Investigating the Effect of Brain Size on Deformation Magnitude Using Subject-Specific Finite Element Models.

Abstract In the last decade, computational models of the brain have become the gold standard tool for investigating traumatic brain injury (TBI) mechanisms and developing novel protective equipment and other safety countermeasures. However, most studies utilizing finite element (FE) models of the brain have been conducted using models developed to represent the average neuroanatomy of a target demographic, such as the 50th percentile male. Although this is an efficient strategy, it neglects normal anatomical variations present within the population and their contributions on the brain's deformation response. As a result, the contributions of structural characteristics of the brain, such as brain volume, on brain deformation are not well understood. The objective of this study was to develop a set of statistical regression models relating measures of the size and shape of the brain to the resulting brain deformation. This was performed using a database of 125 subject-specific models, simulated under six independent head kinematic boundary conditions, spanning a range of impact modes (frontal, oblique, side), severity (non-injurious and injurious), and environments (volunteer, automotive, and American football). Two statistical regression techniques were utilized. First, simple linear regression (SLR) models were trained to relate intracranial volume (ICV) and the 95th percentile of maximum principal strain (MPS-95) for each of the impact cases. Second, a partial least squares regression model was constructed to predict MPS-95 based on the affine transformation parameters from each subject, representing the size and shape of their brain, considering the six impact conditions collectively. Both techniques indicated a strong linear relationship between ICV and MPS-95, with MPS-95 varying by approximately 5% between the smallest and largest brains. This difference represented up to 40% of the mean strain across all subjects. This study represents a comprehensive assessment of the relationships between brain anatomy and deformation, which is crucial for the development of personalized protective equipment, identifying individuals at higher risk of injury, and using computational models to aid clinical diagnostics of TBI.

Quantifying the Effect of Sex and Neuroanatomical Biomechanical Features on Brain Deformation Response in Finite Element Brain Models.

Recent automotive epidemiology studies have concluded that females have significantly higher odds of sustaining a moderate brain injury or concussion than males in a frontal crash after controlling for multiple crash and occupant variables. Differences in neuroanatomical features, such as intracranial volume (ICV), have been shown between male and female subjects, but how these sex-specific neuroanatomical differences affect brain deformation is unknown. This study used subject-specific finite element brain models, generated via registration-based morphing using both male and female magnetic resonance imaging scans, to investigate sex differences of a variety of neuroanatomical features and their effect on brain deformation; additionally, this study aimed to determine the relative importance of these neuroanatomical features and sex on brain deformation metrics for a single automotive loading environment. Based on the Bayesian linear mixed models, sex had a significant effect on ICV, white matter volume and gray matter volume, as well as a section of cortical gray matter regions' thicknesses and volumes; however, after these neuroanatomical features were accounted for in the statistical model, sex was not a significant factor in predicting brain deformation. ICV had the highest relative effect on the brain deformation metrics assessed. Therefore, ICV should be considered when investigating both brain injury biomechanics and injury risk.

Integrating Human and Nonhuman Primate Data to Estimate Human Tolerances for Traumatic Brain Injury.

Traumatic brain injury (TBI) contributes to a significant portion of the injuries resulting from motor vehicle crashes, falls, and sports collisions. The development of advanced countermeasures to mitigate these injuries requires a complete understanding of the tolerance of the human brain to injury. In this study, we developed a new method to establish human injury tolerance levels using an integrated database of reconstructed football impacts, subinjurious human volunteer data, and nonhuman primate data. The human tolerance levels were analyzed using tissue-level metrics determined using harmonized species-specific finite element (FE) brain models. Kinematics-based metrics involving complete characterization of angular motion (e.g., diffuse axonal multi-axial general evaluation (DAMAGE)) showed better power of predicting tissue-level deformation in a variety of impact conditions and were subsequently used to characterize injury tolerance. The proposed human brain tolerances for mild and severe TBI were estimated and presented in the form of injury risk curves based on selected tissue-level and kinematics-based injury metrics. The application of the estimated injury tolerances was finally demonstrated using real-world automotive crash data.

