Enhancing anti-adhesion properties by designing microstructure - the microscopy and spectroscopy study of the intercellular bacterial response.

This study is the first one that investigates in detail the bacterial intercellular response to the high density of crystallographic defects including vacancies created in Cu by high pressure torsion. To this aim, samples were deformed by high pressure torsion and afterward, their antibacterial properties against Staphylococcus aureus were analyzed in adhesion tests. As a reference an annealed sample was applied. To avoid the influence of surface roughness, specially elaborated conditions for surface preparation were employed, which do not introduce defects and assure comparable surface roughness. The analysis of the chemical composition and thickness of passive layers by X-ray photoelectron spectroscopy showed that they were comparable for nanostructured and micrograined samples, consisting of Cu2O and CuO, and a thickness of 6 nm. The interface bacterium-substrate was prepared by a focused ion beam and further analyzed by scanning transmission electron microscopy and energy dispersive spectroscopy. High pressure torsion processed Cu shows enhanced anti-adhesion properties while in contact with S. aureus than micrograined Cu. There is a linear correlation between luminous intensity and grain size-0.5. The bacterial intercellular defence mechanism includes the creation of Cu2O nanoparticles and the increased concentration of sulphur-rich compounds near these nanoparticles.

Lipid droplet-associated hydrolase mobilizes stores of liver X receptor sterol ligands and protects against atherosclerosis.

Foam cells in atheroma are engorged with lipid droplets (LDs) that contain esters of regulatory lipids whose metabolism remains poorly understood. LD-associated hydrolase (LDAH) has a lipase structure and high affinity for LDs of foam cells. Using knockout and transgenic mice of both sexes, here we show that LDAH inhibits atherosclerosis development and promotes stable lesion architectures. Broad and targeted lipidomic analyzes of primary macrophages and comparative lipid profiling of atheroma identified a broad impact of LDAH on esterified sterols, including natural liver X receptor (LXR) sterol ligands. Transcriptomic analyzes coupled with rescue experiments show that LDAH modulates the expression of prototypical LXR targets and leads macrophages to a less inflammatory phenotype with a profibrotic gene signature. These studies underscore the role of LDs as reservoirs and metabolic hubs of bioactive lipids, and suggest that LDAH favorably modulates macrophage activation and protects against atherosclerosis via lipolytic mobilization of regulatory sterols.

Horizontal Gene Transfer of a key Translation Factor and its Role in Polyproline Proteome Evolution.

Prolines cause ribosomes to stall during translation due to their rigid structure. This phenomenon occurs in all domains of life and is exacerbated at polyproline motifs. Such stalling can be eased by the elongation factor P (EF-P) in bacteria. We discovered a potential connection between the loss of ancestral EF-P, the appearance of horizontally transferred EF-P variants, and genomic signs of EF-P dysfunction. Horizontal transfer of the efp gene has occurred several times among bacteria and is associated with the loss of highly conserved polyproline motifs. In this study, we pinpoint cases of horizontal EF-P transfer among a diverse set of bacteria and examine genomic features associated with these events in the phyla Thermotogota and Planctomycetes. In these phyla, horizontal EF-P transfer is also associated with the loss of entire polyproline motif-containing proteins, whose expression is likely dependent on EF-P. In particular, three proteases (Lon, ClpC, and FtsH) and three tRNA synthetases (ValS, IleS1, and IleS2) appear highly sensitive to EF-P transfer. The conserved polyproline motifs within these proteins all reside within close proximity to ATP-binding-regions, some of which are crucial for their function. Our work shows that an ancient EF-P dysfunction has left genomic traces that persist to this day, although it remains unclear whether this dysfunction was strictly due to loss of ancestral EF-P or was related to the appearance of an exogenous variant. The latter possibility would imply that the process of "domesticating" a horizontally transferred efp gene can perturb the overall function of EF-P.

Dose-dependent regulation of immune memory responses against HIV by saponin monophosphoryl lipid A nanoparticle adjuvant.

