Physiological roles of endocytosis and presynaptic scaffold in vesicle replenishment at fast and slow central synapses.

After exocytosis, release sites are cleared of vesicular residues to replenish with transmitter-filled vesicles. Endocytic and scaffold proteins are thought to underlie this site-clearance mechanism. However, the physiological significance of this mechanism at diverse mammalian central synapses remains unknown. Here, we tested this in a physiologically optimized condition using action potential evoked EPSCs at fast calyx synapse and relatively slow hippocampal CA1 synapse, in post-hearing mice brain slices at 37°C and in 1.3 mM [Ca2+]. Pharmacological block of endocytosis enhanced synaptic depression at the calyx synapse, whereas it attenuated synaptic facilitation at the hippocampal synapse. Block of scaffold protein activity likewise enhanced synaptic depression at the calyx but had no effect at the hippocampal synapse. At the fast calyx synapse, block of endocytosis or scaffold protein activity significantly enhanced synaptic depression as early as 10 ms after the stimulation onset. Unlike previous reports, neither endocytic blockers nor scaffold protein inhibitors prolonged the recovery from short-term depression. We conclude that the release-site clearance by endocytosis can be a universal phenomenon supporting vesicle replenishment at both fast and slow synapses, whereas the presynaptic scaffold mechanism likely plays a specialized role in vesicle replenishment predominantly at fast synapses.

Gradient Boosting Machine and Efficient Combination of Features for Speech-Based Detection of COVID-19.

In recent times, speech-based automatic disease detection systems have shown several promising results in biomedical and life science applications, especially in the case of respiratory diseases. It provides a quick, cost-effective, reliable, and non-invasive potential alternative detection option for COVID-19 in the ongoing pandemic scenario since the subject's voice can be remotely recorded and sent for further analysis. The existing COVID-19 detection methods including RT-PCR, and chest X-ray tests are not only costlier but also require the involvement of a trained technician. The present paper proposes a novel speech-based respiratory disease detection scheme for COVID-19 and Asthma using the Gradient Boosting Machine-based classifier. From the recorded speech samples, the spectral, cepstral, and periodicity features, as well as spectral descriptors, are computed and then homogeneously fused to obtain relevant statistical features. These features are subsequently used as inputs to the Gradient Boosting Machine. The various performance matrices of the proposed model have been obtained using thirteen sound categories' speech data collected from more than 50 countries using five standard datasets for accurate diagnosis of respiratory diseases including COVID-19. The overall average accuracy achieved by the proposed model using the stratified k-fold cross-validation test is above 97%. The analysis of various performance matrices demonstrates that under the current pandemic scenario, the proposed COVID-19 detection scheme can be gainfully employed by physicians.

Microtubule assembly by tau impairs endocytosis and neurotransmission via dynamin sequestration in Alzheimer's disease synapse model.

Elevation of soluble wild-type (WT) tau occurs in synaptic compartments in Alzheimer's disease. We addressed whether tau elevation affects synaptic transmission at the calyx of Held in slices from mice brainstem. Whole-cell loading of WT human tau (h-tau) in presynaptic terminals at 10-20 µM caused microtubule (MT) assembly and activity-dependent rundown of excitatory neurotransmission. Capacitance measurements revealed that the primary target of WT h-tau is vesicle endocytosis. Blocking MT assembly using nocodazole prevented tau-induced impairments of endocytosis and neurotransmission. Immunofluorescence imaging analyses revealed that MT assembly by WT h-tau loading was associated with an increased MT-bound fraction of the endocytic protein dynamin. A synthetic dodecapeptide corresponding to dynamin 1-pleckstrin-homology domain inhibited MT-dynamin interaction and rescued tau-induced impairments of endocytosis and neurotransmission. We conclude that elevation of presynaptic WT tau induces de novo assembly of MTs, thereby sequestering free dynamins. As a result, endocytosis and subsequent vesicle replenishment are impaired, causing activity-dependent rundown of neurotransmission.

