Identification of DNase I hypersensitive sites in the human genome by multiple sequence descriptors.

DNase I hypersensitive sites (DHSs) are chromatin regions highly sensitive to DNase I enzymes. Studying DHSs is crucial for understanding complex transcriptional regulation mechanisms and localizing cis-regulatory elements (CREs). Numerous studies have indicated that disease-related loci are often enriched in DHSs regions, underscoring the importance of identifying DHSs. Although wet experiments exist for DHSs identification, they are often labor-intensive. Therefore, there is a strong need to develop computational methods for this purpose. In this study, we used experimental data to construct a benchmark dataset. Seven feature extraction methods were employed to capture information about human DHSs. The F-score was applied to filter the features. By comparing the prediction performance of various classification algorithms through five-fold cross-validation, random forest was proposed to perform the final model construction. The model could produce an overall prediction accuracy of 0.859 with an AUC value of 0.837. We hope that this model can assist scholars conducting DNase research in identifying these sites.

Identification of RNA­dependent liquid­liquid phase separation proteins using an artificial intelligence strategy.

RNA-dependent liquid-liquid phase separation (LLPS) proteins play critical roles in cellular processes such as stress granule formation, DNA repair, RNA metabolism, germ cell development, and protein translation regulation. The abnormal behavior of these proteins is associated with various diseases, particularly neurodegenerative disorders like amyotrophic lateral sclerosis and frontotemporal dementia, making their identification crucial. However, conventional biochemistry-based methods for identifying these proteins are time-consuming and costly. Addressing this challenge, our study developed a robust computational model for their identification. We constructed a comprehensive dataset containing 137 RNA-dependent and 606 non-RNA-dependent LLPS protein sequences, which were then encoded using amino acid composition, composition of K-spaced amino acid pairs, Geary autocorrelation, and conjoined triad methods. Through a combination of correlation analysis, mutual information scoring, and incremental feature selection, we identified an optimal feature subset. This subset was used to train a random forest model, which achieved an accuracy of 90% when tested against an independent dataset. This study demonstrates the potential of computational methods as efficient alternatives for the identification of RNA-dependent LLPS proteins. To enhance the accessibility of the model, a user-centric web server has been established and can be accessed via the link: http://rpp.lin-group.cn.

Operando NMR electrochemical gating studies of ion dynamics in PEDOT:PSS.

Although organic mixed ionic-electronic conductors are widely proposed for use in bioelectronics, energy generation/storage and neuromorphic computing, our fundamental understanding of the charge-compensating interactions between the ionic and electronic carriers and the dynamics of ions remains poor, particularly for hydrated devices and on electrochemical cycling. Here we show that operando 23Na and 1H nuclear magnetic resonance (NMR) spectroscopy can quantify cation and water movement during the doping/dedoping of films comprising the widely used mixed conductor poly(3,4-ethylene dioxythiophene) poly(styrene sulfonate) (PEDOT:PSS). A distinct 23Na quadrupolar splitting is observed due to the partial ordering of the PSS chains within the PEDOT:PSS-rich domains, with respect to the substrate. Operando 23Na NMR studies reveal a close-to-linear correlation between the quadrupolar splitting and the charge stored, which is quantitatively explained by a model in which the holes on the PEDOT backbone are bound to the PSS SO3- groups; an increase in hole concentration during doping inversely correlates with the number of Na+ ions bound to the PSS chains within the PEDOT-rich ordered domains, leading to a decrease in ions within the ordered regions and a decrease in quadrupolar splitting. The Na+-to-electron coupling efficiency, measured via 23Na NMR intensity changes, is close to 100% when using a 1 M NaCl electrolyte. Operando 1H NMR spectroscopy confirms that the Na+ ions injected into/extracted from the wet films are hydrated. These findings shed light on the working principles of organic mixed conductors and demonstrate the utility of operando NMR spectroscopy in revealing structure-property relationships in electroactive polymers.

Mslar: Microbial synthetic lethal and rescue database.

Synthetic lethality (SL) occurs when mutations in two genes together lead to cell or organism death, while a single mutation in either gene does not have a significant impact. This concept can also be extended to three or more genes for SL. Computational and experimental methods have been developed to predict and verify SL gene pairs, especially for yeast and Escherichia coli. However, there is currently a lack of a specialized platform to collect microbial SL gene pairs. Therefore, we designed a synthetic interaction database for microbial genetics that collects 13,313 SL and 2,994 Synthetic Rescue (SR) gene pairs that are reported in the literature, as well as 86,981 putative SL pairs got through homologous transfer method in 281 bacterial genomes. Our database website provides multiple functions such as search, browse, visualization, and Blast. Based on the SL interaction data in the S. cerevisiae, we review the issue of duplications' essentiality and observed that the duplicated genes and singletons have a similar ratio of being essential when we consider both individual and SL. The Microbial Synthetic Lethal and Rescue Database (Mslar) is expected to be a useful reference resource for researchers interested in the SL and SR genes of microorganisms. Mslar is open freely to everyone and available on the web at http://guolab.whu.edu.cn/Mslar/.

