ALEXANDRE FERREIRA RAMOS
Projetos de Pesquisa
Unidades Organizacionais
SIN-86, EACH - Docente
LIM/24 - Laboratório de Oncologia Experimental, Hospital das Clínicas, Faculdade de Medicina - Líder
LIM/26 - Laboratório de Pesquisa em Cirurgia Experimental, Hospital das Clínicas, Faculdade de Medicina
LIM/24 - Laboratório de Oncologia Experimental, Hospital das Clínicas, Faculdade de Medicina - Líder
LIM/26 - Laboratório de Pesquisa em Cirurgia Experimental, Hospital das Clínicas, Faculdade de Medicina
9 resultados
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- A comparative analysis of noise properties of stochastic binary models for a self-repressing and for an externally regulating gene(2020) GIOVANINI, Guilherme; SABINO, Alan U.; BARROS, Luciana R. C.; RAMOS, Alexandre F.This manuscript presents a comparison of noise properties exhibited by two stochastic binary models for: (i) a self-repressing gene; (ii) a repressed or activated externally regulating one. The stochastic models describe the dynamics of probability distributions governing two random variables, namely, protein numbers and the gene state as ON or OFF. In a previous work, we quantify noise in protein numbers by means of its Fano factor and write this quantity as a function of the covariance between the two random variables. Then we show that distributions governing the number of gene products can be super-Fano, Fano or sub-Fano if the covariance is, respectively, positive, null or negative. The latter condition is exclusive for the self-repressing gene and our analysis shows the conditions for which the Fano factor is a sufficient classifier of fluctuations in gene expression. In this work, we present the conditions for which the noise on the number of gene products generated from a self-repressing gene or an externally regulating one are quantitatively similar. That is important for inference of gene regulation from noise in gene expression quantitative data. Our results contribute to a classification of noise function in biological systems by theoretically demonstrating the mechanisms underpinning the higher precision in expression of a self-repressing gene in comparison with an externally regulated one.
- Binary Expression Enhances Reliability of Messaging in Gene Networks(2020) GAMA, Leonardo R.; GIOVANINI, Guilherme; BALAZSI, Gabor; RAMOS, Alexandre F.The promoter state of a gene and its expression levels are modulated by the amounts of transcription factors interacting with its regulatory regions. Hence, one may interpret a gene network as a communicating system in which the state of the promoter of a gene (the source) is communicated by the amounts of transcription factors that it expresses (the message) to modulate the state of the promoter and expression levels of another gene (the receptor). The reliability of the gene network dynamics can be quantified by Shannon's entropy of the message and the mutual information between the message and the promoter state. Here we consider a stochastic model for a binary gene and use its exact steady state solutions to calculate the entropy and mutual information. We show that a slow switching promoter with long and equally standing ON and OFF states maximizes the mutual information and reduces entropy. That is a binary gene expression regime generating a high variance message governed by a bimodal probability distribution with peaks of the same height. Our results indicate that Shannon's theory can be a powerful framework for understanding how bursty gene expression conciliates with the striking spatio-temporal precision exhibited in pattern formation of developing organisms.
- Lessons and perspectives for applications of stochastic models in biological and cancer research(2018) SABINO, Alan U.; VASCONCELOS, Miguel Fs; SITTONI, Misaki Yamada; LAUTENSCHLAGER, Willian W.; QUEIROGA, Alexandre S.; MORAIS, Mauro Cc; RAMOS, Alexandre F.The effects of randomness, an unavoidable feature of intracellular environments, are observed at higher hierarchical levels of living matter organization, such as cells, tissues, and organisms. Additionally, the many compounds interacting as a well-orchestrated network of reactions increase the difficulties of assessing these systems using only experiments. This limitation indicates that elucidation of the dynamics of biological systems is a complex task that will benefit from the establishment of principles to help describe, categorize, and predict the behavior of these systems. The theoretical machinery already available, or ones to be discovered to help solve biological problems, might play an important role in these processes. Here, we demonstrate the application of theoretical tools by discussing some biological problems that we have approached mathematically: fluctuations in gene expression and cell proliferation in the context of loss of contact inhibition. We discuss the methods that have been employed to provide the reader with a biologically motivated phenomenological perspective of the use of theoretical methods. Finally, we end this review with a discussion of new research perspectives motivated by our results.
