Payam Emami Khoonsari

Analysis of the Cerebrospinal Fluid Proteome in Alzheimer's Disease

Alzheimer’s disease is a neurodegenerative disorder accounting for more than 50% of cases of dementia. Diagnosis of Alzheimer’s disease relies on cognitive tests and analysis of amyloid beta, protein tau, and hyperphosphorylated tau in cerebrospinal fluid. Although these markers provide relatively high sensitivity and specificity for early disease detection, they are not suitable for monitor of disease progression. In the present study, we used label-free shotgun mass spectrometry to analyse the cerebrospinal fluid proteome of Alzheimer’s disease patients and non-demented controls to identify potential biomarkers for Alzheimer’s disease. We processed the data using five programs (DecyderMS, Maxquant, OpenMS, PEAKS, and Sieve) and compared their results by means of reproducibility and peptide identification, including three different normalization methods. After depletion of high abundant proteins we found that Alzheimer’s disease patients had lower fraction of low-abundance proteins in cerebrospinal fluid compared to healthy controls (p<0.05). Consequently, global normalization was found to be less accurate compared to using spiked-in chicken ovalbumin for normalization. In addition, we determined that Sieve and OpenMS resulted in the highest reproducibility and PEAKS was the programs with the highest identification performance. Finally, we successfully verified significantly lower levels (p<0.05) of eight proteins (A2GL, APOM, C1QB, C1QC, C1S, FBLN3, PTPRZ, and SEZ6) in Alzheimer’s disease compared to controls using an antibody-based detection method. These proteins are involved in different biological roles spanning from cell adhesion and migration, to regulation of the synapse and the immune system.

Spinal Cord Stimulation Alters Protein Levels in the Cerebrospinal Fluid of Neuropathic Pain Patients : A Proteomic Mass Spectrometric Analysis

Electrical neuromodulation by spinal cord stimulation (SCS) is a well‐established method for treatment of neuropathic pain. However, the mechanism behind the pain relieving effect in patients remains largely unknown. In this study, we target the human cerebrospinal fluid (CSF) proteome, a little investigated aspect of SCS mechanism of action.

Increased Levels of Extracellular Microvesicle Markers and Decreased Levels of Endocytic/Exocytic Proteins in the Alzheimer's Disease Brain

BACKGROUND Alzheimers disease (AD) is a chronic neurodegenerative disorder accounting for more than 50% of all dementia cases. AD neuropathology is characterized by the formation of extracellular plaques and intracellular neurofibrillary tangles consisting of aggregated amyloid-β and tau, respectively. The disease mechanism has only been partially elucidated and is believed to also involve many other proteins. OBJECTIVE This study intended to perform a proteomic profiling of post mortem AD brains and compare it with control brains as well as brains from other neurological diseases to gain insight into the disease pathology. METHODS Here we used label-free shotgun mass spectrometry to analyze temporal neocortex samples from AD, other neurological disorders, and non-demented controls, in order to identify additional proteins that are altered in AD. The mass spectrometry results were verified by antibody suspension bead arrays. RESULTS We found 50 proteins with altered levels between AD and control brains. The majority of these proteins were found at lower levels in AD. Pathway analyses revealed that several of the decreased proteins play a role in exocytic and endocytic pathways, whereas several of the increased proteins are related to extracellular vesicles. Using antibody-based analysis, we verified the mass spectrometry results for five representative proteins from this group of proteins (CD9, HSP72, PI42A, TALDO, and VAMP2) and GFAP, a marker for neuroinflammation. CONCLUSIONS Several proteins involved in exo-endocytic pathways and extracellular vesicle functions display altered levels in the AD brain. We hypothesize that such changes may result in disturbed cellular clearance and a perturbed cell-to-cell communication that may contribute to neuronal dysfunction and cell death in AD.

PhenoMeNal: Processing and analysis of Metabolomics data in the Cloud

Background Metabolomics is the comprehensive study of a multitude of small molecules to gain insight into an organism’s metabolism. The research field is dynamic and expanding with applications across biomedical, biotechnological and many other applied biological domains. Its computationally-intensive nature has driven requirements for open data formats, data repositories and data analysis tools. However, the rapid progress has resulted in a mosaic of independent – and sometimes incompatible – analysis methods that are difficult to connect into a useful and complete data analysis solution. Findings The PhenoMeNal (Phenome and Metabolome aNalysis) e-infrastructure provides a complete, workflow-oriented, interoperable metabolomics data analysis solution for a modern infrastructure-as-a-service (IaaS) cloud platform. PhenoMeNal seamlessly integrates a wide array of existing open source tools which are tested and packaged as Docker containers through the project’s continuous integration process and deployed based on a kubernetes orchestration framework. It also provides a number of standardized, automated and published analysis workflows in the user interfaces Galaxy, Jupyter, Luigi and Pachyderm. Conclusions PhenoMeNal constitutes a keystone solution in cloud infrastructures available for metabolomics. It provides scientists with a ready-to-use, workflow-driven, reproducible and shareable data analysis platform harmonizing the software installation and configuration through user-friendly web interfaces. The deployed cloud environments can be dynamically scaled to enable large-scale analyses which are interfaced through standard data formats, versioned, and have been tested for reproducibility and interoperability. The flexible implementation of PhenoMeNal allows easy adaptation of the infrastructure to other application areas and ‘omics research domains.

