Workflow for Rapidly Extracting Biological Insights from Complex, Multicondition Proteomics Experiments with WGCNA and PloGO2

Analysis and validation of serum biomarkers in brucellosis patients through proteomics and bioinformatics

Introduction

This study aims to utilize proteomics, bioinformatics, and machine learning algorithms to identify diagnostic biomarkers in the serum of patients with acute and chronic brucellosis.

Methods

Proteomic analysis was conducted on serum samples from patients with acute and chronic brucellosis, as well as from healthy controls. Differential expression analysis was performed to identify proteins with altered expression, while Weighted Gene Co-expression Network Analysis (WGCNA) was applied to detect co-expression modules associated with clinical features of brucellosis. Machine learning algorithms were subsequently used to identify the optimal combination of diagnostic biomarkers. Finally, ELISA was employed to validate the identified proteins.

Results

A total of 1,494 differentially expressed proteins were identified, revealing two co-expression modules significantly associated with the clinical characteristics of brucellosis. The Gaussian Mixture Model (GMM) algorithm identified six proteins that were concurrently present in both the differentially expressed and co-expression modules, demonstrating promising diagnostic potential. After ELISA validation, five proteins were ultimately selected.

Discussion

These five proteins are implicated in the innate immune processes of brucellosis, potentially associated with its pathogenic mechanisms and chronicity. Furthermore, we highlighted their potential as diagnostic biomarkers for brucellosis. This study further enhances our understanding of brucellosis at the protein level, paving the way for future research endeavors.

Saliva Proteome, Metabolome and Microbiome Signatures for Detection of Alzheimer's Disease

Background: As the burden of Alzheimer's disease (AD) escalates with an ageing population, the demand for early and accessible diagnostic methods becomes increasingly urgent. Saliva, with its non-invasive and cost-effective nature, presents a promising alternative to cerebrospinal fluid and plasma for biomarker discovery. Methods: In this study, we conducted a comprehensive multi-omics analysis of saliva samples (n = 20 mild cognitive impairment (MCI), n = 20 Alzheimer's disease and age- and n = 40 gender-matched cognitively normal individuals), from the South Australian Neurodegenerative Disease (SAND) cohort, integrating proteomics, metabolomics, and microbiome data with plasma measurements, including pTau181. Results: Among the most promising findings, the protein Stratifin emerged as a top candidate, showing a strong negative correlation with plasma pTau181 (r = -0.49, p < 0.001) and achieving an AUC of 0.95 in distinguishing AD and MCI combined from controls. In the metabolomics analysis, 3-chlorotyrosine and L-tyrosine exhibited high correlations with disease severity progression, with AUCs of 0.93 and 0.96, respectively. Pathway analysis revealed significant alterations in vitamin B12 metabolism, with Transcobalamin-1 levels decreasing in saliva as AD progressed despite an increase in serum vitamin B12 levels (p = 0.008). Microbiome analysis identified shifts in bacterial composition, with a microbiome cluster containing species such as Lautropia mirabilis showing a significant decrease in abundance in MCI and AD samples. The overall findings were reinforced by weighted correlation network analysis, which identified key hubs and enriched pathways associated with AD. Conclusions: Collectively, these data highlight the potential of saliva as a powerful medium for early AD diagnosis, offering a practical solution for large-scale screening and monitoring.

Multiomics data integration, limitations, and prospects to reveal the metabolic activity of the coral holobiont

Since their radiation in the Middle Triassic period ∼240 million years ago, stony corals have survived past climate fluctuations and five mass extinctions. Their long-term survival underscores the inherent resilience of corals, particularly when considering the nutrient-poor marine environments in which they have thrived. However, coral bleaching has emerged as a global threat to coral survival, requiring rapid advancements in coral research to understand holobiont stress responses and allow for interventions before extensive bleaching occurs. This review encompasses the potential, as well as the limits, of multiomics data applications when applied to the coral holobiont. Synopses for how different omics tools have been applied to date and their current restrictions are discussed, in addition to ways these restrictions may be overcome, such as recruiting new technology to studies, utilizing novel bioinformatics approaches, and generally integrating omics data. Lastly, this review presents considerations for the design of holobiont multiomics studies to support lab-to-field advancements of coral stress marker monitoring systems. Although much of the bleaching mechanism has eluded investigation to date, multiomic studies have already produced key findings regarding the holobiont's stress response, and have the potential to advance the field further.