The induction of durable protective immune responses is the main goal of prophylactic vaccines, and adjuvants play an important role as drivers of such responses. Despite advances in vaccine strategies, a safe and effective HIV vaccine remains a significant challenge. The use of an appropriate adjuvant is crucial to the success of HIV vaccines. Here we assessed the saponin/MPLA nanoparticle (SMNP) adjuvant with an HIV envelope (Env) trimer, evaluating the safety and impact of multiple variables including adjuvant dose (16-fold dose range), immunization route, and adjuvant composition on the establishment of Env-specific memory T and B cell responses (T Mem and B Mem ) and long-lived plasma cells in non-human primates. Robust B Mem were detected in all groups, but a 6-fold increase was observed in the highest SMNP dose group vs. the lowest dose group. Similarly, stronger vaccine responses were induced in the highest SMNP dose for CD40L + OX40 + CD4 T Mem (11-fold), IFNγ + CD4 T Mem (15-fold), IL21 + CD4 T Mem (9-fold), circulating T FH (3.6-fold), bone marrow plasma cells (7-fold), and binding IgG (1.3-fold). Substantial tier-2 neutralizing antibodies were only observed in the higher SMNP dose groups. These investigations highlight the dose-dependent potency of SMNP in non-human primates, which are relevant for human use and next-generation vaccines.

Insights into the LiMn2O4 Cathode Stability in Aqueous Electrolytes.

LiMn2O4 (LMO) cathodes present large stability when cycled in aqueous electrolytes, contrasting with their behavior in conventional organic electrolytes in lithium-ion batteries (LIBs). To elucidate the mechanisms underlying this distinctive behavior, we employ unconventional characterization techniques, including variable energy positron annihilation lifetime spectroscopy (VEPALS), tip-enhanced Raman spectroscopy (TERS), and macro-Raman spectroscopy (with tens of µm-size laser spot). These still rather unexplored techniques in the battery field provide complementary information across different length scales, revealing previously hidden features. VEPALS offers atomic-scale insights, uncovering cationic defects and subnanometer pores that tend to collapse with cycling. TERS, operating in the nanometric range at the surface, captured the presence of Mn3O4 and its dissolution with cycling, elucidating dynamic changes during operation. Additionally, TERS highlights the accumulation of SO4 2- at grain boundaries. Macro-Raman spectroscopy focuses on the micrometer scale, depicting small changes in the cathode's long-range order, suggesting a slow but progressive loss of crystalline quality under operation. Integrating these techniques provides a comprehensive assessment of LMO cathode stability in aqueous electrolytes, offering multifaceted insights into phase and defect evolution that can help to rationalize the origin of such stability when compared with conventional organic electrolytes. Our findings advance the understanding of LMO behavior in aqueous environments and provide guidelines for its development for next-generation LIBs.

Identification and Suppression of Point Defects in Bromide Perovskite Single Crystals Enabling Gamma-Ray Spectroscopy.

Methylammonium lead tribromide (MAPbBr3) stands out as the most easily grown wide-band-gap metal halide perovskite. It is a promising semiconductor for room-temperature gamma-ray (γ-ray) spectroscopic detectors, but no operational devices are realized. This can be largely attributed to a lack of understanding of point defects and their influence on detector performance. Here, through a combination of crystal growth design and defect characterization, including positron annihilation and impedance spectroscopy, the presence of specific point defects are identified and correlated to detector performance. Methylammonium (MA) vacancies, MA interstitials, and Pb vacancies are identified as the dominant charge-trapping defects in MAPbBr3 crystals, while Br vacancies caused doping. The addition of excess MABr reduces the MA and Br defects and so enables the detection of energy-resolved γ-ray spectra using a MAPbBr3 single-crystal device. Interestingly, the addition of formamidinium (FA) cations, which converted to methylformamidinium (MFA) cations by reaction with MA+ during crystal growth further reduced MA defects. This enabled an energy resolution of 3.9% for the 662 keV 137Cs line using a low bias of 100 V. The work provides direction toward enabling further improvements in wide-bandgap perovskite-based device performance by reducing detrimental defects.

Pre-treatment with Trichoderma viride: Towards a better understanding of its consequences for anaerobic digestion.

