Biology subjects

Espinosa, E.

Publications and source records attributed to Espinosa, E..

15 recordsLinked to original sources

bioRxiv2024

MatP delays the segregation of chromosome termini independently of cell division and tetramer formation in Vibrio cholerae

Vibrio cholerae harbours a primary chromosome derived from the monochromosomal ancestor of the Vibrionales (ChrI) and a secondary chromosome derived from a megaplasmid (ChrII). The coordinated segregation of the replication terminus of both chromosomes (TerI and TerII) determines when and where cell division occurs. ChrI encodes a homolog of Escherichia coli MatP, a protein that binds to a DNA motif (matS) that is overrepresented in replication termini. Here, we use a combination of deep sequencing and fluorescence microscopy techniques to show that V. cholerae MatP structures TerI and TerII into macrodomains, targets them to mid-cell during replication, and delays their segregation, thus supporting that ChrII behaves as a bona fide chromosome. We further show that the extent of the segregation delay mediated by MatP depends on the number and local density of matS sites, and is independent of its assembly into tetramers and any interaction with the divisome, in contrast to what has been previously observed in E. coli.

microbiology↗

bioRxiv2023

Unsaturated fatty acid synthesis is associated with poor prognosis in pediatric neuroblastoma and is differentially regulated by MYCN and tumor suppressor microRNAs.

MYCN amplification and disruption of tumor suppressor microRNA (TSmiR) function are central drivers of poor outcomes in neuroblastoma (NB). MYC, MYCN, and TSmiRs regulate glucose metabolism; however, their role in unsaturated fatty acid synthesis (UFAS) remains poorly understood. Here we show that de novo and UFAS pathway genes FASN, ELOVL6, SCD, FADS2, and FADS1 are upregulated in high-risk NB and are associated with poor prognosis. RNA-Seq analysis of eight human NB cell lines revealed parallel UFAS gene expression patterns. Consistent with this, we found that NB-related TSmiRs were predicted to extensively target these genes. In addition, we observed that both MYC and MYCN upregulated UFAS pathway genes while suppressing TSmiR host gene expression, thereby creating a possible UFAS regulatory network between MYCN and TSmiRs in NB. Furthermore, NB cells are high in omega 9 ({omega}9) unsaturated fatty acids that can be synthesized de novo and low in both {omega}6 and {omega}3, providing a plausible means for NB to limit cell-autonomous immune stimulation and reactive oxygen species (ROS)-driven apoptosis from {omega}6 and {omega}3 unsaturated fatty acid derivatives, respectively. We propose a model in which the UFAS pathway, through novel regulation by MYCN and TSmiRs, plays a key role in neuroblastoma pathology with implications for other MYC-driven cancers.

cancer biology↗

bioRxiv2021

T helper cell-licensed mast cells promote inflammatory Th17 cells

CD4+ T helper cells (Th) infiltrate sites of inflammation and orchestrate the immune response by instructing local leukocytes. Mast cells (MCs) are tissue sentinel cells particularly abundant in skin and mucosa. Here, we analyzed the interplay between human MCs and Th cells and, through the application of RNAseq and functional assays, showed that Th cells induced a specific transcriptomic program in helped MCs (named here MCTH) driving them toward an inflammatory phenotype. The gene signature of MCTH indicated that MCs helped by Th cell acquired in turn the capacity to regulate effector T cell response through wide-range of soluble and membrane ligands. Accordingly, we showed that MCTH promoted Th17 cells and notably an inflammatory subset of Th17, producing both IFN-{gamma} and GM-CSF, through a PGE2 and IL-1{beta} axis. Our findings demonstrate that activated effector/memory CD4+ T cells activate and instruct resting MCs toward a specific differentiated pro-inflammatory phenotype endowed with the capacity to speak back to effector T cells and to mold their functions.

immunology↗

bioRxiv2021

IL-17+ mast cell/T helper cell axis in the early stages of acne

Acne is a multifactorial disease driven by physiological changes occurring during puberty in the pilosebaceous unit (PSU) that leads to sebum overproduction and a dysbiosis involving notably Cutibacterium acnes. These changes in the PSU microenvironment lead to a shift from a homeostatic to an inflammatory state. Indeed, immunohistochemical analyses have revealed that inflammation and lymphocyte infiltration can be detected even in the infraclinical acneic stages, highlighting the importance of the early stages of the disease. In this study, we utilized a robust multi-pronged approach that included flow cytometry, confocal microscopy, and bioinformatics to comprehensively characterize the evolution of the infiltrating and resident immune cell populations in acneic lesions, beginning in the early stages of their development. Using a discovery cohort of 15 patients, we demonstrated that the composition of immune cell infiltrate is highly dynamic in nature, with the relative abundance of different cell types changing significantly as a function of clinical lesion stage. Within the stages examined, we identified a large population of CD69+ CD4+ T cells, several populations of activated antigen presenting cells, and activated mast cells producing IL-17. IL-17+ mast cells were preferentially located in CD4+ T cell rich areas and we showed that activated CD4+ T cells license mast cells to produce IL-17. Our study reveals that mast cells are the main IL-17 producers in the early stage of acne, underlying the importance of targeting the IL-17+ mast cell/T helper cell axis in therapeutic approaches.

immunology↗

bioRxiv2020

Molecular characterization of triple negative breast cancer formaldehyde-fixed paraffin-embedded samples by data independent acquisition proteomics

Triple negative breast cancer (TNBC) accounts for 15-20% of all breast carcinomas and it is clinically characterized by an aggressive phenotype and bad prognosis. TNBC does not benefit from any targeted therapy, so further characterization is needed to define subgroups with potential therapeutic value. In this work, the proteomes of one hundred twenty-five formalin-fixed paraffin-embedded samples from patients diagnosed with triple negative breast cancer were analyzed by mass spectrometry using data-independent acquisition. Hierarchical clustering, probabilistic graphical models and Significance Analysis of Microarrays were used to characterize molecular groups. Additionally, a predictive signature related with relapse was defined. Two molecular groups with differences in several biological processes as glycolysis, translation and immune response, were defined in this cohort, and a prognostic signature based on the abundance of proteins RBM3 and NIPSNAP1 was defined. This predictor split the population into low-risk and high-risk groups. The differential processes identified between the two molecular groups may serve to design new therapeutic strategies in the future and the prognostic signature could be useful to identify a population at high-risk of relapse that could be directed to clinical trials.

cancer biology↗

bioRxiv2020

Stochastic asymmetric repartition of lytic machinery in dividing human CD8+ T cells generates heterogeneous killing behavior

Cytotoxic immune cells are endowed with a high degree of heterogeneity in their lytic function, but how this heterogeneity is generated is still an open question. We therefore investigated if human CD8+ T cells could segregate their lytic components during telophase, using imaging flow cytometry, confocal microscopy and live cell imaging. We show that CD107a+-intracellular vesicles, perforin and granzyme B unevenly segregate in a constant fraction of telophasic cells during each division round. Mathematical modeling posits that unequal lytic molecule inheritance by daughter cells results from the random distribution of lytic granules on the two sides of the cleavage furrow. Finally, we establish that the level of lytic compartment in individual CTL dictates CTL killing capacity. Together, our results show the stochastic asymmetric distribution of effector molecules in dividing CD8+ T cells. They propose uneven mitotic repartition of pre-packaged lytic components as a mechanism generating non-hereditary functional heterogeneity in CTL.

immunology↗

bioRxiv2020

L-arabinose induces the formation of viable non-proliferating Vibrio cholerae spheroplasts

A general survival strategy of many life forms faced with harmful growth conditions is to enter into a non-proliferating state until conditions suitable for growth are restored. In bacteria, this survival strategy is associated with antimicrobial tolerance, chronic infections and environmental dispersion. In particular, the agent of the deadly human disease cholera, Vibrio cholerae, undergoes a morphological transition from a rod-shaped proliferative form to a spherical non-proliferating form after exposure to cold or cell wall targeting antibiotics. Growth is resumed when the adverse conditions have ceased. Here, we show that a component of the hemicellulose and pectin of terrestrial plants, L-arabinose, triggers the formation of non-proliferating V. cholerae spherical cells, which are able to return to growth when L-arabinose is removed from the growth medium. We found that the cell wall of L-arabinose treated V. cholerae cells has a peptidoglycan composition similar to the cell wall of spheroplasts and that they revert to a wild-type morphology through the formation of branched cells like L-forms. Unlike L-forms, however, they are osmo-resistant. Through a random Tn genetic screen for mutants insensitive to L-arabinose, we identified genes involved in the uptake and catabolism of galactose and in glycolysis. We hypothesize that L-arabinose is enzymatically processed by the galactose catabolism and glycolysis pathways until it is transformed in a product that cannot be further recognized by V. cholerae enzymes. Accumulation of this enzymatic by-product triggers the formation of viable non-dividing cell wall deficient spherical cells.

