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.

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.

Multi-omics revealed anti-fatigue property of polyphenol from areca nut.

BACKGROUND: Areca nut polyphenols (AP) that extracted from areca nut, have been demonstrated for their potential of anti-fatigue effects. However, the underlying mechanisms for the anti-fatigue properties of AP has not been fully elucidated to date. Previous studies have predominantly concentrated on single aspects, such as antioxidation and anti-inflammation, yet have lacked comprehensive multi-dimensional analyses. PURPOSE: To explore the underlying mechanism of AP in exerting anti-fatigue effects. METHODS: In this study, we developed a chronic sleep deprivation-induced fatigue model and used physiological, hematological, and biochemical indicators to evaluate the anti- fatigue efficacy of AP. Additionally, a multi-omics approach was employed to reveal the anti-fatigue mechanisms of AP from the perspective of microbiome, metabolome, and proteome. RESULTS: The detection of physiology, hematology and biochemistry index indicated that AP markedly alleviate mice fatigue state induced by sleep deprivation. The 16S rRNA sequencing showed the AP promoted the abundance of probiotics (Odoribacter, Dubosiella, Marvinbryantia, and Eubacterium) and suppressed harmful bacteria (Ruminococcus). On the other hand, AP was found to regulate the expression of colonic proteins, such as increases of adenosine triphosphate (ATP) synthesis and mitochondrial function related proteins, including ATP5A1, ATP5O, ATP5L, ATP5H, NDUFA, NDUFB, NDUFS, and NDUFV. Serum metabolomic analysis revealed AP upregulated the levels of anti-fatigue amino acids, such as taurine, leucine, arginine, glutamine, lysine, and l-proline. Hepatic proteins express levels, especially tricarboxylic acid (TCA) cycle (CS, SDHB, MDH2, and DLST) and redox-related proteins (SOD1, SOD2, GPX4, and PRDX3), were significantly recovered by AP administration. Spearman correlation analysis uncovered the strong correlation between microbiome, metabolome and proteome, suggesting the anti-fatigue effects of AP is attribute to the energy homeostasis and redox balance through gut-liver axis. CONCLUSION: AP increased colonic ATP production and improve mitochondrial function by regulating gut microbiota, and further upregulated anti-fatigue amino acid levels in the blood. Based on the gut-liver axis, AP upregulated the hepatic tricarboxylic acid cycle and oxidoreductase-related protein expression, regulating energy homeostasis and redox balance, and ultimately exerting anti-fatigue effects. This study provides insights into the anti-fatigue mechanisms of AP, highlighting its potential as a therapeutic agent.

Epigenomic features associated with body temperature stabilize tissues during cold exposure in cold-resistant pigs.

Cold stress in low-temperature environments can trigger changes in gene expression, but epigenomics regulation of temperature stability in vital tissues, including the fat and diencephalon, is still unclear. Here, we explore the cold-induced changes in epigenomic features in the diencephalon and fat tissues of two cold-resistant Chinese pig breeds, Min and Enshi black (ES) pigs, utilizing H3K27ac CUT&Tag, RNA-seq, and selective signature analysis. Our results show significant alterations in H3K27ac modifications in the diencephalon of Min pigs and the fat of ES pigs after cold exposure. Dramatic changes in H3K27ac modifications in Min pigs are primarily associated with genes involved in energy metabolism and hormone regulation, whereas those in ES pigs are primarily associated with immunity-related genes. Moreover, transcription factors PRDM1 and HSF1, which show evidence of selection, are enriched in genomic regions presenting cold-responsive alterations in H3K27ac modification in the Min pig diencephalon and ES pig fat, respectively. Our results indicate the diversity of epigenomic response mechanisms to cold exposure between Min and ES pigs, providing unique epigenetic resources for studies of low-temperature adaptation in large mammals.

Multi-omics analyses and botanical perfumer hypothesis provide insights into the formation and maintenance of aromatic characteristics of Dendrobium loddigesii flowers.

Dendrobium loddigesii, a member of the Orchidaceae family, is a valuable horticultural crop known for its aromatic qualities. However, the mechanisms responsible for the development of its aromatic characteristics remain poorly understood. To elucidate these underlying mechanisms, we assembled the first chromosome-level reference genome of D. loddigesii using PacBio HiFi-reads, Illumina short-reads, and Hi-C data. The assembly comprises 19 pseudochromosomes with N50 contig and N50 scaffold sizes of 55.15 and 89.94 Mb, respectively, estimating the genome size to be 1.68 Gb, larger than that of other sequenced Dendrobium species. During the flowering stages, we conducted a comprehensive analysis combining volatilomics and transcriptomics to understand the characteristics and biosynthetic mechanisms pathways of the floral scent. Our findings emphasize the significant contribution of aromatic terpenoids, especially monoterpenoids, in defining the floral aroma. Furthermore, we identified two crucial terpene synthase (TPS) genes that play a key role in maintaining the aroma during flowering. Through the integration volatilomics data with catalytic assays of DlTPSbs proteins, we identified specific compounds responsible for the aromatic characteristics of D. loddigesii. This integrated analysis of the genome, transcriptome, and volatilome, offers valuable insights into the development and preservation of D. loddigesii's aromatic characteristics, setting the stage for further exploration of the botanical perfumer hypothesis.

Integrative multi-omics analysis unravels the host response landscape and reveals a serum protein panel for early prognosis prediction for ARDS.

BACKGROUND: The multidimensional biological mechanisms underpinning acute respiratory distress syndrome (ARDS) continue to be elucidated, and early biomarkers for predicting ARDS prognosis are yet to be identified. METHODS: We conducted a multicenter observational study, profiling the 4D-DIA proteomics and global metabolomics of serum samples collected from patients at the initial stage of ARDS, alongside samples from both disease control and healthy control groups. We identified 28-day prognosis biomarkers of ARDS in the discovery cohort using the LASSO method, fold change analysis, and the Boruta algorithm. The candidate biomarkers were validated through parallel reaction monitoring (PRM) targeted mass spectrometry in an external validation cohort. Machine learning models were applied to explore the biomarkers of ARDS prognosis. RESULTS: In the discovery cohort, comprising 130 adult ARDS patients (mean age 72.5, 74.6% male), 33 disease controls, and 33 healthy controls, distinct proteomic and metabolic signatures were identified to differentiate ARDS from both control groups. Pathway analysis highlighted the upregulated sphingolipid signaling pathway as a key contributor to the pathological mechanisms underlying ARDS. MAP2K1 emerged as the hub protein, facilitating interactions with various biological functions within this pathway. Additionally, the metabolite sphingosine 1-phosphate (S1P) was closely associated with ARDS and its prognosis. Our research further highlights essential pathways contributing to the deceased ARDS, such as the downregulation of hematopoietic cell lineage and calcium signaling pathways, contrasted with the upregulation of the unfolded protein response and glycolysis. In particular, GAPDH and ENO1, critical enzymes in glycolysis, showed the highest interaction degree in the protein-protein interaction network of ARDS. In the discovery cohort, a panel of 36 proteins was identified as candidate biomarkers, with 8 proteins (VCAM1, LDHB, MSN, FLG2, TAGLN2, LMNA, MBL2, and LBP) demonstrating significant consistency in an independent validation cohort of 183 patients (mean age 72.6 years, 73.2% male), confirmed by PRM assay. The protein-based model exhibited superior predictive accuracy compared to the clinical model in both the discovery cohort (AUC: 0.893 vs. 0.784; Delong test, P < 0.001) and the validation cohort (AUC: 0.802 vs. 0.738; Delong test, P = 0.008). INTERPRETATION: Our multi-omics study demonstrated the potential biological mechanism and therapy targets in ARDS. This study unveiled several novel predictive biomarkers and established a validated prediction model for the poor prognosis of ARDS, offering valuable insights into the prognosis of individuals with ARDS.

Unveiling the molecular landscape of cognitive aging: insights from polygenic risk scores, DNA methylation, and gene expression.