Understanding and optimising biological pre-treatment strategies for enhanced bio-methane production is a central aspect in second-generation biofuel research. In this regard, the application of fungi for pre-treatment seems highly promising; however, understanding the mode of action is crucial. Here, we show how aerobic pre-treatment of crystalline cellulose with the cellulolytic Trichoderma viride affects substrate degradability during mesophilic, anaerobic digestion. It could be demonstrated that fungal pre-treatment resulted in a slightly reduced substrate mass. Nevertheless, no significant impact on the overall methane yield was found during batch fermentation. Short chain organic acids accumulation, thus, overall degradation dynamics including methane production kinetics were affected by the pre-treatment as shown by Gompertz modelling. Finally, 16S rRNA amplicon sequencing followed by ANCOM-BC resulted in up to 53 operative taxonomic units including fermentative, syntrophic and methanogenic taxa, whereby their relative abundances were significantly affected by fungal pre-treatment depending on the duration of the pre-treatment. The results demonstrated the impact of soft rot fungal pre-treatment of cellulose on subsequent anaerobic cellulose hydrolysis as well as on methanogenic activity. To the best of our knowledge, this is the first study to investigate the direct causal effects of pre-treatment with T. viride on basic but crucial anaerobic digestion parameters in a highly standardised approach.

Genotype sampling for deep-learning assisted experimental mapping of a combinatorially complete fitness landscape.

MOTIVATION: Experimental characterization of fitness landscapes, which map genotypes onto fitness, is important for both evolutionary biology and protein engineering. It faces a fundamental obstacle in the astronomical number of genotypes whose fitness needs to be measured for any one protein. Deep learning may help to predict the fitness of many genotypes from a smaller neural network training sample of genotypes with experimentally measured fitness. Here I use a recently published experimentally mapped fitness landscape of more than 260 000 protein genotypes to ask how such sampling is best performed. RESULTS: I show that multilayer perceptrons, recurrent neural networks, convolutional networks, and transformers, can explain more than 90% of fitness variance in the data. In addition, 90% of this performance is reached with a training sample comprising merely ≈103 sequences. Generalization to unseen test data is best when training data is sampled randomly and uniformly, or sampled to minimize the number of synonymous sequences. In contrast, sampling to maximize sequence diversity or codon usage bias reduces performance substantially. These observations hold for more than one network architecture. Simple sampling strategies may perform best when training deep learning neural networks to map fitness landscapes from experimental data. AVAILABILITY AND IMPLEMENTATION: The fitness landscape data analyzed here is publicly available as described previously (Papkou et al. 2023). All code used to analyze this landscape is publicly available at https://github.com/andreas-wagner-uzh/fitness_landscape_sampling.

Gasification chars and activated carbon: Systematic physico-chemical characterization and effect on biogas production.

Gasification residues/chars (GR) and activated carbon (AC) are added to wastewater treatment processes mainly as a fourth purification stage, e.g., to adsorb heavy metals or pharmaceutical residues. However, the effects of GR or AC, which are transferred to the anaerobic digestion (AD) via the sludge, are not yet fully understood. Although, the positive effect of char addition on AD has been demonstrated in several investigations, systematic studies with chemically well described chars are still missing. Therefore, in this study, different chars were characterized in detail, subjected to AD in different concentrations, and their effect on methane production investigated. GR of a gasification plant with a floating fixed bed technology, carbon made by chemical impregnation with ZnCl2 from waste-wood, carbon produced by thermochemical activation with CO2 from GR and commercial powdered AC were used for the experiments. Among others, thermogravimetric analysis, physisorption, pH, and conductivity analysis were used to characterize the chars. Mesophilic AD batch tests with different concentrations (0.025, 0.05, 0.5, 1.0, 7.0, 14.0 gL-1) of all chars (GR and ACs, respectively) were performed with digester sludge from a wastewater treatment plant for a period of 47 d. Volatile fatty acids (VFA) as well as biogas production and CH4 concentrations were monitored. It could be shown, that concentrations below 1.0 g char L-1 did not result in significant effects on CH4 and/or VFA production, whereas high concentrations of GR and AC influenced both, the CH4 yield and kinetics. Depending on the production process and the characteristics of the chars, the effect on AD varied, whereby both, positive and negative effects on biogas yield and methane production were observed. This study provides the first systematic evaluation of char application to AD processes, and therefore allows for better predictions of char applicability and effect.

Rapid evolutionary change in trait correlations of single proteins.

Many organismal traits are genetically determined and covary in evolving populations. The resulting trait correlations can either help or hinder evolvability - the ability to bring forth new and adaptive phenotypes. The evolution of evolvability requires that trait correlations themselves must be able to evolve, but we know little about this ability. To learn more about it, we here study two evolvable systems, a yellow fluorescent protein and the antibiotic resistance protein VIM-2 metallo beta-lactamase. We consider two traits in the fluorescent protein, namely the ability to emit yellow and green light, and three traits in our enzyme, namely the resistance against ampicillin, cefotaxime, and meropenem. We show that correlations between these traits can evolve rapidly through both mutation and selection on short evolutionary time scales. In addition, we show that these correlations are driven by a protein's ability to fold, because single mutations that alter foldability can dramatically change trait correlations. Since foldability is important for most proteins and their traits, mutations affecting protein folding may alter trait correlations mediated by many other proteins. Thus, mutations that affect protein foldability may also help shape the correlations of complex traits that are affected by hundreds of proteins.

