Integrated Data Analysis Research Articles

Ythdf2 facilitates precursor miR-378/miR-378-5p maturation to support myogenic differentiation.

Ythdf2 is known to mediate mRNA degradation in an m6A-dependent manner, and it has been shown to play a role in skeletal muscle differentiation. Recently, Ythdf2 was also found to bind to m6A-modified precursor miRNAs and regulate their maturation. However, it remains unknown whether this mechanism is related to the regulation of myogenesis by Ythdf2. Here, we observed that Ythdf2 knockdown significantly suppressed myotube formation and impacted miRNAs expression during myogenic differentiation. Through integrated analysis of miRNA and mRNA sequencing data, miR-378 and miR-378-5p were identified as important targets of Ythdf2 in myogenesis. Mechanically, Ythdf2 was found to interact with core components of the pre-miRNA processor complex, namely DICER1 and TARBP2, thereby facilitating the maturation of pre-miR-378/miR-378-5p in an m6A-dependent manner and resulting in an increase in the expression levels of mature miR-378 and miR-378-5p. Moreover, the downregulation of either miR-378 or miR-378-5p significantly inhibited myotube formation, while the forced expression of miR-378 or miR-378-5p could partially rescued Ythdf2 knockdown-induced suppression of myogenic differentiation by activating the mTOR pathway. Collectively, our results for the first time suggest that Ythdf2 regulates myogenic differentiation via mediating pre-miR-378/miR-378-5p maturation, which might provide new insights into the molecular mechanisms underlying m6A modification in the regulation of myogenesis.

MIMR: Development of a Web-Based System for miRNA and mRNA Integrated Analysis

The human body is a complex network of systems that is harmonized with multiple biological components. To understand these interactions is very challenging. With rapid development of advanced sequencing technologies, massive amounts of data such as mRNA, miRNA are rapidly accumulated. The integrated analysis of mRNA–miRNA has brought an extensive understanding of complex biological systems and pathological mechanisms. MicroRNAs (miRNAs) are small non-coding RNAs that intricately regulate target gene products, resulting in the inhibition of gene expression. While these miRNAs play crucial roles in essential biological processes—ranging from immunity and metabolism to cell death—their specific impacts on diseases remain unknown. Recent studies have been focused on the integration of miRNA and mRNA expression to reveal the underlying biological pathways and mechanisms responsible for disease manifestation. We proposed a novel approach for integrative analysis of miRNA and mRNA expression data and developed MIMR (Integrative Analysis of miRNA and mRNA), a web-based application that leverages the Random Walk with Restart (RWR) algorithm. MIMR incorporates both direct and indirect interactions by utilizing protein–protein interaction (PPI) networks and experimentally validated mRNA–miRNA target interactions. MIMR provides comprehensive results, including novel pathological pathways associated with a specific disease and interactive network diagrams representing the mRNAs and miRNAs. We applied it to Alzheimer and breast cancer data and successfully identified the novel biological pathways related to these diseases. In summary, MIMR will offer a deeper insight into the hidden mechanisms of diseases and identify potential therapeutic strategies through integrated analysis of miRNAs and mRNAs.

Multi-omics Data Integration Analysis Identified Therapeutic Targets and Potential Reuse Drugs for Osteoporosis.