BACKGROUND: Aging represents a significant risk factor for the occurrence of cerebral small vessel disease, associated with white matter (WM) lesions, and to age-related cognitive alterations, though the precise mechanisms remain largely unknown. This study aimed to investigate the impact of polygenic risk scores (PRS) for WM integrity, together with age-related DNA methylation, and gene expression alterations, on cognitive aging in a cross-sectional healthy aging cohort. The PRSs were calculated using genome-wide association study (GWAS) summary statistics for magnetic resonance imaging (MRI) markers of WM integrity, including WM hyperintensities, fractional anisotropy (FA), and mean diffusivity (MD). These scores were utilized to predict age-related cognitive changes and evaluate their correlation with structural brain changes, which distinguish individuals with higher and lower cognitive scores. To reduce the dimensionality of the data and identify age-related DNA methylation and transcriptomic alterations, Sparse Partial Least Squares-Discriminant Analysis (sPLS-DA) was used. Subsequently, a canonical correlation algorithm was used to integrate the three types of omics data (PRS, DNA methylation, and gene expression data) and identify an individual "omics" signature that distinguishes subjects with varying cognitive profiles. RESULTS: We found a positive association between MD-PRS and long-term memory, as well as a correlation between MD-PRS and structural brain changes, effectively discriminating between individuals with lower and higher memory scores. Furthermore, we observed an enrichment of polygenic signals in genes related to both vascular and non-vascular factors. Age-related alterations in DNA methylation and gene expression indicated dysregulation of critical molecular features and signaling pathways involved in aging and lifespan regulation. The integration of multi-omics data underscored the involvement of synaptic dysfunction, axonal degeneration, microtubule organization, and glycosylation in the process of cognitive aging. CONCLUSIONS: These findings provide valuable insights into the biological mechanisms underlying the association between WM coherence and cognitive aging. Additionally, they highlight how age-associated DNA methylation and gene expression changes contribute to cognitive aging.

Advancing lung adenocarcinoma prognosis and immunotherapy prediction with a multi-omics consensus machine learning approach.

Lung adenocarcinoma (LUAD) is a tumour characterized by high tumour heterogeneity. Although there are numerous prognostic and immunotherapeutic options available for LUAD, there is a dearth of precise, individualized treatment plans. We integrated mRNA, lncRNA, microRNA, methylation and mutation data from the TCGA database for LUAD. Utilizing ten clustering algorithms, we identified stable multi-omics consensus clusters (MOCs). These data were then amalgamated with ten machine learning approaches to develop a robust model capable of reliably identifying patient prognosis and predicting immunotherapy outcomes. Through ten clustering algorithms, two prognostically relevant MOCs were identified, with MOC2 showing more favourable outcomes. We subsequently constructed a MOCs-associated machine learning model (MOCM) based on eight MOCs-specific hub genes. Patients characterized by a lower MOCM score exhibited better overall survival and responses to immunotherapy. These findings were consistent across multiple datasets, and compared to many previously published LUAD biomarkers, our MOCM score demonstrated superior predictive performance. Notably, the low MOCM group was more inclined towards 'hot' tumours, characterized by higher levels of immune cell infiltration. Intriguingly, a significant positive correlation between GJB3 and the MOCM score (R = 0.77, p < 0.01) was discovered. Further experiments confirmed that GJB3 significantly enhances LUAD proliferation, invasion and migration, indicating its potential as a key target for LUAD treatment. Our developed MOCM score accurately predicts the prognosis of LUAD patients and identifies potential beneficiaries of immunotherapy, offering broad clinical applicability.

Progress based on a multi-omics research strategy in the biosynthesis and modernization of active ingredients of Herpetospermum pedunculosum seeds.

Herpetospermum pedunculosum seeds also known as Herpetospermum caudigerum Wall. is the mature seed of the Herpetospermum pedunculosum(Ser.) C. B. Clarke,Cucurbitaceae. Modern pharmacological studies have shown that H. pedunculosum has hepatoprotective, anti-inflammatory, anti-gout and antibacterial pharmacological activities. The biologically active chemical components include lignin compounds such as Herpetin, Herpetetrone, Herpetoriol and so on. The natural product displays considerable skeletal diversity and structural complexity, offering significant opportunities for novel drug discovery. Based on the multi-omics research strategy and the 'gene-protein-metabolite' research framework, the biosynthetic pathway of terpenoids and lignans in H. pedunculosum has has been elucidated at multiple levels. These approaches provide comprehensive genetic information for cloning and identification of pertinent enzyme genes. Furthermore, the application of multi-omics integrative approaches provides a scientific means to elucidate entire secondary metabolic pathways. We investigated the biosynthetic pathways of lignin and terpene components in H. pedunculosum and conducted bioinformatics analysis of the crucial enzyme genes involved in the biosynthetic process using genomic and transcriptomic data. We identified candidate genes for six key enzymes in the biosynthetic pathway. This review reports on the current literature on pharmacological investigations of H. pedunculosum, proposing its potential as an antidiabetic agent. Moreover, we conclude, for the first time, the identification of key enzyme genes potentially involved in the biosynthesis of active compounds in H. pedunculosum. This review provides a scientific foundation for the discovery of novel therapeutic agents from natural sources.