Computational models for prediction of protein-protein interaction in rice and Magnaporthe grisea.

Introduction: Plant-microbe interactions play a vital role in the development of strategies to manage pathogen-induced destructive diseases that cause enormous crop losses every year. Rice blast is one of the severe diseases to rice Oryza sativa (O. sativa) due to Magnaporthe grisea (M. grisea) fungus. Protein-protein interaction (PPI) between rice and fungus plays a key role in causing rice blast disease. Methods: In this paper, four genomic information-based models such as (i) the interolog, (ii) the domain, (iii) the gene ontology, and (iv) the phylogenetic-based model are developed for predicting the interaction between O. sativa and M. grisea in a whole-genome scale. Results and Discussion: A total of 59,430 interacting pairs between 1,801 rice proteins and 135 blast fungus proteins are obtained from the four models. Furthermore, a machine learning model is developed to assess the predicted interactions. Using composition-based amino acid composition (AAC) and conjoint triad (CT) features, an accuracy of 88% and 89% is achieved, respectively. When tested on the experimental dataset, the CT feature provides the highest accuracy of 95%. Furthermore, the specificity of the model is verified with other pathogen-host datasets where less accuracy is obtained, which confirmed that the model is specific to O. sativa and M. grisea. Understanding the molecular processes behind rice resistance to blast fungus begins with the identification of PPIs, and these predicted PPIs will be useful for drug design in the plant science community.

Deep Neural Network and Extreme Gradient Boosting Based Hybrid Classifier for Improved Prediction of Protein-Protein Interaction.

Understanding the behavioral process of life and disease-causing mechanism, knowledge regarding protein-protein interactions (PPI) is essential. In this paper, a novel hybrid approach combining deep neural network (DNN) and extreme gradient boosting classifier (XGB) is employed for predicting PPI. The hybrid classifier (DNN-XGB) uses a fusion of three sequence-based features, amino acid composition (AAC), conjoint triad composition (CT), and local descriptor (LD) as inputs. The DNN extracts the hidden information through a layer-wise abstraction from the raw features that are passed through the XGB classifier. The 5-fold cross-validation accuracy for intraspecies interactions dataset of Saccharomyces cerevisiae (core subset), Helicobacter pylori, Saccharomyces cerevisiae, and Human are 98.35, 96.19, 97.37, and 99.74 percent respectively. Similarly, accuracies of 98.50 and 97.25 percent are achieved for interspecies interaction dataset of Human- Bacillus Anthracis and Human- Yersinia pestis datasets, respectively. The improved prediction accuracies obtained on the independent test sets and network datasets indicate that the DNN-XGB can be used to predict cross-species interactions. It can also provide new insights into signaling pathway analysis, predicting drug targets, and understanding disease pathogenesis. Improved performance of the proposed method suggests that the hybrid classifier can be used as a useful tool for PPI prediction. The datasets and source codes are available at: https://github.com/SatyajitECE/DNN-XGB-for-PPI-Prediction.

ANOVA-particle swarm optimization-based feature selection and gradient boosting machine classifier for improved protein-protein interaction prediction.

Feature fusion and selection strategies have been applied to improve accuracy in the prediction of protein-protein interaction (PPI). In this paper, an embedded feature selection framework is developed by integrating a cost function based on analysis of variance (ANOVA) with the particle swarm optimization (PSO), termed AVPSO. Initially, the features of the protein sequences extracted using pseudo-amino acid composition (PseAAC), conjoint triad composition, and local descriptor are fused. Then, AVPSO is employed to select the optimal set of features. The light gradient boosting machine (LGBM) classifier is used to predict the PPIs using the optimal feature subset. On the five-fold cross-validation analysis, the proposed model (AVPSO-LGBM) achieved an average accuracy of 97.12% and 95.09%, respectively, on the intraspecies PPI datasets Saccharomyces cerevisiae and Helicobacter pylori. On the interspecies, PPI datasets of the Human-Bacillus and Human-Yersinia, an average accuracy of 95.20% and 93.44%, are achieved. Results obtained on independent test datasets, and network datasets show that the prediction accuracy of the AVPSO-LGBM is better than the existing methods, demonstrating its generalization ability. The improved prediction performance obtained by the proposed model makes it a reliable and effective PPI prediction model.

