Spatiotemporal dissection of the cell cycle with single-cell proteogenomics.

The cell cycle, over which cells grow and divide, is a fundamental process of life. Its dysregulation has devastating consequences, including cancer1-3. The cell cycle is driven by precise regulation of proteins in time and space, which creates variability between individual proliferating cells. To our knowledge, no systematic investigations of such cell-to-cell proteomic variability exist. Here we present a comprehensive, spatiotemporal map of human proteomic heterogeneity by integrating proteomics at subcellular resolution with single-cell transcriptomics and precise temporal measurements of individual cells in the cell cycle. We show that around one-fifth of the human proteome displays cell-to-cell variability, identify hundreds of proteins with previously unknown associations with mitosis and the cell cycle, and provide evidence that several of these proteins have oncogenic functions. Our results show that cell cycle progression explains less than half of all cell-to-cell variability, and that most cycling proteins are regulated post-translationally, rather than by transcriptomic cycling. These proteins are disproportionately phosphorylated by kinases that regulate cell fate, whereas non-cycling proteins that vary between cells are more likely to be modified by kinases that regulate metabolism. This spatially resolved proteomic map of the cell cycle is integrated into the Human Protein Atlas and will serve as a resource for accelerating molecular studies of the human cell cycle and cell proliferation.

Renal Mitochondrial ATP Transporter Ablation Ameliorates Obesity-Induced CKD.

SIGNIFICANCE STATEMENT: This study sheds light on the central role of adenine nucleotide translocase 2 (ANT2) in the pathogenesis of obesity-induced CKD. Our data demonstrate that ANT2 depletion in renal proximal tubule cells (RPTCs) leads to a shift in their primary metabolic program from fatty acid oxidation to aerobic glycolysis, resulting in mitochondrial protection, cellular survival, and preservation of renal function. These findings provide new insights into the underlying mechanisms of obesity-induced CKD and have the potential to be translated toward the development of targeted therapeutic strategies for this debilitating condition. BACKGROUND: The impairment in ATP production and transport in RPTCs has been linked to the pathogenesis of obesity-induced CKD. This condition is characterized by kidney dysfunction, inflammation, lipotoxicity, and fibrosis. In this study, we investigated the role of ANT2, which serves as the primary regulator of cellular ATP content in RPTCs, in the development of obesity-induced CKD. METHODS: We generated RPTC-specific ANT2 knockout ( RPTC-ANT2-/- ) mice, which were then subjected to a 24-week high-fat diet-feeding regimen. We conducted comprehensive assessment of renal morphology, function, and metabolic alterations of these mice. In addition, we used large-scale transcriptomics, proteomics, and metabolomics analyses to gain insights into the role of ANT2 in regulating mitochondrial function, RPTC physiology, and overall renal health. RESULTS: Our findings revealed that obese RPTC-ANT2-/- mice displayed preserved renal morphology and function, along with a notable absence of kidney lipotoxicity and fibrosis. The depletion of Ant2 in RPTCs led to a fundamental rewiring of their primary metabolic program. Specifically, these cells shifted from oxidizing fatty acids as their primary energy source to favoring aerobic glycolysis, a phenomenon mediated by the testis-selective Ant4. CONCLUSIONS: We propose a significant role for RPTC-Ant2 in the development of obesity-induced CKD. The nullification of RPTC-Ant2 triggers a cascade of cellular mechanisms, including mitochondrial protection, enhanced RPTC survival, and ultimately the preservation of kidney function. These findings shed new light on the complex metabolic pathways contributing to CKD development and suggest potential therapeutic targets for this condition.

DPI_CDF: druggable protein identifier using cascade deep forest.

