Insights into channel function via channel dysfunction

The nicotinic synapse has been a touchstone for advances in neuroscience ever since Jean Nicot, the French ambassador to Portugal, sent some tobacco seeds home to Paris in 1550 with a note that the New World plant had interesting effects when smoked. Now the muscle nicotinic acetylcholine receptor (nAChR) is a well-studied example of ligand-gated ion channels. After a motor neuron is stimulated, the nerve impulse reaches the presynaptic terminal, where it evokes release of acetylcholine (ACh) into the synapse. The nAChR depolarizes the postsynaptic muscle and triggers muscle action potentials; muscle contraction follows. To date, several nAChR subtypes have been successfully isolated, purified, imaged, and expressed, and unitary currents have been recorded from these channels (1). Researchers continue to unravel the molecular mechanisms of these macromolecules that are embedded in membranes at vertebrate nerve-muscle synapses, at invertebrate nicotinic synapses (which explains why nicotine-producing tobacco plants have a select advantage against invertebrate pests), and in the vertebrate central system (which explains Jean Nicot’s fascination with those leaves). However, the precise structural events that trigger channel opening or "gating" remain mostly unknown

Mutations Linked to Autosomal Dominant Nocturnal Frontal Lobe Epilepsy Affect Allosteric Ca²⁺ Activation of the α4β2 Nicotinic Acetylcholine Receptor

Extracellular Ca²⁺ robustly potentiates the acetylcholine response of α4β2 nicotinic receptors. Rat orthologs of five mutations linked to autosomal dominant nocturnal frontal lobe epilepsy (ADNFLE)—α4(S252F), α4(S256L), α4(+L264), β2(V262L), and β2(V262M)—reduced 2 mM Ca²⁺ potentiation of the α4β2 1 mM acetylcholine response by 55 to 74%. To determine whether altered allosteric Ca²⁺ activation or enhanced Ca²⁺ block caused this reduction, we coexpressed the rat ADNFLE mutations with an α4 N-terminal mutation, α4(E180Q), that abolished α4β2 allosteric Ca²⁺ activation. In each case, Ca²⁺ inhibition of the double mutants was less than that expected from a Ca²⁺ blocking mechanism. In fact, the effects of Ca²⁺ on the ADNFLE mutations near the intracellular end of the M2 region—α4(S252F) and α4(S256L)—were consistent with a straightforward allosteric mechanism. In contrast, the effects of Ca²⁺ on the ADNFLE mutations near the extracellular end of the M2 region—α4(+L264)β2, β2(V262L), and β2(V262M)—were consistent with a mixed mechanism involving both altered allosteric activation and enhanced block. However, the effects of 2 mM Ca²⁺ on the α4β2, α4(+L264)β2, and α4β2(V262L) single-channel conductances, the effects of membrane potential on the β2(V262L)-mediated reduction in Ca²⁺ potentiation, and the effects of eliminating the negative charges in the extracellular ring on this reduction failed to provide any direct evidence of mutant-enhanced Ca²⁺ block. Moreover, analyses of the α4β2, α4(S256L), and α4(+L264) Ca²⁺ concentration-potentiation relations suggested that the ADNFLE mutations reduce Ca²⁺ potentiation of the α4β2 acetylcholine response by altering allosteric activation rather than by enhancing block

Novel Seizure Phenotype and Sleep Disruptions in Knock-In Mice with Hypersensitive α4* Nicotinic Receptors

A leucine to alanine substitution (L9′A) was introduced in the M2 region of the mouse α4 neuronal nicotinic acetylcholine receptor (nAChR) subunit. Expressed in Xenopus oocytes, α4(L9′A)β2 nAChRs were ≥30-fold more sensitive than wild type (WT) to both ACh and nicotine. We generated knock-in mice with the L9′A mutation and studied their cellular responses, seizure phenotype, and sleep-wake cycle. Seizure studies on α4-mutated animals are relevant to epilepsy research because all known mutations linked to autosomal dominant nocturnal frontal lobe epilepsy (ADNFLE) occur in the M2 region of α4or β2 subunits. Thalamic cultures and synaptosomes from L9′A mice were hypersensitive to nicotine-induced ion flux. L9′A mice were ∼15-fold more sensitive to seizures elicited by nicotine injection than their WT littermates. Seizures in L9′A mice differed qualitatively from those in WT: L9′A seizures started earlier, were prevented by nicotine pretreatment, lacked EEG spike-wave discharges, and consisted of fast repetitive movements. Nicotine-induced seizures in L9′A mice were partial, whereas WT seizures were generalized. When L9′A homozygous mice received a 10 mg/kg nicotine injection, there was temporal and phenomenological separation of mutant and WT-like seizures: an initial seizure ∼20 s after injection was clonic and showed no EEG changes. A second seizure began 3-4 min after injection, was tonic-clonic, and had EEG spike-wave activity. No spontaneous seizures were detected in L9′A mice during chronic video/EEG recordings, but their sleep-wake cycle was altered. Our findings show that hypersensitive α4* nicotinic receptors in mice mediate changes in the sleep-wake cycle and nicotine-induced seizures resembling ADNFLE

TRY plant trait database - enhanced coverage and open access

Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

TRY plant trait database – enhanced coverage and open access

Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

TRY plant trait database - enhanced coverage and open access

Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

TRY plant trait database - enhanced coverage and open access

This article has 730 authors, of which I have only listed the lead author and myself as a representative of University of HelsinkiPlant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives.Peer reviewe

TRY plant trait database – enhanced coverage and open access

Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants - determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits - almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives