IMass Time: The Future, in Future!

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.

Conceptualised psycho-medical footprint for health status outcomes and the potential impacts for early detection and prevention of chronic diseases in the context of 3P medicine

Background

The Suboptimal Health Status Questionnaire-25 (SHSQ-25) is a distinctive medical psychometric diagnostic tool designed for the early detection of chronic diseases. However, the synaptic connections between the 25 symptomatic items and their relevance in supporting the monitoring of suboptimal health outcomes, which are precursors for chronic diseases, have not been thoroughly evaluated within the framework of predictive, preventive, and personalised medicine (PPPM/3PM). This baseline study explores the internal structure of the SHSQ-25 and demonstrates its discriminatory power to predict optimal and suboptimal health status (SHS) and develop photogenic representations of their distinct relationship patterns.

Methods

The cross-sectional study involved healthy Ghanaian participants (n = 217; aged 30-80 years; ~ 61% female), who responded to the SHSQ-25. The median SHS score was used to categorise the population into optimal and SHS. Graphical LASSO model and multi-dimensional scaling configuration methods were employed to describe the network structures for the two populations.

Results

We observed differences in the structural, node placement and node distance of the synaptic networks for the optimal and suboptimal populations. A statistically significant variance in connectivity levels was noted between the optimal (58 non-zero edges) and suboptimal (43 non-zero edges) networks (p = 0.024). Fatigue emerged as a prominently central subclinical condition within the suboptimal population, whilst the cardiovascular system domain had the greatest relevance for the optimal population. The contrast in connectivity levels and the divergent prominence of specific subclinical conditions across domain networks shed light on potential health distinctions.

Conclusions

We have demonstrated the feasibility of creating dynamic visualizers of the evolutionary trends in the relationships between the domains of SHSQ-25 relative to health status outcomes. This will provide in-depth comprehension of the conceptual model to inform personalised strategies to circumvent SHS. Additionally, the findings have implications for both health care and disease prevention because at-risk individuals can be predicted and prioritised for monitoring, and targeted intervention can begin before their symptoms reach an irreversible stage.

Supplementary information

The online version contains supplementary material available at 10.1007/s13167-023-00344-2.

Advances in the Application of Artificial Intelligence-Based Spectral Data Interpretation: A Perspective

The interpretation of spectral data, including mass, nuclear magnetic resonance, infrared, and ultraviolet-visible spectra, is critical for obtaining molecular structural information. The development of advanced sensing technology has multiplied the amount of available spectral data. Chemical experts must use basic principles corresponding to the spectral information generated by molecular fragments and functional groups. This is a time-consuming process that requires a solid professional knowledge base. In recent years, the rapid development of computer science and its applications in cheminformatics and the emergence of computer-aided expert systems have greatly reduced the difficulty in analyzing large quantities of data. For expert systems, however, the problem-solving strategy must be known in advance or extracted by human experts and translated into algorithms. Gratifyingly, the development of artificial intelligence (AI) methods has shown great promise for solving such problems. Traditional algorithms, including the latest neural network algorithms, have shown great potential for both extracting useful information and processing massive quantities of data. This Perspective highlights recent innovations covering all of the emerging AI-based spectral interpretation techniques. In addition, the main limitations and current obstacles are presented, and the corresponding directions for further research are proposed. Moreover, this Perspective gives the authors' personal outlook on the development and future applications of spectral interpretation.

Decoding the mechanism of hypertension through multiomics profiling

Hypertension, characterised by a constant high blood pressure, is the primary risk factor for multiple cardiovascular events and a major cause of death in adults. Excitingly, innovations in high-throughput technologies have enabled the global exploration of the whole genome (genomics), revealing dysregulated genes that are linked to hypertension. Moreover, post-genomic biomarkers, from the emerging fields of transcriptomics, proteomics, glycomics and lipidomics, have provided new insights into the molecular underpinnings of hypertension. In this paper, we review the pathophysiology of hypertension, and highlight the multi-omics approaches for hypertension prediction and diagnosis.

