Comprehensive Analysis of Crucial m6A-Related Differentially Expressed Genes in Psoriasis.

BACKGROUND: Psoriasis is a common, chronic, and multifactorial inflammatory cutaneous disorder that involves genetic and epigenetic factors. N6-methyladenosine methylation (m6A) is the most prevalent RNA modification implicated in various diseases; however, its role in psoriasis still needs to be further explored. We aimed to explore the mechanisms underlying the effects of m6A in psoriasis pathogenesis, prompting new therapeutic targets. METHODS: Three psoriasis-related datasets, including GSE155702, GSE109248, and GSE142582, were collected. Differentially m6A methylated genes (DMGs) between psoriasis lesions of psoriasis patients and healthy skin controls were identified from the GSE155702 dataset, and corresponding Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed. Differentially expressed genes (DEGs) and the common DEGs between the two groups were screened from the GSE109248 and GSE142582 datasets; the expression and interactions of the m6A regulators were analyzed. The m6A levels of total RNAs and the protein expression levels of METTL3, WTAP, ALKBH5, FTO, and METTL14 in imiquimod (IMQ)-induced psoriasiform lesions were evaluated. RESULTS: 66 significantly upregulated and 381 significantly downregulated m6A peaks were identified, corresponding to 414 genes which were particularly associated with cell and tissue development processes and cell cycle related items. 271 common DEGs were identified, associating with keratinocyte differentiation, epidermis development, cytokine-cytokine receptor interaction, and fatty acid metabolic processes. 15 crucial m6A related differentially expressed genes were obtained after the intersection of the DMGs and common DEGs, including NEU2, GALNT6, MTCL1, DOC2B, CAMK2N1, SNTB1, RNF150, CGNL1, CCDC102A, MEOX2, EEF2K, OBSCN, SLC46A2, CCDC85A, and DACH1. In addition, we found that m6A methylation and these five m6A regulators were both upregulated in psoriatic lesions. CONCLUSIONS: It revealed that psoriasis pathophysiological processes encompass m6A epigenetic alterations, and that m6A alterations may specifically influence cell proliferation and neural regulation, and closely associated with osteoarticular involvement and metabolic syndrome in psoriasis.

The soil conservation agenda of Brazil: A review of "edge-to-edge" science contributions.

Soil conservation adheres to various United Nations Sustainable Development Goals while in Brazil is a constitutional obligation. To attain the goals and fulfil the obligation, laws, policies, governance and science must be imbricated to deliver suitable conservation solutions for the long term, namely appropriate to positively influence other downstream chains such as the food chain. However, in Brazil, a major world producer and exporter of food, weaknesses were recently diagnosed by judicial authorities concerning soil governance and coordinated land use policies. Integrated scientific assessments on soil conservation and mitigation of degraded soil are also lacking in this country. This was enough motivation and the purpose to present here a holistic view over the soil conservation agenda and promoting policies in Brazil, based on a literature review that followed the guidelines and criteria of PRISMA approach. We termed this analysis a review hinged on "edge-to-edge" science contributions for two reasons. Firstly, the intent of retrieving from the recently published literature solely papers centered on a relevant soil conservation topic (e.g., soil characterization, here called an "edge") but with complementary analyses over boundary topics (frontier "edges", such as soil degradation). Secondly, the intent of underlining the urgency to assist decision-makers with scientific evidence in all dimensions of the soil conservation agenda ("edge-to-edge" science), namely soil characterization (e.g., quality reference values), soil degradation assessment (e.g., anthropogenic-related soil erosion or contamination), soil degradation consequences focused on the carbon cycle (e.g., net CO2 emissions and climate warming), sustainable management practices and production systems (e.g., no-tillage agriculture and integrated crop-livestock-forestry systems), and scientific evaluation of existing laws as well as of governance and policy programs with potential implications on soil quality (e.g., the Forest Code). Thus, this literature review addressed all these topics following a multidisciplinary discourse, which produced an extensive but comprehensive document about soil conservation in Brazil.

