From single- to multi-omics: future research trends in medicinal plants.

Medicinal plants are the main source of natural metabolites with specialised pharmacological activities and have been widely examined by plant researchers. Numerous omics studies of medicinal plants have been performed to identify molecular markers of species and functional genes controlling key biological traits, as well as to understand biosynthetic pathways of bioactive metabolites and the regulatory mechanisms of environmental responses. Omics technologies have been widely applied to medicinal plants, including as taxonomics, transcriptomics, metabolomics, proteomics, genomics, pangenomics, epigenomics and mutagenomics. However, because of the complex biological regulation network, single omics usually fail to explain the specific biological phenomena. In recent years, reports of integrated multi-omics studies of medicinal plants have increased. Until now, there have few assessments of recent developments and upcoming trends in omics studies of medicinal plants. We highlight recent developments in omics research of medicinal plants, summarise the typical bioinformatics resources available for analysing omics datasets, and discuss related future directions and challenges. This information facilitates further studies of medicinal plants, refinement of current approaches and leads to new ideas.

Stress adaptation in Tibetan cashmere goats is governed by inherent metabolic differences and manifested through variable cashmere phenotypes.

Tibetan cashmere goats are not only served as a valuable model for studying adaptation to hypoxia and high-altitude conditions but also playing a pivotal role in bolstering local economies through the provision of premium quality cashmere yarn. In this study, we performed an integration and network analysis of metabolomic, transcriptomic and proteomic to elucidate the role of differentially expressed genes, important metabolites, and relevant cellular and metabolic pathways between the fine (average 12.04 ± 0.03 µm of mean fiber diameter) and coarse cashmere (average 14.88 ± 0.05 µm of mean fber diameter) producing by Tibetan cashmere goats. We identified a distinction of 56 and 71 differential metabolites (DMs) between the F and C cashmere groups under positive and negative ion modes, respectively. The KEGG pathway enrichment analysis of these DMs highlighted numerous pathways predominantly involved in amino acid and protein metabolism, as indicated by the finding that the most impactful pathway was the mammalian target of rapamycin (mTOR) signalling pathway. In the F group, we identified a distinctive metabolic profile where amino acid metabolites including serine, histidine, asparagine, glutamic acid, arginine, valine, aspartic acid, tyrosine, and methionine were upregulated, while lysine, isoleucine, glutamine, tryptophan, and threonine were downregulated. The regulatory network and gene co-expression network revealed crucial genes, metabolites, and metabolic pathways. The integrative omics analysis revealed a high enrichment of several pathways, notably encompassing protein digestion and absorption, sphingolipid signalling, and the synaptic vesicle cycle. Within the sphere of our integrative analysis, DNMT3B was identified as a paramount gene, intricately associated with significant proteins such as HMCN1, CPB2, GNG12, and LRP1. Our present study delineated the molecular underpinnings governing the variations in cashmere characteristics by conducting comprehensive analyses across metabolomic, transcriptomic, and proteomic dimensions. This research provided newly insights into the mechanisms regulating cashmere traits and facilitated the advancement of selective breeding programs aimed at cultivating high-quality superfine Tibetan cashmere goats.

Comprehensive multi-omics analysis and experimental verification reveal PFDN5 is a novel prognostic and therapeutic biomarker for gastric cancer.

Prefoldin Subunit 5 (PFDN5) plays a critical role as a member of the prefoldins (PFDNs) in maintaining a finely tuned equilibrium between protein production and degradation. However, there has been no comprehensive analysis specifically focused on PFDN5 thus far. Here, a comprehensive multi-omics (transcriptomics, genomics, and proteomics) analysis, systematic molecular biology experiments (in vitro and in vivo), transcriptome sequencing and PCR Array were performed for identifying the value of PFDN5 in pan-cancer, especially in Gastric Cancer (GC). We found PFDN5 had the potential to serve as a prognostic and therapeutic biomarker in GC. And PFDN5 could promote the proliferation of GC cells, primarily by affecting the cell cycle, cell death and immune process etc. These findings provide novel insights into the molecular mechanisms and precise treatments of in GC.

Multiple omics analysis reveals the regulation of SIRT5 on mitochondrial function and lipid metabolism during the differentiation of bovine preadipocytes.

