Integrative omics for health and disease

Application of multi-omics techniques to androgenetic alopecia: Current status and perspectives

The rapid advancement of sequencing technologies has enabled the generation of vast datasets, allowing for the in-depth analysis of sequencing data. This analysis has facilitated the validation of novel pathogenesis hypotheses for understanding and treating diseases through ex vivo and in vivo experiments. Androgenetic alopecia (AGA), a common hair loss disorder, has been a key focus of investigators attempting to uncover its underlying mechanisms. Abnormal changes in mRNA, proteins, and metabolites have been identified in individuals with AGA, and future developments in sequencing technologies may reveal new biomarkers for AGA. By integrating multiple omics analysis datasets such as genomics, transcriptomics, proteomics, and metabolomics-along with clinical phenotype data-we can achieve a comprehensive understanding of the molecular underpinnings of AGA. This review summarizes the data-mining studies conducted on various omics analysis datasets as related to AGA that have been adopted to interpret the biological data obtained from different omics layers. We herein discuss the challenges of integrative omics analyses, and suggest that collaborative multi-omics studies can enhance the understanding of the complete pathomechanism(s) of AGA by focusing on the interaction networks comprising DNA, RNA, proteins, and metabolites.

Advancing personalized medicine: Integrating statistical algorithms with omics and nano-omics for enhanced diagnostic accuracy and treatment efficacy

Medical laboratory services enable precise measurement of thousands of biomolecules and have become an inseparable part of high-quality healthcare services, exerting a profound influence on global health outcomes. The integration of omics technologies into laboratory medicine has transformed healthcare, enabling personalized treatments and interventions based on individuals' distinct genetic and metabolic profiles. Interpreting laboratory data relies on reliable reference values. Presently, population-derived references are used for individuals, risking misinterpretation due to population heterogeneity, and leading to medical errors. Thus, personalized references are crucial for precise interpretation of individual laboratory results, and the interpretation of omics data should be based on individualized reference values. We reviewed recent advancements in personalized laboratory medicine, focusing on personalized omics, and discussed strategies for implementing personalized statistical approaches in omics technologies to improve global health and concluded that personalized statistical algorithms for interpretation of omics data have great potential to enhance global health. Finally, we demonstrated that the convergence of nanotechnology and omics sciences is transforming personalized laboratory medicine by providing unparalleled diagnostic precision and innovative therapeutic strategies.

Silica aggravates pulmonary fibrosis through disrupting lung microbiota and amino acid metabolites

Silicosis, recognized as a severe global public health issue, is an irreversible pulmonary fibrosis caused by the long-term inhalation of silica particles. Given the intricate pathogenesis of silicosis, there is no effective intervention measure, which poses a severe threat to public health. Our previous study reported that dysbiosis of lung microbiota is associated with the development of pulmonary fibrosis, potentially involving the lipopolysaccharides/toll-like receptor 4 pathway. Similarly, the process of pulmonary fibrosis is accompanied by alterations in metabolic pathways. This study employed a combined approach of 16S rDNA sequencing and metabolomic analysis to investigate further the role of lung microbiota in silicosis delving deeper into the potential pathogenesis of silicosis. Silica exposure can lead to dysbiosis of the lung microbiota and the occurrence of pulmonary fibrosis, which was alleviated by a combination antibiotic intervention. Additionally, significant metabolic disturbances were found in silicosis, involving 85 differential metabolites among the three groups, which are mainly focused on amino acid metabolic pathways. The changed lung metabolites showed a substantial correlation with lung microbiota. The relative abundance of Pseudomonas negatively correlated with L-Aspartic acid, L-Glutamic acid, and L-Threonine levels. These results indicate that dysbiosis in pulmonary microbiota exacerbates silica-induced fibrosis through impacts on amino acid metabolism, providing new insights into the potential mechanisms and interventions of silicosis.

Multi-omics integration reveals potential stage-specific druggable targets in T-cell acute lymphoblastic leukemia

T-cell acute lymphoblastic leukemia (T-ALL), a heterogeneous hematological malignancy, is caused by the developmental arrest of normal T-cell progenitors. The development of targeted therapeutic regimens is impeded by poor knowledge of the stage-specific aberrances in this disease. In this study, we performed multi-omics integration analysis, which included mRNA expression, chromatin accessibility, and gene-dependency database analyses, to identify potential stage-specific druggable targets and repositioned drugs for this disease. This multi-omics integration helped identify 29 potential pathological genes for T-ALL. These genes exhibited tissue-specific expression profiles and were enriched in the cell cycle, hematopoietic stem cell differentiation, and the AMPK signaling pathway. Of these, four known druggable targets (CDK6, TUBA1A, TUBB, and TYMS) showed dysregulated and stage-specific expression in malignant T cells and may serve as stage-specific targets in T-ALL. The TUBA1A expression level was higher in the early T cell precursor (ETP)-ALL cells, while TUBB and TYMS were mainly highly expressed in malignant T cells arrested at the CD4 and CD8 double-positive or single-positive stage. CDK6 exhibited a U-shaped expression pattern in malignant T cells along the naïve to maturation stages. Furthermore, mebendazole and gemcitabine, which target TUBA1A and TYMS, respectively, exerted stage-specific inhibitory effects on T-ALL cell lines, indicating their potential stage-specific antileukemic role in T-ALL. Collectively, our findings might aid in identifying potential stage-specific druggable targets and are promising for achieving more precise therapeutic strategies for T-ALL.

Multi-omics and multi-stages integration identified a novel variant associated with silicosis risk

Assessing the association between candidate single-nucleotide polymorphisms (SNPs) identified by multi-omics approaches and susceptibility to silicosis. RNA-seq analysis was performed to screen the differentially expressed mRNAs in the fibrotic lung tissues of mice exposed to silica particles. Following this, we integrated the SNPs located in the above human homologenes with the silicosis-related genome-wide association study (GWAS) data to select the candidate SNPs. Then, expression quantitative trait locus (eQTL)-SNPs were identified by the GTEx database. Next, we validated the associations between the functional eQTL-SNPs and silicosis susceptibility by additional case-control study. And the contribution of the identified SNP and its host gene in the fibrosis process was further validated by functional experiments. A total of 12 eQTL-SNPs were identified in the screening stage. The results of the validation stage suggested that the variant T allele of rs419540 located in IL12RB1 significantly increased the risk of developing silicosis [additive model: odds ratio (OR) = 1.78, 95% confidence interval (CI) 1.11-2.85, P = 0.017]. Furthermore, the combination of GWAS and the results of validation stage also indicated that the variant T allele of rs419540 in IL12RB1 was associated with increased silicosis risk (additive model: OR = 2.07, 95% CI 1.38-3.12, P < 0.001). Additionally, after knockdown or overexpression of IL12RB1, the levels of pro-inflammatory factors, such as IL-12, IFN-γ, and other pro-inflammatory factors, were correspondingly decreased or increased. The novel eQTL-SNP, rs419540, might increase the risk of silicosis by modulating the expression levels of IL12RB1.

The advantages of multi-level omics research on stem cell-based therapies for ischemic stroke

Stem cell transplantation is a potential therapeutic strategy for ischemic stroke. However, despite many years of preclinical research, the application of stem cells is still limited to the clinical trial stage. Although stem cell therapy can be highly beneficial in promoting functional recovery, the precise mechanisms of action that are responsible for this effect have yet to be fully elucidated. Omics analysis provides us with a new perspective to investigate the physiological mechanisms and multiple functions of stem cells in ischemic stroke. Transcriptomic, proteomic, and metabolomic analyses have become important tools for discovering biomarkers and analyzing molecular changes under pathological conditions. Omics analysis could help us to identify new pathways mediated by stem cells for the treatment of ischemic stroke via stem cell therapy, thereby facilitating the translation of stem cell therapies into clinical use. In this review, we summarize the pathophysiology of ischemic stroke and discuss recent progress in the development of stem cell therapies for the treatment of ischemic stroke by applying multi-level omics. We also discuss changes in RNAs, proteins, and metabolites in the cerebral tissues and body fluids under stroke conditions and following stem cell treatment, and summarize the regulatory factors that play a key role in stem cell therapy. The exploration of stem cell therapy at the molecular level will facilitate the clinical application of stem cells and provide new treatment possibilities for the complete recovery of neurological function in patients with ischemic stroke.