Improved prediction of protein-protein interaction using a hybrid of functional-link Siamese neural network and gradient boosting machines.

In this paper, for accurate prediction of protein-protein interaction (PPI), a novel hybrid classifier is developed by combining the functional-link Siamese neural network (FSNN) with the light gradient boosting machine (LGBM) classifier. The hybrid classifier (FSNN-LGBM) uses the fusion of features derived using pseudo amino acid composition and conjoint triad descriptors. The FSNN extracts the high-level abstraction features from the raw features and LGBM performs the PPI prediction task using these abstraction features. On performing 5-fold cross-validation experiments, the proposed hybrid classifier provides average accuracies of 98.70 and 98.38%, respectively, on the intraspecies PPI data sets of Saccharomyces cerevisiae and Helicobacter pylori. Similarly, the average accuracies for the interspecies PPI data sets of the Human-Bacillus and Human-Yersinia data sets are 98.52 and 97.40%, respectively. Compared with the existing methods, the hybrid classifier achieves higher prediction accuracy on the independent test sets and network data sets. The improved prediction performance obtained by the FSNN-LGBM makes it a flexible and effective PPI prediction model.

Integrating Resonant Recognition Model and Stockwell Transform for Localization of Hotspots in Tubulin.

Tubulin is a promising target for designing anti-cancer drugs. Identification of hotspots in multifunctional Tubulin protein provides insights for new drug discovery. Although machine learning techniques have shown significant results in prediction, they fail to identify the hotspots corresponding to a particular biological function. This paper presents a signal processing technique combining resonant recognition model (RRM) and Stockwell Transform (ST) for the identification of hotspots corresponding to a particular functionality. The characteristic frequency (CF) representing a specific biological function is determined using the RRM. Then the spectrum of the protein sequence is computed using ST. The CF is filtered from the ST spectrum using a time-frequency mask. The energy peaks in the filtered sequence represent the hotspots. The hotspots predicted by the proposed method are compared with the experimentally detected binding residues of Tubulin stabilizing drug Taxol and destabilizing drug Colchicine present in the Tubulin protein. Out of the 53 experimentally identified hotspots, 60% are predicted by the proposed method whereas around 20% are predicted by existing machine learning based methods. Additionally, the proposed method predicts some new hot spots, which may be investigated.

Dynamin-1 deletion enhances post-tetanic potentiation and quantal size after tetanic stimulation at the calyx of Held.

KEY POINTS: Post-tetanic potentiation (PTP) is attributed mainly to an increase in release probability (Pr ) and/or readily-releasable pool (RRP) in many synapses, but the role of endocytosis in PTP is unknown. Using the calyx of Held synapse from tissue-specific dynamin-1 knockout (cKO) mice (P16-20), we report that cKO synapses show enhanced PTP compared to control. We found significant increases in both spontaneous excitatory postsynaptic current (spEPSC) amplitude and RRP size (estimated by a train of 30 APs at 100 Hz) in cKO over control during PTP. Actin depolymerization blocks the increase in spEPSC amplitude in both control and cKO, and it abolishes the enhancement of PTP in cKO. PTP is sensitive to the PKC inhibitor GF109203X in both control and cKO. We conclude that an activity-dependent quantal size increase contributes to the enhancement of PTP in cKO over control and an altered endocytosis affects short-term plasticity through quantal size changes. ABSTRACT: High-frequency stimulation leads to post-tetanic potentiation (PTP) at many types of synapses. Previous studies suggest that PTP results primarily from a protein kinase C (PKC)-dependent increase in release probability (Pr ) and/or readily-releasable pool (RRP) of synaptic vesicles (SVs), but the role of SV endocytosis in PTP is unknown. Using the mature calyx of Held (P16-20), we report that tissue-specific ablation of dynamin-1 (cKO), an endocytic protein crucial for SV regeneration, enhances PTP in cKO over control. To explore the mechanism of this enhancement, we estimated the changes in paired-pulse ratios (PPRs) and RRP size during PTP. RRP was estimated by the back-extrapolation of cumulative EPSC amplitudes during a train of 30 action potentials at 100 Hz (termed RRPtrain ). We found an increase in RRPtrain during PTP in both control and cKO, but no significant changes in the PPR. Moreover, the amplitude and frequency of spontaneous excitatory postsynaptic currents (spEPSCs) increased during PTP in both control and cKO; however, the spEPSC amplitude in cKO during PTP was significantly larger than in control. Actin depolymerization reagent latrunculin-B (Lat-B) abolished the activity-dependent increase in spEPSC amplitude in both control and cKO, but selectively blocked the enhancement of PTP in cKO, without affecting PTP in control. PKC inhibitor GF109203X nearly abolished PTP in both control and cKO. These data suggest that the quantal size increase contributes to the enhancement of PTP in dynamin-1 cKO, and this change depends on strong synaptic activity and actin polymerization.

