Consistency across multi-omics layers in a drug-perturbed gut microbial community.

Multi-omics analyses are used in microbiome studies to understand molecular changes in microbial communities exposed to different conditions. However, it is not always clear how much each omics data type contributes to our understanding and whether they are concordant with each other. Here, we map the molecular response of a synthetic community of 32 human gut bacteria to three non-antibiotic drugs by using five omics layers (16S rRNA gene profiling, metagenomics, metatranscriptomics, metaproteomics and metabolomics). We find that all the omics methods with species resolution are highly consistent in estimating relative species abundances. Furthermore, different omics methods complement each other for capturing functional changes. For example, while nearly all the omics data types captured that the antipsychotic drug chlorpromazine selectively inhibits Bacteroidota representatives in the community, the metatranscriptome and metaproteome suggested that the drug induces stress responses related to protein quality control. Metabolomics revealed a decrease in oligosaccharide uptake, likely caused by Bacteroidota depletion. Our study highlights how multi-omics datasets can be utilized to reveal complex molecular responses to external perturbations in microbial communities.

Profiling the human intestinal environment under physiological conditions.

The spatiotemporal structure of the human microbiome1,2, proteome3 and metabolome4,5 reflects and determines regional intestinal physiology and may have implications for disease6. Yet, little is known about the distribution of microorganisms, their environment and their biochemical activity in the gut because of reliance on stool samples and limited access to only some regions of the gut using endoscopy in fasting or sedated individuals7. To address these deficiencies, we developed an ingestible device that collects samples from multiple regions of the human intestinal tract during normal digestion. Collection of 240 intestinal samples from 15 healthy individuals using the device and subsequent multi-omics analyses identified significant differences between bacteria, phages, host proteins and metabolites in the intestines versus stool. Certain microbial taxa were differentially enriched and prophage induction was more prevalent in the intestines than in stool. The host proteome and bile acid profiles varied along the intestines and were highly distinct from those of stool. Correlations between gradients in bile acid concentrations and microbial abundance predicted species that altered the bile acid pool through deconjugation. Furthermore, microbially conjugated bile acid concentrations exhibited amino acid-dependent trends that were not apparent in stool. Overall, non-invasive, longitudinal profiling of microorganisms, proteins and bile acids along the intestinal tract under physiological conditions can help elucidate the roles of the gut microbiome and metabolome in human physiology and disease.

Immobility-associated thromboprotection is conserved across mammalian species from bear to human.

Venous thromboembolism (VTE) comprising deep venous thrombosis and pulmonary embolism is a major cause of morbidity and mortality. Short-term immobility-related conditions are a major risk factor for the development of VTE. Paradoxically, long-term immobilized free-ranging hibernating brown bears and paralyzed spinal cord injury (SCI) patients are protected from VTE. We aimed to identify mechanisms of immobility-associated VTE protection in a cross-species approach. Mass spectrometry-based proteomics revealed an antithrombotic signature in platelets of hibernating brown bears with heat shock protein 47 (HSP47) as the most substantially reduced protein. HSP47 down-regulation or ablation attenuated immune cell activation and neutrophil extracellular trap formation, contributing to thromboprotection in bears, SCI patients, and mice. This cross-species conserved platelet signature may give rise to antithrombotic therapeutics and prognostic markers beyond immobility-associated VTE.

Transparent Exploration of Machine Learning for Biomarker Discovery from Proteomics and Omics Data.

Biomarkers are of central importance for assessing the health state and to guide medical interventions and their efficacy; still, they are lacking for most diseases. Mass spectrometry (MS)-based proteomics is a powerful technology for biomarker discovery but requires sophisticated bioinformatics to identify robust patterns. Machine learning (ML) has become a promising tool for this purpose. However, it is sometimes applied in an opaque manner and generally requires specialized knowledge. To enable easy access to ML for biomarker discovery without any programming or bioinformatics skills, we developed "OmicLearn" (http://OmicLearn.org), an open-source browser-based ML tool using the latest advances in the Python ML ecosystem. Data matrices from omics experiments are easily uploaded to an online or a locally installed web server. OmicLearn enables rapid exploration of the suitability of various ML algorithms for the experimental data sets. It fosters open science via transparent assessment of state-of-the-art algorithms in a standardized format for proteomics and other omics sciences.

