CRISPR-Mediated Programmable 3D Genome Positioning and Nuclear Organization.

Programmable control of spatial genome organization is a powerful approach for studying how nuclear structure affects gene regulation and cellular function. Here, we develop a versatile CRISPR-genome organization (CRISPR-GO) system that can efficiently control the spatial positioning of genomic loci relative to specific nuclear compartments, including the nuclear periphery, Cajal bodies, and promyelocytic leukemia (PML) bodies. CRISPR-GO is chemically inducible and reversible, enabling interrogation of real-time dynamics of chromatin interactions with nuclear compartments in living cells. Inducible repositioning of genomic loci to the nuclear periphery allows for dissection of mitosis-dependent and -independent relocalization events and also for interrogation of the relationship between gene position and gene expression. CRISPR-GO mediates rapid de novo formation of Cajal bodies at desired chromatin loci and causes significant repression of endogenous gene expression over long distances (30-600 kb). The CRISPR-GO system offers a programmable platform to investigate large-scale spatial genome organization and function.

Extraction of mRNA from Stalled Ribosomes by the Ski Complex.

The evolutionarily conserved Ski2-Ski3-Ski8 (Ski) complex containing the 3'→5' RNA helicase Ski2 binds to 80S ribosomes near the mRNA entrance and facilitates 3'→5' exosomal degradation of mRNA during ribosome-associated mRNA surveillance pathways. Here, we assayed Ski's activity using an in vitro reconstituted translation system and report that this complex efficiently extracts mRNA from 80S ribosomes in the 3'→5' direction in a nucleotide-by-nucleotide manner. The process is ATP dependent and can occur on pre- and post-translocation ribosomal complexes. The Ski complex can engage productively with mRNA and extract it from 80S complexes containing as few as 19 (but not 13) 3'-terminal mRNA nucleotides starting from the P site. The mRNA-extracting activity of the Ski complex suggests that its role in mRNA quality control pathways is not limited to acceleration of exosomal degradation and could include clearance of stalled ribosomes from mRNA, poising mRNA for degradation and rendering stalled ribosomes recyclable by Pelota/Hbs1/ABCE1.

Translation-coupled mRNA quality control mechanisms.

mRNA surveillance pathways are essential for accurate gene expression and to maintain translation homeostasis, ensuring the production of fully functional proteins. Future insights into mRNA quality control pathways will enable us to understand how cellular mRNA levels are controlled, how defective or unwanted mRNAs can be eliminated, and how dysregulation of these can contribute to human disease. Here we review translation-coupled mRNA quality control mechanisms, including the non-stop and no-go mRNA decay pathways, describing their mechanisms, shared trans-acting factors, and differences. We also describe advances in our understanding of the nonsense-mediated mRNA decay (NMD) pathway, highlighting recent mechanistic findings, the discovery of novel factors, as well as the role of NMD in cellular physiology and its impact on human disease.

A ubiquitin language communicates ribosomal distress.

Cells entrust ribosomes with the critical task of identifying problematic mRNAs and facilitating their degradation. Ribosomes must communicate when they encounter and stall on an aberrant mRNA, lest they expose the cell to toxic and disease-causing proteins, or they jeopardize ribosome homeostasis and cellular translation. In recent years, ribosomal ubiquitination has emerged as a central signaling step in this process, and proteomic studies across labs and experimental systems show a myriad of ubiquitination sites throughout the ribosome. Work from many labs zeroed in on ubiquitination in one region of the small ribosomal subunit as being functionally significant, with the balance and exact ubiquitination sites determined by stall type, E3 ubiquitin ligases, and deubiquitinases. This review discusses the current literature surrounding ribosomal ubiquitination during translational stress and considers its role in committing translational complexes to decay.

PKCα-LSD1-NF-κB-Signaling Cascade Is Crucial for Epigenetic Control of the Inflammatory Response.