A Transcriptomic and Reverse-Engineering Strategy Reveals Molecular Signatures of Arachidonic Acid Metabolism in 12 Cancers

Cancer and arachidonic acid (AA) have important linkages. For example, AA metabolites regulate several critical biological functions associated with carcinogenesis: angiogenesis, apoptosis, and cancer invasion. However, little is known about the comparative changes in metabolite expression of the arachidonic acid pathway (AAP) in carcinogenesis. In this study, we examined transcriptome data from 12 cancers, such as breast invasive carcinoma, colon adenocarcinoma, lung adenocarcinoma, and prostate adenocarcinoma. We also report here a reverse-engineering strategy wherein we estimated metabolic signatures associated with AAP by (1) making deductive inferences through transcriptome-level data extraction, (2) remodeling AA metabolism, and (3) performing a comparative analysis of cancer types to determine the similarities and differences between different cancer types with respect to AA metabolic alterations. We identified 77 AAP gene signatures differentially expressed in cancers and 37 AAP metabolites associated with them. Importantly, the metabolite 15(S)-HETE was identified in almost all cancers, while arachidonate, 5-HETE, PGF2α, 14,15-EET, 8,9-EET, 5,6-EET, and 20-HETE were discovered as other most regulated metabolites. This study shows that the 12 cancers studied herein, although in different branches of the AAP, have altered expression of AAP gene signatures. Going forward, AA related-cancer research generally, and the molecular signatures and their estimated metabolites reported herein specifically, hold broad promise for precision/personalized medicine in oncology as potential therapeutic and diagnostic targets.

A new update of MALDI-TOF mass spectrometry in lipid research

Matrix-assisted laser desorption and ionization (MALDI) mass spectrometry (MS) is an indispensable tool in modern lipid research since it is fast, sensitive, tolerates sample impurities and provides spectra without major analyte fragmentation. We will discuss some methodological aspects, the related ion-forming processes and the MALDI MS characteristics of the different lipid classes (with the focus on glycerophospholipids) and the progress, which was achieved during the last ten years. Particular attention will be given to quantitative aspects of MALDI MS since this is widely considered as the most serious drawback of the method. Although the detailed role of the matrix is not yet completely understood, it will be explicitly shown that the careful choice of the matrix is crucial (besides the careful evaluation of the positive and negative ion mass spectra) in order to be able to detect all lipid classes of interest. Two developments will be highlighted: spatially resolved Imaging MS is nowadays well established and the distribution of lipids in tissues merits increasing interest because lipids are readily detectable and represent ubiquitous compounds. It will also be shown that a combination of MALDI MS with thin-layer chromatography (TLC) enables a fast spatially resolved screening of an entire TLC plate which makes the method competitive with LC/MS.

Glycosylation Profiling as a Biomarker of Suboptimal Health Status for Chronic Disease Stratification

WHO defines health as "a state of complete physical, mental, and social well-being and not merely the absence of disease or infirmity." We coined and defined suboptimal health status (SHS) as a subclinical, reversible stage of the pre-chronic disease. SHS is a physical state between health and disease, characterized by health complaints, general weakness, chronic fatigue, and low energy levels. We have developed an instrument to measure SHS, Suboptimal Health Status Questionnaire-25 (SHSQ-25), a self-reported survey assessing five health components that has been validated in various ethnical populations. Our studies suggest that SHS is associated with the major components of cardiovascular health and the early onset of metabolic diseases. Besides subjective measure of health (SHS), glycans are conceived as objective biomarkers of SHS. Glycans are complex and branching carbohydrate moieties attached to proteins, participating in inflammatory regulation and chronic disease pathogenesis. We have been investigating the role of glycans and SHS in multiple cardiometabolic diseases in different ethnical populations (African, Chinese, and Caucasian). Here we present case studies to prove that a combination of subjective health measure (SHS) with objective health measure (glycans) represents a window of opportunity to halt or reverse the progression of chronic diseases.

