Extracellular matrix stiffness modulates host-bacteria interactions and antibiotic therapy of bacterial internalization

Single-cell SERS imaging of dual cell membrane receptors expression influenced by extracellular matrix stiffness

Membrane receptors perform a diverse range of cellular functions, accounting for more than half of all drug targets. The mechanical microenvironment regulates cell behaviors and phenotype. However, conventional analysis methods of membrane receptors often ignore the effects of the extracellular matrix stiffness, failing to reveal the heterogeneity of cell membrane receptors expression. Herein, we developed an in-situ surface-enhanced Raman scattering (SERS) imaging method to visualize single-cell membrane receptors on substrates with different stiffness. Two SERS substrates, Au@4-mercaptobenzonitrile@Ag@Sgc8c and Au@4-pethynylaniline@Ag@SYL3c, were employed to specifically target protein tyrosine kinase-7 (PTK7) and epithelial cell adhesion molecule (EpCAM), respectively. The polyacrylamide (PA) gels with tunable stiffness (2.5-25 kPa) were constructed to mimic extracellular matrix. The simultaneous SERS imaging of dual membrane receptors on single cancer cells on substrates with different stiffness was achieved. Our findings reveal decreased expression of PTK7 and EpCAM on cells cultured on stiffer substrates and higher migration ability of the cells. The results elucidate the heterogeneity of membrane receptors expression of cells cultured on the substrates with different stiffness. This single-cell analysis method offers an in-situ platform for investigating the impacts of extracellular matrix stiffness on the expression of membrane receptors, providing insights into the role of cell membrane receptors in cancer metastasis.

Advances in Engineered Nano-Biosensors for Bacteria Diagnosis and Multidrug Resistance Inhibition

Bacterial infections continue to pose a significant global health challenge, with the emergence of multidrug-resistant (MDR) bacteria and biofilms further complicating treatment options. The rise of pan-resistant bacteria, coupled with the slow development of new antibiotics, highlights the urgent need for new therapeutic strategies. Nanotechnology-based biosensors offer fast, specific, sensitive, and selective methods for detecting and treating bacteria; hence, it is a promising approach for the diagnosis and treatment of MDR bacteria. Through mechanisms, such as destructive bacterial cell membranes, suppression of efflux pumps, and generation of reactive oxygen species, nanotechnology effectively combats bacterial resistance and biofilms. Nano-biosensors and related technology have demonstrated their importance in bacteria diagnosis and treatment, providing innovative ideas for MDR inhibition. This review focuses on multiple nanotechnology approaches in targeting MDR bacteria and eliminating antimicrobial biofilms, highlighting nano-biosensors via photodynamics-based biosensors, eletrochemistry biosensors, acoustic-dynamics sensors, and so on. Furthermore, the major challenges, opportunities of multi-physical-field biometrics-based biosensors, and relevant nanotechnology in MDR bacterial theranostics are also discussed. Overall, this review provides insights and scientific references to harness the comprehensive and diverse capabilities of nano-biosensors for precise bacteria theranostics and MDR inhibition.

Geometric constraint-triggered collagen expression mediates bacterial-host adhesion

Cells living in geometrically confined microenvironments are ubiquitous in various physiological processes, e.g., wound closure. However, it remains unclear whether and how spatially geometric constraints on host cells regulate bacteria-host interactions. Here, we reveal that interactions between bacteria and spatially constrained cell monolayers exhibit strong spatial heterogeneity, and that bacteria tend to adhere to these cells near the outer edges of confined monolayers. The bacterial adhesion force near the edges of the micropatterned monolayers is up to 75 nN, which is ~3 times higher than that at the centers, depending on the underlying substrate rigidities. Single-cell RNA sequencing experiments indicate that spatially heterogeneous expression of collagen IV with significant edge effects is responsible for the location-dependent bacterial adhesion. Finally, we show that collagen IV inhibitors can potentially be utilized as adjuvants to reduce bacterial adhesion and thus markedly enhance the efficacy of antibiotics, as demonstrated in animal experiments.

Research progress on the mechanism of Treponema pallidum breaking through placental barrier

Congenital syphilis, a significant cause of fetal mortality worldwide, is a congenital infectious disease instigated by the vertical transmission of Treponema pallidum during pregnancy. Clinical manifestations include preterm delivery, stillbirth, neonatal skin lesions, skeletal abnormalities, and central nervous system aberrations. The ongoing increase in the incidence of congenital syphilis, coupled with complexities in diagnosis, necessitates a detailed understanding of its pathogenesis for the development of improved diagnostic approaches, and to interrupt the route of vertical transmission. Drawing from the broader body of research associated with vertical transmission pathogens, we aim to clarify the potential mechanisms by which Treponema pallidum breaches the placental barrier to infect the fetus.

Mechanobiology as a tool for addressing the genotype-to-phenotype problem in microbiology

The central hypothesis of the genotype-phenotype relationship is that the phenotype of a developing organism (i.e., its set of observable attributes) depends on its genome and the environment. However, as we learn more about the genetics and biochemistry of living systems, our understanding does not fully extend to the complex multiscale nature of how cells move, interact, and organize; this gap in understanding is referred to as the genotype-to-phenotype problem. The physics of soft matter sets the background on which living organisms evolved, and the cell environment is a strong determinant of cell phenotype. This inevitably leads to challenges as the full function of many genes, and the diversity of cellular behaviors cannot be assessed without wide screens of environmental conditions. Cellular mechanobiology is an emerging field that provides methodologies to understand how cells integrate chemical and physical environmental stress and signals, and how they are transduced to control cell function. Biofilm forming bacteria represent an attractive model because they are fast growing, genetically malleable and can display sophisticated self-organizing developmental behaviors similar to those found in higher organisms. Here, we propose mechanobiology as a new area of study in prokaryotic systems and describe its potential for unveiling new links between an organism's genome and phenome.