Comprehensive analysis of consensus molecular subtypes for ovarian cancer from bulk to single-cell perspectives.

Molecular subtypes play a pivotal role in guiding preclinical and clinical risk assessment and treatment strategies in cancer. In this study, we extracted whole-tissue transcriptomic data from 1987 ovarian cancer patients spanning 26 independent Gene Expression Omnibus cohorts. A total of four consensus subtypes (C1-C4) were identified, notably, subtype C1 samples exhibited a poor prognosis and higher M2 macrophages infiltration, whereas subtype C2 samples demonstrated the best prognosis and higher CD4 resting T cells infiltration. Additionally, we characterized cancer- and stromal-specific gene expression profiles, and conducted an analysis of ligand-receptor interactions within these compartments. Based on cancer compartment, subtype-specific interactions as well as gene signatures for each molecular subtype were identified. Leveraging single-cell transcriptomic data, we delineated malignant epithelial cells with four molecular subtypes and observed an increase in C1 cell proportions from primary to relapse to metastasis stages, with a corresponding decrease in C2 cell proportions. Furthermore, we investigated subtype-specific interaction with T cells through integrated analysis of bulk and single-cell datasets. Finally, we developed a robust ten-gene risk model based on subtype gene signatures for prognostic evaluation in ovarian cancer, demonstrating its efficacy across independent datasets. In summary, this study systematically explored ovarian cancer molecular subtypes and provided a framework for other cancer types.

Estimand Framework and Statistical Considerations for Integrated Analysis of Clinical Trial Safety Data.

BACKGROUND: Safety analyses play a pivotal role in drug development, ensuring the protection of patients while advancing innovative pharmaceuticals to market. A single study generally does not have sufficient sample size to evaluate all important safety events with reasonable precision and may not cover the full target population for the investigational treatment. Integrated analyses (pooled or meta-analysis) over several studies may be helpful in that regard. But without a structured conscious workflow accompanied with appropriate statistical methods for the integrated analysis, this can easily take a route compromising the interpretation. METHODS: In this article we apply the ICH estimand framework to clinical trial integration and summarize respective critical statistical assumptions to ensure the integrated analyses are interpretable. RESULTS: The estimand framework is valuable for developing principles for a deeper understanding of the critical statistical aspects of planning an integrated safety analysis. Our principles address the clinical question of interest, estimand and estimation. Special focus was given to the criteria for inclusion and exclusion of the component studies in the integrated analysis, and to integration of estimates pertaining to signal detection. CONCLUSION: Performing an integrated analysis and its preparatory steps calls for a good understanding of the clinical question of interest and its estimand, care and sound practice, to enable interpretation and avoid introducing unnecessary bias. It is valuable to use the estimand framework not only for efficacy evaluations, but also for safety evaluations in clinical trials and for integrated safety analyses.

NRP1 knockdown inhibits the invasion and migration of rhabdoid tumor of the kidney cells.

PURPOSE: The aim of this study was to detect candidate oncogenes of rhabdoid tumor of the kidney (RTK) and evaluate their roles in RTK in vitro. METHODS: An integrated analysis of messenger RNA (mRNA) and microRNA (miRNA) sequencing was performed to determine the expression profile of exosome-derived miRNAs and mRNAs in human RTK-derived cell lines and a human embryonic renal cell line. A Gene Ontology enrichment analysis was performed to analyze the functional characteristics of differentially expressed mRNAs in RTK cells. Matrigel invasion and wound-healing assays were performed to evaluate the cell invasion and migration abilities. RESULTS: Forty mRNAs were highly expressed in RTK cells targeted by exosomal miRNAs, the expression of which was lower in RTK cells than in the controls. These mRNAs were primarily related to cell adhesion. Of these mRNAs, we selected neuropilin 1 (NRP1) as a candidate oncogene because its upregulated expression is associated with a poor prognosis of several types of tumors. RTK cells in which NRP1 had been knocked down exhibited decreased invasive and migratory abilities. CONCLUSION: Our study indicates that NRP1 acts as an oncogene by promoting the invasion and migration of RTK cells and that it could serve as a therapeutic target.

