Non-coding RNA in Fragile X Syndrome and Converging Mechanisms Shared by Related Disorders

Fragile X syndrome (FXS) is one of the most common forms of hereditary intellectual disability. It is also a well-known monogenic cause of autism spectrum disorders (ASD). Repetitive trinucleotide expansion of CGG repeats in the 5′-UTR of FMR1 is the pathological mutation. Full mutation CGG repeats epigenetically silence FMR1 and thus lead to the absence of its product, fragile mental retardation protein (FMRP), which is an indispensable translational regulator at synapsis. Loss of FMRP causes abnormal neural morphology, dysregulated protein translation, and distorted synaptic plasticity, giving rise to FXS phenotypes. Non-coding RNAs, including siRNA, miRNA, and lncRNA, are transcribed from DNA but not meant for protein translation. They are not junk sequence but play indispensable roles in diverse cellular processes. FXS is the first neurological disorder being linked to miRNA pathway dysfunction. Since then, insightful knowledge has been gained in this field. In this review, we mainly focus on how non-coding RNAs, especially the siRNAs, miRNAs, and lncRNAs, are involved in FXS pathogenesis. We would also like to discuss several potential mechanisms mediated by non-coding RNAs that may be shared by FXS and other related disorders

Transcriptional Regulation of opaR, qrr2–4 and aphA by the Master Quorum-Sensing Regulator OpaR in Vibrio parahaemolyticus

Background: Vibrio parahaemolyticus is a leading cause of infectious diarrhea and enterogastritis via the fecal-oral route. V. harveyi is a pathogen of fishes and invertebrates, and has been used as a model for quorum sensing (QS) studies. LuxR is the master QS regulator (MQSR) of V. harveyi, and LuxR-dependent expression of its own gene, qrr2–4 and aphA have been established in V. harveyi. Molecular regulation of target genes by the V. parahaemolyticus MQSR OpaR is still poorly understood. Methodology/Principal Findings: The bioinformatics analysis indicated that V. parahaemolyticus OpaR, V. harveyi LuxR, V. vulnificu SmcR, and V. alginolyticus ValR were extremely conserved, and that these four MQSRs appeared to recognize the same conserved cis-acting signals, which was represented by the consensus constructs manifesting as a position frequency matrix and as a 20 bp box, within their target promoters. The MQSR box-like sequences were found within the upstream DNA regions of opaR, qrr2–4 and aphA in V. parahaemolyticus, and the direct transcriptional regulation of these target genes by OpaR were further confirmed by multiple biochemical experiments including primer extension assay, gel mobility shift assay, and DNase I footprinting analysis. Translation and transcription starts, core promoter elements for sigma factor recognition, Shine-Dalgarno sequences for ribosome recognition, and OpaR-binding sites were determined for the five target genes of OpaR, which gave a structural map of the OpaR-dependent promoters. Further computational promote

Gadolinium Enhancement May Indicate a Condition at Risk of Developing Necrosis in Marchiafava–Bignami Disease: A Case Report and Literature Review

Marchiafava–Bignami disease (MBD) is a rare condition characterized by demyelination, necrosis and atrophy of the corpus callosum (CC), and mainly associated with alcoholism. MBD may present with various clinical manifestations. Brain magnetic resonance imaging (MRI) scan is important in prompt diagnosis and treatment of MBD. Here we reported a case of MBD and reviewed literature about the usage of gadolinium-enhanced MRI in MBD. Gadolinium enhancement may indicate a condition at risk of developing necrosis. We therefore recommend a contrast-enhanced MRI study in severe alcoholics with suspected diagnosis of MBD

Isolation and Characterization of 89K Pathogenicity Island-Positive ST-7 Strains of Streptococcus suis Serotype 2 from Healthy Pigs, Northeast China

Streptococcus suis is a swine pathogen which can also cause severe infection, such as meningitis, and streptococcal-like toxic shock syndrome (STSS), in humans. In China, most of the S. suis infections in humans were reported in the southern areas with warm and humid climates, but little attention had been paid to the northern areas. Data presented here showed that the virulent serotypes 1, 2, 7, and 9 of S. suis could be steadily isolated from the healthy pigs in the pig farms in all the three provinces of Northeast China. Notably, a majority of the serotype 2 isolates belonged to the 89K pathogenicity island-positive ST-7 clone that had historically caused the human STSS outbreaks in the Sichuan and Jiangsu provinces of China, although the human STSS case caused by S. suis had never been reported in northern areas of China. Data presented here indicated that the survey of S. suis should be expanded to or reinforced in the northern areas of China

