Unique fractal evaluation and therapeutic implications of mitochondrial morphology in malignant mesothelioma

Higher density of compact B cell clusters in invasive front may contribute to better prognosis in pancreatic ductal adenocarcinoma

While the correlation between T cells and patient survival was widely investigated, the clinical significance of CD20+ B cells in pancreatic ductal adenocarcinoma (PDAC) is less clear. We hypothesized that the spatial pattern of B cells within tumor microenvironment (TME) are more informative, which may reveal the prognostic significance for PDAC patients. Therefore, we developed a computer-based workflow to analyze CD20+ B cells in whole slide images (WSI) from 45 cases of PDAC patients. Depending on this workflow, annotations of each case which were created by pathologists were subdivided for three regions, including invasive front (IF), cancer center (CT) and cancer island (CI) to explore the association between the spatial pattern of CD20+ B cells and patient prognosis outcomes. After that, occupancy rate (as area under curve, occupancy AUC), fractal dimension differences (ΔFD), cluster density and coverage ratio were used to quantify the spatial pattern of B cells in TME. We observed B cells were distributed across different regions, manifesting in both clustered and dispersed patterns. Compared to features of B cells spatial distribution in CT region, B cells in IF region exhibited higher occupancy AUC (p = 0.00004), cluster density (p = 0.000002) and coverage ratio (p = 0.000884). Patients with longer survivals had smaller ΔFD (p = 0.05), higher B-cell cluster density (p = 0.003) and lower coverage ratio (p = 0.02) in IF region. Our study indicated the spatial distribution of B cells in IF and CT was different and the higher density of compact B-cell clusters in IF region may be associated with better prognosis in PDAC.

Application of a Photothermal Microscope To Study the Process of Cutaneous Lesion Formation of Malignant Melanoma

In this study, we applied a photothermal microscope to study the process of malignant melanoma formation. We analyzed benign papilloma tumors, their metastatic carcinomas, and metastatic melanoma on the preparation of mouse skin using a gray-level co-occurrence matrix (GLCM) method. Based on the analysis of nine GLCM parameters investigated, the characteristics during the degenerative and metastatic processes are clarified by the investigation. The determination of characteristic parameters corresponding to three processes before, during, and after the degeneration indicated that this investigation enables the detection of the malignant transformation and related processes.

Teriflunomide/leflunomide synergize with chemotherapeutics by decreasing mitochondrial fragmentation via DRP1 in SCLC

Although up to 80% small cell lung cancer (SCLC) patients' response is good for first-line chemotherapy regimen, most patients develop recurrence of the disease within weeks to months. Here, we report cytostatic effect of leflunomide (Leflu) and teriflunomide (Teri) on SCLC cell proliferation through inhibition of DRP1 phosphorylation at Ser616 and decreased mitochondrial fragmentation. When administered together, Teri and carboplatin (Carbo) act synergistically to significantly inhibit cell proliferation and DRP1 phosphorylation, reduce abundance of intermediates in pyrimidine de novo pathway, and increase apoptosis and DNA damage. Combination of Leflu&Carbo has anti-tumorigenic effect in vivo. Additionally, lurbinectedin (Lur) and Teri potently and synergistically inhibited spheroid growth and depleted uridine and DRP1 phosphorylation in mouse tumors. Our results suggest combinations of Carbo and Lur with Teri or Leflu are efficacious and underscore how the relationship between DRP1/DHODH and mitochondrial plasticity serves as a potential therapeutic target to validate these treatment strategies in SCLC clinical trials.

Molecular mechanism of glutaminase activation through filamentation and the role of filaments in mitophagy protection

Glutaminase (GLS), which deaminates glutamine to form glutamate, is a mitochondrial tetrameric protein complex. Although inorganic phosphate (Pi) is known to promote GLS filamentation and activation, the molecular basis of this mechanism is unknown. Here we aimed to determine the molecular mechanism of Pi-induced mouse GLS filamentation and its impact on mitochondrial physiology. Single-particle cryogenic electron microscopy revealed an allosteric mechanism in which Pi binding at the tetramer interface and the activation loop is coupled to direct nucleophile activation at the active site. The active conformation is prone to enzyme filamentation. Notably, human GLS filaments form inside tubulated mitochondria following glutamine withdrawal, as shown by in situ cryo-electron tomography of cells thinned by cryo-focused ion beam milling. Mitochondria with GLS filaments exhibit increased protection from mitophagy. We reveal roles of filamentous GLS in mitochondrial morphology and recycling.

Spatial topology of organelle is a new breast cancer cell classifier

Genomics and proteomics have been central to identify tumor cell populations, but more accurate approaches to classify cell subtypes are still lacking. We propose a new methodology to accurately classify cancer cells based on their organelle spatial topology. Herein, we developed an organelle topology-based cell classification pipeline (OTCCP), which integrates artificial intelligence (AI) and imaging quantification to analyze organelle spatial distribution and inter-organelle topology. OTCCP was used to classify a panel of human breast cancer cells, grown as 2D monolayer or 3D tumor spheroids using early endosomes, mitochondria, and their inter-organelle contacts. Organelle topology allows for a highly precise differentiation between cell lines of different subtypes and aggressiveness. These findings lay the groundwork for using organelle topological profiling as a fast and efficient method for phenotyping breast cancer function as well as a discovery tool to advance our understanding of cancer cell biology at the subcellular level.

