Integrative omics for health and disease

What Has Longitudinal ‘Omics’ Studies Taught Us about Irritable Bowel Syndrome? A Systematic Review

Irritable bowel syndrome is a prototypical disorder of the brain–gut–microbiome axis, although the underlying pathogenesis and mechanisms remain incompletely understood. With the recent advances in ‘omics’ technologies, studies have attempted to uncover IBS-specific variations in the host–microbiome profile and function. However, no biomarker has been identified to date. Given the high inter-individual and day-to-day variability of the gut microbiota, and a lack of agreement across the large number of microbiome studies, this review focused on omics studies that had sampling at more than one time point. A systematic literature search was performed using various combinations of the search terms “Irritable Bowel Syndrome” and “Omics” in the Medline, EMBASE, and Cochrane Library up to 1 December 2022. A total of 16 original studies were reviewed. These multi-omics studies have implicated Bacteroides, Faecalibacterium prausnitzii, Ruminococcus spp., and Bifidobacteria in IBS and treatment response, found altered metabolite profiles in serum, faecal, or urinary samples taken from IBS patients compared to the healthy controls, and revealed enrichment in the immune and inflammation-related pathways. They also demonstrated the possible therapeutic mechanisms of diet interventions, for example, synbiotics and low fermentable oligosaccharides, disaccharides, monosaccharides, and polyol (FODMAP) diets on microbial metabolites. However, there was significant heterogeneity among the studies and no uniform characteristics of IBS-related gut microbiota. There is a need to further study these putative mechanisms and also ensure that they can be translated to therapeutic benefits for patients with IBS.

Host Response Biomarkers for Sepsis in the Emergency Room

This article is one of ten reviews selected from the Annual Update in Intensive Care and Emergency Medicine 2023. Other selected articles can be found online at https://www.biomedcentral.com/collections/annualupdate2023 . Further information about the Annual Update in Intensive Care and Emergency Medicine is available from https://link.springer.com/bookseries/8901 .

Machine learning based combination of multi-omics data for subgroup identification in non-small cell lung cancer

Non-small Cell Lung Cancer (NSCLC) is a heterogeneous disease with a poor prognosis. Identifying novel subtypes in cancer can help classify patients with similar molecular and clinical phenotypes. This work proposes an end-to-end pipeline for subgroup identification in NSCLC. Here, we used a machine learning (ML) based approach to compress the multi-omics NSCLC data to a lower dimensional space. This data is subjected to consensus K-means clustering to identify the five novel clusters (C1-C5). Survival analysis of the resulting clusters revealed a significant difference in the overall survival of clusters (p-value: 0.019). Each cluster was then molecularly characterized to identify specific molecular characteristics. We found that cluster C3 showed minimal genetic aberration with a high prognosis. Next, classification models were developed using data from each omic level to predict the subgroup of unseen patients. Decision‑level fused classification models were then built using these classifiers, which were used to classify unseen patients into five novel clusters. We also showed that the multi-omics-based classification model outperformed single-omic-based models, and the combination of classifiers proved to be a more accurate prediction model than the individual classifiers. In summary, we have used ML models to develop a classification method and identified five novel NSCLC clusters with different genetic and clinical characteristics.

Plant and microbial sciences as key drivers in the development of metabolomics research

This year marks the 25th anniversary of the coinage of the term metabolome [S. G. Oliver et al., Trends Biotech. 16, 373-378 (1998)]. As the field rapidly advances, it is important to take stock of the progress which has been made to best inform the disciplines future. While a medical-centric perspective on metabolomics has recently been published [M. Giera et al., Cell Metab. 34, 21-34 (2022)], this largely ignores the pioneering contributions made by the plant and microbial science communities. In this perspective, we provide a contemporary overview of all fields in which metabolomics is employed with particular emphasis on both methodological and application breakthroughs made in plant and microbial sciences that have shaped this evolving research discipline from the very early days of its establishment. This will not cover all types of metabolomics assays currently employed but will focus mainly on those utilizing mass spectrometry-based measurements since they are currently by far the most prominent. Having established the historical context of metabolomics, we will address the key challenges currently facing metabolomics and offer potential approaches by which these can be faced. Most salient among these is the fact that the vast majority of mass features are as yet not annotated with high confidence; what we may refer to as definitive identification. We discuss the potential of both standard compound libraries and artificial intelligence technologies to address this challenge and the use of natural variance-based approaches such as genome-wide association studies in attempt to assign specific functions to the myriad of structurally similar and complex specialized metabolites. We conclude by stating our contention that as these challenges are epic and that they will need far greater cooperative efforts from biologists, chemists, and computer scientists with an interest in all kingdoms of life than have been made to date. Ultimately, a better linkage of metabolome and genome data will likely also be needed particularly considering the Earth BioGenome Project.

Honokiol reduces fungal burden and ameliorate inflammation lesions of Aspergillus fumigatus keratitis via Dectin-2 down-regulation

PURPOSE

To screen and identify the mechanism of honokiol on anti-fungi and anti-inflammation in fungal keratitis (FK) through bioinformatic analysis and biological experiments.

METHODS

Transcriptome profile demonstrated differential expression genes (DEGs) of Aspergillus fumigatus keratitis between PBS-treated and honokiol-treated groups via bioinformatics analyses. Inflammatory substances were quantified by qRT-PCR, Western blot and ELISA, and macrophage polarization was examined by flow cytometry. Periodic acid Schiff staining and morphological interference assay were used to detect hyphal distribution in vivo and fungal germination in vitro, respectively. Electron microscopy was to illustrate hyphal microstructure.

RESULTS

Illumina sequencing demonstrated that compared with the honokiol group, 1175 up-regulated and 383 down-regulated genes were induced in C57BL/6 mice Aspergillus fumigatus keratitis with PBS treatment. Through GO analysis, some differential expression proteins (DEPs) played major roles in biological processes, especially fungal defense and immune activation. KEGG analysis provided fungus-related signaling pathways. PPI analysis demonstrated that DEPs from multiple pathways form a close-knit network, providing a broader context for FK treatment. In biological experiments, Dectin-2, NLRP3 and IL-1β were upregulated by Aspergillus fumigatus to evaluate immune response. Honokiol could reverse the trend, comparable to Dectin-2 siRNA interference. Meanwhile, honokiol could also play an anti-inflammatory role via promoting M2 phenotype polarization. Moreover, honokiol reduced hyphal distribution in the stroma, delayed germination, and destroyed the hyphal cell membrane in-vitro.

CONCLUSIONS

Honokiol possesses anti-fungal and anti-inflammatory effects in Aspergillus fumigatus keratitis and may develop a potential and safe therapeutic modality for FK.

