Deciphering the acidophilia and acid resistance in Acetilactobacillus jinshanensis dominating baijiu fermentation through multi-omics analysis.

Lactic acid bacteria (LAB) are pivotal in constructing the intricate bio-catalytic networks underlying traditional fermented foods such as Baijiu. However, LAB and their metabolic mechanisms are partially understood in Moutai flavor Baijiu fermentation. Here, we found that Acetilactobacillus jinshanensis became the· dominant species with relative abundance reaching 92%, where the acid accumulated rapidly and peaked at almost 30 g/kg in Moutai flavor Baijiu. After separation, purification, and cultivation, A. jinshanensis exhibited pronounced acidophilia and higher acid resistance compared to other LAB. Further integrated multi-omics analysis revealed that fatty acid synthesis, cell membrane integrity, pHi and redox homeostasis maintenance, protein and amide syntheses were possibly crucial acid-resistant mechanisms in A. jinshanensis. Structural proteomics indicated that the surfaces of A. jinshanensis proteases contained more positively charged amino acid residues to maintain protein stability in acidic environments. The genes HSP20 and acpP were identified as acid-resistant genes for A. jinshanensis by heterologous expression analysis. These findings not only enhance our understanding of LAB in Baijiu, providing a scientific basis for acid regulation for production process, but also offer valuable insights for studying core species in other fermentation systems.

Deciphering heart failure: an integrated proteomic and transcriptomic approach with experimental validation.

This study analyzed transcriptomic and proteomic data to identify molecular changes during heart failure (HF). Additionally,we embarked on an exploration of the prospect of therapeutic intervention through the manipulation of proteins implicated in ferroptosis. Three publicly available microarray datasets (GSE135055, GSE147236, GSE161472) profiling left ventricular samples from HF patients and healthy controls were obtained. Differentially expressed genes were identified in each dataset and cross-analyzed to determine shared gene signatures. Enrichment analysis of Gene Ontology (GO) terms, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and gene set enrichment analysis were performed. Differentially expressed proteins were obtained from published proteomic studies and integrated with the transcriptomic results. To validate findings, a HF mouse model was generated and ferroptosis-related proteins were evaluated. Additionally, the effect of suppression of ferroptosis on hypoxia-induced ischemia model in HL-1 cardiomyocytes was assessed by knocking down Acyl-CoA synthetase long-chain family member 4 (ACSL4) using small interfering RNA (siRNA).Cross-analysis of differentially expressed genes (DEGs) in the GSE135055, GSE147236 and GSE161472 datasets revealed 224 up-regulated and 187 down-regulated potential genes which showed high enrichment in immune, inflammatory and metabolic pathways. Notably, four proteins, among them ACSL4, displayed consistent alterations at both the transcriptional and protein levels. In the HF mouse model, ACSL4 exhibited an elevation, whereas negative regulators of ferroptosis witnessed a decrement. Subsequently, knockdown of ACSL4 in a hypoxia-induced ischemic HL-1 cardiomyocyte cell model upregulated the expression of ferroptosis inhibitory protein and decreased the levels of reactive oxygen species (ROS), malondialdehyde (MDA)., and free iron and increased cell viability. Comprehensive multi-omics analysis revealed that the expression of the molecular target ACSL4 was increased in HF. Targeting ACSL4 to inhibit ferroptosis may represent a novel therapeutic strategy for HF treatment.

Transcriptomics and proteomics analyses reveal the molecular mechanisms of yeast cells regulated by Phe-Cys against ethanol-oxidation cross-stress.

Antioxidant dipeptide Phe-Cys (FC) could dramatically improve yeast cells resistance to ethanol-oxidation cross-stress, but the regulatory mechanisms remain unclear. Therefore, transcriptomic and proteomic analyses were conducted to investigate the effects of FC treatment on yeast under ethanol-oxidation cross-stress. Following FC supplementation, 875 differential expressed genes (DEGs) and 1296 differential expressed proteins (DEPs) were identified. Integrated analysis revealed a substantial enrichment of DEGs and DEPs in the KEGG pathways of carbon metabolism, amino acid biosynthesis, cofactor biosynthesis, and glycolysis/gluconeogenesis. Furthermore, FC improved yeast cell membrane integrity by promoting fatty acids and steroids biosynthesis, and implemented a high-energy strategy by upregulating glycolysis and oxidative phosphorylation. Additionally, alterations in DEGs and DEPs levels associated with amino acids metabolism accelerated protein synthesis and enhanced cell viability. In conclusion, this study elucidated the response mechanisms of yeast to FC treatment under ethanol-oxidation cross-stress, providing a theoretical basis for the application of FC in high-gravity brewing.

Multiomics approach discloses lipids and metabolites profiles associated to Parkinson's disease stages and applied therapies.