To facilitate drug discovery and development for the treatment of osteoporosis. With global aging, osteoporosis has become a common problem threatening the health of the elderly. It is of important clinical value to explore new targets for drug intervention and develop promising drugs for the treatment of osteoporosis. To understand the major molecules that mediate the communication between the cell populations of bone marrow-derived mesenchymal stem cells (BM-MSCs) in osteoporosis and osteoarthritis patients and identify potential reusable drugs for the treatment of osteoporosis. Single-cell RNA sequencing (scRNA-seq) data of BM-MSCs in GSE147287 dataset were classified using the Seurat package. CellChat was devoted to analyzing the ligand-receptor pairs (LR pairs) contributing to the communication between BM-MSCs subsets. The LR pairs that were differentially expressed between osteoporosis samples and control samples and significantly correlated with immune score were screened in the GSE35959 dataset, and the differentially expressed gene in both GSE35959 and GSE13850 data sets were identified as targets from a single ligand or receptor. The therapeutic drugs for osteoporosis were screened by network proximity method, and the top-ranked drugs were selected for molecular docking and molecular dynamics simulation with the target targets. Twelve subsets of BM-MSCs were identified, of which CD45-BM-MSCS_4, CD45-BM- MSCS_5, and CD45+ BM-MSCs_5 subsets showed significantly different distributions between osteoporosis samples and osteoarthritis samples. Six LR pairs were identified in the bidirectional communication between these three BM-MSCs subsets and other BM-MSCs subsets. Among them, MIF-CD74 and ITGB2-ICAM2 were significantly correlated with the immune score. CD74 was identified as the target, and a total of 48 drugs targeting CD47 protein were identified. Among them, DB01940 had the lowest free energy binding score with CD74 protein and the binding state was very stable. This study provided a new network-based framework for drug reuse and identified initial insights into therapeutic agents targeting CD74 in osteoporosis, which may be meaningful for promoting the development of osteoporosis treatment.

Integrated Metabolome and Transcriptome Analysis Reveals the Key Bitter Substances of Pleioblastus amarus Shoots at Different Developmental Stages

Pleioblastus amarus (bitter bamboo) shoots are a traditional forest vegetable in China, renowned for its nutritional composition and associated health benefits. This study aimed to investigate the dynamic changes in nutritional quality and bitterness-related metabolites, as well as corresponding gene expression in P. amarus shoots at three distinct growth stages, specifically at heights of 10 cm (BT1), 20 cm (BT2), and 40 cm (BT3). The results showed that the content of soluble sugars decreased while the content of total phenols and flavonoids, which contribute to bitterness, increased during the growth of P. amarus shoots. In addition, comparative analysis of BT2 vs. BT1, BT3 vs. BT1, and BT3 vs. BT2 revealed 14, 43, and 36, respectively, differentially accumulated metabolites (DAMs). Notably, BT2 exhibited upregulation of flavonoids, such as apigenin, while bitter amino acids, including phenylalanine and arginine, showed a marked increase in BT3. Moreover, differential gene expression analysis revealed 3161, 13268, and 3742 differentially expressed genes (DEGs) in the BT2 vs. BT1, BT3 vs. BT1, and BT3 vs. BT2 comparisons, respectively. An integrated analysis of metabolomic and transcriptomic data indicated that the metabolites and genes associated with the biosynthesis pathways of phenylalanine, tyrosine, tryptophan, arginine, and the branched-chain amino acids valine, leucine, and isoleucine were significantly enriched during the growth of P. amarus shoots. The study indicates that early-stage (≤20 cm) P. amarus shoots are mainly bitter due to flavonoids like apigenin, while the bitterness in later-stage (around 40 cm) shoots is largely due to bitter amino acids such as phenylalanine, arginine, valine, and isoleucine. This research highlights key compounds and genes related to bitterness, providing a basis for future research on enhancing the nutritional value and flavor of P. amarus shoots.