Noninvasive proteomic biomarkers for alcohol-related liver disease.

Alcohol-related liver disease (ALD) is a major cause of liver-related death worldwide, yet understanding of the three key pathological features of the disease-fibrosis, inflammation and steatosis-remains incomplete. Here, we present a paired liver-plasma proteomics approach to infer molecular pathophysiology and to explore the diagnostic and prognostic capability of plasma proteomics in 596 individuals (137 controls and 459 individuals with ALD), 360 of whom had biopsy-based histological assessment. We analyzed all plasma samples and 79 liver biopsies using a mass spectrometry (MS)-based proteomics workflow with short gradient times and an enhanced, data-independent acquisition scheme in only 3 weeks of measurement time. In plasma and liver biopsy tissues, metabolic functions were downregulated whereas fibrosis-associated signaling and immune responses were upregulated. Machine learning models identified proteomics biomarker panels that detected significant fibrosis (receiver operating characteristic-area under the curve (ROC-AUC), 0.92, accuracy, 0.82) and mild inflammation (ROC-AUC, 0.87, accuracy, 0.79) more accurately than existing clinical assays (DeLong's test, P < 0.05). These biomarker panels were found to be accurate in prediction of future liver-related events and all-cause mortality, with a Harrell's C-index of 0.90 and 0.79, respectively. An independent validation cohort reproduced the diagnostic model performance, laying the foundation for routine MS-based liver disease testing.

Dynamic human liver proteome atlas reveals functional insights into disease pathways.

Deeper understanding of liver pathophysiology would benefit from a comprehensive quantitative proteome resource at cell type resolution to predict outcome and design therapy. Here, we quantify more than 150,000 sequence-unique peptides aggregated into 10,000 proteins across total liver, the major liver cell types, time course of primary cell cultures, and liver disease states. Bioinformatic analysis reveals that half of hepatocyte protein mass is comprised of enzymes and 23% of mitochondrial proteins, twice the proportion of other liver cell types. Using primary cell cultures, we capture dynamic proteome remodeling from tissue states to cell line states, providing useful information for biological or pharmaceutical research. Our extensive data serve as spectral library to characterize a human cohort of non-alcoholic steatohepatitis and cirrhosis. Dramatic proteome changes in liver tissue include signatures of hepatic stellate cell activation resembling liver cirrhosis and providing functional insights. We built a web-based dashboard application for the interactive exploration of our resource (www.liverproteome.org).

A knowledge graph to interpret clinical proteomics data.

Implementing precision medicine hinges on the integration of omics data, such as proteomics, into the clinical decision-making process, but the quantity and diversity of biomedical data, and the spread of clinically relevant knowledge across multiple biomedical databases and publications, pose a challenge to data integration. Here we present the Clinical Knowledge Graph (CKG), an open-source platform currently comprising close to 20 million nodes and 220 million relationships that represent relevant experimental data, public databases and literature. The graph structure provides a flexible data model that is easily extendable to new nodes and relationships as new databases become available. The CKG incorporates statistical and machine learning algorithms that accelerate the analysis and interpretation of typical proteomics workflows. Using a set of proof-of-concept biomarker studies, we show how the CKG might augment and enrich proteomics data and help inform clinical decision-making.

Advances and Utility of the Human Plasma Proteome.

