Windows Scanning Multiomics: Integrated Metabolomics and Proteomics

Metabolomics and proteomics offer significant advantages in understanding biological mechanisms at two hierarchical levels. However, conventional single omics analysis faces challenges due to the high demand for specimens and the complexity of intrinsic associations. To obtain comprehensive and accurate system biological information, we developed a multiomics analytical method called Windows Scanning Multiomics (WSM). In this method, we performed simultaneous extraction of metabolites and proteins from the same sample, resulting in a 10% increase in the coverage of the identified biomolecules. Both metabolomics and proteomics analyses were conducted by using ultrahigh-performance liquid chromatography mass spectrometry (UPLC-MS), eliminating the need for instrument conversions. Additionally, we designed an R-based program (WSM.R) to integrate mathematical and biological correlations between metabolites and proteins into a correlation network. The network created from simultaneously extracted biomolecules was more focused and comprehensive compared to those from separate extractions. Notably, we excluded six pairs of false-positive relationships between metabolites and proteins in the network established using simultaneously extracted biomolecules. In conclusion, this study introduces a novel approach for multiomics analysis and data processing that greatly aids in bioinformation mining from multiomics results. This method is poised to play an indispensable role in systems biology research.

Systematic Multiomic Analysis of PKHD1L1 Gene Expression and Its Role as a Predicting Biomarker for Immune Cell Infiltration in Skin Cutaneous Melanoma and Lung Adenocarcinoma

The identification of genetic factors that regulate the cancer immune microenvironment is important for understanding the mechanism of tumor progression and establishing an effective treatment strategy. Polycystic kidney and hepatic disease 1-like protein 1 (PKHD1L1) is a large transmembrane protein that is highly expressed in immune cells; however, its association with tumor progression remains unclear. Here, we systematically analyzed the clinical relevance of PKHD1L1 in the tumor microenvironment in multiple cancer types using various bioinformatic tools. We found that the PKHD1L1 mRNA expression levels were significantly lower in skin cutaneous melanoma (SKCM) and lung adenocarcinoma (LUAD) than in normal tissues. The decreased expression of PKHD1L1 was significantly associated with unfavorable overall survival (OS) in SKCM and LUAD. Additionally, PKHD1L1 expression was positively correlated with the levels of infiltrating B cells, cluster of differentiation (CD)-8+ T cells, and natural killer (NK) cells, suggesting that the infiltration of immune cells could be associated with a good prognosis due to increased PKHD1L1 expression. Gene ontology (GO) analysis also revealed the relationship between PKHD1L1-co-altered genes and the activation of lymphocytes, including B and T cells. Collectively, this study shows that PKHD1L1 expression is positively correlated with a good prognosis via the induction of immune infiltration, suggesting that PKHD1L1 has potential prognostic value in SKCM and LUAD.

Multiomics Reveals the Key Microorganisms and Metabolites in the Resistance to Root Rot Disease of Paris polyphylla

Root rot of Paris polyphylla has received widespread attention due to its threat to yield and leads to serious economic losses. However, the relationship among the rhizosphere microbial community, metabolites and root rot disease remained largely unexplored. Herein, we used integrated 16S rRNA, ITS, RNA sequencing and UPLC-MS/MS to systematically investigate the differences between healthy and diseased P. polyphylla. We found that root rot reduced the microbial diversity in the diseased P. polyphylla compared with the healthy control. The relative abundance of the bacterial phylum Actinobacteria increased in the diseased rhizome of P. polyphylla. For the fungal community, root rot disease contributed to an increased relative abundance of Ascomycota and decreased Glomeromycota at the phylum level. The transcriptomic results showed that the differently expressed genes were significantly enriched in the "Biosynthesis of various alkaloids", "flavonoid biosynthesis" and "isoflavonoid biosynthesis" and "Phenylpropanoid biosynthesis" was dramatically enriched in healthy P. polyphylla compared with that in diseased P. polyphylla. Likewise, the metabolomic results showed that the biosynthesis of secondary metabolites and metabolic pathways was found to be significantly enriched by differential metabolites. Taken together, the study of combining metabolomics with microbiomes can help us enhance our understanding of the mechanisms of plant resistance to root rot disease, thereby discovering specific metabolites and microorganisms that can resist pathogen infection in P. polyphylla.