Differences in Drug Shortages in the US and Canada

Drug shortages are a persistent public health issue that increased during the COVID-19 pandemic. Both the US and Canada follow similar regulatory standards and require reporting of drug-related supply chain issues that may result in shortages. However, it is unknown what proportion are associated with meaningful shortages (defined by a significant decrease in drug supply) and whether differences exist between Canada and the US. To compare how frequently reports of drug-related supply chain issues in the US vs Canada were associated with drug shortages. Longitudinal cross-sectional study conducted from January 2023 to March 2024 using drug-related reports of supply chain issues from 2017 to 2021 that were less than 180 days apart in Canada and the US. Shortages were assessed using data from the IQVIA Multinational Integrated Data Analysis database, comprising 89% of US and 100% of Canadian drug purchases. Country (Canada vs US), timing of report issuance (before vs after the COVID-19 pandemic), and characteristics of the supply chain prior to the reports of drug-related supply chain issues (including World Health Organization essential medicine status, Health Canada tier 3 medicine [moderate risk classification], whether there was sole-source manufacturing of the drug, the formulation, the price per unit, ≥20 years since drug approval, and the number of therapeutic alternatives). A drug shortage (a decrease of ≥33% in monthly purchased standardized drug units) within 12 months, relative to the average units purchased during the 6 months prior to the report of supply chain issues to a US or Canadian reporting system. Among the 104 drug-related reports of supply chain issues in both countries, 49.0% (95% CI, 39.3%-59.7%) were associated with drug shortages in the US vs 34.0% (95% CI, 25.0%-45.0%) in Canada (adjusted hazard ratio [HR], 0.53 [95% CI, 0.36-0.79]). The lower risk of drug shortages in Canada vs the US was consistent before the COVID-19 pandemic (adjusted HR, 0.47 [95% CI, 0.30-0.75]) and after the pandemic (adjusted HR, 0.31 [95% CI, 0.15-0.66]). After combining reports of supply chain issues in both countries, the shortage risk was double for sole-sourced drugs (adjusted HR, 2.58 [95% CI, 1.57-4.24]) and nearly half for Canadian tier 3 medicines (moderate risk) (adjusted HR, 0.56 [95% CI, 0.32-0.98]). Drug-related reports of supply chain issues were 40% less likely to result in meaningful drug shortages in Canada compared with the US. These findings highlight the need for international cooperation between countries to curb the effects of drug shortages and improve resiliency of the supply chain for drugs.

Prediction of grain shoal reservoirs via seismic forward modeling and waveform classification: Application to the northern slope of the central Sichuan Uplift, Sichuan Basin, SW China

Grain shoals are one of the principal reservoir facies of carbonate rocks in oil and gas exploration domains. With the discovery of oil and gas in the grain shoal reservoirs of the Longwangmiao Formation in the central Sichuan paleo-uplift, the characteristics and distribution of grain shoal reservoirs are still unclear in the northern slope of the central Sichuan paleo-uplift. Through integrated analysis of wireline logs and high-quality 3D seismic data, combined with seismic forward modeling and waveform classification, reservoir seismic facies, reservoir classification, and the reservoir distribution of different grades are investigated. The results show that (1) under the thin thickness (i.e., the thickness is around 80 m) of the Longwangmiao Formation (i) when reservoirs are not developed, the seismic waveform is manifested as a single peak, and the top interface of the Longwangmiao Formation corresponds to 1/8 [Formula: see text] above the single peak. (ii) When a single set of reservoirs is merely developed in the second member of the Longwangmiao Formation (SMLF), the waveform displays a single peak, and the top peak moves downward. In addition, when multiple sets of reservoirs are developed in the SMLF, the seismic reflections of multiple reservoirs are consistent with those of a single reservoir with the same cumulative thickness. (iii) When the reservoirs are developed in the first member of the Longwangmiao Formation (FMLF) and SMLF, the single peak moves downward, and either the reservoir thickness of the SMLF is greater than that of the FMLF or the reservoir velocity of the SMLF is lower than that of the FMLF. (2) Under the thick thickness (i.e., the thickness is approximately 110 m) of the Longwangmiao Formation, (i) when reservoirs are not developed, the waveform of the Longwangmiao Formation exhibits a double peak, and the top interface of the Longwangmiao Formation corresponds to the extreme value of the upper peak. (ii) When only a single set of reservoirs or multiple sets of reservoirs are developed in the SMLF, the Longwangmiao Formation exhibits a single peak, and the top interface of the Longwangmiao Formation corresponds to the trough. (iii) With the reservoir development in the first and second members of the Longwangmiao Formation, the waveform of the Longwangmiao Formation shows an asymmetric low-frequency peak, and either the reservoir thickness of the SMLF is greater than that of the FMLF or the reservoir velocity of the SMLF is lower than that of the FMLF. (3) Using the waveform classification method, seismic waveforms corresponding to the Longwangmiao Formation could be summarized into four types (i.e., two types of waveforms associated with reservoir development and two types of waveforms associated with reservoir un-development). Two reservoir waveforms are distributed as two strips oriented approximately east–west and spread in the central study area, according to the planar study results that combine seismic attributes and waveform classification. The reservoir stripes near well PS1 represent the better-developed reservoir zones in the study area.

