Research

Circuit topology

The Interdisciplinary Medical Innovation group has developed "circuit topology", a unique topology framework that complements the 200-year-old knot theory. Circuit topology refers to the arrangement of contacts and contact-contact interactions within a folded chain, where contacts represent constraints of various origins. Circuit topology identifies the arrangement of contacts established by direct intra-chain interactions within a folded linear chain (so called "hard" contacts). Furthermore, one can express entanglement by a set of 4 fundamental structural units (so called "soft" contacts) subjected to 3 (or 5) binary topological operations. Prime knots, which are viewed by knot theory as undecomposable, are also made of these structural units connected in some specific way. In turn, this kind of connection shows the fundamental reason why prime knots cannot be decomposed in knot theory. 

Their new theory defines the topology of proteins (typically not knotted) and genomic structures as a simple and precise barcode that allows the identification of all types of folds. Furthermore, it reveals the design principles of naturally occurring biomolecular knots. The circuit topology barcode enables among others more profound research into diseases caused by misfolding proteins, such as neuromuscular diseases and some sorts of cancer.

The team is also applying circuit topology to problems in other areas of natural sciences as well as social sciences. 

Are you interested in learning more about circuit Topology? Please see our channel here: Mashaghi Lab Channel 

Intrinsically disordered proteins and molecular condensates

Studying the conformation of highly dynamic intrinsically disordered protein regions (IDRs) is a major challenge in structural biology. The intrinsic disorder poses a challenge not only for experimental analysis of the conformation but also for computational modeling of the chain due to the size of the conformation space and lack of stable folds. For example, the state-of-the art artificial intelligence-based prediction approaches fail to identify the conformation of human androgen receptor. Our group is using innovative single molecule and modelling approaches to study disordered nuclear receptors. 

Importantly, we are interested in studying topological buildup and dynamics of protein condensates and aggregates. Biomolecular condensates are intracellular assemblies which play fundamental roles in cellular organization and physiology, and our understanding of the molecular principles, components and forces underlying their formation is limited. 

Chaperone-assisted protein folding

Folding pathways are traditionally studied for proteins in isolation, even though chaperones and other interacting partners critically interfere with folding and assembly of biomolecules in vivo in constructive or destructive ways. Consequently, our understanding of folding processes in life is fundamentally incomplete. Our group addresses this question with a single-molecule approach and modelling techniques. 

Importantly, our group has performed the first ever single-molecule force spectroscopy of proteins in a cytosolic environment.

Our group develops tools to analyse topology of interactions in complex systems (e.g. genome, signalling network, and human disease networks). We hope to demonstrate how these topology analyses help solving biomedical problems such as medical diagnostic problems, pharmacological challenges and molecular folding puzzles.  

Reaction networks

Our team was among the first that applied graph theory to biomolecular processes. We analyzed the complete protein interactome of a eukaryotic cell several decades ago. Since then, we have been interested in applying graph theory to reaction networks. We discovered that a non-monotonic input-output relation can arise with simple network topologies including coherent and incoherent feed-forward loops. As we demonstrated, fundamental constraints are imposed on the effectiveness and toxicity of any drug independent of its chemical nature and selectivity due to the specific network structure.

Currently, we are investigating the use of reaction network topology guided design of combination therapy as a predictive in silico drug-drug interaction screening approach. The results revealed that drug-drug interactions critically depend on the choice of target arrangement in a given topology, the nature of the drug, and the desired level of change in the network output. 

Disease networks

It is widely believed that cooperation between clinicians and machines may address many of the decisional fragilities intrinsic to current medical practice. However, the realization of this potential will require more precise definitions of disease states as well as their dynamics and interactions. A careful probabilistic examination of symptoms and signs, including the molecular profiles of the relevant biochemical networks, will often be required for building an unbiased and efficient diagnostic approach. Analogous problems have been studied for years by physicists extracting macroscopic states of various physical systems by examining microscopic elements and their interactions. These valuable experiences are now being extended to the medical field. From this perspective, we investigate how recent developments in statistical physics, machine learning and inference algorithms are coming together to improve current medical diagnostic approaches.

Organ chips and bioprinting  

Our group is interested in animal-free innovations for disease modeling. We pioneered the development of organ chips for viral diseases. Currently, we are interested in developing organ chip models for viral hemorrhagic syndromes diseases (e.g., Ebola virus). Viral diseases such as those that cause viral hemorrhagic syndromes or severe acute respiratory syndromes are recognised by World Health Organisation (WHO) as urgent threats to the public. This creates a real urgency for the innovative engineered models for diagnostic, preventive and drug development research. We use the organ chip approach for assessing new candidate drugs for these viral hemorrhagic syndromes together with our industrial partners. 

