Biology subjects

Jafari, R.

Publications and source records attributed to Jafari, R..

13 recordsLinked to original sources

bioRxiv2026

Proteomic profiling reveals pleiotropic antimetabolite activity of triciribine in acute lymphoblastic leukemia

Acute lymphoblastic leukemia (ALL) exhibits marked genetic and metabolic heterogeneity that limits the efficacy of targeted therapies. Antimetabolite strategies remain central to ALL treatment, yet the mechanisms underlying differential drug sensitivity are incompletely defined. Here, we investigated the activity of the purine analog triciribine (TCN) across diverse ALL cellular models. We find that TCN exerts potent cytotoxic effects in multiple ALL cell lines, exceeding those observed with canonical Akt inhibitors in our datasets. Phosphoproteomic analyses indicate that, at early time points, TCN does not primarily suppress Akt signaling but instead induces transient pathway activation accompanied by inhibition of cyclin-dependent kinases in both sensitive and resistant cells. The monophosphorylated metabolite TCN-P represents the predominant intracellular species and requires adenosine kinase (ADK) for activity, with ADK protein levels positively correlating with TCN sensitivity in both cell lines and primary patient samples. Using Proteome Integral Solubility Alteration profiling, we identify candidate protein interactions of TCN-P distributed across multiple cellular pathways, including nucleotide metabolism, DNA replication, and protein synthesis. These interactions are accompanied by impaired purine biosynthesis, DNA damage, translational stress, and cell-cycle arrest, as supported by time-course quantitative proteomics and immunoblot analyses. Together, these findings characterize triciribine as an antileukemic agent with pleiotropic antimetabolite activity in ALL and highlight ADK-dependent metabolism as a key determinant of therapeutic sensitivity, suggesting that ADK levels may serve as a predictive biomarker to stratify patients for triciribine-based precision treatment strategies.

cancer biology↗

bioRxiv2024

Decoding the elite soccer players psychological profile

Soccer is arguably the most widely followed sport worldwide, and many dream of becoming soccer players. However, only a few manage to achieve this dream, which has cast a significant spotlight on elite soccer players who possess exceptional skills to rise above the rest. Originally, such attention was focused on their great physical abilities. However, recently, it a new perspective has emerged, suggesting that being an elite soccer player require a deep understanding of the game, rapid information processing and decision-making. This growing attention has led to several studies suggesting higher executive functions in soccer players compared to the general population. Unfortunately, these studies often had small and non-elite samples, focusing mainly on executive functions alone without employing advanced machine learning techniques. In this study, we used artificial neural networks to comprehensively investigate the personality traits and cognitive abilities of a sample of 328 participants, including 204 elite soccer players from the top teams in Brazil and Sweden. Our findings indicate that elite soccer players demonstrate heightened planning and memory capacities, enhanced executive functions, especially cognitive flexibility, elevated levels of conscientiousness, extraversion, and openness to experience, coupled with reduced neuroticism and agreeableness. This research provides insights into the psychology of elite soccer players, holding significance for talent identification, development strategies in soccer and offering insights into the psychological profiles associated with success. Significance statementThis study explores the psychological profiles of elite soccer players, revealing that success on the field goes beyond physical ability. By analysing a sample of 328 participants, including 204 elite soccer players from the top teams in Brazil and Sweden, we found that elite players have exceptional cognitive abilities, including improved planning, memory, and decision-making skills. They also possess personality traits like high conscientiousness and openness, along with reduced neuroticism. Using artificial intelligence, we identified unique psychological patterns that could help in talent identification and development. These insights can be used to better understand the mental attributes that contribute to success in soccer and other high-performance fields.

neuroscience↗

bioRxiv2024

Sulfoglycodendron Antivirals with Scalable Architectures and Activities

Many viruses initiate their cell-entry by binding their multi-protein receptors to human heparan sulfate proteoglycans (HSPG) and other molecular components present on cellular membranes. These viral interactions could be blocked and the whole viruses could be eliminated by suitable HSPG-mimetics providing multivalent binding to viral protein receptors. Here, large sulfoglycodendron HSPG-mimetics of different topologies, structures, and sizes were designed to this purpose. Atomistic molecular dynamics simulations were used to examine the ability of these broad-spectrum antivirals to block multi-protein HSPG-receptors in HIV, SARS-CoV-2, HPV, and dengue viruses. To characterize the inhibitory potential of these mimetics, their binding to individual and multiple protein receptors was examined. In particular, vectorial distributions of binding energies between the mimetics and viral protein receptors were introduced and calculated along the simulated trajectories. Space-dependent residual analysis of the mimetic-receptor binding was also performed. This analysis revealed detail nature of binding between these antivirals and viral protein receptors, and provided evidence that large inhibitors with multivalent binding might act like a molecular glue initiating the self-assembly of protein receptors in enveloped viruses. TOC FIGURE O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=103 SRC="FIGDIR/small/606251v2_ufig1.gif" ALT="Figure 1"> View larger version (51K): org.highwire.dtl.DTLVardef@55801borg.highwire.dtl.DTLVardef@14dac62org.highwire.dtl.DTLVardef@707fa1org.highwire.dtl.DTLVardef@1cb451a_HPS_FORMAT_FIGEXP M_FIG C_FIG

