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The Vascular Endothelial Growth Factor (VEGF) signaling family has been implicated in neuroprotection and clinical progression in Alzheimer’s disease (AD). Previous work in postmortem human dorsolateral prefrontal cortex demonstrated that higher transcript levels of VEGFB, PGF, FLT1, and FLT4 are associated with AD dementia, worse cognitive outcomes, and higher AD neuropathology. To expand prior work, we leveraged bulk RNA sequencing data, single nucleus RNA (snRNA) sequencing, and both tandem mass tag and selected reaction monitoring mass spectrometry proteomic measures from the post-mortem brain. Outcomes included AD diagnosis, cognition, and AD neuropathology. We replicated previously reported VEGFB and FLT1 results, whereby higher expression was associated with worse outcomes, and snRNA results suggest microglia, oligodendrocytes, and endothelia may play a central role in these associations. Additionally, FLT4 and NRP2 expression were associated with better cognitive outcomes. This study provides a comprehensive molecular picture of the VEGF signaling family in cognitive aging and AD and critical insight towards the biomarker and therapeutic potential of VEGF family members in AD.

Identifying the molecular systems and proteins that modify the progression of Alzheimer’s disease and related dementias (ADRD) is central to drug target selection. However, discordance between mRNA and protein abundance, and the scarcity of proteomic data, has limited our ability to advance candidate targets that are mainly based on gene expression. Therefore, by using a deep neural network that predicts protein abundance from mRNA expression, here we attempt to track the early protein drivers of ADRD. Specifically, by applying the clei2block deep learning model to 1192 brain RNA-seq samples, we identify protein modules and disease-associated expression changes that were not directly observed at the mRNA level. Moreover, pseudo-temporal trajectory inference based on the predicted proteome became more closely correlated with cognitive decline and hippocampal atrophy compared to RNA-based trajectories. This suggests that the predicted changes in protein expression could provide a better molecular representation of ADRD progression. Furthermore, overlaying clinical traits on protein pseudotime trajectory identifies protein modules altered before cognitive impairment. These results demonstrate how our method can be used to identify potential early protein drivers and possible drug targets for treating and/or preventing ADRD.

We examined whether plasma p-tau181 and p-tau217 are specific biomarkers of pathologically confirmed Alzheimer’s disease (AD). In particular, we investigated the utility of plasma p-tau for differentiating AD from primary age-related tauopathy (PART), as well as AD with mixed pathologies. Data came from 269 older adults who participated in the Religious Orders Study or the Rush Memory and Aging Project. Blood samples were collected during annual clinical evaluations. Participants died and underwent brain autopsy. P-tau181 and p-tau217 were quantified in the plasma samples proximate to death (average interval before death: 1.4 years) using Lilly-developed MSD immunoassays. Uniform neuropathologic evaluations assessed AD, PART, and other common degenerative and cerebrovascular conditions. Plasma p-tau217 was more strongly correlated with brain β-amyloid and paired helical filament tau (PHFtau) tangles than p-tau181. Both p-tau markers were associated with greater odds of AD, but p-tau217 had higher accuracy (area under the ROC curve (AUC): 0.83) than p-tau181 (AUC: 0.76). Plasma p-tau markers were almost exclusively associated with AD pathologic indices with the exception of cerebral amyloid angiopathy. Compared to p-tau181, p-tau217 showed a higher AUC (0.82 versus 0.74) in differentiating AD from PART. For either p-tau, we did not observe a level difference between individuals with AD alone and those with mixed AD pathologies. In summary, plasma p-tau181and p-tau217 were specifically associated with AD pathological changes. Further, our data provide initial evidence that p-tau217 may be able to differentiate between AD and PART in individuals with comparable burdens of tau tangle pathology. These results demonstrate the specificity of p-tau217 for AD, supporting its use to identify patients suitable for anti-AD therapies including β-amyloid immunotherapies.

Late-onset Alzheimer's disease (LOAD) is a polygenic disorder with a long prodromal phase, making early diagnosis challenging. Twin studies estimate LOAD as 60-80% heritable, and while common genetic variants can account for 30% of this heritability, nearly 70% remains "missing". Polygenic risk scores (PRS) leverage combined effects of many loci to predict LOAD risk, but often lack sensitivity to preclinical disease changes, limiting clinical utility. Our group has built and published on a resilience phenotype to model better-than-expected cognition give amyloid pathology burden and hypothesized it may assist in preclinical polygenic risk prediction. Thus, we built a LOAD PRS and a resilience PRS and evaluated both in predicting cognition in a dementia-free cohort (N=254). The LOAD PRS had a significant main effect on baseline memory (β=-0.18, P=1.68E-03). Both the LOAD PRS (β=-0.03, P=1.19E-03) and the resilience PRS (β=0.02, P=0.03) had significant main effects on annual memory decline. The resilience PRS interacted with CSF Aβ on baseline memory (β=-6.04E-04, P=0.02), whereby it predicted baseline memory among Aβ+ individuals (β=0.44, P=0.01) but not among Aβ- individuals (β=0.06, P=0.46). Excluding APOE from PRS resulted in mainly LOAD PRS associations attenuating, but notably the resilience PRS interaction with CSF Aβ and selective prediction among Aβ+ individuals was consistent. Although the resilience PRS is currently somewhat limited in scope from the phenotype's cross-sectional nature, our results suggest that the resilience PRS may be a promising tool in assisting in preclinical disease risk prediction among dementia-free and Aβ+ individuals, though replication and fine-tuning are needed.

Alzheimer’s disease (AD) is a heterogeneous disorder with abnormalities in multiple biological domains. In an advanced machine learning analysis of postmortem brain and in vivo blood multi-omics molecular data ( N = 1863), we integrated epigenomic, transcriptomic, proteomic, and metabolomic profiles into a multilevel biological AD taxonomy. We obtained a personalized multilevel molecular index of AD dementia progression that predicts severity of neuropathologies, and identified three robust molecular-based subtypes that explain much of the pathologic and clinical heterogeneity of AD. These subtypes present distinct patterns of alteration in DNA methylation, RNA, proteins, and metabolites, identifiable in the brain and subsequently in blood. In addition, the genetic variations that predispose to the various AD subtypes in brain predict distinct spatial patterns of alteration in cell types, suggesting a unique influence of each putative AD variant on neuropathological mechanisms. These observations support that an individually tailored multi-omics molecular taxonomy of AD may represent distinct targets for preventive or treatment interventions.

Alzheimer’s disease (AD) is defined by the presence of abundant amyloid-β (Aβ) and tau neuropathology. While this neuropathology is necessary for AD diagnosis, it is not sufficient for causing cognitive impairment. Up to one third of community dwelling older adults harbor intermediate to high levels of AD neuropathology at death yet demonstrate no significant cognitive impairment. Conversely, there are individuals who exhibit dementia with no gross explanatory neuropathology. In prior studies, synapse loss correlated with cognitive impairment. To understand how synaptic composition changes in relation to neuropathology and cognition, multiplexed liquid chromatography mass-spectrometry was used to quantify enriched synaptic proteins from the parietal association cortex of 100 subjects with contrasting levels of AD pathology and cognitive performance. 123 unique proteins were significantly associated with diagnostic category. Functional analysis showed enrichment of serotonin release and oxidative phosphorylation categories in normal (cognitively unimpaired, low neuropathology) and “resilient” (unimpaired despite AD pathology) individuals. In contrast, frail individuals, (low pathology, impaired cognition) showed a metabolic shift towards glycolysis and increased presence of proteasome subunits.

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) and fibromyalgia have overlapping neurologic symptoms particularly disabling fatigue. This has given rise to the question whether they are distinct central nervous system (CNS) entities or is one an extension of the other. To investigate this, we used unbiased quantitative mass spectrometry-based proteomics to examine the most proximal fluid to the brain, cerebrospinal fluid (CSF). This was to ascertain if the proteome profile of one was the same or different from the other. We examined two separate groups of ME/CFS, one with (n=15) and one without (n=15) fibromyalgia. We quantified a total of 2,083 proteins using immunoaffinity depletion, tandem mass tag isobaric labeling and offline two-dimensional liquid chromatography coupled to tandem mass spectrometry, including 1,789 that were quantified in all the CSF samples. ANOVA analysis did not yield any proteins with an adjusted p-value < 0.05. This supports the notion that ME/CFS and fibromyalgia as currently defined are not distinct entities.

Proteinaceous aggregates containing α-synuclein protein called Lewy bodies in the substantia nigra is a hallmark of Parkinson's disease. The molecular mechanisms of Lewy body formation and associated neuronal loss remain largely unknown. To gain insights into proteins and pathways associated with Lewy body pathology, we performed quantitative profiling of the proteome. We analyzed substantia nigra tissue from 51 subjects arranged into three groups: cases with Lewy body pathology, Lewy body-negative controls with matching neuronal loss, and controls with no neuronal loss. Using a label-free liquid chromatography-tandem mass spectrometry (LC-MS/MS) approach, we characterized the proteome both in terms of protein abundances and peptide modifications. Statistical testing for differential abundance of the most abundant 2963 proteins, followed by pathway enrichment and Bayesian learning of the causal network structure, was performed to identify likely drivers of Lewy body formation and dopaminergic neuronal loss. The identified pathways include (1) Arp2/3 complex-mediated actin nucleation; (2) synaptic function; (3) poly(A) RNA binding; (4) basement membrane and endothelium; and (5) hydrogen peroxide metabolic process. According to the data, the endothelial/basement membrane pathway is tightly connected with both pathologies and likely to be one of the drivers of neuronal loss. The poly(A) RNA-binding proteins, including the ones relevant to other neurodegenerative disorders (e.g., TDP-43 and FUS), have a strong inverse correlation with Lewy bodies and may reflect an alternative mechanism of nigral neurodegeneration.