Machine Learning Analysis of RNA-seq Data for Diagnostic and Prognostic Prediction of Colon Cancer

Data from omics studies have been used for prediction and classification of various diseases in biomedical and bioinformatics research. In recent years, Machine Learning (ML) algorithms have been used in many different fields related to healthcare systems, especially for disease prediction and classification tasks. Integration of molecular omics data with ML algorithms has offered a great opportunity to evaluate clinical data. RNA sequence (RNA-seq) analysis has been emerged as the gold standard for transcriptomics analysis. Currently, it is being used widely in clinical research. In our present work, RNA-seq data of extracellular vesicles (EV) from healthy and colon cancer patients are analyzed. Our aim is to develop models for prediction and classification of colon cancer stages. Five different canonical ML and Deep Learning (DL) classifiers are used to predict colon cancer of an individual with processed RNA-seq data. The classes of data are formed on the basis of both colon cancer stages and cancer presence (healthy or cancer). The canonical ML classifiers, which are k-Nearest Neighbor (kNN), Logistic Model Tree (LMT), Random Tree (RT), Random Committee (RC), and Random Forest (RF), are tested with both forms of the data. In addition, to compare the performance with canonical ML models, One-Dimensional Convolutional Neural Network (1-D CNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM) DL models are utilized. Hyper-parameter optimizations of DL models are constructed by using genetic meta-heuristic optimization algorithm (GA). The best accuracy in cancer prediction is obtained with RC, LMT, and RF canonical ML algorithms as 97.33%. However, RT and kNN show 95.33% performance. The best accuracy in cancer stage classification is achieved with RF as 97.33%. This result is followed by LMT, RC, kNN, and RT with 96.33%, 96%, 94.66%, and 94%, respectively. According to the results of the experiments with DL algorithms, the best accuracy in cancer prediction is obtained with 1-D CNN as 97.67%. BiLSTM and LSTM show 94.33% and 93.67% performance, respectively. In classification of the cancer stages, the best accuracy is achieved with BiLSTM as 98%. 1-D CNN and LSTM show 97% and 94.33% performance, respectively. The results reveal that both canonical ML and DL models may outperform each other for different numbers of features.

Pulmonary arterial hypertension associated with congenital heart disease: An omics study

Pulmonary arterial hypertension associated with congenital heart disease (PAH-CHD) is a severely progressive condition with uncertain physiological course. Hence, it has become increasingly relevant to clarify the specific mechanisms of molecular modification, which is crucial to identify more treatment strategies. With the rapid development of high-throughput sequencing, omics technology gives access to massive experimental data and advanced techniques for systems biology, permitting comprehensive assessment of disease occurrence and progression. In recent years, significant progress has been made in the study of PAH-CHD and omics. To provide a comprehensive description and promote further in-depth investigation of PAH-CHD, this review attempts to summarize the latest developments in genomics, transcriptomics, epigenomics, proteomics, metabolomics, and multi-omics integration.

Multi-omics analysis identifies potential mechanisms by which high glucose accelerates macrophage foaming

Atherosclerotic morbidity is significantly higher in the diabetic population. Hyperglycemia, a typical feature of diabetes, has been proven to accelerate foam cell formation. However, the molecular mechanisms behind this process remain unclear. In this study, LPS and IFN-γ were used to convert THP-1-derived macrophages into M1 macrophages, which were then activated with ox-LDL in either high glucose or normal condition. We identified lipids within macrophages by Oil red O staining and total cholesterol detection. The genes involved in lipid absorption, efflux, inflammation, and metabolism were analyzed using qRT-PCR. The mechanisms of high glucose-induced foam cell formation were further investigated through metabolomics and transcriptomics analysis. We discovered that high glucose speed up lipid accumulation in macrophages (both lipid droplets and total cholesterol increased), diminished lipid efflux (ABCG1 down-regulation), and aggravated inflammation (IL1B and TNF up-regulation). Following multi-omics analysis, it was determined that glucose altered the metabolic and transcriptional profiles of macrophages, identifying 392 differently expressed metabolites and 293 differentially expressed genes, respectively. Joint pathway analysis suggested that glucose predominantly disrupted the glycerolipid, glycerophospholipid, and arachidonic acid metabolic pathways in macrophages. High glucose in the glyceride metabolic pathway, for instance, suppressed the transcription of triglyceride hydrolase (LIPG and LPL), causing cells to deposit excess triglycerides into lipid droplets and encouraging foam cell formation. More importantly, high glucose triggered the accumulation of pro-atherosclerotic lipids (7-ketocholesterol, lysophosphatidylcholine, and glycerophosphatidylcholine). In conclusion, this work elucidated mechanisms of glucose-induced foam cell formation via a multi-omics approach.

Diagnosis of Unruptured Intracranial Aneurysm by High-Performance Serum Metabolic Fingerprints

Unruptured intracranial aneurysm (UIA) is a high-risk cerebrovascular saccular dilatation, the effective medical management of which depends on high-performance diagnosis. However, most UIAs are diagnosed incidentally during neurovascular imaging modalities, which are time-consuming and harmful (e.g., radiation). Serum metabolic fingerprints is a promising alternative for early diagnosis of UIA. Here, nanoparticle enhanced laser desorption/ionization mass spectrometry is applied to obtain high-performance UIA-specific serum metabolic fingerprints. Diagnostic performance with an area-under-the-curve (AUC) of 0.842 (95% confidence interval (CI): 0.783-0.891) is achieved by the constructed machine learning (ML) model, including ML algorithm selection and feature selection. Lactate, glutamine, homoarginine, and 3-methylglutaconic acid are identified as the metabolic biomarker panel, which showed satisfactory diagnosis (AUC of 0.812, 95% CI: 0.727-0.897) and effective growth risk assessment (p<0.05, two-tailed t-test) of UIAs. This work aims to promote the diagnostics of UIAs and metabolic biomarker screening for medical management.

Lipid dysregulation associated with progression of silica-induced pulmonary fibrosis

Silicosis is an irreversible, progressive, fibrotic lung disease caused by long-term exposure to dust-containing silica particles at the workplace. Despite the precautions enforced, the rising incidence of silicosis continues to occur globally, particularly in developing countries. A better understanding of the disease progression and potential metabolic reprogramming of silicosis is warranted. The low- or high-dose silica-induced pulmonary fibrosis in mice was constructed to mimic chronic or accelerated silicosis. Silica-induced mice lung fibrosis was analyzed by histology, lung function, and computed tomography scans. Non-targeted metabolomics of the lung tissues was conducted by ultra-high-performance liquid chromatography-mass spectrometry to show the temporal metabolic trajectory. The low-dose silica-induced silicosis characterized inflammation for up to 42 days, with the onset of cellular silicon nodules. Conversely, the high-dose silica-induced silicosis characterized inflammation for up to 14 days, after which the disease developed rapidly, with a large volume of collagen deposition, presenting progressive massive fibrosis. Both low- and high silica-induced fibrosis had aberrant lipid metabolism. Combined with the RNA-Seq data, this multiomics study demonstrated alterations in the enzymes involved in sphingolipid metabolism. Time-dependent metabolic reprogramming revealing abnormal glycerophospholipid metabolism was intimately associated with the process of inflammation, whereas sphingolipid metabolism was crucial during lung fibrosis. These findings suggest that lipid dysregulation, especially sphingolipid metabolism, was involved in the process of silicosis.

