The purpose of this mini review is to describe metabolomics in cerebrospinal fluid (CSF) and its potential in aneurysmal subarachnoid hemorrhage (aSAH). In brain injury, patients’ micro dialysis enables detecting biochemical change in brain tissue. Indicators for ischemia were detected such as lactate, pyruvate, glucose, and glutamate. In aSAH patients, the pathophysiology and the factor for poor outcome are not completely understood yet. Routine use of biomarkers in CSF, particularly in aSAH patients, is still lacking.This mini review was performed on the role of metabolomics alterations after aneurysmal subarachnoid hemorrhage.We identified five clinical studies that addressed metabolomics in patients with aneurysmal subarachnoid hemorrhage.There is increasing evidence suggesting that biomarkers can give insight in the pathogenesis and can serve as an outcome predictor. In this mini review, we present a brief overview of metabolomics profiling in neuroscience and wish to discuss the predictive and therapeutic value in aSAH patients.The online version contains supplementary material available at 10.1007/s13760-023-02266-2. Regardless of the extensive experimental and clinical research efforts, aneurysmal subarachnoid hemorrhage (aSAH) remains a devastating disease, frequently striking patients in a fairly young age without any warning signs. Half of patients with aSAH are younger than 55 years old. Most survivors remain significantly impaired in their daily lives . Post-heIn this mini review, we present a brief overview of CSF metabolomics profiling in neuroscience and discuss its potential predictive and therapeutic value in aSAH patients.Apart from numerous vital functions of CSF such as hydromechanical cerebral protection and salubrious homeostasis of the brain, specific CSF conditions may mirror pertinent alterations offering an in-depth insight into the cerebral metabolite production rates and its disorders for instance aSAH. Metabolomics methods have identified appreciable number of biomarkers for cerebral diseases including their progression , 5–7.Metabolites are small molecules (< 1500 Da) in various body fluids which can influence cell response locally and systemically, possibly reflecting a disease progression . MetabolIn 2008, a very first systematic analysis of the human cerebrospinal fluid metabolome demonstrated that 41–70 CSF compounds can be detected with nuclear magnetic resonance spectroscopy (NMR), gas chromatography–mass spectrometry (GC–MS) or Fourier transform mass spectrometry coupled with liquid chromatography (LC–FTMS). The authors of the study also described that CSF is a metabolically diverse fluid with 33 different compound categories. Of note, CSF is rich in amino acids, sugars, and inorganic salts .Nowadays, metabolomic approaches are used in several neurological diseases to particularize diagnosis, prognosis, and monitor treatments. Metabolomics has been applied in many degenerative disorders. For instance, various metabolites are being routinely used in diagnostics and treatment for Alzheimer´s disease, amyotrophic lateral sclerosis, epilepsy, Parkinson’s disease, multiple sclerosis, and stroke .In respect of aSAH, established or clinically verified biomarkers are still lacking or are not yet used routinely in clinical practice. Discovery of novel specific indicators for, i.e., DCI and poor clinical outcome is urgently needed. However, to our best knowledge, there are only three recent studies dealing with metabolomics profiling of CSF after aSAH.In a 2017 European study, Sokół et al. revealed that in their liquid chromatography–mass spectrometry/mass spectrometry (LC–MS/MS) of 49 CSF samples from 23 patients analyzing 33 amino acids and related compounds, 27 of them were significantly elevated within the first 3 days after aSAH. The investigation showed that the patterns differ between poor and good outcome. In particular, during early brain injury (EBI) after aSAH, concentration of nine amino acids was significantly higher in patients with poor outcome compared to those with good outcome: taurine, aspartic acid, citrulline, glutamic acid, gamma-amino-butyric acid, 3-methyl-histidine, ornithine, cystathionine, and isoleucine. On day 5 post-SAH, glutamic acid was the only amino acid that significantly increased. Interestingly, higher levels of excitatory amino acids  appear to predict a poor outcome . SimilarThe first prospective study analyzed endogenous metabolites in patients with ruptured intracranial aneurysms and aSAH Fisher Grade III and IV compared to patients with elective aneurysms. CSF samples were consecutively collected before aneurysm treatment (the time point of EVD placement), intraoperatively, 6 h later, and daily thereafter for 10 days. The study showed that amino acids, biogenic amines, and acylcarnitine levels continuously increased over the time starting as early as 6 h after aSAH onset. Taurine concentration, however, was initially elevated directly after onset and started decreasing after only 6 h. Other analyzed metabolites such as hydroxysphingomyelins, lysophosphatidylcholines, and sphingomyelins showed temporarily increased levels immediately after aSAH with an ensuing decrease to CSF concentrations within the first 6 h that are comparable with the non-hemorrhagic CSF. The study presented significant consecutive alterations of various metabolites in the longitudinal course of aSAH. There was a peak of structural amino acids as early as within the first 6 h after aneurysm treatment. However, the authors concluded that further evaluation of CSF metabolites and compounds and their time-dependent alterations may elucidate pathophysiological processes after aSAH, potentially providing new predictive biomarkers related to aSAH-specific parameters and outcomes .A Chinese study published in 2018 showed data of 40 subarachnoid hemorrhage patients and the control group of 6 patients. Neurological outcome was evaluated 12 months after discharge. Interestingly, the group showed that there is no significant variation among CSF metabolome in subarachnoid hemorrhage patients with different fisher scales (amount and distribution of blood). However, sub-analysis showed that pyruvate metabolism was altered in subarachnoid hemorrhage patients, especially in those with a high Hunt–Hess scale . Pyruvate level is associated with WFNS  grading scale above III. In retrospect, patients with unfavorable outcome had significantly altered amino acid metabolism and lipid biosynthesis .The work of an American group which was published in 2020 aimed to identify the leading CSF metabolites associated with poor outcome, as determined by the modified Rankin Scale (mRS) at discharge and at 90 days after discharge. The authors used 81 CSF samples. Through orthogonal partial least squares-discriminant analysis, symmetric dimethylarginine (SDMA), dimethylguanidine valeric acid (DMGV), and ornithine were detected as the markers associated with poor outcome .All five aforementioned clinical studies dealing with CSF metabolomics after aSAH provide a first very interesting finding of various metabolites that can be instrumental in further innovative research strategies.In summary, metabolomics of post-hemorrhagic CSF provides a unique signature of multifactorial pathophysiologic processes in the brain after aSAH. A published study in 2005 described metabolomics as a systematic study of unique chemical fingerprints that specific cellular processes leave behind that provides dynamic information . While sSeveral significant altered compounds in those studies are found in the cerebral glucose metabolism Fig. . FurtherFuture metabolomics profiling in both untargeted and targeted ways in aSAH patients should allow acquiring further insights into the global cerebral endogenous metabolism after aSAH with in-depth monitoring of pertinent metabolomic changes, and therefore potential identification of aSAH-specific biomarkers.Supplementary file1 (DOCX 110 KB)Below is the link to the electronic supplementary material.


