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Proteomic analysis of cerebrospinal fluid of amyotrophic lateral sclerosis patients in the presence of autologous bone marrow derived mesenchymal stem cells

Abstract

Background

Amyotrophic lateral sclerosis (ALS) is a fatal and rapidly progressive motoneuron degenerative disorder. There are still no drugs capable of slowing disease evolution or improving life quality of ALS patients. Thus, autologous stem cell therapy has emerged as an alternative treatment regime to be investigated in clinical ALS.

Method

Using Proteomics and Protein-Protein Interaction Network analyses combined with bioinformatics, the possible cellular mechanisms and molecular targets related to mesenchymal stem cells (MSCs, 1 × 106 cells/kg, intrathecally in the lumbar region of the spine) were investigated in cerebrospinal fluid (CSF) of ALS patients who received intrathecal infusions of autologous bone marrow-derived MSCs thirty days after cell therapy. Data are available via ProteomeXchange with identifier PXD053129.

Results

Proteomics revealed 220 deregulated proteins in CSF of ALS subjects treated with MSCs compared to CSF collected from the same patients prior to MSCs infusion. Bioinformatics enriched analyses highlighted events of Extracellular matrix and Cell adhesion molecules as well as related key targets APOA1, APOE, APP, C4A, C5, FGA, FGB, FGG and PLG in the CSF of cell treated ALS subjects.

Conclusions

Extracellular matrix and cell adhesion molecules as well as their related highlighted components have emerged as key targets of autologous MSCs in CSF of ALS patients.

Trial registration

Clinicaltrial.gov identifier NCT0291768. Registered 28 September 2016.

Background

Mesenchymal stem cells (MSCs) have been tested clinically as a potential therapy for amyotrophic lateral sclerosis (ALS), a fatal motor neuron degenerative disease [1, 2]. Indeed, indications of MSCs-induced motor neuron protection experimentally [3, 4] as well as clinically [5,6,7,8,9,10] in ALS have been obtained.

Stem cell therapies for neurodegenerative disorders were proposed decades ago [11, 12]. Questions regarding their ability to promote clinically relevant outcomes have been raised since that time [13, 14]. These questions are based on the theory that functional neurons would be produced from these stem cells in vivo [15]. Indeed, despite the ability of adult stem cells to differentiate into neurons in vitro and in vivo under controlled experimental conditions [16], it was hard to accept that new neurons could regenerate long neuronal pathways or fully integrate in such morphologically and physiologically complex nervous tissue parenchyma leading towards functional restoration. That skepticism changed with the demonstration that MSCs could induce neuroprotection, reduce inflammation and contribute to functional neuronal repair in some neurodegenerative disorders [17, 18] via processes that may involve their paracrine ability to interact with the diseased milieu [19, 20]. Indeed, a wide range of complex surface receptors allow MSCs to detect and to react to specific local molecular signals [21, 22] and in response, may produce and secrete soluble bioactive molecules and extracellular vesicles [19, 23] with the potential to positively impact neurodegenerative processes. Remarkably, activated MCSs may modulate glial reaction, endothelial state, immune cell responses and endogenous stem cells, all able to interfere with neuroprotective events [16,17,18,19,20,21,22,23]. Additionally, these paracrine actions of MSCs on the pathophysiology of nervous tissue has highlighted the importance of the Extracellular matrix in local MSCs effects [24].

Studies have explored the regulation of Extracellular matrix proteins and Cell adhesion molecules in the search for specific molecular targets of cellular events related to neurodegeneration [25], neurodegenerative disorders [26, 27] and neuroprotection [26]. However, information on the specific molecular responses and related mechanisms of MSCs in combating neurodegeneration experimentally or clinically are still lacking [28, 29].

Therefore, this study used a large Proteomic analysis in combination with Protein interaction network and molecular modeling to obtain further indications on the cellular mechanisms and related molecular targets in the CSF of ALS subjects thirty days after intrathecal delivery of autologous bone marrow-derived MSCs.

MSCs benefits for the treatment of ALS were tested due to their potential ability to trigger motor neuron protective events [30, 31]. This study investigated whether such benefits might be mediated by MSCs paracrine mechanisms and was designed to identify specific molecules associated with these paracrine interactions [31, 32].

Methods

ALS subjects, MSCs infusion and CSF withdrawal

This study is a subproject of a Phase I/II Clinical Trial (www.clinicaltrials.gov; NCT02917681) that tested the safety and preliminary effects of intrathecal (subarachnoid space of lumbar vertebrae, L3-L5) autologous bone marrow-derived mesenchymal stem cells (MSCs) infusion (106 cells/kg− 1 body weight). The study was conducted (2016–2019) at the Neurology Division of the Clinics Hospital of the Medical School of the University of Sao Paulo, Brazil. The study was approved by the Ethics Committee of the Clinics Hospital of the University of Sao Paulo. Patients were clinically evaluated to inclusion/exclusion criteria and had their ALS diagnosis reconfirmed. Once included, ALS all subjects signed informed consent forms. Subjects were accompanied monthly for three and seven months, respectively, before and after cell infusion. After bone marrow aspiration of ALS subjects, MSCs were individually isolated and expanded at the Core for Cell Technology, Pontifical Catholic of University of Parana, Brazil, according to the previously described protocols [33]. A rigorous analysis of cell quality was performed in the amplified MSCs of ALS patients before injection, in order to obtain information of specific cell identity, cell viability and karyotype, based on the fact that laboratorial handling may impact the final cell set to be injected as it has been demonstrated in experimental animal research [34]. Only high quality MSCs were injected in the ALS subjects, according to well-stablished international criteria [33]. CSF (10 ml) was collected from the subarachnoid lumbar space of ALS subjects immediately before MSCs infusion and also 30 days later. The first 5 ml were delivered for standard clinical laboratory tests, including bacteriological and biochemical analyses, and the remaining 5 ml were used for molecular analysis in this study. CSF samples were centrifuged at 1,000 × g for 10 min at 4 ºC, aliquoted (1 ml) into polypropylene cryogenic tubes and stored at -80 °C until further analyses. All samples were processed within 30 min of collection.

Proteomics

Mass spectrometry-based proteomic analysis of CSF from ALS subjects

CSF (1 ml) of ALS subjects were filtered using ultracentrifugation devices with a molecular cut-off of 10 kDa. Proteins in the retentate were denatured in 8 M urea, reduced by addition of dithiothreitol (final concentration of 10 mM DTT), alkylated with iodoacetamide (final concentration of 40 mM) and digested with trypsin (1:50 enzyme to protein ratio). The reaction was stopped (1% trifluoroacetic acid), resulting peptides were purified (primed Oligo R3 reversed phase SPE micro-column) and dried [35].

Samples were then verified by a nLC-MS/MS analysis using an analytical platform, notably the nanoflow liquid chromatography with linear trap quadrupole (LTQ) Orbitrap mass spectrometers (see below). Peptides were separated by nano ultra-high performance liquid chromatography tandem mass spectrometry (nUHPLC LC-MS/MS) according to the previous description [36,37,38].

The nanoLC was connected online to a QExactive HF Hybrid Quadrupole-Orbitrap mass spectrometer (Thermo Fisher Scientific) operating in positive ion mode and using data-dependent acquisition [37, 38].

The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE [39,40,41] partner repository with the dataset identifier PXD053129.

Deregulated proteins using LTQ Orbitrap

Proteins that were identified to be deregulated in the CSF of ALS subjects 30 days after MSCs infusion in comparison to CSF of same subjects before cell delivery were selected and their proteotypic peptides mapped in the PeptideAtlas database. Selected m/z values were monitored across all gradients and their MS/MS spectra were recorded in order to perform a database search using MaxQuant software [42]. Specifically, http://www.mcponline.org/ downloaded from 17 engine Andromeda [43] was used to search for MS/MS spectra against a database composed from the Uniprot Human Protein Database [44] with a 4.5ppm tolerance level for MS, and 20ppm for MS/MS. Furthermore, ceruloplasmin and reelin proteins, were selected as internal controls. Proteins detected in seven out of eight samples with their peptides identified by at least 6 samples with the MS/MS spectra search were considered for further analyses [37, 38]. Finally, label free quantification normalized values were used. Bioinformatics and statistical details are described below.