Integrated multi-omics reveals the roles of cecal microbiota and its derived bacterial consortium in promoting chicken growth

IMPORTANCE

The improvement of chicken growth performance is one of the major concerns for the poultry industry. Gut microbes are increasingly evidenced to be associated with chicken physiology and metabolism, thereby influencing chicken growth and development. Here, through integrated multi-omics analyses, we showed that chickens from the same line differing in their body weight were very different in their gut microbiota structure and host-microbiota crosstalk; microbes in high body weight (HBW) chickens contributed to chicken growth by regulating the gut function and homeostasis. We also verified that a specific bacterial consortium consisting of isolates from the HBW chickens has the potential to be used as chicken growth promoters. These findings provide new insights into the potential links between gut microbiota and chicken phenotypes, shedding light on future manipulation of chicken gut microbiota to improve chicken growth performance.

Zinc effects on bacteria: insights from Escherichia coli by multi-omics approach

IMPORTANCE

A long-term exposure of bacteria to zinc oxide and zinc oxide nanoparticles leads to major alterations in bacterial morphology and physiology. These included biochemical and physiological processes promoting the emergence of strains with multi-drug resistance and virulence traits. After the removal of zinc pressure, bacterial phenotype reversed back to the original state; however, certain changes at the genomic, transcriptomic, and proteomic level remained. Why is this important? The extensive and intensive use of supplements in animal feed effects the intestinal microbiota of livestock and this may negatively impact the health of animals and people. Therefore, it is crucial to understand and monitor the impact of feed supplements on intestinal microorganisms in order to adequately assess and prevent potential health risks.

Longitudinal multi-omics study reveals common etiology underlying association between plasma proteome and BMI trajectories in adolescent and young adult twins

BACKGROUND

The influence of genetics and environment on the association of the plasma proteome with body mass index (BMI) and changes in BMI remains underexplored, and the links to other omics in these associations remain to be investigated. We characterized protein-BMI trajectory associations in adolescents and adults and how these connect to other omics layers.

METHODS

Our study included two cohorts of longitudinally followed twins: FinnTwin12 (N = 651) and the Netherlands Twin Register (NTR) (N = 665). Follow-up comprised 4 BMI measurements over approximately 6 (NTR: 23-27 years old) to 10 years (FinnTwin12: 12-22 years old), with omics data collected at the last BMI measurement. BMI changes were calculated in latent growth curve models. Mixed-effects models were used to quantify the associations between the abundance of 439 plasma proteins with BMI at blood sampling and changes in BMI. In FinnTwin12, the sources of genetic and environmental variation underlying the protein abundances were quantified by twin models, as were the associations of proteins with BMI and BMI changes. In NTR, we investigated the association of gene expression of genes encoding proteins identified in FinnTwin12 with BMI and changes in BMI. We linked identified proteins and their coding genes to plasma metabolites and polygenic risk scores (PRS) applying mixed-effects models and correlation networks.

RESULTS

We identified 66 and 14 proteins associated with BMI at blood sampling and changes in BMI, respectively. The average heritability of these proteins was 35%. Of the 66 BMI-protein associations, 43 and 12 showed genetic and environmental correlations, respectively, including 8 proteins showing both. Similarly, we observed 7 and 3 genetic and environmental correlations between changes in BMI and protein abundance, respectively. S100A8 gene expression was associated with BMI at blood sampling, and the PRG4 and CFI genes were associated with BMI changes. Proteins showed strong connections with metabolites and PRSs, but we observed no multi-omics connections among gene expression and other omics layers.

CONCLUSIONS

Associations between the proteome and BMI trajectories are characterized by shared genetic, environmental, and metabolic etiologies. We observed few gene-protein pairs associated with BMI or changes in BMI at the proteome and transcriptome levels.

An Integrated Multi-Omics Analysis Identifying Immune Subtypes of Pancreatic Cancer