The Future of Obesity Management through Precision Nutrition: Putting the Individual at the Center

PURPOSE OF REVIEW: The prevalence of obesity continues to rise steadily. While obesity management typically relies on dietary and lifestyle modifications, individual responses to these interventions vary widely. Clinical guidelines for overweight and obesity stress the importance of personalized approaches to care. This review aims to underscore the role of precision nutrition in delivering tailored interventions for obesity management. RECENT FINDINGS: Recent technological strides have expanded our ability to detect obesity-related genetic polymorphisms, with machine learning algorithms proving pivotal in analyzing intricate genomic data. Machine learning algorithms can also predict postprandial glucose, triglyceride, and insulin levels, facilitating customized dietary interventions and ultimately leading to successful weight loss. Additionally, given that adherence to dietary recommendations is one of the key predictors of weight loss success, employing more objective methods for dietary assessment and monitoring can enhance sustained long-term compliance. Biomarkers of food intake hold promise for a more objective dietary assessment. Acknowledging the multifaceted nature of obesity, precision nutrition stands poised to transform obesity management by tailoring dietary interventions to individuals' genetic backgrounds, gut microbiota, metabolic profiles, and behavioral patterns. However, there is insufficient evidence demonstrating the superiority of precision nutrition over traditional dietary recommendations. The integration of precision nutrition into routine clinical practice requires further validation through randomized controlled trials and the accumulation of a larger body of evidence to strengthen its foundation.

Machine learning for the advancement of genome-scale metabolic modeling

Constraint-based modeling (CBM) has evolved as the core systems biology tool to map the interrelations between genotype, phenotype, and external environment. The recent advancement of high-throughput experimental approaches and multi-omics strategies has generated a plethora of new and precise information from wide-ranging biological domains. On the other hand, the continuously growing field of machine learning (ML) and its specialized branch of deep learning (DL) provide essential computational architectures for decoding complex and heterogeneous biological data. In recent years, both multi-omics and ML have assisted in the escalation of CBM. Condition-specific omics data, such as transcriptomics and proteomics, helped contextualize the model prediction while analyzing a particular phenotypic signature. At the same time, the advanced ML tools have eased the model reconstruction and analysis to increase the accuracy and prediction power. However, the development of these multi-disciplinary methodological frameworks mainly occurs independently, which limits the concatenation of biological knowledge from different domains. Hence, we have reviewed the potential of integrating multi-disciplinary tools and strategies from various fields, such as synthetic biology, CBM, omics, and ML, to explore the biochemical phenomenon beyond the conventional biological dogma. How the integrative knowledge of these intersected domains has improved bioengineering and biomedical applications has also been highlighted. We categorically explained the conventional genome-scale metabolic model (GEM) reconstruction tools and their improvement strategies through ML paradigms. Further, the crucial role of ML and DL in omics data restructuring for GEM development has also been briefly discussed. Finally, the case-study-based assessment of the state-of-the-art method for improving biomedical and metabolic engineering strategies has been elaborated. Therefore, this review demonstrates how integrating experimental and in silico strategies can help map the ever-expanding knowledge of biological systems driven by condition-specific cellular information. This multiview approach will elevate the application of ML-based CBM in the biomedical and bioengineering fields for the betterment of society and the environment.

A roadmap to the molecular human linking multiomics with population traits and diabetes subtypes

In-depth multiomic phenotyping provides molecular insights into complex physiological processes and their pathologies. Here, we report on integrating 18 diverse deep molecular phenotyping (omics-) technologies applied to urine, blood, and saliva samples from 391 participants of the multiethnic diabetes Qatar Metabolomics Study of Diabetes (QMDiab). Using 6,304 quantitative molecular traits with 1,221,345 genetic variants, methylation at 470,837 DNA CpG sites, and gene expression of 57,000 transcripts, we determine (1) within-platform partial correlations, (2) between-platform mutual best correlations, and (3) genome-, epigenome-, transcriptome-, and phenome-wide associations. Combined into a molecular network of > 34,000 statistically significant trait-trait links in biofluids, our study portrays "The Molecular Human". We describe the variances explained by each omics in the phenotypes (age, sex, BMI, and diabetes state), platform complementarity, and the inherent correlation structures of multiomics data. Further, we construct multi-molecular network of diabetes subtypes. Finally, we generated an open-access web interface to "The Molecular Human" ( http://comics.metabolomix.com ), providing interactive data exploration and hypotheses generation possibilities.

Combined transcriptome and microbiome analysis reveals the thyrotoxic effects of PM2.5 in female rats

Pervasive environmental pollutants, specifically particulate matter (PM2.5), possess the potential to disrupt homeostasis of female thyroid hormone (TH). However, the precise mechanism underlying this effect remains unclear. In this study, we established a model of PM2.5-induced thyroid damage in female rats through intratracheal instillation and employed histopathological and molecular biological methods to observe the toxic effects of PM2.5 on the thyroid gland. Transcriptome gene analysis and 16S rRNA sequencing were utilized to investigate the impact of PM2.5 exposure on the female rat thyroid gland. Furthermore, based on the PM2.5-induced toxic model in female rats, we evaluated its effects on intestinal microbiota, TH levels, and indicators of thyroid function. The findings revealed that PM2.5 exposure induced histopathological damage to thyroid tissue by disrupting thyroid hormone levels (total T3 [TT3], (P < 0.05); total T4 [TT4], (P < 0.05); and thyrotropin hormone [TSH], (P < 0.05)) and functional indices (urine iodine [UI], P > 0.05), thus further inducing histopathological injuries. Transcriptome analysis identified differentially expressed genes (DEGs), primarily concentrated in interleukin 17 (IL-17), forkhead box O (FOXO), and other signaling pathways. Furthermore, exposure to PM2.5 altered the composition and abundance of intestinal microbes. Transcriptome and microbiome analyses demonstrated a correlation between the DEGs within these pathways and the flora present in the intestines. Moreover, 16 S rRNA gene sequencing analysis or DEGs combined with thyroid function analysis revealed that exposure to PM2.5 significantly induced thyroid hormone imbalance. We further identified key DEGs involved in thyroid function-relevant pathways, which were validated using molecular biology methods for clinical applications. In conclusion, the homeostasis of the "gut-thyroid" axis may serve as the underlying mechanism for PM2.5-induced thyrotoxicity in female rats.

Omics Science and Social Aspects in Detecting Biomarkers for Diagnosis, Risk Prediction, and Outcomes of Carotid Stenosis

Carotid stenosis is characterized by the progressive narrowing of the carotid arteries due to the formation of atherosclerotic plaque, which can lead to stroke and death as major complications. Numerous biomarkers allow for its study and characterization, particularly those related to "omics" sciences. Through the most common research databases, we report representative studies about carotid stenosis biomarkers based on genomics, transcriptomics, proteomics, and metabolomics in a narrative review. To establish a priority among studies based on their internal validity, we used a quality assessment tool, the Scale for the Assessment of Narrative Review Articles (SANRA). Genes, transcriptomes, proteins, and metabolites can diagnose the disease, define plaque connotations, predict consequences after revascularization interventions, and associate carotid stenosis with other patient comorbidities. It also emerged that many aspects determining the patient's psychological and social sphere are implicated in carotid disease. In conclusion, when taking the multidisciplinary approach that combines human sciences with biological sciences, it is possible to comprehensively define a patient's health and thus improve their clinical management through precision medicine.

mosGraphGPT: a foundation model for multi-omic signaling graphs using generative AI

Generative pretrained models represent a significant advancement in natural language processing and computer vision, which can generate coherent and contextually relevant content based on the pre-training on large general datasets and fine-tune for specific tasks. Building foundation models using large scale omic data is promising to decode and understand the complex signaling language patterns within cells. Different from existing foundation models of omic data, we build a foundation model, mosGraphGPT , for multi-omic signaling (mos) graphs, in which the multi-omic data was integrated and interpreted using a multi-level signaling graph. The model was pretrained using multi-omic data of cancers in The Cancer Genome Atlas (TCGA), and fine-turned for multi-omic data of Alzheimer's Disease (AD). The experimental evaluation results showed that the model can not only improve the disease classification accuracy, but also is interpretable by uncovering disease targets and signaling interactions. And the model code are uploaded via GitHub with link: https://github.com/mosGraph/mosGraphGPT.