Unveiling the novel regulatory roles of RpoD-family sigma factors in Salmonella Typhimurium heat shock response through systems biology approaches

Three RpoD-family sigma factors, RpoD, RpoS, and RpoH, play critical roles in transcriptional regulation in Salmonella enterica serovar Typhimurium under heat shock conditions. However, the genome-wide regulatory mechanisms of these sigma factors in response to heat stress have remained elusive. In this study, we comprehensively identified 2,319, 2,226, and 213 genome-wide binding sites for RpoD, RpoS, and RpoH, respectively, under sublethal heat shock conditions (42°C). Machine learning-based transcriptome analysis was employed to infer the relative activity of iModulons, providing valuable insights into the transcriptional impact of heat shock. Integrative data analysis enabled the reconstruction of the transcriptional regulatory network of sigma factors, revealing how they modulate gene expression to adapt to heat stress, including responses to anaerobic and oxidative stresses. Notably, we observed a significant expansion of the RpoS sigmulon from 97 to 301 genes in response to heat shock, underscoring the crucial role of RpoS in regulating various metabolic processes. Moreover, we uncovered a competition mechanism between RpoD and RpoS within RpoS sigmulons, where RpoS significantly increases its binding within promoter regions shared with RpoD under heat shock conditions. These findings illuminate how three RpoD-family sigma factors coordinate multiple cellular processes to orchestrate the overall response of S. Typhimurium to heat stress.

Integrative analysis of bulk and single-cell transcriptomic data reveals novel insights into lipid metabolism and prognostic factors in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is associated with high mortality rate. This study investigated the status of lipid metabolism-related genes in HCC. Bulk transcriptomic and single-cell sequencing data for HCC were retrieved from public databases. The single-cell sequencing data was subjected to dimensionality reduction, which facilitated the annotation of distinct cell subpopulations and marker gene expression analysis within each subpopulation. Genes associated with lipid metabolism in liver cells were identified, and a machine-learning model was developed using the bulk transcriptomic data randomly partitioned into training and validation sets. The efficacy of the model was validated using these two sets. A multifactorial Cox analysis on the model genes combined with clinical features, led to the identification of age, HMGCS2, HNRNPU, and RAN as independent prognostic factors, which were included in the nomogram model construction and validation. A weighted gene co-expression analysis of all genes of the bulk transcriptome samples revealed the correlation between gene modules and risk score. Genes with cor > 0.4 in the highest-expressing module were selected for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functional enrichment analysis. Immune-related analysis was conducted based on seven algorithms for immune cell infiltration prediction. For the genes in the nomogram model, the expression in clinical pathological factors was also analyzed. The drug sensitivity analysis offered a reference for the selection of targeting drugs. This investigation provides novel insights and a theoretical basis for the prognosis, treatment, and pharmaceutical advancements for patients diagnosed with HCC.

Using data fusion with multiple imputation to correct for misclassification in self-reported exposure: a case-control study of cannabis use and homicide victimization

BackgroundCannabis use has been causally linked to violent behaviors in experimental and case studies, but its association with homicide victimization has not been rigorously assessed through epidemiologic research.MethodsWe performed a case-control analysis using two national data systems. Cases were homicide victims from the National Violent Death Reporting System (NVDRS), and controls were participants from the National Survey on Drug Use and Health (NSDUH). While the NVDRS contained toxicological testing data on cannabis use, the NSDUH only collected self-reported data, and thus the potential misclassification in the self-reported data needed to be corrected. We took a data fusion approach by concatenating the NSDUH with a third data system, the National Roadside Survey of Alcohol and Drug Use by Drivers (NRS), which collected toxicological testing and self-reported data on cannabis use for drivers. The data fusion approach provided multiple imputations (MIs) of toxicological testing results on cannabis use for the participants in the NSDUH, which were then used in the case-control analysis. Bootstrap was used to obtain valid statistical inference.ResultsThe analyses revealed that cannabis use was associated with 3.55-fold (95% CI: 2.75–4.35) increased odds of homicide victimization. Alcohol use, being Black, male, aged 21–34 years, and having less than a high school education were also significantly associated with increased odds of homicide victimization.ConclusionsCannabis use is a major risk factor for homicide victimization. The data fusion with MI method is useful in integrative data analysis for harmonizing measures between different data sources.