Comprehensive review of the identification of essential genes using computational methods: focusing on feature implementation and assessment.

Essential genes have attracted increasing attention in recent years due to the important functions of these genes in organisms. Among the methods used to identify the essential genes, accurate and efficient computational methods can make up for the deficiencies of expensive and time-consuming experimental technologies. In this review, we have collected researches on essential gene predictions in prokaryotes and eukaryotes and summarized the five predominant types of features used in these studies. The five types of features include evolutionary conservation, domain information, network topology, sequence component and expression level. We have described how to implement the useful forms of these features and evaluated their performance based on the data of Escherichia coli MG1655, Bacillus subtilis 168 and human. The prerequisite and applicable range of these features is described. In addition, we have investigated the techniques used to weight features in various models. To facilitate researchers in the field, two available online tools, which are accessible for free and can be directly used to predict gene essentiality in prokaryotes and humans, were referred. This article provides a simple guide for the identification of essential genes in prokaryotes and eukaryotes.

Understanding Fluoroethylene Carbonate and Vinylene Carbonate Based Electrolytes for Si Anodes in Lithium Ion Batteries with NMR Spectroscopy.

Fluoroethylene carbonate (FEC) and vinylene carbonate (VC) are widely used as electrolyte additives in lithium ion batteries. Here we analyze the solid electrolyte interphase (SEI) formed on binder-free silicon nanowire (SiNW) electrodes in pure FEC or VC electrolytes containing 1 M LiPF6 by solid-state NMR with and without dynamic nuclear polarization (DNP) enhancement. We find that the polymeric SEIs formed in pure FEC or VC electrolytes consist mainly of cross-linked poly(ethylene oxide) (PEO) and aliphatic chain functionalities along with additional carbonate and carboxylate species. The formation of branched fragments is further confirmed by 13C-13C correlation NMR experiments. The presence of cross-linked PEO-type polymers in FEC and VC correlates with good capacity retention and high Coulombic efficiencies of the SiNWs. Using 29Si DNP NMR, we are able to probe the interfacial region between SEI and the Si surface for the first time with NMR spectroscopy. Organosiloxanes form upon cycling, confirming that some of the organic SEI is covalently bonded to the Si surface. We suggest that both the polymeric structure of the SEI and the nature of its adhesion to the redox-active materials are important for electrochemical performance.

Accurate prediction of human essential genes using only nucleotide composition and association information.

MOTIVATION: Previously constructed classifiers in predicting eukaryotic essential genes integrated a variety of features including experimental ones. If we can obtain satisfactory prediction using only nucleotide (sequence) information, it would be more promising. Three groups recently identified essential genes in human cancer cell lines using wet experiments and it provided wonderful opportunity to accomplish our idea. Here we improved the Z curve method into the λ-interval form to denote nucleotide composition and association information and used it to construct the SVM classifying model. RESULTS: Our model accurately predicted human gene essentiality with an AUC higher than 0.88 both for 5-fold cross-validation and jackknife tests. These results demonstrated that the essentiality of human genes could be reliably reflected by only sequence information. We re-predicted the negative dataset by our Pheg server and 118 genes were additionally predicted as essential. Among them, 20 were found to be homologues in mouse essential genes, indicating that some of the 118 genes were indeed essential, however previous experiments overlooked them. As the first available server, Pheg could predict essentiality for anonymous gene sequences of human. It is also hoped the λ-interval Z curve method could be effectively extended to classification issues of other DNA elements. AVAILABILITY AND IMPLEMENTATION: http://cefg.uestc.edu.cn/Pheg. CONTACT: fbguo@uestc.edu.cn. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Identifying the Structural Basis for the Increased Stability of the Solid Electrolyte Interphase Formed on Silicon with the Additive Fluoroethylene Carbonate.