Changes in microbial community composition as a function of human health and disease states have sparked remarkable interest in the human gut microbiome. However, establishing reproducible insights into the determinants of microbial succession in disease has been a formidable challenge.Here we use fecal microbiota transplantation (FMT) as an in natura experimental model to investigate the association between metabolic independence and resilience in stressed gut environments. Our genome-resolved metagenomics survey suggests that FMT serves as an environmental filter that favors populations with higher metabolic independence, the genomes of which encode complete metabolic modules to synthesize critical metabolites, including amino acids, nucleotides, and vitamins. Interestingly, we observe higher completion of the same biosynthetic pathways in microbes enriched in IBD patients.These observations suggest a general mechanism that underlies changes in diversity in perturbed gut environments and reveal taxon-independent markers of “dysbiosis” that may explain why widespread yet typically low-abundance members of healthy gut microbiomes can dominate under inflammatory conditions without any causal association with disease.The online version contains supplementary material available at 10.1186/s13059-023-02924-x. Understanding the determinants of microbial colonization is one of the fundamental aims of gut microbial ecology , 2. The Inflammatory bowel disease (IBD), a group of increasingly common intestinal disorders that cause inflammation of the gastrointestinal tract , has beeClostridioides difficile infection (CDI) [Fecal microbiota transplantation (FMT), the transfer of stool from a donor into a recipient’s gastrointestinal tract , represeon (CDI)  that canon (CDI) .Here we use FMT as an in natura experimental model to investigate the ecological and functional determinants of successful colonization of the human gut at the level of individual microbial populations using genome-resolved metagenomics. Our findings highlight the importance of environmental selection acting on the biosynthetic capacity for essential nutrients as a key driver of not only colonization outcomes after FMT but also microbial resilience during inflammation, and demonstrate that 'metabolic independence' can serve as a taxonomy-independent determinant of colonization success in the human gut under stress.Our study includes 109 gut metagenomes . Co-assemblies of 24 donor A and 15 donor B metagenomes independently resulted in 53,891 and 54,311 contigs that were longer than 2500 nucleotides, and described 0.70 and 0.79 million genes occurring in 179 and 248 genomes, as estimated by the mode of the frequency of bacterial single-copy core genes , Bacteroidetes (n = 20), Actinobacteria (n = 14), Proteobacteria (n = 7), Verrucomicrobia (n = 2), Cyanobacteria (n = 2), and Patescibacteria (n = 1)  explained the entirety of the Bacteroidetes group, we recovered 265 genomes that represented lower abundance but diverse populations of Firmicutes  population-level microbial colonization dynamics before and after FMT using donor and recipient metagenomes and (2) the distribution of each donor population across geographically distributed humans using 1984 publicly available human gut metagenomes  and donor B (n = 2), respectively. In contrast, in the cases of FMT where donor stool was transplanted via pills, we detected 69.5 and 61.6% donor genomes in the recipients of donor A (n = 2) and donor B (n = 3), respectively.Our metagenomic read recruitment analysis showed that donor A and B genomes recruited on average 77.05 and 83.04%, respectively, of reads from post-FMT metagenomes, suggesting that the collection of donor genomes well represents the recipient metagenomes post-FMT Fig. . As expeOverall, not every donor population in our dataset was detected in each recipient, but the emergence of donor populations in recipients did not appear to be random: while some donor populations colonized all recipients, others colonized none Fig. , providiOf the populations that consistently occurred in donor metagenomes, some were absent in all or most recipient metagenomes after FMT, and others were continuously present throughout the sampling period in both donor and recipient metagenomes Fig. . To gainp = 6.3e − 06 and p = 9.0e − 07)  systematically colonized the majority of FMT recipients, (2) persisted in these environments long-term regardless of host genetics or lifestyle, and (3) were prevalent in public gut metagenomes outside of our study. In contrast, the so-called “poor colonizers” failed to colonize or persist in at least three FMT recipients. These populations were nevertheless viable in the donor gut environment: not only did they occur systematically in donor metagenomes but also they sporadically colonized some FMT recipients. Yet, unlike the good colonizers, the distribution patterns of poor colonizers were sparse within our cohort, as well as within the publicly available metagenomes. In fact, populations identified as poor colonizers were less prevalent than good colonizers in each of the 17 different countries we queried. In countries including the USA, Canada, Austria, China, England, and Australia, microbial populations identified as good colonizers occurred in 5 times more people than poor colonizers in the same country  and averaged to 91 and 93%, respectively. But intriguingly, the genome sizes between the two groups differed dramatically (p = 2.9e − 06): genomes of good colonizers averaged to 2.8 Mbp while those of poor colonizers averaged to 1.6 Mbp. We considered that our bioinformatics analyses may have introduced biases to genome lengths, but found a very high correspondence between the lengths of the genomes and their best matching reference genomes in the Genome Taxonomy Database (GTDB) . Assuming that the generally larger genomes of good colonizers may be an indication of an increased repertoire of core metabolic competencies compared to poor colonizers, we next conducted a metabolic enrichment analysis for quantitative insights  that were enriched in good colonizers and none that were enriched in poor colonizers Fig. . Of all V = 958, p = 5e − 09)  to describe genomic evidence for a population’s ability to synthesize essential compounds , and “low metabolic independence” (LMI) to describe the absence of, or reduction in, such capacity.Our results so far show that while the healthy donor environment could support both HMI and LMI populations Fig. , challenTo test these hypotheses, we compared genomes reconstructed from a cohort of healthy individuals  to genomp = 0.5)   Fig. . In cont07) Fig. . Metabol07) Fig. , a sevenOur findings show that the same set of biosynthetic metabolic modules that distinguish good and poor colonizers during FMT were also differentially associated with populations of IBD patients and healthy individuals. In particular, while healthy individuals harbored microbes with a broad spectrum of metabolic capacity, microbes from individuals who suffer from two different forms of IBD had significantly higher biosynthetic independence. It is conceivable that a stable gut microbial ecosystem is more likely to support LMI populations through metabolic cross-feeding, where vitamins, amino acids, and nucleotides are exchanged between microbes . In contThese observations have implications for our understanding of the hallmarks of healthy gut microbial ecosystems. Defining the “healthy gut microbiome” has been a major goal of human gut microbiome research , which sOur findings offer a new, taxonomy-independent perspective on the determinants of microbial resilience in the human gut environment under stress. Yet, our study is limited to well-known metabolic pathways—which, given the extent of the unknown coding space in microbial genomes , are likOur study identifies high metabolic independence conferred by the biosynthetic capacity for amino acids, nucleotides, and essential micronutrients as a distinguishing hallmark of microbial populations that colonize recipients of FMT and that thrive in IBD patients. These findings highlight the functional complexity of the human gut microbiome whose various interactions with the host are shaped through a network of microbial interactions such as cross-feeding of macro- and micronutrients. Our study offers a simple model that posits the following: microbial populations that are metabolically independent and those that lack the means to synthesize essential metabolites co-occur in a healthy gut environment in harmony, where their differential resilience to stress is indiscernible by their taxonomy or relative abundance. However, the challenges associated with the transfer to a new gut environment through FMT, or with host-mediated stress through IBD, initiate an ecological filter that selects for microbes that can self-sustain in the absence of ecosystem services associated with states of homeostasis. This model provides a hypothesis that explains the dominance of low-abundance members of healthy gut environments under stressful conditions, without any necessary direct causal association with disease state. If the association between particular microbial taxa and disease is solely driven by their superior metabolic independence, microbial therapies that aim to treat complex diseases by adding microbes associated with healthy individuals will be unlikely to compete with the adaptive processes that regulate complex gut microbial ecosystems.C. difficile infection before FMT, and two DA recipients and one DB recipient were also diagnosed with ulcerative colitis (UC). Twenty-four stool samples were collected from the DA donor over a period of 636 days, and 15 stool samples were collected from the DB donor over a period of 532 days. Between 5 and 9 stool samples were collected from each recipient over periods of 187 to 404 days, with at least one sample collected pre-FMT and 4 samples collected post-FMT. This gave us a total of 109 stool samples from all donors and recipients. Samples were stored at − 80 °C . After final precipitation, the DNA samples were resuspended in TE buffer and stored at − 20 °C until further analysis. Sample DNA concentrations were determined by PicoGreen assay. DNA was sheared to ~ 400 bp using the Covaris S2 acoustic platform, and libraries were constructed using the Nugen Ovation Ultralow kit. The products were visualized on an Agilent Tapestation 4200 and size-selected using BluePippin (Sage Biosciences). The final library pool was quantified with the Kapa Biosystems qPCR protocol and sequenced on the Illumina NextSeq500 in a 2 × 150 paired-end sequencing run using dedicated read indexing.http://merenlab.org/anvio-workflows/ [Whenever applicable, we automated and scaled our ‘omics analyses using the bioinformatics workflows implemented by the program “anvi-run-workflow”  in anvi’rkflows/ . The folWe used Kraken2 v2.0.8-beta  with theTo minimize the impact of random sequencing errors in our downstream analyses, we used the program “iu-filter-quality-minoche” to process short metagenomic reads, which is implemented in illumina-utils v2.11  and remoWe use the following strategies to process both sequences we obtained from our assemblies and those we obtained from reference genomes. Briefly, we used (1) “anvi-gen-contigs-database” on contigs to compute k-mer frequencies and identify open reading frames (ORFs) using Prodigal v2.6.3 , (2) “anhttps://doi.org/10.6084/m9.figshare.14331236 [We recruited metagenomic short reads to contigs using Bowtie2 v2.3.5  and conv14331236  gives ac14331236 , “anvi-r14331236  assigned14331236 , but to 14331236 . Finally14331236 .Using mean coverage to assess the occurrence of populations in a given sample based on metagenomic read recruitment can yield misleading insights, since this strategy cannot accurately distinguish reference sequences that represent very low-abundance environmental populations from those sequences that do not represent an environmental population in a sample yet still recruit reads from non-target