Toward Comprehensive Plasma Proteomics by Orthogonal Protease Digestion

Plasma proteomics for novel biomarker discovery in childhood tuberculosis

Failure to rapidly diagnose tuberculosis disease (TB) and initiate treatment is a driving factor of TB as a leading cause of death in children. Current TB diagnostic assays have poor performance in children, and identifying novel non-sputum-based TB biomarkers to improve pediatric TB diagnosis is a global priority. We sought to develop a plasma biosignature for TB by probing the plasma proteome of 511 children stratified by TB diagnostic classification and HIV status from sites in four low- and middle-income countries, using high-throughput data-independent acquisition mass-spectrometry (DIA-PASEF-MS). We identified 47 proteins differentially regulated (BH adjusted p-values < 1%) between children with microbiologically confirmed TB and children with non-TB respiratory diseases (Unlikely TB). We further employed machine learning to derive three parsimonious biosignatures encompassing 4, 5, or 6 proteins that achieved AUCs of 0.86-0.88 all of which exceeded the minimum WHO target product profile accuracy thresholds for a TB screening test (70% specificity at 90% sensitivity, PPV 0.65-0.74, NPV 0.92-0.95). This work provides insights into the unique host response in pediatric TB disease, as well as a non-sputum biosignature that could reduce delays in TB diagnosis and improve detection and management of TB in children worldwide.

Malaria exposure remodels the plasma proteome of Ghanaian children

BACKGROUND

Malaria, caused by Plasmodium falciparum, remains a major public health burden causing ~ 200 million deaths annually, especially among children. Although the lack of an effective vaccine has hindered malaria elimination, studies have reported on individuals acquiring natural immunity to malaria in the context of high malaria exposure. However, the immune correlates of protection in these people who acquire natural immunity against malaria are poorly understood.

METHODS

Symptomatic children residing in high and low malaria transmission areas of Ghana were enrolled into the study and followed for 3 weeks from the day of malaria confirmation. The plasma proteome of these children was profiled using a mass spectrometry-based approach and putative protein-based biomarkers and predictors of immune tolerance to malaria were identified.

RESULTS

We identified several differentially abundant proteins in children living in high malaria transmission areas relative to children in low transmission areas. Differentially abundant proteins were enriched in immune response processes, including complement cascade activities and elevated platelet activation. We found IGKV3D-20 protein to be strongly associated with high malaria exposure.

CONCLUSIONS

Our findings confirm earlier reports and identify putative signature proteins implicated in immune tolerance to malaria. Further large-scale and more mechanistic studies will be needed to reveal the key components of the identified pathways that could explain naturally acquired immunity to malaria and possibly be exploited to develop novel therapeutics against P. falciparum.

A PERTURBATION CELL ATLAS OF HUMAN INDUCED PLURIPOTENT STEM CELLS

Towards comprehensively investigating the genotype-phenotype relationships governing the human pluripotent stem cell state, we generated an expressed genome-scale CRISPRi Perturbation Cell Atlas in KOLF2.1J human induced pluripotent stem cells (hiPSCs) mapping transcriptional and fitness phenotypes associated with 11,739 targeted genes. Using the transcriptional phenotypes, we created a minimum distortion embedding map of the pluripotent state, demonstrating rich recapitulation of protein complexes, such as strong co-clustering of MRPL, BAF, SAGA, and Ragulator family members. Additionally, we uncovered transcriptional regulators that are uncoupled from cell fitness, discovering potential novel pluripotency (JOSD1, RNF7) and metabolic factors (ZBTB41). We validated these findings via phenotypic, protein-interaction, and metabolic tracing assays. Finally, we propose a contrastive human-cell engineering framework (CHEF), a machine learning architecture that learns from perturbation cell atlases to predict perturbation recipes that achieve desired transcriptional states. Taken together, our study presents a comprehensive resource for interrogating the regulatory networks governing pluripotency.

