ACKR1 favors transcellular over paracellular T-cell diapedesis across the blood-brain barrier in neuroinflammation in vitro.

The migration of CD4+ effector/memory T cells across the blood-brain barrier (BBB) is a critical step in MS or its animal model, EAE. T-cell diapedesis across the BBB can occur paracellular, via the complex BBB tight junctions or transcellular via a pore through the brain endothelial cell body. Making use of primary mouse brain microvascular endothelial cells (pMBMECs) as in vitro model of the BBB, we here directly compared the transcriptome profile of pMBMECs favoring transcellular or paracellular T-cell diapedesis by RNA sequencing (RNA-seq). We identified the atypical chemokine receptor 1 (Ackr1) as one of the main candidate genes upregulated in pMBMECs favoring transcellular T-cell diapedesis. We confirmed upregulation of ACKR1 protein in pMBMECs promoting transcellular T-cell diapedesis and in venular endothelial cells in the CNS during EAE. Lack of endothelial ACKR1 reduced transcellular T-cell diapedesis across pMBMECs under physiological flow in vitro. Combining our previous observation that endothelial ACKR1 contributes to EAE pathogenesis by shuttling chemokines across the BBB, the present data support that ACKR1 mediated chemokine shuttling enhances transcellular T-cell diapedesis across the BBB during autoimmune neuroinflammation.

Mean-Field Phase Transitions in Tensorial Group Field Theory Quantum Gravity.

Controlling the continuum limit and extracting effective gravitational physics are shared challenges for quantum gravity approaches based on quantum discrete structures. The description of quantum gravity in terms of tensorial group field theory (TGFT) has recently led to much progress in its application to phenomenology, in particular, cosmology. This application relies on the assumption of a phase transition to a nontrivial vacuum (condensate) state describable by mean-field theory, an assumption that is difficult to corroborate by a full RG flow analysis due to the complexity of the relevant TGFT models. Here, we demonstrate that this assumption is justified due to the specific ingredients of realistic quantum geometric TGFT models: combinatorially nonlocal interactions, matter degrees of freedom, and Lorentz group data, together with the encoding of microcausality. This greatly strengthens the evidence for the existence of a meaningful continuum gravitational regime in group-field and spin-foam quantum gravity, the phenomenology of which is amenable to explicit computations in a mean-field approximation.

A novel logical model of COVID-19 intracellular infection to support therapies development.

In this paper, a logical-based mathematical model of the cellular pathways involved in the COVID-19 infection has been developed to study various drug treatments (single or in combination), in different illness scenarios, providing insights into their mechanisms of action. Drug simulations suggest that the effects of single drugs are limited, or depending on the scenario counterproductive, whereas better results appear combining different treatments. Specifically, the combination of the anti-inflammatory Baricitinib and the anti-viral Remdesivir showed significant benefits while a stronger efficacy emerged from the triple combination of Baricitinib, Remdesivir, and the corticosteroid Dexamethasone. Together with a sensitivity analysis, we performed an analysis of the mechanisms of the drugs to reveal their impact on molecular pathways.

Peptide-Functionalized and Drug-Loaded Tomato Bushy Stunt Virus Nanoparticles Counteract Tumor Growth in a Mouse Model of Shh-Dependent Medulloblastoma.

Sonic hedgehog medulloblastoma (SHH-MB) accounts for 25-30% of all MBs, and conventional therapy results in severe long-term side effects. New targeted therapeutic approaches are urgently needed, drawing also on the fields of nanoparticles (NPs). Among these, plant viruses are very promising, and we previously demonstrated that tomato bushy stunt virus (TBSV), functionalized on the surface with CooP peptide, specifically targets MB cells. Here, we tested the hypothesis that TBSV-CooP can specifically deliver a conventional chemotherapeutic drug (i.e., doxorubicin, DOX) to MB in vivo. To this aim, a preclinical study was designed to verify, by histological and molecular methods, if multiple doses of DOX-TBSV-CooP were able to inhibit tumor progression of MB pre-neoplastic lesions, and if a single dose was able to modulate pro-apoptotic/anti-proliferative molecular signaling in full-blown MBs. Our results demonstrate that when DOX is encapsulated in TBSV-CooP, its effects on cell proliferation and cell death are similar to those obtained with a five-fold higher dose of non-encapsulated DOX, both in early and late MB stages. In conclusion, these results confirm that CooP-functionalized TBSV NPs are efficient carriers for the targeted delivery of therapeutics to brain tumors.