- New strategies for targeting kinase networks in cancer(2021) YESILKANAL, Ali E.; JOHNSON, Gary L.; RAMOS, Alexandre F.; ROSNER, Marsha RichTargeted strategies against specific driver molecules of cancer have brought about many advances in cancer treatment since the early success of the first small-molecule inhibitor Gleevec. Today, there are a multitude of targeted therapies approved by the Food and Drug Administration for the treatment of cancer. However, the initial efficacy of virtually every targeted treatment is often reversed by tumor resistance to the inhibitor through acquisition of new mutations in the target molecule, or reprogramming of the epigenome, transcriptome, or kinome of the tumor cells. At the core of this clinical problem lies the assumption that targeted treatments will only be efficacious if the inhibitors are used at their maximum tolerated doses. Such aggressive regimens create strong selective pressure on the evolutionary progression of the tumor, resulting in resistant cells. High-dose single agent treatments activate alternative mechanisms that bypass the inhibitor, while high-dose combinatorial treatments suffer from increased toxicity resulting in treatment cessation. Although there is an arsenal of targeted agents being tested clinically and preclinically, identifying the most effective combination treatment plan remains a challenge. In this review, we discuss novel targeted strategies with an emphasis on the recent cross-disciplinary studies demonstrating that it is possible to achieve antitumor efficacy without increasing toxicity by adopting low-dose multitarget approaches to treatment of cancer and metastasis.
- A comparative analysis of noise properties of stochastic binary models for a self-repressing and for an externally regulating gene (vol 17, pg 5477, 2020)(2020) GIOVANINI, Guilherme; SABINO, Alan U.; BARROS, Luciana R. C.; RAMOS, Alexandre F.
- A Stochastic Binary Model for the Regulation of Gene Expression to Investigate Responses to Gene Therapy(2022) GIOVANINI, Guilherme; BARROS, Luciana R. C.; GAMA, Leonardo R.; TORTELLI, Tharcisio C.; RAMOS, Alexandre F.Simple Summary Gene editing technologies reached a turning point toward epigenetic modulation for cancer treatment. Gene networks are complex systems composed of multiple non-trivially coupled elements capable of reliably processing dynamical information from the environment despite unavoidable randomness. However, this functionality is lost when the cells are in a diseased state. Hence, gene-editing-based therapeutic design can be viewed as a gene network dynamics modulation toward a healthy state. Enhancement of this control relies on mathematical models capable of effectively describing the regulation of stochastic gene expression. We use a two-state stochastic model for gene expression to investigate treatment response with a switching target gene. We show the necessity of modulating multiple gene-expression-related processes to reach a heterogeneity-reduced specific response using epigenetic-targeting cancer treatment designs. Our approach can be used as an additional tool for developing epigenetic-targeting treatments. In this manuscript, we use an exactly solvable stochastic binary model for the regulation of gene expression to analyze the dynamics of response to a treatment aiming to modulate the number of transcripts of a master regulatory switching gene. The challenge is to combine multiple processes with different time scales to control the treatment response by a switching gene in an unavoidable noisy environment. To establish biologically relevant timescales for the parameters of the model, we select the RKIP gene and two non-specific drugs already known for changing RKIP levels in cancer cells. We demonstrate the usefulness of our method simulating three treatment scenarios aiming to reestablish RKIP gene expression dynamics toward a pre-cancerous state: (1) to increase the promoter's ON state duration; (2) to increase the mRNAs' synthesis rate; and (3) to increase both rates. We show that the pre-treatment kinetic rates of ON and OFF promoter switching speeds and mRNA synthesis and degradation will affect the heterogeneity and time for treatment response. Hence, we present a strategy for reaching increased average mRNA levels with diminished heterogeneity while reducing drug dosage by simultaneously targeting multiple kinetic rates that effectively represent the chemical processes underlying the regulation of gene expression. The decrease in heterogeneity of treatment response by a target gene helps to lower the chances of emergence of resistance. Our approach may be useful for inferring kinetic constants related to the expression of antimetastatic genes or oncogenes and for the design of multi-drug therapeutic strategies targeting the processes underpinning the expression of master regulatory genes.
- Stochastic model of contact inhibition and the proliferation of melanoma in situ(2017) MORAIS, Mauro Cesar Cafundo; STUHL, Izabella; SABINO, Alan U.; LAUTENSCHLAGER, Willian W.; QUEIROGA, Alexandre S.; TORTELLI JR., Tharcisio Citrangulo; CHAMMAS, Roger; SUHOV, Yuri; RAMOS, Alexandre F.Contact inhibition is a central feature orchestrating cell proliferation in culture experiments; its loss is associated with malignant transformation and tumorigenesis. We performed a co-culture experiment with human metastatic melanoma cell line (SKMEL-147) and immortalized keratinocyte cells (HaCaT). After 8 days a spatial pattern was detected, characterized by the formation of clusters of melanoma cells surrounded by keratinocytes constraining their proliferation. In addition, we observed that the proportion of melanoma cells within the total population has increased. To explain our results we propose a spatial stochastic model (following a philosophy of the Widom-Rowlinson model from Statistical Physics and Molecular Chemistry) which considers cell proliferation, death, migration, and cell-to-cell interaction through contact inhibition. Our numerical simulations demonstrate that loss of contact inhibition is a sufficient mechanism, appropriate for an explanation of the increase in the proportion of tumor cells and generation of spatial patterns established in the conducted experiments.
