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.

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.

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.

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.

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.

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.

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.

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.

Integrated multi-omics analyses revealed the association between rheumatoid arthritis and colorectal cancer: MYO9A as a shared gene signature and an immune-related therapeutic target.

BACKGROUND: Our study aims to explore the relationship, shared gene signature, and the underlying mechanisms that connect rheumatoid arthritis (RA) to colorectal cancer (CRC). METHODS: Mendelian randomization (MR) analysis was conducted to assess the causality between RA and CRC. Summary statistic data-based Mendelian randomization (SMR) leveraging eQTL data was employed to identify the CRC-related causal genes. Integrated analyses of single-cell RNA sequencing and bulk RNA sequencing were employed to comprehensively investigate the shared gene signature and potential mechanisms underlying the pathogenesis of both RA and CRC. Predictive analysis of the shared hub gene in CRC immunotherapy response was performed. Pan-cancer analyses were conducted to explore the potential role of MYO9A in 33 types of human tumors. RESULTS: MR analysis suggested that RA might be associated with a slight increased risk of CRC (Odds Ratio = 1.04, 95% Confidence Interval = 1.01-1.07, P = 0.005). SMR analysis combining transcriptome analyses identified MYO9A as a causal gene in CRC and a shared gene signature in both RA and CRC. MYO9A may contribute to tumor suppression, while downregulation of MYO9A may impact CRC tumorigenesis by disrupting epithelial polarity and architecture, resulting in a worse prognosis in CRC. Additionally, MYO9A shows promise as a powerful predictive biomarker for cancer prognosis and immunotherapy response in CRC. Pan-cancer analyses demonstrated MYO9A may have a protective role in the occurrence and progression of various human cancers. CONCLUSION: RA might be associated with a slight increased risk of CRC. MYO9A is a shared gene signature and a potential immune-related therapeutic target for both CRC and RA. Targeting the MYO9A-mediated loss of polarity and epithelial architecture could be a novel therapeutic approach for CRC.

Integrating trans-omics, cellular experiments and clinical validation to identify ILF2 as a diagnostic serum biomarker and therapeutic target in gastric cancer.

BACKGROUND: Gastric cancer (GC) lacks serum biomarkers with clinical diagnostic value. Multi-omics analysis is an important approach to discovering cancer biomarkers. This study aimed to identify and validate serum biomarkers for GC diagnosis by cross-analysis of proteomics and transcriptomics datasets. METHODS: A cross-omics analysis was performed to identify overlapping differentially expressed genes (DEGs) between our previous aptamer-based GC serum proteomics dataset and the GC tissue RNA-Seq dataset in The Cancer Genome Atlas (TCGA) database, followed by lasso regression and random forest analysis to select key overlapping DEGs as candidate biomarkers for GC. The mRNA levels and diagnostic performance of these candidate biomarkers were analyzed in the original and independent GC datasets to select valuable candidate biomarkers. The valuable candidate biomarkers were subjected to bioinformatics analysis to select those closely associated with the biological behaviors of GC as potential biomarkers. The clinical diagnostic value of the potential biomarkers was validated using serum samples, and their expression levels and functions in GC cells were validated using in vitro cell experiments. RESULTS: Four candidate biomarkers (ILF2, PGM2L1, CHD7, and JCHAIN) were selected. Their mRNA levels differed significantly between tumor and normal tissues and showed different diagnostic performances for GC, with areas under the receiver operating characteristic curve (AUROCs) of 0.629-0.950 in the TCGA dataset and 0.736-0.840 in the Gene Expression Omnibus (GEO) dataset. In the bioinformatics analysis, only ILF2 (interleukin enhancer-binding factor 2) gene levels were associated with immune cell infiltration, some checkpoint gene expression, chemotherapy sensitivity, and immunotherapy response. Serum levels of ILF2 were higher in GC patients than in controls, with an AUROC of 0.944 for the diagnosis of GC, and it was also detected in the supernatants of GC cells. Knockdown of ILF2 by siRNA significantly reduced the proliferation and colony formation of GC cells. Overexpression of ILF2 significantly promotes the proliferation and colony formation of gastric cancer cells. CONCLUSIONS: Trans-omics analysis of proteomics and transcriptomics is an efficient approach for discovering serum biomarkers, and ILF2 is a potential diagnostic biomarker and therapeutic target of gastric cancer.

Enhancement of vitamin B6 production driven by omics analysis combined with fermentation optimization.

BACKGROUND: Microbial engineering aims to enhance the ability of bacteria to produce valuable products, including vitamin B6 for various applications. Numerous microorganisms naturally produce vitamin B6, yet the metabolic pathways involved are rigorously controlled. This regulation by the accumulation of vitamin B6 poses a challenge in constructing an efficient cell factory. RESULTS: In this study, we conducted transcriptome and metabolome analyses to investigate the effects of the accumulation of pyridoxine, which is the major commercial form of vitamin B6, on cellular processes in Escherichia coli. Our omics analysis revealed associations between pyridoxine and amino acids, as well as the tricarboxylic acid (TCA) cycle. Based on these findings, we identified potential targets for fermentation optimization, including succinate, amino acids, and the carbon-to-nitrogen (C/N) ratio. Through targeted modifications, we achieved pyridoxine titers of approximately 514 mg/L in shake flasks and 1.95 g/L in fed-batch fermentation. CONCLUSION: Our results provide insights into pyridoxine biosynthesis within the cellular metabolic network for the first time. Our comprehensive analysis revealed that the fermentation process resulted in a remarkable final yield of 1.95 g/L pyridoxine, the highest reported yield to date. This work lays a foundation for the green industrial production of vitamin B6 in the future.

Key role of K+ and Ca2+ in high-yield ethanol production by S. Cerevisiae from concentrated sugarcane molasses.

BACKGROUND: Saccharomyces cerevisiae is an important microorganism in ethanol synthesis, and with sugarcane molasses as the feedstock, ethanol is being synthesized sustainably to meet growing demands. However, high-concentration ethanol fermentation based on high-concentration sugarcane molasses-which is needed for reduced energy consumption of ethanol distillation at industrial scale-is yet to be achieved. RESULTS: In the present study, to identify the main limiting factors of this process, adaptive laboratory evolution and high-throughput screening (Py-Fe3+) based on ARTP (atmospheric and room-temperature plasma) mutagenesis were applied. We identified high osmotic pressure, high temperature, high alcohol levels, and high concentrations of K+, Ca2+, K+ and Ca2+ (K+&Ca2+), and sugarcane molasses as the main limiting factors. The robust S. cerevisiae strains of NGT-F1, NGW-F1, NGC-F1, NGK+, NGCa2+ NGK+&Ca2+-F1, and NGTM-F1 exhibited high tolerance to the respective limiting factor and exhibited increased yield. Subsequently, ethanol synthesis, cell morphology, comparative genomics, and gene ontology (GO) enrichment analysis were performed in a molasses broth containing 250 g/L total fermentable sugars (TFS). Additionally, S. cerevisiae NGTM-F1 was used with 250 g/L (TFS) sugarcane molasses to synthesize ethanol in a 5-L fermenter, giving a yield of 111.65 g/L, the conversion of sugar to alcohol reached 95.53%. It is the highest level of physical mutagenesis yield at present. CONCLUSION: Our results showed that K+ and Ca2+ ions primarily limited the efficient production of ethanol. Then, subsequent comparative transcriptomic GO and pathway analyses showed that the co-presence of K+ and Ca2+ exerted the most prominent limitation on efficient ethanol production. The results of this study might prove useful by promoting the development and utilization of green fuel bio-manufactured from molasses.

Genomic and Transcriptomic Diversification of Flagellin Genes Provides Insight into Environmental Adaptation and Phylogeographic Characteristics in Aeromonas hydrophila.

Aeromonas hydrophila is an opportunistic motile pathogen with a broad host range, infecting both terrestrial and aquatic animals. Environmental and geographical conditions exert selective pressure on both geno- and phenotypes of pathogens. Flagellin, directly exposed to external environments and containing important immunogenic epitopes, may display significant variability in response to external conditions. In this study, we conducted a comparative analysis of ~ 150 A. hydrophila genomes, leading to the identification of six subunits of the flagellin gene (fla-1 to fla-4, flaA, and flaB). Individual strains harbored different composition of flagellin subunits and copies. The composition of subunits showed distinct patterns depending on environmental sources. Strains from aquatic environments were mainly comprised of fla-1 to fla-4 subunits, while terrestrial strains predominated in groups harboring flaA and flaB subunits. Each flagellin showed varying levels of expression, with flaA and flaB demonstrating significantly higher expression compared to others. One of the chemotaxis pathways that control flagellin movement through a two-component system was significantly upregulated in flaA(+ 1)/flaB(+ 1) group, whereas flaA and flaB showed different transcriptomic expressions. The genes positively correlated with flaA expression were relevant to biofilm formation and bacterial chemotaxis, but flaB showed a negative correlation with the genes in ABC transporters and quorum sensing pathway. However, the expression patterns of fla-2 to fla-4 were identical. This suggests various types of flagellin subunits may have different biological functions. The composition and expression levels of flagellin subunits could provide valuable insights into the adaptation of A. hydrophila and the differences among strains in response to various external environments.