Parallelized multidimensional analytic framework applied to mammary epithelial cells uncovers regulatory principles in EMT

A proper understanding of disease etiology will require longitudinal systems-scale reconstruction of the multitiered architecture of eukaryotic signaling. Here we combine state-of-the-art data acquisition platforms and bioinformatics tools to devise PAMAF, a workflow that simultaneously examines twelve omics modalities, i.e., protein abundance from whole-cells, nucleus, exosomes, secretome and membrane; N-glycosylation, phosphorylation; metabolites; mRNA, miRNA; and, in parallel, single-cell transcriptomes. We apply PAMAF in an established in vitro model of TGFβ-induced epithelial to mesenchymal transition (EMT) to quantify >61,000 molecules from 12 omics and 10 timepoints over 12 days. Bioinformatics analysis of this EMT-ExMap resource allowed us to identify; -topological coupling between omics, -four distinct cell states during EMT, -omics-specific kinetic paths, -stage-specific multi-omics characteristics, -distinct regulatory classes of genes, -ligand-receptor mediated intercellular crosstalk by integrating scRNAseq and subcellular proteomics, and -combinatorial drug targets (e.g., Hedgehog signaling and CAMK-II) to inhibit EMT, which we validate using a 3D mammary duct-on-a-chip platform. Overall, this study provides a resource on TGFβ signaling and EMT.

Study on the Relationship between Selenium and Cadmium in Diseased Human Lungs

Cadmium (Cd) is a toxic environmental metal that interacts with selenium (Se) and contributes to many lung diseases. Humans have widespread exposures to Cd through diet and cigarette smoking, and studies in rodent models show that Se can protect against Cd toxicities. We sought to identify whether an antagonistic relationship existed between Se and Cd burdens and determine whether this relationship may associate with metabolic variation within human lungs. We performed metabolomics of 31 human lungs, including 25 with end-stage lung disease due to idiopathic pulmonary fibrosis, cystic fibrosis, chronic obstructive lung disease (COPD)/emphysema and other causes, and 6 non-diseased lungs. Results showed pathway associations with Cd including amino acid, lipid and energy-related pathways. Metabolic pathways varying with Se had considerable overlap with these pathways. Hierarchical cluster analysis (HCA) of individuals according to metabolites associated with Cd showed partial separation of disease types, with COPD/emphysema in the cluster with highest Cd, and non-diseased lungs in the cluster with the lowest Cd. When compared to HCA of metabolites associated with Se, the results showed that the cluster containing COPD/emphysema had the lowest Se, and the non-diseased lungs had the highest Se. A greater number of pathway associations occurred for Cd to Se ratio than either Cd or Se alone, indicating that metabolic patterns were more dependent on Cd to Se ratio than on either alone. Network analysis of interactions of Cd and Se showed network centrality was associated with pathways linked to polyunsaturated fatty acids involved in inflammatory signaling. Overall, the data show that metabolic pathway responses in human lung vary with Cd and Se in a pattern suggesting that Se is antagonistic to Cd toxicity in humans.

Deep Learning of Dual Plasma Fingerprints for High-Performance Infection Classification

Infection classification is the key for choosing the proper treatment plans. Early determination of the causative agents is critical for disease control. Host responses analysis can detect variform and sensitive host inflammatory responses to ascertain the presence and type of the infection. However, traditional host-derived inflammatory indicators are insufficient for clinical infection classification. Fingerprints-based omic analysis has attracted increasing attention globally for analyzing the complex host systemic immune response. A single type of fingerprints is not applicable for infection classification (area under curve (AUC) of 0.550-0.617). Herein, an infection classification platform based on deep learning of dual plasma fingerprints (DPFs-DL) is developed. The DPFs with high reproducibility (coefficient of variation <15%) are obtained at low sample consumption (550 nL native plasma) using inorganic nanoparticle and organic matrix assisted laser desorption/ionization mass spectrometry. A classifier (DPFs-DL) for viral versus bacterial infection discrimination (AUC of 0.775) and coronavirus disease 2019 (COVID-2019) diagnosis (AUC of 0.917) is also built. Furthermore, a metabolic biomarker panel of two differentially regulated metabolites, which may serve as potential biomarkers for COVID-19 management (AUC of 0.677-0.883), is constructed. This study will contribute to the development of precision clinical care for infectious diseases.

Third-generation sequencing-selected Scardovia wiggsiae promotes periodontitis progression in mice

BACKGROUNDS

Periodontitis is an oral-bacteria-directed disease that occurs worldwide. Currently, periodontal pathogens are mostly determined using traditional culture techniques, next-generation sequencing, and microbiological screening system. In addition to the well-known and cultivatable periodontal bacteria, we aimed to discover a novel periodontal pathogen by using DNA sequencing and investigate its role in the progression of periodontitis.

OBJECTIVE

This study identified pathogens from subgingival dental plaque in patients with periodontitis by using the Oxford Nanopore Technology (ONT) third-generation sequencing system and validated the impact of selected pathogen in periodontitis progression by ligature-implanted mice.

METHODS

Twenty-five patients with periodontitis and 25 healthy controls were recruited in this study. Subgingival plaque samples were collected for metagenomic analysis. The ONT third-generation sequencing system was used to confirm the dominant bacteria. A mouse model with ligature implantation and bacterial injection verified the pathogenesis of periodontitis. Neutrophil infiltration and osteoclast activity were evaluated using immunohistochemistry and tartrate-resistant acid phosphatase assays in periodontal tissue. Gingival inflammation was evaluated using pro-inflammatory cytokines in gingival crevicular fluids. Alveolar bone destruction in the mice was evaluated using micro-computed tomography and hematoxylin and eosin staining.

RESULTS

Scardovia wiggsiae (S. wiggsiae) was dominant in the subgingival plaque of the patients with periodontitis. S. wiggsiae significantly deteriorated ligature-induced neutrophil infiltration, osteoclast activation, alveolar bone destruction, and the secretion of interleukin-6, monocyte chemoattractant protein-1, and tumor necrosis factor-α in the mouse model.

CONCLUSION

Our metagenome results suggested that S. wiggsiae is a dominant flora in patients with periodontitis. In mice, the induction of neutrophil infiltration, proinflammatory cytokine secretion, osteoclast activation, and alveolar bone destruction further verified the pathogenic role of S. wiggsiae in the progress of periodontitis. Future studies investigating the metabolic interactions between S. wiggsiae and other periodontopathic bacteria are warranted.

From 'Omics to Multi-omics Technologies: the Discovery of Novel Causal Mediators

PURPOSE OF REVIEW

'Omics studies provide a comprehensive characterisation of a biological entity, such as the genome, epigenome, transcriptome, proteome, metabolome, or microbiome. This review covers the unique properties of these types of 'omics and their roles as causal mediators in cardiovascular disease. Moreover, applications and challenges of integrating multiple types of 'omics data to increase predictive power, improve causal inference, and elucidate biological mechanisms are discussed.