Statistical analysis

All datasets were tested for normal distribution before applying parametric tests. Proteomic data were processed using Perseus computational platform v.1.6.14.0 (https://cox-labs.github.io/coxdocs/). Label Free Quantitation (LFQ) data were log2-transformed, protein reverse, contaminants and only by site were removed. Imputation was performed by replacing missing values from the normal distribution with a width of 0.3 and down shift of 1.8. Statistical analysis of LFQ data, employing the paired t-test and Benjamini-Hochberg correction, FDR < 0.05, p ≤ 0.05 (Graphpad Prism) identified deregulated proteins in CSF of ALS subjects, 30 days after MSCs infusion, compared to CSF of subjects before cell infusions [37, 38].

Protein-protein interaction network

Interactions among identified deregulated proteins in Network were evaluated using Cytoscape GeneMANIA plug-in (version 3.8.2), by highlighting “path” and “physical” interactions [45]. Subsequently, Network nodes were obtained using the centrality parameters “degree” and “betweenness” (Cytoscape CentiScaPe plug-in). Node degree is a measure of local structure in networks that determines the number of edges at each node, and betweenness is a global structure measure in networks that identify the number of shortest paths that pass through a specific node when directly or indirectly connecting pairs of nodes [46]. Furthermore, a set of top 15 proteins was created with the highest betweenness and degree values. After elimination of repetitions, a set of final high representative molecules in Network was created based on 220 deregulated proteins.

Bioinformatics

We next evaluated possible mechanisms of action, and their related molecular targets, of the deregulated proteins identified by our proteomic analysis above. The data were further analyzed for cellular/molecular functional enrichments by employing specific bioinformatics tools described below.

Functional enrichment analysis

Deregulated proteins identified by the proteomic study were analyzed by means of Database for Integrated Annotation, Visualization and Discovery (DAVID, https://david.ncifcrf.gov). This analysis identified pathways (KEGG - Kyoto Encyclopedia of Genes and Genomes) and Gene Ontology categories (Biological Process, Cellular component and Molecular Function) based on their specific set of deregulated proteins [47, 48], according to specific levels of significance for KEGG and Gene Ontology (0.00001 ≤ p ≥ 0.01, see legend of Table 2).

Table 1 Deregulated proteins in CSF of ALS subjects 30 days after MSC infusion

REVIGO

In order to further highlight cellular and molecular mechanisms among described enriched DAVID categories, REVIGO (http://revigo.irb.hr) was applied [49] to group such categories in Superclusters, based on distribution of the SimRel semantic similarity measure (default in REVIGO). REVIGO summarizes long Gene Ontology categories (Biological Process, Cellular Component and Molecular function) by reducing functional redundancies, and also visualizes the remaining Gene Ontology categories.

Highlighted deregulated proteins in CSF

Proteins of REVIGO Gene Ontology Superclusters were identified. Subsequently, intersections of sets of proteins of Superclusters identified common molecules by means of Venn Diagram (http://bioinformatics.psb.ugent.be/webtools/Venn/). Intersections among Superclusters were considered for Biological Process (up to 3), Cellular Component (up to 4), and Molecular Function (up to 2), to reach a maximal 27 molecules of each intersection. Thus, three Supercluster sets of overlapped proteins of Biological Process, Cellular Component, and Molecular Function were created. Subsequently, intersections of these 3 Supercluster sets and the Set of high representative molecules in the Network identified common molecules in the sets, considering their presence in at least 3 sets as well as a presence of a minimal of 1 molecule from each Network set. Following these criteria, highlighted molecules with 100% representation (present in the 4 sets) and 75% representation (present in 3 sets) were identified. Finally, the nine final molecules with the greatest intersections were considered as potentially prominent molecular targets and related molecular/cellular mechanisms in the CSF associated with the presence of MSCs in ALS subjects.

Extracellular matrix and cell adhesion molecules MeSH

Based on the fact that biological/molecular aspects of “Extracellular Matrix” and “Cell Adhesion Molecules” have been well described in biological events related to MSCs function in injured tissues, the Medical Subject Headings (MeSH) “Extracellular Matrix” and “Cell Adhesion Molecules” were used to point out their related categories among all described KEGG, Biological Process, Cellular Component, and Molecular Function categories, whose terms indicated similarity to above MeSH terms. Proteins of those “Matrix extracellular” and “Cell Adhesion Molecules” MeSH-related categories were indicated (symbols) in the list of 220 proteomic identified deregulated proteins (* for “Extracellular Matrix” and # for “Cell Adhesion Molecules”; see results). Subsequently, the number of molecules belonging to those categories were defined and corresponded percentages of total number of deregulated proteins were calculated.

Results

Demographic information of ALS subjects

Demographic Information of 24 ALS subjects included in the study are summarized in Table S1. Subjects were Caucasian (14 males and 10 females), who showed clinical history of spinal (n = 19) and bulbar (n = 5) disease onset. The averages of patient age at the time of disease onset and of disease evolution until the first CSF collection were 52.13 years and 16.88 months, respectively.

Deregulated proteins in CSF of ALS subjects

Mass spectrometry-based proteomics identified two hundred-twenty deregulated proteins [n = 86 (fold > 1.0) upregulated and n = 134 (fold < 1.0) downregulated] in the CSF of ALS subjects 30 days after MSCs intrathecal infusion compared to CSF of subjects collected before cells (Table 1). Deregulated proteins were statistically significant with a q-value of less than 0.1 (Table 1).

Table 2 KEGG pathways and Gene Ontology categories

Functional enrichment analysis

Cellular and molecular events possibly related to MSCs therapy demonstrated by KEGG pathways and Gene Ontology categories are shown in Table 2. Respective number of molecules and p-value are also seen in Table 2. Furthermore, proteins related KEGG and Gene Ontology events are indicated in Table S2. Additionally, Superclusters, as well as their respective protein number, that were formed by REVIGO from Gene Ontology categories of Biological Process, Cell Component and Molecular Function are shown in Table 3. Figure S1 illustrates an image of a REVIGO Biological Processes cluster. Importantly, the overlapped proteins among specific Gene Ontology Superclusters, according to the proposed method, are seen in Table 3, thus highlighting important proteins of the DAVID enriched analysis among those proteomics-indicated 220 deregulated molecules.

Table 3 Highly representative molecules in superclusters

Protein-protein interaction network

Two sets of top 15 protein hubs in the Protein-protein Interaction Network that were ranked according to values of their betweenness and degree nodes in the network, are seen in Table 4. Including elimination of repetitions, the resulting set of twenty-four Network relevant proteins is shown in the legend of Table 4. The Network is illustrated in Figure S2 of supplementary material.

Table 4 Hubs of protein interaction network

Molecular representation of “Extracellular Matrix” and “Cell Adhesion Molecules” MESHs

Two KEGG pathways (K5, K7) and eight Gene Ontology categories (PB1, PB7, PB18, CC1, CC2, CC3, CC6, MF6) that are related to “Extracellular Matrix” or “Cell Adhesion Molecules” MeSHs were described in Table 5. The majority of proteomics-indicated deregulated proteins were encountered in the above described “Extracellular Cellular Matrix” and “Cell Adhesion Molecules” -related pathways/categories (201 molecules, representing 92% of total). Specifically, 186 (84% of total) and 49 (22% of total) molecules corresponded to pathways/categories that are related to “Extracellular Cellular Matrix” and “Cell Adhesion Molecules” MESHs, respectively (Table 5).

Table 5 DAVID representation of “Extracellular Matrix” and “Cell adhesion Molecules” MeSH

Highlighted molecules related to MSCs infusion

Highlighted Molecules with a high presence (100% or 75%, according to defined criteria described in methods) were seen in Table 6. See details also in Table 6 legend. APOA1, APOE, APP, and PLG reached 100% representation. C4A, C5, FGA, FGB, FGG) reached 75% representation (Table 6). Specifically, APOA1, C4A, C5, FGA, FGB, FGG and PLG are upregulated and APOE and APP are downregulated, as indicated by our proteomic analysis. (Table 2). All Highlighted Molecules were verified to belong to pathways/categories related to MESH “Extracellular Matrix” and “Cell Adhesion Molecules” (Table 6).

Table 6 Highlighted molecules

Discussion

MSCs as a promising approach for effective drug discovery in clinical ALS

MSCs have emerged as a promising therapy for the treatment of human ALS [10]. Indeed, recent clinical trials have revealed positive effects of MSCs for the treatment of some neurodegenerative diseases [50, 51], including ALS [7, 8, 52, 53]. However, information on the putative cellular/molecular mechanisms underlying MSCs-induced neuroprotection [18, 20, 54], and thus counteracting motor neuron death in ALS [3, 55] is lacking. Interestingly, the identification of molecules involved in mediating the effects of MSCs on neurons has increased experimentally [17, 20] but our clinical understanding of such phenomena has not kept pace [54, 56]. Given the failure to translate therapeutic targets for ALS from bench to bed side [57, 58], there is a need to explore, in detail, the cellular mechanisms and corresponding molecules related to the use of MSCs in the treatment of ALS. Based on the absence of a reliable, long-lasting cell therapy for chronic neurodegenerative disorders, MSCs infusion in ALS patients may lead to the discovery of critical molecular targets. These could, in turn, contribute to the development of an effective pharmacological therapy to replace cell therapy in ALS and potentially, other neurodegenerative diseases.

