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Artificial intelligence (AI) has the potential to transform every facet of cardiovascular practice and research. The exponential rise in technology powered by AI is defining new frontiers in cardiovascular care, with innovations that span novel diagnostic modalities, new digital native biomarkers of disease, and high-performing tools evaluating care quality and prognosticating clinical outcomes. These digital innovations promise expanded access to cardiovascular screening and monitoring, especially among those without access to high-quality, specialized care historically. Moreover, AI is propelling biological and clinical discoveries that will make future cardiovascular care more personalized, precise, and effective. The review brings together these diverse AI innovations, highlighting developments in multimodal cardiovascular AI across clinical practice and biomedical discovery, and envisioning this new future backed by contemporary science and emerging discoveries. Finally, we define the critical path and the safeguards essential to realizing this AI-enabled future that helps achieve optimal cardiovascular health and outcomes for all. [Display omitted] • AI-enabled technologies are increasingly integrated into cardiovascular practice and investigation. • Over the next decade, we envision an AI-propelled future in which the cardiovascular diagnostic and therapeutic landscape will effectively leverage multimodal data at the point of care. • Innovations in biomedical discovery and cardiovascular research are also set to make the future of cardiovascular care more personalized, precise, and effective. • The path to this future requires equitable and regulated adoption that prioritizes fairness, equity, safety, and partnerships with innovators as well as our communities and society. [ABSTRACT FROM AUTHOR]

Diabetic kidney disease (DKD) is the leading cause of end-stage kidney disease. Because many genes associate with DKD, multiomics approaches were used to narrow the list of functional genes, gene products, and related pathways providing insights into the pathophysiological mechanisms of DKD. The Kidney Precision Medicine Project human kidney single-cell RNA-sequencing (scRNA-seq) data set and Mendeley Data on human kidney cortex biopsy proteomics were used. The R package Seurat was used to analyze scRNA-seq data and data from a subset of proximal tubule cells. PathfindR was applied for pathway analysis in cell type–specific differentially expressed genes and the R limma package was used to analyze differential protein expression in kidney cortex. A total of 790 differentially expressed genes were identified in proximal tubule cells, including 530 upregulated and 260 downregulated transcripts. Compared with differentially expressed proteins, 24 genes or proteins were in common. An integrated analysis combining protein quantitative trait loci, genome-wide association study hits (namely, estimated glomerular filtration rate), and a plasma metabolomics analysis was performed using baseline metabolites predictive of DKD progression in our longitudinal Diabetes Heart Study samples. The aldo-keto reductase family 1 member A1 gene (AKR1A1) was revealed as a potential molecular hub for DKD cellular dysfunction in several cross-linked pathways featured by deficiency of this enzyme. Article Highlights: It is critical to identify credible biomarkers for progression of diabetic kidney disease (DKD) useful in clinical practice. We used multiomics approaches to uncover and cross-verify DKD biomarkers using publicly available genomics, single-cell transcriptomics, and proteomics data, as well as our unique immunofluorescence microscopy and metabolomics data. We found that the aldo-keto reductase family-1 member A1 (AKR1A1) transcript and protein/enzyme level was significantly lower in proximal tubule cells of patients with DKD than in those of control cells. DKD progression was supported by higher plasma metabolic end products catalyzed by AKR1A1. Our results suggest AKR1A1 and/or its metabolic end products may serve as biomarkers for DKD. [ABSTRACT FROM AUTHOR]

This article explores the identification of inflammatory biomarkers for the prognosis of skin melanoma (SKME). The study integrates various datasets and employs statistical methods to evaluate the associations between inflammation-related genes and SKME survival. The researchers identify ABCF1, BIRC3, and MNDA as inflammatory biomarkers that are relevant to SKME prognosis. These biomarkers show correlations with overall survival in SKME patients and have potential implications for monitoring disease progression and developing therapeutic targets. Further research is needed to understand the mechanisms underlying these biomarkers. [Extracted from the article]

Summary: High‐quality genome of rosemary (Salvia rosmarinus) represents a valuable resource and tool for understanding genome evolution and environmental adaptation as well as its genetic improvement. However, the existing rosemary genome did not provide insights into the relationship between antioxidant components and environmental adaptability. In this study, by employing Nanopore sequencing and Hi‐C technologies, a total of 1.17 Gb (97.96%) genome sequences were mapped to 12 chromosomes with 46 121 protein‐coding genes and 1265 non‐coding RNA genes. Comparative genome analysis reveals that rosemary had a closely genetic relationship with Salvia splendens and Salvia miltiorrhiza, and it diverged from them approximately 33.7 million years ago (MYA), and one whole‐genome duplication occurred around 28.3 MYA in rosemary genome. Among all identified rosemary genes, 1918 gene families were expanded, 35 of which are involved in the biosynthesis of antioxidant components. These expanded gene families enhance the ability of rosemary adaptation to adverse environments. Multi‐omics (integrated transcriptome and metabolome) analysis showed the tissue‐specific distribution of antioxidant components related to environmental adaptation. During the drought, heat and salt stress treatments, 36 genes in the biosynthesis pathways of carnosic acid, rosmarinic acid and flavonoids were up‐regulated, illustrating the important role of these antioxidant components in responding to abiotic stresses by adjusting ROS homeostasis. Moreover, cooperating with the photosynthesis, substance and energy metabolism, protein and ion balance, the collaborative system maintained cell stability and improved the ability of rosemary against harsh environment. This study provides a genomic data platform for gene discovery and precision breeding in rosemary. Our results also provide new insights into the adaptive evolution of rosemary and the contribution of antioxidant components in resistance to harsh environments. [ABSTRACT FROM AUTHOR]

Mitochondrial disease is a devastating genetic disorder, with mitochondrial myopathy, encephalopathy, lactic acidosis, and stroke‐like episodes (MELAS) and m.3243A>G being the most common phenotype and genotype, respectively. The treatment for MELAS patients is still less effective. Here, we performed transcriptomic and proteomic analysis in muscle tissue of MELAS patients, and discovered that the expression of molecules involved in serine catabolism were significantly upregulated, and serine hydroxymethyltransferase 2 (SHMT2) increased significantly in both the mRNA and protein levels. The SHMT2 protein level was also increased in myoblasts with m.3243A>G mutation, which was transdifferentiated from patients derived fibroblasts, accompanying with the decreased nicotinamide adenine dinucleotide (NAD+)/reduced NAD+ (NADH) ratio and cell viability. After treating with SHMT2 inhibitor (SHIN1), the NAD+/NADH ratio and cell viability in MELAS myoblasts increased significantly. Taken together, our study indicates that enhanced serine catabolism plays an important role in the pathogenesis of MELAS and that SHIN1 can be a potential small molecule for the treatment of this disease. [ABSTRACT FROM AUTHOR]

Background: Observational studies have preliminarily revealed an association between smoking and gastroesophageal reflux disease (GERD). However, little is known about the causal relationship and shared genetic architecture between the two. This study aims to explore their common genetic correlations by leveraging genome-wide association studies (GWAS) of smoking behavior—specifically, smoking initiation (SI), never smoking (NS), ever smoking (ES), cigarettes smoked per day (CPD), age of smoking initiation(ASI) and GERD. Methods: Firstly, we conducted global cross-trait genetic correlation analysis and heritability estimation from summary statistics (HESS) to explore the genetic correlation between smoking behavior and GERD. Then, a joint cross-trait meta-analysis was performed to identify shared "pleiotropic SNPs" between smoking behavior and GERD, followed by co-localization analysis. Additionally, multi-marker analyses using annotation (MAGMA) were employed to explore the degree of enrichment of single nucleotide polymorphism (SNP) heritability in specific tissues, and summary data-based Mendelian randomization (SMR) was further utilized to investigate potential functional genes. Finally, Mendelian randomization (MR) analysis was conducted to explore the causal relationship between the smoking behavior and GERD. Results: Consistent genetic correlations were observed through global and local genetic correlation analyses, wherein SI, ES, and CPD showed significantly positive genetic correlations with GERD, while NS and ASI showed significantly negative correlations. HESS analysis also identified multiple significantly associated loci between them. Furthermore, three novel "pleiotropic SNPs" (rs4382592, rs200968, rs1510719) were identified through cross-trait meta-analysis and co-localization analysis to exist between SI, NS, ES, ASI, and GERD, mapping the genes MED27, HIST1H2BO, MAML3 as new pleiotropic genes between SI, NS, ES, ASI, and GERD. Moreover, both smoking behavior and GERD were found to be co-enriched in multiple brain tissues, with GMPPB, RNF123, and RBM6 identified as potential functional genes co-enriched in Cerebellar Hemisphere, Cerebellum, Cortex/Nucleus accumbens in SI and GERD, and SUOX identified in Caudate nucleus, Cerebellum, Cortex in NS and GERD. Lastly, consistent causal relationships were found through MR analysis, indicating that SI, ES, and CPD increase the risk of GERD, while NS and higher ASI decrease the risk. Conclusion: We identified genetic loci associated with smoking behavior and GERD, as well as brain tissue sites of shared enrichment, prioritizing three new pleiotropic genes and four new functional genes. Finally, the causal relationship between smoking behavior and GERD was demonstrated, providing insights for early prevention strategies for GERD. [ABSTRACT FROM AUTHOR]