Toward subject-specific evaluation: methods of evaluating finite element brain models using experimental high-rate rotational brain motion.

Computational models of the brain have become the gold standard in biomechanics to understand, predict, and mitigate traumatic brain injuries. Many models have been created and evaluated with limited experimental data and without accounting for subject-specific morphometry of the specimens in the dataset. Recent advancements in the measurement of brain motion using sonomicrometry allow for a comprehensive evaluation of brain model biofidelity using a high-rate, rotational brain motion dataset. In this study, four methods were used to determine the best technique to compare nodal displacement to experimental brain motion, including a new morphing method to match subject-specific inner skull geometry. Three finite element brain models were evaluated in this study: the isotropic GHBMC and SIMon models, as well as an anisotropic model with explicitly embedded axons (UVA-EAM). Using a weighted cross-correlation score (between 0 and 1), the anisotropic model yielded the highest average scores across specimens and loading conditions ranging from 0.53 to 0.63, followed by the isotropic GHBMC with average scores ranging from 0.46 to 0.58, and then the SIMon model with average scores ranging from 0.36 to 0.51. The choice of comparison method did not significantly affect the cross-correlation score, and differences of global strain up to 0.1 were found for the morphed geometry relative to baseline models. The morphed or scaled geometry is recommended when evaluating computational brain models to capture the subject-specific skull geometry of the experimental specimens.

Calibration of a Heterogeneous Brain Model Using a Subject-Specific Inverse Finite Element Approach.

Central to the investigation of the biomechanics of traumatic brain injury (TBI) and the assessment of injury risk from head impact are finite element (FE) models of the human brain. However, many existing FE human brain models have been developed with simplified representations of the parenchyma, which may limit their applicability as an injury prediction tool. Recent advances in neuroimaging techniques and brain biomechanics provide new and necessary experimental data that can improve the biofidelity of FE brain models. In this study, the CAB-20MSym template model was developed, calibrated, and extensively verified. To implement material heterogeneity, a magnetic resonance elastography (MRE) template image was leveraged to define the relative stiffness gradient of the brain model. A multi-stage inverse FE (iFE) approach was used to calibrate the material parameters that defined the underlying non-linear deviatoric response by minimizing the error between model-predicted brain displacements and experimental displacement data. This process involved calibrating the infinitesimal shear modulus of the material using low-severity, low-deformation impact cases and the material non-linearity using high-severity, high-deformation cases from a dataset of in situ brain displacements obtained from cadaveric specimens. To minimize the geometric discrepancy between the FE models used in the iFE calibration and the cadaveric specimens from which the experimental data were obtained, subject-specific models of these cadaveric brain specimens were developed and used in the calibration process. Finally, the calibrated material parameters were extensively verified using independent brain displacement data from 33 rotational head impacts, spanning multiple loading directions (sagittal, coronal, axial), magnitudes (20-40 rad/s), durations (30-60 ms), and severity. Overall, the heterogeneous CAB-20MSym template model demonstrated good biofidelity with a mean overall CORA score of 0.63 ± 0.06 when compared to in situ brain displacement data. Strains predicted by the calibrated model under non-injurious rotational impacts in human volunteers (N = 6) also demonstrated similar biofidelity compared to in vivo measurements obtained from tagged magnetic resonance imaging studies. In addition to serving as an anatomically accurate model for further investigations of TBI biomechanics, the MRE-based framework for implementing material heterogeneity could serve as a foundation for incorporating subject-specific material properties in future models.

Evaluation of Tissue-Level Brain Injury Metrics Using Species-Specific Simulations.