LC-MS metabolomics and lipidomics in cerebrospinal fluid from viral and bacterial CNS infections: a review

There is compelling evidence that a dysregulated immune inflammatory response in neuroinfectious diseases results in modifications in metabolic processes and altered metabolites, directly or indirectly influencing lipid metabolism within the central nervous system (CNS). The challenges in differential diagnosis and the provision of effective treatment in many neuroinfectious diseases are, in part, due to limited understanding of the pathophysiology underlying the disease. Although there are numerous metabolomics studies, there remains a deficit in neurolipidomics research to provide a comprehensive understanding of the connection between altered metabolites and changes in lipid metabolism. The brain is an inherently high-lipid organ; hence, understanding neurolipidomics is the key to future breakthroughs. This review aims to provide an integrative summary of altered cerebrospinal fluid (CSF) metabolites associated with neurolipid metabolism in bacterial and viral CNS infections, with a particular focus on studies that used liquid chromatography-mass spectrometry (LC-MS). Lipid components (phospholipids) and metabolites (carnitine and tryptophan) appear to be the most significant indicators in both bacterial and viral infections. On the basis of our analysis of the literature, we recommend employing neurolipidomics in conjunction with existing neurometabolomics data as a prospective method to enhance our understanding of the cross link between dysregulated metabolites and lipid metabolism in neuroinfectious diseases.

Mass Spectrometry-Based Multiomics Identifies Metabolic Signatures of Sarcopenia in Rhesus Monkey Skeletal Muscle

Sarcopenia is a progressive disorder characterized by age-related loss of skeletal muscle mass and function. Although significant progress has been made over the years to identify the molecular determinants of sarcopenia, the precise mechanisms underlying the age-related loss of contractile function remains unclear. Advances in "omics" technologies, including mass spectrometry-based proteomic and metabolomic analyses, offer great opportunities to better understand sarcopenia. Herein, we performed mass spectrometry-based analyses of the vastus lateralis from young, middle-aged, and older rhesus monkeys to identify molecular signatures of sarcopenia. In our proteomic analysis, we identified proteins that change with age, including those involved in adenosine triphosphate and adenosine monophosphate metabolism as well as fatty acid beta oxidation. In our untargeted metabolomic analysis, we identified metabolites that changed with age largely related to energy metabolism including fatty acid beta oxidation. Pathway analysis of age-responsive proteins and metabolites revealed changes in muscle structure and contraction as well as lipid, carbohydrate, and purine metabolism. Together, this study discovers new metabolic signatures and offers new insights into the molecular mechanisms underlying sarcopenia for the evaluation and monitoring of a therapeutic treatment of sarcopenia.

Unlocking biological mechanisms with integrative functional genomics approaches

Reverse genetics offers precise functional insights into genes through the targeted manipulation of gene expression followed by phenotypic assessment. While these approaches have proven effective in model organisms such as Saccharomyces cerevisiae, large-scale genetic manipulations in human cells were historically unfeasible due to methodological limitations. However, recent advancements in functional genomics, particularly clustered regularly interspaced short palindromic repeats (CRISPR)-based screening technologies and next-generation sequencing platforms, have enabled pooled screening technologies that allow massively parallel, unbiased assessments of biological phenomena in human cells. This review provides a comprehensive overview of cutting-edge functional genomic screening technologies applicable to human cells, ranging from short hairpin RNA screens to modern CRISPR screens. Additionally, we explore the integration of CRISPR platforms with single-cell approaches to monitor gene expression, chromatin accessibility, epigenetic regulation, and chromatin architecture following genetic perturbations at the omics level. By offering an in-depth understanding of these genomic screening methods, this review aims to provide insights into more targeted and effective strategies for genomic research and personalized medicine.

Integrative multi-omics analysis reveals ortho-topolin riboside exhibits anticancer activity by regulating metabolic pathways in radio-resistant triple negative breast cancer cells

Radio-resistant triple negative breast cancer (TNBC) is resistant to conventional drugs and radiation therapy. ortho-topolin riboside (oTR) has been evaluated for its anticancer activity in several types of cancer cells. However, its anti-proliferative activity in radio-resistant TNBC cells has not yet been reported. Therefore, we investigated the anti-proliferative activity of oTR in radio-resistant TNBC cells, and performed metabolome, lipidome, transcriptome, and proteome profiling to reveal the mechanisms of the anticancer activity of oTR. oTR showed cytotoxicity against radio-resistant TNBC cells with an inhibitory concentration (IC50) value of 7.78 μM. Significantly decreased (p value < 0.05) basal and compensatory glycolysis were observed in the oTR-treated group than untreated group. Mitochondrial spare respiratory capacity, which is relevant to cell fitness and flexibility, was significantly decreased (p value < 0.05) in the oTR-treated group. The major metabolic pathways significantly altered by oTR according to metabolome, transcriptome, and proteome profiles were the glycerolipid/glycerophospholipid pathway (log2(FC) of MGLL = -0.13, log2(FC) of acylglycerol lipase = -1.35, log2(FC) of glycerol = -0.81), glycolysis (log2(FC) of EGLN1 = 0.16, log2(FC) of EGLN1 = 0.62, log2(FC) of glucose = -0.76, log2(FC) of lactate = -0.81), and kynurenine pathway (log2(FC) of KYNU = 0.29, log2(FC) of kynureninase = 0.55, log2(FC) of alanine = 0.72). Additionally, proline metabolism (log2(FC) of PYCR1 = -0.17, log2(FC) of proline = -0.73) was significantly altered in the metabolomic and transcriptomic profiles. The MAPK signaling pathway (log2(FC) of CCN1 = -0.15, log2(FC) of CCN family member 1 = -1.02) and Rap 1 signaling pathway (log2(FC) of PARD6B = -0.28, log2(FC) of PAR6B = -3.13) were also significantly altered in transcriptomic and proteomic profiles. The findings of this study revealed that oTR has anticancer activity in radio-resistant TNBC cells by affecting various metabolic pathways, suggesting the potential of oTR as a novel anticancer agent for radio-resistant TNBC patients.

Harnessing Artificial Intelligence in Multimodal Omics Data Integration: Paving the Path for the Next Frontier in Precision Medicine

The integration of multiomics data with detailed phenotypic insights from electronic health records marks a paradigm shift in biomedical research, offering unparalleled holistic views into health and disease pathways. This review delineates the current landscape of multimodal omics data integration, emphasizing its transformative potential in generating a comprehensive understanding of complex biological systems. We explore robust methodologies for data integration, ranging from concatenation-based to transformation-based and network-based strategies, designed to harness the intricate nuances of diverse data types. Our discussion extends from incorporating large-scale population biobanks to dissecting high-dimensional omics layers at the single-cell level. The review underscores the emerging role of large language models in artificial intelligence, anticipating their influence as a near-future pivot in data integration approaches. Highlighting both achievements and hurdles, we advocate for a concerted effort toward sophisticated integration models, fortifying the foundation for groundbreaking discoveries in precision medicine.

BioLadder: A bioinformatic platform primarily focused on proteomic data analysis

BioLadder (https://www.bioladder.cn/) is an online data analysis platform designed for proteomics research, which includes three classes of experimental data analysis modules and four classes of common data analysis modules. It allows for a variety of proteomics analyses to be conducted easily and efficiently. Additionally, most modules can also be utilized for the analysis of other omics data. To facilitate user experience, we have carefully designed four different kinds of functions for customers to quickly and accurately utilize the relevant analysis modules.

Application of spatial omics in gastric cancer

Gastric cancer (GC), a globally prevalent and lethal malignancy, continues to be a key research focus. However, due to its considerable heterogeneity and complex pathogenesis, the treatment and diagnosis of gastric cancer still face significant challenges. With the rapid development of spatial omics technology, which provides insights into the spatial information within tumor tissues, it has emerged as a significant tool in gastric cancer research. This technology affords new insights into the pathology and molecular biology of gastric cancer for scientists. This review discusses recent advances in spatial omics technology for gastric cancer research, highlighting its applications in the tumor microenvironment (TME), tumor heterogeneity, tumor genesis and development mechanisms, and the identification of potential biomarkers and therapeutic targets. Moreover, this article highlights spatial omics' potential in precision medicine and summarizes existing challenges and future directions. It anticipates spatial omics' continuing impact on gastric cancer research, aiming to improve diagnostic and therapeutic approaches for patients. With this review, we aim to offer a comprehensive overview to scientists and clinicians in gastric cancer research, motivating further exploration and utilization of spatial omics technology. Our goal is to improve patient outcomes, including survival rates and quality of life.

