Method For Biomarker Discovery Research Articles

A cloud-based learning module for biomarker discovery.

This manuscript describes the development of a resource module that is part of a learning platform named "NIGMS Sandbox for Cloud-based Learning" https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox at the beginning of this Supplement. This module delivers learning materials on basic principles in biomarker discovery in an interactive format that uses appropriate cloud resources for data access and analyses. In collaboration with Google Cloud, Deloitte Consulting and NIGMS, the Rhode Island INBRE Molecular Informatics Core developed a cloud-based training module for biomarker discovery. The module consists of nine submodules covering various topics on biomarker discovery and assessment and is deployed on the Google Cloud Platform and available for public use through the NIGMS Sandbox. The submodules are written as a series of Jupyter Notebooks utilizing R and Bioconductor for biomarker and omics data analysis. The submodules cover the following topics: 1) introduction to biomarkers; 2) introduction to R data structures; 3) introduction to linear models; 4) introduction to exploratory analysis; 5) rat renal ischemia-reperfusion injury case study; (6) linear and logistic regression for comparison of quantitative biomarkers; 7) exploratory analysis of proteomics IRI data; 8) identification of IRI biomarkers from proteomic data; and 9) machine learning methods for biomarker discovery. Each notebook includes an in-line quiz for self-assessment on the submodule topic and an overview video is available on YouTube (https://www.youtube.com/watch?v=2-Q9Ax8EW84). This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.

Twin Scholarships of Glycomedicine and Precision Medicine in Times of Single-Cell Multiomics.

Systems biology and multiomics research expand the prospects of planetary health innovations. In this context, this mini-review unpacks the twin scholarships of glycomedicine and precision medicine in the current era of single-cell multiomics. A significant growth in glycan research has been observed over the past decade, unveiling and establishing co- and post-translational modifications as dynamic indicators of both pathological and physiological conditions. Systems biology technologies have enabled large-scale and high-throughput glycoprofiling and access to data-intensive biological repositories for global research. These advancements have established glycans as a pivotal third code of life, alongside nucleic acids and amino acids. However, challenges persist, particularly in the simultaneous analysis of the glycome and transcriptome in single cells owing to technical limitations. In addition, holistic views of the complex molecular interactions between glycomics and other omics types remain elusive. We underscore and call for a paradigm shift toward the exploration of integrative glycan platforms and analysis methods for single-cell multiomics research and precision medicine biomarker discovery. The integration of multiple datasets from various single-cell omics levels represents a crucial application of systems biology in understanding complex cellular processes and is essential for advancing the twin scholarships of glycomedicine and precision medicine.

Discovering urinary protein biomarkers for hepatocellular carcinoma in the population of patients with chronic hepatitis B: A single-center case-control study.

e16230 Background: Chronic hepatitis B (CHB) can result in hepatocellular carcinoma (HCC) imposing a substantial health and economic burden worldwide. Early detection of hepatitis B virus-related HCC (HBV-HCC) in CHB with potential biomarkers has emerged as an urgent and challenging task. Recent progression of urinary proteomics provides a promising method for HBV-HCC biomarker discovery. Methods: This single-center case-control study, conducted at Peking Union Medical College Hospital, enrolled 33 patients with HBV-HCC, 26 patients with CHB, 30 healthy controls, and 33 controls with other types of cancer. Urine proteomes were analyzed using liquid chromatography with tandem mass spectrometry by data-dependent acquisition or parallel reaction monitoring, respectively. Differential analysis was carried out with limma package in R software, and the origin of selected proteins was confirmed with immunohistochemistry of surgical specimens. The diagnostic performance of the biomarker panel was evaluated with area under receiver operator curves (AUROC). Results: 21 urinary proteins were significantly upregulated in HBV-HCC compared with CHB samples, playing key roles in vesicular transportation, cytoskeleton-extracellular matrix interaction, and tumor development according to enrichment analysis. Immunohistochemistry validated the differential expression of 4 urinary proteins (4UP: HNRNPM, ADGRG1, APOC1, and RALA) across cancerous and adjacent normal liver tissues. These proteins were specifically found in HBV-HCC but not in CHB, healthy control, or other cancer controls. The AUROCs of 4UP individually (0.642 for APOC1, 0.780 for HNRNPM, 0.755 for ADGRG1, 0.729 for RALA) were no worse than that of serum α-fetoprotein (AUROC = 0.751), and the 4UP panel unified via logistic regression showed an AUROC of 0.828. Conclusions: Urinary proteomics revealed HNRNPM, ADGRG1, APOC1, and RALA as biomarkers to distinguish HBV-HCC from CHB. The 4UP could be traced back to HBV-HCC tumor cells, shedding light on pathogenesis of HBV-HCC. [Table: see text]