pharmacology and toxicology↗

bioRxiv2024

SETD8 inhibition targets cancer cells with increased rates of ribosome biogenesis

SETD8 is a methyltransferase that is overexpressed in several cancers, which monomethylates H4K20 as well as other non-histone targets such as PCNA or p53. We here report novel SETD8 inhibitors, which were discovered while trying to identify chemicals that prevent 53BP1 foci formation, an event mediated by H4K20 methylation. Consistent with previous reports, SETD8 inhibitors induce p53 expression, although they are equally toxic for p53-deficient cells. Thermal stability proteomics revealed that the compounds had a particular impact on nucleoli, which was confirmed by fluorescent and electron microscopy. Similarly, Setd8 deletion generated nucleolar stress and impaired ribosome biogenesis, confirming that this was an on-target effect of SETD8 inhibitors. Furthermore, a genome-wide CRISPR screen identified an enrichment of nucleolar factors among those modulating the toxicity of SETD8 inhibitors. Accordingly, the toxicity of SETD8 inhibitors correlated with MYC or mTOR activity, key regulators of ribosome biogenesis. Together, our study provides a new class of SETD8 inhibitors and a novel biomarker to identify tumors most likely to respond to this therapy.

cancer biology↗

bioRxiv2024

Pathway activation model for personalized prediction of drug synergy

Targeted monotherapies for cancer often fail due to inherent or acquired drug resistance. By aiming at multiple targets simultaneously, drug combinations can produce synergistic interactions that increase drug effectiveness and reduce resistance. Computational models based on the integration of omics data have been used to identify synergistic combinations, but predicting drug synergy remains a challenge. Here, we introduce DIPx, an algorithm for personalized prediction of drug synergy based on biologically motivated tumor- and drug-specific pathway activation scores (PASs). We trained and validated DIPx in the AstraZeneca-Sanger (AZS) DREAM Challenge dataset using two separate test sets: Test Set 1 comprised the combinations already present in the training set, while Test Set 2 contained combinations absent from the training set, thus indicating the models ability to handle novel combinations. The Spearman correlation coefficients between predicted and observed drug synergy were 0.50 (95% CI: 0.47-0.53) in Test Set 1 and 0.26 (95% CI: 0.22-0.30) in Test Set 2, compared to 0.38 (95% CI: 0.34-0.42) and 0.18 (95% CI: 0.16-0.20), respectively, for the best performing method in the Challenge. We show evidence that higher synergy is associated with higher functional interaction between the drug targets, and this functional interaction information is captured by PAS. We illustrate the use of PAS to provide a potential biological explanation in terms of activated pathways that mediate the synergistic effects of combined drugs. In summary, DIPx can be a useful tool for personalized prediction of drug synergy and exploration of activated pathways related to the effects of combined drugs.

bioinformatics↗

bioRxiv2023

Proteoform-level deconvolution reveals a broader spectrum of ibrutinib off-targets

Over the last decade, proteome-wide mapping of drug interactions has revealed that most targeted drugs bind to not only their intended targets, but additional proteins as well. However, the majority of these studies have focused on analyzing proteins as encoded by their genes, thus neglecting the fact that most proteins exist as dynamic populations of multiple proteoforms. Here, we addressed this problem by combining the use of thermal proteome profiling (TPP), a powerful method for proteome analysis, with proteoform detection to refine the target landscape of an approved drug, ibrutinib. We revealed that, in addition to known targets, ibrutinib exhibits an intricate network of interactions involving multiple different proteoforms. Notably, we discovered affinity for specific proteoforms that link ibrutinib to mechanisms in immunomodulation and cellular processes like Golgi trafficking, endosomal trafficking, and glycosylation. These insights provide a framework for interpreting clinically observed off-target and adverse events. More generally, our findings highlight the importance of proteoform-level deconvolution in understanding drug interactions and their functional impacts, and offer a critical perspective for drug mechanism studies and potential applications in precision medicine.

molecular biology↗

bioRxiv2023

Assessment of resting state structural-functional relationships in perisylvian region during the early weeks after birth

This study investigates the structural-functional (S-F) relationships of the perisylvian region during the first weeks after birth in the resting state. Previous joint S-F studies of perisylvian development were mainly conducted on individual structural and functional metrics. By employing a weighted combination of metrics, joint S-F studies can enhance the understanding of perisylvian development in neonates, thereby providing valuable features for the prediction of neurodevelopmental disorders. To this end, we employed both structural and functional metrics derived from 16 perisylvian sub-regions (PSRs). Structural metrics included morphological and myelination measures, while functional metrics encompassed functional connectivity (FC) values between a designated seed PSR and the remaining PSRs. In addition, fractional amplitude of low-frequency fluctuations (fALFF) for all PSRs was computed and used as a distinct group of functional metrics. During statistical procedure, based on sparse canonical correlation analysis (CCA), the structural metrics were correlated with each respective group of functional metrics. Then, CCA was employed to delineate regional interdependencies and derive combined structural-functional features. The findings revealed that combined features outperform individual metrics for characterizing the normal development of the PSRs in term neonates. The myelination is prominently related to both fALFF and FCs, while morphological metrics of the PSRs have very limited contribution in combined structural features. Among the designated PSRs, the FCs of both the right insula and Heschls gyrus with other PSRs demonstrated a more robust correlation with the combined structural features.

bioengineering↗

bioRxiv2022

Deep thermal proteome profiling for detection of proteoforms and drug sensitivity biomarkers

The complexity of the functional proteome extends significantly beyond the protein coding genome resulting in millions of proteoforms. Investigation of proteoforms and their functional roles is important to understand cellular physiology and its deregulation in diseases, but challenging to perform systematically. Here, we apply thermal proteome profiling with deep peptide coverage to detect functional proteoforms in acute lymphoblastic leukemia cell lines with different cytogenetic aberrations. We detect 15,846 proteoforms, capturing differently spliced, post-translationally modified, and cleaved proteins expressed from 9,290 genes. We identify differential coaggregation of proteoform pairs and establish links to disease biology. Moreover, we systematically make use of measured biophysical proteoform states to find specific biomarkers of drug sensitivity. Our approach thus provides a powerful and unique tool for systematic detection and functional annotation of proteoforms.

biochemistry↗

bioRxiv2022

Proteogenomics refines the molecular classification of chronic lymphocytic leukemia

Cancer heterogeneity at the proteome level may explain differences in therapy response and prognosis beyond the currently established genomic and transcriptomic based diagnostics. The relevance of proteomics for disease classifications remains to be established in clinically heterogeneous cancer entities such as chronic lymphocytic leukemia (CLL). Here, we characterized the proteome and transcriptome in-depth alongside genetic and ex-vivo drug response profiling in a clinically well annotated CLL discovery cohort (n= 68). Unsupervised clustering of the proteome data revealed six subgroups. Five of these proteomic groups were associated with genetic features, while one group was only detectable at the proteome level. This new group was characterized by accelerated disease progression, high spliceosomal protein abundances associated with aberrant splicing, and low B cell receptor signaling protein abundances (ASB-CLL). We developed classifiers to identify ASB-CLL based on its characteristic proteome or splicing signature in two independent cohorts (n= 165, n= 169) and confirmed that ASB-CLL comprises about 20 % of CLL patients. The inferior overall survival observed in ASB-CLL was independent of both TP53- and IGHV mutation status. Our multi-omics analysis refines the classification of CLL and highlights the potential of proteomics to improve cancer patient stratification beyond genetic and transcriptomic profiling. Single sentence summaryWe performed the largest proteogenomic analysis of CLL, linked proteomic profiles to clinical outcomes, and discovered a new poor outcome subgroup (ASB-CLL).

cancer biology↗

bioRxiv2021

A hybrid boundary element-finite element method to solve the EEG forward problem

This article presents a hybrid boundary element-finite element (BE-FE) method to solve the EEG forward problem and take advantages of both the boundary element method (BEM) and finite element method (FEM). Although realistic EEG forward problems with heterogeneous and anisotropic regions can be solved by FEM accurately, the FEM modeling of the brain with dipolar sources may lead to singularity. In contrast, the BEM can solve EEG forward problems with isotropic tissue regions and dipolar sources using a suitable integral formulation. This work utilizes both FEM and BEM strengths attained by dividing the regions into some homogeneous BE regions with sources and some heterogeneous and anisotropic FE regions. Furthermore, the BEM is applied for modeling the brain, including dipole sources and the FEM for other head layers. To validate the proposed method, inhomogeneous isotropic/anisotropic three- and four-layer spherical head models are studied. Moreover, a four-layer realistic head model is investigated. Results for six different dipole eccentricities and two different dipole orientations are computed using the BEM, FEM, and hybrid BE-FE method together with statistical analysis and the related error criteria are compared. The proposed method is a promising new approach for solving realistic EEG forward problems.