Late Microaerobic Growth for Efficient Production of Human Cytochrome P450 3A4 in E. coli.

Detailed preclinical characterization of metabolites formed in vivo from candidate drug substances is mandatory prior to the initiation of clinical trials. Therefore, inexpensive and efficient methods for drug metabolite synthesis are of high importance for rapid advancement of the drug development process. A large fraction of small molecule drugs is modified by monooxygenase cytochrome P450 3A4 produced in the human liver and intestine. Therefore, this enzyme is frequently employed to catalyze metabolite synthesis in vitro, making 3A4 availability a critical requirement in early drug development. Unfortunately, the recombinant production of this enzyme in microbial hosts is notoriously difficult. Maintaining low oxygen transfer rates and the use of rich media for host cultivation are required for P450 3A4 production. However, detailed studies on the relationship between oxygen supply and P450 3A4 space-time yields are missing. We describe an improved biotechnological process for the heterologous expression of P450 3A4 together with its redox partner, cytochrome P450 reductase, in Escherichia coli. Enzyme production was most efficient under so-called "late microaerobic" growth conditions, in which the cells have just not yet made the switch to anaerobic metabolism, characterized by a limited oxygen supply leading to oxygen concentrations in the liquid phase that are far below the detection limit of standard oxygen electrodes. Furthermore, feeding the carbon source glycerol as well as controlling cellular acetate formation improved process productivity. The presented protocol resulted in the formation of functional recombinant 3A4 at concentrations up to 680 nmol L-1.

Biocatalytic One-Carbon Ring Expansion of Aziridines to Azetidines via a Highly Enantioselective [1,2]-Stevens Rearrangement.

We report enantioselective one-carbon ring expansion of aziridines to make azetidines as a new-to-nature activity of engineered "carbene transferase" enzymes. A laboratory-evolved variant of cytochrome P450BM3, P411-AzetS, not only exerts unparalleled stereocontrol (99:1 er) over a [1,2]-Stevens rearrangement but also overrides the inherent reactivity of aziridinium ylides, cheletropic extrusion of olefins, to perform a [1,2]-Stevens rearrangement. By controlling the fate of the highly reactive aziridinium ylide intermediates, these evolvable biocatalysts promote a transformation which cannot currently be performed using other catalyst classes.

A comparison of deterministic and stochastic approaches for sensitivity analysis in computational systems biology.

With the recent rising application of mathematical models in the field of computational systems biology, the interest in sensitivity analysis methods had increased. The stochastic approach, based on chemical master equations, and the deterministic approach, based on ordinary differential equations (ODEs), are the two main approaches for analyzing mathematical models of biochemical systems. In this work, the performance of these approaches to compute sensitivity coefficients is explored in situations where stochastic and deterministic simulation can potentially provide different results (systems with unstable steady states, oscillators with population extinction and bistable systems). We consider two methods in the deterministic approach, namely the direct differential method and the finite difference method, and five methods in the stochastic approach, namely the Girsanov transformation, the independent random number method, the common random number method, the coupled finite difference method and the rejection-based finite difference method. The reviewed methods are compared in terms of sensitivity values and computational time to identify differences in outcome that can highlight conditions in which one approach performs better than the other.

A robust computational pipeline for model-based and data-driven phenotype clustering.

MOTIVATION: Precision medicine is a promising field that proposes, in contrast to a one-size-fits-all approach, the tailoring of medical decisions, treatments or products. In this context, it is crucial to introduce innovative methods to stratify a population of patients on the basis of an accurate system-level knowledge of the disease. This is particularly important in very challenging conditions, where the use of standard statistical methods can be prevented by poor data availability or by the need of oversimplifying the processes regulating a complex disease. RESULTS: We define an innovative method for phenotype classification that combines experimental data and a mathematical description of the disease biology. The methodology exploits the mathematical model for inferring additional subject features relevant for the classification. Finally, the algorithm identifies the optimal number of clusters and classifies the samples on the basis of a subset of the features estimated during the model fit. We tested the algorithm in two test cases: an in silico case in the context of dyslipidemia, a complex disease for which a large population of patients has been generated, and a clinical test case, in the context of a lysosomal rare disorder, for which the amount of available data was limited. In both the scenarios, our methodology proved to be accurate and robust, and allowed the inference of an additional phenotype division that the experimental data did not show. AVAILABILITY AND IMPLEMENTATION: The code to reproduce the in silico results has been implemented in MATLAB v.2017b and it is available in the Supplementary Material. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Remote Sensing System for Motor Nerve Impulse.

