-1 and CSF-1R with amounts of AD pathology, but no correlation between these measures and IL-34 mRNA expression. During the course of these studies, it was reported similarly that CSF-1R and CSF-1 mRNA levels were increased in AD temporal cortex, but no change in IL-34 mRNA levels  resulted in much greater proliferation (Lee et al., The significance of altered CSF-1R signaling to microglial survival and activation has been highlighted by a series of studies involving administration of CSF-1R inhibitor agents to experimental rodents. Studies by one group used two related CSF-1R inhibitors, PLX3397, or PLX5622 administered in the animal feed, produced almost complete ablation of microglia from the animal brain at the highest dose (Elmore et al., There are still many unanswered questions about the role of microglia in propagating AD pathology in human subjects. If removal of microglia from brain does not lead to enhanced pathology due to the increased accumulation of Aβ plaques, but does lead to reduced neurotoxicity, synaptic damage, and tau pathology due to decreased neuroinflammation, a reassessment of Aβ reduction as the primary therapeutic targets for AD might be needed. One unexplored area that we have tried to address and may be key to understanding the interrelationship of microglia to AD could be the effects of aging on microglial function. A recent paper showed that media from microglial cultures of young mice could supplement the phagocytosis ability for Aβ of microglia from old animals (Daria et al., DW conceived study, developed experimental design, carried out data collection and analysis, prepared manuscript. TT carried out data collection and analysis. LL aided in experimental design, data collection, provided research materials, and aided in manuscript preparation.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


We aimed to compare various methods for free light chain (fLC) quantitation in cerebrospinal fluid (CSF) and serum and to determine whether quantitative CSF measurements could reliably predict intrathecal fLC synthesis. In addition, we wished to determine the relationship between free kappa and free lambda light chain concentrations in CSF and serum in various disease groups.PLUS), nephelometry , and two different  sandwich ELISAs. The results were compared with oligoclonal fLC detected by affinity-mediated immunoblotting after isoelectric focusing.We analysed 166 paired CSF and serum samples by at least one of the following methods: turbidimetry (Freelite™, SPAAlthough the correlations between quantitative methods were good, both proportional and systematic differences were discerned. However, no major differences were observed in the prediction of positive oligoclonal fLC test. Surprisingly, CSF free kappa/free lambda light chain ratios were lower than those in serum in about 75% of samples with negative oligoclonal fLC test. In about a half of patients with multiple sclerosis and clinically isolated syndrome, profoundly increased free kappa/free lambda light chain ratios were found in the CSF.Our results show that using appropriate method-specific cut-offs, different methods of CSF fLC quantitation can be used for the prediction of intrathecal fLC synthesis. The reason for unexpectedly low free kappa/free lambda light chain ratios in normal CSFs remains to be elucidated. Whereas CSF free kappa light chain concentration is increased in most patients with multiple sclerosis and clinically isolated syndrome, CSF free lambda light chain values show large interindividual variability in these patients and should be investigated further for possible immunopathological and prognostic significance. Although the presence of free kappa light chains (fKLC) in the cerebrospinal fluid (CSF) of multiple sclerosis (MS) patients was already assumed in 1974 –10. AlreAlready in 2004 Fischer et al. published a study on the use of Freelite™ for CSF fKLC measurement on Siemens BN II analyser . One yeaHowever, several problems persist. First, there is still no internationally recognised primary standard. The source of most commercially available purified fLC is either serum or urine of patients with fLC paraproteinaemia or Bence Jones paraproteinuria, respectively. The Binding Site primary standards are light chains purified from intact immunoglobulins by reduction and acetylation, which has been criticised by Nakano et al., who proposed a purification procedure for polyclonal fLC from human urine as an alternative for standard preparation . Normal Third, all of the fLC tests have primarily been developed to help in the diagnosis and monitoring of plasma cell dyscrasias, while their performance in other settings has not yet been sufficiently validated.Finally, fLC concentrations in normal CSF are often so low that they cannot be measured even by the modifications of the original assay. Even with latex particle enhancement, turbidimetry or nephelometry simply come to their limits on principal grounds at concentrations below 0.1 mg/L.Reports on the diagnostic performance of CSF fKLC measurement in the context of MS diagnosis are extraordinarily consistent. Almost all studies showed performance similar to, or even marginally better than, the detection of oligoclonal IgG bands, irrespective of what the qualitative or quantitative methods were used and whether CSF fKLC concentration, CSF/Serum quotient, fKLC index (fKLC quotient/Albumin quotient) or any kind of non-linear relationship between fKLC quotient and albumin quotient was used for evaluation. The value of CSF fLLC measurement is less clear. While the earliest reports found intrathecal fLLC synthesis more frequently than that of fKLC , 12, 17,We therefore aimed at choosing the most suitable method for the routine detection of intrathecal fLC synthesis. After we had succesfully introduced oligoclonal fLC detection , 37, we et al.  was slightly below 1 and the value of 1.0 corresponded approximately to the 75th percentile . About hAccording to Reiber , CSF/SerWe confirmed our earlier observations  that forBesides CSF/Serum quotient, also fKLC/fLLC ratio can be calculated from the data. Since this ratio is used in serum as an aid in diagnosing and monitoring patients with monoclonal gammopathies, it might be interesting to detect a skewed distribution of fLC in CSF.Our results are in accordance with older studies that showed substantially higher CSF fLLC than fKLC concentrations in the control groups , 21 but Since the intrathecal fLLC synthesis is usually accompanied by the fKLC synthesis, it may seem logical to drop fLLC studies and perform only fKLC tests. However, from the immunological point of view, the analysis of both fLC might be useful as widely different fLLC values were seen in MS patients by our group  as well PLUS analyser, which is economic and user-friendly; manual dilution of the CSF sample is rarely required for fKLC and almost never for fLLC. Nevertheless, ELISA based assays might be better applicable in some laboratories worldwide where access to automated instruments is limited. In addition, diluted CSF is used in our in-house ELISA assay, which leads to a much lower amount of sample required for the analysis. Although the sample volume in the automatised assays is low , we have to add the dead volume that is slightly above 100 μl in both cases. Nephelometric method on BN ProSpec (with either Freelite or N Latex FLC) might be best suited for scientific purposes. Besides having marginally better sensitivity for fKLC (about 0.05 mg/L versus 0.1 mg/L on the SPAPLUS instrument), in consequent dilutions it strives to achieve a signal in the sample that is close to the middle of the calibration curve where the measurement can be expected to be most precise. On the other hand, this results in