A novel integrated extraction protocol for multi-omic studies in heavily degraded samples

The combination of multi-omic techniques, such as genomics, transcriptomics, proteomics, metabolomics and epigenomics, has revolutionised studies in medical research. These techniques are employed to support biomarker discovery, better understand molecular pathways and identify novel drug targets. Despite concerted efforts in integrating omic datasets, there is an absence of protocols that integrate all four biomolecules in a single extraction process. Here, we demonstrate for the first time a minimally destructive integrated protocol for the simultaneous extraction of artificially degraded DNA, proteins, lipids and metabolites from pig brain samples. We used an MTBE-based approach to separate lipids and metabolites, followed by subsequent isolation of DNA and proteins. We have validated this protocol against standalone extraction protocols and show comparable or higher yields of all four biomolecules. This integrated protocol is key to facilitating the preservation of irreplaceable samples while promoting downstream analyses and successful data integration by removing bias from univariate dataset noise and varied distribution characteristics.

Biomarker Identification and Risk Prediction Model Development for Differentiated Thyroid Carcinoma Lung Metastasis Based on Primary Lesion Proteomics

PURPOSE

The rising global high incidence of differentiated thyroid carcinoma (DTC) has led to a significant increase in patients presenting with lung metastasis of DTC (LMDTC). This population poses a significant challenge in clinical practice, necessitating the urgent development of effective risk stratification methods and predictive tools for lung metastasis.

EXPERIMENTAL DESIGN

Through proteomic analysis of large samples of primary lesion and dual validation employing parallel reaction monitoring and IHC, we identified eight hub proteins as potential biomarkers. By expanding the sample size and conducting statistical analysis on clinical features and hub protein expression, we constructed three risk prediction models.

RESULTS

This study identified eight hub proteins-SUCLG1/2, DLAT, IDH3B, ACSF2, ACO2, CYCS, and VDAC2-as potential biomarkers for predicting LMDTC risk. We developed and internally validated three risk prediction models incorporating both clinical characteristics and hub protein expression. Our findings demonstrated that the combined prediction model exhibited optimal predictive performance, with the highest discrimination (AUC: 0.986) and calibration (Brier score: 0.043). Application of the combined prediction model within a specific risk threshold (0-0.97) yielded maximal clinical benefit. Finally, we constructed a nomogram based on the combined prediction model.

CONCLUSIONS

As a large sample size study in LMDTC research, the identification of biomarkers through primary lesion proteomics and the development of risk prediction models integrating clinical features and hub protein biomarkers offer valuable insights for predicting LMDTC and establishing personalized treatment strategies.

Neuroprotective Mechanism of MOTS-c in TBI Mice: Insights from Integrated Transcriptomic and Metabolomic Analyses

Background

Traumatic brain injury (TBI) is a condition characterized by structural and physiological disruptions in brain function caused by external forces. However, as the highly complex and heterogenous nature of TBI, effective treatments are currently lacking. Mitochondrial open reading frame of the 12S rRNA-c (MOTS-c) has shown notable antinociceptive and anti-inflammatory effects, yet its detailed neuroprotective effects and mode of action remain incompletely understood. This study investigated the neuroprotective effects and the underlying mechanisms of MOTS-c.

Methods

Adult male C57BL/6 mice were randomly divided into three groups: control (CON) group, MOTS-c group and TBI group. Enzyme-linked immunosorbent assay (ELISA) kit method was used to measure the expression levels of MOTS-c in different groups. Behavioral tests were conducted to assess the effects of MOTS-c. Then, transcriptomics and metabolomics were performed to search Differentially Expressed Genes (DEGs) and Differentially Expressed Metabolites (DEMs), respectively. Moreover, the integrated transcriptomics and metabolomics analysis were employed using R packages and online Kyoto Encyclopedia of Genes and Genomes (KEGG) database.

Results

ELISA kit method showed that TBI resulted in a decrease in the expression of MOTS-c. and peripheral administration of MOTS-c could enter the brain tissue after TBI. Behavioral tests revealed that MOTS-c improved memory, learning, and motor function impairments in TBI mice. Additionally, transcriptomic analysis screened 159 differentially expressed genes. Metabolomic analysis identified 491 metabolites with significant differences. Integrated analysis found 14 KEGG pathways, primarily related to metabolic pathways. Besides, several signaling pathways were enriched, including neuroactive ligand-receptor interaction and retrograde endocannabinoid signaling.

Conclusion

TBI reduced the expression of MOTS-c. MOTS-c reduced inflammatory responses, molecular damage, and cell death by down-regulating macrophage migration inhibitory factor (MIF) expression and activating the retrograde endocannabinoid signaling pathway. In addition, MOTS-c alleviated the response to hypoxic stress and enhanced lipid β-oxidation to provide energy for the body following TBI. Overall, our study offered new insights into the neuroprotective mechanisms of MOTS-c in TBI mice.

AVBAE-MODFR: A novel deep learning framework of embedding and feature selection on multi-omics data for pan-cancer classification

Integration analysis of cancer multi-omics data for pan-cancer classification has the potential for clinical applications in various aspects such as tumor diagnosis, analyzing clinically significant features, and providing precision medicine. In these applications, the embedding and feature selection on high-dimensional multi-omics data is clinically necessary. Recently, deep learning algorithms become the most promising cancer multi-omic integration analysis methods, due to the powerful capability of capturing nonlinear relationships. Developing effective deep learning architectures for cancer multi-omics embedding and feature selection remains a challenge for researchers in view of high dimensionality and heterogeneity. In this paper, we propose a novel two-phase deep learning model named AVBAE-MODFR for pan-cancer classification. AVBAE-MODFR achieves embedding by a multi2multi autoencoder based on the adversarial variational Bayes method and further performs feature selection utilizing a dual-net-based feature ranking method. AVBAE-MODFR utilizes AVBAE to pre-train the network parameters, which improves the classification performance and enhances feature ranking stability in MODFR. Firstly, AVBAE learns high-quality representation among multiple omics features for unsupervised pan-cancer classification. We design an efficient discriminator architecture to distinguish the latent distributions for updating forward variational parameters. Secondly, we propose MODFR to simultaneously evaluate multi-omics feature importance for feature selection by training a designed multi2one selector network, where the efficient evaluation approach based on the average gradient of random mask subsets can avoid bias caused by input feature drift. We conduct experiments on the TCGA pan-cancer dataset and compare it with four state-of-the-art methods for each phase. The results show the superiority of AVBAE-MODFR over SOTA methods.

Molecular subtypes of colorectal cancer in the era of precision oncotherapy: Current inspirations and future challenges

BACKGROUND

Colorectal cancer (CRC) is among the most hackneyed malignancies. Even patients with identical clinical symptoms and the same TNM stage still exhibit radically different clinical outcomes after receiving equivalent treatment regimens, indicating extensive heterogeneity of CRC. Myriad molecular subtypes of CRC have been exploited for decades, including the most compelling consensus molecular subtype (CMS) classification that has been broadly applied for patient stratification and biomarker-drug combination formulation. Encountering barriers to clinical translation, however, CMS classification fails to fully reflect inter- or intra-tumor heterogeneity of CRC. As a consequence, addressing heterogeneity and precisely managing CRC patients with unique characteristics remain arduous tasks for clinicians.

REVIEW

In this review, we systematically summarize molecular subtypes of CRC and further elaborate on their clinical applications, limitations, and future orientations.

CONCLUSION

In recent years, exploration of subtypes through cell lines, animal models, patient-derived xenografts (PDXs), organoids, and clinical trials contributes to refining biological insights and unraveling subtype-specific therapies in CRC. Therapeutic interventions including nanotechnology, clustered regulatory interspaced short palindromic repeat/CRISPR-associated nuclease 9 (CRISPR/Cas9), gut microbiome, and liquid biopsy are powerful tools with the possibility to shift the immunologic landscape and outlook for CRC precise medicine.

Multi-omics data integration using ratio-based quantitative profiling with Quartet reference materials

Characterization and integration of the genome, epigenome, transcriptome, proteome and metabolome of different datasets is difficult owing to a lack of ground truth. Here we develop and characterize suites of publicly available multi-omics reference materials of matched DNA, RNA, protein and metabolites derived from immortalized cell lines from a family quartet of parents and monozygotic twin daughters. These references provide built-in truth defined by relationships among the family members and the information flow from DNA to RNA to protein. We demonstrate how using a ratio-based profiling approach that scales the absolute feature values of a study sample relative to those of a concurrently measured common reference sample produces reproducible and comparable data suitable for integration across batches, labs, platforms and omics types. Our study identifies reference-free 'absolute' feature quantification as the root cause of irreproducibility in multi-omics measurement and data integration and establishes the advantages of ratio-based multi-omics profiling with common reference materials.

