A clinical protocol for a German birth cohort study of the Maturation of Immunity Against respiratory viral Infections (MIAI).

Introduction: Respiratory viral infections (RVIs) are a major global contributor to morbidity and mortality. The susceptibility and outcome of RVIs are strongly age-dependent and show considerable inter-population differences, pointing to genetically and/or environmentally driven developmental variability. The factors determining the age-dependency and shaping the age-related changes of human anti-RVI immunity after birth are still elusive. Methods: We are conducting a prospective birth cohort study aiming at identifying endogenous and environmental factors associated with the susceptibility to RVIs and their impact on cellular and humoral immune responses against the influenza A virus (IAV), respiratory syncytial virus (RSV) and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The MIAI birth cohort enrolls healthy, full-term neonates born at the University Hospital Würzburg, Germany, with follow-up at four defined time-points during the first year of life. At each study visit, clinical metadata including diet, lifestyle, sociodemographic information, and physical examinations, are collected along with extensive biomaterial sampling. Biomaterials are used to generate comprehensive, integrated multi-omics datasets including transcriptomic, epigenomic, proteomic, metabolomic and microbiomic methods. Discussion: The results are expected to capture a holistic picture of the variability of immune trajectories with a focus on cellular and humoral key players involved in the defense of RVIs and the impact of host and environmental factors thereon. Thereby, MIAI aims at providing insights that allow unraveling molecular mechanisms that can be targeted to promote the development of competent anti-RVI immunity in early life and prevent severe RVIs. Clinical trial registration: https://drks.de/search/de/trial/, identifier DRKS00034278.

Liquid biopsy for early cancer detection: technological revolutions and clinical dilemma.

INTRODUCTION: Liquid biopsy is an innovative advancement in oncology, offering a noninvasive method for early cancer detection and monitoring by analyzing circulating tumor cells, DNA, RNA, and other biomarkers in bodily fluids. This technique has the potential to revolutionize precision oncology by providing real-time analysis of tumor dynamics, enabling early detection, monitoring treatment responses, and tailoring personalized therapies based on the molecular profiles of individual patients. AREAS COVERED: In this review, the authors discuss current methodologies, technological challenges, and clinical applications of liquid biopsy. This includes advancements in detecting minimal residual disease, tracking tumor evolution, and combining liquid biopsy with other diagnostic modalities for precision oncology. Key areas explored are the sensitivity, specificity, and integration of multi-omics, AI, ML, and LLM technologies. EXPERT OPINION: Liquid biopsy holds great potential to revolutionize cancer care through early detection and personalized treatment strategies. However, its success depends on overcoming technological and clinical hurdles, such as ensuring high sensitivity and specificity, interpreting results amidst tumor heterogeneity, and making tests accessible and affordable. Continued innovation and collaboration are crucial to fully realize the potential of liquid biopsy in improving early cancer detection, treatment, and monitoring.

Optimized protocol for the multi-omics processing of cryopreserved human kidney tissue.

Biobanking of tissue from clinically obtained kidney biopsies for later use with multi-omic and imaging techniques is an inevitable step to overcome the need of disease model systems and towards translational medicine. Hence, collection protocols ensuring integration into daily clinical routines using preservation media not requiring liquid nitrogen but instantly preserving kidney tissue for clinical and scientific analyses are of paramount importance. Thus, we modified a robust single nucleus dissociation protocol for kidney tissue stored snap frozen or in the preservation media RNAlaterand CellCover. Using porcine kidney tissue as surrogate for human kidney tissue, we conducted single nucleus RNA sequencing with the Chromium 10X Genomics platform. The resulting data sets from each storage condition were analyzed to identify any potential variations in transcriptomic profiles. Furthermore, we assessed the suitability of the preservation media for additional analysis techniques (proteomics, metabolomics) and the preservation of tissue architecture for histopathological examination including immunofluorescence staining. In this study, we show that in daily clinical routines the RNAlater facilitates the collection of highly preserved human kidney biopsies and enables further analysis with cutting-edge techniques like single nucleus RNA sequencing, proteomics, and histopathological evaluation. Only metabolome analysis is currently restricted to snap frozen tissue. This work will contribute to build tissue biobanks with well-defined cohorts of the respective kidney disease that can be deeply molecularly characterized, opening new horizons for the identification of unique cells, pathways and biomarkers for the prevention, early identification, and targeted therapy of kidney diseases.

