Search Results (66)

Bayesian analysis is particularly useful for inferring models and their parameters given data. This is a common task in metabolic modeling, where models of varying complexity are used to interpret data. Nested sampling is a class of probabilistic inference algorithms that are particularly effective for estimating evidence and sampling the parameter posterior probability distributions. However, the practicality of nested sampling for metabolic network inference has yet to be studied. In this technical report, we explore the amalgamation of nested sampling, specifically diffusive nested sampling, with reversible jump Markov chain Monte Carlo. We apply the algorithm to two synthetic problems from the field of metabolic flux analysis. We present run times and share insights into hyperparameter choices, providing a useful point of reference for future applications of nested sampling to metabolic flux problems. Full article

Objectives: The Taihe Black-Boned Silky Fowl (TBSF) is a unique indigenous chicken breed in China, characterized by widespread melanin deposition throughout its body. Fluoroquinolones (FQs) such as enrofloxacin can persist in TBSF for an extended period exceeding 100 days. The aim of this study was to examine the current status and development trends of FQ resistance within the TBSF breeding environment. Methods: Whole-genome sequencing was utilized to identify the molecular presence of quinolone resistance-determining region (QRDR) mutations and plasmid-mediated quinolone resistance (PMQR) genes in Escherichia coli isolates obtained from TBSF farms. Network inference based on strong Spearman correlations (ρ > 0.5) and statistically significant associations (p-value < 0.05) was applied to investigate the co-occurrence patterns among FQ residues, resistance phenotypes, and antibiotic resistance genes. Results: The results showed that FQ residues were identified as the primary contributor to FQ resistance in E. coli isolates. Mutations at QRDR sites were the predominant factor driving FQ resistance, rather than PMQR determinants. This study also reported the first identification of GyrA-S83Q mutation being associated with FQ resistance. Conclusions: It was concluded that E. coli strains in TBSF environments, where chickens have a long-term residual metabolic cycle of antimicrobials, may develop and evolve new mechanisms to adapt to this environment. Further research is warranted to investigate the evolution of FQ resistance in E. coli strains within TBSF environments. Full article

Nutrition plays a fundamental role in promoting health and preventing chronic diseases, with bioactive food components offering a therapeutic potential in biomedical applications. Among these, edible oils are recognised for their functional properties, which contribute to disease prevention and metabolic regulation. The proposed study aims to evaluate the quality of four bioactive oils (olive oil, sunflower oil, tomato seed oil, and pumpkin seed oil) by analysing their thermal behaviour through infrared (IR) imaging. The study designed a customised electronic system to acquire thermographic signals under controlled temperature and humidity conditions. The acquisition system was used to extract thermal data. Analysis of the acquired thermal signals revealed characteristic heat absorption profiles used to infer differences in oil properties related to stability and degradation potential. A hybrid deep learning model that integrates Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) units was used to classify and differentiate the oils based on stability, thermal reactivity, and potential health benefits. A signal analysis showed that the AI-based method improves both the accuracy (achieving an F1-score of 93.66%) and the repeatability of quality assessments, providing a non-invasive and intelligent framework for the validation and traceability of nutritional compounds. Full article

Background: Gastric cancer (GC) represents a significant global health burden with considerable heterogeneity in clinical and molecular behavior. The anatomical site of tumor origin—proximal versus distal—has emerged as a determinant of prognosis and response to therapy. The aim of this paper is to elucidate the transcriptional and regulatory differences between proximal gastric cancer (PGC) and distal gastric cancer (DGC) through master regulator (MR) analysis. Methods: We analyzed RNA-seq data from TCGA-STAD and microarray data from GEO (GSE62254, GSE15459). Differential gene expression and MR analyses were performed using DESeq2, limma, corto, and RegEnrich pipelines. A harmonized matrix of 4785 genes was used for MR inference following normalization and batch correction. Functional enrichment and survival analyses were conducted to explore prognostic associations. Results: Among 364 TCGA and 492 GEO patients, PGC was associated with more aggressive clinicopathological features and poorer outcomes. We identified 998 DEGs distinguishing PGC and DGC. PGC showed increased FOXM1 (a key regulator of cell proliferation), STAT3, and NF-κB1 activity, while DGC displayed enriched GATA6, CDX2 (a marker of intestinal differentiation), and HNF4A signaling. Functional enrichment highlighted proliferative and inflammatory programs in PGC, and differentiation and metabolic pathways in DGC. MR activity stratified survival outcomes, reinforcing prognostic relevance. Conclusions: PGC and DGC are governed by distinct transcriptional regulators and signaling networks. Our findings provide a biological rationale for location-based stratification and inform targeted therapy development. Full article