RECENT FINDINGS

Multi-omics approaches are growing in adoption as they provide orthogonal evidence and overcome the limitations of individual types of 'omics data. Studies with multiple types of 'omics data have improved the diagnosis and prediction of disease states and afforded a deeper understanding of underlying pathophysiological mechanisms, beyond any single type of 'omics data. For instance, disease-associated loci in the genome can be supplemented with other 'omics to prioritise causal genes and understand the function of non-coding variants. Alternatively, techniques, such as Mendelian randomisation, can leverage genetics to provide evidence supporting a causal role for disease-associated molecules, and elucidate their role in disease pathogenesis. As technologies improve, costs for 'omics studies will continue to fall and datasets will become increasingly accessible to researchers. The intrinsically unbiased nature of 'omics data is well-suited to exploratory analyses that discover causal mediators of disease, and multi-omics is an emerging discipline that leverages the strengths of each type of 'omics data to provide insights greater than the sum of its parts.

Simulated annealing for balancing covariates

Covariate balance is one of the fundamental issues in designing experiments for treatment comparisons, especially in randomized clinical trials. In this article, we introduce a new class of covariate-adaptive procedures based on the Simulated Annealing algorithm aimed at balancing the allocations of two competing treatments across a set of pre-specified covariates. Due to the nature of the simulated annealing, these designs are intrinsically randomized, thus completely unpredictable, and very flexible: they can manage both quantitative and qualitative factors and be implemented in a static version as well as sequentially. The properties of the suggested proposal are described, showing a significant improvement in terms of covariate balance and inferential accuracy with respect to all the other procedures proposed in the literature. An illustrative example based on real data is also discussed.

Organic Peroxides in Aerosol: Key Reactive Intermediates for Multiphase Processes in the Atmosphere

Organic peroxides (POs) are organic molecules with one or more peroxide (-O-O-) functional groups. POs are commonly regarded as chemically labile termination products from gas-phase radical chemistry and therefore serve as temporary reservoirs for oxidative radicals (HOx and ROx) in the atmosphere. Owing to their ubiquity, active gas-particle partitioning behavior, and reactivity, POs are key reactive intermediates in atmospheric multiphase processes determining the life cycle (formation, growth, and aging), climate, and health impacts of aerosol. However, there remain substantial gaps in the origin, molecular diversity, and fate of POs due to their complex nature and dynamic behavior. Here, we summarize the current understanding on atmospheric POs, with a focus on their identification and quantification, state-of-the-art analytical developments, molecular-level formation mechanisms, multiphase chemical transformation pathways, as well as environmental and health impacts. We find that interactions with SO2 and transition metal ions are generally the fast PO transformation pathways in atmospheric liquid water, with lifetimes estimated to be minutes to hours, while hydrolysis is particularly important for α-substituted hydroperoxides. Meanwhile, photolysis and thermolysis are likely minor sinks for POs. These multiphase PO transformation pathways are distinctly different from their gas-phase fates, such as photolysis and reaction with OH radicals, which highlights the need to understand the multiphase partitioning of POs. By summarizing the current advances and remaining challenges for the investigation of POs, we propose future research priorities regarding their origin, fate, and impacts in the atmosphere.

Efficient feature extraction from highly sparse binary genotype data for cancer prognosis prediction using an auto-encoder

Genomics involving tens of thousands of genes is a complex system determining phenotype. An interesting and vital issue is how to integrate highly sparse genetic genomics data with a mass of minor effects into a prediction model for improving prediction power. We find that the deep learning method can work well to extract features by transforming highly sparse dichotomous data to lower-dimensional continuous data in a non-linear way. This may provide benefits in risk prediction-associated genotype data. We developed a multi-stage strategy to extract information from highly sparse binary genotype data and applied it for cancer prognosis. Specifically, we first reduced the size of binary biomarkers via a univariable regression model to a moderate size. Then, a trainable auto-encoder was used to learn compact features from the reduced data. Next, we performed a LASSO problem process to select the optimal combination of extracted features. Lastly, we applied such feature combination to real cancer prognostic models and evaluated the raw predictive effect of the models. The results indicated that these compressed transformation features could better improve the model's original predictive performance and might avoid an overfitting problem. This idea may be enlightening for everyone involved in cancer research, risk reduction, treatment, and patient care via integrating genomics data.

Exploring human biology with N-of-1 clinical trials

Studies on humans that exploit contemporary data-intensive, high-throughput 'omic' assay technologies, such as genomics, transcriptomics, proteomics and metabolomics, have unequivocally revealed that humans differ greatly at the molecular level. These differences, which are compounded by each individual's distinct behavioral and environmental exposures, impact individual responses to health interventions such as diet and drugs. Questions about the best way to tailor health interventions to individuals based on their nuanced genomic, physiologic, behavioral, etc. profiles have motivated the current emphasis on 'precision' medicine. This review's purpose is to describe how the design and execution of N-of-1 (or personalized) multivariate clinical trials can advance the field. Such trials focus on individual responses to health interventions from a whole-person perspective, leverage emerging health monitoring technologies, and can be used to address the most relevant questions in the precision medicine era. This includes how to validate biomarkers that may indicate appropriate activity of an intervention as well as how to identify likely beneficial interventions for an individual. We also argue that multivariate N-of-1 and aggregated N-of-1 trials are ideal vehicles for advancing biomedical and translational science in the precision medicine era since the insights gained from them can not only shed light on how to treat or prevent diseases generally, but also provide insight into how to provide real-time care to the very individuals who are seeking attention for their health concerns in the first place.

COMBATdb: a database for the COVID-19 Multi-Omics Blood ATlas

Advances in our understanding of the nature of the immune response to SARS-CoV-2 infection, and how this varies within and between individuals, is important in efforts to develop targeted therapies and precision medicine approaches. Here we present a database for the COvid-19 Multi-omics Blood ATlas (COMBAT) project, COMBATdb (https://db.combat.ox.ac.uk). This enables exploration of multi-modal datasets arising from profiling of patients with different severities of illness admitted to hospital in the first phase of the pandemic in the UK prior to vaccination, compared with community cases, healthy controls, and patients with all-cause sepsis and influenza. These data include whole blood transcriptomics, plasma proteomics, epigenomics, single-cell multi-omics, immune repertoire sequencing, flow and mass cytometry, and cohort metadata. COMBATdb provides access to the processed data in a well-defined framework of samples, cell types and genes/proteins that allows exploration across the assayed modalities, with functionality including browse, search, download, calculation and visualisation via shiny apps. This advances the ability of users to leverage COMBAT datasets to understand the pathogenesis of COVID-19, and the nature of specific and shared features with other infectious diseases.

Multiscale Label-Free Imaging of Fibrillar Collagen in the Tumor Microenvironment

With recent advances in cancer therapeutics, there is a great need for improved imaging methods for characterizing cancer onset and progression in a quantitative and actionable way. Collagen, the most abundant extracellular matrix protein in the tumor microenvironment (and the body in general), plays a multifaceted role, both hindering and promoting cancer invasion and progression. Collagen deposition can defend the tumor with immunosuppressive effects, while aligned collagen fiber structures can enable tumor cell migration, aiding invasion and metastasis. Given the complex role of collagen fiber organization and topology, imaging has been a tool of choice to characterize these changes on multiple spatial scales, from the organ and tumor scale to cellular and subcellular level. Macroscale density already aids in the detection and diagnosis of solid cancers, but progress is being made to integrate finer microscale features into the process. Here we review imaging modalities ranging from optical methods of second harmonic generation (SHG), polarized light microscopy (PLM), and optical coherence tomography (OCT) to the medical imaging approaches of ultrasound and magnetic resonance imaging (MRI). These methods have enabled scientists and clinicians to better understand the impact collagen structure has on the tumor environment, at both the bulk scale (density) and microscale (fibrillar structure) levels. We focus on imaging methods with the potential to both examine the collagen structure in as natural a state as possible and still be clinically amenable, with an emphasis on label-free strategies, exploiting intrinsic optical properties of collagen fibers.

