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      A plasma protein signature associated with cognitive function in men without severe cognitive impairment

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          Abstract

          Background

          A minimally invasive blood-based assessment of cognitive function could be a promising screening strategy to identify high-risk groups for the incidence of Alzheimer’s disease.

          Methods

          The study included 448 cognitively unimpaired men (mean age 64.1 years) drawn from the Geelong Osteoporosis Study. A targeted mass spectrometry-based proteomic assay was performed to measure the abundance levels of 269 plasma proteins followed by linear regression analyses adjusted for age and APOE ε4 carrier status to identify the biomarkers related to overall cognitive function. Furthermore, two-way interactions were conducted to see whether Alzheimer’s disease-linked genetic variants or health conditions modify the association between biomarkers and cognitive function.

          Results

          Ten plasma proteins showed an association with overall cognitive function. This association was modified by allelic variants in genes ABCA7, CLU, BDNF and MS4A6A that have been previously linked to Alzheimer’s disease. Modifiable health conditions such as mood disorders and poor bone health, which are postulated to be risk factors for Alzheimer’s disease, also impacted the relationship observed between protein marker levels and cognition. In addition to the univariate analyses, an 11-feature multianalyte model was created using the least absolute shrinkage and selection operator regression that identified 10 protein features and age associated with cognitive function.

          Conclusions

          Overall, the present study revealed plasma protein candidates that may contribute to the development of a blood-based screening test for identifying early cognitive changes. This study also highlights the importance of considering other risk factors in elucidating the relationship between biomarkers and cognition, an area that remains largely unexplored.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s13195-023-01294-7.

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          The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets

          Damian Szklarczyk, Annika Gable, Katerina C. Nastou … (2020)
          Abstract Cellular life depends on a complex web of functional associations between biomolecules. Among these associations, protein–protein interactions are particularly important due to their versatility, specificity and adaptability. The STRING database aims to integrate all known and predicted associations between proteins, including both physical interactions as well as functional associations. To achieve this, STRING collects and scores evidence from a number of sources: (i) automated text mining of the scientific literature, (ii) databases of interaction experiments and annotated complexes/pathways, (iii) computational interaction predictions from co-expression and from conserved genomic context and (iv) systematic transfers of interaction evidence from one organism to another. STRING aims for wide coverage; the upcoming version 11.5 of the resource will contain more than 14 000 organisms. In this update paper, we describe changes to the text-mining system, a new scoring-mode for physical interactions, as well as extensive user interface features for customizing, extending and sharing protein networks. In addition, we describe how to query STRING with genome-wide, experimental data, including the automated detection of enriched functionalities and potential biases in the user's query data. The STRING resource is available online, at https://string-db.org/.
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            Systematic meta-analyses of Alzheimer disease genetic association studies: the AlzGene database.

            Lars Bertram, Matthew B. McQueen, Kristina Mullin … (2007)
            The past decade has witnessed hundreds of reports declaring or refuting genetic association with putative Alzheimer disease susceptibility genes. This wealth of information has become increasingly difficult to follow, much less interpret. We have created a publicly available, continuously updated database that comprehensively catalogs all genetic association studies in the field of Alzheimer disease (http://www.alzgene.org). We performed systematic meta-analyses for each polymorphism with available genotype data in at least three case-control samples. In addition to identifying the epsilon4 allele of APOE and related effects, we pinpointed over a dozen potential Alzheimer disease susceptibility genes (ACE, CHRNB2, CST3, ESR1, GAPDHS, IDE, MTHFR, NCSTN, PRNP, PSEN1, TF, TFAM and TNF) with statistically significant allelic summary odds ratios (ranging from 1.11-1.38 for risk alleles and 0.92-0.67 for protective alleles). Our database provides a powerful tool for deciphering the genetics of Alzheimer disease, and it serves as a potential model for tracking the most viable gene candidates in other genetically complex diseases.
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              The reactome pathway knowledgebase 2022

