Research

Mechanical phenomena affect nearly every aspect of cellular biology and function, yet the underlying mechanisms of how mechanical forces and biochemical signals interact is not clearly understood. We are interested in understanding the molecular basis of cell mechanics and mechanotransduction and shedding light on the role of these processes in human disease. Our current projects are as follows:

 

I. LOOKING “UNDER THE HOOD” OF CELLULAR MECHANOTRANSDUCTION

Mechanics of Integrin-Mediated Focal Adhesions. The underlying mechanics and mechanisms of mechanotransduction are not yet clearly understood. One hypothesis is that forces transmitted via individual proteins, either at the site of cell adhesion to its surroundings or within the stress-bearing members of the cytoskeleton, cause conformational changes that change the binding affinity of these proteins to other intracellular molecules. This altered equilibrium state can subsequently initiate biochemical signaling cascades or produce immediate structural changes. Force-induced conformational changes (‘deformations’) in proteins may play a critical role in initiating and controlling cell signaling pathways. To understand mechanotransduction at the molecular level requires detailed analysis of protein molecular conformational changes that occur in response to forces, which can be exerted by extracellular matrix through the cellular membrane or the cytoskeleton.
Cell_ECM

Cytoskeletal Organization and Mechanics. We study actin cross-linking by α-actinin and its further reinforcement with vinculin (Shams et al. Submitted).

We also investigate the mechanical properties of microtubules and microtubular bundles, e.g. in neuronal axons under tension (Mehrbod et al. 2011; Peter et al. 2012) along with buckling (Soheilypour et al. 2015) and torsional behavior (Lazarus et al. 2015). Axonal microtubules (MTs) are bundled by interconnections mediated by MT-associated protein (MAP) tau and form polarized arrays located in the interior portion of the axon. While today’s imaging methods are not able to detect these microscopic damages to axons, computational modeling can efficiently simulate the neuronal components subjected to external forces and substantially improve contemporary understanding of the underlying damage mechanisms. We have developed a computational model of axonal MTs to explore the biomechanical behavior of axon components under excessive compressive, tensile, and torsional forces. This work has implications in our understanding of traumatic brain injury.

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The Connection of Cytoskeleton to Nucleus. The genetic information of eukaryotic cells is enclosed within a double-layered nuclear envelope, which comprises an inner and outer nuclear membrane. Several transmembrane proteins locate to the nuclear envelope; however, only two integral protein complexes span the nuclear envelope and connect the inside of the nucleus to the cytoplasm. The nuclear pore complex (NPC) acts as a gateway for molecular exchange between the interior of the nucleus and the cytoplasm, whereas so-called LINC complexes physically link the nucleoskeleton and the cytoskeleton. The assembly of NPCs and their even distribution throughout the nuclear envelope is dependent on components of the LINC complex. Additionally, LINC complex formation is dependent on the successful localization of inner nuclear membrane components of LINC complexes and their transport through the NPC. Furthermore, the architecture of the nuclear envelope depends on both protein complexes. Finally, the LINC complexes can affect nucleocytoplasmic transport through the NPC. Understanding the NPC and LINC complexes, the relation between them and how they govern the mechanics and transport of nucleo-cytoskeletal connection offers important insight into mechanobiology of several human diseases.

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Mechanochemistry of the Nuclear Pore Complex. Nuclear pore complex (NPC) is the sole gateway for bidirectional transport of vital cargos, ranging from different functional proteins to RNAs and ribosomes, between the cytoplasm and the nucleus in eukaryotic cells. The complex, yet delicate, geometry of the NPC and the fine spatiotemporal resolution at which the nucleocytoplasmic transport takes place have so far hindered the direct, experimental investigation of this exquisite nanopore. Using a hybrid of state-of-the-art computational modeling approaches, ranging from finite element (Wolf et al. 2008) to coarse-grained Brownian dynamics (Moussavi-Baygi et al. 2016) to molecular dynamics techniques (Zhao et al. 2014) to new agent-based modeling methods (Azimi et al. 2014), we study the structure and function of the nuclear pore complex and the dynamics of nucleocytoplasmic traffic. Understanding the biomechanics of the nuclear pore complex and nucleocytoplasmic transport will broadly impact our understanding of viral diseases and will revolutionize therapeutic approaches (e.g. gene therapy) and open the door to many industrial applications of biomimetic artificial nanopores.

NPC

mRNA export and quality control. Following transcription, messenger ribonucleic acids (mRNAs) are transported to the cytoplasm to transfer genetic information and direct synthesis of functional proteins. Export of mRNAs into the cytoplasm is meticulously quality controlled by intricate mechanisms in eukaryotic cells. Despite the significance of these processes, many aspects of them are still poorly understood. We are developing and employing different computational techniques, such as agent-based modeling and molecular dynamics, to shed light on these essential processes in mRNA biogenesis.