Nardosinone and aurantio-obtusin, two medicine food homology natural compounds, are anti-influenza agents as indicated by transcriptome signature reversion

BACKGROUND

Medicine food homology (MFH) refers to food that can be used as medicine, and compounds isolated from MFH materials are valuable in novel drug discovery due to their good safety. Transcriptome signature reversion (TSR) is an attractive method for discovering drugs through transcriptional reverse matching; namely, the changes in transcriptional signatures induced by compounds are matched to a certain disease. This strategy can be used to discover anti-influenza agents among MFH natural compounds.

PURPOSE

MFH natural compounds with anti-influenza activities were identified through analyses of the reversal in the expression of multiple informative genes followed by in vitro evaluation of the cytopathic effect (CPE) caused by influenza infection and relative quantification of the nucleoprotein (NP) gene in viral RNA (vRNA). The combined effect of active compounds was determined through network-based separation score prediction followed by quantification of the viral hemagglutinin (HA) level.

METHODS

The transcriptome profiles of 4 lung or airway cell lines infected with 7 influenza virus strains were analyzed by robust rank aggregation (RRA) to identify informative genes in the signature of influenza virus infection. The identified informative genes were then matched to a transcriptomic profile library of MFH natural compounds. The anti-influenza activities of MFH natural compounds with negative enrichment scores (ESs) were evaluated in vitro using a CPE assay and relative quantification of the NP gene in the vRNA in the supernatant and cytoplasm to identify anti-influenza agents. The effects of combinations of active compounds were analyzed using network-based calculations followed by confirmation through bioassays for quantifying the viral HA levels.

RESULTS

Among the 159 MFH natural compounds, 54 compounds had negative ESs, as determined through TSR, and the anti-influenza activities of nardosinone and aurantio-obtusin were confirmed by bioassays. The half-maximal effective concentrations (EC₅₀) of nardosinone and aurantio-obtusin were 4.3-84.4 μM and 31.9-113.6 μM, respectively. The separation score between the informative genes with expression that was negatively regulated by nardosinone and aurantio-obtusin in the human protein-protein interaction (PPI) network was calculated to be 0.10, which indicated that the two compounds potentially exert a synergistic effect, and this effect was confirmed by the finding that the combination indexes (CIs) were calculated to equal 0.86 at inhibition level of 50% and 0.44 at inhibition level of 90%.

CONCLUSION

The TSR analysis and in vitro evaluation identified nardosinone and aurantio-obtusin as anti-influenza agents. Their antiviral activities were exerted by reversing the expression of multiple informative genes of the host cells. The separation analysis between the informative genes that were reversely regulated by nardosinone and aurantio-obtusin indicated that their combination may exert a synergistic effect, which was confirmed in vitro.

Translational research into frailty from bench to bedside: Salivary biomarkers for inflammaging

Frailty is a complex physiological syndrome associated with adverse ageing and decreased physiological reserves. Frailty leads to cognitive and physical disability and is a significant cause of morbidity, mortality and economic costs. The underlying cause of frailty is multifaceted, including immunosenescence and inflammaging, changes in microbiota and metabolic dysfunction. Currently, salivary biomarkers are used as early predictors for some clinical diseases, contributing to the effective prevention and treatment of diseases, including frailty. Sample collection for salivary analysis is non-invasive and simple, which are paramount factors for testing in the vulnerable frail population. The aim of this review is to describe the current knowledge on the association between frailty and the inflammatory process and discuss methods to identify putative biomarkers in salivary fluids to predict this syndrome. This study describes the relationship between i.-inflammatory process and frailty; ii.-infectious, chronic, skeletal, metabolic and cognitive diseases with inflammation and frailty; iii.-inflammatory biomarkers and salivary fluids. There is a limited number of previous studies focusing on the analysis of inflammatory salivary biomarkers and frailty syndrome; hence, the study of salivary fluids as a source for biomarkers is an open area of research with the potential to address the increasing demands for frailty-associated biomarkers.

Targeted Therapy and Personalized Medicine

Targeted therapy and personalized medicine are novel emerging disciplines of cancer research intended for treatment and prevention. One of the most significant advancements in modern oncology is the shift from an organ-centric strategy to a personalized strategy guided by deep molecular analysis. This shift in view, which focuses on the tumour's precise molecular changes, has paved the way for individualized treatment. Researchers and clinicians are using targeted therapies to select the best treatment available based on the molecular characterization of malignant cancer. In the treatment of a cancer, personalized medicine entails the use of genetic, immunological, and proteomic profiling to provide therapeutic alternatives as well as prognostic information about cancer. In this book, targeted therapies and personalized medicine have been covered for specific malignancies, including latest FDA-approved targeted therapies and it also sheds light on effective anti-cancer regimens and drug resistance. This will help to enhance our ability to conduct individualized health planning, make early diagnoses, and choose optimal medications for each cancer patient with predictable side effects and outcomes in a quickly evolving era. Various applications and tools' capacity have been improved for early diagnosis of cancer and the growing number of clinical trials that choose specific molecular targets reflects this predicament. Nevertheless, there are several limitations that must need to be addressed. Hence, in this chapter, we will discuss recent advancements, challenges, and opportunities in personalized medicine for various cancers, with a specific emphasis on target therapies in diagnostics and therapeutics.

Chemotherapy-Induced Peripheral Neuropathy

Chemotherapy-induced peripheral neuropathy (CIPN) is a debilitating side effect of many common anti-cancer agents that can lead to dose reduction or treatment discontinuation, which decrease chemotherapy efficacy. Long-term CIPN can interfere with activities of daily living and diminish the quality of life. The mechanism of CIPN is not yet fully understood, and biomarkers are needed to identify patients at high risk and potential treatment targets. Metabolomics can capture the complex behavioral and pathophysiological processes involved in CIPN. This chapter is to review the CIPN metabolomics studies to find metabolic pathways potentially involved in CIPN. These potential CIPN metabolites are then investigated to determine whether there is evidence from studies of other neuropathy etiologies such as diabetic neuropathy and Leber hereditary optic neuropathy to support the importance of these pathways in peripheral neuropathy. Six potential biomarkers and their putative mechanisms in peripheral neuropathy were reviewed. Among these biomarkers, histidine and phenylalanine have clear roles in neurotransmission or neuroinflammation in peripheral neuropathy. Further research is needed to discover and validate CIPN metabolomics biomarkers in large clinical studies.

