Biology subjects

Bahmani, A.

Publications and source records attributed to Bahmani, A..

4 recordsLinked to original sources

bioRxiv2026

Multiscale fracture resistance of human annulus fibrosus and its deterioration with degeneration

Soft fibrous tissues must resist fracture under repeated loading, yet how degeneration compromises this resistance remains unclear. We investigated human annulus fibrosus using fracture and fatigue testing, proteoglycan depletion, microscopy, and X-ray scattering. Non-degenerate tissue exhibited pronounced fracture anisotropy, with axial toughness nearly fourfold higher than interlamellar delamination toughness. Degeneration reduced axial and radial toughness despite comparatively preserved tensile modulus and strength. Proteoglycan depletion reduced toughness most strongly in radial and delamination loading. Neither tissue state showed measurable macroscopic crack extension over 20,000 cycles at 70% of its respective monotonic failure strain. Nevertheless, fatigue produced localized structural changes, and degenerate tissue exhibited secondary delamination and diffuse collagen damage. X-ray scattering revealed fatigue- and degeneration-dependent changes in collagen periodicity and intermolecular packing. These findings connect tissue fracture resistance to collagen-matrix interactions across scales and inform the design of crack-resistant biomaterials.

bioengineering↗

bioRxiv2026

Bacteria-mimetic bioadhesives with multivalent mucoadhesion and drug-compatible delivery

The ability to adhere to mucus-lined tissues underpins a range of biomedical devices and therapies. However, many existing strategies rely on covalent bonding chemistries and can be unstable, cytotoxic, or incompatible with therapeutics. Here, we present a bacteria-mimetic bioadhesion strategy inspired by Vibrio cholerae. A short Bap1-derived adhesion peptide is grafted onto chitosan to strengthen mucus interactions through multivalent, cooperative secondary bonding, while preserving pH-triggered interfacial bridging behavior. Bacterial peptide grafting significantly increases adhesion energy on porcine intestine, and when paired with a tough hydrogel matrix achieves adhesion energies >400 J/m2 without forming covalent bonds to tissue. Confocal imaging reveals deep tissue penetration ([~]80 m) with markedly enhanced mucin binding and no loss of cytocompatibility. Ex vivo intestinal delivery and in vitro drug release tests demonstrate improved drug transport and tissue exposure compared to carbodiimide-mediated covalent bonding strategy. These findings establish a bacteria-mimetic bioadhesion strategy for tissue repair and drug delivery. Novelty StatementBioadhesive designs have drawn inspiration from nature such as mussel-inspired catechol chemistry and gecko-inspired dry adhesion. In contrast, bacterial adhesion mechanisms, despite enabling robust colonization of mucus-lined tissues under demanding conditions, remain largely overlooked. This work introduces a bacteria-mimetic bioadhesion strategy that selectively repurposes a short, non-pathogenic peptide derived from a Vibrio cholerae adhesin to enhance bioadhesion through multivalent, cooperative physical interactions rather than covalent bonding. This strategy significantly toughens adhesion even on chitosan, a polymer already rich in adhesive functional groups. By decoupling bacterial adhesion function from pathogenic risk, this study establishes bacterial adhesion peptides as a safe, modular, and mechanistically distinct foundation for next generation bioadhesives with improved drug compatibility.

bioengineering↗

bioRxiv2023

Data-driven discovery of innate immunomodulators via machine learning-guided high throughput screening

The innate immune response is vital for the success of prophylactic vaccines and immunotherapies. Control of signaling in innate immune pathways can improve prophylactic vaccines by inhibiting unfavorable systemic inflammation and immunotherapies by enhancing immune stimulation. In this work, we developed a machine learning-enabled active learning pipeline to guide in vitro experimental screening and discovery of small molecule immunomodulators that improve immune responses by altering the signaling activity of innate immune responses stimulated by traditional pattern recognition receptor agonists. Molecules were tested by in vitro high throughput screening (HTS) where we measured modulation of the nuclear factor{kappa} -light-chain-enhancer of activated B-cells (NF-{kappa}B) and the interferon regulatory factors (IRF) pathways. These data were used to train data-driven predictive models linking molecular structure to modulation of the NF-{kappa}B and IRF responses using deep representational learning, Gaussian process regression, and Bayesian optimization. By interleaving successive rounds of model training and in vitro HTS, we performed an active learning-guided traversal of a 139,998 molecule library. After sampling only[~] 2% of the library, we discovered viable molecules with unprecedented immunomodulatory capacity, including those capable of suppressing NF-{kappa}B activity by up to 15-fold, elevating NF-{kappa}B activity by up to 5-fold, and elevating IRF activity by up to 6-fold. We extracted chemical design rules identifying particular chemical fragments as principal drivers of specific immunomodulation behaviors. We validated the immunomodulatory effect of a subset of our top candidates by measuring cytokine release profiles. Of these, one molecule induced a 3-fold enhancement in IFN-{beta} production when delivered with a cyclic di-nucleotide stimulator of interferon genes (STING) agonist. In sum, our machine learning-enabled screening approach presents an efficient immunomodulator discovery pipeline that has furnished a library of novel small molecules with a strong capacity to enhance or suppress innate immune signaling pathways to shape and improve prophylactic vaccination and immunotherapies.

immunology↗

bioRxiv2020

Molecular Choreography of Acute Exercise

Exercise testing is routinely used in clinical practice to assess fitness - a strong predictor of survival - as well as causes of exercise limitations. While these studies often focus on cardiopulmonary response and selected molecular pathways, the dynamic system-wide molecular response to exercise has not been fully characterized. We performed a longitudinal multi-omic profiling of plasma and peripheral blood mononuclear cells including transcriptome, immunome, proteome, metabolome and lipidome in 36 well-characterized volunteers before and after a controlled bout of acute exercise (2, 15, 30 min and 1 hour in recovery). Integrative analysis revealed an orchestrated choreography of biological processes across key tissues. Most of these processes were dampened in insulin resistant participants. Finally, we discovered biological pathways involved in exercise capacity and developed prediction models revealing potential resting blood-based biomarkers of fitness.

systems biology↗