a high consumption of reagents for samples with high CSF fLC concentrations, which is a major drawback of this method.Each method as well as each analyser has its own limits and advantages. Finally, we chose the Freelite turbidimetric assay on SPAWe are aware of several weaknesses of our study. First, it was impossible to perform all tests on every sample, mainly due to insufficient amounts of CSFs and/or sera needed for the assays. Testing by the N Latex FLC method on BN ProSpec had to be interrupted prematurely because of quality control failure regarding a new lot of reagents that was unfortunately only discovered after thawing a proportion of samples. Simultaneously performing in-house ELISA methods with monoclonal and polyclonal (Freelite™) standards, we found out there were rare cases when one sample showed a signal higher than that of the highest standard in one method, but within the calibration range in the other method. However, the analysis could not be repeated in higher dilution either due to insufficient amount of sample or for logistic reasons.Second, although theoretically sound, the assumption that the detection of CSF-restricted oligoclonal immunoglobulin or fLC bands represents a gold standard to prove intrathecal synthesis, against which quantitative measures should be compared, was only sufficiently validated for IgG. The negative result of oligoclonal IgG detection is often used as a reference method for detecting the absence of intrathecal humoral immune reaction, but it was shown that o-fLC could sometimes be detected in the absence of o-IgG, especially in other inflammatory CNS diseases rather than MS , 37. TheIn spite of considerable proportional as well as systematic differences between various quantitative fLC assays compared in this study, no major differences were observed regarding the prediction of a positive o-fLC test or the diagnostic performance in the context of inflammatory nervous system disease diagnosis. The performance of o-fKLC as well as quantitative fKLC tests is comparable to that of o-IgG, but not superior. In the context of MS diagnosis, o-IgG seems to be more specific than fKLC tests. We therefore conclude that it is too early to recommend abandoning the o-IgG test as suggested in some recent reports , 8 untilS1 FigST2 and ST0.2, fKLC and fLLC standards  as positive controls; F, IgG preparation  as a negative control; C1+S1, C2 + S2: CIS (case 1 subsequently converted into definite MS). Both CSF samples are positive for fKLC, while only the second sample is positive for fLLC. CSF fKLC concentrations were 5.09 and 1.74 mg/L, while CSF fLLC concentrations 0.10 and 1.06 mg/L, respectively.C3 + S3: myelitis of unknown aetiology (diagnosis group 3); both fKLC and fLLC OCBs can be seen in the CSF. CSF concentration of fKLC was 2.02 mg/L and that of fLLC 1.37 mg/L.At control 7 months later, no fLC bands were seen and the CSF fLC concentrations fell to normal . Control sample was not included in the analysis.C4 + S4: cryptogenic oculomotor mononeuropathy (diagnosis group 4); normal CSF, negative result of both o-fKLC and o-fLLC  Anode is at the top.(GIF)Click here for additional data file.S2 FigStandards of methods (A)–(D) analysed by the in-house ELISA method.Calibrators of methods (A)–(D) together with Freelite® calibrators for serum assays  in the figure legend) were diluted to similar fLC concentrations and analyzed by in-house ELISA (D). Please note that method (E) used the same calibrator as method (A). Dilution curves were compared. Optical densities (OD) were very similar for both Freelite® calibrators. Otherwise, however, there were considerable differences among the calibrators, especially for fKLC.(PDF)Click here for additional data file.S3 FigA clear correlation can be seen between fLC quotients and albumin quotients in the group of o-fLC negative samples, whereas no such correlation is observed when there is intrathecal fLC synthesis.(TIFF)Click here for additional data file.S1 FileTable A. Comparison of CSF fLC concentrations measured by methods (A)—(E) by means of Passing and Bablok regression and Spearman´s correlation coefficient. (A), Freelite™ assay on the SPAPLUS analyser; (B) N Latex FLC™ assay on BN ProSpec analyser; (C) commercially available ELISA (BioVendor); (D), in-house ELISA using monoclonal standards (Bethyl Laboratories); (E), in-house ELISA using Freelite™ standards; fLC, free light chains; fKLC, free kappa light chains; fLLC, free lambda light chains; CI, confidence interval Table B. Comparison of serum fLC concentrations measured by methods (A)—(E) by means of Passing and Bablok regression and Spearman´s correlation coefficient. (A), Freelite™ assay on the SPAPLUS analyser; (B) N Latex FLC™ assay on BN ProSpec analyser; (C) commercially available ELISA (BioVendor); (D), in-house ELISA using monoclonal standards (Bethyl Laboratories); (E), in-house ELISA using Freelite™ standards; fLC, free light chains; fKLC, free kappa light chains; fLLC, free lambda light chains; CI, confidence interval Table C. Comparison of CSF/Serum fLC quotients by means of Passing and Bablok regression and Spearman´s correlation coefficient.(A), Freelite™ assay on the SPAPLUS analyser; (B) N Latex FLC™ assay on BN ProSpec analyser; (C) commercially available ELISA (BioVendor); (D), in-house ELISA using monoclonal standards (Bethyl Laboratories); (E), in-house ELISA using Freelite™ standards; fLC, free light chains; fKLC, free kappa light chains; fLLC, free lambda light chains; CI, confidence interval(ZIP)Click here for additional data file.S2 File3) F. Q fLLC (∙ 103)Method comparison (scatter diagrams). A. CSF fKLC (mg/L) B. Serum fKLC (mg/L) C. CSF fLLC (mg/L) D. Serum fLLC (mg/L) E. Q fKLC , Freelite™ assay on the SPAPLUS analyser; (B) N Latex FLC™ assay on BN ProSpec analyser; (C) commercially available ELISA (BioVendor); (D), in-house ELISA using monoclonal standards (Bethyl Laboratories); (E), in-house ELISA using Freelite™ standards(ZIP)Click here for additional data file.S3 File3) F. Q fLLC (∙ 103)A. CSF fKLC (mg/L) B. Serum fKLC (mg/L) C. CSF fLLC (mg/L) D. Serum fLLC (mg/L) E. Q fKLC , Freelite™ assay on the SPAPLUS analyser; (B) N Latex FLC™ assay on BN ProSpec analyser; (C) commercially available ELISA (BioVendor); (D), in-house ELISA using monoclonal standards (Bethyl Laboratories); (E), in-house ELISA using Freelite™ standards.(ZIP)Click here for additional data file.S1 Tablea. according to the diagnosis group b. according to MS and IND status MS, multiple sclerosis; CIS, clinically isolated syndrome; OIND, other (than MS) inflammatory neurological diseases; NIND, non-inflammatory neurological diseases; IND, inflammatory neurological diseases.(RTF)Click here for additional data file.S2 Tablea. Free kappa light chains b. Free lambda light chains MS, multiple sclerosis; non-MS, other diagnoses than multiple sclerosis or clinically isolated syndrome It should be noted that the number of samples examined by the N Latex FLC™ and BioVendor ELISA is too small to permit definitive conclusions regarding the performance of these tests in the context of MS diagnosis.* The second best cut-off value in case the calculated cut-off resulted in ≤50% sensitivity or specificity that was considered as unacceptable.(RTF)Click here for additional data file.S3 Tablea. Free kappa light chains b. Free lambda light chains IND, inflammatory neurological diseases; NIND, non-inflammatory neurological diseases and controls * The second best cut-off value in case the calculated cut-off resulted in ≤50% sensitivity or specificity that was considered as unacceptable. ** The second best cut-off value in case the calculated cut-off resulted in ≤50% specificity that was considered as unacceptable.(RTF)Click here for additional data file.