BACKGROUND: Drug targets in living beings perform pivotal roles in the discovery of potential drugs. Conventional wet-lab characterization of drug targets is although accurate but generally expensive, slow, and resource intensive. Therefore, computational methods are highly desirable as an alternative to expedite the large-scale identification of druggable proteins (DPs); however, the existing in silico predictor's performance is still not satisfactory. METHODS: In this study, we developed a novel deep learning-based model DPI_CDF for predicting DPs based on protein sequence only. DPI_CDF utilizes evolutionary-based (i.e., histograms of oriented gradients for position-specific scoring matrix), physiochemical-based (i.e., component protein sequence representation), and compositional-based (i.e., normalized qualitative characteristic) properties of protein sequence to generate features. Then a hierarchical deep forest model fuses these three encoding schemes to build the proposed model DPI_CDF. RESULTS: The empirical outcomes on 10-fold cross-validation demonstrate that the proposed model achieved 99.13 % accuracy and 0.982 of Matthew's-correlation-coefficient (MCC) on the training dataset. The generalization power of the trained model is further examined on an independent dataset and achieved 95.01% of maximum accuracy and 0.900 MCC. When compared to current state-of-the-art methods, DPI_CDF improves in terms of accuracy by 4.27% and 4.31% on training and testing datasets, respectively. We believe, DPI_CDF will support the research community to identify druggable proteins and escalate the drug discovery process. AVAILABILITY: The benchmark datasets and source codes are available in GitHub: http://github.com/Muhammad-Arif-NUST/DPI_CDF .

Unified mRNA Subcellular Localization Predictor based on machine learning techniques.

BACKGROUND: The mRNA subcellular localization bears substantial impact in the regulation of gene expression, cellular migration, and adaptation. However, the methods employed for experimental determination of this localization are arduous, time-intensive, and come with a high cost. METHODS: In this research article, we tackle the essential challenge of predicting the subcellular location of messenger RNAs (mRNAs) through Unified mRNA Subcellular Localization Predictor (UMSLP), a machine learning (ML) based approach. We embrace an in silico strategy that incorporate four distinct feature sets: kmer, pseudo k-tuple nucleotide composition, nucleotide physicochemical attributes, and the 3D sequence depiction achieved via Z-curve transformation for predicting subcellular localization in benchmark dataset across five distinct subcellular locales, encompassing nucleus, cytoplasm, extracellular region (ExR), mitochondria, and endoplasmic reticulum (ER). RESULTS: The proposed ML model UMSLP attains cutting-edge outcomes in predicting mRNA subcellular localization. On independent testing dataset, UMSLP ahcieved over 87% precision, 94% specificity, and 94% accuracy. Compared to other existing tools, UMSLP outperformed mRNALocator, mRNALoc, and SubLocEP by 11%, 21%, and 32%, respectively on average prediction accuracy for all five locales. SHapley Additive exPlanations analysis highlights the dominance of k-mer features in predicting cytoplasm, nucleus, ER, and ExR localizations, while Z-curve based features play pivotal roles in mitochondria subcellular localization detection. AVAILABILITY: We have shared datasets, code, Docker API for users in GitHub at: https://github.com/smusleh/UMSLP .

Multivalent Sulfur Vacancy-Rich NiCo2 S4 @MnO2 Urchin-Like Heterostructures for Ambient Electrochemical N2 Reduction to NH3.

Innovative advances in the exploitation of effective electrocatalytic materials for the reduction of nitrogen (N2 ) to ammonia (NH3 ) are highly required for the sustainable production of fertilizers and zero-carbon emission fuel. In order to achieve zero-carbon footprints and renewable NH3 production, electrochemical N2 reduction reaction (NRR) provides a favorable energy-saving alternative but it requires more active, efficient, and selective catalysts. In current work, sulfur vacancy (Sv)-rich NiCo2 S4 @MnO2 heterostructures are efficaciously fabricated via a facile hydrothermal approach followed by heat treatment. The urchin-like Sv-NiCo2 S4 @MnO2 heterostructures serve as cathodes, which demonstrate an optimal NH3 yield of 57.31 µg h-1  mgcat -1 and Faradaic efficiency of 20.55% at -0.2 V versus reversible hydrogen electrode (RHE) in basic electrolyte owing to the synergistic interactions between Sv-NiCo2 S4 and MnO2 . Density functional theory (DFT) simulation further verifies that Co-sites of urchin-like Sv-NiCo2 S4 @MnO2 heterostructures are beneficial to lowering the energy threshold for N2 adsorption and successive protonation. Distinctive micro/nano-architectures exhibit high NRR electrocatalytic activities that might motivate researchers to explore and concentrate on the development of heterostructures for ambient electrocatalytic NH3 generation.