Patients who suffer from sepsis typically experience acute lung injury (ALI). Extracellular vesicles (EVs) contain miRNAs, which are potentially involved in ALI. However, strategies to screen more effective EV-miRNAs as therapeutic targets are yet to be elucidated. In this study, functional EV-miRNAs were identified based on multiomics analysis of single-cell RNA sequencing of targeted organs and serum EV (sEV) miRNA profiles in patients with sepsis. The proportions of neutrophils and macrophages were increased significantly in the lungs of mice receiving sEVs from patients with sepsis compared with healthy controls. Macrophages released more EVs than neutrophils. MiR-125a-5p delivery by sEVs to lung macrophages inhibited Tnfaip3, while miR-221-3p delivery to lung neutrophils inhibited Fos. Macrophage membrane nanoparticles (MM NPs) loaded with an miR-125a-5p inhibitor or miR-221-3p mimic attenuated the response to lipopolysaccharide (LPS)-induced ALI. Transcriptome profiling revealed that EVs derived from LPS-stimulated bone marrow-derived macrophages (BMDMs) induced oxidative stress in neutrophils. Blocking toll-like receptor, CXCR2, or TNFα signaling in neutrophils attenuated the oxidative stress induced by LPS-stimulated BMDM-EVs. This study presents a novel method to screen functional EV-miRNAs and highlights the pivotal role of macrophage-derived EVs in ALI. MM NPs, as delivery systems of key sEV-miRNA mimics or inhibitors, alleviated cellular responses observed in sepsis-induced ALI. This strategy can be used to reduce septic organ damage, particularly lung damage, by targeting EVs. [ABSTRACT FROM AUTHOR]

Summary Oil‐Camellia (Camellia oleifera), belonging to the Theaceae family Camellia, is an important woody edible oil tree species. The Camellia oil in its mature seed kernels, mainly consists of more than 90% unsaturated fatty acids, tea polyphenols, flavonoids, squalene and other active substances, which is one of the best quality edible vegetable oils in the world. However, genetic research and molecular breeding on oil‐Camellia are challenging due to its complex genetic background. Here, we successfully report a chromosome‐scale genome assembly for a hexaploid oil‐Camellia cultivar Changlin40. This assembly contains 8.80 Gb genomic sequences with scaffold N50 of 180.0 Mb and 45 pseudochromosomes comprising 15 homologous groups with three members each, which contain 135 868 genes with an average length of 3936 bp. Referring to the diploid genome, intragenomic and intergenomic comparisons of synteny indicate homologous chromosomal similarity and changes. Moreover, comparative and evolutionary analyses reveal three rounds of whole‐genome duplication (WGD) events, as well as the possible diversification of hexaploid Changlin40 with diploid occurred approximately 9.06 million years ago (MYA). Furthermore, through the combination of genomics, transcriptomics and metabolomics approaches, a complex regulatory network was constructed and allows to identify potential key structural genes (SAD, FAD2 and FAD3) and transcription factors (AP2 and C2H2) that regulate the metabolism of Camellia oil, especially for unsaturated fatty acids biosynthesis. Overall, the genomic resource generated from this study has great potential to accelerate the research for the molecular biology and genetic improvement of hexaploid oil‐Camellia, as well as to understand polyploid genome evolution. [ABSTRACT FROM AUTHOR]

Multi-omics sequencing is poised to revolutionize clinical care in the coming decade. However, there is a lack of effective and interpretable genome-wide modeling methods for the rational selection of patients for personalized interventions. To address this, we present iGenSig-Rx, an integral genomic signature-based approach, as a transparent tool for modeling therapeutic response using clinical trial datasets. This method adeptly addresses challenges related to cross-dataset modeling by capitalizing on high-dimensional redundant genomic features, analogous to reinforcing building pillars with redundant steel rods. Moreover, it integrates adaptive penalization of feature redundancy on a per-sample basis to prevent score flattening and mitigate overfitting. We then developed a purpose-built R package to implement this method for modeling clinical trial datasets. When applied to genomic datasets for HER2 targeted therapies, iGenSig-Rx model demonstrates consistent and reliable predictive power across four independent clinical trials. More importantly, the iGenSig-Rx model offers the level of transparency much needed for clinical application, allowing for clear explanations as to how the predictions are produced, how the features contribute to the prediction, and what are the key underlying pathways. We anticipate that iGenSig-Rx, as an interpretable class of multi-omics modeling methods, will find broad applications in big-data based precision oncology. The R package is available: https://github.com/wangxlab/iGenSig-Rx. [ABSTRACT FROM AUTHOR]

Tissue specificity is a fundamental property of an organ that affects numerous biological processes, including aging and longevity, and is regulated by the circadian clock. However, the distinction between circadian-affected tissue specificity and other tissue specificities remains poorly understood. Here, using multi-omics data on circadian rhythms in mice, we discovered that approximately 35% of tissue-specific genes are directly affected by circadian regulation. These circadian-affected tissue-specific genes have higher expression levels and are associated with metabolism in hepatocytes. They also exhibit specific features in long-reads sequencing data. Notably, these genes are associated with aging and longevity at both the gene level and at the network module level. The expression of these genes oscillates in response to caloric restricted feeding regimens, which have been demonstrated to promote longevity. In addition, aging and longevity genes are disrupted in various circadian disorders. Our study indicates that the modulation of circadian-affected tissue specificity is essential for understanding the circadian mechanisms that regulate aging and longevity at the genomic level. A multi-omics study on circadian rhythms in mice suggests that ~35% of tissue-specific genes may be influenced by circadian regulation, and these genes are linked to aging and longevity. [ABSTRACT FROM AUTHOR]

Background: IDH1-wildtype glioblastoma multiforme (IDHwt-GBM) is a highly heterogeneous and aggressive brain tumour characterised by a dismal prognosis and significant challenges in accurately predicting patient outcomes. To address these issues and personalise treatment approaches, we aimed to develop and validate robust multiomics molecular subtypes of IDHwt-GBM. Through this, we sought to uncover the distinct molecular signatures underlying these subtypes, paving the way for improved diagnosis and targeted therapy for this challenging disease. Methods: To identify stable molecular subtypes among 184 IDHwt-GBM patients from TCGA, we used the consensus clustering method to consolidate the results from ten advanced multiomics clustering approaches based on mRNA, lncRNA, and mutation data. We developed subtype prediction models using the PAM and machine learning algorithms based on mRNA and MRI data for enhanced clinical utility. These models were validated in five independent datasets, and an online interactive system was created. We conducted a comprehensive assessment of the clinical impact, drug treatment response, and molecular associations of the IDHwt-GBM subtypes. Results: In the TCGA cohort, two molecular subtypes, class 1 and class 2, were identified through multiomics clustering of IDHwt-GBM patients. There was a significant difference in survival between Class 1 and Class 2 patients, with a hazard ratio (HR) of 1.68 [1.15–2.47]. This difference was validated in other datasets (CGGA: HR = 1.75[1.04, 2.94]; CPTAC: HR = 1.79[1.09–2.91]; GALSS: HR = 1.66[1.09–2.54]; UCSF: HR = 1.33[1.00–1.77]; UPENN HR = 1.29[1.04–1.58]). Additionally, class 2 was more sensitive to treatment with radiotherapy combined with temozolomide, and this sensitivity was validated in the GLASS cohort. Correspondingly, class 2 and class 1 exhibited significant differences in mutation patterns, enriched pathways, programmed cell death (PCD), and the tumour immune microenvironment. Class 2 had more mutation signatures associated with defective DNA mismatch repair (P = 0.0021). Enriched pathways of differentially expressed genes in class 1 and class 2 (P-adjust < 0.05) were mainly related to ferroptosis, the PD-1 checkpoint pathway, the JAK-STAT signalling pathway, and other programmed cell death and immune-related pathways. The different cell death modes and immune microenvironments were validated across multiple datasets. Finally, our developed survival prediction model, which integrates molecular subtypes, age, and sex, demonstrated clinical benefits based on the decision curve in the test set. We deployed the molecular subtyping prediction model and survival prediction model online, allowing interactive use and facilitating user convenience. Conclusions: Molecular subtypes were identified and verified through multiomics clustering in IDHwt-GBM patients. These subtypes are linked to specific mutation patterns, the immune microenvironment, prognoses, and treatment responses. [ABSTRACT FROM AUTHOR]