Data Integration and Predictive Analysis for Healthcare: Survey

In modern healthcare, handling of medical data is often divided, with sensitive information stored in various formats in documents like prescriptions, lab reports and patient summaries. The lack of standardization and management of such data can lead to errors and inefficiency in patient care. Various techniques like HCR, OCR, machine learning algorithms are for the purpose of data integration and predictive analysis. The aim of this paper is to conduct the survey of various techniques used in healthcare systems and domain for data handling and performing operations like predictive analysis on that data. We have reviewed and shortlisted 7 papers and the methods used in those studies. This survey is beneficial to those who wish to manage and store data of healthcare and use the same for their respective purposes.

Molecular phylogeny reveals a new genus and species of freshwater mussel (Bivalvia, Unionidae, Gonideinae) from Jiangxi, China

Freshwater mussels of the tribe Gonideini (Bivalvia: Unionidae: Gonideinae) constitute one of the most taxonomically diverse groups and serve as keystone species in riverine and lacustrine ecosystems across East Asia. A new genus and species of Gonideini (Bivalvia: Unionidae) is described from Jiangxi, China, as Pseudopostolata angulagen. et sp. nov. based on an integrative analysis of shell morphology and molecular data. The validity of the new genus and species is supported by distinct conchological features: a short, rounded anterior; a long, wide posterior; a slightly downward-curved dorsal margin; and a distinctly obtuse angle at the center of the posterior margin. The multi-locus (COI + 16S rRNA + 28S rRNA) phylogeny showed that the species formed a monophyletic group in the tribe Gonideini of the subfamily Gonideinae. Pseudopostolata angulagen. et sp. nov. is identified as the sister group to a clade comprising the genera Postolata, Cosmopseudodon, Obovalis, Ptychorhynchus, Parvasolenaia, and Koreosolenaia. We emphasize the significant morphological convergence in freshwater mussels, particularly within Gonideini, highlighting the necessity of an integrated taxonomic approach for accurate generic classification of this group.

Integrating Molecular Perspectives: Strategies for Comprehensive Multi-Omics Integrative Data Analysis and Machine Learning Applications in Transcriptomics, Proteomics, and Metabolomics

With the advent of high-throughput technologies, the field of omics has made significant strides in characterizing biological systems at various levels of complexity. Transcriptomics, proteomics, and metabolomics are the three most widely used omics technologies, each providing unique insights into different layers of a biological system. However, analyzing each omics data set separately may not provide a comprehensive understanding of the subject under study. Therefore, integrating multi-omics data has become increasingly important in bioinformatics research. In this article, we review strategies for integrating transcriptomics, proteomics, and metabolomics data, including co-expression analysis, metabolite–gene networks, constraint-based models, pathway enrichment analysis, and interactome analysis. We discuss combined omics integration approaches, correlation-based strategies, and machine learning techniques that utilize one or more types of omics data. By presenting these methods, we aim to provide researchers with a better understanding of how to integrate omics data to gain a more comprehensive view of a biological system, facilitating the identification of complex patterns and interactions that might be missed by single-omics analyses.

Microbial metabolomics in acute myeloid leukemia: From pathogenesis to treatment.