In this article, we present our research achievements regarding the development of a remote sensing system for motor pulse acquisition, as a first step towards a complete neuroprosthetic arm. We present the fabrication process of an implantable electrode for nerve impulse acquisition, together with an innovative wirelessly controlled system. In our study, these were combined into an implantable device for attachment to peripheral nerves. Mechanical and biocompatibility tests were performed, as well as in vivo testing on pigs using the developed system. This testing and the experimental results are presented in a comprehensive manner, demonstrating that the system is capable of accomplishing the requirements of its designed application. Most significantly, neural electrical signals were acquired and transmitted out of the body during animal experiments, which were conducted according to ethical regulations in the field.

Multi-omics integration-a comparison of unsupervised clustering methodologies.

With the recent developments in the field of multi-omics integration, the interest in factors such as data preprocessing, choice of the integration method and the number of different omics considered had increased. In this work, the impact of these factors is explored when solving the problem of sample classification, by comparing the performances of five unsupervised algorithms: Multiple Canonical Correlation Analysis, Multiple Co-Inertia Analysis, Multiple Factor Analysis, Joint and Individual Variation Explained and Similarity Network Fusion. These methods were applied to three real data sets taken from literature and several ad hoc simulated scenarios to discuss classification performance in different conditions of noise and signal strength across the data types. The impact of experimental design, feature selection and parameter training has been also evaluated to unravel important conditions that can affect the accuracy of the result.

Histone deacetylase 1 controls CD4+ T cell trafficking in autoinflammatory diseases.

CD4+ T cell trafficking is a fundamental property of adaptive immunity. In this study, we uncover a novel role for histone deacetylase 1 (HDAC1) in controlling effector CD4+ T cell migration, thereby providing mechanistic insight into why a T cell-specific deletion of HDAC1 protects against experimental autoimmune encephalomyelitis (EAE). HDAC1-deficient CD4+ T cells downregulated genes associated with leukocyte extravasation. In vitro, HDAC1-deficient CD4+ T cells displayed aberrant morphology and migration on surfaces coated with integrin LFA-1 ligand ICAM-1 and showed an impaired ability to arrest on and to migrate across a monolayer of primary mouse brain microvascular endothelial cells under physiological flow. Moreover, HDAC1 deficiency reduced homing of CD4+ T cells into the intestinal epithelium and lamina propria preventing weight-loss, crypt damage and intestinal inflammation in adoptive CD4+ T cell transfer colitis. This correlated with reduced expression levels of LFA-1 integrin chains CD11a and CD18 as well as of selectin ligands CD43, CD44 and CD162 on transferred circulating HDAC1-deficient CD4+ T cells. Our data reveal that HDAC1 controls T cell-mediated autoimmunity via the regulation of CD4+ T cell trafficking into the CNS and intestinal tissues.

Modeling the effect of immunotherapies on human castration-resistant prostate cancer.

In this paper we analyze the potential effect of immunotherapies on castration-resistant form of human Prostate Cancer (PCa). In particular, we examine the potential effect of the dendritic vaccine sipuleucel-T, the only currently available immunotherapy option for advanced PCa, and of ipilimumab, a drug targeting the Cytotoxic T-Lymphocyte Antigen 4 (CTLA4), exposed on the CTLs membrane, currently under Phase II clinical trial. The model, building on the one by Rutter and Kuang, includes different types of immune cells and interactions and is parameterized on available data. Our results show that the vaccine has only a very limited effect on PCa, while repeated treatments with ipilimumab appear potentially capable of controlling and even eradicating an androgen-independent prostate cancer. From a mathematical analysis of a simplified model, it seems likely that, under continuous administration of ipilimumab, the system lies in a bistable situation where both the no-tumor equilibrium and the high-tumor equilibrium are attractive. The schedule of periodic treatments could then determine the outcome, and mathematical models could help determine an optimal schedule.

Tomato Bushy Stunt Virus Nanoparticles as a Platform for Drug Delivery to Shh-Dependent Medulloblastoma.