MDDOmics: multi-omics resource of major depressive disorder

Major depressive disorder (MDD) is a pressing global health issue. Its pathogenesis remains elusive, but numerous studies have revealed its intricate associations with various biological factors. Consequently, there is an urgent need for a comprehensive multi-omics resource to help researchers in conducting multi-omics data analysis for MDD. To address this issue, we constructed the MDDOmics database (Major Depressive Disorder Omics, (https://www.csuligroup.com/MDDOmics/), which integrates an extensive collection of published multi-omics data related to MDD. The database contains 41 222 entries of MDD research results and several original datasets, including Single Nucleotide Polymorphisms, genes, non-coding RNAs, DNA methylations, metabolites and proteins, and offers various interfaces for searching and visualization. We also provide extensive downstream analyses of the collected MDD data, including differential analysis, enrichment analysis and disease-gene prediction. Moreover, the database also incorporates multi-omics data for bipolar disorder, schizophrenia and anxiety disorder, due to the challenge in differentiating MDD from similar psychiatric disorders. In conclusion, by leveraging the rich content and online interfaces from MDDOmics, researchers can conduct more comprehensive analyses of MDD and its similar disorders from various perspectives, thereby gaining a deeper understanding of potential MDD biomarkers and intricate disease pathogenesis. Database URL: https://www.csuligroup.com/MDDOmics/.

Advances in Integrated Multi-omics Analysis for Drug-Target Identification

As an essential component of modern drug discovery, the role of drug-target identification is growing increasingly prominent. Additionally, single-omics technologies have been widely utilized in the process of discovering drug targets. However, it is difficult for any single-omics level to clearly expound the causal connection between drugs and how they give rise to the emergence of complex phenotypes. With the progress of large-scale sequencing and the development of high-throughput technologies, the tendency in drug-target identification has shifted towards integrated multi-omics techniques, gradually replacing traditional single-omics techniques. Herein, this review centers on the recent advancements in the domain of integrated multi-omics techniques for target identification, highlights the common multi-omics analysis strategies, briefly summarizes the selection of multi-omics analysis tools, and explores the challenges of existing multi-omics analyses, as well as the applications of multi-omics technology in drug-target identification.

Application of omics in the diagnosis, prognosis, and treatment of acute myeloid leukemia

Acute myeloid leukemia (AML) is the most frequent leukemia in adults with a high mortality rate. Current diagnostic criteria and selections of therapeutic strategies are generally based on gene mutations and cytogenetic abnormalities. Chemotherapy, targeted therapies, and hematopoietic stem cell transplantation (HSCT) are the major therapeutic strategies for AML. Two dilemmas in the clinical management of AML are related to its poor prognosis. One is the inaccurate risk stratification at diagnosis, leading to incorrect treatment selections. The other is the frequent resistance to chemotherapy and/or targeted therapies. Genomic features have been the focus of AML studies. However, the DNA-level aberrations do not always predict the expression levels of genes and proteins and the latter is more closely linked to disease phenotypes. With the development of high-throughput sequencing and mass spectrometry technologies, studying downstream effectors including RNA, proteins, and metabolites becomes possible. Transcriptomics can reveal gene expression and regulatory networks, proteomics can discover protein expression and signaling pathways intimately associated with the disease, and metabolomics can reflect precise changes in metabolites during disease progression. Moreover, omics profiling at the single-cell level enables studying cellular components and hierarchies of the AML microenvironment. The abundance of data from different omics layers enables the better risk stratification of AML by identifying prognosis-related biomarkers, and has the prospective application in identifying drug targets, therefore potentially discovering solutions to the two dilemmas. In this review, we summarize the existing AML studies using omics methods, both separately and combined, covering research fields of disease diagnosis, risk stratification, prognosis prediction, chemotherapy, as well as targeted therapy. Finally, we discuss the directions and challenges in the application of multi-omics in precision medicine of AML. Our review may inspire both omics researchers and clinical physicians to study AML from a different angle.

nipalsMCIA: Flexible Multi-Block Dimensionality Reduction in R via Non-linear Iterative Partial Least Squares

Motivation

With the increased reliance on multi-omics data for bulk and single cell analyses, the availability of robust approaches to perform unsupervised analysis for clustering, visualization, and feature selection is imperative. Joint dimensionality reduction methods can be applied to multi-omics datasets to derive a global sample embedding analogous to single-omic techniques such as Principal Components Analysis (PCA). Multiple co-inertia analysis (MCIA) is a method for joint dimensionality reduction that maximizes the covariance between block- and global-level embeddings. Current implementations for MCIA are not optimized for large datasets such such as those arising from single cell studies, and lack capabilities with respect to embedding new data.

Results

We introduce nipalsMCIA, an MCIA implementation that solves the objective function using an extension to Non-linear Iterative Partial Least Squares (NIPALS), and shows significant speed-up over earlier implementations that rely on eigendecompositions for single cell multi-omics data. It also removes the dependence on an eigendecomposition for calculating the variance explained, and allows users to perform out-of-sample embedding for new data. nipalsMCIA provides users with a variety of pre-processing and parameter options, as well as ease of functionality for down-stream analysis of single-omic and global-embedding factors.

Availability

nipalsMCIA is available as a BioConductor package at https://bioconductor.org/packages/release/bioc/html/nipalsMCIA.html, and includes detailed documentation and application vignettes. Supplementary Materials are available online.

Mendelian randomization reveals interactions of the blood proteome and immunome in mitral valve prolapse

BACKGROUND

Mitral valve prolapse (MVP) is a common heart disorder characterized by an excessive production of proteoglycans and extracellular matrix in mitral valve leaflets. Large-scale genome-wide association study (GWAS) underlined that MVP is heritable. The molecular underpinnings of the disease remain largely unknown.

METHODS

We interrogated cross-modality data totaling more than 500,000 subjects including GWAS, 4809 molecules of the blood proteome, and genome-wide expression of mitral valves to identify candidate drivers of MVP. Data were investigated through Mendelian randomization, network analysis, ligand-receptor inference and digital cell quantification.

RESULTS

In this study, Mendelian randomization identify that 33 blood proteins, enriched in networks for immunity, are associated with the risk of MVP. MVP- associated blood proteins are enriched in ligands for which their cognate receptors are differentially expressed in mitral valve leaflets during MVP and enriched in cardiac endothelial cells and macrophages. MVP-associated blood proteins are involved in the renewal-polarization of macrophages and regulation of adaptive immune response. Cytokine activity profiling and digital cell quantification show in MVP a shift toward cytokine signature promoting M2 macrophage polarization. Assessment of druggability identify CSF1R, CX3CR1, CCR6, IL33, MMP8, ENPEP and angiotensin receptors as actionable targets in MVP.

CONCLUSIONS

Hence, integrative analysis identifies networks of candidate molecules and cells involved in immune control and remodeling of the extracellular matrix, which drive the risk of MVP.

Multi-omics data integration and drug screening of AML cancer using Generative Adversarial Network

In the era of precision medicine, accurate disease phenotype prediction for heterogeneous diseases, such as cancer, is emerging due to advanced technologies that link genotypes and phenotypes. However, it is difficult to integrate different types of biological data because they are so varied. In this study, we focused on predicting the traits of a blood cancer called Acute Myeloid Leukemia (AML) by combining different kinds of biological data. We used a recently developed method called Omics Generative Adversarial Network (GAN) to better classify cancer outcomes. The primary advantages of a GAN include its ability to create synthetic data that is nearly indistinguishable from real data, its high flexibility, and its wide range of applications, including multi-omics data analysis. In addition, the GAN was effective at combining two types of biological data. We created synthetic datasets for gene activity and DNA methylation. Our method was more accurate in predicting disease traits than using the original data alone. The experimental results provided evidence that the creation of synthetic data through interacting multi-omics data analysis using GANs improves the overall prediction quality. Furthermore, we identified the top-ranked significant genes through statistical methods and pinpointed potential candidate drug agents through in-silico studies. The proposed drugs, also supported by other independent studies, might play a crucial role in the treatment of AML cancer. The code is available on GitHub; https://github.com/SabrinAfroz/omicsGAN_codes?fbclid=IwAR1-/stuffmlE0hyWgSu2wlXo6dYlKUei3faLdlvpxTOOUPVlmYCloXf4Uk9ejK4I.

