Development and Validation of a Proximity Labeling Fusion Protein Construct to Identify the Protein-Protein Interactions of Transcription Factors.

Dynamic interactions between transcription factors govern changes in gene expression that mediate changes in cell state accompanying injury response and regeneration. Transcription factors frequently function as obligate dimers whose activity is often modulated by post-translational modifications. These critical and often transient interactions are not easily detected by traditional methods to investigate protein-protein interactions. This chapter discusses the design and validation of a fusion protein involving a transcription factor tethered to a proximity labeling ligase, APEX2. In this technique, proteins are biotinylated within a small radius of the transcription factor of interest, regardless of time of interaction. Here we discuss the validations required to ensure proper functioning of the transcription factor proximity labeling tool and the sample preparation of biotinylated proteins for mass spectrometry analysis of putative protein interactors.

Interaction Analysis by Fragment Molecular Orbital Method for Drug Discovery Research.

The use of computational methods in drug discovery research has increased substantially in recent years. Computational chemistry techniques, such as quantum chemical calculations and molecular dynamics simulations, continue to be widely used. In this review, we focused on drug discovery-related studies that employ fragment molecular orbital methods. Furthermore, we focused on inhibitor discovery, protein-protein interaction analysis, including antigen-antibody interaction analysis, and integration with molecular dynamics simulations.

A Novel Tetravalent CD95/Fas Fusion Protein With Superior CD95L/FasL Antagonism.

Inhibition of CD95/Fas activation is currently under clinical investigation as a therapy for glioblastoma multiforme and preclinical studies suggest that disruption of the CD95-CD95L interaction could also be a strategy to treat inflammatory and neurodegenerative disorders. Besides neutralizing anti-CD95L/FasL antibodies, mainly CD95ed-Fc, a dimeric Fc fusion protein of the extracellular domain of CD95 (CD95ed), is used to prevent CD95 activation. In view of the fact that full CD95 activation requires CD95L-induced CD95 trimerization and clustering of the resulting liganded CD95 trimers, we investigated whether fusion proteins of the extracellular domain of CD95 with a higher valency than CD95ed-Fc have an improved CD95L-neutralization capacity. We evaluated an IgG1(N297A)-based tetravalent CD95ed fusion protein which was obtained by replacing the variable domains of IgG1(N297A) with CD95ed (CD95ed-IgG1(N297A)) and a hexavalent variant obtained by fusion of CD95ed with a TNC-Fc(DANA) scaffold (CD95ed-TNC-Fc(DANA)) promoting hexamerization. The established N297A and DANA mutations were used to minimize FcγR binding of the constructs under maintenance of neonatal Fc receptor (FcRn) binding. Size exclusion high-performance liquid chromatography indicated effective assembly of CD95ed-IgG1(N297A). More important, CD95ed-IgG1(N297A) was much more efficient than CD95ed-Fc in protecting cells from cell death induction by human and murine CD95L. Surprisingly, despite its hexavalent structure, CD95ed-TNC-Fc(DANA) displayed an at best minor improvement of the capacity to neutralize CD95L suggesting that besides valency, other factors, such as spatial organization and agility of the CD95ed domains, play also a role in neutralization of CD95L trimers by CD95ed fusion proteins. More studies are now required to evaluate the superior CD95L-neutralizing capacity of CD95ed-IgG1(N297A) in vivo.

Allosteric site identification, virtual screening and discovery of a sulfonamide Hsp110-STAT3 interaction inhibitor for the treatment of hypoxic pulmonary arterial hypertension.