To elucidate the role of fluoroethylene carbonate (FEC) as an additive in the standard carbonate-based electrolyte for Li-ion batteries, the solid electrolyte interphase (SEI) formed during electrochemical cycling on silicon anodes was analyzed with a combination of solution and solid-state NMR techniques, including dynamic nuclear polarization. To facilitate characterization via 1D and 2D NMR, we synthesized 13C-enriched FEC, ultimately allowing a detailed structural assignment of the organic SEI. We find that the soluble poly(ethylene oxide)-like linear oligomeric electrolyte breakdown products that are observed after cycling in the standard ethylene carbonate-based electrolyte are suppressed in the presence of 10 vol% FEC additive. FEC is first defluorinated to form soluble vinylene carbonate and vinoxyl species, which react to form both soluble and insoluble branched ethylene-oxide-based polymers. No evidence for branched polymers is observed in the absence of FEC.

Accurately identifying positive and negative regulation of apoptosis using fusion features and machine learning methods.

Apoptotic proteins play a crucial role in the apoptosis process, ensuring a balance between cell proliferation and death. Thus, further elucidating the regulatory mechanisms of apoptosis will enhance our understanding of their functions. However, the development of computational methods to accurately identify positive and negative regulation of apoptosis remains a significant challenge. This work proposes a machine learning model based on multi-feature fusion to effectively identify the roles of positive and negative regulation of apoptosis. Initially, we constructed a reliable benchmark dataset containing 200 positive regulation of apoptosis and 241 negative regulation of apoptosis proteins. Subsequently, we developed a classifier that combines the support vector machine (SVM) with pseudo composition of k-spaced amino acid pairs (PseCKSAAP), composition transition distribution (CTD), dipeptide deviation from expected mean (DDE), and PSSM-composition to identify these proteins. Analysis of variance (ANOVA) was employed to select optimized features that could yield the maximum prediction performance. Evaluating the proposed model on independent data revealed and achieved an accuracy of 0.781 with an AUROC of 0.837, demonstrating our model's potent capabilities.

A protein pre-trained model-based approach for the identification of the liquid-liquid phase separation (LLPS) proteins.

Liquid-liquid phase separation (LLPS) regulates many biological processes including RNA metabolism, chromatin rearrangement, and signal transduction. Aberrant LLPS potentially leads to serious diseases. Therefore, the identification of the LLPS proteins is crucial. Traditionally, biochemistry-based methods for identifying LLPS proteins are costly, time-consuming, and laborious. In contrast, artificial intelligence-based approaches are fast and cost-effective and can be a better alternative to biochemistry-based methods. Previous research methods employed word2vec in conjunction with machine learning or deep learning algorithms. Although word2vec captures word semantics and relationships, it might not be effective in capturing features relevant to protein classification, like physicochemical properties, evolutionary relationships, or structural features. Additionally, other studies often focused on a limited set of features for model training, including planar π contact frequency, pi-pi, and ß-pairing propensities. To overcome such shortcomings, this study first constructed a reliable dataset containing 1206 protein sequences, including 603 LLPS and 603 non-LLPS protein sequences. Then a computational model was proposed to efficiently identify the LLPS proteins by perceiving semantic information of protein sequences directly; using an ESM2-36 pre-trained model based on transformer architecture in conjunction with a convolutional neural network. The model could achieve an accuracy of 85.68% and 89.67%, respectively on training data and test data, surpassing the accuracy of previous studies. The performance demonstrates the potential of our computational methods as efficient alternatives for identifying LLPS proteins.

Clarifying the Temperature-Dependent Lithium Deposition/Stripping Process and the Evolution of Inactive Li in Lithium Metal Batteries.

The deposition/stripping behavior of lithium metal is intriguing, and the associated formation of inactive lithium at various temperatures remains elusive, which hinders the practical application of lithium metal batteries. Here, utilizing the variable-temperature operando solid-state nuclear magnetic resonance (SS NMR) technique, we reveal the temperature effects on the lithium microstructure evolution in a carbonate-based electrolyte system. In addition, the mass spectrometry titration (MST) method is used to quantify the evolution of inactive lithium components, including dead lithium, solid electrolyte interface (SEI), and lithium hydride (LiH). Combined SS NMR and MST results show that the morphology of lithium metal is reasonably correlated to the amount of inactive Li formed. At low/ambient temperature, the lithium microstructure has a similar evolution pattern, and its poor morphology leads to a large amount of dead lithium, which dominates capacity loss; however, at high temperature large and dense lithium deposits form with less dead Li detected, and the intensified electrolyte consumption in SEI formation is the major cause for capacity loss. Our phase-field simulation results reveal that the compact lithium deposition formed at higher temperature is due to the more uniformly distributed electric field and Li+ concentration. Lastly, two strategies in forming a dense Li deposit are proposed and tested that show performance-enhancing results.

Understanding the failure process of sulfide-based all-solid-state lithium batteries via operando nuclear magnetic resonance spectroscopy.