Integration of magnetic resonance imaging and protein and metabolite CSF measurements to enable early diagnosis of secondary progressive multiple sclerosis

Molecular networks in neurological diseases are complex. Despite this fact, contemporary biomarkers are in most cases interpreted in isolation, leading to a significant loss of information and power. We present an analytical approach to scrutinize and combine information from biomarkers originating from multiple sources with the aim of discovering a condensed set of biomarkers that in combination could distinguish the progressive degenerative phenotype of multiple sclerosis (SPMS) from the relapsing-remitting phenotype (RRMS). Methods: Clinical and magnetic resonance imaging (MRI) data were integrated with data from protein and metabolite measurements of cerebrospinal fluid, and a method was developed to sift through all the variables to establish a small set of highly informative measurements. This prospective study included 16 SPMS patients, 30 RRMS patients and 10 controls. Protein concentrations were quantitated with multiplexed fluorescent bead-based immunoassays and ELISA. The metabolome was recorded using liquid chromatography-mass spectrometry. Clinical follow-up data of the SPMS patients were used to assess disease progression and development of disability. Results: Eleven variables were in combination able to distinguish SPMS from RRMS patients with high confidence superior to any single measurement. The identified variables consisted of three MRI variables: the size of the spinal cord and the third ventricle and the total number of T1 hypointense lesions; six proteins: galectin-9, monocyte chemoattractant protein-1 (MCP-1), transforming growth factor alpha (TGF-α), tumor necrosis factor alpha (TNF-α), soluble CD40L (sCD40L) and platelet-derived growth factor AA (PDGF-AA); and two metabolites: 20β-dihydrocortisol (20β-DHF) and indolepyruvate. The proteins myelin basic protein (MBP) and macrophage-derived chemokine (MDC), as well as the metabolites 20β-DHF and 5,6-dihydroxyprostaglandin F1a (5,6-DH-PGF1), were identified as potential biomarkers of disability progression. Conclusion: Our study demonstrates, in a limited but well-defined and data-rich cohort, the importance and value of combining multiple biomarkers to aid diagnostics and track disease progression.

Spinal injection of newly identified cerebellin-1 and cerebellin-2 peptides induce mechanical hypersensitivity in mice

By screening for neuropeptides in the mouse spinal cord using mass spectrometry (MS), we have previously demonstrated that one of the 78 peptides that is expressed predominantly (> 6-fold) in the dorsal horn compared to the ventral spinal cord is the atypical peptide desCER [des-Ser1]-cerebellin, which originates from the precursor protein cerebellin 1 (CBLN1). Furthermore, we found that intrathecal injection of desCER induces mechanical hypersensitivity in a dose dependent manner. The current study was designed to further investigate the relative expression of other CBLN derived peptides in the spinal cord and to examine whether they share similar nociceptive properties. In addition to the peptides cerebellin (CER) and desCER we identified and relatively quantified nine novel peptides originating from cerebellin precursor proteins CBLN1 (two peptides), CBLN2 (three peptides) and CBLN4 (four peptides). Ten out of eleven peptides displayed statistically significantly (p < 0.05) higher expression levels (200-350%) in the dorsal horn compared to the ventral horn. Intrathecal injection of three of the four CBLN1 and two of the three CBLN2 derived peptides induced mechanical hypersensitivity in response to von Frey filament testing in mice during the first 6 h post-injection compared to saline injected mice, while none of the four CBLN4 derived peptides altered withdrawal thresholds. This study demonstrates that high performance MS is an effective tool for detecting novel neuropeptides in CNS tissues. We show the presence of nine novel atypical peptides originating from CBLN1, CBLN2 and CBLN4 precursor proteins in the mouse dorsal horn, whereof five peptides induce pain-like behavior upon intrathecal injection. Further studies are required to investigate the mechanisms by which CBLN1 and CBLN2 derived peptides facilitate nociceptive signal transmission.