The study of proteins circulating in blood offers tremendous opportunities to diagnose, stratify, or possibly prevent diseases. With recent technological advances and the urgent need to understand the effects of COVID-19, the proteomic analysis of blood-derived serum and plasma has become even more important for studying human biology and pathophysiology. Here we provide views and perspectives about technological developments and possible clinical applications that use mass-spectrometry(MS)- or affinity-based methods. We discuss examples where plasma proteomics contributed valuable insights into SARS-CoV-2 infections, aging, and hemostasis and the opportunities offered by combining proteomics with genetic data. As a contribution to the Human Proteome Organization (HUPO) Human Plasma Proteome Project (HPPP), we present the Human Plasma PeptideAtlas build 2021-07 that comprises 4395 canonical and 1482 additional nonredundant human proteins detected in 240 MS-based experiments. In addition, we report the new Human Extracellular Vesicle PeptideAtlas 2021-06, which comprises five studies and 2757 canonical proteins detected in extracellular vesicles circulating in blood, of which 74% (2047) are in common with the plasma PeptideAtlas. Our overview summarizes the recent advances, impactful applications, and ongoing challenges for translating plasma proteomics into utility for precision medicine.

High-resolution serum proteome trajectories in COVID-19 reveal patient-specific seroconversion.

A deeper understanding of COVID-19 on human molecular pathophysiology is urgently needed as a foundation for the discovery of new biomarkers and therapeutic targets. Here we applied mass spectrometry (MS)-based proteomics to measure serum proteomes of COVID-19 patients and symptomatic, but PCR-negative controls, in a time-resolved manner. In 262 controls and 458 longitudinal samples of 31 patients, hospitalized for COVID-19, a remarkable 26% of proteins changed significantly. Bioinformatics analyses revealed co-regulated groups and shared biological functions. Proteins of the innate immune system such as CRP, SAA1, CD14, LBP, and LGALS3BP decreased early in the time course. Regulators of coagulation (APOH, FN1, HRG, KNG1, PLG) and lipid homeostasis (APOA1, APOC1, APOC2, APOC3, PON1) increased over the course of the disease. A global correlation map provides a system-wide functional association between proteins, biological processes, and clinical chemistry parameters. Importantly, five SARS-CoV-2 immunoassays against antibodies revealed excellent correlations with an extensive range of immunoglobulin regions, which were quantified by MS-based proteomics. The high-resolution profile of all immunoglobulin regions showed individual-specific differences and commonalities of potential pathophysiological relevance.

Cohort profile: the MUNICH Preterm and Term Clinical study (MUNICH-PreTCl), a neonatal birth cohort with focus on prenatal and postnatal determinants of infant and childhood morbidity.

PURPOSE: The MUNICH Preterm and Term Clinical (MUNICH-PreTCl) birth cohort was established to uncover pathological processes contributing to infant/childhood morbidity and mortality. We collected comprehensive medical information of healthy and sick newborns and their families, together with infant blood samples for proteomic analysis. MUNICH-PreTCl aims to identify mechanism-based biomarkers in infant health and disease to deliver more precise diagnostic and predictive information for disease prevention. We particularly focused on risk factors for pregnancy complications, family history of genetically influenced health conditions such as diabetes and paediatric long-term health-all to be further monitored and correlated with proteomics data in the future. PARTICIPANTS: Newborns and their parents were recruited from the Perinatal Center at the LMU University Hospital, Munich, between February 2017 and June 2019. Infants without congenital anomalies, delivered at 23-41 weeks of gestation, were eligible. FINDINGS: Findings to date concern the clinical data and extensive personal patient information. A total of 662 infants were recruited, 44% were female (36% in preterm, 46% in term). 90% of approached families agreed to participate. Neonates were grouped according to gestational age: extremely preterm (<28 weeks, N=28), very preterm (28 to <32 weeks, N=36), late preterm (32 to <37 weeks, N=97) and term infants (>37+0 weeks, N=501). We collected over 450 data points per child-parent set, (family history, demographics, pregnancy, birth and daily follow-ups throughout hospitalisation) and 841 blood samples longitudinally. The completion rates for medical examinations and blood samples were 100% and 95% for the questionnaire. FUTURE PLANS: The correlation of large clinical datasets with proteomic phenotypes, together with the use of medical registries, will enable future investigations aiming to decipher mechanisms of disorders in a systems biology perspective. TRIAL REGISTRATION NUMBER: DRKS (00024189); Pre-results.