populations due to the presence of conserved genomic regions. Thus, we relied upon the “detection” metric, which is a measure of the proportion of the nucleotides in a given sequence that are covered by at least one short read. We considered a population to be detected in a metagenome if anvi’o reported a detection value of at least 0.25 for its genome (whether it was a metagenome-assembled or isolate genome). Values of detection in metagenomic read recruitment results often follow a bimodal distribution for populations that are present and absent  information for all MAGs after read recruitment. We then used DESMAN v2.1.1  to analyWe applied the set of criteria described in Additional file Bifidobacterium MAGs and reference genomes, we ran the anvi’o command “anvi-get-sequences-for-hmm-hits” with the “–return-best-hit,” “–get-aa-sequence,” and “—concatenate” flags, and the “–align-with” flag set to “muscle” to use MUSCLE v3.8.1551 [To concatenate and align amino acid sequences of 46 single-copy core  ribosoma3.8.1551  for alig3.8.1551 .https://anvio.org/m/anvi-estimate-metabolism [p-values, and corrected q-values. We considered any function or metabolic module with a q-value less than 0.05 to be “enriched” in its associated group if it was also at least 75% complete and present in at least 50% of the group members.We calculated the level of completeness for a given KEGG module , 86 in otabolism  serves atabolism , but briWe classified MAGs as good colonizers if, in all 5 recipients, they were detected in the donor sample used for transplantation as well as the recipient more than 7 days post-FMT. We classified MAGs as poor colonizers as those that, in at least 3 recipients, were detected in the donor sample used for FMT but were not detected in the recipient at least 7 days post-FMT. We reduced the number of good colonizer MAGs to be the same as the number of poor colonizer MAGs for metabolic enrichment analysis by selecting only those populations that were the most prevalent in the Canadian gut metagenomes.https://anvio.org/m/anvi-script-estimate-metabolic-independence [We developed a script to calculate the pathwise completeness of the 33 KEGG modules that were enriched in good colonizers in this study to determine whether a given genome resembles HMI or LMI populations. The URL pendence  serves mWe used the R vegan v2.4–2 package “metaMDS” function to perform nonmetric multidimensional scaling (NMDS) with Horn-Morisita dissimilarity distance to compare taxonomic composition between donor, recipient, and global metagenomes. We visualized ordination plots using R ggplot2.Additional file 1. Description of FMT study and stool samples collected. a Description of FMT donor stool samples and SRA accession numbers. b Description of FMT recipient samples and SRA accession numbers. c Description of transplantation events.Additional file 2: Supplementary figures S1, S2, S3 and S4. Fig. S1. Timeline of stool samples collected from FMT study. Each circle represents a stool sample collected from either an FMT donor or FMT recipient. The thicker, red vertical line at day 0 represents the FMT event for each recipient. FMT method (pill or colonoscopy) and FMT recipient health and disease state  are indicated on the right. Fig. S2. Nonmetric multidimensional scaling (NMDS) ordination of the taxonomic composition of donor, recipient, and Canadian gut metagenomes at the genus level based on Morisita-Horn dissimilarity. Samples from the same participant are joined by lines with the earliest time point labeled. CAN: Canadian gut metagenomes, DA: donor A, DB: donor B, POST: recipients post-FMT, PRE: recipients pre-FMT. Fig. S3. Nonmetric multidimensional scaling (NMDS) ordination of the taxonomic composition of the donor and recipient metagenomes at genus level based on Morisita-Horn dissimilarity. Samples from the same participant are joined by lines with the earliest time point labeled. DA_POST: donor A recipients post-FMT, DA_PRE: donor A recipients pre-FMT, DA: donor A, DB_POST: donor B recipients post-FMT, DB_PRE: donor B recipients pre-FMT, DB: donor B. Fig. S4. A flowchart outlining our method to assign successful colonization, failed colonization, or undetermined colonization phenotypes to donor-derived populations in the recipients of that donor’s stool.Additional file 3. Description of FMT metagenomes and co-assemblies. a Metagenome SRA accession numbers and number of metagenomic short-reads sequenced and mapped to co-assemblies and MAGs. b) Phylum level taxonomic composition of metagenomes. c) Genus level taxonomic composition of metagenomes. d) Summary statistics for contigs from metagenome co-assemblies. Additional file 4. Description of MAGs. a Summary statistics and taxonomic assignments for MAGs. b and c Detection of Donor A and Donor B MAGs in FMT metagenomes, respectively. d and e Detection of Donor A and Donor B MAGs in global gut metagenomes, respectively. f and g Detection summary statistics of Donor A and Donor B MAGs in global gut metagenomes, respectively. h and i Mean non-outlier coverage of Donor A and Donor B MAG single-copy core genes in FMT metagenomes.Additional file 5. Accession numbers of gut metagenomes from 17 countries.Additional file 6. Additional discussion and supplementary figures for A adaptive ecological forces are the primary drivers of microbial colonization; B considerations of annotation bias that reduce the number of genes with functional annotations for microbial populations that typically occur in healthy individuals.Additional file 7. MAG subpopulation information. a and b Number of Donor A and Donor B MAG subpopulations detected in FMT metagenomes, respectively. c and d Subpopulation composition of Donor A and Donor B MAGs in FMT metagenomes, respectively.Additional file 8. MAG/recipient pair colonization outcomes and MAG mean coverage in the 2nd and 3rd quartiles in stool samples used for transplantation.Additional file 9. Description of HMI vs. LMI populations. a Taxonomic assignments and genome size estimates for high- and low-metabolic independence populations. b KEGG module completeness information for high- and low-metabolic independence populations. c Raw KEGG module enrichment information for high- and low-metabolic independence populations. d KEGG module enrichment and categorical information for the 33 modules enriched in high-metabolic independence populations. e and f Completeness information for the 33 modules enriched in high-metabolic independence populations in all high- and low-metabolic independence populations.Additional file 10.a List of genomes from healthy individuals and individuals with IBD. b Module completion values across genomes.Additional file 11. Review history.


Allogeneic hematopoietic stem cell transplantation  recipients show lower humoral vaccine responsiveness than immunocompetent individuals. HLA diversity, measured by the HLA evolutionary divergence (HED) metrics, reflects the diversity of the antigenic repertoire presented to T cells, and has been shown to predict response to cancer immunotherapy. We retrospectively investigated the association of HED with humoral response to SARS-CoV-2 vaccine in allo-HSCT recipients. HED was calculated as pairwise genetic distance between alleles at HLA-A, -B, -C, -DRB1, -DQB1, and -DPB1 loci in recipients and their donors. Low anti-spike IgG levels (<30 BAU/mL) were associated with short time from allo-SCT and low donor DPB1-HED, mostly related to donor DPB1 homozygosity. The diversity of donor HLA-DP molecules, assessed by heterozygosity or sequence divergence, may thus impact the efficacy of donor-derived CD4 T cells to sustain vaccine-mediated antibody response in allo-HSCT recipients.  •Hematopoietic stem cell transplant recipients have impaired response to SARS-CoV-2 vaccine•HLA-DPB1 divergence in the donor is associated with low levels of anti-spike IgG•Donor HLA-DPB1 homozygosity is associated with no SARS-CoV-2 vaccine response Highlights Hematopoietic stem cell transplant recipients have impaired response to SARS-CoV-2 vaccine HLA-DPB1 divergence in the donor is associated with low levels of anti-spike IgG Donor HLA-DPB1 homozygosity is associated with no SARS-CoV-2 vaccine responseImmunology; Virology  Clinical and biological characteristics of the patients are shown in HLA-A, -B, -C, -DRB1, -DQB1, and -DPB1 typing at second field resolution was previously performed in all recipients and their donors. The protein sequence divergence (HED) was calculated for all allele pairs at each locus in both recipients and donors.In univariate analysis comparing responders to nonresponders, shorter time from allo-HSCT and homozygosity or low HED at the DPB1 locus of the donor were associated with no response . Donor DNotably, the response rate did not differ by donor type , immunosuppression status , presence of active acute or chronic GVHD, or immunological recovery  at the time of vaccination .In multivariate logistic analysis, lack of vaccine response was associated with shorter time from allo-SCT (p = 0.033), donor DPB1 homozygosity (p = 0.031) or low donor DPB1-HED (p = 0.010). When excluding the 37 DPB1 homozygous donors, the significant effect of DPB1-HED was lost, suggesting that the lower response rate was mainly related to DPB1 homozygosity.,FH) cell responses which support germinal center formation and differentiation of memory and antibody-secreting B cells.,FH responses against immunodominant spike peptides in establishing a robust and persistent immunity after BNT162b2 vaccine in healthy individuals.The development of anti-SARS-CoV-2 immunity is particularly important in allo-SCT recipients, as they have poor outcome after COVID-19 infection. We found that 88.5% of allo-SCT recipients developed an anti-S IgG response after 2 doses of the BNT162b2, a high rate in line with other studies with this vaccine.Our study is limited by the relatively small sample size of our cohort, lack of baseline serology in most patients, and antibody titers measured with three different commercial assays. However, it helps in understanding mechanisms of impaired vaccine immune response in immunosuppressed patients. Notably, the 18 nonresponders received a third vaccine dose, of whom 16 had serological evaluation: 4 still failed to mount a serological response and 3 of them (75%) had a DPB1 homozygous donor, compared to 5 of the 12 responders (41.7%). If confirmed in a replication cohort, these data suggest that allo-HSCT recipients of DPB1 homozygous donors should be offered a different platform for a subsequent vaccination.sophie.caillat-zucman@aphp.fr).Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Sophie Caillat-Zucman  resolution was available for all recipients and their donors. All patients gave their consent for data collection before transplantation.Response to SARS-CoV-2 vaccine was defined according to quantitative serological assays using the anti-SARS-CoV-2 S assay (Roche Diagnostics), the SARS-CoV-2 IgG II Quant assay (Abbott Diagnostics) or the SARS-CoV-2 Trimeric S IgG assay (Diasorin). For standardization of the results, the concentrations were transformed using conversion factors (cf.) from the arbitrary units (AU) of each manufacturer to Binding Antibody Units (BAU = cf. x AU), according to the first WHO international standard (NIBSC code 20/136).The respective protein sequences of the peptide-binding groove  were extracted from the IMGT/HLA database.Standard descriptive statistics were used to summarize baseline demographic, clinical and biological characteristics. The outcome was the vaccinal response defined as an ordinal categorial variable: no response, moderate response, and strong response. Ordered logistic regression was used for the univariable and multivariable analysis of relevant covariates. All analyses were performed with R 4.0 using the “Grantham” and “MASS” packages. The threshold for significance was p value <0.05.Associations of HLA allelic frequencies with the vaccinal response were evaluated using a two-sided Fisher’s exact test with the Bonferroni correction.


