The guanine nucleotide exchange factor RapGEF2 is required for ERK-dependent immediate-early gene (Egr1) activation during fear memory formation.

The MAP kinase ERK is important for neuronal plasticity underlying associative learning, yet specific molecular pathways for neuronal ERK activation are undetermined. RapGEF2 is a neuron-specific cAMP sensor that mediates ERK activation. We investigated whether it is required for cAMP-dependent ERK activation leading to other downstream neuronal signaling events occurring during associative learning, and if RapGEF2-dependent signaling impairments affect learned behavior. Camk2α-cre+/-::RapGEF2fl/fl mice with depletion of RapGEF2 in hippocampus and amygdala exhibit impairments in context- and cue-dependent fear conditioning linked to corresponding impairment in Egr1 induction in these two brain regions. Camk2α-cre+/-::RapGEF2fl/fl mice show decreased RapGEF2 expression in CA1 and dentate gyrus associated with abolition of pERK and Egr1, but not of c-Fos induction, following fear conditioning, impaired freezing to context after fear conditioning, and impaired cAMP-dependent long-term potentiation at perforant pathway and Schaffer collateral synapses in hippocampal slices ex vivo. RapGEF2 expression is largely eliminated in basolateral amygdala, also involved in fear memory, in Camk2α-cre+/-::RapGEF2fl/fl mice. Neither Egr1 nor c-fos induction in BLA after fear conditioning, nor cue-dependent fear learning, are affected by ablation of RapGEF2 in BLA. However, Egr1 induction (but not that of c-fos) in BLA is reduced after restraint stress-augmented fear conditioning, as is freezing to cue after restraint stress-augmented fear conditioning, in Camk2α-cre+/-::RapGEF2fl/fl mice. Cyclic AMP-dependent GEFs have been genetically associated as risk factors for schizophrenia, a disorder associated with cognitive deficits. Here we show a functional link between one of them, RapGEF2, and cognitive processes involved in associative learning in amygdala and hippocampus.

Surgery duration: Optimized prediction and causality analysis.

Accurate estimation of duration of surgery (DOS) can lead to cost-effective utilization of surgical staff and operating rooms and decrease patients' waiting time. In this study, we present a supervised DOS nonlinear regression prediction model whose accuracy outperforms earlier results. In addition, unlike previous studies, we identify the features that influence DOS prediction. Further, in difference from others, we study the causal relationship between the feature set and DOS. The feature sets used in prior studies included a subset of the features presented in this study. This study aimed to derive influential effectors of duration of surgery via optimized prediction and causality analysis. We implemented an array of machine learning algorithms and trained them on datasets comprising surgery-related data, to derive DOS prediction models. The datasets we acquired contain patient, surgical staff, and surgery features. The datasets comprised 23,293 surgery records of eight surgery types performed over a 10-year period in a public hospital. We have introduced new, unstudied features and combined them with features adopted from previous studies to generate a comprehensive feature set. We utilized feature importance methods to identify the influential features, and causal inference methods to identify the causal features. Our model demonstrates superior performance in comparison to DOS prediction models in the art. The performance of our DOS model in terms of the mean absolute error (MAE) was 14.9 minutes. The algorithm that derived the model with the best performance was the gradient boosted trees (GBT). We identified the 10 most influential features and the 10 most causal features. In addition, we showed that 40% of the influential features have a significant (p-value = 0.05) causal relationship with DOS. We developed a DOS prediction model whose accuracy is higher than that of prior models. This improvement is achieved via the introduction of a novel feature set on which the model was trained. Utilizing our prediction model, hospitals can improve the efficiency of surgery schedules, and by exploiting the identified causal relationship, can influence the DOS. Further, the feature importance methods we used can help explain the model's predictions.

cAMP-Dependent Synaptic Plasticity at the Hippocampal Mossy Fiber Terminal.