Traumatic brain injury (TBI) is a significant public health burden, and the development of advanced countermeasures to mitigate and prevent these injuries during automotive, sports, and military impact events requires an understanding of the intracranial mechanisms related to TBI. In this study, the efficacy of tissue-level injury metrics for predicting TBI was evaluated using finite element reconstructions from a comprehensive, multi-species TBI database. The database consisted of human volunteer tests, laboratory-reconstructed head impacts from sports, in vivo non-human primate (NHP) tests, and in vivo pig tests. Eight tissue-level metrics related to brain tissue strain, axonal strain, and strain-rate were evaluated using survival analysis for predicting mild and severe TBI risk. The correlation between TBI risk and most of the assessed metrics were statistically significant, but when injury data was analyzed by species, the best metric was often inconclusive and limited by the small datasets. When the human and animal datasets were combined, the injury analysis was able to delineate maximum axonal strain as the best predictor of injury for all species and TBI severities, with maximum principal strain as a suitable alternative metric. The current study is the first to provide evidence to support the assumption that brain strain response between human, pig, and NHP result in similar injury outcomes through a multi-species analysis. This assumption is the biomechanical foundation for translating animal brain injury findings to humans. The findings in the study provide fundamental guidelines for developing injury criteria that would contribute towards the innovation of more effective safety countermeasures.

The Effect of Muscle Activation on Head Kinematics During Non-injurious Head Impacts in Human Subjects.

In this study, twenty volunteers were subjected to three, non-injurious lateral head impacts delivered by a 3.7 kg padded impactor at 2 m/s at varying levels of muscle activation (passive, co-contraction, and unilateral contraction). Electromyography was used to quantify muscle activation conditions, and resulting head kinematics were recorded using a custom-fit instrumented mouthpiece. A multi-modal battery of diagnostic tests (evaluated using neurocognitive, balance, symptomatic, and neuroimaging based assessments) was performed on each subject pre- and post-impact. The passive muscle condition resulted in the largest resultant head linear acceleration (12.1 ± 1.8 g) and angular velocity (7.3 ± 0.5 rad/s). Compared to the passive activation, increasing muscle activation decreased both peak resultant linear acceleration and angular velocity in the co-contracted (12.1 ± 1.5 g, 6.8 ± 0.7 rad/s) case and significantly decreased in the unilateral contraction (10.7 ± 1.7 g, 6.5 ± 0.7 rad/s) case. The duration of angular velocity was decreased with an increase in neck muscle activation. No diagnostic metric showed a statistically or clinically significant alteration between baseline and post-impact assessments, confirming these impacts were non-injurious. This study demonstrated that isometric neck muscle activation prior to impact can reduce resulting head kinematics. This study also provides the data necessary to validate computational models of head impact.

An Image Registration-Based Morphing Technique for Generating Subject-Specific Brain Finite Element Models.

Finite element (FE) models of the brain are crucial for investigating the mechanisms of traumatic brain injury (TBI). However, FE brain models are often limited to a single neuroanatomy because the manual development of subject-specific models is time consuming. The objective of this study was to develop a pipeline to automatically generate subject-specific FE brain models using previously developed nonlinear image registration techniques, preserving both external and internal neuroanatomical characteristics. To verify the morphing-induced mesh distortions did not influence the brain deformation response, strain distributions predicted using the morphed model were compared to those from manually created voxel models of the same subject. Morphed and voxel models were generated for 44 subjects ranging in age, and simulated using head kinematics from a football concussion case. For each subject, brain strain distributions predicted by each model type were consistent, and differences in strain prediction was less than 4% between model type. This automated technique, taking approximately 2 h to generate a subject-specific model, will facilitate interdisciplinary research between the biomechanics and neuroimaging fields and could enable future use of biomechanical models in the clinical setting as a tool for improving diagnosis.

Predicting Concussion Outcome by Integrating Finite Element Modeling and Network Analysis.

