Proteomic associations with forced expiratory volume: a Mendelian randomisation study.

BACKGROUND: A decline in forced expiratory volume (FEV1) is a hallmark of respiratory diseases that are an important cause of morbidity among the elderly. While some data exist on biomarkers that are related to FEV1, we sought to do a systematic analysis of causal relations of biomarkers with FEV1. METHODS: Data from the population-based AGES-Reykjavik study were used. Serum proteomic measurements were done using 4782 DNA aptamers (SOMAmers). Data from 1479 participants with spirometric data were used to assess the association of SOMAmer measurements with FEV1 using linear regression. Bi-directional two-sample Mendelian randomisation (MR) analyses were done to assess causal relations of observationally associated SOMAmers with FEV1, using genotype and SOMAmer data from 5368 AGES-Reykjavik participants and genetic associations with FEV1 from a publicly available GWAS (n = 400,102). RESULTS: In observational analyses, 530 SOMAmers were associated with FEV1 after multiple testing adjustment (FDR < 0.05). The most significant were Retinoic Acid Receptor Responder 2 (RARRES2), R-Spondin 4 (RSPO4) and Alkaline Phosphatase, Placental Like 2 (ALPPL2). Of the 257 SOMAmers with genetic instruments available, eight were associated with FEV1 in MR analyses. Three were directionally consistent with the observational estimate, Thrombospondin 2 (THBS2), Endoplasmic Reticulum Oxidoreductase 1 Beta (ERO1B) and Apolipoprotein M (APOM). THBS2 was further supported by a colocalization analysis. Analyses in the reverse direction, testing whether changes in SOMAmer levels were caused by changes in FEV1, were performed but no significant associations were found after multiple testing adjustments. CONCLUSIONS: In summary, this large scale proteogenomic analyses of FEV1 reveals circulating protein markers of FEV1, as well as several proteins with potential causality to lung function.

A proteomic analysis of atrial fibrillation in a prospective longitudinal cohort (AGES-Reykjavik study).

AIMS: Atrial fibrillation (AF) is associated with high risk of comorbidities and mortality. Our aim was to examine causal and predictive relationships between 4137 serum proteins and incident AF in the prospective population-based Age, Gene/Environment Susceptibility-Reykjavik (AGES-Reykjavik) study. METHODS AND RESULTS: The study included 4765 participants, of whom 1172 developed AF. Cox proportional hazards regression models were fitted for 4137 baseline protein measurements adjusting for known risk factors. Protein associations were tested for replication in the Cardiovascular Health Study (CHS). Causal relationships were examined in a bidirectional, two-sample Mendelian randomization analysis. The time-dependent area under the receiver operating characteristic curve (AUC)-statistic was examined as protein levels and an AF-polygenic risk score (PRS) were added to clinical risk models. The proteomic signature of incident AF consisted of 76 proteins, of which 63 (83%) were novel and 29 (38%) were replicated in CHS. The signature included both N-terminal prohormone of brain natriuretic peptide (NT-proBNP)-dependent (e.g. CHST15, ATP1B1, and SVEP1) and independent components (e.g. ASPN, AKR1B, and LAMA1/LAMB1/LAMC1). Nine causal candidates were identified (TAGLN, WARS, CHST15, CHMP3, COL15A1, DUSP13, MANBA, QSOX2, and SRL). The reverse causal analysis suggested that most AF-associated proteins were affected by the genetic liability to AF. N-terminal prohormone of brain natriuretic peptide improved the prediction of incident AF events close to baseline with further improvements gained by the AF-PRS at all time points. CONCLUSION: The AF proteomic signature includes biologically relevant proteins, some of which may be causal. It mainly reflects an NT-proBNP-dependent consequence of the genetic liability to AF. N-terminal prohormone of brain natriuretic peptide is a promising marker for incident AF in the short term, but risk assessment incorporating a PRS may improve long-term risk assessment.

The Proteomic Profile of Interstitial Lung Abnormalities.

