Looking "Under the Hood" of Cellular Mechanotransduction with Computational Tools: A Systems Biomechanics Approach across Multiple Scales

TripletProt: Deep Representation Learning of Proteins Based On Siamese Networks

Pretrained representations have recently gained attention in various machine learning applications. Nonetheless, the high computational costs associated with training these models have motivated alternative approaches for representation learning. Herein we introduce TripletProt, a new approach for protein representation learning based on the Siamese neural networks. Representation learning of biological entities which capture essential features can alleviate many of the challenges associated with supervised learning in bioinformatics. The most important distinction of our proposed method is relying on the protein-protein interaction (PPI) network. The computational cost of the generated representations for any potential application is significantly lower than comparable methods since the length of the representations is significantly smaller than that in other approaches. TripletProt offers great potentials for the protein informatics tasks and can be widely applied to similar tasks. We evaluate TripletProt comprehensively in protein functional annotation tasks including sub-cellular localization (14 categories) and gene ontology prediction (more than 2000 classes), which are both challenging multi-class, multi-label classification machine learning problems. We compare the performance of TripletProt with the state-of-the-art approaches including a recurrent language model-based approach (i.e., UniRep), as well as a protein-protein interaction (PPI) network and sequence-based method (i.e., DeepGO). Our TripletProt showed an overall improvement of F1 score in the above mentioned comprehensive functional annotation tasks, solely relying on the PPI network. Availability: The source code and datasets are available at https://github.com/EsmaeilNourani/TripletProt.

Adhesion strength between cells regulate nonmonotonic growth by a biomechanical feedback mechanism

We determine how intercellular interactions and mechanical pressure experienced by single cells regulate cell proliferation using a minimal computational model for three-dimensional multicellular spheroid (MCS) growth. We discover that emergent spatial variations in the cell division rate, depending on the location of the cells either at the core or periphery within the MCS, is regulated by intercellular adhesion strength (fad). Varying fad results in nonmonotonic proliferation of cells in the MCS. A biomechanical feedback mechanism coupling the fad and microenvironment-dependent pressure fluctuations relative to a threshold value (pc) determines the onset of a dormant phase, and explains the nonmonotonic proliferation response. Increasing fad from low values enhances cell proliferation because pressure on individual cells is smaller compared with pc. However, at high fad, cells readily become dormant and cannot rearrange effectively in spacetime, leading to arrested cell proliferation. Utilizing our theoretical predictions, we explain experimental data on the impact of adhesion strength on cell proliferation and find good agreement. Our work, which shows that proliferation is regulated by pressure-adhesion feedback mechanism, may be a general feature of multicellular growth.

On modeling the multiscale mechanobiology of soft tissues: Challenges and progress

Tissues grow and remodel in response to mechanical cues, extracellular and intracellular signals experienced through various biological events, from the developing embryo to disease and aging. The macroscale response of soft tissues is typically nonlinear, viscoelastic anisotropic, and often emerges from the hierarchical structure of tissues, primarily their biopolymer fiber networks at the microscale. The adaptation to mechanical cues is likewise a multiscale phenomenon. Cell mechanobiology, the ability of cells to transform mechanical inputs into chemical signaling inside the cell, and subsequent regulation of cellular behavior through intra- and inter-cellular signaling networks, is the key coupling at the microscale between the mechanical cues and the mechanical adaptation seen macroscopically. To fully understand mechanics of tissues in growth and remodeling as observed at the tissue level, multiscale models of tissue mechanobiology are essential. In this review, we summarize the state-of-the art modeling tools of soft tissues at both scales, the tissue level response, and the cell scale mechanobiology models. To help the interested reader become more familiar with these modeling frameworks, we also show representative examples. Our aim here is to bring together scientists from different disciplines and enable the future leap in multiscale modeling of tissue mechanobiology.

On the nuclear pore complex and its emerging role in cellular mechanotransduction

The nuclear pore complex (NPC) is a large protein assembly that perforates the nuclear envelope and provides a sole gateway for traffic between the cytoplasm and the nucleus. The NPC controls the nucleocytoplasmic transport by selectively allowing cargoes such as proteins and mRNA to pass through its central channel, thereby playing a vital role in protecting the nuclear component and regulating gene expression and protein synthesis. The selective transport through the NPC originates from its exquisite molecular structure featuring a large scaffold and the intrinsically disordered central channel domain, but the exact mechanism underlying the selective transport remains elusive and is the subject of various, often conflicting, hypotheses. Moreover, recent studies have suggested a new role for the NPC as a mechanosensor, where the NPC changes its channel diameter depending on the nuclear envelope tension, altering the molecular transportability through this nanopore. In this mini-review, we summarize the current understandings of the selective nature of the NPC and discuss its emerging role in cellular mechanotransduction.