Concussion is a significant public health problem affecting 1.6-2.4 million Americans annually. An alternative to reducing the burden of concussion is to reduce its incidence with improved protective equipment and injury mitigation systems. Finite element (FE) models of the brain response to blunt trauma are often used to estimate injury potential and can lead to improved helmet designs. However, these models have yet to incorporate how the patterns of brain connectivity disruption after impact affects the relay of information in the injured brain. Furthermore, FE brain models typically do not consider the differences in individual brain structural connectivities and their purported role in concussion risk. Here, we use graph theory techniques to integrate brain deformations predicted from FE modeling with measurements of network efficiency to identify brain regions whose connectivity characteristics may influence concussion risk. We computed maximum principal strain in 129 brain regions using head kinematics measured from 53 professional football impact reconstructions that included concussive and non-concussive cases. In parallel, using diffusion spectrum imaging data from 30 healthy subjects, we simulated structural lesioning of each of the same 129 brain regions. We simulated lesioning by removing each region one at a time along with all its connections. In turn, we computed the resultant change in global efficiency to identify regions important for network communication. We found that brain regions that deformed the most during an impact did not overlap with regions most important for network communication (Pearson's correlation, ρ = 0.07; p = 0.45). Despite this dissimilarity, we found that predicting concussion incidence was equally accurate when considering either areas of high strain or of high importance to global efficiency. Interestingly, accuracy for concussion prediction varied considerably across the 30 healthy connectomes. These results suggest that individual network structure is an important confounding variable in concussion prediction and that further investigation of its role may improve concussion prediction and lead to the development of more effective protective equipment.

Finite Element Model of a Deformable American Football Helmet Under Impact.

Despite the use of helmets in American football, brain injuries are still prevalent. To reduce the burden of these injuries, novel impact mitigation systems are needed. The Vicis Zero1 (VZ1) American football helmet is unique in its use of multi-directional buckling structures sandwiched between a deformable outer shell and a stiff inner shell. The objective of this study was to develop a model of the VZ1 and to assess this unique characteristic for its role in mitigating head kinematics. The VZ1 model was developed using a bottom-up framework that emphasized material testing, constitutive model calibration, and component-level validation. Over 50 experimental tests were simulated to validate the VZ1 model. CORrelation and Analysis (CORA) was used to quantify the similarity between experimental and model head kinematics, neck forces, and impactor accelerations and forces. The VZ1 model demonstrated good correlation with an overall mean CORA score of 0.86. A parametric analysis on helmet compliance revealed that the outer shell and column stiffness influenced translational head kinematics more than rotational. For the material parameters investigated, head linear acceleration ranged from 80 to 220 g, whereas angular velocity ranged from 37 to 40 rad/s. This helmet model is open-source and serves as an in silico design platform for helmet innovation.

Biomechanics of the Human Brain during Dynamic Rotation of the Head.

Traumatic brain injuries (TBI) are a substantial societal burden. The development of better technologies and systems to prevent and/or mitigate the severity of brain injury requires an improved understanding of the mechanisms of brain injury, and more specifically, how head impact exposure relates to brain deformation. Biomechanical investigations have used computational models to identify these relations, but more experimental brain deformation data are needed to validate these models and support their conclusions. The objective of this study was to generate a dataset describing in situ human brain motion under rotational loading at impact conditions considered injurious. Six head-neck human post-mortem specimens, unembalmed and never frozen, were instrumented with 24 sonomicrometry crystals embedded throughout the parenchyma that can directly measure dynamic brain motion. Dynamic brain displacement, relative to the skull, was measured for each specimen with four loading severities in the three directions of controlled rotation, for a total of 12 tests per specimen. All testing was completed 42-72 h post-mortem for each specimen. The final dataset contains approximately 5,000 individual point displacement time-histories that can be used to validate computational brain models. Brain motion was direction-dependent, with axial rotation resulting in the largest magnitude of displacement. Displacements were largest in the mid-cerebrum, and the inferior regions of the brain-the cerebellum and brainstem-experienced relatively lower peak displacements. Brain motion was also found to be positively correlated to peak angular velocity, and negatively correlated with angular velocity duration, a finding that has implications related to brain injury risk-assessment methods. This dataset of dynamic human brain motion will form the foundation for the continued development and refinement of computational models of the human brain for predicting TBI.

Investigation of Cross-Species Scaling Methods for Traumatic Brain Injury Using Finite Element Analysis.