Metabolic Effects of New Glucose Transporter (GLUT-1) and Lactate Dehydrogenase-A (LDH-A) Inhibitors against Chemoresistant Malignant Mesothelioma

Malignant mesothelioma (MM) is a highly aggressive and resistant tumor. The prognostic role of key effectors of glycolytic metabolism in MM prompted our studies on the cytotoxicity of new inhibitors of glucose transporter type 1 (GLUT-1) and lactate dehydrogenase-A (LDH-A) in relation to ATP/NAD+ metabolism, glycolysis and mitochondrial respiration. The antiproliferative activity of GLUT-1 (PGL13, PGL14) and LDH-A (NHI-1, NHI-2) inhibitors, alone and in combination, were tested with the sulforhodamine-B assay in peritoneal (MESO-II, STO) and pleural (NCI-H2052 and NCI-H28) MM and non-cancerous (HMEC-1) cells. Effects on energy metabolism were measured by both analysis of nucleotides using RP-HPLC and evaluation of glycolysis and respiration parameters using a Seahorse Analyzer system. All compounds reduced the growth of MM cells in the µmolar range. Interestingly, in H2052 cells, PGL14 decreased ATP concentration from 37 to 23 and NAD+ from 6.5 to 2.3 nmol/mg protein. NHI-2 reduced the ATP/ADP ratio by 76%. The metabolic effects of the inhibitors were stronger in pleural MM and in combination, while in HMEC-1 ATP reduction was 10% lower compared to that of the H2052 cells, and we observed a minor influence on mitochondrial respiration. To conclude, both inhibitors showed cytotoxicity in MM cells, associated with a decrease in ATP and NAD+, and were synergistic in the cells with the highest metabolic modulation. This underlines cellular energy metabolism as a potential target for combined treatments in selected cases of MM.

Universal dynamics of mitochondrial networks: a finite-size scaling analysis

Evidence from models and experiments suggests that the networked structure observed in mitochondria emerges at the critical point of a phase transition controlled by fission and fusion rates. If mitochondria are poised at criticality, the relevant network quantities should scale with the system's size. However, whether or not the expected finite-size effects take place has not been demonstrated yet. Here, we first provide a theoretical framework to interpret the scaling behavior of mitochondrial network quantities by analyzing two conceptually different models of mitochondrial dynamics. Then, we perform a finite-size scaling analysis of real mitochondrial networks extracted from microscopy images and obtain scaling exponents comparable with critical exponents from models and theory. Overall, we provide a universal description of the structural phase transition in mammalian mitochondria.

Microtubule-Based Mitochondrial Dynamics as a Valuable Therapeutic Target in Cancer

Mitochondria constitute an ever-reorganizing dynamic network that plays a key role in several fundamental cellular functions, including the regulation of metabolism, energy production, calcium homeostasis, production of reactive oxygen species, and programmed cell death. Each of these activities can be found to be impaired in cancer cells. It has been reported that mitochondrial dynamics are actively involved in both tumorigenesis and metabolic plasticity, allowing cancer cells to adapt to unfavorable environmental conditions and, thus, contributing to tumor progression. The mitochondrial dynamics include fusion, fragmentation, intracellular trafficking responsible for redistributing the organelle within the cell, biogenesis, and mitophagy. Although the mitochondrial dynamics are driven by the cytoskeleton-particularly by the microtubules and the microtubule-associated motor proteins dynein and kinesin-the molecular mechanisms regulating these complex processes are not yet fully understood. More recently, an exchange of mitochondria between stromal and cancer cells has also been described. The advantage of mitochondrial transfer in tumor cells results in benefits to cell survival, proliferation, and spreading. Therefore, understanding the molecular mechanisms that regulate mitochondrial trafficking can potentially be important for identifying new molecular targets in cancer therapy to interfere specifically with tumor dissemination processes.

Adhesion-mediated mechanosignaling forces mitohormesis

Mitochondria control eukaryotic cell fate by producing the energy needed to support life and the signals required to execute programed cell death. The biochemical milieu is known to affect mitochondrial function and contribute to the dysfunctional mitochondrial phenotypes implicated in cancer and the morbidities of aging. However, the physical characteristics of the extracellular matrix are also altered in cancerous and aging tissues. Here, we demonstrate that cells sense the physical properties of the extracellular matrix and activate a mitochondrial stress response that adaptively tunes mitochondrial function via solute carrier family 9 member A1-dependent ion exchange and heat shock factor 1-dependent transcription. Overall, our data indicate that adhesion-mediated mechanosignaling may play an unappreciated role in the altered mitochondrial functions observed in aging and cancer.