Advantage of large proteomic analysis of CSF from MSCs treated ALS patients over sampled immune assays

The present study was the first to combine large omics analyses, specifically Proteomics and Protein Interaction Network, with well-defined criteria for molecular modeling in order to identify novel cellular mechanisms and their related molecules in the CSF of ALS subjects 30 days after intrathecal infusion of autologous bone marrow-derived MSCs. Detailed methods and the set of proteomically identified deregulated proteins described herein have also been shown in preprint form [59] according to BMC’s publication policy. Importantly, our results are in agreement with those of previous reports that investigated the molecular responses in CSF after local delivery of MSCs in ALS patients by applying different methodologies [2, 6062]. Moreover, while previous investigations have identified the molecular responses to MSCs in blood serum in clinical ALS [63, 64] and also in striatal muscles in experimental ALS [65, 66], the CSF is considered a more physiologically and clinically relevant body compartment for molecular investigation. This is due to the CSF’s anatomical proximity to diseased neurons as well as for its containment of bio molecular signatures of altered biochemical processes related to central nervous system pathophysiology [67, 68].

MSCs performed in this trial may facilitate cell signals reaching neurodegeneration zones in ALS subjects, as discussed elsewhere [3, 17, 61]. This is in contrast to previous clinical designs that analyzed molecular responses to MSCs after intra-muscular delivery [30, 69]. It should be mentioned that this study has employed an endogenous control (CSF before MSC infusion) to evaluate molecular changes in the CSF after cell therapy, instead of CSF from healthy subjects, based on the well-known difficulty to eliminate the influence of the specific clinical situation that led a CSF withdrawn on the molecular signaling in central nervous system [70].

Furthermore, this study is the first to employ Proteomics by means of mass spectrometry to determine the molecular profile of CSF after intrathecal autologous MSCs treatment of ALS subjects. Recently, a similar methodology has been employed in a biomarker discovery program in CSF of ALS patients [7173]. Previously, the molecular regulation within CSF of MSCs-treated ALS patients has been performed using classical, non-proteomic methodology [2, 62]. As with our study, the majority of ALS clinical trials on MSC-delivered to CSF have employed autologous bone marrow-derived MSCs [2, 6, 60, 74, 75], rather than stem cells derived from adipose tissue, umbilical cord or other sources that are mainly employed in experimental investigations [61]. The advantages of bone marrow-derived MSCs for clinical application, especially in neurodegenerative disorders, have been well described, particularly their ability to interact in an autocrine/paracrine manner with injured tissue [17,18,19,20, 76]. Indeed, it has been proposed that the paracrine molecular crosstalk between MSCs and nervous tissue cells might interfere with inflammatory events at the wound site, with the potential to modify the progression of neurodegeneration [77, 78]. Indeed, MSCs paracrine signaling might be important in counteracting neuronal cell death in progressive neurodegenerative disorders like ALS [77, 78]. Thus, it is clear that there are distinct advantages of employing large omics analyses (like proteomics), over sampled immune assays of predefined proteins, in the search for effective and reliably translatable therapeutic targets in the CSF of ALS patients. The power of large proteomics may be amplified with the combination of specific criteria for molecular modeling in the search for key molecules among all deregulated proteins.

Our proteomic analysis has identified 220 deregulated proteins in the CSF of ALS subjects 30 days after autologous bone marrow-derived MSCs intrathecal delivery. These data provide an extensive set of molecular responses to the presence of MSCs in ALS subjects. In addition, our results provide a much more extensive database than the set of deregulated molecules described by similar clinical trials on ALS that have not applied omics technology in the screening of molecular biomarkers [2, 60]. Among those deregulated proteins identified in our study, upregulated and downregulated molecules might contribute to our understanding of the mechanisms related to the efficacy of MSCs therapy for ALS. Moreover, our results may identify important biomarkers of MSCs effects on the progression of ALS in future investigations.

Enrichment analysis described key mechanisms and targets related to cell therapy

The present study has contributed useful data to the original descriptions of the cellular mechanisms and related molecular targets associated with intrathecal infusion of MSCs in ALS patients by employing enrichment analysis of deregulated molecules [48, 49]. REVIGO analysis of deregulated proteins has identified a set of clusters and superclusters of cellular/molecular mechanisms possibly related to MSCs actions 30 days after intrathecal MSCs delivery in ALS patients. Interestingly, Extracellular matrix and Cell adhesion terms were highlighted among these superclusters, thus highlighting the usefulness of this powerful methodology. In fact, despite the development of REVIGO clusterization analyses 13 years ago [49], our study is the first to employ this approach to identify mechanisms related to MSCs effects in ALS patients. Moreover, the literature analysis of “Extracellular matrix” and “Cell adhesion molecules” MeSHs indicated a significant involvement of such factors in the context of ALS as well as MSCs. Our study determined that 92% of the 220 deregulated molecules identified by proteomic analysis belonged to Pathways/Categories related to “Extracellular matrix” and/or “Cell adhesion molecules” MeSHs. Our data thus clearly implicate Extracellular matrix and Cell adhesion molecules as factors associated with the putative paracrine signaling of MSCs delivered to the CSF of ALS subjects.

We are still unable to address whether the Extracellular matrix/Cell adhesion molecules (or some interaction of both) identified in this study are associated with the putative MSCs effects (after intrathecal delivery in ALS patients) on ongoing motor neuron degeneration. This question remains the subject of further investigation. Extracellular matrix and Cell adhesion molecules have been previously described in the context of ALS motor neuron degeneration [7982] as well as with MSCs mechanisms of action [10, 55, 83]. Indeed, Extracellular Matrix and Cell adhesion molecules have been frequently associated with the mechanisms underlying both autocrine [84] and paracrine [85] cellular effects correlated with MSCs actions [54] and motor neuron degeneration/protection [51, 86]. These observations suggest the possibility that an integrated mechanism involving Extracellular matrix and Cell adhesion molecules might underlie the effects of MSCs in the injury sites of ALS. This interaction, in turn, could possibly interfere with motor neuron degeneration in this disorder. In sum, this study has highlighted, for the first time, the importance of the Extracellular matrix and Cell adhesion molecules as contributors to the neuroprotective effects of MSCs in ALS.

The identification of factors such as APOA1, APOE, APP, PLG, C4A, C5, FGA, FGB and FGG as possible key proteins in the CSF from ALS subjects treated with MSCs is an additional important contribution of this study. Moreover, we must highlight the critical contribution of the Protein Interaction Network analysis [87, 88] in establishing criteria for the identification and establishment of possible interactions between these identified molecules. Importantly, it is the first time APOA1, APOE, APP, C4A, C5, FGA, FGB, FGG and PLG have been mentioned in the context of biomarkers in CSF of ALS subjects 30 days after intrathecal MSCs cell delivery.

Indeed, all nine highlighted molecules are associated with elements of the Extracellular matrix and Cell adhesion [8993] and have also been demonstrated to be associated with mechanisms of action of MSCs [94,95,96,97]. Thus, the Extracellular matrix and Cell adhesion molecules represent important factors associated with the paracrine actions of these stem cells. These findings are a major and original contribution of this study. Additionally, they will lead to further studies addressing the precise roles of Extracellular Matrix and Cell Adhesion molecules in paracrine signaling associated with the neurotrophic effects of MSCs, with the ultimate goal of providing clinical benefits for ALS patients [53].

Furthermore, some of our highlighted molecules have been described in the context of neuronal degeneration/survival or with neurodegenerative disorders, as is the case for C4A, FGB, FGG and PLG [98,99,100,101,102]. Unfortunately little information is presently available on their involvement in these processes in ALS. Despite the fact that the majority of these key proteins (APOA1, APOE, APP, C4A, C5, FGA, FGG and PLG) have been investigated in the context of ALS [103,104,105,106,107,108,109], there is a lack of information on their roles associated with specific cellular/molecular mechanisms of motoneuron death in this disease. More specifically, despite the identification of APOE as a biomarker for the rare, genetic forms of ALS associated with dementia [53, 110], the role of APOE in neuronal death in the more prevalent sporadic form of ALS is still lacking. Nevertheless, APOE polymorphisms have been correlated with lysosomal dysfunction-induced neuronal death in ALS [110]. Also, activation of microglial APOE signaling has been associated with motoneuron cell death in ALS [111] and APOE signaling over nuclear TDP-43 seems to trigger motor neuron death in this disorder [112]. Importantly these detrimental effects of APOE on motoneuron survival in ALS correlate with a possible protective action of MSCs delivered to ALS subjects in our clinical trial, as a downregulation of APOE was seen in the CSF of ALS patients treated with MSCs.