The inflammatory response mediated by nuclear factor κB (NF-κB) signaling is essential for host defense against pathogens. Although the regulatory mechanism of NF-κB signaling has been well studied, the molecular basis for epigenetic regulation of the inflammatory response is poorly understood. Here we identify a new signaling axis of PKCα-LSD1-NF-κB, which is critical for activation and amplification of the inflammatory response. In response to excessive inflammatory stimuli, PKCα translocates to the nucleus and phosphorylates LSD1. LSD1 phosphorylation is required for p65 binding and facilitates p65 demethylation, leading to enhanced stability. In vivo genetic analysis using Lsd1SA/SA mice with ablation of LSD1 phosphorylation and chemical approaches in wild-type mice with inhibition of PKCα or LSD1 activity show attenuated sepsis-induced inflammatory lung injury and mortality. Together, we demonstrate that the PKCα-LSD1-NF-κB signaling cascade is crucial for epigenetic control of the inflammatory response, and targeting this signaling could be a powerful therapeutic strategy for systemic inflammatory diseases, including sepsis.

Lost in translation: How neurons cope with tRNA decoding.

Post-transcriptional tRNA modifications contribute to the decoding efficiency of tRNAs by supporting codon recognition and tRNA stability. Recent work shows that the molecular and cellular functions of tRNA modifications and tRNA-modifying-enzymes are linked to brain development and neurological disorders. Lack of these modifications affects codon recognition and decoding rate, promoting protein aggregation and translational stress response pathways with toxic consequences to the cell. In this review, we discuss the peculiarity of local translation in neurons, suggesting a role for fine-tuning of translation performed by tRNA modifications. We provide several examples of tRNA modifications involved in physiology and pathology of the nervous system, highlighting their effects on protein translation and discussing underlying mechanisms, like the unfolded protein response (UPR), ribosome quality control (RQC), and no-go mRNA decay (NGD), which could affect neuronal functions. We aim to deepen the understanding of the roles of tRNA modifications and the coordination of these modifications with the protein translation machinery in the nervous system.

A calibrated functional patch-clamp assay to enhance clinical variant interpretation in KCNH2-related long QT syndrome.

Modern sequencing technologies have revolutionized our detection of gene variants. However, in most genes, including KCNH2, the majority of missense variants are currently classified as variants of uncertain significance (VUSs). The aim of this study was to investigate the utility of an automated patch-clamp assay for aiding clinical variant classification in KCNH2. The assay was designed according to recommendations proposed by the Clinical Genome Sequence Variant Interpretation Working Group. Thirty-one variants (17 pathogenic/likely pathogenic, 14 benign/likely benign) were classified internally as variant controls. They were heterozygously expressed in Flp-In HEK293 cells for assessing the effects of variants on current density and channel gating in order to determine the sensitivity and specificity of the assay. All 17 pathogenic variant controls had reduced current density, and 13 of 14 benign variant controls had normal current density, which enabled determination of normal and abnormal ranges for applying evidence of moderate or supporting strength for VUS reclassification. Inclusion of functional assay evidence enabled us to reclassify 6 out of 44 KCNH2 VUSs as likely pathogenic. The high-throughput patch-clamp assay can provide moderate-strength evidence for clinical interpretation of clinical KCNH2 variants and demonstrates the value of developing automated patch-clamp assays for functional characterization of ion channel gene variants.

Quantification of elongation stalls and impact on gene expression in yeast.

Ribosomal pauses are a critical part of cotranslational events including protein folding and localization. However, extended ribosome pauses can lead to ribosome collisions, resulting in the activation of ribosome rescue pathways and turnover of protein and mRNA. While this relationship has been known, there has been little exploration of how ribosomal stalls impact translation duration at a quantitative level. We have taken a method used to measure elongation time and adapted it for use in Saccharomyces cerevisiae to quantify the impact of elongation stalls. We find, in transcripts containing Arg CGA codon repeat-induced stalls, a Hel2-mediated dose-dependent decrease in protein expression and mRNA level and an elongation delay on the order of minutes. In transcripts that contain synonymous substitutions to nonoptimal Leu codons, there is a decrease in protein and mRNA levels, as well as similar elongation delay, but this occurs through a non-Hel2-mediated mechanism. Finally, we find that Dhh1 selectively increases protein expression, mRNA level, and elongation rate. This indicates that distinct poorly translated mRNAs will activate different rescue pathways despite similar elongation stall durations. Taken together, these results provide new quantitative mechanistic insight into the surveillance of translation and the roles of Hel2 and Dhh1 in mediating ribosome pausing events.

Increased regional Hurst exponent reflects response inhibition related neural complexity alterations in pediatric bipolar disorder patients during an emotional Go-Nogo task.

Fractal patterns have been shown to change in resting- and task-state blood oxygen level-dependent signals in bipolar disorder patients. However, fractal characteristics of brain blood oxygen level-dependent signals when responding to external emotional stimuli in pediatric bipolar disorder remain unclear. Blood oxygen level-dependent signals of 20 PBD-I patients and 17 age- and sex-matched healthy controls were extracted while performing an emotional Go-Nogo task. Neural responses relevant to the task and Hurst exponent of the blood oxygen level-dependent signals were assessed. Correlations between clinical indices and Hurst exponent were estimated. Significantly increased activations were found in regions covering the frontal lobe, parietal lobe, temporal lobe, insula, and subcortical nuclei in PBD-I patients compared to healthy controls in contrast of emotional versus neutral distractors. PBD-I patients exhibited higher Hurst exponent in regions that involved in action control, such as superior frontal gyrus, inferior frontal gyrus, inferior temporal gyrus, and insula, with Hurst exponent of frontal orbital gyrus correlated with onset age. The present study exhibited overactivation, increased self-similarity and decreased complexity in cortical regions during emotional Go-Nogo task in patients relative to healthy controls, which provides evidence of an altered emotional modulation of cognitive control in pediatric bipolar disorder patients. Hurst exponent may be a fractal biomarker of neural activity in pediatric bipolar disorder.

EEG biomarkers of behavioral inhibition in patients with depression who committed violent offenses: a Go/NoGo ERP study.

Understanding the neurobiological correlates of behavioral inhibition in patients with depression who committed violent offenses could contribute to the prediction and prevention of violence. The present study recruited 29 depressed patients with violent offenses (VD group), 27 depressed patients without violent behavior (NVD group), and 28 healthy controls (HC group) to complete a visual Go/NoGo task, during which their responses and electroencephalography were simultaneously recorded using an event-related potentiometer. The results showed that the VD group made more commission errors and responded more slowly relative to the NVD and HC groups. The P3 amplitude of the VD group was reduced in the frontal and central brain regions compared to the HC group and increased in the parietal regions compared to the NVD group. In comparison to Go stimuli, NoGo stimuli induced longer P3 latencies in frontal regions in both the VD and NVD groups; however, this difference was not statistically significant in the HC group. These results provide electrophysical evidence of behavioral inhibition deficits in patients with depression, especially in those with violent behaviors. The reduced P3 amplitude in the frontal-central regions, increased P3 amplitude in the parietal regions, and increased NoGo P3 latency may be potential electrophysiological features that can predict violent behavior in patients with depression.

Ribosome Collision Is Critical for Quality Control during No-Go Decay.

No-go decay (NGD) is a eukaryotic quality control mechanism that evolved to cope with translational arrests. The process is characterized by an endonucleolytic cleavage near the stall sequence, but the mechanistic details are unclear. Our analysis of cleavage sites indicates that cleavage requires multiple ribosomes on the mRNA. We also show that reporters harboring stall sequences near the initiation codon, which cannot accommodate multiple ribosomes, are not subject to NGD. Consistent with our model, we uncover an inverse correlation between ribosome density per mRNA and cleavage efficiency. Furthermore, promoting global ribosome collision in vivo resulted in ubiquitination of ribosomal proteins, suggesting that collision is sensed by the cell to initiate downstream quality control processes. Collectively, our data suggest that NGD and subsequent quality control are triggered by ribosome collision. This model provides insight into the regulation of quality control processes and the manner by which they reduce off-target effects.

Comparative profiling of whole-cell and exosome samples reveals protein signatures that stratify breast cancer subtypes.