Comparative analysis of the fatty acid profiles in goat milk during different lactation periods and their interactions with volatile compounds and metabolites.

This study aimed to compare the composition of fatty acids in goat milk during lactation with human milk, as well as analyze the differences in their interaction with odor and metabolites. Polyunsaturated fatty acids content was higher in human milk, while odd-chain, branched-chain, and monounsaturated fatty acids content were higher in goat milk with a decreasing trend during lactation. PUFAs in human milk undergo auto-oxidation to produce aldehydes (hexanal), giving it a mild aroma. Butyric acid in goat colostrum mediates the synthesis and auto-oxidation of PUFA, while taurine mediated the hydrolysis of amino acids. They produce a furanone compound (2(5H)-furanone) with a buttery flavor. The presence of butyric acid in goat transitional milk had an impact on flavor and metabolites. The medium chain fatty acid composition of the goat mature milk was affected by nucleic acid compounds, which then oxidized to produce methyl ketone (2-nonanone), giving it an unpleasant flavor.

Integrated in silico analysis of transcriptomic alterations in nanoparticle toxicity across human and mouse models.

Nanoparticles, due to their exceptional physicochemical properties are used in our day-to-day environment. They are currently not regulated which might lead to increased levels in the biological systems causing adverse effects. However, the overall mechanism behind nanotoxicity remains elusive. Previously, we analysed the transcriptome datasets of copper oxide nanoparticles using in silico tools and identified IL-17, chemokine signaling pathway, and cytokine-cytokine receptor interaction as the key pathways altered. Based on the findings, we hypothesized a common pathway could be involved in transition metal oxide nanoparticles toxicity irrespective of the variables. Further, there could be unique transcriptome changes between metal oxide nanoparticles and other nanoparticles. To accomplish this, the overall transcriptome datasets of nanoparticles consisting of microarray and RNA-Seq were obtained. >90 studies for 17 different nanoparticles, performed in humans, rats, and mice were assessed. After initial screening, 24 mouse studies (with 196 datasets) and 34 human studies (with 200 datasets) were used for further analyses. The common genes that are dysregulated upon NPs exposure were identified for human and mouse datasets separately. Further, an overrepresentation functional enrichment analysis was performed. The common genes, their gene ontology, gene-gene, and protein-protein interactions were assessed. The overall results suggest that IL-17 and its related pathways might be commonly altered in nanoparticle exposure with lung as one of the major organs affected.

Mechanism of ERBB2 gene overexpression by the formation of super-enhancer with genomic structural abnormalities in lung adenocarcinoma without clinically actionable genetic alterations.

BACKGROUND: In an extensive genomic analysis of lung adenocarcinomas (LUADs), driver mutations have been recognized as potential targets for molecular therapy. However, there remain cases where target genes are not identified. Super-enhancers and structural variants are frequently identified in several hundred loci per case. Despite this, most cancer research has approached the analysis of these data sets separately, without merging and comparing the data, and there are no examples of integrated analysis in LUAD. METHODS: We performed an integrated analysis of super-enhancers and structural variants in a cohort of 174 LUAD cases that lacked clinically actionable genetic alterations. To achieve this, we conducted both WGS and H3K27Ac ChIP-seq analyses using samples with driver gene mutations and those without, allowing for a comprehensive investigation of the potential roles of super-enhancer in LUAD cases. RESULTS: We demonstrate that most genes situated in these overlapped regions were associated with known and previously unknown driver genes and aberrant expression resulting from the formation of super-enhancers accompanied by genomic structural abnormalities. Hi-C and long-read sequencing data further corroborated this insight. When we employed CRISPR-Cas9 to induce structural abnormalities that mimicked cases with outlier ERBB2 gene expression, we observed an elevation in ERBB2 expression. These abnormalities are associated with a higher risk of recurrence after surgery, irrespective of the presence or absence of driver mutations. CONCLUSIONS: Our findings suggest that aberrant gene expression linked to structural polymorphisms can significantly impact personalized cancer treatment by facilitating the identification of driver mutations and prognostic factors, contributing to a more comprehensive understanding of LUAD pathogenesis.

