Investigations on the electrical current-voltage response in protein light receptors

We report a theoretical/computational approach for modeling the current-voltage characteristics of sensing proteins. The modeling is applied to a couple of transmembrane proteins, bacteriorhodopsin and proteorhodopsin, sensitive to visible light and promising biomaterials for the development of a new generation of photo-transducers. The agreement between theory and experiments sheds new light on the microscopic interpretation of charge transfer in proteins and biological materials in general.Comment: 4 pages, 3 figures To be published in J Phys. C: Conf Ser. Proceeding of the Conference IC-MCSQUARE, PRAGUE 201

Photoreceptors for a light biotransducer: a comparative study of the electrical responses of two (type-1)-opsins

The increasing interest in photoactivated proteins as natural replacement of standard inorganic materials in photocells drives to the compared analysis of bacteriorhodopsin and proteorhodopsin, two widely diffused proteins belonging to the family of \textit{type-1} opsins. These proteins share similar behaviours but exhibit relevant differences in the sequential chain of the amino acids constituting their tertiary structure. The use of an impedance network analogue to model the protein main features provides a microscopic interpretation of a set of experiments on their photoconductance properties. In particular, this model links the protein electrical responses to the tertiary structure and to the interactions among neighbouring amino acids. The same model is also used to predict the small-signal response in terms of the Nyquist plot. Interesting enough, these rhodopsins are found to behave like a wide gap semiconductor with intrinsic conductivities of the order of $10^{-7}$ S/cm.Comment: 19 pages, 15 figure

Current voltage characteristics and excess noise at the trap filling transition in polyacenes

Experiments in organic semiconductors (polyacenes) evidence a strong super quadratic increase of the current-voltage (I-V) characteristic at voltages in the transition region between linear (Ohmic) and quadratic (trap free space-charge-limited-current) behaviours. Similarly, excess noise measurements at a given frequency and increasing voltages evidence a sharp peak of the relative spectral density of the current noise in concomitance with the strong super-quadratic I-V characteristics. Here we discuss the physical interpretation of these experiments in terms of an essential contribution from field assisted trapping-detrapping processes of injected carriers. To this purpose, the fraction of filled traps determined by the I-V characteristics is used to evaluate the excess noise in the trap filled transition (TFT) regime. We have found an excellent agreement between the predictions of our model and existing experimental results in tetracene and pentacene thin films of different length in the range $0.65 \div 35 \ \mu m$.Comment: 20 pg, 13 figures, in pres

Proteotronics: Electronic devices based on proteins

The convergent interests of different scientific disciplines, from biochemistry to electronics, toward the investigation of protein electrical properties, has promoted the development of a novel bailiwick, the so called proteotronics. The main aim of proteotronics is to propose and achieve innovative electronic devices, based on the selective action of specific proteins. This paper gives a sketch of the fields of applications of proteotronics, by using as significant example the detection of a specific odorant molecule carried out by an olfactory receptor. The experiment is briefly reviewed and its theoretical interpretation given. Further experiments are envisioned and expected results discussed in the perspective of an experimental validation.Comment: 4 pages, 3 figures; Proceedings of the II national meeting on sensors, Rome (Iatly), February 19-21, 201

Heterogeneity of Microglial Activation in the Innate Immune Response in the Brain

The immune response in the brain has been widely investigated and while many studies have focused on the proinflammatory cytotoxic response, the brain’s innate immune system demonstrates significant heterogeneity. Microglia, like other tissue macrophages, participate in repair and resolution processes after infection or injury to restore normal tissue homeostasis. This review examines the mechanisms that lead to reduction of self-toxicity and to repair and restructuring of the damaged extracellular matrix in the brain. Part of the resolution process involves switching macrophage functional activation to include reduction of proinflammatory mediators, increased production and release of anti-inflammatory cytokines, and production of cytoactive factors involved in repair and reconstruction of the damaged brain. Two partially overlapping and complimentary functional macrophage states have been identified and are called alternative activation and acquired deactivation. The immunosuppressive and repair processes of each of these states and how alternative activation and acquired deactivation participate in chronic neuroinflammation in the brain are discussed