Optimized high-throughput protocols for comprehensive metabolomic and lipidomic profiling of brain sample.

Establishing direct causal and functional links between genotype and phenotype requires thoroughly analyzing metabolites and lipids in systems biology. Tissue samples, which provide localized and direct information and contain unique compounds, play a significant role in objectively classifying diseases, predicting prognosis, and deciding personalized therapeutic strategies. Comprehensive metabolomic and lipidomic analyses in tissue samples need efficient sample preparation steps, optimized analysis conditions, and the integration of orthogonal analytical platforms because of the physicochemical diversities of biomolecules. Here, we propose simple, rapid, and robust high-throughput analytical protocols based on the design of experiment (DoE) strategies, with the various parameters systematically tested for comprehensively analyzing the heterogeneous brain samples. The suggested protocols present a systematically DoE-based strategy for performing the most comprehensive analysis for integrated GC-MS and LC-qTOF-MS from brain samples. The five different DoE models, including D-optimal, full factorial, fractional, and Box-Behnken, were applied to increase extraction efficiency for metabolites and lipids and optimize instrumental parameters, including sample preparation and chromatographic parameters. The superior simultaneous extraction of metabolites and lipids from brain samples was achieved by the methanol-water-dichloromethane (2:1:3, v/v/v) mixture. For GC-MS based metabolomics analysis, incubation time, temperature, and methoxyamine concentration (10 mg/mL) affected metabolite coverage significantly. For LC-qTOF-MS based metabolomics analysis, the extraction solvent (methanol-water; 2:1, v/v) and the reconstitution solvent (%0.1 FA in acetonitrile) were superior on the metabolite coverage. On the other hand, the ionic strength and column temperature were critical and significant parameters for high throughput metabolomics and lipidomics studies using LC-qTOF-MS. In conclusion, DoE-based optimization strategies for a three-in-one single-step extraction enabled rapid, comprehensive, high-throughput, and simultaneous analysis of metabolites, lipids, and even proteins from a 10 mg brain sample. Under optimized conditions, 475 lipids and 158 metabolites were identified in brain samples.

Methods for multi-omic data integration in cancer research.

Multi-omics data integration is a term that refers to the process of combining and analyzing data from different omic experimental sources, such as genomics, transcriptomics, methylation assays, and microRNA sequencing, among others. Such data integration approaches have the potential to provide a more comprehensive functional understanding of biological systems and has numerous applications in areas such as disease diagnosis, prognosis and therapy. However, quantitative integration of multi-omic data is a complex task that requires the use of highly specialized methods and approaches. Here, we discuss a number of data integration methods that have been developed with multi-omics data in view, including statistical methods, machine learning approaches, and network-based approaches. We also discuss the challenges and limitations of such methods and provide examples of their applications in the literature. Overall, this review aims to provide an overview of the current state of the field and highlight potential directions for future research.

Unravelling the molecular regulation network of carbon metabolism and lipid metabolism during seed development in Akebia trifoliata via integrated multi-omics analysis.

Akebia trifoliata is a medicinal plant with high oil content and broad pharmacological effects. To investigate the regulatory mechanisms of key metabolic pathways during seed development, we conducted an integrated multi-omics analysis, including transcriptomics, proteomics, and metabolomics, exploring the dynamic changes in carbon and lipid metabolism. Metabolomics analysis revealded that glucose and sucrose levels decreased, while glycolytic intermediate phosphoenolpyruvate and fatty acids increased with seed development, indicating a shift in carbon flux towards fatty acid synthesis. Integrated transcriptomic and proteomic analyses showed that 70 days after flowering, the expression levels of genes and proteins associated with carbon and fatty acid metabolism were upregulated, suggesting an increased energy demand. Additionally, LEC2, LEC1, WRI1, FUS3, and ABI3 were identified as vital regulators of lipid synthesis. By constructing a multi-omics co-expression network, we identified hub genes such as aroE, GAPDH, KCS, TPS, and hub proteins like PGM, PDH, ENO, PFK, PK, ACCase, SAD, PLC, and OGDH that play critical regulatory roles in seed lipid synthesis. This study provides new ideas for the molecular basis of lipid synthesis in Akebia trifoliata seeds and can facilitate future research on the genetic improvement through molecular-assisted breeding.