Identifying novel breast cancer biomarkers will improve patient stratification, enhance therapeutic outcomes, and help develop non-invasive diagnostics. We compared the proteomic profiles of whole-cell and exosomal samples of representative breast cancer cell subtypes to evaluate the potential of extracellular vesicles as non-invasive disease biomarkers in liquid biopsies. Overall, differentially-expressed proteins in whole-cell and exosome samples (which included markers for invasion, metastasis, angiogenesis, and drug resistance) effectively discriminated subtypes; furthermore, our results confirmed that the proteomic profile of exosomes reflects breast cancer cell-of-origin, which underscores their potential as disease biomarkers. Our study will contribute to identifying biomarkers that support breast cancer patient stratification and developing novel therapeutic strategies. We include an open, interactive web tool to explore the data as a molecular resource that can explain the role of these protein signatures in breast cancer classification.

Graphene Oxide Membrane Reactor for Electrochemical Deuteration Reactions.

The deuteration of organic molecules is considerably important in organic and medicinal chemistry. An electrochemical membrane reactor using proton-conducting graphene oxide (GO) nanosheets was developed to synthesize valuable deuterium-labeled products via an efficient hydrogen-to-deuterium (H/D) exchange under mild conditions at ambient temperature and atmospheric pressure. Deuterons (D+) formed by the anodic oxidation of heavy water (D2O) at the Pt/C anode permeate through the GO membrane to the Pt/C cathode, where organic molecules with functional groups (C≡C and C═O) are deuterated with adsorbed atomic D species. Deuteration occurs in outstanding yields with high levels of D incorporation. We also achieved the electrodeuteration of a drug molecule, ibuprofen, demonstrating the promising feasibility of the GO membrane reactor in the pharmaceutical industry.

SGCP: a spectral self-learning method for clustering genes in co-expression networks.

BACKGROUND: A widely used approach for extracting information from gene expression data employs the construction of a gene co-expression network and the subsequent computational detection of gene clusters, called modules. WGCNA and related methods are the de facto standard for module detection. The purpose of this work is to investigate the applicability of more sophisticated algorithms toward the design of an alternative method with enhanced potential for extracting biologically meaningful modules. RESULTS: We present self-learning gene clustering pipeline (SGCP), a spectral method for detecting modules in gene co-expression networks. SGCP incorporates multiple features that differentiate it from previous work, including a novel step that leverages gene ontology (GO) information in a self-leaning step. Compared with widely used existing frameworks on 12 real gene expression datasets, we show that SGCP yields modules with higher GO enrichment. Moreover, SGCP assigns highest statistical importance to GO terms that are mostly different from those reported by the baselines. CONCLUSION: Existing frameworks for discovering clusters of genes in gene co-expression networks are based on relatively simple algorithmic components. SGCP relies on newer algorithmic techniques that enable the computation of highly enriched modules with distinctive characteristics, thus contributing a novel alternative tool for gene co-expression analysis.

Deep learning-assisted prediction of protein-protein interactions in Arabidopsis thaliana.

Currently, the experimentally identified interactome of Arabidopsis (Arabidopsis thaliana) is still far from complete, suggesting that computational prediction methods can complement experimental techniques. Motivated by the prosperity and success of deep learning algorithms and natural language processing techniques, we introduce an integrative deep learning framework, DeepAraPPI, allowing us to predict protein-protein interactions (PPIs) of Arabidopsis utilizing sequence, domain and Gene Ontology (GO) information. Our current DeepAraPPI comprises: (i) a word2vec encoding-based Siamese recurrent convolutional neural network (RCNN) model; (ii) a Domain2vec encoding-based multiple-layer perceptron (MLP) model; and (iii) a GO2vec encoding-based MLP model. Finally, DeepAraPPI combines the prediction results of the three individual predictors through a logistic regression model. Compiling high-quality positive and negative training and test samples by applying strict filtering strategies, DeepAraPPI shows superior performance compared with existing state-of-the-art Arabidopsis PPI prediction methods. DeepAraPPI also provides better cross-species predictive ability in rice (Oryza sativa) than traditional machine learning methods, although the overall performance in cross-species prediction remains to be improved. DeepAraPPI is freely accessible at http://zzdlab.com/deeparappi/. In the meantime, we have also made the source code and data sets of DeepAraPPI available at https://github.com/zjy1125/DeepAraPPI.

