Structural basis for the sequence-specific RNA-recognition mechanism of human CUG-BP1 RRM3

The CUG-binding protein 1 (CUG-BP1) is a member of the CUG-BP1 and ETR-like factors (CELF) family or the Bruno-like family and is involved in the control of splicing, translation and mRNA degradation. Several target RNA sequences of CUG-BP1 have been predicted, such as the CUG triplet repeat, the GU-rich sequences and the AU-rich element of nuclear pre-mRNAs and/or cytoplasmic mRNA. CUG-BP1 has three RNA-recognition motifs (RRMs), among which the third RRM (RRM3) can bind to the target RNAs on its own. In this study, we solved the solution structure of the CUG-BP1 RRM3 by hetero-nuclear NMR spectroscopy. The CUG-BP1 RRM3 exhibited a noncanonical RRM fold, with the four-stranded b-sheet surface tightly associated with the N-terminal extension. Furthermore, we determined the solution structure of the CUG-BP1 RRM3 in the complex with (UG)3 RNA, and discovered that the UGU trinucleotide is specifically recognized through extensive stacking interactions and hydrogen bonds within the pocket formed by the b-sheet surface and the N-terminal extension. This study revealed the unique mechanism that enables the CUG-BP1 RRM3 to discriminate the short RNA segment from other sequences, thus providing the molecular basis for the comprehension of the role of the RRM3s in the CELF/Bruno-like family

Molecular basis for the dosing time-dependency of anti-allodynic effects of gabapentin in a mouse model of neuropathic pain

<p>Abstract</p> <p>Background</p> <p>Neuropathic pain is characterized by hypersensitivity to innocuous stimuli (tactile allodynia) that is nearly always resistant to NSAIDs or even opioids. Gabapentin, a GABA analogue, was originally developed to treat epilepsy. Accumulating clinical evidence supports the effectiveness of this drug for diverse neuropathic pain. In this study, we showed that the anti-allodynic effect of gabapentin was changed by the circadian oscillation in the expression of its target molecule, the calcium channel α2δ-1 subunit.</p> <p>Results</p> <p>Mice were underwent partial sciatic nerve ligation (PSL) to create a model of neuropathic pain. The paw withdrawal threshold (PWT) in PSL mice significantly decreased and fluctuated with a period length about 24 h. The PWT in PSL mice was dose-dependently increased by intraperitoneal injection of gabapentin, but the anti-allodynic effects varied according to its dosing time. The protein levels of α2δ-1 subunit were up-regulated in the DRG of PSL mice, but the protein levels oscillated in a circadian time-dependent manner. The time-dependent oscillation of α2δ-1 subunit protein correlated with fluctuations in the maximal binding capacity of gabapentin. The anti-allodynic effect of gabapentin was attenuated at the times of the day when α2δ-1 subunit protein was abundant.</p> <p>Conclusions</p> <p>These findings suggest that the dosing time-dependent difference in the anti-allodynic effects of gabapentin is attributable to the circadian oscillation of α2δ-1 subunit expression in the DRG and indicate that the optimizing its dosing schedule helps to achieve rational pharmacotherapy for neuropathic pain.</p

Spontaneous Pneumomediastinum: Report of a Case and Review of Literature

Spontaneous pneumomediastinum, defined as the presence of free air in the mediastinal structures, is a rare disease that usually take a good course with conservative therapy. However, clinicians are usually unfamiliar with the disease because of its infrequency. We report herein a case of spontaneous pneumomediastinum following an episode of violent cough. The patient was treated conservatively after hospitalization and got well without any medical problems. In addition we reviewed characteristics of spontaneous pneumomediastinum reported so far in the Japanese literature. The result of our review suggests that the typical patient with spontaneous pneumomediastinum is a young man with a slender build. In most patients, spontaneous pneumomediastinum occurs in conjunction with condition causing high airway pressure. None of the patients described in the literature developed serious complications such as cardiac insufficiency or airway compromise, and none of the cases without concomitant pneumothorax required any medical care. In conclusion, familiarity with this rare disease is crucial to provide appropriate treatment

Random regression for modeling soybean plant response to irrigation changes using time-series multispectral data

Plant response to drought is an important yield-related trait under abiotic stress, but the method for measuring and modeling plant responses in a time series has not been fully established. The objective of this study was to develop a method to measure and model plant response to irrigation changes using time-series multispectral (MS) data. We evaluated 178 soybean (Glycine max (L.) Merr.) accessions under three irrigation treatments at the Arid Land Research Center, Tottori University, Japan in 2019, 2020 and 2021. The irrigation treatments included W5: watering for 5 d followed by no watering 5 d, W10: watering for 10 d followed by no watering 10 d, D10: no watering for 10 d followed by watering 10 d, and D: no watering. To capture the plant responses to irrigation changes, time-series MS data were collected by unmanned aerial vehicle during the irrigation/non-irrigation switch of each irrigation treatment. We built a random regression model (RRM) for each of combination of treatment by year using the time-series MS data. To test the accuracy of the information captured by RRM, we evaluated the coefficient of variation (CV) of fresh shoot weight of all accessions under a total of nine different drought conditions as an indicator of plant’s stability under drought stresses. We built a genomic prediction model (MTRRM model) using the genetic random regression coefficients of RRM as secondary traits and evaluated the accuracy of each model for predicting CV. In 2020 and 2021,the mean prediction accuracies of MTRRM models built in the changing irrigation treatments (r = 0.44 and 0.49, respectively) were higher than that in the continuous drought treatment (r = 0.34 and 0.44, respectively) in the same year. When the CV was predicted using the MTRRM model across 2020 and 2021 in the changing irrigation treatment, the mean prediction accuracy (r = 0.46) was 42% higher than that of the simple genomic prediction model (r =0.32). The results suggest that this RRM method using the time-series MS data can effectively capture the genetic variation of plant response to drought

Structural basis for the dual RNA-recognition modes of human Tra2-beta RRM

Human Transformer2-beta (hTra2-beta) is an important member of the serine/arginine-rich protein family, and contains one RNA recognition motif (RRM). It controls the alternative splicing of several pre-mRNAs, including those of the calcitonin/calcitonin gene-related peptide (CGRP), the survival motor neuron 1 (SMN1) protein and the tau protein. Accordingly, the RRM of hTra2-beta specifically binds to two types of RNA sequences [the CAA and (GAA)2 sequences]. We determined the solution structure of the hTra2-beta RRM (spanning residues Asn110–Thr201), which not only has a canonical RRM fold, but also an unusual alignment of the aromatic amino acids on the beta-sheet surface. We then solved the complex structure of the hTra2-beta RRM with the (GAA)2 sequence, and found that the AGAA tetra-nucleotide was specifically recognized through hydrogen-bond formation with several amino acids on the N- and C-terminal extensions, as well as stacking interactions mediated by the unusually aligned aromatic rings on the beta-sheet surface. Further NMR experiments revealed that the hTra2-beta RRM recognizes the CAA sequence when it is integrated in the stem-loop structure. This study indicates that the hTra2-beta RRM recognizes two types of RNA sequences in different RNA binding modes