Improving protein-protein interaction prediction using protein language model and protein network features.

Interactions between proteins are ubiquitous in a wide variety of biological processes. Accurately identifying the protein-protein interaction (PPI) is of significant importance for understanding the mechanisms of protein functions and facilitating drug discovery. Although the wet-lab technological methods are the best way to identify PPI, their major constraints are their time-consuming nature, high cost, and labor-intensiveness. Hence, lots of efforts have been made towards developing computational methods to improve the performance of PPI prediction. In this study, we propose a novel hybrid computational method (called KSGPPI) that aims at improving the prediction performance of PPI via extracting the discriminative information from protein sequences and interaction networks. The KSGPPI model comprises two feature extraction modules. In the first feature extraction module, a large protein language model, ESM-2, is employed to exploit the global complex patterns concealed within protein sequences. Subsequently, feature representations are further extracted through CKSAAP, and a two-dimensional convolutional neural network (CNN) is utilized to capture local information. In the second feature extraction module, the query protein acquires its similar protein from the STRING database via the sequence alignment tool NW-align and then captures the graph embedding feature for the query protein in the protein interaction network of the similar protein using the algorithm of Node2vec. Finally, the features of these two feature extraction modules are efficiently fused; the fused features are then fed into the multilayer perceptron to predict PPI. The results of five-fold cross-validation on the used benchmarked datasets demonstrate that KSGPPI achieves an average prediction accuracy of 88.96 %. Additionally, the average Matthews correlation coefficient value (0.781) of KSGPPI is significantly higher than that of those state-of-the-art PPI prediction methods. The standalone package of KSGPPI is freely downloaded at https://github.com/rickleezhe/KSGPPI.

Microorganism-mediated biodegradation for effective management and/or removal of micro-plastics from the environment: a comprehensive review.

Micro- plastics (MPs) pose significant global threats, requiring an environment-friendly mode of decomposition. Microbial-mediated biodegradation and biodeterioration of micro-plastics (MPs) have been widely known for their cost-effectiveness, and environment-friendly techniques for removing MPs. MPs resistance to various biocidal microbes has also been reported by various studies. The biocidal resistance degree of biodegradability and/or microbiological susceptibility of MPs can be determined by defacement, structural deformation, erosion, degree of plasticizer degradation, metabolization, and/or solubilization of MPs. The degradation of microplastics involves microbial organisms like bacteria, mold, yeast, algae, and associated enzymes. Analytical and microbiological techniques monitor microplastic biodegradation, but no microbial organism can eliminate microplastics. MPs can pose environmental risks to aquatic and human life. Micro-plastic biodegradation involves fragmentation, assimilation, and mineralization, influenced by abiotic and biotic factors. Environmental factors and pre-treatment agents can naturally degrade large polymers or induce bio-fragmentation, which may impact their efficiency. A clear understanding of MPs pollution and the microbial degradation process is crucial for mitigating its effects. The study aimed to identify deteriogenic microorganism species that contribute to the biodegradation of micro-plastics (MPs). This knowledge is crucial for designing novel biodeterioration and biodegradation formulations, both lab-scale and industrial, that exhibit MPs-cidal actions, potentially predicting MPs-free aquatic and atmospheric environments. The study emphasizes the urgent need for global cooperation, research advancements, and public involvement to reduce micro-plastic contamination through policy proposals and improved waste management practices.