NPC

II. BACTERIAL MECHANOTRANSDUCTION AND MICROBIOMES

Bacterial cells involve mechanisms that are similar to mammalian cells to some extent but are also different in many aspects. Bacterial adhesion, which is subject to biochemical and mechanical factors, is relevant in the pathogenesis of bacterial infections and community-level dynamics such as the human microbiome. Bacteria are lined with cell wall anchored proteins, which play a critical role in the molecular basis of adhesion to many surfaces and proteins. A broad range of human diseases is associated with bacterial infections, often initiated by specific adhesion of a bacterium to the target environment. Despite the significant role of bacterial adhesion in human infectious diseases, details and mechanisms of bacterial adhesion have remained elusive. We combine experimental and computational methods to study the adhesion characteristics of Staphylococcus aureus.

Staphylococcus Aureus Adhesion at the Cellular and Molecular Levels.We are exploring the interaction of S. Aureus bacterial cells with nanostructures of various size and geometry (Jahed et al. 2014), and studying force sensing of S. Aureus surface proteins by examining the interplay of fibronectin binding protein A (FnBPA) of S. Aureus with fibrinogen, fibronectin and integrin. We are also examining the structural mechanics of a characteristic bacterial collagen-binding adhesion (CNA) of Staphylococcus Aureus (Madani et al. 2017).

AV

New Approaches for Big-data Analysis of Microbial Communities.Microbial communities have important functions relevant to supporting life. It has recently been shown that microbial life plays a key role in regulating a broad range of systems, ranging from the human body to the soil and water microbiomes in the environment. Studies on the characterization of healthy versus unhealthy human microbiomes in different body sites and disease conditions have demonstrated a high potential for microbial community profiling techniques in the diagnosis and treatment of diseases. We are developing novel scalable approaches for the analysis of microbial communities using 16S metagenomic data, demonstrating that our methods can effectively distinguish between microbiomes from different body sites, healthy and diseased oral microbiomes, and infant microbiomes over varying times (Asgari et al. 2016).

AV

III. DEEP LEARNING FOR BIOLOGY AND MEDICINE

Deep Genomics and Proteomics. Biophysical and biochemical principles are associated with biological sequences (e.g., DNA, RNA, and protein amino-acid sequences) much similar to how the conceptual meaning of a natural language is linked to the structure of clauses and sentences. This analogy has motivated the ‘life language processing’ in our lab, where we treat biological sequences as the output of a certain language and adopt/develop language processing methods to discover functions encoded within biological sequences. Specifically, we use advanced techniques in deep learning for language processing and bioinformatics. For more information, see Asgari and Mofrad (2015).

AV

Deep Learning for Clinical Classifications and Diagnostics. Advances in artificial intelligence have the potential for transformative change in the field of medicine. We are using computer vision techniques for varying imaging modalities from CT, MR, ultrasound, and X-ray to automate disease classification and segmentation (Madani et al. 2017). In addition, through the use of unsupervised learning, we are gaining further insights in analyzing cardiovascular disease. Many of our deep learning models outperform clinician performance for routine tasks. However, we hope to focus our computational research efforts to assist and expand the capabilities of modern medicine to improve patient care.

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IV. MECHANICS OF HUMAN DISEASE

A long-term goal of our research program has been to understand the role of mechanics and mechanotransduction in human diseases, in particular, cardiovascular diseases like atherosclerosis and aortic valve calcification, and cancer.

Multiscale Mechanics of the Aortic Heart Valve: A Mechanotransduction Perspective on Calcific Aortic Stenosis. The human heart is a pump system consisting of four chambers and four valves. When functioning correctly, the valves open widely to allow blood through and seal securely shut. Valvular disease inhibits a valve’s ability to open and close, decreasing the efficiency of the heart and leading to a variety of other cardiovascular disorders. Aortic valves (AV) control the flow of oxygen-rich blood from the left ventricle to the aorta and thereby the rest of the body. Normally, the aortic valve functions very efficiently, providing negligible resistance to forward flow and allowing minimal backflow. The mechanical function of the aortic valve can, however, be affected by pathological conditions. The most common valvular disease is calcific aortic stenosis (CAS). In CAS, the aortic valve undergoes changes very similar to those seen in atherosclerosis. First, inflammatory cells migrate to the site. Monocytes adhere to the endothelial layer, infiltrate it, and differentiate into macrophages. The macrophages send intracellular signals to nearby fibroblasts, causing the fibroblasts to promote cellular proliferation and matrix remodeling. Macrophages add calcium deposits to the matrix. Eventually, the remodeled matrix and calcium deposits build up to yield a thickened, stiffened leaflet. These changes can affect mechanical function, a result known as stenosis. To understand the mechanisms of calcific aortic stenosis, and to evaluate methods of prevention and treatment for this disease, we develop computational models of aortic valve mechanics.

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Role of Mechanics and Mechanotransduction in Arterial Disease. We have a long-term interest in understanding and characterization of atherosclerotic plaques. Due to focal nature of this disease, mechanical factors (namely, arterial wall mechanics, hemodynamics, and mass transport patterns) are widely believed to play a key role in initiation and progression of atherosclerosis. Rigorous assessment of potential links between such mechanical factors and atherosclerosis, however, requires detailed studies using realistic, subject-specific models. Over the past several years, we have developed models for computational simulation of hemodynamics and wall mechanics in patient-specific carotid bifurcations.