Sampling Real-Time Atomic Dynamics in Metal Nanoparticles by Combining Experiments, Simulations, and Machine Learning.

Even at low temperatures, metal nanoparticles (NPs) possess atomic dynamics that are key for their properties but challenging to elucidate. Recent experimental advances allow obtaining atomic-resolution snapshots of the NPs in realistic regimes, but data acquisition limitations hinder the experimental reconstruction of the atomic dynamics present within them. Molecular simulations have the advantage that these allow directly tracking the motion of atoms over time. However, these typically start from ideal/perfect NP structures and, suffering from sampling limits, provide results that are often dependent on the initial/putative structure and remain purely indicative. Here, by combining state-of-the-art experimental and computational approaches, how it is possible to tackle the limitations of both approaches and resolve the atomistic dynamics present in metal NPs in realistic conditions is demonstrated. Annular dark-field scanning transmission electron microscopy enables the acquisition of ten high-resolution images of an Au NP at intervals of 0.6 s. These are used to reconstruct atomistic 3D models of the real NP used to run ten independent molecular dynamics simulations. Machine learning analyses of the simulation trajectories allow resolving the real-time atomic dynamics present within the NP. This provides a robust combined experimental/computational approach to characterize the structural dynamics of metal NPs in realistic conditions.

Detecting dynamic domains and local fluctuations in complex molecular systems via timelapse neighbors shuffling.

It is known that the behavior of many complex systems is controlled by local dynamic rearrangements or fluctuations occurring within them. Complex molecular systems, composed of many molecules interacting with each other in a Brownian storm, make no exception. Despite the rise of machine learning and of sophisticated structural descriptors, detecting local fluctuations and collective transitions in complex dynamic ensembles remains often difficult. Here, we show a machine learning framework based on a descriptor which we name Local Environments and Neighbors Shuffling (LENS), that allows identifying dynamic domains and detecting local fluctuations in a variety of systems in an abstract and efficient way. By tracking how much the microscopic surrounding of each molecular unit changes over time in terms of neighbor individuals, LENS allows characterizing the global (macroscopic) dynamics of molecular systems in phase transition, phases-coexistence, as well as intrinsically characterized by local fluctuations (e.g., defects). Statistical analysis of the LENS time series data extracted from molecular dynamics trajectories of, for example, liquid-like, solid-like, or dynamically diverse complex molecular systems allows tracking in an efficient way the presence of different dynamic domains and of local fluctuations emerging within them. The approach is found robust, versatile, and applicable independently of the features of the system and simply provided that a trajectory containing information on the relative motion of the interacting units is available. We envisage that "such a LENS" will constitute a precious basis for exploring the dynamic complexity of a variety of systems and, given its abstract definition, not necessarily of molecular ones.

Automatic Optimization of Lipid Models in the Martini Force Field Using SwarmCG.

After two decades of continued development of the Martini coarse-grained force field (CG FF), further refinment of the already rather accurate Martini lipid models has become a demanding task that could benefit from integrative data-driven methods. Automatic approaches are increasingly used in the development of accurate molecular models, but they typically make use of specifically designed interaction potentials that transfer poorly to molecular systems or conditions different than those used for model calibration. As a proof of concept, here, we employ SwarmCG, an automatic multiobjective optimization approach facilitating the development of lipid force fields, to refine specifically the bonded interaction parameters in building blocks of lipid models within the framework of the general Martini CG FF. As targets of the optimization procedure, we employ both experimental observables (top-down references: area per lipid and bilayer thickness) and all-atom molecular dynamics simulations (bottom-up reference), which respectively inform on the supra-molecular structure of the lipid bilayer systems and on their submolecular dynamics. In our training sets, we simulate at different temperatures in the liquid and gel phases up to 11 homogeneous lamellar bilayers composed of phosphatidylcholine lipids spanning various tail lengths and degrees of (un)saturation. We explore different CG representations of the molecules and evaluate improvements a posteriori using additional simulation temperatures and a portion of the phase diagram of a DOPC/DPPC mixture. Successfully optimizing up to ∼80 model parameters within still limited computational budgets, we show that this protocol allows the obtainment of improved transferable Martini lipid models. In particular, the results of this study demonstrate how a fine-tuning of the representation and parameters of the models may improve their accuracy and how automatic approaches, such as SwarmCG, may be very useful to this end.

TimeSOAP: Tracking high-dimensional fluctuations in complex molecular systems via time variations of SOAP spectra.