Caryocar brasiliense Camb.) is an evergreen tree typical of the biodiversity in the Brazilian cerrado biome and it represents an important source of income for communities that sell its fruit and related products, such as PO (pequi oil). Pequi oil has antioxidant properties as a result of its high concentration of carotenoids, and said properties have been investigated in in vitro studies and animal models. However, up until now, the effects of dietary supplementation with PO have not been investigated in broiler chickens, which raised our research interest. We found that the levels of pequi oil used in broiler chickens submitted to high temperatures had a hepatoprotective effect; in addition, higher levels reduced the concentration of malondialdehyde in their livers. Corroborating these results, birds fed with higher levels of pequi oil showed a 92% reduction in the concentration of their Hsp 70 mRNA in comparison to birds with supplementation, whereas the Nrf2 gene was upregulated (37%). Therefore, supplementation with PO relieves alterations in the antioxidant system caused by heat stress in broiler chickens, acting as a potential antioxidant additive for use in poultry production. Furthermore, our findings can inform future studies.Pequi (p = 0.006), triglycerides (p < 0.001), and HDL (p = 0.042) at 21 days and reduced (p = 0.005) serum total cholesterol at 42 days. The results also showed that HS increased the contents of alanine aminotransferase (ALT) and aspartate aminotransferase (AST). In contrast, PO linearly decreased AST (p = 0.048) and ALT (p = 0.020) at 21 and 42 days, respectively. The heterophil-to-lymphocyte ratio in the birds under HS was higher than in those in the TN environment (p = 0.046). Heat stress decreased (p = 0.032) the relative weight of their livers at 21 days. The superoxide dismutase activity increased (p = 0.010) in the HS treatments in comparison to the TN treatments, while the glutathione peroxidase activity in the liver decreased (p < 0.001) at 42 days; however, the activity of catalase had no significant effects. Meanwhile, increasing the dietary PO levels linearly decreased plasma malondialdehyde (p < 0.001) in the birds in the HS environment. In addition, PO reduced (p = 0.027) the expression of Hsp 70 in the liver by 92% when compared to the TN treatment without PO, mainly at the 6.0 g/kg diet level. The expression of Nrf2 was upregulated by 37% (p = 0.049) in response to PO with the 6.0 g/kg diet compared to the HS treatment without PO. In conclusion, PO supplementation alleviated the adverse effects of HS on broilers due to its antioxidant action and modulation of the genes related to oxidative stress, providing insights into its application as a potential feed additive in broiler production.The present study was conducted to determine the possible antioxidant protection of pequi oil (PO) against cyclic heat stress in broiler chickens and to highlight the application of PO as a promising additive in broiler feed. A total of 400 one-day-old male broiler chicks (Cobb 500) were randomly assigned to 2 × 5 factorially arranged treatments: two temperature-controlled rooms  and five dietary PO levels  for 42 days. Each treatment consisted of eight replicates of five birds. The results showed that HS increased glucose ( Heat stress (HS) in poultry farming is a worldwide issue, particularly in tropical and subtropical areas ,2. In adAnimal cells have developed a sophisticated defense system that is able to neutralize ROS ,7, featuCaryocar brasiliense Camb.) is an oily fruit native to the Brazilian cerrado biome and is considered to be one of the species of the greatest socioeconomic relevance to the region [Pequi  and the regeneration of biomolecules damaged by oxidative lesions . MirandaTherefore, the current study was conducted to investigate the effects of graded levels of PO supplementation on the plasma biochemical indices, relative organ weights, antioxidant statuses, and gene expressions of Hsp 70 and Nrf2 in broiler chickens subjected to cyclic heat stress. The results of this research could provide guidance and a reference for the further application of PO as a promising phytogenic feed additive in broiler feed.All the experimental procedures were approved by the Animal Ethics Committee of the Veterinary and Animal Science College, São Paulo State University, Botucatu, SP, Brazil (protocol 0192/2018—CEUA).C. brasiliense fruits were collected during the harvesting period in January 2019. The oil was extracted from the pulp using a manual process employing hot water and was purchased from Grande Sertão Cooperative . The pequi oil composition is shown in A total of 400 one-day-old commercial male broiler chicks (Cobb 500) with an average initial body weight of 47.5 ± 1.27 g were obtained from a local commercial hatchery . The broilers were vaccinated upon hatching for Marek’s disease, infectious bronchitis, and Gumboro disease. On day 1, the broiler chicks were assigned in a fully randomized design with a 2 × 5 factorial arrangement to treatments with two temperature-controlled rooms  and five dietary pequi oil (PO) levels, with eight replicates of five birds each. The experimental period lasted for 42 days.In each of the temperature-controlled rooms, the birds were housed in 40 battery cages (8 cages/treatment) with wire floors . Water was supplied in nipple drinkers and trough feeders made of galvanized steel were placed in front of the cages. The experimental diets (in mash form) and fresh water were provided ad libitum. The lighting program was applied according to the management guides and recommendations of the company .The experimental diets were prepared by including levels of PO into the basal diets. The five dietary treatments were as follows: control diet , CON + 1.5 g PO/kg diet; CON + 3.0 g PO/kg diet; CON + 4.5 g PO/kg diet; and CON + 6.0 g PO/kg diet. The PO was first mixed intensively with the associated soybean oil and then gradually added to the diet. The basal diet was formulated to correspond to nutrient requirements that were equal to or slightly lower than those recommended by Rostagno et al.  for broiThe birds were reared in temperature-controlled rooms with independent temperature control. During the experimental period (from 1 to 42 days of age), all the broiler chickens in the thermoneutral room (TN) were reared under the temperature conditions recommended by the Cobb Broiler Management Guide , but adaBioplus Produtos para Laboratórios Ltd.a, Barueri, SP, Brazil): glucose , total cholesterol—CHO , triglycerides—TG , high-density lipoprotein—HDL , aspartate aminotransferase—AST , and alanine aminotransferase—ALT .At 21 and 42 days of age, blood samples were collected from the broiler chicks (n = 8 birds per treatment) into clean sterile tubes and left to coagulate before being centrifuged at 3500 rpm for 15 min to separate the serum. The serum samples were stored in Eppendorf tubes at −20 °C until the analysis. The following parameters were determined spectrophotometrically in the serum using commercial reagent kits  via a semi-automated biochemical analyzer  was sampled at random. Blood samples were taken with a 23-gauge needle from the jugular vein and collected into two tubes. The first tube, containing EDTA, was centrifuged  to obtain plasma for a determination of the leukocyte profile. The second tube, containing heparin, was used to obtain the serum malondialdehyde (MDA) concentrations and was centrifuged at 3500 rpm for 15 min at 4 °C before being stored at −80 °C until further analysis. For the leukocyte population, the blood samples were stained according to Lucas and Jamroz ,21, and At 21 and 42 days, one bird per cage (n = eight per treatment) was euthanized via cervical dislocation and the whole spleen, bursa of the Fabricius, thymus, pancreas, and liver were immediately removed and weighed. The relative weights of these organs were expressed as percentages of the live weight of the birds.On day 42, the broilers were slaughtered via cervical dislocation and liver samples were collected (n = 8 samples per treatment), immediately immersed in liquid nitrogen, and stored at −80 °C until the activity of their antioxidant enzymes was determined . For the activities of SOD , CAT , and GSH-Px , 1 g of a liver sample was homogenized in 5 mL of 0.05 M phosphate buffer (pH = 7.0). The homogenate was centrifuged at 5000 rpm for 20 min at 4 °C. The supernatant was used for the enzyme activity and protein content analyses. The activities of SOD, CAT, and GSH-Px were determined as previously described by Vicente et al. . The absRNA was extracted from a 50 mg liver sample from four birds per treatment . The ext−ΔΔCT method [After this, a real-time PCR analysis (RT-PCR) was performed using an Applied Biosystems StepOnePlus Real-Time PCR System  and a SYBR Green PCR Master Mix kit . The primer sequences for the target and reference genes are listed in T method , which wij = μ + Ti + POj + (T × PO)ij + eij, where y = the response variable, µ = the population mean, Ti = the main effect of the temperature, PO = the main effect of the dietary pequi oil level, T × PO = the interaction effect of the temperature with the dietary pequi oil levels, and eij = the residual error. Orthogonal polynomial contrasts were also applied to determine the linear and quadratic responses to the different levels of PO supplementation. The results of the relative expressions of each target gene in the HS treatments, i.e., Hsp 70 and Nrf2, were compared to the treatment with no PO under thermoneutral ambient conditions . The significant differences among the treatment means were compared using Tukey’s test. The differences were considered statistically significant when the p-value was less than 0.05. The results were presented as means with their pooled standard errors.The data were analyzed as a completely randomized design using the MIXED procedure in the software SAS  . The modp = 0.006), TG (p < 0.001), and HDL (p = 0.042) concentrations in comparison to those of the birds housed in the thermoneutral (TN) condition. Additionally, variations in the serum CHO in response to the dietary PO levels were independent of the environmental temperature (p = 0.003). On day 42, the HS condition significantly reduced (p = 0.005) the serum CHO and did not change the plasma glucose, TG, or HDL concentrations. Furthermore, the birds challenged by HS had an increased serum enzyme activity of ALT at day 21 (p = 0.020) and increased activities of AST (p = 0.012) and ALT (p = 0.015) at day 42 in comparison to the TN condition. Supplementation with PO linearly reduced the serum enzyme activities of AST  and ALT  at days 21 and 42, respectively.No effect of the interaction of the dietary PO levels with the environmental temperature was found on the serum biochemical parameters . On day p = 0.022). However, increases in the H/L in response to the environmental temperature were independent of the dietary PO levels (p = 0.046). The data on the leukocyte subsets are shown in p = 0.032) the relative weight of the liver at day 21. The dietary PO levels had no significant effect on the relative organ weights. p = 0.010) in response to exposure to HS, whereas the GSH-Px activity decreased (p < 0.001) when compared to the birds housed under the TN condition. No significant difference was observed (p > 0.05) in the hepatic CAT activity among all the treatments. However, the serum MDA concentration responses to the increasing dietary PO levels in the birds housed in either the TN or HS conditions were different (interaction effect: p = 0.012). The serum MDA content was 42.5% lower in the broilers that received supplementation with 6.0 PO under the HS condition than in those that were only challenged with HS (18.99 vs. 10.93 nmol/L). Furthermore, the reduction in the MDA concentration was more notable in the HS environment with broilers supplemented with PO than in the TN environment. Increasing dietary PO levels linearly decreased the MDA concentrations  in the birds housed in the HS condition  in response to the HS environment. The dietary PO levels reduced the liver Hsp 70 levels by 92% compared to the birds reared in the HS environment, mainly at the 6.0 g PO/kg diet level (4.02 vs. 0.32 relative gene expression). In addition, the hepatic Nrf2 levels were not affected in the HS groups compared to those in the TN treatments. However, compared to the HS treatment without PO, the treatment using the 6.0 g PO/kg diet enhanced the liver Nrf2 expression by 37% .The present study was carried out to investigate the hypothesis that supplementation with PO alleviates alterations in the antioxidant system caused by heat stress (HS) in broiler chickens. The high cyclical temperatures during the day in the HS rooms were above the recommendations for the lineage according to the rearing phases . In poulThe results of this study show that the birds under HS had lower plasma concentrations of glucose, triglyceride, and HDL at 21 days of age, which corroborates the results observed by Puvadolpirod and Thaxton . AnimalsThe levels of ALT and AST in circulation may be high when liver damage occurs; thus, these enzymes are employed as sensitive markers of hepatocellular lesions . The preThe heterophil–lymphocyte ratio (H/L) is a primary indicator of stress in birds . In agreSome studies have shown that HS decreases the relative weights of immunologic organs  ,40. The 2O2) into water is regulated by the CAT and GSH-Px enzymes, but the GSH-Px activity was reduced by HS in this study. This allows for the inference that H2O2 takes part in the formation of more reactive radicals, such as the hydroxyl radical in Fenton reactions [As the temperature increased, the activities of the antioxidant enzymes in the liver and plasma may have been upregulated as a protective response against oxidative stress . Likewiseactions . It migheactions . Such a The antioxidant action of PO was able to linearly reduce the lipid peroxidation in the broilers submitted to HS, which was a result of the decrease in the MDA content, particularly at the PO level of 6.0 g/kg diet (18.99 HS vs. 10.93 TN nmol/L). In accordance with this finding, Vale et al.  reportedExposure to HS upregulates the synthesis of the HSPs (heat shock proteins) produced in all cells and tissues in response to stress, which are aimed at facilitating the production, conformation, and renovation of other proteins ,54. The To further explore the mechanisms subjacent to the antioxidant effects of PO, the Nrf2 expression in the liver was determined. Nrf2 is a sensitive redox transcription factor that regulates the numerous genes that code phase II detoxifying enzymes and antioxidant enzymes such as GSH-Px, SOD, CAT, heme oxygenase-1, and glutathione-S-transferase . The preIn conclusion, adding PO, especially at the level of a 6.0 g/kg diet, alleviated the oxidative stress induced by a high ambient temperature and had a hepatoprotective effect in broiler chickens. Moreover, the PO supplementation resulted in low levels of Hsp 70 expression and induced the expression of Nrf2-mediated genes. Overall, such results suggest supplementing broiler chickens with PO in a 6.0 g/kg diet may activate the defense mechanisms of the organism, thus decreasing the molecular oxidative effects. Such antioxidant actions of PO alleviated the negative effects of HS, particularly when the birds were exposed to stress in hot climates, acting as an excellent antioxidant additive in poultry production.