              Marc Gillespie, Bijay Jassal, Ralf Stephan … (2021)
              The Reactome Knowledgebase ( https://reactome.org ), an Elixir core resource, provides manually curated molecular details across a broad range of physiological and pathological biological processes in humans, including both hereditary and acquired disease processes. The processes are annotated as an ordered network of molecular transformations in a single consistent data model. Reactome thus functions both as a digital archive of manually curated human biological processes and as a tool for discovering functional relationships in data such as gene expression profiles or somatic mutation catalogs from tumor cells. Recent curation work has expanded our annotations of normal and disease-associated signaling processes and of the drugs that target them, in particular infections caused by the SARS-CoV-1 and SARS-CoV-2 coronaviruses and the host response to infection. New tools support better simultaneous analysis of high-throughput data from multiple sources and the placement of understudied (‘dark’) proteins from analyzed datasets in the context of Reactome’s manually curated pathways.
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                Author and article information

                Contributors
                Veer Bala Gupta: veer.gupta@deakin.edu.au
                Journal
                Journal ID (nlm-ta): Alzheimers Res Ther
                Journal ID (iso-abbrev): Alzheimers Res Ther
                Title: Alzheimer's Research & Therapy
                Publisher: BioMed Central (London )
                ISSN (Electronic): 1758-9193
                Publication date (Electronic): 1 September 2023
                Publication date PMC-release: 1 September 2023
                Publication date Collection: 2023
                Volume: 15
                Electronic Location Identifier: 148
                Affiliations
                [1 ]GRID grid.1021.2, ISNI 0000 0001 0526 7079, Deakin University, IMPACT – The Institute for Mental and Physical Health and Clinical Translation, School of Medicine, ; Geelong, VIC 3216 Australia
                [2 ]GRID grid.1051.5, ISNI 0000 0000 9760 5620, Baker Heart and Diabetes Institute, ; Melbourne, VIC Australia
                [3 ]GRID grid.1021.2, ISNI 0000 0001 0526 7079, Biostatistics Unit, Faculty of Health, , Deakin University, ; Burwood, VIC Australia
                [4 ]GRID grid.1008.9, ISNI 0000 0001 2179 088X, Department of Medicine-Western Health, , The University of Melbourne, ; St Albans, VIC Australia
                [5 ]GRID grid.1002.3, ISNI 0000 0004 1936 7857, Department of Epidemiology and Preventive Medicine, , Monash University, ; Prahran, VIC Australia
                [6 ]GRID grid.414257.1, ISNI 0000 0004 0540 0062, Barwon Health, ; Geelong, VIC Australia
                [7 ]GRID grid.414257.1, ISNI 0000 0004 0540 0062, Department of Geriatric Medicine, , Barwon Health, ; Geelong, VIC Australia
                Article
                Publisher ID: 1294
                DOI: 10.1186/s13195-023-01294-7
                PMC ID: 10472730
                PubMed ID: 37658429
                SO-VID: a3786df9-00cf-46bf-ae58-699c84f9ef00
                Copyright © © BioMed Central Ltd., part of Springer Nature 2023
                License:

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                Date received : 5 May 2023
                Date accepted : 21 August 2023
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100001778, Deakin University;
                Award ID: Postgraduate Research Scholarship
                Funded by: FundRef http://dx.doi.org/10.13039/501100000925, National Health and Medical Research Council;
                Award ID: 1174060
                Award ID: RM34909
                Categories
                Subject: Research
                Custom metadata
                issue-copyright-statement © BioMed Central Ltd., part of Springer Nature 2023

                ScienceOpen disciplines: Neurology
                Keywords: cognitive function,alzheimer’s disease,proteomic analysis,genotyping,risk factors
                Data availability:
                ScienceOpen disciplines: Neurology
                Keywords: cognitive function, alzheimer’s disease, proteomic analysis, genotyping, risk factors

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