microbiology↗

bioRxiv2019

High-resolution whole-genome analysis of sister-chromatid cohesion

Sister-chromatid cohesion describes the orderly association of newly-replicated DNA molecules behind replication forks. It plays an essential role in the maintenance and the faithful transmission of genetic information. It is created by DNA topological links and proteinaceous bridges, whose formation and deposition could be potentially affected by many different DNA-binding proteins. However, a mean to analyse local variations in the duration of cohesion on a whole genome was lacking. Here, we present a High-throughput methodology to monitor Sister Chromatid Contacts (Hi-SC2), and show that it permits to analyse locus-specific variations in sister-chromatid cohesion over the whole length of chromosomes.

genomics↗

bioRxiv2018

Computational metabolism modeling predicts risk of distant relapse-free survival in breast cancer patients

AimsDifferences in metabolism among breast cancer subtypes suggest that metabolism plays an important role in this disease. Flux Balance Analysis is used to explore these differences as well as drug response.\n\nMaterials & MethodsProteomics data from breast tumors were obtained by mass-spectrometry. Flux Balance Analysis was performed to study metabolic networks. Flux activities from metabolic pathways were calculated and used to build prognostic models.\n\nResultsFlux activities of vitamin A, tetrahydrobiopterin and beta-alanine metabolism pathways split our population into low- and high-risk patients. Additionally, flux activities of glycolysis and glutamate metabolism split triple negative tumors into low- and high-risk groups.\n\nConclusionsFlux activities summarize Flux Balance Analysis data and can be associated with prognosis in cancer.

cancer biology↗

bioRxiv2018

Probabilistic graphical models and computational metabolic models applied to the analysis of metabolomics data in breast cancer

Metabolomics has a great potential in the development of new biomarkers in cancer. In this study, metabolomics and gene expression data from breast cancer tumor samples were analyzed, using (1) probabilistic graphical models to define associations using quantitative data without other a priori information; and (2) Flux Balance Analysis and flux activities to characterize differences in metabolic pathways. On the one hand, both analyses highlighted the importance of glutamine in breast cancer. Moreover, cell experiments showed that treating breast cancer cells with drugs targeting glutamine metabolism significantly affects cell viability. On the other hand, these computational methods suggested some hypotheses and have demonstrated their utility in the analysis of metabolomics data and in associating metabolomics with patients clinical outcome.

systems biology↗

bioRxiv2018

Novel Molecular Classification of Muscle-Invasive Bladder Cancer Opens New Treatment Opportunities

BackgroundMuscle-invasive bladder tumors are associated with high risk of relapse and metastasis even after neoadjuvant chemotherapy and radical cystectomy. Therefore, further therapeutic options are needed and molecular characterization of the disease may help to identify new targets.\n\nObjectiveThe aim of this work is to characterize muscle-invasive bladder tumors at molecular levels using computational analyses.\n\nDesign, Settings and ParticipantsThe TCGA cohort of muscle-invasive bladder cancer patients was used to describe these tumors.\n\nOutcome Measurements and Statistical AnalysisProbabilistic graphical models, layer analyses based on sparse k-means coupled with Consensus Cluster, and Flux Balance Analysis were applied to characterize muscle-invasive bladder tumors at functional level.\n\nResultsLuminal and Basal groups were identified, and an immune molecular layer with independent value was also described. Luminal tumors had decreased activity in the nodes of epidermis development and extracellular matrix, and increased activity in the node of steroid metabolism leading to a higher expression of androgen receptor.\n\nThis fact points to androgen receptor as a therapeutic target in this group. Basal tumors were highly proliferative according to Flux Balance Analysis, which make these tumors good candidates for neoadjuvant chemotherapy. Immune-high group had higher expression of immune biomarkers, suggesting that this group may benefit from immune therapy.\n\nConclusionsOur approach, based on layer analyses, established a Luminal group candidate for androgen receptor inhibitor therapy, a proliferative Basal group which seems to be a good candidate for chemotherapy, and an immune-high group candidate for immunotherapy.\n\nPatient SummaryMuscle-invasive bladder cancer has a poor prognosis in spite of appropriate therapy. Therefore, it is still necessary to characterize these tumors to propose new therapeutic targets. In this work we used computational analyses to characterize these tumors and propose treatments.

cancer biology↗

bioRxiv2018

Directed Bayesian Networks established functional differences between breast cancer subtypes

