Integrated multi-omics analysis reveals molecular changes associated with chronic lipid accumulation following contusive spinal cord injury.

Functional and pathological recovery after spinal cord injury (SCI) is often incomplete due to the limited regenerative capacity of the central nervous system (CNS), which is further impaired by several mechanisms that sustain tissue damage. Among these, the chronic activation of immune cells can cause a persistent state of local CNS inflammation and damage. However, the mechanisms that sustain this persistent maladaptive immune response in SCI have not been fully clarified yet. In this study, we integrated histological analyses with proteomic, lipidomic, transcriptomic, and epitranscriptomic approaches to study the pathological and molecular alterations that develop in a mouse model of cervical spinal cord hemicontusion. We found significant pathological alterations of the lesion rim with myelin damage and axonal loss that persisted throughout the late chronic phase of SCI. This was coupled by a progressive lipid accumulation in myeloid cells, including resident microglia and infiltrating monocyte-derived macrophages. At tissue level, we found significant changes of proteins indicative of glycolytic, tricarboxylic acid cycle (TCA), and fatty acid metabolic pathways with an accumulation of triacylglycerides with C16:0 fatty acyl chains in chronic SCI. Following transcriptomic, proteomic, and epitranscriptomic studies identified an increase of cholesterol and m6A methylation in lipid-droplet-accumulating myeloid cells as a core feature of chronic SCI. By characterizing the multiple metabolic pathways altered in SCI, our work highlights a key role of lipid metabolism in the chronic response of the immune and central nervous system to damage.

Exploring the anti-ovarian aging mechanism of He's Yangchao formula: Insights from multi-omics analysis in naturally aged mice.

BACKGROUND: The rapid acceleration of female reproductive aging has become a major public health concern. He's Yangchao formula (HSYC), a compound comprising eight herbs, has demonstrated efficacy in enhancing ovarian function. Thus, an in-depth study of its anti-ovarian aging mechanism is required. PURPOSE: To evaluate the anti-ovarian aging effect of HSYC in naturally aged mice and investigate the underlying mechanism by analyzing the gut microbiota (GM), metabolome, and transcriptome. METHODS: Young and advanced maternal age (AMA) mice were selected for this study. Hematoxylin and eosin staining, fluorescence staining, western blotting, and qPCR analyses were used to detect the phenotypes associated with ovarian aging. Subsequently, analyses of the GM, transcriptome, and metabolome analyses were performed to explore the potential mechanisms of action of HSYC. Finally, in vivo and in vitro experiments were performed to verify potential therapeutic mechanisms. RESULTS: HSYC promoted follicular development in AMA mice and ameliorated age-related mitochondrial dysfunction, apoptosis, and defects in DNA damage repair. GM analysis revealed that HSYC treatment significantly increased the abundance of Akkermansia and Turicibacter. Transcriptome and metabolome analyses showed that HSYC might mitigate ovarian aging by regulating metabolic pathways, amino acid metabolism, glutathione metabolism, and the synthesis of pantothenic acid and coenzyme A. Combined transcriptomic and metabolomic analyses identified the glutathione metabolic pathway as the key pathway through which HSYC counteracts ovarian aging. Additional experimental verification confirmed that HSYC upregulated the glutathione metabolic genes GPX8, GSTA1, and GSTA4, increased glutathione-related products (GSH), and reduced ROS levels. CONCLUSIONS: HSYC exerts beneficial therapeutic effects on ovarian aging by regulating multiple endogenous metabolites, targets, and metabolic pathways, with an emphasis on its anti-ovarian aging effects through the glutathione metabolic pathway. These findings underscore the innovative potential of HSYC in addressing ovarian aging and offer a novel therapeutic approach that targets multiple biological pathways to improve the reproductive health of women with AMA..

Interspecies synergistic interactions mediated by cofactor exchange enhance stress tolerance by inducing biofilm formation.