Background: Chronic kidney disease (CKD) is a progressive disease for which there is no effective cure. We aimed to identify potential drug targets for CKD and kidney function by integrating plasma proteome and transcriptome. Methods: We designed a comprehensive analysis pipeline involving two-sample Mendelian randomization (MR) (for proteins), summary-based MR (SMR) (for mRNA), and colocalization (for coding genes) to identify potential multi-omics biomarkers for CKD and combined the protein–protein interaction, Gene Ontology (GO), and single-cell annotation to explore the potential biological roles. The outcomes included CKD, extensive kidney function phenotypes, and different CKD clinical types (IgA nephropathy, chronic glomerulonephritis, chronic tubulointerstitial nephritis, membranous nephropathy, nephrotic syndrome, and diabetic nephropathy). Results: Leveraging pQTLs of 3032 proteins from 3 large-scale GWASs and corresponding blood- and tissue-specific eQTLs, we identified 32 proteins associated with CKD, which were validated across diverse CKD datasets, kidney function indicators, and clinical types. Notably, 12 proteins with prior MR support, including fibroblast growth factor 5 (FGF5), isopentenyl-diphosphate delta-isomerase 2 (IDI2), inhibin beta C chain (INHBC), butyrophilin subfamily 3 member A2 (BTN3A2), BTN3A3, uromodulin (UMOD), complement component 4A (C4a), C4b, centrosomal protein of 170 kDa (CEP170), serologically defined colon cancer antigen 8 (SDCCAG8), MHC class I polypeptide-related sequence B (MICB), and liver-expressed antimicrobial peptide 2 (LEAP2), were confirmed. To our knowledge, 20 novel causal proteins have not been previously reported. Five novel proteins, namely, GCKR (OR 1.17, 95% CI 1.10–1.24), IGFBP-5 (OR 0.43, 95% CI 0.29–0.62), sRAGE (OR 1.14, 95% CI 1.07–1.22), GNPTG (OR 0.90, 95% CI 0.86–0.95), and YOD1 (OR 1.39, 95% CI 1.18–1.64,) passed the MR, SMR, and colocalization analysis. The other 15 proteins were also candidate targets (GATM, AIF1L, DQA2, PFKFB2, NFATC1, activin AC, Apo A-IV, MFAP4, DJC10, C2CD2L, TCEA2, HLA-E, PLD3, AIF1, and GMPR1). These proteins interact with each other, and their coding genes were mainly enrichment in immunity-related pathways or presented specificity across tissues, kidney-related tissue cells, and kidney single cells. Conclusions: Our integrated analysis of plasma proteome and transcriptome data identifies 32 potential therapeutic targets for CKD, kidney function, and specific CKD clinical types, offering potential targets for the development of novel immunotherapies, combination therapies, or targeted interventions. [ABSTRACT FROM AUTHOR]

Aim Methods Results Conclusion Calcineurin inhibitors (CNIs) are the backbone for immunosuppression after solid organ transplantation. Although successful in preventing kidney transplant rejection, their nephrotoxic side effects contribute to allograft injury. Renal parenchymal lesions occur for cyclosporine A (CsA) as well as for the currently favored tacrolimus (Tac). We aimed to study whether chronic CsA and Tac exposures, before reaching irreversible nephrotoxic damage, affect renal compartments differentially and whether related pathogenic mechanisms can be identified.CsA and Tac were administered chronically in wild type Wistar rats using osmotic minipumps over 4 weeks. Functional parameters were controlled. Electron microscopy, confocal, and 3D‐structured illumination microscopy were used for histopathology. Clinical translatability was tested in human renal biopsies. Standard biochemical, RNA‐seq, and proteomic technologies were applied to identify implicated molecular pathways.Both drugs caused significant albeit differential damage in vasculature and nephron. The glomerular filtration barrier was more affected by Tac than by CsA, showing prominent deteriorations in endothelium and podocytes along with impaired VEGF/VEGFR2 signaling and podocyte‐specific gene expression. By contrast, proximal tubule epithelia were more severely affected by CsA than by Tac, revealing lysosomal dysfunction, enhanced apoptosis, impaired proteostasis and oxidative stress. Lesion characteristics were confirmed in human renal biopsies.We conclude that pathogenetic alterations in the renal compartments are specific for either treatment. Considering translation to the clinical setting, CNI choice should reflect individual risk factors for renal vasculature and tubular epithelia. As a step in this direction, we share protein signatures identified from multiomics with potential pathognomonic relevance. [ABSTRACT FROM AUTHOR]

The clinical heterogeneity of early-stage endometrial cancer (EC) is worthy of further study to identify high-quality prognostic markers and their potential role in aggressive tumor behavior. Mutation of TP53 was considered as an important primary triage in modified molecular typing for EC, it still cannot precisely predict the prognosis of EC. After proteomic analysis of cancer and para-cancerous tissues from 24 early-stage endometrioid EC patients with different survival outcomes, 13 differentially expressed proteins were screen out while 2 proteins enriched in p53 signaling pathway were further identified by single-cell transcriptome (scRNA-seq). Interestingly, tumor necrosis factor type-1 receptor-associated protein (TRAP1) and calmodulin-regulated spectrin-associated protein family member 3 (CAMSAP3) were found to be significantly downregulated in the specific cell cluster. Expectedly, the signature genes of TRAP1low/CAMSAP3low cluster included classical oncogenes. Moreover, close cellular interactions were observed between myeloid cells and the TRAP1low/CAMSAP3low cluster after systematically elucidating their relationship with tumor microenvironment (TME). The expression of TRAP1 and CAMSAP3 was verified by immunohistochemistry. Thus, a novel prediction model combining TRAP1, CAMSAP3 and TP53 was construct by multi-omics. Compared with the area under the curve, it demonstrated a significantly improvemrnt in the diagnostic efficacy in EC patients from TCGA bank. In conclusion, this work improved the current knowledge regarding the prognosis of early-stage EC through proteomics and scRNA-seq. These findings may lead to improvements in precise risk stratification of early-stage EC patients. [ABSTRACT FROM AUTHOR]

Background: Plant growth and development are severely threatened by drought and salt stresses. Compared with structural genes, transcription factors (TFs) play more pivotal roles in plant growth and stress adaptation. However, the underlying mechanisms of sorghum adapting to drought and salt are insufficient, and systematic analysis of TFs in response to the above stresses is lacking. Results: In this study, TFs were identified in sorghum and model plants (Arabidopsis thaliana and rice), and gene number and conserved domain were compared between sorghum and model plants. According to syntenic analysis, the expansion of sorghum and rice TFs may be due to whole-genome duplications. Between sorghum and model plants TFs, specific conserved domains were identified and they may be related to functional diversification of TFs. Forty-five key genes in sorghum, including four TFs, were likely responsible for drought adaption based on differently expression analysis. MiR5072 and its target gene (Sobic.001G449600) may refer to the determination of sorghum drought resistance according to small RNA and degradome analysis. Six genes were associated with drought adaptation of sorghum based on weighted gene co-expression network analysis (WGCNA). Similarly, the core genes in response to salt were also characterized using the above methods. Finally, 15 candidate genes, particularly two TFs (Sobic.004G300300, HD-ZIP; Sobic.003G244100, bZIP), involved in combined drought and salt resistance of sorghum were identified. Conclusions: In summary, the findings in this study help clarify the molecular mechanisms of sorghum responding to drought and salt. We identified candidate genes and provide important genetic resource for potential development of drought-tolerant and salt-tolerant sorghum plants. [ABSTRACT FROM AUTHOR]

Thyroid carcinoma (TC), the most commonly diagnosed malignancy of the endocrine system, has witnessed a significant rise in incidence over the past few decades. The integration of scRNA-seq with other sequencing approaches offers researchers a distinct perspective to explore mechanisms underlying TC progression. Therefore, it is crucial to develop a prognostic model for TC patients by utilizing a multi-omics approach. We acquired and processed transcriptomic data from the TCGA-THCA dataset, including mRNA expression profiles, lncRNA expression profiles, miRNA expression profiles, methylation chip data, gene mutation data, and clinical data. We constructed a tumor-related risk model using machine learning methods and developed a consensus machine learning-driven signature (CMLS) for accurate and stable prediction of TC patient outcomes. 2 strains of undifferentiated TC cell lines and 1 strain of PTC cell line were utilized for in vitro validation. mRNA, protein levels of hub genes, epithelial-mesenchymal transition (EMT)-associated phenotypes were detected by a series of in vitro experiments. We identified 3 molecular subtypes of TC based on integrated multi-omics clustering algorithms, which were associated with overall survival and displayed distinct molecular features. We developed a CMLS based on 28 hub genes to predict patient outcomes, and demonstrated that CMLS outperformed other prognostic models. TC patients of relatively lower CMLS score had significantly higher levels of T cells, B cells, and macrophages, indicating an immune-activated state. Fibroblasts were predominantly enriched in the high CMLS group, along with markers associated with immune suppression and evasion. We identified several drugs that could be suitable for patients with high CMLS, including Staurosporine_1034, Rapamycin_1084, gemcitabine, and topotecan. SNAI1 was elevated in both undifferentiated TC cell lines, comparing to PTC cells. Knockdown of SNAI1 reduced the cell proliferation and EMT phenotypes of undifferentiated TC cells. Our findings highlight the importance of multi-omics analysis in understanding the molecular subtypes and immune characteristics of TC, and provide a novel prognostic model and potential therapeutic targets for this disease. Moreover, we identified SNAI1 in mediating TC progression through EMT in vitro. [ABSTRACT FROM AUTHOR]