Olaris Global Panel (OGP): A Highly Accurate and Reproducible Triple Quadrupole Mass Spectrometry-Based Metabolomics Method for Clinical Biomarker Discovery.

Mass spectrometry (MS)-based clinical metabolomics is very promising for the discovery of new biomarkers and diagnostics. However, poor data accuracy and reproducibility limit its true potential, especially when performing data analysis across multiple sample sets. While high-resolution mass spectrometry has gained considerable popularity for discovery metabolomics, triple quadrupole (QqQ) instruments offer several benefits for the measurement of known metabolites in clinical samples. These benefits include high sensitivity and a wide dynamic range. Here, we present the Olaris Global Panel (OGP), a HILIC LC-QqQ MS method for the comprehensive analysis of ~250 metabolites from all major metabolic pathways in clinical samples. For the development of this method, multiple HILIC columns and mobile phase conditions were compared, the robustness of the leading LC method assessed, and MS acquisition settings optimized for optimal data quality. Next, the effect of U-13C metabolite yeast extract spike-ins was assessed based on data accuracy and precision. The use of these U-13C-metabolites as internal standards improved the goodness of fit to a linear calibration curve from r2 < 0.75 for raw data to >0.90 for most metabolites across the entire clinical concentration range of urine samples. Median within-batch CVs for all metabolite ratios to internal standards were consistently lower than 7% and less than 10% across batches that were acquired over a six-month period. Finally, the robustness of the OGP method, and its ability to identify biomarkers, was confirmed using a large sample set.

DeepKEGG: a multi-omics data integration framework with biological insights for cancer recurrence prediction and biomarker discovery.

Deep learning-based multi-omics data integration methods have the capability to reveal the mechanisms of cancer development, discover cancer biomarkers and identify pathogenic targets. However, current methods ignore the potential correlations between samples in integrating multi-omics data. In addition, providing accurate biological explanations still poses significant challenges due to the complexity of deep learning models. Therefore, there is an urgent need for a deep learning-based multi-omics integration method to explore the potential correlations between samples and provide model interpretability. Herein, we propose a novel interpretable multi-omics data integration method (DeepKEGG) for cancer recurrence prediction and biomarker discovery. In DeepKEGG, a biological hierarchical module is designed for local connections of neuron nodes and model interpretability based on the biological relationship between genes/miRNAs and pathways. In addition, a pathway self-attention module is constructed to explore the correlation between different samples and generate the potential pathway feature representation for enhancing the prediction performance of the model. Lastly, an attribution-based feature importance calculation method is utilized to discover biomarkers related to cancer recurrence and provide a biological interpretation of the model. Experimental results demonstrate that DeepKEGG outperforms other state-of-the-art methods in 5-fold cross validation. Furthermore, case studies also indicate that DeepKEGG serves as an effective tool for biomarker discovery. The code is available at https://github.com/lanbiolab/DeepKEGG.

Mechanistic insight into glioma through spatially multidimensional proteomics.

Characterizing the tumor microenvironment at the molecular level is essential for understanding the mechanisms of tumorigenesis and evolution. However, the specificity of the blood proteome in localized region of the tumor and its linkages with other systems is difficult to investigate. Here, we propose a spatially multidimensional comparative proteomics strategy using glioma as an example. The blood proteome signature of tumor microenvironment was specifically identified by in situ collection of arterial and venous blood from the glioma region of the brain for comparison with peripheral blood. Also, by integrating with different dimensions of tissue and peripheral blood proteomics, the information on the genesis, migration, and exchange of glioma-associated proteins was revealed, which provided a powerful method for tumor mechanism research and biomarker discovery. The study recruited multidimensional clinical cohorts, allowing the proteomic results to corroborate each other, reliably revealing biological processes specific to gliomas, and identifying highly accurate biomarkers.

Proteome Analysis of Serum Purified Using Solanum tuberosum and Lycopersicon esculentum Lectins.

Serum and plasma exhibit a broad dynamic range of protein concentrations, posing challenges for proteome analysis. Various technologies have been developed to reduce this complexity, including high-abundance depletion methods utilizing antibody columns, extracellular vesicle enrichment techniques, and trace protein enrichment using nanobead cocktails. Here, we employed lectins to address this, thereby extending the scope of biomarker discovery in serum or plasma using a novel approach. We enriched serum proteins using 37 different lectins and subjected them to LC-MS/MS analysis with data-independent acquisition. Solanum tuberosum lectin (STL) and Lycopersicon esculentum lectin (LEL) enabled the detection of more serum proteins than the other lectins. STL and LEL bind to N-acetylglucosamine oligomers, emphasizing the significance of capturing these oligomer-binding proteins when analyzing serum trace proteins. Combining STL and LEL proved more effective than using them separately, allowing us to identify over 3000 proteins from serum through single-shot proteome analysis. We applied the STL/LEL trace-protein enrichment method to the sera of systemic lupus erythematosus model mice. This revealed differences in >1300 proteins between the systemic lupus erythematosus model and control mouse sera, underscoring the utility of this method for biomarker discovery.

CoNIC Challenge: Pushing the frontiers of nuclear detection, segmentation, classification and counting

Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome. To drive innovation in this area, we setup a community-wide challenge using the largest available dataset of its kind to assess nuclear segmentation and cellular composition. Our challenge, named CoNIC, stimulated the development of reproducible algorithms for cellular recognition with real-time result inspection on public leaderboards. We conducted an extensive post-challenge analysis based on the top-performing models using 1,658 whole-slide images of colon tissue. With around 700 million detected nuclei per model, associated features were used for dysplasia grading and survival analysis, where we demonstrated that the challenge’s improvement over the previous state-of-the-art led to significant boosts in downstream performance. Our findings also suggest that eosinophils and neutrophils play an important role in the tumour microevironment. We release challenge models and WSI-level results to foster the development of further methods for biomarker discovery.

PlateletSeq: A novel method for discovery of blood-based biomarkers.

Platelets are small circulating fragments of cells that play important roles in thrombosis, haemostasis, immune response, inflammation and cancer growth. Although anucleate, they contain a rich RNA repertoire which offers an opportunity to characterise changes in platelet gene expression in health and disease. Whilst this can be achieved with conventional RNA sequencing, a large input of high-quality RNA, and hence blood volume, is required (unless a pre-amplification step is added), along with specialist bioinformatic skills for data analysis and interpretation. We have developed a transcriptomics next-generation sequencing-based approach that overcomes these limitations. Termed PlateletSeq, this method requires very low levels of RNA input and does not require specialist bioinformatic analytical skills. Here we describe the methodology, from sample collection to processing and data analysis. Specifically, blood samples can be stored for up to 8days at 4°C prior to analysis. Platelets are isolated using multi-step centrifugation and a purity of≤1 leucocyte per 0.26x106 platelets is optimal for gene expression analysis. We have applied PlateletSeq to normal adult blood samples and show there are no age-associated variations and only minor gender-associated differences. In contrast, platelets from patients with myeloproliferative neoplasms show differences in platelet transcript profiles from normal and between disease subtypes. This illustrates the potential applicability of PlateletSeq for biomarker discovery and studying platelet biology in patient samples. It also opens avenues for assessing platelet quality in other fields such as transfusion research.

Biomarkers for clinical use in psychiatry: where are we and will we ever get there?

Biomarkers for clinical use in psychiatry: where are we and will we ever get there?