Profiling circulating lipids and metabolites in Parkinson's disease (PD) patients could be useful not only to highlight new pathways affected in PD condition but also to identify sensitive and effective biomarkers for early disease detection and potentially effective therapeutic interventions. In this study we adopted an untargeted omics approach in three groups of patients (No L-Dopa, L-Dopa and DBS) to disclose whether long-term levodopa treatment with or without deep brain stimulation (DBS) could reflect a characteristic lipidomic and metabolomic signature at circulating level. Our findings disclosed a wide up regulation of the majority of differentially regulated lipid species that increase with disease progression and severity. We found a relevant modulation of triacylglycerols and acyl-carnitines, together with an altered profile in adiponectin and leptin, that can differentiate the DBS treated group from the others PD patients. We found a highly significant increase of exosyl ceramides (Hex2Cer) and sphingoid bases (SPB) in PD patients mainly in DBS group (p < 0.0001), which also resulted in a highly accurate diagnostic performance. At metabolomic level, we found a wide dysregulation of pathways involved in the biosynthesis and metabolism of several amino acids. The most interesting finding was the identification of a specific modulation of L-glutamic acid in the three groups of patients. L-glutamate levels increased slightly in No L-Dopa and highly in L-Dopa patients while decreased in DBS, suggesting that DBS therapy might have a beneficial effect on the glutamatergic cascade. All together, these data provide novel insights into the molecular and metabolic alterations underlying PD therapy and might be relevant for PD prediction, diagnosis and treatment.

Pentachlorophenol increases diabetes risk by damaging ß-cell secretion and disrupting gut microbial-related amino acids and fatty acids biosynthesis.

Pentachlorophenol (PCP), a ubiquitous environmental pollutant, has been reported as a possible contributor to diabetes. However, evidence for general population is scarce while related mechanisms are largely unknown. Using a representative population-based case-control study in Beijing (n = 1796), we found a positive association between PCP exposure and diabetes risk with the odds ratio reaching 1.68 (95 % confidence interval: 1.30 to 2.18). A further rat experiment revealed that low-dose PCP mimicking real-world human exposure can significantly impair glycemic homeostasis by inducing pancreatic ß-cell dysfunction, with non-linear dose-response relationships. Subsequent multi-omics analysis suggested that low-dose PCP led to notable gut microbiota dysbiosis (especially the species from genus Prevotella, such as intermedia, dentalis, ruminicola, denticola, melaninogenica, and oris), decreased serum amino acids (L-phenylalanine, L-tyrosine, and L-tryptophan) and increased serum fatty acids (oleic and palmitic acid) in rats, while strong correlations were observed among alterations of gut microbes, serum metabolites and glycemic-related biomarkers (e.g., fasting blood glucose and insulin). Collectively, these results imply PCP may increase diabetes risk by disrupting gut microbial-related amino acids and fatty acids biosynthesis. This will help guide future in-depth studies on the roles of PCP in the development of human diabetes.

Ovarian multi-omics analysis reveals key rate-limiting enzymes FASN, SCD5, FADS1, 3BHSD, and STAR as potential targets for regulating kidding traits in goats.

The kidding traits of goats are an important index of production. However, the molecular regulatory mechanisms of kidding traits in goats have not been fully elucidated. This study aimed to investigate the molecular regulatory network of kidding traits in goats. Multi-omics revealed the enrichment of 10 signaling pathways, with fatty acid biosynthesis, biosynthesis of unsaturated fatty acids, and steroid hormone biosynthesis pathways being closely related to reproduction. Interestingly, the key rate-limiting enzymes, fatty acid synthase (FASN), stearoyl-CoA desaturase 5 (SCD5), fatty acid desaturase 1 (FADS1), 3ß-hydroxysteroid dehydrogenase/isomerase (3BHSD), and steroidogenic acute regulatory protein (STAR) enriched in these pathways regulate changes in reproduction-related metabolites. In interference experiments, it was observed that suppressing these key rate-limiting enzymes inhibited the expression of CYP19A1, ESR2, and FSHR. Furthermore, interference inhibited granulosa cell proliferation, caused cell cycle arrest, and promoted apoptosis. Thus, these results suggest that the specific markers of nanny goats with multiple kids are the key rate-limiting enzymes FASN, SCD5, FADS1, 3BHSD, and STAR. These findings may greatly enhance the understanding of regulatory mechanisms that govern goat parturition.

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

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

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

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

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

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

NAT10 functions as a pivotal regulator in gastric cancer metastasis and tumor immunity.