Pulmonary arterial hypertension (PAH) is a severe pulmonary vascular disorder marked by vascular remodeling, which is linked to the malignant phenotypes of pulmonary vascular cells. The prevailing therapeutic approaches for PAH tend to neglect the potential role of vascular remodeling, leading to the clinical prognosis remains poor. Previously, we first demonstrated that heat shock protein (Hsp110) was significantly activated to boost Hsp110-STAT3 interaction, which resulted in abnormal proliferation and migration of human pulmonary arterial endothelial cells (HPAECs) under hypoxia. In the present study, we initially postulated the allosteric site of Hsp110, performed a virtual screening and biological evaluation studies to discover novel Hsp110-STAT3 interaction inhibitors. Here, we identified compound 29 (AN-329/43448068) as the effective inhibitor of HPAECs proliferation and the Hsp110-STAT3 association with good druggability. In vitro, 29 significantly impeded the chaperone function of Hsp110 and the malignant phenotypes of HPAECs. In vivo, 29 remarkably attenuated pulmonary vascular remodeling and right ventricular hypertrophy in hypoxia-induced PAH rats (i.g). Altogether, our data support the conclusion that it not only provides a novel lead compound but also presents a promising approach for subsequent inhibitor development targeting Hsp110-STAT3 interaction.

TMEM100 acts as a TAK1 receptor that prevents pathological cardiac hypertrophy progression.

Pathological cardiac hypertrophy is the primary cause of heart failure, yet its underlying mechanisms remain incompletely understood. Transmembrane protein 100 (TMEM100) plays a role in various disorders, such as nervous system disease, pain and tumorigenesis, but its function in pathological cardiac hypertrophy is still unknown. In this study, we observed that TMEM100 is upregulated in cardiac hypertrophy. Functional investigations have shown that adeno-associated virus 9 (AAV9) mediated-TMEM100 overexpression mice attenuates transverse aortic constriction (TAC)-induced cardiac hypertrophy, including cardiomyocyte enlargement, cardiac fibrosis, and impaired heart structure and function. We subsequently demonstrated that adenoviral TMEM100 (AdTMEM100) mitigates phenylephrine (PE)-induced cardiomyocyte hypertrophy and downregulates the expression of cardiac hypertrophic markers in vitro, whereas TMEM100 knockdown exacerbates cardiomyocyte hypertrophy. The RNA sequences of the AdTMEM100 group and control group revealed that TMEM100 was involved in oxidative stress and the MAPK signaling pathway after PE stimulation. Mechanistically, we revealed that the transmembrane domain of TMEM100 (amino acids 53-75 and 85-107) directly interacts with the C-terminal region of TAK1 (amino acids 1-300) and inhibits the phosphorylation of TAK1 and its downstream molecules JNK and p38. TAK1-binding-defective TMEM100 failed to inhibit the activation of the TAK1-JNK/p38 pathway. Finally, the application of a TAK1 inhibitor (iTAK1) revealed that TAK1 is necessary for TMEM100-mediated cardiac hypertrophy. In summary, TMEM100 protects against pathological cardiac hypertrophy through the TAK1-JNK/p38 pathway and may serve as a promising target for the treatment of cardiac hypertrophy.

A Systems Biology Approach to the Pathogenesis of Celiac Disease: Identification of Potential Protective and Promoting Mechanisms.

Background: Celiac disease (CeD) is an autoimmune enteropathy triggered by dietary gluten. Almost 90% of CeD patients have HLA-DQ2 or -DQ8 haplotypes. As a high proportion of first-degree relatives (FDRs) of CeD patients have the same haplotype, it is assumed that they are at a higher risk of disease development than the general population. Nevertheless, the prevalence of CeD among FDRs is considerably low (7.5%). Materials and Methods: In order to figure out this discrepancy, a microarray dataset of intestinal mucosal biopsies of CeD patients, FDRs, and control groups was reanalyzed, and a protein-protein interaction network was constructed. Results: Principal component analysis showed that CeD and FDR groups are far away in terms of gene expression. Comparing differentially expressed genes of both networks demonstrated inverse expression of some genes mainly related to cell cycle mechanisms. Moreover, analysis of the modular structures of up- and downregulated gene networks determined activation of protein degradation mechanisms and inhibition of ribosome-related protein synthesis in celiac patients with an upside-down pattern in FDRs. Conclusions: The top-down systems biology approach determined some regulatory pathways with inverse function in CeD and FDR groups. These genes and molecular mechanisms could be a matter of investigation as potential druggable targets or prognostic markers in CeD.