Rationale: Knowledge on biomarkers of interstitial lung disease is incomplete. Interstitial lung abnormalities (ILAs) are radiologic changes that may present in its early stages. Objectives: To uncover blood proteins associated with ILAs using large-scale proteomics methods. Methods: Data from two prospective cohort studies, the AGES-Reykjavik (Age, Gene/Environment Susceptibility-Reykjavik) study (N = 5,259) for biomarker discovery and the COPDGene (Genetic Epidemiology of COPD) study (N = 4,899) for replication, were used. Blood proteins were measured using DNA aptamers, targeting more than 4,700 protein analytes. The association of proteins with ILAs and ILA progression was assessed with regression modeling, as were associations with genetic risk factors. Adaptive Least Absolute Shrinkage and Selection Operator models were applied to bootstrap data samples to discover sets of proteins predictive of ILAs and their progression. Measurements and Main Results: Of 287 associations, SFTPB (surfactant protein B) (odds ratio [OR], 3.71 [95% confidence interval (CI), 3.20-4.30]; P = 4.28 × 10-67), SCGB3A1 (Secretoglobin family 3A member 1) (OR, 2.43 [95% CI, 2.13-2.77]; P = 8.01 × 10-40), and WFDC2 (WAP four-disulfide core domain protein 2) (OR, 2.42 [95% CI, 2.11-2.78]; P = 4.01 × 10-36) were most significantly associated with ILA in AGES-Reykjavik and were replicated in COPDGene. In AGES-Reykjavik, concentrations of SFTPB were associated with the rs35705950 MUC5B (mucin 5B) promoter polymorphism, and SFTPB and WFDC2 had the strongest associations with ILA progression. Multivariate models of ILAs in AGES-Reykjavik, ILAs in COPDGene, and ILA progression in AGES-Reykjavik had validated areas under the receiver operating characteristic curve of 0.880, 0.826, and 0.824, respectively. Conclusions: Novel, replicated associations of ILA, its progression, and genetic risk factors with numerous blood proteins are demonstrated as well as machine-learning-based models with favorable predictive potential. Several proteins are revealed as potential markers of early fibrotic lung disease.

Human gut microbes impact host serum metabolome and insulin sensitivity.

Insulin resistance is a forerunner state of ischaemic cardiovascular disease and type 2 diabetes. Here we show how the human gut microbiome impacts the serum metabolome and associates with insulin resistance in 277 non-diabetic Danish individuals. The serum metabolome of insulin-resistant individuals is characterized by increased levels of branched-chain amino acids (BCAAs), which correlate with a gut microbiome that has an enriched biosynthetic potential for BCAAs and is deprived of genes encoding bacterial inward transporters for these amino acids. Prevotella copri and Bacteroides vulgatus are identified as the main species driving the association between biosynthesis of BCAAs and insulin resistance, and in mice we demonstrate that P. copri can induce insulin resistance, aggravate glucose intolerance and augment circulating levels of BCAAs. Our findings suggest that microbial targets may have the potential to diminish insulin resistance and reduce the incidence of common metabolic and cardiovascular disorders.

Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota.

In recent years, several associations between common chronic human disorders and altered gut microbiome composition and function have been reported. In most of these reports, treatment regimens were not controlled for and conclusions could thus be confounded by the effects of various drugs on the microbiota, which may obscure microbial causes, protective factors or diagnostically relevant signals. Our study addresses disease and drug signatures in the human gut microbiome of type 2 diabetes mellitus (T2D). Two previous quantitative gut metagenomics studies of T2D patients that were unstratified for treatment yielded divergent conclusions regarding its associated gut microbial dysbiosis. Here we show, using 784 available human gut metagenomes, how antidiabetic medication confounds these results, and analyse in detail the effects of the most widely used antidiabetic drug metformin. We provide support for microbial mediation of the therapeutic effects of metformin through short-chain fatty acid production, as well as for potential microbiota-mediated mechanisms behind known intestinal adverse effects in the form of a relative increase in abundance of Escherichia species. Controlling for metformin treatment, we report a unified signature of gut microbiome shifts in T2D with a depletion of butyrate-producing taxa. These in turn cause functional microbiome shifts, in part alleviated by metformin-induced changes. Overall, the present study emphasizes the need to disentangle gut microbiota signatures of specific human diseases from those of medication.

The genome of a Late Pleistocene human from a Clovis burial site in western Montana.