Breast cancer is a heterogeneous disease. In clinical practice, tumors are classified as hormonal receptor positive, Her2 positive and triple negative tumors. In previous works, our group defined a new hormonal receptor positive subgroup, the TN-like subtype, which has a prognosis and a molecular profile more similar to triple negative tumors. In this study, proteomics and Bayesian networks were used to characterize protein relationships in 106 breast tumor samples. Components obtained by these methods had a clear functional structure. The analysis of these components suggested differences in processes such as metastasis or proliferation between breast cancer subtypes, including our new subtype TN-like. In addition, one of the components, mainly related with metastasis, had prognostic value in this cohort. Functional approaches allow to build hypotheses about regulatory mechanisms and to establish new relationships among proteins in the breast cancer context.\n\nAuthor SummaryBreast cancer classification in the clinical practice is defined by three biomarkers (estrogen receptor, progesterone receptor and HER2) into hormone receptor positive, HER2+ and triple negative breast cancer (TNBC). Our group recently described a new ER+ subtype with molecular characteristics and prognosis similar to TNBC. In this study we propose a mathematical method, the Bayesian networks, as a useful tool to study protein interactions and differential biological processes in breast cancer subtypes, characterizing differences in relevant processes such as proliferation or metastasis and associated them with patient prognosis.

cancer biology↗

bioRxiv2017

Probabilistic Graphical Models Relate Immune Status With Response To Neoadjuvant Chemo-Therapy In Breast Cancer

Breast cancer is the most frequent tumor in women and its incidence is increasing. Neoadjuvant chemotherapy has become standard of care as a complement to surgery in locally advanced or poor-prognosis early stage disease. The achievement of a complete response to neoadjuvant chemotherapy correlates with prognosis but it is not possible to predict who will obtain an excellent response. The molecular analysis of the tumor offers a unique opportunity to unveil predictive factors. In this work, gene expression profiling in 279 tumor samples from patients receiving neoadjuvant chemotherapy was performed and probabilistic graphical models were used. This approach enables addressing biological and clinical questions from a Systems Biology perspective, allowing to deal with large gene expression data and their interactions. Tumors presenting complete response to neoadjuvant chemotherapy had a higher activity of immune related functions compared to resistant tumors. Similarly, samples from complete responders presented higher expression of lymphocyte cell lineage markers, immune-activating and immune-suppressive markers, which may correlate with tumor infiltration by lymphocytes (TILs). These results suggest that the patients immune system plays a key role in tumor response to neoadjuvant treatment. However, future studies with larger cohorts are necessary to validate these hypotheses.

cancer biology↗

bioRxiv2017

Melanoma proteomics unravels major differences related to mutational status

The aim of the study was to explore the molecular differences between melanoma tumor subtypes, based on BRAF and NRAS mutational status. Fourteen formalin-fixed, paraffin- embedded melanoma samples were analyzed using a high-throughput proteomics approach, coupled with probabilistic graphical models and Flux Balance Analysis, to characterize these differences. Proteomics analyses showed differences in expression of proteins related with fatty acid metabolism, melanogenesis and extracellular space between BRAF mutated and BRAF non-mutated melanoma tumors. Additionally, probabilistic graphical models showed differences between melanoma subgroups at biological processes such as melanogenesis or metabolism. On the other hand, Flux Balance Analysis predicts a higher tumor growth rate in BRAF mutated melanoma samples. In conclusion, differential biological processes between melanomas showing a specific mutational status can be detected using combined proteomics and computational approaches.

cancer biology↗

bioRxiv2017

Molecular characterization of breast cancer cell response to metabolic drugs

Metabolic reprogramming is a hallmark of cancer. We and other authors have previously shown that breast cancer subtypes present metabolism differences. In this study, breast cancer cell lines were treated with metformin and rapamycin. The response was heterogeneous across various breast cancer cells, leading to cell cycle disruption in specific conditions. The molecular effects of these treatments were characterized using sublethal doses, SNP genotyping and mass spectrometry-based proteomics. Protein expression was analyzed using probabilistic graphical models, showing that treatments elicit various responses in some biological processes, providing insights into cell responses to metabolism drugs. Moreover, a flux balance analysis approach using protein expression values was applied, showing that predicted growth rates were comparable with cell viability measurements and suggesting an increase in reactive oxygen species response enzymes due to metformin treatment. In addition, a method to assess flux differences in whole pathways was proposed. Our results show that these various approaches provide complementary information, which can be used to suggest hypotheses about the drugs mechanisms of action and the response to drugs that target metabolism.

systems biology↗