This is the first study to demonstrate the successful implementation of a printing strategy for embedding conductive traces with a homogeneous cross-sectional area in FFF 3D printing.A novel approach to manufacture components with integrated conductor paths involves embedding and sintering an isotropic conductive adhesive (ICA) during fused filament fabrication (FFF). However, the molten plastic is deposited directly onto the adhesive path which causes an inhomogeneous displacement of the uncured ICA. This paper presents a 3D printing strategy to achieve a homogeneous cross-section of the conductor path. The approach involves embedding the ICA into a printed groove and sealing it with a wide extruded plastic strand. Three parameter studies are conducted to obtain a consistent cavity for uniform formation of the ICA path. Specimens made of polylactic acid (PLA) with embedded ICA paths are printed and evaluated. The optimal parameters include a groove printed with a layer height of 0.1 mm, depth of 0.4 mm, and sealed with a PLA strand of 700 µm diameter. This resulted in a conductor path with a homogeneous cross-section, measuring 660 µm ± 22 µm in width (relative standard deviation: 3.3%) and a cross-sectional area of 0.108 mm In the context of Industry 4.0 and the Internet of Things (IoT), components and assemblies with integrated electrical circuits are becoming deeply significant . The int® IC4753  filled with silver microparticles was first dispensed in the immediate vicinity of the extruder nozzle and the molten and hot plastic were deposited directly on top [3D system described by Espalin et al. [In a previous work, the possibility of embedding and sintering conductive fluids in FFF printed PLA  structures in a single process step was presented. An isotropic conductive adhesive (ICA) DELO DUALBONDy on top . Howevery on top  or the mn et al. . HoweverThe integration of conductive paths into FFF printing involves two essential aspects. First, the printing strategy to embed the conductive ICA homogeneously. And secondly the curing and sintering of the ICA. This paper reports on the development of a printing strategy to embed the conductive ICA with a focus on the cross-sectional geometry of the conductor path. The aim is to determine the process parameters to achieve conductor paths with a homogeneous cross-section along its length.® IC4753 . This ICA is based on acrylate and filled with silver micro particles. The density specified in the datasheet is 3.17 g/cm3 [−1 shear rate [The conductive material used is ICA DELO DUALBOND17 g/cm3 . Accordiear rate .3 [White PLA  is selected as the substrate material, which has a lower density than the IC4753, i.e., 1.24 g/cm3 . The ICA(i)A Cartesian XY-head, single extruder FFF printer . The printhead moves along a X and Y gantry. The distance of the nozzle to the non-heated build platform is set by the movement of the print bed in Z direction.(ii)A digital dispenser  for dosing the ICA using the time-pressure technique.(iii)A 5 cc syringe barrel and a dispensing tip with an inner cannula diameter of 0.34 mm , and both are lightproof against UV light.(iv)A 3D-printed holder to attach the dispensing system to the print head. The dispensing needle is aligned with the nozzle by four thrust screws. The ICA is applied in front of the plastic deposition. Due to this arrangement, the embedding of the ICA is done linearly from right to left a,b. SincLiquid (conductive) materials can be applied through various methods, for example, contactless methods such as ink-jetting or aerosol jetting . HoweverAn endoscope camera  was used to visualize the embedding process and to monitor the plastic deposition, e.g., to detect leakage of the ICA during embedding. Specimens were designed in the computer-aided design (CAD) software SolidWorks (2020 SP5.0)  and converted to a Standard Triangulation Language (STL) file. From this, g-codes were generated using the slicer software Simplify3D (v4.1.2) , in which the printing parameters can be modified layer by layer.The new 3D printing strategy is to embed the ICA in a printed groove and seal it with a wide single plastic strand. In FFF printing, and especially in the process described, there is a variety of parameters which influence the embedding process and the geometry of the conductor paths. During plastic deposition, extrusion forces are generated that act on the already printed layers. In the ordinary  FFF process, the underlying layers are already cured, and the forces have no significant influence on them. In the new printing strategy, however, the ICA is not yet cured at the time of plastic deposition. The viscosity of the IC4753 stated in the datasheet is 30 Pa·s . The zerThe extrusion forces acting normally to the deposited layer are the weight force caused by the weight of the molten plastic, the impulse force caused by the molten plastic decelerating, and the force caused by the interaction of the nozzle with the deposited plastic when the nozzle compresses the strand . Plott eThe flow rates of the plastic and the ICA can only be considered in conjunction with the printing speed. In this work, the amount of extruded plastic is described by the strand diameter t result . If neceThe 3D printing strategy pursued consisted of three process steps, as shown in In order to realize the described embedding strategy, three parameter studies were carried out in sequential order. The results of each study are included as fixed parameters in the next study. First, the layer height of the groove is investigated with regard to the flatness of the groove sides and the dimensional accuracy of the groove. Second, the strand diameter is varied so that the strand seals the groove. And third, the groove depth is increased until a defined cavity is formed. Finally, specimens with embedded ICA are printed with the determined parameters to evaluate the strategy. The dosing parameters are listed in In FFF printing, the cross-section of the deposited plastic does not have a perfectly rectangular geometry. Rather, the sides of the strands are rounded and can each be approximated by a semicircle. The diameter of the semicircle corresponds to the layer height . TherefoIn the embedding process described, the groove limits the lateral deformation of the ICA. Thus, the layer height influences the cross-sectional geometry of the embedded conductor path. In order to determine the layer height that leads to the most homogeneous cross-section, 40 mm long and 3.8 mm wide specimens with 600 µm wide grooves without the sealing strand were printed. The layer height of the groove is varied between 0.1 mm (8 layers), 0.2 mm (4 layers) and 0.3 mm (3 layers), with two 0.2 mm thick layers each as bottom and top layers . The priIn g-code, the command for extrusion  printed a. The prTo ensure that the extrusion forces are dissipated via the groove sides and do not act on the ICA, a cavity must be formed after strand deposition. For that purpose, three specimens with a varying groove depth between 300 µm, 400 µm and 500 µm were printed, as seen in With the determined groove layer height, strand diameter, and groove depth, five specimens with embedded ICA were printed. The fabrication of a specimen is shown in The g-code commands of the embedding process are written manually and added to the code generated by the slicer software. The schematic of the overall printing process is shown in Microscope images of the groove cross-sections are presented in s et al.  and Desws et al. . The pris et al. . HoweverDeposition of the plastic strand and the resulting cross-sections under variation of Specimens for the investigation of the groove depth were printed with 2. With the defined print speed during embedding of 300 mm/min, the required dosing flow rate is 30 µL/min.Equation (1) is used to determine the dosing flow rate of the ICA. The cross-sectional area of the cavity 2 with a total SD of 0.008 mm2 (RSD = 7.2%).A microscopic evaluation of the cross-sections shows that the ICA was completely embedded a. All adThe results show that embedding of ICA paths with a homogeneous cross-section along its length is possible with the presented printing strategy based on groove sealing and the determined parameters. The minor variations in the width of the embedded path are in line with the observed dimensional accuracy of the groove printed with a 0.1 mm layer height. Compared to Espalin et al. , the fin2 with a relative standard deviation of 7.2%. The mean width of the paths is 660 µm with a relative standard deviation of 3.3%.A novel printing strategy for embedding conductor paths into 3D printed parts was presented. It was successfully demonstrated that the groove sealing strategy can be used to embed conductor paths made of ICA in FFF printed parts with homogeneous cross-sections in one process step. A groove layer height of 0.1 mm results in the flattest groove sides and the highest dimensional accuracy of the groove width. With a sealing strand diameter of 700 µm, a groove width of 600 µm, and a groove depth of 400 µm, a defined cavity is created in which the ICA path is uniformly formed. The conductor paths printed with the described parameters have a mean cross-sectional area of 0.108 mmIn the present study, the electrical properties of the conductors were not investigated. In order to be able to undertake these investigations, the next step must focus on the thermal post-processing of the ICA, i.e., the curing and sintering of the ICA. The sealing strand is deposited directly onto the conductive liquid, and both share a considerable large contact area. Therefore, it should be investigated whether sufficient heat transfer occurs between the sealing strand and the conductor path during FFF 3D printing to cure and sinter the conductive fluid so that high electrical conductivity is achieved. Further sintering methods which could be considered are non-selective methods such as thermal sintering and intense pulsed light sintering or selective sintering methods such as laser sintering or ohmic sintering. In addition, the investigations were limited to the realization of simple unidirectional conductor paths. Technical extensions and experimental investigations are also required on this point in order to be able to realize two-dimensional or even three-dimensional conductor paths in complex 3D printed components.