Scaling methods are used to relate animal exposure data to humans by determining equivalent biomechanical impact conditions that result in similar tissue-level mechanics for different species. However, existing scaling methods for traumatic brain injury (TBI) do not account for the anatomical and morphological complexity of the brains for different species and have not been validated based on accurate anatomy and realistic material properties. In this study, the relationship between the TBI condition and brain tissue deformation was investigated using human, baboon, and macaque brain finite element (FE) models, which featured macro- and mesoscale anatomical details. The aim was to evaluate existing scaling methods in predicting similar biomechanical responses in the different species using both idealized and real-world TBI pulses. A second aim was to develop a new method to improve how animal data are scaled to humans. As previously found in humans, the animal's brain response to the rotational head motion was well characterized by single-degree-of-freedom (sDOF) mechanical systems with resonance at certain natural frequency, and this concept was leveraged to develop a new TBI scaling method based the natural frequency of the sDOF models representing each species. Previously described biomechanical scaling methods based on mass or inertia ratios were poor predictors of equivalent strain. The novel frequency-based scaling method was an improved approach to scaling the equivalent loading conditions. The findings of this study enable better interpretation of mechanical-trauma responses obtained from animal data to the human, thus effectively advancing the development of human injury criteria and contributing toward the mitigation of TBI.

More than a messenger: Alternative splicing as a therapeutic target.

Alternative splicing of pre-mRNA is an essential post- and co-transcriptional mechanism of gene expression regulation that produces multiple mature mRNA transcripts from a single gene. Genetic mutations that affect splicing underlie numerous devastating diseases. The complexity of splicing regulation allows for multiple therapeutic approaches to correct disease-associated mis-splicing events. In this review, we first highlight recent findings from therapeutic strategies that have used splice switching antisense oligonucleotides and small molecules that bind directly to RNA. Second, we summarize different genetic and chemical approaches to target components of the spliceosome to correct splicing defects in pathological conditions. Finally, we present an overview of compounds that target kinases and accessory pathways that intersect with the splicing machinery. Advancements in the understanding of disease-specific defects caused by mis-regulation of alternative splicing will certainly increase the development of therapeutic options for the clinic. This article is part of a Special Issue entitled: RNA structure and splicing regulation edited by Francisco Baralle, Ravindra Singh and Stefan Stamm.

Explicit Modeling of White Matter Axonal Fiber Tracts in a Finite Element Brain Model.

Many human brain finite element (FE) models lack mesoscopic (~ 1 mm) white matter structures, which may limit their capability in predicting TBI and assessing tissue-based injury metrics such as axonal strain. This study investigated an embedded method to explicitly incorporate white matter axonal fibers into an existing 50th percentile male brain model. The white matter was decomposed into myelinated axon tracts and an isotropic ground substance that had similar material properties to gray matter. The axon tract bundles were derived from a population-based tractography template explicitly modeled using 1-D cable elements. The axonal fibers and ground substance material were implemented using hyper-viscoelastic constitutive models, which were calibrated using white and gray matter brain tissue material testing data available in the literature. Finally, the new axon-based model was extensively validated for brain-skull relative deformation under various loading conditions (n = 17) and showed good biofidelity compared to other brain models. Through these analyses, we demonstrated the applicability of this method for incorporating axonal fiber tracts into an existing FE brain model. The axon-based model will be a useful tool for understanding the mechanisms of TBI, evaluating tissue-based injury metrics, and developing injury mitigation systems.

An Analytical Review of the Numerical Methods used for Finite Element Modeling of Traumatic Brain Injury.

Dozens of finite element models of the human brain have been developed for providing insight into the mechanical response of the brain during impact. Many models used in traumatic brain injury research are based on different computational techniques and approaches. In this study, a comprehensive review of the numerical methods implemented in 16 brain models was performed. Differences in element type, mesh size, element formulation, hourglass control, and solver were found. A parametric study using the SIMon FE brain model was performed to quantify the sensitivity of model outputs to differences in numerical implementation. Model outputs investigated in this study included nodal displacement (commonly used for validation) and maximum principal strain (commonly used for injury assessment), and these results were demonstrated using the loading characteristics of a reconstructed football concussion event. Order-of-magnitude differences in brain response were found when only changing the characteristics of the numerical method. Mesh type and mesh size had the largest effect on model response. These differences have important implications on the interpretation of results among different models simulating the same impacts, and of the results between model and in vitro experiments. Additionally, future studies need to better report the numerical methods used in the models.

