MarkerDB: an online database of molecular biomarkers

PubChem 2025 update

PubChem (https://pubchem.ncbi.nlm.nih.gov) is a large and highly-integrated public chemical database resource at NIH. In the past two years, significant updates were made to PubChem. With additions from over 130 new sources, PubChem contains >1000 data sources, 119 million compounds, 322 million substances and 295 million bioactivities. New interfaces, such as the consolidated literature panel and the patent knowledge panel, were developed. The consolidated literature panel combines all references about a compound into a single list, allowing users to easily find, sort, and export all relevant articles for a chemical in one place. The patent knowledge panels for a given query chemical or gene display chemicals, genes, and diseases co-mentioned with the query in patent documents, helping users to explore relationships between co-occurring entities within patent documents. PubChemRDF was expanded to include the co-occurrence data underlying the literature knowledge panel, enabling users to exploit semantic web technologies to explore entity relationships based on the co-occurrences in the scientific literature. The usability and accessibility of information on chemicals with non-discrete structures (e.g. biologics, minerals, polymers, UVCBs and glycans) were greatly improved with dedicated web pages that provide a comprehensive view of all available information in PubChem for these chemicals.

MarkerDB 2.0: a comprehensive molecular biomarker database for 2025

MarkerDB (https://markerdb.ca) has become a leading resource for comprehensive information on molecular biomarkers. Over the past 3 years, the database has evolved significantly, reflecting the dynamic landscape of biomarker research and increasing demands from its user community. This year's update, which is called MarkerDB 2.0, introduces key improvements to enhance the database's usability, consistency and the range of biomarkers covered. These improvements include (i) the addition of thousands of new biomarkers and associated health conditions, (ii) the inclusion of many new biomarker types and categories, (iii) upgraded searches and data filtering functionalities, (iv) new features for exploring and understanding biomarker panels and (v) significantly expanded and improved descriptions. These upgrades, along with numerous minor improvements in content, interface, layout and overall website performance, have greatly enhanced MarkerDB's usability and capacity to facilitate biomarker interpretation across various research domains. MarkerDB remains committed to providing a free, publicly accessible platform for consolidated information on a wide range of molecular (protein, genetic, chromosomal and chemical/small molecule) biomarkers, covering diagnostic, prognostic, risk, monitoring, safety and response-related biomarkers. We are confident that these upgrades and updates will improve MarkerDB's user friendliness, increase its utility and greatly expand its potential applications to many other areas of clinical medicine and biomedical research.

Decision Tree for Protein Biomarker Selection for Clinical Applications

Discovery of novel protein biomarkers for clinical applications is an active research field across a manifold of diseases. Despite some successes and progress, the biomarker development pipeline still frequently ends in failure. Biomarker candidates that are discovered by appropriate technologies such as unbiased mass spectrometry cannot be validated or translated to immunoassays in many cases. Selection of strong disease biomarker candidates that further constitute suitable targets for antibody binding in immunoassays is thus important to allow routine clinical use. This essential selection step can be supported and rationalized using bioinformatics tools such as protein databases. Here, we present a workflow in the form of decision trees to computationally investigate biomarker candidates and their available affinity reagents in depth. This analysis can identify the most promising biomarker candidates for assay development, while minimal time and effort are required.

Multidisciplinary approaches to study anaemia with special mention on aplastic anaemia (Review)

Anaemia is a common health problem worldwide that disproportionately affects vulnerable groups, such as children and expectant mothers. It has a variety of underlying causes, some of which are genetic. A comprehensive strategy combining physical examination, laboratory testing (for example, a complete blood count), and molecular tools for accurate identification is required for diagnosis. With nearly 400 varieties of anaemia, accurate diagnosis remains a challenging task. Red blood cell abnormalities are largely caused by genetic factors, which means that a thorough understanding requires interpretation at the molecular level. As a result, precision medicine has become a key paradigm, utilising artificial intelligence (AI) techniques, such as deep learning and machine learning, to improve prognostic evaluation, treatment prediction, and diagnostic accuracy. Furthermore, exploring the immunomodulatory role of vitamin D along with biomarker‑based molecular techniques offers promising avenues for insight into anaemia's pathophysiology. The intricacy of aplastic anaemia makes it particularly noteworthy as a topic deserving of concentrated molecular research. Given the complexity of anaemia, an integrated strategy integrating clinical, laboratory, molecular, and AI techniques shows a great deal of promise. Such an approach holds promise for enhancing global anaemia management options in addition to advancing our understanding of the illness.

Absolute Quantitative Targeted Monitoring of Potential Plasma Protein Biomarkers: A Pilot Study on Healthy Individuals

BACKGROUND/OBJECTIVES

The development of blood tests for the early detection of individual predisposition to socially significant diseases remains a pressing issue.

METHODS

In this pilot study, multiple reaction monitoring mass spectrometry (MRM-MS) with a BAK-270 assay was applied for protein concentrations analysis in blood plasma from 21 healthy volunteers of the European cohort.

RESULTS

The levels of 138 plasma proteins were reliably and precisely quantified in no less than 50% of samples. The quantified proteins included 66 FDA-approved markers of cardiovascular diseases (CVD), and other potential biomarkers of pathologies such as cancer, diabetes mellitus, and Alzheimer's disease. The analysis of individual variations of the plasma proteins revealed significant differences between the male (11) and female (10) groups. In total, fifteen proteins had a significantly different concentration in plasma; this included four proteins that exhibited changes greater than ±1.5-fold, three proteins (RBP4, APCS, and TTR) with higher levels in males, and one (SHBG) elevated in females. The obtained results demonstrated considerable agreement with the data collected from 20 samples of a North American cohort, which were analyzed with the similar MRM assay. The most significant differences between the cohorts of the two continents were observed in the level of 42 plasma proteins (including 24 FDA markers), of which 17 proteins showed a ≥1.5-fold change, and included proteins increased in North Americans (APOB, CRTAC1, C1QB, C1QC, C9, CRP, HP, IGHG1, IGKV4-1, SERPING1, RBP4, and AZGP1), as well as those elevated in Europeans (APOF, CD5L, HBG2, SELPLG, and TNA).

CONCLUSIONS

The results suggest a different contribution of specific (patho)physiological pathways (e.g., immune system and blood coagulation) to the development of socially significant diseases in Europeans and North Americans, and they should be taken into account when refining diagnostic panels.

"My Biomarkers Are Fine, Thank You": On the Biomarkerization of Modern Medicine

Biomarkers are becoming crucial in ever more medical tasks and are proposed to change medicine in profound ways. By biomarking ever more attributes of human life, they tend to blur the distinction between health and disease and come to characterize life as such. Not only do biomarkers strongly influence the professional conception of disease by pervading ever more diagnoses, but they also impact patients' experience of illness. To manage how biomarkers influence patients, professionals, and societies, we urgently need to move from identifying potentially relevant biomarkers to determine their meaning and value to individuals, professionals, and public health.

Missing Values in Longitudinal Proteome Dynamics Studies: Making a Case for Data Multiple Imputation

Temporal proteomics data sets are often confounded by the challenges of missing values. These missing data points, in a time-series context, can lead to fluctuations in measurements or the omission of critical events, thus hindering the ability to fully comprehend the underlying biomedical processes. We introduce a Data Multiple Imputation (DMI) pipeline designed to address this challenge in temporal data set turnover rate quantifications, enabling robust downstream analysis to gain novel discoveries. To demonstrate its utility and generalizability, we applied this pipeline to two use cases: a murine cardiac temporal proteomics data set and a human plasma temporal proteomics data set, both aimed at examining protein turnover rates. This DMI pipeline significantly enhanced the detection of protein turnover rate in both data sets, and furthermore, the imputed data sets captured new representation of proteins, leading to an augmented view of biological pathways, protein complex dynamics, as well as biomarker-disease associations. Importantly, DMI exhibited superior performance in benchmark data sets compared to single imputation methods (DSI). In summary, we have demonstrated that this DMI pipeline is effective at overcoming challenges introduced by missing values in temporal proteome dynamics studies.

