Integrative Multi-omics Analysis Research Articles

DRPPM-EASY: A Web-Based Framework for Integrative Analysis of Multi-Omics Cancer Datasets.

Simple SummaryWith the influx of multi-omics profiling, effective integration of these data remains the bottleneck for omics-driven discovery. Thus, we developed DRPPM-EASY, an R Shiny framework for integrative multi-omics analysis of cancer datasets. Our tool enables the exploration of multi-omics data by providing a simple user interface that minimizes the need for computational experience. Furthermore, the interface can be deployed locally or on a webserver to facilitate scientific collaboration and discovery.High-throughput transcriptomic and proteomic analyses are now routinely applied to study cancer biology. However, complex omics integration remains challenging and often time-consuming. Here, we developed DRPPM-EASY, an R Shiny framework for integrative multi-omics analysis. We applied our application to analyze RNA-seq data generated from a USP7 knockdown in T-cell acute lymphoblastic leukemia (T-ALL) cell line, which identified upregulated expression of a TAL1-associated proliferative signature in T-cell acute lymphoblastic leukemia cell lines. Next, we performed proteomic profiling of the USP7 knockdown samples. Through DRPPM-EASY-Integration, we performed a concurrent analysis of the transcriptome and proteome and identified consistent disruption of the protein degradation machinery and spliceosome in samples with USP7 silencing. To further illustrate the utility of the R Shiny framework, we developed DRPPM-EASY-CCLE, a Shiny extension preloaded with the Cancer Cell Line Encyclopedia (CCLE) data. The DRPPM-EASY-CCLE app facilitates the sample querying and phenotype assignment by incorporating meta information, such as genetic mutation, metastasis status, sex, and collection site. As proof of concept, we verified the expression of TP53 associated DNA damage signature in TP53 mutated ovary cancer cells. Altogether, our open-source application provides an easy-to-use framework for omics exploration and discovery.

SETD2 loss perturbs the kidney cancer epigenetic landscape to promote metastasis and engenders actionable dependencies on histone chaperone complexes.

SETD2 is a H3K36 trimethyltransferase that is mutated with high prevalence (13%) in clear cell renal cell carcinoma (ccRCC). Genomic profiling of primary ccRCC tumors reveals a positive correlation between SETD2 mutations and metastasis. However, whether and how SETD2 loss promotes metastasis remains unclear. In this study, we used SETD2-mutant metastatic ccRCC patient-derived cell line and xenograft models and showed that H3K36me3 restoration greatly reduced distant metastases of ccRCC in mice in an MMP1-dependent manner. An integrated multi-omics analysis using ATAC-seq, ChIP-seq, and RNA-seq established a tumor suppressor model in which loss of SETD2-mediated H3K36me3 activates enhancers to drive oncogenic transcriptional output through regulation of chromatin accessibility. Furthermore, we uncovered mechanism-based therapeutic strategies for SETD2-deficient cancer through the targeting of specific histone chaperone complexes including ASF1A/B and SPT16. Overall, SETD2 loss creates a permissive epigenetic landscape for cooperating oncogenic drivers to amplify transcriptional output, providing unique therapeutic opportunities.

An integrative multi-omics analysis based on liquid\u2013liquid phase separation delineates distinct subtypes of lower-grade glioma and identifies a prognostic signature

