The Complement Anaphylatoxin C5a Induces Apoptosis in Adrenomedullary Cells during Experimental Sepsis

Sepsis remains a poorly understood, enigmatic disease. One of the cascades crucially involved in its pathogenesis is the complement system. Especially the anaphylatoxin C5a has been shown to have numerous harmful effects during sepsis. We have investigated the impact of high levels of C5a on the adrenal medulla following cecal ligation and puncture (CLP)-induced sepsis in rats as well as the role of C5a on catecholamine production from pheochromocytoma-derived PC12 cells. There was significant apoptosis of adrenal medulla cells in rats 24 hrs after CLP, as assessed by the TUNEL technique. These effects could be reversed by dual-blockade of the C5a receptors, C5aR and C5L2. When rats were subjected to CLP, levels of C5a and norepinephrine were found to be antipodal as a function of time. PC12 cell production of norepinephrine and dopamine was significantly blunted following exposure to recombinant rat C5a in a time-dependent and dose-dependent manner. This impaired production could be related to C5a-induced initiation of apoptosis as defined by binding of Annexin V and Propidium Iodine to PC12 cells. Collectively, we describe a C5a-dependent induction of apoptotic events in cells of adrenal medulla in vivo and pheochromocytoma PC12 cells in vitro. These data suggest that experimental sepsis induces apoptosis of adrenomedullary cells, which are responsible for the bulk of endogenous catecholamines. Septic shock may be linked to these events. Since blockade of both C5a receptors virtually abolished adrenomedullary apoptosis in vivo, C5aR and C5L2 become promising targets with implications on future complement-blocking strategies in the clinical setting of sepsis

A kilonova as the electromagnetic counterpart to a gravitational-wave source

Gravitational waves were discovered with the detection of binary black-hole mergers1 and they should also be detectable from lower-mass neutron-star mergers. These are predicted to eject material rich in heavy radioactive isotopes that can power an electromagnetic signal. This signal is luminous at optical and infrared wavelengths and is called a kilonova2,3,4,5. The gravitational-wave source GW170817 arose from a binary neutron-star merger in the nearby Universe with a relatively well confined sky position and distance estimate6. Here we report observations and physical modelling of a rapidly fading electromagnetic transient in the galaxy NGC 4993, which is spatially coincident with GW170817 and with a weak, short γ-ray burst7,8. The transient has physical parameters that broadly match the theoretical predictions of blue kilonovae from neutron-star mergers. The emitted electromagnetic radiation can be explained with an ejected mass of 0.04 ± 0.01 solar masses, with an opacity of less than 0.5 square centimetres per gram, at a velocity of 0.2 ± 0.1 times light speed. The power source is constrained to have a power-law slope of −1.2 ± 0.3, consistent with radioactive powering from r-process nuclides. (The r-process is a series of neutron capture reactions that synthesise many of the elements heavier than iron.) We identify line features in the spectra that are consistent with light r-process elements (atomic masses of 90–140). As it fades, the transient rapidly becomes red, and a higher-opacity, lanthanide-rich ejecta component may contribute to the emission. This indicates that neutron-star mergers produce gravitational waves and radioactively powered kilonovae, and are a nucleosynthetic source of the r-process element

Molecular and Evolutionary Bases of Within-Patient Genotypic and Phenotypic Diversity in Escherichia coli Extraintestinal Infections

Although polymicrobial infections, caused by combinations of viruses, bacteria, fungi and parasites, are being recognised with increasing frequency, little is known about the occurrence of within-species diversity in bacterial infections and the molecular and evolutionary bases of this diversity. We used multiple approaches to study the genomic and phenotypic diversity among 226 Escherichia coli isolates from deep and closed visceral infections occurring in 19 patients. We observed genomic variability among isolates from the same site within 11 patients. This diversity was of two types, as patients were infected either by several distinct E. coli clones (4 patients) or by members of a single clone that exhibit micro-heterogeneity (11 patients); both types of diversity were present in 4 patients. A surprisingly wide continuum of antibiotic resistance, outer membrane permeability, growth rate, stress resistance, red dry and rough morphotype characteristics and virulence properties were present within the isolates of single clones in 8 of the 11 patients showing genomic micro-heterogeneity. Many of the observed phenotypic differences within clones affected the trade-off between self-preservation and nutritional competence (SPANC). We showed in 3 patients that this phenotypic variability was associated with distinct levels of RpoS in co-existing isolates. Genome mutational analysis and global proteomic comparisons in isolates from a patient revealed a star-like relationship of changes amongst clonally diverging isolates. A mathematical model demonstrated that multiple genotypes with distinct RpoS levels can co-exist as a result of the SPANC trade-off. In the cases involving infection by a single clone, we present several lines of evidence to suggest diversification during the infectious process rather than an infection by multiple isolates exhibiting a micro-heterogeneity. Our results suggest that bacteria are subject to trade-offs during an infectious process and that the observed diversity resembled results obtained in experimental evolution studies. Whatever the mechanisms leading to diversity, our results have strong medical implications in terms of the need for more extensive isolate testing before deciding on antibiotic therapies

Innovations in research ethics governance in humanitarian settings.