Knowledge translation and knowledge mobilization from the FoodBAll project

This report describes the knowledge mobilization and translation outcomes of the Canadian-funded portion of a large, international project called the Food Biomarker Alliance (FoodBAll), which ran from 2015 to 2019. This remarkably successful project led to a large number of important findings, outputs, and impacts. In particular, FoodBAll unequivocally demonstrated that metabolomics could be used to not only discover biomarkers of food intake (BFIs), but also to measure diet in a more objective manner. FoodBAll also created standards for assessing and validating BFIs, papers and databases describing BFIs, and kits for measuring BFIs and it laid the groundwork for many global studies exploring food composition and precision nutrition.

Multiomics: Functional Molecular Biomarkers of Micronutrients for Public Health Application

Adequate micronutrient intake and status are global public health goals. Vitamin and mineral deficiencies are widespread and known to impair health and survival across the life stages. However, knowledge of molecular effects, metabolic pathways, biological responses to variation in micronutrient nutriture, and abilities to assess populations for micronutrient deficiencies and their pathology remain lacking. Rapidly evolving methodological capabilities in genomics, epigenomics, proteomics, and metabolomics offer unparalleled opportunities for the nutrition research community to link micronutrient exposure to cellular health; discover new, arguably essential micronutrients of microbial origin; and integrate methods of molecular biology, epidemiology, and intervention trials to develop novel approaches to assess and prevent micronutrient deficiencies in populations. In this review article, we offer new terminology to specify nutritional application of multiomic approaches and encourage collaboration across the basic to public health sciences to advance micronutrient deficiency prevention.

MetaboAnalyst 6.0: towards a unified platform for metabolomics data processing, analysis and interpretation

We introduce MetaboAnalyst version 6.0 as a unified platform for processing, analyzing, and interpreting data from targeted as well as untargeted metabolomics studies using liquid chromatography - mass spectrometry (LC-MS). The two main objectives in developing version 6.0 are to support tandem MS (MS2) data processing and annotation, as well as to support the analysis of data from exposomics studies and related experiments. Key features of MetaboAnalyst 6.0 include: (i) a significantly enhanced Spectra Processing module with support for MS2 data and the asari algorithm; (ii) a MS2 Peak Annotation module based on comprehensive MS2 reference databases with fragment-level annotation; (iii) a new Statistical Analysis module dedicated for handling complex study design with multiple factors or phenotypic descriptors; (iv) a Causal Analysis module for estimating metabolite - phenotype causal relations based on two-sample Mendelian randomization, and (v) a Dose-Response Analysis module for benchmark dose calculations. In addition, we have also improved MetaboAnalyst's visualization functions, updated its compound database and metabolite sets, and significantly expanded its pathway analysis support to around 130 species. MetaboAnalyst 6.0 is freely available at https://www.metaboanalyst.ca.

Evolutionary screening of precision oncology biomarkers and its applications in prognostic model construction

Biomarker screening is critical for precision oncology. However, one of the main challenges in precision oncology is that the screened biomarkers often fail to achieve the expected clinical effects and are rarely approved by regulatory authorities. Considering the close association between cancer pathogenesis and the evolutionary events of organisms, we first explored the evolutionary feature underlying clinically approved biomarkers, and two evolutionary features of approved biomarkers (Ohnologs and specific evolutionary stages of genes) were identified. Subsequently, we utilized evolutionary features for screening potential prognostic biomarkers in four common cancers: head and neck squamous cell carcinoma, liver hepatocellular carcinoma, lung adenocarcinoma, and lung squamous cell carcinoma. Finally, we constructed an evolution-strengthened prognostic model (ESPM) for cancers. These models can predict cancer patients' survival time across different cancer cohorts effectively and perform better than conventional models. In summary, our study highlights the application potentials of evolutionary information in precision oncology biomarker screening.

Identification of key mitochondria-related genes and their relevance to the immune system linking Parkinson's disease and primary Sjögren's syndrome through integrated bioinformatics analyses

BACKGROUND

Mitochondria are the metabolic hubs of cells, regulating energy production and antigen presentation, which are essential for activation, proliferation, and function of immune cells. Recent evidence indicates that mitochondrial antigen presentation may have an impact on diseases such as Parkinson's disease (PD) and autoimmune diseases. However, there is limited knowledge about the mechanisms that regulate the presentation of mitochondrial antigens in these diseases.

METHODS

In the current study, RNA sequencing was performed on labial minor salivary gland (LSG) from 25 patients with primary Sjögren's syndrome (pSS) and 14 non-pSS aged controls. Additionally, we obtained gene expression omnibus datasets associated with PD patients from NCBI Gene Expression Omnibus (GEO) databases. Single-sample Gene Set Enrichment Analysis (ssGSEA), ESTIMATE and Spearman correlations were conducted to explore the association between mitochondrial related genes and the immune system. Furthermore, we applied weighted Gene Co-expression Network Analysis (WGCNA) to identify hub mitochondria-related genes and investigate the correlated networks in both diseases. Single cell transcriptome analysis, immunohistochemical (IHC) staining and quantitative real-time PCR (qRT-PCR) were used to verify the activation of the hub mitochondria-related pathway. Pearson correlations and the CIBERSORT algorithms were employed to further reveal the correlation between hub mitochondria-related pathways and immune infiltration.

RESULTS

The transcriptome analysis revealed the presence of overlapping mitochondria-related genes and mitochondrial DNA damage in patients with pSS and PD. Reactive oxygen species (ROS), the senescence marker p53, and the inflammatory markers CD45 and Bcl-2 were found to be regionally distributed in LSGs of pSS patients. WGCNA analysis identified the STING pathway as the central mitochondria-related pathway closely associated with the immune system. Single cell analysis, IHC staining, and qRT-PCR confirmed the activation of the STING pathway. Subsequent, bioinformatic analysis revealed the proportion of infiltrating immune cells in the STING-high and STING-low groups of pSS and PD. Furthermore, the study demonstrated the association of the STING pathway with innate and adaptive immune cells, as well as functional cells, in the immune microenvironment of PD and pSS.

CONCLUSION

Our study uncovered a central pathway that connects mitochondrial dysfunction and the immune microenvironment in PD and pSS, potentially offering valuable insights into therapeutic targets for these conditions.

Biomarkers for Early Cancer Detection: A Landscape View of Recent Advancements, Spotlighting Pancreatic and Liver Cancers

Cancer is one of the leading causes of death worldwide. Early cancer detection is critical because it can significantly improve treatment outcomes, thus saving lives, reducing suffering, and lessening psychological and economic burdens. Cancer biomarkers provide varied information about cancer, from early detection of malignancy to decisions on treatment and subsequent monitoring. A large variety of molecular, histologic, radiographic, or physiological entities or features are among the common types of cancer biomarkers. Sizeable recent methodological progress and insights have promoted significant developments in the field of early cancer detection biomarkers. Here we provide an overview of recent advances in the knowledge related to biomolecules and cellular entities used for early cancer detection. We examine data from the CAS Content Collection, the largest human-curated collection of published scientific information, as well as from the biomarker datasets at Excelra, and analyze the publication landscape of recent research. We also discuss the evolution of key concepts and cancer biomarkers development pipelines, with a particular focus on pancreatic and liver cancers, which are known to be remarkably difficult to detect early and to have particularly high morbidity and mortality. The objective of the paper is to provide a broad overview of the evolving landscape of current knowledge on cancer biomarkers and to outline challenges and evaluate growth opportunities, in order to further efforts in solving the problems that remain. The merit of this review stems from the extensive, wide-ranging coverage of the most up-to-date scientific information, allowing unique, unmatched breadth of landscape analysis and in-depth insights.

The multifaceted roles of COL4A4 in lung adenocarcinoma: An integrated bioinformatics and experimental study

BACKGROUND

Abnormal expression of collagen IV subunits has been reported in cancers, but the significance is not clear. No study has reported the significance of COL4A4 in lung adenocarcinoma (LUAD).

METHODS

COL4A4 expression data, single-cell sequencing data and clinical data were downloaded from public databases. A range of bioinformatics and experimental methods were adopted to analyze the association of COL4A4 expression with clinical parameters, tumor microenvironment (TME), drug resistance and immunotherapy response, and to investigate the roles and underlying mechanism of COL4A4 in LUAD.

RESULTS

COL4A4 is differentially expressed in most of cancers analyzed, being associated with prognosis, tumor stemness, immune checkpoint gene expression and TME parameters. In LUAD, COL4A4 expression is down-regulated and associated with various TME parameters, response to immunotherapy and drug resistance. LUAD patients with lower COL4A4 have worse prognosis. Knockdown of COL4A4 significantly inhibited the expression of cell-cycle associated genes, and the expression and activation of signaling pathways including JAK/STAT3, p38, and ERK pathways, and induced quiescence in LUAD cells. Besides, it significantly induced the expression of a range of bioactive molecule genes that have been shown to have critical roles in TME remodeling and immune regulation.

CONCLUSIONS

COL4A4 is implicated in the pathogenesis of cancers including LUAD. Its function may be multifaceted. It can modulate the activity of LUAD cells, TME remodeling and tumor stemness, thus affecting the pathological process of LUAD. COL4A4 may be a prognostic molecular marker and a potential therapeutic target.

Exploring the Potential of Exosomes as Biomarkers in Tuberculosis and Other Diseases

Tuberculosis (TB) is a major cause of morbidity and mortality and remains an important public health issue in developing countries worldwide. The existing methods and techniques available for the diagnosis of TB are based on combinations of laboratory (chemical and biological), radiological, and clinical tests. These methods are sophisticated and laborious and have limitations in terms of sensitivity, specificity, and accuracy. Clinical settings need improved diagnostic biomarkers to accurately detect biological changes due to pathogen invasion and pharmacological responses. Exosomes are membrane-bound vesicles and mediators of intercellular signaling processes that play a significant role in the pathogenesis of various diseases, such as tuberculosis, and can act as promising biomarkers for the monitoring of TB infection. Compared to conventional biomarkers, exosome-derived biomarkers are advantageous because they are easier to detect in different biofluids, are more sensitive and specific, and may be useful in tracking patients' reactions to therapy. This review provides insights into the types of biomarkers, methods of exosome isolation, and roles of the cargo (proteins) present in exosomes isolated from patients through omics studies, such as proteomics. These findings will aid in developing new prognostic and diagnostic biomarkers and could lead to the identification of new therapeutic targets in the clinical setting.

CBD2: A functional biomarker database for colorectal cancer

The rapidly evolving landscape of biomarkers for colorectal cancer (CRC) necessitates an integrative, updated repository. In response, we constructed the Colorectal Cancer Biomarker Database (CBD), which collected and displayed the curated biomedicine information for 870 CRC biomarkers in the previous study. Building on CBD, we have now developed CBD2, which includes information on 1569 newly reported biomarkers derived from different biological sources (DNA, RNA, protein, and others) and clinical applications (diagnosis, treatment, and prognosis). CBD2 also incorporates information on nonbiomarkers that have been identified as unsuitable for use as biomarkers in CRC. A key new feature of CBD2 is its network analysis function, by which users can investigate the visible and topological network between biomarkers and identify their relevant pathways. CBD2 also allows users to query a series of chemicals, drug combinations, or multiple targets, to enable multidrug, multitarget, multipathway analyses, toward facilitating the design of polypharmacological treatments for CRC. CBD2 is freely available at http://www.eyeseeworld.com/cbd.

PathBank 2.0-the pathway database for model organism metabolomics

PathBank (https://pathbank.org) and its predecessor database, the Small Molecule Pathway Database (SMPDB), have been providing comprehensive metabolite pathway information for the metabolomics community since 2010. Over the past 14 years, these pathway databases have grown and evolved significantly to meet the needs of the metabolomics community and respond to continuing changes in computing technology. This year's update, PathBank 2.0, brings a number of important improvements and upgrades that should make the database more useful and more appealing to a larger cross-section of users. In particular, these improvements include: (i) a significant increase in the number of primary or canonical pathways (from 1720 to 6951); (ii) a massive increase in the total number of pathways (from 110 234 to 605 359); (iii) significant improvements to the quality of pathway diagrams and pathway descriptions; (iv) a strong emphasis on drug metabolism and drug mechanism pathways; (v) making most pathway images more slide-compatible and manuscript-compatible; (vi) adding tools to support better pathway filtering and selecting through a more complete pathway taxonomy; (vii) adding pathway analysis tools for visualizing and calculating pathway enrichment. Many other minor improvements and updates to the content, the interface and general performance of the PathBank website have also been made. Overall, we believe these upgrades and updates should greatly enhance PathBank's ease of use and its potential applications for interpreting metabolomics data.

BioKA: a curated and integrated biomarker knowledgebase for animals

Biomarkers play an important role in various area such as personalized medicine, drug development, clinical care, and molecule breeding. However, existing animals' biomarker resources predominantly focus on human diseases, leaving a significant gap in non-human animal disease understanding and breeding research. To address this limitation, we present BioKA (Biomarker Knowledgebase for Animals, https://ngdc.cncb.ac.cn/bioka), a curated and integrated knowledgebase encompassing multiple animal species, diseases/traits, and annotated resources. Currently, BioKA houses 16 296 biomarkers associated with 951 mapped diseases/traits across 31 species from 4747 references, including 11 925 gene/protein biomarkers, 1784 miRNA biomarkers, 1043 mutation biomarkers, 773 metabolic biomarkers, 357 circRNA biomarkers and 127 lncRNA biomarkers. Furthermore, BioKA integrates various annotations such as GOs, protein structures, protein-protein interaction networks, miRNA targets and so on, and constructs an interactive knowledge network of biomarkers including circRNA-miRNA-mRNA associations, lncRNA-miRNA associations and protein-protein associations, which is convenient for efficient data exploration. Moreover, BioKA provides detailed information on 308 breeds/strains of 13 species, and homologous annotations for 8784 biomarkers across 16 species, and offers three online application tools. The comprehensive knowledge provided by BioKA not only advances human disease research but also contributes to a deeper understanding of animal diseases and supports livestock breeding.

TheMarker: a comprehensive database of therapeutic biomarkers

Distinct from the traditional diagnostic/prognostic biomarker (adopted as the indicator of disease state/process), the therapeutic biomarker (ThMAR) has emerged to be very crucial in the clinical development and clinical practice of all therapies. There are five types of ThMAR that have been found to play indispensable roles in various stages of drug discovery, such as: Pharmacodynamic Biomarker essential for guaranteeing the pharmacological effects of a therapy, Safety Biomarker critical for assessing the extent or likelihood of therapy-induced toxicity, Monitoring Biomarker indispensable for guiding clinical management by serially measuring patients' status, Predictive Biomarker crucial for maximizing the clinical outcome of a therapy for specific individuals, and Surrogate Endpoint fundamental for accelerating the approval of a therapy. However, these data of ThMARs has not been comprehensively described by any of the existing databases. Herein, a database, named 'TheMarker', was therefore constructed to (a) systematically offer all five types of ThMAR used at different stages of drug development, (b) comprehensively describe ThMAR information for the largest number of drugs among available databases, (c) extensively cover the widest disease classes by not just focusing on anticancer therapies. These data in TheMarker are expected to have great implication and significant impact on drug discovery and clinical practice, and it is freely accessible without any login requirement at: https://idrblab.org/themarker.

Enrichment of sweat-derived extracellular vesicles of human and bacterial origin for biomarker identification

Sweat contains biomarkers for real-time non-invasive health monitoring, but only a few relevant analytes are currently used in clinical practice. In the present study, we investigated whether sweat-derived extracellular vesicles (EVs) can be used as a source of potential protein biomarkers of human and bacterial origin. Methods: By using ExoView platform, electron microscopy, nanoparticle tracking analysis and Western blotting we characterized EVs in the sweat of eight volunteers performing rigorous exercise. We compared the presence of EV markers as well as general protein composition of total sweat, EV-enriched sweat and sweat samples collected in alginate skin patches. Results: We identified 1209 unique human proteins in EV-enriched sweat, of which approximately 20% were present in every individual sample investigated. Sweat derived EVs shared 846 human proteins (70%) with total sweat, while 368 proteins (30%) were captured by medical grade alginate skin patch and such EVs contained the typical exosome marker CD63. The majority of identified proteins are known to be carried by EVs found in other biofluids, mostly urine. Besides human proteins, EV-enriched sweat samples contained 1594 proteins of bacterial origin. Bacterial protein profiles in EV-enriched sweat were characterized by high interindividual variability, that reflected differences in total sweat composition. Alginate-based sweat patch accumulated only 5% proteins of bacterial origin. Conclusion: We showed that sweat-derived EVs provide a rich source of potential biomarkers of human and bacterial origin. Use of commercially available alginate skin patches selectively enrich for human derived material with very little microbial material collected.

A Systematic Review of Lipid-Focused Cardiovascular Disease Research: Trends and Opportunities

Lipids are important modifiers of protein function, particularly as parts of lipoproteins, which transport lipophilic substances and mediate cellular uptake of circulating lipids. As such, lipids are of particular interest as blood biological markers for cardiovascular disease (CVD) as well as for conditions linked to CVD such as atherosclerosis, diabetes mellitus, obesity and dietary states. Notably, lipid research is particularly well developed in the context of CVD because of the relevance and multiple causes and risk factors of CVD. The advent of methods for high-throughput screening of biological molecules has recently resulted in the generation of lipidomic profiles that allow monitoring of lipid compositions in biological samples in an untargeted manner. These and other earlier advances in biomedical research have shaped the knowledge we have about lipids in CVD. To evaluate the knowledge acquired on the multiple biological functions of lipids in CVD and the trends in their research, we collected a dataset of references from the PubMed database of biomedical literature focused on plasma lipids and CVD in human and mouse. Using annotations from these records, we were able to categorize significant associations between lipids and particular types of research approaches, distinguish non-biological lipids used as markers, identify differential research between human and mouse models, and detect the increasingly mechanistic nature of the results in this field. Using known associations between lipids and proteins that metabolize or transport them, we constructed a comprehensive lipid-protein network, which we used to highlight proteins strongly connected to lipids found in the CVD-lipid literature. Our approach points to a series of proteins for which lipid-focused research would bring insights into CVD, including Prostaglandin G/H synthase 2 (PTGS2, a.k.a. COX2) and Acylglycerol kinase (AGK). In this review, we summarize our findings, putting them in a historical perspective of the evolution of lipid research in CVD.

Proteomic comparison between non-purified cerebrospinal fluid and cerebrospinal fluid-derived extracellular vesicles from patients with Alzheimer's, Parkinson's and Lewy body dementia

Dementia is a leading cause of death worldwide, with increasing prevalence as global life expectancy increases. The most common neurodegenerative disorders are Alzheimer's disease (AD), dementia with Lewy bodies (DLB) and Parkinson's disease dementia (PDD). With this study, we took an in-depth look at the proteome of the (non-purified) cerebrospinal fluid (CSF) and the CSF-derived extracellular vesicles (EVs) of AD, PD, PD-MCI (Parkinson's disease with mild cognitive impairment), PDD and DLB patients analysed by label-free mass spectrometry. This has led to the discovery of differentially expressed proteins that may be helpful for differential diagnosis. We observed a greater number of differentially expressed proteins in CSF-derived EV samples (N = 276) compared to non-purified CSF (N = 169), with minimal overlap between both datasets. This finding suggests that CSF-derived EV samples may be more suitable for the discovery phase of a biomarker study, due to the removal of more abundant proteins, resulting in a narrower dynamic range. As disease-specific markers, we selected a total of 39 biomarker candidates identified in non-purified CSF, and 37 biomarker candidates across the different diseases under investigation in the CSF-derived EV data. After further exploration and validation of these proteins, they can be used to further differentiate between the included dementias and may offer new avenues for research into more disease-specific pharmacological therapeutics.

Detection, mechanisms, and therapeutic implications of oncometabolites

Metabolic abnormalities are a hallmark of cancer cells and are essential to tumor progression. Oncometabolites have pleiotropic effects on cancer biology and affect a plethora of processes, from oncogenesis and metabolism to therapeutic resistance. Targeting oncometabolites, therefore, could offer promising therapeutic avenues against tumor growth and resistance to treatments. Recent advances in characterizing the metabolic profiles of cancer cells are shedding light on the underlying mechanisms and associated metabolic networks. This review summarizes the diverse detection methods, molecular mechanisms, and therapeutic targets of oncometabolites, which may lead to targeting oncometabolism for cancer therapy.

A Metabolites Merging Strategy (MMS): Harmonization to Enable Studies' Intercomparison

Metabolomics encounters challenges in cross-study comparisons due to diverse metabolite nomenclature and reporting practices. To bridge this gap, we introduce the Metabolites Merging Strategy (MMS), offering a systematic framework to harmonize multiple metabolite datasets for enhanced interstudy comparability. MMS has three steps. Step 1: Translation and merging of the different datasets by employing InChIKeys for data integration, encompassing the translation of metabolite names (if needed). Followed by Step 2: Attributes' retrieval from the InChIkey, including descriptors of name (title name from PubChem and RefMet name from Metabolomics Workbench), and chemical properties (molecular weight and molecular formula), both systematic (InChI, InChIKey, SMILES) and non-systematic identifiers (PubChem, CheBI, HMDB, KEGG, LipidMaps, DrugBank, Bin ID and CAS number), and their ontology. Finally, a meticulous three-step curation process is used to rectify disparities for conjugated base/acid compounds (optional step), missing attributes, and synonym checking (duplicated information). The MMS procedure is exemplified through a case study of urinary asthma metabolites, where MMS facilitated the identification of significant pathways hidden when no dataset merging strategy was followed. This study highlights the need for standardized and unified metabolite datasets to enhance the reproducibility and comparability of metabolomics studies.

NULISA: a proteomic liquid biopsy platform with attomolar sensitivity and high multiplexing

The blood proteome holds great promise for precision medicine but poses substantial challenges due to the low abundance of most plasma proteins and the vast dynamic range of the plasma proteome. Here we address these challenges with NUcleic acid Linked Immuno-Sandwich Assay (NULISA™), which improves the sensitivity of traditional proximity ligation assays by ~10,000-fold to attomolar level, by suppressing assay background via a dual capture and release mechanism built into oligonucleotide-conjugated antibodies. Highly multiplexed quantification of both low- and high-abundance proteins spanning a wide dynamic range is achieved by attenuating signals from abundant targets with unconjugated antibodies and next-generation sequencing of barcoded reporter DNA. A 200-plex NULISA containing 124 cytokines and chemokines and other proteins demonstrates superior sensitivity to a proximity extension assay in detecting biologically important low-abundance biomarkers in patients with autoimmune diseases and COVID-19. Fully automated NULISA makes broad and in-depth proteomic analysis easily accessible for research and diagnostic applications.