Proteomic investigations of Alzheimer’s and Parkinson’s disease have provided valuable insights into neurodegenerative disorders. Thus far, these investigations have largely been restricted to bottom-up approaches, hindering the degree to which one can characterize a protein’s “intact” state. Top-down proteomics (TDP) overcomes this limitation; however, it is typically limited to observing only the most abundant proteoforms and of a relatively small size. Therefore, fractionation techniques are commonly used to reduce sample complexity. Here, we investigate gas-phase fractionation through high-field asymmetric waveform ion mobility spectrometry (FAIMS) within TDP. Utilizing a high complexity sample derived from Alzheimer’s disease (AD) brain tissue, we describe how the addition of FAIMS to TDP can robustly improve the depth of proteome coverage. For example, implementation of FAIMS with external compensation voltage (CV) stepping at −50, −40, and −30 CV could more than double the mean number of non-redundant proteoforms, genes, and proteome sequence coverage compared to without FAIMS. We also found that FAIMS can influence the transmission of proteoforms and their charge envelopes based on their size. Importantly, FAIMS enabled the identification of intact amyloid beta (Aβ) proteoforms, including the aggregation-prone Aβ(1–42) variant which is strongly linked to AD. Raw data and associated files have been deposited to the ProteomeXchange Consortium via the MassIVE data repository with data set identifier PXD023607.

Age-related neuropathologies progressively impair cognitive abilities by damaging synaptic function. We aimed to identify key components within the presynaptic SNARE (soluble N-ethylmaleimide-sensitive factor attachment protein receptor) machinery associated with cognitive performance and estimate their potential contribution to brain reserve in old age. We used targeted SRM proteomics to quantify amounts of 60 peptides, encoded in 30 different genes, in postmortem specimens of the prefrontal cortex from 1209 participants of two aging studies, with available antemortem cognitive evaluations and postmortem neuropathologic assessments. We found that select (but not all) proteoforms are strongly associated with cognitive function and the burden of Alzheimer's disease (AD) pathology. Specifically, greater abundance of STX1A (but not other syntaxins), SYT12, full-length SNAP25, and the GABAergic STXBP1 variant were robustly associated with better cognitive performance. By contrast, greater abundance of other presynaptic proteins (e.g., STXBP5 or tomosyn, STX7, or SYN2) showed a negative influence on cognition. Regression models adjusting for demographic and pathologic variables showed that altered levels of these protein species explained 7.7% additional between-subject variance in cognition (more than any individual age-related neuropathology in the model), suggesting that these molecules constitute key elements of brain reserve. Network analyses indicated that those peptides associated with brain reserve, and closest to the SNARE fusogenic activity, showed greater centrality measures and were better connected in the network. Validation assays confirmed the selective loss of the STX1A (but not STX1B) isoform in cognitively impaired cases. In rodent and human brains, STX1A was selectively located at glutamatergic terminals. However, in AD brains, STX1A was redistributed adjacent to neuritic pathology, and markedly expressed in astrocytes. Our study provides strong evidence, indicating that select presynaptic proteins are key in maintaining brain reserve. Compromised ability to sustain expression levels of these proteins may trigger synaptic dysfunction and concomitant cognitive impairment.

Background: Approximately 85% of the U.S. military active duty population is male and less than 50 years of age, with elevated levels of known risk factors for oropharyngeal squamous cell carcinoma (OPSCC), including smoking, excessive use of alcohol, and greater numbers of sexual partners, and elevated prevalence of human papilloma virus (HPV). Given the recent rise in incidence of OPSCC related to the HPV, the Department of Defense Serum Repository provides an unparalleled resource for longitudinal studies of OPSCC in the military for the identification of early detection biomarkers. Methods: We identified 175 patients diagnosed with OPSCC with 175 matched healthy controls and retrieved a total of 978 serum samples drawn at the time of diagnosis, 2 and 4 years prior to diagnosis, and 2 years after diagnosis. Following immunoaffinity depletion, serum samples were analyzed by targeted proteomics assays for multiplexed quantification of a panel of 146 candidate protein biomarkers from the curated literature. Results: Using a Random Forest machine learning approach, we derived a 13-protein signature that distinguishes cases versus controls based on longitudinal changes in serum protein concentration. The abundances of each of the 13 proteins remain constant over time in control subjects. The AUC for the derived Random Forest classifier was 0.90. Conclusions: This 13-protein classifier is highly promising for detection of OPSCC prior to overt symptoms. Impact: Use of longitudinal samples has significant potential to identify biomarkers for detection and risk stratification.

Literature suggests vascular endothelial growth factor A (VEGFA) is protective among those at highest risk for Alzheimer’s disease (AD). Apolipoprotein E (APOE) ε4 allele carriers represent a highly susceptible population for cognitive decline, and VEGF may confer distinct protection among APOE-ε4 carriers. We evaluated interactions between cortical expression of 10 VEGF gene family members and APOE-ε4 genotype to clarify which VEGF genes modify the association between APOE-ε4 and cognitive decline. Data were obtained from the Religious Orders Study and Rush Memory and Aging Project (N=531). Linear regression assessed interactions on global cognition. VEGF genes NRP1 and VEGFA interacted with APOE-ε4 on cognitive performance (p.fdr

Currently, no oral medications are available for type 1 diabetes (T1D). While our recent randomized placebo-controlled T1D trial revealed that oral verapamil had short-term beneficial effects, their duration and underlying mechanisms remained elusive. Now, our global T1D serum proteomics analysis identified chromogranin A (CHGA), a T1D-autoantigen, as the top protein altered by verapamil and as a potential therapeutic marker and revealed that verapamil normalizes serum CHGA levels and reverses T1D-induced elevations in circulating proinflammatory T-follicular-helper cell markers. RNA-sequencing further confirmed that verapamil regulates the thioredoxin system and promotes an anti-oxidative, anti-apoptotic and immunomodulatory gene expression profile in human islets. Moreover, continuous use of oral verapamil delayed T1D progression, promoted endogenous beta-cell function and lowered insulin requirements and serum CHGA levels for at least 2 years and these benefits were lost upon discontinuation. Thus, the current studies provide crucial mechanistic and clinical insight into the beneficial effects of verapamil in T1D.

26 pages, 11 figures.-- This article is licensed under a Creative Commons Attribution 4.0 International License, [Background] Theoretical analysis of signaling pathways can provide a substantial amount of insight into their function. One particular area of research considers signaling pathways capable of assuming two or more stable states given the same amount of signaling ligand. This phenomenon of bistability can give rise to switch-like behavior, a mechanism that governs cellular decision making. Investigation of whether or not a signaling pathway can confer bistability and switch-like behavior, without knowledge of specific kinetic rate constant values, is a mathematically challenging problem. Recently a technique based on optimization has been introduced, which is capable of finding example parameter values that confer switch-like behavior for a given pathway. Although this approach has made it possible to analyze moderately sized pathways, it is limited to reaction networks that presume a uniterminal structure. It is this limited structure we address by developing a general technique that applies to any mass action reaction network with conservation laws., [Results] In this paper we developed a generalized method for detecting switch-like bistable behavior in any mass action reaction network with conservation laws. The method involves (1) construction of a constrained optimization problem using the determinant of the Jacobian of the underlying rate equations, (2) minimization of the objective function to search for conditions resulting in a zero eigenvalue, (3) computation of a confidence level that describes if the global minimum has been found and (4) evaluation of optimization values, using either numerical continuation or directly simulating the ODE system, to verify that a bistability region exists. The generalized method has been tested on three motifs known to be capable of bistability., [Conclusions] We have developed a variation of an optimization-based method for the discovery of bistability, which is not limited to uniterminal chemical reaction networks. Successful completion of the method provides an S-shaped bifurcation diagram, which indicates that the network acts as a bistable switch for the given optimization parameters, This work was supported by the “Capturing Pathway Activity and Linking Outcomes through Multi-omic Data Integration” project (P.I. Jason McDermott) under the Laboratory Directed Research and Development Program at Pacific Northwest National Laboratory

Global and phosphoproteome profiling has demonstrated great utility for the analysis of clinical specimens. One barrier to the broad clinical application of proteomic profiling is the large amount of biological material required, particularly for phosphoproteomics─currently on the order of 25 mg wet tissue weight. For hematopoietic cancers such as acute myeloid leukemia (AML), the sample requirement is ≥10 million peripheral blood mononuclear cells (PBMCs). Across large study cohorts, this requirement will exceed what is obtainable for many individual patients/time points. For this reason, we were interested in the impact of differential peptide loading across multiplex channels on proteomic data quality. To achieve this, we tested a range of channel loading amounts (approximately the material obtainable from 5E5, 1E6, 2.5E6, 5E6, and 1E7 AML patient cells) to assess proteome coverage, quantification precision, and peptide/phosphopeptide detection in experiments utilizing isobaric tandem mass tag (TMT) labeling. As expected, fewer missing values were observed in TMT channels with higher peptide loading amounts compared to lower loadings. Moreover, channels with a lower loading have greater quantitative variability than channels with higher loadings. A statistical analysis showed that decreased loading amounts result in an increase in the type I error rate. We then examined the impact of differential loading on the detection of known differences between distinct AML cell lines. Similar patterns of increased data missingness and higher quantitative variability were observed as loading was decreased resulting in fewer statistical differences; however, we found good agreement in features identified as differential, demonstrating the value of this approach.

The associations of four proteins – AK4, ITPK1, HSPB2, and IGFBP5 – with cognitive function in older adults were largely unexplained by known brain pathologies. We examined the extent to which individual protein associations with cognitive decline were attributable to microstructural changes in the brain. This study included 521 participants (mean age 90.3, 65.9–108.3) with the postmortem reciprocal of transverse relaxation time (R(2)) magnetic resonance image. All participants came from one of two ongoing longitudinal cohorts of aging and dementia, the Religious Orders Study and Rush Memory and Aging Project. Higher abundance of AK4, HSPB2, and IGFBP5 was associated with faster cognitive decline, and mediated through lower postmortem R(2) in the frontal and temporal white matter regions. In contrast, higher abundance of ITPK1 was associated with slower cognitive decline, and mediated through higher postmortem R(2) in the frontal and temporal white matter regions. The association of four proteins - AK4, ITPK1, IGFBP5, and HSPB2 - with cognition in late life were explained via microstructural changes in the brain.

Phosphorylation of proteins is a key way cells regulate function, both at the individual protein level and at the level of signaling pathways. Kinases are responsible for phosphorylation of substrates, generally on serine, threonine, or tyrosine residues. Though particular sequence patterns can be identified that dictate whether a residue will be phosphorylated by a specific kinase, these patterns are not highly predictive of phosphorylation. The availability of large scale proteomic and phosphoproteomic data sets generated using mass-spectrometry-based approaches provides an opportunity to study the important relationship between kinase activity, substrate specificity, and phosphorylation. In this study, we analyze relationships between protein abundance and phosphopeptide abundance across more than 150 tumor samples and show that phosphorylation at specific phosphosites is not well correlated with overall kinase abundance. However, individual kinases show a clear and statistically significant difference in correlation among known phosphosite targets for that kinase and randomly selected phosphosites. We further investigate relationships between phosphorylation of known activating or inhibitory sites on kinases and phosphorylation of their target phosphosites. Combined with motif-based analysis, this approach can predict novel kinase targets and show which subsets of a kinase's target repertoire are specifically active in one condition versus another.

BACKGROUND: Brain proteins, including Insulin-like Growth Factor Binding Protein 5 (IGFBP-5), have been associated with cognitive dysfunction in aging. Mechanisms linking depression with cognition are poorly understood. We hypothesize that the association of depressive symptoms with cognition is mediated or modified by brain proteins. METHODS: IGFBP-5, HSPB2, AK4, ITPK1 and PLXNB1 were measured in dorsolateral prefrontal cortex in 1057 deceased participants, who underwent annual assessments of depressive symptoms and cognition for a mean of 8.9 years. The average number of depressive symptoms per year before a dementia diagnosis was calculated for each person. RESULTS: A one standard deviation above the mean IGFBP-5 was associated with a 14% higher odds of having more depressive symptoms (p0.070). LIMITATIONS: Participants were volunteers and self-selection bias limits the generalizability of our findings. In addition, we used self-reported data on depressive symptoms. However, we also used data on depression medications as sensitivity analyses to confirm findings. CONCLUSIONS: In old age, brain IGFBP-5 is associated with depressive symptoms and cognition. The association of depressive symptoms with cognitive decline is conditional on IGFBP-5.

Accumulation of β-amyloid (Aβ) is one of the hallmarks of Alzheimer’s disease. The deposition of β-amyloid plaques is likely to start years in advance of manifestation of clinical symptoms, although the exact timing is unknown. Over the years, Aβ peptides undergo both post-translational modification and stereoisomerization. Analysis of the resulting stereoisomers is particularly challenging because of their identical elemental composition and similar physicochemical properties. Herein, we have utilized our recently developed structures for lossless ion manipulations ion mobility-mass spectrometry platform (SLIM IM-MS), in conjunction with serpentine ultralong path with extended routing (SUPER), to baseline resolve four distinct sets of Aβ17–28 tryptic peptide epimers on a rapid (~1 s) time scale. We discovered that sodium adduct ions, [M + H + Na](2+), allowed baseline SLIM SUPER IM resolution for all Aβ epimer sets assessed, while such baseline separations were unachievable for their [M + 2H](2+) doubly protonated ions.

SummaryMitochondrial respiratory chain (RC) function requires the stoichiometric interaction among dozens of proteins but their co-regulation has not been defined in the human brain. Here, using quantitative proteomics across three independent cohorts we systematically characterized the co-regulation patterns of mitochondrial RC proteins in the human dorsolateral prefrontal cortex (DLPFC). Whereas the abundance of RC protein subunits that physically assemble into stable complexes were correlated, indicating their co-regulation, RC assembly factors exhibited modest co-regulation. Within complex I, nuclear DNA-encoded subunits exhibited >2.5-times higher co-regulation than mitochondrial (mt)DNA-encoded subunits. Moreover, mtDNA copy number was unrelated to mtDNA-encoded subunits abundance, suggesting that mtDNA content is not limiting. Alzheimer’s disease (AD) brains exhibited reduced abundance of complex I RC subunits, an effect largely driven by a 2-4% overall lower mitochondrial protein content. These findings provide foundational knowledge to identify molecular mechanisms contributing to age- and disease-related erosion of mitochondrial function in the human brain.

Motor resilience proteins may be a high value therapeutic target that offset the negative effects of pathologies on motor function. This study sought to identify cortical proteins associated with motor decline unexplained by brain pathologies that provide motor resilience. We studied 1226 older decedents with annual motor testing, postmortem brain pathologies and quantified 226 proteotypic peptides in prefrontal cortex. Twenty peptides remained associated with motor decline in models controlling for ten brain pathologies (FDR

Currently, no oral medications are available for type 1 diabetes (T1D). While our recent randomized placebo-controlled T1D trial revealed that oral verapamil had short-term beneficial effects, their duration and underlying mechanisms remained elusive. Now, our global T1D serum proteomics analysis identified chromogranin A (CHGA), a T1D-autoantigen, as the top protein altered by verapamil and as a potential therapeutic marker and revealed that verapamil normalizes serum CHGA levels and reverses T1D-induced elevations in circulating proinflammatory T-follicular-helper cell markers. RNA-sequencing further confirmed that verapamil regulates the thioredoxin system and promotes an anti-oxidative, anti-apoptotic and immunomodulatory gene expression profile in human islets. Moreover, continuous use of oral verapamil delayed T1D progression, promoted endogenous beta-cell function and lowered insulin requirements and serum CHGA levels for at least 2 years and these benefits were lost upon discontinuation. Thus, the current studies provide crucial mechanistic and clinical insight into the beneficial effects of verapamil in T1D.

Glioblastoma (GBM) is the most aggressive nervous system cancer. Understanding its molecular pathogenesis is crucial to improving diagnosis and treatment. Integrated analysis of genomic, proteomic, post-translational modification and metabolomic data on 99 treatment-naive GBMs provides insights to GBM biology. We identify key phosphorylation events (e.g., phosphorylated PTPN11 and PLCG1) as potential switches mediating oncogenic pathway activation, as well as potential targets for EGFR-, TP53-, and RB1-altered tumors. Immune subtypes with distinct immune cell types are discovered using bulk omics methodologies, validated by snRNA-seq, and correlated with specific expression and histone acetylation patterns. Histone H2B acetylation in classical-like and immune-low GBM is driven largely by BRDs, CREBBP, and EP300. Integrated metabolomic and proteomic data identify specific lipid distributions across subtypes and distinct global metabolic changes in IDH-mutated tumors. This work highlights biological relationships that could contribute to stratification of GBM patients for more effective treatment. Wang et al. perform integrated proteogenomic analysis of adult glioblastoma (GBM), including metabolomics, lipidomics, and single nuclei RNA-Seq, revealing insights into the immune landscape of GBM, cell-specific nature of EMT signatures, histone acetylation in classical GBM, and the existence of signaling hubs which could provide therapeutic vulnerabilities.

Background: Global and phosphoproteome profiling has demonstrated great utility for the analysis of clinical specimens. One major barrier to the broad clinical application of proteomic profiling is the large amount of biological material required, particularly for phosphoproteomics—currently on the order of 25 mg wet tissue weight, depending on tissue type. For hematopoietic cancers such as acute myeloid leukemia (AML), the sample requirement is in excess of 10 million (1E7) peripheral blood mononuclear cells (PBMCs). Throughout the course of a prospective study, this requirement will certainly exceed what is obtainable from many of the individual patients/timepoints. For this reason, we were interested in examining the impact of differential peptide loading across multiplex channels on proteomic data quality. Methods: To achieve this, we tested a range of channel loading amounts (20, 40, 100, 200, and 400 μg of tryptic peptides, or approximately the material obtainable from 5E5, 1E6, 2.5E6, 5E6, and 1E7 AML patient cells) to assess proteome coverage, quantification precision, and peptide/phosphopeptide detection in experiments utilizing isobaric tandem mass tag (TMT) labeling. Results: As expected, we found that fewer missing values are observed in TMT channels with higher peptide loading amounts compared to those with lower loading. Moreover, channels with lower loading amounts have greater quantitative variability than channels with higher loading amounts. Statistical analysis of the differences in means among the five loading groups showed that the 20 μg loading group was significantly different from the 400 μg loading group. However, no significant differences were detected among the 40, 100, 200 and 400 μg loading groups. Conclusions: These assessment data demonstrate the practical limits of loading differential quantities of peptides across channels in TMT multiplexes, and provide a basis for designing the optimal clinical proteomics study when specimen quantities are limited.

Age-related neuropathologies progressively impair cognitive abilities by damaging synaptic function. We aimed to identify key components within the presynaptic SNARE (soluble N-ethylmaleimide-sensitive factor attachment protein receptor) machinery associated with cognitive performance and estimate their potential contribution to brain reserve in old age. We used targeted SRM proteomics to quantify amounts of 60 peptides, encoded in 30 different genes, in postmortem specimens of the prefrontal cortex from 1209 participants of two aging studies, with available antemortem cognitive evaluations and postmortem neuropathologic assessments. We found that select (but not all) proteoforms are strongly associated with cognitive function and the burden of Alzheimer's disease (AD) pathology. Specifically, greater abundance of STX1A (but not other syntaxins), SYT12, full-length SNAP25, and the GABAergic STXBP1 variant were robustly associated with better cognitive performance. By contrast, greater abundance of other presynaptic proteins (e.g., STXBP5 or tomosyn, STX7, or SYN2) showed a negative influence on cognition. Regression models adjusting for demographic and pathologic variables showed that altered levels of these protein species explained 7.7% additional between-subject variance in cognition (more than any individual age-related neuropathology in the model), suggesting that these molecules constitute key elements of brain reserve. Network analyses indicated that those peptides associated with brain reserve, and closest to the SNARE fusogenic activity, showed greater centrality measures and were better connected in the network. Validation assays confirmed the selective loss of the STX1A (but not STX1B) isoform in cognitively impaired cases. In rodent and human brains, STX1A was selectively located at glutamatergic terminals. However, in AD brains, STX1A was redistributed adjacent to neuritic pathology, and markedly expressed in astrocytes. Our study provides strong evidence, indicating that select presynaptic proteins are key in maintaining brain reserve. Compromised ability to sustain expression levels of these proteins may trigger synaptic dysfunction and concomitant cognitive impairment.