BackgroundEmerging evidences have indicated that the aberrant liquid–liquid phase separation (LLPS) leads to the dysfunction of biomolecular condensates, thereby contributing to the tumorigenesis and progression. Nevertheless, it remains unclear whether or how the LLPS of specific molecules affects the prognosis and tumor immune microenvironment (TIME) of patients with lower-grade glioma (LGG).MethodsWe integrated the transcriptome information of 3585 LLPS-related genes to comprehensively evaluate the LLPS patterns of 423 patients with LGG in The Cancer Genome Atlas (TCGA) cohort. Then, we systematically demonstrated the differences among four LLPS subtypes based on multi-omics analyses. In addition, we constructed the LLPS-related prognostic risk score (LPRS) for individualized integrative assessment.ResultsBased on the expression profiles of 85 scaffolds, 355 regulators, and 3145 clients in LGG, we identified four LLPS subtypes, namely LS1, LS2, LS3 and LS4.We confirmed that there were significant differences in prognosis, clinicopathological features, cancer hallmarks, genomic alterations, TIME patterns and immunotherapeutic responses among four LLPS subtypes. In addition, a prognostic signature called LPRS was constructed for individualized integrative assessment. LPRS exhibited a robust predictive capacity for prognosis of LGG patients in multiple cohorts. Moreover, LPRS was found to be correlated with clinicopathological features, cancer hallmarks, genomic alterations and TIME patterns of LGG patients. The predictive power of LPRS in response to immune checkpoint inhibitor (ICI) therapy was also prominent.ConclusionsThis study provided a novel classification of LGG patients based on LLPS. The constructed LPRS might facilitate individualized prognosis prediction and better immunotherapy options for LGG patients.

Integrative Multi-Omics Analysis Reveals Candidate Biomarkers for Oral Squamous Cell Carcinoma.

Oral squamous cell carcinoma (OSCC) is one of the most common types of cancer worldwide. Due to the lack of early detection and treatment, the survival rate of OSCC remains poor and the incidence of OSCC has not decreased during the past decades. To explore potential biomarkers and therapeutic targets for OSCC, we analyzed differentially expressed genes (DEGs) associated with OSCC using RNA sequencing technology. Methylation−regulated and differentially expressed genes (MeDEGs) of OSCC were further identified via an integrative approach by examining publicly available methylomic datasets together with our transcriptomic data. Protein−protein interaction (PPI) networks of MeDEGs were constructed and highly connected hub MeDEGs were identified from these PPI networks. Subsequently, expression and survival analyses of hub genes were performed using The Cancer Genome Atlas (TCGA) database and the Gene Expression Profiling Interactive Analysis (GEPIA) online tool. A total of 56 upregulated MeDEGs and 170 downregulated MeDEGs were identified in OSCC. Eleven hub genes with high degree of connectivity were picked out from the PPI networks constructed by those MeDEGs. Among them, the expression level of four hub genes (CTLA4, CDSN, ACTN2, and MYH11) were found to be significantly changed in the head and neck squamous carcinoma (HNSC) patients. Three hypomethylated hub genes (CTLA4, GPR29, and TNFSF11) and one hypermethylated hub gene (ISL1) were found to be significantly associated with overall survival (OS) of HNSC patients. Therefore, these hub genes may serve as potential DNA methylation biomarkers and therapeutic targets of OSCC.

Multi-Omics Integration and Network Analysis Reveal Potential Hub Genes and Genetic Mechanisms Regulating Bovine Mastitis

Mastitis, inflammation of the mammary gland, is the most prevalent disease in dairy cattle that has a potential impact on profitability and animal welfare. Specifically designed multi-omics studies can be used to prioritize candidate genes and identify biomarkers and the molecular mechanisms underlying mastitis in dairy cattle. Hence, the present study aimed to explore the genetic basis of bovine mastitis by integrating microarray and RNA-Seq data containing healthy and mastitic samples in comparative transcriptome analysis with the results of published genome-wide association studies (GWAS) using a literature mining approach. The integration of different information sources resulted in the identification of 33 common and relevant genes associated with bovine mastitis. Among these, seven genes—CXCR1, HCK, IL1RN, MMP9, S100A9, GRO1, and SOCS3—were identified as the hub genes (highly connected genes) for mastitis susceptibility and resistance, and were subjected to protein-protein interaction (PPI) network and gene regulatory network construction. Gene ontology annotation and enrichment analysis revealed 23, 7, and 4 GO terms related to mastitis in the biological process, molecular function, and cellular component categories, respectively. Moreover, the main metabolic-signalling pathways responsible for the regulation of immune or inflammatory responses were significantly enriched in cytokine–cytokine-receptor interaction, the IL-17 signaling pathway, viral protein interaction with cytokines and cytokine receptors, and the chemokine signaling pathway. Consequently, the identification of these genes, pathways, and their respective functions could contribute to a better understanding of the genetics and mechanisms regulating mastitis and can be considered a starting point for future studies on bovine mastitis.