Integrated multi-omics reveals anaplerotic rewiring in methylmalonyl-CoA mutase deficiency

Methylmalonic aciduria (MMA) is an inborn error of metabolism with multiple monogenic causes and a poorly understood pathogenesis, leading to the absence of effective causal treatments. Here we employ multi-layered omics profiling combined with biochemical and clinical features of individuals with MMA to reveal a molecular diagnosis for 177 out of 210 (84%) cases, the majority (148) of whom display pathogenic variants in methylmalonyl-CoA mutase (MMUT). Stratification of these data layers by disease severity shows dysregulation of the tricarboxylic acid cycle and its replenishment (anaplerosis) by glutamine. The relevance of these disturbances is evidenced by multi-organ metabolomics of a hemizygous Mmut mouse model as well as through identification of physical interactions between MMUT and glutamine anaplerotic enzymes. Using stable-isotope tracing, we find that treatment with dimethyl-oxoglutarate restores deficient tricarboxylic acid cycling. Our work highlights glutamine anaplerosis as a potential therapeutic intervention point in MMA.

Integration of metabolomics and proteomics analysis to explore the mechanism of neurotoxicity induced by receipt of isoniazid and rifampicin in mice

Isoniazid (INH) and rifampicin (RIF) are co-administered in tuberculosis treatment but can cause neurotoxicity, and the mechanism is not known. To explore this mechanism, we employed an integrated approach using metabolomics analysis (MA) and proteomics analysis (PA). Male mice were divided into three groups and administered vehicle (control group), or co-administered INH (120 mg/kg) and RIF (240 mg/kg), for 7 or 14 days. Mice brains were collected for mass spectrometry-based PA and MA plus lipidomics analysis. Measurement of brain levels of malondialdehyde and superoxide dismutase revealed time-dependent brain injury after exposure to INH+RIF for 7 and 14 days. Also, 422 proteins, 35 metabolites, and 21 lipids were dysregulated and identified. MA demonstrated "purine metabolism," "phenylalanine, tyrosine and tryptophan biosynthesis," "biosynthesis of unsaturated fatty acids," "phenylalanine metabolism," and "arginine biosynthesis" to be disturbed significantly. PA demonstrated pathways such as "lipids," "amino acids," and "energy metabolism" to be disrupted. Peroxisome proliferator-activated receptor (PPAR) pathways were changed in energy metabolism, which led to the neurotoxicity induced by INH+RIF. Immunohistochemical analyses of PPARs in mice brains verified that PPAR-α and -γ expression was downregulated. PPAR-α and -γ activation might be a key target for alleviating INH+RIF-induced neurotoxicity.

Current State and Future Perspectives on Personalized Metabolomics

Metabolomics is one of the most promising 'omics' sciences for the implementation in medicine by developing new diagnostic tests and optimizing drug therapy. Since in metabolomics, the end products of the biochemical processes in an organism are studied, which are under the influence of both genetic and environmental factors, the metabolomics analysis can detect any changes associated with both lifestyle and pathological processes. Almost every case-controlled metabolomics study shows a high diagnostic accuracy. Taking into account that metabolomics processes are already described for most nosologies, there are prerequisites that a high-speed and comprehensive metabolite analysis will replace, in near future, the narrow range of chemical analyses used today, by the medical community. However, despite the promising perspectives of personalized metabolomics, there are currently no FDA-approved metabolomics tests. The well-known problem of complexity of personalized metabolomics data analysis and their interpretation for the end-users, in addition to a traditional need for analytical methods to address the quality control, standardization, and data treatment are reported in the review. Possible ways to solve the problems and change the situation with the introduction of metabolomics tests into clinical practice, are also discussed.

Detecting latent topics and trends of digital twins in healthcare: A structural topic model-based systematic review

Objective

Digital twins (DTs) have received widespread attention recently, providing new ideas and possibilities for future healthcare. This review aims to provide a quantitative review to analyze specific study contents, research focus, and trends of DT in healthcare. Simultaneously, this review intends to expand the connotation of "healthcare" into two directions, namely "Disease treatment" and "Health enhancement" to analyze the content within the "DT + healthcare" field thoroughly.

Methods

A data mining method named Structure Topic Modeling (STM) was used as the analytical tool due to its topic analysis ability and versatility. Google Scholar, Web of Science, and China National Knowledge Infrastructure supplied the material papers in this review.

Results

A total of 94 high-quality papers published between 2018 and 2022 were gathered and categorized into eight topics, collectively covering the transformative impact across a broader spectrum in healthcare. Three main findings have emerged: (1) papers published in healthcare predominantly concentrate on technology development (artificial intelligence, Internet of Things, etc.) and application scenarios(personalized, precise, and real-time health service); (2) the popularity of research topics is influenced by various factors, including policies, COVID-19, and emerging technologies; and (3) the preference for topics is diverse, with a general inclination toward the attribute of "Health enhancement."

Conclusions

This review underscores the significance of real-time capability and accuracy in shaping the future of DT, where algorithms and data transmission methods assume central importance in achieving these goals. Moreover, technological advancements, such as omics and Metaverse, have opened up new possibilities for DT in healthcare. These findings contribute to the existing literature by offering quantitative insights and valuable guidance to keep researchers ahead of the curve.

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.

Discovery of AHCY as an Off-Target of Doxorubicin by Integrative Analysis of Photoaffinity Labeling Chemoproteomics and Untargeted Metabolomics

Target identification is critically important for understanding the mechanism of action of drugs. Here, we reported a new strategy for deconvolution of drug targets (or off-targets) with photoaffinity labeling chemoproteomics in combination with untargeted metabolomics by using doxorubicin (DOX) as a model. The DOX-derived photoaffinity probes were prepared and applied to capture DOX-interacting proteins in living cells. The captured DOX-interacting proteins were then identified by label-free quantitative proteomics. Totally, 151 significant proteins were identified with high confidence (fold change >4, p-value < 0.005). The gene ontology enrichment analysis suggested that the proteins were mainly involved in carbon metabolism, citrate cycle, fatty acid metabolism, and metabolic pathways. Therefore, untargeted metabolomics was applied to quantify the significantly altered metabolites in cells upon drug treatment. The pathway enrichment analysis suggested that DOX mainly interrupted with the processes of pyrimidine and purine metabolism, carbon metabolism, methionine metabolism, and phosphatidylcholine biosynthesis. Integrative analysis of chemoproteomics and metabolomics indicated that adenosylhomocysteinase (AHCY) is a new target (off-target) of DOX leading to the accumulation of S-adenosyl homocysteine. This deduced DOX target was confirmed by the cellular thermal shift assay, affinity competitive pull-down assay, biochemical assay, and siRNA knock down experiments. Our result suggested that AHCY is the uncovered off-target of DOX.

A population-based study of precision health assessments using multi-omics network-derived biological functional modules

Recent technological advances in multi-omics and bioinformatics provide an opportunity to develop precision health assessments, which require big data and relevant bioinformatic methods. Here we collect multi-omics data from 4,277 individuals. We calculate the correlations between pairwise features from cross-sectional data and then generate 11 biological functional modules (BFMs) in males and 12 BFMs in females using a community detection algorithm. Using the features in the BFM associated with cardiometabolic health, carotid plaques can be predicted accurately in an independent dataset. We developed a model by comparing individual data with the health baseline in BFMs to assess health status (BFM-ash). Then we apply the model to chronic patients and modify the BFM-ash model to assess the effects of consuming grape seed extract as a dietary supplement. Finally, anomalous BFMs are identified for each subject. Our BFMs and BFM-ash model have huge prospects for application in precision health assessment.