Background: Innate/adaptive immunity is the key to anti-tumor therapy. However, its causal relationship to Gastrointestinal (GI) cancer remains unclear. Methods: Immunity genes were extracted from the MSigDB database. The Genome-wide association studies (GWAS) summary data of GI cancer were integrated with expression quantitative trait loci (eQTL) and DNA methylation quantitative trait loci (mQTL) associated with genes. Summary-data-based Mendelian randomization (SMR) and co-localization analysis were used to reveal causal relationships between genes and GI cancer. Two-sample MR analysis was used for sensitivity analysis. Single cell analysis clarified the enrichment of genes. Results: Three-step SMR analysis showed that a putative mechanism, cg17294865 CpG site regulating HLA-DRA expression was negatively associated with gastric cancer risk. HLA-DRA was significantly differentially expressed in monocyte/macrophage and myeloid cells in gastric cancer. Conclusion: This study provides evidence that upregulating the expression level of HLA-DRA can reduce the risk of gastric cancer. [ABSTRACT FROM AUTHOR]

Background: Studies have highlighted a possible crosstalk between the pathogeneses of COVID-19 and systemic lupus erythematosus (SLE); however, the interactive mechanisms remain unclear. We aimed to elucidate the impact of COVID-19 on SLE using clinical information and the underlying mechanisms of both diseases. Methods: RNA-seq datasets were used to identify shared hub gene signatures between COVID-19 and SLE, while genome-wide association study datasets were used to delineate the interaction mechanisms of the key signaling pathways. Finally, single-cell RNA-seq datasets were used to determine the primary target cells expressing the shared hub genes and key signaling pathways. Results: COVID-19 may affect patients with SLE through hematologic involvement and exacerbated inflammatory responses. We identified 14 shared hub genes between COVID-19 and SLE that were significantly associated with interferon (IFN)-I/II. We also screened and obtained four core transcription factors related to these hub genes, confirming the regulatory role of the IFN-I/II-mediated Janus kinase/signal transducers and activators of transcription (JAK-STAT) signaling pathway on these hub genes. Further, SLE and COVID-19 can interact via IFN-I/II and IFN-I/II receptors, promoting the levels of monokines, including interleukin (IL)-6/10, tumor necrosis factor-α, and IFN-γ, and elevating the incidence rate and risk of cytokine release syndrome. Therefore, in SLE and COVID-19, both hub genes and core TFs are enriched within monocytes/macrophages. Conclusions: The interaction between SLE and COVID-19 promotes the activation of the IFN-I/II-triggered JAK-STAT signaling pathway in monocytes/macrophages. These findings provide a new direction and rationale for diagnosing and treating patients with SLE–COVID-19 comorbidity. [ABSTRACT FROM AUTHOR]

Aortic root aneurysm is a potentially life‐threatening condition that may lead to aortic rupture and is often associated with genetic syndromes, such as Marfan syndrome (MFS). Although studies with MFS animal models have provided valuable insights into the pathogenesis of aortic root aneurysms, this understanding of the transcriptomic and epigenomic landscape in human aortic root tissue remains incomplete. This knowledge gap has impeded the development of effective targeted therapies. Here, this study performs the first integrative analysis of single‐nucleus multiomic (gene expression and chromatin accessibility) and spatial transcriptomic sequencing data of human aortic root tissue under healthy and MFS conditions. Cell‐type‐specific transcriptomic and cis‐regulatory profiles in the human aortic root are identified. Regulatory and spatial dynamics during phenotypic modulation of vascular smooth muscle cells (VSMCs), the cardinal cell type, are delineated. Moreover, candidate key regulators driving the phenotypic modulation of VSMC, such as FOXN3, TEAD1, BACH2, and BACH1, are identified. In vitro experiments demonstrate that FOXN3 functions as a novel key regulator for maintaining the contractile phenotype of human aortic VSMCs through targeting ACTA2. These findings provide novel insights into the regulatory and spatial dynamics during phenotypic modulation in the aneurysmal aortic root of humans. [ABSTRACT FROM AUTHOR]

Choroid plexus tumors (CPTs) are intraventricular tumors derived from the choroid plexus epithelium and occur frequently in children. The aim of this study was to investigate the genomic and epigenomic characteristics of CPT and identify the differences between choroid plexus papilloma (CPP) and choroid plexus carcinoma (CPC). We conducted multiomics analyses of 20 CPT patients including CPP and CPC. Multiomics analysis included whole-genome sequencing, whole-transcriptome sequencing, and methylation sequencing. Mutually exclusive TP53 and EPHA7 point mutations, coupled with the amplification of chromosome 1, were exclusively identified in CPC. In contrast, amplification of chromosome 9 was specific to CPP. Differential gene expression analysis uncovered a significant overexpression of genes related to cell cycle regulation and epithelial-mesenchymal transition pathways in CPC compared to CPP. Overexpression of genes associated with tumor metastasis and progression was observed in the CPC subgroup with leptomeningeal dissemination. Furthermore, methylation profiling unveiled hypomethylation in major repeat regions, including long interspersed nuclear elements, short interspersed nuclear elements, long terminal repeats, and retrotransposons in CPC compared to CPP, implying that the loss of epigenetic silencing of transposable elements may play a role in tumorigenesis of CPC. Finally, the differential expression of AK1, regulated by both genomic and epigenomic factors, emerged as a potential contributing factor to the histological difference of CPP against CPC. Our results suggest pronounced genomic and epigenomic disparities between CPP and CPC, providing insights into the pathogenesis of CPT at the molecular level. [ABSTRACT FROM AUTHOR]

The genome folds into complex configurations and structures thought to profoundly impact its function. The intricacies of this dynamic structure-function relationship are not well understood particularly in the context of viral infection. To unravel this interplay, here we provide a comprehensive investigation of simultaneous host chromatin structural (via Hi-C and ATAC-seq) and functional changes (via RNA-seq) in response to vaccinia virus infection. Over time, infection significantly impacts global and local chromatin structure by increasing long-range intra-chromosomal interactions and B compartmentalization and by decreasing chromatin accessibility and inter-chromosomal interactions. Local accessibility changes are independent of broad-scale chromatin compartment exchange (~12% of the genome), underscoring potential independent mechanisms for global and local chromatin reorganization. While infection structurally condenses the host genome, there is nearly equal bidirectional differential gene expression. Despite global weakening of intra-TAD interactions, functional changes including downregulated immunity genes are associated with alterations in local accessibility and loop domain restructuring. Therefore, chromatin accessibility and local structure profiling provide impactful predictions for host responses and may improve development of efficacious anti-viral counter measures including the optimization of vaccine design. The three-dimensional architecture of the host genome condenses, reorganizes, and alters its accessibility resulting in bidirectional gene expression changes in response to viral infection over time. [ABSTRACT FROM AUTHOR]

Background: Deciphering the role of plasma proteins in pancreatic cancer (PC) susceptibility can aid in identifying novel targets for diagnosis and treatment. Methods: We examined the relationship between genetically determined levels of plasma proteins and PC through a systemic proteome-wide Mendelian randomization (MR) analysis utilizing cis-pQTLs from multiple centers. Rigorous sensitivity analyses, colocalization, reverse MR, replications with varying instrumental variable selections and additional datasets, as well as subsequent meta-analysis, were utilized to confirm the robustness of significant findings. The causative effect of corresponding protein-coding genes' expression and their expression pattern in single-cell types were then investigated. Enrichment analysis, between-protein interaction and causation, knock-out mice models, and mediation analysis with established PC risk factors were applied to indicate the pathogenetic pathways. These candidate targets were ultimately prioritized upon druggability and potential side effects predicted by a phenome-wide MR. Results: Twenty-one PC-related circulating proteins were identified in the exploratory phase with no evidence for horizontal pleiotropy or reverse causation. Of these, 11 were confirmed in a meta-analysis integrating external validations. The causality at a transcription level was repeated for neutrophil elastase, hydroxyacylglutathione hydrolase, lipase member N, protein disulfide-isomerase A5, xyloside xylosyltransferase 1. The carbohydrate sulfotransferase 11 and histo-blood group ABO system transferase exhibited high-support genetic colocalization evidence and were found to affect PC carcinogenesis partially through modulating body mass index and type 2 diabetes, respectively. Approved drugs have been established for eight candidate targets, which could potentially be repurposed for PC therapies. The phenome-wide investigation revealed 12 proteins associated with 51 non-PC traits, and interference on protein disulfide-isomerase A5 and cystatin-D would increase the risk of other malignancies. Conclusions: By employing comprehensive methodologies, this study demonstrated a genetic predisposition linking 21 circulating proteins to PC risk. Our findings shed new light on the PC etiology and highlighted potential targets as priorities for future efforts in early diagnosis and therapeutic strategies of PC. [ABSTRACT FROM AUTHOR]

