Background: Breast cancer (BC) is a significant cause of morbidity and mortality in women. Although the important role of metabolism in the molecular pathogenesis of BC is known, there is still a need for robust metabolomic biomarkers and predictive models that will enable the detection and prognosis of BC. This study aims to identify targeted metabolomic biomarker candidates based on explainable artificial intelligence (XAI) for the specific detection of BC.
Methods: Data obtained after targeted metabolomics analyses using plasma samples from BC patients (n = 102) and healthy controls (n = 99) were used. Machine learning (ML) models based on raw data were developed, then feature selection methods were applied, and the results were compared. SHapley Additive exPlanations (SHAP), an XAI method, was used to clinically explain the decisions of the optimal model in BC prediction.
Results: The results revealed that variable selection increased the performance of ML models in BC classification, and the optimal model was obtained with the logistic regression (LR) classifier after support vector machine (SVM)-SHAP-based feature selection. SHAP annotations of the LR model revealed that Leucine, isoleucine, L-alloisoleucine, norleucine, and homoserine acids were the most important potential BC diagnostic biomarkers. Combining the identified metabolite markers provided robust BC classification measures with precision, recall, and specificity of 89.50%, 88.38%, and 83.67%, respectively.
Conclusion: In conclusion, this study adds valuable information to the discovery of BC biomarkers and underscores the potential of targeted metabolomics-based diagnostic advances in the management of BC.
Thyroid diseases, including functional and neoplastic diseases, bring a huge burden to people's health. Therefore, a timely and accurate diagnosis is necessary. Mass spectrometry (MS) based multi-omics has become an effective strategy to reveal the complex biological mechanisms of thyroid diseases. The exponential growth of biomedical data has promoted the applications of machine learning (ML) techniques to address new challenges in biology and clinical research. In this review, we presented the detailed review of applications of ML for MS-based multi-omics in thyroid disease. It is primarily divided into two sections. In the first section, MS-based multi-omics, primarily proteomics and metabolomics, and their applications in clinical diseases are briefly discussed. In the second section, several commonly used unsupervised learning and supervised algorithms, such as principal component analysis, hierarchical clustering, random forest, and support vector machines are addressed, and the integration of ML techniques with MS-based multi-omics data and its application in thyroid disease diagnosis is explored.
Oleate hydratase (OhyA), a flavoenzyme that catalyzes the hydration of unsaturated fatty acids, has been identified in various Bacillales organisms, including those in the Listeria, Lysinibacillus, Paenibacillus, and Staphylococcus genera. In this study, we combine structural biology with molecular and phylogenetic analyses to investigate the evolutionary dynamics of the OhyA protein family within the Bacillales order. Our evolutionary analysis reveals two distinct OhyA clades (clade I and clade II) within Bacillales that, while sharing catalytic function, exhibit significant genomic and structural differences. Our findings suggest that these OhyA clades originated from independent evolutionary processes through convergent evolution rather than gene duplication. We also show that the evolutionary divergence in OhyA is likely due to intrinsic sequence variations rather than being strictly linked to functional domain changes. Furthermore, within the Staphylococcus genus, we observed that the evolution of the ohyA gene aligns with the species tree, supporting a common ancestral origin. This study enhances our understanding of the impact of evolutionary history on the structure and function of OhyA across the Bacillales order.
Hemorphins are short atypical opioid peptide fragments embedded in the β-chain of hemoglobin. They have received considerable attention recently due to their interaction with opioid receptors. The affinity of hemorphins to opioid receptors μ-opioid receptor (MOR), δ-opioid receptor (DOR), and κ-opioid receptor (KOR) has been well established. However, the underlying binding mode and molecular interactions of hemorphins in opioid receptors remain largely unknown. Here, we report the pattern of interaction of camel and other mammalian hemorphins with DOR. Extensive in silico docking and molecular dynamics simulations were employed to identify intermolecular interactions and binding energies were calculated to determine the affinity of these peptides for DOR. Longer forms of hemorphins - hemorphin-7, hemorphin-6, camel hemorphin-7, and camel hemorphin-6 had strong interactions with DOR. However, camel hemorphin-7 and camel hemorphin-6 had high binding affinity towards DOR. Thus, the findings of this study provide molecular insights into how hemorphins, particularly camel hemorphin variants, could be a therapeutic agent for pain regulation, stress management, and analgesia.
Introduction: Heart failure is a leading global cause of mortality, with ischemic heart failure (IHF) being a major contributor. IHF is primarily driven by coronary artery disease, and its underlying mechanisms are not fully understood, particularly the role of immune responses and inflammation in cardiac muscle remodeling. This study aims to elucidate the immune landscape of heart failure using multi-omics data to identify biomarkers for preventing cardiac fibrosis and disease progression.
Methods: We utilized multi-omics data to elucidate the intricate immune landscape of heart failure at various regulatory levels. Given the substantial size of our transcriptomic dataset, we used diverse machine learning techniques to identify key mRNAs. For smaller datasets such as our proteomic dataset, we applied multilevel data cleansing and enhancement using principles from network biology. This comprehensive analysis led to the development of a scalable, integrated -omics analysis pipeline.
Results: Pleiotrophin (PTN) had shown significant upregulation in multiple datasets and the activation of various molecules associated with dysplastic cardiac remodeling. By synthesizing these data with experimental validations, PTN was identified as a potential biomarker.
Discussion: The present study not only provides a comprehensive perspective on immune dynamics in IHF but also offers valuable insights for the identification of biomarkers, discovery of therapeutic targets, and development of drugs.
