Horizontal Cell Biology: Monitoring Global Changes of Protein Interaction States with the Proteome-Wide Cellular Thermal Shift Assay (CETSA).

The cellular thermal shift assay (CETSA) is a biophysical technique allowing direct studies of ligand binding to proteins in cells and tissues. The proteome-wide implementation of CETSA with mass spectrometry detection (MS-CETSA) has now been successfully applied to discover targets for orphan clinical drugs and hits from phenotypic screens, to identify off-targets, and to explain poly-pharmacology and drug toxicity. Highly sensitive multidimensional MS-CETSA implementations can now also access binding of physiological ligands to proteins, such as metabolites, nucleic acids, and other proteins. MS-CETSA can thereby provide comprehensive information on modulations of protein interaction states in cellular processes, including downstream effects of drugs and transitions between different physiological cell states. Such horizontal information on ligandmodulation in cells is largely orthogonal to vertical information on the levels of different proteins and therefore opens novel opportunities to understand operational aspects of cellular proteomes.

Aging and comprehensive molecular profiling in acute myeloid leukemia.

Acute myeloid leukemia (AML) is an aging-related and heterogeneous hematopoietic malignancy. In this study, a total of 1,474 newly diagnosed AML patients with RNA sequencing data were enrolled, and targeted or whole exome sequencing data were obtained in 94% cases. The correlation of aging-related factors including age and clonal hematopoiesis (CH), gender, and genomic/transcriptomic profiles (gene fusions, genetic mutations, and gene expression networks or pathways) was systematically analyzed. Overall, AML patients aged 60 y and older showed an apparently dismal prognosis. Alongside age, the frequency of gene fusions defined in the World Health Organization classification decreased, while the positive rate of gene mutations, especially CH-related ones, increased. Additionally, the number of genetic mutations was higher in gene fusion-negative (GF-) patients than those with GF. Based on the status of CH- and myelodysplastic syndromes (MDS)-related mutations, three mutant subgroups were identified among the GF- AML cohort, namely, CH-AML, CH-MDS-AML, and other GF- AML. Notably, CH-MDS-AML demonstrated a predominance of elderly and male cases, cytopenia, and significantly adverse clinical outcomes. Besides, gene expression networks including HOXA/B, platelet factors, and inflammatory responses were most striking features associated with aging and poor prognosis in AML. Our work has thus unraveled the intricate regulatory circuitry of interactions among different age, gender, and molecular groups of AML.

Linking discoveries, mechanisms, and technologies to develop a clearer perspective on plant long noncoding RNAs.

Long noncoding RNAs (lncRNAs) are a large and diverse class of genes in eukaryotic genomes that contribute to a variety of regulatory processes. Functionally characterized lncRNAs play critical roles in plants, ranging from regulating flowering to controlling lateral root formation. However, findings from the past decade have revealed that thousands of lncRNAs are present in plant transcriptomes, and characterization has lagged far behind identification. In this setting, distinguishing function from noise is challenging. However, the plant community has been at the forefront of discovery in lncRNA biology, providing many functional and mechanistic insights that have increased our understanding of this gene class. In this review, we examine the key discoveries and insights made in plant lncRNA biology over the past two and a half decades. We describe how discoveries made in the pregenomics era have informed efforts to identify and functionally characterize lncRNAs in the subsequent decades. We provide an overview of the functional archetypes into which characterized plant lncRNAs fit and speculate on new avenues of research that may uncover yet more archetypes. Finally, this review discusses the challenges facing the field and some exciting new molecular and computational approaches that may help inform lncRNA comparative and functional analyses.

Development of a mobile, high-throughput, and low-cost image-based plant growth phenotyping system.