neuroscience↗

bioRxiv2021

Normal development of the brain: a survey of joint structural-functional brain studies

Joint structural-functional (S-F) developmental studies present a novel approach to address the complex neuroscience questions on how the human brain works and how it matures. Joint S-F biomarkers have the inherent potential to model effectively the brains maturation, fill the information gap in temporal brain atlases, and demonstrate how the brains performance matures during the lifespan. This review presents the current state of knowledge on heterochronous and heterogeneous development of S-F links during the maturation period. The S-F relationship has been investigated in early-matured unimodal and prolonged-matured transmodal regions of the brain using a variety of structural and functional biomarkers and data acquisition modalities. Joint S-F unimodal studies have employed auditory and visual stimuli, while the main focus of joint S-F transmodal studies has been resting-state networks and working memory. However, non-significant associations between some structural and functional biomarkers and their maturation show that designing and developing effective S-F biomarkers is still a challenge in the field. Maturational characteristics of brain asymmetries have been poorly investigated by the joint S-F studies, and the results were inconsistent with previous non-joint ones. The inherent complexity of the brain performance can be modeled using multifactorial and nonlinear techniques as promising methods to simulate the impact of age on S-F relations considering their analysis challenges.

neuroscience↗

bioRxiv2021

Inhibition of the ubiquitin-proteasome system by a bioactivatable compound

Malignant cells display an increased sensitivity towards drugs that reduce the function of the ubiquitin-proteasome system (UPS), which is the primary proteolytic system for destruction of aberrant proteins. Here, we report on the discovery of the bioactivatable compound CBK77, which causes an irreversible collapse of the UPS, accompanied by a general accumulation of ubiquitylated proteins and caspase-dependent cell death. CBK77 caused accumulation of ubiquitin-dependent, but not ubiquitin-independent, reporter substrates of the UPS, suggesting a selective effect on ubiquitin-dependent proteolysis. In a genome-wide CRISPR interference screen, we identified the redox enzyme NAD(P)H:quinone oxidoreductase 1 (NQO1) as a critical mediator of CBK77 activity, and further demonstrated its role as the compound bioactivator. Through affinity-based proteomics, we found that CBK77 covalently interacts with ubiquitin. In vitro experiments showed that CBK77-treated ubiquitin conjugates were less susceptible to disassembly by deubiquitylating enzymes. In vivo efficacy of CBK77 was validated by reduced growth of NQO1-proficient human adenocarcinoma cells in nude mice treated with CBK77. This first-in-class NQO1-activatable UPS inhibitor suggests that it may be possible to exploit the intracellular environment in malignant cells for leveraging the impact of compounds that impair the UPS.

cell biology↗

bioRxiv2019

A new immunotherapeutic concept for pancreatic cancer using childhood vaccine recall antigens and Listeria

Pancreatic ductal adenocarcinoma is highly metastatic, poorly immunogenic, and immune suppression prevents T cell activation in the tumor microenvironment. We developed a microbial-based immunotherapeutic concept for selective delivery of a highly immunogenic tetanus toxoid protein (TT856-1313), into tumor cells by attenuated Listeria monocytogenes, and reactivation of pre-existing TT-specific memory T cells (generated during childhood) to kill infected tumor cells. Thus, TT here functions as an alternative for neoantigens. Treatment of KPC mice with Listeria-TT resulted in TT accumulation in tumors and inside tumor cells, and attraction of predominantly TT-specific memory CD4 T cells. Moreover, gemcitabine (GEM) combined with Listeria-TT significantly improved the migration of CD4 T cells into tumors and the production of perforin and granzyme B, turning cold tumors into immunological hot tumors. In vivo depletion of T cells in Listeria-TT+GEM-treated mice demonstrated CD4 T cell-mediated eradication of tumors and metastases (Mann-Whitney p<0.05). In addition, peritumoral lymph node like structures (LNS) were observed in close contact with the pancreatic tumors displaying CD4 T cells and CD8 T cells of KPC mice treated with Listeria-TT or Listeria-TT+GEM. The production of perforin and granzyme B was observed in LNS of KPC mice that received Listeria-TT+GEM. This combination not only reduced tumor burden (80%) and metastases (87%) significantly (p<0.05, Mann-Whitney), but also improved the survival time of KPC mice with advanced pancreatic cancer substantially (Mantel-Cox p<0.0001). Our results unveil new mechanisms of Listeria and GEM improving immunotherapy for PDAC.

immunology↗