404-error "Disease not found": Unleashing the translational potential of -omics approaches beyond traditional disease classification in heart failure research

The emergence of personalized medicine, facilitated by the progress in -omics technologies, has initiated a new era in medical diagnostics and treatment. This review examines the potential of -omics approaches in heart failure, a condition that has not yet fully capitalized on personalized strategies compared to other medical fields like cancer therapy. Here, we argue that integrating multi-omics technology with systems medicine approaches could fundamentally transform heart failure management, moving away from the traditional paradigm of 'one size fits all'. Our review examines how omics can enhance understanding of heart failure's molecular foundations and contribute to a more comprehensive disease classification. We draw attention to the current state of medical practice that only relies on clinical evidence and a number of standard laboratory tests. At the same time, we propose a shift towards a universal approach that uses quantitative data from multi-omics to unravel complex molecular interactions. The discussion centres around the potential of the transition as a means to enhance individual risk assessment and emphasizes management within clinical settings. While the use of omics in cardiovascular research is not recent, many past studies have focused only on a single omics approach. In order to achieve a better understanding of disease mechanisms, we explore more holistic approaches using genomics, transcriptomics, epigenomics, and proteomics. This review concludes with a call to action to adopt multi-omics in clinical trials and practice to pave the way for more personalized disease management and more effective heart failure interventions.

A Review of the Application of Spatial Transcriptomics in Neuroscience

Since spatial transcriptomics can locate and distinguish the gene expression of functional genes in special regions and tissue, it is important for us to investigate the brain development, the development mechanism of brain diseases, and the relationship between brain structure and function in Neuroscience (or Brain science). While previous studies have introduced the crucial spatial transcriptomic techniques and data analysis methods, there are few studies to comprehensively overview the key methods, data resources, and technological applications of spatial transcriptomics in Neuroscience. For these reasons, we first investigate several common spatial transcriptomic data analysis approaches and data resources. Second, we introduce the applications of the spatial transcriptomic data analysis approaches in Neuroscience. Third, we summarize the integrating spatial transcriptomics with other technologies in Neuroscience. Finally, we discuss the challenges and future research directions of spatial transcriptomics in Neuroscience.

From flesh to bones: Multi-omics approaches in forensic science

Recent advancements in omics techniques have revolutionised the study of biological systems, enabling the generation of high-throughput biomolecular data. These innovations have found diverse applications, ranging from personalised medicine to forensic sciences. While the investigation of multiple aspects of cells, tissues or entire organisms through the integration of various omics approaches (such as genomics, epigenomics, metagenomics, transcriptomics, proteomics and metabolomics) has already been established in fields like biomedicine and cancer biology, its full potential in forensic sciences remains only partially explored. In this review, we have presented a comprehensive overview of state-of-the-art analytical platforms employed in omics research, with specific emphasis on their application in the forensic field for the identification of the cadaver and the cause of death. Moreover, we have conducted a critical analysis of the computational integration of omics approaches, and highlighted the latest advancements in employing multi-omics techniques for forensic investigations.

Modulation of Metal Homeostasis for Cancer Therapy

Metal ions such as iron, zinc, copper, manganese, and calcium are essential for normal cellular processes, including DNA synthesis, enzyme activity, cellular signaling, and oxidative stress regulation. When the balance of metal homeostasis is disrupted, it can lead to various pathological conditions, including cancer. Thus, understanding the role of metal homeostasis in cancer has led to the development of anti-tumor strategies that specifically target the metal imbalance. Up to now, diverse small molecule-based chelators, ionophores, metal complexes, and metal-based nanomaterials have been developed to restore the normal balance of metals or exploit the dysregulation for therapeutic purposes. They hold great promise in inhibiting tumor growth, preventing metastasis, and enhancing the effectiveness of existing cancer therapies. In this review, we aim to provide a comprehensive summary of the strategies employed to modulate the homeostasis of iron, zinc, copper, manganese, and calcium for cancer therapy. Their modulation mechanisms for metal homeostasis are succinctly described, and their recent applications in the field of cancer therapy are discussed. At the end, the limitations of these approaches are addressed, and potential avenues for future developments are explored.

ProMENDA: an updated resource for proteomic and metabolomic characterization in depression

Depression is a prevalent mental disorder with a complex biological mechanism. Following the rapid development of systems biology technology, a growing number of studies have applied proteomics and metabolomics to explore the molecular profiles of depression. However, a standardized resource facilitating the identification and annotation of the available knowledge from these scattered studies associated with depression is currently lacking. This study presents ProMENDA, an upgraded resource that provides a platform for manual annotation of candidate proteins and metabolites linked to depression. Following the establishment of the protein dataset and the update of the metabolite dataset, the ProMENDA database was developed as a major extension of its initial release. A multi-faceted annotation scheme was employed to provide comprehensive knowledge of the molecules and studies. A new web interface was also developed to improve the user experience. The ProMENDA database now contains 43,366 molecular entries, comprising 20,847 protein entries and 22,519 metabolite entries, which were manually curated from 1370 human, rat, mouse, and non-human primate studies. This represents a significant increase (more than 7-fold) in molecular entries compared to the initial release. To demonstrate the usage of ProMENDA, a case study identifying consistently reported proteins and metabolites in the brains of animal models of depression was presented. Overall, ProMENDA is a comprehensive resource that offers a panoramic view of proteomic and metabolomic knowledge in depression. ProMENDA is freely available at https://menda.cqmu.edu.cn .

Spatiotemporal multi-omics: exploring molecular landscapes in aging and regenerative medicine

Aging and regeneration represent complex biological phenomena that have long captivated the scientific community. To fully comprehend these processes, it is essential to investigate molecular dynamics through a lens that encompasses both spatial and temporal dimensions. Conventional omics methodologies, such as genomics and transcriptomics, have been instrumental in identifying critical molecular facets of aging and regeneration. However, these methods are somewhat limited, constrained by their spatial resolution and their lack of capacity to dynamically represent tissue alterations. The advent of emerging spatiotemporal multi-omics approaches, encompassing transcriptomics, proteomics, metabolomics, and epigenomics, furnishes comprehensive insights into these intricate molecular dynamics. These sophisticated techniques facilitate accurate delineation of molecular patterns across an array of cells, tissues, and organs, thereby offering an in-depth understanding of the fundamental mechanisms at play. This review meticulously examines the significance of spatiotemporal multi-omics in the realms of aging and regeneration research. It underscores how these methodologies augment our comprehension of molecular dynamics, cellular interactions, and signaling pathways. Initially, the review delineates the foundational principles underpinning these methods, followed by an evaluation of their recent applications within the field. The review ultimately concludes by addressing the prevailing challenges and projecting future advancements in the field. Indubitably, spatiotemporal multi-omics are instrumental in deciphering the complexities inherent in aging and regeneration, thus charting a course toward potential therapeutic innovations.

Prediction of disease-free survival for precision medicine using cooperative learning on multi-omic data

In precision medicine, both predicting the disease susceptibility of an individual and forecasting its disease-free survival are areas of key research. Besides the classical epidemiological predictor variables, data from multiple (omic) platforms are increasingly available. To integrate this wealth of information, we propose new methodology to combine both cooperative learning, a recent approach to leverage the predictive power of several datasets, and polygenic hazard score models. Polygenic hazard score models provide a practitioner with a more differentiated view of the predicted disease-free survival than the one given by merely a point estimate, for instance computed with a polygenic risk score. Our aim is to leverage the advantages of cooperative learning for the computation of polygenic hazard score models via Cox's proportional hazard model, thereby improving the prediction of the disease-free survival. In our experimental study, we apply our methodology to forecast the disease-free survival for Alzheimer's disease (AD) using three layers of data. One layer contains epidemiological variables such as sex, APOE (apolipoprotein E, a genetic risk factor for AD) status and 10 leading principal components. Another layer contains selected genomic loci, and the last layer contains methylation data for selected CpG sites. We demonstrate that the survival curves computed via cooperative learning yield an AUC of around $0.7$, above the state-of-the-art performance of its competitors. Importantly, the proposed methodology returns (1) a linear score that can be easily interpreted (in contrast to machine learning approaches), and (2) a weighting of the predictive power of the involved data layers, allowing for an assessment of the importance of each omic (or other) platform. Similarly to polygenic hazard score models, our methodology also allows one to compute individual survival curves for each patient.

Advancing drug-response prediction using multi-modal and -omics machine learning integration (MOMLIN): a case study on breast cancer clinical data

The inherent heterogeneity of cancer contributes to highly variable responses to any anticancer treatments. This underscores the need to first identify precise biomarkers through complex multi-omics datasets that are now available. Although much research has focused on this aspect, identifying biomarkers associated with distinct drug responders still remains a major challenge. Here, we develop MOMLIN, a multi-modal and -omics machine learning integration framework, to enhance drug-response prediction. MOMLIN jointly utilizes sparse correlation algorithms and class-specific feature selection algorithms, which identifies multi-modal and -omics-associated interpretable components. MOMLIN was applied to 147 patients' breast cancer datasets (clinical, mutation, gene expression, tumor microenvironment cells and molecular pathways) to analyze drug-response class predictions for non-responders and variable responders. Notably, MOMLIN achieves an average AUC of 0.989, which is at least 10% greater when compared with current state-of-the-art (data integration analysis for biomarker discovery using latent components, multi-omics factor analysis, sparse canonical correlation analysis). Moreover, MOMLIN not only detects known individual biomarkers such as genes at mutation/expression level, most importantly, it correlates multi-modal and -omics network biomarkers for each response class. For example, an interaction between ER-negative-HMCN1-COL5A1 mutations-FBXO2-CSF3R expression-CD8 emerge as a multimodal biomarker for responders, potentially affecting antimicrobial peptides and FLT3 signaling pathways. In contrast, for resistance cases, a distinct combination of lymph node-TP53 mutation-PON3-ENSG00000261116 lncRNA expression-HLA-E-T-cell exclusions emerged as multimodal biomarkers, possibly impacting neurotransmitter release cycle pathway. MOMLIN, therefore, is expected advance precision medicine, such as to detect context-specific multi-omics network biomarkers and better predict drug-response classifications.

System-level analysis of genes mutated in muscular dystrophies reveals a functional pattern associated with muscle weakness distribution

Muscular dystrophies (MDs) are inherited genetic diseases causing weakness and degeneration of muscles. The distribution of muscle weakness differs between MDs, involving distal muscles or proximal muscles. While the mutations in most of the MD-associated genes lead to either distal or proximal onset, there are also genes whose mutations can cause both types of onsets. We hypothesized that the genes associated with different MD onsets code proteins with distinct cellular functions. To investigate this, we collected the MD-associated genes and assigned them to three onset groups: genes mutated only in distal onset dystrophies, genes mutated only in proximal onset dystrophies, and genes mutated in both types of onsets. We then systematically evaluated the cellular functions of these gene sets with computational strategies based on functional enrichment analysis and biological network analysis. Our analyses demonstrate that genes mutated in either distal or proximal onset MDs code proteins linked with two distinct sets of cellular processes. Interestingly, these two sets of cellular processes are relevant for the genes that are associated with both onsets. Moreover, the genes associated with both onsets display high centrality and connectivity in the network of muscular dystrophy genes. Our findings support the hypothesis that the proteins associated with distal or proximal onsets have distinct functional characteristics, whereas the proteins associated with both onsets are multifunctional.

Modern Electrospray Ionization Mass Spectrometry Techniques for the Characterization of Supramolecules and Coordination Compounds

Mass spectrometry is routinely used for myriad applications in clinical, industrial, and research laboratories worldwide. Developments in the areas of ionization sources, high-resolution mass analyzers, tandem mass spectrometry, and ion mobility have significantly extended the repertoire of mass spectrometrists; however, for coordination compounds and supramolecules, mass spectrometry remains underexplored and arguably underappreciated. Here, the reader is guided through different tools of modern electrospray ionization mass spectrometry that are suitable for larger inorganic complexes. All steps, from sample preparation and technical details to data analysis and interpretation are discussed. The main target audience of this tutorial is synthetic chemists as well as technicians/mass spectrometrists with little experience in characterizing labile inorganic compounds.

An integrated multi-omics analysis reveals osteokines involved in global regulation

Bone secretory proteins, termed osteokines, regulate bone metabolism and whole-body homeostasis. However, fundamental questions as to what the bona fide osteokines and their cellular sources are and how they are regulated remain unclear. In this study, we analyzed bone and extraskeletal tissues, osteoblast (OB) conditioned media, bone marrow supernatant (BMS), and serum, for basal osteokines and those responsive to aging and mechanical loading/unloading. We identified 375 candidate osteokines and their changes in response to aging and mechanical dynamics by integrating data from RNA-seq, scRNA-seq, and proteomic approaches. Furthermore, we analyzed their cellular sources in the bone and inter-organ communication facilitated by them (bone-brain, liver, and aorta). Notably, we discovered that senescent OBs secrete fatty-acid-binding protein 3 to propagate senescence toward vascular smooth muscle cells (VSMCs). Taken together, we identified previously unknown candidate osteokines and established a dynamic regulatory network among them, thus providing valuable resources to further investigate their systemic roles.

Metabolic characterization of neurogenetic disorders involving glutamatergic neurotransmission

The study of inborn errors of neurotransmission has been mostly focused on monoamine disorders, GABAergic and glycinergic defects. The study of the glutamatergic synapse using the same approach than classic neurotransmitter disorders is challenging due to the lack of biomarkers in the CSF. A metabolomic approach can provide both insight into their molecular basis and outline novel therapeutic alternatives. We have performed a semi-targeted metabolomic analysis on CSF samples from 25 patients with neurogenetic disorders with an important expression in the glutamatergic synapse and 5 controls. Samples from patients diagnosed with MCP2, CDKL5-, GRINpathies and STXBP1-related encephalopathies were included. We have performed univariate (UVA) and multivariate statistical analysis (MVA), using Wilcoxon rank-sum test, principal component analysis (PCA), and OPLS-DA. By using the results of both analyses, we have identified the metabolites that were significantly altered and that were important in clustering the respective groups. On these, we performed pathway- and network-based analyses to define which metabolic pathways were possibly altered in each pathology. We have observed alterations in the tryptophan and branched-chain amino acid metabolism pathways, which interestingly converge on LAT1 transporter-dependency to cross the blood-brain barrier (BBB). Analysis of the expression of LAT1 transporter in brain samples from a mouse model of Rett syndrome (MECP2) revealed a decrease in the transporter expression, that was already noticeable at pre-symptomatic stages. The study of the glutamatergic synapse from this perspective advances the understanding of their pathophysiology, shining light on an understudied feature as is their metabolic signature.

Multi-omics pan-cancer study of SPTBN2 and its value as a potential therapeutic target in pancreatic cancer

SPTBN2 is a protein-coding gene that is closely related to the development of malignant tumors. However, its prognostic value and biological function in pan-cancer, especially pancreatic cancer (PAAD), have not been reported. In the present study, a novel exploration of the value and potential mechanism of SPTBN2 in PAAD was conducted using multi-omics in the background of pan-cancer. Via various database analysis, up-regulated expression of SPTBN2 was detected in most of the tumor tissues examined. Overexpression of SPTBN2 in PAAD and kidney renal clear cell cancer patients potentially affected overall survival, disease-specific survival, and progression-free interval. In PAAD, SPTBN2 can be used as an independent factor affecting prognosis. Mutations and amplification of SPTBN2 were detected, with abnormal methylation of SPTBN2 affecting its expression and the survival outcome of PAAD patients. Immunoassay results demonstrate that SPTBN2 was a potential biomarker for predicting therapeutic response in PAAD, and may influence the immunotherapy efficacy of PAAD by regulating levels of CD8 + T cells and neutrophil infiltration. Results from an enrichment analysis indicated that SPTBN2 may regulate the development of PAAD via immune pathways. Thus, SPTBN2 is a potential prognostic biomarker and immunotherapy target based on its crucial role in the development of PAAD.