Screening Automation in Systematic Reviews: Analysis of Tools and Their Machine Learning Capabilities.

Systematic reviews provide robust evidence but require significant human labor, a challenge that can be mitigated with digital tools. This paper focuses on machine learning (ML) support for the title and abstract screening phase, the most time-intensive aspect of the systematic review process. The existing literature was systematically reviewed and five promising tools were analyzed, focusing on their ability to reduce human workload and their application of ML. This paper details the current state of automation capabilities and highlights significant research findings that point towards further improvements in the field. Directions for future research in this evolving field are outlined, with an emphasis on the need for a cautious application of existing systems.

Revisiting Metal-Organic Frameworks Porosimetry by Positron Annihilation: Metal Ion States and Positronium Parameters.

Metal-organic frameworks (MOFs) stand as pivotal porous materials with exceptional surface areas, adaptability, and versatility. Positron Annihilation Lifetime Spectroscopy (PALS) is an indispensable tool for characterizing MOF porosity, especially micro- and mesopores in both open and closed phases. Notably, PALS offers porosity insights independent of probe molecules, which is vital for detailed characterization without structural transformations. This study explores how metal ion states in MOFs affect PALS results. We find significant differences in measured porosity due to paramagnetic or oxidized metal ions compared to simulated values. By analyzing CPO-27(M) (M = Mg, Co, Ni), with identical pore dimensions, we observe distinct PALS data alterations based on metal ions. Paramagnetic Co and Ni ions hinder and quench positronium (Ps) formation, resulting in smaller measured pore volumes and sizes. Mg only quenches Ps, leading to underestimated pore sizes without volume distortion. This underscores the metal ions' pivotal role in PALS outcomes, urging caution in interpreting MOF porosity.

Prospective Observational Study of Volatile Sedation with Sevoflurane After Aneurysmal Subarachnoid Hemorrhage Using the Sedaconda Anesthetic Conserving Device.

BACKGROUND: Volatile sedation is still used with caution in patients with acute brain injury because of safety concerns. We analyzed the effects of sevoflurane sedation on systemic and cerebral parameters measured by multimodal neuromonitoring in patients after aneurysmal subarachnoid hemorrhage (aSAH) with normal baseline intracranial pressure (ICP). METHODS: In this prospective observational study, we analyzed a 12-h period before and after the switch from intravenous to volatile sedation with sevoflurane using the Sedaconda Anesthetic Conserving Device with a target Richmond Agitation Sedation Scale score of - 5 to - 4. ICP, cerebral perfusion pressure (CPP), brain tissue oxygenation (PBrO2), metabolic values of cerebral microdialysis, systemic cardiopulmonary parameters, and the administered drugs before and after the sedation switch were analyzed. RESULTS: We included 19 patients with a median age of 61 years (range 46-78 years), 74% of whom presented with World Federation of Neurosurgical Societies grade 4 or 5 aSAH. We observed no significant changes in the mean ICP (9.3 ± 4.2 vs. 9.7 ± 4.2 mm Hg), PBrO2 (31.0 ± 13.2 vs. 32.2 ± 12.4 mm Hg), cerebral lactate (5.0 ± 2.2 vs. 5.0 ± 1.9 mmol/L), pyruvate (136.6 ± 55.9 vs. 134.1 ± 53.6 µmol/L), and lactate/pyruvate ratio (37.4 ± 8.7 vs. 39.8 ± 9.2) after the sedation switch to sevoflurane. We found a significant decrease in mean arterial pressure (MAP) (88.6 ± 7.6 vs. 86.3 ± 5.8 mm Hg) and CPP (78.8 ± 8.5 vs. 76.6 ± 6.6 mm Hg) after the initiation of sevoflurane, but the decrease was still within the physiological range requiring no additional hemodynamic support. CONCLUSIONS: Sevoflurane appears to be a feasible alternative to intravenous sedation in patients with aSAH without intracranial hypertension, as our study did not show negative effects on ICP, cerebral oxygenation, or brain metabolism. Nevertheless, the risk of a decrease of MAP leading to a consecutive CPP decrease should be considered.