Limited studies have explored novel pancreatic cancer (PC) subtypes or prognostic biomarkers based on the altered activity of relevant signaling pathway gene sets. Here, we employed non-negative matrix factorization (NMF) to identify three immune subtypes of PC based on C7 immunologic signature gene set activity in PC and normal samples. Cluster 1, the immune-inflamed subtype, showed a higher response rate to immune checkpoint blockade (ICB) and had the lowest tumor immune dysfunction and exclusion (TIDE) scores. Cluster 2, the immune-excluded subtype, exhibited strong associations with stromal activation, characterized by elevated expression levels of transforming growth factor (TGF)-β, cell adhesion, extracellular matrix remodeling, and epithelial-to-mesenchymal transition (EMT) related genes. Cluster 3, the immune-desert subtype, displayed limited immune activity. For prognostic prediction, we developed an immune-related prognostic risk model (IRPM) based on four immune-related prognostic genes in pancreatic cancer, RHOF, CEP250, TSC1, and KIF20B. The IRPM demonstrated excellent prognostic efficacy and successful validation in an external cohort. Notably, the key gene in the prognostic model, RHOF, exerted significant influence on the proliferation, migration, and invasion of pancreatic cancer cells through in vitro experiments. Furthermore, we conducted a comprehensive analysis of somatic mutational landscapes and immune landscapes in PC patients with different IRPM risk scores. Our findings accurately stratified patients based on their immune microenvironment and predicted immunotherapy responses, offering valuable insights for clinicians in developing more targeted clinical strategies.

Single-cell multi-omics analysis of human testicular germ cell tumor reveals its molecular features and microenvironment

Seminoma is the most common malignant solid tumor in 14 to 44 year-old men. However, its molecular features and tumor microenvironment (TME) is largely unexplored. Here, we perform a series of studies via genomics profiling (single cell multi-omics and spatial transcriptomics) and functional examination using seminoma samples and a seminoma cell line. We identify key gene expression programs share between seminoma and primordial germ cells, and further characterize the functions of TFAP2C in promoting tumor invasion and migration. We also identify 15 immune cell subtypes in TME, and find that subtypes with exhaustion features were located closer to the tumor region through combined spatial transcriptome analysis. Furthermore, we identify key pathways and genes that may facilitate seminoma disseminating beyond the seminiferous tubules. These findings advance our knowledge of seminoma tumorigenesis and produce a multi-omics atlas of in situ human seminoma microenvironment, which could help discover potential therapy targets for seminoma.

Exploring cell aggregation as a defense strategy against perchlorate stress in Chlamydomonas reinhardtii through multi-omics analysis

Perchlorate (ClO4-) is a type of novel, widely distributed, and persistent inorganic pollutant. However, the impacts of perchlorate on freshwater algae remain unclear. In this study, the response and defense mechanisms of microalgae (Chlamydomonas reinhardtii) under perchlorate stress were investigated by integrating physiological and biochemical monitoring, transcriptomics, and metabolomics. Weighted gene co-expression network analysis (WGCNA) of transcriptome data was used to analyze the relationship between genes and phenotype and screen the key pathways. C. reinhardtii exhibited aggregate behavior when exposed to 100- and 200-mM perchlorate but was restored to its unicellular lifestyle when transferred to fresh medium. WGCNA results found that the "carbohydrate metabolism" and "lipid metabolism" pathways were closely related to cell aggregation phenotype. The differential expression genes (DEGs) and differentially accumulated metabolites (DAMs) of these pathways were upregulated, indicating that the lipid and carbohydrate metabolisms were enhanced in aggregated cells. Additionally, most genes and metabolites related to phytohormone abscisic acid (ABA) biosynthesis and the mitogen-activated protein kinase (MAPK) signaling pathway were significantly upregulated, indicating their crucial roles in the signal transmission of aggregated cells. Meanwhile, in aggregated cells, extracellular polymeric substances (EPS) and lipid contents increased, photosynthesis activity decreased, and the antioxidant system was activated. These characteristics contributed to C. reinhardtii's improved resistance to perchlorate stress. Above results demonstrated that cell aggregation behavior was the principal defense strategy of C. reinhardtii against perchlorate. Overall, this study sheds new light on the impact mechanisms of perchlorate to aquatic microalgae and provides multi-omics insights into the research of multicellular-like aggregation as an adaptation strategy to abiotic stress. These results are beneficial for assessing the risk of perchlorate in aquatic environments.

Multi-omics computational analysis unveils the involvement of AP-1 and CTCF in hysteresis of chromatin states during macrophage polarization

Macrophages display extreme plasticity, and the mechanisms and applications of polarization and de-/repolarization of macrophages have been extensively investigated. However, the regulation of macrophage hysteresis after de-/repolarization remains unclear. In this study, by using a large-scale computational analysis of macrophage multi-omics data, we report a list of hysteresis genes that maintain their expression patterns after polarization and de-/repolarization. While the polarization in M1 macrophages leads to a higher level of hysteresis in genes associated with cell cycle progression, cell migration, and enhancement of the immune response, we found weak levels of hysteresis after M2 polarization. During the polarization process from M0 to M1 and back to M0, the factors IRFs/STAT, AP-1, and CTCF regulate hysteresis by altering their binding sites to the chromatin. Overall, our results show that a history of polarization can lead to hysteresis in gene expression and chromatin accessibility over a given period. This study contributes to the understanding of de-/repolarization memory in macrophages.