Many molecular systems and physical phenomena are controlled by local fluctuations and microscopic dynamical rearrangements of the constitutive interacting units that are often difficult to detect. This is the case, for example, of phase transitions, phase equilibria, nucleation events, and defect propagation, to mention a few. A detailed comprehension of local atomic environments and of their dynamic rearrangements is essential to understand such phenomena and also to draw structure-property relationships useful to unveil how to control complex molecular systems. Considerable progress in the development of advanced structural descriptors [e.g., Smooth Overlap of Atomic Position (SOAP), etc.] has certainly enhanced the representation of atomic-scale simulations data. However, despite such efforts, local dynamic environment rearrangements still remain difficult to elucidate. Here, exploiting the structurally rich description of atomic environments of SOAP and building on the concept of time-dependent local variations, we developed a SOAP-based descriptor, TimeSOAP (τSOAP), which essentially tracks time variations in local SOAP environments surrounding each molecule (i.e., each SOAP center) along ensemble trajectories. We demonstrate how analysis of the time-series τSOAP data and of their time derivatives allows us to detect dynamic domains and track instantaneous changes of local atomic arrangements (i.e., local fluctuations) in a variety of molecular systems. The approach is simple and general, and we expect that it will help shed light on a variety of complex dynamical phenomena.

Tumor heterogeneity: preclinical models, emerging technologies, and future applications.

Heterogeneity describes the differences among cancer cells within and between tumors. It refers to cancer cells describing variations in morphology, transcriptional profiles, metabolism, and metastatic potential. More recently, the field has included the characterization of the tumor immune microenvironment and the depiction of the dynamics underlying the cellular interactions promoting the tumor ecosystem evolution. Heterogeneity has been found in most tumors representing one of the most challenging behaviors in cancer ecosystems. As one of the critical factors impairing the long-term efficacy of solid tumor therapy, heterogeneity leads to tumor resistance, more aggressive metastasizing, and recurrence. We review the role of the main models and the emerging single-cell and spatial genomic technologies in our understanding of tumor heterogeneity, its contribution to lethal cancer outcomes, and the physiological challenges to consider in designing cancer therapies. We highlight how tumor cells dynamically evolve because of the interactions within the tumor immune microenvironment and how to leverage this to unleash immune recognition through immunotherapy. A multidisciplinary approach grounded in novel bioinformatic and computational tools will allow reaching the integrated, multilayered knowledge of tumor heterogeneity required to implement personalized, more efficient therapies urgently required for cancer patients.

Unsupervised Data-Driven Reconstruction of Molecular Motifs in Simple to Complex Dynamic Micelles.

The reshuffling mobility of molecular building blocks in self-assembled micelles is a key determinant of many their interesting properties, from emerging morphologies and surface compartmentalization, to dynamic reconfigurability and stimuli-responsiveness. However, the microscopic details of such complex structural dynamics are typically nontrivial to elucidate, especially in multicomponent assemblies. Here we show a machine-learning approach that allows us to reconstruct the structural and dynamic complexity of mono- and bicomponent surfactant micelles from high-dimensional data extracted from equilibrium molecular dynamics simulations. Unsupervised clustering of smooth overlap of atomic position (SOAP) data enables us to identify, in a set of multicomponent surfactant micelles, the dominant local molecular environments that emerge within them and to retrace their dynamics, in terms of exchange probabilities and transition pathways of the constituent building blocks. Tested on a variety of micelles differing in size and in the chemical nature of the constitutive self-assembling units, this approach effectively recognizes the molecular motifs populating them in an exquisitely agnostic and unsupervised way, and allows correlating them to their composition in terms of constitutive surfactant species.

Differential angiogenesis of bone and muscle endothelium in aging and inflammatory processes.

Different tissues have different endothelial features, however, the implications of this heterogeneity in pathological responses are not clear yet. "Inflamm-aging" has been hypothesized as a possible trigger of diseases, including osteoarthritis (OA) and sarcopenia, often present in the same patient. To highlight a possible contribution of organ-specific endothelial cells (ECs), we compare ECs derived from bone and skeletal muscle of the same OA patients. OA bone ECs show a pro-inflammatory signature and higher angiogenic sprouting as compared to muscle ECs, in control conditions and stimulated with TNFα. Furthermore, growth of muscle but not bone ECs decreases with increasing patient age and systemic inflammation. Overall, our data demonstrate that inflammatory conditions in OA patients differently affect bone and muscle ECs, suggesting that inflammatory processes increase angiogenesis in subchondral bone while associated systemic low-grade inflammation impairs angiogenesis in muscle, possibly highlighting a vascular trigger linking OA and sarcopenia.

A microfluidic model of human vascularized breast cancer metastasis to bone for the study of neutrophil-cancer cell interactions.