Typical approach for increasing apheresis platelet collections is to recruit new donors. Here, we investigated the effectiveness of an alternative strategy: optimizing donor scheduling, prior to recruitment, at a hospital-based blood donor center.Analysis of collections, during the 89 consecutive months since opening of donor center, was performed. Linear regression and segmented time-series analyses were performed to calculate growth rates of collections and to test for statistical differences, respectively.Pre-intervention donor scheduling capacity was 39/month. In the absence of active donor recruitment, during the first 29 months, the number of collections rose gradually to 24/month (growth-rate of 0.70/month). However, between month-30 and -55, collections exhibited a plateau at 25.6 ± 3.0 (growth-rate of -0.09/month) (p<0.0001). This plateau-phase coincided with donor schedule approaching saturation (65.6 ± 7.6% schedule booked). Scheduling capacity was increased by following two interventions: adding an apheresis instrument (month-56) and adding two more collection days/week (month-72). Consequently, the scheduling capacity increased to 130/month. Post-interventions, apheresis platelet collections between month-56 and -81 exhibited a spontaneous renewed growth at a rate of 0.62/month (p<0.0001), in absence of active donor recruitment. Active donor recruitment in month-82 and -86, when the donor schedule had been optimized to accommodate further growth, resulted in a dramatic but transient surge in collections.rd of the scheduling capacity. Optimizing the scheduling capacity prior to active donor recruitment is an effective strategy to increase platelet collections at a hospital-based donor center.Apheresis platelet collections plateau at nearly 2/3 Hospital-based donor centers do not always have sufficient whole-blood donors to pool platelets from the same ABO-groups to create a full adult-dose of platelets. These insufficient doses may be used for pediatric patients, but are often not utilized.Platelets play a major role in primary hemostasis and are transfused to patients for a number of reasons, primarily to stop or prevent life-threatening bleeding. AlthoughIncreasingly, platelets are collected by apheresis rather than whole blood collections. ApheresiPlatelets obtained from regional blood centers are expensive, which is a major financial burden. AdditionThe typical reflexive approach to increasing platelet collections at a hospital-based donor center is to actively recruit more apheresis platelet donors. In this report, we document the effectiveness of an alternative approach: optimizing the apheresis donor scheduling capacity prior to donor recruitment efforts. We show that even with a modest number of apheresis donors at a 615-bed tertiary care academic hospital, we were able to significantly increase platelet collections by optimizing the apheresis donor scheduling capacity, in absence of active donor recruitment. When donor recruitment efforts are timed with optimized scheduling capacity, it results in a dramatic increase in apheresis platelet collections. We provide a summary of practical considerations associated with the strategy and a flowchart to guide decision between donor recruitment and expanding scheduling capacity to maximize platelet collections at hospital-based donor centers.The study was approved by the Institution Review Board of Pennsylvania State University College of Medicine (Study 990), Hershey, Pennslvania USA. Informed consent was not required as only non-identifying data were collected and analyzed.An analysis of apheresis collections at the hospital-based donor center was performed from November 2007 (the first day of apheresis collection at our institution) through March 2015 (89-months). Local and regional apheresis platelet donor information was obtained from the Blood Bank Control System (BBCS) database, maintained by the regional blood bank.Platelets were collected using the Trima Accel® Automated Blood Collection System and tubing sets (Terumo BCT), per manufacturer’s protocols.The 89-month period was separated into four phases: growth-phase 1, plateau-phase, growth-phase 2 and donor-recruitment phase. We calculated the growth rate (slope) of apheresis collections during each phase by performing a simple linear regression model. We utilized segmented time-series analysis to perform a pair-wise comparison of the growth-trends among the first three phases to test if they were statistically different. The assumption of autocorrelation was investigated using a generalized Durbin-Watson test and a correction was performed if autocorrelation was detected. Autocorrelation correction was necessary when comparing growth-phase 1 with plateau-phase, and comparing growth-phase 1 with growth-phase 2; correction was not required in the comparison between the plateau-phase and growth-phase 2. A p-value of < 0.05 was considered significant for all statistical tests. All statistical analyses were performed using SAS software, version 9.4.2 of 0.80). However, between month-30 and month-55, apheresis collections stopped growing and averaged approximately 25.58 collections per month . Time-analysis showed that the growth rate during plateau-phase was statistically different when compared to growth-phase 1 . This “plateau” in our apheresis collections may be explained by one of two possibilities: insufficient number of active donors or insufficient donor scheduling capacity (i.e. schedule filled approaching saturation).We performed an analysis of all apheresis platelet collections between November 2007 (month-1) and March 2015 (month-89) at our institution , Table 1During the plateau-phase, which lasted 26 months, we performed 665 apheresis procedures at our center from 80 platelet donors, an average of 3.84 collections/donor/year. The FDA regulation allows for a maximum of 24 collections/donor/year. In comparison, at a regional blood center, platelet donors donated an average of 8.64 collections/donor/year between August 2013 and July 2014. Therefore, donors at our hospital-based donor center were donating less frequently than the regional donor center, presumably due to restrictive scheduling. This dissimilarity may also be explained by potential differences in donor demographics or more aggressive donor recruitment by the regional donor center.% schedule filled) identifies the likely reason for the plateau. During growth-phase 1, the % schedule filled increased from 15% to 80% (average of 44.4%). However, during the plateau-phase the % schedule filled averaged 65.6% (within a narrow range from 54% - 80%) (A retrospective look at the percentage of the apheresis schedule capacity filled by our donors (hereafter referred to as % - 80%) . TherefoWe increased our total donor scheduling capacity by the following two interventions. The overall effects of these interventions are summarized in % schedule filled  during growth-phase 2. Concurrently, our monthly apheresis collections during growth-phase 2 increased from an average of 25.58 collections/month during the plateau-phase to 42.0 collections on month-81, at a growth rate of 0.62 collections/month , in absence of active donor recruitment. The growth rate during growth-phase 2 was significantly different than the plateau-phase (p <0.0001), but not significantly different when compared to growth-phase 1 (p = 0.43).Collectively these interventions reduced We acquired the second apheresis machine on a reagent-rental contract, wherein there was no upfront capital expenditure. However, the cost of the apheresis machine was captured in the adjusted price of the apheresis kits. During the 26 months of growth-phase 2, our apheresis collections averaged 34.46/month, which was an increase from an average of 25.58/month during the plateau-phase. An additional 230.88 ((34.46–25.58)/month x 26 months) apheresis platelet collections were performed during growth-phase 2. This equates to 344 adult doses, based on our split-rate of 1.49 doses/collection. The direct costs associated with in-house platelet collection was $297.68/dose, which included the technologist’s time, the cost of apheresis kits, cost of infectious disease testing, maintenance cost of the second apheresis machine, marketing cost and the cost associated with the usage of the electronic system. Indirect costs associated with platelet collections were not included in our analysis, as they were largely unchanged from plateau-phase to growth phase-2. Each dose of platelet collected in-house yielded a savings of approximately $212.32 to our institution when compared to purchasing those units from the regional blood center ($510.00/dose at the time). Therefore, our institution saved approximately $73,038.08 in direct costs during the growth-phase 2 ($212.32 x 344 doses).