Biology subjects

Vakili, S.

Publications and source records attributed to Vakili, S..

4 recordsLinked to original sources

bioRxiv2026

Exploratory Network Analysis of Oral Bacteria Taste Signaling Autophagy Crosstalk in Oral Squamous Cell Carcinoma and Multi-Target Ligand Design for the MAPK1 STAT3 mTOR Axis

G protein-coupled receptor (GPCR) signaling represents a critical interface between oral bacteria and host cellular regulation in oral squamous cell carcinoma (OSCC). Here, we integrated systems biology, exploratory machine learning, and structure-based drug design to characterize potential associations between bacteria-related signaling and autophagy and to identify candidate therapeutic targets. Taste-associated signaling genes belonging to the GPCR superfamily were curated from KEGG, while OSCC- and autophagy-associated proteins were obtained from STRING, Reactome, UniProt, KEGG, and HMDB. Ten bacteria-associated host-interaction datasets were integrated using NetworkAnalyst to construct protein- protein interaction networks, and key hub nodes were identified through degree and betweenness centrality. Feature matrices derived from network topology were analyzed using exploratory dimensionality reduction (PCA), hierarchical clustering, and supervised models (SVM and Gradient Boosting) to assess whether network-derived features showed separability according to literature-informed bacterial reference categories; a Dysbiosis Index was additionally calculated. Results suggested that bacterial sensing through taste-associated GPCR signaling may converge on a MAPK1-centered axis linking calcium signaling, autophagy, and oncogenic pathways. Pathobiont-associated networks showed greater representation of inflammatory and terminal-autophagy-related signaling through MAPK1-STAT3, whereas commensal-associated networks were more closely aligned with cytoprotective autophagy through balanced MAPK1-TP53/PTEN networks. Exploratory machine learning analyses highlighted MDM2 and AKT3 as high-contribution, network-associated candidate features linked to group separability within the current dataset. A dual-target MTDL (SG101) was designed to target downstream nodes (MDM2 and JAK2), showing favorable predicted docking interactions and computationally predicted ADMET properties. In conclusion, bacteria-associated host taste signaling may be linked to differing autophagy-related network states in OSCC, and targeting downstream regulatory hubs with multi-target ligands represents a hypothesis-generating strategy that warrants experimental validation for pathway-oriented therapy.

cancer biology↗

bioRxiv2025

Hierarchical Machine Learning Uncovers Topological Signatures of Autophagy Regulation by Oral Bacteria in Oral Squamous Cell Carcinoma

Oral squamous cell carcinoma (OSCC) progression has been increasingly linked to dysbiosis of the oral microbiome. We hypothesized that pathogenic versus commensal bacteria differentially rewire host autophagy networks to either promote or inhibit OSCC progression. To test this, we constructed host-bacterium autophagy interactomes from KEGG, STRING, and curated databases, identifying key network hubs (e.g., MAPK1, STAT3) via graph-theoretic metrics. We then applied a hierarchical unsupervised machine learning pipeline, combining two-stage principal component analysis with permutation testing and linear discriminant analysis (LDA), to interrogate differences in network topology. This multi-layer approach revealed a clear separation between pro-cancer (pathogenic) and anti-cancer (commensal) bacterial network signatures, with Fusobacterium nucleatum and Streptococcus mitis emerging as dominant global outliers. Pathogenic taxa activated inflammatory-metabolic autophagy signatures (e.g., NFKB1, MYC, ACACA), whereas commensals stabilized kinase-homeostasis signaling (EGFR, PTEN, HSP90AA1). Permutation testing confirmed that these network differences were highly significant and non-random (p < 0.001). We also derived a Dysbiosis Index that robustly distinguished the pro- versus anti-cancer bacterial cohorts with high predictive power. Collectively, our findings highlight oral microbiota-autophagy network topologies as potential biomarkers of OSCC dysbiosis and as novel therapeutic targets. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=189 SRC="FIGDIR/small/696881v1_ufig1.gif" ALT="Figure 1"> View larger version (53K): org.highwire.dtl.DTLVardef@13eb27forg.highwire.dtl.DTLVardef@138bccborg.highwire.dtl.DTLVardef@1f2e651org.highwire.dtl.DTLVardef@1eee140_HPS_FORMAT_FIGEXP M_FIG C_FIG Lay summaryHealthy mouth bacteria help cells stay balanced and protected. When harmful bacteria take over, they disrupt cell recycling (autophagy), increase inflammation, and causing cells to become more aggressive, which can promote oral cancer development.

cancer biology↗

bioRxiv2025

Mapping and reprogramming microenvironment-induced cell states in human disease using generative AI

Tissue microenvironments reprogram local cellular states in disease, yet current computational spatial methods remain descriptive and do not simulate tissue perturbation. We present MintFlow, a generative AI algorithm that learns how the tissue microenvironment influences cell states and predicts how tissue perturbations can reprogram them. Applied to three human diseases, MintFlow uncovered distinct pathogenic spatial reprogramming in inflammatory and tumor microenvironments. In atopic dermatitis, MintFlow identified a novel, spatially-imprinted, type 2 (IL13+ITGAE+) epidermal T resident memory cell population (type 2 TRM), and decoded signaling pathways within the perivascular lymphoid niche. In melanoma, MintFlow identified fibrotic stroma resembling keloid scar tissue. In kidney cancer, MintFlow resolved immunosuppressed CD8+ T cell states within tertiary lymphoid structures. Furthermore, MintFlow enabled in silico perturbations of disease-relevant cell states and tissue environments. Regulatory T cell modulation in atopic dermatitis was predicted to suppress the pro-inflammatory tissue environment, supporting manipulation of these cells as a therapeutic target. In kidney cancer, in silico T cell replacement recapitulated immune checkpoint blockade, while spatially targeted macrophage depletion reverted immunosuppressed T cell states. The corresponding gene programs correlated with survival in large kidney cancer patient cohorts. Together, these findings position MintFlow as a tool for unbiased disease mechanism prediction and in silico perturbation, accelerating translational hypothesis generation and guiding therapeutic strategies.

genomics↗

bioRxiv2022

Development of Multi-Bundle Virtual Ligaments to Simulate Knee Mechanics after Total Knee Arthroplasty

Preclinical evaluation of total knee arthroplasty (TKA) components is essential to understanding their mechanical behavior and developing strategies for improving joint stability. While preclinical testing of TKA components has been useful in quantifying their effectiveness, such testing can be criticized for lacking clinical relevance, as the important contributions of surrounding soft tissues are either neglected or greatly simplified. The purpose of our study was to develop and determine if subject-specific virtual ligaments reproduce the same kinematics as native ligaments surrounding TKA joints. Five TKA knees were mounted to a motion simulator. Each was subjected to tests of anterior-posterior (AP), internal-external (IE), and varus-valgus (VV) laxity. The forces transmitted through major ligaments were measured using a sequential resection technique. By tuning the measured ligament forces and elongations to a generic non-linear elastic ligament model, virtual ligaments were designed and used to simulate the soft tissue envelope around isolated TKA components. The average root mean square error (RMSE) between the laxity results of TKA joints with native versus virtual ligaments was 2.9 mm during AP translation, 6.5{degrees} during IE rotations, and 2.0{degrees} during VV rotations, and there was no statistically significant difference between the results of both methods. Interclass correlation coefficients (ICCs) indicated a good level of reliability for AP and IE laxity (0.85 and 0.84). To conclude, a virtual ligament envelope around TKA joints can mimic natural knee behavior and is an effective method for the preclinical testing of TKA components.

bioengineering↗