Metabolic exchange plays a crucial role in shaping microbial community interactions and functions, including the exchange of small molecules such as cofactors. Cofactors are fundamental to enzyme catalytic activities; however, the role of cofactors in microbial stress tolerance is unclear. Here, we constructed a synergistic consortium containing two strains that could efficiently mineralize di-(2-ethylhexyl) phthalate under hyperosmotic stress. Integration of transcriptomic analysis, metabolic profiling, and a genome-scale metabolic model (GEM) facilitated the discovery of the potential mechanism of microbial interactions. Multi-omics analysis revealed that the vitamin B12-dependent methionine-folate cycle could be a key pathway for enhancing the hyperosmotic stress tolerance of synergistic consortium. Further GEM simulations revealed interspecies exchange of S-adenosyl-L-methionine and riboflavin, cofactors needed for vitamin B12 biosynthesis, which was confirmed by in vitro experiments. Overall, we proposed a new mechanism of bacterial hyperosmotic stress tolerance: bacteria might promote the production of vitamin B12 to enhance biofilm formation, and the species collaborate with each other by exchanging cofactors to improve consortium hyperosmotic stress tolerance. These findings offer new insights into the role of cofactors in microbial interactions and stress tolerance and are potentially exploitable for environmental remediation. IMPORTANCE: Metabolic interactions (also known as cross-feeding) are thought to be ubiquitous in microbial communities. Cross-feeding is the basis for many positive interactions (e.g., mutualism) and is a primary driver of microbial community assembly. In this study, a combination of multi-omics analysis and metabolic modeling simulation was used to reveal the metabolic interactions of a synthetic consortium under hyperosmotic stress. Interspecies cofactor exchange was found to promote biofilm formation under hyperosmotic stress. This provides a new perspective for understanding the role of metabolic interactions in microbial communities to enhance environmental adaptation, which is significant for improving the efficiency of production activities and environmental bioremediation.

Multi-omics analysis of antiviral interactions of Elizabethkingia anophelis and Zika virus.

The microbial communities residing in the mosquito midgut play a key role in determining the outcome of mosquito pathogen infection. Elizabethkingia anophelis, originally isolated from the midgut of Anopheles gambiae possess a broad-spectrum antiviral phenotype, yet a gap in knowledge regarding the mechanistic basis of its interaction with viruses exists. The current study aims to identify pathways and genetic factors linked to E. anophelis antiviral activity. The understanding of E. anophelis antiviral mechanism could lead to novel transmission barrier tools to prevent arboviral outbreaks. We utilized a non-targeted multi-omics approach, analyzing extracellular lipids, proteins, metabolites of culture supernatants coinfected with ZIKV and E. anophelis. We observed a significant decrease in arginine and phenylalanine levels, metabolites that are essential for viral replication and progression of viral infection. This study provides insights into the molecular basis of E. anophelis antiviral phenotype. The findings lay a foundation for in-depth mechanistic studies.

Multi-omics analysis reveals genes and metabolites involved in Streptococcus suis biofilm formation.

BACKGROUND: Streptococcus suis is an important zoonotic pathogen. Biofilm formation largely explains the difficulty in preventing and controlling S. suis. However, little is known about the molecular mechanism of S. suis biofilm formation. RESULTS: In this study, transcriptomic and metabolomic analyses of S. suis in biofilm and planktonic states were performed to identify key genes and metabolites involved in biofilm formation. A total of 789 differential genes and 365 differential metabolites were identified. By integrating transcriptomics and metabolomics, five main metabolic pathways were identified, including amino acid pathway, nucleotide metabolism pathway, carbon metabolism pathway, vitamin and cofactor metabolism pathway, and aminoacyl-tRNA biosynthesis metabolic pathway. CONCLUSIONS: These results provide new insights for exploring the molecular mechanism of S. suis biofilm formation.

Single-Cell Sequencing: High-Resolution Analysis of Cellular Heterogeneity in Autoimmune Diseases.