Codonopsis pilosula polysaccharides have demonstrated multiple biological activities including immune regulation, antitumor, and antioxidant properties. The rapid development and integrated application of multi-omics can facilitate the unraveling of the complex network of immune system regulation. In this study, C. pilosula alkali-extracted polysaccharide (CPAP) were prepared, and their effects on gut microbiota compositions, metabolic pathways, and protein expressions in peripheral blood and solid tumors in mice were further evaluated. The 16S rDNA sequencing results showed that CPAP could effectively promote the enrichment of intestinal Lactobacillus in tumor-bearing mice. In addition, it could be inferred from peripheral blood and solid tumor proteomics results that CPAP might activate T cell-mediated antitumor immune functions by regulating purine metabolism and alleviate tumor-caused inflammation by promoting neutrophil degranulation, finally inducing apoptosis in tumor cells by increasing oxidative stress. These results will provide a theoretical foundation and data support for the further development of CPAP as dietary adjuvants targeting immune deficiency-related diseases. Full article

Understanding how transcription factors regulate biomass accumulation and sucrose storage is essential for improving sugarcane productivity. In this study, we quantified transcription factor protein (TFP) abundance in sugarcane internodes across developmental stages and growth rates. These profiles were correlated with key biochemical traits, including lignin, glucan, hemicellulose, and sucrose content. From 7333 identified proteins, 205 were annotated as transcription factors spanning 22 families. By applying Pearson correlation followed by Partial Correlation with Information Theory (PCIT), we identified 46 high-confidence TFP-trait associations. Key regulators, such as ScMYB113, ScMADS15, and ScbZIP85, exhibited trait-specific roles, influencing sucrose storage and cell wall biosynthesis. Network topology revealed distinct transcriptional modules linked to biomass production, polysaccharide deposition, and intermediary metabolism. Notably, sucrose and lignin accumulation intensified after internode elongation ceased, highlighting shifts in transcriptional control during maturation. This study delivers the first protein-level regulatory map linking transcription factors to metabolic traits in sugarcane and provides a framework for targeting candidate regulators to enhance biomass quality and yield in bioenergy crops such as sugarcane. Full article

Background/Objectives: Organ size is a critical target trait in fruit-tree breeding programs, as it significantly impacts the economic value of plants by influencing their biomass, yield, and quality. Understanding the molecular mechanisms underlying organ size in citrus is essential for breeding new cultivars with superior fruit quality. Methods: In this study, we investigated the regulatory network involved in organ size using the Citrus sinensis ‘Newhall’ navel orange variety and its large-organ mutant, ‘M25’. Results: Ploidy analysis indicated that the organ enlargement observed in ‘M25’ was not attributable to changes in chromosome ploidy. Furthermore, RNA sequencing of tender leaves and young fruits from both ‘M25’ and ‘Newhall’ oranges identified 1817 and 1605 differentially expressed genes (DEGs), respectively. Functional enrichment analysis revealed that these DEGs were enriched in pathways associated with organ size regulation, including those related to cell division, DNA replication, protein biosynthesis, plant hormone signal transduction, and cell wall metabolism. Weighted gene co-expression network analysis identified the grey 60 and orange modules as the key modules influencing organ enlargement; from these modules, we identified 51 and 35 hub genes, respectively. Combined homologous function annotation and expression analysis identified four transcription-factor-encoding hub genes (Cs_ont_6g005380, Cs_ont_8g025330, Cs_ont_9g019400, and Cs_ont_9g008010) as candidate genes potentially related to organ size. Conclusions: Among these, Cs_ont_8g025330 (CsMYB73) was inferred to be the key gene influencing organ size through auxin and cytokinin regulation. These findings lay the foundation for further investigations of the regulatory mechanism of organ size in navel orange varieties. Full article