High temperature delays and low temperature accelerates evolution of a new protein phenotype.

Since the origin of life, temperatures on earth have fluctuated both on short and long time scales. How such changes affect the rate at which Darwinian evolution can bring forth new phenotypes remains unclear. On the one hand, high temperature may accelerate phenotypic evolution because it accelerates most biological processes. On the other hand, it may slow phenotypic evolution, because proteins are usually less stable at high temperatures and therefore less evolvable. Here, to test these hypotheses experimentally, we evolved a green fluorescent protein in E. coli towards the new phenotype of yellow fluorescence at different temperatures. Yellow fluorescence evolved most slowly at high temperature and most rapidly at low temperature, in contradiction to the first hypothesis. Using high-throughput population sequencing, protein engineering, and biochemical assays, we determined that this is due to the protein-destabilizing effect of neofunctionalizing mutations. Destabilization is highly detrimental at high temperature, where neofunctionalizing mutations cannot be tolerated. Their detrimental effects can be mitigated through excess stability at low temperature, leading to accelerated adaptive evolution. By modifying protein folding stability, temperature alters the accessibility of mutational paths towards high-fitness genotypes. Our observations have broad implications for our understanding of how temperature changes affect evolutionary adaptations and innovations.

Frustration can Limit the Adaptation of Promiscuous Enzymes Through Gene Duplication and Specialisation.

Virtually all enzymes catalyse more than one reaction, a phenomenon known as enzyme promiscuity. It is unclear whether promiscuous enzymes are more often generalists that catalyse multiple reactions at similar rates or specialists that catalyse one reaction much more efficiently than other reactions. In addition, the factors that shape whether an enzyme evolves to be a generalist or a specialist are poorly understood. To address these questions, we follow a three-pronged approach. First, we examine the distribution of promiscuity in empirical enzymes reported in the BRENDA database. We find that the promiscuity distribution of empirical enzymes is bimodal. In other words, a large fraction of promiscuous enzymes are either generalists or specialists, with few intermediates. Second, we demonstrate that enzyme biophysics is not sufficient to explain this bimodal distribution. Third, we devise a constraint-based model of promiscuous enzymes undergoing duplication and facing selection pressures favouring subfunctionalization. The model posits the existence of constraints between the catalytic efficiencies of an enzyme for different reactions and is inspired by empirical case studies. The promiscuity distribution predicted by our constraint-based model is consistent with the empirical bimodal distribution. Our results suggest that subfunctionalization is possible and beneficial only in certain enzymes. Furthermore, the model predicts that conflicting constraints and selection pressures can cause promiscuous enzymes to enter a 'frustrated' state, in which competing interactions limit the specialisation of enzymes. We find that frustration can be both a driver and an inhibitor of enzyme evolution by duplication and subfunctionalization. In addition, our model predicts that frustration becomes more likely as enzymes catalyse more reactions, implying that natural selection may prefer catalytically simple enzymes. In sum, our results suggest that frustration may play an important role in enzyme evolution.

Regnase-1 overexpression as a therapeutic approach of Marfan syndrome.

Rupture or dissection of thoracic aortic aneurysms is still the leading cause of death for patients diagnosed with Marfan syndrome. Inflammation and matrix digestion regulated by matrix metalloproteases (MMPs) play a major role in the pathological remodeling of the aortic media. Regnase-1 is an endoribonuclease shown to cleave the mRNA of proinflammatory cytokines, such as interleukin-6. Considering the major anti-inflammatory effects of regnase-1, here, we aimed to determine whether adeno-associated virus (AAV)-mediated vascular overexpression of the protein could provide protection from the development and progression of aortic aneurysms in Marfan syndrome. The overexpression of regnase-1 resulted in a marked decrease in inflammatory parameters and elastin degradation in aortic smooth muscle cells in vitro. Intravenous injection of a vascular-targeted AAV vector resulted in the efficient transduction of the aortic wall and overexpression of regnase-1 in a murine model of Marfan syndrome, associated with lower circulating levels of proinflammatory cytokines and decreased MMP expression and activity. Regnase-1 overexpression strongly improved elastin architecture in the media and reduced aortic diameter at distinct locations. Therefore, AAV-mediated regnase-1 overexpression may represent a novel gene therapy approach for inhibiting aortic aneurysms in Marfan syndrome.