Gastric cancer (GC) presents a significant global health burden, with metastasis being the leading cause of treatment failure and mortality. NAT10, a regulatory protein involved in mRNA acetylation, has been implicated in various cancers. However, its role in GC, especially concerning metastasis and immune interactions, remains unclear. Utilizing multi-omics data from gastric cancer samples, we conducted comprehensive analyses to investigate NAT10 expression, its correlation with clinical parameters and immune relevance. Bioinformatics analysis and digital image processing were employed for this purpose. Furthermore, in vitro and in vivo experiments were conducted to elucidate the functional role of NAT10 in gastric cancer progression, aiming to provide deeper biological insights. Our findings reveal a significant association between NAT10 expression and various aspects of transcriptional, protein, as well as tumor immunity in GC patients. Additionally, we demonstrated that NAT10 promotes gastric cancer cell proliferation and migration, both in cellular models and in animal studies, suggesting its involvement in early tumor microvascular metastasis. NAT10 emerges as a promising molecular target, offering potential avenues for further research into molecular mechanisms and therapeutic strategies for GC.

Innate immune response in COVID-19: single-cell multi-omics profile of NK lymphocytes in a clinical case series.

BACKGROUND: COVID-19 pandemic caused by the Severe Acute Respiratory Syndrome-Coronavirus-2 (SARS-CoV-2) represents the biggest global health emergency in recent decades. The host immune response to SARS-CoV-2 seems to play a key role in disease pathogenesis and clinical manifestations, with Natural Killer (NK) lymphocytes being among the targets of virus-induced regulation. METHODS: This study performed a single-cell multi-omics analysis of transcripts and proteins of NK lymphocytes in COVID-19 patients, for the characterization of the innate immunological response to infection. NK cells were isolated from peripheral blood samples collected from adult subjects divided into 3 study groups: (1) non-infected subjects (Naïve group, n = 3), (2) post COVID-19 convalescent subjects (Healed group, n = 3) and (3) patients that were vaccinated against SARS-CoV-2 (Vaccine group, n = 3). Cells were then analysed by the BD Rhapsody System for the single-cell multi-omics investigation of transcriptome and membrane proteins. RESULTS: The bioinformatic analysis identified 5 cell clusters which differentially expressed gene/protein markers, defining NK cell subsets as "Active NK cells" and "Mature NK cells". Calculating the relative proportion of each cluster within patient groups, more than 40% of the Naïve group cell population was found to belong to Mature NKs, whereas more than 75% of the Vaccine group cell population belonged to the cluster of Active NKs. Regarding the Healed group, it seemed to show intermediate phenotype between Active and Mature NK cells. Differential expression of specific genes, proteins and signaling pathways was detected comparing the profile of the 3 experimental groups, revealing a more activated NK cell phenotype in vaccinated patients versus recovered individuals. CONCLUSIONS: The present study detected differential expression of NK cell markers in relation to SARS-CoV-2 infection and vaccine administration, suggesting the possibility to identify key molecular targets for clinical-diagnostic use of the individual response to viral infection and/or re-infection.

Multi-omics reveals lactylation-driven regulatory mechanisms promoting tumor progression in oral squamous cell carcinoma.

BACKGROUND: Lactylation, a post-translational modification, is increasingly recognized for its role in cancer progression. This study investigates its prevalence and impact in oral squamous cell carcinoma (OSCC). RESULTS: Immunohistochemical staining of 81 OSCC cases shows lactylation levels correlate with malignancy grading. Proteomic analyses of six OSCC tissue pairs reveal 2765 lactylation sites on 1033 proteins, highlighting its extensive presence. These modifications influence metabolic processes, molecular synthesis, and transport. CAL27 cells are subjected to cleavage under targets and tagmentation assay for accessible-chromatin with high-throughput sequencing, and transcriptomic sequencing pre- and post-lactate treatment, with 217 genes upregulated due to lactylation. Chromatin immunoprecipitation-quantitative PCR and real-time fluorescence quantitative PCR confirm the regulatory role of lactylation at the K146 site of dexh-box helicase 9 (DHX9), a key factor in OSCC progression. CCK8, colony formation, scratch healing, and Transwell assays demonstrate that lactylation mitigates the inhibitory effect of DHX9 on OSCC, thereby promoting its occurrence and development. CONCLUSIONS: Lactylation actively modulates gene expression in OSCC, with significant effects on chromatin structure and cellular processes. This study provides a foundation for developing targeted therapies against OSCC, leveraging the role of lactylation in disease pathogenesis.

Boosting succinic acid production of Yarrowia lipolytica at low pH through enhancing product tolerance and glucose metabolism.