Molecular Origin of Blood-Based Infrared Spectroscopic Fingerprints*.

Infrared spectroscopy of liquid biopsies is a time- and cost-effective approach that may advance biomedical diagnostics. However, the molecular nature of disease-related changes of infrared molecular fingerprints (IMFs) remains poorly understood, impeding the method's applicability. Here we probe 148 human blood sera and reveal the origin of the variations in their IMFs. To that end, we supplemented infrared spectroscopy with biochemical fractionation and proteomic profiling, providing molecular information about serum composition. Using lung cancer as an example of a medical condition, we demonstrate that the disease-related differences in IMFs are dominated by contributions from twelve highly abundant proteins-that, if used as a pattern, may be instrumental for detecting malignancy. Tying proteomic to spectral information and machine learning advances our understanding of the infrared spectra of liquid biopsies, a framework that could be applied to probing of any disease.

A New Parallel High-Pressure Packing System Enables Rapid Multiplexed Production of Capillary Columns.

Reversed-phase HPLC is the most commonly applied peptide-separation technique in MS-based proteomics. Particle-packed capillary columns are predominantly used in nanoflow HPLC systems. Despite being the broadly applied standard for many years, capillary columns are still expensive and suffer from short lifetimes, particularly in combination with ultra-high-pressure chromatography systems. For this reason, and to achieve maximum performance, many laboratories produce their own in-house packed columns. This typically requires a considerable amount of time and trained personnel. Here, we present a new packing system for capillary columns enabling rapid, multiplexed column packing with pressures reaching up to 3000 bar. Requiring only a conventional gas pressure supply and methanol as the driving fluid, our system replaces the traditional setup of helium-pressured packing bombs. By using 10× multiplexing, we have reduced the production time to just under 2 min for several 50 cm columns with 1.9-µm particle size, speeding up the process of column production 40 to 800 times. We compare capillary columns with various inner diameters and lengths packed under different pressure conditions with our newly designed, broadly accessible high-pressure packing station.

Integrative analysis of cell state changes in lung fibrosis with peripheral protein biomarkers.

The correspondence of cell state changes in diseased organs to peripheral protein signatures is currently unknown. Here, we generated and integrated single-cell transcriptomic and proteomic data from multiple large pulmonary fibrosis patient cohorts. Integration of 233,638 single-cell transcriptomes (n = 61) across three independent cohorts enabled us to derive shifts in cell type proportions and a robust core set of genes altered in lung fibrosis for 45 cell types. Mass spectrometry analysis of lung lavage fluid (n = 124) and plasma (n = 141) proteomes identified distinct protein signatures correlated with diagnosis, lung function, and injury status. A novel SSTR2+ pericyte state correlated with disease severity and was reflected in lavage fluid by increased levels of the complement regulatory factor CFHR1. We further discovered CRTAC1 as a biomarker of alveolar type-2 epithelial cell health status in lavage fluid and plasma. Using cross-modal analysis and machine learning, we identified the cellular source of biomarkers and demonstrated that information transfer between modalities correctly predicts disease status, suggesting feasibility of clinical cell state monitoring through longitudinal sampling of body fluid proteomes.

Plasma Proteomes Can Be Reidentifiable and Potentially Contain Personally Sensitive and Incidental Findings.