Interestingly, ablation of the APP gene in the murine model of ALS was able to counteract disease severity [113]. Furthermore, APP accumulation in the endoplasmic reticulum of motoneurons was correlated with cytoskeleton disruption, neurite retraction, accumulation of toxic molecules, i.e. TDP-43, SOD1 FUS, and motoneuron death in ALS [114]. In fact, misfolded APP toxicity might spread among motoneurons thus amplifying cell death in ALS [115]. These descriptions of APP toxicity in ALS are interesting, given our finding that APP is downregulated in the CSF of MSCs treated ALS subjects. This downregulation may thus be related to a protective effect of MSCs on motoneurons, thus representing another significant contribution of the present study.

Furthermore, the upregulation of complement 5 (C5) in the CSF of ALS subjects 30 days after MSCs infusion may be an indication of ongoing neurodegenerative events rather than a response to cell infusion. This conclusion is in view of the recent findings on the promising effects of the monoclonal antibody C5 complement inhibitor ravulizumab in a clinical ALS trial [116]. Interestingly, Mantovani et al. 2014 [106] have described increases of C4 and C5 in the blood of ALS patients while our proteomic analysis has shown upregulation of C4, C5 and several other molecules of complement system in the CSF of MSC-treated ALS subjects. Based on these observations, we speculate that a combination of monoclonal antibodies against several complement system molecules might amplify neurological benefits achieved with the C5 complement inhibitor ravulizumab [116].

The identification of the molecules APOA1, FGA, FGB, FGG and PLG represent an original contribution to our understanding of the molecular signaling associated with the Extracellular matrix and Cell adhesion underlying the putative protective effects of MSCs treatment of ALS. This represents another direction for further analysis.

Finally, additional criteria can be applied in future studies for molecular modeling among our 220 deregulated proteins. These studies could identify new key mechanisms and associated molecules related to MSCs treatment in clinical ALS. Thus, our present proteomic analysis provides an initial framework for further study and establishes the importance of a set of 220 deregulated proteins in the CSF of ALS patients 30 days after MSCs delivery.

Conclusion

A complex proteomics and network analysis revealed 220 deregulated proteins in the CSF of ALS subjects 30 days after intrathecal autologous MSCs infusion. Extracellular matrix and Cell adhesion molecules were identified as potential therapeutic targets related to stem cell therapy for the ongoing neurodegenerative events in ALS chronic progressive disorder. APOA1, APOE, APP, PLG, C4A, C5, FGA, FGB and FGG were identified as key molecules associated with those events.

Data availability

Raw data are available via ProteomeXchange with identifier PXD053129. Additional data used and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Goutman SA, Hardiman O, Al-Chalabi A, Chio A, Savelieff MG, Kiernan MC, et al. Recent advances in the diagnosis and prognosis of amyotrophic lateral sclerosis. Lancet Neurol. 2022;21(5):480–93. https://doi.org/10.1016/S1474-4422(21)00465-8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Oh KW, Noh MY, Kwon MS, Kim HY, Oh SI, Park J, et al. Repeated Intrathecal Mesenchymal Stem cells for amyotrophic lateral sclerosis. Ann Neurol. 2018;84(3):361–73. https://doi.org/10.1002/ana.25302.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Gugliandolo A, Bramanti P, Mazzon E. Mesenchymal stem cells: a potential Therapeutic Approach for Amyotrophic lateral sclerosis? Stem Cells Int. 2019;2019:3675627. https://doi.org/10.1155/2019/3675627.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Sironi F, De Marchi F, Mazzini L, Bendotti C. Cell therapy in ALS: an update on preclinical and clinical studies. Brain Res Bull. 2023;194:64–81. https://doi.org/10.1016/j.brainresbull.2023.01.008.

    Article  CAS  PubMed  Google Scholar 

  5. Boucherie C, Schafer S, Lavand’homme P, Maloteaux JM, Hermans E. Chimerization of astroglial population in the lumbar spinal cord after mesenchymal stem cell transplantation prolongs survival in a rat model of amyotrophic lateral sclerosis. J Neurosci Res. 2009;87(9):2034–46. https://doi.org/10.1002/jnr.22038.

    Article  CAS  PubMed  Google Scholar 

  6. Oh KW, Moon C, Kim HY, Oh SI, Park J, Lee JH, et al. Phase I trial of repeated intrathecal autologous bone marrow-derived mesenchymal stromal cells in amyotrophic lateral sclerosis. Stem Cells Transl Med. 2015;4(6):590–7. https://doi.org/10.5966/sctm.2014-0212.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Petrou P, Gothelf Y, Argov Z, Gotkine M, Levy YS, Kassis I, et al. Safety and Clinical effects of mesenchymal stem cells secreting neurotrophic factor transplantation in patients with amyotrophic lateral sclerosis: results of phase 1/2 and 2a clinical trials. JAMA Neurol. 2016;73(3):337–44. https://doi.org/10.1001/jamaneurol.2015.4321.

    Article  PubMed  Google Scholar 

  8. Petrou P, Kassis I, Yaghmour NE, Ginzberg A, Karussis D. A phase II clinical trial with repeated intrathecal injections of autologous mesenchymal stem cells in patients with amyotrophic lateral sclerosis. Front Biosci (Landmark Ed). 2021;26(10):693–706. https://doi.org/10.52586/4980.

    Article  CAS  PubMed  Google Scholar 

  9. Siwek T, Jezierska-Wozniak K, Maksymowicz S, Barczewska M, Sowa M, Badowska W, et al. Repeat administration of bone marrow-derived mesenchymal stem cells for treatment of amyotrophic lateral sclerosis. Med Sci Monit. 2020;26:e927484. https://doi.org/10.12659/MSM.927484.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Sykova E, Cizkova D, Kubinova S. Mesenchymal stem cells in treatment of spinal cord Injury and Amyotrophic lateral sclerosis. Front Cell Dev Biol. 2021;9:695900. https://doi.org/10.3389/fcell.2021.695900.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Lindvall O, Kokaia Z, Martinez-Serrano A. Stem cell therapy for human neurodegenerative disorders-how to make it work. Nat Med. 2004;10. https://doi.org/10.1038/nm1064. Suppl:S42-50.

  12. Kim IK, Park JH, Kim B, Hwang KC, Song BW. Recent advances in stem cell therapy for neurodegenerative disease: three dimensional tracing and its emerging use. World J Stem Cells. 2021;13(9):1215–30. https://doi.org/10.4252/wjsc.v13.i9.1215.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Volarevic V, Markovic BS, Gazdic M, Volarevic A, Jovicic N, Arsenijevic N, et al. Ethical and Safety issues of Stem Cell-based therapy. Int J Med Sci. 2018;15(1):36–45. https://doi.org/10.7150/ijms.21666.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Hoang DM, Pham PT, Bach TQ, Ngo ATL, Nguyen QT, Phan TTK, et al. Stem cell-based therapy for human diseases. Signal Transduct Target Ther. 2022;7(1):272. https://doi.org/10.1038/s41392-022-01134-4.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Cendelin J, Mitoma H. Neurotransplantation therapy. Handb Clin Neurol. 2018;155:379–91. https://doi.org/10.1016/B978-0-444-64189-2. 00025 – 1.

    Article  PubMed  Google Scholar 

  16. Bueno C, Martinez-Morga M, Garcia-Bernal D, Moraleda JM, Martinez S. Differentiation of human adult-derived stem cells towards a neural lineage involves a dedifferentiation event prior to differentiation to neural phenotypes. Sci Rep. 2021;11(1):12034. https://doi.org/10.1038/s41598-021-91566-9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Andrzejewska A, Dabrowska S, Lukomska B, Janowski M. Mesenchymal stem cells for neurological disorders. Adv Sci (Weinh). 2021;8(7):2002944. https://doi.org/10.1002/advs.202002944.

