Potassium channels in behavioral brain disorders. Molecular mechanisms and therapeutic potential: A narrative review

In Silico Methods for the Discovery of Kv7.2/7.3 Channels Modulators: A Comprehensive Review

The growing interest in Kv7.2/7.3 agonists originates from the involvement of these channels in several brain hyperexcitability disorders. In particular, Kv7.2/7.3 mutants have been clearly associated with epileptic encephalopathies (DEEs) as well as with a spectrum of focal epilepsy disorders, often associated with developmental plateauing or regression. Nevertheless, there is a lack of available therapeutic options, considering that retigabine, the only molecule used in clinic as a broad-spectrum Kv7 agonist, has been withdrawn from the market in late 2016. This is why several efforts have been made both by both academia and industry in the search for suitable chemotypes acting as Kv7.2/7.3 agonists. In this context, in silico methods have played a major role, since the precise structures of different Kv7 homotetramers have been only recently disclosed. In the present review, the computational methods used for the design of Kv.7.2/7.3 small molecule agonists and the underlying medicinal chemistry are discussed in the context of their biological and structure-function properties.

Early statin exposure influences cardiac and skeletal development with implications for ion channel transcriptomes in zebrafish

Statins, widely prescribed for cholesterol management by inhibiting HMG-CoA reductase in the cholesterol biosynthesis pathway, may also influence vertebrate development. In this study, we investigated the developmental effects of two widely used statins, atorvastatin (ATO) and pravastatin (PRA), on zebrafish offspring. For ATO, we administered doses classified as low (1 μM), medium (5 μM), and high (10 μM), while for PRA, the corresponding concentrations were set at low (18 μM), medium (180 μM), and high (270 μM). Our results showed significant reductions in birth and hatching rates, along with decreased body length in offspring at all ATO concentrations and medium to high PRA concentrations. A notable increase in malformation rates, especially in the spine and heart, was observed across all ATO treatments and in medium and high PRA groups. Additionally, we observed reduced heart contraction rates, decreased heart size, lower bone volumes, and diminished expression of mRNA osteogenic markers. Elevated venous sinus-artery bulb (SV-BA) ratios, increased thoracic area, and abnormal cartilage development were also prominent in all ATO-treated groups. Transcriptome analysis revealed alterations in genes predominantly associated with ion channels. These findings provide insights into the potential impacts of specific concentrations of statins on offspring development and highlight potential gene interactions with statins.

Integrating across behaviors and timescales to understand the neural control of movement

The nervous system evolved to enable navigation throughout the environment in the pursuit of resources. Evolutionarily newer structures allowed increasingly complex adaptations but necessarily added redundancy. A dominant view of movement neuroscientists is that there is a one-to-one mapping between brain region and function. However, recent experimental data is hard to reconcile with the most conservative interpretation of this framework, suggesting a degree of functional redundancy during the performance of well-learned, constrained behaviors. This apparent redundancy likely stems from the bidirectional interactions between the various cortical and subcortical structures involved in motor control. We posit that these bidirectional connections enable flexible interactions across structures that change depending upon behavioral demands, such as during acquisition, execution or adaptation of a skill. Observing the system across both multiple actions and behavioral timescales can help isolate the functional contributions of individual structures, leading to an integrated understanding of the neural control of movement.

Use of big data and machine learning algorithms to extract possible treatment targets in neurodevelopmental disorders

Neurodevelopmental disorders (NDDs) impact multiple aspects of an individual's functioning, including social interactions, communication, and behaviors. The underlying biological mechanisms of NDDs are not yet fully understood, and pharmacological treatments have been limited in their effectiveness, in part due to the complex nature of these disorders and the heterogeneity of symptoms across individuals. Identifying genetic loci associated with NDDs can help in understanding biological mechanisms and potentially lead to the development of new treatments. However, the polygenic nature of these complex disorders has made identifying new treatment targets from genome-wide association studies (GWAS) challenging. Recent advances in the fields of big data and high-throughput tools have provided radically new insights into the underlying biological mechanism of NDDs. This paper reviews various big data approaches, including classical and more recent techniques like deep learning, which can identify potential treatment targets from GWAS and other omics data, with a particular emphasis on NDDs. We also emphasize the increasing importance of explainable and causal machine learning (ML) methods that can aid in identifying genes, molecular pathways, and more complex biological processes that may be future targets of intervention in these disorders. We conclude that these new developments in genetics and ML hold promise for advancing our understanding of NDDs and identifying novel treatment targets.