Nondestructive plant phenotyping forms a key technique for unraveling molecular processes underlying plant development and response to the environment. While the emergence of high-throughput phenotyping facilities can further our understanding of plant development and stress responses, their high costs greatly hinder scientific progress. To democratize high-throughput plant phenotyping, we developed sets of low-cost image- and weight-based devices to monitor plant shoot growth and evapotranspiration. We paired these devices to a suite of computational pipelines for integrated and straightforward data analysis. The developed tools were validated for their suitability for large genetic screens by evaluating a cowpea (Vigna unguiculata) diversity panel for responses to drought stress. The observed natural variation was used as an input for a genome-wide association study, from which we identified nine genetic loci that might contribute to cowpea drought resilience during early vegetative development. The homologs of the candidate genes were identified in Arabidopsis (Arabidopsis thaliana) and subsequently evaluated for their involvement in drought stress by using available T-DNA insertion mutant lines. These results demonstrate the varied applicability of this low-cost phenotyping system. In the future, we foresee these setups facilitating the identification of genetic components of growth, plant architecture, and stress tolerance across a wide variety of plant species.

Identification and functional annotation of long intergenic non-coding RNAs in Brassicaceae.

Long intergenic noncoding RNAs (lincRNAs) are a large yet enigmatic class of eukaryotic transcripts that can have critical biological functions. The wealth of RNA-sequencing (RNA-seq) data available for plants provides the opportunity to implement a harmonized identification and annotation effort for lincRNAs that enables cross-species functional and genomic comparisons as well as prioritization of functional candidates. In this study, we processed >24 Tera base pairs of RNA-seq data from >16,000 experiments to identify ∼130,000 lincRNAs in four Brassicaceae: Arabidopsis thaliana, Camelina sativa, Brassica rapa, and Eutrema salsugineum. We used nanopore RNA-seq, transcriptome-wide structural information, peptide data, and epigenomic data to characterize these lincRNAs and identify conserved motifs. We then used comparative genomic and transcriptomic approaches to highlight lincRNAs in our data set with sequence or transcriptional conservation. Finally, we used guilt-by-association analyses to assign putative functions to lincRNAs within our data set. We tested this approach on a subset of lincRNAs associated with germination and seed development, observing germination defects for Arabidopsis lines harboring T-DNA insertions at these loci. LincRNAs with Brassicaceae-conserved putative miRNA binding motifs, small open reading frames, or abiotic-stress modulated expression are a few of the annotations that will guide functional analyses into this cryptic portion of the transcriptome.

GPRASP1 loss-of-function links to arteriovenous malformations by endothelial activating GPR4 signals.

Arteriovenous malformations (AVMs) are fast-flow vascular malformations and refer to important causes of intracerebral haemorrhage in young adults. Getting deep insight into the genetic pathogenesis of AVMs is necessary. Herein, we identified two vital missense variants of G protein-coupled receptor (GPCR) associated sorting protein 1 (GPRASP1) in AVM patients for the first time and congruously determined to be loss-of-function variants in endothelial cells. GPRASP1 loss-of-function caused endothelial dysfunction in vitro and in vivo. Endothelial Gprasp1 knockout mice suffered a high probability of cerebral haemorrhage, AVMs and exhibited vascular anomalies in multiple organs. GPR4 was identified to be an effective GPCR binding with GPRASP1 to develop endothelial disorders. GPRASP1 deletion activated GPR4/cAMP/MAPK signalling to disturb endothelial functions, thus contributing to vascular anomalies. Mechanistically, GPRASP1 promoted GPR4 degradation. GPRASP1 enabled GPR4 K63-linked ubiquitination, enhancing the binding of GPR4 and RABGEF1 to activate RAB5 for conversions from endocytic vesicles to endosomes, and subsequently increasing the interactions of GPR4 and ESCRT members to package GPR4 into multivesicular bodies or late endosomes for lysosome degradation. Notably, the GPR4 antagonist NE 52-QQ57 and JNK inhibitor SP600125 effectively rescued the vascular phenotype caused by endothelial Gprasp1 deletion. Our findings provided novel insights into the roles of GPRASP1 in AVMs and hinted at new therapeutic strategies.

Feedback-related brain activity in individual decision: evidence from a gambling EEG study.