    Article  CAS  PubMed  Google Scholar 

  18. Nair S, Rocha-Ferreira E, Fleiss B, Nijboer CH, Gressens P, Mallard C, et al. Neuroprotection offered by mesenchymal stem cells in perinatal brain injury: role of mitochondria, inflammation, and reactive oxygen species. J Neurochem. 2021;158(1):59–73. https://doi.org/10.1111/jnc.15267.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Han Y, Yang J, Fang J, Zhou Y, Candi E, Wang J, et al. The secretion profile of mesenchymal stem cells and potential applications in treating human diseases. Signal Transduct Target Ther. 2022;7(1):92. https://doi.org/10.1038/s41392-022-00932-0.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Yari H, Mikhailova MV, Mardasi M, Jafarzadehgharehziaaddin M, Shahrokh S, Thangavelu L, et al. Emerging role of mesenchymal stromal cells (MSCs)-derived exosome in neurodegeneration-associated conditions: a groundbreaking cell-free approach. Stem Cell Res Ther. 2022;13(1):423. https://doi.org/10.1186/s13287-022-03122-5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Gomez-Salazar M, Gonzalez-Galofre ZN, Casamitjana J, Crisan M, James AW, Peault B. Five decades later, are mesenchymal stem cells still relevant? Front Bioeng Biotechnol. 2020;8:148. https://doi.org/10.3389/fbioe.2020.00148.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Markov A, Thangavelu L, Aravindhan S, Zekiy AO, Jarahian M, Chartrand MS, et al. Mesenchymal stem/stromal cells as a valuable source for the treatment of immune-mediated disorders. Stem Cell Res Ther. 2021;12(1):192. https://doi.org/10.1186/s13287-021-02265-1.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Liang X, Ding Y, Zhang Y, Tse HF, Lian Q. Paracrine mechanisms of mesenchymal stem cell-based therapy: current status and perspectives. Cell Transpl. 2014;23(9):1045–59. https://doi.org/10.3727/096368913X667709.

    Article  Google Scholar 

  24. Novoseletskaya E, Grigorieva O, Nimiritsky P, Basalova N, Eremichev R, Milovskaya I, et al. Mesenchymal stromal cell-produced components of Extracellular Matrix Potentiate Multipotent Stem Cell response to differentiation stimuli. Front Cell Dev Biol. 2020;8:555378. https://doi.org/10.3389/fcell.2020.555378.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Doroszkiewicz J, Groblewska M, Mroczko B. Molecular biomarkers and their implications for the early diagnosis of selected neurodegenerative diseases. Int J Mol Sci. 2022;23(9). https://doi.org/10.3390/ijms23094610.

  26. Hernandez SJ, Fote G, Reyes-Ortiz AM, Steffan JS, Thompson LM. Cooperation of cell adhesion and autophagy in the brain: functional roles in development and neurodegenerative disease. Matrix Biol Plus. 2021;12:100089. https://doi.org/10.1016/j.mbplus.2021.100089.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Soles A, Selimovic A, Sbrocco K, Ghannoum F, Hamel K, Moncada EL, et al. Extracellular Matrix Regulation in Physiology and in Brain Disease. Int J Mol Sci. 2023;24(8). https://doi.org/10.3390/ijms24087049.

  28. Bonneh-Barkay D, Wiley CA. Brain extracellular matrix in neurodegeneration. Brain Pathol. 2009;19(4):573–85. https://doi.org/10.1111/j.1750-3639.2008.00195.x.

    Article  CAS  PubMed  Google Scholar 

  29. Pang X, He X, Qiu Z, Zhang H, Xie R, Liu Z, et al. Targeting integrin pathways: mechanisms and advances in therapy. Signal Transduct Target Ther. 2023;8(1):1. https://doi.org/10.1038/s41392-022-01259-6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Lewis CM, Suzuki M. Therapeutic applications of mesenchymal stem cells for amyotrophic lateral sclerosis. Stem Cell Res Ther. 2014;5(2):32. https://doi.org/10.1186/scrt421.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Lin TJ, Cheng GC, Wu LY, Lai WY, Ling TY, Kuo YC, et al. Potential of Cellular Therapy for ALS: current strategies and future prospects. Front Cell Dev Biol. 2022;10:851613. https://doi.org/10.3389/fcell.2022.851613.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Tang BL. The use of mesenchymal stem cells (MSCs) for amyotrophic lateral sclerosis (ALS) therapy - a perspective on cell biological mechanisms. Rev Neurosci. 2017;28(7):725–38. https://doi.org/10.1515/revneuro-2017-0018.

    Article  PubMed  Google Scholar 

  33. de Oliveira HG, Cruz FF, Antunes MA, de Macedo Neto AV, Oliveira GA, Svartman FM, et al. Combined bone marrow-derived mesenchymal stromal cell therapy and one-way endobronchial valve Placement in patients with Pulmonary Emphysema: A Phase I Clinical Trial. Stem Cells Transl Med. 2017;6(3):962–9. https://doi.org/10.1002/sctm.16-0315.

    Article  PubMed  Google Scholar 

  34. Dar ER, Gugjoo MB, Javaid M, Hussain S, Fazili MR, Dhama K, et al. Adipose tissue- and bone marrow-derived mesenchymal stem cells from Sheep: culture characteristics. Anim (Basel). 2021;11(8). https://doi.org/10.3390/ani11082153.

  35. Palmisano G, Lendal SE, Engholm-Keller K, Leth-Larsen R, Parker BL, Larsen MR. Selective enrichment of sialic acid-containing glycopeptides using titanium dioxide chromatography with analysis by HILIC and mass spectrometry. Nat Protoc. 2010;5(12):1974–82. https://doi.org/10.1038/nprot.2010.167.

    Article  CAS  PubMed  Google Scholar 

  36. Carneiro A, Macedo-da-Silva J, Santiago VF, de Oliveira GS, Guimaraes T, Mendonca CF, et al. Urine proteomics as a non-invasive approach to monitor exertional rhabdomyolysis during military training. J Proteom. 2022;258:104498. https://doi.org/10.1016/j.jprot.2022.104498.

    Article  CAS  Google Scholar 

  37. Kawahara R, Rosa-Fernandes L, Dos Santos AF, Bandeira CL, Dombrowski JG, Souza RM, et al. Integrated Proteomics Reveals Apoptosis-Related Mechanisms Associated with placental malaria. Mol Cell Proteom. 2019;18(2):182–99. https://doi.org/10.1074/mcp.RA118.000907.

    Article  CAS  Google Scholar 

  38. Rosa-Fernandes L, Cugola FR, Russo FB, Kawahara R, de Melo Freire CC, Leite PEC, et al. Zika Virus impairs neurogenesis and synaptogenesis pathways in human neural stem cells and neurons. Front Cell Neurosci. 2019;13:64. https://doi.org/10.3389/fncel.2019.00064.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Deutsch EW, Bandeira N, Perez-Riverol Y, Sharma V, Carver JJ, Mendoza L, et al. The ProteomeXchange consortium at 10 years: 2023 update. Nucleic Acids Res. 2023;51(D1):D1539–48. https://doi.org/10.1093/nar/gkac1040.

    Article  PubMed  Google Scholar 

  40. Perez-Riverol Y, Bai J, Bandla C, Garcia-Seisdedos D, Hewapathirana S, Kamatchinathan S, et al. The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences. Nucleic Acids Res. 2022;50(D1):D543–52. https://doi.org/10.1093/nar/gkab1038.

    Article  CAS  PubMed  Google Scholar 

  41. Perez-Riverol Y, Xu QW, Wang R, Uszkoreit J, Griss J, Sanchez A, et al. Mol Cell Proteom. 2016;15(1):305–17. https://doi.org/10.1074/mcp.O115.050229. PRIDE Inspector Toolsuite: Moving Toward a Universal Visualization Tool for Proteomics Data Standard Formats and Quality Assessment of ProteomeXchange Datasets.

  42. Cox J, Mann M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat Biotechnol. 2008;26(12):1367–72. https://doi.org/10.1038/nbt.1511.

    Article  CAS  PubMed  Google Scholar 

  43. Cox J, Neuhauser N, Michalski A, Scheltema RA, Olsen JV, Mann M. Andromeda: a peptide search engine integrated into the MaxQuant environment. J Proteome Res. 2011;10(4):1794–805. https://doi.org/10.1021/pr101065j.

    Article  CAS  PubMed  Google Scholar 

  44. UniProt C. UniProt: the Universal protein knowledgebase in 2023. Nucleic Acids Res. 2023;51(D1):D523–31. https://doi.org/10.1093/nar/gkac1052.

    Article  CAS  Google Scholar 

  45. Salavaty A, Ramialison M, Currie P. Integrated Value of Influence: an integrative method for the identification of the most influential nodes within networks. Patterns (N Y). 2020;1(5):100052. https://doi.org/10.1016/j.patter.2020.100052.