Extracellular Matrix Rigidities Regulate the Tricarboxylic Acid Cycle and Antibiotic Resistance of Three-Dimensionally Confined Bacterial Microcolonies

As a major cause of clinical chronic infection, microbial biofilms/microcolonies in host tissues essentially live in 3D-constrained microenvironments, which potentially modulate their spatial self-organization and morphodynamics. However, it still remains unclear whether and how mechanical cues of 3D confined microenvironments, for example, extracellular matrix (ECM) stiffness, exert an impact on antibiotic resistance of bacterial biofilms/microcolonies. With a high-throughput antibiotic sensitivity testing (AST) platform, it is revealed that 3D ECM rigidities greatly modulate their resistance to diverse antibiotics. The microcolonies in 3D ECM with human tissue-specific rigidities varying from 0.5 to 20 kPa show a ≈2-10 000-fold increase in minimum inhibitory concentration, depending on the types of antibiotics. The authors subsequently identified that the increase in 3D ECM rigidities leads to the downregulation of the tricarboxylic acid (TCA) cycle, which is responsible for enhanced antibiotic resistance. Further, it is shown that fumarate, as a potentiator of TCA cycle activity, can alleviate the elevated antibiotic resistance and thus remarkably improve the efficacy of antibiotics against bacterial microcolonies in 3D confined ECM, as confirmed in the chronic infection mice model. These findings suggest fumarate can be employed as an antibiotic adjuvant to effectively treat infections induced by bacterial biofilms/microcolonies in a 3D-confined environment.

Escherichia coli Biofilm Formation, Motion and Protein Patterns on Hyaluronic Acid and Polydimethylsiloxane Depend on Surface Stiffness

The surface stiffness of the microenvironment is a mechanical signal regulating biofilm growth without the risks associated with the use of bioactive agents. However, the mechanisms determining the expansion or prevention of biofilm growth on soft and stiff substrates are largely unknown. To answer this question, we used PDMS (polydimethylsiloxane, 9-574 kPa) and HA (hyaluronic acid gels, 44 Pa-2 kPa) differing in their hydration. We showed that the softest HA inhibited Escherichia coli biofilm growth, while the stiffest PDMS activated it. The bacterial mechanical environment significantly regulated the MscS mechanosensitive channel in higher abundance on the least colonized HA-44Pa, while Type-1 pili (FimA) showed regulation in higher abundance on the most colonized PDMS-9kPa. Type-1 pili regulated the free motion (the capacity of bacteria to move far from their initial position) necessary for biofilm growth independent of the substrate surface stiffness. In contrast, the total length travelled by the bacteria (diffusion coefficient) varied positively with the surface stiffness but not with the biofilm growth. The softest, hydrated HA, the least colonized surface, revealed the least diffusive and the least free-moving bacteria. Finally, this shows that customizing the surface elasticity and hydration, together, is an efficient means of affecting the bacteria's mobility and attachment to the surface and thus designing biomedical surfaces to prevent biofilm growth.

Antibacterial Modes of Herbal Flavonoids Combat Resistant Bacteria

The increasing dissemination of multidrug resistant (MDR) bacterial infections endangers global public health. How to develop effective antibacterial agents against resistant bacteria is becoming one of the most urgent demands to solve the drug resistance crisis. Traditional Chinese medicine (TCM) with multi-target antibacterial actions are emerging as an effective way to combat the antibacterial resistance. Based on the innovative concept of organic wholeness and syndrome differentiation, TCM use in antibacterial therapies is encouraging. Herein, advances on flavonoid compounds of heat-clearing Chinese medicine exhibit their potential for the therapy of resistant bacteria. In this review, we focus on the antibacterial modes of herbal flavonoids. Additionally, we overview the targets of flavonoid compounds and divide them into direct-acting antibacterial compounds (DACs) and host-acting antibacterial compounds (HACs) based on their modes of action. We also discuss the associated functional groups of flavonoid compounds and highlight recent pharmacological activities against diverse resistant bacteria to provide the candidate drugs for the clinical infection.

Deep learning-based 3D cellular force reconstruction directly from volumetric images

The forces exerted by single cells in the three-dimensional (3D) environments play a crucial role in modulating cellular functions and behaviors closely related to physiological and pathological processes. Cellular force microscopy (CFM) provides a feasible solution for quantifying the mechanical interactions, which usually regains cellular forces from deformation information of extracellular matrices embedded with fluorescent beads. Owing to computational complexity, the traditional 3D-CFM is usually extremely time-consuming, which makes it challenging for efficient force recovery and large-scale sample analysis. With the aid of deep neural networks, this study puts forward a novel data-driven 3D-CFM to reconstruct 3D cellular force fields directly from volumetric images with random fluorescence patterns. The deep learning (DL)-based network is established through stacking deep convolutional neural network (DCNN) and specific function layers. Some necessary physical information associated with constitutive relation of extracellular matrix material is coupled to the data-driven network. The mini-batch stochastic gradient descent and back-propagation algorithms are introduced to ensure its convergence and training efficiency. The network not only have good generalization ability and robustness, but also can recover 3D cellular forces directly from the input fluorescence image pairs. Particularly, the computational efficiency of the DL-based network is at least one to two orders of magnitude higher than that of the traditional 3D-CFM. This study provides a novel scheme for developing high-performance 3D cellular force microscopy to quantitatively characterize mechanical interactions between single cells and surrounding extracellular matrices, which is of vital importance for quantitative investigations in biomechanics and mechanobiology.