Acute myeloid leukemia (AML), the most common leukemia in adults, is a biologically heterogeneous disease arising from clonally proliferating hematopoietic stem cells. Increased appreciation of novel genetic methods has improved the understanding of AML biology. Recently, the emerging field of metabolomics has indicated qualitative and quantitative alterations in metabolic profiles in AML pathogenesis, progression and treatment. Multiple metabolic and molecular pathways regulate human metabolism and host-microbiome interactions may significantly affect this biochemical machinery. Microbiota have been found to play a significant role in hematopoietic function, metabolism and immunity, contributing to AML occurrence. A large number of studies have highlighted the importance of the composition and diversity of the gut microbiota (GM) in response to treatment and prognosis in AML. Moreover, strong evidence emphasizes the detrimental link between dysbiosis and infectious complications, a leading cause of morbidity and mortality for patients with AML. Several microbiota-related mechanisms have been linked to particular changes in host physiology so far, and microbial-derived metabolites belong to one of the most important. Circulating in the body, they modulate human conditions both locally and systemically. The extensive and diverse repertoire of bacterial metabolic functions plays a critical role in numerous processes, including leukemogenesis. Integrative analysis of microbiome and metabolome data is a promising avenue for better understanding the complex relationship between the microbiota, biochemical alterations and AML pathogenesis to effectively prevent, treat and mitigate its outcomes. This review concentrates on the pathologic roles and therapeutic implications of microbe-derived metabolites in AML settings.

Transcriptome and Metabolome Reveal Accumulation of Key Metabolites with Medicinal Properties of Phylloporia pulla.

Phylloporia pulla, a macrofungal species in the Hymenochaetales, Basidiomycota, is known to enhance the nutritional and bioactive properties of rice through co-fermentation; however, its own secondary metabolites are not well understood. In this study, an integrative analysis of transcriptome and metabolome data revealed that the accumulation of steroids, steroid derivatives, and triterpenoids in P. pulla peaks during the mid-growth stage, while the genes associated with these metabolites show higher expression levels from the early to mid-growth stages. Weighted gene co-expression network analysis identified several modules containing candidate genes involved in the synthesis of steroids, steroid derivatives, and triterpenoids. Specifically, six key hub genes were identified, along with their connectivity to other related genes, as potential catalysts in converting the precursor lanosterol to celastrol. This study enhances our understanding of the secondary metabolites of P. pulla and is essential for the selective utilization of these bioactive compounds.

Multidimensional analysis of the impact of Gemmatimonas, Rhodothermus, and Sutterella on drug and treatment response in colorectal cancer.

Colorectal cancer is the third most prevalent cancer across the globe. Despite a diversity of treatment methods, the recurrence and mortality rates of the disease remain high. Recent studies have revealed a close association of the gut microbiota with the occurrence, development, treatment response, and prognosis of colorectal cancer. This study aims to integrate transcriptome and microbiome data to identify colorectal cancer subtypes associated with different gut microbiota and evaluate their roles in patient survival prognosis, tumor microenvironment (TME), and drug treatment response. An integrated analysis of microbiome data was conducted on samples of colorectal cancer from public databases. Based on this, two tumor subtypes (C1 and C2) closely associated with patient survival prognosis were identified and a risk score model was constructed. The survival status, clinical parameters, immune scores, and other features were analyzed in-depth, and the sensitivity of various potential drugs was examined. A thorough examination of microbiome information obtained from colorectal cancer patients led to the identification of two primary tumor clusters (C1 and C2), exhibiting notable variations in survival outcomes. Patients with the C1 subtype were closely associated with better prognosis, while those with the C2 subtype had higher gut microbial richness and poorer survival prognosis. A predictive model utilizing the microbiome data was developed to accurately forecast the survival outcome of patients with colorectal cancer. The TME scores provided a biological basis for risk assessment in high-risk (similar to the C2 subtype) patient cohorts. Evaluation of the sensitivity of different subtypes to various potential drugs, indicated the critical importance of personalized treatment. Further analysis showed good potential of the developed risk-scoring model in predicting immune checkpoint functions and treatment response of patients, which may be crucial in guiding the selection of immunotherapy strategies for patients with colorectal cancer. This study, through a comprehensive analysis of colorectal cancer microbiome, immune microenvironment, and drug sensitivity, enhances the current understanding of the multidimensional interactions of colorectal cancer and provides important clinical indications for improving future treatment strategies. The findings offer a new perspective on improving treatment response and long-term prognosis of patients with CRC through the regulation of microbiota or the utilization of biomarkers provided by it.