Autoimmune diseases (AIDs) are complex in etiology and diverse in classification but clinically show similar symptoms such as joint pain and skin problems. As a result, the diagnosis is challenging, and usually, only broad treatments can be available. Consequently, the clinical responses in patients with different types of AIDs are unsatisfactory. Therefore, it is necessary to conduct more research to figure out the pathogenesis and therapeutic targets of AIDs. This requires research technologies with strong extraction and prediction capabilities. Single-cell sequencing technology analyses the genomic, epigenomic, or transcriptomic information at the single-cell level. It can define different cell types and states in greater detail, further revealing the molecular mechanisms that drive disease progression. These advantages enable cell biology research to achieve an unprecedented resolution and scale, bringing a whole new vision to life science research. In recent years, single-cell technology especially single-cell RNA sequencing (scRNA-seq) has been widely used in various disease research. In this paper, we present the innovations and applications of single-cell sequencing in the medical field and focus on the application contributing to the differential diagnosis and precise treatment of AIDs. Despite some limitations, single-cell sequencing has a wide range of applications in AIDs. We finally present a prospect for the development of single-cell sequencing. These ideas may provide some inspiration for subsequent research.

Multi-omics analysis and experimental validation of the value of monocyte-associated features in prostate cancer prognosis and immunotherapy.

Background: Monocytes play a critical role in tumor initiation and progression, with their impact on prostate adenocarcinoma (PRAD) not yet fully understood. This study aimed to identify key monocyte-related genes and elucidate their mechanisms in PRAD. Method: Utilizing the TCGA-PRAD dataset, immune cell infiltration levels were assessed using CIBERSORT, and their correlation with patient prognosis was analyzed. The WGCNA method pinpointed 14 crucial monocyte-related genes. A diagnostic model focused on monocytes was developed using a combination of machine learning algorithms, while a prognostic model was created using the LASSO algorithm, both of which were validated. Random forest and gradient boosting machine singled out CCNA2 as the most significant gene related to prognosis in monocytes, with its function further investigated through gene enrichment analysis. Mendelian randomization analysis of the association of HLA-DR high-expressing monocytes with PRAD. Molecular docking was employed to assess the binding affinity of CCNA2 with targeted drugs for PRAD, and experimental validation confirmed the expression and prognostic value of CCNA2 in PRAD. Result: Based on the identification of 14 monocyte-related genes by WGCNA, we developed a diagnostic model for PRAD using a combination of multiple machine learning algorithms. Additionally, we constructed a prognostic model using the LASSO algorithm, both of which demonstrated excellent predictive capabilities. Analysis with random forest and gradient boosting machine algorithms further supported the potential prognostic value of CCNA2 in PRAD. Gene enrichment analysis revealed the association of CCNA2 with the regulation of cell cycle and cellular senescence in PRAD. Mendelian randomization analysis confirmed that monocytes expressing high levels of HLA-DR may promote PRAD. Molecular docking results suggested a strong affinity of CCNA2 for drugs targeting PRAD. Furthermore, immunohistochemistry experiments validated the upregulation of CCNA2 expression in PRAD and its correlation with patient prognosis. Conclusion: Our findings offer new insights into monocyte heterogeneity and its role in PRAD. Furthermore, CCNA2 holds potential as a novel targeted drug for PRAD.

Biodegradation and bioaugmentation of tetracycline by Providencia stuartii TX2: Performance, degradation pathway, genetic background, key enzymes, and application risk assessment.

The antibiotic tetracycline (TC) is an emerging pollutant frequently detected in various environments. Biodegradation is a crucial approach for eliminating TC contamination. However, only a few efficient TC-degrading bacteria have been isolated, and the molecular mechanisms of TC degradation, as well as their application potential, remain poorly understood. This study isolated a novel TC-degrading bacterium, Providencia stuartii TX2, from the intestine of black soldier fly larvae. TX2 exhibited remarkable performance, degrading 72.17 % of 400 mg/L TC within 48 h. Genomic analysis of TX2 unveiled the presence of antibiotic resistance genes and TC degradation enzymes. Transcriptomic analysis highlighted the roles of proteins related to efflux pumps, enzymatic transformation, adversity resistance, and unknown functions. Three TC degradation pathways were proposed, with TC being transformed into 27 metabolites through epimerization, hydroxylation, oxygenation, ring opening, and de-grouping, reducing TC toxicity. Additionally, TX2 significantly enhanced TC biodegradation in four TC-contaminated environmental samples and reduced antibiotic resistance genes and mobile genetic elements in chicken manure. This research provides insights into the survival and biodegradation mechanisms of Providencia stuartii TX2 and evaluates its potential for environmental bioremediation.