The organ-specific metastatization of breast cancer to bone is driven by specific interactions between the host microenvironment and cancer cells (CCs). However, it is still unclear the role that circulating immune cells, including neutrophils, play during bone colonization (i.e. pro-tumoral vs. anti-tumoral). Here, we aimed at analyzing the migratory behavior of neutrophils when exposed to breast CCs colonizing the bone and their contribution to the growth of breast cancer micrometastases. Based on our previous bone metastasis models, we designed a microfluidic system that allows to independently introduce human vascularized breast cancer metastatic seeds within a bone-mimicking microenvironment containing osteo-differentiated mesenchymal stromal cells and endothelial cells (ECs). ECs self-assembled into microvascular networks and connected the bone-mimicking microenvironment with the metastatic seed. Compared to controls without CCs, metastatic seeds compromised the architecture of microvascular networks resulting in a lower number of junctions (5.7 â€‹± â€‹1.2 vs. 18.8 â€‹± â€‹4.5, p â€‹= â€‹0.025) and shorter network length (10.5 â€‹± â€‹1.0 vs. 13.4 â€‹± â€‹0.8 [mm], p â€‹= â€‹0.042). Further, vascular permeability was significantly higher with CCs (2.60 â€‹× â€‹10-8 â€‹± â€‹3.59 â€‹× â€‹10-8 â€‹vs. 0.53 â€‹× â€‹10-8 â€‹± â€‹0.44 â€‹× â€‹10-8 [cm/s], p â€‹= â€‹0.05). Following metastatic seed maturation, neutrophils were injected into microvascular networks resulting in a higher extravasation rate when CCs were present (27.9 â€‹± â€‹13.7 vs. 14.7 â€‹± â€‹12.4 [%], p â€‹= â€‹0.01). Strikingly, the percentage of dying CCs increased in presence of neutrophils, as confirmed by confocal imaging and flow cytometry on isolated cells from the metastatic seeds. The biofabricated metastatic niche represents a powerful tool to analyze the mechanisms of interaction between circulating immune cells and organ-specific micrometastases and to test novel drug combinations targeting the metastatic microenvironment.

Molecular communications in complex systems of dynamic supramolecular polymers.

Supramolecular polymers are composed of monomers that self-assemble non-covalently, generating distributions of monodimensional fibres in continuous communication with each other and with the surrounding solution. Fibres, exchanging molecular species, and external environment constitute a sole complex system, which intrinsic dynamics is hard to elucidate. Here we report coarse-grained molecular simulations that allow studying supramolecular polymers at the thermodynamic equilibrium, explicitly showing the complex nature of these systems, which are composed of exquisitely dynamic molecular entities. Detailed studies of molecular exchange provide insights into key factors controlling how assemblies communicate with each other, defining the equilibrium dynamics of the system. Using minimalistic and finer chemically relevant molecular models, we observe that a rich concerted complexity is intrinsic in such self-assembling systems. This offers a new dynamic and probabilistic (rather than structural) picture of supramolecular polymer systems, where the travelling molecular species continuously shape the assemblies that statistically emerge at the equilibrium.

The driving role of the Cdk5/Tln1/FAKS732 axis in cancer cell extravasation dissected by human vascularized microfluidic models.

BACKGROUND: Understanding the molecular mechanisms of metastatic dissemination, the leading cause of death in cancer patients, is required to develop novel, effective therapies. Extravasation, an essential rate-limiting process in the metastatic cascade, includes three tightly coordinated steps: cancer cell adhesion to the endothelium, trans-endothelial migration, and early invasion into the secondary site. Focal adhesion proteins, including Tln1 and FAK, regulate the cytoskeleton dynamics: dysregulation of these proteins is often associated with metastatic progression and poor prognosis. METHODS: Here, we studied the previously unexplored role of these targets in each extravasation step using engineered 3D in vitro models, which recapitulate the physiological vascular niche experienced by cancer cells during hematogenous metastasis. RESULTS: Human breast cancer and fibrosarcoma cell lines respond to Cdk5/Tln1/FAK axis perturbation, impairing their metastatic potential. Vascular breaching requires actin polymerization-dependent invadopodia formation. Invadopodia generation requires the structural function of FAK and Tln1 rather than their activation through phosphorylation. Our data support that the inhibition of FAKS732 phosphorylation delocalizes ERK from the nucleus, decreasing ERK phosphorylated form. These findings indicate the critical role of these proteins in driving trans-endothelial migration. In fact, both knock-down experiments and chemical inhibition of FAK dramatically reduces lung colonization in vivo and TEM in microfluidic setting. Altogether, these data indicate that engineered 3D in vitro models coupled to in vivo models, genetic, biochemical, and imaging tools represent a powerful weapon to increase our understanding of metastatic progression. CONCLUSIONS: These findings point to the need for further analyses of previously overlooked phosphorylation sites of FAK, such as the serine 732, and foster the development of new effective antimetastatic treatments targeting late events of the metastatic cascade.