Integrative Analysis of ATAC-Seq and RNA-Seq through Machine Learning Identifies 10 Signature Genes for Breast Cancer Intrinsic Subtypes.

Breast cancer is a heterogeneous disease composed of various biologically distinct subtypes, each characterized by unique molecular features. Its formation and progression involve a complex, multistep process that includes the accumulation of numerous genetic and epigenetic alterations. Although integrating RNA-seq transcriptome data with ATAC-seq epigenetic information provides a more comprehensive understanding of gene regulation and its impact across different conditions, no classification model has yet been developed for breast cancer intrinsic subtypes based on such integrative analyses. In this study, we employed machine learning algorithms to predict intrinsic subtypes through the integrative analysis of ATAC-seq and RNA-seq data. We identified 10 signature genes (CDH3, ERBB2, TYMS, GREB1, OSR1, MYBL2, FAM83D, ESR1, FOXC1, and NAT1) using recursive feature elimination with cross-validation (RFECV) and a support vector machine (SVM) based on SHAP (SHapley Additive exPlanations) feature importance. Furthermore, we found that these genes were primarily associated with immune responses, hormone signaling, cancer progression, and cellular proliferation.

Integrated miRNA and mRNA omics reveal neferine induced autophagy-dependent ferroptosis in human endometrial cancer in vivo and in vitro

The most frequent type of gynecological cancer, endometrial cancer, seriously jeopardizes the health of women. Lotus seed embryos contain large amounts of the compound neferine (Nef), which has demonstrated strong anticancer properties. Nevertheless, its impacts and defense mechanisms against Endometrial cancer (EC) are still unknown. In order to identify the putative anti-cancer pathways that Nef targets, we combined mRNA-seq and miRNA-seq analyses and assessed the effects of Nef on EC both in vivo and in vitro. The integrative analysis of miRNA and mRNA omics data identified autophagy and ferroptosis as the primary anti-cancer pathways targeted by Nef. Nef stimulated the production of autophagosomes and autolysosomes, so initiating cell autophagy, as demonstrated by transmission electron microscopy. Furthermore, Nef altered Fe2+ concentration, GSH, and MDA levels, promoting ferroptosis. Molecular data indicated that Nef activated the AMPK/mTOR and SLC7A11/GPX4 pathways in vitro and in vivo, inducing autophagy and ferroptosis. The use of the 3-Methyladenine (3-MA) and Ferrostatin-1 (Fer-1) reversed Nef-induced reactive oxygen species (ROS) generation and GPX4 expression levels, confirming that Nef suppresses Ishikawa cell growth through autophagy-dependent ferroptosis. These results indicate that Nef is a viable functional food for the treatment of EC, as it can dramatically promote autophagy-dependent ferroptosis in Ishikawa cells.

Optimizing reservoir characterization: insights from integrated data analysis

The Onshore Hydrocarbon prospectivity of the Niger Delta X-field is examined through the integration of 3D seismic and recorded information from well log data. Probable reservoirs of hydrocarbon-bearing were delineated to tackle the non-uniqueness in the identification of hydrocarbon quantity of concern. Three significant faulting patterns or systems were delineated and their architectural attributes depict a typical Niger Delta embedded anticlinal structural pattern. The study field exhibited both synthetic and antithetic structures, hence only fault on delineated reservoirs was used for structural modeling. All well locations were sited within the fault-supported synthetic and anticlinal structures and the static characteristics within the well coordinates were analyzed through petrophysical assessment. Depicted reservoir sands show low gamma ray readings, low volume of shale, considerate hydrocarbon saturation and low water saturation. The established petrophysical models showed a better net-to-gross (NTG) ratio, for the entire delineated reservoir units. This has contributed to the assessment of hydrocarbon-bearing reservoir units before employing the strategy for field development scenario, in order to eliminate dry hole drilling campaign. These will contribute to the reduction of operational costs, having delineated the accurate geometry and petrophysical model of hydrocarbon reservoir units.