Background: Pterygium is a complex ocular surface disease characterized by the abnormal proliferation and growth of conjunctival and fibrovascular tissues at the corneal-scleral margin. Understanding the underlying molecular mechanisms of pterygium is crucial for developing effective diagnostic and therapeutic strategies.
Methods: To elucidate the molecular mechanisms of pterygium, we conducted a differential gene expression analysis between pterygium and normal conjunctival tissues using high-throughput RNA sequencing. We identified differentially expressed genes (DEGs) with statistical significance (adjust p < 0.05, |logFC| > 1). Enrichment analyses were performed to assess the biological processes and signaling pathways associated with these DEGs. Additionally, we utilized weighted correlation network analysis (WGCNA) to select module genes and applied Random Forest (RF) and Support Vector Machine (SVM) algorithms to identify pivotal feature genes influencing pterygium progression. The diagnostic potential of these genes was validated using external datasets (GSE2513 and GSE51995). Immune cell infiltration analysis was conducted using CIBERSORT to compare immune cell populations between pterygium and normal conjunctival tissues. Quantitative PCR (qPCR) was used to confirm the expression levels of the identified feature genes. Furthermore, we identified key miRNAs and candidate drugs targeting these feature genes.
Results: A total of 718 DEGs were identified in pterygium tissues compared to normal conjunctival tissues, with 254 genes showing upregulated expression and 464 genes exhibiting downregulated expression. Enrichment analyses revealed that these DEGs were significantly associated with inflammatory processes and key signaling pathways, notably leukocyte migration and IL-17 signaling. Using WGCNA, RF, and SVM, we identified KRT10 and NGEF as pivotal feature genes influencing pterygium progression. The diagnostic potential of these genes was validated using external datasets. Immune cell infiltration analysis demonstrated significant differences in immune cell populations between pterygium and normal conjunctival tissues, with an increased presence of M1 macrophages and resting dendritic cells in pterygium samples. qPCR analysis confirmed the elevated expression of KRT10 and NGEF in pterygium tissues.
Conclusion: Our findings emphasize the importance of gene expression profiling in unraveling the pathogenesis of pterygium. The identification of pivotal feature gene KRT10 and NGEF provide valuable insights into the molecular mechanisms underlying pterygium progression.
[This corrects the article DOI: 10.3389/fmolb.2021.665199.].
Introduction: Largemouth bass is an economically important farmed freshwater fish species that has delicious meat, no intermuscular thorns, and rapid growth rates. However, the molecular regulatory mechanisms underlying the different growth and developmental stages of this fish have not been reported.
Methods: In this study, we performed histological and transcriptomic analyses on the brain and dorsal muscles of largemouth bass at different growth periods. The brain and muscle tissue were dehydrated, embedded, sliced and stained with hematoxylin-eosin. Images were captured under a microscope and acquired using a microphotographic system. Differential expression between groups was analyzed using DESeq2. GO functional analysis and KEGG pathway analysis were then performed for differentially expressed genes. RT-qPCR validates the reliability of transcriptome sequencing data.
Result: Smaller fish had more new muscle fiber numbers and wider intermuscular spaces compared to big specimens. Axons and nerve fibers were more pronounced in the telencephalons of big fish than in small fish. A total of 19,225 differentially expressed genes (DEGs) were detected in the muscle tissue, among which 7,724 were upregulated and 11,501 were downregulated, while a total of 5,373 DEGs were detected in the brain, among which 2,923 were upregulated and 2,450 were downregulated. GO and KEGG enrichment analyses indicated that nucleic acid binding, cytoskeletal motor activity, DNA binding, circadian rhythm, glycolysis/gluconeogenesis, and osteoclast differentiation were related to brain development while binding, cytoskeletal protein binding, biological processes, c-type lectin receptors, mitogen-activated protein kinase (MAPK) signaling pathways, and osteoclast differentiation were related to muscle growth. Stat3, pparg, akt1, mapk3, and mapk1 genes were mainly involved in the growth and development of largemouth bass.
Conclusion: These results provide novel perspectives for deepening our understanding of the mechanisms underlying the growth and development and performing genetic selection in largemouth bass.
Introduction: The plasma membrane-bound protein, multi-drug resistance-associated protein 4 (MRP4/ABCC4), has gained attention for its pivotal role in facilitating the efflux of a wide range of endogenous and xenobiotic molecules. Its significance in adipogenesis and fatty acid metabolism has been brought to light by recent studies. Notably, research on ABCC4 knockout (ABCC4 -/- ) mice has established a link between the absence of ABCC4 and the development of obesity and diabetes. Nevertheless, the specific contribution of ABCC4 within adipose tissue remains largely unexplored.
Methods: To address this gap, we conducted a study to elucidate the role of the ABCC4 transporter in mature adipocytes, using siRNA constructs to silence its gene function.
Results: The successful knockdown of ABCC4 significantly altered lipid status and adipogenic gene expression in mature 3T3-L1 adipocytes. Intriguingly, this knockdown also altered the gene expression patterns of other ABCC transporter family members in 3T3-L1 cells. The downregulation of ABCC5 expression was particularly noteworthy, suggesting potential crosstalk between ABCC transporters in mature adipocytes. Additionally, knocking down ABCC5 resulted in significantly higher adipogenic and lipogenic gene expression levels. Oil Red O staining confirmed increased lipid accumulation following the knockdown of ABCC4 and ABCC5. Surprisingly, the simultaneous knockdown of both transporters did not show a cumulative effect on adipogenesis, rather it led to higher levels of intracellular cAMP and extracellular prostaglandin metabolite, both of which are essential signaling molecules in adipogenesis.
Conclusion: These results highlight the complex interplay between ABCC4 and ABCC5 transporters in adipocyte function and suggest their individual contributions toward obesity and related disorders.
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