Spatial distribution of B cells and lymphocyte clusters as a predictor of triple-negative breast cancer outcome

While tumor infiltration by CD8+ T cells is now widely accepted to predict outcomes, the clinical significance of intratumoral B cells is less clear. We hypothesized that spatial distribution rather than density of B cells within tumors may provide prognostic significance. We developed statistical techniques (fractal dimension differences and a box-counting method 'occupancy') to analyze the spatial distribution of tumor-infiltrating lymphocytes (TILs) in human triple-negative breast cancer (TNBC). Our results indicate that B cells in good outcome tumors (no recurrence within 5 years) are spatially dispersed, while B cells in poor outcome tumors (recurrence within 3 years) are more confined. While most TILs are located within the stroma, increased numbers of spatially dispersed lymphocytes within cancer cell islands are associated with a good prognosis. B cells and T cells often form lymphocyte clusters (LCs) identified via density-based clustering. LCs consist either of T cells only or heterotypic mixtures of B and T cells. Pure B cell LCs were negligible in number. Compared to tertiary lymphoid structures (TLS), LCs have fewer lymphocytes at lower densities. Both types of LCs are more abundant and more spatially dispersed in good outcomes compared to poor outcome tumors. Heterotypic LCs in good outcome tumors are smaller and more numerous compared to poor outcome. Heterotypic LCs are also closer to cancer islands in a good outcome, with LC size decreasing as they get closer to cancer cell islands. These results illuminate the significance of the spatial distribution of B cells and LCs within tumors.

S616-p-DRP1 associates with locally invasive behavior of follicular cell-derived thyroid carcinoma

PURPOSE

Dynamin-related protein 1 (DRP1), a mitochondrial fission protein, and its active form phosphorylated at Serine 616 (S616-p-DRP1) have been increasingly associated with tumorigenesis and invasion in various tumor models, including oncocytic thyroid cancer (TC). In this study, the expression of DRP1 and S616-p-DRP1 and its relationship with patients' clinicopathological characteristics, tumor genetic profiles, and clinical outcomes were assessed in a large series of follicular cell-derived TC (FCDTC).

METHODS

Retrospective biomarker study characterizing the clinicopathological and immunochemistry DRP1 and S616-p-DRP1 expression of a series of 259 patients with FCDTC followed in two University Hospitals.

RESULTS

DRP1 expression was positive in 65.3% (169/259) of the cases, while the expression of the S616-p-DRP1 was positive in only 17.3% (17/98). DRP1-positive expression was significantly associated with differentiated tumors (67.7 vs. 48.0%; P = 0.049), non-encapsulated tumors (73.8 vs. 57.4%; P = 0.011) and thyroid capsule invasion (73.4 vs. 57.5%; P = 0.013). S616-p-DRP1-positive expression was significantly associated with tumor infiltrative margins (88.9 vs. 11.1%; P = 0.033), thyroid capsule invasion (29.8 vs. 3.1%; P = 0.043), lymph node metastases (23.3 vs. 8.1%; P = 0.012), and higher mean cumulative radioiodine dosage (317.4 ± 265.0 mCi vs. 202.5 ± 217.7 mCi; P = 0.038). S616-p-DRP1 expression was negatively associated with oncocytic phenotype (0.0 vs. 26.2%; P = 0.028).

CONCLUSIONS

S616-p-DRP1 is a better candidate than DRP1 to identify tumors with locally invasive behavior. Prospective studies should be pursued to assess S616-p-DRP1 role as a molecular marker of malignancy in TC and in patients' risk assessment.

Mitochondria: The metabolic switch of cellular oncogenic transformation

Mitochondria, well recognized as the "powerhouse" of cells, are maternally inherited organelles with bacterial ancestry that play essential roles in a myriad of cellular functions. It has become profoundly evident that mitochondria regulate a wide array of cellular and metabolic functions, including biosynthetic metabolism, cell signaling, redox homeostasis, and cell survival. Correspondingly, defects in normal mitochondrial functioning have been implicated in various human malignancies. Cancer development involves the activation of oncogenes, inactivation of tumor suppressor genes, and impairment of apoptotic programs in cells. Mitochondria have been recognized as the site of key metabolic switches for normal cells to acquire a malignant phenotype. This review outlines the role of mitochondria in human malignancies and highlights potential aspects of mitochondrial metabolism that could be targeted for therapeutic development.

Physics approaches to the spatial distribution of immune cells in tumors

The goal of immunotherapy is to mobilize the immune system to kill cancer cells. Immunotherapy is more effective and, in general, the prognosis is better, when more immune cells infiltrate the tumor. We explore the question of whether the spatial distribution rather than just the density of immune cells in the tumor is important in forecasting whether cancer recurs. After reviewing previous work on this issue, we introduce a novel application of maximum entropy to quantify the spatial distribution of discrete point-like objects. We apply our approach to B and T cells in images of tumor tissue taken from triple negative breast cancer patients. We find that the immune cells are more spatially dispersed in good clinical outcome (no recurrence of cancer within at least 5 years of diagnosis) compared to poor clinical outcome (recurrence within 3 years of diagnosis). Our results highlight the importance of spatial distribution of immune cells within tumors with regard to clinical outcome, and raise new questions on their role in cancer recurrence.