Integrative multi-omics analysis reveals cellular and molecular insights into primary Sjögren's syndrome.

Objective: This study aims to comprehensively analyze genomic, transcriptomic, proteomic, and single-cell sequencing data to unravel the molecular basis of primary Sjögren's syndrome (pSS) and explore potential therapeutic targets. Methods: Mendelian randomization and single-cell RNA sequencing were employed to analyze pSS data. Differentially expressed genes specific to different blood cell types were identified. Integration of multiomics data facilitated the exploration of genetic regulatory relationships. Results: The analysis revealed distinct cell clusters representing various immune cell subsets. Several genes, including cathepsin S (CTSS) and glutathione S-transferase omega 1 (GSTO1), were identified as potential biomarkers and therapeutic targets for pSS. Diagnostic utility analysis demonstrated the discriminatory power of CTSS and GSTO1 in distinguishing pSS patients from healthy controls. Conclusion: The findings highlight the importance of integrating multiomics data for understanding pSS pathogenesis. CTSS and GSTO1 show promise as diagnostic biomarkers and potential therapeutic targets for pSS. Further investigations are warranted to elucidate the underlying mechanisms and develop targeted therapies for this complex autoimmune disease.

Biodegradation of humic acids by Streptomyces rochei to promote the growth and yield of corn.

Humic acids (HAs) are organic macromolecules that play an important role in improving soil properties, plant growth and agronomic parameters. However, the feature of relatively complex aromatic structure makes it difficult to be degraded, which restricts the promotion to the crop growth. Thus, exploring microorganisms capable of degrading HAs may be a potential solution. Here, a HAs-degrading strain, Streptomyces rochei L1, and its potential for biodegradation was studied by genomics, transcriptomics, and targeted metabolomics analytical approaches. The results showed that the high molecular weight HAs were cleaved to low molecular aliphatic and aromatic compounds and their derivatives. This cleavage may be associated with the laccase (KatE). In addition, the polysaccharide deacetylase (PdgA) catalyzes the removal of acetyl groups from specific sites on the HAs molecule, resulting in structural changes. The field experiment showed that the degraded HAs significantly promote the growth of corn seedlings and increase the corn yield by 3.6 %. The HAs-degrading products, including aromatic and low molecular weight aliphatic substances as well as secondary metabolites from S. rochei L1, might be the key components responsible for the corn promotion. Our findings will advance the application of HAs as soil nutrients for the green and sustainable agriculture.

Integrated transcriptomic and proteomic analysis of exogenous abscisic acid regulation on tuberous root development in Pseudostellaria heterophylla.

Abscisic acid (ABA) significantly regulates plant growth and development, promoting tuberous root formation in various plants. However, the molecular mechanisms of ABA in the tuberous root development of Pseudostellaria heterophylla are not yet fully understood. This study utilized Illumina sequencing and de novo assembly strategies to obtain a reference transcriptome associated with ABA treatment. Subsequently, integrated transcriptomic and proteomic analyses were used to determine gene expression profiles in P. heterophylla tuberous roots. ABA treatment significantly increases the diameter and shortens the length of tuberous roots. Clustering analysis identified 2,256 differentially expressed genes and 679 differentially abundant proteins regulated by ABA. Gene co-expression and protein interaction networks revealed ABA positively induced 30 vital regulators. Furthermore, we identified and assigned putative functions to transcription factors (PhMYB10, PhbZIP2, PhbZIP, PhSBP) that mediate ABA signaling involved in the regulation of tuberous root development, including those related to cell wall metabolism, cell division, starch synthesis, hormone metabolism. Our findings provide valuable insights into the complex signaling networks of tuberous root development modulated by ABA. It provided potential targets for genetic manipulation to improve the yield and quality of P. heterophylla, which could significantly impact its cultivation and medicinal value.