CAPRISA, 2015.Abstract available in pdf

Multi-messenger observations of a binary neutron star merger

On 2017 August 17 a binary neutron star coalescence candidate (later designated GW170817) with merger time 12:41:04 UTC was observed through gravitational waves by the Advanced LIGO and Advanced Virgo detectors. The Fermi Gamma-ray Burst Monitor independently detected a gamma-ray burst (GRB 170817A) with a time delay of ~1.7 s with respect to the merger time. From the gravitational-wave signal, the source was initially localized to a sky region of 31 deg2 at a luminosity distance of 40+8-8 Mpc and with component masses consistent with neutron stars. The component masses were later measured to be in the range 0.86 to 2.26 Mo. An extensive observing campaign was launched across the electromagnetic spectrum leading to the discovery of a bright optical transient (SSS17a, now with the IAU identification of AT 2017gfo) in NGC 4993 (at ~40 Mpc) less than 11 hours after the merger by the One- Meter, Two Hemisphere (1M2H) team using the 1 m Swope Telescope. The optical transient was independently detected by multiple teams within an hour. Subsequent observations targeted the object and its environment. Early ultraviolet observations revealed a blue transient that faded within 48 hours. Optical and infrared observations showed a redward evolution over ~10 days. Following early non-detections, X-ray and radio emission were discovered at the transient’s position ~9 and ~16 days, respectively, after the merger. Both the X-ray and radio emission likely arise from a physical process that is distinct from the one that generates the UV/optical/near-infrared emission. No ultra-high-energy gamma-rays and no neutrino candidates consistent with the source were found in follow-up searches. These observations support the hypothesis that GW170817 was produced by the merger of two neutron stars in NGC4993 followed by a short gamma-ray burst (GRB 170817A) and a kilonova/macronova powered by the radioactive decay of r-process nuclei synthesized in the ejecta

A Graphical and Computational Modelling Platform for Biological Pathways

A major endeavor of systems biology is the construction of graphical and computational models of biological pathways as a means to better understand their structure and function. Here, we present a protocol for a biologist-friendly graphical modeling scheme that facilitates the construction of detailed network diagrams, summarizing the components of a biological pathway (such as proteins and biochemicals) and illustrating how they interact. These diagrams can then be used to simulate activity flow through a pathway, thereby modeling its dynamic behavior. The protocol is divided into four sections: (i) assembly of network diagrams using the modified Edinburgh Pathway Notation (mEPN) scheme and yEd network editing software with pathway information obtained from published literature and databases of molecular interaction data; (ii) parameterization of the pathway model within yEd through the placement of 'tokens' on the basis of the known or imputed amount or activity of a component; (iii) model testing through visualization and quantitative analysis of the movement of tokens through the pathway, using the network analysis tool Graphia Professional and (iv) optimization of model parameterization and experimentation. This is the first modeling approach that combines a sophisticated notation scheme for depicting biological events at the molecular level with a Petri net–based flow simulation algorithm and a powerful visualization engine with which to observe the dynamics of the system being modeled. Unlike many mathematical approaches to modeling pathways, it does not require the construction of a series of equations or rate constants for model parameterization. Depending on a model's complexity and the availability of information, its construction can take days to months, and, with refinement, possibly years. However, once assembled and parameterized, a simulation run, even on a large model, typically takes only seconds. Models constructed using this approach provide a means of knowledge management, information exchange and, through the computation simulation of their dynamic activity, generation and testing of hypotheses, as well as prediction of a system's behavior when perturbed

Modelling the structure and dynamics of biological pathways

There is a need for formalised diagrams that both summarise current biological pathway knowledge and support modelling approaches that explain and predict their behaviour. Here, we present a new, freely available modelling framework that includes a biologist-friendly pathway modelling language (mEPN), a simple but sophisticated method to support model parameterisation using available biological information; a stochastic flow algorithm that simulates the dynamics of pathway activity; and a 3-D visualisation engine that aids understanding of the complexities of a system's dynamics. We present example pathway models that illustrate of the power of approach to depict a diverse range of systems