AutoCore: A network-based definition of the core module of human autoimmunity and autoinflammation

Although research on rare autoimmune and autoinflammatory diseases has enabled definition of nonredundant regulators of homeostasis in human immunity, because of the single gene-single disease nature of many of these diseases, contributing factors were mostly unveiled in sequential and noncoordinated individual studies. We used a network-based approach for integrating a set of 186 inborn errors of immunity with predominant autoimmunity/autoinflammation into a comprehensive map of human immune dysregulation, which we termed "AutoCore." The AutoCore is located centrally within the interactome of all protein-protein interactions, connecting and pinpointing multidisease markers for a range of common, polygenic autoimmune/autoinflammatory diseases. The AutoCore can be subdivided into 19 endotypes that correspond to molecularly and phenotypically cohesive disease subgroups, providing a molecular mechanism-based disease classification and rationale toward systematic targeting for therapeutic purposes. Our study provides a proof of concept for using network-based methods to systematically investigate the molecular relationships between individual rare diseases and address a range of conceptual, diagnostic, and therapeutic challenges.

Principles of metabolome conservation in animals

Metabolite levels shape cellular physiology and disease susceptibility, yet the general principles governing metabolome evolution are largely unknown. Here, we introduce a measure of conservation of individual metabolite levels among related species. By analyzing multispecies tissue metabolome datasets in phylogenetically diverse mammals and fruit flies, we show that conservation varies extensively across metabolites. Three major functional properties, metabolite abundance, essentiality, and association with human diseases predict conservation, highlighting a striking parallel between the evolutionary forces driving metabolome and protein sequence conservation. Metabolic network simulations recapitulated these general patterns and revealed that abundant metabolites are highly conserved due to their strong coupling to key metabolic fluxes in the network. Finally, we show that biomarkers of metabolic diseases can be distinguished from other metabolites simply based on evolutionary conservation, without requiring any prior clinical knowledge. Overall, this study uncovers simple rules that govern metabolic evolution in animals and implies that most tissue metabolome differences between species are permitted, rather than favored by natural selection. More broadly, our work paves the way toward using evolutionary information to identify biomarkers, as well as to detect pathogenic metabolome alterations in individual patients.

NULISA: a novel proteomic liquid biopsy platform with attomolar sensitivity and high multiplexing

The blood proteome holds great promise for precision medicine but poses substantial challenges due to the low abundance of most plasma proteins and the vast dynamic range across the proteome. We report a novel proteomic technology - NUcleic acid Linked Immuno-Sandwich Assay (NULISA™) - that incorporates a dual capture and release mechanism to suppress the assay background and improves the sensitivity of the proximity ligation assay by over 10,000-fold to the attomolar level. It utilizes pairs of antibodies conjugated to DNA oligonucleotides that enable immunocomplex purification and generate reporter DNA containing target- and sample-specific barcodes for a next-generation sequencing-based, highly multiplexed readout. A 200-plex NULISA targeting 124 cytokines and chemokines and 80 other immune response-related proteins demonstrated superior sensitivity for detecting low-abundance proteins and high concordance with other immunoassays. The ultrahigh sensitivity allowed the detection of previously difficult-to-detect, but biologically important, low-abundance biomarkers in patients with autoimmune diseases and COVID-19. Fully automated NULISA addresses longstanding challenges in proteomic analysis of liquid biopsies and makes broad and in-depth proteomic analysis accessible to the general research community and future diagnostic applications.

An Inflection Point in Cancer Protein Biomarkers: What Was and What's Next

Biomarkers remain the highest value proposition in cancer medicine today - especially protein biomarkers. Yet despite decades of evolving regulatory frameworks to facilitate the review of emerging technologies, biomarkers have been mostly about promise with very little to show for improvements in human health. Cancer is an emergent property of a complex system and deconvoluting the integrative and dynamic nature of the overall system through biomarkers is a daunting proposition. The last two decades have seen an explosion of multi-omics profiling and a range of advanced technologies for precision medicine, including the emergence of liquid biopsy, exciting advances in single cell analysis, artificial intelligence (machine and deep learning) for data analysis and many other advanced technologies that promise to transform biomarker discovery. Combining multiple omics modalities to acquire a more comprehensive landscape of the disease state, we are increasingly developing biomarkers to support therapy selection and patient monitoring. Furthering precision medicine, especially in oncology, necessitates moving away from the lens of reductionist thinking towards viewing and understanding that complex diseases are, in fact, complex adaptive systems. As such, we believe it is necessary to re-define biomarkers as representations of biological system states at different hierarchical levels of biological order. This definition could include traditional molecular, histologic, radiographic, or physiological characteristics, as well as emerging classes of digital markers and complex algorithms. To succeed in the future, we must move past purely observational individual studies and instead start building a mechanistic framework to enable integrative analysis of new studies within the context of prior studies. Identifying information in complex systems and applying theoretical constructs, such as information theory, to study cancer as a disease of dysregulated communication could prove to be "game changing" for the clinical outcome of cancer patients.

The role of nanotechnology-based approaches for clinical infectious diseases and public health

Given the high incidence of infection and the growing resistance of bacterial and viral infections to the traditional antiseptic, the need for novel antiseptics is critical. Therefore, novel approaches are urgently required to reduce the activity of bacterial and viral infections. Nanotechnology is increasingly being exploited for medical purposes and is of significant interest in eliminating or limiting the activity of various pathogens. Due to the increased surface-to-volume ratio of a given mass of particles, the antimicrobial properties of some naturally occurring antibacterial materials, such as zinc and silver, increase as particle size decreases into the nanometer regime. However, the physical structure of a nanoparticle and the way it interacts with and penetrates the bacteria also appear to provide unique bactericidal mechanisms. To measure the efficacy of nanoparticles (diameter 100 nm) as antimicrobial agents, it is necessary to comprehend the range of approaches for evaluating the viability of bacteria; each of them has its advantages and disadvantages. The nanotechnology-based disinfectants and sensors for SARS-CoV-2 provide a roadmap for creating more effective sensors and disinfectants for detecting and preventing coronaviruses and other infections. Moreover, there is an increasing role of nanotechnology-based approaches in various infections, including wound healing and related infection, nosocomial infections, and various bacterial infections. To meet the demand for patient care, nanotechnology-based disinfectants need to be further advanced with optimum approaches. Herein, we review the current burden of infectious diseases with a focus on SARS-CoV-2 and bacterial infection that significantly burdens developed healthcare systems and small healthcare communities. We then highlight how nanotechnology could aid in improving existing treatment modalities and diagnosis of those infectious agents. Finally, we conclude the current development and future perspective of nanotechnology for combating infectious diseases. The overall goal is to update healthcare providers on the existing role and future of nanotechnology in tackling those common infectious diseases.

Bioinformatics approaches and big data analytics opportunities in improving fisheries and aquaculture

Aquaculture has witnessed an excellent growth rate during the last two decades and offers huge potential to provide nutritional as well as livelihood security. Genomic research has contributed significantly toward the development of beneficial technologies for aquaculture. The existing high throughput technologies like next-generation technologies generate oceanic data which requires extensive analysis using appropriate tools. Bioinformatics is a rapidly evolving science that involves integrating gene based information and computational technology to produce new knowledge for the benefit of aquaculture. Bioinformatics provides new opportunities as well as challenges for information and data processing in new generation aquaculture. Rapid technical advancements have opened up a world of possibilities for using current genomics to improve aquaculture performance. Understanding the genes that govern economically relevant characteristics, necessitates a significant amount of additional research. The various dimensions of data sources includes next-generation DNA sequencing, protein sequencing, RNA sequencing gene expression profiles, metabolic pathways, molecular markers, and so on. Appropriate bioinformatics tools are developed to mine the biologically relevant and commercially useful results. The purpose of this scoping review is to present various arms of diverse bioinformatics tools with special emphasis on practical translation to the aquaculture industry.