Combinatorial, additive and dose-dependent drug–microbiome associations

Data availability: The source data for the figures are provided at Zenodo (https://doi.org/10.5281/zenodo.4728981). Raw shotgun sequencing data that support the findings of this study have been deposited at the ENA under accession codes PRJEB41311, PRJEB38742 and PRJEB37249 with public access. Raw spectra for metabolomics have been deposited in the MassIVE database under the accession codes MSV000088043 (UPLC–MS/MS) and MSV000088042 (GC–MS). The metadata on disease groups and drug intake are provided in Supplementary Tables 1–3. The demographic, clinical and phenotype metadata, and processed microbiome and metabolome data for French, German and Danish participants are available at Zenodo (https://doi.org/10.5281/zenodo.4674360).Code availability: The new drug-aware univariate biomarker testing pipeline is available as an R package (metadeconfoundR; Birkner et al., manuscript in preparation) at Github (https://github.com/TillBirkner/metadeconfoundR) and at Zenodo (https://doi.org/10.5281/zenodo.4721078). The latest version (0.1.8) of this package was used to generate the data shown in this publication. The code used for multivariate analysis based on the VpThemAll package is available at Zenodo (https://doi.org/10.5281/zenodo.4719526). The phenotype and drug intake metadata, processed microbiome, and metabolome data and code resources are available for download at Zenodo (https://doi.org/10.5281/zenodo.4674360). The code for reproducing the figures is provided at Zenodo (https://doi.org/10.5281/zenodo.4728981).During the transition from a healthy state to cardiometabolic disease, patients become heavily medicated, which leads to an increasingly aberrant gut microbiome and serum metabolome, and complicates biomarker discovery1,2,3,4,5. Here, through integrated multi-omics analyses of 2,173 European residents from the MetaCardis cohort, we show that the explanatory power of drugs for the variability in both host and gut microbiome features exceeds that of disease. We quantify inferred effects of single medications, their combinations as well as additive effects, and show that the latter shift the metabolome and microbiome towards a healthier state, exemplified in synergistic reduction in serum atherogenic lipoproteins by statins combined with aspirin, or enrichment of intestinal Roseburia by diuretic agents combined with beta-blockers. Several antibiotics exhibit a quantitative relationship between the number of courses prescribed and progression towards a microbiome state that is associated with the severity of cardiometabolic disease. We also report a relationship between cardiometabolic drug dosage, improvement in clinical markers and microbiome composition, supporting direct drug effects. Taken together, our computational framework and resulting resources enable the disentanglement of the effects of drugs and disease on host and microbiome features in multimedicated individuals. Furthermore, the robust signatures identified using our framework provide new hypotheses for drug–host–microbiome interactions in cardiometabolic disease.This work was supported by the European Union’s Seventh Framework Program for research, technological development and demonstration under grant agreement HEALTH-F4-2012-305312 (METACARDIS). Part of this work was also supported by the EMBL, by the Metagenopolis grant ANR-11-DPBS-0001, by the H2020 European Research Council (ERC-AdG-669830) (to P.B.), and by grants from the Deutsche Forschungsgemeinschaft (SFB1365 to S.K.F. and L.M.; and SFB1052/3 A1 MS to M.S. (209933838)). Assistance Publique-Hôpitaux de Paris is the promoter of the clinical investigation (MetaCardis). M.-E.D. is supported by the NIHR Imperial Biomedical Research Centre and by grants from the French National Research Agency (ANR-10-LABX-46 (European Genomics Institute for Diabetes)), from the National Center for Precision Diabetic Medicine – PreciDIAB, which is jointly supported by the French National Agency for Research (ANR-18-IBHU-0001), by the European Union (FEDER), by the Hauts-de-France Regional Council (Agreement 20001891/NP0025517) and by the European Metropolis of Lille (MEL, Agreement 2019_ESR_11) and by Isite ULNE (R-002-20-TALENT-DUMAS), also jointly funded by ANR (ANR-16-IDEX-0004-ULNE), the Hauts-de-France Regional Council (20002845) and by the European Metropolis of Lille (MEL). R.J.A. is a member of the Collaboration for joint PhD degree between EMBL and Heidelberg University, Faculty of Bioscience. The Novo Nordisk Foundation Center for Basic Metabolic Research is an independent research institution at the University of Copenhagen partially funded by an unrestricted donation from the Novo Nordisk Foundation

Combinatorial, additive and dose-dependent drug–microbiome associations

During the transition from a healthy state to cardiometabolic disease, patients become heavily medicated, which leads to an increasingly aberrant gut microbiome and serum metabolome, and complicates biomarker discovery. Here, through integrated multi-omics analyses of 2,173 European residents from the MetaCardis cohort, we show that the explanatory power of drugs for the variability in both host and gut microbiome features exceeds that of disease. We quantify inferred effects of single medications, their combinations as well as additive effects, and show that the latter shift the metabolome and microbiome towards a healthier state, exemplified in synergistic reduction in serum atherogenic lipoproteins by statins combined with aspirin, or enrichment of intestinal Roseburia by diuretic agents combined with beta-blockers. Several antibiotics exhibit a quantitative relationship between the number of courses prescribed and progression towards a microbiome state that is associated with the severity of cardiometabolic disease. We also report a relationship between cardiometabolic drug dosage, improvement in clinical markers and microbiome composition, supporting direct drug effects. Taken together, our computational framework and resulting resources enable the disentanglement of the effects of drugs and disease on host and microbiome features in multimedicated individuals. Furthermore, the robust signatures identified using our framework provide new hypotheses for drug–host–microbiome interactions in cardiometabolic disease