Multi-omics differences in the bone marrow between essential thrombocythemia and prefibrotic primary myelofibrosis.

Essential thrombocythemia (ET) and prefibrotic primary myelofibrosis (pre-PMF) are Philadelphia chromosome-negative myeloproliferative neoplasms. These conditions share overlapping clinical presentations; however, their prognoses differ significantly. Current morphological diagnostic methods lack reliability in subtype differentiation, underlining the need for improved diagnostics. The aim of this study was to investigate the multi-omics alterations in bone marrow biopsies of patients with ET and pre-PMF to improve our understanding of the nuanced diagnostic characteristics of both diseases. We performed proteomic analysis with 4D direct data-independent acquisition and microbiome analysis with 2bRAD-M sequencing technology to identify differential protein and microbe levels between untreated patients with ET and pre-PMF. Laboratory and multi-omics differences were observed between ET and pre-PMF, encompassing diverse pathways, such as lipid metabolism and immune response. The pre-PMF group showed an increased neutrophil-to-lymphocyte ratio and decreased high-density lipoprotein and cholesterol levels. Protein analysis revealed significantly higher CXCR2, CXCR4, and MX1 levels in pre-PMF, while APOC3, APOA4, FABP4, C5, and CFB levels were elevated in ET, with diagnostic accuracy indicated by AUC values ranging from 0.786 to 0.881. Microbiome assessment identified increased levels of Mycobacterium, Xanthobacter, and L1I39 in pre-PMF, whereas Sphingomonas, Brevibacillus, and Pseudomonas_E were significantly decreased, with AUCs for these genera ranging from 0.833 to 0.929. Our study provides preliminary insights into the proteomic and microbiome variations in the bone marrow of patients with ET and pre-PMF, identifying specific proteins and bacterial genera that warrant further investigation as potential diagnostic indicators. These observations contribute to our evolving understanding of the multi-omics variations and possible mechanisms underlying ET and pre-PMF.

Evidence of synergistic mechanisms of hepatoprotective botanical herbal preparation of Pueraria montana var. lobata and Schisandra sphenanthera.

Background: Pueraria montana var. lobata (Willd.) Maesen & S.M.Almeida ex Sanjappa & Predeep (syn. Pueraria lobata (Willd.) Ohwi) and Schisandra sphenanthera Rehder & E.H. Wilson are traditional edible and medicinal hepatoprotective botanical drugs. Studies have shown that the combination of two botanical drugs enhanced the effects of treating acute liver injury (ALI), but the synergistic effect and its action mechanisms remain unclear. This study aimed to investigate the synergistic effect and its mechanism of the combination of Pueraria montana var. lobata (Willd.) Maesen & S.M.Almeida ex Sanjappa & Predeep (syn. Pueraria lobata (Willd.) Ohwi) (PM) and Schisandra sphenanthera Rehder & E.H. Wilson (SS) in the treatment of ALI. Methods: High performance liquid chromatography (HPLC) were utilized to conduct the chemical interaction analysis. Then the synergistic effects of botanical hybrid preparation of PM-SS (BHP PM-SS) against ALI were comprehensively evaluated by the CCl4 induced ALI mice model. Afterwards, symptom-oriented network pharmacology, transcriptomics and metabolomics were applied to reveal the underlying mechanism of action. Finally, the key target genes were experimentally by RT-qPCR. Results: Chemical analysis and pharmacodynamic experiments revealed that BHP PM-SS was superior to the single botanical drug, especially at 2:3 ratio, with a better dissolution rate of active ingredients and synergistic anti-ALI effect. Integrated symptom-oriented network pharmacology combined with transcriptomics and metabolomics analyses showed that the active ingredients of BHP PM-SS could regulate Glutathione metabolism, Pyrimidine metabolism, Arginine biosynthesis and Amino acid sugar and nucleotide sugar metabolism, by acting on the targets of AKT1, TNF, EGFR, JUN, HSP90AA1 and STAT3, which could be responsible for the PI3K-AKT signaling pathway, MAPK signaling pathway and Pathway in cancer to against ALI. Conclusion: Our study has provided compelling evidence for the synergistic effect and its mechanism of the combination of BHP PM-SS, and has contributed to the development and utilization of BHP PM-SS dietary supplements.