Discovering pathway biomarkers of hepatocellular carcinoma occurrence and development by dynamic network entropy analysis

ObjectiveGene expression profiling techniques measure the transcription of thousands of genes in a parallel manner. With more and more hepatocellular carcinoma (HCC) transcriptomic data becoming available, the high-throughput data provides an unprecedented opportunity to discover HCC diagnostic biomarkers. In this work, we propose a bioinformatics method based on dynamic network entropy analysis, called DNEA, to identify potential pathway biomarkers for HCC occurrence and development by integrating transcriptome and interactome. MethodsWe firstly collect the pathways documented in different knowledge-bases and then impose the genome-wide human transcriptomic data of multistage cancerous tissues during the development and progression of HCC. After linking the gene sets of pathways into individual connected networks, we map the corresponding gene expression information onto these pathways. The dynamic network entropy of individual pathways is calculated to evaluate its activities and dysfunctionalities during the disease occurrence and development. We use the overall significant difference in the entropic dynamics during the time course to prioritize distinctive pathways during disease progression. Then machine learning classification methods are employed to screen out pathway biomarkers with the classification ability to distinguish different-stage samples of HCC progression. ResultsPathway biomarkers discovered based on DNEA demonstrate good classification performance in measuring HCC progression. The classification accuracy is as follows: DNA replication pathway (mean AUC = 0.82, 20 genes) from KEGG, FMLP pathway (mean AUC = 0.84, 14 genes) from BioCarta, and downstream signaling of activated FGFR pathway (mean AUC = 0.80, 15 genes) from Reactome. At the same time, previous studies have shown that these genes and pathways screened are closely related to the occurrence and development of HCC in terms of oncogenesis dysfunctions. ConclusionsOur method for cancer biomarker discovery based on dynamic network entropy analysis is effective and efficient in identifying pathway biomarkers related to the progression of complex diseases.

Sequence-Based Platforms for Discovering Biomarkers in Liquid Biopsy of Non-Small-Cell Lung Cancer.

Lung cancer detection and monitoring are hampered by a lack of sensitive biomarkers, which results in diagnosis at late stages and difficulty in tracking response to treatment. Recent developments have established liquid biopsies as promising non-invasive methods for detecting biomarkers in lung cancer patients. With concurrent advances in high-throughput sequencing technologies and bioinformatics tools, new approaches for biomarker discovery have emerged. In this article, we survey established and emerging biomarker discovery methods using nucleic acid materials derived from bodily fluids in the context of lung cancer. We introduce nucleic acid biomarkers extracted from liquid biopsies and outline biological sources and methods of isolation. We discuss next-generation sequencing (NGS) platforms commonly used to identify novel biomarkers and describe how these have been applied to liquid biopsy. We highlight emerging biomarker discovery methods, including applications of long-read sequencing, fragmentomics, whole-genome amplification methods for single-cell analysis, and whole-genome methylation assays. Finally, we discuss advanced bioinformatics tools, describing methods for processing NGS data, as well as recently developed software tailored for liquid biopsy biomarker detection, which holds promise for early diagnosis of lung cancer.

Abstract 4115: KLRG1 marks tumor-infiltrating CD4 T cell subsets associated with immune escape and immunotherapy response