Our lab is also equipped with several state-of-the-art bioprinters (BioX and LumenX+). Bioprinters enable us to engineer complex tissues and organs. Bioprinted tissues are expected to revolutionise the biomedical field by eliminating the need for laboratory animals and enabling high-tech innovations such as next generation organ chips.

Single cell analysis of mechanics and metabolism

Our ambition at the Mashaghi lab is to develop and use innovative technologies that allow mechanical analysis of biological systems down to single cell and single organelle resolution, and to find the interdependencies between mechanical parameters and chemical biomarkers. 

The lab is equipped with advanced single cell technologies including state-of-the art optical tweezers, acoustic force spectroscopy and CellHesion, enabling mechanical analysis of single cells and cellular interactions. Furthermore, we use micro-engineered chips and micropillar array systems to learn about mechanics of cells and tissues. Mechanically heterogenous cells can also be sampled using a special microsampler available in the lab and the cellular content can then be subjected to high resolution chemical analysis using live single cell mass spectrometric methods. 

This innovative single cell platform will help us to understand the mechanisms of disease and will open up our way towards research into mechano-pharmacology and mechanotoxicity.

Immunoengineering 

We are interested in engineering immune cells for therapeutic purposes, as well as in studying single immune cells in biologically inspired engineered environments.  

Macrophages are crucial drivers of inflammatory corneal neovascularization and thus are potential targets for immunomodulatory therapies. We found that mesenchymal stromal cells can modulate the phenotype and angiogenic function of macrophages. Macrophages “educated" by mesenchymal stromal cells express significantly higher levels of anti-angiogenic and anti-inflammatory factors compared with control macrophages. Furthermore, we have discovered that mesenchymal stromal cells inhibit neutrophil effector functions via direct cell-cell contact interaction during inflammation. Our findings could have implications for the treatment of inflammatory ocular disorders caused by excessive neutrophil activation.

Our team uses lab-on-chip platforms to study mechanobiology and migratory behavior of single immune cells, or the collective migration of immune cells.  We use experimental and modeling approaches to study cellular trafficking, to understand cancer immunity and transplant rejection.  

Membrane engineering 

For years we have been very active in the area of lipid nanotechnology and membrane engineering. We fabricated the first bacterial lipid membrane model on a chip and developed label-free detection technologies for chip-based membrane sensing, including dual polarization interferometry and quartz crystal microbalance based membrane analysis. We also pioneered the use of quantum mechanical approches for studying electrical properties and vibrational dynamics of lipid bilayers. 

Mashaghi lab is also involved in basic and clinical research in the field of ophthalmology. We are particularly interested in studying ocular surface diseases, for which we are developing in vitro models and physics-based approaches. Our interest lies mainly in promoting interdisciplinary eye research and training a new generation of clinician-scientists. 

Corneal transplantation is one of the most common human organ transplantations worldwide. Although the 1-year survival rate is as high as 90%, more than half of transplantation patients suffer various types of corneal rejection, such as epithelial rejection, chronic stromal rejection, and endothelial rejection. In 2017, Mashaghi and his co-workers developed an immunotherapy strategy to improve survival of cornea grafts. The approach provides hope for patients with inflamed cornea bed, typically suffering from high graft rejection rates. The team also discovered that corneal graft rejection occurs via regulation of MHC class I-related chain A (MICA) expression in human corneal epithelial cells (HCECs). MICA is a non-classical MHC molecule that can (co-)stimulate CD8+ T cells or NK cells, thus affecting allograft survival.

The Mashaghi team contributed to the use of omics technology, and systems approaches in ophthalmology. Metabolomics has been applied to diagnose diseases, predict disease progression, and design therapeutic strategies in various areas of medicine. However, it remains to be applied to the ocular surface diseases, where biological samples are often of limited quantities. We successfully performed proof-of-concept metabolomics assessment of volume-limited cytology samples from a clinical form of chronic inflammatory cicatrizing conjunctivitis and discovered metabolic changes of signaling lipid mediators upon disease onset and progression. 

Finally, we were among the first to help the emergence of the field of mathematical ophthalmology, and in particular the use of mathematical approaches to the study of corneal diseases.