Mito Hacker: a set of tools to enable high-throughput analysis of mitochondrial network morphology

Mitochondria are highly dynamic organelles that can exhibit a wide range of morphologies. Mitochondrial morphology can differ significantly across cell types, reflecting different physiological needs, but can also change rapidly in response to stress or the activation of signaling pathways. Understanding both the cause and consequences of these morphological changes is critical to fully understanding how mitochondrial function contributes to both normal and pathological physiology. However, while robust and quantitative analysis of mitochondrial morphology has become increasingly accessible, there is a need for new tools to generate and analyze large data sets of mitochondrial images in high throughput. The generation of such datasets is critical to fully benefit from rapidly evolving methods in data science, such as neural networks, that have shown tremendous value in extracting novel biological insights and generating new hypotheses. Here we describe a set of three computational tools, Cell Catcher, Mito Catcher and MiA, that we have developed to extract extensive mitochondrial network data on a single-cell level from multi-cell fluorescence images. Cell Catcher automatically separates and isolates individual cells from multi-cell images; Mito Catcher uses the statistical distribution of pixel intensities across the mitochondrial network to detect and remove background noise from the cell and segment the mitochondrial network; MiA uses the binarized mitochondrial network to perform more than 100 mitochondria-level and cell-level morphometric measurements. To validate the utility of this set of tools, we generated a database of morphological features for 630 individual cells that encode 0, 1 or 2 alleles of the mitochondrial fission GTPase Drp1 and demonstrate that these mitochondrial data could be used to predict Drp1 genotype with 87% accuracy. Together, this suite of tools enables the high-throughput and automated collection of detailed and quantitative mitochondrial structural information at a single-cell level. Furthermore, the data generated with these tools, when combined with advanced data science approaches, can be used to generate novel biological insights.

Mitochondrial ROS Induce Partial Dedifferentiation of Human Mesothelioma via Upregulation of NANOG

The expression of pluripotency factors is a key regulator of tumor differentiation status and cancer stem cells. The purpose of this study was to examine the expression of pluripotency factors and differentiation status of human mesothelioma and the role of mitochondria in their regulation. We tested the expression of OCT4/POU5F1, NANOG, SOX2, PI3K-AKT pathway and BCL2 genes and proteins in 65 samples of human mesothelioma and 19 samples of normal mesothelium. Mitochondrial membrane potential, reactive oxygen species (ROS) generation and expression of pluripotency factors were also tested in human mesothelioma cell line. Human mesothelium and mesothelioma expressed SOX2, NANOG, PI3K and AKT genes and proteins and POU5F1 gene, whereby NANOG, SOX2 and phosphorylated (activated) AKT were upregulated in mesothelioma. NANOG protein expression was elevated in less differentiated samples of human mesothelioma. The expression of genes of PI3K-AKT pathway correlated with pluripotency factor genes. Mesothelioma cells had functional, but depolarized mitochondria with large capacity to generate ROS. Mitochondrial ROS upregulated NANOG and mitoTEMPO abrogated it. In conclusion, human mesothelioma displays enhanced expression of NANOG, SOX2 and phosphorylated AKT proteins, while elevated NANOG expression correlates with poor differentiation of human mesothelioma. Mitochondria of mesothelioma cells have a large capacity to form ROS and thereby upregulate NANOG, leading to dedifferentiation of mesothelioma.

Prognostic Significance of Lacunarity in Preoperative Biopsy of Colorectal Cancer

The quantity and quality of preoperative material in colorectal cancer is often limiting factor in determination of risk factors and therapy planning. The most important negative prognostic factors are intravascular and perineural invasion, as well as tumor budding. Usually, the only parameter available in preoperative biopsy is tumor budding. However, the growing body of evidence suggests that cancer differentiation based on the poorly differentiated clusters has better prognostic value. The limiting factor in applying of these new parameters is reproducible, simple, cheap and fast method of their determination. In this paper we investigated the prognostic value of lacunarity, determined in preoperative biopsy. Lacunarity is a measure of spatial heterogeneity (inhomogeneity) in an image. It quantifies how objects fill the space, and enables analysis of gaps distribution, homogeneity of gaps, and presence of structures. It was shown that lacunarity and the total number of buds could be combined in a model which clearly divides colorectal cancer patients in low, medium and high risk subgroups. The paper also points out that the quantitative numerical methods are superior to semiquantitative methods, and that individual methods should be combined using algorithms to obtain a more accurate prediction. Because the study described is designed as a pilot study, verification is needed on a larger sample of patients from independent researchers.

Occupancy and Fractal Dimension Analyses of the Spatial Distribution of Cytotoxic (CD8+) T Cells Infiltrating the Tumor Microenvironment in Triple Negative Breast Cancer

Favorable outcomes have been associated with high densities of tumor infiltrating lymphocytes (TILs) such as cytotoxic ([Formula: see text]) T cells. However, the clinical significance of the spatial distribution of TILs is less well understood. We have developed novel statistical techniques to characterize the spatial distribution of TILs at various length scales. These include a box counting method that we call “occupancy” and novel applications of fractal dimensions. We apply these techniques to the spatial distribution of [Formula: see text] T cells in the tumor microenvironment of tissue resected from 35 triple negative breast cancer patients. We find that there is a distinct difference in the spatial distribution of [Formula: see text] T cells between good clinical outcome (no recurrence within at least 5 years of diagnosis) and poor clinical outcome (recurrence within 3 years of diagnosis). The statistical significance of the difference between good and poor outcome in the occupancy, fractal dimension (FD), and FD difference of [Formula: see text] T cells is comparable to that of the [Formula: see text] T cell density. Even when we randomly exclude some of the cells so that the images have the same cell density, we still find that the fractal dimension at short length scales is correlated with cancer recurrence, implying that the actual spatial distribution of [Formula: see text] cells, and not just the [Formula: see text] cell density, is associated with clinical outcome. The occupancy and FD difference indicate that the [Formula: see text] T cells are more spatially dispersed in good outcome and more aggregated in poor outcome. We discuss possible interpretations.