Clovis, with its distinctive biface, blade and osseous technologies, is the oldest widespread archaeological complex defined in North America, dating from 11,100 to 10,700 (14)C years before present (bp) (13,000 to 12,600 calendar years bp). Nearly 50 years of archaeological research point to the Clovis complex as having developed south of the North American ice sheets from an ancestral technology. However, both the origins and the genetic legacy of the people who manufactured Clovis tools remain under debate. It is generally believed that these people ultimately derived from Asia and were directly related to contemporary Native Americans. An alternative, Solutrean, hypothesis posits that the Clovis predecessors emigrated from southwestern Europe during the Last Glacial Maximum. Here we report the genome sequence of a male infant (Anzick-1) recovered from the Anzick burial site in western Montana. The human bones date to 10,705 ± 35 (14)C years bp (approximately 12,707-12,556 calendar years bp) and were directly associated with Clovis tools. We sequenced the genome to an average depth of 14.4× and show that the gene flow from the Siberian Upper Palaeolithic Mal'ta population into Native American ancestors is also shared by the Anzick-1 individual and thus happened before 12,600 years bp. We also show that the Anzick-1 individual is more closely related to all indigenous American populations than to any other group. Our data are compatible with the hypothesis that Anzick-1 belonged to a population directly ancestral to many contemporary Native Americans. Finally, we find evidence of a deep divergence in Native American populations that predates the Anzick-1 individual.

Metabolite ratios as potential biomarkers for type 2 diabetes: a DIRECT study.

AIMS/HYPOTHESIS: Circulating metabolites have been shown to reflect metabolic changes during the development of type 2 diabetes. In this study we examined the association of metabolite levels and pairwise metabolite ratios with insulin responses after glucose, glucagon-like peptide-1 (GLP-1) and arginine stimulation. We then investigated if the identified metabolite ratios were associated with measures of OGTT-derived beta cell function and with prevalent and incident type 2 diabetes. METHODS: We measured the levels of 188 metabolites in plasma samples from 130 healthy members of twin families (from the Netherlands Twin Register) at five time points during a modified 3 h hyperglycaemic clamp with glucose, GLP-1 and arginine stimulation. We validated our results in cohorts with OGTT data (n = 340) and epidemiological case-control studies of prevalent (n = 4925) and incident (n = 4277) diabetes. The data were analysed using regression models with adjustment for potential confounders. RESULTS: There were dynamic changes in metabolite levels in response to the different secretagogues. Furthermore, several fasting pairwise metabolite ratios were associated with one or multiple clamp-derived measures of insulin secretion (all p < 9.2 × 10-7). These associations were significantly stronger compared with the individual metabolite components. One of the ratios, valine to phosphatidylcholine acyl-alkyl C32:2 (PC ae C32:2), in addition showed a directionally consistent positive association with OGTT-derived measures of insulin secretion and resistance (p ≤ 5.4 × 10-3) and prevalent type 2 diabetes (ORVal_PC ae C32:2 2.64 [ß 0.97 ± 0.09], p = 1.0 × 10-27). Furthermore, Val_PC ae C32:2 predicted incident diabetes independent of established risk factors in two epidemiological cohort studies (HRVal_PC ae C32:2 1.57 [ß 0.45 ± 0.06]; p = 1.3 × 10-15), leading to modest improvements in the receiver operating characteristics when added to a model containing a set of established risk factors in both cohorts (increases from 0.780 to 0.801 and from 0.862 to 0.865 respectively, when added to the model containing traditional risk factors + glucose). CONCLUSIONS/INTERPRETATION: In this study we have shown that the Val_PC ae C32:2 metabolite ratio is associated with an increased risk of type 2 diabetes and measures of insulin secretion and resistance. The observed effects were stronger than that of the individual metabolites and independent of known risk factors.

Early differences in islets from prediabetic NOD mice: combined microarray and proteomic analysis.