Medulloblastoma (MB) is a primary central nervous system tumor affecting mainly young children. New strategies of drug delivery are urgent to treat MB and, in particular, the SHH-dependent subtype-the most common in infants-in whom radiotherapy is precluded due to the severe neurological side effects. Plant virus nanoparticles (NPs) represent an innovative solution for this challenge. Tomato bushy stunt virus (TBSV) was functionally characterized as a carrier for drug targeted delivery to a murine model of Shh-MB. The TBSV NPs surface was genetically engineered with peptides for brain cancer cell targeting, and the modified particles were produced on a large scale using Nicotiana benthamiana plants. Tests on primary cultures of Shh-MB cells allowed us to define the most efficient peptides able to induce specific uptake of TBSV. Immunofluorescence and molecular dynamics simulations supported the hypothesis that the specific targeting of the NPs was mediated by the interaction of the peptides with their natural partners and reinforced by the presentation in association with the virus. In vitro experiments demonstrated that the delivery of Doxorubicin through the chimeric TBSV allowed reducing the dose of the chemotherapeutic agent necessary to induce a significant decrease in tumor cells viability. Moreover, the systemic administration of TBSV NPs in MB symptomatic mice, independently of sex, confirmed the ability of the virus to reach the tumor in a specific manner. A significant advantage in the recognition of the target appeared when TBSV NPs were functionalized with the CooP peptide. Overall, these results open new perspectives for the use of TBSV as a vehicle for the targeted delivery of chemotherapeutics to MB in order to reduce early and late toxicity.

Emergence of a Negative Activation Heat Capacity during Evolution of a Designed Enzyme.

Temperature influences the reaction kinetics and evolvability of all enzymes. To understand how evolution shapes the thermodynamic drivers of catalysis, we optimized the modest activity of a computationally designed enzyme for an elementary proton-transfer reaction by nearly 4 orders of magnitude over 9 rounds of mutagenesis and screening. As theorized for primordial enzymes, the catalytic effects of the original design were almost entirely enthalpic in origin, as were the rate enhancements achieved by laboratory evolution. However, the large reductions in ΔH⧧ were partially offset by a decrease in TΔS⧧ and unexpectedly accompanied by a negative activation heat capacity, signaling strong adaptation to the operating temperature. These findings echo reports of temperature-dependent activation parameters for highly evolved natural enzymes and are relevant to explanations of enzymatic catalysis and adaptation to changing thermal environments.

Tissue oxygen saturation changes and postoperative complications in cardiac surgery: a prospective observational study.

BACKGROUND: Cardiac surgery with extracorporeal circulation (ECC) can induce microvascular dysfunction and tissue hypoperfusion. We hypothesized that the alterations in near-infrared spectroscopy (NIRS)-derived parameters would be associated with post-operative complications in cardiac surgery patients. METHODS: Prospective observational study performed at two University Hospitals. Ninety patients undergoing cardiac surgery with ECC were enrolled. The NIRS sensor was applied on the thenar eminence. A vascular occlusion test (VOT, 3-min ischemia) was performed at baseline (t0), at Intensive Care Unit (ICU) admission (t1), 3 (t2) and 6 (t3) hours later. Baseline tissue oxygen saturation (StO2), oxygen extraction rate and microvascular reactivity indices were calculated. RESULTS: In the first hours after cardiac surgery, StO2 tended to increase (86% [80-89] at T3 versus 82% [79-86] at T0, p = ns), while both tissue oxygen extraction and microvascular reactivity tended to decrease, as indicated by increasing occlusion slope (- 8.1%/min [- 11.2 to - 7] at T3 versus - 11.2%/min [- 13.9 to - 7.9] at T0, p = ns) and decreasing recovery slope (1.9%/sec [1.1-2.9] at T3 versus 3.1%/sec [2.3-3.9] at T0, p = ns). No substantial differences were found in NIRS-derived variables and their changes over time between patients with complications and those without complications. CONCLUSIONS: Peripheral tissue oxygen extraction and microvascular reactivity were reduced during the first hours after cardiac surgery. NIRS-derived parameters were not able to predict complications in this population of cardiac surgery patients.

Myocarditis requiring extracorporeal membrane oxygenation support following Influenza B infection: a case report and literature review.

Seasonal influenza A (IA) and B (IB) viruses co-circulate every year, causing respiratory tract infections in individuals of all ages. Recently, the association between laboratory-confirmed influenza infection and acute myocardial infarction has been clearly demonstrated. However, most of the reported cases of fulminant myocarditis had been associated with influenza virus type A infection. Here we report the case of a 44 y/o man who experienced myocarditis with cardiogenic shock [requiring percutaneous extracorporeal membrane oxygenation (ECMO) support], following influenza B virus infection, which circulated widely in Italy in 2017-18.