Development of Open-Source Dummy and Impactor Models for the Assessment of American Football Helmet Finite Element Models.

The objective of this study was to develop and validate a set of Hybrid-III head and neck (HIII-HN) and impactor models that can be used to assess American football design modifications with established dummy-based injury metrics. The model was validated in two bare-head impact test configurations used by the National Football League and research groups to rank and assess helmet performance. The first configuration was a rigid pendulum impact to three locations on the HIII head (front, rear, side) at 3 m/s. The second configuration was a set of eight 5.5 m/s impacts to the HIII head at different locations using a linear impactor with a compliant end cap. The ISO/TS 18571 objective rating metric was used to quantify the correlation between the experimental and model head kinematics (linear and rotational velocity and acceleration) and neck kinetics (neck force and moment). The HIII-HN model demonstrated good correlation with overall mean ISO scores of 0.69-0.78 in the pendulum impacts and 0.65-0.81 in the linear impacts. These publically available models will serve as an in silico design platform that will be useful for investigating novel football helmet designs and studying human head impact biomechanics, in general.

A Cortical Thickness Mapping Method for the Coxal Bone Using Morphing.

As human body finite element models become more integrated with the design of safety countermeasures and regulations, novel models need to be developed that reflect the variation in the population's anthropometry. However, these new models may be missing information which will need to be translated from existing models. During the development of a 5th percentile female occupant model (F05), cortical thickness information of the coxal bone was unavailable due to resolution limits in the computed tomography (CT) scans. In this study, a method for transferring cortical thickness information from a source to a target model with entirely different geometry and architecture is presented. The source and target models were the Global Human Body Models Consortium (GHBMC) 50th percentile male (M50) and F05 coxal bones, respectively. To project the coxal bone cortical thickness from the M50 to the F05, the M50 model was first morphed using a Kriging method with 132 optimized control points to the F05 anthropometry. This technique was found to be accurate with a mean nodal discrepancy of 1.27 mm between the F05 and morphed M50 (mM50) coxal bones. Cortical thickness at each F05 node was determined by taking the average cortical thickness of every mM50 node, non-linearly weighted by its distance to the F05 nodes. The non-linear weighting coefficient, ß, had a large effect on the accuracy and smoothness of the projected cortical bone thickness. The optimal projection had ß = 4 and was defined when the tradeoff between projection accuracy and smoothness was equal. Finally, a quasi-static pelvis compression was simulated to examine to effect of ß. As ß, increased from 0 to 4, the failure force decreased by ~100 N, whereas the failure displacement increased by 0.9 mm. Results from quasi-static compression tests of the F05 pelvis were comparable to experimental results. This method could be applied to other anatomical regions where cortical thickness variation is important, such as the femur and ribs and is not limited to GHBMC-family models. Furthermore, this process will aid the development of subject-specific finite element models where accurate cortical bone thickness measurements cannot be obtained.

A Novel Method for Quantifying Human In Situ Whole Brain Deformation under Rotational Loading Using Sonomicrometry.

Traumatic brain injuries (TBI) are one of the least understood injuries to the body. Finite element (FE) models of the brain have been crucial for understanding concussion and for developing injury mitigation systems; however, the experimental brain deformation data currently used to validate these models are limited. The objective of this study was to develop a methodology for the investigation of in situ three-dimensional brain deformation during pure rotational loading of the head, using sonomicrometry. Sonomicrometry uses ultrasonic pulses to measure the dynamic distances between piezoelectric crystals implanted in any sound-transmitting media. A human cadaveric head-neck specimen was acquired 14 h postmortem and was instrumented with an array of 32 small sonomicrometry crystals embedded in the head: 24 crystals were implanted in the brain, and 8 were fixed to the inner skull. A dynamic rotation was then applied to the head using a closed-loop controlled test device. Four pulses with different severity levels were applied around three orthogonal anatomical axes of rotation. A repeated test of the highest severity rotation was conducted in each axis to assess repeatability. All tests were completed within 56 h postmortem. Overall, the combined experimental and sonomicrometry methods were demonstrated to reliably and repeatedly capture three-dimensional dynamic deformation of an intact human brain. These methods provide a framework for using sonomicrometry to acquire multidimensional experimental data required for FE model development and validation, and will lend insight into the deformations sustained by the brain during impact.