% schedule filled reached 65.6%. Because our collection practices have not changed significantly, we therefore predict that our new schedule will also reach a plateau when nearly two-thirds of the schedule has been filled (i.e. 85.3 collections per month), given adequate number of donors. However, we would exhaust our apheresis platelet donors before achieving this new theoretical plateau. At the end of growth-phase 2, we had 72 active donors. Assuming the donation behavior of regional donors who donated 8.64 times/year, we expect to exhaust our current pool of donors at approximately 56.16 apheresis collections/month.We attempt to predict the next plateau in our collections. Our new schedule capacity was 130 collections per month. Previously, we reached a plateau when our Post-interventions, our total scheduling capacity increased significantly. We argued that we might have the potential to accommodate rapid growth of apheresis collections. We tested the effect of actively recruiting apheresis donors at this stage. We solicited platelet donation among eligible blood donors using scripted phone calls (intervention #3) on month-82 and posting donor recruitment flyers around the hospital (intervention #4) on month-86 respectively. Immediately following each recruitment strategy, we saw a surge in platelet collections: 21.9% increase in the month after intervention #3 (50 collections) and a cumulative 63.4% increment (67 collections) in the month following intervention #4, compared to the average collections of the last three months in growth-phase 2 (41 collections/month). However, the effect of our temporary phone calls and the advertisement flyers were short-lived, as the number of apheresis collections gradually drifted back to the pre-recruitment growth rate. The surge in collections indicate that a donor schedule with optimal scheduling capacity has the potential for significant growth when paired with appropriate and timely recruitment strategies.In this study, we document the utility of optimizing the apheresis scheduling capacity on platelet collections, prior to donor recruitment, at a hospital-based donor center. There are several limitations of this study. Donors who cancelled (or did not show up) after scheduling are not captured in our study. The effect of interventions during growth-phase 2 are overlapping, which limits our ability to evaluate the impact of each intervention separately. Several confounding factors might affect our analysis, including seasonal variations in donations, changes in donor demographics over time, increased awareness of apheresis donations in the community, etc. Because our individual study periods  are each multi-year long, we predict that the effect of any seasonal or transient confounding factor would likely be minimal, if any.Hospital-based donor centers have limited resources to dedicate towards donor recruitment and management. Few studies till date have focused on computer simulations for blood donors scheduling.–8 Pratt % schedule filled reached an average of 65.6% (nearly 2/3rd) of total scheduling capacity. Although the exact % schedule filled that no longer supports growth will vary by institution, we postulate that the pattern of schedule saturation will likely be similar, i.e. decreased collections/donor/year compared to other regional centers. Ability to schedule donors at their convenience is expected to be critical in collection growth. Donor scheduling capacity can be increased in one of two ways: either by obtaining an additional apheresis machine or by increasing donor hours. Both methods have their advantages and disadvantages. Purchasing an additional apheresis machine allows a donor center to instantly increase their scheduling capacity by as much as two-fold. This approach is useful for donor centers that are unable to expand their current donor hours any further, or that have several donors who prefer the same time slot. The disadvantage is that buying an additional machine might require upfront capital commitment. The advantage of the second approach of increasing donor hours is that the donor center can cater their hours according to donor preferences.In this report, we provide the means to identify the lack of growth in apheresis collections which may be due to either donor scheduling approaching saturation or due to the lack of apheresis donors. Surprisingly, with a modest platelet donor database at our institution of <70 active donors (defined as donors who donated in the previous 12 months) at the time, collections reached a plateau likely due to lack of donor scheduling capacity before exhausting our donor pool. This might be true at other institutions as well. We reached a plateau when our Although, we experienced financial savings at our institution, there are certain limitations with our analysis. We have not included indirect costs in our analysis as they were largely unchanged by the interventions. At other institutions, however, the indirect costs such as donor recruitment, donor retention, administration costs, rent, depreciation, etc. may significantly affect the financial analysis. The cost of training additional technologists is also not included in our analysis. The financial considerations for making the decision will likely vary by institution. Our analysis also does not capture the impact of our interventions on the ability to fulfil patient’s demands, or the freshness of platelets given to the patients.We predict that if a donor center is approaching schedule saturation, then either of the above two approaches will provide a significant increase in apheresis collections. We advise caution about generalizing interpretations from such a project. First, the operation of any individual donor center could be significantly different; therefore, the recommendation of increasing scheduling capacity is not meant as a panacea. The flowchart should be used in the context of the individual donor center’s practice. Second, our predictions are based on the assumption that the donor behavior at our institution reflects the regional average. However, the demographics of the donors at a particular donor center could be different than the regional average. Moreover, each institution and donor center has its own challenges. The prediction, while simplistic, cannot recapitulate all the possibilities or hurdles that will be encountered at any institution. For example, some hospital-based donor centers might have partnered with their regional blood centers for donor recruitment. Also, seasonal variations could be significant and dependent on the region. Finally, before expanding donor schedules, institutions should consider additional factors, particularly whether donors are available and willing to donate on the proposed new schedule. A donor survey might be useful in the planning process before implementing any changes.S1 File(XLSX)Click here for additional data file.