Effects of selected deubiquitinating enzyme inhibitors on the proliferation and motility of lung cancer and mesothelioma cell lines

The post‑translational modification of proteins by ubiquitinating enzymes plays a central role in a number of cellular functions, such as cell proteolysis, DNA repair, and cell signaling and communication. Deubiquitinating enzymes (DUBs) disassemble ubiquitin chains and remove ubiquitin moieties from proteins. Targeting DUBs in cancer models has revealed an important role for these enzymes in tumorigenesis, and they therefore have emerged as attractive therapeutic targets. In the present study, the effects of three DUB inhibitors, PR‑619, RA‑9 and LDN‑91946, on a non‑small cell lung cancer cell line (A549) and a mesothelioma cell line (H2373) were investigated. PR‑619 significantly inhibited cell adhesion and the proliferation of both cell lines. RA‑9 exerted an inhibitory effect on the adhesion and proliferation of H2373 cells, whereas it had no effect on A549 cells. Notably, however, while PR‑619 attenuated the proliferation of both cell lines, it exerted an opposite effect on cell motility; in the case of A549 cells, there was a significant increase in cell motility, while for the H2373 cells, there was a significant decrease. Furthermore, protein phosphorylation kinetic analyses revealed that the effects were cell line‑specific. In H2373 cells, the phosphorylation of only one peptide corresponding to the P85A protein was significantly affected, and while LDN‑91946 treatment increased phosphorylation, treatment with RA‑9 or PR‑619 decreased its phosphorylation compared to the DMSO control. By contrast, in the case of A549 cells, the phosphorylation of 21 peptides was significantly affected by the same compounds. In light of the potential for the negative side‑effects of DUB inhibition, such as increased cancer cell motility, the data presented herein underscore the dire need for the development of specific DUB inhibitors and to elucidate the individual role of DUB family members in cancer biology before they can be specifically pharmacologically targeted.

Automated Machine Learning Diagnostic Support System as a Computational Biomarker for Detecting Drug-Induced Liver Injury Patterns in Whole Slide Liver Pathology Images

Drug-induced liver injury (DILI) is a challenging disease to diagnose, a leading cause of acute liver failure, and responsible for drug withdrawal from the market. There is no symptom, no biomarker or test for detection, no therapy, but discontinuation of the drug. Pharmaceutical companies spend huge money, time, and scientific research efforts to test DILI effects and drug efficacy. A preclinical diagnostic support system is designed and proposed for DILI detection and classification on liver biopsy histopathology images. Heterogeneity features and automated machine learning (AutoML) models were tested to classify DILI injury patterns on whole slide image. Fractal and lacunarity values were used to detect hepatocellular necrotic injury patterns caused on a rat liver (in vivo) by 10 drugs at four dose levels. Correlations between fractal and lacunarity values were statistically analyzed for the 10 drugs; the Pearson correlation (r = 0.9809), p-value (1.6612E-06), and R2 (0.9582) were found to be high in the case of carbon tetrachloride. The AutoML model was tested to understand the injury patterns on a subset of 1,277 histology images. The AutoML algorithm was able to classify necrotic injury patterns accurately with an average precision of 98.6% on a score threshold of 0.5.

Monitoring and Determining Mitochondrial Network Parameters in Live Lung Cancer Cells

Mitochondria are dynamic organelles that constantly fuse and divide, forming dynamic tubular networks. Abnormalities in mitochondrial dynamics and morphology are linked to diverse pathological states, including cancer. Thus, alterations in mitochondrial parameters could indicate early events of disease manifestation or progression. However, finding reliable and quantitative tools for monitoring mitochondria and determining the network parameters, particularly in live cells, has proven challenging. Here, we present a 2D confocal imaging-based approach that combines automatic mitochondrial morphology and dynamics analysis with fractal analysis in live small cell lung cancer (SCLC) cells. We chose SCLC cells as a test case since they typically have very little cytoplasm, but an abundance of smaller mitochondria compared to many of the commonly used cell types. The 2D confocal images provide a robust approach to quantitatively measure mitochondrial dynamics and morphology in live cells. Furthermore, we performed 3D reconstruction of electron microscopic images and show that the 3D reconstruction of the electron microscopic images complements this approach to yield better resolution. The data also suggest that the parameters of mitochondrial dynamics and fractal dimensions are sensitive indicators of cellular response to subtle perturbations, and hence, may serve as potential markers of drug response in lung cancer.

