Mitochondrial RNA modification-based signature to predict prognosis of lower grade glioma: a multi-omics exploration and verification study

Mitochondrial RNA modification (MRM) plays a crucial role in regulating the expression of key mitochondrial genes and promoting tumor metastasis. Despite its significance, comprehensive studies on MRM in lower grade gliomas (LGGs) remain unknown. Single-cell RNA-seq data (GSE89567) was used to evaluate the distribution functional status, and correlation of MRM-related genes in different cell types of LGG microenvironment. We developed an MRM scoring system by selecting potential MRM-related genes using LASSO regression analysis and the Random Survival Forest algorithm, based on multiple bulk RNA-seq datasets from TCGA, CGGA, GSE16011, and E-MTAB-3892. Analysis was performed on prognostic and immunological features, signaling pathways, metabolism, somatic mutations and copy number variations (CNVs), treatment responses, and forecasting of potential small-molecule agents. A total of 35 MRM-related genes were selected from the literature. Differential expression analysis of 1120 normal brain tissues and 529 LGGs revealed that 22 and 10 genes were upregulated and downregulated, respectively. Most genes were associated with prognosis of LGG. METLL8, METLL2A, TRMT112, and METTL2B were extensively expressed in all cell types and different cell cycle of each cell type. Almost all cell types had clusters related to mitochondrial RNA processing, ribosome biogenesis, or oxidative phosphorylation. Cell-cell communication and Pearson correlation analyses indicated that MRM may promoting the development of microenvironment beneficial to malignant progression via modulating NCMA signaling pathway and ICP expression. A total of 11 and 9 MRM-related genes were observed by LASSO and the RSF algorithm, respectively, and finally 6 MRM-related genes were used to establish MRM scoring system (TRMT2B, TRMT11, METTL6, METTL8, TRMT6, and TRUB2). The six MRM-related genes were then validated by qPCR in glioma and normal tissues. MRM score can predict the malignant clinical characteristics, abundance of immune infiltration, gene variation, clinical outcome, the enrichment of signaling pathways and metabolism. In vitro experiments demonstrated that silencing METTL8 significantly curbs glioma cell proliferation and enhances apoptosis. Patients with a high MRM score showed a better response to immunotherapies and small-molecule agents such as arachidonyl trifluoromethyl ketone, MS.275, AH.6809, tacrolimus, and TTNPB. These novel insights into the biological impacts of MRM within the glioma microenvironment underscore its potential as a target for developing precise therapies, including immunotherapeutic approaches.

An integrative multi-omics approach aimed to gain insight on the effect of composition, style, yeast, and wheat species on wheat craft beer flavour

This study was aimed to unravel the effect of raw materials (barley and wheat), wheat concentration (0, 25, 40, and 100 %), wheat species (common and durum), beer style (Blanche and Weiss), and yeast (US-05 and WB-06) on the chemical composition, volatiles, and sensory profile of wheat craft beers by using a multivariate statistical approach. Beer samples were analysed for their composition, volatiles and sensory profile and data were processed using unsupervised multivariate analyses, PLS regression and a multi-omics approach using multi-block PLS-DA. Multi-block variable sparsification was used as an embedded dimension reduction step. The adopted multi-omics approach permitted to correctly classify beers with different styles and wheat concentration, and to accurate classify (95 % accuracy) beers according to yeast type. Wheat species was of lower importance since it permitted a classification with 49 % accuracy which increased to 74 % in Blanche beers, thus suggesting that malting flattened differences determined by wheat species.

