Spatial and temporal diversity of glycome expression in mammalian brain.

Mammalian brain glycome remains a relatively poorly understood area compared to other large-scale "omics" studies, such as genomics and transcriptomics due to the inherent complexity and heterogeneity of glycan structure and properties. Here, we first performed spatial and temporal analysis of glycome expression patterns in the mammalian brain using a cutting-edge experimental tool based on liquid chromatography-mass spectrometry, with the ultimate aim to yield valuable implications on molecular events regarding brain functions and development. We observed an apparent diversity in the glycome expression patterns, which is spatially well-preserved among nine different brain regions in mouse. Next, we explored whether the glycome expression pattern changes temporally during postnatal brain development by examining the prefrontal cortex (PFC) at different time point across six postnatal stages in mouse. We found that glycan expression profiles were dynamically regulated during postnatal developments. A similar result was obtained in PFC samples from humans ranging in age from 39 d to 49 y. Novel glycans unique to the brain were also identified. Interestingly, changes primarily attributed to sialylated and fucosylated glycans were extensively observed during PFC development. Finally, based on the vast heterogeneity of glycans, we constructed a core glyco-synthesis map to delineate the glycosylation pathway responsible for the glycan diversity during the PFC development. Our findings reveal high levels of diversity in a glycosylation program underlying brain region specificity and age dependency, and may lead to new studies exploring the role of glycans in spatiotemporally diverse brain functions.

Diagnostic prediction model development using data from dried blood spot proteomics and a digital mental health assessment to identify major depressive disorder among individuals presenting with low mood.

With less than half of patients with major depressive disorder (MDD) correctly diagnosed within the primary care setting, there is a clinical need to develop an objective and readily accessible test to enable earlier and more accurate diagnosis. The aim of this study was to develop diagnostic prediction models to identify MDD patients among individuals presenting with subclinical low mood, based on data from dried blood spot (DBS) proteomics (194 peptides representing 115 proteins) and a novel digital mental health assessment (102 sociodemographic, clinical and personality characteristics). To this end, we investigated 130 low mood controls, 53 currently depressed individuals with an existing MDD diagnosis (established current MDD), 40 currently depressed individuals with a new MDD diagnosis (new current MDD), and 72 currently not depressed individuals with an existing MDD diagnosis (established non-current MDD). A repeated nested cross-validation approach was used to evaluate variation in model selection and ensure model reproducibility. Prediction models that were trained to differentiate between established current MDD patients and low mood controls (AUC = 0.94 ± 0.01) demonstrated a good predictive performance when extrapolated to differentiate between new current MDD patients and low mood controls (AUC = 0.80 ± 0.01), as well as between established non-current MDD patients and low mood controls (AUC = 0.79 ± 0.01). Importantly, we identified DBS proteins A1AG1, A2GL, AL1A1, APOE and CFAH as important predictors of MDD, indicative of immune system dysregulation; as well as poor self-rated mental health, BMI, reduced daily experiences of positive emotions, and tender-mindedness. Despite the need for further validation, our preliminary findings demonstrate the potential of such prediction models to be used as a diagnostic aid for detecting MDD in clinical practice.

Evidence of microglial activation following exposure to serum from first-onset drug-naïve schizophrenia patients.

Abnormal activation of brain microglial cells is widely implicated in the pathogenesis of schizophrenia. Previously the pathophysiology of microglial activation was considered to be intrinsic to the central nervous system. We hypothesised that due to their perivascular localization, microglia can also be activated by factors present in circulating blood. Through application of high-content functional screening, we show that peripheral blood serum from first-onset drug-naïve schizophrenia patients is sufficient to provoke microglial cell signalling network responses in vitro which are indicative of proinflammatory activation. We further explore the composition of the serum for the presence of analytes, with the potential to activate microglia, and the utility of the resultant microglial cellular phenotype for novel drug discovery.

Altered apolipoprotein C expression in association with cognition impairments and hippocampus volume in schizophrenia and bipolar disorder.