ALS-related p97 R155H mutation disrupts lysophagy in iPSC-derived motor neurons

Mutations in the AAA+ ATPase p97 cause multisystem proteinopathy 1, which includes amyotrophic lateral sclerosis; however, the pathogenic mechanisms that contribute to motor neuron loss remain obscure. Here, we use two induced pluripotent stem cell models differentiated into spinal motor neurons to investigate how p97 mutations perturb the motor neuron proteome. Using quantitative proteomics, we find that motor neurons harboring the p97 R155H mutation have deficits in the selective autophagy of lysosomes (lysophagy). p97 R155H motor neurons are unable to clear damaged lysosomes and have reduced viability. Lysosomes in mutant motor neurons have increased pH compared with wild-type cells. The clearance of damaged lysosomes involves UBXD1-p97 interaction, which is disrupted in mutant motor neurons. Finally, inhibition of the ATPase activity of p97 using the inhibitor CB-5083 rescues lysophagy defects in mutant motor neurons. These results add to the evidence that endo-lysosomal dysfunction is a key aspect of disease pathogenesis in p97-related disorders.

A transient protein folding response targets aggregation in the early phase of TDP-43-mediated neurodegeneration

Understanding the mechanisms that drive TDP-43 pathology is integral to combating amyotrophic lateral sclerosis (ALS), frontotemporal lobar degeneration (FTLD) and other neurodegenerative diseases. Here we generated a longitudinal quantitative proteomic map of the cortex from the cytoplasmic TDP-43 rNLS8 mouse model of ALS and FTLD, and developed a complementary open-access webtool, TDP-map ( https://shiny.rcc.uq.edu.au/TDP-map/ ). We identified distinct protein subsets enriched for diverse biological pathways with temporal alterations in protein abundance, including increases in protein folding factors prior to disease onset. This included increased levels of DnaJ homolog subfamily B member 5, DNAJB5, which also co-localized with TDP-43 pathology in diseased human motor cortex. DNAJB5 over-expression decreased TDP-43 aggregation in cell and cortical neuron cultures, and knockout of Dnajb5 exacerbated motor impairments caused by AAV-mediated cytoplasmic TDP-43 expression in mice. Together, these findings reveal molecular mechanisms at distinct stages of ALS and FTLD progression and suggest that protein folding factors could be protective in neurodegenerative diseases.

Deep Proteomics Network and Machine Learning Analysis of Human Cerebrospinal Fluid in Japanese Encephalitis Virus Infection

Japanese encephalitis virus is a leading cause of neurological infection in the Asia-Pacific region with no means of detection in more remote areas. We aimed to test the hypothesis of a Japanese encephalitis (JE) protein signature in human cerebrospinal fluid (CSF) that could be harnessed in a rapid diagnostic test (RDT), contribute to understanding the host response and predict outcome during infection. Liquid chromatography and tandem mass spectrometry (LC-MS/MS), using extensive offline fractionation and tandem mass tag labeling (TMT), enabled comparison of the deep CSF proteome in JE vs other confirmed neurological infections (non-JE). Verification was performed using data-independent acquisition (DIA) LC-MS/MS. 5,070 proteins were identified, including 4,805 human proteins and 265 pathogen proteins. Feature selection and predictive modeling using TMT analysis of 147 patient samples enabled the development of a nine-protein JE diagnostic signature. This was tested using DIA analysis of an independent group of 16 patient samples, demonstrating 82% accuracy. Ultimately, validation in a larger group of patients and different locations could help refine the list to 2-3 proteins for an RDT. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD034789 and 10.6019/PXD034789.

Biomarkers and Coronary Microvascular Dysfunction in Women With Angina and No Obstructive Coronary Artery Disease

Background

Coronary microvascular dysfunction (CMD) is a major cause of ischemia with no obstructed coronary arteries.

Objectives

The authors sought to assess protein biomarker signature for CMD.

Methods

We quantified 184 unique cardiovascular proteins with proximity extension assay in 1,471 women with angina and no obstructive coronary artery disease characterized for CMD by coronary flow velocity reserve (CFVR) by transthoracic echo Doppler. We performed Pearson's correlations of CFVR and each of the 184 biomarkers, and principal component analyses and weighted correlation network analysis to identify clusters linked to CMD. For prediction of CMD (CFVR < 2.25), we applied logistic regression and machine learning algorithms (least absolute shrinkage and selection operator, random forest, extreme gradient boosting, and adaptive boosting) in discovery and validation cohorts.

Results

Sixty-one biomarkers were correlated with CFVR with strongest correlations for renin (REN), growth differentiation factor 15, brain natriuretic protein (BNP), N-terminal-proBNP (NT-proBNP), and adrenomedullin (ADM) (all P < 1e-06). Two principal components with highest loading on BNP/NTproBNP and interleukin 6, respectively, were strongly associated with low CFVR. Weighted correlation network analysis identified 2 clusters associated with low CFVR reflecting involvement of hypertension/vascular function and immune modulation. The best prediction model for CFVR <2.25 using clinical data had area under the receiver operating characteristic curve (ROC-AUC) of 0.61 (95% CI: 0.56-0.66). ROC-AUC was 0.66 (95% CI: 0.62-0.71) with addition of biomarkers (P for model improvement = 0.01). Stringent two-layer cross-validated machine learning models had ROC-AUC ranging from 0.58 to 0.66; the most predictive biomarkers were REN, BNP, NT-proBNP, growth differentiation factor 15, and ADM.