Health literacy and cognitive function in people with diabetic foot ulcer with focus on knowledge, attitude, and practice in relation to foot self-care.

Introduction: Preventative foot self-care is vital for avoiding diabetic foot ulcer episodes and lowering the risk of amputations. Yet, it demands high levels of health literacy and cognitive function. Objective: To investigate health literacy and cognitive function in persons presenting with a diabetic foot ulcer. Methods: Participants with type 2 diabetes were recruited from the tertiary foot clinic at Steno Diabetes Center North Denmark. The European Health Literacy Survey Questionnaire and Addenbrooke's Cognitive Examination were applied. A semi-structured interview guide was developed to evaluate foot self-care knowledge, attitude, and practice. The qualitative data were analyzed with a deductive approach based on a qualitative thematic analysis model. Subsequently, an integrated analysis of the quantitative and qualitative results was conducted. Results: The participants (n = 12) had a mean age of 62.6 ± 8.4 years, and 11 were males. The mean diabetes duration was 15.9 ± 8.9 years. Eight participants had a recurrent diabetic foot ulcer. The health literacy level was sufficient in nine participants, and cognitive function was normal in five participants. Three different profiles related to foot self-care (proactive, active, or passive, respectively) were constructed by the final integrated analysis: a proactive profile refers to taking preventative action in concordance with knowledge and attitude, an active profile to taking action in response to a situation, but challenged by conflicting levels of knowledge and attitude, and a passive profile to not taking action. Conclusion: The study suggests that people presenting with a diabetic foot ulcer have different foot self-care profiles based on person-specific health literacy, cognitive function, and knowledge, attitude, and practice element characteristics, highlighting the need for individualized education and intervention strategy instead of a one-size-fits-all approach.

Biochemical, Histological, and Transcriptomic Analyses Reveal Underlying Differences in Flesh Quality between Wild and Farmed Ricefield Eel (Monopterus albus).

The present study aimed to systematically investigate the underlying differences in flesh quality between wild and farmed Monopterus albus. Fifteen healthy M. albus per group with an average body weight of 45 g were sampled to analyze muscle parameters by biochemical indicators, histomorphology, and molecular biology. Compared with the wild fish, the farmed M. albus in flesh had lower crude protein, collagen, lysine, histidine, total amino acids, SFA, n-3 PUFA contents, and n-3/n-6 ratio (p < 0.05), and higher moisture, crude lipid, crude ash, MUFA, n-6PUFA, and total PUFA contents (p < 0.05). The thawing loss, drip loss, steaming loss, and boiling loss in the farmed group were significantly higher, and hardness, springiness, cohesiveness, gumminess, chewiness, and resilience were significantly lower than those in the wild group (p < 0.05). In addition, higher muscle fiber density and lower muscle fiber diameter were observed in wild M. albus (p < 0.05). In muscle transcriptome profiling, differentially expressed genes and enriched pathways are primarily associated with muscle development, protein synthesis, catabolism, lipid metabolism, and immunity. To the best of our knowledge, this is the first investigation that compares the flesh quality between wild and farmed M. albus in terms of biochemistry, histology, and molecular biology levels. Overall, wild M. albus had a higher nutritional value and texture quality than farmed M. albus.

Comparative proteomic and metabolomic analyses reveal stress responses of hemp to salinity.