Multi-Omics Characterization of E3 Regulatory Patterns in Different Cancer Types.

Ubiquitination, a post-translational modification, refers to the covalent attachment of ubiquitin molecules to substrates. This modification plays a critical role in diverse cellular processes such as protein degradation. The specificity of ubiquitination for substrates is regulated by E3 ubiquitin ligases. Dysregulation of ubiquitination has been associated with numerous diseases, including cancers. In our study, we first investigated the protein expression patterns of E3 ligases across 12 cancer types. Our findings indicated that E3 ligases tend to be up-regulated and exhibit reduced tissue specificity in tumors. Moreover, the correlation of protein expression between E3 ligases and substrates demonstrated significant changes in cancers, suggesting that E3-substrate specificity alters in tumors compared to normal tissues. By integrating transcriptome, proteome, and ubiquitylome data, we further characterized the E3-substrate regulatory patterns in lung squamous cell carcinoma. Our analysis revealed that the upregulation of the SKP2 E3 ligase leads to excessive degradation of BRCA2, potentially promoting tumor cell proliferation and metastasis. Furthermore, the upregulation of E3 ubiquitin-protein ligase TRIM33 was identified as a biomarker associated with a favorable prognosis by inhibiting the cell cycle. This work exemplifies how leveraging multi-omics data to analyze E3 ligases across various cancers can unveil prognosis biomarkers and facilitate the identification of potential drug targets for cancer therapy.

Diagnostic and prognostic role of LINC01767 in hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is a primary contributor to cancer-related mortality on a global scale. However, the underlying molecular mechanisms are still poorly understood. Long noncoding RNAs are emerging markers for HCC diagnosis, prognosis, and therapeutic target. No study of LINC01767 in HCC was published. AIM: To conduct a multi-omics analysis to explore the roles of LINC01767 in HCC for the first time. METHODS: DESeq2 Package was used to analyze different gene expressions. Receiver operating characteristic curves assessed the diagnostic performance. Kaplan-Meier univariate and Cox multivariate analyses were used to perform survival analysis. The least absolute shrinkage and selection operator (LASSO)-Cox was used to identify the prediction model. Subsequent to the validation of LINC01767 expression in HCC fresh frozen tissues through quantitative real time polymerase chain reaction, next generation sequencing was performed following LINC01767 over expression (GSE243371), and Gene Ontology/Kyoto Encyclopedia of Genes and Genomes/Gene Set Enrichment Analysis/ingenuity pathway analysis was carried out. In vitro experiment in Huh7 cell was carried out. RESULTS: LINC01767 was down-regulated in HCC with a log fold change = 1.575 and was positively correlated with the cancer stemness. LINC01767 was a good diagnostic marker with area under the curve (AUC) [0.801, 95% confidence interval (CI): 0.751-0.852, P = 0.0106] and an independent predictor for overall survival (OS) with hazard ratio = 1.899 (95%CI: 1.01-3.58, P = 0.048). LINC01767 nomogram model showed a satisfied performance. The top-ranked regulatory network analysis of LINC01767 showed the regulation of genes participating various pathways. LASSO regression identified the 9-genes model showing a more satisfied performance than 5-genes model to predict the OS with AUC > 0.75. LINC01767 was down-expressed obviously in tumor than para-tumor tissues in our cohort as well as in cancer cell line; the over expression of LINC01767 inhibit cell proliferation and clone formation of Huh7 in vitro. CONCLUSION: LINC01767 was an important tumor suppressor gene in HCC with good diagnostic and prognostic performance.

