Biology subjects

Solorzano, J.

Publications and source records attributed to Solorzano, J..

4 recordsLinked to original sources

bioRxiv2024

Src promotes tumor cell invasion by hijacking the translation machinery

The Src oncogene controls cancer cell invasiveness by promoting invadosome formation and extracellular matrix degradation (ECM). Invadosomes are enriched in the eukaryotic translation initiation factor 3 (eIF3) complex associated with a local mRNA translation activity mandatory for their maintenance. Here, we show that Src regulates mRNA translation by controlling the expression of eIF3 subunits. Among them, eIF3h/e/d are essential for invadosome formation and ECM degradation. We demonstrate that Src controls the canonical mTOR/eIF4E and the non-canonical eIF3d cap-dependent translation initiation pathways. We show that both pathways are necessary for invadosome formation and their ECM degradation function. Finally, we highlighted a correlation between Src and eIF3h/e/d overexpression, which is associated with poor prognosis in hepatocellular carcinoma (HCC) patients and controls the ECM degradation and invasive properties of HCC cells. These findings identify Src as a major regulator of translation initiation pathways, which leads to invadosome formation, ECM degradation and tumor cell invasion. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=152 SRC="FIGDIR/small/606119v1_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@1804203org.highwire.dtl.DTLVardef@16ddae8org.highwire.dtl.DTLVardef@13ecca5org.highwire.dtl.DTLVardef@1ed685a_HPS_FORMAT_FIGEXP M_FIG Graphical abstract C_FIG

cell biology↗

bioRxiv2023

Digitize your Biology! Modeling multicellular systems through interpretable cell behavior

Cells are fundamental units of life, constantly interacting and evolving as dynamical systems. While recent spatial multi-omics can quantitate individual cells characteristics and regulatory programs, forecasting their evolution ultimately requires mathematical modeling. We develop a conceptual framework--a cell behavior hypothesis grammar--that uses natural language statements (cell rules) to create mathematical models. This allows us to systematically integrate biological knowledge and multi-omics data to make them computable. We can then perform virtual "thought experiments" that challenge and extend our understanding of multicellular systems, and ultimately generate new testable hypotheses. In this paper, we motivate and describe the grammar, provide a reference implementation, and demonstrate its potential through a series of examples in tumor biology and immunotherapy. Altogether, this approach provides a bridge between biological, clinical, and systems biology researchers for mathematical modeling of biological systems at scale, allowing the community to extrapolate from single-cell characterization to emergent multicellular behavior.

systems biology↗

bioRxiv2021

GEM-DeCan: Improving tumor immune microenvironment profiling by the integration of novel gene expression and DNA methylation deconvolution signatures

Quantifying the proportion of the different cell types present in tumor biopsies remains a priority in cancer research. So far, a number of deconvolution methods have emerged for estimating cell composition using reference signatures, either based on gene expression or on DNA methylation from purified cells. These two deconvolution approaches could be complementary to each other, leading to even more performant signatures, in cases where both data types are available. However, the potential relationship between signatures based on gene expression and those based on DNA methylation remains underexplored. Here we present five new deconvolution signature matrices, based RNAseq data or on DNA methylation, which can estimate the proportion of immune cells and cancer cells in a tumour sample. We test these signature matrices on available datasets for in-silico and in-vitro mixtures, peripheral blood, cancer samples from TCGA, and a single-cell melanoma dataset. Cell proportions estimates based on deconvolution performed using our signature matrices, implemented within the EpiDISH framework, show comparable or better correlation with FACS measurements of immune cell-type abundance and with various estimates of cancer sample purity and composition than existing methods. Using publicly available data of 3D chromatin structure in haematopoietic cells, we expanded the list of genes to be included in the RNAseq signature matrices by considering the presence of methylated CpGs in gene promoters or in genomic regions which are in 3D contact with these promoters. Our expanded signature matrices have improved performance compared to our initial RNAseq signature matrix. Finally, we show the value of our signatures in predicting patient response to immune checkpoint inhibitors in three melanoma cancer cohorts, based on bulk tumour sample gene expression. We also provide GEM-DeCan: a snakemake pipeline, able to run an analysis from raw sequencing data to deconvolution based on various gene expression signature matrices, both for bulk RNASeq and DNA methylation data.

bioinformatics↗

bioRxiv2020

Translatome-based classification reveals a dual metabolic dependency of a new tumor subtype of pancreatic cancer.

Molecular profiling of Pancreatic Ductal Adenocarcinoma (PDA), based on transcriptomic analyses, identifies two main prognostic subtypes (basal-like and classical), but does not allow personalized first-line treatment. To date, tumors have not been profiled based on protein synthesis rates, yet the step of mRNA translation is highly deregulated in both PDA cancer cells and their microenvironment. Using a collection of twenty-seven pancreatic Patient-Derived Xenografts (PDX), we performed genome-wide analysis of translated mRNA (translatome). Unsupervised bioinformatics analysis revealed a new tumor subtype harboring a low protein synthesis rate, but associated with a robust translation of mRNAs encoding effectors of the integrated stress response (ISR), including the transcription factor ATF4. Functional characterization of the "ISR-activated" human cancer cells revealed a high resistance to drugs, low autophagic capacities, and importantly, metabolic impairments in the serine synthesis and transsulfuration pathways. Overall, our study highlights the strength of translatomic profiling on PDA, which here revealed an unforeseen drug-resistant cancer cell phenotype, whose auxotrophy to both serine and cysteine may be amenable to targeted therapy.

cancer biology↗