Subversion of the host immune system by viruses is often mediated by molecular decoys that sequester host proteins pivotal to mounting effective immune responses. The widespread mammalian pathogen parapox Orf virus deploys GIF, a member of the poxvirus immune evasion superfamily, to antagonize GM-CSF (granulocyte macrophage colony-stimulating factor) and IL-2 (interleukin-2), two pleiotropic cytokines of the mammalian immune system. However, structural and mechanistic insights into the unprecedented functional duality of GIF have remained elusive. Here we reveal that GIF employs a dimeric binding platform that sequesters two copies of its target cytokines with high affinity and slow dissociation kinetics to yield distinct complexes featuring mutually exclusive interaction footprints. We illustrate how GIF serves as a competitive decoy receptor by leveraging binding hotspots underlying the cognate receptor interactions of GM-CSF and IL-2, without sharing any structural similarity with the cytokine receptors. Our findings contribute to the tracing of novel molecular mimicry mechanisms employed by pathogenic viruses. Viruses often subvert the host immune system using molecular decoys to prevent an effective immune response. Here, the authors examine the structural details of the viral decoy receptor GIF and its antagnosim of GM-CSF and IL-2. Throughout evolution, mammalian viruses co-evolved with their hosts and developed numerous immunomodulatory strategies aimed at countering and evading antiviral responses by the host immune system. Among the many different immune evasion tactics deployed by viruses, are the use of antigenic variation, latency, interference with antigen presentation, inhibition of apoptosis of viral infected cells and viral mimicry134The orf virus, a 140 kb double-stranded DNA (dsDNA) virus from the parapoxfamily infects ruminants and humans worldwide causing contagious ecthyma, also known as orf, a highly spreadable skin condition characterized by severe lesions and ulcerations67810128The Orf virus subverts the immune response by secreting virulence proteins that inhibit or mimic key anti-inflammatory host effector moleculesThe latter, GIF, is arguably one of the most intriguing viral proteins known because it targets two distinct host proteins pivotal to the host's ability to mount an effective immune response. For instance, GM-CSF is involved in the survival and activation of macrophages, neutrophils and eosinophils, the maturation of dendritic cells and differentiation of invariant natural killer T cells222425In this study, we employ an integrative structural biology approach in an effort to elucidate the structural and mechanistic principles of how a single viral protein can sequester two different host proteins. Via a series of cross-validated structural snapshots including the crystal structure of GIF in complex with GM-CSF, two-dimensional (2D) classifications of GIF:GM-CSF and GIF:IL-2 complexes by negative-stain electron microscopy, the ensuing three-dimensional (3D) reconstruction of the GIF:IL-2 complex, and modeling of small-angle X-ray scattering (SAXS) data in conjunction with multi-angle laser light scattering (MALLS) measurements, we show that GIF adopts an obligate dimeric structure that can bind two molecules of its target cytokines. We further show via structure-based mutagenesis in combination with interaction studies that GIF does so with high affinity using a mutually exclusive interaction site for both cytokines and plays into binding principles utilized by the cognate receptors of GM-CSF and IL-2. Together, our findings help to unmask the molecular virtuosity gained during viral protein evolution towards new protein ligand specificities and the molecular mechanisms developed by viruses to subvert the host immune response.in situ reconstitution of GIF:GM-CSF and GIF:IL-2 complexes that could be purified to homogeneity for further biophysical and structural studies. Indeed, crystallization trials and subsequent crystal optimization efforts yielded crystals of the GIF:GM-CSF complex that enabled determination of the crystal structure to 2.8 Å resolution  and size-exclusion chromatography (SEC) initially led to prohibitory low amounts of purified GIF protein and uncovered the tendency for recombinant GIF to aggregate in standard purification buffers. However, addition of a molar excess of either recombinant ovine GM-CSF or IL-2 to culture medium containing secreted recombinant GIF enabled solution  allowingOur structural analyses reveal that GIF is an obligatory dimer, consisting of two GIF subunits with an 11-strand β-sandwich topology stabilized by three intramolecular disulfide bonds. Two GIF protomers interact via β-strand complementation mediated by strand βB and the C-terminal strand βK, resulting in an extended and concave β-sandwich platform . The ens272829302 of solvent-accessible surface area  using purified complexes following co-expression of GIF with each of its target cytokines in HEK293T cells . The ensTo provide additional evidence for the molecular basis and stoichiometry of the two structurally distinct GIF–cytokine complexes we carried out a comparative analysis by SEC and MALLS . Our anaThus, our structural analyses of GIF:GM-CSF and GIF:IL-2 complexes by several orthogonal approaches including X-ray crystallography, EM, SAXS and SEC-MALLS illustrate that the two GIF–cytokine complexes are structurally distinct based on the ability of dimeric GIF to sequester two copies of each cytokine to yield stable assemblies obeying 2:2 stoichiometry.KD=27 nM, whereas IL-2 binds to GIF with ∼60-fold higher affinity (KD=0.47 nM) that can be recapitulated by fast association kinetics and moderately slow dissociation kinetics  (Novagen) Escherichia coli cells and produced from inclusion bodies by rapid dilution refolding  and eluted with 10 column volumes elution buffer . After elution protein fractions were pooled, concentrated to a volume of 1 ml and injected onto a Prep-Grade Hiload 16/600 SD 75 size exclusion column  equilibrated with HEPES-buffered saline buffer . oGM-CSF and oIL-2 expressed in HEK293T cells were purified using the same purification protocol as described above, but instead of Ni-NTA cartridges, a Co2+-packed TALON FF column (Clontech) was used for IMAC.Sequence optimized constructs containing residues 18–144 of ovine GM-CSF (Uniprot code: P28773) and residues 21–155 of ovine IL-2 (Uniprot code: P19114) were cloned in the pET-15b vector (Novagen) and pHLsec vector6-tag2+-packed TALON FF column (Clontech). During IMAC, the same buffers as described above were used, supplemented with 0.15% (weight per volume) CHAPS. Eluted GIF was pooled, concentrated and injected onto a Prep-Grade Hiload 16/600 SD 200 size exclusion column  equilibrated with HBS supplemented with 0.15% (weight per volume) CHAPS.A construct containing residues 1-265 of the Orf virus GM-CSF/IL-2 inhibition factor  was cloned into the pHLsec vector encoding a C-terminal His2+-packed TALON FF column (Clontech) or Ni2+-packed complete column (Roche) for oIL-2 and oGM-CSF, respectively. Subsequent IMAC and SEC purification steps were performed using the protocol as described for unliganded GIF, but without