Incorporating extrinsic noise into the stochastic simulation of biochemical reactions: A comparison of approaches.

The stochastic simulation algorithm (SSA) has been widely used for simulating biochemical reaction networks. SSA is able to capture the inherently intrinsic noise of the biological system, which is due to the discreteness of species population and to the randomness of their reciprocal interactions. However, SSA does not consider other sources of heterogeneity in biochemical reaction systems, which are referred to as extrinsic noise. Here, we extend two simulation approaches, namely, the integration-based method and the rejection-based method, to take extrinsic noise into account by allowing the reaction propensities to vary in time and state dependent manner. For both methods, new efficient implementations are introduced and their efficiency and applicability to biological models are investigated. Our numerical results suggest that the rejection-based method performs better than the integration-based method when the extrinsic noise is considered.

MpTheory Java library: a multi-platform Java library for systems biology based on the Metabolic P theory.

UNLABELLED: MpTheory Java library is an open-source project collecting a set of objects and algorithms for modeling observed dynamics by means of the Metabolic P (MP) theory, that is, a mathematical theory introduced in 2004 for modeling biological dynamics. By means of the library, it is possible to model biological systems both at continuous and at discrete time. Moreover, the library comprises a set of regression algorithms for inferring MP models starting from time series of observations. To enhance the modeling experience, beside a pure Java usage, the library can be directly used within the most popular computing environments, such as MATLAB, GNU Octave, Mathematica and R. AVAILABILITY AND IMPLEMENTATION: The library is open-source and licensed under the GNU Lesser General Public License (LGPL) Version 3.0. Source code, binaries and complete documentation are available at http://mptheory.scienze.univr.it. CONTACT: luca.marchetti@univr.it, marchetti@cosbi.eu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Modeling time-dependent transcription effects of HER2 oncogene and discovery of a role for E2F2 in breast cancer cell-matrix adhesion.

MOTIVATION: Oncogenes are known drivers of cancer phenotypes and targets of molecular therapies; however, the complex and diverse signaling mechanisms regulated by oncogenes and potential routes to targeted therapy resistance remain to be fully understood. To this end, we present an approach to infer regulatory mechanisms downstream of the HER2 driver oncogene in SUM-225 metastatic breast cancer cells from dynamic gene expression patterns using a succession of analytical techniques, including a novel MP grammars method to mathematically model putative regulatory interactions among sets of clustered genes. RESULTS: Our method highlighted regulatory interactions previously identified in the cell line and a novel finding that the HER2 oncogene, as opposed to the proto-oncogene, upregulates expression of the E2F2 transcription factor. By targeted gene knockdown we show the significance of this, demonstrating that cancer cell-matrix adhesion and outgrowth were markedly inhibited when E2F2 levels were reduced. Thus, validating in this context that upregulation of E2F2 represents a key intermediate event in a HER2 oncogene-directed gene expression-based signaling circuit. This work demonstrates how predictive modeling of longitudinal gene expression data combined with multiple systems-level analyses can be used to accurately predict downstream signaling pathways. Here, our integrated method was applied to reveal insights as to how the HER2 oncogene drives a specific cancer cell phenotype, but it is adaptable to investigate other oncogenes and model systems. AVAILABILITY AND IMPLEMENTATION: Accessibility of various tools is listed in methods; the Log-Gain Stoichiometric Stepwise algorithm is accessible at http://www.cbmc.it/software/Software.php.

International consensus on use of focused ultrasound for painful bone metastases: Current status and future directions.

Focused ultrasound surgery (FUS), in particular magnetic resonance guided FUS (MRgFUS), is an emerging non-invasive thermal treatment modality in oncology that has recently proven to be effective for the palliation of metastatic bone pain. A consensus panel of internationally recognised experts in focused ultrasound critically reviewed all available data and developed consensus statements to increase awareness, accelerate the development, acceptance and adoption of FUS as a treatment for painful bone metastases and provide guidance towards broader application in oncology. In this review, evidence-based consensus statements are provided for (1) current treatment goals, (2) current indications, (3) technical considerations, (4) future directions including research priorities, and (5) economic and logistical considerations.