The biological bases of longevity are not well understood, and there are limited biomarkers for the prediction of long life. We used a high‐throughput, discovery‐based proteomics approach to identify serum peptides and proteins that were associated with the attainment of longevity in a longitudinal study of community‐dwelling men age ≥65 years. Baseline serum in 1196 men were analyzed using liquid chromatography–ion mobility–mass spectrometry, and lifespan was determined during ~12 years of follow‐up. Men who achieved longevity (≥90% expected survival) were compared to those who died earlier. Rigorous statistical methods that controlled for false positivity were utilized to identify 25 proteins that were associated with longevity. All these proteins were in lower abundance in long‐lived men and included a variety involved in inflammation or complement activation. Lower levels of longevity‐associated proteins were also associated with better health status, but as time to death shortened, levels of these proteins increased. Pathway analyses implicated a number of compounds as important upstream regulators of the proteins and implicated shared networks that underlie the observed associations with longevity. Overall, these results suggest that complex pathways, prominently including inflammation, are linked to the likelihood of attaining longevity. This work may serve to identify novel biomarkers for longevity and to understand the biology underlying lifespan., In a cohort of older men, discovery‐based serum proteomics at baseline revealed protein biomarkers that were robustly associated with the achievement of longevity during ~12 years of subsequent follow‐up. Longevity‐associated proteins were frequently associated with inflammatory pathways.

The majority of methods for detecting differentially abundant proteins between samples in label-free LC-MS bottom-up proteomics experiments rely on statistically testing inferred protein abundances derived from peptide ionization intensities or averaging peptide level statistics. Here, we statistically test peptide ionization intensities directly and combine the resulting dependent P-values using the Empirical Brown's Method (EBM), avoiding error introduced through the estimation of protein abundances or summarizing test statistics. We show that on a spike-in proteomics dataset, a peptide level approach using EBM outperforms differential abundance detection using a protein level approach and several analysis workflows, including MSstats. Additionally, we demonstrate the effectiveness of this approach by detecting enriched proteins from an activity-based protein profiling dataset.

Background Identified as an Alzheimer’s disease (AD) susceptibility gene by genome wide-association studies, BIN1 has 10 isoforms that are expressed in the Central Nervous System (CNS). The distribution of these isoforms in different cell types, as well as their role in AD pathology still remains unclear. Methods Utilizing antibodies targeting specific BIN1 epitopes in human post-mortem tissue and analyzing mRNA expression data from purified microglia, we identified three isoforms expressed in neurons and astrocytes (isoforms 1, 2 and 3) and four isoforms expressed in microglia (isoforms 6, 9, 10 and 12). The abundance of selected peptides, which correspond to groups of BIN1 protein isoforms, was measured in dorsolateral prefrontal cortex, and their relation to neuropathological features of AD was assessed. Results Peptides contained in exon 7 of BIN1’s N-BAR domain were found to be significantly associated with AD-related traits and, particularly, tau tangles. Decreased expression of BIN1 isoforms containing exon 7 is associated with greater accumulation of tangles and subsequent cognitive decline, with astrocytic rather than neuronal BIN1 being the more likely culprit. These effects are independent of the BIN1 AD risk variant. Conclusions Exploring the molecular mechanisms of specific BIN1 isoforms expressed by astrocytes may open new avenues for modulating the accumulation of Tau pathology in AD.

Label-free quantitative proteomics has become an increasingly popular tool for profiling global protein abundances. However, one major limitation is the potential performance drift of the LC-MS platform over time, which, in turn, limits its utility for analyzing large-scale sample sets. To address this, we introduce an experimental and data analysis scheme based on a block design with common references within each block for enabling large-scale label-free quantification. In this scheme, a large number of samples (e.g., >100 samples) are analyzed in smaller and more manageable blocks, minimizing instrument drift and variability within individual blocks. Each designated block also contains common reference samples (e.g., controls) for normalization across all blocks. We demonstrated the robustness of this approach by profiling the proteome response of human macrophage THP-1 cells to 11 engineered nanomaterials at two different doses. A total of 116 samples were analyzed in six blocks, yielding an average coverage of 4500 proteins per sample. Following a common reference-based correction, 2537 proteins were quantified with high reproducibility without any imputation of missing values from 116 data sets. The data revealed the consistent quantification of proteins across all six blocks, as illustrated by the highly consistent abundances of house-keeping proteins in all samples and the high levels of correlation among samples from different blocks. The data also demonstrated that label-free quantification is robust and accurate enough to quantify even very subtle abundance changes as well as large fold-changes. Our streamlined workflow is easy to implement and can be readily adapted to other large cohort studies for reproducible label-free proteome quantification.