AIMS/HYPOTHESIS: Type 1 diabetes is an endocrine disease where a long preclinical phase, characterised by immune cell infiltration in the islets of Langerhans, precedes elevated blood glucose levels and disease onset. Although several studies have investigated the role of the immune system in this process of insulitis, the importance of the beta cells themselves in the initiation of type 1 diabetes is less well understood. The aim of this study was to investigate intrinsic differences present in the islets from diabetes-prone NOD mice before the onset of insulitis. METHODS: The islet transcriptome and proteome of 2-3-week-old mice was investigated by microarray and 2-dimensional difference gel electrophoresis (2D-DIGE), respectively. Subsequent analyses using sophisticated pathway analysis and ranking of differentially expressed genes and proteins based on their relevance in type 1 diabetes were performed. RESULTS: In the preinsulitic period, alterations in general pathways related to metabolism and cell communication were already present. Additionally, our analyses pointed to an important role for post-translational modifications (PTMs), especially citrullination by PAD2 and protein misfolding due to low expression levels of protein disulphide isomerases (PDIA3, 4 and 6), as causative mechanisms that induce beta cell stress and potential auto-antigen generation. CONCLUSIONS/INTERPRETATION: We conclude that the pancreatic islets, irrespective of immune differences, may contribute to the initiation of the autoimmune process. DATA AVAILABILITY: All microarray data are available in the ArrayExpress database ( www.ebi.ac.uk/arrayexpress ) under accession number E-MTAB-5264.

Proteomic prediction of incident heart failure and its main subtypes.

AIM: To examine the ability of serum proteins in predicting future heart failure (HF) events, including HF with reduced or preserved ejection fraction (HFrEF or HFpEF), in relation to event time, and with or without considering established HF-associated clinical variables. METHODS AND RESULTS: In the prospective population-based Age, Gene/Environment Susceptibility Reykjavik Study (AGES-RS), 440 individuals developed HF after their first visit with a median follow-up of 5.45 years. Among them, 167 were diagnosed with HFrEF and 188 with HFpEF. A least absolute shrinkage and selection operator regression model with non-parametric bootstrap were used to select predictors from an analysis of 4782 serum proteins, and several pre-established clinical parameters linked to HF. A subset of 8-10 distinct or overlapping serum proteins predicted different future HF outcomes, and C-statistics were used to assess discrimination, revealing proteins combined with a C-index of 0.80 for all incident HF, 0.78 and 0.80 for incident HFpEF or HFrEF, respectively. In the AGES-RS, protein panels alone encompassed the risk contained in the clinical information and improved the performance characteristics of prediction models based on N-terminal pro-B-type natriuretic peptide and clinical risk factors. Finally, the protein predictors performed particularly well close to the time of an HF event, an outcome that was replicated in the Cardiovascular Health Study. CONCLUSION: A small number of circulating proteins accurately predicted future HF in the AGES-RS cohort of older adults, and they alone encompass the risk information found in a collection of clinical data. Incident HF events were predicted up to 8 years, with predictor performance significantly improving for events occurring less than 1 year ahead, a finding replicated in an external cohort study.

Serum proteomics reveal APOE-ε4-dependent and APOE-ε4-independent protein signatures in Alzheimer's disease.

A deeper understanding of the molecular processes underlying late-onset Alzheimer's disease (LOAD) could aid in biomarker and drug target discovery. Using high-throughput serum proteomics in the prospective population-based Age, Gene/Environment Susceptibility-Reykjavik Study (AGES) cohort of 5,127 older Icelandic adults (mean age, 76.6 ± 5.6 years), we identified 303 proteins associated with incident LOAD over a median follow-up of 12.8 years. Over 40% of these proteins were associated with LOAD independently of APOE-ε4 carrier status, were implicated in neuronal processes and overlapped with LOAD protein signatures in brain and cerebrospinal fluid. We identified 17 proteins whose associations with LOAD were strongly dependent on APOE-ε4 carrier status, with mostly consistent associations in cerebrospinal fluid. Remarkably, four of these proteins (TBCA, ARL2, S100A13 and IRF6) were downregulated by APOE-ε4 yet upregulated due to LOAD, a finding replicated in external cohorts and possibly reflecting a response to disease onset. These findings highlight dysregulated pathways at the preclinical stages of LOAD, including those both independent of and dependent on APOE-ε4 status.

Proteomic analysis of Alzheimer's disease cerebrospinal fluid reveals alterations associated with APOE ε4 and atomoxetine treatment.