Tissue-specific dynamin-1 deletion at the calyx of Held decreases short-term depression through a mechanism distinct from vesicle resupply.

Dynamin is a large GTPase with a crucial role in synaptic vesicle regeneration. Acute dynamin inhibition impairs neurotransmitter release, in agreement with the protein's established role in vesicle resupply. Here, using tissue-specific dynamin-1 knockout [conditional knockout (cKO)] mice at a fast central synapse that releases neurotransmitter at high rates, we report that dynamin-1 deletion unexpectedly leads to enhanced steady-state neurotransmission and consequently less synaptic depression during brief periods of high-frequency stimulation. These changes are also accompanied by increased transmission failures. Interestingly, synaptic vesicle resupply and several other synaptic properties remain intact, including basal neurotransmission, presynaptic Ca(2+) influx, initial release probability, and postsynaptic receptor saturation and desensitization. However, acute application of Latrunculin B, a reagent known to induce actin depolymerization and impair bulk and ultrafast endocytosis, has a stronger effect on steady-state depression in cKO than in control and brings the depression down to a control level. The slow phase of presynaptic capacitance decay following strong stimulation is impaired in cKO; the rapid capacitance changes immediately after strong depolarization are also different between control and cKO and sensitive to Latrunculin B. These data raise the possibility that, in addition to its established function in regenerating synaptic vesicles, the endocytosis protein dynamin-1 may have an impact on short-term synaptic depression. This role comes into play primarily during brief high-frequency stimulation.

Equal sensitivity of Cav1.2 and Cav1.3 channels to the opposing modulations of PKA and PKG in mouse chromaffin cells.

Mouse chromaffin cells (MCCs) express high densities of L-type Ca2+ channels (LTCCs), which control pacemaking activity and catecholamine secretion proportionally to their density of expression. In vivo phosphorylation of LTCCs by cAMP-PKA and cGMP­PKG, regulate LTCC gating in two opposing ways: the cAMP-PKA pathway potentiates while the cGMP­PKG cascade inhibits LTCCs. Despite this, no attempts have been made to answer three key questions related to the two Cav1 isoforms expressed in MCCs (Cav1.2 and Cav1.3): (i) how much are the two Cav1 channels basally modulated by PKA and PKG?, (ii) to what extent can Cav1.2 and Cav1.3 be further regulated by PKA or PKG activation?, and (iii) are the effects of both kinases cumulative when simultaneously active? Here, by comparing the size of L-type currents of wild-type (WT; Cav1.2+Cav1.3) and Cav1.3−/− KO (Cav1.2) MCCs, we provide new evidence that both PKA and PKG pathways affect Cav1.2 and Cav1.3 to the same extent either under basal conditions or induced stimulation. Inhibition of PKA by H89 (5 µM) reduced the L-type current in WT and KO MCCs by∼60%,while inhibition of PKG by KT 5823 (1 µM) increased by∼40% the same current in both cell types. Given that Cav1.2 and Cav1.3 carry the same quantity of Ca2+ currents, this suggests equal sensitivity of Cav1.2 and Cav1.3 to the two basal modulatory pathways. Maximal stimulation of cAMP­PKA by forskolin (100 µM) and activation of cGMP­PKG by pCPT-cGMP (1mM) uncovered a∼25% increase of L-type currents in the first case and∼65% inhibition in the second case in both WT and KO MCCs, suggesting equal sensitivity of Cav1.2 and Cav1.3 during maximal PKA or PKG stimulation. The effects of PKA and PKG were cumulative and most evident when one pathway was activated and the other was inhibited. The two extreme combinations(PKA activation­PKG inhibition vs. PKG activation-PKA inhibition) varied the size of L-type currents by one order of magnitude (from 180% to 18% of control size). Taken together our data suggest that: (i) Cav1.2 and Cav1.3 are equally sensitive to PKA and PKG action under both basal conditions and maximal stimulation, and (ii) PKA and PKG act independently on both Cav1.2 and Cav1.3, producing cumulative effects when opposingly activated. These extreme Cav1 channel modulations may occur either during high-frequency sympathetic stimulation to sustain prolonged catecholamine release (maximal L-type current) or following activation of the NO­cGMP­PKG signalling pathway (minimal L-type current) to limit the steady release of catecholamines.

Are Ca(v)1.3 pacemaker channels in chromaffin cells? Possible bias from resting cell conditions and DHP blockers usage.

Mouse and rat chromaffin cells (MCCs, RCCs) fire spontaneously at rest and their activity is mainly supported by the two L-type Ca(2+) channels expressed in these cells (Ca(v)1.2 and Ca(v)1.3). Using Ca(v)1.3(-/-) KO MCCs we have shown that Ca(v)1.3 possess all the prerequisites for carrying subthreshold currents that sustain low frequency cell firing near resting (0.5 to 2 Hz at -50 mV): low-threshold and steep voltage dependence of activation, slow and incomplete inactivation during pulses of several hundreds of milliseconds. Ca(v)1.2 contributes also to pacemaking MCCs and possibly even Na(+) channels may participate in the firing of a small percentage of cells. We now show that at potentials near resting (-50 mV), Ca(v)1.3 carries equal amounts of Ca(2+) current to Ca(v)1.2 but activates at 9 mV more negative potentials. MCCs express only TTX-sensitive Na(v)1 channels that activate at 24 mV more positive potentials than Ca(v)1.3 and are fully inactivating. Their blockade prevents the firing only in a small percentage of cells (13%). This suggests that the order of importance with regard to pacemaking MCCs is: Ca(v)1.3, Ca(v)1.2 and Na(v)1. The above conclusions, however, rely on the proper use of DHPs, whose blocking potency is strongly holding potential dependent. We also show that small increases of KCl concentration steadily depolarize the MCCs causing abnormally increased firing frequencies, lowered and broadened AP waveforms and an increased facility of switching "non-firing" into "firing" cells that may lead to erroneous conclusions about the role of Ca(v)1.3 and Ca(v)1.2 as pacemaker channels in MCCs.

CaV1.3 as pacemaker channels in adrenal chromaffin cells: specific role on exo- and endocytosis?

Voltage-gated L-type calcium channels (LTCCs) are expressed in adrenal chromaffin cells. Besides shaping the action potential (AP), LTCCs are involved in the excitation-secretion coupling controlling catecholamine release and in Ca (2+) -dependent vesicle retrieval. Of the two LTCCs expressed in chromaffin cells (CaV1.2 and CaV1.3), CaV1.3 possesses the prerequisites for pacemaking spontaneously firing cells: low-threshold, steep voltage-dependence of activation and slow inactivation. By using CaV1 .3 (-/-) KO mice and the AP-clamp it has been possible to resolve the time course of CaV1.3 pacemaker currents, which is similar to that regulating substantia nigra dopaminergic neurons. In mouse chromaffin cells CaV1.3 is coupled to fast-inactivating BK channels within membrane nanodomains and controls AP repolarization. The ability to carry subthreshold Ca (2+) currents and activate BK channels confers to CaV1.3 the unique feature of driving Ca (2+) loading during long interspike intervals and, possibly, to control the Ca (2+) -dependent exocytosis and endocytosis processes that regulate catecholamine secretion and vesicle recycling.