In this study, based on scalp electroencephalogram (EEG), we conducted cortical source localization and functional network analyses to investigate the underlying mechanism explaining the decision processes when individuals anticipate maximizing gambling benefits, particularly in situations where the decision outcomes are inconsistent with the profit goals. The findings shed light on the feedback monitoring process, wherein incongruity between outcomes and gambling goals triggers a more pronounced medial frontal negativity and activates the frontal lobe. Moreover, long-range theta connectivity is implicated in processing surprise and uncertainty caused by inconsistent feedback conditions, while middle-range delta coupling reflects a more intricate evaluation of feedback outcomes, which subsequently modifies individual decision-making for optimizing future rewards. Collectively, these findings deepen our comprehension of decision-making under circumstances where the profit goals are compromised by decision outcomes and provide electrophysiological evidence supporting adaptive adjustments in individual decision strategies to achieve maximum benefit.

Transcriptome-based molecular subtypes and differentiation hierarchies improve the classification framework of acute myeloid leukemia.

The current classification of acute myeloid leukemia (AML) relies largely on genomic alterations. Robust identification of clinically and biologically relevant molecular subtypes from nongenomic high-throughput sequencing data remains challenging. We established the largest multicenter AML cohort (n = 655) in China, with all patients subjected to RNA sequencing (RNA-Seq) and 619 (94.5%) to targeted or whole-exome sequencing (TES/WES). Based on an enhanced consensus clustering, eight stable gene expression subgroups (G1-G8) with unique clinical and biological significance were identified, including two unreported (G5 and G8) and three redefined ones (G4, G6, and G7). Apart from four well-known low-risk subgroups including PML::RARA (G1), CBFB::MYH11 (G2), RUNX1::RUNX1T1 (G3), biallelic CEBPA mutations or -like (G4), four meta-subgroups with poor outcomes were recognized. The G5 (myelodysplasia-related/-like) subgroup enriched clinical, cytogenetic and genetic features mimicking secondary AML, and hotspot mutations of IKZF1 (p.N159S) (n = 7). In contrast, most NPM1 mutations and KMT2A and NUP98 fusions clustered into G6-G8, showing high expression of HOXA/B genes and diverse differentiation stages, from hematopoietic stem/progenitor cell down to monocyte, namely HOX-primitive (G7), HOX-mixed (G8), and HOX-committed (G6). Through constructing prediction models, the eight gene expression subgroups could be reproduced in the Cancer Genome Atlas (TCGA) and Beat AML cohorts. Each subgroup was associated with distinct prognosis and drug sensitivities, supporting the clinical applicability of this transcriptome-based classification of AML. These molecular subgroups illuminate the complex molecular network of AML, which may promote systematic studies of disease pathogenesis and foster the screening of targeted agents based on omics.

High-Pressure Electro-Fenton Driving CH4 Conversion by O2 at Room Temperature.

Electrochemical conversion of CH4 to easily transportable and value-added liquid fuels is highly attractive for energy-efficient CH4 utilization, but it is challenging due to the low reactivity and solubility of CH4 in the electrolyte. Herein, we report a high-pressure electro-Fenton (HPEF) strategy to establish a hetero-homogeneous process for the electrocatalytic conversion of CH4 by O2 at room temperature. In combination with elevation of reactant pressure to accelerate reaction kinetics, it delivers an unprecedented HCOOH productivity of 11.5 mmol h-1 gFe-1 with 220 times enhancement compared to that under ambient pressure. Remarkably, an HCOOH Faradic efficiency of 81.4% can be achieved with an ultralow cathodic overpotential of 0.38 V. The elevated pressure not only promotes the electrocatalytic reduction of O2 to H2O2 but also increases the reaction collision probability between CH4 and •OH, which is in situ generated from the Fe2+-facilitated decomposition of H2O2.

Bacterial polyynes uncovered: a journey through their bioactive properties, biosynthetic mechanisms, and sustainable production strategies.

Covering: up to 2023Conjugated polyynes are natural compounds characterized by alternating single and triple carbon-carbon bonds, endowing them with distinct physicochemical traits and a range of biological activities. While traditionally sourced mainly from plants, recent investigations have revealed many compounds originating from bacterial strains. This review synthesizes current research on bacterial-derived conjugated polyynes, delving into their biosynthetic routes, underscoring the variety in their molecular structures, and examining their potential applications in biotechnology. Additionally, we outline future directions for metabolic and protein engineering to establish more robust and stable platforms for their production.