Integrated analysis of metabolomic and transcriptomic profiles elucidates the core role of glycerophospholipid metabolism in hypertension related to metabolic syndrome

BackgroundMetabolic syndrome (MetS) represents a comprehensive framework that encompasses conditions such as diabetes, hypertension (HBP), obesity, and dyslipidemia. MetS without hypertension significantly contributes to the development of refractory hypertension. However, the underlying molecular regulatory mechanisms of hypertension associated with MetS remain incompletely elucidated. The objective of this study was to identify differences in plasma metabolome and leukocyte transcriptome profiles between patients with MetS comorbid HBP compared to those with MetS with hypertension and HBP alone. MethodsIn this study, whole blood samples were collected from 104 participants, including patients with HBP, MetS without hypertension (MS), and MetS with hypertension (MS-HBP). Ultra-high-performance liquid chromatography was employed to perform metabolomic analysis of plasma samples. Comparative analysis was conducted to identify distinct metabolites among the three groups, while MetaboAnalyst 5.0 was utilized for enrichment and pathway analysis. Clinical correlation analysis and receiver operating characteristic analyses were applied to select biomarker metabolites for MS-HBP. Transcriptomic profiling of white blood cells was performed to identify dysregulated genes and KEGG/GO pathways. Integrative analysis of metabolomics and transcriptomics was used to construct molecular interaction networks. Finally, blood pressure was monitored every 2 weeks during the treatment period for experimental validation. ResultsThe metabolomic results indicated that lipid metabolic pathways, particularly glycerophospholipid metabolic pathways, played a crucial role in MS-HBP. The area under the curve of tryptophan-isoleucine for diagnosis in the MS-HBP group was 0.82. Tryptophyl-isoleucine demonstrated a negative correlation with both HbA1c and diastolic blood pressure. Transcriptomic analysis revealed the involvement of MS-HBP in numerous immune and inflammatory pathways. Furthermore, the integrated analysis of metabolomic and transcriptomic data emphasized the importance of glycerophospholipid metabolism in MS-HBP. PPC exhibited a comparable effect on lowering blood pressure in SHR rats. ConclusionThe findings of this study demonstrated that glycerophospholipid metabolism plays a pivotal role in the pathophysiological processes of MS-HBP, providing novel insights into early clinical diagnosis and the optimization of therapeutic strategies.

Improving Stroke Outcome Prediction Using Molecular and Machine Learning Approaches in Large Vessel Occlusion.

Introduction: Predicting stroke outcomes in acute ischemic stroke (AIS) can be challenging, especially for patients with large vessel occlusion (LVO). Available tools such as infarct volume and the National Institute of Health Stroke Scale (NIHSS) have shown limited accuracy in predicting outcomes for this specific patient population. The present study aimed to confirm whether sudden metabolic changes due to blood-brain barrier (BBB) disruption during LVO reflect differences in circulating metabolites and RNA between small and large core strokes. The second objective was to evaluate whether integrating molecular markers with existing neurological and imaging tools can enhance outcome predictions in LVO strokes. Methods: The infarction volume in patients was measured using magnetic resonance diffusion-weighted images, and the 90-day stroke outcome was defined by a modified Rankin Scale (mRS). Differential expression patterns of miRNAs were identified by RNA sequencing of serum-driven exosomes. Nuclear magnetic resonance (NMR) spectroscopy was used to identify metabolites associated with AIS with small and large infarctions. Results: We identified 41 miRNAs and 11 metabolites to be significantly associated with infarct volume in a multivariate regression analysis after adjusting for the confounders. Eight miRNAs and ketone bodies correlated significantly with infarct volume, NIHSS (severity), and mRS (outcome). Through integrative analysis of clinical, radiological, and omics data using machine learning, our study identified 11 top features for predicting stroke outcomes with an accuracy of 0.81 and AUC of 0.91. Conclusions: Our study provides a future framework for advancing stroke therapeutics by incorporating molecular markers into the existing neurological and imaging tools to improve predictive efficacy and enhance patient outcomes.