Insight into the chemical compositions of Anhua dark teas derived from identical tea materials: A multi-omics, electronic sensory, and microbial sequencing analysis

Anhua dark teas (DTs), including Tianjian tea, Qianliang tea, Hei brick tea, and Fu brick tea, are unique fermented teas from China's Anhua County; yet their chemical composition differences remain unclear. Herein, metabolomics, volatolomics, and electronic sensory assessments were employed to analyze and compare chemical compositions and sensory characteristics of five types of Anhua DTs. All of these teas were derived from identical tea materials. Chemical compositions differed significantly among Anhua DTs, with Tianjian tea remarkable. Long-lasting fermentation and complex processing methods led to transformation of multiple compounds, particularly catechins. Eighteen volatile compounds with OVA > 1 were key aroma contributors in Anhua DTs. Internal transcribed spacer and 16S ribosomal DNA sequencing showed that Eurotium, Pseudomonas, and Bacillus are dominant microorganisms in Anhua DTs. Furthermore, this study unveiled notable differences in chemical compositions between Anhua DTs and five other traditional types of tea. This research enhances our understanding of Anhua DTs processing.

A guided network estimation approach using multi-omic information

INTODUCTION

In systems biology, an organism is viewed as a system of interconnected molecular entities. To understand the functioning of organisms it is essential to integrate information about the variations in the concentrations of those molecular entities. This information can be structured as a set of networks with interconnections and with some hierarchical relations between them. Few methods exist for the reconstruction of integrative networks.

OBJECTIVE

In this work, we propose an integrative network reconstruction method in which the network organization for a particular type of omics data is guided by the network structure of a related type of omics data upstream in the omic cascade. The structure of these guiding data can be either already known or be estimated from the guiding data themselves.

METHODS

The method consists of three steps. First a network structure for the guiding data should be provided. Next, responses in the target set are regressed on the full set of predictors in the guiding data with a Lasso penalty to reduce the number of predictors and an L2 penalty on the differences between coefficients for predictors that share edges in the network for the guiding data. Finally, a network is reconstructed on the fitted target responses as functions of the predictors in the guiding data. This way we condition the target network on the network of the guiding data.

CONCLUSIONS

We illustrate our approach on two examples in Arabidopsis. The method detects groups of metabolites that have a similar genetic or transcriptomic basis.

Integrative omics analysis elucidates the genetic basis underlying seed weight and oil content in soybean

Synergistic optimization of key agronomic traits by traditional breeding has dramatically enhanced crop productivity in the past decades. However, the genetic basis underlying coordinated regulation of yield- and quality-related traits remains poorly understood. Here, we dissected the genetic architectures of seed weight and oil content by combining genome-wide association studies (GWAS) and transcriptome-wide association studies (TWAS) using 421 soybean (Glycine max) accessions. We identified 26 and 33 genetic loci significantly associated with seed weight and oil content by GWAS, respectively, and detected 5,276 expression quantitative trait loci (eQTLs) regulating expression of 3,347 genes based on population transcriptomes. Interestingly, a gene module (IC79), regulated by two eQTL hotspots, exhibited significant correlation with both seed weigh and oil content. Twenty-two candidate causal genes for seed traits were further prioritized by TWAS, including Regulator of Weight and Oil of Seed 1 (GmRWOS1), which encodes a sodium pump protein. GmRWOS1 was verified to pleiotropically regulate seed weight and oil content by gene knockout and overexpression. Notably, allelic variations of GmRWOS1 were strongly selected during domestication of soybean. This study uncovers the genetic basis and network underlying regulation of seed weight and oil content in soybean and provides a valuable resource for improving soybean yield and quality by molecular breeding.

Multi-omics in nasal epithelium reveals three axes of dysregulation for asthma risk in the African Diaspora populations

Asthma has striking disparities across ancestral groups, but the molecular underpinning of these differences is poorly understood and minimally studied. A goal of the Consortium on Asthma among African-ancestry Populations in the Americas (CAAPA) is to understand multi-omic signatures of asthma focusing on populations of African ancestry. RNASeq and DNA methylation data are generated from nasal epithelium including cases (current asthma, N = 253) and controls (never-asthma, N = 283) from 7 different geographic sites to identify differentially expressed genes (DEGs) and gene networks. We identify 389 DEGs; the top DEG, FN1, was downregulated in cases (q = 3.26 × 10-9) and encodes fibronectin which plays a role in wound healing. The top three gene expression modules implicate networks related to immune response (CEACAM5; p = 9.62 × 10-16 and CPA3; p = 2.39 × 10-14) and wound healing (FN1; p = 7.63 × 10-9). Multi-omic analysis identifies FKBP5, a co-chaperone of glucocorticoid receptor signaling known to be involved in drug response in asthma, where the association between nasal epithelium gene expression is likely regulated by methylation and is associated with increased use of inhaled corticosteroids. This work reveals molecular dysregulation on three axes - increased Th2 inflammation, decreased capacity for wound healing, and impaired drug response - that may play a critical role in asthma within the African Diaspora.