This study applies the socio-metabolic approach and relatedly the concept of planetary urbanization understanding to detect the identity of the “alternative zones” embedded in the food supply chain of cities (FSC). To achieve shortened and sustainable FSCs for cities, strong alternative food networks (AFNs) should be developed and sustained. The precious element of a strong AFN is its urban areas, which serve as niche alternative food initiatives (AFIs) for sustainability transitions in food supply chains (FSCs). To achieve shorter and more sustainable FSCs in cities, it is crucial to develop and sustain empowered alternative food networks (AFNs) by deploying their AFIs. Within this context, this study examines AFIs in 12 European FUSILLI cities to understand the potential of the intrinsic AFN to accelerate the sustainable transition in FSCs. Considering the results of AFNs in accelerating sustainability transitions in FSCs. Results through spatial analyses of food ecosystems of FUSILLI cities, although there are prominent examples with a strong short and alternative food network, it is obvious that the sustainable transition into an alternative food network has proceeded; however, the analysis of AFNs in FUSILLI cities demonstrates that sustainability transitions have advanced through vigorous AFNs. However, extended urban areas still have room to supersede their place in conventional/industrial agricultural production, which remains embedded in these spaces. The same inference applies to urban—rural linkages, which need to be strengthened to support the relocation of the food system in the development of AFNs in urban areas and to create more sustainable and shortened FSCs. Also, it is obvious that cities with greater extended AFNs, for example, Rome, due to its great number of AFIs and geographical extent of AFN covering concentrated urban areas and to strengthen the rural–urban linkage for shortened food supply chains, as well as extended urban areas, and Oslo, due to its great variety of AFIs embedded in concentrated urban areas with alternative food production areas in its (erstwhile rural areas) extended urban areas. Full article

Background/Objectives: Determining appropriate cellular objectives is crucial for the system-scale modeling of biological networks for metabolic engineering, cellular reprogramming, and drug discovery applications. The mathematical representation of metabolic objectives can describe how cells manage limited resources to achieve biological goals within mechanistic and environmental constraints. While rapidly proliferating cells like tumors are often assumed to prioritize biomass production, mammalian cell types can exhibit objectives beyond growth, such as supporting tissue functions, developmental processes, and redox homeostasis. Methods: This review addresses the challenge of determining metabolic objectives and trade-offs from multiomics data. Results: Recent advances in single-cell omics, metabolic modeling, and machine/deep learning methods have enabled the inference of cellular objectives at both the transcriptomic and metabolic levels, bridging gene expression patterns with metabolic phenotypes. Conclusions: These in silico models provide insights into how cells adapt to changing environments, drug treatments, and genetic manipulations. We further explore the potential application of incorporating cellular objectives into personalized medicine, drug discovery, tissue engineering, and systems biology. Full article

Background/Objectives: Vitamin D (VD) plays a crucial role in age-related diseases, and its influence on cellular senescence (CS) could help clarify its function in aging. Considering VD’s pleiotropic effects and the heterogeneity of CS. Methods: we utilized single-cell RNA sequencing (scRNA-seq) to explore these dynamics across multiple tissues. We analyzed three murine tissue datasets (bone, prostate, and skin) obtained from public repositories, enriching for senescence gene signatures. We then inferred gene regulatory networks (GRNs) at the tissue and cell-type levels and performed two cell communication analyses: one for senescent cells and another for interactions between senescent and non-senescent cells. Results: VD supplementation significantly decreased senescence scores in the skin (p = $3.96\times {10}^{-134}$) and prostate ($p=1.56\times {10}^{-34}$). GRN analysis of the prostate revealed an altered macrophage–fibroblast regulatory relationship. In bone, distinct aging-related modules emerged for different bone lineages. In skin, contrary differentiation patterns between suprabasal and basal cells were observed. The main VD-modulated pathways were involved in inflammation, extracellular matrix remodeling, protein metabolism, and translation. VD reduced fibroblast–macrophage interactions in the prostate and skin but increased overall cellular crosstalk in bone. Conclusions: Our findings demonstrate that VD alleviates CS burden across tissues by modulating inflammation and metabolic processes and promoting differentiation. Key aging-related genes modulated by VD were linked to anabolism and cellular differentiation, suggesting VD’s potential for therapeutic interventions targeting age-related diseases. Full article