A combined analysis of multi-omics data reveals the prognostic values and immunotherapy response of LAG3 in human cancers

Lymphocyte-activation gene 3 (LAG3) is a highly anticipated immune checkpoint in the context of cancer, exerting regulatory control over immune cell proliferation and function to reinforce the advancement of cancers. However, the comprehensive functional analysis of LAG3 across various cancer types remains undisclosed; thus, this study aims to investigate the pan-cancer expression profile of LAG3. We have investigated the expression profile, prognostic significance, and genetic alterations of LAG3 in various cancers while elucidating its characteristic in immune response regulation. Our findings demonstrated that elevated LAG3 expression is significantly associated with favorable prognosis in patients with cutaneous melanoma (SKCM), and it may be a potential biomarker for SKCM. Furthermore, multiple immune algorithms have highlighted the important regulatory role of LAG3 for the tumor-infiltrating immune cells including CD8 + T cells, B cells, dendritic cells (DCs), macrophages, and natural killer (NK) cells. We also examined the distribution of LAG3 at the single-cell level and explored its functional significance. A comprehensive and systematic analysis of LAG3 would facilitate a comprehensive evaluation of LAG3 in cancer biology and provide valuable insights for cancer management.

Integration of multiomics analysis to reveal the major pathways of vitamin A deficiency aggravates acute respiratory distress syndrome in neonatal rats

Acute respiratory distress syndrome (ARDS) is a major disease that threatens the life and health of neonates. Vitamin A (VA) can participate in early fetal lung development and affect lung immune function. Researches revealed that the serum VA level in premature infants with ARDS was lower than that in premature infants without ARDS of the same gestational age, and premature infants with VA deficiency (VAD) were more likely to develop ARDS. Moreover, the VA levels can be used as a predictor of the development and severity of neonatal ARDS. However, the critical question here is; Does ARDS develop due to VAD in these systemic diseases? Or does ARDS develop because these diseases cause VAD? We hypothesize that VAD may aggravate neonatal ARDS by affecting immunity, metabolism, barriers and other pathways. In this article, we used multiomics analysis to find that VAD may aggravate ARDS mainly through the Fc epsilon RI signaling pathway, the HIF-1 signaling pathway, glutathione metabolism, and valine, leucine and isoleucine degradation signaling pathways, which may provide the molecular pathogenic mechanism behind the pathology of VAD-aggravated ARDS and can also provide potential molecular targets for subsequent research on ARDS.

The effect of fructose exposure on amino acid metabolism among Chinese community residents and its possible multi-omics mechanisms

The consumption of fructose has increased dramaticly during the last few decades, inducing a great increase in the risk of intrahepatic lipid accumulation, hypertriglyceridemia, hyperuricemia and cancer. However, the underlying mechanism has not yet been fully elucidated. Amino acid metabolism may play an important role in the process of the diseases caused by fructose, but there is still a lack of corresponding evidence. In present study, we provide an evidence of how fructose affects amino acids metabolism in 1895 ordinary residents in Chinese community using UPLC-QqQMS based amino acid targeted metabolomics and the underlying mechanism of fructose exposure how interferes with amino acid metabolism related genes and acetylated modification of proteome in the liver of rats model. We found people with high fructose exposure had higher levels of Asa, EtN, Asp, and Glu, and lower levels of 1MHis, PEtN, Arg, Gln, GABA, Aad, Hyl and Cys. The further mechanism study displayed amino acid metabolic genes of Aspa, Cndp1, Dbt, Dmgdh, and toxic metabolites such as N-acetylethanolamines accumulation, interference of urea cycle, as well as acetylated modification of key enzymes in glutamine metabolic network and glutamine derived NEAAs synthesis pathway in liver may play important roles in fructose caused reprogramming in amino acid metabolism. This research provides novel insights of the mechanism of amino acid metabolic disorder caused by fructose and supplies new targets for clinical therapy.