Metabolic Regulation of Longevity and Immune Response in Caenorhabditis elegans by Ingestion of Lacticaseibacillus rhamnosus IDCC 3201 Using Multi-Omics Analysis

Probiotics, specifically Lacticaseibacillus rhamnosus, have garnered attention for their potential health benefits. This study focuses on evaluating the probiotic properties of candidate probiotics L. rhamnosus IDCC 3201 (3201) using the Caenorhabditis elegans surrogate animal model, a well-established in vivo system for studying host-bacteria interactions. The adhesive ability to the host's gastrointestinal tract is a crucial criterion for selecting potential probiotic bacteria. Our findings demonstrated that 3201 exhibits significantly higher adhesive capabilities compared with Escherichia coli OP50 (OP50), a standard laboratory food source for C. elegans and is comparable with the widely recognized probiotic L. rhamnosus GG (LGG). In lifespan assay, 3201 significantly increased the longevity of C. elegans compared with OP50. In addition, preconditioning with 3201 enhanced C. elegans immune response against four different foodborne pathogenic bacteria. To uncover the molecular basis of these effects, transcriptome analysis elucidated that 3201 modulates specific gene expression related to the innate immune response in C. elegans. C-type lectin-related genes and lysozyme-related genes, crucial components of the immune system, showed significant upregulation after feeding 3201 compared with OP50. These results suggested that preconditioning with 3201 may enhance the immune response against pathogens. Metabolome analysis revealed increased levels of fumaric acid and succinic acid, metabolites of the citric acid cycle, in C. elegans fed with 3201 compared with OP50. Furthermore, there was an increase in the levels of lactic acid, a well-known antimicrobial compound. This rise in lactic acid levels may have contributed to the robust defense mechanisms against pathogens. In conclusion, this study demonstrated the probiotic properties of the candidate probiotic L. rhamnosus IDCC 3201 by using multi-omics analysis.

Machine Learning-Based Integrated Multiomics Characterization of Colorectal Cancer Reveals Distinctive Metabolic Signatures

The metabolic signature identification of colorectal cancer is critical for its early diagnosis and therapeutic approaches that will significantly block cancer progression and improve patient survival. Here, we combined an untargeted metabolic analysis strategy based on internal extractive electrospray ionization mass spectrometry and the machine learning approach to analyze metabolites in 173 pairs of cancer samples and matched normal tissue samples to build robust metabolic signature models for diagnostic purposes. Screening and independent validation of metabolic signatures from colorectal cancers via machine learning methods (Logistic Regression_L1 for feature selection and eXtreme Gradient Boosting for classification) was performed to generate a panel of seven signatures with good diagnostic performance (the accuracy of 87.74%, sensitivity of 85.82%, and specificity of 89.66%). Moreover, seven signatures were evaluated according to their ability to distinguish between cancer and normal tissues, with the metabolic molecule PC (30:0) showing good diagnostic performance. In addition, genes associated with PC (30:0) were identified by multiomics analysis (combining metabolic data with transcriptomic data analysis) and our results showed that PC (30:0) could promote the proliferation of colorectal cancer cell SW480, revealing the correlation between genetic changes and metabolic dysregulation in cancer. Overall, our results reveal potential determinants affecting metabolite dysregulation, paving the way for a mechanistic understanding of altered tissue metabolites in colorectal cancer and design interventions for manipulating the levels of circulating metabolites.