Do Added Microplastics, Native Soil Properties, and Prevailing Climatic Conditions Have Consequences for Carbon and Nitrogen Contents in Soil? A Global Data Synthesis of Pot and Greenhouse Studies.

Microplastics threaten soil ecosystems, strongly influencing carbon (C) and nitrogen (N) contents. Interactions between microplastic properties and climatic and edaphic factors are poorly understood. We conducted a meta-analysis to assess the interactive effects of microplastic properties (type, shape, size, and content), native soil properties (texture, pH, and dissolved organic carbon (DOC)) and climatic factors (precipitation and temperature) on C and N contents in soil. We found that low-density polyethylene reduced total nitrogen (TN) content, whereas biodegradable polylactic acid led to a decrease in soil organic carbon (SOC). Microplastic fragments especially depleted TN, reducing aggregate stability, increasing N-mineralization and leaching, and consequently increasing the soil C/N ratio. Microplastic size affected outcomes; those <200 µm reduced both TN and SOC contents. Mineralization-induced nutrient losses were greatest at microplastic contents between 1 and 2.5% of soil weight. Sandy soils suffered the highest microplastic contamination-induced nutrient depletion. Alkaline soils showed the greatest SOC depletion, suggesting high SOC degradability. In low-DOC soils, microplastic contamination caused 2-fold greater TN depletion than in soils with high DOC. Sites with high precipitation and temperature had greatest decrease in TN and SOC contents. In conclusion, there are complex interactions determining microplastic impacts on soil health. Microplastic contamination always risks soil C and N depletion, but the severity depends on microplastic characteristics, native soil properties, and climatic conditions, with potential exacerbation by greenhouse emission-induced climate change.

Could soil microplastic pollution exacerbate climate change? A meta-analysis of greenhouse gas emissions and global warming potential.

Microplastics pollution and climate change are primarily investigated in isolation, despite their joint threat to the environment. Greenhouse gases (GHGs) are emitted during: the production of plastic and rubber, the use and degradation of plastic, and after contamination of environment. This is the first meta-analysis to assess underlying causal relationships and the influence of likely mediators. We included 60 peer-reviewed empirical studies; estimating GHGs emissions effect size and global warming potential (GWP), according to key microplastics properties and soil conditions. We investigated interrelationships with microbe functional gene expression. Overall, microplastics contamination was associated with increased GHGs emissions, with the strongest effect (60%) on CH4 emissions. Polylactic-acid caused 32% higher CO2 emissions, but only 1% of total GWP. Phenol-formaldehyde had the greatest (175%) GWP via 182% increased N2O emissions. Only polystyrene resulted in reduced GWP by 50%, due to N2O mitigation. Polyethylene caused the maximum (60%) CH4 emissions. Shapes of microplastics differed in GWP: fiber had the greatest GWP (66%) whereas beads reduced GWP by 53%. Films substantially increased emissions of all GHGs: 14% CO2, 10% N2O and 60% CH4. Larger-sized microplastics had higher GWP (125%) due to their 9% CO2 and 63% N2O emissions. GWP rose sharply if soil microplastics content exceeded 0.5%. Higher CO2 emissions, ranging from 4% to 20%, arose from soil which was either fine, saturated or had high-carbon content. Higher N2O emissions, ranging from 10% to 95%, arose from soils that had either medium texture, saturated water content or low-carbon content. Both CO2 and N2O emissions were 43%-56% higher from soils with neutral pH. We conclude that microplastics contamination can cause raised GHGs emissions, posing a risk of exacerbating climate-change. We show clear links between GHGs emissions, microplastics properties, soil characteristics and soil microbe functional gene expression. Further research is needed regarding underlying mechanisms and processes.

Toxicological assessment of dietary exposure of polyethylene microplastics on growth, nutrient digestibility, carcass and gut histology of Nile Tilapia (Oreochromis niloticus) fingerlings.

