One of the most potent insecticidal venom peptides described to date is Aps III from the venom of the trapdoor spider Apomastus schlingeri. Aps III is highly neurotoxic to lepidopteran crop pests, making it a promising candidate for bioinsecticide development. However, its disulfideconnectivity, three-dimensional structure, and mode of action have not been determined. Here we show that recombinant Aps III (rAps III) is an atypical knottin peptide; three of the disulfide bridges form a classical inhibitor cystine knot motif while the fourth disulfide acts as a molecular staple that restricts the flexibility of an unusually large β hairpin loop that often houses the pharmacophore in this class of toxins. We demonstrate that the irreversible paralysis induced in insects by rAps III results from a potent block of insect voltage-gated sodium channels. Channel block by rAps III is voltage-independent insofar as it occurs without significant alteration in the voltage-dependence of channel activation or steady-state inactivation. Thus, rAps III appears to be a pore blocker that plugs the outer vestibule of insect voltage-gated sodium channels. This mechanism of action contrasts strikingly with virtually all other sodium channel modulators isolated from spider venoms that act as gating modifiers by interacting with one or more of the four voltage-sensing domains of the channel.

Aminoacyl-tRNA synthetase-interacting multifunctional proteins (AIMPs) are nonenzymatic scaffolding proteins that comprise multisynthetase complex (MSC) with nine aminoacyl-tRNA synthetases in higher eukaryotes. Among the three AIMPs, AIMP3/p18 is strongly anchored to methionyl-tRNA synthetase (MRS) in the MSC. MRS attaches methionine (Met) to initiator tRNA (tRNA(i)(Met)) and plays an important role in translation initiation. It is known that AIMP3 is dispatched to nucleus or nuclear membrane to induce DNA damage response or senescence; however, the role of AIMP3 in translation as a component of MSC and the meaning of its interaction with MRS are still unclear. Herein, we observed that AIMP3 specifically interacted with Met-tRNA(i)(Met)in vitro, while it showed little or reduced interaction with unacylated or lysine-charged tRNA(i)(Met). In addition, AIMP3 discriminates Met-tRNA(i)(Met) from Met-charged elongator tRNA based on filter-binding assay. Pull-down assay revealed that AIMP3 and MRS had noncompetitive interaction with eukaryotic initiation factor 2 (eIF2) γ subunit (eIF2γ), which is in charge of binding with Met-tRNA(i)(Met) for the delivery of Met-tRNA(i)(Met) to ribosome. AIMP3 recruited active eIF2γ to the MRS-AIMP3 complex, and the level of Met-tRNA(i)(Met) bound to eIF2 complex was reduced by AIMP3 knockdown resulting in reduced protein synthesis. All these results suggested the novel function of AIMP3 as a critical mediator of Met-tRNA(i)(Met) transfer from MRS to eIF2 complex for the accurate and efficient translation initiation.

BackgroundWhen a cell is exposed to a time-varying magnetic field, this leads to an induced voltage on the cytoplasmic membrane, as well as on the membranes of the internal organelles, such as mitochondria. These potential changes in the organelles could have a significant impact on their functionality. However, a quantitative analysis on the magnetically-induced membrane potential on the internal organelles has not been performed.MethodsUsing a two-shell model, we provided the first analytical solution for the transmembrane potential in the organelle membrane induced by a time-varying magnetic field. We then analyzed factors that impact on the polarization of the organelle, including the frequency of the magnetic field, the presence of the outer cytoplasmic membrane, and electrical and geometrical parameters of the cytoplasmic membrane and the organelle membrane.ResultsThe amount of polarization in the organelle was less than its counterpart in the cytoplasmic membrane. This was largely due to the presence of the cell membrane, which "shielded" the internal organelle from excessive polarization by the field. Organelle polarization was largely dependent on the frequency of the magnetic field, and its polarization was not significant under the low frequency band used for transcranial magnetic stimulation (TMS). Both the properties of the cytoplasmic and the organelle membranes affect the polarization of the internal organelle in a frequency-dependent manner.ConclusionsThe work provided a theoretical framework and insights into factors affecting mitochondrial function under time-varying magnetic stimulation, and provided evidence that TMS does not affect normal mitochondrial functionality by altering its membrane potential.

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The advent of next-generation sequencing (NGS) has accelerated biomedical research by enabling the high-throughput analysis of DNA sequences at a very low cost. However, NGS has limitations in detecting rare-frequency variants (< 1%) because of high sequencing errors (> 0.1~1%). NGS errors could be filtered out using molecular barcodes, by comparing read replicates among those with the same barcodes. Accordingly, these barcoding methods require redundant reads of non-target sequences, resulting in high sequencing cost. Here, we present a cost-effective NGS error validation method in a barcode-free manner. By physically extracting and individually amplifying the DNA clones of erroneous reads, we distinguish true variants of frequency > 0.003% from the systematic NGS error and selectively validate NGS error after NGS. We achieve a PCR-induced error rate of 2.5×10 −6 per base per doubling event, using 10 times less sequencing reads compared to those from previous studies.

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It has been previously shown that wavelet artificial neural networks (WANNs) are able to classify the different states of epileptiform activity and predict the onsets of seizure-like events (SLEs) by offline processing (Ann. Biomed. Eng. 33(6):798-810, 2005) of the electrical data from the in-vitro hippocampal slice model of recurrent spontaneous SLEs. The WANN design entailed the assumption that time-varying frequency information from the biological recordings can be used to estimate the times at which onsets of SLEs would most likely occur in the future. Progressions of different frequency components were captured by the artificial neural network (ANN) using selective frequency inputs from the initial wavelet transform of the biological data. The training of the WANN had been established using 184 SLE episodes in 34 slices from 21 rats offline. Nine of these rats also exhibited periods of interictal bursts (IBs). These IBs were included as part of the training to help distinguish the difference in dynamics of bursting activities between the preictal- and interictal type. In this paper, we present the results of an online processing using WANN on 23 in-vitro rat hippocampal slices from 9 rats having 93 spontaneous SLE episodes generated under low magnesium conditions. Over the test cases, three of the nine rats exhibited over 30 min of IB activities. We demonstrated that the WANN was able to classify the different states, namely, interictal, preictal, ictal, and IB activities with an accuracy of 86.6, 72.6, 84.5, and 69.1%, respectively. Prediction of state transitions into ictal events was achieved using regression of initial "normalized time-to-onset" estimates. The SLE onsets can be estimated up to 36.4 s ahead of their actual occurrences, with a mean error of 14.3 +/- 27.0 s. The prediction errors decreased progressively as the actual time-to-onset decreased and more initial "normalized time-to-onset" estimates were used for the regression procedure.

Neural rhythms are associated with different brain functions and pathological conditions. These rhythms are often clinically relevant for purposes of diagnosis or treatment, though their complex, time-varying features make them difficult to isolate. The wavelet packet transform has proven itself to be versatile and effective with respect to resolving signal features in both time and frequency. We propose a signal analysis technique, called neural rhythm extraction (NRE) that incorporates wavelet packet analysis along with a threshold-based scheme for separating rhythmic neural features from non-rhythmic ones. We applied NRE to rat in vitro intracellular recordings and human scalp electroencephalogram (EEG) signals, and were able to isolate and classify individual neural rhythms in signals containing large amplitude spikes and other artifacts. NRE is capable of discriminating signal features sharing similar time or frequency localization, as well as extracting low-amplitude, low-power rhythms otherwise masked by spectrally dominant signal components. The algorithm allows for independent retention and reconstruction of rhythmic features, which may serve to enhance other analysis techniques such as independent component analysis (ICA), and aid in application-specific tasks such as detection, classification or tracking.