    Article  PubMed  Google Scholar 

  46. Scardoni G, Petterlini M, Laudanna C. Analyzing biological network parameters with CentiScaPe. Bioinformatics. 2009;25(21):2857–9. https://doi.org/10.1093/bioinformatics/btp517.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Huang da W, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009;4(1):44–57. https://doi.org/10.1038/nprot.2008.211.

    Article  CAS  PubMed  Google Scholar 

  48. Sherman BT, Hao M, Qiu J, Jiao X, Baseler MW, Lane HC, et al. DAVID: a web server for functional enrichment analysis and functional annotation of gene lists (2021 update). Nucleic Acids Res. 2022;50(W1):W216–21. https://doi.org/10.1093/nar/gkac194.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Supek F, Bosnjak M, Skunca N, Smuc T. REVIGO summarizes and visualizes long lists of gene ontology terms. PLoS ONE. 2011;6(7):e21800. https://doi.org/10.1371/journal.pone.0021800.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Pittenger MF, Discher DE, Peault BM, Phinney DG, Hare JM, Caplan AI. Mesenchymal stem cell perspective: cell biology to clinical progress. NPJ Regen Med. 2019;4:22. https://doi.org/10.1038/s41536-019-0083-6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Staff NP, Jones DT, Singer W. Mesenchymal stromal cell therapies for neurodegenerative diseases. Mayo Clin Proc. 2019;94(5):892–905. https://doi.org/10.1016/j.mayocp.2019.01.001.

    Article  PubMed  Google Scholar 

  52. Abati E, Bresolin N, Comi G, Corti S. Advances, challenges, and perspectives in translational stem cell therapy for amyotrophic lateral sclerosis. Mol Neurobiol. 2019;56(10):6703–15. https://doi.org/10.1007/s12035-019-1554-x.

    Article  CAS  PubMed  Google Scholar 

  53. Cudkowicz ME, Lindborg SR, Goyal NA, Miller RG, Burford MJ, Berry JD, et al. A randomized placebo-controlled phase 3 study of mesenchymal stem cells induced to secrete high levels of neurotrophic factors in amyotrophic lateral sclerosis. Muscle Nerve. 2022;65(3):291–302. https://doi.org/10.1002/mus.27472.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Fan XL, Zhang Y, Li X, Fu QL. Mechanisms underlying the protective effects of mesenchymal stem cell-based therapy. Cell Mol Life Sci. 2020;77(14):2771–94. https://doi.org/10.1007/s00018-020-03454-6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Najafi S, Najafi P, Kaffash Farkhad N, Hosseini Torshizi G, Assaran Darban R, Boroumand AR, et al. Mesenchymal stem cell therapy in amyotrophic lateral sclerosis (ALS) patients: a comprehensive review of disease information and future perspectives. Iran J Basic Med Sci. 2023;26(8):872–81. https://doi.org/10.22038/IJBMS.2023.66364.14572.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Song N, Scholtemeijer M, Shah K. Mesenchymal stem cell immunomodulation: mechanisms and therapeutic potential. Trends Pharmacol Sci. 2020;41(9):653–64. https://doi.org/10.1016/j.tips.2020.06.009.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Schroder S, Litscher G, Pan W, Editorial. Translational study for amyotrophic lateral sclerosis treatment. Front Neurol. 2022;13:1105360. https://doi.org/10.3389/fneur.2022.1105360.

    Article  PubMed  Google Scholar 

  58. Xu X, Shen D, Gao Y, Zhou Q, Ni Y, Meng H, et al. A perspective on therapies for amyotrophic lateral sclerosis: can disease progression be curbed? Transl Neurodegener. 2021;10(1):29. https://doi.org/10.1186/s40035-021-00250-5.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Reis ALG, Maximino JR, Lage Covas LAP, Pereira J, Brofman PR, Senegaglia AC, et al. Proteomic analysis of cerebrospinal fluid of amyotrophic lateral sclerosis patients in the presence of autologous bone marrow derived mesenchymal stem cells. Preprint. 2023. https://doi.org/10.21203/rs.3.rs-3665197/v1.

    Article  Google Scholar 

  60. Berry JD, Cudkowicz ME, Windebank AJ, Staff NP, Owegi M, Nicholson K, et al. NurOwn, phase 2, randomized, clinical trial in patients with ALS: Safety, clinical, and biomarker results. Neurology. 2019;93(24):e2294–305. https://doi.org/10.1212/WNL.0000000000008620.

    Article  PubMed  PubMed Central  Google Scholar 

  61. Krull AA, Setter DO, Gendron TF, Hrstka SCL, Polzin MJ, Hart J, et al. Alterations of mesenchymal stromal cells in cerebrospinal fluid: insights from transcriptomics and an ALS clinical trial. Stem Cell Res Ther. 2021;12(1):187. https://doi.org/10.1186/s13287-021-02241-9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Sobus A, Baumert B, Litwinska Z, Golab-Janowska M, Stepniewski J, Kotowski M, et al. Safety and feasibility of Lin- cells administration to ALS patients: a novel view on humoral factors and miRNA profiles. Int J Mol Sci. 2018;19(5). https://doi.org/10.3390/ijms19051312.

  63. Chio A, Mora G, La Bella V, Caponnetto C, Mancardi G, Sabatelli M, et al. Repeated courses of granulocyte colony-stimulating factor in amyotrophic lateral sclerosis: clinical and biological results from a prospective multicenter study. Muscle Nerve. 2011;43(2):189–95. https://doi.org/10.1002/mus.21851.

    Article  CAS  PubMed  Google Scholar 

  64. Tarella C, Rutella S, Gualandi F, Melazzini M, Scime R, Petrini M, et al. Consistent bone marrow-derived cell mobilization following repeated short courses of granulocyte-colony-stimulating factor in patients with amyotrophic lateral sclerosis: results from a multicenter prospective trial. Cytotherapy. 2010;12(1):50–9. https://doi.org/10.3109/14653240903300682.

    Article  CAS  PubMed  Google Scholar 

  65. Kook MG, Lee S, Shin N, Kong D, Kim DH, Kim MS, et al. Repeated intramuscular transplantations of hUCB-MSCs improves motor function and survival in the SOD1 G(93)a mice through activation of AMPK. Sci Rep. 2020;10(1):1572. https://doi.org/10.1038/s41598-020-58221-1.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Suzuki M, McHugh J, Tork C, Shelley B, Hayes A, Bellantuono I, et al. Direct muscle delivery of GDNF with human mesenchymal stem cells improves motor neuron survival and function in a rat model of familial ALS. Mol Ther. 2008;16(12):2002–10. https://doi.org/10.1038/mt.2008.197.

    Article  CAS  PubMed  Google Scholar 

  67. Krause K, Wulf M, Sommer P, Barkovits K, Vorgerd M, Marcus K, et al. CSF Diagnostics: A potentially Valuable Tool in neurodegenerative and inflammatory disorders Involving Motor neurons: a review. Diagnostics (Basel). 2021;11(9). https://doi.org/10.3390/diagnostics11091522.

  68. Verber N, Shaw PJ. Biomarkers in amyotrophic lateral sclerosis: a review of new developments. Curr Opin Neurol. 2020;33(5):662–8. https://doi.org/10.1097/WCO.0000000000000854.

    Article  CAS  PubMed  Google Scholar 

  69. Geijo-Barrientos E, Pastore-Olmedo C, De Mingo P, Blanquer M, Gomez Espuch J, Iniesta F, et al. Intramuscular injection of bone marrow stem cells in amyotrophic lateral sclerosis patients: a Randomized Clinical Trial. Front Neurosci. 2020;14:195. https://doi.org/10.3389/fnins.2020.00195.

    Article  PubMed  PubMed Central  Google Scholar 

  70. Teunissen CE, Tumani H, Engelborghs S, Mollenhauer B. Biobanking of CSF: international standardization to optimize biomarker development. Clin Biochem. 2014;47(4–5):288–92. https://doi.org/10.1016/j.clinbiochem.2013.12.024.

    Article  PubMed  Google Scholar 

  71. Oeckl P, Weydt P, Thal DR, Weishaupt JH, Ludolph AC, Otto M. Proteomics in cerebrospinal fluid and spinal cord suggests UCHL1, MAP2 and GPNMB as biomarkers and underpins importance of transcriptional pathways in amyotrophic lateral sclerosis. Acta Neuropathol. 2020;139(1):119–34. https://doi.org/10.1007/s00401-019-02093-x.

    Article  CAS  PubMed  Google Scholar 

  72. Oh S, Jang Y, Na CH. Discovery of biomarkers for amyotrophic lateral sclerosis from human cerebrospinal fluid using Mass-Spectrometry-based proteomics. Biomedicines. 2023;11(5). https://doi.org/10.3390/biomedicines11051250.

  73. Pasinetti GM, Ungar LH, Lange DJ, Yemul S, Deng H, Yuan X, et al. Identification of potential CSF biomarkers in ALS. Neurology. 2006;66(8):1218–22. https://doi.org/10.1212/01.wnl.0000203129.82104.07.

    Article  CAS  PubMed  Google Scholar 

  74. Mazzini L, Ferrero I, Luparello V, Rustichelli D, Gunetti M, Mareschi K, et al. Mesenchymal stem cell transplantation in amyotrophic lateral sclerosis: a phase I clinical trial. Exp Neurol. 2010;223(1):229–37. https://doi.org/10.1016/j.expneurol.2009.08.007.

    Article  CAS  PubMed  Google Scholar 

  75. Sykova E, Rychmach P, Drahoradova I, Konradova S, Ruzickova K, Vorisek I, et al. Transplantation of mesenchymal stromal cells in patients with amyotrophic lateral sclerosis: results of phase I/IIa clinical trial. Cell Transpl. 2017;26(4):647–58. https://doi.org/10.3727/096368916X693716.

    Article  Google Scholar 

  76. Qin C, Bai L, Li Y, Wang K. The functional mechanism of bone marrow-derived mesenchymal stem cells in the treatment of animal models with Alzheimer’s disease: crosstalk between autophagy and apoptosis. Stem Cell Res Ther. 2022;13(1):90. https://doi.org/10.1186/s13287-022-02765-8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Mason HD, McGavern DB. How the immune system shapes neurodegenerative diseases. Trends Neurosci. 2022;45(10):733–48. https://doi.org/10.1016/j.tins.2022.08.001.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Mead RJ, Shan N, Reiser HJ, Marshall F, Shaw PJ. Amyotrophic lateral sclerosis: a neurodegenerative disorder poised for successful therapeutic translation. Nat Rev Drug Discov. 2023;22(3):185–212. https://doi.org/10.1038/s41573-022-00612-2.

    Article  CAS  PubMed  Google Scholar 

  79. Le Gall L, Anakor E, Connolly O, Vijayakumar UG, Duddy WJ, Duguez S. Molecular and Cellular mechanisms affected in ALS. J Pers Med. 2020;10(3). https://doi.org/10.3390/jpm10030101.

  80. Lin J, Huang P, Chen W, Ye C, Su H, Yao X. Key molecules and pathways underlying sporadic amyotrophic lateral sclerosis: Integrated Analysis on Gene expression profiles of motor neurons. Front Genet. 2020;11:578143. https://doi.org/10.3389/fgene.2020.578143.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Lin JZ, Duan MR, Lin N, Zhao WJ. The emerging role of the chondroitin sulfate proteoglycan family in neurodegenerative diseases. Rev Neurosci. 2021;32(7):737–50. https://doi.org/10.1515/revneuro-2020-0146.

    Article  CAS  PubMed  Google Scholar 

  82. Rabin SJ, Kim JM, Baughn M, Libby RT, Kim YJ, Fan Y, et al. Sporadic ALS has compartment-specific aberrant exon splicing and altered cell-matrix adhesion biology. Hum Mol Genet. 2010;19(2):313–28. https://doi.org/10.1093/hmg/ddp498.

    Article  CAS  PubMed  Google Scholar 

  83. Oskarsson B, Gendron TF, Staff NP. Amyotrophic Lateral Sclerosis: An Update for 2018. Mayo Clin Proc. 2018;93(11):1617-28. https://doi.org/10.1016/j.mayocp.2018.04.007.

  84. Valiente-Alandi I, Schafer AE, Blaxall BC. Extracellular matrix-mediated cellular communication in the heart. J Mol Cell Cardiol. 2016;91:228–37. https://doi.org/10.1016/j.yjmcc.2016.01.011.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Bandzerewicz A, Gadomska-Gajadhur A. Into the tissues: Extracellular Matrix and its Artificial substitutes: cell signalling mechanisms. Cells. 2022;11(5). https://doi.org/10.3390/cells11050914.

  86. Rahbaran M, Zekiy AO, Bahramali M, Jahangir M, Mardasi M, Sakhaei D, et al. Therapeutic utility of mesenchymal stromal cell (MSC)-based approaches in chronic neurodegeneration: a glimpse into underlying mechanisms, current status, and prospects. Cell Mol Biol Lett. 2022;27(1):56. https://doi.org/10.1186/s11658-022-00359-z.

    Article  PubMed  PubMed Central  Google Scholar 

  87. Calabrese G, Molzahn C, Mayor T. Protein interaction networks in neurodegenerative diseases: from physiological function to aggregation. J Biol Chem. 2022;298(7):102062. https://doi.org/10.1016/j.jbc.2022.102062.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Robin V, Bodein A, Scott-Boyer MP, Leclercq M, Perin O, Droit A. Overview of methods for characterization and visualization of a protein-protein interaction network in a multi-omics integration context. Front Mol Biosci. 2022;9:962799. https://doi.org/10.3389/fmolb.2022.962799.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Pfundstein G, Nikonenko AG, Sytnyk V. Amyloid precursor protein (APP) and amyloid beta (abeta) interact with cell adhesion molecules: implications in Alzheimer’s disease and normal physiology. Front Cell Dev Biol. 2022;10:969547. https://doi.org/10.3389/fcell.2022.969547.

    Article  PubMed  PubMed Central  Google Scholar 

  90. Shao X, Taha IN, Clauser KR, Gao YT, Naba A. MatrisomeDB: the ECM-protein knowledge database. Nucleic Acids Res. 2020;48(D1):D1136–44. https://doi.org/10.1093/nar/gkz849.

    Article  CAS  PubMed  Google Scholar 

  91. Woodruff TM, Ager RR, Tenner AJ, Noakes PG, Taylor SM. The role of the complement system and the activation fragment C5a in the central nervous system. Neuromolecular Med. 2010;12(2):179–92. https://doi.org/10.1007/s12017-009-8085-y.

    Article  CAS  PubMed  Google Scholar 

  92. Peoples N, Strang C. Complement activation in the Central Nervous System: a Biophysical Model for Immune Dysregulation in the Disease State. Front Mol Neurosci. 2021;14:620090. https://doi.org/10.3389/fnmol.2021.620090.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Martinez-Martinez AB, Torres-Perez E, Devanney N, Del Moral R, Johnson LA, Arbones-Mainar JM. Beyond the CNS: the many peripheral roles of APOE. Neurobiol Dis. 2020;138:104809. https://doi.org/10.1016/j.nbd.2020.104809.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Ferlin KM, Prendergast ME, Miller ML, Nguyen BN, Kaplan DS, Fisher JP. Development of a dynamic stem cell culture platform for mesenchymal stem cell adhesion and evaluation. Mol Pharm. 2014;11(7):2172–81. https://doi.org/10.1021/mp500062n.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Gregory SH, Cousens LP, van Rooijen N, Dopp EA, Carlos TM, Wing EJ. Complementary adhesion molecules promote neutrophil-kupffer cell interaction and the elimination of bacteria taken up by the liver. J Immunol. 2002;168(1):308–15. https://doi.org/10.4049/jimmunol.168.1.308.

    Article  CAS  PubMed  Google Scholar 

  96. Jassal B, Matthews L, Viteri G, Gong C, Lorente P, Fabregat A, et al. The reactome pathway knowledgebase. Nucleic Acids Res. 2020;48(D1):D498–503. https://doi.org/10.1093/nar/gkz1031.

    Article  CAS  PubMed  Google Scholar 

  97. Novoseletskaya ES, Evdokimov PV, Efimenko AY. Extracellular matrix-induced signaling pathways in mesenchymal stem/stromal cells. Cell Commun Signal. 2023;21(1):244. https://doi.org/10.1186/s12964-023-01252-8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Bian Z, Yamashita T, Shi X, Feng T, Yu H, Hu X, et al. Accelerated accumulation of fibrinogen peptide chains with Abeta deposition in Alzheimer’s disease (AD) mice and human AD brains. Brain Res. 2021;1767:147569. https://doi.org/10.1016/j.brainres.2021.147569.

    Article  CAS  PubMed  Google Scholar 

  99. Cheng SX, Xu ZW, Yi TL, Sun HT, Yang C, Yu ZQ, et al. iTRAQ-Based quantitative Proteomics reveals the New evidence base for traumatic brain Injury treated with targeted temperature management. Neurotherapeutics. 2018;15(1):216–32. https://doi.org/10.1007/s13311-017-0591-2.