This study was conducted to ascertain the negative effects of dietary low-density polyethylene microplastics (LDPE-MPs) exposure on growth, nutrient digestibility, body composition and gut histology of Nile tilapia (Oreochromis niloticus). Six sunflower meal-based diets (protein 30.95%; fat 8.04%) were prepared; one was the control (0%) and five were incorporated with LDPE-MPs at levels of 2, 4, 6, 8 and 10% in sunflower meal-based diets. A total of eighteen experimental tanks, each with 15 fingerlings, were used in triplicates. Fish were fed at the rate of 5% biomass twice a day for 60 days. Results revealed that best values of growth, nutrient digestibility, body composition and gut histology were observed by control diet, while 10% exposure to LDPE-MPs significantly (P < 0.05) reduced weight gain (WG%, 85.04%), specific growth rate (SGR%, 0.68%), and increased FCR (3.92%). The findings showed that higher level of LDPE-MPs (10%) exposure in the diet of O. niloticus negatively affects nutrient digestibility. Furthermore, the results revealed that the higher concentration of LDPE-MPs (10%) had a detrimental impact on crude protein (11.92%) and crude fat (8.04%). A high number of histological lesions were seen in gut of fingerlings exposed to LDPE-MPs. Hence, LDPE-MPs potentially harm the aquatic health.

Dam inundation reduces ecosystem multifunctionality following riparian afforestation in the Three Gorges Reservoir Region.

Afforestation is an acknowledged method for rehabilitating deteriorated riparian ecosystems, presenting multiple functions to alleviate the repercussions of river damming and climate change. However, how ecosystem multifunctionality (EMF) responds to inundation in riparian afforestation ecosystems remains relatively unexplored. Thus, this article aimed to disclose how EMF alters with varying inundation intensities and to elucidate the key drivers of this variation based on riparian reforestation experiments in the Three Gorges Reservoir Region in China. Our EMF analysis encompassed wood production, carbon storage, nutrient cycling, decomposition, and water regulation under different inundation intensities. We examined their correlation with soil properties and microbial diversity. The results indicated a substantial reduction in EMF with heightened inundation intensity, which was primarily due to the decline in most individual functions. Notably, soil bacterial diversity (23.02%), soil properties such as oxidation-reduction potential (ORP, 11.75%), and temperature (5.85%) emerged as pivotal variables elucidating EMF changes under varying inundation intensities. Soil bacterial diversity and ORP declined as inundation intensified but were positively associated with EMF. In contrast, soil temperature rose with increased inundation intensity and exhibited a negative correlation with EMF. Further insights gleaned from structural equation modeling revealed that inundation reduced EMF directly and indirectly by reducing soil ORP and bacterial diversity and increasing soil temperature. This work underscores the adverse effects of dam inundation on riparian EMF and the crucial role soil characteristics and microbial diversity play in mediating EMF in response to inundation. These insights are pivotal for the conservation of biodiversity and functioning following afforestation in dam-induced riparian habitats.

3D Hollow Hierarchical Porous Carbon with Fe-N4 -OH Single-Atom Sites for High-Performance Zn-Air Batteries.

The development of innovative and efficient Fe-N-C catalysts is crucial for the widespread application of zinc-air batteries (ZABs), where the inherent oxygen reduction reaction (ORR) activity of Fe single-atom sites needs to be optimized to meet the practical application. Herein, a three-dimensional (3D) hollow hierarchical porous electrocatalyst (ZIF8@FePMPDA-920) rich in asymmetric Fe-N4 -OH moieties as the single atomic sites is reported. The Fe center is in a penta-coordinated geometry with four N atoms and one O atom to form Fe-N4 -OH configuration. Compared to conventional Fe-N4 configuration, this unique structure can weaken the adsorption of intermediates by reducing the electron density of the Fe center for oxygen binding, which decreases the energy barrier of the rate-determining steps (RDS) to accelerate the ORR and oxygen evolution reaction (OER) processes for ZABs. The rechargeable liquid ZABs (LZABs) equipped with ZIF8@FePMPDA-920 display a high power density of 123.11 mW cm-2 and a long cycle life (300 h). The relevant flexible all-solid-state ZABs (FASSZABs) also display outstanding foldability and cyclical stability. This work provides a new perspective for the structural design of single-atom catalysts in the energy conversion and storage areas.