Digging Deeper into Precision/Personalized Medicine: Cracking the Sugar Code, the Third Alphabet of Life, and Sociomateriality of the Cell

Precision/personalized medicine is a hot topic in health care. Often presented with the motto "the right drug, for the right patient, at the right dose, and the right time," precision medicine is a theory for rational therapeutics as well as practice to individualize health interventions (e.g., drugs, food, vaccines, medical devices, and exercise programs) using biomarkers. Yet, an alien visitor to planet Earth reading the contemporary textbooks on diagnostics might think precision medicine requires only two biomolecules omnipresent in the literature: nucleic acids (e.g., DNA) and proteins, known as the first and second alphabet of biology, respectively. However, the precision/personalized medicine community has tended to underappreciate the third alphabet of life, the "sugar code" (i.e., the information stored in glycans, glycoproteins, and glycolipids). This article brings together experts in precision/personalized medicine science, pharmacoglycomics, emerging technology governance, cultural studies, contemporary art, and responsible innovation to critically comment on the sociomateriality of the three alphabets of life together. First, the current transformation of targeted therapies with personalized glycomedicine and glycan biomarkers is examined. Next, we discuss the reasons as to why unraveling of the sugar code might have lagged behind the DNA and protein codes. While social scientists have historically noted the importance of constructivism (e.g., how people interpret technology and build their values, hopes, and expectations into emerging technologies), life scientists relied on the material properties of technologies in explaining why some innovations emerge rapidly and are more popular than others. The concept of sociomateriality integrates these two explanations by highlighting the inherent entanglement of the social and the material contributions to knowledge and what is presented to us as reality from everyday laboratory life. Hence, we present a hypothesis based on a sociomaterial conceptual lens: because materiality and synthesis of glycans are not directly driven by a template, and thus more complex and open ended than sequencing of a finite length genome, social construction of expectations from unraveling of the sugar code versus the DNA code might have evolved differently, as being future-uncertain versus future-proof, respectively, thus potentially explaining the "sugar lag" in precision/personalized medicine diagnostics over the past decades. We conclude by introducing systems scientists, physicians, and biotechnology industry to the concept, practice, and value of responsible innovation, while glycomedicine and other emerging biomarker technologies (e.g., metagenomics and pharmacomicrobiomics) transition to applications in health care, ecology, pharmaceutical/diagnostic industries, agriculture, food, and bioengineering, among others.

Next-Generation (Glycomic) Biomarkers for Cardiometabolic Health: A Community-Based Study of Immunoglobulin G N-Glycans in a Chinese Han Population

Cardiovascular disease is a common complex trait that calls for next-generation biomarkers for precision diagnostics and therapeutics. The most common type of post-translational protein modification involves glycosylation. Glycans participate in key intercellular and intracellular functions, such as protein quality control, cell adhesion, cell-cell recognition, signal transduction, cell proliferation, and cell differentiation. In this context, immunoglobulin G (IgG) N-glycans affect the anti-inflammatory and proinflammatory responses of IgG, and are associated with cardiometabolic risk factors such as aging, central obesity, dyslipidemia, and hyperglycemia. Yet, the role of such glycomic biomarkers requires evaluation in diverse world populations. We report here original observations on association of IgG N-glycan biosignatures with 15 cardiometabolic risk factors in a community-based cross-sectional study conducted in 701 Chinese Han participants. After controlling for age and sex, we found that the 16, 21, and 18 IgG N-glycan traits were significantly different in participants with and without metabolic syndrome, hypertriglyceridemic waist phenotype, or abdominal obesity, respectively. The canonical correlation analysis showed that IgG N-glycan profiles were significantly associated with cardiometabolic risk factors (r = 0.469, p < 0.001). Classification models based on IgG N-glycan traits were able to differentiate participants with (1) metabolic syndrome, (2) hypertriglyceridemic waist phenotype, or (3) abdominal obesity from controls, with an area under receiver operating characteristic curves (AUC) of 0.632 (95% confidence interval [CI], 0.574-0.691, p < 0.001), 0.659 (95% CI, 0.587-0.730, p < 0.001), and 0.610 (95% CI, 0.565-0.656, p < 0.001), respectively. These new data suggest that IgG N-glycans may play an important role in cardiometabolic disease pathogenesis by regulating the proinflammatory or anti-inflammatory responses of IgG. Looking into the future, IgG N-glycan biosignatures warrant further research in other world population samples with a view to applications in clinical cardiology and public health practice.