Background/Objectives: Research on pharmacogenetic variability in response to prescribed drugs and across ethnic groups is essential for personalized medicine, particularly in admixed and unstudied populations. For the first time, this study examines CYP2D6, CYP2C9, and CYP2C19 alleles and genotypes in 197 healthy volunteers from the Dominican Republic, as part of the RIBEF-CEIBA collaborative network. Methods: The analysis focuses on the participants’ tri-hybrid genomic ancestry, with CYP alleles determined by real-time PCR and molecular ancestry inferred using 90 AIMs. Linear regression was used to associate ancestry components with CYP frequencies. Results: The average ancestry was 23.8% European, 42.6% Native American, and 33.6% African, the latter being higher than in most Latin American populations. Native American ancestry was also higher than expected. Predicted phenotype frequencies based on genotypes were 4.2% poor metabolizers (gPMs) and 3.6% ultrarapid metabolizers (gUMs) for CYP2D6, as well as 3% gPMs, 22.8% rapid metabolizers (gRMs), and 1.5% gUMs for CYP2C19. No gPM individuals were observed for CYP2C9. Certain alleles associated with decreased CYP2D6 activity (*17 and *29) and increased CYP2C19 activity (*17 and gUMs) were positively linked with African ancestry and negatively with Native American ancestry. Rare CYP2C9 alleles (*5 and *6) with clinical relevance were additionally found. Conclusions: These findings build on previous results from the RIBEF-CEIBA collaborative network, demonstrating differences in allele frequencies of CYP2D6, CYP2C9, and CYP2C19 in relation to genomic ancestry. In summary, ethnicity must be considered in the development of pharmacogenetic guidelines for clinical application, research, and regulation to avoid widening the biotechnology gap and to allow Personalized Medicine to reach the entire world population. Full article

Alzheimer’s disease (AD) is a multifaceted neurodegenerative disorder characterized by cognitive decline and neuronal loss, representing a most challenging health issue. We present a computational analysis of transcriptomic data of AD tissues vs. healthy controls, focused on the elucidation of functional roles played by long non-coding RNAs (lncRNAs) throughout the AD progression. We first assembled our own lncRNA transcripts from the raw RNA-Seq data generated from 527 samples of the dorsolateral prefrontal cortex, resulting in the identification of 31,574 novel lncRNA genes. Based on co-expression analyses between mRNAs and lncRNAs, a co-expression network was constructed. Maximal subnetworks with dense connections were identified as functional clusters. Pathway enrichment analyses were conducted over mRNAs and lncRNAs in each cluster, which served as the basis for the inference of functional roles played by lncRNAs involved in each of the key steps in an AD development model that we have previously built based on transcriptomic data of protein-encoding genes. Detailed information is presented about the functional roles of lncRNAs in activities related to stress response, reprogrammed metabolism, cell polarity, and development. Our analyses also revealed that lncRNAs have the discerning power to distinguish between AD samples of each stage and healthy controls. This study represents the first of its kind. Full article