Multi-omics approaches for revealing the complexity of cardiovascular disease.

The development and progression of cardiovascular disease (CVD) can mainly be attributed to the narrowing of blood vessels caused by atherosclerosis and thrombosis, which induces organ damage that will result in end-organ dysfunction characterized by events such as myocardial infarction or stroke. It is also essential to consider other contributory factors to CVD, including cardiac remodelling caused by cardiomyopathies and co-morbidities with other diseases such as chronic kidney disease. Besides, there is a growing amount of evidence linking the gut microbiota to CVD through several metabolic pathways. Hence, it is of utmost importance to decipher the underlying molecular mechanisms associated with these disease states to elucidate the development and progression of CVD. A wide array of systems biology approaches incorporating multi-omics data have emerged as an invaluable tool in establishing alterations in specific cell types and identifying modifications in signalling events that promote disease development. Here, we review recent studies that apply multi-omics approaches to further understand the underlying causes of CVD and provide possible treatment strategies by identifying novel drug targets and biomarkers. We also discuss very recent advances in gut microbiota research with an emphasis on how diet and microbial composition can impact the development of CVD. Finally, we present various biological network analyses and other independent studies that have been employed for providing mechanistic explanation and developing treatment strategies for end-stage CVD, namely myocardial infarction and stroke.

Improving DNA-Binding Protein Prediction Using Three-Part Sequence-Order Feature Extraction and a Deep Neural Network Algorithm.

Identification of the DNA-binding protein (DBP) helps dig out information embedded in the DNA-protein interaction, which is significant to understanding the mechanisms of DNA replication, transcription, and repair. Although existing computational methods for predicting the DBPs based on protein sequences have obtained great success, there is still room for improvement since the sequence-order information is not fully mined in these methods. In this study, a new three-part sequence-order feature extraction (called TPSO) strategy is developed to extract more discriminative information from protein sequences for predicting the DBPs. For each query protein, TPSO first divides its primary sequence features into N- and C-terminal fragments and then extracts the numerical pseudo features of three parts including the full sequence and these two fragments, respectively. Based on TPSO, a novel deep learning-based method, called TPSO-DBP, is proposed, which employs the sequence-based single-view features, the bidirectional long short-term memory (BiLSTM) and fully connected (FC) neural networks to learn the DBP prediction model. Empirical outcomes reveal that TPSO-DBP can achieve an accuracy of 87.01%, covering 85.30% of all DBPs, while achieving a Matthew's correlation coefficient value (0.741) that is significantly higher than most existing state-of-the-art DBP prediction methods. Detailed data analyses have indicated that the advantages of TPSO-DBP lie in the utilization of TPSO, which helps extract more concealed prominent patterns, and the deep neural network framework composed of BiLSTM and FC that learns the nonlinear relationships between input features and DBPs. The standalone package and web server of TPSO-DBP are freely available at https://jun-csbio.github.io/TPSO-DBP/.

MMPatho: Leveraging Multilevel Consensus and Evolutionary Information for Enhanced Missense Mutation Pathogenic Prediction.