Recognition of C4 Olefins by an Ultramicroporous ftw-Type Yttrium-Based Metal-Organic Framework with Distorted Cages.

Developing porous adsorbents for efficient separation of C4 olefins is significant but challenging in the petrochemical industry due to their similar molecular sizes and physical properties. The separation efficiency is often limited when separating C4 olefins by a single separation mechanism. Herein, an ultramicroporous yttrium-based MOF, Y-dbai, is reported featuring cage-like pores connected by small windows, for recognition and efficient separation of C4 olefins through a synergistic effect of thermodynamic and kinetic mechanisms. At 298 K and 1 bar, the adsorption capacities of Y-dbai for C4H6, 1-C4H8, and i-C4H8 are 2.88, 1.07, and 0.14 mmol g-1, respectively, indicating a molecular sieving effect toward i-C4H8. The C4H6/i-C4H8 and 1-C4H8/i-C4H8 uptake selectivities of Y-dbai are 20.6 and 7.6, respectively, outperforming most of the reported adsorbents. The static and kinetic adsorption experiments coupled with DFT calculations indicate the separation should be attributed to a combined effect of thermodynamically and kinetically controlled mechanism. Breakthrough experiments have confirmed the excellent separation capability of Y-dbai toward C4H6/1-C4H8, C4H6/i-C4H8, and C4H6/1-C4H8/i-C4H8 mixtures.

Guard Cell-Inspired Ion Channels: Harnessing the Photomechanical Effect via Supramolecular Assembly of Cross-Linked Azobenzene/Polymers.

Stimuli-responsive ion nanochannels have attracted considerable attention in various fields because of their remote controllability of ionic transportation. For photoresponsive ion nanochannels, however, achieving precise regulation of ion conductivity is still challenging, primarily due to the difficulty of programmable structural changes in confined environments. Moreover, the relationship between noncontact photo-stimulation in nanoscale and light-induced ion conductivity has not been well understood. In this work, a versatile design for fabricating guard cell-inspired photoswitchable ion channels is presented by infiltrating azobenzene-cross-linked polymer (AAZO-PDAC) into nanoporous anodic aluminum oxide (AAO) membranes. The azobenzene-cross-linked polymer is formed by azobenzene chromophore (AAZO)-cross-linked poly(diallyldimethylammonium chloride) (PDAC) with electrostatic interactions. Under UV irradiation, the trans-AAZO isomerizes to the cis-AAZO, causing the volume compression of the polymer network, whereas, in darkness, the cis-AAZO reverts to the trans-AAZO, leading to the recovery of the structure. Consequently, the resultant nanopore sizes can be manipulated by the photomechanical effect of the AAZO-PDAC polymers. By adding ionic liquids, the ion conductivity of the light-driven ion nanochannels can be controlled with good repeatability and fast responses (within seconds) in multiple cycles. The ion channels have promising potential in the applications of biomimetic materials, sensors, and biomedical sciences.

MiRNA-disease association prediction based on meta-paths.

Since miRNAs can participate in the posttranscriptional regulation of gene expression, they may provide ideas for the development of new drugs or become new biomarkers for drug targets or disease diagnosis. In this work, we propose an miRNA-disease association prediction method based on meta-paths (MDPBMP). First, an miRNA-disease-gene heterogeneous information network was constructed, and seven symmetrical meta-paths were defined according to different semantics. After constructing the initial feature vector for the node, the vector information carried by all nodes on the meta-path instance is extracted and aggregated to update the feature vector of the starting node. Then, the vector information obtained by the nodes on different meta-paths is aggregated. Finally, miRNA and disease embedding feature vectors are used to calculate their associated scores. Compared with the other methods, MDPBMP obtained the highest AUC value of 0.9214. Among the top 50 predicted miRNAs for lung neoplasms, esophageal neoplasms, colon neoplasms and breast neoplasms, 49, 48, 49 and 50 have been verified. Furthermore, for breast neoplasms, we deleted all the known associations between breast neoplasms and miRNAs from the training set. These results also show that for new diseases without known related miRNA information, our model can predict their potential miRNAs. Code and data are available at https://github.com/LiangYu-Xidian/MDPBMP.