Multi-omics analysis of macrophage-associated receptor and ligand reveals a strong prognostic signature and subtypes in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a significant contributor to morbidity and mortality worldwide. The interaction between receptors and ligands is the primary mode of intercellular signaling and plays a vital role in the progression of HCC. This study aimed to identify the macrophage-related receptor ligand marker genes associated with HCC and further explored the molecular immune mechanisms attributed to altered biomarkers. Single-cell RNA sequencing data containing primary and recurrent samples were downloaded from the China National GeneBank. Cell types were first identified to explore differences between immune cells from different sample sources. CellChat analysis was used to infer and analyze intercellular communication networks quantitatively. Three molecular subtypes were constructed based on the screened twenty macrophage-associated receptor ligand genes. Bulk RNA-Seq data were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases. After the screening, the minor absolute shrinkage and selection operator (LASSO) regression model was employed to identify key markers. After collecting peripheral blood and clinical information from patients, an enzyme-linked immunosorbent assay (ELISA) was used to detect the correlation between key markers and IL-10, one of the macrophage markers. After developing a new HCC risk adjustment model and conducting analysis, it was found that there were significant differences in immune status and gene mutations between the high-risk and low-risk groups of patients based on macrophage-associated receptor and ligand genes. This study identified SPP1, ANGPT2, and NCL as key biological targets for HCC. The drug-gene interaction network analysis identified wortmannin, ribavirin, and tarnafloxin as potential therapeutic drugs for the three key markers. In a clinical cohort study, patients with immune checkpoint inhibitor (ICI) resistance had significantly higher expression levels of OPN, ANGPT2, NCL, and IL-10 than patients with ICI-responsiveness. These three key markers were positively correlated with the expression level of IL-10. The signature based on macrophage-associated receptor and ligand genes can accurately predict the prognosis of patients with HCC and the sensitivity to immunotherapy. These results may help guide the development of targeted prevention and personalized treatment of HCC.

Systematic characterization of multi-omics landscape between gut microbial metabolites and GPCRome in Alzheimer's disease

Shifts in the magnitude and nature of gut microbial metabolites have been implicated in Alzheimer's disease (AD), but the host receptors that sense and respond to these metabolites are largely unknown. Here, we develop a systems biology framework that integrates machine learning and multi-omics to identify molecular relationships of gut microbial metabolites with non-olfactory G-protein-coupled receptors (termed the "GPCRome"). We evaluate 1.09 million metabolite-protein pairs connecting 408 human GPCRs and 335 gut microbial metabolites. Using genetics-derived Mendelian randomization and integrative analyses of human brain transcriptomic and proteomic profiles, we identify orphan GPCRs (i.e., GPR84) as potential drug targets in AD and that triacanthine experimentally activates GPR84. We demonstrate that phenethylamine and agmatine significantly reduce tau hyperphosphorylation (p-tau181 and p-tau205) in AD patient induced pluripotent stem cell-derived neurons. This study demonstrates a systems biology framework to uncover the GPCR targets of human gut microbiota in AD and other complex diseases if broadly applied.

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

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

UHPLC-HRMS-based Multiomics to Explore the Potential Mechanisms and Biomarkers for Colorectal Cancer

BACKGROUND

Understanding the metabolic changes in colorectal cancer (CRC) and exploring potential diagnostic biomarkers is crucial for elucidating its pathogenesis and reducing mortality. Cancer cells are typically derived from cancer tissues and can be easily obtained and cultured. Systematic studies on CRC cells at different stages are still lacking. Additionally, there is a need to validate our previous findings from human serum.

METHODS

Ultrahigh-performance liquid chromatography tandem high-resolution mass spectrometry (UHPLC-HRMS)-based metabolomics and lipidomics were employed to comprehensively measure metabolites and lipids in CRC cells at four different stages and serum samples from normal control (NR) and CRC subjects. Univariate and multivariate statistical analyses were applied to select the differential metabolites and lipids between groups. Biomarkers with good diagnostic efficacy for CRC that existed in both cells and serum were screened by the receiver operating characteristic curve (ROC) analysis. Furthermore, potential biomarkers were validated using metabolite standards.