Proteomic analyses facilitate the interpretation of molecular biomarker probes which are very helpful in diagnosing schizophrenia (SZ). In the current study, we attempt to test whether potential differences in plasma protein expressions in SZ and bipolar disorder (BD) are associated with cognitive deficits and their underlying brain structures. Forty-two plasma proteins of 29 SZ patients, 25 BD patients and 93 non-clinical controls were quantified and analysed using multiple reaction monitoring-based triple quadrupole mass spectrometry approach. We also computed group comparisons of protein expressions between patients and controls, and between SZ and BD patients, as well. Potential associations of protein levels with cognitive functioning (psychomotor speed, executive functioning, crystallised intelligence) as well as underlying brain volume in the hippocampus were explored, using bivariate correlation analyses. The main finding of this study was that apolipoprotein expression differed between patients and controls and that these alterations in both disease groups were putatively related to cognitive impairments as well as to hippocampus volumes. However, none of the protein level differences were related to clinical symptom severity. In summary, altered apolipoprotein expression in BD and SZ was linked to cognitive decline and underlying morphological changes in both disorders. Our results suggest that the detection of molecular patterns in association with cognitive performance and its underlying brain morphology is of great importance for understanding of the pathological mechanisms of SZ and BD, as well as for supporting the diagnosis and treatment of both disorders.

In-depth method for the characterization of glycosylation in manufactured recombinant monoclonal antibody drugs.

The glycosylation in recombinant monoclonal antibody (rMab) drugs is a major concern in the biopharmaceutical industry as it impacts the drugs' many attributes. Characterization is important but complicated by the intricate structures, microheterogeneity, and the limitations of current tools for structural analysis. In this study, we developed a liquid chromatography-mass spectrometry (LC-MS) N-glycan library based on eight commercial rMab drugs. A library of over 70 structures was developed for the rapid characterization of rMab. N-Glycans were separated on a porous graphitized carbon (PGC) column incorporated on a chip and then analyzed by an electrospray ionization hybrid quadrupole time-of-flight (ESI-Q-TOF) MS. The retention time and accurate mass for each N-glycan were recorded in the library. The complete structures were obtained through exoglycosidase sequencing. The results showed that most of the N-glycans between different antibodies are nearly the same with different abundances. The utility of this library enables one to identify structures in a rapid manner by matching LC retention times and accurate masses.

Structural proteomics of a bacterial mega membrane protein complex: FtsH-HflK-HflC.

Recent advances in mass spectrometry (MS) yielding sensitive and accurate measurements along with developments in software tools have enabled the characterization of complex systems routinely. Thus, structural proteomics and cross-linking mass spectrometry (XL-MS) have become a useful method for structural modeling of protein complexes. Here, we utilized commonly used XL-MS software tools to elucidate the protein interactions within a membrane protein complex containing FtsH, HflK, and HflC, over-expressed in E. coli. The MS data were processed using MaxLynx, MeroX, MS Annika, xiSEARCH, and XlinkX software tools. The number of identified inter- and intra-protein cross-links varied among software. Each interaction was manually checked using the raw MS and MS/MS data and distance restraints to verify inter- and intra-protein cross-links. A total of 37 inter-protein and 148 intra-protein cross-links were determined in the FtsH-HflK-HflC complex. The 59 of them were new interactions on the lacking region of recently published structures. These newly identified interactions, when combined with molecular docking and structural modeling, present opportunities for further investigation. The results provide valuable information regarding the complex structure and function to decipher the intricate molecular mechanisms underlying the FtsH-HflK-HflC complex.

Characterization of novel O-glycans isolated from tear and saliva of ocular rosacea patients.

O-Glycans in saliva and tear isolated from patients suffering from ocular rosacea, a form of inflammatory ocular surface disease, were profiled, and their structures were elucidated using high resolution mass spectrometry. We have previously shown that certain structures, particularly sulfated oligosaccharides, increased in the tear and saliva of rosacea patients. In this study, the structures of these glycans were elucidated using primarily tandem mass spectrometry. There were important similarities in the glycan profiles of tears and saliva with the majority of the structures in common. The structures of the most abundant species common to both tear and saliva, which were also the most abundant species in both, were elucidated. For sulfated species, the positions of the sulfate groups were localized. The majority of the structures were new, with the sulfated glycans comprising mucin core 1- and core 2-type structures. As both saliva and tear are rich in mucins, it is suggested that the O-glycans are mainly components of mucins. The study further illustrates the strong correspondence between the glycans in the tear and saliva of ocular rosacea patients.

A single protein to multiple peptides: Investigation of protein-peptide correlations using targeted alpha-2-macroglobulin analysis.