Systematic analysis of the cerebrospinal fluid proteome of fibromyalgia patients

Fibromyalgia (FM) is a syndrome characterized by widespread muscular pain, fatigue and functional symptoms, which is known to be difficult to diagnose as the various symptoms overlap with many other conditions. Currently, there are no biomarkers for FM, and the diagnosis is made subjectively by the clinicians. We have performed shotgun proteomics on cerebrospinal fluid (CSF) from FM patients and non-pain controls to find potential biomarker candidates for this syndrome. Based on our multivariate and univariate analyses, we found that the relative differences in the CSF proteome between FM patients and controls were moderate. Four proteins, important to discriminate FM patients from non-pain controls, were found: Apolipoprotein C-III, Galectin-3-binding protein, Malate dehydrogenase cytoplasmic and the neuropeptide precursor protein ProSAAS. These proteins are involved in lipoprotein lipase (LPL) activity, inflammatory signaling, energy metabolism and neuropeptide signaling. SIGNIFICANCE: Fibromyalgia is present in as much as 2% of the population, causing pain, stiffness, and tenderness of the muscles. Upon accurate diagnostic, nonpharmacological and pharmacological therapies can be used to alleviate pain and manage other symptoms. However, lack of objective, universal applicable diagnostic criteria as well as vague and diffused symptoms, have made diagnosis difficult. In this context, our findings can shed light on potential value of CSF proteome for objectively diagnosing FM.

Corrigendum to “Low molar excess of 4-oxo-2-nonenal and 4-hydroxy-2-nonenal promote oligomerization of alpha-synuclein through different pathways” [Free Rad. Biol. Med. (2017) 421–431]

Corrigendum to “Low molar excess of 4-oxo-2-nonenal and 4-hydroxy-2-nonenal promote oligomerization of alpha-synuclein through different pathways” [Free Rad. Biol. Med. (2017) 421–431]

Container-based bioinformatics with Pachyderm

Motivation: Computational biologists face many challenges related to data size, and they need to manage complicated analyses often including multiple stages and multiple tools, all of which must be deployed to modern infrastructures. To address these challenges and maintain reproducibility of results, researchers need (i) a reliable way to run processing stages in any computational environment, (ii) a well‐defined way to orchestrate those processing stages and (iii) a data management layer that tracks data as it moves through the processing pipeline. Results: Pachyderm is an open‐source workflow system and data management framework that fulfils these needs by creating a data pipelining and data versioning layer on top of projects from the container ecosystem, having Kubernetes as the backbone for container orchestration. We adapted Pachyderm and demonstrated its attractive properties in bioinformatics. A Helm Chart was created so that researchers can use Pachyderm in multiple scenarios. The Pachyderm File System was extended to support block storage. A wrapper for initiating Pachyderm on cloud‐agnostic virtual infrastructures was created. The benefits of Pachyderm are illustrated via a large metabolomics workflow, demonstrating that Pachyderm enables efficient and sustainable data science workflows while maintaining reproducibility and scalability. Availability and implementation: Pachyderm is available from https://github.com/pachyderm/pachyderm. The Pachyderm Helm Chart is available from https://github.com/kubernetes/charts/tree/master/stable/pachyderm. Pachyderm is available out‐of‐the‐box from the PhenoMeNal VRE (https://github.com/phnmnl/KubeNow‐plugin) and general Kubernetes environments instantiated via KubeNow. The code of the workflow used for the analysis is available on GitHub (https://github.com/pharmbio/LC‐MS‐Pachyderm). Supplementary information: Supplementary data are available at Bioinformatics online.

Interoperable and scalable metabolomics data analysis with microservices

Developing a robust and performant data analysis workflow that integrates all necessary components whilst still being able to scale over multiple compute nodes is a challenging task. We introduce a generic method based on the microservice architecture, where software tools are encapsulated as Docker containers that can be connected into scientific workflows and executed in parallel using the Kubernetes container orchestrator. The access point is a virtual research environment which can be launched on-demand on cloud resources and desktop computers. IT-expertise requirements on the user side are kept to a minimum, and established workflows can be re-used effortlessly by any novice user. We validate our method in the field of metabolomics on two mass spectrometry studies, one nuclear magnetic resonance spectroscopy study and one fluxomics study, showing that the method scales dynamically with increasing availability of computational resources. We achieved a complete integration of the major software suites resulting in the first turn-key workflow encompassing all steps for mass-spectrometry-based metabolomics including preprocessing, multivariate statistics, and metabolite identification. Microservices is a generic methodology that can serve any scientific discipline and opens up for new types of large-scale integrative science.Developing a robust and performant data analysis workflow that integrates all necessary components whilst still being able to scale over multiple compute nodes is a challenging task. We here presen ...