RESULTS

Metabolite and lipid profiles differed significantly among CRC cells at stages A, B, C, and D. Dysregulation of glycerophospholipid (GPL), fatty acid (FA), and amino acid (AA) metabolism played a crucial role in the CRC progression, particularly GPL metabolism dominated by phosphatidylcholine (PC). A total of 46 differential metabolites and 29 differential lipids common to the four stages of CRC cells were discovered. Eight metabolites showed the same trends in CRC cells and serum from CRC patients compared to the control groups. Among them, palmitoylcarnitine and sphingosine could serve as potential biomarkers with the values of area under the curve (AUC) more than 0.80 in the serum and cells. Their panel exhibited excellent performance in discriminating CRC cells at different stages from normal cells (AUC = 1.00).

CONCLUSIONS

To our knowledge, this is the first research to attempt to validate the results of metabolism studies of serum from CRC patients using cell models. The metabolic disorders of PC, FA, and AA were closely related to the tumorigenesis of CRC, with PC being the more critical factor. The panel composed of palmitoylcarnitine and sphingosine may act as a potential biomarker for the diagnosis of CRC, aiding in its prevention.

Multi-omics analysis of a case of congenital microtia reveals aldob and oxidative stress associated with microtia etiology

BACKGROUND

Microtia is reported to be one of the most common congenital craniofacial malformations. Due to the complex etiology and the ethical barrier of embryonic study, the precise mechanisms of microtia remain unclear. Here we report a rare case of microtia with costal chondrodysplasia based on bioinformatics analysis and further verifications on other sporadic microtia patients.

RESULTS

One hundred fourteen deleterious insert and deletion (InDel) and 646 deleterious SNPs were screened out by WES, candidate genes were ranked in descending order according to their relative impact with microtia. Label-free proteomic analysis showed that proteins significantly different between the groups were related with oxidative stress and energy metabolism. By real-time PCR and immunohistochemistry, we further verified the candidate genes between other sporadic microtia and normal ear chondrocytes, which showed threonine aspartase, cadherin-13, aldolase B and adiponectin were significantly upregulated in mRNA levels but were significantly lower in protein levels. ROS detection and mitochondrial membrane potential (∆ Ψ m) detection proved that oxidative stress exists in microtia chondrocytes.

CONCLUSIONS

Our results not only spot new candidate genes by WES and label-free proteomics, but also speculate for the first time that metabolism and oxidative stress may disturb cartilage development and this might become therapeutic targets and potential biomarkers with clinical usefulness in the future.

Multi-omics profiling reveals dysregulated ribosome biogenesis and impaired cell proliferation following knockout of CDR2L

BACKGROUND

Cerebellar degeneration-related (CDR) proteins are associated with paraneoplastic cerebellar degeneration (PCD) - a rare, neurodegenerative disease caused by tumour-induced autoimmunity against neural antigens resulting in degeneration of Purkinje neurons in the cerebellum. The pathogenesis of PCD is unknown, in large part due to our limited understanding of the functions of CDR proteins. To this end, we performed an extensive, multi-omics analysis of CDR-knockout cells focusing on the CDR2L protein, to gain a deeper understanding of the properties of the CDR proteins in ovarian cancer.

METHODS

Ovarian cancer cell lines lacking either CDR1, CDR2, or CDR2L were analysed using RNA sequencing and mass spectrometry-based proteomics to assess changes to the transcriptome, proteome and secretome in the absence of these proteins.

RESULTS

For each knockout cell line, we identified sets of differentially expressed genes and proteins. CDR2L-knockout cells displayed a distinct expression profile compared to CDR1- and CDR2-knockout cells. Knockout of CDR2L caused dysregulation of genes involved in ribosome biogenesis, protein translation, and cell cycle progression, ultimately causing impaired cell proliferation in vitro. Several of these genes showed a concurrent upregulation at the transcript level and downregulation at the protein level.

CONCLUSIONS

Our study provides the first integrative multi-omics analysis of the impact of knockout of the CDR genes, providing both new insights into the biological properties of the CDR proteins in ovarian cancer, and a valuable resource for future investigations into the CDR proteins.