STAT3 interactome predicts presence of proteins that regulates immune system in oral squamous cell carcinoma.

OBJECTIVES: Signal transducer and activator of transcription 3 (STAT3) is one of the key proliferation mechanism-related proteins that helps in oral squamous cell carcinoma (OSCC) progression. Immune evasion by STAT3 is mediated by the JAK2/STAT3/PDL1 signaling axis. Based on previous findings, we hypothesized that STAT3-binding partners participate in the inhibition of anti-tumor activity in OSCC. METHODS: A 3D cancer-immune co-culture model was constructed using oral cancer cell lines SCC4, SCC9, SCC25, and CAL27 and normal oral cell line OKF6. The cells were co-cultured with natural killer (NK-92) and Jurkat cells. The target protein STAT3 was chosen based on SWATH data, and co-immunoprecipitation (Co-IP)-based proteomics was conducted. The Co-IP LC-MS/MS output was analyzed to determine the protein interaction network, gene ontology, pathway analysis, and protein cluster annotation. RESULTS: STAT3 in oral cancer cell lines interacts with the epidermal growth factor receptor (EGFR) and other proteins that participate in proliferation and immune mechanisms. Proteome analysis showed that some STAT3-binding proteins found in this study are known immune system regulators. CONCLUSION: Overall, STAT3 interactive proteins regulate the immune system in oral squamous cell carcinoma cells.

Identification of molecular subtypes of dementia by using blood-proteins interaction-aware graph propagational network.

Plasma protein biomarkers have been considered promising tools for diagnosing dementia subtypes due to their low variability, cost-effectiveness, and minimal invasiveness in diagnostic procedures. Machine learning (ML) methods have been applied to enhance accuracy of the biomarker discovery. However, previous ML-based studies often overlook interactions between proteins, which are crucial in complex disorders like dementia. While protein-protein interactions (PPIs) have been used in network models, these models often fail to fully capture the diverse properties of PPIs due to their local awareness. This drawback increases the chance of neglecting critical components and magnifying the impact of noisy interactions. In this study, we propose a novel graph-based ML model for dementia subtype diagnosis, the graph propagational network (GPN). By propagating the independent effect of plasma proteins on PPI network, the GPN extracts the globally interactive effects between proteins. Experimental results showed that the interactive effect between proteins yielded to further clarify the differences between dementia subtype groups and contributed to the performance improvement where the GPN outperformed existing methods by 10.4% on average.

Integrating network analysis with differential expression to uncover therapeutic and prognostic biomarkers in esophageal squamous cell carcinoma.

Introduction: Esophageal squamous cell carcinoma (ESCC) accounts for over 90% of all esophageal tumors. However, the molecular mechanism underlying ESCC development and prognosis remains unclear, and there are still no effective molecular biomarkers for diagnosing or predicting the clinical outcome of patients with ESCC. Here, we used bioinformatics analysis to identify potential biomarkers and therapeutic targets for ESCC. Methodology: Differentially expressed genes (DEGs) between ESCC and normal esophageal tissue samples were obtained by comprehensively analyzing publicly available RNA-seq datasets from the TCGA and GTEX. Gene Ontology (GO) annotation and Reactome pathway analysis identified the biological roles of the DEGs. Moreover, the Cytoscape 3.10.1 platform and subsidiary tools such as CytoHubba were used to visualize the DEGs' protein-protein interaction (PPI) network and identify hub genes, Furthermore our results are validated by using Single-cell RNA analysis. Results: Identification of 2524 genes exhibiting altered expression enriched in pathways including keratinization, epidermal cell differentiation, G alpha(s) signaling events, and biological process of cell proliferation and division, extracellular matrix (ECM) disassembly, and muscle function. Moreover, upregulation of hallmarks E2F targets, G2M checkpoints, and TNF signaling. CytoHubba revealed 20 hub genes that had a valuable influence on the progression of ESCC in these patients. Among these, the high expression levels of four genes, CDK1 MAD2L1, PLK1, and TOP2A, were associated with critical dependence for cell survival in ESCC cell lines, as indicated by CRISPR dependency scores, gene expression data, and cell line metadata. We also identify the molecules targeting these essential hub genes, among which GSK461364 is a promising inhibitor of PLK1, BMS265246, and Valrubicin inhibitors of CDK1 and TOP2A, respectively. Moreover, we identified that elevated expression of MMP9 is associated with worse overall survival in ESCC patients, which may serve as potential prognostic biomarker or therapeutic target for ESCC. The single-cell RNA analysis showed MMP9 is highly expressed in myeloid, fibroblast, and epithelial cells, but low in T cells, endothelial cells, and B cells. This suggests MMP9's role in tumor progression and matrix remodeling, highlighting its potential as a prognostic marker and therapeutic target. Discussion: Our study identified key hub genes in ESCC, assessing their potential as therapeutic targets and biomarkers through detailed expression and dependency analyses. Notably, MMP9 emerged as a significant prognostic marker with high expression correlating with poor survival, underscoring its potential for targeted therapy. These findings enhance our understanding of ESCC pathogenesis and highlight promising avenues for treatment.