Mesenchymal Stem Cells are ideal seed cells for tissue repair and cell therapy and have promising applications in regenerative medicine and tissue engineering. Using Platelet-Rich Plasma as an adjuvant to create and improve the microenvironment for Mesenchymal Stem Cells growth can enhance the biological properties of Mesenchymal Stem Cells and improve the efficacy of cell therapy. However, the mechanism by which Platelet-Rich Plasma improves the biological performance of Mesenchymal Stem Cells is still unknown. In this study, by examining the effects of Platelet-Rich Plasma on the biological performance of Mesenchymal Stem Cells, combined with multiomics analysis (Transcriptomics, Proteomics and Metabolomics) and related tests, we analyzed the specific pathways, related mechanisms and metabolic pathways of Platelet-Rich Plasma to improve the biological performance of Mesenchymal Stem Cells. In an in vitro cell culture system, the biological performance of Mesenchymal Stem Cells was significantly improved after replacing Foetal Bovine Serum with Platelet-Rich Plasma, and the genes (ESM1, PDGFB, CLEC7A, CCR1 and ITGA6 et al.) related to cell proliferation, adhesion, growth, migration and signal transduction were significantly upregulated. Platelet-Rich Plasma can enhance the secretion function of MSC exosomes, significantly upregulate many proteins related to tissue repair, immune regulation and anti-infection, and enhance the repair effect of exosomes on skin injury. After replacing Foetal Bovine Serum with Platelet-Rich Plasma, Mesenchymal Stem Cells underwent metabolic reprogramming, the metabolism of amino acids and fatty acids and various signaling pathways were changed, the anabolic pathways of various proteins were enhanced. These results provide a theoretical and technical reference for optimizing the Mesenchymal Stem Cells culture system, improving the biological characteristics and clinical application effects of Mesenchymal Stem Cells. [ABSTRACT FROM AUTHOR]

Large-scale microbiome studies are progressively utilizing multiomics designs, which include the collection of microbiome samples together with host genomics and metabolomics data. Despite the increasing number of data sources, there remains a bottleneck in understanding the relationships between different data modalities due to the limited number of statistical and computational methods for analyzing such data. Furthermore, little is known about the portability of general methods to the metagenomic setting and few specialized techniques have been developed. In this review, we summarize and implement some of the commonly used methods. We apply these methods to real data sets where shotgun metagenomic sequencing and metabolomics data are available for microbiome multiomics data integration analysis. We compare results across methods, highlight strengths and limitations of each, and discuss areas where statistical and computational innovation is needed. [ABSTRACT FROM AUTHOR]

Fructosamine-3-kinases (FN3Ks) are a conserved family of repair enzymes that phosphorylate reactive sugars attached to lysine residues in peptides and proteins. Although FN3Ks are present across the Tree of Life and share detectable sequence similarity to eukaryotic protein kinases, the biological processes regulated by these kinases are largely unknown. To address this knowledge gap, we leveraged the FN3K CRISPR Knock-Out (KO) HepG2 cell line alongside an integrative multi-omics study combining transcriptomics, metabolomics, and interactomics to place these enzymes in a pathway context. The integrative analyses revealed the enrichment of pathways related to oxidative stress response, lipid biosynthesis (cholesterol and fatty acids), and carbon and co-factor metabolism. Moreover, enrichment of nicotinamide adenine dinucleotide (NAD) binding proteins and localization of human FN3K (HsFN3K) to mitochondria suggests potential links between FN3K and NAD-mediated energy metabolism and redox balance. We report specific binding of HsFN3K to NAD compounds in a metal and concentration-dependent manner and provide insight into their binding mode using modeling and experimental site-directed mutagenesis. Our studies provide a framework for targeting these understudied kinases in diabetic complications and metabolic disorders where redox balance and NAD-dependent metabolic processes are altered. [ABSTRACT FROM AUTHOR]

BACKGROUND: Discovering determinants of cardiomyocyte maturity is critical for deeply understanding the maintenance of differentiated states and potentially reawakening endogenous regenerative programs in adult mammalian hearts as a therapeutic strategy. Forced dedifferentiation paired with oncogene expression is sufficient to drive cardiac regeneration, but elucidation of endogenous developmental regulators of the switch between regenerative and mature cardiomyocyte cell states is necessary for optimal design of regenerative approaches for heart disease. MBNL1 (muscleblind-like 1) regulates fibroblast, thymocyte, and erythroid differentiation and proliferation. Hence, we examined whether MBNL1 promotes and maintains mature cardiomyocyte states while antagonizing cardiomyocyte proliferation. METHODS: MBNL1 gain- and loss-of-function mouse models were studied at several developmental time points and in surgical models of heart regeneration. Multi-omics approaches were combined with biochemical, histological, and in vitro assays to determine the mechanisms through which MBNL1 exerts its effects. RESULTS: MBNL1 is coexpressed with a maturation-association genetic program in the heart and is regulated by the MEIS1/ calcineurin signaling axis. Targeted MBNL1 overexpression early in development prematurely transitioned cardiomyocytes to hypertrophic growth, hypoplasia, and dysfunction, whereas loss of MBNL1 function increased cardiomyocyte cell cycle entry and proliferation through altered cell cycle inhibitor transcript stability. Moreover, MBNL1-dependent stabilization of estrogen-related receptor signaling was essential for maintaining cardiomyocyte maturity in adult myocytes. In accordance with these data, modulating MBNL1 dose tuned the temporal window of neonatal cardiac regeneration, where increased MBNL1 expression arrested myocyte proliferation and regeneration and MBNL1 deletion promoted regenerative states with prolonged myocyte proliferation. However, MBNL1 deficiency was insufficient to promote regeneration in the adult heart because of cell cycle checkpoint activation. CONCLUSIONS: Here, MBNL1 was identified as an essential regulator of cardiomyocyte differentiated states, their developmental switch from hyperplastic to hypertrophic growth, and their regenerative potential through controlling an entire maturation program by stabilizing adult myocyte mRNAs during postnatal development and throughout adulthood. Targeting loss of cardiomyocyte maturity and downregulation of cell cycle inhibitors through MBNL1 deletion was not sufficient to promote adult regeneration. [ABSTRACT FROM AUTHOR]

Phytopathogenic Fusarium graminearum poses significant threats to crop health and soil quality. Although our laboratory-cultivated Pseudomonas sp. P13 exhibited potential biocontrol capacities, its effectiveness against F. graminearum and underlying antifungal mechanisms are still unclear. In light of this, our study investigated a significant inhibitory effect of P13 on F. graminearum T1, both in vitro and in a soil environment. Conducting genomic, metabolomic, and transcriptomic analyses of P13, we sought to identify evidence supporting its antagonistic effects on T1. The results revealed the potential of P13, a novel Pseudomonas species, to produce active antifungal components, including phenazine-1-carboxylate (PCA), hydrogen cyanide (HCN), and siderophores [pyoverdine (Pvd) and histicorrugatin (Hcs)], as well as the dynamic adaptive changes in the metabolic pathways of P13 related to these active ingredients. During the logarithmic growth stage, T1-exposed P13 strategically upregulated PCA and HCN biosynthesis, along with transient inhibition of the tricarboxylic acid (TCA) cycle. However, with growth stabilization, upregulation of PCA and HCN synthesis ceased, whereas the TCA cycle was enhanced, increasing siderophores secretion (Pvd and Hcs), suggesting that this mechanism might have caused continuous inhibition of T1. These findings improved our comprehension of the biocontrol mechanisms of P13 and provided the foundation for potential application of Pseudomonas strains in the biocontrol of phytopathogenic F. graminearum. [ABSTRACT FROM AUTHOR]

Bioproduction of short chain carboxylic acids (SCCAs) and medium chain carboxylic acids (MCCAs) has emerged as an alternative strategy to upgrade low-value organic waste and reduce fossil fuels requirement. Using synthesis gas (syngas) to generate SCCAs and MCCAs by microbial communities would be an option to address part of the current energy challenge. Syngas fermentation offers a pathway for the sustainable synthesis of fuels and chemicals with advantages over catalytic syngas conversion. In the same way, chain elongation is an anaerobic microbial process driven by ethanol, carbohydrates, and SCCAs (e.g., acetate, lactate) to obtain high-value MCCAs (e.g., caproate, valerate). Because these technologies use organic wastes as feedstocks, mixed microbial communities are often considered biocatalysts. However, the management of microbial communities is the biggest bottleneck for efficient and simultaneous production of SCCAs and MCCAs. Understanding and steering these microbiomes is critical to optimize bioprocess performance. Therefore, this review discusses the metabolic pathways of both syngas fermentation and chain elongation. Also, to examine the overall performance of microbial communities involved in syngas fermentation and chain elongation, the influence of reactor parameters on the growth and metabolic activity of the key microorganisms is presented. The experimental strategies for simultaneous syngas fermentation and chain elongation processes are also presented and discussed. Finally, the use of multi-omics to better understand both syngas fermentation and chain elongation processes is discussed to steer these bioproduction processes towards full-scale applications. [ABSTRACT FROM AUTHOR]

The skin is a complex and dynamic organ where homeostasis is maintained through the intricate interplay between the immune system and metabolism, particularly cholesterol metabolism. Various factors such as cytokines, inflammatory mediators, cholesterol metabolites, and metabolic enzymes play crucial roles in facilitating these interactions. Dysregulation of this delicate balance contributes to the pathogenic pathways of inflammatory skin conditions, notably psoriasis. In this article, we provide an overview of omics biomarkers associated with psoriasis in relation to cholesterol metabolism. We explore multi-omics approaches that reveal the communication between immunometabolism and psoriatic inflammation. Additionally, we summarize the use of multi-omics strategies to uncover the complexities of multifactorial and heterogeneous inflammatory diseases. Finally, we highlight potential future perspectives related to targeted drug therapies and research areas that can advance precise medicine. This review aims to serve as a valuable resource for those investigating the role of cholesterol metabolism in psoriasis. [ABSTRACT FROM AUTHOR]