Alzheimer's disease (AD) is currently defined by the aggregation of amyloid-ß (Aß) and tau proteins in the brain. Although biofluid biomarkers are available to measure Aß and tau pathology, few markers are available to measure the complex pathophysiology that is associated with these two cardinal neuropathologies. Here, we characterized the proteomic landscape of cerebrospinal fluid (CSF) changes associated with Aß and tau pathology in 300 individuals using two different proteomic technologies-tandem mass tag mass spectrometry and SomaScan. Integration of both data types allowed for generation of a robust protein coexpression network consisting of 34 modules derived from 5242 protein measurements, including disease-relevant modules associated with autophagy, ubiquitination, endocytosis, and glycolysis. Three modules strongly associated with the apolipoprotein E ε4 (APOE ε4) AD risk genotype mapped to oxidant detoxification, mitogen-associated protein kinase signaling, neddylation, and mitochondrial biology and overlapped with a previously described lipoprotein module in serum. Alterations of all three modules in blood were associated with dementia more than 20 years before diagnosis. Analysis of CSF samples from an AD phase 2 clinical trial of atomoxetine (ATX) demonstrated that abnormal elevations in the glycolysis CSF module-the network module most strongly correlated to cognitive function-were reduced by ATX treatment. Clustering of individuals based on their CSF proteomic profiles revealed heterogeneity of pathological changes not fully reflected by Aß and tau.

Serum proteomics reveals APOE dependent and independent protein signatures in Alzheimer's disease.

The current demand for early intervention, prevention, and treatment of late onset Alzheimer's disease (LOAD) warrants deeper understanding of the underlying molecular processes which could contribute to biomarker and drug target discovery. Utilizing high-throughput proteomic measurements in serum from a prospective population-based cohort of older adults (n = 5,294), we identified 303 unique proteins associated with incident LOAD (median follow-up 12.8 years). Over 40% of these proteins were associated with LOAD independently of APOE-ε4 carrier status. These proteins were implicated in neuronal processes and overlapped with protein signatures of LOAD in brain and cerebrospinal fluid. We found 17 proteins which LOAD-association was strongly dependent on APOE-ε4 carrier status. Most of them showed consistent associations with LOAD in cerebrospinal fluid and a third had brain-specific gene expression. Remarkably, four proteins in this group (TBCA, ARL2, S100A13 and IRF6) were downregulated by APOE-ε4 yet upregulated as a consequence of LOAD as determined in a bi-directional Mendelian randomization analysis, reflecting a potential response to the disease onset. Accordingly, the direct association of these proteins to LOAD was reversed upon APOE-ε4 genotype adjustment, a finding which we replicate in an external cohort (n = 719). Our findings provide an insight into the dysregulated pathways that may lead to the development and early detection of LOAD, including those both independent and dependent on APOE-ε4. Importantly, many of the LOAD-associated proteins we find in the circulation have been found to be expressed - and have a direct link with AD - in brain tissue. Thus, the proteins identified here, and their upstream modulating pathways, provide a new source of circulating biomarker and therapeutic target candidates for LOAD.

A Large-Scale Genome-Wide Study of Gene-Sleep Duration Interactions for Blood Pressure in 811,405 Individuals from Diverse Populations.

Although both short and long sleep duration are associated with elevated hypertension risk, our understanding of their interplay with biological pathways governing blood pressure remains limited. To address this, we carried out genome-wide cross-population gene-by-short-sleep and long-sleep duration interaction analyses for three blood pressure traits (systolic, diastolic, and pulse pressure) in 811,405 individuals from diverse population groups. We discover 22 novel gene-sleep duration interaction loci for blood pressure, mapped to genes involved in neurological, thyroidal, bone metabolism, and hematopoietic pathways. Non-overlap between short sleep (12) and long sleep (10) interactions underscores the plausibility of distinct influences of both sleep duration extremes in cardiovascular health. With several of our loci reflecting specificity towards population background or sex, our discovery sheds light on the importance of embracing granularity when addressing heterogeneity entangled in gene-environment interactions, and in therapeutic design approaches for blood pressure management.

A Large-Scale Genome-Wide Study of Gene-Sleep Duration Interactions for Blood Pressure in 811,405 Individuals from Diverse Populations.

