Arbovirus Transmission Predictions Are Affected by Both Temperature Data Source and Modeling Methodologies across Cities in Colombia.

Weather variables has been described as major drivers of vector proliferation and arbovirus transmission. Among them, temperature has consistently been found to be impactful in transmission dynamics, and models that incorporate temperature have been widely used to evaluate and forecast transmission or arboviruses like dengue, zika, or chikungunya virus. Further, there is growing evidence of the importance of micro-environmental temperatures in driving transmission of Aedes aegypti-borne viruses, as these mosquitoes tend to live within domiciles. Yet there is still a considerable gap in our understanding of how accounting for micro-environmental temperatures in models varies from the use of other widely-used, macro-level temperature measures. This effort combines field-collected data of both indoor and outdoor household associated temperatures and weather station temperature data from three Colombian cities to describe the relationship between the measures representing temperature at the micro- and macro-levels. These data indicate that weather station data may not accurately capture the temperature profiles of indoor micro-environments. However, using these data sources, the basic reproductive number for arboviruses was calculated by means of three modeling efforts to investigate whether temperature measure differences translated to differential transmission predictions. Across all three cities, it was determined that the modeling method was more often impactful rather than the temperature data-source, though no consistent pattern was immediately clear. This suggests that temperature data sources and modeling methods are important for precision in arbovirus transmission predictions, and more studies are needed to parse out this complex interaction.

Novel evaluation approach for molecular signature-based deconvolution methods.

The tumoral immune microenvironment (TIME) plays a key role in prognosis, therapeutic approach and pathophysiological understanding over oncological processes. Several computational immune cell-type deconvolution methods (DM), supported by diverse molecular signatures (MS), have been developed to uncover such TIME interplay from RNA-seq tumor biopsies. MS-DM pairs were benchmarked against each other by means of different metrics, such as Pearson's correlation, R2 and RMSE, but these only evaluate the linear association of the estimated proportion related to the expected one, missing the analysis of prediction-dependent bias trends and cell identification accuracy. We present a novel protocol composed of four tests allowing appropriate evaluation of the cell type identification performance and proportion prediction accuracy of molecular signature-deconvolution method pair by means of certainty and confidence cell-type identification scores (F1-score, distance to the optimal point and error rates) as well the Bland-Altman method for error-trend analysis. Our protocol was used to benchmark six state-of-the-art DMs (CIBERSORTx, DCQ, DeconRNASeq, EPIC, MIXTURE and quanTIseq) paired to five murine tissue-specific MSs, revealing a systematic overestimation of the number of different cell types across almost all methods.

Modeling cancer immunoediting in tumor microenvironment with system characterization through the ising-model Hamiltonian.

BACKGROUND AND OBJECTIVE: Cancer Immunoediting (CI) describes the cellular-level interaction between tumor cells and the Immune System (IS) that takes place in the Tumor Micro-Environment (TME). CI is a highly dynamic and complex process comprising three distinct phases (Elimination, Equilibrium and Escape) wherein the IS can both protect against cancer development as well as, over time, promote the appearance of tumors with reduced immunogenicity. Herein we present an agent-based model for the simulation of CI in the TME, with the objective of promoting the understanding of this process. METHODS: Our model includes agents for tumor cells and for elements of the IS. The actions of these agents are governed by probabilistic rules, and agent recruitment (including cancer growth) is modeled via logistic functions. The system is formalized as an analogue of the Ising model from statistical mechanics to facilitate its analysis. The model was implemented in the Netlogo modeling environment and simulations were performed to verify, illustrate and characterize its operation. RESULTS: A main result from our simulations is the generation of emergent behavior in silico that is very difficult to observe directly in vivo or even in vitro. Our model is capable of generating the three phases of CI; it requires only a couple of control parameters and is robust to these. We demonstrate how our simulated system can be characterized through the Ising-model energy function, or Hamiltonian, which captures the "energy" involved in the interaction between agents and presents it in clear and distinct patterns for the different phases of CI. CONCLUSIONS: The presented model is very flexible and robust, captures well the behaviors of the target system and can be easily extended to incorporate more variables such as those pertaining to different anti-cancer therapies. System characterization via the Ising-model Hamiltonian is a novel and powerful tool for a better understanding of CI and the development of more effective treatments. Since data of CI at the cellular level is very hard to procure, our hope is that tools such as this may be adopted to shed light on CI and related developing theories.

Tumour-derived transforming growth factor-ß signalling contributes to fibrosis in patients with cancer cachexia.

