Biology subjects

Ashtiani, M.

Publications and source records attributed to Ashtiani, M..

6 recordsLinked to original sources

bioRxiv2026

SPAFESTWDILK, a plant-derived dodecapeptide from Zingiber officinale, as a predicted inhibitor of the MDM2-p53 interaction: computational discovery and multi-method evaluation

The MDM2-p53 protein-protein interaction is a validated oncology target, yet no food-derived linear peptide has been documented to engage the canonical three-anchor MDM2-p53 interface. We developed a multi-stage computational pipeline (PepVeg) to screen 22 plant and fungal proteomes (337,646 proteins) for MDM2-binding peptides, applying sequential in silico hydrolysis, physicochemical filtering, ESM-2 embedding-based dimensionality reduction, and pharmacophore-driven selection. Twenty-six candidates were evaluated by AlphaFold 3 (AF3) co-folding against MDM2(25-109), yielding 15 binders (iPTM >= 0.75; 58% of evaluated). A 36-peptide benchmark with 29 hard negatives confirmed AF3 discriminative power (Cohens d = 3.41; 95% CI: 1.94-4.88; Hedges g = 3.32; zero overlap). The lead candidate, SPAFESTWDILK -- a tryptic fragment of Zingiber officinale histone deacetylase (UniProt A0A8J5FLH2) -- was evaluated by eight computational assessments: AF3 Server (iPTM 0.83, SD 0.01), Protenix (iPTM 0.923), Chai-1 (iPTM 0.891), EvoEF2 (-55.57 EEU), two GROMACS simulations (no dissociation across two force fields), and two MM-PBSA calculations (-75.30 (SD 4.92) and -55.07 (SD 2.86) kcal/mol). The W8A point mutant produced an iPTM drop of 0.201, closely paralleling the p53 W23A drop of 0.193; we predict W8A substitution will abolish binding. SPAFESTWDILK ranked only #890/2,000 by ESM-2 similarity and was recovered solely through pharmacophore matching, demonstrating that no single pipeline stage alone is sufficient. To our knowledge, this is the first food-database-derived linear peptide with multi-convergent computational evidence supporting engagement of the canonical three-anchor MDM2-p53 interface. Experimental validation by SPR/ITC is warranted.

biochemistry↗

bioRxiv2025

RUNX1::RUNX1T1 Depletion Eliminates Stemness and Induces Bidirectional Differentiation of Acute Myeloid Leukemia

Chromosomal rearrangements that generate novel fusion genes are a hallmark of acute myeloid leukemia (AML). Depletion experiments in cell line models have suggested that continued expression is required for maintaining their leukemic phenotype and they therefore represent ideal cancer-specific therapeutic targets. However, to which extent this result holds true for the different stages of hematopoietic development in primary cells and whether therapeutic agents can be efficiently delivered to those cells is still unclear. In this study, we demonstrate that primary AML cells harboring the chromosomal translocation t(8;21) are critically dependent on the corresponding fusion gene, RUNX1::RUNX1T1, to suppress differentiation and maintain stemness. Silencing RUNX1::RUNX1T1 expression using siRNA-loaded lipid nanoparticles induces substantial changes in chromatin accessibility, thereby redirecting the leukemia-associated transcriptional network towards a myeloid differentiation program. Single-cell analyses reveal that this transcriptional reprogramming is associated with the depletion of immature stem and progenitor-like cell populations, accompanied by an expansion of granulocytic and eosinophilic/mast cell-like populations with impaired self-renewal capacity. These findings underscore the essential role of RUNX1::RUNX1T1 in sustaining AML and highlight the therapeutic potential of targeting fusion gene expression in primary AML cells. O_FIG O_LINKSMALLFIG WIDTH=167 HEIGHT=200 SRC="FIGDIR/small/637937v1_ufig1.gif" ALT="Figure 1"> View larger version (53K): org.highwire.dtl.DTLVardef@cf4838org.highwire.dtl.DTLVardef@8e6d90org.highwire.dtl.DTLVardef@bb46c5org.highwire.dtl.DTLVardef@12357e4_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2024

Repurposing CD19-directed immunotherapies for pediatric t(8;21) acute myeloid leukemia