Multi-omics analysis and response prediction of PD-1 monoclonal antibody containing regimens in patients with relapsed/refractory diffuse large B-cell lymphoma.

Patients with relapsed/refractory (r/r) diffuse large B-cell lymphoma (DLBCL) show varied responses to PD-1 monoclonal antibody (mAb) containing regimens. The mechanisms and predictive biomarkers for the efficacy of this regimen are unclear. This study retrospectively collected r/r DLBCL patients who received PD-1 mAb and rituximab regimens as salvage therapy. Clinical and genomic features were collected, and mechanisms were explored by multiplex immunofluorescence and digital spatial profiling. An artificial neural network (ANN) model was constructed to predict the response. Between October 16th, 2018 and May 4th, 2023, 50 r/r DLBCL patients were collected, 29 were response patients and 21 were non-response patients. CREBBP (p = 0.029) and TP53 (p = 0.015) alterations were statistically higher in non-response patients. Patients with PD-L1 CPS ≥ 5 were correlated with a longer overall survival (OS) than those with PD-L1 CPS < 5 (median OS: not reached vs. 9.7 months, hazard ratio [HR]: 3.8, 95% confidence interval [CI] 0.64-22.44, p = 0.016). Immune-related pathways were activated in response patients. The proportion and spatial organization of tumor-infiltrating immune cells affect the response. PD-L1 CPS level, age, and alterations of TP53, MYD88, CREBBP, EP300, GNA13 were used to build an ANN predictive model that showed high prediction efficiency (training set area under curve [AUC] of 0.97 and test set AUC of 0.94). The proportion and spatial distribution of tumor-infiltrating immune cells may be related to the function of immune-related pathways, thereby influencing the efficacy of PD-1 mAb containing regimens. The ANN predictive model showed potential value in predicting the responses of r/r DLBCL patients received PD-1 mAb and rituximab regimens.

Mechanisms of Berberine in anti-pancreatic ductal adenocarcinoma revealed by integrated multi-omics profiling.

This study integrates pharmacology databases with bulk RNA-seq and scRNA-seq to reveal the latent anti-PDAC capacities of BBR. Target genes of BBR were sifted through TargetNet, CTD, SwissTargetPrediction, and Binding Database. Based on the GSE183795 dataset, DEG analysis, GSEA, and WGCNA were sequentially run to build a disease network. Through sub-network filtration acquired PDAC-related hub genes. A PPI network was established using the shared genes. Degree algorithm from cytoHubba screened the key cluster in the network. Analysis of differential mRNA expression and ROC curves gauged the diagnostic performance of clustered genes. CYBERSORT uncovered the potential role of the key cluster on PDAC immunomodulation. ScRNA-seq analysis evaluated the distribution and expression profile of the key cluster at the single-cell level, assessing enrichment within annotated cell subpopulations to delineate the target distribution of BBR in PDAC. We identified 425 drug target genes and 771 disease target genes, using 57 intersecting genes to construct the PPI network. CytoHubba anchored the top 10 highest contributing genes to be the key cluster. mRNA expression levels and ROC curves confirmed that these genes showed good robustness for PDAC. CYBERSORT revealed that the key cluster influenced immune pathways predominantly associated with Macrophages M0, CD8 T cells, and naïve B cells. ScRNA-seq analysis clarified that BBR mainly acted on epithelial cells and macrophages in PDAC tissues. BBR potentially targets CDK1, CCNB1, CTNNB1, CDK2, TOP2A, MCM2, RUNX2, MYC, PLK1, and AURKA to exert therapeutic effects on PDAC. The mechanisms of action appear to significantly involve macrophage polarization-related immunological responses.

Deep learning-based approaches for multi-omics data integration and analysis.