Cyclic adenosine monophosphate (cAMP) is a crucial second messenger involved in both pre- and postsynaptic plasticity in many neuronal types across species. In the hippocampal mossy fiber (MF) synapse, cAMP mediates presynaptic long-term potentiation and depression. The main cAMP-dependent signaling pathway linked to MF synaptic plasticity acts via the activation of the protein kinase A (PKA) molecular cascade. Accordingly, various downstream putative synaptic PKA target proteins have been linked to cAMP-dependent MF synaptic plasticity, such as synapsin, rabphilin, synaptotagmin-12, RIM1a, tomosyn, and P/Q-type calcium channels. Regulating the expression of some of these proteins alters synaptic release probability and calcium channel clustering, resulting in short- and long-term changes to synaptic efficacy. However, despite decades of research, the exact molecular mechanisms by which cAMP and PKA exert their influences in MF terminals remain largely unknown. Here, we review current knowledge of different cAMP catalysts and potential downstream PKA-dependent molecular cascades, in addition to non-canonical cAMP-dependent but PKA-independent cascades, which might serve as alternative, compensatory or competing pathways to the canonical PKA cascade. Since several other central synapses share a similar form of presynaptic plasticity with the MF, a better description of the molecular mechanisms governing MF plasticity could be key to understanding the relationship between the transcriptional and computational levels across brain regions.

Effect of SARS-CoV-2 proteins on vascular permeability.

Severe acute respiratory syndrome (SARS)-CoV-2 infection leads to severe disease associated with cytokine storm, vascular dysfunction, coagulation, and progressive lung damage. It affects several vital organs, seemingly through a pathological effect on endothelial cells. The SARS-CoV-2 genome encodes 29 proteins, whose contribution to the disease manifestations, and especially endothelial complications, is unknown. We cloned and expressed 26 of these proteins in human cells and characterized the endothelial response to overexpression of each, individually. Whereas most proteins induced significant changes in endothelial permeability, nsp2, nsp5_c145a (catalytic dead mutant of nsp5), and nsp7 also reduced CD31, and increased von Willebrand factor expression and IL-6, suggesting endothelial dysfunction. Using propagation-based analysis of a protein-protein interaction (PPI) network, we predicted the endothelial proteins affected by the viral proteins that potentially mediate these effects. We further applied our PPI model to identify the role of each SARS-CoV-2 protein in other tissues affected by coronavirus disease (COVID-19). While validating the PPI network model, we found that the tight junction (TJ) proteins cadherin-5, ZO-1, and ß-catenin are affected by nsp2, nsp5_c145a, and nsp7 consistent with the model prediction. Overall, this work identifies the SARS-CoV-2 proteins that might be most detrimental in terms of endothelial dysfunction, thereby shedding light on vascular aspects of COVID-19.

Single Cell ADNP Predictive of Human Muscle Disorders: Mouse Knockdown Results in Muscle Wasting.

Activity-dependent neuroprotective protein (ADNP) mutations are linked with cognitive dysfunctions characterizing the autistic-like ADNP syndrome patients, who also suffer from delayed motor maturation. We thus hypothesized that ADNP is deregulated in versatile myopathies and that local ADNP muscle deficiency results in myopathy, treatable by the ADNP fragment NAP. Here, single-cell transcriptomics identified ADNP as a major constituent of the developing human muscle. ADNP transcript concentrations further predicted multiple human muscle diseases, with concentrations negatively correlated with the ADNP target interacting protein, microtubule end protein 1 (EB1). Reverting back to modeling at the single-cell level of the male mouse transcriptome, Adnp mRNA concentrations age-dependently correlated with motor disease as well as with sexual maturation gene transcripts, while Adnp expressing limb muscle cells significantly decreased with aging. Mouse Adnp heterozygous deficiency exhibited muscle microtubule reduction and myosin light chain (Myl2) deregulation coupled with motor dysfunction. CRISPR knockdown of adult gastrocnemius muscle Adnp in a Cas9 mouse resulted in treadmill (male) and gait (female) dysfunctions that were specifically ameliorated by treatment with the ADNP snippet, microtubule interacting, Myl2-regulating, NAP (CP201). Taken together, our studies provide new hope for personalized diagnosis/therapeutics in versatile myopathies.