Detection of simple proteins by direct surface-enhanced Raman scattering based on the Hofmeister ion-specific effect

Surface-enhanced Raman scattering (SERS) spectroscopy has unique advantages in detecting biomolecules, but label-free determination of proteins with low scattering cross-sections remains challenging. In this study, such proteins' SERS signals have been optimized using the Hofmeister effect between protein molecules and CsI solution at physiological concentrations (A 100 mmol/L Cesium iodide, CsI). Cs⁺ as chaotro cation ion has a complex interaction mechanism with protein, can not only deprive hydrated water molecules on the surface of protein but also penetrate into the hydrophobic interior of protein. In addition to the above advantages, I^- in excess CsI solution with appropriate concentration can removes the interference of citric acid-based impurities on the surface of silver nanoparticles, and Cs⁺ in excess CsI solution attracts the aggregation of negatively charged silver nanoparticles and cause local electromagnetic field enhancement to achieve high sensitivity in protein detection. This has been combined with principal component analysis to perform a comprehensive analysis of several proteins. Molecular dynamics simulations have been performed to study the mechanism of interaction between CsI and proteins. In addition, the vibrational peak of water has been used as an internal standard to quantify the protein content, and a good linear relationship between peak intensity and concentration was obtained.

Genomic and proteomic biomarker landscape in Clinical Trials

The use of molecular biomarkers to support disease diagnosis, monitor its progression, and guide drug treatment has gained traction in the last decades. While only a dozen biomarkers have been approved for their exploitation in the clinic by the FDA, many more are evaluated in the context of translational research and clinical trials. Furthermore, the information on which biomarkers are measured, for which purpose, and in relation to which conditions are not readily accessible: biomarkers used in clinical studies available through resources such as ClinicalTrials.gov are described as free text, posing significant challenges in finding, analyzing, and processing them by both humans and machines. We present a text mining strategy to identify proteomic and genomic biomarkers used in clinical trials and classify them according to the methodologies by which they are measured. We find more than 3000 biomarkers used in the context of 2600 diseases. By analyzing this dataset, we uncover patterns of use of biomarkers across therapeutic areas over time, including the biomarker type and their specificity. These data are made available at the Clinical Biomarker App at https://www.disgenet.org/biomarkers/, a new portal that enables the exploration of biomarkers extracted from the clinical studies available at ClinicalTrials.gov and enriched with information from the scientific literature. The App features several metrics that assess the specificity of the biomarkers, facilitating their selection and prioritization. Overall, the Clinical Biomarker App is a valuable and timely resource about clinical biomarkers, to accelerate biomarker discovery, development, and application.

Biomarkers: Promising and valuable tools towards diagnosis, prognosis and treatment of Covid-19 and other diseases

The use of biomarkers as early warning systems in the evaluation of disease risk has increased markedly in the last decade. Biomarkers are indicators of typical biological processes, pathogenic processes, or pharmacological reactions to therapy. The application and identification of biomarkers in the medical and clinical fields have an enormous impact on society. In this review, we discuss the history, various definitions, classifications, characteristics, and discovery of biomarkers. Furthermore, the potential application of biomarkers in the diagnosis, prognosis, and treatment of various diseases over the last decade are reviewed. The present review aims to inspire readers to explore new avenues in biomarker research and development.

Suggesting disease associations for overlooked metabolites using literature from metabolic neighbors

In human health research, metabolic signatures extracted from metabolomics data have a strong added value for stratifying patients and identifying biomarkers. Nevertheless, one of the main challenges is to interpret and relate these lists of discriminant metabolites to pathological mechanisms. This task requires experts to combine their knowledge with information extracted from databases and the scientific literature. However, we show that most compounds (>99%) in the PubChem database lack annotated literature. This dearth of available information can have a direct impact on the interpretation of metabolic signatures, which is often restricted to a subset of significant metabolites. To suggest potential pathological phenotypes related to overlooked metabolites that lack annotated literature, we extend the "guilt-by-association" principle to literature information by using a Bayesian framework. The underlying assumption is that the literature associated with the metabolic neighbors of a compound can provide valuable insights, or an a priori, into its biomedical context. The metabolic neighborhood of a compound can be defined from a metabolic network and correspond to metabolites to which it is connected through biochemical reactions. With the proposed approach, we suggest more than 35,000 associations between 1,047 overlooked metabolites and 3,288 diseases (or disease families). All these newly inferred associations are freely available on the FORUM ftp server (see information at https://github.com/eMetaboHUB/Forum-LiteraturePropagation).

Applying multi-omics techniques to the discovery of biomarkers for acute aortic dissection

Acute aortic dissection (AAD) is a cardiovascular disease that manifests suddenly and fatally. Due to the lack of specific early symptoms, many patients with AAD are often overlooked or misdiagnosed, which is undoubtedly catastrophic for patients. The particular pathogenic mechanism of AAD is yet unknown, which makes clinical pharmacological therapy extremely difficult. Therefore, it is necessary and crucial to find and employ unique biomarkers for Acute aortic dissection (AAD) as soon as possible in clinical practice and research. This will aid in the early detection of AAD and give clear guidelines for the creation of focused treatment agents. This goal has been made attainable over the past 20 years by the quick advancement of omics technologies and the development of high-throughput tissue specimen biomarker screening. The primary histology data support and add to one another to create a more thorough and three-dimensional picture of the disease. Based on the introduction of the main histology technologies, in this review, we summarize the current situation and most recent developments in the application of multi-omics technologies to AAD biomarker discovery and emphasize the significance of concentrating on integration concepts for integrating multi-omics data. In this context, we seek to offer fresh concepts and recommendations for fundamental investigation, perspective innovation, and therapeutic development in AAD.

Enhanced Competition at the Nano-Bio Interface Enables Comprehensive Characterization of Protein Corona Dynamics and Deep Coverage of Proteomes

Introducing engineered nanoparticles (NPs) into a biofluid such as blood plasma leads to the formation of a selective and reproducible protein corona at the particle-protein interface, driven by the relationship between protein-NP affinity and protein abundance. This enables scalable systems that leverage protein-nano interactions to overcome current limitations of deep plasma proteomics in large cohorts. Here the importance of the protein to NP-surface ratio (P/NP) is demonstrated and protein corona formation dynamics are modeled, which determine the competition between proteins for binding. Tuning the P/NP ratio significantly modulates the protein corona composition, enhancing depth and precision of a fully automated NP-based deep proteomic workflow (Proteograph). By increasing the binding competition on engineered NPs, 1.2-1.7× more proteins with 1% false discovery rate are identified on the surface of each NP, and up to 3× more proteins compared to a standard plasma proteomics workflow. Moreover, the data suggest P/NP plays a significant role in determining the in vivo fate of nanomaterials in biomedical applications. Together, the study showcases the importance of P/NP as a key design element for biomaterials and nanomedicine in vivo and as a powerful tuning strategy for accurate, large-scale NP-based deep proteomic studies.