Recent advances in proteomics technologies have enabled the analysis of thousands of proteins in a high-throughput manner. Mass spectrometry (MS) based proteomics uses a peptide-centric approach where biological samples undergo specific proteolytic digestion and then only unique peptides are used for protein identification and quantification. Considering the fact that a single protein may have multiple unique peptides and a number of different forms, it becomes essential to understand dynamic protein-peptide relationships to ensure robust and reliable peptide-centric protein analysis. In this study, we investigated the correlation between protein concentration and corresponding unique peptide responses under a conventional proteolytic digestion condition. Protein-peptide correlation, digestion efficiency, matrix-effect, and concentration-effect were evaluated. Twelve unique peptides of alpha-2-macroglobulin (A2MG) were monitored using a targeted MS approach to acquire insights into protein-peptide dynamics. Although the peptide responses were reproducible between replicates, protein-peptide correlation was moderate in protein standards and low in complex matrices. The results suggest that reproducible peptide signal could be misleading in clinical studies and a peptide selection could dramatically change the outcome at protein level. This is the first study investigating quantitative protein-peptide correlations in biological samples using all unique peptides representing the same protein and opens a discussion on peptide-based proteomics.

Proteomics Approach to Differentiate Protein Extraction Methods in Sugar Beet Leaves.

Interest in alternative plant-based protein sources is continuously growing. Sugar beet leaves have the potential to satisfy that demand due to their high protein content. They are considered as agricultural waste and utilizing them as protein sources can bring them back to the food chain. In this study, isoelectric-point-precipitation, heat-coagulation, ammonium-sulfate precipitation, high-pressure-assisted isoelectric-point precipitation, and high-pressure-assisted heat coagulation methods were used to extract proteins from sugar beet leaves. A mass spectrometry-based proteomic approach was used for comprehensive protein characterization. The analyses yielded 817 proteins, the most comprehensive protein profile on sugar beet leaves to date. Although the total protein contents were comparable, there was a significant difference between the methods for low-abundance proteins. High-pressure-assisted methods showed elevated levels of proteins predominantly located in the chloroplast. Here we showed for the first time that the extraction/precipitation methods may result in different protein profiles that potentially affect the physical and nutritional properties of functional products.

Glycomic analysis of tear and saliva in ocular rosacea patients: the search for a biomarker.

The purpose of this study was to study changes in glycosylation in tear and saliva obtained from control and ocular rosacea patients in order to identify potential biomarkers for rosacea. Tear fluid was collected from 51 subjects (28 healthy controls and 23 patients with ocular rosacea). Saliva was collected from 42 of the same subjects (25 controls and 17 patients). Pooled and individual samples were examined to determine overall glycan profiles and individual variations in glycosylation. O-and N- glycans were released from both patients and control subjects. Released glycans were purified and enriched by solid-phase extraction (SPE) with graphitized carbon. Glycans were eluted based on glycan size and polarity. SPE fractions were then analyzed by high-resolution mass spectrometry. Glycan compositions were assigned by accurate masses. Their structures were further elucidated by tandem mass spectrometric using collision-induced dissociation (CID), and specific linkage information was obtained by exoglycosidase digestion. N- and O-glycans were released from 20-µL samples without protein identification, separation, and purification. Approximately 50 N-glycans and 70 O-glycans were globally profiled by mass spectrometry. Most N-glycans were highly fucosylated, while O-glycans were sulfated. Normal tear fluid and saliva contain highly fucosylated glycans. The numbers of sulfated glycans were dramatically increased in tear and saliva of rosacea patients compared to controls. Glycans found in tear and saliva from roseatic patients present highly quantitative similarity. The abundance of highly fucosylated N-glycans in the control samples and sulfated O-glycans in ocular rosacea patient samples may lead to the discovery of an objective diagnostic marker for the disease.

Comprehensive native glycan profiling with isomer separation and quantitation for the discovery of cancer biomarkers.

Glycosylation is highly sensitive to the biochemical environment and has been implicated in many diseases including cancer. Glycan compositional profiling of human serum with mass spectrometry has already identified potential biomarkers for several types of cancer and diseases; however, composition alone does not fully describe glycan stereo- and regioisomeric diversity. The vast structural heterogeneity of glycans presents a formidable analytical challenge. We have developed a method to identify and quantify isomeric native glycans using nanoflow liquid chromatography (nano-LC)/mass spectrometry. A microfluidic chip packed with graphitized carbon was used to chromatographically separate the glycans. To determine the utility of this method for structure-specific biomarker discovery, we analyzed serum samples from two groups of prostate cancer patients with different prognoses. More than 300 N-glycan species (including isomeric structures) were identified, corresponding to over 100 N-glycan compositions. Statistical tests established significant differences in glycan abundances between patient groups. This method provides comprehensive, selective, and quantitative glycan profiling.