Fragment ion intensity prediction improves the identification rate of non-tryptic peptides in timsTOF

Immunopeptidomics is crucial for immunotherapy and vaccine development. Because the generation of immunopeptides from their parent proteins does not adhere to clear-cut rules, rather than being able to use known digestion patterns, every possible protein subsequence within human leukocyte antigen (HLA) class-specific length restrictions needs to be considered during sequence database searching. This leads to an inflation of the search space and results in lower spectrum annotation rates. Peptide-spectrum match (PSM) rescoring is a powerful enhancement of standard searching that boosts the spectrum annotation performance. We analyze 302,105 unique synthesized non-tryptic peptides from the ProteomeTools project on a timsTOF-Pro to generate a ground-truth dataset containing 93,227 MS/MS spectra of 74,847 unique peptides, that is used to fine-tune the deep learning-based fragment ion intensity prediction model Prosit. We demonstrate up to 3-fold improvement in the identification of immunopeptides, as well as increased detection of immunopeptides from low input samples.

Understanding the value of biobank attributes to researchers using a conjoint experiment

Biobanks are important in biomedical and public health research, and future healthcare research relies on their strength and capacity. However, there are financial challenges related to the operation of commercial biobanks and concerns around the commercialization of biobanks. Non-commercial biobanks depend on grant funding to operate and could be valuable to researchers if they can enable access to quality specimens at lower costs. The objective of this study is to estimate the value of specific biobank attributes. We used a rating-based conjoint experiment approach to study how researchers valued handling fee, access, quality, characterization, breadth of consent, access to key endemics, and time taken to fulfil requests. We found that researchers placed the greatest relative importance on the quality of specimens (26%), followed by the characterization of specimens (21%). Researchers with prior experience purchasing biological samples also valued access to key endemic in-country sites (11.6%) and low handling fees (5.5%) in biobanks.

Identification of Plasma Biomarkers from Rheumatoid Arthritis Patients Using an Optimized Sequential Window Acquisition of All THeoretical Mass Spectra (SWATH) Proteomics Workflow

Rheumatoid arthritis (RA) is a systemic autoimmune and inflammatory disease. Plasma biomarkers are critical for understanding disease mechanisms, treatment effects, and diagnosis. Mass spectrometry-based proteomics is a powerful tool for unbiased biomarker discovery. However, plasma proteomics is significantly hampered by signal interference from high-abundance proteins, low overall protein coverage, and high levels of missing data from data-dependent acquisition (DDA). To achieve quantitative proteomics analysis for plasma samples with a balance of throughput, performance, and cost, we developed a workflow incorporating plate-based high abundance protein depletion and sample preparation, comprehensive peptide spectral library building, and data-independent acquisition (DIA) SWATH mass spectrometry-based methodology. In this study, we analyzed plasma samples from both RA patients and healthy donors. The results showed that the new workflow performance exceeded that of the current state-of-the-art depletion-based plasma proteomic platforms in terms of both data quality and proteome coverage. Proteins from biological processes related to the activation of systemic inflammation, suppression of platelet function, and loss of muscle mass were enriched and differentially expressed in RA. Some plasma proteins, particularly acute-phase reactant proteins, showed great power to distinguish between RA patients and healthy donors. Moreover, protein isoforms in the plasma were also analyzed, providing even deeper proteome coverage. This workflow can serve as a basis for further application in discovering plasma biomarkers of other diseases.

Next-generation proteomics for quantitative Jumbophage-bacteria interaction mapping

Host-pathogen interactions are pivotal in regulating establishment, progression, and outcome of an infection. While affinity-purification mass spectrometry has become instrumental in characterizing such interactions, it suffers from limitations in scalability and biological authenticity. Here we present the use of co-fractionation mass spectrometry for high throughput analysis of host-pathogen interactions from native viral infections of two jumbophages (ϕKZ and ϕPA3) in Pseudomonas aeruginosa. This approach enabled the detection of > 6000 unique host-pathogen interactions for each phage, encompassing > 50% of their respective proteomes. This deep coverage provided evidence for interactions between KZ-like phage proteins and the host ribosome, and revealed protein complexes for previously undescribed phage ORFs, including a ϕPA3 complex showing strong structural and sequence similarity to ϕKZ non-virion RNA polymerase. Interactome-wide comparison across phages showed similar perturbed protein interactions suggesting fundamentally conserved mechanisms of phage predation within the KZ-like phage family. To enable accessibility to this data, we developed PhageMAP, an online resource for network query, visualization, and interaction prediction ( https://phagemap.ucsf.edu/ ). We anticipate this study will lay the foundation for the application of co-fractionation mass spectrometry for the scalable profiling of host-pathogen interactomes and protein complex dynamics upon infection.