BACKGROUND: Succinic acid (SA) is an important bio-based C4 platform chemical with versatile applications, including the production of 1,4-butanediol, tetrahydrofuran, and γ-butyrolactone. The non-conventional yeast Yarrowia lipolytica has garnered substantial interest as a robust cell factory for SA production at low pH. However, the high concentrations of SA, especially under acidic conditions, can impose significant stress on microbial cells, leading to reduced glucose metabolism viability and compromised production performance. Therefore, it is important to develop Y. lipolytica strains with enhanced SA tolerance for industrial-scale SA production. RESULTS: An SA-tolerant Y. lipolytica strain E501 with improved SA production was obtained through adaptive laboratory evolution (ALE). In a 5-L bioreactor, the evolved strain E501 produced 89.62 g/L SA, representing a 7.2% increase over the starting strain Hi-SA2. Genome resequencing and transcriptome analysis identified a mutation in the 26S proteasome regulatory subunit Rpn1, as well as genes involved in transmembrane transport, which may be associated with enhanced SA tolerance. By further fine-tuning the glycolytic pathway flux, the highest SA titer of 112.54 g/L to date at low pH was achieved, with a yield of 0.67 g/g glucose and a productivity of 2.08 g/L/h. CONCLUSION: This study provided a robust engineered Y. lipolytica strain capable of efficiently producing SA at low pH, thereby reducing the cost of industrial SA fermentation.

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

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

Mutual Interactions of Silymarin and Colon Microbiota in Healthy Young and Healthy Elder Subjects.

SCOPE: This multi-omic study investigates the bidirectional interactions between gut microbiota and silymarin metabolism, highlighting the differential effects across various age groups. Silymarin, the extract from Silybum marianum (milk thistle), is commonly used for its hepatoprotective effects. METHODS AND RESULTS: An in vitro fermentation colon model was used with microbiota from 20 stool samples obtained from healthy donors divided into two age groups. A combination of three analytical advanced techniques, namely proton nuclear magnetic resonance (1H NMR), next-generation sequencing (NGS), and liquid chromatography-mass spectrometry (LC-MS) was used to determine silymarin microbial metabolites over 24 h, overall metabolome, and microbiota composition. Silymarin at a low diet-relevant dose of 50 µg mL-1 significantly altered gut microbiota metabolism, reducing short-chain fatty acid (acetate, butyrate, propionate) production, glucose utilization, and increasing alpha-diversity. Notably, the study reveals age-related differences in silymarin catabolism. Healthy elderly donors (70-80 years) exhibited a significant increase in a specific catabolite associated with Oscillibacter sp., whereas healthy young donors (12-45 years) showed a faster breakdown of silymarin components, particularly isosilybin B, which is associated with higher abundance of Faecalibacterium and Erysipelotrichaceae UCG-003. CONCLUSION: This study provides insights into microbiome functionality in metabolizing dietary flavonolignans, highlighting implications for age-specific nutritional strategies, and advancing our understanding of dietary (poly)phenol metabolism.

Mass spectrometry-based multi-omics analysis reveals distinct molecular features in early and advanced stages of hepatocellular carcinoma.

Hepatocellular Carcinoma (HCC) is a serious primary solid tumor that is prevalent worldwide. Due to its high mortality rate, it is crucial to explore both early diagnosis and advanced treatment for HCC. In recent years, multi-omics approaches have emerged as promising tools to identify biomarkers and investigate molecular mechanisms of biological processes and diseases. In this study, we performed proteomics, phosphoproteomics, metabolomics, and lipidomics to reveal the molecular features of early- and advanced-stage HCC. The data obtained from these omics were analyzed separately and then integrated to provide a comprehensive understanding of the disease. The multi-omics results unveiled intricate biological pathways and interaction networks underlying the initiation and progression of HCC. Moreover, we proposed specific potential biomarker panels for both early- and advanced-stage HCC by overlapping our data with CPTAC database for HCC diagnosis, and deduced novel insights and mechanisms related to HCC origination and development, such as glucose depletion during tumor progression, ROCK1 deactivation and GSK3A activation.

Visual analysis of multi-omics data.

We present a tool for multi-omics data analysis that enables simultaneous visualization of up to four types of omics data on organism-scale metabolic network diagrams. The tool's interactive web-based metabolic charts depict the metabolic reactions, pathways, and metabolites of a single organism as described in a metabolic pathway database for that organism; the charts are constructed using automated graphical layout algorithms. The multi-omics visualization facility paints each individual omics dataset onto a different "visual channel" of the metabolic-network diagram. For example, a transcriptomics dataset might be displayed by coloring the reaction arrows within the metabolic chart, while a companion proteomics dataset is displayed as reaction arrow thicknesses, and a complementary metabolomics dataset is displayed as metabolite node colors. Once the network diagrams are painted with omics data, semantic zooming provides more details within the diagram as the user zooms in. Datasets containing multiple time points can be displayed in an animated fashion. The tool will also graph data values for individual reactions or metabolites designated by the user. The user can interactively adjust the mapping from data value ranges to the displayed colors and thicknesses to provide more informative diagrams.