(In)Distinctive Role of Long Non-Coding RNAs in Common and Rare Ovarian Cancers.

Rare ovarian cancers (ROCs) are OCs with an annual incidence of fewer than 6 cases per 100,000 women. They affect women of all ages, but due to their low incidence and the potential clinical inexperience in management, there can be a delay in diagnosis, leading to a poor prognosis. The underlying causes for these tumors are varied, but generally, the tumors arise due to alterations in gene/protein expression in cellular processes that regulate normal proliferation and its checkpoints. Dysregulation of the cellular processes that lead to cancer includes gene mutations, epimutations, non-coding RNA (ncRNA) regulation, posttranscriptional and posttranslational modifications. Long non-coding RNA (lncRNA) are defined as transcribed RNA molecules, more than 200 nucleotides in length which are not translated into proteins. They regulate gene expression through several mechanisms and therefore add another level of complexity to the regulatory mechanisms affecting tumor development. Since few studies have been performed on ROCs, in this review we summarize the mechanisms of action of lncRNA in OC, with an emphasis on ROCs.

Ultimate COVID-19 Detection Protocol Based on Saliva Sampling and qRT-PCR with Risk Probability Assessment.

In the era of COVID-19 outbreak, various efforts are undertaken to develop a quick, easy, inexpensive, and accurate way for diagnosis. Although many commercial diagnostic kits are available, detailed scientific evaluation is lacking, making the public vulnerable to fear of false-positive results. Moreover, current tissue sampling method from respiratory tract requires personal contact of medical staff with a potential asymptomatic SARSCOV-2 carrier and calls for safe and less invasive sampling method. Here, we have developed a convenient detection protocol for SARS-COV-2 based on a non-invasive saliva self-sampling method by extending our previous studies on development of a laboratory-safe and low-cost detection protocol based on qRT-PCR. We tested and compared various self-sampling methods of self-pharyngeal swab and self-saliva sampling from non-carrier volunteers. We found that the self-saliva sampling procedure gave expected negative results from all of the non-carrier volunteers within 2 hours, indicating cost-effectiveness, speed and reliability of the saliva-based method. For an automated assessment of the sampling quality and degree of positivity for COVID-19, we developed scalable formulae based on a logistic classification model using both cycle threshold and melting temperature from the qRT-PCR results. Our newly developed protocol will allow easy sampling and spatial-separation between patient and experimenter for guaranteed safety. Furthermore, our newly established risk assessment formula can be applied to a large-scale diagnosis in health institutions and agencies around the world.

A machine learning algorithm to differentiate bipolar disorder from major depressive disorder using an online mental health questionnaire and blood biomarker data.

The vast personal and economic burden of mood disorders is largely caused by their under- and misdiagnosis, which is associated with ineffective treatment and worsening of outcomes. Here, we aimed to develop a diagnostic algorithm, based on an online questionnaire and blood biomarker data, to reduce the misdiagnosis of bipolar disorder (BD) as major depressive disorder (MDD). Individuals with depressive symptoms (Patient Health Questionnaire-9 score ≥5) aged 18-45 years were recruited online. After completing a purpose-built online mental health questionnaire, eligible participants provided dried blood spot samples for biomarker analysis and underwent the World Health Organization World Mental Health Composite International Diagnostic Interview via telephone, to establish their mental health diagnosis. Extreme Gradient Boosting and nested cross-validation were used to train and validate diagnostic models differentiating BD from MDD in participants who self-reported a current MDD diagnosis. Mean test area under the receiver operating characteristic curve (AUROC) for separating participants with BD diagnosed as MDD (N = 126) from those with correct MDD diagnosis (N = 187) was 0.92 (95% CI: 0.86-0.97). Core predictors included elevated mood, grandiosity, talkativeness, recklessness and risky behaviour. Additional validation in participants with no previous mood disorder diagnosis showed AUROCs of 0.89 (0.86-0.91) and 0.90 (0.87-0.91) for separating newly diagnosed BD (N = 98) from MDD (N = 112) and subclinical low mood (N = 120), respectively. Validation in participants with a previous diagnosis of BD (N = 45) demonstrated sensitivity of 0.86 (0.57-0.96). The diagnostic algorithm accurately identified patients with BD in various clinical scenarios, and could help expedite accurate clinical diagnosis and treatment of BD.