A comprehensive multi-omics analysis reveals unique signatures to predict Alzheimer's disease.

Background: Complex disorders, such as Alzheimer's disease (AD), result from the combined influence of multiple biological and environmental factors. The integration of high-throughput data from multiple omics platforms can provide system overviews, improving our understanding of complex biological processes underlying human disease. In this study, integrated data from four omics platforms were used to characterise biological signatures of AD. Method: The study cohort consists of 455 participants (Control:148, Cases:307) from the Religious Orders Study and Memory and Aging Project (ROSMAP). Genotype (SNP), methylation (CpG), RNA and proteomics data were collected, quality-controlled and pre-processed (SNP = 130; CpG = 83; RNA = 91; Proteomics = 119). Using a diagnosis of Mild Cognitive Impairment (MCI)/AD combined as the target phenotype, we first used Partial Least Squares Regression as an unsupervised classification framework to assess the prediction capabilities for each omics dataset individually. We then used a variation of the sparse generalized canonical correlation analysis (sGCCA) to assess predictions of the combined datasets and identify multi-omics signatures characterising each group of participants. Results: Analysing datasets individually we found methylation data provided the best predictions with an accuracy of 0.63 (95%CI = [0.54-0.71]), followed by RNA, 0.61 (95%CI = [0.52-0.69]), SNP, 0.59 (95%CI = [0.51-0.68]) and proteomics, 0.58 (95%CI = [0.51-0.67]). After integration of the four datasets, predictions were dramatically improved with a resulting accuracy of 0.95 (95% CI = [0.89-0.98]). Conclusion: The integration of data from multiple platforms is a powerful approach to explore biological systems and better characterise the biological signatures of AD. The results suggest that integrative methods can identify biomarker panels with improved predictive performance compared to individual platforms alone. Further validation in independent cohorts is required to validate and refine the results presented in this study.

Elucidating the role of angiogenesis-related genes in colorectal cancer: a multi-omics analysis.

Background: Angiogenesis plays a pivotal role in colorectal cancer (CRC), yet its underlying mechanisms demand further exploration. This study aimed to elucidate the significance of angiogenesis-related genes (ARGs) in CRC through comprehensive multi-omics analysis. Methods: CRC patients were categorized according to ARGs expression to form angiogenesis-related clusters (ARCs). We investigated the correlation between ARCs and patient survival, clinical features, consensus molecular subtypes (CMS), cancer stem cell (CSC) index, tumor microenvironment (TME), gene mutations, and response to immunotherapy. Utilizing three machine learning algorithms (LASSO, Xgboost, and Decision Tree), we screen key ARGs associated with ARCs, further validated in independent cohorts. A prognostic signature based on key ARGs was developed and analyzed at the scRNA-seq level. Validation of gene expression in external cohorts, clinical tissues, and blood samples was conducted via RT-PCR assay. Results: Two distinct ARC subtypes were identified and were significantly associated with patient survival, clinical features, CMS, CSC index, and TME, but not with gene mutations. Four genes (S100A4, COL3A1, TIMP1, and APP) were identified as key ARCs, capable of distinguishing ARC subtypes. The prognostic signature based on these genes effectively stratified patients into high- or low-risk categories. scRNA-seq analysis showed that these genes were predominantly expressed in immune cells rather than in cancer cells. Validation in two external cohorts and through clinical samples confirmed significant expression differences between CRC and controls. Conclusion: This study identified two ARG subtypes in CRC and highlighted four key genes associated with these subtypes, offering new insights into personalized CRC treatment strategies.

Identification of tumor-specific T cell signature predicting cancer immunotherapy response in bladder cancer by multi-omics analysis and experimental verification.

