Microstates of the cortical brain-heart axis

Electroencephalographic (EEG) microstates are brain states with quasi-stable scalp topography. Whether such states extend to the body level, that is, the peripheral autonomic nerves, remains unknown. We hypothesized that microstates extend at the brain-heart axis level as a functional state of the central autonomic network. Thus, we combined the EEG and heartbeat dynamics series to estimate the directional information transfer originating in the cortex targeting the sympathovagal and parasympathetic activity oscillations and vice versa for the afferent functional direction. Data were from two groups of participants: 36 healthy volunteers who were subjected to cognitive workload induced by mental arithmetic, and 26 participants who underwent physical stress induced by a cold pressure test. All participants were healthy at the time of the study. Based on statistical testing and goodness-of-fit evaluations, we demonstrated the existence of microstates of the functional brain-heart axis, with emphasis on the cerebral cortex, since the microstates are derived from EEG. Such nervous-system microstates are spatio-temporal quasi-stable states that exclusively refer to the efferent brain-to-heart direction. We demonstrated brain-heart microstates that could be associated with specific experimental conditions as well as brain-heart microstates that are non-specific to tasks.

Methods for the purification and detection of single nucleotide KRAS mutations on extrachromosomal circular DNA in human plasma

BACKGROUNDS

Despite recent advances, many cancers are still detected too late for curative treatment. There is, therefore, a need for the development of new diagnostic methods and biomarkers. One approach may arise from the detection of extrachromosomal circular DNA (eccDNA), which is part of cell-free DNA in human plasma.

AIMS

First, we assessed and compared two methods for the purification of eccDNA from plasma. Second, we tested for an easy diagnostic application of eccDNA liquid biopsy-based assays.

MATERIALS & METHODS

For the comparison we tested a solid-phase silica purification method and a phenol/chloroform method with salt precipitation. For the diagnostic application of eccDNA we developed and tested a qPCR primer-based SNP detection system, for the detection of two well-established cancer-causing KRAS mutations (G12V and G12R) on circular DNA. This investigation was supported by purifying, sequencing, and analysing clinical plasma samples for eccDNAs containing KRAS mutant alleles in 0.5 mL plasma from 16 pancreatic ductal adenocarcinoma patients and 19 healthy controls.

RESULTS

In our method comparison we observed, that following exonuclease treatment a lower eccDNA yield was found for the phenol/chloroform method (15.7%-26.7%) compared with the solid-phase purification approach (47.8%-65.9%). For the diagnostic application of eccDNA tests, the sensitivity of the tested qPCR assay only reached ~10-3 in a background of 105 wild type (wt) KRAS circular entities, which was not improved by general amplification or primer-based inhibition of wt KRAS amplification. Furthermore, we did not detect eccDNA containing KRAS in any of the clinical samples.

DISCUSSION

A potential explanation for our inability to detect any KRAS mutations in the clinical samples may be related to the general low abundance of eccDNA in plasma.

CONCLUSION

Taken together our results provide a benchmark for eccDNA purification methods while raising the question of what is required for the optimal fast and sensitive detection of SNP mutations on eccDNA with greater sensitivity than primer-based qPCR detection.

Distinct non-coding RNA cargo of extracellular vesicles from M1 and M2 human primary macrophages

Macrophages are important antigen presenting cells which can release extracellular vesicles (EVs) carrying functional cargo including non-coding RNAs. Macrophages can be broadly classified into M1 'classical' and M2 'alternatively-activated' macrophages. M1 macrophages have been linked with inflammation-associated pathologies, whereas a switch towards an M2 phenotype indicates resolution of inflammation and tissue regeneration. Here, we provide the first comprehensive analysis of the small RNA cargo of EVs from human M1 and M2 primary macrophages. Using small RNA sequencing, we identified several types of small non-coding RNAs in M1 and M2 macrophage EVs including miRNAs, isomiRs, tRNA fragments, piRNA, snRNA, snoRNA and Y-RNA fragments. Distinct differences were observed between M1 and M2 EVs, with higher relative abundance of miRNAs, and lower abundance of tRNA fragments in M1 compared to M2 EVs. MicroRNA-target enrichment analysis identified several gene targets involved in gene expression and inflammatory signalling pathways. EVs were also enriched in tRNA fragments, primarily originating from the 5' end or the internal region of the full length tRNAs, many of which were differentially abundant in M1 and M2 EVs. Similarly, several other small non-coding RNAs, namely snRNAs, snoRNAs and Y-RNA fragments, were differentially enriched in M1 and M2 EVs; we discuss their putative roles in macrophage EVs. In conclusion, we show that M1 and M2 macrophages release EVs with distinct RNA cargo, which has the potential to contribute to the unique effect of these cell subsets on their microenvironment.