Human adipose tissue from normal and tumoral breast regulaltes the behavior of mammary ephithelial cells

INTRODUCTION: Stromal-epithelial interactions mediate both breast development and breast cancer progression. In the present work, we evaluated the effects of conditioned media (CMs) of human adipose tissue explants from normal (hATN) and tumor (hATT) breast on proliferation, adhesion, migration and metalloproteases activity on tumor (MCF-7 and IBH-7) and non-tumor (MCF-10A) human breast epithelial cell lines. MATERIALS AND METHODS: Human adipose tissues were obtained from patients and the conditioned medium from hATN and hATT collected after 24 h of incubation. MCF-10A, MCF-7 and IBH-7 cells were grown and incubated with CMs and proliferation and adhesion, as well as migration ability and metalloprotease activity, of epithelial cells after exposing cell cultures to hATN- or hATT-CMs were quantified. The statistical significance between different experimental conditions was evaluated by one-way ANOVA. Tukey's post hoc tests were performed. RESULTS: Tumor and non-tumor breast epithelial cells significantly increased their proliferation activity after 24 h of treatment with hATT-CMs compared to control-CMs. Furthermore, cellular adhesion of these two tumor cell lines was significantly lower with hATT-CMs than with hATN-CMs. Therefore, hATT-CMs seem to induce significantly lower expression or less activity of the components involved in cellular adhesion than hATN-CMs. In addition, hATT-CMs induced pro-MMP-9 and MMP-9 activity and increased the migration of MCF-7 and IBH-7 cells compared to hATN-CMs. CONCLUSIONS: We conclude that the microenvironment of the tumor interacts in a dynamic way with the mutated epithelium. This evidence leads to the possibility to modify the tumor behavior/phenotype through the regulation or modification of its microenvironment. We developed a model in which we obtained CMs from adipose tissue explants completely, either from normal or tumor breast. In this way, we studied the contribution of soluble factors independently of the possible effects of direct cell contact (AU)

Heme oxygenase 1 (HO-1) challenges the angiogenic switch in prostate cancer.

Prostate cancer (PCa) is the second leading cause of cancer-associated death in men. Once a tumor is established it may attain further characteristics via mutations or hypoxia, which stimulate new blood vessels. Angiogenesis is a hallmark in the pathogenesis of cancer and inflammatory diseases that may predispose to cancer. Heme oxygenase-1 (HO-1) counteracts oxidative and inflammatory damage and was previously reported to play a key role in prostate carcinogenesis. To gain insight into the anti-tumoral properties of HO-1, we investigated its capability to modulate PCa associated-angiogenesis. In the present study, we identified in PC3 cells a set of inflammatory and pro-angiogenic genes down-regulated in response to HO-1 overexpression, in particular VEGFA, VEGFC, HIF1α and α5ß1 integrin. Our results indicated that HO-1 counteracts oxidative imbalance reducing ROS levels. An in vivo angiogenic assay showed that intradermal inoculation of PC3 cells stable transfected with HO-1 (PC3HO-1) generated tumours less vascularised than controls, with decreased microvessel density and reduced CD34 and MMP9 positive staining. Interestingly, longer term grown PC3HO-1 xenografts displayed reduced neovascularization with the subsequent down-regulation of VEGFR2 expression. Additionally, HO-1 repressed nuclear factor κB (NF-κB)-mediated transcription from an NF-κB responsive luciferase reporter construct, which strongly suggests that HO-1 may regulate angiogenesis through this pathway. Taken together, these data supports a key role of HO-1 as a modulator of the angiogenic switch in prostate carcinogenesis ascertaining it as a logical target for intervention therapy.