Plasma proteomics for risk prediction of Alzheimer's disease in the general population.

We aimed to develop and validate a protein risk score for predicting Alzheimer's disease (AD) and compare its performance with a validated clinical risk model (Cognitive Health and Dementia Risk Index for AD [CogDrisk-AD]) and apolipoprotein E (APOE) genotypes. The development cohort, consisting of 35,547 participants from England in the UK Biobank, was randomly divided into a 7:3 training-testing ratio. The validation cohort included 4667 participants from Scotland and Wales in the UK Biobank. In the training set, an AD protein risk score was constructed using 31 proteins out of 2911 proteins. In the testing set, the AD protein risk score had a C-index of 0.867 (95% CI, 0.828, 0.906) for AD prediction, followed by CogDrisk-AD risk factors (C-index, 0.856; 95% CI, 0.823, 0.889), and APOE genotypes (C-index, 0.705; 95% CI, 0.660, 0.750). Adding the AD protein risk score to CogDrisk-AD risk factors (C-index increase, 0.050; 95% CI, 0.008, 0.093) significantly improved the predictive performance for AD. However, adding CogDrisk-AD risk factors (C-index increase, 0.040; 95% CI, -0.007, 0.086) or APOE genotypes (C-index increase, 0.000; 95% CI, -0.054, 0.055) to the AD protein risk score did not significantly improve the predictive performance for AD. The top 10 proteins with the highest coefficients in the AD protein risk score contributed most of the predictive power for AD risk. These results were verified in the external validation cohort. EGFR, GFAP, and CHGA were identified as key proteins within the protein network. Our result suggests that the AD protein risk score demonstrated a good predictive performance for AD risk.

Identification of SLC25A46 interaction interfaces with mitochondrial membrane fusogens Opa1 and Mfn2.

Mitochondrial fusion requires the sequential merger of four bilayers to two. The outer-membrane solute carrier protein SLC25A46 interacts with both the outer and inner-membrane dynamin family GTPases Mfn1/2 and Opa1. While SLC25A46 levels are known to affect mitochondrial morphology, how SLC25A46 interacts with Mfn1/2 and Opa1 to regulate membrane fusion is not understood. In this study, we use crosslinking mass-spectrometry and AlphaFold 2 modeling to identify interfaces mediating a SLC25A46 interactions with Opa1 and Mfn2. We reveal that the bundle signaling element of Opa1 interacts with SLC25A46, and present evidence of a Mfn2 interaction involving the SLC25A46 cytosolic face. We validate these newly identified interaction interfaces and show that they play a role in mitochondrial network maintenance.

Structural Basis for Evasion of New SARS-CoV-2 Variants from the Potent Virus-Neutralizing Nanobody Targeting the S-Protein Receptor-Binding Domain.