A healthy dose of chaos: Using fractal frameworks for engineering higher-fidelity biomedical systems

Optimal levels of chaos and fractality are distinctly associated with physiological health and function in natural systems. Chaos is a type of nonlinear dynamics that tends to exhibit seemingly random structures, whereas fractality is a measure of the extent of organization underlying such structures. Growing bodies of work are demonstrating both the importance of chaotic dynamics for proper function of natural systems, as well as the suitability of fractal mathematics for characterizing these systems. Here, we review how measures of fractality that quantify the dose of chaos may reflect the state of health across various biological systems, including: brain, skeletal muscle, eyes and vision, lungs, kidneys, tumours, cell regulation, skin and wound repair, bone, vasculature, and the heart. We compare how reports of either too little or too much chaos and fractal complexity can be damaging to normal biological function, and suggest that aiming for the healthy dose of chaos may be an effective strategy for various biomedical applications. We also discuss rising examples of the implementation of fractal theory in designing novel materials, biomedical devices, diagnostics, and clinical therapies. Finally, we explain important mathematical concepts of fractals and chaos, such as fractal dimension, criticality, bifurcation, and iteration, and how they are related to biology. Overall, we promote the effectiveness of fractals in characterizing natural systems, and suggest moving towards using fractal frameworks as a basis for the research and development of better tools for the future of biomedical engineering.

The Mitochondrion as an Emerging Therapeutic Target in Cancer

Mitochondria have emerged as important pharmacological targets because of their key role in cellular proliferation and death. In tumor tissues, mitochondria can switch metabolic phenotypes to meet the challenges of high energy demand and macromolecular synthesis. Furthermore, mitochondria can engage in crosstalk with the tumor microenvironment, and signals from cancer-associated fibroblasts can impinge on mitochondria. Cancer cells can also acquire a hybrid phenotype in which both glycolysis and oxidative phosphorylation (OXPHOS) can be utilized. This hybrid phenotype can facilitate metabolic plasticity of cancer cells more specifically in metastasis and therapy-resistance. In light of the metabolic heterogeneity and plasticity of cancer cells that had until recently remained unappreciated, strategies targeting cancer metabolic dependency appear to be promising in the development of novel and effective cancer therapeutics.

Small Cell Lung Cancer Therapeutic Responses Through Fractal Measurements: From Radiology to Mitochondrial Biology

Small cell lung cancer (SCLC) is an aggressive neuroendocrine disease with an overall 5 year survival rate of ~7%. Although patients tend to respond initially to therapy, therapy-resistant disease inevitably emerges. Unfortunately, there are no validated biomarkers for early-stage SCLC to aid in early detection. Here, we used readouts of lesion image characteristics and cancer morphology that were based on fractal geometry, namely fractal dimension (FD) and lacunarity (LC), as novel biomarkers for SCLC. Scanned tumors of patients before treatment had a high FD and a low LC compared to post treatment, and this effect was reversed after treatment, suggesting that these measurements reflect the initial conditions of the tumor, its growth rate, and the condition of the lung. Fractal analysis of mitochondrial morphology showed that cisplatin-treated cells showed a discernibly decreased LC and an increased FD, as compared with control. However, treatment with mdivi-1, the small molecule that attenuates mitochondrial division, was associated with an increase in FD as compared with control. These data correlated well with the altered metabolic functions of the mitochondria in the diseased state, suggesting that morphological changes in the mitochondria predicate the tumor’s future ability for mitogenesis and motogenesis, which was also observed on the CT scan images. Taken together, FD and LC present ideal tools to differentiate normal tissue from malignant SCLC tissue as a potential diagnostic biomarker for SCLC.

Deep Analysis of Mitochondria and Cell Health Using Machine Learning

There is a critical need for better analytical methods to study mitochondria in normal and diseased states. Mitochondrial image analysis is typically done on still images using slow manual methods or automated methods of limited types of features. MitoMo integrated software overcomes these bottlenecks by automating rapid unbiased quantitative analysis of mitochondrial morphology, texture, motion, and morphogenesis and advances machine-learning classification to predict cell health by combining features. Our pixel-based approach for motion analysis evaluates the magnitude and direction of motion of: (1) molecules within mitochondria, (2) individual mitochondria, and (3) distinct morphological classes of mitochondria. MitoMo allows analysis of mitochondrial morphogenesis in time-lapse videos to study early progression of cellular stress. Biological applications are presented including: (1) establishing normal phenotypes of mitochondria in different cell types; (2) quantifying stress-induced mitochondrial hyperfusion in cells treated with an environmental toxicant, (3) tracking morphogenesis in mitochondria undergoing swelling, and (4) evaluating early changes in cell health when morphological abnormalities are not apparent. MitoMo unlocks new information on mitochondrial phenotypes and dynamics by enabling deep analysis of mitochondrial features in any cell type and can be applied to a broad spectrum of research problems in cell biology, drug testing, toxicology, and medicine.