adding CHAPS to purification buffers.To generate GIF:oGM-CSF and GIF:oIL-2 complexes, GIF was either co-expressed with oGM-CSF or oIL-2 in HEK293T cells by transient transfection−1 before initiating crystallization trials. Large plate-like crystals were obtained at 293 K in a crystallization condition containing 0.2 M sodium acetate trihydrate, 0.1 M TRIS hydrochloride pH 8.5 and 30% (weight per volume) PEG 4,000 . Crystals were scooped, transferred to mother liquor supplemented with 25% ethylene glycol, and subsequently flash-cooled in liquid nitrogen. Diffraction data were collected to 2.0 Å at the P14 microfocus beam line at PETRA III, Hamburg. The obtained data set was processed using XDSAll crystallization trials were set up using a Mosquito robot (TTP LabTech) in a sitting drop vapour diffusion set-up with drop volumes of 200 nl (100 nl protein solution+100 nl reservoir solution). Refolded oGM-CSF was concentrated to 5 mg mlin vitro refolded oGM-CSF produced in E. coli was concentrated to 6 mg ml−1. Small and highly mosaic cube-like crystals were obtained at 293 K in a crystallization condition containing 0.15 M ammonium sulfate, 0.1 M MES pH 5.5 and 25% (weight per volume) PEG4000 . After optimization of this initial hit, new crystals with dimensions of 40 × 40 × 250 μm slowly grew over a period of 3 months in a condition containing 0.15 M ammonium sulfate, 0.1 M MES pH 6 and 24% (weight per volume) PEG 4,000. Before X-ray data collection, crystals were scooped, transferred to mother liquor supplemented with 25% ethylene glycol, and flash cooled in liquid nitrogen. Diffraction data were collected to 2.84 Å at the P14 microfocus beam line at PETRA III, Hamburg, and subsequently processed using XDSPurified complex between GIF produced in HEK293T cells and P21 . Structure determination was performed by maximum-likelihood molecular replacement (MR) in PhaserDiffraction data for ovine GM-CSF was processed in spacegroup C2 . After manual inspection of the collected data, severe anisotropy was observed. The data were ellipsoidally truncated and anisotropically scaled on-line using the anisotropy servera*, b* and c* axes of 3.3 Å, 2.8 Å and 2.7 Å, respectively. As no homologues of the Orf GIF protein are available, a trimmed ensemble model was made with the program Ensembler of the Phenix package by using two proteins distantly related to GIF, vaccinia virus A41L (ref. The obtained data set for the GIF:oGM-CSF complex was processed in space group 41L ref.  (PDB cod41L ref.  (PDB cod41L ref. .E. coli biotin ligase (BirA) cloned in the pDisplay vector  were cloned in the pHLsec vector containing a C-terminal AviTag2HPO4, 1.8 mM KH2PO4, pH 7.4) supplemented with 0.01% (w/v) bovine serum albumin and 0.002% Tween-20, using an Octet RED96 instrument (ForteBio), operating at 298 K. Streptavidin-coated biosensors were functionalized with biotinylated GIF, quenched with a 10 μg ml−1 biocytin solution and then exposed to different concentrations of ligand . To verify that no nonspecific binding was present during the interaction assay, non-functionalized biosensors were used as a control by measuring in parallel all ligand concentrations as well as running buffer. The measurements presented in KD, kd and ka values represent the averages of these triplicates.All experiments were performed in 1 × PBS  uranyl acetate. Images were recorded under low-dose conditions with a JEOL 1,200 EX II microscope at 100 kV and at a nominal magnification of 40,000x. Negatives were digitized on a Zeiss scanner (Photoscan TD) to a pixel size of 3.5 Å at the object scale . A semi-GIF:GM-CSF was applied to the clear side of carbon on a carbon–mica interface and stained with 2% (w/v) sodium silicotungstate, pH 7.5. Images were recorded under low-dose conditions with a FEI Tecnai12 microscope operating at 120 kV and at a nominal magnification of 49,000 × using a Gatan Orius SC1,000 CCD camera. The CTF for each micrograph was determined with CTFFIND3 ref. . Using pThe crystal structure of dimeric GIF and two copies of an oIL-2 homology model generated by MODELLER−1 respectively before injection on a Superdex Increase 10/300 GL size exclusion column  equilibrated with HBS (pH 7.5) using an injection volume of 100 μl and a flow rate of 0.5 ml min−1. Measurements were performed at 293 K, within a momentum transfer range of 0.01 Å−1<s<0.550 Å−1 (GIF:GM-CSF) and 0.01 Å−1<s<0.622 Å−1 (GIF:IL-2), where s=4πsin(θ)/λ and 2θ is the scattering angle. Eighty nine buffer frames with an exposure time of 1 s were averaged and subtracted from 10 × 1 s frames (GIF:GM-CSF) or 16 × 1 s frames (GIF:IL-2) taken from the eluted protein peaks. Scattering data were collected on an AVIEX PCCD170170 detector. Initial data integration, averaging and subtraction were performed using the Foxtrot software at SWING. The forward scattering (I0) and radius of gyration (Rg) were determined by PRIMUS-QTI0 and Rg calculated by SCÅTTERVp) was evaluated using AutoporodDmax and corresponding distance distribution function P(r) were evaluated using SCÅTTERSEC-SAXS data were collected at the SWING beamline at SOLEIL (France) using an integrated online HPLC set-up. Purified samples of GIF:oGM-CSF and GIF:oIL-2 co-expressed in HEK293T cells were first concentrated to 10 and 8.8 mg ml−1 was used. Fits of the resulting SAXS models of GIF:GM-CSF and GIF:IL-2 complexes to the experimental SAXS data were calculated using the FoXS softwareThe crystal structure of the GIF:oGM-CSF complex and the model of GIF:IL-2 based on the fit in the EM map were used as an input for the Allosmod-FoXS web server−1 and injected onto a Superdex Increase 10/300 GL size exclusion column  equilibrated with HBS (pH 7.5), coupled to an online UV-detector (Shimadzu), a mini DAWN TREOS (Wyatt) multi-angle laser light scattering detector and an Optilab T-rEX refractometer (Wyatt) at 298 K. Following refractive index (RI) increment values (dn/dc) were used for protein concentration and molecular mass determination: ovine GM-CSF: 0.182 ml g−1, ovine IL-2: 0.182 ml g−1, GIF:oGM-CSF: 0.181 ml g−1, GIF:oIL-2: 0.182 ml g−1. Data analysis was carried out using the ASTRA6.1 software. Theoretical and measured molecular weights relevant to this study are summarized in Purified oGM-CSF, oIL-2, GIF:oGM-CSF and GIF:oIL-2 samples expressed in HEK293T cells were concentrated to 1–2 mg mlCoordinates and structure factors for ovine GM-CSF and the GIF:GM-CSF complex have been deposited in the Protein Data Bank (PDB) with accession codes 5D22 and 5D28 respectively. EM maps obtained after 3D reconstruction of the GIF:IL-2 complex have been deposited in the Electron microscopy Data Bank (EMDB) with accession code EMD-3230. Experimental SAXS data, a SAXS model for the GIF:GM-CSF and GIF:IL-2 complexes after loop and N-glycan modeling in Allosmod-FoXS, and the fit with experimental SAXS curves have been deposited in the Small-Angle Scattering Biological Data Bank (SASBDB) with accession codes SASDA89 (GIF:GM-CSF) and SASDA99 (GIF:IL-2).How to cite this article: Felix, J. et al. Structural basis of GM-CSF and IL-2 sequestration by the viral decoy receptor GIF. Nat. Commun.7, 13228 doi: 10.1038/ncomms13228 (2016).Supplementary Figures 1-6 and Supplementary Tables 1-5.