BACKGROUND: The rapid growth of deep learning, as well as the vast and ever-growing amount of available data, have provided ample opportunity for advances in fusion and analysis of complex and heterogeneous data types. Different data modalities provide complementary information that can be leveraged to gain a more complete understanding of each subject. In the biomedical domain, multi-omics data includes molecular (genomics, transcriptomics, proteomics, epigenomics, metabolomics, etc.) and imaging (radiomics, pathomics) modalities which, when combined, have the potential to improve performance on prediction, classification, clustering and other tasks. Deep learning encompasses a wide variety of methods, each of which have certain strengths and weaknesses for multi-omics integration. METHOD: In this review, we categorize recent deep learning-based approaches by their basic architectures and discuss their unique capabilities in relation to one another. We also discuss some emerging themes advancing the field of multi-omics integration. RESULTS: Deep learning-based multi-omics integration methods were categorized broadly into non-generative (feedforward neural networks, graph convolutional neural networks, and autoencoders) and generative (variational methods, generative adversarial models, and a generative pretrained model). Generative methods have the advantage of being able to impose constraints on the shared representations to enforce certain properties or incorporate prior knowledge. They can also be used to generate or impute missing modalities. Recent advances achieved by these methods include the ability to handle incomplete data as well as going beyond the traditional molecular omics data types to integrate other modalities such as imaging data. CONCLUSION: We expect to see further growth in methods that can handle missingness, as this is a common challenge in working with complex and heterogeneous data. Additionally, methods that integrate more data types are expected to improve performance on downstream tasks by capturing a comprehensive view of each sample.

Multi-omics study reveals Shuangshen Pingfei formula regulates EETs metabolic reprogramming to exert its therapeutic effect on pulmonary fibrosis.

As a clinical formula derived from Renshen Pingfei San, Shuangshen Pingfei formula (SSPF) has been used to treat pulmonary fibrosis (PF). However, its in-depth mechanism of action remains unknown. In this study, the effect of SSPF was evaluated by applying a rat model of PF caused by intratracheal drip bleomycin. To characterize the molecular changes related to PF and reveal therapeutic targets for SSPF, we performed transcriptomic and metabolomic analyses on rat lung. Finally, western blotting and qPCR experiments were used to validate the multi-omics results. As a result, a significant reduction in inflammation and fibrosis caused by BLM was observed when SSPF was administered. Widespread changes in gene expression and metabolic programming were observed in the lungs of PF rats through RNA-seq and untargeted metabolomic analysis. Combined transcriptomic and metabolomic analyses revealed the involvement of arachidonic acid (AA) metabolism pathways in PF. Further validation of AA metabolite synthase genes and protein levels showed a significant decrease in the levels of epoxyeicosatrienoic acids (EETs) synthases, including Cyp2j2, Cyp2b1, in the PF lungs. SSPF treatment regulated the above changes in gene expression and metabolic programming, particularly the regulation of EETs. This study is the first to investigate the mechanism of action of SSPF in the treatment of PF from the perspective of regulating the synthesis of EETs in AA metabolism.

Investigation of Human Aging at the Single-Cell Level.

Human aging is characterized by a gradual decline in physiological functions and an increased susceptibility to various diseases. The complex mechanisms underlying human aging are still not fully elucidated. Single-cell sequencing (SCS) technologies have revolutionized aging research by providing unprecedented resolution and detailed insights into cellular diversity and dynamics. In this review, we discuss the application of various SCS technologies in human aging research, encompassing single-cell, genomics, transcriptomics, epigenomics, and proteomics. We also discuss the combination of multiple omics layers within single cells and the integration of SCS technologies with advanced methodologies like spatial transcriptomics and mass spectrometry. These approaches have been essential in identifying aging biomarkers, elucidating signaling pathways associated with aging, discovering novel aging cell subpopulations, uncovering tissue-specific aging characteristics, and investigating aging-related diseases. Furthermore, we provide an overview of aging-related databases that offer valuable resources for enhancing our understanding of the human aging process.

CD8+ T cell associated scoring model helps prognostic diagnosis and immunotherapy selection in patients with colon adenocarcinoma.