Abstract Current methods for biomarker discovery and target identification in immuno-oncology rely on static snapshots of tumor immunity. To better capture the dynamic and compartmentalized nature of antitumor immune responses, we generated longitudinal “temporal atlases” of productive versus non-productive antitumor immune responses in murine tumor models. We utilized a 34-parameter full spectrum flow cytometry panel to comprehensively profile immune composition within tumors, draining and non-draining lymph nodes, and blood in and around key inflection points of tumor regression or progression. We leveraged two distinct preclinical models for this; the NPK-C1 ectopic prostate cancer model to map dynamics of spontaneous cancer immunoediting, and anti-PD-1 treated MC38 tumors to study response or non-response to immune checkpoint blockade (ICB). We utilized UMAP and FlowSOM algorithms for iterative dimensionality reduction and clustering, respectively, to reveal novel phenotypes associated with productive versus non-productive immunity across model systems, tissues, and time points. We discovered expression of KLRG1 within the intratumoral CD4 T cell compartment was highly associated with tumor progression and response to ICB. Specifically, both FoxP3+ Tregs and FoxP3- Tconv cells within tumors accumulated KLRG1 expression through disease progression, but this was not observed in CD4 T cell or other immune subsets residing in lymph nodes or circulating in blood. Among all intratumoral clusters, KLRG1+ Tconv were the only subset significantly correlated with tumor burden at each time point tested and across both models. KLRG1+ Tconv were significantly enriched in NPK-C1 tumors undergoing progression to escape versus those under immune-mediated equilibrium (p=0.0004) and were lost in animals undergoing curative responses to ICB (p=0.003). In the Treg compartment, unsupervised clustering revealed a KLRG1+Helios- tumor Treg subset that was positively correlated with transition from equilibrium to escape in the NPK-C1 model (p=0.005). Also indicating a potential functional significance, this phenotype was absent in tumors undergoing curative responses to ICB (p=0.0002). Systematic investigation of the functional characteristics, transcriptional programming, and translational significance of intratumoral KLRG1+ CD4 T cell subsets is ongoing. Together, these findings identify KLRG1+ CD4 T cell populations as subsets for further investigation in cancer and demonstrate the utility of longitudinal full spectrum flow cytometry profiling as an engine of dynamic biomarker and/or target discovery in immuno-oncology. Citation Format: Casey Ager, Matthew Chaimowitz, Shruti Bansal, Meri Rogava, Johannes Melms, Catherine Spina, Cory Abate-Shen, Charles G. Drake, Matthew Dallos, Benjamin Izar. KLRG1 marks tumor-infiltrating CD4 T cell subsets associated with immune escape and immunotherapy response. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4115.

Abstract 5410: Predictive biomarker discovery method to bridge the gap between preclinical disease model dose-response and clinical trials

Abstract The translational gap of drug and biomarker discovery remains one of the biggest challenges in the pharmaceutical industry. On one hand high throughput screening and omics methods facilitate the generation of in-vitro and in-vivo data of dose-response for a particular drug or combinations. On the other hand, clinical omics techniques allow for the creation of large scale treatment-naive clinical datasets, with the TCGA and CPTAC as prominent examples. However, the question of which disease model best represents the response/resistance mechanisms of an oncology patient remains challenging. In this study we present a machine learning (ML) based computational technique for integrating omics from preclinical dose-response studies with clinical treatment-naive samples to create putative predictive biomarkers, and exemplify its application 2 case studies - PARP or AKT inhibitors. We utilize the Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) as a resource for in-vitro dose response data for PARPi and AKTi, coupled with multi-omic molecular data. We utilize the treatment naive molecular characterization from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) data of Breast cancer patients. Multi-omic analysis of these datasets derived 2 putative predictive biomarkers for PARPi and AKTi. The multi-omic analysis resembles the data availability at the critical drug development stage of transition from preclinical response models to a clinical trial. These biomarkers are tested on a validation dataset obtained from the iSPY adaptive clinical trial and achieve superior results compared to the original enrolment biomarkers and even the retrospectively derived biomarkers. This study presents a novel computational approach to bridge the gap between preclinical dose-response and clinical datasets and suggests an efficient way to discover predictive biomarkers based on data accessible in a preclinical setting. Citation Format: David Futorian, Oren Fischman, Gali Arad, Nitzan Simchi, Omri Erez, Eran Seger, Rozanne Groen, Kirill Pevzner. Predictive biomarker discovery method to bridge the gap between preclinical disease model dose-response and clinical trials. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5410.