Pannexin1 channels-a potential therapeutic target in inflammation

An exaggerated inflammatory response is the hallmark of a plethora of disorders. ATP is a central signaling molecule that orchestrates the initiation and resolution of the inflammatory response by enhancing activation of the inflammasome, leukocyte recruitment and activation of T cells. ATP can be released from cells through pannexin (Panx) channels, a family of glycoproteins consisting of three members, Panx1, Panx2, and Panx3. Panx1 is ubiquitously expressed and forms heptameric channels in the plasma membrane mediating paracrine and autocrine signaling. Besides their involvement in the inflammatory response, Panx1 channels have been shown to contribute to different modes of cell death (i.e., pyroptosis, necrosis and apoptosis). Both genetic ablation and pharmacological inhibition of Panx1 channels decrease inflammation in vivo and contribute to a better outcome in several animal models of inflammatory disease involving various organs, including the brain, lung, kidney and heart. Up to date, several molecules have been identified to inhibit Panx1 channels, for instance probenecid (Pbn), mefloquine (Mfq), flufenamic acid (FFA), carbenoxolone (Cbx) or mimetic peptides like 10Panx1. Unfortunately, the vast majority of these compounds lack specificity and/or serum stability, which limits their application. The recent availability of detailed structural information on the Panx1 channel from cryo-electron microscopy studies may open up innovative approaches to acquire new classes of synthetic Panx1 channel blockers with high target specificity. Selective inhibition of Panx1 channels may not only limit acute inflammatory responses but may also prove useful in chronic inflammatory diseases, thereby improving human health. Here, we reviewed the current knowledge on the role of Panx1 in the initiation and resolution of the inflammatory response, we summarized the effects of Panx1 inhibition in inflammatory pathologies and recapitulate current Panx1 channel pharmacology with an outlook towards future approaches.

From Biomass-Derived p-Hydroxycinnamic Acids to Novel Sustainable and Non-Toxic Phenolics-Based UV-Filters: A Multidisciplinary Journey

Although organic UV-filters are extensively used in cosmetics to protect consumers from the deleterious effects of solar UV radiation-exposure, they suffer from some major drawbacks such as their fossil origin and their toxicity to both humans and the environment. Thus, finding sustainable and non-toxic UV-filters is becoming a topic of great interest for the cosmetic industry. A few years ago, sinapoyl malate was shown to be a powerful naturally occurring UV-filter. Building on these findings, we decided to design and optimize an entire value chain that goes from biomass to innovative biobased and non-toxic lignin-derived UV-filters. This multidisciplinary approach relies on: 1) The production of phenolic synthons using either metabolite extraction from biomass or their bioproduction through synthetic biology/fermentation/in stream product recovery; 2) their functionalization using green chemistry to access sinapoyl malate and analogues; 3) the study of their UV-filtering activity, their photostability, their biological properties; and 4) their photodynamics. This mini-review aims at demonstrating that combining biotechnology, green chemistry, downstream process and photochemistry is a powerful approach to transform biomass and, in particular lignins, into high value-added innovative UV-filters.