Objective: T cell-mediated immunity plays a crucial role in the immune response against tumors, with CD 8+ T cells playing a leading role in the eradication of cancer cells. Material and methods: A total of 5 datasets were included in this study. Single cell transcriptome data were used to discover CD8+ T cell marker genes, and Bulk transcriptome data from TCGA and GEO were jointly analyzed to screen candidate prognostic genes. lasso regression was performed to construct prognostic models. Immunotherapy cohort (IMvigor 210 and GSE78220) was applied to validate the diagnostic power of markers. Result: Single-cell transcriptome data identified 65 CD8+ T cell marker genes, highlighting their importance in T cell-mediated immune responses. Among these, 11 genes were identified as CD8+ T-associated differential genes through analysis of bulk data from TCGA and GEO. A prognostic model for 5 genes was identified based on Lasso regression, dividing colon adenocarcinoma (COAD) patients into high- and low-risk groups. This model exhibited higher prognostic accuracy compared to traditional clinicopathological characteristics (age, pathological stage, histological grading). Moreover, the risk score derived from this model successfully differentiated patient responses to immunotherapy, as validated in the IMvigor 210 and GSE78220 cohorts. Conclusion: Our research introduces a novel prognostic signature based on CD8+ T cell marker genes, demonstrating significant predictive power for prognosis and immunotherapy response in COAD patients. This model offers a potential tool for improving patient stratification and personalizing treatment strategies.

Multi-omics analysis reveals phenylalanine enhance mitochondrial function and hypoxic endurance via LKB1/AMPK activation.

Many studies have focused on the effects of small molecules, such as amino acids, on metabolism under hypoxia. Recent findings have indicated that phenylalanine levels were markedly elevated in adaptation to chronic hypoxia. This raises the possibility that phenylalanine treatment could markedly improve the hypoxic endurance. However, the importance of hypoxia-regulated phenylalanine is still unclear. This study investigates the role of phenylalanine in hypoxia adaptation using a hypoxic zebrafish model and multi-omics analysis. We found that phenylalanine-related metabolic pathways are significantly up-regulated under hypoxia, contributing to enhanced hypoxic endurance. Phenylalanine treatment reduced ROS levels, improved mitochondrial oxygen consumption rate (OCR), and extracellular acidification rate (ECAR) in hypoxic cells. Western blotting revealed increased phenylalanine uptake via L-type amino transporters (LAT1), activating the LKB1/AMPK signaling pathway. This activation up-regulated peroxisome proliferator-activated receptor gamma coactivator-1 alpha (PGC-1α) and the Bcl-2/Bax ratio, while down-regulating uncoupling protein 2 (UCP2), thereby improving mitochondrial function under hypoxia. This is the first comprehensive multi-omics analysis to demonstrate phenylalanine's crucial role in hypoxia adaptation, providing insights for the development of anti-hypoxic drugs.

Leveraging Multi-Omics Approaches and Advanced Technologies to Unravel the Molecular Complexities, Modifiers, and Precision Medicine Strategies for Hemoglobin H Disease.

Hemoglobin H (HbH) disease, a form of alpha-thalassemia, poses significant clinical challenges due to its complex molecular underpinnings. It is characterized by reduced synthesis of the alpha-globin chain. The integration of multi-omics and precision medicine holds immense potential to comprehensively understand and capture interactions at the molecular and genetic levels. This review integrates current multi-omics approaches and advanced technologies in HbH research. Furthermore, it delves into detailed pathophysiology and possible therapeutics in the upcoming future. We explore the role of genomics, transcriptomics, proteomics, and metabolomics studies, alongside bioinformatics tools and gene-editing technologies like CRISPR/Cas9, to identify genetic modifiers, decipher molecular pathways, and discover therapeutic targets. Recent advancements are unveiling novel genetic and epigenetic modifiers impacting HbH disease severity, paving the way for personalized precision medicine interventions. The significance of multi-omics research in unraveling the complexities of rare diseases like HbH is underscored, highlighting its potential to revolutionize clinical practice through precision medicine approaches. This paradigm shift can pave the way for a deeper understanding of HbH complexities and improved disease management.

Corrigendum: Alterations in cellular metabolism under different grades of glioma staging identified based on a multi-omics analysis strategy.

[This corrects the article DOI: 10.3389/fendo.2023.1292944.].

Integrative multi-omic and machine learning approach for prognostic stratification and therapeutic targeting in lung squamous cell carcinoma.