Auditory discrimination learning and acoustic cue weighing in female zebra finches with localized FoxP1 knockdowns.

Rare disruptions of the transcription factor FOXP1 are implicated in a human neurodevelopmental disorder characterized by autism and/or intellectual disability with prominent problems in speech and language abilities. Avian orthologues of this transcription factor are evolutionarily conserved and highly expressed in specific regions of songbird brains, including areas associated with vocal production learning and auditory perception. Here, we investigated possible contributions of FoxP1 to song discrimination and auditory perception in juvenile and adult female zebra finches. They received lentiviral knockdowns of FoxP1 in one of two brain areas involved in auditory stimulus processing, HVC (proper name) or CMM (caudomedial mesopallium). Ninety-six females, distributed over different experimental and control groups were trained to discriminate between two stimulus songs in an operant Go/Nogo paradigm and subsequently tested with an array of stimuli. This made it possible to assess how well they recognized and categorized altered versions of training stimuli and whether localized FoxP1 knockdowns affected the role of different features during discrimination and categorization of song. Although FoxP1 expression was significantly reduced by the knockdowns, neither discrimination of the stimulus songs nor categorization of songs modified in pitch, sequential order of syllables or by reversed playback were affected. Subsequently, we analyzed the full dataset to assess the impact of the different stimulus manipulations for cue weighing in song discrimination. Our findings show that zebra finches rely on multiple parameters for song discrimination, but with relatively more prominent roles for spectral parameters and syllable sequencing as cues for song discrimination.NEW & NOTEWORTHY In humans, mutations of the transcription factor FoxP1 are implicated in speech and language problems. In songbirds, FoxP1 has been linked to male song learning and female preference strength. We found that FoxP1 knockdowns in female HVC and caudomedial mesopallium (CMM) did not alter song discrimination or categorization based on spectral and temporal information. However, this large dataset allowed to validate different cue weights for spectral over temporal information for song recognition.

Low-beta versus high-beta band cortico-subcortical coherence in movement inhibition and expectation.

Beta band oscillations in the sensorimotor cortex and subcortical structures, such as the subthalamic nucleus (STN) and internal pallidum (GPi), are closely linked to motor control. Recent research suggests that low-beta (14.5-23.5 Hz) and high-beta (23.5-35 Hz) cortico-STN coherence arise through distinct networks, possibly reflecting indirect and hyperdirect pathways. In this study, we sought to probe whether low- and high-beta coherence also exhibit different functional roles in facilitating and inhibiting movement. Twenty patients with Parkinson's disease who had deep brain stimulation electrodes implanted in either STN or GPi performed a classical go/nogo task while undergoing simultaneous magnetoencephalography and local field potentials recordings. Subjects' expectations were manipulated by presenting go- and nogo-trials with varying probabilities. We identified a lateral source in the sensorimotor cortex for low-beta coherence, as well as a medial source near the supplementary motor area for high-beta coherence. Task-related coherence time courses for these two sources revealed that low-beta coherence was more strongly implicated than high-beta coherence in the performance of go-trials. Accordingly, average pre-stimulus low-beta but not high-beta coherence or spectral power correlated with overall reaction time across subjects. High-beta coherence during unexpected nogo-trials was higher compared to expected nogo-trials at a relatively long latency of 3 s after stimulus presentation. Neither low- nor high-beta coherence showed a significant correlation with patients' symptom severity at baseline assessment. While low-beta cortico-subcortical coherence appears to be related to motor output, the role of high-beta coherence requires further investigation.

Reconstruction of Eriocheir sinensis Protein-Protein Interaction Network Based on DGO-SVM Method.