BACKGROUND: Cachexia is a paraneoplastic syndrome related with poor prognosis. The tumour micro-environment contributes to systemic inflammation and increased oxidative stress as well as to fibrosis. The aim of the present study was to characterise the inflammatory circulating factors and tumour micro-environment profile, as potentially contributing to tumour fibrosis in cachectic cancer patients. METHODS: 74 patients (weight stable cancer n = 31; cachectic cancer n = 43) diagnosed with colorectal cancer were recruited, and tumour biopsies were collected during surgery. Multiplex assay was performed to study inflammatory cytokines and growth factors. Immunohistochemistry analysis was carried out to study extracellular matrix components. RESULTS: Higher protein expression of inflammatory cytokines and growth factors such as epidermal growth factor, granulocyte-macrophage colony-stimulating factor, interferon-α, and interleukin (IL)-8 was observed in the tumour and serum of cachectic cancer patients in comparison with weight-stable counterparts. Also, IL-8 was positively correlated with weight loss in cachectic patients (P = 0.04; r = 0.627). Immunohistochemistry staining showed intense collagen deposition (P = 0.0006) and increased presence of α-smooth muscle actin (P < 0.0001) in tumours of cachectic cancer patients, characterizing fibrosis. In addition, higher transforming growth factor (TGF)-ß1, TGF-ß2, and TGF-ß3 expression (P = 0.003, P = 0.05, and P = 0.047, respectively) was found in the tumour of cachectic patients, parallel to p38 mitogen-activated protein kinase alteration. Hypoxia-inducible factor-1α mRNA content was significantly increased in the tumour of cachectic patients, when compared with weight-stable group (P = 0.005). CONCLUSIONS: Our results demonstrate TGF-ß pathway activation in the tumour in cachexia, through the (non-canonical) mitogen-activated protein kinase pathway. The results show that during cachexia, intratumoural inflammatory response contributes to the onset of fibrosis. Tumour remodelling, probably by TGF-ß-induced transdifferentiation of fibroblasts to myofibroblasts, induces unbalanced inflammatory cytokine profile, angiogenesis, and elevation of extracellular matrix components (EMC). We speculate that these changes may affect tumour aggressiveness and present consequences in peripheral organs.

Emergence of Three Dimensional Printed Cardiac Tissue: Opportunities and Challenges in Cardiovascular Diseases.

Three-dimensional (3D) printing, also known as additive manufacturing, was developed originally for engineering applications. Since its early advancements, there has been a relentless development in enthusiasm for this innovation in biomedical research. It allows for the fabrication of structures with both complex geometries and heterogeneous material properties. Tissue engineering using 3D bio-printers can overcome the limitations of traditional tissue engineering methods. It can match the complexity and cellular microenvironment of human organs and tissues, which drives much of the interest in this technique. However, most of the preliminary evaluations of 3Dprinted tissues and organ engineering, including cardiac tissue, relies extensively on the lessons learned from traditional tissue engineering. In many early examples, the final printed structures were found to be no better than tissues developed using traditional tissue engineering methods. This highlights the fact that 3D bio-printing of human tissue is still very much in its infancy and more work needs to be done to realise its full potential. This can be achieved through interdisciplinary collaboration between engineers, biomaterial scientists and molecular cell biologists. This review highlights current advancements and future prospects for 3D bio-printing in engineering ex vivo cardiac tissue and associated vasculature, such as coronary arteries. In this context, the role of biomaterials for hydrogel matrices and choice of cells are discussed. 3D bio-printing has the potential to advance current research significantly and support the development of novel therapeutics which can improve the therapeutic outcomes of patients suffering fatal cardiovascular pathologies.

Role of Cytokine Combinations on CD4+ T Cell Differentiation, Partial Polarization, and Plasticity: Continuous Network Modeling Approach.

Purpose: We put forward a theoretical and dynamical approach for the semi-quantitative analysis of CD4+ T cell differentiation, the process by which cells with different functions are derived from activated CD4+ T naïve lymphocytes in the presence of particular cytokine microenvironments. We explore the system-level mechanisms that underlie CD4+ T plasticity-the conversion of polarized cells to phenotypes different from those originally induced. Methods: In this paper, we extend a previous study based on a Boolean network to a continuous framework. The network includes transcription factors, signaling pathways, as well as autocrine and exogenous cytokines, with interaction rules derived using fuzzy logic. Results: This approach allows us to assess the effect of relative differences in the concentrations and combinations of exogenous and endogenous cytokines, as well as of the expression levels of diverse transcription factors. We found either abrupt or gradual differentiation patterns between observed phenotypes depending on critical concentrations of single or multiple environmental cytokines. Plastic changes induced by environmental cytokines were observed in conditions of partial phenotype polarization in the T helper 1 to T helper 2 transition. On the other hand, the T helper 17 to induced regulatory T-cells transition was highly dependent on cytokine concentrations, with TGFß playing a prime role. Conclusion: The present approach is useful to further understand the system-level mechanisms underlying observed patterns of CD4+ T differentiation and response to changing immunological challenges.

Cancer and the metastatic substrate.

Seventy percent of cancer patients have detectable metastases when they receive a diagnosis and 90% of cancer deaths result from metastases. These two facts emphasise the urgency for research to study the mechanisms and processes that enable metastasis. We need to develop a greater understanding of the cellular and molecular mechanisms that cause metastasis and also we need to do more. We must also consider the micro- and macro-environmental factors that influence this disease. Studying this environmental context has led us to update the 'seed and soil' hypothesis which dates back to the 19th century. This theory describes cancerous cells as seeds and the substrate as the soil in target organs though this may seem antiquated. Nonetheless, the tissue specificity that researchers have recently observed in metastatic colonisation supports the validity of the seed and soil theory. We now know that the metastatic potential of a tumour cell depends on multiple, reciprocal interactions between the primary tumour and distant sites. These interactions determine tumour progression. Studies of metastasis have allowed us to develop treatments that focus on therapeutic effectiveness. These new treatments account for the frequent metastasis of some tumours to target organs such as bones, lungs, brain, and liver. The purpose of this review is first to describe interactions between the cellular and molecular entities and the target organ tumour environment that enables metastasis. A second aim is to describe the complex mechanisms that mediate these interactions.