Grafting with rootstocks promotes phenolic compound accumulation in grape berry skin during development based on integrative multi-omics analysis.

In viticulture, grafting has been practiced widely and influences grape development as well as berry and wine quality. However, there is limited understanding of the effects of rootstocks on grape phenolic compounds, which are located primarily in the berry skin and contribute to certain sensory attributes of wine. In this study, scion–rootstock interactions were investigated at the green-berry stage and the veraison stage when grapevines were hetero-grafted with three commonly used rootstock genotypes (5BB, 101-14MG, and SO4). Physiological investigations showed that hetero-grafts, especially CS/5BB, contained higher concentrations of total proanthocyanidins (PAs) and various PA components in berry skins compared with the auto-grafted grapevines. Further metabolomics analysis identified 105 differentially accumulated flavonoid compounds, the majority of which, including anthocyanins, PAs, and flavonols, were significantly increased in the berry skins of hetero-grafted grapevines compared with auto-grafted controls. In addition, transcriptomic analysis of the same samples identified several thousand differentially expressed genes between hetero-grafted and auto-grafted vines. The three rootstocks not only increased the transcript levels of stilbene, anthocyanin, PA, and flavonol synthesis genes but also affected the expression of numerous transcription factor genes. Taken together, our results suggest that hetero-grafting can promote phenolic compound accumulation in grape berry skin during development. These findings provide new insights for improving the application value of grafting by enhancing the accumulation of nutritious phenolic components in grape.

An Integrative Multi-Omics Analysis of The Molecular Links between Aging and Aggressiveness in Thyroid Cancers.

Aging modifies risk in all cancers, but age is used as a clinical staging criterion uniquely in thyroid cancer (TC). The molecular drivers of age-dependent TC onset and aggressiveness remain poorly understood. We applied an integrative, multi-omics data analysis approach to characterize these signatures. Our analysis reveals that aging, independent of BRAFV600E mutational status, drives a significant accumulation of aggressiveness-related markers and poorer survival outcomes, most noticeably at age 55 and over. We identified that chromosomal alterations in loci 1p/1q as aging-associated drivers of aggressiveness, and that depleted infiltration with tumor surveillant CD8+T and follicular helper T cells, dysregulation of proteostasis- and senescence-related processes, and ERK1/2 signaling cascade are key features of the aging thyroid and TC onset/progression and aggressiveness in aging patients but not in young individuals. A panel of 23 genes, including those related to cell division such as CENPF, ERCC6L, and the kinases MELK and NEK2, were identified and rigorously characterized as aging-dependent and aggressiveness-specific markers. These genes effectively stratified patients into aggressive clusters with distinct phenotypic enrichment and genomic/transcriptomic profiles. This panel also showed excellent performance in predicting metastasis stage, BRAFV600E, TERT promoter mutation, and survival outcomes and was superior to the American Thyroid Association (ATA) methodology in predicting aggressiveness risk. Our analysis established clinically relevant biomarkers for TC aggressiveness factoring in aging as an important component.

MLSP: A bioinformatics tool for predicting molecular subtypes and prognosis in patients with breast cancer

The molecular landscape in breast cancer is characterized by large biological heterogeneity and variable clinical outcomes. Here, we performed an integrative multi-omics analysis of patients diagnosed with breast cancer. Using transcriptomic analysis, we identified three subtypes (cluster A, cluster B and cluster C) of breast cancer with distinct prognosis, clinical features, and genomic alterations: Cluster A was associated with higher genomic instability, immune suppression and worst prognosis outcome; cluster B was associated with high activation of immune-pathway, increased mutations and middle prognosis outcome; cluster C was linked to Luminal A subtype patients, moderate immune cell infiltration and best prognosis outcome. Combination of the three newly identified clusters with PAM50 subtypes, we proposed potential new precision strategies for 15 subtypes using L1000 database. Then, we developed a robust gene pair (RGP) score for prognosis outcome prediction of patients with breast cancer. The RGP score is based on a novel gene-pairing approach to eliminate batch effects caused by differences in heterogeneous patient cohorts and transcriptomic data distributions, and it was validated in ten cohorts of patients with breast cancer. Finally, we developed a user-friendly web-tool (https://sujiezhulab.shinyapps.io/BRCA/) to predict subtype, treatment strategies and prognosis states for patients with breast cancer.