KEY MESSAGE: Integrated omics analyses outline the cellular and metabolic events of hemp plants in response to salt stress and highlight several photosynthesis and energy metabolism related pathways as key regulatory points. Soil salinity affects many physiological processes of plants and leads to crop yield losses worldwide. For hemp, a crop that is valued for multiple aspects, such as its medical compounds, fibre, and seed, a comprehensive understanding of its salt stress responses is a prerequisite for resistance breeding and tailoring its agronomic performance to suit certain industrial applications. Here, we first observed the phenotype of salt-stressed hemp plants and found that under NaCl treatment, hemp plants displayed pronounced growth defects, as indicated by the significantly reduced average height, number of leaves, and chlorophyll content. Next, we conducted comparative proteomics and metabolomics to dissect the complex salt-stress response mechanisms. A total of 314 proteins and 649 metabolites were identified to be differentially behaving upon NaCl treatment. Functional classification and enrichment analysis unravelled that many differential proteins were proteases associated with photosynthesis. Through metabolic pathway enrichment, several energy-related pathways were found to be altered, such as the biosynthesis and degradation of branched-chain amino acids, and our network analysis showed that many ribosomal proteins were involved in these metabolic adaptations. Taken together, for hemp plants, influences on chloroplast function probably represent a major toxic effect of salinity, and modulating several energy-producing pathways possibly through translational regulation is presumably a key protective mechanism against the negative impacts. Our data and analyses provide insights into our understanding of hemp's stress biology and may lay a foundation for future functional genomics studies.

Imidacloprid-induced lung injury in mice: Activation of the PI3K/AKT/NF-κB signaling pathway via TLR4 receptor engagement.

Significant impairment of pulmonary function has been demonstrated through long-term exposure to neonicotinoid insecticides, such as imidacloprid (IMI). However, the underlying mechanisms of lung injury induced by IMI remain unclear. In this study, a mouse model of IMI-induced pulmonary injury was established, and the toxicity and lung damage were assessed through mouse body weight, organ index, hematological parameters, and histopathological analysis of lung tissues. Furthermore, metabolomics and transcriptomics techniques were employed to explore the mechanistic aspects. Results from the toxicity assessments indicated that mouse body weight was significantly reduced by IMI, organ index was disturbed, and hematological parameters were disrupted, resulting in pulmonary injury. The mechanistic experimental results indicate that the differences in metabolites and gene expression in mouse lungs could be altered by IMI. Validation of the results through combined analysis of metabolomics and transcriptomics revealed that the mechanism by which IMI induces lung injury in mice might be associated with the activation of the TLR4 receptor, thereby activating the PI3K/AKT/NF-κB signaling pathway to induce inflammation in mouse lungs. This study provided valuable insights into the mechanisms underlying IMI-induced pulmonary damage, potentially contributing to the development of safer pest control strategies. The knowledge gained served as a robust scientific foundation for the prevention and treatment of IMI-related pulmonary injuries.

Consensus gene modules strategy identifies candidate blood-based biomarkers for primary Sjögren's disease.

Primary Sjögren disease (pSD) is an autoimmune disease characterized by lymphoid infiltration of exocrine glands leading to dryness of the mucosal surfaces and by the production of autoantibodies. The pathophysiology of pSD remains elusive and no treatment with demonstrated efficacy is available yet. To better understand the biology underlying pSD heterogeneity, we aimed at identifying Consensus gene Modules (CMs) that summarize the high-dimensional transcriptomic data of whole blood samples in pSD patients. We performed unsupervised gene classification on four data sets and identified thirteen CMs. We annotated and interpreted each of these CMs as corresponding to cell type abundances or biological functions by using gene set enrichment analyses and transcriptomic profiles of sorted blood cell subsets. Correlation with independently measured cell type abundances by flow cytometry confirmed these annotations. We used these CMs to reconcile previously proposed patient stratifications of pSD. Importantly, we showed that the expression of modules representing lymphocytes and erythrocytes before treatment initiation is associated with response to hydroxychloroquine and leflunomide combination therapy in a clinical trial. These consensus modules will help the identification and translation of blood-based predictive biomarkers for the treatment of pSD.

Identification of potential therapeutic targets for plaque vulnerability based on an integrated analysis.

