GO Enrichment Analysis for Differential Proteomics Using ProteoRE

Utilizing integrated bioinformatics and machine learning approaches to elucidate biomarkers linking sepsis to fatty acid metabolism-associated genes

Sepsis, characterized as a systemic inflammatory response triggered by the invasion of pathogens, represents a continuum that may escalate from mild systemic infection to severe sepsis, potentially resulting in septic shock and multiple organ dysfunction syndrome. Advancements in lipidomics and metabolomics have unveiled the complex role of fatty acid metabolism (FAM) in both healthy and pathological states. Leveraging bioinformatics, this investigation aimed to identify and substantiate potential FAM-related genes (FAMGs) implicated in sepsis. The approach encompassed a differential expression analysis across a pool of 36 candidate FAMGs. GSEA and GSVA were employed to assess the biological significance and pathways associated with these genes. Furthermore, Lasso regression and SVM-RFE methodologies were implemented to determine key hub genes and assess the diagnostic prowess of nine selected FAMGs in sepsis identification. The study also investigated the correlation between these hub FAMGs. Validation was conducted through expression-level analysis using the GSE13904 and GSE65682 datasets. The study identified 13 sepsis-associated FAMGs, including ABCD2, ACSL3, ACSM1, ACSS1, ACSS2, ACOX1, ALDH9A1, ACACA, ACACB, FASN, OLAH, PPT1, and ELOVL4. As demonstrated by functional enrichment analysis results, these genes played key roles in several critical biological pathways, such as the Peroxisome, PPAR signaling pathway, and Insulin signaling pathway, all of which are intricately linked to metabolic regulation and inflammatory responses. The diagnostic potential of these FAMGs was further highlighted. In short, the expression patterns of these FAMGs c effectively distinguished sepsis cases from non-septic controls, which suggested that they may be promising biomarkers for early sepsis detection. This discovery not only enhanced our understanding of the molecular mechanisms underpinning sepsis but also paved the way for developing novel diagnostic tools and therapeutic strategies targeting metabolic dysregulation in septic patients. This research sheds light on 13 FAMGs associated with sepsis, providing valuable insights into novel biomarkers for this condition and facilitating the monitoring of its progression. These findings underscore the significance of purine metabolism in sepsis pathogenesis and open avenues for further investigation into therapeutic targets.

Metabolism-associated molecular classification and prognosis signature of head and neck squamous cell carcinoma

Although the fundamental processes and chemical changes in metabolic programs have been elucidated in many cancers, the expression patterns of metabolism-related genes in head and neck squamous cell carcinoma (HNSCC) remain unclear. The mRNA expression profiles from the Cancer Genome Atlas included 502 tumour and 44 normal samples were extracted. We explored the biological functions and prognosis roles of metabolism-associated genes in patients with HNSCC. The results indicated that patients with HNSCC could be divided into three molecular subtypes (C1, C2 and C3) based on 249 metabolism-related genes. There were markedly different clinical characteristics, prognosis outcomes, and biological functions among the three subtypes. Different molecular subtypes also have different tumour microenvironments and immune infiltration levels. The established prognosis model with 17 signature genes could predict the prognosis of patients with HNSCC and was validated using an independent cohort dataset. An individual risk scoring tool was developed using the risk score and clinical parameters; the risk score was an independent prognostic factor for patients with HNSCC. Different risk stratifications have different clinical characteristics, biological features, tumour microenvironments and immune infiltration levels. Our study could be used for clinical risk management and to help conduct precision medicine for patients with HNSCC.

Analysis of the mechanism of exogenous indole-3-acetic acid on the enrichment of d-glucose in Chlorococcum humicola cultured by sludge extracts

Addressing problems of high organic toxicity in the wastewater treatment process, microalgae have been used to reduce the toxicity in sludge and to synthesize non-toxic and recoverable biomass of resources. Phytohormone is a core regulator of plant growth and current research has generally focused on their promotion of cell division and cell expansion. Effects of phytohormone on the enrichment mechanism of microalgae directional polysaccharides accumulation remain poorly elucidated. This study was carried out to investigate the effects of exogenous indole-3-acetic acid (IAA) on growth characteristics, biomass accumulation, and photosynthesis capacity of Chlorococcum humicola cultured in sludge extract and further find the d-glucose enrichment mechanism of it through proteomic. The results indicated that the optimal culture conditions were the 75 % sludge extract and 25 % selenite enrichment (SE) medium with 5 × 10-6 mol/L indole-3-acetic acid. Polysaccharides increased significantly from day 20 and accumulated to (326.59 ± 13.06) mg/L on day 30, in which the d-glucose proportion increased to 61.53 %. Most notably, proteomic tests were performed and found that the photosynthesis-related proteins including the differential proteins of photosystem electron transport, ATP and NADPH catalytic synthesis were significantly up-regulated. At the end of the path, three pathways of d-glucose enrichment with α-d-Glucose-1P as a precursor were summarized through indole-3-acetic acid activation on amylase, endoglucanase and Beta-glucosidase, etc. These results provide insights to explore the directed enrichment of biomass in Chlorococcum humicola by indole-3-acetic acid.