A Combined Digital and Biomarker Diagnostic Aid for Mood Disorders (the Delta Trial): Protocol for an Observational Study.

BACKGROUND: Mood disorders affect hundreds of millions of people worldwide, imposing a substantial medical and economic burden. Existing diagnostic methods for mood disorders often result in a delay until accurate diagnosis, exacerbating the challenges of these disorders. Advances in digital tools for psychiatry and understanding the biological basis of mood disorders offer the potential for novel diagnostic methods that facilitate early and accurate diagnosis of patients. OBJECTIVE: The Delta Trial was launched to develop an algorithm-based diagnostic aid combining symptom data and proteomic biomarkers to reduce the misdiagnosis of bipolar disorder (BD) as a major depressive disorder (MDD) and achieve more accurate and earlier MDD diagnosis. METHODS: Participants for this ethically approved trial were recruited through the internet, mainly through Facebook advertising. Participants were then screened for eligibility, consented to participate, and completed an adaptive digital questionnaire that was designed and created for the trial on a purpose-built digital platform. A subset of these participants was selected to provide dried blood spot (DBS) samples and undertake a World Health Organization World Mental Health Composite International Diagnostic Interview (CIDI). Inclusion and exclusion criteria were chosen to maximize the safety of a trial population that was both relevant to the trial objectives and generalizable. To provide statistical power and validation sets for the primary and secondary objectives, 840 participants were required to complete the digital questionnaire, submit DBS samples, and undertake a CIDI. RESULTS: The Delta Trial is now complete. More than 3200 participants completed the digital questionnaire, 924 of whom also submitted DBS samples and a CIDI, whereas a total of 1780 participants completed a 6-month follow-up questionnaire and 1542 completed a 12-month follow-up questionnaire. The analysis of the trial data is now underway. CONCLUSIONS: If a diagnostic aid is able to improve the diagnosis of BD and MDD, it may enable earlier treatment for patients with mood disorders. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/18453.

Integrating proteomic, sociodemographic and clinical data to predict future depression diagnosis in subthreshold symptomatic individuals.

Individuals with subthreshold depression have an increased risk of developing major depressive disorder (MDD). The aim of this study was to develop a prediction model to predict the probability of MDD onset in subthreshold individuals, based on their proteomic, sociodemographic and clinical data. To this end, we analysed 198 features (146 peptides representing 77 serum proteins (measured using MRM-MS), 22 sociodemographic factors and 30 clinical features) in 86 first-episode MDD patients (training set patient group), 37 subthreshold individuals who developed MDD within two or four years (extrapolation test set patient group), and 86 subthreshold individuals who did not develop MDD within four years (shared reference group). To ensure the development of a robust and reproducible model, we applied feature extraction and model averaging across a set of 100 models obtained from repeated application of group LASSO regression with ten-fold cross-validation on the training set. This resulted in a 12-feature prediction model consisting of six serum proteins (AACT, APOE, APOH, FETUA, HBA and PHLD), three sociodemographic factors (body mass index, childhood trauma and education level) and three depressive symptoms (sadness, fatigue and leaden paralysis). Importantly, the model demonstrated a fair performance in predicting future MDD diagnosis of subthreshold individuals in the extrapolation test set (AUC = 0.75), which involved going beyond the scope of the model. These findings suggest that it may be possible to detect disease indications in subthreshold individuals up to four years prior to diagnosis, which has important clinical implications regarding the identification and treatment of high-risk individuals.

Multimodel inference for biomarker development: an application to schizophrenia.

In the present study, to improve the predictive performance of a model and its reproducibility when applied to an independent data set, we investigated the use of multimodel inference to predict the probability of having a complex psychiatric disorder. We formed training and test sets using proteomic data (147 peptides from 77 proteins) from two-independent collections of first-onset drug-naive schizophrenia patients and controls. A set of prediction models was produced by applying lasso regression with repeated tenfold cross-validation to the training set. We used feature extraction and model averaging across the set of models to form two prediction models. The resulting models clearly demonstrated the utility of a multimodel based approach to make good (training set AUC > 0.80) and reproducible predictions (test set AUC > 0.80) for the probability of having schizophrenia. Moreover, we identified four proteins (five peptides) whose effect on the probability of having schizophrenia was modified by sex, one of which was a novel potential biomarker of schizophrenia, foetal haemoglobin. The evidence of effect modification suggests that future schizophrenia studies should be conducted in males and females separately. Future biomarker studies should consider adopting a multimodel approach and going beyond the main effects of features.