[Multi-omics research progress in early-onset colorectal cancer]

Globally, the incidence of early-onset colorectal cancer (EOCRC) among individuals younger than 50 is escalating. Compared to late-onset colorectal cancer, EOCRC exhibits distinct clinical, pathological, and molecular features, with a higher prevalence in the left colon and rectum. However, the occurrence and development of EOCRC is a multi-factor and multi-stage evolution process, which is the result of the mutual effect of environmental, genetic and biological factors, and involves the multi-level regulation mechanism of other organisms. With the development and improvement of high-throughput sequencing technology, the application of multi-omics analysis has become an important development direction to resolve the pathogenesis of complex diseases and individualized treatment plans. This article aims to review the research progress of EOCRC at the multi-omics level, providing a theoretical foundation for earlier diagnosis and more precise treatment of this diseases.

Differences between psoriatic arthritis and psoriasis in multi-omics

We aim to systemically review the genomics, transcriptomics, epigenetics, proteomics, metabonomics and microbiota of psoriatic arthritis and psoriasis, illustrating the differences of these two diseases, broadening our understanding of the pathogenesis of them and providing important clues for valuable biomarkers of earlier diagnosis and treatments. To our knowledge, this is the first study that combine all omics studies from genomics to microbiota and may serve as a reference for future studies to identify the key underlying pathways in psoriatic arthritis.

Multi-omic analysis tools for microbial metabolites prediction

How to resolve the metabolic dark matter of microorganisms has long been a challenging problem in discovering active molecules. Diverse omics tools have been developed to guide the discovery and characterization of various microbial metabolites, which make it gradually possible to predict the overall metabolites for individual strains. The combinations of multi-omic analysis tools effectively compensates for the shortcomings of current studies that focus only on single omics or a broad class of metabolites. In this review, we systematically update, categorize and sort out different analysis tools for microbial metabolites prediction in the last five years to appeal for the multi-omic combination on the understanding of the metabolic nature of microbes. First, we provide the general survey on different updated prediction databases, webservers, or software that based on genomics, transcriptomics, proteomics, and metabolomics, respectively. Then, we discuss the essentiality on the integration of multi-omics data to predict metabolites of different microbial strains and communities, as well as stressing the combination of other techniques, such as systems biology methods and data-driven algorithms. Finally, we identify key challenges and trends in developing multi-omic analysis tools for more comprehensive prediction on diverse microbial metabolites that contribute to human health and disease treatment.

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

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

[Multi-omics prediction of lymph node metastasis status in breast cancer]

Lymph node metastasis status stands as a pivotal prognostic indicator in forecasting the outlook for breast cancer patients. Consequently, precise evaluation of this status holds paramount importance in the staging, treatment, and prognosis of breast cancer. The utilization of radiomics, genomics, proteomics, transcriptomics, and histopathology methodologies has notably enhanced the precision of lymph node metastasis status prediction in breast cancer. This review provides an overview of recent advancements in omics-based lymph node metastasis prediction for breast cancer, elucidating the significance of various omics prediction models and integrated multi-omics models in this predictive endeavor. The overarching goal is to augment the accuracy of preoperative lymph node metastasis status prediction in breast cancer, thereby aiding clinicians in the selection of efficacious personalized treatment strategies, while concurrently averting undertreatment of patients with a heightened risk of metastasis.

Multiomics profiling reveals VDR as a central regulator of mesenchymal stem cell senescence with a known association with osteoporosis after high-fat diet exposure

The consumption of a high-fat diet (HFD) has been linked to osteoporosis and an increased risk of fragility fractures. However, the specific mechanisms of HFD-induced osteoporosis are not fully understood. Our study shows that exposure to an HFD induces premature senescence in bone marrow mesenchymal stem cells (BMSCs), diminishing their proliferation and osteogenic capability, and thereby contributes to osteoporosis. Transcriptomic and chromatin accessibility analyses revealed the decreased chromatin accessibility of vitamin D receptor (VDR)-binding sequences and decreased VDR signaling in BMSCs from HFD-fed mice, suggesting that VDR is a key regulator of BMSC senescence. Notably, the administration of a VDR activator to HFD-fed mice rescued BMSC senescence and significantly improved osteogenesis, bone mass, and other bone parameters. Mechanistically, VDR activation reduced BMSC senescence by decreasing intracellular reactive oxygen species (ROS) levels and preserving mitochondrial function. Our findings not only elucidate the mechanisms by which an HFD induces BMSC senescence and associated osteoporosis but also offer new insights into treating HFD-induced osteoporosis by targeting the VDR-superoxide dismutase 2 (SOD2)-ROS axis.

