The Salmonella Mutagenicity Assay: The Stethoscope of Genetic Toxicology for the 21st Century

Objectives: According to the 2007 National Research Council report Toxicology for the Twenty-First Century, modern methods (e.g., "omics," in vitro assays, high-throughput testing, computational methods) will lead to the emergence of a new approach to toxicology. The Salmonella mammalian microsome mutagenicity assay has been central to the field of genetic toxicology since the 1970s. Here we document the paradigm shifts engendered by the assay, the validation and applications of the assay, and how the assay is a model for future in vitro toxicology assays. Data sources: We searched PubMed, Scopus, and Web of Knowledge using key words relevant to the Salmonella assay and additional genotoxicity assays. Data extraction: We merged the citations, removing duplicates, and categorized the papers by year and topic. Data synthesis: The Salmonella assay led to two paradigm shifts: that some carcinogens were mutagens and that some environmental samples (e.g., air, water, soil, food, combustion emissions) were mutagenic. Although there are > 10,000 publications on the Salmonella assay, covering tens of thousands of agents, data on even more agents probably exist in unpublished form, largely as proprietary studies by industry. The Salmonella assay is a model for the development of 21st century in vitro toxicology assays in terms of the establishment of standard procedures, ability to test various agents, transferability across laboratories, validation and testing, and structure-activity analysis. Conclusions: Similar to a stethoscope as a first-line, inexpensive tool in medicine, the Salmonella assay can serve a similar, indispensable role in the foreseeable future of 21st century toxicology

Spike-Based Bayesian-Hebbian Learning of Temporal Sequences

Many cognitive and motor functions are enabled by the temporal representation and processing of stimuli, but it remains an open issue how neocortical microcircuits can reliably encode and replay such sequences of information. To better understand this, a modular attractor memory network is proposed in which meta-stable sequential attractor transitions are learned through changes to synaptic weights and intrinsic excitabilities via the spike-based Bayesian Confidence Propagation Neural Network (BCPNN) learning rule. We find that the formation of distributed memories, embodied by increased periods of firing in pools of excitatory neurons, together with asymmetrical associations between these distinct network states, can be acquired through plasticity. The model's feasibility is demonstrated using simulations of adaptive exponential integrate-and-fire model neurons (AdEx). We show that the learning and speed of sequence replay depends on a confluence of biophysically relevant parameters including stimulus duration, level of background noise, ratio of synaptic currents, and strengths of short-term depression and adaptation. Moreover, sequence elements are shown to flexibly participate multiple times in the sequence, suggesting that spiking attractor networks of this type can support an efficient combinatorial code. The model provides a principled approach towards understanding how multiple interacting plasticity mechanisms can coordinate hetero-associative learning in unison

Mesoscopic segregation of excitation and inhibition in a brain network model

Neurons in the brain are known to operate under a careful balance of excitation and inhibition, which maintains neural microcircuits within the proper operational range. How this balance is played out at the mesoscopic level of neuronal populations is, however, less clear. In order to address this issue, here we use a coupled neural mass model to study computationally the dynamics of a network of cortical macrocolumns operating in a partially synchronized, irregular regime. The topology of the network is heterogeneous, with a few of the nodes acting as connector hubs while the rest are relatively poorly connected. Our results show that in this type of mesoscopic network excitation and inhibition spontaneously segregate, with some columns acting mainly in an excitatory manner while some others have predominantly an inhibitory effect on their neighbors. We characterize the conditions under which this segregation arises, and relate the character of the different columns with their topological role within the network. In particular, we show that the connector hubs are preferentially inhibitory, the more so the larger the node's connectivity. These results suggest a potential mesoscale organization of the excitation-inhibition balance in brain networks.This work was supported by the Ministerio de Economia y Competividad (Spain, project FIS2012-37655-C02- 01), and by the Generalitat de Catalunya (project 2009SGR1168). JGO acknowledges support from the ICREA Academia programme. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript