Biology subjects

Behrouzi, P.

Publications and source records attributed to Behrouzi, P..

4 recordsLinked to original sources

bioRxiv2026

Conjunctive Targeting Links Drug Synergy to Emergent Proteome Structural States

Combinatorial therapies are widely used in the treatment of acute myeloid leukemia (AML) to address disease heterogeneity, adaptive resistance, and rewired signaling and metabolic states. Yet drug prioritization remains largely guided by clinical or phenotypic evidence, while the molecular mechanisms underlying effective drug combinations remain incompletely defined. To narrow this gap, we developed Combinatorial high-ratio Partial proteolysis with reference PRoteome Analysis (CoPPRA), a structural proteomics workflow based on limited proteolysis of cell lysates that profiles drug-associated changes in regional protein accessibility at peptide-level resolution. Here, we applied CoPPRA to ruxolitinib and ulixertinib, individually and in combination, in AML-related cell lysates. Our findings extend conjunctive targeting (CT), a recently proposed mechanism of combinatorial drug action in which combined exposure produces protein targeting patterns not observed with either drug alone. Previously identified through combination-associated changes in protein solubility/stability, CT is examined here at peptide-level resolution through regional differences in proteolytic accessibility. The ruxolitinib-ulixertinib combination produced broad peptide-level accessibility changes, including a subset meeting the predefined criteria for CT. CT candidates predominantly exhibited regional accessibility changes, with altered peptide regions occurring against comparatively small changes across the remaining quantified peptides from the same proteins. MAP2K1 and ATP6V1G1 showed pronounced differences between overlapping peptide sequences, highlighting localized variation in combination-associated accessibility, including an ATP6V1G1 peptide mapping to an annotated helical region. Combination-associated increases in peptide signals were also observed in PIK3R1, BRD4, and PTPN11, linking regional accessibility changes to signaling and transcriptional regulators relevant to AML. Functional enrichment and network analyses further implicated nucleotide and glucose metabolism, ficolin-1-rich granules, ribosome-associated processes, and phagocytic vesicles. These results extend conjunctive targeting from protein-level solubility/stability changes to regional differences in proteolytic accessibility, showing that combination-associated effects can be concentrated within specific peptide regions rather than distributed uniformly across proteins. More broadly, CoPPRA provides a peptide-resolved approach for investigating the molecular features of combinatorial drug action and prioritizing protein regions for subsequent mechanistic validation.

biochemistry↗

bioRxiv2024

Complementary approaches to dissect late leaf rust resistance in an interspecific raspberry population

Over the last ten years, global raspberry production has increased by 47.89%, based on the red species (Rubus idaeus). However, the black raspberry species (Rubus occidentalis), although less consumed, is resistant to one of the most important diseases for the crop, the late leaf rust caused by Acculeastrum americanum fungus, to which the red ones are susceptible. In this context, genetic resistance is the most sustainable way to control the disease, mainly because there are no registered fungicides for late leaf rust in the crop in Brazil. Therefore, the aim was to understand the genetic architecture that controls resistance to late rust in raspberries. For that, we used an interspecific diversity panel between the cited above species, two different statistical approaches to associate the phenotypes to the markers (GWAS and copula graphical models), and two phenotyping methodologies from the first to the seventeenth day after inoculation (high-throughput phenotyping with a multispectral camera and traditional phenotyping by disease severity scores). Our findings indicate that a locus of higher effect possibly controls the resistance to late leaf rust, as both GWAS and the network suggested the same marker. Furthermore, a candidate defense-related gene cluster is close to this marker. Finally, the best stage to evaluate for disease severity is thirteen days after inoculation, confirmed by both traditional and high-throughput phenotyping. Although the network and GWAS indicated the same higher effect genomic region, the network identified other different regions complementing the genetic control comprehension.

genetics↗

bioRxiv2023

Natural variation in salt-induced root growth phases and their contribution to root architecture plasticity

During salt stress, the root system architecture of a plant is important for survival. Different accessions of Arabidopsis thaliana have adopted different strategies in remodeling their root architecture during salt stress. Salt induces a multiphase growth response in roots, consisting of a stop phase, quiescent phase, recovery phase and eventually a new level of homeostasis. We explored natural variation in the length of and growth rate during these phases in both main and lateral roots and find that some accessions lack the quiescent phase. Using mathematical models and correlation-based network, allowed us to correlate dynamic traits to overall root architecture and discover that both the main root growth rate during homeostasis and lateral root appearance are the strongest determinants of overall root architecture. In addition, this approach revealed a trade-off between investing in main or lateral root length during salt stress. By studying natural variation in high-resolution temporal root growth using mathematical modeling, we gained new insights in the interactions between dynamic root growth traits and we identified key traits that modulate overall root architecture during salt stress. Summary statementBy studying natural variation in salt-induced root growth phases in Arabidopsis, we show that main root growth rate during homeostasis and lateral root appearance contribute most to root architecture and we reveal a trade-off between investing in main and lateral root growth during salt stress.

plant biology↗

bioRxiv2019

Reconstruction of networks with direct and indirect genetic effects

Genetic variance of a phenotypic trait can originate from direct genetic effects, or from indirect effects, i.e., through genetic effects on other traits, affecting the trait of interest. This distinction is often of great importance, for example when trying to improve crop yield and simultaneously controlling plant height. As suggested by Sewall Wright, assessing contributions of direct and indirect effects requires knowledge of (1) the presence or absence of direct genetic effects on each trait, and (2) the functional relationships between the traits. Because experimental validation of such relationships is often unfeasible, it is increasingly common to reconstruct them using causal inference methods. However, most of the current methods require all genetic variance to be explained by a small number of QTLs with fixed effects. Only few authors considered the missing heritability case, where contributions of many undetectable QTLs are modelled with random effects. Usually, these are treated as nuisance terms, that need to be eliminated by taking residuals from a multi-trait mixed model (MTM). But fitting such MTM is challenging, and it is impossible to infer the presence of direct genetic effects. Here we propose an alternative strategy, where genetic effects are formally included in the graph. This has important advantages: (1) genetic effects can be directly incorporated in causal inference, implemented via our PCgen algorithm, which can analyze many more traits and (2) we can test the existence of direct genetic effects and improve the orientation of edges between traits. Finally, we show that reconstruction is much more accurate if individual plant or plot data are used, instead of genotypic means. We have implemented the PCgen-algorithm in the R-package pcgen.

genetics↗