Cerebrospinal fluid(CSF) is an essential matrix for the discoveryof neurological disease biomarkers. However, the high dynamic rangeof protein concentrations in CSF hinders the detection of the leastabundant protein biomarkers by untargeted mass spectrometry. It isthus beneficial to gain a deeper understanding of the secretion processeswithin the brain. Here, we aim to explore if and how the secretionof brain proteins to the CSF can be predicted. By combining a curatedCSF proteome and the brain elevated proteome of the Human ProteinAtlas, brain proteins were classified as CSF or non-CSF secreted.A machine learning model was trained on a range of sequence-basedfeatures to differentiate between CSF and non-CSF groups and effectivelypredict the brain origin of proteins. The classification model achievesan area under the curve of 0.89 if using high confidence CSF proteins.The most important prediction features include the subcellular localization,signal peptides, and transmembrane regions. The classifier generalizedwell to the larger brain detected proteome and is able to correctlypredict novel CSF proteins identified by affinity proteomics. In additionto elucidating the underlying mechanisms of protein secretion, thetrained classification model can support biomarker candidate selection. CSF contains thousands of proteinswith a highly dynamic concentration range of an estimated 9 ordersof magnitude.4 Further, only an estimated20% of CSF proteins originate from the brain and are able to provideinsight into pathological processes of the CNS.6Forthe discovery of central nervous system (CNS) fluid biomarkers,cerebrospinal fluid (CSF) is the preferred matrix as it is in closecontact with the brain tissue,7 These studiesare based on “bottom up” mass spectrometry—ahypothesis-free and untargeted method able to identify a high numberof unique peptides and subsequently proteins within a sample.8 This renders mass spectrometry the most suitableapproach to build the proteome of a fluid or tissue of interest.2 However, inherent limitations of this technologyregarding proteome coverage have to be considered during the biomarkerdiscovery phase.10 Whilegreat advances have been made in recent years that allow researchersto overcome many issues, untargeted mass spectrometry workflows stillstruggle to identify the least abundant proteins in complex biologicalfluids.11 Importantly, steps used to improve the detectionof the low abundance proteome, e.g., protein depletion and fractionation,have their own drawbacks including decreased workflow reproducibility.9 This issue is of high importance as these difficultto detect proteins are often of interest as biomarkers.4 It is thus beneficial to exploit alternativeapproaches to identify low abundance CSF proteins that might constitutenovel biomarker candidates.Multiple studies have probed the CSF proteome inhealthy humansvia discovery proteomics.12 Machine learning-based approaches might helppredict proteins with the potential to be secreted to and thus bepresent in a fluid of interest but that are not detectable by untargetedmass spectrometry. These potential biomarkers might subsequently bemeasured with more sensitive targeted approaches to confirm the prediction.In addition, machine learning approaches to protein secretion canprovide a deeper understanding of the cellular processes.A way to augment the experimentalstudy of body fluid proteomesfor biomarker discovery is in silico protein secretion prediction.13 but only rarely was CSF-specific secretion predictionpursued. A recent prediction model, DeepSec, reported an area underthe curve (AUC) of 0.9 demonstrating that the movement of proteinsto the CSF is encoded in the protein amino acid sequence.14 This study and other recently published methodshave applied the increasingly popular approach of deep learning tothe task of secretion prediction.13 Whilereporting high model accuracy, biological insight from these modelsis very limited; the infamous “black box” characterof deep learning hinders us to understand how the model makes itsdecisions. Thus, if gaining biological insights from a predictionmodel is an additional research aim, use of “shallow”but interpretable models might be more beneficiary.15Multipleprotein secretion predictors have been developed,Accordingly, we were interested in developing a CSFsecretion predictorconsidering two important aspects: 1) By limiting model training tolikely CNS-originating proteins, our study focuses on specificallydistinguishing between proteins secreted from the brain to CSF andthose confined to the brain; 2) We prioritize model interpretabilityby utilizing an explainable machine learning model and biologicallyinformative features to investigate how the model makes its decisionsand to explore the biology behind CSF secretion. Note that while werefer here and throughout this study to protein secretion, this termis meant to include all physiological processes that lead to a brainprotein’s presence in CSF.In this study, we integratedmultiple CSF proteomics studies todefine and analyze the healthy human CSF proteome. Using this comprehensiveCSF proteome, we annotated the brain elevated proteome of the HumanProtein Atlas (HPA) regarding CSF presence 1. We arg20 which are summarized in supplement.To curate a comprehensive proteomeof healthy human CSF, previously published CSF proteomics studieswere searched and included based on the following requirements: 1)Healthy CSF samples, i.e., without any known diagnoses of neurologicaldisease, were measured; 2) The study was exploratory, not targeted;3) The study provided a high coverage of the CSF proteome by detectinga minimum of 1000 proteins; 4) The full data set and peptide countwere publicly available. These specifications led to the inclusionof six CSF proteomics studies,23 that respectively identifiedmore than 1000 CSF proteins were identified (Table S1). Further details on the data retrieval can be found inthe Supporting Information.A CSF proteomeof AD was curated to compare the CSF proteome in health and diseaseand corroborate that neurological disease does not systematicallyaffect which set of proteins is present in the CSF, but rather altersthe abundance of proteins. As AD is a well-researched neurologicaldisease, multiple studies were available that compared the CSF proteinabundances of AD patients with those of healthy controls. Three exploratorymass spectrometry studies25 The HPA also providesinformation about the average gene expression within the brain. Ofthe 2709 proteins of the brain elevated HPA proteome, 2546 proteinswith a known and unique Uniprot ID were kept. For entries that mappedto more than one Uniprot ID, only the first identifier was retained.Entries with no associated canonical protein sequence or a sequencecontaining nonstandard amino acids were discarded. Human proteinsthat have never been detected by any mass spectrometry study accordingto the ProteomicsDB were excluded as for these proteins it would notbe possible to conclude if they are not identified by mass spectrometrystudies because of their absence or undetectability.27 Subsequently, 2079 brain elevated proteins were retained. We annotatedthis filtered brain elevated HPA proteome as CSF secreted (positiveclass) and as non-CSF secreted (negative class) if the proteins werepresent or absent in the CSF proteome .proteome 1. A secohttp://www.pantherdb.org/).28 The list of 892 CSF proteins was used as thetarget set, and the filtered brain elevated HPA proteome of 2079 proteinswas used as the background set to identify enriched and depleted GOterms in the CSF brain protein set. PANTHER successfully mapped 884and 2053 proteins of the target and background set, respectively.Fisher’s Exact test was used to test for statistical significance.The false discovery rate was set at .05.Gene ontology(GO) term enrichment analysis was performed using PANTHER -anchors36 were included. The PROSITE protein domain database and the ScanPrositetool were used to identify group enriched sequence pattern motifsand annotate the motif-containing proteins.38 The use of sequence-based features largely obtained from predictiontools was preferred over curated database annotations to limit theannotation bias in our results.39 However,curated protein annotations of ectodomain shedding41 and extracellular vesicle (EV) association27 were taken from previous studies on these properties and investigatedto support the interpretation of our model’s findings. Note,however, that these latter annotations were not included in the featuredata set used for training the machine learning model. A detaileddescription of the feature generation process is provided in the Supporting Information.We generated a collection of protein(sequence) properties, so-called features, to characterize the proteinsin our data set and to train a classifier on. We derived featuresdirectly from the canonical amino acid sequence: proportions of eachamino acid type, the protein’s physicochemical properties,and instability index.42 First, the data was randomly split into a training(80%) and a hold-out test (20%) set. All continuous features werescaled, and the training set was balanced to ensure the same numberof proteins in the CSF and non-CSF class. We wished to exclude uninformativefeatures to attain a prediction model as simple as possible. Logisticregression with L1 regularization was implemented, as this model allowsthe weight of feature coefficients to be set to zero, effectivelyperforming feature selection. To determine the optimal degree of regularization,10-fold cross-validation of the training set was used to find an optimalregularization parameter C for the model. A C value of 0.5 led tothe smallest set of features without a drop in the model performance.The entire training set was subsequently used to produce a trainedclassifier that is able to distinguish between CSF and non-CSF proteinsbased on the generated features. The held-back test set provided anestimate how well the trained model would perform on unseen proteinsthat were not considered during model optimization and training.43 The classifier outputs a probability score between0 and 1 for each protein, with a higher value indicating a strongerprobability for this protein to belong to the positive class, i.e.,to be secreted from the brain to CSF. The threshold of the model’spredicted probability to be scored as positive (CSF secreted) is setat 0.5. A probability score between 0 and 0.5 indicates that a proteinbelongs to the negative class and is thus predicted not to be secretedto CSF. Model performance was measured by AUC. Feature importancewas studied by extracting the feature weight coefficients of the trainedmodels. A higher absolute coefficient value indicates a higher importancefor the model.Theannotated brain elevated HPA proteome was used to build a machinelearning model for the CSF protein secretion prediction. The entiremodel training workflow described hereinafter was performed both forthe full CSF and the high confidence CSF annotations respectivelyand was carried out using the Python module Scikit-learn.14 a body fluid-specific protein secretion predictor utilizing neuralnetworks. DeepSec was trained on 6260 CSF proteins collected fromprevious studies, while the non-CSF proteins were collected from Pfam-definedprotein families that are not present in the positive CSF proteinset. Note that the training data used by DeepSec is