Preadipocyte differentiation represents a critical stage in adipogenesis, with mitochondria playing an undeniable pivotal role. Given the intricate interplay between transcription and metabolic signaling during adipogenesis, the regulation of sirtuin 5 (SIRT5) on mitochondrial function and lipid metabolism was revealed via multiple omics analysis. The findings suggest that SIRT5 plays a crucial role in promoting mitochondrial biosynthesis and maintaining mitochondrial function during preadipocyte differentiation. Moreover, SIRT5 modulates the metabolic levels of numerous bioactive substances by extensively regulating genes expression associated with differentiation, energy metabolism, lipid synthesis, and mitochondrial function. Finally, SIRT5 was found to suppress triacylglycerols (TAG) accumulation while enhancing the proportion and diversity of unsaturated fatty acids, and providing conditions for the expansion and stability of membrane structure during mitochondrial biosynthesis through numerous gene regulations. Our findings provide a foundation for the identification of crucial functional genes, signaling pathways, and metabolic substances associated with adipose tissue differentiation and metabolism.

Multiomics analysis identified IL-4-induced IL1RL1high eosinophils characterized by prominent cysteinyl leukotriene metabolism.

BACKGROUND: Clinical studies have demonstrated that IL-4, a type 2 cytokine, plays an important role in the pathogenesis of chronic rhinosinusitis and eosinophilic asthma. However, the direct effect of IL-4 on eosinophils remains unclear. OBJECTIVE: We aimed to elucidate the inflammatory effects of IL-4 on the functions of human eosinophils. METHODS: A multiomics analysis comprising transcriptomics, proteomics, lipidomics, quantitative RT-PCR, and flow cytometry was performed by using blood eosinophils from healthy subjects stimulated with IL-4, IL-5, or a combination thereof. RESULTS: Transcriptomic and proteomic analyses revealed that both IL-4 and IL-5 upregulate the expression of γ-gultamyl transferase 5, a fatty acid-metabolizing enzyme that converts leukotriene C4 into leukotriene D4. In addition, IL-4 specifically upregulates the expression of IL-1 receptor-like 1 (IL1RL1), a receptor for IL-33 and transglutaminase-2. Additional transcriptomic analysis of cells stimulated with IL-13 revealed altered gene expression profiles, characterized by the upregulation of γ-gultamyl transferase 5, transglutaminase-2, and IL1RL1. The IL-13-induced changes were not totally different from the IL-4-induced changes. Lipidomic analysis revealed that IL-5 and IL-4 additively increased the extracellular release of leukotriene D4. In vitro experiments revealed that STAT6 and IL-4 receptor-α control the expression of these molecules in the presence of IL-4 and IL-13. Analysis of eosinophils derived from patients with allergic disorders indicated the involvement of IL-4 and IL-13 at the inflamed sites. CONCLUSIONS: IL-4 induces the proallergic phenotype of IL1RL1high eosinophils, with prominent cysteinyl leukotriene metabolism via STAT6. These cellular changes represent potential therapeutic targets for chronic rhinosinusitis and eosinophilic asthma.

Using flux theory in dynamic omics data sets to identify differentially changing signals using DPoP.

BACKGROUND: Derivative profiling is a novel approach to identify differential signals from dynamic omics data sets. This approach applies variable step-size differentiation to time dynamic omics data. This work assumes that there is a general omics derivative that is a useful and descriptive feature of dynamic omics experiments. We assert that this omics derivative, or omics flux, is a valuable descriptor that can be used instead of, or with, fold change calculations. RESULTS: The results of derivative profiling are compared to established methods such as Multivariate Adaptive Regression Splines, significance versus fold change analysis (Volcano), and an adjusted ratio over intensity (M/A) analysis to find that there is a statistically significant similarity between the results. This comparison is repeated for transcriptomic and phosphoproteomic expression profiles previously characterized in Aspergillus nidulans. This method has been packaged in an open-source, GUI-based MATLAB app, the Derivative Profiling omics Package (DPoP). Gene Ontology (GO) term enrichment has been included in the app so that a user can automatically/programmatically describe the over/under-represented GO terms in the derivative profiling results using domain specific knowledge found in their organism's specific GO database file. The advantage of the DPoP analysis is that it is computationally inexpensive, it does not require fold change calculations, it describes both instantaneous as well as overall behavior, and it achieves statistical confidence with signal trajectories of a single bio-replicate over four or more points. CONCLUSIONS: While we apply this method to time dynamic transcriptomic and phosphoproteomic datasets, it is a numerically generalizable technique that can be applied to any organism and any field interested in time series data analysis. The app described in this work enables omics researchers with no computer science background to apply derivative profiling to their data sets, while also allowing multidisciplined users to build on the nascent idea of profiling derivatives in omics.

Identification and characterization of structural variants related to meat quality in pigs using chromosome-level genome assemblies.

BACKGROUND: Many studies have been performed to identify various genomic loci and genes associated with the meat quality in pigs. However, the full genetic architecture of the trait still remains unclear in part because of the lack of accurate identification of related structural variations (SVs) which resulted from the shortage of target breeds, the limitations of sequencing data, and the incompleteness of genome assemblies. The recent generation of a new pig breed with superior meat quality, called Nanchukmacdon, and its chromosome-level genome assembly (the NCMD assembly) has provided new opportunities. RESULTS: By applying assembly-based SV calling approaches to various genome assemblies of pigs including Nanchukmacdon, the impact of SVs on meat quality was investigated. Especially, by checking the commonality of SVs with other pig breeds, a total of 13,819 Nanchukmacdon-specific SVs (NSVs) were identified, which have a potential effect on the unique meat quality of Nanchukmacdon. The regulatory potentials of NSVs for the expression of nearby genes were further examined using transcriptome- and epigenome-based analyses in different tissues. CONCLUSIONS: Whole-genome comparisons based on chromosome-level genome assemblies have led to the discovery of SVs affecting meat quality in pigs, and their regulatory potentials were analyzed. The identified NSVs will provide new insights regarding genetic architectures underlying the meat quality in pigs. Finally, this study confirms the utility of chromosome-level genome assemblies and multi-omics analysis to enhance the understanding of unique phenotypes.

Multi-omics and clustering analyses reveal the mechanisms underlying unmet needs for patients with lung adenocarcinoma and identify potential therapeutic targets.

BACKGROUND: The cancer genome contains several driver mutations. However, in some cases, no known drivers have been identified; these remaining areas of unmet needs, leading to limited progress in cancer therapy. Whole-genome sequencing (WGS) can identify non-coding alterations associated with the disease. Consequently, exploration of non-coding regions using WGS and other omics data such as ChIP-sequencing (ChIP-seq) to discern novel alterations and mechanisms related to tumorigenesis have been attractive these days. METHODS: Integrated multi-omics analyses, including WGS, ChIP-seq, DNA methylation, and RNA-sequencing (RNA-seq), were conducted on samples from patients with non-clinically actionable genetic alterations (non-CAGAs) in lung adenocarcinoma (LUAD). Second-level cluster analysis was performed to reinforce the correlations associated with patient survival, as identified by RNA-seq. Subsequent differential gene expression analysis was performed to identify potential druggable targets. RESULTS: Differences in H3K27ac marks in non-CAGAs LUAD were found and confirmed by analyzing RNA-seq data, in which mastermind-like transcriptional coactivator 2 (MAML2) was suppressed. The down-regulated genes whose expression was correlated to MAML2 expression were associated with patient prognosis. WGS analysis revealed somatic mutations associated with the H3K27ac marks in the MAML2 region and high levels of DNA methylation in MAML2 were observed in tumor samples. The second-level cluster analysis enabled patient stratification and subsequent analyses identified potential therapeutic target genes and treatment options. CONCLUSIONS: We overcome the persistent challenges of identifying alterations or driver mutations in coding regions related to tumorigenesis through a novel approach combining multi-omics data with clinical information to reveal the molecular mechanisms underlying non-CAGAs LUAD, stratify patients to improve patient prognosis, and identify potential therapeutic targets. This approach may be applicable to studies of other cancers with unmet needs.

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

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

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

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

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

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

Multi-omics analysis revealed the mechanism underlying flavonol biosynthesis during petal color formation in Camellia Nitidissima.

BACKGROUND: Camellia nitidissima is a rare, prized camellia species with golden-yellow flowers. It has a high ornamental, medicinal, and economic value. Previous studies have shown substantial flavonol accumulation in C. nitidissima petals during flower formation. However, the mechanisms underlying the golden flower formation in C. nitidissima remain largely unknown. RESULTS: We performed an integrative analysis of the transcriptome, proteome, and metabolome of the petals at five flower developmental stages to construct the regulatory network underlying golden flower formation in C. nitidissima. Metabolome analysis revealed the presence of 323 flavonoids, and two flavonols, quercetin glycosides and kaempferol glycosides, were highly accumulated in the golden petals. Transcriptome and proteome sequencing suggested that the flavonol biosynthesis-related genes and proteins upregulated and the anthocyanin and proanthocyanidin biosynthesis-related genes and proteins downregulated in the golden petal stage. Further investigation revealed the involvement of MYBs and bHLHs in flavonoid biosynthesis. Expression analysis showed that flavonol synthase 2 (CnFLS2) was highly expressed in the petals, and its expression positively correlated with flavonol content at all flower developmental stages. Transient overexpression of CnFLS2 in the petals increased flavonol content. Furthermore, correlation analysis showed that the jasmonate (JA) pathways positively correlated with flavonol biosynthesis, and exogenous methyl jasmonate (MeJA) treatment promoted CnFLS2 expression and flavonol accumulation. CONCLUSIONS: Our findings showed that the JA-CnFLS2 module regulates flavonol biosynthesis during golden petal formation in C. nitidissima.

Application of non-negative matrix factorization in oncology: one approach for establishing precision medicine.

The increase in the expectations of artificial intelligence (AI) technology has led to machine learning technology being actively used in the medical field. Non-negative matrix factorization (NMF) is a machine learning technique used for image analysis, speech recognition, and language processing; recently, it is being applied to medical research. Precision medicine, wherein important information is extracted from large-scale medical data to provide optimal medical care for every individual, is considered important in medical policies globally, and the application of machine learning techniques to this end is being handled in several ways. NMF is also introduced differently because of the characteristics of its algorithms. In this review, the importance of NMF in the field of medicine, with a focus on the field of oncology, is described by explaining the mathematical science of NMF and the characteristics of the algorithm, providing examples of how NMF can be used to establish precision medicine, and presenting the challenges of NMF. Finally, the direction regarding the effective use of NMF in the field of oncology is also discussed.

Bibliometric review of ATAC-Seq and its application in gene expression.

With recent advances in high-throughput next-generation sequencing, it is possible to describe the regulation and expression of genes at multiple levels. An assay for transposase-accessible chromatin using sequencing (ATAC-seq), which uses Tn5 transposase to sequence protein-free binding regions of the genome, can be combined with chromatin immunoprecipitation coupled with deep sequencing (ChIP-seq) and ribonucleic acid sequencing (RNA-seq) to provide a detailed description of gene expression. Here, we reviewed the literature on ATAC-seq and described the characteristics of ATAC-seq publications. We then briefly introduced the principles of RNA-seq, ChIP-seq and ATAC-seq, focusing on the main features of the techniques. We built a phylogenetic tree from species that had been previously studied by using ATAC-seq. Studies of Mus musculus and Homo sapiens account for approximately 90% of the total ATAC-seq data, while other species are still in the process of accumulating data. We summarized the findings from human diseases and other species, illustrating the cutting-edge discoveries and the role of multi-omics data analysis in current research. Moreover, we collected and compared ATAC-seq analysis pipelines, which allowed biological researchers who lack programming skills to better analyze and explore ATAC-seq data. Through this review, it is clear that multi-omics analysis and single-cell sequencing technology will become the mainstream approach in future research.

Intestinal microbiota and metabolome perturbations in ischemic and idiopathic dilated cardiomyopathy.

BACKGROUND: Various clinical similarities are present in ischemic (ICM) and idiopathic dilated cardiomyopathy (IDCM), leading to ambiguity on some occasions. Previous studies have reported that intestinal microbiota appeared dysbiosis in ICM, whether implicating in the IDCM remains unclear. The aim of this study was to assess the alterations in intestinal microbiota and fecal metabolites in ICM and IDCM. METHODS: ICM (n = 20), IDCM (n = 22), and healthy controls (HC, n = 20) were enrolled in this study. Stool samples were collected for 16S rRNA gene sequencing and gas chromatography-mass spectrometry (GC-MS) analysis. RESULTS: Both ICM and IDCM exhibited reduced alpha diversity and altered microbial community structure compared to HC. At the genus level, nine taxa including Blautia, [Ruminococcus]_torques_group, Christensenellaceae_R-7_group, UCG-002, Corynebacterium, Oceanobacillus, Gracilibacillus, Klebsiella and Citrobacter was specific to ICM, whereas one taxa Alistipes uniquely altered in IDCM. Likewise, these changes were accompanied by significant metabolic differences. Further differential analysis displayed that 18 and 14 specific metabolites uniquely changed in ICM and IDCM, respectively. The heatmap was generated to display the association between genera and metabolites. Receiver operating characteristic curve (ROC) analysis confirmed the predictive value of the distinct microbial-metabolite features in disease status. The results showed that microbial (area under curve, AUC = 0.95) and metabolic signatures (AUC = 0.84) were effective in discriminating ICM from HC. Based on the specific microbial and metabolic features, the patients with IDCM could be separated from HC with an AUC of 0.80 and 0.87, respectively. Furthermore, the gut microbial genus (AUC = 0.88) and metabolite model (AUC = 0.89) were comparable in predicting IDCM from ICM. Especially, the combination of fecal microbial-metabolic features improved the ability to differentiate IDCM from ICM with an AUC of 0.96. CONCLUSION: Our findings highlighted the alterations of gut microbiota and metabolites in different types of cardiomyopathies, providing insights into the pathophysiological mechanisms of myocardial diseases. Moreover, multi-omics analysis of fecal samples holds promise as a non-invasive tool for distinguishing disease status.