- Myokines in treatment-na & iuml;ve patients with cancer-associated cachexia(2021) CASTRO, Gabriela S. de; CORREIA-LIMA, Joanna; SIMOES, Estefania; ORSSO, Camila E.; XIAO, Jingjie; GAMA, Leonardo R.; GOMES, Silvio P.; GONCALVES, Daniela Caetano; COSTA, Raquel G. F.; RADLOFF, Katrin; LENZ, Ulrike; TARANKO, Anna E.; BIN, Fang Chia; FORMIGA, Fernanda B.; GODOY, Louisie G. L. de; SOUZA, Rafael P. de; NUCCI, Luis H. A.; FEITOZA, Mario; CASTRO, Claudio C. de; TOKESHI, Flavio; ALCANTARA, Paulo S. M.; OTOCH, Jose P.; RAMOS, Alexandre F.; LAVIANO, Alessandro; COLETTI, Dario; MAZURAK, Vera C.; PRADO, Carla M.; SEELAENDER, MariliaCancer-associated cachexia is a complex metabolic syndrome characterized by weight loss and systemic inflammation. Muscle loss and fatty infiltration into muscle are associated with poor prognosis in cancer patients. Skeletal muscle secretes myokines, factors with autocrine, paracrine and/or endocrine action, which may be modified by or play a role in cachexia. This study examined myokine content in the plasma, skeletal muscle and tumor homogenates from treatment-na & iuml;ve patients with gastric or colorectal stages I-IV cancer with cachexia (CC, N = 62), or not (weight stable cancer, WSC, N = 32). Myostatin, interleukin (IL) 15, follistatin-like protein 1 (FSTL-1), fatty acid binding protein 3 (FABP3), irisin and brain-derived neurotrophic factor (BDNF) protein content in samples was measured with Multiplex technology; body composition and muscle lipid infiltration were evaluated in computed tomography, and quantification of triacylglycerol (TAG) in the skeletal muscle. Cachectic patients presented lower muscle FSTL-1 expression (p = 0.047), higher FABP3 plasma content (p = 0.0301) and higher tumor tissue expression of FABP3 (p = 0.0182), IL-15 (p = 0.007) and irisin (p = 0.0110), compared to WSC. Neither muscle TAG content, nor muscle attenuation were different between weight stable and cachectic patients. Lumbar adipose tissue (AT) index, visceral AT index and subcutaneous AT index were lower in CC (p = 0.0149, p = 0.0455 and p = 0.0087, respectively), who also presented lower muscularity in the cohort (69.2% of patients; p = 0.0301), compared to WSC. The results indicate the myokine profile in skeletal muscle, plasma and tumor is impacted by cachexia. These findings show that myokines eventually affecting muscle wasting may not solely derive from the muscle itself (as the tumor also may contribute to the systemic scenario), and put forward new perspectives on cachexia treatment targeting myokines and associated receptors and pathways. (c) 2020 The Author(s).
- Synthetic enhancer design by in silico compensatory evolution reveals flexibility and constraint in cis-regulation(2017) BARR, Kenneth A.; MARTINEZ, Carlos; MORAN, Jennifer R.; KIM, Ah-Ram; RAMOS, Alexandre F.; REINITZ, JohnBackground: Models that incorporate specific chemical mechanisms have been successful in describing the activity of Drosophila developmental enhancers as a function of underlying transcription factor binding motifs. Despite this, the minimum set of mechanisms required to reconstruct an enhancer from its constituent parts is not known. Synthetic biology offers the potential to test the sufficiency of known mechanisms to describe the activity of enhancers, as well as to uncover constraints on the number, order, and spacing of motifs. Results: Using a functional model and in silico compensatory evolution, we generated putative synthetic even-skipped stripe 2 enhancers with varying degrees of similarity to the natural enhancer. These elements represent the evolutionary trajectories of the natural stripe 2 enhancer towards two synthetic enhancers designed ab initio. In the first trajectory, spatially regulated expression was maintained, even after more than a third of binding sites were lost. In the second, sequences with high similarity to the natural element did not drive expression, but a highly diverged sequence about half the length of the minimal stripe 2 enhancer drove ten times greater expression. Additionally, homotypic clusters of Zelda or Stat92E motifs, but not Bicoid, drove expression in developing embryos. Conclusions: Here, we present a functional model of gene regulation to test the degree to which the known transcription factors and their interactions explain the activity of the Drosophila even-skipped stripe 2 enhancer. Initial success in the first trajectory showed that the gene regulation model explains much of the function of the stripe 2 enhancer. Cases where expression deviated from prediction indicates that undescribed factors likely act to modulate expression. We also showed that activation driven Bicoid and Hunchback is highly sensitive to spatial organization of binding motifs. In contrast, Zelda and Stat92E drive expression from simple homotypic clusters, suggesting that activation driven by these factors is less constrained. Collectively, the 40 sequences generated in this work provides a powerful training set for building future models of gene regulation.