not publicly available;thus, potential overlap between the proteins DeepSec was trained onand the proteins we tested on could not be removed. Proteins for whichwe wished to compare model performance were submitted to the DeepSecWeb server (https://bmbl.bmi.osumc.edu/deepsec/index.php/Home/Index/index.html), and the predicted probability for CSF secretion was obtained foreach protein. The model performance was compared by the AUC and sensitivity.We compared thepredictions on thehold-out test set from our model to DeepSec,25 The proteome is composed of 16,507 proteins, of which 16,021 weremapped successfully to a known Uniprot ID. For a fair evaluation ofour model’s generalizability, we removed all proteins thatare part of the brain elevated HPA proteome from the brain detectedHPA proteome, as the model has already seen the brain elevated proteinsduring training and testing. We removed non-mass spectrometry detectableproteins according to ProteomicsDB;26 thisleft 12,583 proteins in the data set. The brain detected HPA proteomewas overlapped with our CSF proteome to count the number of CSF studiesin which a protein was found in the same manner as described for thebrain elevated HPA proteome. The model was then applied to the braindetected HPA proteome to receive predicted probabilities regardingCSF secretion. For comparison of differences in abundance and expressiondistribution, data from PaxDB44 was usedto annotate proteins regarding their average brain abundance; annotationsregarding RNA tissue distribution were taken from the HPA.To evaluate how well the model generalized toward allproteins known to be expressed in the brain, features were also generatedfor the entire brain detected proteome of the HPA (version 21.0).45 measured hundreds of CSF proteins in a large cohort ofdementia patients by proximity extension assay (PEA). PEA is an antibody-basedtechnology with high sensitivity and medium multiplex capabilities46 which has previously been suggested to be usedcomplementarily with untargeted mass spectrometry for biomarker discovery.47 We collected the study’s confidentlydetected CSF proteins and examined our model’s predicted probabilityfor these proteins with a focus on the novel detected CSF proteins,i.e., the proteins not found in the CSF proteome collected from exploratorymass spectrometry studies.We wishedto confirm that the trained classifier is able to identifyCSF proteins that can only be detected by a targeted approach despitebeing trained on protein annotations from untargeted mass spectrometry.Successful prediction of such CSF proteins would corroborate thatthe model truly learned signals of CSF secretion instead of mass spectrometrydetectability. A recent study by Del Campo et al.50 We then removed these proteins from our brain elevated proteome,if present, to allow for a fair evaluation of our classifier. Theclassification model was retrained in the same way as described above.The newly trained model was then used to obtain the predicted probabilitiesfor the AD biomarkers.To demonstrate the potential of a machinelearning approach toidentify the presence of biomarker candidates in CSF, we investigatedwhether known CSF biomarkers of AD would be predicted as secretedwith our approach. A confirmation of established biomarkers wouldincrease the confidence in the model to be able to select novel biomarkers.Seventeen AD biomarkers were collected from recent reviews.Studiesof healthy CSF were combined to define the full and highconfidence CSF proteome. We annotated the brain elevated HPA proteomeregarding its presence in CSF, created relevant features for theseproteins, and trained a machine learning model to correctly classifyCSF and non-CSF secreted brain proteins. To understand the processesof protein secretion to CSF, features important for differentiationbetween the CSF and the non-CSF class were identified. To illustrateour model’s utility, we applied it to the brain detected HPAproteome and an affinity proteomics detected protein set.51 If only high confidence CSF proteins that weredetected in three or more studies were included, the fraction of proteinsidentified by a single peptide drops to 0.07%, starkly limiting thepossibility of spurious protein identifications. There is high interstudyvariability with overlaps of the identified proteins ranging between61% and 90% . Integration ofmultiple CSF proteomics studies thus enables curation of a more comprehensiveand high confidence CSF proteome compared with single study results.From the six included massspectrometry studies of healthy CSF . The overlap increases from 87.26% in the respectivefull CSF sets to 99.06% in the high confidence CSF sets. A pairwisecomparison of the relative overlap of identified proteins for eachstudy with the other included healthy and AD CSF studies showed nosystematic difference in protein overlap between healthy CSF studiesand AD CSF studies . Theseresults indicate that the CSF protein set is not systematically differentin studies of disease, and we thus continued with the healthy CSFproteome for subsequent steps.Further, we investigated if a systematic bias in CSF protein presenceexists in neuropathology, as it would limit the application of a machinelearning model trained on the healthy CSF proteome for the selectionof disease biomarker candidates. We compared the composition of thehealthy CSF proteome with the AD CSF proteome . This observation indicates that the constant subset ofthe CSF is increasingly composed of CNS-derived proteins, while theheterogeneity between CSF studies stems from other sources, i.e.,plasma-originating proteins.The overlapbetween brain elevated and CSF proteome increases when including onlyCSF proteins detected in multiple studies  was trained to distinguish between the positive(CSF secreted brain proteins) and negative classes (non-CSF secretedbrain proteins). Evaluation on a held-out test set produced an AUCof 0.81, clearly indicating that there is a discrepancy between thetwo classes to be learned from the included features , anda negative value indicates correlation with the negative class (non-CSFsecreted). The 20 most important features of the high confidence CSFmodel are shown in 52The presence of a signalpeptide within the protein’s sequenceis a highly important feature utilized by the high confidence CSFmodel to identify secreted proteins 4A. This The subcellularlocalization of a protein, e.g., in the nucleus,the Golgi apparatus, or the extracellular space, is highly importantfor correct classification of CSF and non-CSF brain proteins. Examiningthe distribution of predicted subcellular locations illustrates thedifferences between CSF secreted and non-CSF secreted proteins 4C. The sFigure S3). Note that these annotations were not included in the predictionmodel, as they cannot be considered sequence-based.Interestingly,the presence of transmembrane regions as a binaryfeature is positively correlated with CSF secreted proteins, whilethe number of transmembrane regions shows a negative correlation.To understand this apparent paradox, we investigated the fractionsof CSF and non-CSF proteins per number of transmembrane regions. Figure S4). These motifs affirm theproperties of CSF and non-CSF brain proteins: EGF domains are foundin membrane-associated and extracellular proteins.53 Cadherins are known to be glycosylated and involved incell–cell adhesion processes through their extracellular domain.55 G protein-coupled receptors are firmly incorporated into the membranethrough their seven transmembrane regions.56 Both the Zinc finger and “Homeobox” domain are involvedin nucleotide binding.58We identifiedthree motifs that are enriched in CSF brain elevatedproteins and three motifs that are enriched in non-CSF brain proteins.Associated with the CSF class are the EGF-like domain signature 1,EGF-like domain signature 2, and the Cadherin domain signature. Inthe non-CSF class, we identified the G protein-coupled receptors family1 signature, the Zinc finger C2H2 type domain signature, and the “Homeobox”domain signature. While none of these patterns are associated witha great number of proteins, enrichment in their respective proteingroup is strong and is still observed in the higher stringency datasets , performed on the brain detected HPA proteome .Note that while brain detected proteins are expressed in the brain,their origin when detected in CSF is less certain, as these can beexpressed highly in other tissue(s) as well. Nevertheless, the modelshould be able to identify the proteins with the potential to be secretedfrom the brain to the CSF. To evaluate the model’s performancemore fairly, we removed the proteins present in the brain elevatedHPA proteome used during training from the brain detected HPA proteome,leading to the model being applied to 12,583 proteins with 3679 ofthese overlapping with our full CSF proteome 1. In FigFigure S5). Thus, these proteinsmight have a high chance to reach the CSF (and other body fluids)owing to their high concentration levels and ubiquity, despite notcarrying the typical properties of CSF secretion according to themachine learning model.To better understand misclassification of our model,we investigatedfalse negatively predicted proteins, i.e., proteins that were detectedin CSF by proteomics studies but with low probability to be CSF secretedaccording to the prediction model. We were able to retrieve averagebrain abundance values for 7796 out of the 12,583 proteins from PaxDB;RNA tissue distribution classification from the HPA was availablefor the full protein set. Comparison of average abundance and RNAtissue distribution suggests that the falsely negative predicted proteinsare highly abundant in the brain and expressed widely across all humantissues (Table S3).Of strong interest for CSF biomarkerresearch is the false positivepredicted proteins with a high probability score. If a protein hasnot yet been detected in exploratory mass spectrometry studies andis thus annotated as a non-CSF protein but is confidently predictedto be secreted to CSF by our model, this might indicate that the proteinis present in CSF but is not easily detected by mass spectrometry.An obvious reason might be low abundance, which would require ultrasensitivemethods to measure this protein in a complex matrix. We provide theprobability score of our models for almost every protein in the humanproteome including information on its presence in the included CSFstudies, the brain detected and the brain elevated HPA proteome  that are not part of the high confidenceCSF set  scans of the brain. Note thatwhile these two proteins, SV2A and TSPO, are protein biomarkers, theyare not necessarily expected to be present in CSF as they are notfluid biomarkers. The predicted secretion scores for these 17 biomarkersare shown in 12Toidentify urgently needed novel CSF biomarkers, a deeper knowledgeof brain protein secretion and leakage processes will be of greathelp. Interpretable machine learning is a valuable approach to widenour understanding as well as to discover novel biomarker candidatesthat might be difficult to detect by the conventional workflow, i.e.,using “bottom up” proteomics, because of their low abundancein CSF. Utilizing information gained from prediction tools as theone presented here is a rapid, effortless and cost-effective approachto support biomarker candidate identification and selection.Here, we built a CSF-specific proteinsecretion prediction