Multi-omic profiling reveals the endogenous and neoplastic responses to immunotherapies in cutaneous T cell lymphoma

Cutaneous T cell lymphomas (CTCLs) are skin cancers with poor survival rates and limited treatments. While immunotherapies have shown some efficacy, the immunological consequences of administering immune-activating agents to CTCL patients have not been systematically characterized. We apply a suite of high-dimensional technologies to investigate the local, cellular, and systemic responses in CTCL patients receiving either mono- or combination anti-PD-1 plus interferon-gamma (IFN-γ) therapy. Neoplastic T cells display no evidence of activation after immunotherapy. IFN-γ induces muted endogenous immunological responses, while anti-PD-1 elicits broader changes, including increased abundance of CLA+CD39+ T cells. We develop an unbiased multi-omic profiling approach enabling discovery of immune modules stratifying patients. We identify an enrichment of activated regulatory CLA+CD39+ T cells in non-responders and activated cytotoxic CLA+CD39+ T cells in leukemic patients. Our results provide insights into the effects of immunotherapy in CTCL patients and a generalizable framework for multi-omic analysis of clinical trials.

Molecular insights into the catabolism of dibutyl phthalate in Pseudomonas aeruginosa PS1 based on biochemical and multi-omics approaches

A comprehensive understanding of the molecular mechanisms underlying microbial catabolism of dibutyl phthalate (DBP) is still lacking. Here, we newly isolated a bacterial strain identified as Pseudomonas aeruginosa PS1 with high efficiency of DBP degradation. The degradation ratios of DBP at 100-1000 mg/L by this strain reached 80-99 % within 72 h without a lag phase. A rare DBP-degradation pathway containing two monobutyl phthalate-catabolism steps was proposed based on intermediates identified by HPLC-TOF-MS/MS. In combination with genomic and transcriptomic analyses, we identified 66 key genes involved in DBP biodegradation and revealed the genetic basis for a new complete catabolic pathway from DBP to Succinyl-CoA or Acetyl-CoA in the genus Pseudomonas for the first time. Notably, we found that a series of homologous genes in Pht and Pca clusters were simultaneously activated under DBP exposure and some key intermediate degradation related gene clusters including Pht, Pca, Xyl, Ben, and Cat exhibited a favorable coexisting pattern, which contributed the high-efficient DBP degradation ability and strong adaptability to this strain. Overall, these results broaden the knowledge of the catabolic diversity of DBP in microorganisms and enhance our understanding of the molecular mechanism underlying DBP biodegradation.

[Research progress on pneumoconiosis markers based on multi-omics analysis]

The etiology of pneumoconiosis is relatively clear, but the pathogenic mechanism is not fully understood, and there is no effective cure for pneumoconiosis. Clarifying the pathogenesis of pneumoconiosis and exploring relevant markers can help screen high-risk groups of dust exposure, and relevant markers can also be used as targets to intervene in the process of pulmonary fibrosis. The in-depth development of genomics, transcriptomics and proteomics has provided a new way to discover more potential markers of pneumoconiosis. In the future, the combination of multi-omics and multi-stage interactive analysis can systematically and comprehensively identify key genes (proteins) , metabolites and metabolic pathways in the occurrence and development of pneumoconiosis, build a core regulatory network, and then screen out sensitive markers related to early diagnosis and treatment of pneumoconiosis. This article summarizes the research progress of pneumoconiosis markers from the perspective of multi-omics, hoping to provide more basic data for the early prevention and diagnosis of pneumoconiosis, pathogenesis research, and therapeutic intervention.