Ca(v)1.3 and BK channels for timing and regulating cell firing.

L-type Ca(2+) channels (LTCCs, Ca(v)1) open readily during membrane depolarization and allow Ca(2+) to enter the cell. In this way, LTCCs regulate cell excitability and trigger a variety of Ca(2+)-dependent physiological processes such as: excitation-contraction coupling in muscle cells, gene expression, synaptic plasticity, neuronal differentiation, hormone secretion, and pacemaker activity in heart, neurons, and endocrine cells. Among the two major isoforms of LTCCs expressed in excitable tissues (Ca(v)1.2 and Ca(v)1.3), Ca(v)1.3 appears suitable for supporting a pacemaker current in spontaneously firing cells. It has steep voltage dependence and low threshold of activation and inactivates slowly. Using Ca(v)1.3(-/-) KO mice and membrane current recording techniques such as the dynamic and the action potential clamp, it has been possible to resolve the time course of Ca(v)1.3 pacemaker currents that regulate the spontaneous firing of dopaminergic neurons and adrenal chromaffin cells. In several cell types, Ca(v)1.3 is selectively coupled to BK channels within membrane nanodomains and controls both the firing frequency and the action potential repolarization phase. Here we review the most critical aspects of Ca(v)1.3 channel gating and its coupling to large conductance BK channels recently discovered in spontaneously firing neurons and neuroendocrine cells with the aim of furnishing a converging view of the role that these two channel types play in the regulation of cell excitability.

Loss of Cav1.3 channels reveals the critical role of L-type and BK channel coupling in pacemaking mouse adrenal chromaffin cells.

We studied wild-type (WT) and Cav1.3(-/-) mouse chromaffin cells (MCCs) with the aim to determine the isoform of L-type Ca(2+) channel (LTCC) and BK channels that underlie the pacemaker current controlling spontaneous firing. Most WT-MCCs (80%) were spontaneously active (1.5 Hz) and highly sensitive to nifedipine and BayK-8644 (1,4-dihydro-2,6-dimethyl-5-nitro-4-[2-(trifluoromethyl)phenyl]-3-pyridinecarboxylic acid, methyl ester). Nifedipine blocked the firing, whereas BayK-8644 increased threefold the firing rate. The two dihydropyridines and the BK channel blocker paxilline altered the shape of action potentials (APs), suggesting close coupling of LTCCs to BK channels. WT-MCCs expressed equal fractions of functionally active Cav1.2 and Cav1.3 channels. Cav1.3 channel deficiency decreased the number of normally firing MCCs (30%; 2.0 Hz), suggesting a critical role of these channels on firing, which derived from their slow inactivation rate, sizeable activation at subthreshold potentials, and close coupling to fast inactivating BK channels as determined by using EGTA and BAPTA Ca(2+) buffering. By means of the action potential clamp, in TTX-treated WT-MCCs, we found that the interpulse pacemaker current was always net inward and dominated by LTCCs. Fast inactivating and non-inactivating BK currents sustained mainly the afterhyperpolarization of the short APs (2-3 ms) and only partially the pacemaker current during the long interspike (300-500 ms). Deletion of Cav1.3 channels reduced drastically the inward Ca(2+) current and the corresponding Ca(2+)-activated BK current during spikes. Our data highlight the role of Cav1.3, and to a minor degree of Cav1.2, as subthreshold pacemaker channels in MCCs and open new interesting features about their role in the control of firing and catecholamine secretion at rest and during sustained stimulations matching acute stress.