Meso- and thermophilic posttreatment of press water coming from a thermophilic municipal solid waste digester.

An efficient biogas production out of organic (waste) materials is important to contribute to a carbon-neutral future. In this study, thermophilic press water (PW) coming from an organic fraction of the municipal solid waste digester was further digested in a thermo- and mesophilic posttreatment approach using two semicontinuous 14 L digesters. The results showed that the PW can still have considerable high biogas potential-at least during the touristic high season in central Europe. The change in temperature led to an increase in volatile fatty acid concentrations and a decrease in biogas production in the mesophilic approach in the first days. However, the losses in biogas production at the beginning could be compensated thus there were no considerable differences in biogas production between thermo- and mesophilic posttreatment at the end of incubation. This can most probably be contributed to a change in the microbial community, and potentially problematic intermediates like valerate could be better degraded in the mesophilic reactor. Especially the abundance of representatives of the phylum Bacteroidota, like Fermentimonas spp., increased during mesophilic anaerobic digestion.

A phase-field model for non-small cell lung cancer under the effects of immunotherapy.

Formulating mathematical models that estimate tumor growth under therapy is vital for improving patient-specific treatment plans. In this context, we present our recent work on simulating non-small-scale cell lung cancer (NSCLC) in a simple, deterministic setting for two different patients receiving an immunotherapeutic treatment. At its core, our model consists of a Cahn-Hilliard-based phase-field model describing the evolution of proliferative and necrotic tumor cells. These are coupled to a simplified nutrient model that drives the growth of the proliferative cells and their decay into necrotic cells. The applied immunotherapy decreases the proliferative cell concentration. Here, we model the immunotherapeutic agent concentration in the entire lung over time by an ordinary differential equation (ODE). Finally, reaction terms provide a coupling between all these equations. By assuming spherical, symmetric tumor growth and constant nutrient inflow, we simplify this full 3D cancer simulation model to a reduced 1D model. We can then resort to patient data gathered from computed tomography (CT) scans over several years to calibrate our model. Our model covers the case in which the immunotherapy is successful and limits the tumor size, as well as the case predicting a sudden relapse, leading to exponential tumor growth. Finally, we move from the reduced model back to the full 3D cancer simulation in the lung tissue. Thereby, we demonstrate the predictive benefits that a more detailed patient-specific simulation including spatial information as a possible generalization within our framework could yield in the future.

Impact of the -1T>C single-nucleotide polymorphism of the CD40 gene on the development of endothelial dysfunction in a pro-diabetic microenvironment.

BACKGROUND AND AIMS: Hyperglycemia reinforces pro-inflammatory conditions that enhance CD40 expression in endothelial cells (EC). Thymine to cytosine transition (-1T > C) in the promoter of the CD40 gene (rs1883832) further increases the abundance of CD40 protein on the EC surface. This study examines potential associations of the -1T > C SNP of the CD40 gene with type 1 (T1D) or type 2 (T2D) diabetes. Moreover, it investigates the impact of a pro-inflammatory diabetic microenvironment on gene expression in human cultured umbilical vein EC (HUVEC) derived from CC- vs. TT-genotype donors. METHODS: Tetra-ARMS-PCR was used to compare genotype distribution in 252 patients with diabetes. Soluble CD40 ligand (sCD40L) and soluble CD40 receptor (sCD40) plasma levels were monitored using ELISA. RNA-sequencing was performed with sCD40L-stimulated CC- and TT-genotype HUVEC. Quantitative PCR, Western blot, multiplex-sandwich ELISA array, and immunocytochemistry were used to analyse changes in gene expression in these cells. RESULTS: Homozygosity for the C-allele was associated with a significant 4.3-fold higher odds of developing T2D as compared to individuals homozygous for the T-allele. Inflammation and endothelial-to-mesenchymal transition (EndMT) driving genes were upregulated in CC-genotype but downregulated in TT-genotype HUVEC when exposed to sCD40L. Expression of EndMT markers significantly increased while that of endothelial markers decreased in HUVEC following exposure to hyperglycemia, tumour necrosis factor-α and sCD40L. CONCLUSIONS: The -1T > C SNP of the CD40 gene is a risk factor for T2D. Depending on the genotype, it differentially affects gene expression in human cultured EC. CC-genotype HUVEC adopt a pro-inflammatory and intermediate EndMT-like phenotype in a pro-diabetic microenvironment.