Even Visually Intact Cell Walls in Waterlogged Archaeological Wood Are Chemically Deteriorated and Mechanically Fragile : A Case of a 170 Year-Old Shipwreck

Structural and chemical deterioration and its impact on cell wall mechanics were investigated for visually intact cell walls (VICWs) in waterlogged archaeological wood (WAW). Cell wall mechanical properties were examined by nanoindentation without prior embedding. WAW showed more than 25% decrease of both hardness and elastic modulus. Changes of cell wall composition, cellulose crystallite structure and porosity were investigated by ATR-FTIR imaging, Raman imaging, wet chemistry, C-13-solid state NMR, pyrolysis-GC/MS, wide angle X-ray scattering, and N-2 nitrogen adsorption. VICWs in WAW possessed a cleavage of carboxyl in side chains of xylan, a serious loss of polysaccharides, and a partial breakage of beta -O-4 interlinks in lignin. This was accompanied by a higher amount of mesopores in cell walls. Even VICWs in WAW were severely deteriorated at the nanoscale with impact on mechanics, which has strong implications for the conservation of archaeological shipwrecks.Peer reviewe

Even Visually Intact Cell Walls in Waterlogged Archaeological Wood Are Chemically Deteriorated and Mechanically Fragile: A Case of a 170 Year-Old Shipwreck

Structural and chemical deterioration and its impact on cell wall mechanics were investigated for visually intact cell walls (VICWs) in waterlogged archaeological wood (WAW). Cell wall mechanical properties were examined by nanoindentation without prior embedding. WAW showed more than 25% decrease of both hardness and elastic modulus. Changes of cell wall composition, cellulose crystallite structure and porosity were investigated by ATR-FTIR imaging, Raman imaging, wet chemistry, 13C-solid state NMR, pyrolysis-GC/MS, wide angle X-ray scattering, and N2 nitrogen adsorption. VICWs in WAW possessed a cleavage of carboxyl in side chains of xylan, a serious loss of polysaccharides, and a partial breakage of β-O-4 interlinks in lignin. This was accompanied by a higher amount of mesopores in cell walls. Even VICWs in WAW were severely deteriorated at the nanoscale with impact on mechanics, which has strong implications for the conservation of archaeological shipwrecks

Phenotypic and transcriptional analysis of the osmotic regulator OmpR in Yersinia pestis

<p>Abstract</p> <p>Background</p> <p>The osmotic regulator OmpR in <it>Escherichia coli </it>regulates differentially the expression of major porin proteins OmpF and OmpC. In <it>Yersinia enterocolitica </it>and <it>Y. pseudotuberculosis</it>, OmpR is required for both virulence and survival within macrophages. However, the phenotypic and regulatory roles of OmpR in <it>Y. pestis </it>are not yet fully understood.</p> <p>Results</p> <p><it>Y. pestis </it>OmpR is involved in building resistance against phagocytosis and controls the adaptation to various stressful conditions met in macrophages. The <it>ompR </it>mutation likely did not affect the virulence of <it>Y. pestis </it>strain 201 that was a human-avirulent enzootic strain. The microarray-based comparative transcriptome analysis disclosed a set of 224 genes whose expressions were affected by the <it>ompR </it>mutation, indicating the global regulatory role of OmpR in <it>Y. pestis</it>. Real-time RT-PCR or <it>lacZ </it>fusion reporter assay further validated 16 OmpR-dependent genes, for which OmpR consensus-like sequences were found within their upstream DNA regions. <it>ompC</it>, <it>F</it>, <it>X</it>, and <it>R </it>were up-regulated dramatically with the increase of medium osmolarity, which was mediated by OmpR occupying the target promoter regions in a tandem manner.</p> <p>Conclusion</p> <p>OmpR contributes to the resistance against phagocytosis or survival within macrophages, which is conserved in the pathogenic yersiniae. <it>Y. pestis </it>OmpR regulates <it>ompC</it>, <it>F</it>, <it>X</it>, and <it>R </it>directly through OmpR-promoter DNA association. There is an inducible expressions of the pore-forming proteins OmpF, C, and × at high osmolarity in <it>Y. pestis</it>, in contrast to the reciprocal regulation of them in <it>E. coli</it>. The main difference is that <it>ompF </it>expression is not repressed at high osmolarity in <it>Y. pestis</it>, which is likely due to the absence of a promoter-distal OmpR-binding site for <it>ompF</it>.</p