ChemFOnt: the chemical functional ontology resource

The Chemical Functional Ontology (ChemFOnt), located at https://www.chemfont.ca, is a hierarchical, OWL-compatible ontology describing the functions and actions of >341 000 biologically important chemicals. These include primary metabolites, secondary metabolites, natural products, food chemicals, synthetic food additives, drugs, herbicides, pesticides and environmental chemicals. ChemFOnt is a FAIR-compliant resource intended to bring the same rigor, standardization and formal structure to the terms and terminology used in biochemistry, food chemistry and environmental chemistry as the gene ontology (GO) has brought to molecular biology. ChemFOnt is available as both a freely accessible, web-enabled database and a downloadable Web Ontology Language (OWL) file. Users may download and deploy ChemFOnt within their own chemical databases or integrate ChemFOnt into their own analytical software to generate machine readable relationships that can be used to make new inferences, enrich their omics data sets or make new, non-obvious connections between chemicals and their direct or indirect effects. The web version of the ChemFOnt database has been designed to be easy to search, browse and navigate. Currently ChemFOnt contains data on 341 627 chemicals, including 515 332 terms or definitions. The functional hierarchy for ChemFOnt consists of four functional 'aspects', 12 functional super-categories and a total of 173 705 functional terms. In addition, each of the chemicals are classified into 4825 structure-based chemical classes. ChemFOnt currently contains 3.9 million protein-chemical relationships and ∼10.3 million chemical-functional relationships. The long-term goal for ChemFOnt is for it to be adopted by databases and software tools used by the general chemistry community as well as the metabolomics, exposomics, metagenomics, genomics and proteomics communities.

EZCancerTarget: an open-access drug repurposing and data-collection tool to enhance target validation and optimize international research efforts against highly progressive cancers

The expanding body of potential therapeutic targets requires easily accessible, structured, and transparent real-time interpretation of molecular data. Open-access genomic, proteomic and drug-repurposing databases transformed the landscape of cancer research, but most of them are difficult and time-consuming for casual users. Furthermore, to conduct systematic searches and data retrieval on multiple targets, researchers need the help of an expert bioinformatician, who is not always readily available for smaller research teams. We invite research teams to join and aim to enhance the cooperative work of more experienced groups to harmonize international efforts to overcome devastating malignancies. Here, we integrate available fundamental data and present a novel, open access, data-aggregating, drug repurposing platform, deriving our searches from the entries of Clue.io. We show how we integrated our previous expertise in small-cell lung cancer (SCLC) to initiate a new platform to overcome highly progressive cancers such as triple-negative breast and pancreatic cancer with data-aggregating approaches. Through the front end, the current content of the platform can be further expanded or replaced and users can create their drug-target list to select the clinically most relevant targets for further functional validation assays or drug trials. EZCancerTarget integrates searches from publicly available databases, such as PubChem, DrugBank, PubMed, and EMA, citing up-to-date and relevant literature of every target. Moreover, information on compounds is complemented with biological background information on eligible targets using entities like UniProt, String, and GeneCards, presenting relevant pathways, molecular- and biological function and subcellular localizations of these molecules. Cancer drug discovery requires a convergence of complex, often disparate fields. We present a simple, transparent, and user-friendly drug repurposing software to facilitate the efforts of research groups in the field of cancer research.

Meta-Analysis Reveals Both the Promises and the Challenges of Clinical Metabolomics

Clinical metabolomics is a rapidly expanding field focused on identifying molecular biomarkers to aid in the efficient diagnosis and treatment of human diseases. Variations in study design, metabolomics methodologies, and investigator protocols raise serious concerns about the accuracy and reproducibility of these potential biomarkers. The explosive growth of the field has led to the recent availability of numerous replicate clinical studies, which permits an evaluation of the consistency of biomarkers identified across multiple metabolomics projects. Pancreatic ductal adenocarcinoma (PDAC) is the third-leading cause of cancer-related death and has the lowest five-year survival rate primarily due to the lack of an early diagnosis and the limited treatment options. Accordingly, PDAC has been a popular target of clinical metabolomics studies. We compiled 24 PDAC metabolomics studies from the scientific literature for a detailed meta-analysis. A consistent identification across these multiple studies allowed for the validation of potential clinical biomarkers of PDAC while also highlighting variations in study protocols that may explain poor reproducibility. Our meta-analysis identified 10 metabolites that may serve as PDAC biomarkers and warrant further investigation. However, 87% of the 655 metabolites identified as potential biomarkers were identified in single studies. Differences in cohort size and demographics, p-value choice, fold-change significance, sample type, handling and storage, data collection, and analysis were all factors that likely contributed to this apparently large false positive rate. Our meta-analysis demonstrated the need for consistent experimental design and normalized practices to accurately leverage clinical metabolomics data for reliable and reproducible biomarker discovery.

Unlocking the Potential of the Human Microbiome for Identifying Disease Diagnostic Biomarkers

The human microbiome encodes more than three million genes, outnumbering human genes by more than 100 times, while microbial cells in the human microbiota outnumber human cells by 10 times. Thus, the human microbiota and related microbiome constitute a vast source for identifying disease biomarkers and therapeutic drug targets. Herein, we review the evidence backing the exploitation of the human microbiome for identifying diagnostic biomarkers for human disease. We describe the importance of the human microbiome in health and disease and detail the use of the human microbiome and microbiota metabolites as potential diagnostic biomarkers for multiple diseases, including cancer, as well as inflammatory, neurological, and metabolic diseases. Thus, the human microbiota has enormous potential to pave the road for a new era in biomarker research for diagnostic and therapeutic purposes. The scientific community needs to collaborate to overcome current challenges in microbiome research concerning the lack of standardization of research methods and the lack of understanding of causal relationships between microbiota and human disease.

Metabolomics application for the design of an optimal diet

Precision nutrition is an emerging branch of nutrition science that aims to use modern omics technologies (genomics, proteomics, and metabolomics) to assess an individual's response to specific foods or dietary patterns and thereby determine the most effective diet or lifestyle interventions to prevent or treat specific diseases. Metabolomics is vital to nearly every aspect of precision nutrition. It can be targeted or untargeted, and it has many applications. Indeed, it can be used to comprehensively characterize the thousands of chemicals in foods, identify food by-products in human biofluids or tissues, characterize nutrient deficiencies or excesses, monitor biochemical responses to dietary interventions, track long- or short-term dietary habits, and guide the development of nutritional therapies. Indeed, metabolomics can be coupled with genomics and proteomics to study and advance the field of precision nutrition. Integrating omics with epidemiological and clinical data will begin to define the beneficial effects of human food metabolites. In this review, we present the metabolome and its relationship to precision nutrition. Moreover, we describe the different techniques used in metabolomics and present how metabolomics has been applied to advance the field of precision nutrition by providing notable examples and cases.

MarkerGenie: an NLP-enabled text-mining system for biomedical entity relation extraction

Motivation

Natural language processing (NLP) tasks aim to convert unstructured text data (e.g. articles or dialogues) to structured information. In recent years, we have witnessed fundamental advances of NLP technique, which has been widely used in many applications such as financial text mining, news recommendation and machine translation. However, its application in the biomedical space remains challenging due to a lack of labeled data, ambiguities and inconsistencies of biological terminology. In biomedical marker discovery studies, tools that rely on NLP models to automatically and accurately extract relations of biomedical entities are valuable as they can provide a more thorough survey of all available literature, hence providing a less biased result compared to manual curation. In addition, the fast speed of machine reader helps quickly orient research and development.

Results

To address the aforementioned needs, we developed automatic training data labeling, rule-based biological terminology cleaning and a more accurate NLP model for binary associative and multi-relation prediction into the MarkerGenie program. We demonstrated the effectiveness of the proposed methods in identifying relations between biomedical entities on various benchmark datasets and case studies.