Conclusions

CMD was associated with pathways particularly involving inflammation (interleukin 6), blood pressure (REN, ADM), and ventricular remodeling (BNP/NT-proBNP) independently of clinical risk factors. Model prediction improved with biomarkers, but prediction remained moderate.

An Eight-mRNA Prognostic Model to Predict Survival in Hepatic Cellular Cancer

Background. Transcriptional dysregulation plays a critical role in the onset and development of malignant tumors. Employing gene dysregulation to forecast the change of tumors is valuable for cancer diagnosis. However, the prognostic prediction for HCC using combined gene models remains insufficient. Methods. The expression profiles of GSE103512 and TCGA-LIHC were downloaded. Gene Ontology (Go) was used to evaluate the overlapping differential genes (DEG) in TCGA and GSE103512. The core genes in the critical module most significantly related to HCC were obtained by WGCNA. Eight genes most significantly related to HCC and OS were identified by reweighted coexpression network analysis and Cox regression. Results. We selected eight genes, FZEB1, CDK1, RAD54L, COL1A2, ATP1B3, CASP8, USP39, and HOXB7. Moreover, we constructed an eight-gene model and forecasted the prognosis of HCC. ROC curve of the eight-mRNA prognostic model was screened out ( $\text{AUC}=0.635$ ), suggesting that this model exhibited a good prediction performance. Survival analysis showed that the survival rate of patients in the high-risk group was significantly lower than that in the low-risk group. Conclusion. The eight-mRNAs model might forecast the OS of HCC patients and advance remedial decision-making.

Application of WGCNA and PloGO2 in the Analysis of Complex Proteomic Data

In this protocol we describe our workflow for analyzing complex, multi-condition quantitative proteomic experiments, with the aim to extract biological insights. The tool we use is an R package, PloGO2, contributed to Bioconductor, which we can optionally precede by running correlation network analysis with WGCNA. We describe the data required and the steps we take, including detailed code examples and outputs explanation. The package was designed to generate gene ontology or pathway summaries for many data subsets at the same time, visualize protein abundance summaries for each biological category examined, help determine enriched protein subsets by comparing them all to a reference set, and suggest key highly correlated hub proteins, if the optional network analysis is employed.

Proteomic Analysis of Whole Blood Using Volumetric Absorptive Microsampling for Precision Medicine Biomarker Studies

Microsampling of patient blood promises several benefits over conventional phlebotomy practices to facilitate precision medicine studies. These include at-home patient blood collection, supporting telehealth monitoring, minimal postcollection processing, and compatibility with nonrefrigerated transport and storage. However, for proteomic biomarker studies, mass spectrometry of whole blood has generally been avoided in favor of using plasma or serum obtained from venepuncture. We evaluated the use of a volumetric absorptive microsampling (VAMS) device as a sample preparation matrix to enable LC-MS proteomic analyses of dried whole blood. We demonstrated the detection and robust quantitation of up to 1600 proteins from single-shot shotgun-LC-MS analysis of dried whole blood, greatly enhancing proteome depth compared with conventional single-shot LC-MS analyses of undepleted plasma. Some proteins not previously reported in blood were detected using this approach. Various washing reagents were used to demonstrate that proteins can be preferentially removed from VAMS devices prior to downstream analyses. We provide a demonstration that archival frozen blood cell pellets housed under long-term storage (exceeding 5 years) are compatible with VAMS to enable quantitation of potential biomarker proteins from biobank repositories. These demonstrations are important steps in establishing viable analysis workflows to underpin large-scale precision medicine studies. Data are available via ProteomeXchange with the identifier PXD028605.

MetaNetwork Enhances Biological Insights from Quantitative Proteomics Differences by Combining Clustering and Enrichment Analyses

Interpreting proteomics data remains challenging due to the large number of proteins that are quantified by modern mass spectrometry methods. Weighted gene correlation network analysis (WGCNA) can identify groups of biologically related proteins using only protein intensity values by constructing protein correlation networks. However, WGCNA is not widespread in proteomic analyses due to challenges in implementing workflows. To facilitate the adoption of WGCNA by the proteomics field, we created MetaNetwork, an open-source, R-based application to perform sophisticated WGCNA workflows with no coding skill requirements for the end user. We demonstrate MetaNetwork's utility by employing it to identify groups of proteins associated with prostate cancer from a proteomic analysis of tumor and adjacent normal tissue samples. We found a decrease in cytoskeleton-related protein expression, a known hallmark of prostate tumors. We further identified changes in module eigenproteins indicative of dysregulation in protein translation and trafficking pathways. These results demonstrate the value of using MetaNetwork to improve the biological interpretation of quantitative proteomics experiments with 15 or more samples.