Integrated omics landscape of hepatocellular carcinoma suggests proteomic subtypes for precision therapy

Patients with hepatocellular carcinoma (HCC) at the same clinical stage can have extremely different prognoses, and molecular subtyping provides an opportunity for individualized precision treatment. In this study, genomic, transcriptomic, proteomic, and phosphoproteomic profiling of primary tumor tissues and paired para-tumor tissues from HCC patients (N = 160) are integrated. Proteomic profiling identifies three HCC subtypes with different clinical prognosis, which are validated in three publicly available external validation sets. A simplified panel of nine proteins associated with metabolic reprogramming is further identified as a potential subtype-specific biomarker for clinical application. Multi-omics analysis further reveals that three proteomic subtypes have significant differences in genetic alterations, microenvironment dysregulation, kinase-substrate regulatory networks, and therapeutic responses. Patient-derived cell-based drug tests (N = 26) show personalized responses for sorafenib in three proteomic subtypes, which can be predicted by a machine-learning response prediction model. Overall, this study provides a valuable resource for better understanding of HCC subtypes for precision clinical therapy.

Identification of Incomplete Annotations of Biosynthesis Pathways in Rhodophytes Using a Multi-Omics Approach

Rhodophytes (red algae) are an important source of natural products and are, therefore, a current research focus in terms of metabolite production. The recent increase in publicly available Rhodophyte whole genome and transcriptome assemblies provides the resources needed for in silico metabolic pathway analysis. Thus, this study aimed to create a Rhodophyte multi-omics resource, utilising both genomes and transcriptome assemblies with functional annotations to explore Rhodophyte metabolism. The genomes and transcriptomes of 72 Rhodophytes were functionally annotated and integrated with metabolic reconstruction and phylogenetic inference, orthology prediction, and gene duplication analysis to analyse their metabolic pathways. This resource was utilised via two main investigations: the identification of bioactive sterol biosynthesis pathways and the evolutionary analysis of gene duplications for known enzymes. We report that sterol pathways, including campesterol, β-sitosterol, ergocalciferol and cholesterol biosynthesis pathways, all showed incomplete annotated pathways across all Rhodophytes despite prior in vivo studies showing otherwise. Gene duplication analysis revealed high rates of duplication of halide-associated haem peroxidases in Florideophyte algae, which are involved in the biosynthesis of drug-related halogenated secondary metabolites. In summary, this research revealed trends in Rhodophyte metabolic pathways that have been under-researched and require further functional analysis. Furthermore, the high duplication of haem peroxidases and other peroxidase enzymes offers insight into the potential drug development of Rhodophyte halogenated secondary metabolites.

Multiomics Sequencing and AlphaFold2 Analysis of the Stereoselective Behavior of Mefentrifluconazole for Bioactivity Improvement and Risk Reduction

As the first isopropanol chiral triazole fungicide, mefentrifluconazole has broad prospects for application. In this study, the stereoselective stability, bioactivity, fate, and biotoxicity were systematically investigated. Our results indicated that the stability of mefentrifluconazole enantiomers differed between environmental media, and they were stable in water and sediment in the dark. The bactericidal activity of R-mefentrifluconazole against the four target pathogens was 4.6-43 times higher than that of S-mefentrifluconazole. In the water-sediment system, S-mefentrifluconazole dissipated faster than R-mefentrifluconazole in water; however, its accumulation capacity was higher than that of R-mefentrifluconazole in sediment and zebrafish. S-Mefentrifluconazole induced more differentially expressed genes (DEGs) and differentially expressed proteins (DEPs) in zebrafish than did R-mefentrifluconazole. Multiomics sequencing results showed that S-mefentrifluconazole enhanced the antioxidant, detoxification, immune, and metabolic functions of zebrafish by interacting with related proteins. Based on AlphaFold2 modeling and molecular docking, mefentrifluconazole enantiomers had different binding modes with key target proteins in pathogens and zebrafish, which may be the main reason for the stereoselective differences in bioactivity and biotoxicity. Based on its excellent bioactivity and low biotoxicity, the R-enantiomer can be developed to improve the bioactivity and reduce the risk of mefentrifluconazole.

Multiomics characterization of fatty acid metabolism for the clinical management of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a prevalent malignancy and there is a lack of effective biomarkers for HCC diagnosis. Living organisms are complex, and different omics molecules interact with each other to implement various biological functions. Genomics and metabolomics, which are the top and bottom of systems biology, play an important role in HCC clinical management. Fatty acid metabolism is associated with malignancy, prognosis, and immune phenotype in cancer, which is a potential hallmark in malignant tumors. In this study, the genes and metabolites related to fatty acid metabolism were thoroughly investigated by a dynamic network construction algorithm named EWS-DDA for the early diagnosis and prognosis of HCC. Three gene ratios and eight metabolite ratios were identified by EWS-DDA as potential biomarkers for HCC clinical management. Further analysis using biological analysis, statistical analysis and document validation in the discovery and validation sets suggested that the selected potential biomarkers had great clinical prognostic value and helped to achieve effective early diagnosis of HCC. Experimental results suggested that in-depth evaluation of fatty acid metabolism from different omics viewpoints can facilitate the further understanding of pathological alterations associated with HCC characteristics, improving the performance of early diagnosis and clinical prognosis.

Potent latency reversal by Tat RNA-containing nanoparticle enables multi-omic analysis of the HIV-1 reservoir

The development of latency reversing agents that potently reactivate HIV without inducing global T cell activation would benefit the field of HIV reservoir research and could pave the way to a functional cure. Here, we explore the reactivation capacity of a lipid nanoparticle containing Tat mRNA (Tat-LNP) in CD4 T cells from people living with HIV undergoing antiretroviral therapy (ART). When combined with panobinostat, Tat-LNP induces latency reversal in a significantly higher proportion of latently infected cells compared to PMA/ionomycin (≈ 4-fold higher). We demonstrate that Tat-LNP does not alter the transcriptome of CD4 T cells, enabling the characterization of latently infected cells in their near-native state. Upon latency reversal, we identify transcriptomic differences between infected cells carrying an inducible provirus and non-infected cells (e.g. LINC02964, GZMA, CCL5). We confirm the transcriptomic differences at the protein level and provide evidence that the long non-coding RNA LINC02964 plays a role in active HIV infection. Furthermore, p24+ cells exhibit heightened PI3K/Akt signaling, along with downregulation of protein translation, suggesting that HIV-infected cells display distinct signatures facilitating their long-term persistence. Tat-LNP represents a valuable research tool for in vitro reservoir studies as it greatly facilitates the in-depth characterization of HIV reservoir cells' transcriptome and proteome profiles.

TCGAplot: an R package for integrative pan-cancer analysis and visualization of TCGA multi-omics data

BACKGROUND

Pan-cancer analysis examines both the commonalities and heterogeneity among genomic and cellular alterations across numerous types of tumors. Pan-cancer analysis of gene expression, tumor mutational burden (TMB), microsatellite instability (MSI), and tumor immune microenvironment (TIME), and methylation becomes available based on the multi-omics data from The Cancer Genome Atlas Program (TCGA). Some online tools provide analysis of gene and protein expression, mutation, methylation, and survival for TCGA data. However, these online tools were either Uni-functional or were not able to perform analysis of user-defined functions. Therefore, we created the TCGAplot R package to facilitate perform pan-cancer analysis and visualization of the built-in multi-omic TCGA data.

RESULTS

TCGAplot provides several functions to perform pan-cancer paired/unpaired differential gene expression analysis, pan-cancer correlation analysis between gene expression and TMB, MSI, TIME, and promoter methylation. Functions for visualization include paired/unpaired boxplot, survival plot, ROC curve, heatmap, scatter, radar chart, and forest plot. Moreover, gene set based pan-cancer and tumor specific analyses were also available. Finally, all these built-in multi-omic data could be extracted for implementation for user-defined functions, making the pan-cancer analysis much more convenient.\ CONCLUSIONS: We developed an R-package for integrative pan-cancer analysis and visualization of TCGA multi-omics data. The source code and pre-built package are available at GitHub ( https://github.com/tjhwangxiong/TCGAplot ).

Comprehensive multi-omics analysis reveals the core role of glycerophospholipid metabolism in rheumatoid arthritis development

OBJECTIVES

Rheumatoid arthritis (RA) is a chronic autoimmune disease with complex causes and recurrent attacks that can easily develop into chronic arthritis and eventually lead to joint deformity. Our study aims to elucidate potential mechanism among control, new-onset RA (NORA) and chronic RA (CRA) with multi-omics analysis.

METHODS

A total of 113 RA patients and 75 controls were included in our study. Plasma and stool samples were obtained for 16S rRNA sequencing, internally transcribed spacer (ITS) sequencing and metabolomics analysis. And PBMCs were obtained for RNA sequencing. We used three models, logistic regression, least absolute shrinkage and selection operator (LASSO), and random forest, respectively, to distinguish NORA from CRA, and finally we validated model performance using an external cohort of 26 subjects.

RESULTS

Our results demonstrated intestinal flora disturbance in RA development, with significantly increased abundance of Escherichia-Shigella and Proteobacteria in NORA. We also found that the diversity was significantly reduced in CRA compared to NORA through fungi analysis. Moreover, we identified 29 differential metabolites between NORA and CRA. Pathway enrichment analysis revealed significant dysregulation of glycerophospholipid metabolism and phenylalanine metabolism pathways in RA patients. Next, we identified 40 differentially expressed genes between NORA and CRA, which acetylcholinesterase (ACHE) was the core gene and significantly enriched in glycerophospholipid metabolism pathway. Correlation analysis showed a strong negatively correlation between glycerophosphocholine and inflammatory characteristics. Additionally, we applied three approaches to develop disease classifier models that were based on plasma metabolites and gut microbiota, which effectively distinguished between new-onset and chronic RA patients in both discovery cohort and external validation cohort.

CONCLUSIONS

These findings revealed that glycerophospholipid metabolism plays a crucial role in the development and progression of RA, providing new ideas for early clinical diagnosis and optimizing treatment strategies.

Multi-omics integrative modelling for stereotactic body radiotherapy in early-stage non-small cell lung cancer: clinical trial protocol of the MONDRIAN study

BACKGROUND

Currently, main treatment strategies for early-stage non-small cell lung cancer (ES-NSCLC) disease are surgery or stereotactic body radiation therapy (SBRT), with successful local control rates for both approaches. However, regional and distant failure remain critical in SBRT, and it is paramount to identify predictive factors of response to identify high-risk patients who may benefit from more aggressive approaches. The main endpoint of the MONDRIAN trial is to identify multi-omic biomarkers of SBRT response integrating information from the individual fields of radiomics, genomics and proteomics.

METHODS

MONDRIAN is a prospective observational explorative cohort clinical study, with a data-driven, bottom-up approach. It is expected to enroll 100 ES-NSCLC SBRT candidates treated at an Italian tertiary cancer center with well-recognized expertise in SBRT and thoracic surgery. To identify predictors specific to SBRT, MONDRIAN will include data from 200 patients treated with surgery, in a 1:2 ratio, with comparable clinical characteristics. The project will have an overall expected duration of 60 months, and will be structured into five main tasks: (i) Clinical Study; (ii) Imaging/ Radiomic Study, (iii) Gene Expression Study, (iv) Proteomic Study, (v) Integrative Model Building.

DISCUSSION

Thanks to its multi-disciplinary nature, MONDRIAN is expected to provide the opportunity to characterize ES-NSCLC from a multi-omic perspective, with a Radiation Oncology-oriented focus. Other than contributing to a mechanistic understanding of the disease, the study will assist the identification of high-risk patients in a largely unexplored clinical setting. Ultimately, this would orient further clinical research efforts on the combination of SBRT and systemic treatments, such as immunotherapy, with the perspective of improving oncological outcomes in this subset of patients.

TRIAL REGISTRATION

The study was prospectively registered at clinicaltrials.gov (NCT05974475).

Identification of the role of MCM6 in bladder cancer prognosis, immunotherapy response, and in vitro experimental investigation using multi-omics analysis

BACKGROUND

The tumor-promoting effects of MCM6 in numerous tumors have been widely revealed, yet its specific role in bladder cancer (BLCA) is still elusive. The objective of this research was to explore the underlying impact of MCM6 on BLCA.

METHODS

Integrating transcriptomic and proteomic data, MCM6 was identified to be strongly correlated with BLCA through weighted gene co-expression network analysis(WGCNA) and venn analyses. Then, the clinical value of MCM6 was validated with public database data. The different molecular/immune characteristics and the benefit of immunotherapy were also found in MCM6-defined subgroups. Additionally, single-cell RNA sequencing (scRNA-seq) data was choose for quantify MCM6 expression in the distinct BLCA cell types. The biological role of MCM6 were evaluated via in vitro functional experiments.