Although both short and long sleep duration are associated with elevated hypertension risk, our understanding of their interplay with biological pathways governing blood pressure remains limited. To address this, we carried out genome-wide cross-population gene-by-short-sleep and long-sleep duration interaction analyses for three blood pressure traits (systolic, diastolic, and pulse pressure) in 811,405 individuals from diverse population groups. We discover 22 novel gene-sleep duration interaction loci for blood pressure, mapped to 23 genes. Investigating these genes' functional implications shed light on neurological, thyroidal, bone metabolism, and hematopoietic pathways that necessitate future investigation for blood pressure management that caters to sleep health lifestyle. Non-overlap between short sleep (12) and long sleep (10) interactions underscores the plausible nature of distinct influences of both sleep duration extremes in cardiovascular health. Several of our loci are specific towards a particular population background or sex, emphasizing the importance of addressing heterogeneity entangled in gene-environment interactions, when considering precision medicine design approaches for blood pressure management.

Proteomic Analysis Identifies Circulating Proteins Associated With Plasma Amyloid-ß and Incident Dementia.

Background: Plasma amyloid-ß (Aß) (Aß42, Aß40, and Aß42/Aß40), biomarkers of the Alzheimer's form of dementia, are under consideration for clinical use. The associations of these peptides with circulating proteins may identify novel plasma biomarkers of dementia and inform peripheral factors influencing the levels of these peptides. Methods: We analyzed the association of these 3 plasma Aß measures with 4638 circulating proteins among a subset of the participants of the Atherosclerosis Risk in Communities (ARIC) study (midlife: n = 1955; late life: n = 2082), related the Aß-associated proteins with incident dementia in the overall ARIC cohort (midlife: n = 11,069, late life: n = 4110) with external replication in the Age, Gene/Environment Susceptibility (AGES)-Reykjavik Study (n = 4973), estimated the proportion of Aß variance explained, and conducted enrichment analyses to characterize the proteins associated with the plasma Aß peptides. Results: At midlife, of the 296 Aß-associated proteins, 8 were associated with incident dementia from midlife and late life in the ARIC study, and NPPB, IBSP, and THBS2 were replicated in the AGES-Reykjavik Study. At late life, of the 34 Aß-associated proteins, none were associated with incident dementia at midlife, and kidney function explained 10%, 12%, and 0.2% of the variance of Aß42, Aß40, and Aß42/Aß40, respectively. Aß42-associated proteins at midlife were found to be enriched in the liver, and those at late life were found to be enriched in the spleen. Conclusions: This study identifies circulating proteins associated with plasma Aß levels and incident dementia and informs peripheral factors associated with plasma Aß levels.

Proteomic Network Analysis of Alzheimer's Disease Cerebrospinal Fluid Reveals Alterations Associated with APOE ε4 Genotype and Atomoxetine Treatment.

Alzheimer's disease (AD) is currently defined at the research level by the aggregation of amyloid-ß (Aß) and tau proteins in brain. While biofluid biomarkers are available to measure Aß and tau pathology, few biomarkers are available to measure the complex pathophysiology that is associated with these two cardinal neuropathologies. Here we describe the proteomic landscape of cerebrospinal fluid (CSF) changes associated with Aß and tau pathology in 300 individuals as assessed by two different proteomic technologies-tandem mass tag (TMT) mass spectrometry and SomaScan. Harmonization and integration of both data types allowed for generation of a robust protein co-expression network consisting of 34 modules derived from 5242 protein measurements, including disease-relevant modules associated with autophagy, ubiquitination, endocytosis, and glycolysis. Three modules strongly associated with the apolipoprotein E ε4 (APOE ε4) AD risk genotype mapped to oxidant detoxification, mitogen associated protein kinase (MAPK) signaling, neddylation, and mitochondrial biology, and overlapped with a previously described lipoprotein module in serum. Neddylation and oxidant detoxification/MAPK signaling modules had a negative association with APOE ε4 whereas the mitochondrion module had a positive association with APOE ε4. The directions of association were consistent between CSF and blood in two independent longitudinal cohorts, and altered levels of all three modules in blood were associated with dementia over 20 years prior to diagnosis. Dual-proteomic platform analysis of CSF samples from an AD phase 2 clinical trial of atomoxetine (ATX) demonstrated that abnormal elevations in the glycolysis CSF module-the network module most strongly correlated to cognitive function-were reduced by ATX treatment. Individuals who had more severe glycolytic changes at baseline responded better to ATX. Clustering of individuals based on their CSF proteomic network profiles revealed ten groups that did not cleanly stratify by Aß and tau status, underscoring the heterogeneity of pathological changes not fully reflected by Aß and tau. AD biofluid proteomics holds promise for the development of biomarkers that reflect diverse pathologies for use in clinical trials and precision medicine.

Serum proteomics reveals APOE dependent and independent protein signatures in Alzheimer's disease.

The current demand for early intervention, prevention, and treatment of late onset Alzheimer's disease (LOAD) warrants deeper understanding of the underlying molecular processes which could contribute to biomarker and drug target discovery. Utilizing high-throughput proteomic measurements in serum from a prospective population-based cohort of older adults (n=5,294), we identified 303 unique proteins associated with incident LOAD (median follow-up 12.8 years). Over 40% of these proteins were associated with LOAD independently of APOE-ε4 carrier status. These proteins were implicated in neuronal processes and overlapped with protein signatures of LOAD in brain and cerebrospinal fluid. We found 17 proteins which LOAD-association was strongly dependent on APOE-ε4 carrier status. Most of them showed consistent associations with LOAD in cerebrospinal fluid and a third had brain-specific gene expression. Remarkably, four proteins in this group (TBCA, ARL2, S100A13 and IRF6) were downregulated by APOE-ε4 yet upregulated as a consequence of LOAD as determined in a bi-directional Mendelian randomization analysis, reflecting a potential response to the disease onset. Accordingly, the direct association of these proteins to LOAD was reversed upon APOE-ε4 genotype adjustment, a finding which we replicate in an external cohort (n=719). Our findings provide an insight into the dysregulated pathways that may lead to the development and early detection of LOAD, including those both independent and dependent on APOE-ε4. Importantly, many of the LOAD-associated proteins we find in the circulation have been found to be expressed - and have a direct link with AD - in brain tissue. Thus, the proteins identified here, and their upstream modulating pathways, provide a new source of circulating biomarker and therapeutic target candidates for LOAD.

Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models.

The application of multiple omics technologies in biomedical cohorts has the potential to reveal patient-level disease characteristics and individualized response to treatment. However, the scale and heterogeneous nature of multi-modal data makes integration and inference a non-trivial task. We developed a deep-learning-based framework, multi-omics variational autoencoders (MOVE), to integrate such data and applied it to a cohort of 789 people with newly diagnosed type 2 diabetes with deep multi-omics phenotyping from the DIRECT consortium. Using in silico perturbations, we identified drug-omics associations across the multi-modal datasets for the 20 most prevalent drugs given to people with type 2 diabetes with substantially higher sensitivity than univariate statistical tests. From these, we among others, identified novel associations between metformin and the gut microbiota as well as opposite molecular responses for the two statins, simvastatin and atorvastatin. We used the associations to quantify drug-drug similarities, assess the degree of polypharmacy and conclude that drug effects are distributed across the multi-omics modalities.

Identification of circulating proteins associated with general cognitive function among middle-aged and older adults.

Identifying circulating proteins associated with cognitive function may point to biomarkers and molecular process of cognitive impairment. Few studies have investigated the association between circulating proteins and cognitive function. We identify 246 protein measures quantified by the SomaScan assay as associated with cognitive function (p < 4.9E-5, n up to 7289). Of these, 45 were replicated using SomaScan data, and three were replicated using Olink data at Bonferroni-corrected significance. Enrichment analysis linked the proteins associated with general cognitive function to cell signaling pathways and synapse architecture. Mendelian randomization analysis implicated higher levels of NECTIN2, a protein mediating viral entry into neuronal cells, with higher Alzheimer's disease (AD) risk (p = 2.5E-26). Levels of 14 other protein measures were implicated as consequences of AD susceptibility (p < 2.0E-4). Proteins implicated as causes or consequences of AD susceptibility may provide new insight into the potential relationship between immunity and AD susceptibility as well as potential therapeutic targets.