Integrative multiomics analysis identifies a metastasis-related gene signature and the potential oncogenic role of EZR in breast cancer.

Distant metastasis is a major cause of increased mortality in breast cancer patients, but the mechanisms underlying breast cancer metastasis remain poorly understood. In this study, we aimed to identify a metastasis-related gene (MRG) signature for predicting progression in breast cancer. By screening using three regression analysis methods, a 9-gene signature (NOTCH1, PTP4A3, MMP13, MACC1, EZR, NEDD9, PIK3CA, F2RL1 and CCR7) was constructed based on an MRG set in the BRCA cohort from TCGA. This signature exhibited strong robustness, and its generalizability was verified in the Metabric and GEO cohorts. Of the nine MRGs, EZR is an oncogenic gene with a well-documented role in cell adhesion and cell migration, but it has rarely been investigated in breast cancer. Based on a search of different databases, EZR was found to be significantly more highly expressed in both breast cancer cells and breast cancer tissue. EZR knockdown significantly inhibited cell proliferation, invasion, chemoresistance and EMT in breast cancer. Mechanistically, RhoA activation assays confirmed that EZR knockdown inhibited the activity of RhoA, Rac1 and Cdc42. In summary, we identified a nine-MRG signature that can be used as an efficient prognostic indicator for breast cancer patients, and owing to its involvement in regulating breast cancer metastasis, EZR might serve as a therapeutic target.

A multi-omics deep learning model for hypoxia phenotype to predict tumor aggressiveness and prognosis in uveal melanoma for rationalized hypoxia-targeted therapy

Uveal melanoma (UM) represents the most common primary intraocular malignancy in adults and is characterized by aggressive behaviors and a lack of targeted therapies. Hypoxia-targeted therapy has become a promising new therapeutic strategy in tumors. Therefore, a better understanding of the tumor hypoxia microenvironment iscritical to improve the treatment efficacy of UM. In this study, we conducted an extensive multi-omics analysis to explore the heterogeneity and prognostic significance of the hypoxia microenvironment. We found that UM revealed the most significant degree of intertumoral heterogeneity in hypoxia by quantifying tumor hypoxia compared with other solid tumor types. Then we systematically correlated the hypoxia phenotypes with clinicopathological features and found that hypoxic UM tumors were associated with an increased risk of metastasis, more aggressive phenotypes, and unfavorable clinical outcomes. Integrative multi-omics analyses identified multidimensional molecular alterations related to hypoxia phenotypes, including elevated genome instability, co-occurring of 8q arm gains and loss of chromosome 3, and BAP1 mutations. Furthermore, hypoxic UM tumors could be characterized by increased CD8+ T cell infiltration and decreased naïve B cell and dysregulated metabolic pathways. Finally, we introduced DNN2HM, an interpretable deep neural network model to decode hypoxia phenotypes from multi-omics data. We showed that the DNN2HM improves hypoxia phenotype prediction and robustly predicts tumor aggressiveness and prognosis in different multi-center datasets. In conclusion, our study provides novel insight into UM tumor microenvironment, which may have clinical implications for future rationalized hypoxia-targeted therapy.

N6-Methyladenosine Reader YTHDF1 Promotes ARHGEF2 Translation and RhoA Signaling in Colorectal Cancer