Next-generation interaction proteomics for quantitative Jumbophage-bacteria interaction mapping

Host-pathogen interactions (HPIs) are pivotal in regulating establishment, progression, and outcome of an infection. Affinity-purification mass spectrometry has become instrumental for the characterization of HPIs, however the targeted nature of exogenously expressing individual viral proteins has limited its utility to the analysis of relatively small pathogens. Here we present the use of co-fractionation mass spectrometry (SEC-MS) for the high-throughput analysis of HPIs from native viral infections of two jumbophages ( ϕ KZ and ϕ PA3) in Pseudomonas aeruginosa . This enabled the detection > 6000 unique host-pathogen and > 200 pathogen-pathogen interactions for each phage, encompassing > 50% of the phage proteome. Interactome-wide comparison across phages showed similar perturbed protein interactions suggesting fundamentally conserved mechanisms of phage predation within the KZ-like phage family. Prediction of novel ORFs revealed a ϕ PA3 complex showing strong structural and sequence similarity to ϕ KZ nvRNAp, suggesting ϕ PA3 also possesses two RNA polymerases acting at different stages of the infection cycle. We further expanded our understanding on the molecular organization of the virion packaged and injected proteome by identifying 23 novel virion components and 5 novel injected proteins, as well as providing the first evidence for interactions between KZ-like phage proteins and the host ribosome. To enable accessibility to this data, we developed PhageMAP, an online resource for network query, visualization, and interaction prediction ( https://phagemap.ucsf.edu/ ). We anticipate this study will lay the foundation for the application of co-fractionation mass spectrometry for the scalable profiling of hostpathogen interactomes and protein complex dynamics upon infection.

Overview and considerations in bottom-up proteomics

Proteins are the key biological actors within cells, driving many biological processes integral to both healthy and diseased states. Understanding the depth of complexity represented within the proteome is crucial to our scientific understanding of cellular biology and to provide disease specific insights for clinical applications. Mass spectrometry-based proteomics is the premier method for proteome analysis, with the ability to both identify and quantify proteins. Although proteomics continues to grow as a robust field of bioanalytical chemistry, advances are still necessary to enable a more comprehensive view of the proteome. In this review, we provide a broad overview of mass spectrometry-based proteomics in general, and highlight four developing areas of bottom-up proteomics: (1) protein inference, (2) alternative proteases, (3) sample-specific databases and (4) post-translational modification discovery.

Deciphering the disturbance mechanism of BaP on the symbiosis of Montipora digitata via 4D-Proteomics approach

The coral holobiont is mainly composed of coral polyps, zooxanthellae, and coral symbiotic microorganisms, which form the basis of coral reef ecosystems. In recent years, the severe degradation of coral reefs caused by climate warming and environmental pollution has aroused widespread concern. Benzo(a)pyrene (BaP) is a widely distributed pollutant in the environment. However, the underlying mechanisms of coral symbiosis destruction due to the stress of BaP are not well understood. In this study, diaPASEF proteomics and 16S rRNA amplicon pyrosequencing technology were used to reveal the effects of 50 μg/L BaP on Montipora digitate. Data analysis was performed from the perspective of the main symbionts of M. digitata (coral polyps, zooxanthellae, and coral symbiotic microorganisms). The results showed that BaP impaired cellular antioxidant capacity by disrupting the GSH/GSSG cycle, and sustained stress causes severe impairment of energy metabolism and protein degradation in coral polyps. In zooxanthellae, BaP downregulated the protein expression of SOD2 and mtHSP70, which then resulted in oxidative free radical accumulation and apoptosis. For coral symbiotic microorganisms, BaP altered the community structure of microorganisms and decreased immunity. Coral symbiotic microorganisms adapted to the stress of BaP by adjusting energy metabolism and enhancing extracellular electron transfer. BaP adversely affected the three main symbionts of M. digitata via different mechanisms. Decreased antioxidant capacity is a common cause of damages to coral polyps and zooxanthellae, whereas coral symbiotic microorganisms are able to appropriately adapt to oxidative stress. This study assessed the effects of BaP on corals from a symbiotic perspective, which is more comprehensive and reliable. At the same time, data from the study supports new directions for coral research and coral reef protection.