Win, Lose, or Tie: Mathematical Modeling of Ligand Competition at the Cell-Extracellular Matrix Interface

Integrin transmembrane proteins conduct mechanotransduction at the cell–extracellular matrix (ECM) interface. This process is central to cellular homeostasis and therefore is particularly important when designing instructive biomaterials and organoid culture systems. Previous studies suggest that fine-tuning the ECM composition and mechanical properties can improve organoid development. Toward the bigger goal of fully functional organoid development, we hypothesize that resolving the dynamics of ECM–integrin interactions will be highly instructive. To this end, we developed a mathematical model that enabled us to simulate three main interactions, namely integrin activation, ligand binding, and integrin clustering. Different from previously published computational models, we account for the binding of more than one type of ligand to the integrin. This competition between ligands defines the fate of the system. We have demonstrated that an increase in the initial concentration of ligands does not ensure an increase in the steady state concentration of ligand-bound integrins. The ligand with higher binding rate occupies more integrins at the steady state than does the competing ligand. With cell type specific, quantitative input on integrin-ligand binding rates, this model can be used to develop instructive cell culture systems.

Revealing hidden information in osteoblast's mechanotransduction through analysis of time patterns of critical events

BACKGROUND

Mechanotransduction in bone cells plays a pivotal role in osteoblast differentiation and bone remodelling. Mechanotransduction provides the link between modulation of the extracellular matrix by mechanical load and intracellular activity. By controlling the balance between the intracellular and extracellular domains, mechanotransduction determines the optimum functionality of skeletal dynamics. Failure of this relationship was suggested to contribute to bone-related diseases such as osteoporosis.

RESULTS

A hybrid mechanical and agent-based model (Mech-ABM), simulating mechanotransduction in a single osteoblast under external mechanical perturbations, was utilised to simulate and examine modulation of the activation dynamics of molecules within mechanotransduction on the cellular response to mechanical stimulation. The number of molecules and their fluctuations have been analysed in terms of recurrences of critical events. A numerical approach has been developed to invert subordination processes and to extract the direction processes from the molecular signals in order to derive the distribution of recurring events. These predict that there are large fluctuations enclosing information hidden in the noise which is beyond the dynamic variations of molecular baselines. Moreover, studying the system under different mechanical load regimes and altered dynamics of feedback loops, illustrate that the waiting time distributions of each molecule are a signature of the system's state.

CONCLUSIONS

The behaviours of the molecular waiting times change with the changing of mechanical load regimes and altered dynamics of feedback loops, presenting the same variation of patterns for similar interacting molecules and identifying specific alterations for key molecules in mechanotransduction. This methodology could be used to provide a new tool to identify potent molecular candidates to modulate mechanotransduction, hence accelerate drug discovery towards therapeutic targets for bone mass upregulation.

The "Stressful" Life of Cell Adhesion Molecules: On the Mechanosensitivity of Integrin Adhesome

Cells have evolved into complex sensory machines that communicate with their microenvironment via mechanochemical signaling. Extracellular mechanical cues trigger complex biochemical pathways in the cell, which regulate various cellular processes. Integrin-mediated focal adhesions (FAs) are large multiprotein complexes, also known as the integrin adhesome, that link the extracellular matrix (ECM) to the actin cytoskeleton, and are part of powerful intracellular machinery orchestrating mechanotransduction pathways. As forces are transmitted across FAs, individual proteins undergo structural and functional changes that involve a conversion of chemical to mechanical energy. The local composition of early adhesions likely defines the regional stress levels and determines the type of newly recruited proteins, which in turn modify the local stress distribution. Various approaches have been used for detecting and exploring molecular mechanisms through which FAs are spatiotemporally regulated, however, many aspects are yet to be understood. Current knowledge on the molecular mechanisms of mechanosensitivity in adhesion proteins is discussed herein along with important questions yet to be addressed, are discussed.

Integrated Multiscale Biomaterials Experiment and Modeling

The integration of modeling and experimentation is an integral component of all engineering design. Developing the technologies to achieve this represents a critical challenge in biomaterials because of the hierarchical structures that comprise them and the spectra of timescales upon which they operate. Progress in integrating modeling and experiment in biomaterials represents progress towards harnessing them for engineering application. We present here a summary of the state of the art, and outlooks for the field as a whole.

α-Actinin Induces a Kink in the Transmembrane Domain of β3-Integrin and Impairs Activation via Talin

Integrin-mediated signaling is crucial for cell-substrate adhesion and can be triggered from both intra- and extracellular interactions. Although talin binding is sufficient for inside-out activation of integrin, other cytoplasmic proteins such as α-actinin and filamin can directly interfere with talin-mediated integrin activation. Specifically, α-actinin plays distinct roles in regulating αIIbβ3 versus α5β1 integrin. It has been shown that α-actinin competes with talin for binding to the cytoplasmic tail of β3-integrin, whereas it cooperates with talin for activating integrin α5β1. In this study, molecular dynamics simulations were employed to compare and contrast molecular mechanisms of αIIbβ3 and α5β1 activation in the presence and absence of α-actinin. Our results suggest that α-actinin impairs integrin signaling by both undermining talin binding to the β3-integrin cytoplasmic tail and inducing a kink in the transmembrane domain of β3-integrin. Furthermore, we showed that α-actinin promote talin association with β1-integrin by restricting the motion of the cytoplasmic tail and reducing the entropic barrier for talin binding. Taken together, our results showed that the interplay between talin and α-actinin regulates signal transmission via controlling the conformation of the transmembrane domain and altering natural response modes of integrins in a type-specific manner.