NmRF: identification of multispecies RNA 2'-O-methylation modification sites from RNA sequences.

2'-O-methylation (Nm) is a post-transcriptional modification of RNA that is catalyzed by 2'-O-methyltransferase and involves replacing the H on the 2'-hydroxyl group with a methyl group. The 2'-O-methylation modification site is detected in a variety of RNA types (miRNA, tRNA, mRNA, etc.), plays an important role in biological processes and is associated with different diseases. There are few functional mechanisms developed at present, and traditional high-throughput experiments are time-consuming and expensive to explore functional mechanisms. For a deeper understanding of relevant biological mechanisms, it is necessary to develop efficient and accurate recognition tools based on machine learning. Based on this, we constructed a predictor called NmRF based on optimal mixed features and random forest classifier to identify 2'-O-methylation modification sites. The predictor can identify modification sites of multiple species at the same time. To obtain a better prediction model, a two-step strategy is adopted; that is, the optimal hybrid feature set is obtained by combining the light gradient boosting algorithm and incremental feature selection strategy. In 10-fold cross-validation, the accuracies of Homo sapiens and Saccharomyces cerevisiae were 89.069 and 93.885%, and the AUC were 0.9498 and 0.9832, respectively. The rigorous 10-fold cross-validation and independent tests confirm that the proposed method is significantly better than existing tools. A user-friendly web server is accessible at http://lab.malab.cn/∼acy/NmRF.

A network embedding framework based on integrating multiplex network for drug combination prediction.

Drug combination is a sensible strategy for disease treatment because it improves the treatment efficacy and reduces concomitant side effects. Due to the large number of possible combinations among candidate compounds, exhaustive screening is prohibitive. Currently, a large number of studies have focused on predicting potential drug combinations. However, these methods are not entirely satisfactory in terms of performance and scalability. In this paper, we proposed a Network Embedding frameWork in MultIplex Network (NEWMIN) to predict synthetic drug combinations. Based on a multiplex drug similarity network, we offered alternative methods to integrate useful information from different aspects and to decide the quantitative importance of each network. For drug combination prediction, we found seven novel drug combinations that have been validated by external sources among the top-ranked predictions of our model. To verify the feasibility of NEWMIN, we compared NEWMIN with other five methods, for which it showed better performance than other methods in terms of the area under the precision-recall curve and receiver operating characteristic curve.

Multiview network embedding for drug-target Interactions prediction by consistent and complementary information preserving.

Accurate prediction of drug-target interactions (DTIs) can reduce the cost and time of drug repositioning and drug discovery. Many current methods integrate information from multiple data sources of drug and target to improve DTIs prediction accuracy. However, these methods do not consider the complex relationship between different data sources. In this study, we propose a novel computational framework, called MccDTI, to predict the potential DTIs by multiview network embedding, which can integrate the heterogenous information of drug and target. MccDTI learns high-quality low-dimensional representations of drug and target by preserving the consistent and complementary information between multiview networks. Then MccDTI adopts matrix completion scheme for DTIs prediction based on drug and target representations. Experimental results on two datasets show that the prediction accuracy of MccDTI outperforms four state-of-the-art methods for DTIs prediction. Moreover, literature verification for DTIs prediction shows that MccDTI can predict the reliable potential DTIs. These results indicate that MccDTI can provide a powerful tool to predict new DTIs and accelerate drug discovery. The code and data are available at: https://github.com/ShangCS/MccDTI.

Research progress of miRNA-disease association prediction and comparison of related algorithms.