Detergent-Assisted Protein Digestion-On the Way to Avoid the Key Bottleneck of Shotgun Bottom-Up Proteomics

Gel-free bottom-up shotgun proteomics is the principal methodological platform for the state-of-the-art proteome research. This methodology assumes quantitative isolation of the total protein fraction from a complex biological sample, its limited proteolysis with site-specific proteases, analysis of the resulted peptides with nanoscaled reversed-phase high-performance liquid chromatography-(tandem) mass spectrometry (nanoRP-HPLC-MS and MS/MS), protein identification by sequence database search and peptide-based quantitative analysis. The most critical steps of this workflow are protein reconstitution and digestion; therefore, detergents and chaotropic agents are strongly mandatory to ensure complete solubilization of complex protein isolates and to achieve accessibility of all protease cleavage sites. However, detergents are incompatible with both RP separation and electrospray ionization (ESI). Therefore, to make LC-MS analysis possible, several strategies were implemented in the shotgun proteomics workflow. These techniques rely either on enzymatic digestion in centrifugal filters with subsequent evacuation of the detergent, or employment of MS-compatible surfactants, which can be degraded upon the digestion. In this review we comprehensively address all currently available strategies for the detergent-assisted proteolysis in respect of their relative efficiency when applied to different biological matrices. We critically discuss the current progress and the further perspectives of these technologies in the context of its advances and gaps.

Mirror proteases of Ac-Trypsin and Ac-LysargiNase precisely improve novel event identifications in Mycolicibacterium smegmatis MC2 155 by proteogenomic analysis

Accurate identification of novel peptides remains challenging because of the lack of evaluation criteria in large-scale proteogenomic studies. Mirror proteases of trypsin and lysargiNase can generate complementary b/y ion series, providing the opportunity to efficiently assess authentic novel peptides in experiments other than filter potential targets by different false discovery rates (FDRs) ranking. In this study, a pair of in-house developed acetylated mirror proteases, Ac-Trypsin and Ac-LysargiNase, were used in Mycolicibacterium smegmatis MC² 155 for proteogenomic analysis. The mirror proteases accurately identified 368 novel peptides, exhibiting 75–80% b and y ion coverages against 65–68% y or b ion coverages of Ac-Trypsin (38.9% b and 68.3% y) or Ac-LysargiNase (65.5% b and 39.6% y) as annotated peptides from M. smegmatis MC² 155. The complementary b and y ion series largely increased the reliability of overlapped sequences derived from novel peptides. Among these novel peptides, 311 peptides were annotated in other public M. smegmatis strains, and 57 novel peptides with more continuous b and y pairs were obtained for further analysis after spectral quality assessment. This enabled mirror proteases to successfully correct six annotated proteins' N-termini and detect 17 new coding open reading frames (ORFs). We believe that mirror proteases will be an effective strategy for novel peptide detection in both prokaryotic and eukaryotic proteogenomics.

Data-Independent Acquisition Protease-Multiplexing Enables Increased Proteome Sequence Coverage Across Multiple Fragmentation Modes

The use of multiple proteases has been shown to increase protein sequence coverage in proteomics experiments; however, due to the additional analysis time required, it has not been widely adopted in routine data-dependent acquisition (DDA) proteomic workflows. Alternatively, data-independent acquisition (DIA) has the potential to analyze multiplexed samples from different protease digests, but has been primarily optimized for fragmenting tryptic peptides. Here we evaluate a DIA multiplexing approach that combines three proteolytic digests (Trypsin, AspN, and GluC) into a single sample. We first optimize data acquisition conditions for each protease individually with both the canonical DIA fragmentation mode (beam type CID), as well as resonance excitation CID, to determine optimal consensus conditions across proteases. Next, we demonstrate that application of these conditions to a protease-multiplexed sample of human peptides results in similar protein identifications and quantitative performance as compared to trypsin alone, but enables up to a 63% increase in peptide detections, and a 45% increase in nonredundant amino acid detections. Nontryptic peptides enabled noncanonical protein isoform determination and resulted in 100% sequence coverage for numerous proteins, suggesting the utility of this approach in applications where sequence coverage is critical, such as protein isoform analysis.