The goal of clinical proteomics is to identify, quantify, and characterize proteins in body fluids or tissue to assist diagnosis, prognosis, and treatment of patients. In this way, it is similar to more mature omics technologies, such as genomics, that are increasingly applied in biomedicine. We argue that, similar to those fields, proteomics also faces ethical issues related to the kinds of information that is inherently obtained through sample measurement, although their acquisition was not the primary purpose. Specifically, we demonstrate the potential to identify individuals both by their characteristic, individual-specific protein levels and by variant peptides reporting on coding single nucleotide polymorphisms. Furthermore, it is in the nature of blood plasma proteomics profiling that it broadly reports on the health status of an individual-beyond the disease under investigation. Finally, we show that private and potentially sensitive information, such as ethnicity and pregnancy status, can increasingly be derived from proteomics data. Although this is potentially valuable not only to the individual, but also for biomedical research, it raises ethical questions similar to the incidental findings obtained through other omics technologies. We here introduce the necessity of-and argue for the desirability for-ethical and human-rights-related issues to be discussed within the proteomics community. Those thoughts are more fully developed in our accompanying manuscript. Appreciation and discussion of ethical aspects of proteomic research will allow for deeper, better-informed, more diverse, and, most importantly, wiser guidelines for clinical proteomics.

Ethical Principles, Constraints and Opportunities in Clinical Proteomics.

Recent advances in mass spectrometry (MS)-based proteomics have vastly increased the quality and scope of biological information that can be derived from human samples. These advances have rendered current workflows increasingly applicable in biomedical and clinical contexts. As proteomics is poised to take an important role in the clinic, associated ethical responsibilities increase in tandem with impacts on the health, privacy, and wellbeing of individuals. We conducted and here report a systematic literature review of ethical issues in clinical proteomics. We add our perspectives from a background of bioethics, the results of our accompanying paper extracting individual-sensitive results from patient samples, and the literature addressing similar issues in genomics. The spectrum of potential issues ranges from patient re-identification to incidental findings of clinical significance. The latter can be divided into actionable and unactionable findings. Some of these have the potential to be employed in discriminatory or privacy-infringing ways. However, incidental findings may also have great positive potential. A plasma proteome profile, for instance, could inform on the general health or disease status of an individual regardless of the narrow diagnostic question that prompted it. We suggest that early discussion of ethical issues in clinical proteomics can ensure that eventual healthcare practices and regulations reflect the considered judgment of the community and anticipate opportunities and problems that may arise as the technology matures.

The proteome landscape of the kingdoms of life.

Proteins carry out the vast majority of functions in all biological domains, but for technological reasons their large-scale investigation has lagged behind the study of genomes. Since the first essentially complete eukaryotic proteome was reported1, advances in mass-spectrometry-based proteomics2 have enabled increasingly comprehensive identification and quantification of the human proteome3-6. However, there have been few comparisons across species7,8, in stark contrast with genomics initiatives9. Here we use an advanced proteomics workflow-in which the peptide separation step is performed by a microstructured and extremely reproducible chromatographic system-for the in-depth study of 100 taxonomically diverse organisms. With two million peptide and 340,000 stringent protein identifications obtained in a standardized manner, we double the number of proteins with solid experimental evidence known to the scientific community. The data also provide a large-scale case study for sequence-based machine learning, as we demonstrate by experimentally confirming the predicted properties of peptides from Bacteroides uniformis. Our results offer a comparative view of the functional organization of organisms across the entire evolutionary range. A remarkably high fraction of the total proteome mass in all kingdoms is dedicated to protein homeostasis and folding, highlighting the biological challenge of maintaining protein structure in all branches of life. Likewise, a universally high fraction is involved in supplying energy resources, although these pathways range from photosynthesis through iron sulfur metabolism to carbohydrate metabolism. Generally, however, proteins and proteomes are remarkably diverse between organisms, and they can readily be explored and functionally compared at www.proteomesoflife.org.

Proteome profiling in cerebrospinal fluid reveals novel biomarkers of Alzheimer's disease.