The proliferation, metastasis, and drug resistance of cancer cells pose significant challenges to the treatment of lung squamous cell carcinoma (LUSC). However, there is a lack of optimal predictive models that can accurately forecast patient prognosis and guide the selection of targeted therapies. The extensive multi-omic data obtained from multi-level molecular biology provides a unique perspective for understanding the underlying biological characteristics of cancer, offering potential prognostic indicators and drug sensitivity biomarkers for LUSC patients. We integrated diverse datasets encompassing gene expression, DNA methylation, genomic mutations, and clinical data from LUSC patients to achieve consensus clustering using a suite of 10 multi-omics integration algorithms. Subsequently, we employed 10 commonly used machine learning algorithms, combining them into 101 unique configurations to design an optimal performing model. We then explored the characteristics of high- and low-risk LUSC patient groups in terms of the tumor microenvironment and response to immunotherapy, ultimately validating the functional roles of the model genes through in vitro experiments. Through the application of 10 clustering algorithms, we identified two prognostically relevant subtypes, with CS1 exhibiting a more favorable prognosis. We then constructed a subtype-specific machine learning model, LUSC multi-omics signature (LMS) based on seven key hub genes. Compared to previously published LUSC biomarkers, our LMS score demonstrated superior predictive performance. Patients with lower LMS scores had higher overall survival rates and better responses to immunotherapy. Notably, the high LMS group was more inclined toward "cold" tumors, characterized by immune suppression and exclusion, but drugs like dasatinib may represent promising therapeutic options for these patients. Notably, we also validated the model gene SERPINB13 through cell experiments, confirming its role as a potential oncogene influencing the progression of LUSC and as a promising therapeutic target. Our research provides new insights into refining the molecular classification of LUSC and further optimizing immunotherapy strategies.

Unveiling the role of IGF1R in autism spectrum disorder: a multi-omics approach to decipher common pathogenic mechanisms in the IGF signaling pathway.

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition marked by impairments in social interaction, communication, and repetitive behaviors. Emerging evidence suggests that the insulin-like growth factor (IGF) signaling pathway plays a critical role in ASD pathogenesis; however, the precise pathogenic mechanisms remain elusive. This study utilizes multi-omics approaches to investigate the pathogenic mechanisms of ASD susceptibility genes within the IGF pathway. Whole-exome sequencing (WES) revealed a significant enrichment of rare variants in key IGF signaling components, particularly the IGF receptor 1 (IGF1R), in a cohort of Chinese Han individuals diagnosed with ASD, as well as in ASD patients from the SFARI SPARK WES database. Subsequent single-cell RNA sequencing (scRNA-seq) of cortical tissues from children with ASD demonstrated elevated expression of IGF receptors in parvalbumin (PV) interneurons, suggesting a substantial impact on their development. Notably, IGF1R appears to mediate the effects of IGF2R on these neurons. Additionally, transcriptomic analysis of brain organoids derived from ASD patients indicated a significant association between IGF1R and ASD. Protein-protein interaction (PPI) and gene regulatory network (GRN) analyses further identified ASD susceptibility genes that interact with and regulate IGF1R expression. In conclusion, IGF1R emerges as a central node within the IGF signaling pathway, representing a potential common pathogenic mechanism and therapeutic target for ASD. These findings highlight the need for further investigation into the modulation of this pathway as a strategy for ASD intervention.

Breakthroughs in AI and multi-omics for cancer drug discovery: A review.

Cancer is one of the biggest medical challenges we face today. It is characterized by abnormal, uncontrolled growth of cells that can spread to different parts of the body. Cancer is extremely complex, with genetic variations and the ability to adapt and evolve. This means we must continuously pursue innovative approaches to developing new cancer drugs. While traditional drug discovery methods have led to important breakthroughs, they also have significant limitations that make it difficult to efficiently create new, cost-effective cancer therapies. Integrating computational tools into the cancer drug discovery process is a major step forward. By harnessing computing power, we can overcome some of the inherent barriers of traditional methods. This review examines the range of computational techniques now being used, such as molecular docking, QSAR models, virtual screening, and pharmacophore modeling. It looks at recent advances in areas like machine learning and molecular simulations. The review also discusses the current challenges with these technologies and envisions future directions, underscoring how transformative these computational tools can be for creating targeted, new cancer treatments.