Quinolone Antibiotics Inhibit the Rice Photosynthesis by Targeting Photosystem II Center Protein: Generational Differences and Mechanistic Insights.

Soil antibiotic pollution profoundly influences plant growth and photosynthetic performance, yet the main disturbed processes and the underlying mechanisms remain elusive. This study explored the photosynthetic toxicity of quinolone antibiotics across three generations on rice plants and clarified the mechanisms through experimental and computational studies. Marked variations across antibiotic generations were noted in their impact on rice photosynthesis with the level of inhibition intensifying from the second to the fourth generation. Omics analyses consistently targeted the light reaction phase of photosynthesis as the primary process impacted, emphasizing the particular vulnerability of photosystem II (PS II) to the antibiotic stress, as manifested by significant interruptions in the photon-mediated electron transport and O2 production. PS II center D2 protein (psbD) was identified as the primary target of the tested antibiotics, with the fourth-generation quinolones displaying the highest binding affinity to psbD. A predictive machine learning method was constructed to pinpoint antibiotic substructures that conferred enhanced affinity. As antibiotic generations evolve, the positive contribution of the carbonyl and carboxyl groups on the 4-quinolone core ring in the affinity interaction gradually intensified. This research illuminates the photosynthetic toxicities of antibiotics across generations, offering insights for the risk assessment of antibiotics and highlighting their potential threats to carbon fixation of agroecosystems.

Epigenetic and integrative cross-omics analyses of cerebral white matter hyperintensities on MRI.

Cerebral white matter hyperintensities on MRI are markers of cerebral small vessel disease, a major risk factor for dementia and stroke. Despite the successful identification of multiple genetic variants associated with this highly heritable condition, its genetic architecture remains incompletely understood. More specifically, the role of DNA methylation has received little attention. We investigated the association between white matter hyperintensity burden and DNA methylation in blood at ∼450 000 cytosine-phosphate-guanine (CpG) sites in 9732 middle-aged to older adults from 14 community-based studies. Single CpG and region-based association analyses were carried out. Functional annotation and integrative cross-omics analyses were performed to identify novel genes underlying the relationship between DNA methylation and white matter hyperintensities. We identified 12 single CpG and 46 region-based DNA methylation associations with white matter hyperintensity burden. Our top discovery single CpG, cg24202936 (P = 7.6 × 10-8), was associated with F2 expression in blood (P = 6.4 × 10-5) and co-localized with FOLH1 expression in brain (posterior probability = 0.75). Our top differentially methylated regions were in PRMT1 and in CCDC144NL-AS1, which were also represented in single CpG associations (cg17417856 and cg06809326, respectively). Through Mendelian randomization analyses cg06809326 was putatively associated with white matter hyperintensity burden (P = 0.03) and expression of CCDC144NL-AS1 possibly mediated this association. Differentially methylated region analysis, joint epigenetic association analysis and multi-omics co-localization analysis consistently identified a role of DNA methylation near SH3PXD2A, a locus previously identified in genome-wide association studies of white matter hyperintensities. Gene set enrichment analyses revealed functions of the identified DNA methylation loci in the blood-brain barrier and in the immune response. Integrative cross-omics analysis identified 19 key regulatory genes in two networks related to extracellular matrix organization, and lipid and lipoprotein metabolism. A drug-repositioning analysis indicated antihyperlipidaemic agents, more specifically peroxisome proliferator-activated receptor-alpha, as possible target drugs for white matter hyperintensities. Our epigenome-wide association study and integrative cross-omics analyses implicate novel genes influencing white matter hyperintensity burden, which converged on pathways related to the immune response and to a compromised blood-brain barrier possibly due to disrupted cell-cell and cell-extracellular matrix interactions. The results also suggest that antihyperlipidaemic therapy may contribute to lowering risk for white matter hyperintensities possibly through protection against blood-brain barrier disruption.

Comprehensive transcriptomic and metabolomic insights into simultaneous CO2 sequestration and nitrate removal by the Chlorella vulgaris and Pseudomonas sp. consortium.