BACKGROUND: Numerous gene signatures predicting the prognosis of bladder cancer have been identified. However, a tumor-specific T cell signature related to immunotherapy response in bladder cancer remains under investigation. METHODS: Single-cell RNA and TCR sequencing from the Gene expression omnibus (GEO) database were used to identify tumor-specific T cell-related genes in bladder cancer. Subsequently, we constructed a tumor-specific T cell signature (TstcSig) and validated its clinical relevance for predicting immunotherapy response in multiple immunotherapy cohorts. Further analyses explored the immune characteristics of TstcSig in bladder cancer patients from other cohorts in the TCGA and GEO databases. Western blot (WB), multicolor immunofluorescence (MIF), qRT-PCR and flow cytometry assays were performed to validate the results of bioinformatics analysis. RESULTS: The established TstcSig, based on five tumor-specific T cell-related genes, could predict outcomes in a bladder cancer immunotherapy cohort. This was verified using two additional immunotherapy cohorts and showed better predictive performance compared to 109 published T cell signatures. TstcSig was strongly correlated with immune characteristics such as immune checkpoint gene expression, tumor mutation burden, and T cell infiltration, as validated by single-cell and spatial transcriptomics datasets. Notably, the positive correlation between TstcSig and T cell infiltration was confirmed in the TCGA cohort. Furthermore, pan-cancer analysis demonstrated the heterogeneity of the prognostic value of TstcSig. Tumor-specific T cells highly expressed CD27, IFNG, GZMB and CXCL13 and secreted more effector cytokines for tumor cell killing, as validated experimentally. CONCLUSION: We developed a five-gene signature (including VAMP5, TIGIT, LCK, CD27 and CACYBP) based on tumor-specific T cell-related genes to predict the immunotherapy response in bladder cancer patients.

Multi-omic characterization of antibody-producing CHO cell lines elucidates metabolic reprogramming and nutrient uptake bottlenecks.

Characterizing the phenotypic diversity and metabolic capabilities of industrially relevant manufacturing cell lines is critical to bioprocess optimization and cell line development. Metabolic capabilities of production hosts limit nutrient and resource channeling into desired cellular processes and can have a profound impact on productivity. These limitations cannot be directly inferred from measured data such as spent media concentrations or transcriptomics. Here, we present an integrated multi-omic analysis pipeline combining exo-metabolomics, transcriptomics, and genome-scale metabolic network analysis and apply it to three antibody-producing Chinese Hamster Ovary cell lines to identify reprogramming features associated with high-producing clones and metabolic bottlenecks limiting product formation in an industrial bioprocess. Analysis of individual datatypes revealed a decreased nitrogenous byproduct secretion in high-producing clones and the topological changes in peripheral metabolic pathway expression associated with phase shifts. An integrated omics analysis in the context of the genome-scale metabolic model elucidated the differences in central metabolism and identified amino acid utilization bottlenecks limiting cell growth and antibody production that were not evident from exo-metabolomics or transcriptomics alone. Thus, we demonstrate the utility of a multi-omics characterization in providing an in-depth understanding of cellular metabolism, which is critical to efforts in cell engineering and bioprocess optimization.

Multi-omics integration identifies ferroptosis involved in black phosphorus quantum dots-induced renal injury.

Black phosphorus quantum dots (BPQDs) have recently emerged as a highly promising contender in biomedical applications ranging from drug delivery systems to cancer therapy modalities. Nevertheless, the potential toxicity and its effects on human health need to be thoroughly investigated. In this study, we utilized multi-omics integrated approaches to explore the complex mechanisms of BPQDs-induced kidney injury. First, histological examination showed severe kidney injury in male mice after subacute exposure to 1 mg/kg BPQDs for 28 days. Subsequently, transcriptomic and metabolomic analyses of kidney tissues exposed to BPQDs identified differentially expressed genes and metabolites associated with ferroptosis, an emerging facet of regulated cell death. Our findings highlight the utility of the multi-omics integrated approach in predicting and elucidating potential toxicological outcomes of nanomaterials. Furthermore, our study provides a comprehensive understanding of the mechanisms driving BPQDs-induced kidney injury, underscoring the importance of recognizing ferroptosis as a potential toxic mechanism associated with BPQDs.