Comparative analysis of integrative classification methods for multi-omics data.

Recent advances in sequencing, mass spectrometry, and cytometry technologies have enabled researchers to collect multiple 'omics data types from a single sample. These large datasets have led to a growing consensus that a holistic approach is needed to identify new candidate biomarkers and unveil mechanisms underlying disease etiology, a key to precision medicine. While many reviews and benchmarks have been conducted on unsupervised approaches, their supervised counterparts have received less attention in the literature and no gold standard has emerged yet. In this work, we present a thorough comparison of a selection of six methods, representative of the main families of intermediate integrative approaches (matrix factorization, multiple kernel methods, ensemble learning, and graph-based methods). As non-integrative control, random forest was performed on concatenated and separated data types. Methods were evaluated for classification performance on both simulated and real-world datasets, the latter being carefully selected to cover different medical applications (infectious diseases, oncology, and vaccines) and data modalities. A total of 15 simulation scenarios were designed from the real-world datasets to explore a large and realistic parameter space (e.g. sample size, dimensionality, class imbalance, effect size). On real data, the method comparison showed that integrative approaches performed better or equally well than their non-integrative counterpart. By contrast, DIABLO and the four random forest alternatives outperform the others across the majority of simulation scenarios. The strengths and limitations of these methods are discussed in detail as well as guidelines for future applications.

An Integrated Multi-omics prediction model for stroke recurrence based on Lnet transformer layer and dynamic weighting mechanism.

BACKGROUND AND OBJECTIVE: Stroke is a disease with high mortality and disability. Importantly, the fatality rate demonstrates a significant increase among patients afflicted by recurrent strokes compared to those experiencing their initial stroke episode. Currently, the existing research encounters three primary challenges. The first is the lack of a reliable, multi-omics image dataset related to stroke recurrence. The second is how to establish a high-performance feature extraction model and eliminate noise from continuous magnetic resonance imaging (MRI) data. The third is how to integration multi-omics data and dynamically weighted for different omics data. METHODS: We systematically compiled MRI and conventional detection data from a cohort comprising 737 stroke patients and established PSTSZC, a multi-omics dataset for predicting stroke recurrence. We introduced the first-ever Integrated Multi-omics Prediction Model for Stroke Recurrence, MPSR, which is based on ResNet, Lnet-transformer, LSTM and dynamically weighted DNN. The MPSR model comprises two principal modules, the Feature Extraction Module, and the Integrated Multi-Omics Prediction Module. In the Feature Extraction module, we proposed a novel Lnet regularization layer, which effectively addresses noise issues in MRI data. In the Integrated Multi-omics Prediction Module, we propose a dynamic weighted mechanism based on evaluators, which mitigates the noise impact brought about by low-performance omics. RESULTS: We compared seven single-omics models and six state-of-the-art multi-omics stroke recurrence models. The experimental results demonstrate that the MPSR model exhibited superior performance. The accuracy, AUROC, specificity, and sensitivity of the MPSR model can reach 0.96, 0.97, 1, and 0.94, respectively, which is higher than the results of contrast model. CONCLUSION: MPSR is the first available high-performance multi-omics prediction model for stroke recurrence. We assert that the MPSR model holds the potential to function as a valuable tool in assisting clinicians in accurately diagnosing individuals with a predisposition to stroke recurrence.

Targeting histone deacetylase in cardiac diseases.

Histone deacetylases (HDAC) catalyze the removal of acetylation modifications on histones and non-histone proteins, which regulates gene expression and other cellular processes. HDAC inhibitors (HDACi), approved anti-cancer agents, emerge as a potential new therapy for heart diseases. Cardioprotective effects of HDACi are observed in many preclinical animal models of heart diseases. Genetic mouse models have been developed to understand the role of each HDAC in cardiac functions. Some of the findings are controversial. Here, we provide an overview of how HDACi and HDAC impact cardiac functions under physiological or pathological conditions. We focus on in vivo studies of zinc-dependent classical HDACs, emphasizing disease conditions involving cardiac hypertrophy, myocardial infarction (MI), ischemic reperfusion (I/R) injury, and heart failure. In particular, we review how non-biased omics studies can help our understanding of the mechanisms underlying the cardiac effects of HDACi and HDAC.