In contrast to patients with B cell precursor acute lymphoblastic leukemia (BCP-ALL), patients with acute myeloid leukemia (AML) have not yet benefited from recent advances in targeted immunotherapy. Repurposing immunotherapies that have been successfully used to target other hematological malignancies could, in case of a shared target antigen, represent a promising opportunity to expand the immunotherapeutic options for AML. Here, we evaluated the expression of CD19 in a large pediatric AML cohort, assessed the ex vivo AML killing efficacy of CD19-directed immunotherapies, and characterized the bone marrow immune microenvironment in pediatric AML, BCP-ALL, and non-leukemic controls. Out of 167 newly diagnosed de novo pediatric AML patients, 18 patients (11%) had CD19+ AML, with 61% carrying the translocation t(8;21)(q22;q22). Among CD19+ samples, we observed a continuum of CD19 expression levels on AML cells. In individuals exhibiting unimodal and high CD19 expression, the antigen was consistently present on nearly all CD34+CD38- and CD34+CD38+ subpopulations. In ex vivo AML-T cell co-cultures, blinatumomab demonstrated substantial AML killing, with an efficacy similar to BCP-ALL. In addition, CAR T cells could effectively eliminate CD19+ AML cells ex vivo. Furthermore, our immunogenomic assessment of the bone marrow immune microenvironment of newly diagnosed pediatric t(8;21) AML revealed that T- and NK cells had a less exhausted and senescent phenotype in comparison to pediatric BCP-ALL. Altogether, our study underscores the promise of CD19-directed immunotherapies for the treatment of pediatric CD19+ AML.

immunology↗

bioRxiv2022

Nanoparticle-mediated Targeting of the Fusion Gene RUNX1/ETO in t(8;21)-positive Acute Myeloid Leukaemia

A hallmark of acute myeloid leukaemias (AMLs) are chromosomal rearrangements that give rise to novel leukaemia-specific fusion genes. Most of these fusion genes are both initiating and driving events in AML and therefore constitute ideal therapeutic targets but are challenging to target by conventional drug development. siRNAs are frequently used for the specific suppression of fusion gene expression but require special formulations for efficient in vivo delivery. Here we describe the use of siRNA-loaded lipid nanoparticles for the specific therapeutic targeting of the leukaemic fusion gene RUNX1/ETO. Transient knockdown of RUNX1/ETO reduces its binding to its target genes and alters the binding of RUNX1 and its co-factor CBF{beta}. Transcriptomic changes in vivo were associated with substantially increased median survival of a t(8;21)-AML mouse model. Importantly, transient knockdown in vivo causes long-lasting inhibition of leukaemic proliferation and clonogenicity, induction of myeloid differentiation and a markedly impaired re-engraftment potential in vivo. These data strongly suggest that temporary inhibition of RUNX1/ETO results in long-term restriction of leukaemic self-renewal. Our results provide proof for the feasibility of targeting RUNX1/ETO in a pre-clinical setting and support the further development of siRNA-LNPs for the treatment of fusion gene-driven malignancies.

cancer biology↗

bioRxiv2017

CINNA: Deciphering Central Informative Nodes in Network Analysis

In network science, usually there is a critical step known as centrality analysis. This is an important step, since by using centrality measures, a large number of vertices with low priority are set aside and only a few ones remain to be used for further inferential outcomes. In the other words, these measures help us to sieve our large network and distinguish coarse vertices. By that, important decisions could be made based on the circumstances of these vertices on the overall behavior of networks. These vertices are potentially assumed as central or essential nodes. However, the centrality analysis has always been accompanied by a series of ambiguities, since there are a large number of well-known centrality measures, with different algorithms pointing to these essential nodes and there is no well-defined preference. Which measure explore more information in a given network about node essentiality according to the topological features? While here, we tried to provide a pipeline to have a comparison among all proper centrality measures regarding the network structure and choose the most informative one according to dimensional reduction methods. Central Informative Nodes in Network Analysis (CINNA) package is prepared to gather all required function for centrality analysis in the weighted/unweighted and directed/undirected networks.\n\nAvailability and implementationCINNA is available in CRAN, including a tutorial. URL: https://cran.r-proiect.org/web/packages/CINNA/index.html\n\nContact: miafari@pasteur.ac.ir

systems biology↗

bioRxiv2017

Revisiting the General Concept of Network Centralities: A Propose for Centrality Analysis in Network Science

BackgroundNumerous centrality measures have been introduced to identify \"central\" nodes in large networks. The availability of a wide range of measures for ranking influential nodes leaves the user to decide which measure may best suit the analysis of a given network. The choice of a suitable measure is furthermore complicated by the impact of the network topology on ranking influential nodes by centrality measures. To approach this problem systematically, we examined the centrality profile of nodes of yeast protein-protein interaction networks (PPINs) in order to detect which centrality measure is succeeding in predicting influential proteins. We studied how different topological network features are reflected in a large set of commonly used centrality measures.\n\nResultsWe used yeast PPINs to compare 27 common of centrality measures. The measures characterize and assort influential nodes of the networks. We applied principal component analysis (PCA) and hierarchical clustering and found that the most informative measures depend on the networks topology. Interestingly, some measures had a high level of contribution in comparison to others in all PPINs, namely Latora closeness, Decay, Lin, Freeman closeness, Diffusion, Residual closeness and Average distance centralities.\n\nConclusionsThe choice of a suitable set of centrality measures is crucial for inferring important functional properties of a network. We concluded that undertaking data reduction using unsupervised machine learning methods helps to choose appropriate variables (centrality measures). Hence, we proposed identifying the contribution proportions of the centrality measures with PCA as a prerequisite step of network analysis before inferring functional consequences, e.g., essentiality of a node.

systems biology↗