With an in-depth understanding of noncoding ribonucleic acid (RNA), many studies have shown that microRNA (miRNA) plays an important role in human diseases. Because traditional biological experiments are time-consuming and laborious, new calculation methods have recently been developed to predict associations between miRNA and diseases. In this review, we collected various miRNA-disease association prediction models proposed in recent years and used two common data sets to evaluate the performance of the prediction models. First, we systematically summarized the commonly used databases and similarity data for predicting miRNA-disease associations, and then divided the various calculation models into four categories for summary and detailed introduction. In this study, two independent datasets (D5430 and D6088) were compiled to systematically evaluate 11 publicly available prediction tools for miRNA-disease associations. The experimental results indicate that the methods based on information dissemination and the method based on scoring function require shorter running time. The method based on matrix transformation often requires a longer running time, but the overall prediction result is better than the previous two methods. We hope that the summary of work related to miRNA and disease will provide comprehensive knowledge for predicting the relationship between miRNA and disease and contribute to advanced computation tools in the future.

PTMdyna: exploring the influence of post-translation modifications on protein conformational dynamics.

Protein post-translational modifications (PTM) play vital roles in cellular regulation, modulating functions by driving changes in protein structure and dynamics. Exploring comprehensively the influence of PTM on conformational dynamics can facilitate the understanding of the related biological function and molecular mechanism. Currently, a series of excellent computation tools have been designed to analyze the time-dependent structural properties of proteins. However, the protocol aimed to explore conformational dynamics of post-translational modified protein is still a blank. To fill this gap, we present PTMdyna to visually predict the conformational dynamics differences between unmodified and modified proteins, thus indicating the influence of specific PTM. PTMdyna exhibits an AUC of 0.884 tested on 220 protein-protein complex structures. The case of heterochromatin protein 1α complexed with lysine 9-methylated histone H3, which is critical for genomic stability and cell differentiation, was used to demonstrate its applicability. PTMdyna provides a reliable platform to predict the influence of PTM on protein dynamics, making it easier to interpret PTM functionality at the structure level. The web server is freely available at http://ccbportal.com/PTMdyna.

Potent antibiotic design via guided search from antibacterial activity evaluations.

MOTIVATION: The emergence of drug-resistant bacteria makes the discovery of new antibiotics an urgent issue, but finding new molecules with the desired antibacterial activity is an extremely difficult task. To address this challenge, we established a framework, MDAGS (Molecular Design via Attribute-Guided Search), to optimize and generate potent antibiotic molecules. RESULTS: By designing the antibacterial activity latent space and guiding the optimization of functional compounds based on this space, the model MDAGS can generate novel compounds with desirable antibacterial activity without the need for extensive expensive and time-consuming evaluations. Compared with existing antibiotics, candidate antibacterial compounds generated by MDAGS always possessed significantly better antibacterial activity and ensured high similarity. Furthermore, although without explicit constraints on similarity to known antibiotics, these candidate antibacterial compounds all exhibited the highest structural similarity to antibiotics of expected function in the DrugBank database query. Overall, our approach provides a viable solution to the problem of bacterial drug resistance. AVAILABILITY AND IMPLEMENTATION: Code of the model and datasets can be downloaded from GitHub (https://github.com/LiangYu-Xidian/MDAGS). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Syntrichia ruralis: emerging model moss genome reveals a conserved and previously unknown regulator of desiccation in flowering plants.

Water scarcity, resulting from climate change, poses a significant threat to ecosystems. Syntrichia ruralis, a dryland desiccation-tolerant moss, provides valuable insights into survival of water-limited conditions. We sequenced the genome of S. ruralis, conducted transcriptomic analyses, and performed comparative genomic and transcriptomic analyses with existing genomes and transcriptomes, including with the close relative S. caninervis. We took a genetic approach to characterize the role of an S. ruralis transcription factor, identified in transcriptomic analyses, in Arabidopsis thaliana. The genome was assembled into 12 chromosomes encompassing 21 169 protein-coding genes. Comparative analysis revealed copy number and transcript abundance differences in known desiccation-associated gene families, and highlighted genome-level variation among species that may reflect adaptation to different habitats. A significant number of abscisic acid (ABA)-responsive genes were found to be negatively regulated by a MYB transcription factor (MYB55) that was upstream of the S. ruralis ortholog of ABA-insensitive 3 (ABI3). We determined that this conserved MYB transcription factor, uncharacterized in Arabidopsis, acts as a negative regulator of an ABA-dependent stress response in Arabidopsis. The new genomic resources from this emerging model moss offer novel insights into how plants regulate their responses to water deprivation.