Availability and implementation

MarkerGenie is available at https://www.genegeniedx.com/markergenie/. Data for model training and evaluation, term lists of biomedical entities, details of the case studies and all trained models are provided at https://drive.google.com/drive/folders/14RypiIfIr3W_K-mNIAx9BNtObHSZoAyn?usp=sharing.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

FAIR Genomes metadata schema promoting Next Generation Sequencing data reuse in Dutch healthcare and research

The genomes of thousands of individuals are profiled within Dutch healthcare and research each year. However, this valuable genomic data, associated clinical data and consent are captured in different ways and stored across many systems and organizations. This makes it difficult to discover rare disease patients, reuse data for personalized medicine and establish research cohorts based on specific parameters. FAIR Genomes aims to enable NGS data reuse by developing metadata standards for the data descriptions needed to FAIRify genomic data while also addressing ELSI issues. We developed a semantic schema of essential data elements harmonized with international FAIR initiatives. The FAIR Genomes schema v1.1 contains 110 elements in 9 modules. It reuses common ontologies such as NCIT, DUO and EDAM, only introducing new terms when necessary. The schema is represented by a YAML file that can be transformed into templates for data entry software (EDC) and programmatic interfaces (JSON, RDF) to ease genomic data sharing in research and healthcare. The schema, documentation and MOLGENIS reference implementation are available at https://fairgenomes.org .

A Bioinformatics-Assisted Review on Iron Metabolism and Immune System to Identify Potential Biomarkers of Exercise Stress-Induced Immunosuppression

The immune function is closely related to iron (Fe) homeostasis and allostasis. The aim of this bioinformatics-assisted review was twofold; (i) to update the current knowledge of Fe metabolism and its relationship to the immune system, and (ii) to perform a prediction analysis of regulatory network hubs that might serve as potential biomarkers during stress-induced immunosuppression. Several literature and bioinformatics databases/repositories were utilized to review Fe metabolism and complement the molecular description of prioritized proteins. The Search Tool for the Retrieval of Interacting Genes (STRING) was used to build a protein-protein interactions network for subsequent network topology analysis. Importantly, Fe is a sensitive double-edged sword where two extremes of its nutritional status may have harmful effects on innate and adaptive immunity. We identified clearly connected important hubs that belong to two clusters: (i) presentation of peptide antigens to the immune system with the involvement of redox reactions of Fe, heme, and Fe trafficking/transport; and (ii) ubiquitination, endocytosis, and degradation processes of proteins related to Fe metabolism in immune cells (e.g., macrophages). The identified potential biomarkers were in agreement with the current experimental evidence, are included in several immunological/biomarkers databases, and/or are emerging genetic markers for different stressful conditions. Although further validation is warranted, this hybrid method (human-machine collaboration) to extract meaningful biological applications using available data in literature and bioinformatics tools should be highlighted.

Nanoporous Substrates with Molecular-Level Perfluoroalkyl/Alkylamide Surface for Laser Desorption/Ionization Mass Spectrometry of Small Proteins

The rapid detection of biomolecules greatly contributes to health management, clinical diagnosis, and prevention of diseases. Mass spectrometry (MS) is effective for detecting and analyzing various molecules at high throughput. However, there are problems with the MS analysis of biological samples, including complicated separation operations and essential pretreatments. In this study, a nanostructured organosilica substrate for laser desorption/ionization mass spectrometry (LDI-MS) is designed and synthesized to detect peptides and small proteins efficiently and rapidly. The surface functionality of the substrate is tuned by perfluoroalkyl/alkylamide groups mixed at a molecular level. This contributes to both lowering the surface free energy and introducing weak anchoring sites for peptides and proteins. Analyte molecules applied onto the substrate are homogeneously distributed and readily desorbed by the laser irradiation. The organosilica substrate enables the efficient LDI of various compounds, including peptides, small proteins, phospholipids, and drugs. An amyloid β protein fragment, which is known as a biomarker for Alzheimer's disease, is detectable at 0.05 fmol μL^-1. The detection of the amyloid β at 0.2 fmol μL^-1 is also confirmed in the presence of blood components. Nanostructured organosilica substrates incorporating a molecular-level surface design have the potential to enable easy detection of a wide range of biomolecules.

TF-Marker: a comprehensive manually curated database for transcription factors and related markers in specific cell and tissue types in human

Transcription factors (TFs) play key roles in biological processes and are usually used as cell markers. The emerging importance of TFs and related markers in identifying specific cell types in human diseases increases the need for a comprehensive collection of human TFs and related markers sets. Here, we developed the TF-Marker database (TF-Marker, http://bio.liclab.net/TF-Marker/), aiming to provide cell/tissue-specific TFs and related markers for human. By manually curating thousands of published literature, 5905 entries including information about TFs and related markers were classified into five types according to their functions: (i) TF: TFs which regulate expression of the markers; (ii) T Marker: markers which are regulated by the TF; (iii) I Marker: markers which influence the activity of TFs; (iv) TFMarker: TFs which play roles as markers and (v) TF Pmarker: TFs which play roles as potential markers. The 5905 entries of TF-Marker include 1316 TFs, 1092 T Markers, 473 I Markers, 1600 TFMarkers and 1424 TF Pmarkers, involving 383 cell types and 95 tissue types in human. TF-Marker further provides a user-friendly interface to browse, query and visualize the detailed information about TFs and related markers. We believe TF-Marker will become a valuable resource to understand the regulation patterns of different tissues and cells.

HMDB 5.0: the Human Metabolome Database for 2022

The Human Metabolome Database or HMDB (https://hmdb.ca) has been providing comprehensive reference information about human metabolites and their associated biological, physiological and chemical properties since 2007. Over the past 15 years, the HMDB has grown and evolved significantly to meet the needs of the metabolomics community and respond to continuing changes in internet and computing technology. This year's update, HMDB 5.0, brings a number of important improvements and upgrades to the database. These should make the HMDB more useful and more appealing to a larger cross-section of users. In particular, these improvements include: (i) a significant increase in the number of metabolite entries (from 114 100 to 217 920 compounds); (ii) enhancements to the quality and depth of metabolite descriptions; (iii) the addition of new structure, spectral and pathway visualization tools; (iv) the inclusion of many new and much more accurately predicted spectral data sets, including predicted NMR spectra, more accurately predicted MS spectra, predicted retention indices and predicted collision cross section data and (v) enhancements to the HMDB's search functions to facilitate better compound identification. Many other minor improvements and updates to the content, the interface, and general performance of the HMDB website have also been made. Overall, we believe these upgrades and updates should greatly enhance the HMDB's ease of use and its potential applications not only in human metabolomics but also in exposomics, lipidomics, nutritional science, biochemistry and clinical chemistry.

W. Emerging technologies and their impact on regulatory science

There is an evolution and increasing need for the utilization of emerging cellular, molecular and in silico technologies and novel approaches for safety assessment of food, drugs, and personal care products. Convergence of these emerging technologies is also enabling rapid advances and approaches that may impact regulatory decisions and approvals. Although the development of emerging technologies may allow rapid advances in regulatory decision making, there is concern that these new technologies have not been thoroughly evaluated to determine if they are ready for regulatory application, singularly or in combinations. The magnitude of these combined technical advances may outpace the ability to assess fit for purpose and to allow routine application of these new methods for regulatory purposes. There is a need to develop strategies to evaluate the new technologies to determine which ones are ready for regulatory use. The opportunity to apply these potentially faster, more accurate, and cost-effective approaches remains an important goal to facilitate their incorporation into regulatory use. However, without a clear strategy to evaluate emerging technologies rapidly and appropriately, the value of these efforts may go unrecognized or may take longer. It is important for the regulatory science field to keep up with the research in these technically advanced areas and to understand the science behind these new approaches. The regulatory field must understand the critical quality attributes of these novel approaches and learn from each other's experience so that workforces can be trained to prepare for emerging global regulatory challenges. Moreover, it is essential that the regulatory community must work with the technology developers to harness collective capabilities towards developing a strategy for evaluation of these new and novel assessment tools.