Integrating Machine Learning in Metabolomics: A Path to Enhanced Diagnostics and Data Interpretation

Metabolomics, leveraging techniques like NMR and MS, is crucial for understanding biochemical processes in pathophysiological states. This field, however, faces challenges in metabolite sensitivity, data complexity, and omics data integration. Recent machine learning advancements have enhanced data analysis and disease classification in metabolomics. This study explores machine learning integration with metabolomics to improve metabolite identification, data efficiency, and diagnostic methods. Using deep learning and traditional machine learning, it presents advancements in metabolic data analysis, including novel algorithms for accurate peak identification, robust disease classification from metabolic profiles, and improved metabolite annotation. It also highlights multiomics integration, demonstrating machine learning's potential in elucidating biological phenomena and advancing disease diagnostics. This work contributes significantly to metabolomics by merging it with machine learning, offering innovative solutions to analytical challenges and setting new standards for omics data analysis.

Integrative analysis of metabolomics and transcriptomics to uncover biomarkers in sepsis

To utilize metabolomics in conjunction with RNA sequencing to identify biomarkers in the blood of sepsis patients and discover novel targets for diagnosing and treating sepsis. In January 2019 and December 2020, blood samples were collected from a cohort of 16 patients diagnosed with sepsis and 11 patients diagnosed with systemic inflammatory response syndrome (SIRS). Non-targeted metabolomics analysis was conducted using liquid chromatography coupled with mass spectrometry (LC-MS/MS technology), while gene sequencing was performed using RNA sequencing. Afterward, the metabolite data and sequencing data underwent quality control and difference analysis, with a fold change (FC) greater than or equal to 2 and a false discovery rate (FDR) less than 0.05.Co-analysis was then performed to identify differential factors with consistent expression trends based on the metabolic pathway context; KEGG enrichment analysis was performed on the crossover factors, and Meta-analysis of the targets was performed at the transcriptome level using the public dataset. In the end, a total of five samples of single nucleated cells from peripheral blood (two normal controls, one with systemic inflammatory response syndrome, and two with sepsis) were collected and examined to determine the cellular location of the essential genes using 10× single cell RNA sequencing (scRNA-seq). A total of 485 genes and 1083 metabolites were found to be differentially expressed in the sepsis group compared to the SIRS group. Among these, 40 genes were found to be differentially expressed in both the metabolome and transcriptome. Functional enrichment analysis revealed that these genes were primarily involved in biological processes related to inflammatory response, immune regulation, and amino acid metabolism. Furthermore, a meta-analysis identified four genes, namely ITGAM, CD44, C3AR1, and IL2RG, which were highly expressed in the sepsis group compared to the normal group (P < 0.05). Additionally, scRNA-seq analysis revealed that the core genes ITGAM and C3AR1 were predominantly localized within the macrophage lineage. The primary genes ITGAM and C3AR1 exhibit predominant expression in macrophages, which play a significant role in inflammatory and immune responses. Moreover, these genes show elevated expression levels in the plasma of individuals with sepsis, indicating their potential as valuable subjects for further research in sepsis.

lncRNA-mRNA Co-Expression and Regulation Analysis in Lung Fibroblasts from Idiopathic Pulmonary Fibrosis

Idiopathic pulmonary fibrosis (IPF) is a progressive lung disease marked by abnormal accumulation of extracellular matrix (ECM) due to dysregulated expression of various RNAs in pulmonary fibroblasts. This study utilized RNA-seq data meta-analysis to explore the regulatory network of hub long non-coding RNAs (lncRNAs) and messenger RNAs (mRNAs) in IPF fibroblasts. The meta-analysis unveiled 584 differentially expressed mRNAs (DEmRNA) and 75 differentially expressed lncRNAs (DElncRNA) in lung fibroblasts from IPF. Among these, BCL6, EFNB1, EPHB2, FOXO1, FOXO3, GNAI1, IRF4, PIK3R1, and RXRA were identified as hub mRNAs, while AC008708.1, AC091806.1, AL442071.1, FAM111A-DT, and LINC01989 were designated as hub lncRNAs. Functional characterization revealed involvement in TGF-β, PI3K, FOXO, and MAPK signaling pathways. Additionally, this study identified regulatory interactions between sequences of hub mRNAs and lncRNAs. In summary, the findings suggest that AC008708.1, AC091806.1, FAM111A-DT, LINC01989, and AL442071.1 lncRNAs can regulate BCL6, EFNB1, EPHB2, FOXO1, FOXO3, GNAI1, IRF4, PIK3R1, and RXRA mRNAs in fibroblasts bearing IPF and contribute to fibrosis by modulating crucial signaling pathways such as FoxO signaling, chemical carcinogenesis, longevity regulatory pathways, non-small cell lung cancer, and AMPK signaling pathways.

Metabolomics assisted by transcriptomics analysis to reveal metabolic characteristics and potential biomarkers associated with treatment response of neoadjuvant therapy with TCbHP regimen in HER2 + breast cancer

BACKGROUND

This study aimed to explore potential indicators associated with the neoadjuvant efficacy of TCbHP regimen (taxane, carboplatin, trastuzumab, and pertuzumab) in HER2 + breast cancer (BrCa) patients.

METHODS

A total of 120 plasma samples from 40 patients with HER2 + BrCa were prospectively collected at three treatment times of neoadjuvant therapy (NAT) with TCbHP regimen. Serum metabolites were analyzed based on LC-MS and GC-MS data. Random forest was used to establish predictive models based on pre-therapeutic differentially expressed metabolites. Time series analysis was used to obtain potential monitors for treatment response. Transcriptome analysis was performed in nine available pre‑therapeutic specimens of core needle biopsies. Integrated analyses of metabolomics and transcriptomics were also performed in these nine patients. qRT-PCR was used to detect altered genes in trastuzumab-sensitive and trastuzumab-resistant cell lines.

RESULTS

Twenty-one patients achieved pCR, and 19 patients achieved non-pCR. There were significant differences in plasma metabolic profiles before and during treatment. A total of 100 differential metabolites were identified between pCR patients and non-pCR patients at baseline; these metabolites were markedly enriched in 40 metabolic pathways. The area under the curve (AUC) values for discriminating the pCR and non-PCR groups from the NAT of the single potential metabolite [sophorose, N-(2-acetamido) iminodiacetic acid, taurine and 6-hydroxy-2-aminohexanoic acid] or combined panel of these metabolites were greater than 0.910. Eighteen metabolites exhibited potential for monitoring efficacy. Several validated genes might be associated with trastuzumab resistance. Thirty-nine altered pathways were found to be abnormally expressed at both the transcriptional and metabolic levels.

CONCLUSION

Serum-metabolomics could be used as a powerful tool for exploring informative biomarkers for predicting or monitoring treatment efficacy. Metabolomics integrated with transcriptomics analysis could assist in obtaining new insights into biochemical pathophysiology and might facilitate the development of new treatment targets for insensitive patients.

The wedish initiative for the stdy of ary sclerosing cholangitis (SUPRIM)

Background

Despite more than 50 years of research and parallel improvements in hepatology and oncology, there is still today neither a treatment to prevent disease progression in primary sclerosing cholangitis (PSC), nor reliable early diagnostic tools for the associated hepatobiliary cancers. Importantly, the limited understanding of the underlying biological mechanisms in PSC and its natural history not only affects the identification of new drug targets but implies a lack of surrogate markers that hampers the design of clinical trials and the evaluation of drug efficacy. The lack of easy access to large representative well-characterised prospective resources is an important contributing factor to the current situation.

Methods

We here present the SUPRIM cohort, a national multicentre prospective longitudinal study of unselected PSC patients capturing the representative diversity of PSC phenotypes. We describe the 10-year effort of inclusion and follow-up, an intermediate analysis report including original results, and the associated research resource. All included patients gave written informed consent (recruitment: November 2011-April 2016).

Findings

Out of 512 included patients, 452 patients completed the five-year follow-up without endpoint outcomes. Liver transplantation was performed in 54 patients (10%) and hepatobiliary malignancy was diagnosed in 15 patients (3%). We draw a comprehensive landscape of the multidimensional clinical and biological heterogeneity of PSC illustrating the diversity of PSC phenotypes. Performances of available predictive scores are compared and perspectives on the continuation of the SUPRIM cohort are provided.

Interpretation

We envision the SUPRIM cohort as an open-access collaborative resource to accelerate the generation of new knowledge and independent validations of promising ones with the aim to uncover reliable diagnostics, prognostic tools, surrogate markers, and new treatment targets by 2040.

Funding

This work was supported by the Swedish Cancer Society, Stockholm County Council, and the Cancer Research Funds of Radiumhemmet.