RESULTS

It was testified that the MCM6 could distinguish patients outcome in TCGA and GEO cohorts. Moreover, compared with the MCM6 low-expression group, the MCM6 high-expression group was related to more tumor-promoting related pathways, aggressive phenotypes, and benefit from immunotherapy. Analysis of scRNA-seq data resulted in MCM6 was mainly expressed in BLCA epithelial cells and the proportion of MCM6-expressing tumor epithelial cells is higher than the normal epithelial cells. Moreover, vitro experiments demonstrated that MCM6 knockdown repressed proliferation, cell cycle, migration, and invasion of BLCA cells.

CONCLUSION

This research indicated MCM6 is a promising marker for both prognosis and immunotherapy benefit and could promote the cells proliferation, invasion and migration in BLCA.

From bench to bedside via bytes: Multi-omic immunoprofiling and integration using machine learning and network approaches

A significant surge in research endeavors leverages the vast potential of high-throughput omic technology platforms for broad profiling of biological responses to vaccines and cutting-edge immunotherapies and stem-cell therapies under development. These profiles capture different aspects of core regulatory and functional processes at different scales of resolution from molecular and cellular to organismal. Systems approaches capture the complex and intricate interplay between these layers and scales. Here, we summarize experimental data modalities, for characterizing the genome, epigenome, transcriptome, proteome, metabolome, and antibody-ome, that enable us to generate large-scale immune profiles. We also discuss machine learning and network approaches that are commonly used to analyze and integrate these modalities, to gain insights into correlates and mechanisms of natural and vaccine-mediated immunity as well as therapy-induced immunomodulation.

Multiomics reveals the formation pathway of volatile compounds in preserved egg yolk (PEY) induced by NaCl: Based on the model of PEY and salted egg yolk (SEY) treated with/without NaCl

The models of preserved egg yolk (PEY) and salted egg yolk both treated with or without NaCl were performed to explore the effect of NaCl on the characteristic volatile compounds (VOCs) in PEY. 1-hexanol, 2-heptanone, isoamyl acetate, etc., compounds were confirmed as the characteristic VOCs in PEY mainly induced by NaCl and the formation of 1-octanol, 2-pentylfuran, ammonia, etc., characteristic VOCs induced by NaCl may depend on the combined effect of Cu2+ and OH-. Among them, 1-hexanol and 2-heptanone were formed from linoleic acid in PS(18:0_18:2) and oleic acid in PG(22:6_18:1), respectively, through multi-omics and correlation analysis. Meanwhile, 1-octanol may originated from β-oxidation of oleic acid in PS(18:1); 2-pentylfuran and ammonia maybe derived from the derivative of aspartate and the degradation of l-methionine, respectively. Moreover, this study provides a new insight to parse the influence of NaCl with/without other exogenous factors on the formation of VOCs in food products.

Multiomic approach and Mendelian randomization analysis identify causal associations between blood biomarkers and subcortical brain structure volumes

Alterations in subcortical brain structure volumes have been found to be associated with several neurodegenerative and psychiatric disorders. At the same time, genome-wide association studies (GWAS) have identified numerous common variants associated with brain structure. In this study, we integrate these findings, aiming to identify proteins, metabolites, or microbes that have a putative causal association with subcortical brain structure volumes via a two-sample Mendelian randomization approach. This method uses genetic variants as instrument variables to identify potentially causal associations between an exposure and an outcome. The exposure data that we analyzed comprised genetic associations for 2994 plasma proteins, 237 metabolites, and 103 microbial genera. The outcome data included GWAS data for seven subcortical brain structure volumes including accumbens, amygdala, caudate, hippocampus, pallidum, putamen, and thalamus. Eleven proteins and six metabolites were found to have a significant association with subcortical structure volumes, with nine proteins and five metabolites replicated using independent exposure data. We found causal associations between accumbens volume and plasma protease c1 inhibitor as well as strong association between putamen volume and Agouti signaling protein. Among metabolites, urate had the strongest association with thalamic volume. No significant associations were detected between the microbial genera and subcortical brain structure volumes. We also observed significant enrichment for biological processes such as proteolysis, regulation of the endoplasmic reticulum apoptotic signaling pathway, and negative regulation of DNA binding. Our findings provide insights to the mechanisms through which brain volumes may be affected in the pathogenesis of neurodevelopmental and psychiatric disorders and point to potential treatment targets for disorders that are associated with subcortical brain structure volumes.