Responsible implementation of engineered nanomaterials (ENMs) into commercial applications is an important societal issue, driving demand for new approaches for rapid and comprehensive evaluation of their bioactivity and safety. An essential part of any research focused on identifying potential hazards of ENMs is the appropriate selection of biological endpoints to evaluate. Herein, we use a tiered strategy employing both targeted biological assays and untargeted quantitative proteomics to elucidate the biological responses of human THP-1 derived macrophages across a library of metal/metal oxide ENMs, raised as priority ENMs for investigation by NIEHS's Nanomaterial Health Implications Research (NHIR) program. Our results show that quantitative cellular proteome profiles readily distinguish ENM types based on their cytotoxic potential according to induction of biological processes and pathways involved in the cellular antioxidant response, TCA cycle, oxidative stress, endoplasmic reticulum stress, and immune responses as major processes impacted. Interestingly, bioinformatics analysis of differentially expressed proteins also revealed new biological processes that were influenced by all ENMs independent of their cytotoxic potential. These included biological processes that were previously implicated as mechanisms cells employ as adaptive responses to low levels of oxidative stress, including cell adhesion, protein translation and protein targeting. Unsupervised clustering revealed the most striking proteome changes that differentiated ENM classes highlight a small subset of proteins involved in the oxidative stress response (HMOX1), protein chaperone functions (HS71B, DNJB1), and autophagy (SQSTM), providing a potential new panel of markers of ENM-induced cellular stress. To our knowledge, the results represent the most comprehensive profiling of the biological responses to a library of ENMs conducted using quantitative mass spectrometry-based proteomics. The results provide a basis to identify the patterns of a diverse set of cellular pathways and biological processes impacted by ENM exposure in an important immune cell type, laying the foundation for multivariate, pathway-level structure activity assessments of ENMs in the future.

Background Mass spectrometry-based proteomics has become a powerful tool for the identification and quantification of proteins from a wide variety of biological specimens. To date, the majority of studies utilizing tissue samples have been carried out on prospectively collected fresh frozen or optimal cutting temperature (OCT) embedded specimens. However, such specimens are often difficult to obtain, in limited in supply, and clinical information and outcomes on patients are inherently delayed as compared to banked samples. Annotated formalin fixed, paraffin embedded (FFPE) tumor tissue specimens are available for research use from a variety of tissue banks, such as from the surveillance, epidemiology and end results (SEER) registries’ residual tissue repositories. Given the wealth of outcomes information associated with such samples, the reuse of archived FFPE blocks for deep proteomic characterization with mass spectrometry technologies would provide a valuable resource for population-based cancer studies. Further, due to the widespread availability of FFPE specimens, validation of specimen integrity opens the possibility for thousands of studies that can be conducted worldwide. Methods To examine the suitability of the SEER repository tissues for proteomic and phosphoproteomic analysis, we analyzed 60 SEER patient samples, with time in storage ranging from 7 to 32 years; 60 samples with expression proteomics and 18 with phosphoproteomics, using isobaric labeling. Linear modeling and gene set enrichment analysis was used to evaluate the impacts of collection site and storage time. Results All samples, regardless of age, yielded suitable protein mass after extraction for expression analysis and 18 samples yielded sufficient mass for phosphopeptide analysis. Although peptide, protein, and phosphopeptide identifications were reduced by 50, 20 and 76% respectively, from comparable OCT specimens, we found no statistically significant differences in protein quantitation correlating with collection site or specimen age. GSEA analysis of GO-term level measurements of protein abundance differences between FFPE and OCT embedded specimens suggest that the formalin fixation process may alter representation of protein categories in the resulting dataset. Conclusions These studies demonstrate that residual FFPE tissue specimens, of varying age and collection site, are a promising source of protein for proteomic investigations if paired with rigorously verified mass spectrometry workflows. Electronic supplementary material The online version of this article (10.1186/s12014-018-9202-4) contains supplementary material, which is available to authorized users.

Here we present an optimized workflow for global proteome and phosphoproteome analysis of tissues or cell lines that uses isobaric tags (TMT (tandem mass tags)-10) for multiplexed analysis and relative quantification, and provides 3× higher throughput than iTRAQ (isobaric tags for absolute and relative quantification)-4-based methods with high intra- and inter-laboratory reproducibility. The workflow was systematically characterized and benchmarked across three independent laboratories using two distinct breast cancer subtypes from patient-derived xenograft models to enable assessment of proteome and phosphoproteome depth and quantitative reproducibility. Each plex consisted of ten samples, each being 300 μg of peptide derived from 0.88. The maximum deviation for the phosphoproteome coverage was 37,000 quantified phosphosites per sample and differential quantification correlations of r > 0.72. The full procedure, including sample processing and data generation, can be completed within 10 d for ten tissue samples, and 100 samples can be analyzed in −4 months using a single LC-MS/MS instrument. The high quality, depth, and reproducibility of the data obtained both within and across laboratories should enable new biological insights to be obtained from mass spectrometry-based proteomics analyses of cells and tissues together with proteogenomic data integration.