We investigated the frequency, predictors, and evolution of acute lymphoblastic leukemia (ALL) in patients with CNS relapse and introduced a novel method for studying BCR-ABL1 protein variants in cDNA from bone marrow (BM) and cerebrospinal fluid (CSF) blast cells. A total of 128 patients were analyzed in two PETHEMA clinical trials. All achieved complete remission after imatinib treatment. Of these, 30 (23%) experienced a relapse after achieving complete remission, and 13 (10%) had an isolated CNS relapse or combined CNS and BM relapses. We compared the characteristics of patients with and without CNS relapse and further analyzed CSF and BM samples from two of the 13 patients with CNS relapse. In both patients, classical sequencing analysis of the kinase domain of BCR-ABL1 from the cDNA of CSF blasts revealed the pathogenic variant p.L387M. We also performed ultra-deep next-generation sequencing (NGS) in three samples from one of the relapsed patients. We did not find the mutation in the BM sample, but we did find it in CSF blasts with 45% of reads at the time of relapse. These data demonstrate the feasibility of detecting BCR-ABL1 mutations in CSF blasts by NGS and highlight the importance of monitoring clonal evolution over time. About 25% of adults and about 5% of children with acute lymphoblastic leukemia (ALL) have the Philadelphia chromosome or the BCR-ABL1 rearrangement. The BCR-ABL1 genetic fusion typically encodes a 190-kDa constitutively active tyrosine kinase protein, but the protein is 210 kDa in some cases. Although the prognosis of ALL has improved with the introduction of tyrosine kinase inhibitor (TKI) therapy, relapses still occur, especially in elderly patients.The CNS is frequently a reservoir for tumor cells that avoid exposure to therapeutic agents. The frequency of CNS relapse in BCR-ABL1-positive ALL ranges from 8 to 17% . Since tUntil very recently, Sanger sequencing (SS) was the most widely used method for evaluating the kinase domain mutation status of the chimeric BCR-ABL1 protein. However, the development of next-generation sequencing (NGS) technologies has overcome some of the limitations of SS, allowing sequencing of the entire kinase domain thousands or even millions of times at once. Ultra-deep NGS allows several sequence reads, each one corresponding to a single DNA clone, which detect the sequence at a single nucleotide position multiple times with high sensitivity .Our primary goal was to study the frequency, predictors, and evolution of the disease in patients with CNS relapse in two consecutive clinical trials of Philadelphia chromosome-positive ALL that were conducted by the Spanish PETHEMA (Programa Español de Tratamientos en Hematología) Group. A secondary aim was to evaluate the use of a novel method for studying the pathogenic mechanisms and variants of uncertain significance (VUS) in the BCR-ABL1 kinase domain using cDNA from samples of bone marrow (BM) and CSF blasts with the idea of selecting optimal TKI treatment based on clonal evolution from BM to CSF cells.We reviewed the CNS relapse data of patients who were included in two consecutive clinical trials, LAL-OPH-2007  and LAL-We evaluated BM and CSF samples from two patients that were taken either at diagnosis or at recurrence. Patient 1 was treated within the LAL-PH-2008 protocol, and patient 2 was treated according to the LAL-OPH-2007 protocol.227 to residue Gly514. Both PCR reactions were performed using Taq1b FastStart DNA MasterPLUS HybProbe . Following the manufacturer’s protocols, the amplicons were purified with Agencourt AMPure XP Beads  at an initial ratio of 1.8× and visualized using the Bioanalyzer 2100 . Further enzymatic fragmentation of the kinase domain was performed using Ion ShearPlus  to obtain ∼270-bp fragments. After another identical purification step with AMPure XP Beads, we ligated the adaptors and performed nick repair. Each library was barcoded with Ion Xpress Barcode Adapters (Thermo Fisher). The resulting new fragments were purified with AMPure XP beads (1.2× ratio) and selected with E-Gel® SizeSelect™ agarose gels (Invitrogen™ Thermo Fisher). Later, we re-amplified the fragments, purified them with beads (1.5× ratio), and quantified them with the Ion Library Quantitation Kit (Thermo Fisher). The final barcoded libraries were pooled and adjusted to a final concentration of 50 pM.RNA was extracted from fresh BM using a standard TRIzol® reagent-based protocol. For the CSF specimen, RNA was extracted using the RNeasy® Mini Kit . cDNA was obtained using the High-capacity cDNA Reverse Transcription Kit . The kinase domain of the cDNA was amplified in two steps from the following samples: BM at diagnosis and BM or CSF at relapse. In the first step of amplification, we used 5′-CTGGCCCAACGATGGCGA-3′ (for BCR exon 1) and 5′-GTACTCACAGCCCCACGGA-3′ (for ABL1 exon 2) as the forward and reverse primers, respectively. This excluded the untranslocated ABL1 allele from the analysis. The kinase domain was amplified from the first PCR product using the primers 5′-AAGCGCAACAAGCCCACTGTCTAT-3′ and 5′-CTTCGTCTGAGATACTGGATTCCTG-3′, which cover the entire kinase domain, from residue GlyThe kinase domain libraries theoretically covered almost a third of an Ion Proton Chip, which would provide ∼20 million reads. Template preparation, enrichment, and chip loading were performed using the Ion Chef™ system (Thermo Fisher). Sequencing was carried out on the Ion Proton™ System for next-generation sequencing (Thermo Fisher). The final read data set was analyzed using Torrent Suite Software V.5.0.3 (Thermo Fisher), resulting in two to four million reads per sample. The detected variants were reviewed manually using the Integrative Genomics Viewer .2 test, Fisher’s exact test, or the non-parametric median test as appropriate.Baseline characteristics were reported as frequency and percentage for categorical variables and as median and range for quantitative variables. Comparisons of proportions and the medians of variables between different groups were performed using the χOS was measured from the time of diagnosis to the time of the last follow-up or death from any cause. The complete response (CR) duration was defined as the time from the first CR achievement to the relapse date, which was considered an event, or as the last follow-up. Actuarial survival curves were performed using the Kaplan-Meier estimation, and the log-rank test was used for comparisons between groups. Binary logistic regression was used for univariate analysis to study the association of predictive factors with CNS relapse.P values <0.05 were considered statistically significant. The statistical package SPSS version 23.0  was used for all analyses.Two-sided n = 9) or at an unknown site (n = 1). In the LAL-PH08 trial, five out of 12 relapsed patients had combined (n = 4) or isolated (n = 1) CNS involvement  achieved complete remission (CR), 39 (68%) of who had not relapsed at the time of the analysis. All patients from the LAL-PH08 trial also achieved CR, and 59 (83%) had not relapsed at the time of the analysis. Of the LAL-OPH07 patients, 18 (32%) relapsed: 8 had CNS recurrence (isolated in 6), and 10 relapsed either in the BM (n = 13) vs. patients with relapses at sites other than the CNS (n = 16) (median CR duration (95% CI) 22.6 (7.8–37.3) vs. 10.6 (6.5–14.6) months, respectively, P = 0.639). The treatment administered to the 13 patients with CNS relapse was as follows: intrathecal chemotherapy (ITC) plus dasatinib (n = 3) or imatinib (n = 1); ITC and systemic chemotherapy, including high-dose methotrexate (HD-MTX) and high-dose ARA-C (HD-ARAC) (n = 4); ITC, HD-MTX, HD-ARAC, and nilotinib (n = 1) or dasatinib (n = 2); palliative care (n = 1); and unknown treatment (n = 1). A second allogeneic HSCT was performed in one patient. Of the 13 patients, seven achieved CR, one had an early death, one withdrew from treatment because of toxicity, one was lost to follow-up, and the other three died due to progression. There was a trend for longer OS in patients with CNS relapse vs. those with relapse outside the CNS: median 39.2 (20.8–57.6) vs. 18.4 (5.8–31.0) months, P = 0.067  was performed in 62/71 (87%) patients in the LAL-PH08 trial (two had relapsed before HSCT and the other seven patients were in the consolidation phase of chemotherapy at the time of data review). HSCT was performed in only four (7%) patients in the LAL-OPH07 trial. No statistically significant differences in CR duration were found for cases with CNS relapse and combined or isolated relapse . The CSF blasts cleared after IT triple therapy and rescue chemotherapy with the TKI switched to dasatinib . After 2The second patient was a 68-year-old man diagnosed with p190 BCR-ABL1-positive ALL after a routine hematological test. Morphologic assessment and flow cytometry found no CNS involvement at diagnosis. He was treated with the LAL-OPH-2007 protocol and achieved complete morphologic and molecular response. Two years later, he experienced a combined BM and CNS relapse. The p.L387M mutation was detected in the CSF sample but, surprisingly, not in the BM sample. A second BM and CSF CR were attained with TIT, systemic chemotherapy, and dasatinib. Currently, he receives dasatinib (70 mg twice a day) without adverse effects. Ultra-deep NGS of two BM samples (one at diagnosis and one at relapse) and a CSF sample taken at relapse confirmed the pathogenic variant c.1159T>A, p.L387M only in CSF blasts in the relapse sample  in a complex with imatinib [We used several pathogenicity predictor servers to predict the effect that the p.K245E mutation would have on the stability of the protein. Given the primary sequence of the protein, MutationTaster predicted the variant to be disease causing (score = 0.999) , while Pimatinib  with theThe frequency of CNS involvement in BCR-ABL1-positive ALL is variable among study populations. Notably, it is somewhat age-dependent, and its frequency is higher than in other B cell precursor ALL diseases. In pediatric and adolescent populations, CNS recurrence occurs in 3–11% of cases –18. ThesWe found the pathogenic variant p.L387M in the CSF blast cells of two patients with CNS recurrence. Notably, this mutation was not detected in BM samples at diagnosis or at relapse in the second patient. We may not have detected a mutation in BCR-ABL1 in the BM because the disease level was minimal at the time of CNS relapse (as measured using multiparametric flow cytometry). It seems likely that the selective emergence of mutated clones only in the CNS is associated with CNS imatinib concentrations that are too low to effectively prevent CNS relapse. In contrast, the independent mutation observed at BM relapse in the second patient is more likely to be associated with other mechanisms of resistance to imatinib. The mechanism(s) of resistance in CSF relapse patients that involve BCR-ABL1 mutations could be either imatinib-dependent or imatinib-independent, since the imatinib concentrations in CSF are lower than in plasma.The mutations identified at relapse were sensitive to dasatinib and nilotinib. These TKIs, particularly dasatinib, show better penetration into the CSF than other TKIs. Based on these results, it seems prudent to perform an NGS mutational study of the BCR-ABL1 kinase domain in CSF blast cells, despite the difficulties of extracting RNA from leukocytes in the CSF. This should be integrated into the work-up protocol of these patients in order to select the best TKI based on clonal evolution of the CSF blast cells. This technique allows the identification of newly emergent clones in the CNS with high sensitivity (∼s1% mutated); notably, such mutations would be undetectable by routine SS.In silico tools such as MutationTaster, PolyPhen-2, and Site Directed Mutator helped us to investigate the pathogenicity of the VUS p.K245E. The consensus of the in silico results plus the external position of the lysine (i.e., its location away from the imatinib binding site; Fig. In summary, in BCR-ABL1 ALL patients treated with imatinib and chemotherapy, CNS relapse was an important feature despite CNS prophylaxis. Taken together, the results suggest that genomic analysis of the BCR-ABL1 kinase domain in cells from CSF should be integrated into the study of these patients in order to detect independent mutations that could be exclusive to this site.