Understanding the pathogenicity of missense mutation (MM) is essential for shed light on genetic diseases, gene functions, and individual variations. In this study, we propose a novel computational approach, called MMPatho, for enhancing missense mutation pathogenic prediction. First, we established a large-scale nonredundant MM benchmark data set based on the entire Ensembl database, complemented by a focused blind test set specifically for pathogenic GOF/LOF MM. Based on this data set, for each mutation, we utilized Ensembl VEP v104 and dbNSFP v4.1a to extract variant-level, amino acid-level, individuals' outputs, and genome-level features. Additionally, protein sequences were generated using ENSP identifiers with the Ensembl API, and then encoded. The mutant sites' ESM-1b and ProtTrans-T5 embeddings were subsequently extracted. Then, our model group (MMPatho) was developed by leveraging upon these efforts, which comprised ConsMM and EvoIndMM. To be specific, ConsMM employs individuals' outputs and XGBoost with SHAP explanation analysis, while EvoIndMM investigates the potential enhancement of predictive capability by incorporating evolutionary information from ESM-1b and ProtT5-XL-U50, large protein language embeddings. Through rigorous comparative experiments, both ConsMM and EvoIndMM were capable of achieving remarkable AUROC (0.9836 and 0.9854) and AUPR (0.9852 and 0.9902) values on the blind test set devoid of overlapping variations and proteins from the training data, thus highlighting the superiority of our computational approach in the prediction of MM pathogenicity. Our Web server, available at http://csbio.njust.edu.cn/bioinf/mmpatho/, allows researchers to predict the pathogenicity (alongside the reliability index score) of MMs using the ConsMM and EvoIndMM models and provides extensive annotations for user input. Additionally, the newly constructed benchmark data set and blind test set can be accessed via the data page of our web server.

An ncRNA transcriptomics-based approach to design siRNA molecules against SARS-CoV-2 double membrane vesicle formation and accessory genes.

BACKGROUND: The corona virus SARS-CoV-2 is the causative agent of recent most global pandemic. Its genome encodes various proteins categorized as non-structural, accessory, and structural proteins. The non-structural proteins, NSP1-16, are located within the ORF1ab. The NSP3, 4, and 6 together are involved in formation of double membrane vesicle (DMV) in host Golgi apparatus. These vesicles provide anchorage to viral replicative complexes, thus assist replication inside the host cell. While the accessory genes coded by ORFs 3a, 3b, 6, 7a, 7b, 8a, 8b, 9b, 9c, and 10 contribute in cell entry, immunoevasion, and pathological progression. METHODS: This in silico study is focused on designing sequence specific siRNA molecules as a tool for silencing the non-structural and accessory genes of the virus. The gene sequences of NSP3, 4, and 6 along with ORF3a, 6, 7a, 8, and 10 were retrieved for conservation, phylogenetic, and sequence logo analyses. siRNA candidates were predicted using siDirect 2.0 targeting these genes. The GC content, melting temperatures, and various validation scores were calculated. Secondary structures of the guide strands and siRNA-target duplexes were predicted. Finally, tertiary structures were predicted and subjected to structural validations. RESULTS: This study revealed that NSP3, 4, and 6 and accessory genes ORF3a, 6, 7a, 8, and 10 have high levels of conservation across globally circulating SARS-CoV-2 strains. A total of 71 siRNA molecules were predicted against the selected genes. Following rigorous screening including binary validations and minimum free energies, final siRNAs with high therapeutic potential were identified, including 7, 2, and 1 against NSP3, NSP4, and NSP6, as well as 3, 1, 2, and 1 targeting ORF3a, ORF7a, ORF8, and ORF10, respectively. CONCLUSION: Our novel in silico pipeline integrates effective methods from previous studies to predict and validate siRNA molecules, having the potential to inhibit viral replication pathway in vitro. In total, this study identified 17 highly specific siRNA molecules targeting NSP3, 4, and 6 and accessory genes ORF3a, 7a, 8, and 10 of SARS-CoV-2, which might be used as an additional antiviral treatment option especially in the cases of life-threatening urgencies.

Risk factors associated with uncontrolled asthma in children - a systematic review and meta-analysis.