Objective: Jining Grey goat is a local Chinese goat breed that is well known for its high fertility and excellent meat quality but shows low meat production performance. Numerous studies have focused on revealing the genetic mechanism of its high fertility, but its highlighting meat quality and muscle growth mechanism still need to be studied. Methods: In this research, an integrative analysis of the genomics and transcriptomics of Jining Grey goats compared with Boer goats was performed to identify candidate genes and pathways related to the mechanisms of meat quality and muscle development. Results: Our results overlap among five genes (ABHD2, FN1, PGM2L1, PRKAG3, RAVER2) and detected a set of candidate genes associated with fatty acid metabolism (PRKAG3, HADHB, FASN, ACADM), amino acid metabolism (KMT2C, PLOD3, NSD2, SETDB1, STT3B, MAN1A2, BCKDHB, NAT8L, P4HA3) and muscle development (MSTN, PPARGC1A, ANKRD2). Several pathways have also been detected, such as the FoxO signaling pathway and Apelin signaling pathway that play roles in lipid metabolism, lysine degradation, N-glycan biosynthesis, valine, leucine and isoleucine degradation that involving with amino acid metabolism. Conclusion: The comparative genomic and transcriptomic analysis of Jining Grey goat and Boer goat revealed the mechanisms underlying the meat quality and meat productive performance of goats. These results provide valuable information for future breeding of goats. [ABSTRACT FROM AUTHOR]

The transformative role of artificial intelligence (AI) and multiomics could enhance the diagnostic and prognostic capabilities of liquid biopsy (LB) for lung cancer (LC). Despite advances, the transition from tissue biopsies to more sophisticated, non‐invasive methods like LB has been impeded by challenges such as the heterogeneity of biomarkers and the low concentration of tumour‐related analytes. The advent of multiomics – enabled by deep learning algorithms – offers a solution by allowing the simultaneous analysis of various analytes across multiple biological fluids, presenting a paradigm shift in cancer diagnostics. Through multi‐marker, multi‐analyte and multi‐source approaches, this review showcases how AI and multiomics are identifying clinically valuable biomarker combinations that correlate with patients' health statuses. However, the path towards clinical implementation is fraught with challenges, including study reproducibility and lack of methodological standardization, thus necessitating urgent solutions to solve these common issues. [ABSTRACT FROM AUTHOR]

Gastric cancer (GC) is one of the most common cancers worldwide. Most patients are diagnosed at the progressive stage of the disease, and current anticancer drug advancements are still lacking. Therefore, it is crucial to find relevant biomarkers with the accurate prediction of prognoses and good predictive accuracy to select appropriate patients with GC. Recent advances in molecular profiling technologies, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics, have enabled the approach of GC biology at multiple levels of omics interaction networks. Systemic biological analyses, such as computational inference of "big data" and advanced bioinformatic approaches, are emerging to identify the key molecular biomarkers of GC, which would benefit targeted therapies. This review summarizes the current status of how bioinformatics analysis contributes to biomarker discovery for prognosis and prediction of therapeutic efficacy in GC based on a search of the medical literature. We highlight emerging individual multi-omics datasets, such as genomics, epigenomics, transcriptomics, proteomics, and metabolomics, for validating putative markers. Finally, we discuss the current challenges and future perspectives to integrate multi-omics analysis for improving biomarker implementation. The practical integration of bioinformatics analysis and multi-omics datasets under complementary computational analysis is having a great impact on the search for predictive and prognostic biomarkers and may lead to an important revolution in treatment. [ABSTRACT FROM AUTHOR]

Soybean, a major source of oil and protein, has seen an annual increase in consumption when used in soybean-derived products and the broadening of its cultivation range. The demand for soybean necessitates a better understanding of the regulatory networks driving storage protein accumulation and oil biosynthesis to broaden its positive impact on human health. In this study, we selected a chromosome segment substitution line (CSSL) with high protein and low oil contents to investigate the underlying effect of donor introgression on seed storage through multi-omics analysis. In total, 1479 differentially expressed genes (DEGs), 82 differentially expressed proteins (DEPs), and 34 differentially expressed metabolites (DEMs) were identified in the CSSL compared to the recurrent parent. Based on Gene Ontology (GO) term analysis and the Kyoto Encyclopedia of Genes and Genomes enrichment (KEGG), integrated analysis indicated that 31 DEGs, 24 DEPs, and 13 DEMs were related to seed storage functionality. Integrated analysis further showed a significant decrease in the contents of the seed storage lipids LysoPG 16:0 and LysoPC 18:4 as well as an increase in the contents of organic acids such as L-malic acid. Taken together, these results offer new insights into the molecular mechanisms of seed storage and provide guidance for the molecular breeding of new favorable soybean varieties. [ABSTRACT FROM AUTHOR]

A quiet quadruple revolution has been in the making in systems science with convergence of (1) artificial intelligence, machine learning, and other digital technologies; (2) multiomics big data integration; (3) growing interest in the "variability science" of precision/personalized medicine that aims to account for patient-to-patient and between-population differences in disease susceptibilities and responses to health interventions such as drugs, nutrition, vaccines, and radiation; and (4) planetary health scholarship that both scales up and integrates biological, clinical, and ecological contexts of health and disease. Against this overarching background, this article presents and highlights some of the salient challenges and prospects of multiomics research, emphasizing the attendant pivotal role of systems medicine and systems biology. In addition, we emphasize the rapidly growing importance of planetary health research for systems medicine, particularly amid climate emergency, ecological degradation, and loss of planetary biodiversity. Looking ahead, we anticipate that the integration and utilization of multiomics big data and artificial intelligence will drive further progress in systems medicine and systems biology, heralding a promising future for both human and planetary health. [ABSTRACT FROM AUTHOR]

This article discusses a study conducted on Chinese patients with small cell lung cancer (SCLC) to understand the mechanisms of chemotherapy response and resistance. The study analyzed the multi-omics characteristics of tumor tissue, circulating tumor cells (CTCs), and circulating tumor DNA (ctDNA) in SCLC patients. The results showed that patients who responded well to chemotherapy had better survival outcomes. The study also identified potential molecular mechanisms, such as KDR mutation, PI3K amplification, and activation of VEGF and KRAS pathways, that contribute to chemotherapy resistance. The findings suggest that targeted therapies and immunotherapy may benefit patients who are resistant to chemotherapy. Further research is needed to determine the most appropriate treatments for recurring patients. [Extracted from the article]

Background: The liver is anatomically divided into eight segments based on the distribution of Glisson's triad. However, the molecular mechanisms underlying each segment and its association with hepatocellular carcinoma (HCC) heterogeneity are not well understood. In this study, our objective is to conduct a comprehensive multiomics profiling of the segmentation atlas in order to investigate potential subtypes and therapeutic approaches for HCC. Methods: A high throughput liquid chromatography‐tandem mass spectrometer strategy was employed to comprehensively analyse proteome, lipidome and metabolome data, with a focus on segment‐resolved multiomics profiling. To classify HCC subtypes, the obtained data with normal reference profiling were integrated. Additionally, potential therapeutic targets for HCC were identified using immunohistochemistry assays. The effectiveness of these targets were further validated through patient‐derived organoid (PDO) assays. Results: A multiomics profiling of 8536 high‐confidence proteins, 1029 polar metabolites and 3381 nonredundant lipids was performed to analyse the segmentation atlas of HCC. The analysis of the data revealed that in normal adjacent tissues, the left lobe was primarily involved in energy metabolism, while the right lobe was associated with small molecule metabolism. Based on the normal reference atlas, HCC patients with segment‐resolved classification were divided into three subtypes. The C1 subtype showed enrichment in ribosome biogenesis, the C2 subtype exhibited an intermediate phenotype, while the C3 subtype was closely associated with neutrophil degranulation. Furthermore, using the PDO assay, exportin 1 (XPO1) and 5‐lipoxygenase (ALOX5) were identified as potential targets for the C1 and C3 subtypes, respectively. Conclusion: Our extensive analysis of the segmentation atlas in multiomics profiling defines molecular subtypes of HCC and uncovers potential therapeutic strategies that have the potential to enhance the prognosis of HCC. [ABSTRACT FROM AUTHOR]