COVID-19 has caused millions of deaths and many times more infections worldwide, emphasizing the unpreparedness of the global health system in the face of new infections and the key role for vaccines and therapeutics, including virus-neutralizing antibodies, in prevention and containment of the disease. Continuous evolution of the SARS-CoV-2 coronavirus has been causing its new variants to evade the action of the immune system, which highlighted the importance of detailed knowledge of the epitopes of already selected potent virus-neutralizing antibodies. A single-chain antibody ("nanobody") targeting the SARS-CoV-2 receptor-binding domain (RBD), clone P2C5, had exhibited robust virus-neutralizing activity against all SARS-CoV-2 variants and, being a major component of the anti-COVID-19 formulation "GamCoviMab", had successfully passed Phase I of clinical trials. However, after the emergence of the Delta and XBB variants, a decrease in the neutralizing activity of this nanobody was observed. Here we report on the successful crystal structure determination of the RBD:P2C5 complex at 3.1 Å, which revealed the intricate protein-protein interface, sterically occluding full ACE2 receptor binding by the P2C5-neutralized RBD. Moreover, the structure revealed the developed RBD:P2C5 interface centered around residues Leu452 and Phe490, thereby explaining the evasion of the Delta or Omicron XBB, but not Omicron B.1.1.529 variant, as a result of the single L452R or F490S mutations, respectively, from the action of P2C5. The structure obtained is expected to foster nanobody engineering in order to rescue neutralization activity and will facilitate epitope mapping for other neutralizing nanobodies by competition assays.

Structure-activity relationship study of small-molecule inhibitor of Atg12-Atg3 protein-protein interaction.

Autophagy is a catabolic process that was described to play a critical role in advanced stages of cancer, wherein it maintains tumor cell homeostasis and growth by supplying nutrients. Autophagy is also described to support alternative cellular trafficking pathways, providing a non-canonical autophagy-dependent inflammatory cytokine secretion mechanism. Therefore, autophagy inhibitors have high potential in the treatment of cancer and acute inflammation. In our study, we identified compound 1 as an inhibitor of the ATG12-ATG3 protein-protein interaction. We focused on the systematic modification of the original hit 1, a casein kinase 2 (CK2) inhibitor, to find potent disruptors of ATG12-ATG3 protein-protein interaction. A systematic modification of the hit structure led us to a wide plethora of compounds that maintain its ATG12-ATG3 inhibitory activity, which could act as a viable starting point to design new compounds with diverse therapeutic applications.

Unravelling the role of NDUFAF4 in Colon Cancer: Insights from multi-omics analysis.

Colon cancer is a significant public health issue, and a deeper understanding of the molecular fundamentals [16] ehind is required to improve sensitivity and curability. This research explored the gene NDUFAF4 as a target of concern due to its link to a mitochondrial function and protein "Relatively of liver tumorigenesis", which remains unclear is attributable to its inclusion into the complex I (CI) pathway. The gene ontology analysis, in turn, showed that NDUFAF4 is a key player in several critical biological phases linked to mitochondrial function and energy metabolism. Furthermore, survival analysis displayed that there was a strong correlation between NDUFAF4 expression and the patients' longevity suggesting that this factor may be important in colon cancer prognosis as well. The TCGA data proved that NDUFAF4 is elevated in colon cancer making the results of the analysis reported credible. All of the above justified the understanding of the role and importance of NDUFAF4 in treating each colon cancer patient as a molecular target. The findings help in understanding the colon cancer pathogenesis and suggest ways for developing more efficient diagnosis and treatment of the disease. SIGNIFICANCE: This research explored the gene NDUFAF4 as a target of concern due to its link to a mitochondrial function and protein "Relatively of liver tumorigenesis", which remains unclear is attributable to its inclusion into the complex I (CI) pathway. Using a comprehensive approach to Gene Ontology analysis, Protein-Protein Interaction network modelling, survival analysis, KEGG pathway analysis, and validation using TCGA data, we identified the activities of NDUFAF4 in colon cancer. The Gene Ontology analysis, in turn, showed that NDUFAF4 is a key player in several critical biological phases linked to mitochondrial function and energy metabolism. The construction of the PPI network illustrates the interactors of NDUFAF4, the functional association protein within the cellular regulatory networks. In addition, survival analysis indicated that there was a considerable relationship between the expression of NDUFAF4 and patient survival, indicating its potential role as a prognostic factor in colon cancer. KEGG pathway analysis suggested that NDUFAF4 plays a role in thermogenesis and mitochondrial biogenesis, biological processes that should be targeted due to their implication in cellular metabolism and cancer onset. The use of TCGA information confirmed the upregulation of NDUFAF4 in colon cancer, thus making the findings of the analysis reported dependable. Overall, our study provided necessary information on the role and significance of NDUFAF4, a potential molecular target in colon cancer cases. These present findings enhance our knowledge of the pathogenesis of colon cancer and open new opportunities for designing novel diagnostic and therapeutic approaches to improve patient outcomes.

ATP synthase subunit ATP5B interacts with TGEV Nsp2 and acts as a negative regulator of TGEV replication.

Coronavirus nonstructural protein 2 (Nsp2) is regarded as a virulence determinant and plays a critical role in virus replication, and innate immunity. Screening and identifying host cell proteins that interact with viral proteins is an effective way to reveal the functions of viral proteins. In this study, the host proteins that interacted with transmissible gastroenteritis virus (TGEV) Nsp2 were identified using immunoprecipitation combined with LC-MS/MS. 77 host cell proteins were identified as putative Nsp2 interaction host cell proteins and a protein-protein interaction (PPI) was constructed. The identified proteins were found to be associated with various subcellular locations and functional categories through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. It is hypothesized that the host cell proteins interacting with TGEV Nsp2 are mainly involved in the formation of the cytoplasmic translation initiation complex, mRNA binding, ribosomes, and proteasomes. Among these, the ATP5B, a core subunit of the mitochondrial ATP synthase was further studied. The Coimmunoprecipitation (Co-IP) and indirect immunofluorescence (IFA) results confirmed that TGEV Nsp2 interacted with ATP5B. Furthermore, the downregulation of ATP5B expression was found to promote TGEV replication, suggesting that ATP5B might function as a negative regulator of TGEV replication. Collectively, our results offer additional insights into the functions of Nsp2 and provide a novel antiviral target against TGEV.

Plant Phytochrome Interactions Decode Light and Temperature Signals.

Plant phytochromes perceive red and far-red light to elicit adaptations to the changing environment. Downstream physiological responses revolve around red-light-induced interactions with phytochrome-interacting factors (PIF). Phytochromes double as thermoreceptors, owing to the pronounced temperature dependence of thermal reversion from the light-adapted Pfr to the dark-adapted Pr state. Here, we assess whether thermoreception may extend to the phytochrome:PIF interactions. While the association between Arabidopsis (Arabidopsis thaliana) PHYTOCHROME B (PhyB) and several PHYTOCHROME-INTERACTING FACTOR (PIF) variants moderately accelerates with temperature, the dissociation does more so, thus causing net destabilization of the phytochrome:PIF complex. Markedly different temperature profiles of PIF3 and PIF6 might underlie stratified temperature responses in plants. Accidentally, we identify a photoreception mechanism under strong continuous light, where the extent of phytochrome:PIF complexation decreases with red-light intensity rather than increases. Mathematical modeling rationalizes this attenuation mechanism and ties it to rapid red-light-driven Pr⇄Pfr interconversion and complex dissociation out of Pr. Varying phytochrome abundance, e.g., during diurnal and developmental cycles, and interaction dynamics, e.g., across different PIFs, modify the nature and extent of attenuation, thus permitting light-response profiles more malleable than possible for the phytochrome Pr⇄Pfr interconversion alone. Our data and analyses reveal a photoreception mechanism with implications for plant physiology, optogenetics, and biotechnological applications.

Elucidating the assembly of gas vesicles by systematic protein-protein interaction analysis.

Gas vesicles (GVs) are gas-filled microbial organelles formed by unique 3-nm thick, amphipathic, force-bearing protein shells, which can withstand multiple atmospheric pressures and maintain a physically stable air bubble with megapascal surface tension. However, the molecular process of GV assembly remains elusive. To begin understanding this process, we have devised a high-throughput in vivo assay to determine the interactions of all 11 proteins in the pNL29 GV operon. Complete or partial deletions of the operon establish interdependent relationships among GV proteins during assembly. We also examine the tolerance of the GV assembly process to protein mutations and the cellular burdens caused by GV proteins. Clusters of GV protein interactions are revealed, proposing plausible protein complexes that are important for GV assembly. We anticipate our findings will set the stage for designing GVs that efficiently assemble in heterologous hosts during biomedical applications.

Knowledge-slanted random forest method for high-dimensional data and small sample size with a feature selection application for gene expression data.

The use of prior knowledge in the machine learning framework has been considered a potential tool to handle the curse of dimensionality in genetic and genomics data. Although random forest (RF) represents a flexible non-parametric approach with several advantages, it can provide poor accuracy in high-dimensional settings, mainly in scenarios with small sample sizes. We propose a knowledge-slanted RF that integrates biological networks as prior knowledge into the model to improve its performance and explainability, exemplifying its use for selecting and identifying relevant genes. knowledge-slanted RF is a combination of two stages. First, prior knowledge represented by graphs is translated by running a random walk with restart algorithm to determine the relevance of each gene based on its connection and localization on a protein-protein interaction network. Then, each relevance is used to modify the selection probability to draw a gene as a candidate split-feature in the conventional RF. Experiments in simulated datasets with very small sample sizes ( n ≤ 30 ) comparing knowledge-slanted RF against conventional RF and logistic lasso regression, suggest an improved precision in outcome prediction compared to the other methods. The knowledge-slanted RF was completed with the introduction of a modified version of the Boruta feature selection algorithm. Finally, knowledge-slanted RF identified more relevant biological genes, offering a higher level of explainability for users than conventional RF. These findings were corroborated in one real case to identify relevant genes to calcific aortic valve stenosis.

Unlocking the potential of edible Ulva sp. seaweeds: Metabolomic profiling, neuroprotective mechanisms, and implications for Parkinson's disease management.

Green seaweed (Ulva sp.) is frequently used as a food component and nutraceutical agent because of its high polysaccharide and natural fiber content in Asian countries. This study investigates both metabolomic profiling of Ulva sp. and the neuroprotective efficacy of its ethanol extract and its underlying mechanisms in a rotenone-induced rat model of neurodegeneration, mimicking Parkinson's disease (PD) in humans. Metabolomic profiling of Ulva sp. extract was done using liquid chromatography high resolution electrospray ionization mass spectrometry and led to the identification of 22 compounds belonging to different chemical classes.Catenin Beta Additionally, this study demonstrated the neuroprotective properties against rotenone-induced PD, which was achieved through the suppression of elevated levels of tumor necrosis factor-α (TNF-α), interleukin-1ß (IL-1ß), and IL-6 together with the inhibition of reactive oxygen species (ROS) generation, apoptosis, inflammatory mediators, and the phosphoinositide 3-kinases/serine/threonine protein kinase (PI3K/AKT) pathway. Using a protein-protein interaction network, AKT1, GAPDH, TNF-α, IL-6, caspase 3, signal transducer and activator of transcription 3, Catenin Beta 1, epidermal growth factor receptor, B-cell lymphoma -2, and HSP90AA1 were identified as the top 10 most significant genes. Finally, molecular docking results showed that compounds 1, 3, and 7 might possess a promising anti-parkinsonism effect by binding to active sites of selected hub genes. Therefore, it is hypothesized that the Ulva sp. extract has the potential to be further developed as a potential therapeutic agent for the treatment of PD.