Mass spectrometry and machine learning in the identification of COVID-19 biomarkers

Identifying specific diagnostic and prognostic biological markers of COVID-19 can improve disease surveillance and therapeutic opportunities. Mass spectrometry combined with machine and deep learning techniques has been used to identify pathways that could be targeted therapeutically. Moreover, circulating biomarkers have been identified to detect individuals infected with SARS-CoV-2 and at high risk of hospitalization. In this review, we have surveyed studies that have combined mass spectrometry-based omics techniques (proteomics, lipdomics, and metabolomics) and machine learning/deep learning to understand COVID-19 pathogenesis. After a literature search, we show 42 studies that applied reproducible, accurate, and sensitive mass spectrometry-based analytical techniques and machine/deep learning methods for COVID-19 biomarker discovery and validation. We also demonstrate that multiomics data results in classification models with higher performance. Furthermore, we focus on the combination of MALDI-TOF Mass Spectrometry and machine learning as a diagnostic and prognostic tool already present in the clinics. Finally, we reiterate that despite advances in this field, more optimization in the analytical and computational parts, such as sample preparation, data acquisition, and data analysis, will improve biomarkers that can be used to obtain more accurate diagnostic and prognostic tools.

Identifying Hair Biomarker Candidates for Alzheimer's Disease Using Three High Resolution Mass Spectrometry-Based Untargeted Metabolomics Strategies.

High-resolution mass spectrometry (HRMS)-based untargeted metabolomics strategies have emerged as an effective tool for discovering biomarkers of Alzheimer's disease (AD). There are various HRMS-based untargeted metabolomics strategies for biomarker discovery, including the data-dependent acquisition (DDA) method, the combination of full scan and target MS/MS, and the all ion fragmentation (AIF) method. Hair has emerged as a potential biospecimen for biomarker discovery in clinical research since it might reflect the circulating metabolic profiles over several months, while the analytical performances of the different data acquisition methods for hair biomarker discovery have been rarely investigated. Here, the analytical performances of three data acquisition methods in HRMS-based untargeted metabolomics for hair biomarker discovery were evaluated. The human hair samples from AD patients (N = 23) and cognitively normal individuals (N = 23) were used as an example. The most significant number of discriminatory features was acquired using the full scan (407), which is approximately 10-fold higher than that using the DDA strategy (41) and 11% higher than that using the AIF strategy (366). Only 66% of discriminatory chemicals discovered in the DDA strategy were discriminatory features in the full scan dataset. Moreover, compared to the deconvoluted MS/MS spectra with coeluted and background ions from the AIF method, the MS/MS spectrum obtained from the targeted MS/MS approach is cleaner and purer. Therefore, an untargeted metabolomics strategy combining the full scan with the targeted MS/MS method could obtain most discriminatory features along with a high quality MS/MS spectrum for discovering the AD biomarkers.

Critical Assessment of the Biomarker Discovery and Classification Methods for Multiclass Metabolomics.

Multiclass metabolomics has been widely applied in clinical practice to understand pathophysiological processes involved in disease progression and diagnostic biomarkers of various disorders. In contrast to the binary problem, the multiclass classification problem is more difficult in terms of obtaining reliable and stable results due to the increase in the complexity of determining exact class decision boundaries. In particular, methods of biomarker discovery and classification have a significant effect on the multiclass model because different methods with significantly varied theories produce conflicting results even for the same dataset. However, a systematic assessment for selecting the most appropriate methods of biomarker discovery and classification for multiclass metabolomics is still lacking. Therefore, a comprehensive assessment is essential to measure the suitability of methods in multiclass classification models from multiple perspectives. In this study, five biomarker discovery methods and nine classification methods were assessed based on four benchmark datasets of multiclass metabolomics. The performance assessment of the biomarker discovery and classification methods was performed using three evaluation criteria: assessment a (cluster analysis of sample grouping), assessment b (biomarker consistency in multiple subgroups), and assessment c (accuracy in the classification model). As a result, 13 combining strategies with superior performance were selected under multiple criteria based on these benchmark datasets. In conclusion, superior strategies that performed consistently well are suggested for the discovery of biomarkers and the construction of a classification model for multiclass metabolomics.

Early warning and diagnosis of liver cancer based on dynamic network biomarker and deep learning

BackgroundEarly detection of complex diseases like hepatocellular carcinoma remains challenging due to their network-driven pathology. Dynamic network biomarkers (DNB) based on monitoring changes in molecular correlations may enable earlier predictions. However, DNB analysis often overlooks disease heterogeneity. MethodsWe integrated DNB analysis with graph convolutional neural networks (GCN) to identify critical transitions during hepatocellular carcinoma development in a mouse model. A DNB-GCN model was constructed using transcriptomic data and gene expression levels as node features. ResultsDNB analysis identified a critical transition point at 7 weeks of age despite histological examinations being unable to detect cancerous changes at that time point. The DNB-GCN model achieved 100% accuracy in classifying healthy and cancerous mice, and was able to accurately predict the health status of newly introduced mice. ConclusionThe integration of DNB analysis and GCN demonstrates potential for the early detection of complex diseases by capturing network structures and molecular features that conventional biomarker discovery methods overlook. The approach warrants further development and validation.

Breath response following a nutritional challenge monitored by secondary electrospray ionization high-resolution mass spectrometry

On-line breath analysis using secondary electrospray ionization coupled to high-resolution mass spectrometry (SESI-HRMS) is a sensitive method for biomarker discovery. The strengths of this technology have already been demonstrated in the clinical environment. For the first time, this study demonstrates the application of SESI-HRMS in the field of nutritional science using a standardized nutritional intervention, consisting of a high-energy shake (950 kcal, 8% protein, 35% sugar and 57% fat). Eleven subjects underwent the intervention on three separate days and their exhaled breath was monitored up to six hours postprandially. In addition, sampling was performed during equivalent fasting conditions for selected subjects. To estimate the impact of inter- and intra-individual variability, analysis of variance simultaneous component analysis was conducted, revealing that the inter-individual variability accounted for 30% of the data variation. To distinguish the effect of the intervention from fasting conditions, partial least squares discriminant analysis was performed. Candidate compound annotation was performed with pathway analysis and collision-induced dissociation (CID) experiments. Pathway analysis highlighted, among others, features associated with the metabolism of linoleate, butanoate and amino sugars. Tentative compounds annotated through CID measurements include fatty acids, amino acids, and amino acid derivatives, some of them likely derived from nutrients by the gut microbiome (e.g. propanoate, indoles), as well as organic acids from the Krebs cycle. Time-series clustering showed an overlap of observed kinetic trends with those reported previously in blood plasma.

Patient‐specific Autoantigen Sample Preparation and Analysis Using the Reversible Protein Tag ProMTag

Autoimmune diseases affect &gt;20 million people in the US today. Currently, disease‐specific autoantibodies are thought to be the best biomarkers for diagnosis. Conventional immunoprecipitation methods have been used to identify autoantigens from the most common autoimmune diseases. However, these diseases account for only 6.5 million of the 20 million patients suffering from autoimmune diseases, leaving many without diagnoses until irreversible damage occurs. The remaining 13.5 million patients have &gt;70 autoimmune disorders without well characterized autoantibodies. The state‐of‐the‐art diagnostic test of these remaining diseases relies on gel electrophoresis of immunoprecipitated radiolabeled proteins, which cannot be identified by MS due to safety issues and the overwhelming presence of immunoglobulins.We have created an immunoprecipitation method that uses serum from patients with any autoimmune disorder to identify patient‐specific autoantigen proteins. This method uses a reversible click chemistry tag, called ProMTag. One end of the ProMTag forms a reversible, covalent bond with protein by coupling to lysines and amino termini. The other end of the ProMTag can form an irreversible, covalent bond with a solid bead support via a click chemistry, methyltetrazine‐TCO, pairing. In this study, the proteins of cell lysates that contain potential autoantigens were labeled with ProMTag. The ProMTagged‐proteins were exposed to patient antibodies bound to Protein A beads, thus capturing the ProMTagged autoantigens. All proteins were released from the Protein A beads, including ProMTagged‐autoantigens and untagged‐antibodies. The ProMTagged‐autoantigens were subsequently coupled to TCO beads, and the untagged‐antibodies were washed away. The linkage between the ProMTag and autoantigens was then reversed, yielding autoantigen proteins with greatly reduced antibody contamination ready for MS analysis. MS analysis successfully identified autoantigens from patient serums with rheumatoid arthri. This autoimmune biomarker discovery method can accelerate sample testing for known autoantigens and facilitating rapid discovery of novel autoantigens for both diagnostic and predictive biomarkers.