Background: Bacterial vaginosis (BV) is a common clinical manifestation of a perturbed vaginal ecology associated with adverse sexual and reproductive health outcomes if left untreated. The existing diagnostic modalities are either cumbersome or require skilled expertise, warranting alternate tests. Application of machine‐learning tools to heterogeneous and high‐dimensional multi‐omics datasets finds promising potential in data integration and may aid biomarker discovery. Objectives: The present study aimed to evaluate the potential of the microbiome and metabolome‐derived biomarkers in BV diagnosis. Interpretable machine‐learning algorithms were used to evaluate the utility of an integrated‐omics‐derived classification model. Methods: Vaginal samples obtained from reproductive‐age group women with (n = 40) and without BV (n = 40) were subjected to 16S rRNA amplicon sequencing and LC–MS‐based metabolomics. The vaginal microbiome and metabolome were characterized, and machine‐learning analysis was performed to build a classification model using biomarkers with the highest diagnostic accuracy. Results: Microbiome‐based diagnostic model exhibited a ROC‐AUC (10‐fold CV) of 0.84 ± 0.21 and accuracy of 0.79 ± 0.18, and important features were Aerococcus spp., Mycoplasma hominis, Sneathia spp., Lactobacillus spp., Prevotella spp., Gardnerella spp. and Fannyhessea vaginae. The metabolome‐derived model displayed superior performance with a ROC‐AUC of 0.97 ± 0.07 and an accuracy of 0.92 ± 0.08. Beta‐leucine, methylimidazole acetaldehyde, dimethylethanolamine, L‐arginine and beta cortol were among key predictive metabolites for BV. A predictive model combining both microbial and metabolite features exhibited a high ROC‐AUC of 0.97 ± 0.07 and accuracy of 0.94 ± 0.08 with diagnostic performance only slightly superior to the metabolite‐based model. Conclusion: Application of machine‐learning tools to multi‐omics datasets aid biomarker discovery with high predictive performance. Metabolome‐derived classification models were observed to have superior diagnostic performance in predicting BV than microbiome‐based biomarkers. [ABSTRACT FROM AUTHOR]

Actinomycetes are a phylogenetically diverse bacterial group which are widely distributed across terrestrial and aquatic ecosystems. Within this order, the genus Pseudonocardia and their specialised metabolites have been the focus of previous ecological studies due to their antagonistic interactions with other microorganisms and their mutualistic interactions with insects. However, the chemical ecology of free‐living Pseudonocardia remains understudied. This study applies a multi‐omics approach to investigate the chemical ecology of free‐living actinomycetes from the genus Pseudonocardia. In a comparative genomics analysis, it was observed that the biosynthetic gene cluster family distribution was influenced mainly by phylogenetic distance rather than the geographic or ecological origin of strains. This finding was also observed in the mass spectrometry‐based metabolomic profiles of nine Pseudonocardia species isolated from marine sediments and two terrestrial species. Antagonist interactions between these 11 species were examined, and matrix‐assisted laser desorption/ionisation‐mass spectrometry imaging was used to examine in situ chemical interactions between the Southern Ocean strains and their phylogenetically close relatives. Overall, it was demonstrated that phylogeny was the main predictor of antagonistic interactions among free‐living Pseudonocardia. Moreover, two features at m/z 441.15 and m/z 332.20 were identified as metabolites related to these interspecies interactions. [ABSTRACT FROM AUTHOR]

Background: Tumor immunotherapy brings new light and vitality to breast cancer patients, but low response rate and limitations of therapeutic targets become major obstacles to its clinical application. Recent studies have shown that CD24 is involved in an important process of tumor immune regulation in breast cancer and is a promising target for immunotherapy. Methods: In this study, singleR was used to annotate each cell subpopulation after t-distributed stochastic neighbor embedding (t-SNE) methods. Pseudo-time trace analysis and cell communication were analyzed by Monocle2 package and CellChat, respectively. A prognostic model based on CD24-related genes was constructed using several machine learning methods. Multiple quantitative immunofluorescence (MQIF) was used to evaluate the spatial relationship between CD24+PANCK+cells and exhausted CD8+T cells. Results: Based on the scRNA-seq analysis, 1488 CD24-related differential genes were identified, and a risk model consisting of 15 prognostic characteristic genes was constructed by combining the bulk RNA-seq data. Patients were divided into high- and low-risk groups based on the median risk score. Immune landscape analysis showed that the low-risk group showed higher infiltration of immune-promoting cells and stronger immune reactivity. The results of cell communication demonstrated a strong interaction between CD24+epithelial cells and CD8+T cells. Subsequent MQIF demonstrated a strong interaction between CD24+PANCK+ and exhausted CD8+T cells with FOXP3+ in breast cancer. Additionally, CD24+PANCK+ and CD8+FOXP3+T cells were positively associated with lower survival rates. Conclusion: This study highlights the importance of CD24+breast cancer cells in clinical prognosis and immunosuppressive microenvironment, which may provide a new direction for improving patient outcomes. [ABSTRACT FROM AUTHOR]

Venn diagrams serve as invaluable tools for visualizing set relationships due to their ease of interpretation. Widely applied across diverse disciplines such as metabolomics, genomics, transcriptomics, and proteomics, their utility is undeniable. However, the operational complexity has been compounded by the absence of standardized data formats and the need to switch between various platforms for generating different Venn diagrams. To address these challenges, we introduce the EVenn platform, a versatile tool offering a unified interface for efficient data exploration and visualization of diverse Venn diagrams. EVenn (http://www.ehbio.com/test/venn) streamlines the data upload process with a standardized format, enhancing the capabilities for multimodule analysis. This comprehensive protocol outlines various applications of EVenn, featuring representative results of multiple Venn diagrams, data uploads in the centralized data center, and step‐by‐step case demonstrations. Through these functionalities, EVenn emerges as a valuable and user‐friendly tool for the in‐depth exploration of multiomics data. Highlights: Comprehensive Venn functionality: EVenn introduces a unified platform with diverse Venn diagram tools, from interactive diagrams to network representations, catering to the intricate needs of multiomics data analysis across metabolomics, genomics, transcriptomics, and proteomics.Efficient data exploration: EVenn's data center streamlines exploration by supporting a standardized data format, allowing researchers to effortlessly upload and analyze data. The manuscript demonstrates its practical utility through representative results and detailed case demonstrations.User‐friendly interface: With a user‐friendly interface, EVenn simplifies the generation of various Venn diagrams, Euler diagrams, UpSet plots, and more. This protocol establishes EVenn as a valuable resource for researchers seeking a cohesive and accessible tool for multiomics data interpretation. [ABSTRACT FROM AUTHOR]

The Gut Universe Database (GutUDB) is a comprehensive multiomics database that allows researchers to manage, analyze, and visualize information about intestinal diseases. It includes various types of omics data and covers a wide range of intestinal diseases across different species. GutUDB provides detailed information on disease-gene associations, therapeutic targets, and diagnostic markers. It also includes spatial and single-cell omics data, as well as interactive visualizations. The database aims to improve our understanding of intestinal diseases and support further research in this field. This document, written by a group of authors, offers a comprehensive report on a specific topic, presenting various perspectives and insights. It is culturally sensitive and respectful of diverse viewpoints, making it a valuable resource for library patrons conducting research on this subject. [Extracted from the article]

In the era of precision medicine, accurate disease phenotype prediction for heterogeneous diseases, such as cancer, is emerging due to advanced technologies that link genotypes and phenotypes. However, it is difficult to integrate different types of biological data because they are so varied. In this study, we focused on predicting the traits of a blood cancer called Acute Myeloid Leukemia (AML) by combining different kinds of biological data. We used a recently developed method called Omics Generative Adversarial Network (GAN) to better classify cancer outcomes. The primary advantages of a GAN include its ability to create synthetic data that is nearly indistinguishable from real data, its high flexibility, and its wide range of applications, including multi-omics data analysis. In addition, the GAN was effective at combining two types of biological data. We created synthetic datasets for gene activity and DNA methylation. Our method was more accurate in predicting disease traits than using the original data alone. The experimental results provided evidence that the creation of synthetic data through interacting multi-omics data analysis using GANs improves the overall prediction quality. Furthermore, we identified the top-ranked significant genes through statistical methods and pinpointed potential candidate drug agents through in-silico studies. The proposed drugs, also supported by other independent studies, might play a crucial role in the treatment of AML cancer. The code is available on GitHub; https://github.com/SabrinAfroz/omicsGAN_codes?fbclid=IwAR1-/stuffmlE0hyWgSu2wlXo6dYlKUei3faLdlvpxTOOUPVlmYCloXf4Uk9ejK4I • Proposing a Generative Adversarial Network (GAN) to classify cancer outcomes more accurately. • Generated synthetic datasets for gene expression and DNA methylation. • Identified potential candidate drug agents through in-silico studies. • The proposed drugs might play a vital role in the treatment of AML cancer. [ABSTRACT FROM AUTHOR]

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

SUMMARY: The SID2 (SA INDUCTION‐DEFICIENT2) gene that encodes ICS1 (isochorismate synthase), plays a central role in salicylic acid biosynthesis in Arabidopsis. The sid2 and NahG (encoding a bacterial SA hydroxylase) overexpressing mutants (NahG‐OE) have currently been shown to outperform wild type, presenting delayed leaf senescence, higher plant biomass and better seed yield. When grown under sulfate‐limited conditions (low‐S), sid2 mutants exhibited early leaf yellowing compared to the NahG‐OE, the npr1 mutant affected in SA signaling pathway, and WT. This indicated that the hypersensitivity of sid2 to sulfate limitation was independent of the canonical npr1 SA‐signaling pathway. Transcriptomic and proteomic analyses revealed that major changes occurred in sid2 when cultivated under low‐S, changes that were in good accordance with early senescence phenotype and showed the exacerbation of stress responses. The sid2 mutants displayed a lower sulfate uptake capacity when cultivated under low‐S and lower S concentrations in their rosettes. Higher glutathione concentrations in sid2 rosettes under low‐S were in good accordance with the higher abundance of proteins involved in glutathione and ascorbate redox metabolism. Amino acid and lipid metabolisms were also strongly modified in sid2 under low‐S. Depletion of total fatty acids in sid2 under low‐S was consistent with the fact that S‐metabolism plays a central role in lipid synthesis. Altogether, our results show that functional ICS1 is important for plants to cope with S limiting conditions. [ABSTRACT FROM AUTHOR]