A multi-omics investigation of the lung injury induced by PM2.5 at environmental levels via the lung-gut axis

Long-term exposure to fine particulate matter (PM2.5) posed injury for gastrointestinal and respiratory systems, ascribing with the lung-gut axis. However, the cross-talk mechanisms remain unclear. Here, we attempted to establish the response networks of lung-gut axis in mice exposed to PM2.5 at environmental levels. Male Balb/c mice were exposed to PM2.5 (dose of 0.1, 0.5, and 1.0 mg/kg) collected from Chengdu, China for 10 weeks, through intratracheally instillation, and examined the effect of PM2.5 on lung functions of mice. The changes of lung and gut microbiota and metabolic profiles of mice in different groups were determined. Furthermore, the results of multi-omics were conjointly analyzed to elucidate the primary microbes and the associated metabolites in lung and gut responsible for PM2.5 exposure. Accordingly, the cross-talk network and key pathways between lung-gut axis were established. The results indicated that exposed to PM2.5 0.1 mg/kg induced obvious inflammations in mice lung, while emphysema was observed at 1.0 mg/kg. The levels of metabolites guanosine, hypoxanthine, and hepoxilin B3 increased in the lung might contribute to lung inflammations in exposure groups. For microbiotas in lung, PM2.5 exposure significantly declined the proportions of Halomonas and Lactobacillus. Meanwhile, the metabolites in gut including L-tryptophan, serotonin, and spermidine were up-regulated in exposure groups, which were linked to the decreasing of Oscillospira and Helicobacter in gut. Via lung-gut axis, the activations of pathways including Tryptophan metabolism, ABC transporters, Serotonergic synapse, and Linoleic acid metabolism contributed to the cross-talk between lung and gut tissues of mice mediated by PM2.5. In summary, the microbes including Lactobacillus, Oscillospira, and Parabacteroides, and metabolites including hepoxilin B3, guanosine, hypoxanthine, L-tryptophan, and spermidine were the main drivers. In this lung-gut axis study, we elucidated some pro- and pre-biotics in lung and gut microenvironments contributed to the adverse effects on lung functions induced by PM2.5 exposure.

Integrated multi-omics analysis reveals liver metabolic reprogramming by fish iridovirus and antiviral function of alpha-linolenic acid

Iridovirus poses a substantial threat to global aquaculture due to its high mortality rate; however, the molecular mechanisms underpinning its pathogenesis are not well elucidated. Here, a multi-omics approach was applied to groupers infected with Singapore grouper iridovirus (SGIV), focusing on the roles of key metabolites. Results showed that SGIV induced obvious histopathological damage and changes in metabolic enzymes within the liver. Furthermore, SGIV significantly reduced the contents of lipid droplets, triglycerides, cholesterol, and lipoproteins. Metabolomic analysis indicated that the altered metabolites were enriched in 19 pathways, with a notable down-regulation of lipid metabolites such as glycerophosphates and alpha-linolenic acid (ALA), consistent with disturbed lipid homeostasis in the liver. Integration of transcriptomic and metabolomic data revealed that the top enriched pathways were related to cell growth and death and nucleotide, carbohydrate, amino acid, and lipid metabolism, supporting the conclusion that SGIV infection induced liver metabolic reprogramming. Further integrative transcriptomic and proteomic analysis indicated that SGIV infection activated crucial molecular events in a phagosome-immune depression-metabolism dysregulation-necrosis signaling cascade. Of note, integrative multi-omics analysis demonstrated the consumption of ALA and linoleic acid (LA) metabolites, and the accumulation of L-glutamic acid (GA), accompanied by alterations in immune, inflammation, and cell death-related genes. Further experimental data showed that ALA, but not GA, suppressed SGIV replication by activating antioxidant and anti-inflammatory responses in the host. Collectively, these findings provide a comprehensive resource for understanding host response dynamics during fish iridovirus infection and highlight the antiviral potential of ALA in the prevention and treatment of iridoviral diseases.