Characterizing neuroinflammation and identifying prenatal diagnostic markers for neural tube defects through integrated multi-omics analysis.

BACKGROUND: Neural Tube Defects (NTDs) are congenital malformations of the central nervous system resulting from the incomplete closure of the neural tube during early embryonic development. Neuroinflammation refers to the inflammatory response in the nervous system, typically resulting from damage to neural tissue. Immune-related processes have been identified in NTDs, however, the detailed relationship and underlying mechanisms between neuroinflammation and NTDs remain largely unclear. In this study, we utilized integrated multi-omics analysis to explore the role of neuroinflammation in NTDs and identify potential prenatal diagnostic markers using a murine model. METHODS: Nine public datasets from Gene Expression Omnibus (GEO) and ArrayExpress were mined using integrated multi-omics analysis to characterize the molecular landscape associated with neuroinflammation in NTDs. Special attention was given to the involvement of macrophages in neuroinflammation within amniotic fluid, as well as the dynamics of macrophage polarization and their interactions with neural cells at single-cell resolution. We also used qPCR assay to validate the key TFs and candidate prenatal diagnostic genes identified through the integrated analysis in a retinoic acid-induced NTDs mouse model. RESULTS: Our analysis indicated that neuroinflammation is a critical pathological feature of NTDs, regulated both transcriptionally and epigenetically within central nervous system tissues. Key alterations in gene expression and pathways highlighted the crucial role of STATs molecules in the JAK-STAT signaling pathway in regulating NTDs-associated neuroinflammation. Furthermore, single-cell resolution analysis revealed significant polarization of macrophages and their interaction with neural cells in amniotic fluid, underscoring their central role in mediating neuroinflammation associated with NTDs. Finally, we identified a set of six potential prenatal diagnostic genes, including FABP7, CRMP1, SCG3, SLC16A10, RNASE6 and RNASE1, which were subsequently validated in a murine NTDs model, indicating their promise as prospective markers for prenatal diagnosis of NTDs. CONCLUSIONS: Our study emphasizes the pivotal role of neuroinflammation in the progression of NTDs and underlines the potential of specific inflammatory and neural markers as novel prenatal diagnostic tools. These findings provide important clues for further understanding the underlying mechanisms between neuroinflammation and NTDs, and offer valuable insights for the future development of prenatal diagnostics.

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

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

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

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

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

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

Uncovering the cellular and omics characteristics of natural killer cells in the bone marrow microenvironment of patients with acute myeloid leukemia.

BACKGROUND: Acute myeloid leukemia (AML) is a highly heterogeneous hematologic malignancy and the most frequently acute leukemia of stem cell precursors and the myeloid derivatives in adult. Longitudinal studies have indicated the therapeutic landscape and drug resistance for patients with AML are still intractable, which largely attribute to the deficiency of detailed information upon the pathogenesis. METHODS: In this study, we compared the cellular phenotype of resident NK cells (rAML-NKs, rHD-NKs) and expanded NK cells (eAML-NKs, eHD-NKs) from bone marrow of AML patients (AML) and healthy donors (HD). Then, we took advantage of the co-culture strategy for the evaluation of the in vitro cytotoxicity of NK cells upon diverse tumor cell lines (e.g., K562, Nalm6, U937). With the aid of RNA-sequencing (RNA-SEQ) and bioinformatics analyses (e.g., GOBP analysis, KEGG analysis, GSEA, volcano plot), we verified the similarities and differences of the omics features between eAML-NKs and eHD-NKs. RESULTS: Herein, we verified the sharp decline in the content of total resident NK cells (CD3-CD56+) in rAML-NKs compared to rHD-NKs. Differ from the expanded eHD-NKs, eAML-NKs revealed decline in diverse NK cell subsets (NKG2D+, CD25+, NKp44+, NKp46+) and alterations in cellular vitality but conservations in cytotoxicity. According to transcriptomic analysis, AML-NKs and HD-NKs showed multifaceted distinctions in gene expression profiling and genetic variations. CONCLUSIONS: Collectively, our data revealed the variations in the cytobiological and transcriptomic features between AML-NKs and HD-NKs in bone marrow environment. Our findings would benefit the further development of novel biomarkers for AML diagnosis and NK cell-based cytotherapy in future.