    Article  CAS  PubMed  Google Scholar 

  100. Shi L, Buckley NJ, Bos I, Engelborghs S, Sleegers K, Frisoni GB, et al. Plasma proteomic biomarkers relating to Alzheimer’s Disease: a Meta-analysis based on our own studies. Front Aging Neurosci. 2021;13:712545. https://doi.org/10.3389/fnagi.2021.712545.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Yepes M, Woo Y, Martin-Jimenez C. Plasminogen activators in neurovascular and neurodegenerative disorders. Int J Mol Sci. 2021;22(9). https://doi.org/10.3390/ijms22094380.

  102. Zorzetto M, Datturi F, Divizia L, Pistono C, Campo I, De Silvestri A, et al. Complement C4A and C4B Gene Copy Number Study in Alzheimer’s Disease patients. Curr Alzheimer Res. 2017;14(3):303–8. https://doi.org/10.2174/1567205013666161013091934.

    Article  CAS  PubMed  Google Scholar 

  103. Chen L, Wang N, Zhang Y, Li D, He C, Li Z, et al. Proteomics analysis indicates the involvement of immunity and inflammation in the onset stage of SOD1-G93A mouse model of ALS. J Proteom. 2023;272:104776. https://doi.org/10.1016/j.jprot.2022.104776.

    Article  CAS  Google Scholar 

  104. Garbuzova-Davis S, Willing AE, Borlongan CV. Apolipoprotein A1 enhances endothelial cell survival in an in vitro model of ALS. eNeuro. 2022;9(4). https://doi.org/10.1523/ENEURO.0140-22.2022.

  105. Genge A, van den Berg LH, Frick G, Han S, Abikoff C, Simmons A, et al. Efficacy and safety of Ravulizumab, a complement C5 inhibitor, in adults with amyotrophic lateral sclerosis: a Randomized Clinical Trial. JAMA Neurol. 2023;80(10):1089–97. https://doi.org/10.1001/jamaneurol.2023.2851.

    Article  PubMed  PubMed Central  Google Scholar 

  106. Mantovani S, Gordon R, Macmaw JK, Pfluger CM, Henderson RD, Noakes PG, et al. Elevation of the terminal complement activation products C5a and C5b-9 in ALS patient blood. J Neuroimmunol. 2014;276(1–2):213–8. https://doi.org/10.1016/j.jneuroim.2014.09.005.

    Article  CAS  PubMed  Google Scholar 

  107. Nguyen KV. beta-amyloid precursor protein (APP) and the human diseases. AIMS Neurosci. 2019;6(4):273–81. https://doi.org/10.3934/Neuroscience.2019.4.273.

    Article  PubMed  PubMed Central  Google Scholar 

  108. Stanga S, Brambilla L, Tasiaux B, Dang AH, Ivanoiu A, Octave JN, et al. A role for GDNF and Soluble APP as biomarkers of amyotrophic lateral sclerosis pathophysiology. Front Neurol. 2018;9:384. https://doi.org/10.3389/fneur.2018.00384.

    Article  PubMed  PubMed Central  Google Scholar 

  109. Thompson AG, Talbot K, Turner MR. Higher blood high density lipoprotein and apolipoprotein A1 levels are associated with reduced risk of developing amyotrophic lateral sclerosis. J Neurol Neurosurg Psychiatry. 2022;93(1):75–81. https://doi.org/10.1136/jnnp-2021-327133.

    Article  PubMed  Google Scholar 

  110. Borrego-Ecija S, Turon-Sans J, Ximelis T, Aldecoa I, Molina-Porcel L, Povedano M, et al. Cognitive decline in amyotrophic lateral sclerosis: neuropathological substrate and genetic determinants. Brain Pathol. 2021;31(3):e12942. https://doi.org/10.1111/bpa.12942.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Butovsky O, Weiner HL. Microglial signatures and their role in health and disease. Nat Rev Neurosci. 2018;19(10):622–35. https://doi.org/10.1038/s41583-018-0057-5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  112. Wang MJ, Kang L, Wang YZ, Yang BR, Zhang C, Lu YF, et al. Microglia in motor neuron disease: Signaling evidence from last 10 years. Dev Neurobiol. 2022;82(7–8):625–38. https://doi.org/10.1002/dneu.22905.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  113. Bryson JB, Hobbs C, Parsons MJ, Bosch KD, Pandraud A, Walsh FS, et al. Amyloid precursor protein (APP) contributes to pathology in the SOD1(G93A) mouse model of amyotrophic lateral sclerosis. Hum Mol Genet. 2012;21(17):3871–82. https://doi.org/10.1093/hmg/dds215.

    Article  CAS  PubMed  Google Scholar 

  114. Muresan V, Ladescu Muresan Z. Shared Molecular mechanisms in Alzheimer’s disease and amyotrophic lateral sclerosis: neurofilament-dependent transport of sAPP, FUS, TDP-43 and SOD1, with endoplasmic Reticulum-Like Tubules. Neurodegener Dis. 2016;16(1–2):55–61. https://doi.org/10.1159/000439256.

    Article  CAS  PubMed  Google Scholar 

  115. Allison WT, DuVal MG, Nguyen-Phuoc K, Leighton PLA. Reduced abundance and subverted functions of proteins in Prion-Like diseases: gained functions Fascinate but Lost functions Affect Aetiology. Int J Mol Sci. 2017;18(10). https://doi.org/10.3390/ijms18102223.

  116. Chen JJ. Overview of current and emerging therapies for amytrophic lateral sclerosis. Am J Manag Care. 2020;26(9 Suppl):S191–7. https://doi.org/10.37765/ajmc.2020.88483.

    Article  PubMed  Google Scholar 

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Acknowledgements

The authors wish to thank our collaborator and friend, Professor Robert Carlone of Brock University, Canada, for reading the manuscript and for his valuable suggestions. Authors also wish to thank staff of Clinics Hospital of University of Sao Paulo Medical School and also of Core for Cell Technology, School of Medicine and Life Sciences, Pontifícia Universidade Catolica of Parana for the enormous assistance regarding patients care, MSCs production, general secretarial care. Special thanks to BIOMASS-Core Facility for Scientific Research (CEFAP-USP), University of Sao Paulo, Brazil, for the Mass Spectrometry and Proteome Research analysis, which were conducted under a direct supervision of Professor Giuseppe Palmisano, head of the Core Facility and Associate Professor of Parasitology Department, Institute of Biomedical Sciences, USP.

Funding

This study was funded by National Council for Scientific and Technological Development (CNPq; #401922/2014-6), Ministry of Health (Agreement # 836458/2016), National Financier of Studies and Projects of Ministry of Science and Technology (FINEP, #1701/22), and State of São Paulo Research Foundation (FAPESP).

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Contributions

GC, PRSB contributed to conception and design, manuscript writing, final approval of manuscript, general interpretation and responsibilities. ALGR was responsible for proteomics and corresponded data interpretation, review of literature, and manuscript writing. LAPCL, JP were responsible for bone marrow aspiration, HRG for CSF aspiration and WSP for MSCs infusion. ACS, CLKR, DRG were responsible for MSCs production and quality control. JRM and GC were responsible for bioinformatics, general statistical analysis, interpretation of data and manuscript writing. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Gerson Chadi.

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Ethics approval and consent to participate

The study was approved by the Ethics Committee of the Clinics Hospital of the University of Sao Paulo (Approval Number: 1.616.004; Date of Approval: June, 30th 2016). Project Registration Number: 37772414.0.0000.0068. Original Title of Approved Project: Estudo fase 1/2 da segurança e eficácia de duas doses intratecais de células-tronco mesenquimais autólogas (CTM), obtidas de células estromais da medula óssea, em pacientes com Esclerose Lateral Amiotrófica. English Title of Approved Project (Translated): Phase 1/2 clinical trial on security and efficiency of 2 doses of intrathecal infusion of bone marrow derived autologous Mesenchymal Stem Cell (MSC) in Amyotrophic Lateral Sclerosis ALS patients provided informed consent in accordance with the Helsinki declaration.

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Not applicable.

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The authors declare that they have no conflict of interest.

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Reis, A.L.G., Maximino, J.R., Lage, L.A.d.P.C. et al. Proteomic analysis of cerebrospinal fluid of amyotrophic lateral sclerosis patients in the presence of autologous bone marrow derived mesenchymal stem cells. Stem Cell Res Ther 15, 301 (2024). https://doi.org/10.1186/s13287-024-03820-2

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