Biology subjects

Saber, H.

Publications and source records attributed to Saber, H..

4 recordsLinked to original sources

bioRxiv2026

Explainable Machine Learning and Epigenomic Profiling Decipher the Topological Determinants of Lentiviral Integration and Longitudinal Persistence in SCID-X1 Gene Therapy

Lentiviral vectors (LVs) are clinically established for SCID-X1 gene therapy, yet the quantitative epigenomic and spatial determinants governing integration targeting and long-term clonal persistence across chromosomes remain incompletely characterized. Using clinical multi-omics datasets from SCID-X1 trials, we curated 274,959 unique clinical integration sites (VIS) across 276,839 clonal records from 10 patients (hg38). Balanced against length-weighted genomic controls (total N = 549,918), five epigenomic and topological features were modeled. We evaluated Logistic Regression, XGBoost, and a Deep Genomic ResNet using inner 5-fold chromosome-grouped cross-validation and evaluation on held-out test chromosomes (chr19-22, chrX). Statistical uncertainty was quantified via 1-Mb block-bootstrap (5,000 replicates) and 1-Mb block permutations (10,000 replicates). On held-out chromosomes, Deep ResNet achieved an ROC-AUC of 0.7857 [95% CI: 0.7672-0.8029] and PR-AUC of 0.8027 [95% CI: 0.7640-0.8337] over the 0.5755 prevalence baseline, outperforming linear baselines. 1-Mb block permutations confirmed significant proximity to scATAC-seq peaks (Cliff's {delta}=-0.3364, P_perm<0.0001) and TSSs ({delta}=-0.2446, P_perm<0.0001), alongside 82.71% gene body overlap (OR = 3.47). Targeted analysis showed 99.91% of VIS reside distal (>100 kb) from proto-oncogenes without proximal enrichment (OR = 1.05, P = 0.617). Furthermore, longitudinal tracking identified 79,587 persistent clones ([≥]2 time points), which exhibited a significant 31% depletion near proto-oncogenes (OR = 0.69, P = 0.014). Compact topological and epigenomic features robustly predict lentiviral integration without spatial data leakage. The long-term depletion of persistent clones near oncogenes confirms the insertional safety of SIN lentiviral gene therapy.

bioinformatics↗

bioRxiv2026

MRI-Based Blood Clot Phenotyping: An In Vitro Study

Background and PurposeNeurointerventional outcomes depend on clot composition and may be influenced by clot contraction. Thus, a priori identification of clot composition and contraction could inform procedural strategies and improve outcomes. The goal of our work is to conduct an in vitro test to determine whether MRI can reliably predict both clot composition and contractile state. Materials and MethodsTo this end, we prepared blood clots spanning clinically observed compositions (0-80% red blood cells (RBCs)) in both contracted and uncontracted states. Contraction was controlled by coagulating blood with or without thrombin. We imaged these clots using quantitative, clinical, and investigational MRI sequences. Using these data, we then determined whether MRI signal intensities, quantitative parameters, and radiomic features capturing intensity and texture patterns can (i) predict clot hematocrit and (ii) classify clots by composition (RBC-rich vs. fibrin-rich) and contraction state. ResultsQuantitative MRI parameters (T1, T2, ADC) decreased with increasing hematocrit (R2 = 0.56-0.85, p < 0.001), while signal intensities from clinical sequences showed weaker correlations (R2 = 0.46-0.62, p < 0.001). Radiomic models predicted hematocrit with performance comparable to MRI parameters. When applied to classification, radiomic features accurately discriminated RBC-versus fibrin-rich clots, with AUCs exceeding 0.90 across nearly all sequences. In contrast, classification of contraction state showed greater variability in AUCs across sequences but remained high for quantitative T1 and T2 values (AUCs up to 0.88). Trends were consistent across clots coagulated with and without thrombin. Pooling features across sequences did not outperform the best individual sequence for either regression or classification. ConclusionsWe demonstrate that MRI-based radiomic analysis quantitatively characterizes clot composition and contraction in vitro. These findings support the feasibility of using MRI for pre-interventional clot phenotyping, with potential to inform thrombolytic and mechanical thrombectomy strategies. Thus, in vivo studies validating these results are warranted.

bioengineering↗

bioRxiv2025

In vitro blood clot mechanical properties depend on fibrinogen and white blood cell subtypes in addition to hematocrit

BackgroundUnderstanding the role of blood composition in clot mechanics may provide critical cues toward their diagnosis and treatment. To this end, we previously showed that sex and standard blood composition measures explain some variability in clot mechanics, but much remains unaccounted for. Ours and others studies ignored the roles of fibrinogen and white blood cell (WBC) subtypes, which is surprising given their physiological importance. ObjectiveTo develop a more complete understanding of what determines clots mechanical properties, we now study the role of previously untested factors, namely fibrinogen levels and WBC subtypes, in addition to sex and standard blood composition measures. MethodsWe drew blood from healthy young adults and prepared in vitro clot samples. Using pure shear and mode-I fracture tests, we measured clots stiffness, fracture toughness, strength, and work to rupture. We then used linear regressions to quantify how blood composition influences clot mechanical properties, reporting R2 to assess explanatory power. ResultsWe found that fibrinogen is the strongest predictor of clot mechanical properties, positively correlating with all four metrics and achieving the highest average R2. For example, fibrinogen accounts for 70% of the variation in stiffness and 78% in fracture toughness. Among WBC subtypes, neutrophils positively correlate with strength and work to rupture, whereas eosinophils negatively correlate with strength. ConclusionOur work shows that fibrinogen levels and WBC subtypes are previously untested but major determinants of in vitro clot mechanical properties. Future studies should connect these findings to in vivo clot behavior and clinical outcomes.

bioengineering↗

bioRxiv2019

Dynamic Modeling of Signal Transduction by mTOR Complexes in Cancer

Signal integration in the mTOR pathway plays a vital role in cell fate decision making in cancer cells. As a signal integrator, mTOR shows a complex dynamical behavior which determines the cell fate at different cellular processes levels including cell cycle progression, cell survival, cell death, metabolic reprogramming, and aging. The dynamics of the complex responses to rapamycin in cancer cells have been attributed to its differential time-dependent inhibitory effects on mTORC1 and mTORC2, the two main complexes of mTOR. Two explanations were previously provided for this phenomenon: 1-Rapamycin does not inhibit mTORC2 directly, whereas it prevents mTORC2 formation by sequestering free mTOR protein. 2-Components like Phosphatidic Acid further stabilize mTORC2 compared with mTORC1. To understand the mechanism by which rapamycin differentially inhibits the mTOR complexes, we present a mathematical model of rapamycin mode of action based on the first explanation, i.e., Le Chateliers principle. Translating the interactions among components of mTORC1 and mTORC2 into a mathematical model revealed the dynamics of rapamycin action in different doses and time-intervals of rapamycin treatment. The model shows that rapamycin has stronger effects on mTORC1 compared with mTORC2, simply due to its direct interaction with free mTOR and mTORC1, but not mTORC2, without the need to consider other components that might further stabilize mTORC2. Based on our results, even when mTORC2 is less stable compared with mTORC1, it can be less inhibited by rapamycin.

systems biology↗