Exploration of the underlying mechanisms of isoniazid/rifampicin-induced liver injury in mice using an integrated proteomics and metabolomics approach

The hepatotoxic mechanism resulting from coadministration of isoniazid (INH) and rifampicin (RIF) are complex and studies remain inconclusive. To systematically explore the underlying mechanisms, an integrated mass-based untargeted metabolomics and label-free quantitative proteomics approach was used to clarify the mechanism of INH/RIF-induced liver injury. Thirty male mice were randomly divided into three groups: control (receiving orally administered vehicle solution), INH (150 mg/kg) + RIF (300 mg/kg) orally administered for either 7 or 14 days, respectively. Serum was collected for the analysis of biochemical parameters and liver samples were obtained for mass spectrum-based proteomics, metabolomics, and lipidomics analysis. Overall, 511 proteins, 31 metabolites, and 23 lipids were dysregulated and identified, and disordered biological pathways were identified. The network of integrated multiomics showed that glucose, lipid, and amino acid metabolism as well as energy metabolism were mainly dysregulated and led to oxidative stress, inflammation, liver steatosis, and cell death induced by INH and RIF. Coadministration of INH and RIF can induce liver injury by oxidative stress, inflammation, liver steatosis, and cell death, and the reduction in glutathione levels may play a critical role in these systematic changes and warrants further study.

Simultaneous metabolite MALDI-MSI, whole exome and transcriptome analysis from formalin-fixed paraffin-embedded tissue sections

Matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) allows spatial analysis of proteins, metabolites, or small molecules from tissue sections. Here, we present the simultaneous generation and analysis of MALDI-MSI, whole-exome sequencing (WES), and RNA-sequencing data from the same formalin-fixed paraffin-embedded (FFPE) tissue sections. Genomic DNA and total RNA were extracted from (i) untreated, (ii) hematoxylin-eosin (HE) stained, and (iii) MALDI-MSI-analyzed FFPE tissue sections from three head and neck squamous cell carcinomas. MALDI-MSI data were generated by a time-of-flight analyzer prior to preprocessing and visualization. WES data were generated using a low-input protocol followed by detection of single-nucleotide variants (SNVs), tumor mutational burden, and mutational signatures. The transcriptome was determined using 3'-RNA sequencing and was examined for similarities and differences between processing stages. All data met the commonly accepted quality criteria. Besides SNVs commonly identified between differently processed tissues, FFPE-typical artifactual variants were detected. Tumor mutational burden was in the same range for tissues from the same patient and mutational signatures were highly overlapping. Transcriptome profiles showed high levels of correlation. Our data demonstrate that simultaneous molecular profiling of MALDI-MSI-processed FFPE tissue sections at the transcriptome and exome levels is feasible and reliable.

Genetic heterogeneity: Challenges, impacts, and methods through an associative lens

Genetic heterogeneity describes the occurrence of the same or similar phenotypes through different genetic mechanisms in different individuals. Robustly characterizing and accounting for genetic heterogeneity is crucial to pursuing the goals of precision medicine, for discovering novel disease biomarkers, and for identifying targets for treatments. Failure to account for genetic heterogeneity may lead to missed associations and incorrect inferences. Thus, it is critical to review the impact of genetic heterogeneity on the design and analysis of population level genetic studies, aspects that are often overlooked in the literature. In this review, we first contextualize our approach to genetic heterogeneity by proposing a high-level categorization of heterogeneity into "feature," "outcome," and "associative" heterogeneity, drawing on perspectives from epidemiology and machine learning to illustrate distinctions between them. We highlight the unique nature of genetic heterogeneity as a heterogeneous pattern of association that warrants specific methodological considerations. We then focus on the challenges that preclude effective detection and characterization of genetic heterogeneity across a variety of epidemiological contexts. Finally, we discuss systems heterogeneity as an integrated approach to using genetic and other high-dimensional multi-omic data in complex disease research.

Multi-omics to predict changes during cold pressor test

Background

The cold pressor test (CPT) is a widely used pain provocation test to investigate both pain tolerance and cardiovascular responses. We hypothesize, that performing multi-omic analyses during CPT gives the opportunity to home in on molecular mechanisms involved. Twenty-two females were phenotypically assessed before and after a CPT, and blood samples were taken. RNA-Sequencing, steroid profiling and untargeted metabolomics were performed. Each ‘omic level was analyzed separately at both single-feature and systems-level (principal component [PCA] and partial least squares [PLS] regression analysis) and all ‘omic levels were combined using an integrative multi-omics approach, all using the paired-sample design.

Results

We showed that PCA was not able to discriminate time points, while PLS did significantly distinguish time points using metabolomics and/or transcriptomic data, but not using conventional physiological measures. Transcriptomic and metabolomic data revealed at feature-, systems- and integrative- level biologically relevant processes involved during CPT, e.g. lipid metabolism and stress response.

Conclusion

Multi-omics strategies have a great potential in pain research, both at feature- and systems- level. Therefore, they should be exploited in intervention studies, such as pain provocation tests, to gain knowledge on the biological mechanisms involved in complex traits.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08981-z.

Computational approaches for network-based integrative multi-omics analysis

Advances in omics technologies allow for holistic studies into biological systems. These studies rely on integrative data analysis techniques to obtain a comprehensive view of the dynamics of cellular processes, and molecular mechanisms. Network-based integrative approaches have revolutionized multi-omics analysis by providing the framework to represent interactions between multiple different omics-layers in a graph, which may faithfully reflect the molecular wiring in a cell. Here we review network-based multi-omics/multi-modal integrative analytical approaches. We classify these approaches according to the type of omics data supported, the methods and/or algorithms implemented, their node and/or edge weighting components, and their ability to identify key nodes and subnetworks. We show how these approaches can be used to identify biomarkers, disease subtypes, crosstalk, causality, and molecular drivers of physiological and pathological mechanisms. We provide insight into the most appropriate methods and tools for research questions as showcased around the aetiology and treatment of COVID-19 that can be informed by multi-omics data integration. We conclude with an overview of challenges associated with multi-omics network-based analysis, such as reproducibility, heterogeneity, (biological) interpretability of the results, and we highlight some future directions for network-based integration.

Artificial intelligence for precision medicine in autoimmune liver disease

Autoimmune liver diseases (AiLDs) are rare autoimmune conditions of the liver and the biliary tree with unknown etiology and limited treatment options. AiLDs are inherently characterized by a high degree of complexity, which poses great challenges in understanding their etiopathogenesis, developing novel biomarkers and risk-stratification tools, and, eventually, generating new drugs. Artificial intelligence (AI) is considered one of the best candidates to support researchers and clinicians in making sense of biological complexity. In this review, we offer a primer on AI and machine learning for clinicians, and discuss recent available literature on its applications in medicine and more specifically how it can help to tackle major unmet needs in AiLDs.

