Vascular smooth muscle cells in low SYNTAX scores coronary artery disease exhibit proinflammatory transcripts and proteins correlated with IL1B activation.

BACKGROUND AND AIMS: The SYNTAX score is clinically validated to stratify number of lesions and pattern of CAD. A better understanding of the underlying molecular mechanisms influencing the pattern and complexity of coronary arteries lesions among CAD patients is needed. METHODS: Human arterial biopsies from 49 patients (16 low-SYNTAX-score (LSS, <23), 16 intermediate-SYNTAX-score (ISS, 23 to 32) and 17 high-SYNTAX-score (HSS, >32)) were evaluated using Affymetrix GeneChip® Human Genome U133 Plus 2.0 microarray. The data were validated by Next-Generation Sequencing (NGS). Primary VSMC from patients with low and high SYNTAX scores were isolated and compared using immunohistochemistry, qPCR and immunoblotting to confirm mRNA and proteomic results. RESULTS: The IL1B was verified as the top upstream regulator of 47 inflammatory DEGs in LSS patients and validated by another sets of patient samples using NGS analysis. The upregulated expression of IL1B was translated to increased level of IL1ß protein in the LSS tissue based on immunohistochemical quantitative analysis. Plausibility of idea that IL1B in the arterial wall could be originated from VSMC was checked by exposing culture to proinflammatory conditions where IL1B came out as the top DEG (logFC = 7.083, FDR = 1.38 × 10-114). The LSS patient-derived primary VSMCs confirmed higher levels of IL1B mRNA and protein. CONCLUSIONS: LSS patients could represent a group of patients where IL1B could play a substantial role in disease pathogenesis. The LSS group could represent a plausible cohort of patients for whom anti-inflammatory therapy could be considered.

Antimicrobial-free graphene nanocoating decreases fungal yeast-to-hyphal switching and maturation of cross-kingdom biofilms containing clinical and antibiotic-resistant bacteria.

Candida albicans and methicillin-resistant Staphylococcus aureus (MRSA) synergize in cross-kingdom biofilms to increase the risk of mortality and morbidity due to high resistance to immune and antimicrobial defenses. Biomedical devices and implants made with titanium are vulnerable to infections that may demand their surgical removal from the infected sites. Graphene nanocoating (GN) has promising anti-adhesive properties against C. albicans. Thus, we hypothesized that GN could prevent fungal yeast-to-hyphal switching and the development of cross-kingdom biofilms. Herein, titanium (Control) was coated with high-quality GN (coverage > 99%). Thereafter, mixed-species biofilms (C. albicans combined with S. aureus or MRSA) were allowed to develop on GN and Control. There were significant reductions in the number of viable cells, metabolic activity, and biofilm biomass on GN compared with the Control (CFU counting, XTT reduction, and crystal violet assays). Also, biofilms on GN were sparse and fragmented, whereas the Control presented several bacterial cells co-aggregating with intertwined hyphal elements (confocal and scanning electronic microscopy). Finally, GN did not induce hemolysis, an essential characteristic for blood-contacting biomaterials and devices. Thus, GN significantly inhibited the formation and maturation of deadly cross-kingdom biofilms, which can be advantageous to avoid infection and surgical removal of infected devices.

Transcompp: understanding phenotypic plasticity by estimating Markov transition rates for cell state transitions.

MOTIVATION: Gradual population-level changes in tissues can be driven by stochastic plasticity, meaning rare stochastic transitions of single-cell phenotype. Quantifying the rates of these stochastic transitions requires time-intensive experiments, and analysis is generally confounded by simultaneous bidirectional transitions and asymmetric proliferation kinetics. To quantify cellular plasticity, we developed Transcompp (Transition Rate ANalysis of Single Cells to Observe and Measure Phenotypic Plasticity), a Markov modeling algorithm that uses optimization and resampling to compute best-fit rates and statistical intervals for stochastic cell-state transitions. RESULTS: We applied Transcompp to time-series datasets in which purified subpopulations of stem-like or non-stem cancer cells were exposed to various cell culture environments, and allowed to re-equilibrate spontaneously over time. Results revealed that commonly used cell culture reagents hydrocortisone and cholera toxin shifted the cell population equilibrium toward stem-like or non-stem states, respectively, in the basal-like breast cancer cell line MCF10CA1a. In addition, applying Transcompp to patient-derived cells showed that transition rates computed from short-term experiments could predict long-term trajectories and equilibrium convergence of the cultured cell population. AVAILABILITY AND IMPLEMENTATION: Freely available for download at http://github.com/nsuhasj/Transcompp. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.