BACKGROUND AND AIMS: This study aimed to explore potential hub genes and pathways of plaque vulnerability and to investigate possible therapeutic targets for acute coronary syndrome (ACS). METHODS AND RESULTS: Four microarray datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs), weighted gene coexpression networks (WGCNA) and immune cell infiltration analysis (IIA) were used to identify the genes for plaque vulnerability. Then, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, Disease Ontology, Gene Ontology annotation and protein-protein interaction (PPI) network analyses were performed to explore the hub genes. Random forest and artificial neural networks were constructed for validation. Furthermore, the CMap and Herb databases were employed to explore possible therapeutic targets. A total of 168 DEGs with an adjusted P < 0.05 and approximately 1974 IIA genes were identified in GSE62646. Three modules were detected and associated with CAD-Class, including 891 genes that can be found in GSE90074. After removing duplicates, 114 hub genes were used for functional analysis. GO functions identified 157 items, and 6 pathways were enriched for the KEGG pathway at adjusted P < 0.05 (false discovery rate, FDR set at < 0.05). Random forest and artificial neural network models were built based on the GSE48060 and GSE34822 datasets, respectively, to validate the previous hub genes. Five genes (GZMA, GZMB, KLRB1, KLRD1 and TRPM6) were selected, and only two of them (GZMA and GZMB) were screened as therapeutic targets in the CMap and Herb databases. CONCLUSION: We performed a comprehensive analysis and validated GZMA and GZMB as a target for plaque vulnerability, which provides a therapeutic strategy for the prevention of ACS. However, whether it can be used as a predictor in blood samples requires further experimental verification.

Integrated evaluation of the biological response of the earthworm Eisenia fetida using two glyphosate exposure strategies: soil enriched and soils collected from crops in Southeastern Mexico.

Under laboratory conditions, the toxicological effects of pesticides tend to be less variable and realistic than under field conditions, limiting their usefulness in environmental risk assessment. In the current study, the earthworm Eisenia fetida was selected as a bioindicator for assessing glyphosate toxic effects in two different trials to solve this dilemma. In Trial 1, the worms were exposed for 7 and 14 days to concentrations of a commercial glyphosate formulation (1 to 500 mg a.i. kg-1) currently used in the field. In Trial 2, the worms were kept in nine soils collected from different plots with crops for 14 days of exposure. In both experiments, glutathione S-transferase (GST), superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (GPx), and acetylcholinesterase (AChE) activities and contents of lipid peroxidation (LPO) were evaluated. In T1, the glyphosate formulation produced a 40% inhibition of AChE activity and a significant increase in GST, SOD, CAT, and GPx activities and LPO contents in E. fetida on day 7. In T2, higher concentrations of glyphosate were detected in the soils of soybean, papaya, and corn (0.92, 0.87, and 0.85 mg kg-1), which induced a positive correlation between the levels of glyphosate residues with GST, SOD, CAT, GPx, and LPO and a negative correlation with AChE. These findings indicate that crop soils polluted with glyphosate elicited higher oxidative stress than under laboratory conditions, confirmed by IBRv2, PCA, and AHC analyses.

Integrated Proteomics and Metabolomics of Safflower Petal Wilting and Seed Development.

Safflower (Carthamus tinctorius L.) is an ancient oilseed crop of interest due to its diversity of end-use industrial and food products. Proteomic and metabolomic profiling of its organs during seed development, which can provide further insights on seed quality attributes to assist in variety and product development, has not yet been undertaken. In this study, an integrated proteome and metabolic analysis have shown a high complexity of lipophilic proteins and metabolites differentially expressed across organs and tissues during seed development and petal wilting. We demonstrated that these approaches successfully discriminated safflower reproductive organs and developmental stages with the identification of 2179 unique compounds and 3043 peptides matching 724 unique proteins. A comparison between cotyledon and husk tissues revealed the complementarity of using both technologies, with husks mostly featuring metabolites (99%), while cotyledons predominantly yielded peptides (90%). This provided a more complete picture of mechanisms discriminating the seed envelope from what it protected. Furthermore, we showed distinct molecular signatures of petal wilting and colour transition, seed growth, and maturation. We revealed the molecular makeup shift occurring during petal colour transition and wilting, as well as the importance of benzenoids, phenylpropanoids, flavonoids, and pigments. Finally, our study emphasizes that the biochemical mechanisms implicated in the growing and maturing of safflower seeds are complex and far-reaching, as evidenced by AraCyc, PaintOmics, and MetaboAnalyst mapping capabilities. This study provides a new resource for functional knowledge of safflower seed and potentially further enables the precision development of novel products and safflower varieties with biotechnology and molecular farming applications.