Determination of fumonisins B1 and B2 in corn by liquid chromatography/mass spectrometry with immunoaffinity column cleanup: single-laboratory method validation.

A single-laboratory method validation was conducted to establish the effectiveness of an immunoaffinity column cleanup procedure followed by liquid chromatography/mass spectrometry (LCIMS) for the determination of fumonisins B1 and B2 (FBI + FB2) in corn. The test portion is extracted with acetonitrile-methanol-water (25 + 25 + 50). The extract is filtered, diluted with phosphate-buffered saline solution, and applied to an immunoaffinity column. FB1 + FB2 are removed with methanol and directly determined by reversed-phase LC with MS detection using selected-ion monitoring of 2 characteristic ions in each case. Test portions of blank corn samples were spiked with a mixture of FB1 + FB2 to give total levels of 200 and 500 ng/g, respectively. Recoveries of both FB1 and FB2 from spiked samples averaged 90.4-101%. Based on results for spiked raw corn (triplicates at 2 levels), the relative standard deviation for repeatability ranged from 2.8 to 7.1%. The accuracy of the method was demonstrated by analysis of Food Analysis Performance Assessment Scheme (FAPAS) test material. The method was also applied to a small survey of processed corn products such as corn chips, cornflakes, and popcorn.

Temporal proteomic profiling of postnatal human cortical development.

Healthy cortical development depends on precise regulation of transcription and translation. However, the dynamics of how proteins are expressed, function and interact across postnatal human cortical development remain poorly understood. We surveyed the proteomic landscape of 69 dorsolateral prefrontal cortex samples across seven stages of postnatal life and integrated these data with paired transcriptome data. We detected 911 proteins by liquid chromatography-mass spectrometry, and 83 were significantly associated with postnatal age (FDR < 5%). Network analysis identified three modules of co-regulated proteins correlated with age, including two modules with increasing expression involved in gliogenesis and NADH metabolism and one neurogenesis-related module with decreasing expression throughout development. Integration with paired transcriptome data revealed that these age-related protein modules overlapped with RNA modules and displayed collinear developmental trajectories. Importantly, RNA expression profiles that are dynamically regulated throughout cortical development display tighter correlations with their respective translated protein expression compared to those RNA profiles that are not. Moreover, the correspondence between RNA and protein expression significantly decreases as a function of cortical aging, especially for genes involved in myelination and cytoskeleton organization. Finally, we used this data resource to elucidate the functional impact of genetic risk loci for intellectual disability, converging on gliogenesis, myelination and ATP-metabolism modules in the proteome and transcriptome. We share all data in an interactive, searchable companion website. Collectively, our findings reveal dynamic aspects of protein regulation and provide new insights into brain development, maturation, and disease.

Parallel changes in serum proteins and diffusion tensor imaging in methamphetamine-associated psychosis.

Methamphetamine-associated psychosis (MAP) involves widespread neurocognitive and molecular deficits, however accurate diagnosis remains challenging. Integrating relationships between biological markers, brain imaging and clinical parameters may provide an improved mechanistic understanding of MAP, that could in turn drive the development of better diagnostics and treatment approaches. We applied selected reaction monitoring (SRM)-based proteomics, profiling 43 proteins in serum previously implicated in the etiology of major psychiatric disorders, and integrated these data with diffusion tensor imaging (DTI) and psychometric measurements from patients diagnosed with MAP (N = 12), methamphetamine dependence without psychosis (MA; N = 14) and healthy controls (N = 16). Protein analysis identified changes in APOC2 and APOH, which differed significantly in MAP compared to MA and controls. DTI analysis indicated widespread increases in mean diffusivity and radial diffusivity delineating extensive loss of white matter integrity and axon demyelination in MAP. Upon integration, several co-linear relationships between serum proteins and DTI measures reported in healthy controls were disrupted in MA and MAP groups; these involved areas of the brain critical for memory and social emotional processing. These findings suggest that serum proteomics and DTI are sensitive measures for detecting pathophysiological changes in MAP and describe a potential diagnostic fingerprint of the disorder.