Target repositioning using multi-layer networks and machine learning: The case of prostate cancer.

The discovery of novel therapeutic targets, defined as proteins which drugs can interact with to induce therapeutic benefits, typically represent the first and most important step of drug discovery. One solution for target discovery is target repositioning, a strategy which relies on the repurposing of known targets for new diseases, leading to new treatments, less side effects and potential drug synergies. Biological networks have emerged as powerful tools for integrating heterogeneous data and facilitating the prediction of biological or therapeutic properties. Consequently, they are widely employed to predict new therapeutic targets by characterizing potential candidates, often based on their interactions within a Protein-Protein Interaction (PPI) network, and their proximity to genes associated with the disease. However, over-reliance on PPI networks and the assumption that potential targets are necessarily near known genes can introduce biases that may limit the effectiveness of these methods. This study addresses these limitations in two ways. First, by exploiting a multi-layer network which incorporates additional information such as gene regulation, metabolite interactions, metabolic pathways, and several disease signatures such as Differentially Expressed Genes, mutated genes, Copy Number Alteration, and structural variants. Second, by extracting relevant features from the network using several approaches including proximity to disease-associated genes, but also unbiased approaches such as propagation-based methods, topological metrics, and module detection algorithms. Using prostate cancer as a case study, the best features were identified and utilized to train machine learning algorithms to predict 5 novel promising therapeutic targets for prostate cancer: IGF2R, C5AR, RAB7, SETD2 and NPBWR1.

Multi-omic validation of the cuproptosis-sphingolipid metabolism network: modulating the immune landscape in osteosarcoma.

Background: The current understanding of the mechanisms by which metal ion metabolism promotes the progression and drug resistance of osteosarcoma remains incomplete. This study aims to elucidate the key roles and mechanisms of genes involved in cuproptosis-related sphingolipid metabolism (cuproptosis-SPGs) in regulating the immune landscape, tumor metastasis, and drug resistance in osteosarcoma cells. Methods: This study employed multi-omics approaches to assess the impact of cuproptosis-SPGs on the prognosis of osteosarcoma patients. Lasso regression analysis was utilized to construct a prognostic model, while multivariate regression analysis was applied to identify key core genes and generate risk coefficients for these genes, thereby calculating a risk score for each osteosarcoma patient. Patients were then stratified into high-risk and low-risk groups based on their risk scores. The ESTIMATE and CIBERSORT algorithms were used to analyze the level of immune cell infiltration within these risk groups to construct the immune landscape. Single-cell analysis was conducted to provide a more precise depiction of the expression patterns of cuproptosis-SPGs among immune cell subtypes. Finally, experiments on osteosarcoma cells were performed to validate the role of the cuproptosis-sphingolipid signaling network in regulating cell migration and apoptosis. Results: In this study, seven cuproptosis-SPGs were identified and used to construct a prognostic model for osteosarcoma patients. In addition to predicting survival, the model also demonstrated reliability in forecasting the response to chemotherapy drugs. The results showed that a high cuproptosis-sphingolipid metabolism score was closely associated with reduced CD8 T cell infiltration and indicated poor prognosis in osteosarcoma patients. Cellular functional assays revealed that cuproptosis-SPGs regulated the LC3B/ERK signaling pathway, thereby triggering cell death and impairing migration capabilities in osteosarcoma cells. Conclusion: The impact of cuproptosis-related sphingolipid metabolism on the survival and migration of osteosarcoma cells, as well as on CD8 T cell infiltration, highlights the potential of targeting copper ion metabolism as a promising strategy for osteosarcoma patients.

Tiger Milk Mushroom: A Comprehensive Review of Nutritional Composition, Phytochemicals, Health Benefits, and Scientific Advancements with Emphasis on Chemometrics and Multi-Omics.