How Do Postgenomic Innovations Emerge? Building Legitimacy by Proteomics Standards and Informing the Next-Generation Technology Policy

How do postgenomic innovations emerge and become legitimate? Proteomics, a frequently utilized postgenomic technology, provides a valuable case study of the sociotechnical strategies used by an emergent scientific field to establish its legitimacy and assert political power. Chief among these strategies is standard making, an inherently political process that requires examination through a critical social science lens. We report in this study an original case study from interviews with proteomics scientists and observations at conferences of the Human Proteome Organization and Australasian Proteomics Society over a 5-year period (2011-2015). The study contributes new knowledge on how an emerging postgenomic science uses standard-setting practices to politically legitimize a hitherto contested technology. Drawing on legitimacy theory, we show how proteomics scientists and organizations used standards as strategic tools to establish the legitimacy of this postgenomic field and affirm that proteomics can generate verifiable and reproducible results, thereby establishing it as a legitimate scientific field. Notably, legitimacy can be leveraged, at the same time, to maximize political power vis-à-vis other fields of science and as such embodies power relationships. These data collectively inform the broader context, in which postgenomic innovations emerge and legitimize, both technically and politically, through standards making. These findings have relevance for the design of next generation technology policies by demonstrating that standards are not "just" standards or neutral constructs but also tools to leverage political power of and by science and innovation actors, as shown in this case study of the emerging early phase of proteomics from 2011 to 2015.

The Urinary Peptidome as a Noninvasive Biomarker Development Strategy for Prenatal Screening of Down's Syndrome

Prenatal screening for Down's syndrome based on maternal age, ultrasound measures, and maternal serum biomarkers is recommended worldwide, but the false-positive rate and poor diagnostic performance of these screening tests remain problematic. Genetic analysis of cell-free DNA in maternal blood has been developed as a new prenatal screening for Down's syndrome, but it has a number of limitations, including turnaround time and cost. Prenatal screening diagnostic innovation calls for new tests that are noninvasive, accurate, and affordable. We report original observations on potential peptide biomarkers in maternal urine for screening of fetal Down's syndrome. The peptidome of urine samples from 23 pregnant women carrying Down's syndrome fetuses and 30 pregnant women carrying fetuses with normal karyotype was fractionated by weak cation exchange magnetic beads and analyzed by MALDI-TOF mass spectrometry. Levels of six peptides (m/z 1022.1, 1032.1, 1099.5, 1155.9, 1306.6, and 2365.6) were significantly altered between the case and control groups after controlling for maternal and gestational age. A classification model was constructed based on these candidate peptides that could differentiate fetuses with Down's syndrome from controls with a sensitivity of 95.7%, a specificity of 70.0%, and an area under receiver operating characteristic curves of 0.909 (95% confidence interval, 0.835-0.984). Peptide peaks at m/z 1099.5 and 1155.9 were identified as the partial sequences of alpha-1-antitrypsin and heat shock protein beta-1, respectively. These new findings support the new idea that maternal urinary peptidome offers prospects for noninvasive biomarker discovery and development for the prenatal screening of fetal Down's syndrome.

Serum Proteomic Changes as Candidate Biomarkers of Intermediate Liver Fibrosis in Chronic Hepatitis B Infection

Chronic hepatitis B (CHB) is a major global health burden. Liver fibrosis, an insidious process, is the main histopathological change in CHB that might lead to the end-stage liver disease if left untreated. The intermediate liver fibrosis (S2) is the optimal time to start antiviral therapy. The aim of the present study was to examine the proteomic changes in patients with CHB at different fibrotic stages, with a view to identify future serum biomarkers for S2. Ninety CHB patients were grouped into mild (S0-1), intermediate (S2), and severe liver fibrosis (S3-4) (61 men and 29 women; age 25-63 years). Isobaric tagging for relative and absolute quantitation was applied to screen proteins differentially expressed among the patient groups. Another 46 patients with CHB (age 25-59 years; 31 men and 15 women), and 16 healthy controls (age 26-61 years; 11 men and 5 women) were enrolled in a validation group. Enzyme-linked immunosorbent assay was used to verify the diagnostic value of the candidate biomarkers. We found 139 proteins that were differentially expressed between various fibrotic stage-paired comparisons. Five protein candidates were selected as potential biomarkers of S2 for further verification. Notably, ficolin-2 (FCN2) and carboxypeptidase B2 (CPB2) showed differential expression between patients and healthy controls. In conclusion, serum proteomic changes reported here offer new molecular leads for future research on biomarker candidates to identify liver fibrotic stages in CHB. In particular, FCN2 and CPB2 warrant further research on their possible mechanistic involvement in CHB pathogenesis.