Exploring the allelopathic autotoxicity mechanism of ginsenosides accumulation under ginseng decomposition based on integrated analysis of transcriptomics and metabolomics.

Continuous cropping obstacles seriously constrained the sustainable development of the ginseng industry. The allelopathic autotoxicity of ginsenosides is the key "trigger" of continuous cropping obstacles in ginseng. During harvest, the ginseng plants could be broken and remain in the soil. The decomposition of ginseng residue in soil is one of the important release ways of ginsenosides. Therefore, the allelopathic mechanism of ginsenosides through the decomposed release pathway needs an in-depth study. To investigate this allelopathic regulation mechanism, the integrated analysis of transcriptomics and metabolomics was applied. The prototype ginsenosides in ginseng were detected converse to rare ginsenosides during decomposition. The rare ginsenosides caused more serious damage to ginseng hairy root cells and inhibited the growth of ginseng hairy roots more significantly. By high-throughput RNA sequencing gene transcriptomics study, the significantly differential expressed genes (DEGs) were obtained under prototype and rare ginsenoside interventions. These DEGs were mainly enriched in the biosynthesis of secondary metabolites and metabolic pathways, phytohormone signal transduction, and protein processing in endoplasmic reticulum pathways. Based on the functional enrichment of DEGs, the targeted metabolomics analysis based on UPLC-MS/MS determination was applied to screen endogenous differential metabolized phytohormones (DMPs). The influence of prototype and rare ginsenosides on the accumulation of endogenous phytohormones was studied. These were mainly involved in the biosynthesis of diterpenoid, zeatin, and secondary metabolites, phytohormone signal transduction, and metabolic pathways. After integrating the transcriptomics and metabolomics analysis, ginsenosides could regulate the genes in phytohormone signaling pathways to influence the accumulation of JA, ABA, and SA. The conclusion was that the prototype ginsenosides were converted into rare ginsenosides by ginseng decomposition and released into the soil, which aggravated its allelopathic autotoxicity. The allelopathic mechanism was to intervene in the response regulation of genes related to the metabolic accumulation of endogenous phytohormones in ginseng. This result provides a reference for the in-depth study of continuous cropping obstacles of ginseng.

Supervised Parametric Learning in the Identification of Composite Biomarker Signatures of Type 1 Diabetes in Integrated Parallel Multi-Omics Datasets.