Regulatory effects of cAMP receptor protein (CRP) on porin genes and its own gene in Yersinia pestis

<p>Abstract</p> <p>Background</p> <p>The cAMP receptor protein (CRP) is a global bacterial regulator that controls many target genes. The CRP-cAMP complex regulates the <it>ompR-envZ </it>operon in <it>E. coli </it>directly, involving both positive and negative regulations of multiple target promoters; further, it controls the production of porins indirectly through its direct action on <it>ompR-envZ</it>. Auto-regulation of CRP has also been established in <it>E. coli</it>. However, the regulation of porin genes and its own gene by CRP remains unclear in <it>Y. pestis</it>.</p> <p>Results</p> <p><it>Y. pestis </it>employs a distinct mechanism indicating that CRP has no regulatory effect on the <it>ompR-envZ </it>operon; however, it stimulates <it>ompC </it>and <it>ompF </it>directly, while repressing <it>ompX</it>. No transcriptional regulatory association between CRP and its own gene can be detected in <it>Y. pestis</it>, which is also in contrast to the fact that CRP acts as both repressor and activator for its own gene in <it>E. coli</it>. It is likely that <it>Y. pestis </it>OmpR and CRP respectively sense different signals (medium osmolarity, and cellular cAMP levels) to regulate porin genes independently.</p> <p>Conclusion</p> <p>Although the CRP of <it>Y. pestis </it>shows a very high homology to that of <it>E. coli</it>, and the consensus DNA sequence recognized by CRP is shared by the two bacteria, the <it>Y. pestis </it>CRP can recognize the promoters of <it>ompC</it>, <it>F</it>, and <it>X </it>directly rather than that of its own gene, which is different from the relevant regulatory circuit of <it>E. coli</it>. Data presented here indicate a remarkable remodeling of the CRP-mediated regulation of porin genes and of its own one between these two bacteria.</p

ERKT-Net: Implementing Efficient and Robust Knowledge Distillation for Remote Sensing Image Classification

The classification of Remote Sensing Images (RSIs) poses a significant challenge due to the presence of clustered ground objects and noisy backgrounds. While many approaches rely on scaling models to enhance accuracy, the deployment of RSI classifiers often requires substantial computational and storage resources, thus necessitating the use of lightweight algorithms. In this paper, we present an efficient and robust knowledge transfer network named ERKT-Net, which is designed to provide a lightweight yet accurate Convolutional Neural Network (CNN) classifier. This method utilizes innovative yet simple concepts to better accommodate the inherent nature of RSIs, thereby significantly improving the efficiency and robustness of traditional Knowledge Distillation (KD) techniques developed on ImageNet-1K. We evaluated ERKT-Net on three benchmark RSI datasets and found that it demonstrated superior accuracy and a very compact volume compared to 40 other advanced methods published between 2020 and 2023. On the most challenging NWPU45 dataset, ERKT-Net outperformed other KD-based methods with a maximum Overall Accuracy (OA) value of 22.4%. Using the same criterion, it also surpassed the first-ranked multi-model method with a minimum OA value of 0.7 but presented at least an 82% reduction in parameters. Furthermore, ablation experiments indicated that our training approach has significantly improved the efficiency and robustness of classic DA techniques. Notably, it can reduce the time expenditure in the distillation phase by at least 80%, with a slight sacrifice in accuracy. This study confirmed that a logit-based KD technique can be more efficient and effective in developing lightweight yet accurate classifiers, especially when the method is tailored to the inherent characteristics of RSIs

Comprehensive evaluation of deep and graph learning on drug-drug interactions prediction

Recent advances and achievements of artificial intelligence (AI) as well as deep and graph learning models have established their usefulness in biomedical applications, especially in drug-drug interactions (DDIs). DDIs refer to a change in the effect of one drug to the presence of another drug in the human body, which plays an essential role in drug discovery and clinical research. DDIs prediction through traditional clinical trials and experiments is an expensive and time-consuming process. To correctly apply the advanced AI and deep learning, the developer and user meet various challenges such as the availability and encoding of data resources, and the design of computational methods. This review summarizes chemical structure based, network based, NLP based and hybrid methods, providing an updated and accessible guide to the broad researchers and development community with different domain knowledge. We introduce widely-used molecular representation and describe the theoretical frameworks of graph neural network models for representing molecular structures. We present the advantages and disadvantages of deep and graph learning methods by performing comparative experiments. We discuss the potential technical challenges and highlight future directions of deep and graph learning models for accelerating DDIs prediction.Comment: Accepted by Briefings in Bioinformatic