Neurodegenerative diseases are a growing burden, and there is an urgent need for better biomarkers for diagnosis, prognosis, and treatment efficacy. Structural and functional brain alterations are reflected in the protein composition of cerebrospinal fluid (CSF). Alzheimer's disease (AD) patients have higher CSF levels of tau, but we lack knowledge of systems-wide changes of CSF protein levels that accompany AD. Here, we present a highly reproducible mass spectrometry (MS)-based proteomics workflow for the in-depth analysis of CSF from minimal sample amounts. From three independent studies (197 individuals), we characterize differences in proteins by AD status (> 1,000 proteins, CV < 20%). Proteins with previous links to neurodegeneration such as tau, SOD1, and PARK7 differed most strongly by AD status, providing strong positive controls for our approach. CSF proteome changes in Alzheimer's disease prove to be widespread and often correlated with tau concentrations. Our unbiased screen also reveals a consistent glycolytic signature across our cohorts and a recent study. Machine learning suggests clinical utility of this proteomic signature.

Multiparametric Assays for Accelerating Early Drug Discovery.

Drug discovery campaigns are hampered by substantial attrition rates largely due to a lack of efficacy and safety reasons associated with candidate drugs. This is true in particular for genetically complex diseases, where insufficient knowledge of the modulatory actions of candidate drugs on targets and entire target pathways further adds to the problem of attrition. To better profile compound actions on targets, potential off-targets, and disease-linked pathways, new innovative technologies need to be developed that can elucidate the complex cellular signaling networks in health and disease. Here, we discuss progress in genetically encoded multiparametric assays and mass spectrometry (MS)-based proteomics, which both represent promising toolkits to profile multifactorial actions of drug candidates in disease-relevant cellular systems to promote drug discovery and personalized medicine.

Proteomics of Cytochrome c Oxidase-Negative versus -Positive Muscle Fiber Sections in Mitochondrial Myopathy.

The mosaic distribution of cytochrome c oxidase+ (COX+) and COX- muscle fibers in mitochondrial disorders allows the sampling of fibers with compensated and decompensated mitochondrial function from the same individual. We apply laser capture microdissection to excise individual COX+ and COX- fibers from the biopsies of mitochondrial myopathy patients. Using mass spectrometry-based proteomics, we quantify >4,000 proteins per patient. While COX+ fibers show a higher expression of respiratory chain components, COX- fibers display protean adaptive responses, including upregulation of mitochondrial ribosomes, translation proteins, and chaperones. Upregulated proteins include C1QBP, required for mitoribosome formation and protein synthesis, and STOML2, which organizes cardiolipin-enriched microdomains and the assembly of respiratory supercomplexes. Factoring in fast/slow fiber type, COX- slow fibers show a compensatory upregulation of beta-oxidation, the AAA+ protease AFG3L1, and the OPA1-dependent cristae remodeling program. These findings reveal compensatory mechanisms in muscle fibers struggling with energy shortage and metabolic stress.

Plasma Proteome Profiling to detect and avoid sample-related biases in biomarker studies.

Plasma and serum are rich sources of information regarding an individual's health state, and protein tests inform medical decision making. Despite major investments, few new biomarkers have reached the clinic. Mass spectrometry (MS)-based proteomics now allows highly specific and quantitative readout of the plasma proteome. Here, we employ Plasma Proteome Profiling to define quality marker panels to assess plasma samples and the likelihood that suggested biomarkers are instead artifacts related to sample handling and processing. We acquire deep reference proteomes of erythrocytes, platelets, plasma, and whole blood of 20 individuals (> 6,000 proteins), and compare serum and plasma proteomes. Based on spike-in experiments, we determine sample quality-associated proteins, many of which have been reported as biomarker candidates as revealed by a comprehensive literature survey. We provide sample preparation guidelines and an online resource ( www.plasmaproteomeprofiling.org) to assess overall sample-related bias in clinical studies and to prevent costly miss-assignment of biomarker candidates.