Establishing an oxidative stress mitochondria-related prognostic model in hepatocellular carcinoma based on multi-omics characteristics and machine learning computational framework.

Hepatocellular carcinoma (HCC) has high incidence and mortality rates worldwide. Damaged mitochondria are characterized by the overproduction of reactive oxygen species (ROS), which can promote cancer development. The prognostic value of the interplay between mitochondrial function and oxidative stress in HCC requires further investigation. Gene expression data of HCC samples were collected from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and International Cancer Genome Consortium (ICGC). We screened prognostic oxidative stress mitochondria-related (OSMT) genes at the bulk transcriptome level. Based on multiple machine learning algorithms, we constructed a consensus oxidative stress mitochondria-related signature (OSMTS), which contained 26 genes. In addition, we identified six of these genes as having a suitable prognostic value for OSMTS to reduce the difficulty of clinical application. Univariate and multivariate analyses verified the OSMTS as an independent prognostic factor for overall survival (OS) in HCC patients. The OSMTS-related nomogram demonstrated to be a powerful tool for the clinical diagnosis of HCC. We observed differences in biological function and immune cell infiltration in the tumor microenvironment between the high- and low-risk groups. The highest expression of the OSMTS was detected in hepatocytes at the single-cell transcriptome level. Hepatocytes in the high- and low-risk groups differed significantly in terms of biological function and intercellular communication. Moreover, at the spatial transcriptome level, high expression of OSMTS was mainly in regions enriched in hepatocytes and B cells. Potential drugs targeting specific risk subgroups were identified. Our study revealed that the OSMTS can serve as a promising tool for prognosis prediction and precise intervention in HCC patients.

Action of circulating and infiltrating B cells in the immune microenvironment of colorectal cancer by single-cell sequencing analysis.

BACKGROUND: The complexity of the immune microenvironment has an impact on the treatment of colorectal cancer (CRC), one of the most prevalent malignancies worldwide. In this study, multi-omics and single-cell sequencing techniques were used to investigate the mechanism of action of circulating and infiltrating B cells in CRC. By revealing the heterogeneity and functional differences of B cells in cancer immunity, we aim to deepen our understanding of immune regulation and provide a scientific basis for the development of more effective cancer treatment strategies. AIM: To explore the role of circulating and infiltrating B cell subsets in the immune microenvironment of CRC, explore the potential driving mechanism of B cell development, analyze the interaction between B cells and other immune cells in the immune microenvironment and the functions of communication molecules, and search for possible regulatory pathways to promote the anti-tumor effects of B cells. METHODS: A total of 69 paracancer (normal), tumor and peripheral blood samples were collected from 23 patients with CRC from The Cancer Genome Atlas database (https://portal.gdc.cancer.gov/). After the immune cells were sorted by multicolor flow cytometry, the single cell transcriptome and B cell receptor group library were sequenced using the 10X Genomics platform, and the data were analyzed using bioinformatics tools such as Seurat. The differences in the number and function of B cell infiltration between tumor and normal tissue, the interaction between B cell subsets and T cells and myeloid cell subsets, and the transcription factor regulatory network of B cell subsets were explored and analyzed. RESULTS: Compared with normal tissue, the infiltrating number of CD20+B cell subsets in tumor tissue increased significantly. Among them, germinal center B cells (GCB) played the most prominent role, with positive clone expansion and heavy chain mutation level increasing, and the trend of differentiation into memory B cells increased. However, the number of plasma cells in the tumor microenvironment decreased significantly, and the plasma cells secreting IgA antibodies decreased most obviously. In addition, compared with the immune microenvironment of normal tissues, GCB cells in tumor tissues became more closely connected with other immune cells such as T cells, and communication molecules that positively regulate immune function were significantly enriched. CONCLUSION: The role of GCB in CRC tumor microenvironment is greatly enhanced, and its affinity to tumor antigen is enhanced by its significantly increased heavy chain mutation level. Meanwhile, GCB has enhanced its association with immune cells in the microenvironment, which plays a positive anti-tumor effect.