OBJECTIVE: We aim to assess the risk factors of uncontrolled asthma in children and adolescents. METHODS: A systemic search was conducted from electronic databases (PubMed/Medline, Cochrane Library, and Google Scholar) from inception to July 17, 2023. All statistical analyses were conducted in Review Manager 5.4.1. Studies meeting inclusion criteria were selected. A random-effects model was used when heterogeneity was seen to pool the studies, and the result was reported in the odds ratio and the corresponding 95% confidence interval. We also used a narrative approach where it was not feasible to quantitatively assess the outcome. RESULTS: Ten observational studies were used to conduct this systematic review and meta-analysis. A quantitative analysis of five factors was done. Pooled analysis showed a statistically significant risk of uncontrolled asthma in association with past hypersensitivity reactions (standardized mean difference [SMD] = 1.51 (1.16, 1.98); p = .002; I2 = 84%) and incomplete controller adherence (SMD = 3.15 (1.83, 5.41); p < .0001; I2 = 94%). While non-significant relation was seen in parental asthma (SMD = 1.23 (0.98, 1.55); p = .07; I2 = 15%), oral corticosteroid use (SMD = 0.99 (0.72, 1.36); p = .96; I2 = 81%) and education of caregivers (SMD = 0.99 (0.72, 1.36); p = .96; I2 = 81%). Some other factors were also discussed qualitatively. CONCLUSION: Our study shows that some significant risk factors might cause uncontrolled asthma in children and adolescents like past hypersensitivity reactions and incomplete controller adherence.

iNetModels 2.0: an interactive visualization and database of multi-omics data.

It is essential to reveal the associations between various omics data for a comprehensive understanding of the altered biological process in human wellness and disease. To date, very few studies have focused on collecting and exhibiting multi-omics associations in a single database. Here, we present iNetModels, an interactive database and visualization platform of Multi-Omics Biological Networks (MOBNs). This platform describes the associations between the clinical chemistry, anthropometric parameters, plasma proteomics, plasma metabolomics, as well as metagenomics for oral and gut microbiome obtained from the same individuals. Moreover, iNetModels includes tissue- and cancer-specific Gene Co-expression Networks (GCNs) for exploring the connections between the specific genes. This platform allows the user to interactively explore a single feature's association with other omics data and customize its particular context (e.g. male/female specific). The users can also register their data for sharing and visualization of the MOBNs and GCNs. Moreover, iNetModels allows users who do not have a bioinformatics background to facilitate human wellness and disease research. iNetModels can be accessed freely at https://inetmodels.com without any limitation.

Evaluation of aerobic biodegradation of phenanthrene using Pseudomonas turukhanskensis: an optimized study.

The ability of Pseudomonas turukhanskensis GEEL-01 to degrade the phenanthrene (PHE) was optimized by response surface methodology (RSM). Three factors as independent variables (including temperature, pH, and inoculum) were studied at 600 mg/L PHE where the highest growth of P. turukhanskensis GEEL-01 was observed. The optimum operating conditions were evaluated through the fit summary analysis, model summary statistics, fit statistics, ANOVA analysis, and model graphs. The degradation of PHE was monitored by high-performance liquid chromatography (HPLC) and the metabolites were identified by gas chromatography-mass spectrometry (GC-MS). The results showed that the correlation among independent variables with experimental and predicted responses was significant (p < 0.0001). The optimal temperature, pH, and inoculum were 30 ℃, 8, and 6 mL respectively. The HPLC peaks exhibited a reduction in PHE concentration from 600 mg/L to 4.97 mg/L with 99% degradation efficiency. The GC-MS peaks indicated that the major end products of PHE degradation were 1-Hydroxy-2-naphthoic acid, salicylic acid, phthalic acid, and catechol. This study demonstrated that the optimized parameters by RSM for P. turukhanskensis GEEL-01 could degrade PHE by phthalic and salicylic acid pathways.