Inhibiting crosstalk between MET signaling and mitochondrial dynamics and morphology: a novel therapeutic approach for lung cancer and mesothelioma

The receptor tyrosine kinase MET is frequently involved in malignant transformation and inhibiting its activity in MET-dependent cancers is associated with improved clinical outcomes. Emerging evidence also suggests that mitochondria play an essential role in tumorigenesis and Dynamin Related Protein (DRP1), a key component of the mitochondrial fission machinery, has emerged as an attractive therapeutic target. Here, we report that inhibiting MET activity with the tyrosine kinase inhibitor MGCD516 attenuates viability, migration, and invasion of non-small cell lung cancer (NSCLC) and malignant pleural mesothelioma (MPM) cell lines in vitro, and significantly retards tumor growth in vivo. Interestingly, MGCD516 treatment also results in altered mitochondrial morphology in these cell lines. Furthermore, inhibiting MET pharmacologically or knocking down its expression using siRNA, decreases DRP1 activity alluding to possible crosstalk between them in these two cancers. Consistently, a combination of MGCD516 and mdivi-1, a quinazolinone reported to inhibit mitochondrial fission, is more effective in attenuating proliferation of NSCLC and MPM cell lines than either drug alone. Considered together, the present study has uncovered a novel mechanism underlying mitochondrial regulation by MET that involves crosstalk with DRP1, and suggests that a combination therapy targeting both MET and DRP1 could be a novel strategy for NSCLC and MPM.

Atrial Rotor Dynamics Under Complex Fractional Order Diffusion

The mechanisms of atrial fibrillation (AF) are a challenging research topic. The rotor hypothesis states that the AF is sustained by a reentrant wave that propagates around an unexcited core. Cardiac tissue heterogeneities, both structural and cellular, play an important role during fibrillatory dynamics, so that the ionic characteristics of the currents, their spatial distribution and their structural heterogeneity determine the meandering of the rotor. Several studies about rotor dynamics implement the standard diffusion equation. However, this mathematical scheme carries some limitations. It assumes the myocardium as a continuous medium, ignoring, therefore, its discrete and heterogeneous aspects. A computational model integrating both, electrical and structural properties could complement experimental and clinical results. A new mathematical model of the action potential propagation, based on complex fractional order derivatives is presented. The complex derivative order appears of considering the myocardium as discrete-scale invariant fractal. The main aim is to study the role of a myocardial, with fractal characteristics, on atrial fibrillatory dynamics. For this purpose, the degree of structural heterogeneity is described through derivatives of complex order γ = α + jβ. A set of variations for γ is tested. The real part α takes values ranging from 1.1 to 2 and the imaginary part β from 0 to 0.28. Under this scheme, the standard diffusion is recovered when α = 2 and β = 0. The effect of γ on the action potential propagation over an atrial strand is investigated. Rotors are generated in a 2D model of atrial tissue under electrical remodeling due to chronic AF. The results show that the degree of structural heterogeneity, given by γ, modulates the electrophysiological properties and the dynamics of rotor-type reentrant mechanisms. The spatial stability of the rotor and the area of its unexcited core are modulated. As the real part decreases and the imaginary part increases, simulating a higher structural heterogeneity, the vulnerable window to reentrant is increased, as the total meandering of the rotor tip. This in silico study suggests that structural heterogeneity, described by means of complex order derivatives, modulates the stability of rotors and that a wide range of rotor dynamics can be generated.

Dynamin-Related Protein 1 at the Crossroads of Cancer

Mitochondrial dynamics are known to have an important role in so-called age-related diseases, including cancer. Mitochondria is an organelle involved in many key cellular functions and responds to physiologic or stress stimuli by adapting its structure and function. Perhaps the most important structural changes involve mitochondrial dynamics (fission and fusion), which occur in normal cells as well as in cells under dysregulation, such as cancer cells. Dynamin-related protein 1 (DRP1), a member of the dynamin family of guanosine triphosphatases (GTPases), is the key component of mitochondrial fission machinery. Dynamin-related protein 1 is associated with different cell processes such as apoptosis, mitochondrial biogenesis, mitophagy, metabolism, and cell proliferation, differentiation, and transformation. The role of DRP1 in tumorigenesis may seem to be paradoxical, since mitochondrial fission is a key mediator of two very different processes, cellular apoptosis and cell mitosis. Dynamin-related protein 1 has been associated with the development of distinct human cancers, including changes in mitochondrial energetics and cellular metabolism, cell proliferation, and stem cell maintenance, invasion, and promotion of metastases. However, the underlying mechanism for this association is still being explored. Herein, we review the published knowledge on the role of DRP1 in cancer, exploring its interaction with different biological processes in the tumorigenesis context.

Mitochondrial network complexity emerges from fission/fusion dynamics

Mitochondrial networks exhibit a variety of complex behaviors, including coordinated cell-wide oscillations of energy states as well as a phase transition (depolarization) in response to oxidative stress. Since functional and structural properties are often interwinded, here we characterized the structure of mitochondrial networks in mouse embryonic fibroblasts using network tools and percolation theory. Subsequently we perturbed the system either by promoting the fusion of mitochondrial segments or by inducing mitochondrial fission. Quantitative analysis of mitochondrial clusters revealed that structural parameters of healthy mitochondria laid in between the extremes of highly fragmented and completely fusioned networks. We confirmed our results by contrasting our empirical findings with the predictions of a recently described computational model of mitochondrial network emergence based on fission-fusion kinetics. Altogether these results offer not only an objective methodology to parametrize the complexity of this organelle but also support the idea that mitochondrial networks behave as critical systems and undergo structural phase transitions.