Nitrogen (N) is essential for sugar beet (Beta vulgaris L.), a highly N-demanding sugar crop. This study investigated the morphological, subcellular, and microRNA-regulated responses of sugar beet roots to low N (LN) stress (0.5 mmol/L N) to better understand the N perception, uptake, and utilization in this species. The results showed that LN led to decreased dry weight of roots, N accumulation, and N dry matter production efficiency, along with damage to cell walls and membranes and a reduction in organelle numbers (particularly mitochondria). Meanwhile, there was an increase in root length (7.2%) and branch numbers (29.2%) and a decrease in root surface area (6.14%) and root volume (6.23%) in sugar beet after 7 d of LN exposure compared to the control (5 mmol/L N). Transcriptomics analysis was confirmed by qRT-PCR for 6 randomly selected microRNAs, and we identified 22 differentially expressed microRNAs (DEMs) in beet root under LN treatment. They were primarily enriched in functions related to binding (1125), ion binding (641), intracellular (437) and intracellular parts (428), and organelles (350) and associated with starch and sucrose metabolism, tyrosine metabolism, pyrimidine metabolism, amino sugar and nucleotide sugar metabolism, and isoquinoline alkaloid biosynthesis, as indicated by the GO and KEGG analyses. Among them, the upregulated miR156a, with conserved sequences, was identified as a key DEM that potentially targets and regulates squamosa promoter-binding-like proteins (SPLs, 104889216 and 104897537) through the microRNA-mRNA network. Overexpression of miR156a (MIR) promoted root growth in transgenic Arabidopsis, increasing the length, surface area, and volume. In contrast, silencing miR156a (STTM) had the opposite effect. Notably, the fresh root weight decreased by 45.6% in STTM lines, while it increased by 27.4% in MIR lines, compared to the wild type (WT). It can be inferred that microRNAs, especially miR156, play crucial roles in sugar beet root’s development and acclimation to LN conditions. They likely facilitate active responses to N deficiency through network regulation, enabling beet roots to take up nutrients from the environment and sustain their vital life processes. Full article

Alkaline coal mine drainage represents one of the most critical issues in the coal industry, driven by complex hydro-biogeochemical processes. However, the interplay of hydrogeochemical and biogeochemical interactions in alkaline coal mine drainage is still poorly understood. To this end, water samples were systematically collected from alkaline coal mine drainage sites from five coal mining areas in Chongqing coal mining district, located in southwestern China. Hydrogeochemical analyses showed that the main water type of the coal mine drainage sample was HCO₃-SO₄~K-Na, which primarily originated from local meteoric water. The microbial community compositions in the studied alkaline coal drainage were critically associated with sulfate, bicarbonate, DOC, nitrate, and pH, and linked to three putative keystone genera via network analysis (Thiothrix, Methylophilaceae_MM1, and an unclassified genus from Comamonadaceae family). Functional predictions from FAPROTAX suggested a high abundance of metabolic pathways involving the oxidation of sulfide and sulfur compounds, potentially underscoring their importance in controlling sulfate enrichment in alkaline coal mine drainage. Interestingly, members of the Methylomonadaceae family (methanotrophs) and the Methylotenera genus (methylotrophs) had positive Spearman correlations with both ammonium and sulfate, potentially inferring that the enhanced activities of methanotrophs might help capture methane in the alkaline coal mine drainage. This study further enhances our comprehension of the intricate interplay between hydrogeochemical and biogeochemical interactions in alkaline coal mine drainage, contributing to the carbon budget. Full article

Despite substantial evidence supporting the efficacy of prebiotics for promoting host health and stress resilience, few experiments present evidence documenting the dynamic changes in microbial ecology and fecal microbially modified metabolites over time. Furthermore, the literature reports a lack of reproducible effects of prebiotics on specific bacteria and bacterial-modified metabolites. The current experiments examined whether consumption of diets enriched in prebiotics (galactooligosaccharides (GOS) and polydextrose (PDX)), compared to a control diet, would consistently impact the gut microbiome and microbially modified bile acids over time and between two research sites. Male Sprague Dawley rats were fed control or prebiotic diets for several weeks, and their gut microbiomes and metabolomes were examined using 16S rRNA gene sequencing and untargeted LC–MS/MS analysis. Dietary prebiotics altered the beta diversity, relative abundance of bacterial genera, and microbially modified bile acids over time. PICRUSt2 analyses identified four inferred functional metabolic pathways modified by the prebiotic diet. Correlational network analyses between inferred metabolic pathways and microbially modified bile acids revealed deoxycholic acid as a potential network hub. All these reported effects were consistent between the two research sites, supporting the conclusion that dietary prebiotics robustly changed the gut microbial ecosystem. Consistent with our previous work demonstrating that GOS/PDX reduces the negative impacts of stressor exposure, we propose that ingesting a diet enriched in prebiotics facilitates the development of a health-promoting gut microbial ecosystem. Full article