N6-methyladenosine (m6A) governs the fate of RNAs through m6A readers. Colorectal cancer (CRC) exhibits aberrant m6A modifications and expression of m6A regulators. However, how m6A readers interpret oncogenic m6A methylome to promote malignant transformation remains to be illustrated. YTH N6-methyladenosine RNA binding protein 1 (Ythdf1) knockout mouse was generated to determine the effect of Ythdf1 in CRC tumorigenesis invivo. Multiomic analysis of RNA-sequencing, m6A methylated RNA immunoprecipitation sequencing, YTHDF1 RNA immunoprecipitation sequencing, and proteomics were performed to unravel targets of YTHDF1 in CRC. The therapeutic potential of targeting YTHDF1-m6A-Rho/Rac guanine nucleotide exchange factor 2 (ARHGEF2) was evaluated using small interfering RNA (siRNA) encapsulated by lipid nanoparticles (LNP). DNA copy number gain of YTHDF1 is a frequent event in CRC and contributes to its overexpression. High expression of YTHDF1 is significantly associated with metastatic gene signature in patient tumors. Ythdf1 knockout in mice dampened tumor growth in an inflammatory CRC model. YTHDF1 promotes cell growth in CRCcell lines and primary organoids and lung and liver metastasis invivo. Integrative multiomics analysis identified RhoA activator ARHGEF2 as a key downstream target of YTHDF1. YTHDF1 binds to m6A sites of ARHGEF2 messenger RNA, resulting in enhanced translation of ARHGEF2. Ectopic expression of ARHGEF2 restored impaired RhoA signaling, cell growth, and metastatic ability both invitro and invivo caused by YTHDF1 loss, verifying that ARHGEF2 is a key target of YTHDF1. Finally, ARHGEF2 siRNA delivered by LNP significantly suppressed tumor growth and metastasis invivo. We identify a novel oncogenic epitranscriptome axis of YTHDF1-m6A-ARHGEF2, which regulates CRC tumorigenesis and metastasis. siRNA-delivering LNP drug validated the therapeutic potential of targeting this axis in CRC.

Proteomic and metabolomic profiling of urine uncovers immune responses in patients with COVID-19.

The utility of the urinary proteome in infectious diseases remains unclear. Here, we analyzed the proteome and metabolome of urine and serum samples from patients with COVID-19 and healthy controls. Our data show that urinary proteins effectively classify COVID-19 by severity. We detect 197 cytokines and their receptors in urine, but only 124 in serum using TMT-based proteomics. The decrease in urinary ESCRT complex proteins correlates with active SARS-CoV-2 replication. The downregulation of urinary CXCL14 in severe COVID-19 cases positively correlates with blood lymphocyte counts. Integrative multiomics analysis suggests that innate immune activation and inflammation triggered renal injuries in patients with COVID-19. COVID-19-associated modulation of the urinary proteome offers unique insights into the pathogenesis of this disease. This study demonstrates the added value of including the urinary proteome in a suite of multiomics analytes in evaluating the immune pathobiology and clinical course of COVID-19 and, potentially, other infectious diseases.

Multiomic Metabolic Enrichment Network Analysis Reveals Metabolite–Protein Physical Interaction Subnetworks Altered in Cancer

Metabolism is recognized as an important driver of cancer progression and other complex diseases, but global metabolite profiling remains a challenge. Protein expression profiling is often a poor proxy since existing pathway enrichment models provide an incomplete mapping between the proteome and metabolism. To overcome these gaps, we introduce multiomic metabolic enrichment network analysis (MOMENTA), an integrative multiomic data analysis framework for more accurately deducing metabolic pathway changes from proteomics data alone in a gene set analysis context by leveraging protein interaction networks to extend annotated metabolic models. We apply MOMENTA to proteomic data from diverse cancer cell lines and human tumors to demonstrate its utility at revealing variation in metabolic pathway activity across cancer types, which we verify using independent metabolomics measurements. The novel metabolic networks we uncover in breast cancer and other tumors are linked to clinical outcomes, underscoring the pathophysiological relevance of the findings.