Simultaneous CO2 sequestration and nitrate removal can be achieved by co-cultivation of Chlorella vulgaris with Pseudomonas sp. However, a comprehensive understanding of the synergistic mechanism between C. vulgaris and Pseudomonas sp. remains unknown. In this study, transcriptomics and metabolomics analysis were employed to elucidate the synergistic mechanism of C. vulgaris and Pseudomonas sp. Transcriptomic and metabolomic analyses identified 3664 differentially expressed genes and 314 metabolites. Transcriptome analysis revealed that co-culture with Pseudomonas sp. promoted the photosynthesis of C. vulgaris by promoting the synthesis of photosynthetic pigments and photosynthesis-antenna proteins. Furthermore, it stimulated pathways associated with energy metabolism from carbon sources, such as the Calvin cycle, glycolytic pathway, and TCA cycle. Additionally, Pseudomonas sp. reduced nitrate levels in the co-culture system by denitrification, and microalgae regulated nitrate uptake by down-regulating the transcript levels of nitrate transporter genes. Metabolomic analysis indicated that nutrient exchange was conducted between algae and bacteria, and amino acids, phytohormones, and organic heterocyclic compounds secreted by the bacteria promoted the growth metabolism of microalgae. After supplementation with differential metabolites, the carbon fixation rate and nitrate removal rate of the co-culture system reached 0.549 g L-1 d-1 and 135.4 mg L-1 d-1, which were increased by 20% and 8%, respectively. This study provides a theoretical insight into microalgae-bacteria interaction and its practical application, as well as a novel perspective on flue gas treatment management.

Microcystin-RR promote lipid accumulation through CD36 mediated signal pathway and fatty acid uptake in HepG2 cells.

Microcystins (MC)-RR is a significant analogue of MC-LR, which has been identified as a hepatotoxin capable of influencing lipid metabolism and promoting the progression of liver-related metabolic diseases. However, the toxicity and biological function of MC-RR are still not well understood. In this study, the toxic effects and its role in lipid metabolism of MC-RR were investigated in hepatoblastoma cells (HepG2cells). The results demonstrated that MC-RR dose-dependently reduced cell viability and induced apoptosis. Additionally, even at low concentrations, MC-RR promoted lipid accumulation through up-regulating levels of triglyceride, total cholesterol, phosphatidylcholines and phosphatidylethaolamine in HepG2 cells, with no impact on cell viability. Proteomics and transcriptomics analysis further revealed significant alterations in the protein and gene expression profiles in HepG2 cells treated with MC-RR. Bioinformatic analysis, along with subsequent validation, indicated the upregulation of CD36 and activation of the AMPK and PI3K/AKT/mTOR in response to MC-RR exposure. Finally, knockdown of CD36 markedly ameliorated MC-RR-induced lipid accumulation in HepG2 cells. These findings collectively suggest that MC-RR promotes lipid accumulation in HepG2 cells through CD36-mediated signal pathway and fatty acid uptake. Our findings provide new insights into the hepatotoxic mechanism of MC-RR.

Transcriptional and metabolic effects of aspartate-glutamate carrier isoform 1 (AGC1) downregulation in mouse oligodendrocyte precursor cells (OPCs).

Aspartate-glutamate carrier isoform 1 (AGC1) is a carrier responsible for the export of mitochondrial aspartate in exchange for cytosolic glutamate and is part of the malate-aspartate shuttle, essential for the balance of reducing equivalents in the cells. In the brain, mutations in SLC25A12 gene, encoding for AGC1, cause an ultra-rare genetic disease, reported as a neurodevelopmental encephalopathy, whose symptoms include global hypomyelination, arrested psychomotor development, hypotonia and seizures. Among the biological components most affected by AGC1 deficiency are oligodendrocytes, glial cells responsible for myelination processes, and their precursors [oligodendrocyte progenitor cells (OPCs)]. The AGC1 silencing in an in vitro model of OPCs was documented to cause defects of proliferation and differentiation, mediated by alterations of histone acetylation/deacetylation. Disrupting AGC1 activity could possibly reduce the availability of acetyl groups, leading to perturbation of many biological pathways, such as histone modifications and fatty acids formation for myelin production. Here, we explore the transcriptome of mouse OPCs partially silenced for AGC1, reporting results of canonical analyses (differential expression) and pathway enrichment analyses, which highlight a disruption in fatty acids synthesis from both a regulatory and enzymatic stand. We further investigate the cellular effects of AGC1 deficiency through the identification of most affected transcriptional networks and altered alternative splicing. Transcriptional data were integrated with differential metabolite abundance analysis, showing downregulation of several amino acids, including glutamine and aspartate. Taken together, our results provide a molecular foundation for the effects of AGC1 deficiency in OPCs, highlighting the molecular mechanisms affected and providing a list of actionable targets to mitigate the effects of this pathology.