A Galaxy of informatics resources for MS-based proteomics

INTRODUCTION

Continuous advances in mass spectrometry (MS) technologies have enabled deeper and more reproducible proteome characterization and a better understanding of biological systems when integrated with other 'omics data. Bioinformatic resources meeting the analysis requirements of increasingly complex MS-based proteomic data and associated multi-omic data are critically needed. These requirements included availability of software that would span diverse types of analyses, scalability for large-scale, compute-intensive applications, and mechanisms to ease adoption of the software.

AREAS COVERED

The Galaxy ecosystem meets these requirements by offering a multitude of open-source tools for MS-based proteomics analyses and applications, all in an adaptable, scalable, and accessible computing environment. A thriving global community maintains these software and associated training resources to empower researcher-driven analyses.

EXPERT OPINION

The community-supported Galaxy ecosystem remains a crucial contributor to basic biological and clinical studies using MS-based proteomics. In addition to the current status of Galaxy-based resources, we describe ongoing developments for meeting emerging challenges in MS-based proteomic informatics. We hope this review will catalyze increased use of Galaxy by researchers employing MS-based proteomics and inspire software developers to join the community and implement new tools, workflows, and associated training content that will add further value to this already rich ecosystem.

Molecular Mechanism of the Role of Apigenin in the Treatment of Hyperlipidemia: A Network Pharmacology Approach

The therapeutic effect of apigenin (APG) on hyperlipidemia was investigated using network pharmacology combined with molecular docking strategy, and the potential targets of APG in the treatment of hyperlipidemia were explored. Genetic Ontology Biological Process (GOBP) and Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway enrichment analysis of common targets were performed. Then, molecular docking was used to predict the binding mode of APG to the target. Finally, Sprague Dawley rats were used to establish a hyperlipidemia model. The expression levels of insulin (INS) and vascular endothelial growth factor A (VEGFA) mRNA in each group were detected by quantitative reverse transcription-polymerase chain reaction. Network pharmacological studies revealed that the role of APG in the treatment of hyperlipidemia was through the regulation of INS, VEGFA, tumor necrosis factor, epidermal growth factor receptor, matrix metalloprotein 9, and other targets, as well as through the regulation of the hypoxia-inducible factor 1 (HIF-1) signaling pathway, fluid shear stress, and atherosclerosis signaling pathways, vascular permeability; APG also participated in the regulation of glucose metabolism and lipid metabolism, and acted on vascular endothelial cells, and regulated vascular tone. Molecular docking showed that APG binds to the target with good efficiency. Experiments showed that after APG treatment, the expression levels of INS and VEGFA mRNA in the model group were significantly decreased (p<0.01). In conclusion, APG has multiple targets and affects pathways involved in the treatment of hyperlipidemia by regulating the HIF-1 signaling pathway, fluid shear stress, and the atherosclerosis pathway.

The Eutopic Endometrium Proteome in Endometriosis Reveals Candidate Markers and Molecular Mechanisms of Physiopathology

Endometriosis is a common chronic gynaecological disease causing various symptoms, such as infertility and chronic pain. The gold standard for its diagnosis is still laparoscopy and the biopsy of endometriotic lesions. Here, we aimed to compare the eutopic endometrium from women with or without endometriosis to identify proteins that may be considered as potential biomarker candidates. Eutopic endometrium was collected from patients with endometriosis (n = 4) and women without endometriosis (n = 5) during a laparoscopy surgery during the mid-secretory phase of their menstrual cycle. Total proteins from tissues were extracted and digested before LC-MS-MS analysis. Among the 5301 proteins identified, 543 were differentially expressed and enriched in two specific KEGG pathways: focal adhesion and PI3K/AKT signaling. Integration of our data with a large-scale proteomics dataset allowed us to highlight 11 proteins that share the same trend of dysregulation in eutopic endometrium, regardless of the phase of the menstrual cycle. Our results constitute the first step towards the identification of potential promising endometrial diagnostic biomarkers. They provide new insights into the mechanisms underlying endometriosis and its etiology. Our results await further confirmation on a larger sample cohort.

ProMetIS, deep phenotyping of mouse models by combined proteomics and metabolomics analysis

Genes are pleiotropic and getting a better knowledge of their function requires a comprehensive characterization of their mutants. Here, we generated multi-level data combining phenomic, proteomic and metabolomic acquisitions from plasma and liver tissues of two C57BL/6 N mouse models lacking the Lat (linker for activation of T cells) and the Mx2 (MX dynamin-like GTPase 2) genes, respectively. Our dataset consists of 9 assays (1 preclinical, 2 proteomics and 6 metabolomics) generated with a fully non-targeted and standardized approach. The data and processing code are publicly available in the ProMetIS R package to ensure accessibility, interoperability, and reusability. The dataset thus provides unique molecular information about the physiological role of the Lat and Mx2 genes. Furthermore, the protocols described herein can be easily extended to a larger number of individuals and tissues. Finally, this resource will be of great interest to develop new bioinformatic and biostatistic methods for multi-omics data integration.