Selecting the right therapeutic target for kidney disease

Kidney disease is a complex disease with several different etiologies and underlying associated pathophysiology. This is reflected by the lack of effective treatment therapies in chronic kidney disease (CKD) that stop disease progression. However, novel strategies, recent scientific breakthroughs, and technological advances have revealed new possibilities for finding novel disease drivers in CKD. This review describes some of the latest advances in the field and brings them together in a more holistic framework as applied to identification and validation of disease drivers in CKD. It uses high-resolution ‘patient-centric’ omics data sets, advanced in silico tools (systems biology, connectivity mapping, and machine learning) and ‘state-of-the-art‘ experimental systems (complex 3D systems in vitro, CRISPR gene editing, and various model biological systems in vivo). Application of such a framework is expected to increase the likelihood of successful identification of novel drug candidates based on strong human target validation and a better scientific understanding of underlying mechanisms.

Prognostic Scoring Systems in Primary Biliary Cholangitis: An Update

Primary biliary cholangitis (PBC) is a complex, chronic disease with a heterogeneous presentation, disease progression, and response to therapy. Several prognostic models based on disease stage and/or treatment response enhance risk stratification and therapeutic management. Recent work on disease modeling proposed early prediction of outcomes at PBC onset, yet this has not been implemented in clinical practice. Although early stratification of patients based on their individual risk of developing end-stage liver disease may prove cost-effective and actually become matter of medical deontology to timely offer the best therapeutic option, given the forthcoming availability of novel, disease-modifying drugs. This review outlines established and novel prognostic systems in PBC and provides some perspectives on the potential role of omics-derived biomarkers in developing reliable risk prediction models and promoting the implementation of personalized medicine in PBC.

Medical care of rare and undiagnosed diseases: Prospects and challenges

Rare and undiagnosed diseases tend to be diverse, misdiagnosed, and difficult to diagnose. In some cases, the disease is progressive and life-threatening. Yet, to date, an estimated 95% of rare diseases have no approved therapy. Therefore, rare and undiagnosed diseases are considered the ultimate challenges for understanding human diseases. Here, we review the research progress, research frontiers, and important scientific issues related to rare and undiagnosed diseases. We mainly focus on five topics: (1) the identification and functional analysis of disease-causing genes; (2) the construction of cells, organoids, and animal models for mechanism validation; (3) subtyping and diagnosis; (4) treatment and drug screening based on causative genes and mutations; and (5) new technologies and methods for studying rare and undiagnosed diseases. In this review, we briefly update and discuss the pathogenic mechanisms and precision medicine for rare and undiagnosed diseases.

A New Mechanism for Ginsenoside Rb1 to Promote Glucose Uptake, Regulating Riboflavin Metabolism and Redox Homeostasis

Glucose absorption promoters perform insulin mimic functions to enhance blood glucose transport to skeletal muscle cells and accelerate glucose consumption, thereby reducing blood glucose levels. In our screening exploration of food ingredients for improving glucose transportation and metabolism, we found that the saponins in American ginseng (Panaxquinquefolius L.) showed potential activity to promote glucose uptake, which can be used for stabilizing levels of postprandial blood glucose. The aim of this study was to identify key components of American ginseng with glucose uptake-promoting activity and to elucidate their metabolic regulatory mechanisms. Bio-guided isolation using zebrafish larvae and 2-NBDG indicator identified ginsenoside Rb1 (GRb1) as the most potential promotor of glucose uptake. Using UPLC-QTOF-MS/MS combined with RT-qPCR and phenotypic verification, we found that riboflavin metabolism is the hinge for GRb1-mediated facilitation of glucose transport. GRb1-induced restoration of redox homeostasis was mediated by targeting riboflavin transporters (SLC52A1 and SLC52A3) and riboflavin kinase (RFK).

Tackling the translational challenges of multi-omics research in the realm of European personalised medicine: A workshop report

Personalised medicine (PM) presents a great opportunity to improve the future of individualised healthcare. Recent advances in -omics technologies have led to unprecedented efforts characterising the biology and molecular mechanisms that underlie the development and progression of a wide array of complex human diseases, supporting further development of PM. This article reflects the outcome of the 2021 EATRIS-Plus Multi-omics Stakeholder Group workshop organised to 1) outline a global overview of common promises and challenges that key European stakeholders are facing in the field of multi-omics research, 2) assess the potential of new technologies, such as artificial intelligence (AI), and 3) establish an initial dialogue between key initiatives in this space. Our focus is on the alignment of agendas of European initiatives in multi-omics research and the centrality of patients in designing solutions that have the potential to advance PM in long-term healthcare strategies.

Multi-Omics Reveals Mechanisms of Partial Modulation of COVID-19 Dysregulation by Glucocorticoid Treatment

Treatments for COVID-19 infections have improved dramatically since the beginning of the pandemic, and glucocorticoids have been a key tool in improving mortality rates. The UK's National Institute for Health and Care Excellence guidance is for treatment to be targeted only at those requiring oxygen supplementation, however, and the interactions between glucocorticoids and COVID-19 are not completely understood. In this work, a multi-omic analysis of 98 inpatient-recruited participants was performed by quantitative metabolomics (using targeted liquid chromatography-mass spectrometry) and data-independent acquisition proteomics. Both 'omics datasets were analysed for statistically significant features and pathways differentiating participants whose treatment regimens did or did not include glucocorticoids. Metabolomic differences in glucocorticoid-treated patients included the modulation of cortisol and bile acid concentrations in serum, but no alleviation of serum dyslipidemia or increased amino acid concentrations (including tyrosine and arginine) in the glucocorticoid-treated cohort relative to the untreated cohort. Proteomic pathway analysis indicated neutrophil and platelet degranulation as influenced by glucocorticoid treatment. These results are in keeping with the key role of platelet-associated pathways and neutrophils in COVID-19 pathogenesis and provide opportunity for further understanding of glucocorticoid action. The findings also, however, highlight that glucocorticoids are not fully effective across the wide range of 'omics dysregulation caused by COVID-19 infections.

Multiscale profiling of protease activity in cancer

Diverse processes in cancer are mediated by enzymes, which most proximally exert their function through their activity. High-fidelity methods to profile enzyme activity are therefore critical to understanding and targeting the pathological roles of enzymes in cancer. Here, we present an integrated set of methods for measuring specific protease activities across scales, and deploy these methods to study treatment response in an autochthonous model of Alk-mutant lung cancer. We leverage multiplexed nanosensors and machine learning to analyze in vivo protease activity dynamics in lung cancer, identifying significant dysregulation that includes enhanced cleavage of a peptide, S1, which rapidly returns to healthy levels with targeted therapy. Through direct on-tissue localization of protease activity, we pinpoint S1 cleavage to the tumor vasculature. To link protease activity to cellular function, we design a high-throughput method to isolate and characterize proteolytically active cells, uncovering a pro-angiogenic phenotype in S1-cleaving cells. These methods provide a framework for functional, multiscale characterization of protease dysregulation in cancer.