model,using a curated CSF proteome data set and the brain elevated HPA proteome,which provides valuable insights for fluid biomarker research. Ourpredictor is able to distinguish between CSF secreted and non-CSFsecreted proteins, with an AUC between 0.81 and 0.89 depending onthe stringency used to define the CSF proteome. Our model outperformedthe deep learning-based DeepSec predictor on two independent testsets. Interpretation of the model gave insights into the propertiesthat distinguish CSF and non-CSF proteins, including signal peptidesand subcellular localization. Finally, we demonstrated the generalizabilityof this model for the brain detected proteome and for proteins detectedby targeted, sensitive technology.59 While we only included proteins of CSF samples thatwere deemed healthy, the true absence of any neurological diseasefrom a CSF sample is difficult to determine. Thus, it was essentialto confirm that protein presence in CSF is not systematically differentin disease . We provide thelargest publicly available collection of healthy CSF proteins, asthe integration of several studies provides higher proteome coverage.Note that while the CSF data set reported by DeepSec is larger (6260proteins), it has not been made publicly available. Wrongly includedproteins were less likely to appear in our high confidence CSF set.The positive effect of data filtering on model performance affirmedthe importance of a clean data set for successful implementation ofmachine learning approaches. Correct predictions on a larger brainproteome . While membrane-associatedproteins might initially not appear as good biomarker candidates,this study strongly suggests otherwise. The results are in line withreports on a few established membrane-associated CSF biomarkers, e.g.,the ectodomain shedding protein TREM2.60 Adhesion proteins were also identified as a specific group of interestbased on GO term and motif enrichment results. Their potential asbiomarkers for neurodegenerative disease has already been reported.62 Our results strongly imply that nucleotide-interacting proteinsrarely constitute good fluid CSF biomarkers, which may explain thedifficulty to develop fluid biomarker tests for the frontotemporaldementia-related protein TDP-43.63While the conventional protein secretion pathway clearlycontributeshighly to the content of the CNS-derived CSF proteome 4, our re39 we actively decided against other strategies as the research aimincluded biological interpretation. Nevertheless, as a deep learning-basedpredictor did not perform better than our model, the prediction taskinvestigated here might not warrant the use of such sophisticatedmodels because of the limited amount of data available. Still, a drawbackof our approach is the extensive feature generation required for themodel. While we do provide the probability scores for almost the fullhuman proteome (Table S3), prediction ona novel sequence would require effort beforehand to engineer the features.The predictions for known AD biomarkers (Limitations of our approach and thus of the model’s applicabilityhave to be considered when interpreting results. The use of a simplemachine learning approach as well as only sequence-based featuresmight limit the accuracy of the model. It is important to keep inmind that while features derived from other machine learning predictiontools are usually more comprehensive, they might not be as accurateas observations, e.g., from mass spectrometry studies. However, tocircumvent annotation bias and lost interpretability,omarkers 7 indicat1 this detailed information would provide further insights.The development of a fragment- or residue-specific predictor of CSFpresence would therefore be an interesting way to build on this study.A more in-depth analysis on thepeptide, instead of protein, levelcould give insights into the specific proteoforms present in CSF,i.e., specific splicing isoforms or post-translationally modifiedresidues. As biomarkers can be proteoform-specific, with phosphorylatedtau presenting one of the hallmark AD biomarkers,The biological insights derived from the model can be easily understoodby biomarker researchers without the need for strong machine learningdomain knowledge. To the best of our knowledge, such an in-depth analysisof the differences between CSF-secreted and brain-confined proteinshas not been done previously.While biomarker researchhas been able to make immense progressin recent years because of advances in mass spectrometry-based discoveryproteomics, approaches to augment these results should still be pursued.Effort must be put into identifying so far potentially overlookedcandidates that could improve the diagnosis and treatment of patients.This study provides one approach to increasing our knowledge of brain-to-CSFprotein secretion and can support the search for the next fluid CNS-derivedbiomarkers for neurological diseases.


The sample was drawn from the Aiginition Longitudinal Biomarker Investigation of Neurodegeneration study, comprising 163 individuals aged 40–75 years: 112 cognitively normal (CN) and 51 with mild cognitive impairment (MCI). Physical function parameters were measured at baseline, a lumbar puncture was performed the same day and CSF biomarkers were analyzed using automated methods. The association between walking time, handgrip strength and CSF biomarkers was evaluated by linear correlation, followed by multivariate linear regression models adjusted for age, sex, education and APOEe4 genotype. Walking time was inversely related to CSF Aβ42  in all participants (p < 0.05). Subgroup analysis showed that this association was stronger in individuals with MCI and participants older than 60 years old, a result which remained statistically significant after adjustment for the aforementioned confounding factors. These findings may open new perspectives regarding the role of mobility in the AD continuum. Cognitive and physical decline, both indicators of aging, seem to be associated with each other. The aim of the present study was to investigate whether physical function parameters  are related to cerebrospinal fluid (CSF) biomarkers (amyloid-beta Aβ Regarding CSF biomarkers, CSF Aβ42 was lower (p = 0.005) and CSF PhTau was higher (p < 0.001) in the MCI group, whereas CSF Tau did not differ between the groups (p = 0.171).In total, 163 individuals of ALBION’s first evaluation were included in our analysis. Their main characteristics are presented in 42 decreased with age (p = 0.007). In contrast, CSF Tau (p = 0.009) and CSF PhTau (p < 0.001) values increased with age. No difference was found between males and females, while ApoE4 carriers presented reduced CSF Aβ42, increased CSF PhTau and CSF Tau . Regarding physical function parameters, age subgroups presented no difference. Walking time was higher in females (p = 0.045) and grip strength was lower, as expected (p < 0.001). Grip strength was lower in ApoE ε4 carriers, as well (p = 0.034).Subgroup analysis of CSF biomarkers and physical function parameters was performed, as shown in 42 in all subgroups (p < 0.05), except CN individuals and participants younger than 60 years old. Regarding grip strength, subgroup analysis showed a negative correlation with CSF Tau and CSF PhTau in males (p < 0.05). Scatter plots of the linear correlation between walking time and CSF Aβ42 in all subgroups are presented in Simple (unadjusted) linear correlations between physical function parameters and CSF biomarkers are presented in Further statistical analysis of the effect of CSF biomarkers in physical function parameters was performed via multiple adjusted regression models , in orde42 in all ALBION participants. Subgroup analysis showed that this association concerned mainly individuals with MCI and participants older than 60 years old, a result which did not change after controlling for the confounding factors . This is the first study to examine CSF biomarkers in relation to physical function parameters in a relatively large number of participants, with a big proportion of them  being CN.In our analysis walking time was inversely related to CSF Aβ42 and walking speed. To be precise, studies using CSF analysis did not provide statistically significant relations between gait speed and Aβ42 . Continuous variables were expressed as mean values and standard deviations, while categorical variables were referred to as frequencies and percentages. Statistical significance was set at 42, Tau and PhTau values. To compare continuous variables such as age, education, cognitive scores, and physical function parameters between the two groups, we used analysis of variance (ANOVA). To compare CSF biomarkers’ values we used the Wilcoxon Rank Sum Test. For categorical variables  we used the Fisher exact test.For statistical purposes, walking time and handgrip strength measurements were converted to an average score from the individual trials. ApoE genotyping data were dichotomized into ε4 carriers  and non-carriers. CSF biomarkers were treated as continuous variables, using the exact CSF AβSubgroup analysis was performed for CSF biomarkers as well as physical function parameters. Age subgroups (<65 and ≥65 years old) were formed according to the median (which was 64.6 years). Sex groups included males and females, while ApoE status was separated in ε4 carriers and non-carriers. Walking time and handgrip strength were compared via ANOVA in subgroups, while CSF biomarkers were compared via Wilcoxon Rank Sum Test.In order to examine the association between physical function parameters and CSF biomarkers, we first performed linear correlational analyses between each parameter and each biomarker, using Spearman’s rank correlation coefficient since some variables had a non-parametric distribution. The above-mentioned subgroups were used, as well as additional age subgroups  which were created for sensitivity analysis.42, CSF Tau and CSF PhTau independent variables) in all subgroups. All models were adjusted for age, sex, education and ApoE status.We then proceeded with multivariate linear regression analyses, in order to adjust for the main confounding factors of such correlations. Walking time and handgrip strength were treated as dependent variables and we performed separate models for each CSF biomarker (CSF Aβ42 in individuals in the course of the AD continuum, mainly those older than 60 years old and participants with MCI. The detailed mechanisms underlying this association are yet to be determined, especially in the context of whether amyloid accumulation influences gait performance per se, or as one of many aspects of neurodegeneration. Our study expands knowledge concerning the relation between walking performance and CSF amyloid, an already recognized AD biomarker, contributing significantly to the existing literature, and opening new perspectives for exploration in research concerning mobility in patients in the AD continuum and the possible identification of new biomarkers.Cerebrospinal fluid biomarkers of the AD continuum precede clinical manifestations of dementia, as they are present in pre-clinical stages as well. In the absence of effective treatment, research has focused on the early detection of increased risk and the prevention of AD dementia. Lifestyle factors seem to prevent clinical progression, including physical activity which seems really promising. Our analysis highlighted that walking time is inversely related to CSF Aβ