A Minimally Invasive Approach Towards “Ecosystem Hacking” With Honeybees

Honey bees live in colonies of thousands of individuals, that not only need to collaborate with each other but also to interact intensively with their ecosystem. A small group of robots operating in a honey bee colony and interacting with the queen bee, a central colony element, has the potential to change the collective behavior of the entire colony and thus also improve its interaction with the surrounding ecosystem. Such a system can be used to study and understand many elements of bee behavior within hives that have not been adequately researched. We discuss here the applicability of this technology for ecosystem protection: A novel paradigm of a minimally invasive form of conservation through “Ecosystem Hacking”. We discuss the necessary requirements for such technology and show experimental data on the dynamics of the natural queen’s court, initial designs of biomimetic robotic surrogates of court bees, and a multi-agent model of the queen bee court system. Our model is intended to serve as an AI-enhanceable coordination software for future robotic court bee surrogates and as a hardware controller for generating nature-like behavior patterns for such a robotic ensemble. It is the first step towards a team of robots working in a bio-compatible way to study honey bees and to increase their pollination performance, thus achieving a stabilizing effect at the ecosystem level.

Quantitative Structure-Property Relationship Modelling for the Prediction of Singlet Oxygen Generation by Heavy-Atom-Free BODIPY Photosensitizers*

Heavy-atom-free sensitizers forming long-living triplet excited states via the spin-orbit charge transfer intersystem crossing (SOCT-ISC) process have recently attracted attention due to their potential to replace costly transition metal complexes in photonic applications. The efficiency of SOCT-ISC in BODIPY donor-acceptor dyads, so far the most thoroughly investigated class of such sensitizers, can be finely tuned by structural modification. However, predicting the triplet state yields and reactive oxygen species (ROS) generation quantum yields for such compounds in a particular solvent is still very challenging due to a lack of established quantitative structure-property relationship (QSPR) models. In this work, the available data on singlet oxygen generation quantum yields (ΦΔ ) for a dataset containing >70 heavy-atom-free BODIPY in three different solvents (toluene, acetonitrile, and tetrahydrofuran) were analyzed. In order to build reliable QSPR model, a series of new BODIPYs were synthesized that bear different electron donating aryl groups in the meso position, their optical and structural properties were studied along with the solvent dependence of singlet oxygen generation, which confirmed the formation of triplet states via the SOCT-ISC mechanism. For the combined dataset of BODIPY structures, a total of more than 5000 quantum-chemical descriptors was calculated including quantum-chemical descriptors using density functional theory (DFT), namely M06-2X functional. QSPR models predicting ΦΔ values were developed using multiple linear regression (MLR), which perform significantly better than other machine learning methods and show sufficient statistical parameters (R=0.88-0.91 and q2 =0.62-0.69) for all three solvents. A small root mean squared error of 8.2 % was obtained for ΦΔ values predicted using MLR model in toluene. As a result, we proved that QSPR and machine learning techniques can be useful for predicting ΦΔ values in different media and virtual screening of new heavy-atom-free BODIPYs with improved photosensitizing ability.

Application of the ESMACS Binding Free Energy Protocol to a Multi-Binding Site Lactate Dehydogenase A Ligand Dataset

Over the past two decades, the use of fragment-based lead generation has become a common, mature approach to identify tractable starting points in chemical space for the drug discovery process. This approach naturally involves the study of the binding properties of highly heterogeneous ligands. Such datasets challenge computational techniques to provide comparable binding free energy estimates from different binding modes. The performance of a range of statistically robust ensemble-based binding free energy calculation protocols, called ESMACS (enhanced sampling of molecular dynamics with approximation of continuum solvent), is evaluated. Ligands designed to target two binding pockets in the lactate dehydogenase, a target protein, which vary in size, charge, and binding mode, are studied. When compared to experimental results, excellent statistical rankings are obtained across this highly diverse set of ligands. In addition, three approaches to account for entropic contributions are investigated: 1) normal mode analysis, 2) weighted solvent accessible surface area (WSAS), and 3) variational entropy. Normal mode analysis and WSAS correlate strongly with each other-although the latter is computationally far cheaper-but do not improve rankings. Variational entropy corrects exaggerated discrimination of ligands bound in different pockets but creates three outliers which reduce the quality of the overall ranking.