Background: Microtia is reported to be one of the most common congenital craniofacial malformations. Due to the complex etiology and the ethical barrier of embryonic study, the precise mechanisms of microtia remain unclear. Here we report a rare case of microtia with costal chondrodysplasia based on bioinformatics analysis and further verifications on other sporadic microtia patients. Results: One hundred fourteen deleterious insert and deletion (InDel) and 646 deleterious SNPs were screened out by WES, candidate genes were ranked in descending order according to their relative impact with microtia. Label-free proteomic analysis showed that proteins significantly different between the groups were related with oxidative stress and energy metabolism. By real-time PCR and immunohistochemistry, we further verified the candidate genes between other sporadic microtia and normal ear chondrocytes, which showed threonine aspartase, cadherin-13, aldolase B and adiponectin were significantly upregulated in mRNA levels but were significantly lower in protein levels. ROS detection and mitochondrial membrane potential (∆ Ψ m) detection proved that oxidative stress exists in microtia chondrocytes. Conclusions: Our results not only spot new candidate genes by WES and label-free proteomics, but also speculate for the first time that metabolism and oxidative stress may disturb cartilage development and this might become therapeutic targets and potential biomarkers with clinical usefulness in the future. [ABSTRACT FROM AUTHOR]

Background: Cerebellar degeneration-related (CDR) proteins are associated with paraneoplastic cerebellar degeneration (PCD) – a rare, neurodegenerative disease caused by tumour-induced autoimmunity against neural antigens resulting in degeneration of Purkinje neurons in the cerebellum. The pathogenesis of PCD is unknown, in large part due to our limited understanding of the functions of CDR proteins. To this end, we performed an extensive, multi-omics analysis of CDR-knockout cells focusing on the CDR2L protein, to gain a deeper understanding of the properties of the CDR proteins in ovarian cancer. Methods: Ovarian cancer cell lines lacking either CDR1, CDR2, or CDR2L were analysed using RNA sequencing and mass spectrometry-based proteomics to assess changes to the transcriptome, proteome and secretome in the absence of these proteins. Results: For each knockout cell line, we identified sets of differentially expressed genes and proteins. CDR2L-knockout cells displayed a distinct expression profile compared to CDR1- and CDR2-knockout cells. Knockout of CDR2L caused dysregulation of genes involved in ribosome biogenesis, protein translation, and cell cycle progression, ultimately causing impaired cell proliferation in vitro. Several of these genes showed a concurrent upregulation at the transcript level and downregulation at the protein level. Conclusions: Our study provides the first integrative multi-omics analysis of the impact of knockout of the CDR genes, providing both new insights into the biological properties of the CDR proteins in ovarian cancer, and a valuable resource for future investigations into the CDR proteins. [ABSTRACT FROM AUTHOR]

Background: Understanding the metabolic changes in colorectal cancer (CRC) and exploring potential diagnostic biomarkers is crucial for elucidating its pathogenesis and reducing mortality. Cancer cells are typically derived from cancer tissues and can be easily obtained and cultured. Systematic studies on CRC cells at different stages are still lacking. Additionally, there is a need to validate our previous findings from human serum. Methods: Ultrahigh-performance liquid chromatography tandem high-resolution mass spectrometry (UHPLC-HRMS)-based metabolomics and lipidomics were employed to comprehensively measure metabolites and lipids in CRC cells at four different stages and serum samples from normal control (NR) and CRC subjects. Univariate and multivariate statistical analyses were applied to select the differential metabolites and lipids between groups. Biomarkers with good diagnostic efficacy for CRC that existed in both cells and serum were screened by the receiver operating characteristic curve (ROC) analysis. Furthermore, potential biomarkers were validated using metabolite standards. Results: Metabolite and lipid profiles differed significantly among CRC cells at stages A, B, C, and D. Dysregulation of glycerophospholipid (GPL), fatty acid (FA), and amino acid (AA) metabolism played a crucial role in the CRC progression, particularly GPL metabolism dominated by phosphatidylcholine (PC). A total of 46 differential metabolites and 29 differential lipids common to the four stages of CRC cells were discovered. Eight metabolites showed the same trends in CRC cells and serum from CRC patients compared to the control groups. Among them, palmitoylcarnitine and sphingosine could serve as potential biomarkers with the values of area under the curve (AUC) more than 0.80 in the serum and cells. Their panel exhibited excellent performance in discriminating CRC cells at different stages from normal cells (AUC = 1.00). Conclusions: To our knowledge, this is the first research to attempt to validate the results of metabolism studies of serum from CRC patients using cell models. The metabolic disorders of PC, FA, and AA were closely related to the tumorigenesis of CRC, with PC being the more critical factor. The panel composed of palmitoylcarnitine and sphingosine may act as a potential biomarker for the diagnosis of CRC, aiding in its prevention. [ABSTRACT FROM AUTHOR]

Transformer‐based models have revolutionized single cell RNA‐seq (scRNA‐seq) data analysis. However, their applicability is challenged by the complexity and scale of single‐cell multi‐omics data. Here a novel single‐cell multi‐modal/multi‐task transformer (scmFormer) is proposed to fill up the existing blank of integrating single‐cell proteomics with other omics data. Through systematic benchmarking, it is demonstrated that scmFormer excels in integrating large‐scale single‐cell multimodal data and heterogeneous multi‐batch paired multi‐omics data, while preserving shared information across batchs and distinct biological information. scmFormer achieves 54.5% higher average F1 score compared to the second method in transferring cell‐type labels from single‐cell transcriptomics to proteomics data. Using COVID‐19 datasets, it is presented that scmFormer successfully integrates over 1.48 million cells on a personal computer. Moreover, it is also proved that scmFormer performs better than existing methods on generating the unmeasured modality and is well‐suited for spatial multi‐omic data. Thus, scmFormer is a powerful and comprehensive tool for analyzing single‐cell multi‐omics data. [ABSTRACT FROM AUTHOR]

Neuroblastoma (NB) is one of the leading causes of cancer-associated death in children. MYCN amplification is a prominent genetic marker for NB, and its targeting to halt NB progression is difficult to achieve. Therefore, an in-depth understanding of the molecular interactome of NB is needed to improve treatment outcomes. Analysis of NB multi-omics unravels valuable insight into the interplay between MYCN transcriptional and miRNA post-transcriptional modulation. Moreover, it aids in the identification of various miRNAs that participate in NB development and progression. This study proposes an integrated computational framework with three levels of high-throughput NB data (mRNA-seq, miRNA-seq, and methylation array). Similarity Network Fusion (SNF) and ranked SNF methods were utilized to identify essential genes and miRNAs. The specified genes included both miRNA-target genes and transcription factors (TFs). The interactions between TFs and miRNAs and between miRNAs and their target genes were retrieved where a regulatory network was developed. Finally, an interaction network-based analysis was performed to identify candidate biomarkers. The candidate biomarkers were further analyzed for their potential use in prognosis and diagnosis. The candidate biomarkers included three TFs and seven miRNAs. Four biomarkers have been previously studied and tested in NB, while the remaining identified biomarkers have known roles in other types of cancer. Although the specific molecular role is yet to be addressed, most identified biomarkers possess evidence of involvement in NB tumorigenesis. Analyzing cellular interactome to identify potential biomarkers is a promising approach that can contribute to optimizing efficient therapeutic regimens to target NB vulnerabilities. [ABSTRACT FROM AUTHOR]

Background: Recent Alzheimer's disease (AD) discoveries are increasingly based on studies from a variety of omics technologies on large cohorts. Currently, there is no easily accessible resource for neuroscientists to browse, query, and visualize these complex datasets in a harmonized manner. Objective: Create an online portal of public omics datasets for AD research. Methods: We developed Alzheimer DataLENS, a web-based portal, using the R Shiny platform to query and visualize publicly available transcriptomics and genetics studies of AD on human cohorts. To ensure consistent representation of AD findings, all datasets were processed through a uniform bioinformatics pipeline. Results: Alzheimer DataLENS currently houses 2 single-nucleus RNA sequencing datasets, over 30 bulk RNA sequencing datasets from 19 brain regions and 3 cohorts, and 2 genome-wide association studies (GWAS). Available visualizations for single-nucleus data include bubble plots, heatmaps, and UMAP plots; for bulk expression data include box plots and heatmaps; for pathways include protein-protein interaction network plots; and for GWAS results include Manhattan plots. Alzheimer DataLENS also links to two other knowledge resources: the AD Progression Atlas and the Astrocyte Atlas. Conclusions: Alzheimer DataLENS is a valuable resource for investigators to quickly and systematically explore omics datasets and is freely accessible at . [ABSTRACT FROM AUTHOR]