This article presents a comprehensive overview of tiger milk mushroom (TMM), covering its nutritional composition, phytochemicals, health benefits, and related scientific advancements. It describes various potential positive health benefits of TMM, including anticancer, anti-inflammatory, respiratory function enhancement, antioxidant, anti-aging, neuroprotective, photoprotective, antidiabetic, wound-healing, and anti-HIV, among others. This article also underlines the importance of further research into the phytochemicals present in TMM for additional discoveries. It underscores the importance of further research into phytochemicals content of TMM for additional discoveries and emphasizes the potential applications of TMM in nutrition, health, and well-being. Sophisticated techniques, such as chemometrics and multi-omics technologies revealed latest scientific advancements of TMM. This comprehensive overview provides a foundation for future research and development in harnessing TMM's potential for human health.

The role of bound polyphenols in the anti-obesity effects of defatted rice bran insoluble dietary fiber: An insight from multi-omics.

Considering the high abundance of bound polyphenols (BP) in whole grain dietary fiber (DF), this study utilized multi-omics approach to evaluate the impact of BP of defatted rice bran insoluble DF (RIDF) in modulating obesity. Mice on high-fat diet were gavage-administered RIDF, BP-removed or formulated RIDF. The results indicated that DF significantly reduced serum total cholesterol, triglycerides, high-density and low-density lipoprotein cholesterol levels. Moreover, hepatic lipid accumulation and damage induced by high-fat diet were significantly ameliorated with DF intervention. The presence of BP increased the abundance of beneficial bacteria g_Akkermansia and g_Butyricicocus, as well as the expression of butyric acid/propionic acid. Furthermore, the expression of hepatic lipids and lipid-like molecules was significantly decreased under the combined intervention of BP and DF, and this was accompanied by alterations in genes related to lipid, sterol, and cholesterol metabolic biological processes. These findings suggest that BP contribute to the anti-obesity effects of DF.

PPARγ/NF-κB axis contributes to cold-induced resolution of experimental colitis and preservation of intestinal barrier.

BACKGROUND: Environmental stress is a significant contributor to the development of inflammatory bowel disease (IBD). The involvement of temperature stimulation in the development of IBD remains uncertain. Our preliminary statistical data suggest that the prevalence of IBD is slightly lower in colder regions compared to non-cold regions. The observation indicates that temperature changes may play a key role in the occurrence and progression of IBD. Here, we hypothesized that cold stress has a protective effect on IBD. METHODS: The cold exposure model for mice was placed in a constant temperature and humidity chamber, maintained at a temperature of 4 °C. Colitis models were induced in the mice using TNBS or DSS. To promote the detection methods more clinically, fluorescence confocal endoscopy was used to observe the mucosal microcirculation status of the colon in the live model. Changes in the colonic wall of the mice were detected using 9.4 T Magnetic Resonance Imaging (MRI) imaging and in vivo fluorescence imaging. Hematoxylin and eosin (H&E) and Immunofluorescence (IF) staining confirmed the pathological alterations in the colons of sacrificed mice. Molecular changes at the protein level were assessed through Western blotting and Enzyme-Linked Immunosorbent Assay (ELISA) assays. RNA sequencing (RNA-seq) and metabolomics (n = 18) were jointly analyzed to investigate the biological changes in the colon of mice treated by cold exposure. RESULTS: Cold exposure decreased the pathologic and disease activity index scores in a mouse model. Endomicroscopy revealed that cold exposure preserved colonic mucosal microcirculation, and 9.4 T MRI imaging revealed alleviation of intestinal wall thickness. In addition, the expression of the TLR4 and PP65 proteins was downregulated and epithelial cell junctions were strengthened after cold exposure. Intriguingly, we found that cold exposure reversed the decrease in ZO-1 and occludin protein levels in dextran sulfate sodium (DSS)- and trinitrobenzenesulfonic acid-induced colitis mouse models. Multi-omics analysis revealed the biological landscape of DSS-induced colitis under cold exposure and identified that the peroxisome proliferator-activated receptor (PPAR) signaling pathway mediates the effects of cold on colitis. Subsequent administration of rosiglitazone (PPAR agonist) enhanced the protective effect of cold exposure on colitis, whereas GW9662 (PPAR antagonist) administration mitigated these protective effects. Overall, cold exposure ameliorated the progression of mouse colitis through the PPARγ/NF-κB signaling axis and preserved the intestinal mucosal barrier. CONCLUSION: Our study provides a mechanistic link between intestinal inflammation and cold exposure, providing a theoretical framework for understanding the differences in the prevalence of IBD between the colder regions and non-cold regions, and offering new insights into IBD therapy.