Correction: A microphysiological early metastatic niche on a chip reveals how heterotypic cell interactions and inhibition of integrin subunit ß3 impact breast cancer cell extravasation.

Correction for 'A microphysiological early metastatic niche on a chip reveals how heterotypic cell interactions and inhibition of integrin subunit ß3 impact breast cancer cell extravasation' by Martina Crippa et al., Lab Chip, 2021, DOI: .

A microphysiological early metastatic niche on a chip reveals how heterotypic cell interactions and inhibition of integrin subunit ß3 impact breast cancer cell extravasation.

During metastatic progression multiple players establish competitive mechanisms, whereby cancer cells (CCs) are exposed to both pro- and anti-metastatic stimuli. The early metastatic niche (EMN) is a transient microenvironment which forms in the circulation during CC dissemination. EMN is characterized by the crosstalk among CCs, platelets, leukocytes and endothelial cells (ECs), increasing CC ability to extravasate and colonize secondary tissues. To better understand this complex crosstalk, we designed a human "EMN-on-a-chip" which involves the presence of blood cells as compared to standard metastases-on-chip models, hence providing a microenvironment more similar to the in vivo situation. We showed that CC transendothelial migration (TEM) was significantly increased in the presence of neutrophils and platelets in the EMN-on-a-chip compared to CC alone. Moreover, exploiting the EMN-on-chip in combination with multi-culture experiments, we showed that platelets increased the expression of epithelial to mesenchymal transition (EMT) markers in CCs and that the addition of a clinically approved antiplatelet drug (eptifibatide, inhibiting integrin ß3) impaired platelet aggregation and decreased CC expression of EMT markers. Inhibition of integrin ß3 in the co-culture system modulated the activation of the Src-FAK-VE-cadherin signaling axis and partially restored the architecture of inter-endothelial junctions by limiting VE-cadherinY658 phosphorylation and its nuclear localization. These observations correlate with the decreased CC TEM observed in the presence of integrin ß3 inhibitor. Our EMN-on-a-chip can be easily implemented for drug repurposing studies and to investigate new candidate molecules counteracting CC extravasation.

Evolution of frustrated and stabilising contacts in reconstructed ancient proteins.

Energetic properties of a protein are a major determinant of its evolutionary fitness. Using a reconstruction algorithm, dating the reconstructed proteins and calculating the interaction network between their amino acids through a coevolutionary approach, we studied how the interactions that stabilise 890 proteins, belonging to five families, evolved for billions of years. In particular, we focused our attention on the network of most strongly attractive contacts and on that of poorly optimised, frustrated contacts. Our results support the idea that the cluster of most attractive interactions extends its size along evolutionary time, but from the data, we cannot conclude that protein stability or that the degree of frustration tends always to decrease.

Effective model of loop extrusion predicts chromosomal domains.

An active loop-extrusion mechanism is regarded as the main out-of-equilibrium mechanism responsible for the structuring of megabase-sized domains in chromosomes. We developed a model to study the dynamics of the chromosome fiber by solving the kinetic equations associated with the motion of the extruder. By averaging out the position of the extruder along the chain, we build an effective equilibrium model capable of reproducing experimental contact maps based solely on the positions of extrusion-blocking proteins. We assessed the quality of the effective model using numerical simulations of chromosomal segments and comparing the results with explicit-extruder models and experimental data.

Advanced Microfluidic Models of Cancer and Immune Cell Extravasation: A Systematic Review of the Literature.

Extravasation is a multi-step process implicated in many physiological and pathological events. This process is essential to get leukocytes to the site of injury or infection but is also one of the main steps in the metastatic cascade in which cancer cells leave the primary tumor and migrate to target sites through the vascular route. In this perspective, extravasation is a double-edged sword. This systematic review analyzes microfluidic 3D models that have been designed to investigate the extravasation of cancer and immune cells. The purpose of this systematic review is to provide an exhaustive summary of the advanced microfluidic 3D models that have been designed to study the extravasation of cancer and immune cells, offering a perspective on the current state-of-the-art. To this end, we set the literature search cross-examining PUBMED and EMBASE databases up to January 2020 and further included non-indexed references reported in relevant reviews. The inclusion criteria were defined in agreement between all the investigators, aimed at identifying studies which investigate the extravasation process of cancer cells and/or leukocytes in microfluidic platforms. Twenty seven studies among 174 examined each step of the extravasation process exploiting 3D microfluidic devices and hence were included in our review. The analysis of the results obtained with the use of microfluidic models allowed highlighting shared features and differences in the extravasation of immune and cancer cells, in view of the setup of a common framework, that could be beneficial for the development of therapeutic approaches fostering or hindering the extravasation process.