Mice lacking NF-κB1 exhibit marked DNA damage responses and more severe gastric pathology in response to intraperitoneal tamoxifen administration

Tamoxifen (TAM) has recently been shown to cause acute gastric atrophy and metaplasia in mice. We have previously demonstrated that the outcome of Helicobacter felis infection, which induces similar gastric lesions in mice, is altered by deletion of specific NF-κB subunits. Nfkb1-/- mice developed more severe gastric atrophy than wild-type (WT) mice 6 weeks after H. felis infection. In contrast, Nfkb2-/- mice were protected from this pathology. We therefore hypothesized that gastric lesions induced by TAM may be similarly regulated by signaling via NF-κB subunits. Groups of five female C57BL/6 (WT), Nfkb1-/-, Nfkb2-/- and c-Rel-/- mice were administered 150 mg/kg TAM by IP injection. Seventy-two hours later, gastric corpus tissues were taken for quantitative histological assessment. In addition, groups of six female WT and Nfkb1-/- mice were exposed to 12 Gy γ-irradiation. Gastric epithelial apoptosis was quantified 6 and 48 h after irradiation. TAM induced gastric epithelial lesions in all strains of mice, but this was more severe in Nfkb1-/- mice than in WT mice. Nfkb1-/- mice exhibited more severe parietal cell loss than WT mice, had increased gastric epithelial expression of Ki67 and had an exaggerated gastric epithelial DNA damage response as quantified by γH2AX. To investigate whether the difference in gastric epithelial DNA damage response of Nfkb1-/- mice was unique to TAM-induced DNA damage or a generic consequence of DNA damage, we also assessed gastric epithelial apoptosis following γ-irradiation. Six hours after γ-irradiation, gastric epithelial apoptosis was increased in the gastric corpus and antrum of Nfkb1-/- mice. NF-κB1-mediated signaling regulates the development of gastric mucosal pathology following TAM administration. This is associated with an exaggerated gastric epithelial DNA damage response. This aberrant response appears to reflect a more generic sensitization of the gastric mucosa of Nfkb1-/- mice to DNA damage

Relational agency: Relational sociology, agency and interaction

yesThis article explores how the concept of agency in social theory changes when it is conceptualised as a relational rather than an individual phenomenon. I begin with a critique of the structure/agency debate, particularly of how this emerges in the critical realist approach to agency typified by Margaret Archer. It is argued that this approach, and the critical realist version of relational sociology that has grown from it, reifies social relations as a third entity to which agents have a cognitive, reflexive relation, playing down the importance of interaction. This upholds the Western moral and political view of agents as autonomous, independent, and reflexive individuals. Instead of this I consider agency from a different theoretical tradition in relational sociology in which agents are always located in manifold social relations. From this I create an understanding of agents as interactants, ones who are interdependent, vulnerable, intermittently reflexive, possessors of capacities that can only be practiced in joint actions, and capable of sensitive responses to others and to the situations of interaction. Instead of agency resting on the reflexive monitoring of action or the reflexive deliberation on structurally defined choices, agency emerges from our emotional relatedness to others as social relations unfold across time and space

Extracting non-linear integrate-and-fire models from experimental data using dynamic I–V curves

The dynamic I–V curve method was recently introduced for the efficient experimental generation of reduced neuron models. The method extracts the response properties of a neuron while it is subject to a naturalistic stimulus that mimics in vivo-like fluctuating synaptic drive. The resulting history-dependent, transmembrane current is then projected onto a one-dimensional current–voltage relation that provides the basis for a tractable non-linear integrate-and-fire model. An attractive feature of the method is that it can be used in spike-triggered mode to quantify the distinct patterns of post-spike refractoriness seen in different classes of cortical neuron. The method is first illustrated using a conductance-based model and is then applied experimentally to generate reduced models of cortical layer-5 pyramidal cells and interneurons, in injected-current and injected- conductance protocols. The resulting low-dimensional neuron models—of the refractory exponential integrate-and-fire type—provide highly accurate predictions for spike-times. The method therefore provides a useful tool for the construction of tractable models and rapid experimental classification of cortical neurons

Eigenvalue spectral properties of sparse random matrices obeying Dale's law

Understanding the dynamics of large networks of neurons with heterogeneous connectivity architectures is a complex physics problem that demands novel mathematical techniques. Biological neural networks are inherently spatially heterogeneous, making them difficult to mathematically model. Random recurrent neural networks capture complex network connectivity structures and enable mathematically tractability. Our paper generalises previous classical results to sparse connectivity matrices which have distinct excitatory (E) or inhibitory (I) neural populations. By investigating sparse networks we construct our analysis to examine the impacts of all levels of network sparseness, and discover a novel nonlinear interaction between the connectivity matrix and resulting network dynamics, in both the balanced and unbalanced cases. Specifically, we deduce new mathematical dependencies describing the influence of sparsity and distinct E/I distributions on the distribution of eigenvalues (eigenspectrum) of the networked Jacobian. Furthermore, we illustrate that the previous classical results are special cases of the more general results we have described here. Understanding the impacts of sparse connectivities on network dynamics is of particular importance for both theoretical neuroscience and mathematical physics as it pertains to the structure-function relationship of networked systems and their dynamics. Our results are an important step towards developing analysis techniques that are essential to studying the impacts of larger scale network connectivity on network function, and furthering our understanding of brain function and dysfunction.Comment: 18 pages, 6 figure