Eriocheir sinensis is an economically important aquatic animal. Its regulatory mechanisms underlying many biological processes are still vague due to the lack of systematic analysis tools. The protein-protein interaction network (PIN) is an important tool for the systematic analysis of regulatory mechanisms. In this work, a novel machine learning method, DGO-SVM, was applied to predict the protein-protein interaction (PPI) in E. sinensis, and its PIN was reconstructed. With the domain, biological process, molecular functions and subcellular locations of proteins as the features, DGO-SVM showed excellent performance in Bombyx mori, humans and five aquatic crustaceans, with 92-96% accuracy. With DGO-SVM, the PIN of E. sinensis was reconstructed, containing 14,703 proteins and 7,243,597 interactions, in which 35,604 interactions were associated with 566 novel proteins mainly involved in the response to exogenous stimuli, cellular macromolecular metabolism and regulation. The DGO-SVM demonstrated that the biological process, molecular functions and subcellular locations of proteins are significant factors for the precise prediction of PPIs. We reconstructed the largest PIN for E. sinensis, which provides a systematic tool for the regulatory mechanism analysis. Furthermore, the novel-protein-related PPIs in the PIN may provide important clues for the mechanism analysis of the underlying specific physiological processes in E. sinensis.

In Silico and In Vitro Study of Isoquercitrin against Kidney Cancer and Inflammation by Triggering Potential Gene Targets.

Kidney cancer has emerged as a major medical problem in recent times. Multiple compounds are used to treat kidney cancer by triggering cancer-causing gene targets. For instance, isoquercitrin (quercetin-3-O-ß-d-glucopyranoside) is frequently present in fruits, vegetables, medicinal herbs, and foods and drinks made from plants. Our previous study predicted using protein-protein interaction (PPI) and molecular docking analysis that the isoquercitrin compound can control kidney cancer and inflammation by triggering potential gene targets of IGF1R, PIK3CA, IL6, and PTGS2. So, the present study is about further in silico and in vitro validation. We performed molecular dynamic (MD) simulation, gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, cytotoxicity assay, and RT-PCR and qRT-PCR validation. According to the MD simulation (250 ns), we found that IGF1R, PIK3CA, and PTGS2, except for IL6 gene targets, show stable binding energy with a stable complex with isoquercitrin. We also performed gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of the final targets to determine their regulatory functions and signaling pathways. Furthermore, we checked the cytotoxicity effect of isoquercitrin (IQ) and found that 5 µg/mL and 10 µg/mL doses showed higher cell viability in a normal kidney cell line (HEK 293) and also inversely showed an inhibition of cell growth at 35% and 45%, respectively, in the kidney cancer cell line (A498). Lastly, the RT-PCR and qRT-PCR findings showed a significant decrease in PTGS2, PIK3CA, and IGF1R gene expression, except for IL6 expression, following dose-dependent treatments with IQ. Thus, we can conclude that isoquercitrin inhibits the expression of PTGS2, PIK3CA, and IGF1R gene targets, which in turn controls kidney cancer and inflammation.

Multiomics Analysis of the PHLDA Gene Family in Different Cancers and Their Clinical Prognostic Value.

The PHLDA (pleckstrin homology-like domain family) gene family is popularly known as a potential biomarker for cancer identification, and members of the PHLDA family have become considered potentially viable targets for cancer treatments. The PHLDA gene family consists of PHLDA1, PHLDA2, and PHLDA3. The predictive significance of PHLDA genes in cancer remains unclear. To determine the role of pleckstrin as a prognostic biomarker in human cancers, we conducted a systematic multiomics investigation. Through various survival analyses, pleckstrin expression was evaluated, and their predictive significance in human tumors was discovered using a variety of online platforms. By analyzing the protein-protein interactions, we also chose a collection of well-known functional protein partners for pleckstrin. Investigations were also carried out on the relationship between pleckstrins and other cancers regarding mutations and copy number alterations. The cumulative impact of pleckstrin and their associated genes on various cancers, Gene Ontology (GO), and pathway analyses were used for their evaluation. Thus, the expression profiles of PHLDA family members and their prognosis in various cancers may be revealed by this study. During this multiomics analysis, we found that among the PHLDA family, PHLDA1 may be a therapeutic target for several cancers, including kidney, colon, and brain cancer, while PHLDA2 can be a therapeutic target for cancers of the colon, esophagus, and pancreas. Additionally, PHLDA3 may be a useful therapeutic target for ovarian, renal, and gastric cancer.