The Genetic Association Identified Between Intervertebral Disc Degeneration and Associated Risk Factors Based on a Systems Biology Approach

Genetic cross-over study of intervertebral disc degeneration (IDD) and its associated risk factors. The purpose is to identify molecular biomarkers that may be involved in the IDD process and to provide effective recommendations in combination with drug analysis via systems biology methods. The pathogenesis and genetic links of IDD are still unclear. Related research is mainly based on a single data set or gene, and the impact of related risk factors on IDD is often ignored. Identifying disease-associated biomarkers and therapeutic targets through a systems biology approach that Integrative network-based gene and multi-omics analysis. In this study, the differential expression of genes was analyzed through NCBI datasets, followed by enrichment analysis. The central protein was identified through the establishment of protein-protein interaction (PPI) network. Key genes were screened out by VENN diagram and reasonable suggestions were put forward based on gene-chemical drug analysis. The IDD database analysis revealed 669 differentially expressed genes (DEGs) which were 22, 26, 168, 5, 38, 36, and 16 common DEGs with AG, SM, DEP, NAD, CED, OB, and HFD, respectively. GO and KEGG enrichment analysis may reveal the pathway by which these DEGs were involved. PPI network identified 10 central proteins including CCNB1, RETN, HMMR, BUB1, MPO, OIP5, HP, KIF11, BUB1B, and CDC25A. Three key genes BUB1, BUB1B, and CCNB1 were screened out and their expression might be related to the pathogenesis of IDD. According to the three chemical Dexamethasone, Nicotine, and Resvera-trol obtained from the analysis of genes-chemical drugs, reasonable treatment suggestions were put forward. Genetic association between IDD and risk factors in the general population was revealed by association network. Important gene-related molecular pathways and chemical drugs closely related to IDD have been found. Further study can provide guidance for the treatment and prognosis of IDD.Level of Evidence: N/A.

Multi-Omics Analysis of Microglial Extracellular Vesicles From Human Alzheimer's Disease Brain Tissue Reveals Disease-Associated Signatures.

Alzheimer’s disease (AD) is the most common cause of dementia, yet there is no cure or diagnostics available prior to the onset of clinical symptoms. Extracellular vesicles (EVs) are lipid bilayer-delimited particles that are released from almost all types of cell. Genome-wide association studies have linked multiple AD genetic risk factors to microglia-specific pathways. It is plausible that microglia-derived EVs may play a role in the progression of AD by contributing to the dissemination of insoluble pathogenic proteins, such as tau and Aβ. Despite the potential utility of EVs as a diagnostic tool, our knowledge of human brain EV subpopulations is limited. Here we present a method for isolating microglial CD11b-positive small EVs from cryopreserved human brain tissue, as well as an integrated multiomics analysis of microglial EVs enriched from the parietal cortex of four late-stage AD (Braak V-VI) and three age-matched normal/low pathology (NL) cases. This integrated analysis revealed 1,000 proteins, 594 lipids, and 105 miRNAs using shotgun proteomics, targeted lipidomics, and NanoString nCounter technology, respectively. The results showed a significant reduction in the abundance of homeostatic microglia markers P2RY12 and TMEM119, and increased levels of disease-associated microglia markers FTH1 and TREM2, in CD11b-positive EVs from AD brain compared to NL cases. Tau abundance was significantly higher in AD brain-derived microglial EVs. These changes were accompanied by the upregulation of synaptic and neuron-specific proteins in the AD group. Levels of free cholesterol were elevated in microglial EVs from the AD brain. Lipidomic analysis also revealed a proinflammatory lipid profile, endolysosomal dysfunction, and a significant AD-associated decrease in levels of docosahexaenoic acid (DHA)-containing polyunsaturated lipids, suggesting a potential defect in acyl-chain remodeling. Additionally, four miRNAs associated with immune and cellular senescence signaling pathways were significantly upregulated in the AD group. Our data suggest that loss of the homeostatic microglia signature in late AD stages may be accompanied by endolysosomal impairment and the release of undigested neuronal and myelin debris, including tau, through extracellular vesicles. We suggest that the analysis of microglia-derived EVs has merit for identifying novel EV-associated biomarkers and providing a framework for future larger-scale multiomics studies on patient-derived cell-type-specific EVs.

Single cell sequencing analysis identifies genetics-modulated ORMDL3+ cholangiocytes having higher metabolic effects on primary biliary cholangitis

BackgroundPrimary biliary cholangitis (PBC) is a classical autoimmune disease, which is highly influenced by genetic determinants. Many genome-wide association studies (GWAS) have reported that numerous genetic loci were significantly associated with PBC susceptibility. However, the effects of genetic determinants on liver cells and its immune microenvironment for PBC remain unclear.ResultsWe constructed a powerful computational framework to integrate GWAS summary statistics with scRNA-seq data to uncover genetics-modulated liver cell subpopulations for PBC. Based on our multi-omics integrative analysis, 29 risk genes including ORMDL3, GSNK2B, and DDAH2 were significantly associated with PBC susceptibility. By combining GWAS summary statistics with scRNA-seq data, we found that cholangiocytes exhibited a notable enrichment by PBC-related genetic association signals (Permuted P < 0.05). The risk gene of ORMDL3 showed the highest expression proportion in cholangiocytes than other liver cells (22.38%). The ORMDL3+ cholangiocytes have prominently higher metabolism activity score than ORMDL3− cholangiocytes (P = 1.38 × 10–15). Compared with ORMDL3− cholangiocytes, there were 77 significantly differentially expressed genes among ORMDL3+ cholangiocytes (FDR < 0.05), and these significant genes were associated with autoimmune diseases-related functional terms or pathways. The ORMDL3+ cholangiocytes exhibited relatively high communications with macrophage and monocyte. Compared with ORMDL3− cholangiocytes, the VEGF signaling pathway is specific for ORMDL3+ cholangiocytes to interact with other cell populations.ConclusionsTo the best of our knowledge, this is the first study to integrate genetic information with single cell sequencing data for parsing genetics-influenced liver cells for PBC risk. We identified that ORMDL3+ cholangiocytes with higher metabolism activity play important immune-modulatory roles in the etiology of PBC.Graphical

Integrative host microbiota multi-omics analysis reveals distinctive systemic inflammation and metabolic disorder across Alzheimer's disease continuum.

There is a growing appreciation that systemic factors, such as chronic inflammation, metabolic disorders, vascular dysfunction and gut microbiota dysbiosis, are highly connected and traverse a complex route across multiple pathophysiological processes that play crucial roles in Alzheimer's disease (AD) pathogenesis. Yet, understanding the pathophysiology of the disease is still lacking. Herein, we used 16s sequencing and short-gun metagenomics for gut microbiota structure and functional analysis, employed mass cytometry technology to examine the blood immune cell profiles, applied antibody array to quantify the expression level of 440 plasma proteins and applied targeted metabolomics approach to detect up to 630 metabolites in the blood. Through these multi-omics analysis, we unraveled the complex interplay among these systemic factors that were functionally linked to brain insults in the discrete stages along disease continuum, and delineated stage-specific systemic characteristics associated with the clinical-pathological changes. Notably, amnesic mild cognitive impairment (aMCI) exhibited a Th1-skewed profile, whereas mild-to-moderate AD showed a higher Th17/Treg ratio, which accompanies the structural and functional alteration of gut microbiota and blood metabolic and protein changes to disease-specific immune response. We further developed diagnostic models discriminating the discrete disease stages and revealed heterogeneity of AD by integrating the systemic factors, which empowered the prediction of aMCI subset at high-risk of progression into AD, and early stage of AD to a more severe one. Our study uncovered a complex interplay of systemic factors in AD development, and provided valuable information for developing the disease-modifying drug in future.

Genome-wide methylation and expression analyses reveal the epigenetic landscape of immune-related diseases for tobacco smoking