BACKGROUND: Type 1 diabetes (T1D) is a devastating autoimmune disease, and its rising prevalence in the United States and around the world presents a critical problem in public health. While some treatment options exist for patients already diagnosed, individuals considered at risk for developing T1D and who are still in the early stages of their disease pathogenesis without symptoms have no options for any preventive intervention. This is because of the uncertainty in determining their risk level and in predicting with high confidence who will progress, or not, to clinical diagnosis. Biomarkers that assess one's risk with high certainty could address this problem and will inform decisions on early intervention, especially in children where the burden of justifying treatment is high. Single omics approaches (e.g., genomics, proteomics, metabolomics, etc.) have been applied to identify T1D biomarkers based on specific disturbances in association with the disease. However, reliable early biomarkers of T1D have remained elusive to date. To overcome this, we previously showed that parallel multi-omics provides a more comprehensive picture of the disease-associated disturbances and facilitates the identification of candidate T1D biomarkers. METHODS: This paper evaluated the use of machine learning (ML) using data augmentation and supervised ML methods for the purpose of improving the identification of salient patterns in the data and the ultimate extraction of novel biomarker candidates in integrated parallel multi-omics datasets from a limited number of samples. We also examined different stages of data integration (early, intermediate, and late) to assess at which stage supervised parametric models can learn under conditions of high dimensionality and variation in feature counts across different omics. In the late integration scheme, we employed a multi-view ensemble comprising individual parametric models trained over single omics to address the computational challenges posed by the high dimensionality and variation in feature counts across the different yet integrated multi-omics datasets. RESULTS: the multi-view ensemble improves the prediction of case vs. control and finds the most success in flagging a larger consistent set of associated features when compared with chance models, which may eventually be used downstream in identifying a novel composite biomarker signature of T1D risk. CONCLUSIONS: the current work demonstrates the utility of supervised ML in exploring integrated parallel multi-omics data in the ongoing quest for early T1D biomarkers, reinforcing the hope for identifying novel composite biomarker signatures of T1D risk via ML and ultimately informing early treatment decisions in the face of the escalating global incidence of this debilitating disease.

Multi-omic profiling of follicular lymphoma reveals changes in tissue architecture and enhanced stromal remodeling in high-risk patients.

Follicular lymphoma (FL) is a generally incurable malignancy that evolves from developmentally blocked germinal center (GC) B cells. To promote survival and immune escape, tumor B cells undergo significant genetic changes and extensively remodel the lymphoid microenvironment. Dynamic interactions between tumor B cells and the tumor microenvironment (TME) are hypothesized to contribute to the broad spectrum of clinical behaviors observed among FL patients. Despite the urgent need, existing clinical tools do not reliably predict disease behavior. Using a multi-modal strategy, we examined cell-intrinsic and -extrinsic factors governing progression and therapeutic outcomes in FL patients enrolled onto a prospective clinical trial. By leveraging the strengths of each platform, we identify several tumor-specific features and microenvironmental patterns enriched in individuals who experience early relapse, the most high-risk FL patients. These features include stromal desmoplasia and changes to the follicular growth pattern present 20 months before first progression and first relapse.

Bioinformatics analysis of a disease-specific lncRNA-miRNA-mRNA regulatory network in recurrent spontaneous abortion (RSA).

BACKGROUND: This study investigated the molecular mechanisms of long non-coding RNAs (lncRNAs) in RSA using the lncRNA-miRNA-mRNA regulatory network. METHODS: The present study obtained expression datasets of long non-coding RNAs (lncRNAs), messenger RNAs (mRNAs), and microRNAs (miRNAs) from blood samples of individuals with unexplained recurrent spontaneous abortion (RSA) and healthy controls. Differentially expressed lncRNAs (DELs), mRNAs (DEMs), and miRNAs (DEmiRs) were identified. A regulatory network comprising lncRNA, miRNA, and mRNA was constructed, and Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted to analyze the biological functions of DEM. Also, a protein-protein interaction (PPI) network was made and key genes were identified. RESULTS: A total of 57 DELs, 212 DEmiRs, and 301 DEMs regarding RSA were identified. Later analysis revealed a lncRNA-miRNA-mRNA network comprising nine lncRNAs, 14 miRNAs, and 65 mRNAs. Then, the ceRNA network genes were subjected to functional enrichment and pathway analysis, which showed their association with various processes, such as cortisol and thyroid hormone synthesis and secretion, human cytomegalovirus infection, and parathyroid hormone synthesis. In addition, ten hub genes (ITGB3, GNAI2, GNAS, SRC, PLEC, CDC42, RHOA, RAC1, CTNND1, and FN1) were identified based on the PPI network results. CONCLUSION: In summary, the outcomes of our study provided some data regarding the alteration genes involved in RSA pathogenic mechanism via the lncRNA-miRNA-mRNA network and reveal the possibility of identifying new lncRNAs and miRNAs as promising molecular biomarkers.