Universal fractality of morphological transitions in stochastic growth processes

Stochastic growth processes give rise to diverse and intricate structures everywhere in nature, often referred to as fractals. In general, these complex structures reflect the non-trivial competition among the interactions that generate them. In particular, the paradigmatic Laplacian-growth model exhibits a characteristic fractal to non-fractal morphological transition as the non-linear effects of its growth dynamics increase. So far, a complete scaling theory for this type of transitions, as well as a general analytical description for their fractal dimensions have been lacking. In this work, we show that despite the enormous variety of shapes, these morphological transitions have clear universal scaling characteristics. Using a statistical approach to fundamental particle-cluster aggregation, we introduce two non-trivial fractal to non-fractal transitions that capture all the main features of fractal growth. By analyzing the respective clusters, in addition to constructing a dynamical model for their fractal dimension, we show that they are well described by a general dimensionality function regardless of their space symmetry-breaking mechanism, including the Laplacian case itself. Moreover, under the appropriate variable transformation this description is universal, i.e., independent of the transition dynamics, the initial cluster configuration, and the embedding Euclidean space.

The Concerted Action of Mitochondrial Dynamics and Positioning: New Characters in Cancer Onset and Progression

Mitochondria are dynamic organelles whose morphology and activity are extremely variable, depending on the metabolic state of the cell. In particular, their shape and movements within the cell are finely regulated by an increasing number of proteins, which take part in the process of mitochondrial fission/fusion and connect the organelles to the cytoskeleton. As to their activities, mitochondria are considered to be at the crossroad between cell life and death since, on the one hand, they are essential in ATP production and in multiple metabolic pathways but, on the other, they are involved in the intrinsic apoptotic cascade, triggered by different stress conditions. Importantly, the process of mitochondrial Ca²⁺ uptake, as well as the morphology and the dynamics of these organelles, is known to deeply impact on both pro-survival and pro-death mitochondrial activities. Recently, increasing evidence has accrued on a central role of deregulated mitochondrial functionalities in the onset and progression of different pathologies, ranging from neurodegenerative diseases to cancer. In this contribution, we will present the latest findings connecting alterations in the machineries that control mitochondrial dynamics and localization to specific cancer hallmarks, highlighting the importance of mitochondria for the viability of cancer cells and discussing their role as promising targets for the development of novel anticancer therapies.

Connecting mitochondrial dynamics and life-or-death events via Bcl-2 family proteins

The morphology of a population of mitochondria is the result of several interacting dynamical phenomena, including fission, fusion, movement, elimination and biogenesis. Each of these phenomena is controlled by underlying molecular machinery, and when defective can cause disease. New understanding of the relationships between form and function of mitochondria in health and disease is beginning to be unraveled on several fronts. Studies in mammals and model organisms have revealed that mitochondrial morphology, dynamics and function appear to be subject to regulation by the same proteins that regulate apoptotic cell death. One protein family that influences mitochondrial dynamics in both healthy and dying cells is the Bcl-2 protein family. Connecting mitochondrial dynamics with life-death pathway forks may arise from the intersection of Bcl-2 family proteins with the proteins and lipids that determine mitochondrial shape and function. Bcl-2 family proteins also have multifaceted influences on cells and mitochondria, including calcium handling, autophagy and energetics, as well as the subcellular localization of mitochondrial organelles to neuronal synapses. The remarkable range of physical or functional interactions by Bcl-2 family proteins is challenging to assimilate into a cohesive understanding. Most of their effects may be distinct from their direct roles in apoptotic cell death and are particularly apparent in the nervous system. Dual roles in mitochondrial dynamics and cell death extend beyond BCL-2 family proteins. In this review, we discuss many processes that govern mitochondrial structure and function in health and disease, and how Bcl-2 family proteins integrate into some of these processes.

Mitochondrial dynamics as regulators of cancer biology

Mitochondria are dynamic organelles that supply energy required to drive key cellular processes, such as survival, proliferation, and migration. Critical to all of these processes are changes in mitochondrial architecture, a mechanical mechanism encompassing both fusion and fragmentation (fission) of the mitochondrial network. Changes to mitochondrial shape, size, and localization occur in a regulated manner to maintain energy and metabolic homeostasis, while deregulation of mitochondrial dynamics is associated with the onset of metabolic dysfunction and disease. In cancers, oncogenic signals that drive excessive proliferation, increase intracellular stress, and limit nutrient supply are all able to alter the bioenergetic and biosynthetic requirements of cancer cells. Consequently, mitochondrial function and shape rapidly adapt to these hostile conditions to support cancer cell proliferation and evade activation of cell death programs. In this review, we will discuss the molecular mechanisms governing mitochondrial dynamics and integrate recent insights into how changes in mitochondrial shape affect cellular migration, differentiation, apoptosis, and opportunities for the development of novel targeted cancer therapies.