BackgroundSmoking is a major causal risk factor for lung cancer, chronic obstructive pulmonary disease (COPD), cardiovascular disease (CVD), and is the main preventable cause of deaths in the world. The components of cigarette smoke are involved in immune and inflammatory processes, which may increase the prevalence of cigarette smoke-related diseases. However, the underlying molecular mechanisms linking smoking and diseases have not been well explored. This study was aimed to depict a global map of DNA methylation and gene expression changes induced by tobacco smoking and to explore the molecular mechanisms between smoking and human diseases through whole-genome bisulfite sequencing (WGBS) and RNA-sequencing (RNA-seq).ResultsWe performed WGBS on 72 samples (36 smokers and 36 nonsmokers) and RNA-seq on 75 samples (38 smokers and 37 nonsmokers), and cytokine immunoassay on plasma from 22 males (9 smokers and 13 nonsmokers) who were recruited from the city of Jincheng in China. By comparing the data of the two groups, we discovered a genome-wide methylation landscape of differentially methylated regions (DMRs) associated with smoking. Functional enrichment analyses revealed that both smoking-related hyper-DMR genes (DMGs) and hypo-DMGs were related to synapse-related pathways, whereas the hypo-DMGs were specifically related to cancer and addiction. The differentially expressed genes (DEGs) revealed by RNA-seq analysis were significantly enriched in the “immunosuppression” pathway. Correlation analysis of DMRs with their corresponding gene expression showed that genes affected by tobacco smoking were mostly related to immune system diseases. Finally, by comparing cytokine concentrations between smokers and nonsmokers, we found that vascular endothelial growth factor (VEGF) was significantly upregulated in smokers.ConclusionsIn sum, we found that smoking-induced DMRs have different distribution patterns in hypermethylated and hypomethylated areas between smokers and nonsmokers. We further identified and verified smoking-related DMGs and DEGs through multi-omics integration analysis of DNA methylome and transcriptome data. These findings provide us a comprehensive genomic map of the molecular changes induced by smoking which would enhance our understanding of the harms of smoking and its relationship with diseases.

Patient similarity network of newly diagnosed multiple myeloma identifies patient subgroups with distinct genetic features and clinical implications.

The remarkable genetic heterogeneity of multiple myeloma poses a substantial challenge for proper prognostication and clinical management of patients. Here, we introduce MM-PSN, the first multiomics patient similarity network of myeloma. MM-PSN enabled accurate dissection of the genetic and molecular landscape of the disease and determined 12 distinct subgroups defined by five data types generated from genomic and transcriptomic profiling of 655 patients. MM-PSN identified patient subgroups not previously described defined by specific patterns of alterations, enriched for specific gene vulnerabilities, and associated with potential therapeutic options. Our analysis revealed that co-occurrence of t(4;14) and 1q gain identified patients at significantly higher risk of relapse and shorter survival as compared to t(4;14) as a single lesion. Furthermore, our results show that 1q gain is the most important single lesion conferring high risk of relapse and that it can improve on the current International Staging Systems (ISS and R-ISS).

The multiomics landscape of serum exosomes during the development of sepsis

IntroductionSepsis is an infection-induced severe inflammatory disorder leading to multiple organ dysfunction. It remains a highly lethal condition for which early diagnosis and therapy achieve unsatisfactory results. Circulating exosomes containing biomarkers and mediators of sepsis have recently received attention, but the progress has been far from optimal. ObjectivesThe present study focuses on the profiles of molecular dynamics in serum exosomes and explores the potential molecular mechanisms on serum exosomes during the process of sepsis. MethodsWe used high-performance liquid chromatography-tandem mass spectrometry and RNA-seq to detect the dynamic profiles of exosome proteins and RNAs (including mRNAs, lncRNAs and miRNAs) in serum exosomes from 3 healthy individuals and 9 septic patients at the different stages. Then integrative multiomics analyses were performed and the results were validated by qRT-PCR, LiquiChip assay and metabolomics analysis on mice subjected to cecal ligation and puncture (CLP) modeling. ResultsA total of 354 proteins, 195 mRNAs, 82 lncRNAs and 55 miRNAs were identified as differentially expressed molecules in serum exosomes from septic patients. Integrative multiomics analysis showed that exosome components were associated with cytokine storm, complement and clotting cascades, the endothelial barrier, 20S proteasome-dependent protein degradation and vitamin metabolism. Importantly, pretreatment with serum exosomes derived from mice subjected to CLP significantly restrained proinflammatory cytokine expression and alleviated tissue injury in septic mice. Further metabolomics analysis demonstrated that pretreatment with septic serum exosomes significantly affected the metabolites associated with vitamin digestion and absorption in CLP mice. ConclusionOur study for the first time describes the landscape of the molecular dynamics of serum exosomes during the development of sepsis and proposes some hypothetical molecular mechanisms by integrative multiomics analysis, which may provide helpful diagnostic and therapeutic insights for the ongoing battle against sepsis.