The performance of all-solid-state lithium metal batteries (SSLMBs) is affected by the presence of electrochemically inactive (i.e., electronically and/or ionically disconnected) lithium metal and solid electrolyte interphase (SEI), which are jointly termed inactive lithium. However, the differentiation and quantification of inactive lithium during cycling are challenging, and their lack limits the fundamental understanding of SSLMBs failure mechanisms. To shed some light on these crucial aspects, here, we propose operando nuclear magnetic resonance (NMR) spectroscopy measurements for real-time quantification and evolution-tracking of inactive lithium formed in SSLMBs. In particular, we examine four different sulfide-based solid electrolytes, namely, Li10GeP2S12, Li9.54Si1.74P1.44S11.7Cl0.3, Li6PS5Cl and Li7P3S11. We found that the chemistry of the solid electrolyte influences the activity of lithium. Furthermore, we demonstrate that electronically disconnected lithium metal is mainly found in the interior of solid electrolytes, and ionically disconnected lithium metal is found at the negative electrode surface. Moreover, by monitoring the Li NMR signal during cell calendar ageing, we prove the faster corrosion rate of mossy/dendritic lithium than flat/homogeneous lithium in SSLMBs.

Efficient Reduction of Cr(VI) with Carbon Quantum Dots.

Hexavalent chromium (Cr(VI)) pollution is a global problem, and the reduction of highly toxic Cr(VI) to less toxic Cr(III) is considered to be an effective method to address Cr(VI) pollution. In this study, low-toxicity carbon quantum dots (CQDs) were used to reduce Cr(VI) in wastewater. The results show that CQDs can directly reduce Cr(VI) at pH 2 and can achieve a reduction efficiency of 94% within 120 min. It is observed that under pH higher than 2, CQDs can activate peroxymonosulfate (PMS) to produce reactive oxygen species (ROS) for the reduction of Cr(VI) and the reduction efficiency can reach 99% within 120 min even under neutral conditions. The investigation of the mechanism shows that the hydroxyl groups on the surface of CQDs can be directly oxidized by Cr(VI) because of the higher redox potential of Cr(VI) at pH 2. As the pH increases, the carbonyl groups on the surface of CQDs can activate PMS to generate ROS, O2 •-, and 1O2, which result in Cr(VI) being reduced. To facilitate the practical application of CQDs, the treatment of Cr(VI) in real water samples by CQDs was simulated and the method reduced Cr(VI) from an initial concentration of 5 mg/L to only 8 µg/L in 150 min, which is below the California water quality standard of 10 µg/L. The study provides a new method for the removal of Cr(VI) from wastewater and a theoretical basis for practical application.

T-G-A Deficiency Pattern in Protein-Coding Genes and Its Potential Reason.

If a stop codon appears within one gene, then its translation will be terminated earlier than expected. False folding of premature protein will be adverse to the host; hence, all functional genes would tend to avoid the intragenic stop codons. Therefore, we hypothesize that there will be less frequency of nucleotides corresponding to stop codons at each codon position of genes. Here, we validate this inference by investigating the nucleotide frequency at a large scale and results from 19,911 prokaryote genomes revealed that nucleotides coinciding with stop codons indeed have the lowest frequency in most genomes. Interestingly, genes with three types of stop codons all tend to follow a T-G-A deficiency pattern, suggesting that the property of avoiding intragenic termination pressure is the same and the major stop codon TGA plays a dominant role in this effect. Finally, a positive correlation between the TGA deficiency extent and the base length was observed in start-experimentally verified genes of Escherichia coli (E. coli). This strengthens the proof of our hypothesis. The T-G-A deficiency pattern observed would help to understand the evolution of codon usage tactics in extant organisms.

Consistent Clustering Pattern of Prokaryotic Genes Based on Base Frequency at the Second Codon Position and its Association with Functional Category Preference.

In 2002, our research group observed a gene clustering pattern based on the base frequency of A versus T at the second codon position in the genome of Vibrio cholera and found that the functional category distribution of genes in the two clusters was different. With the availability of a large number of sequenced genomes, we performed a systematic investigation of A2-T2 distribution and found that 2694 out of 2764 prokaryotic genomes have an optimal clustering number of two, indicating a consistent pattern. Analysis of the functional categories of the coding genes in each cluster in 1483 prokaryotic genomes indicated, that 99.33% of the genomes exhibited a significant difference (p < 0.01) in function distribution between the two clusters. Specifically, functional category P was overrepresented in the small cluster of 98.65% of genomes, whereas categories J, K, and L were overrepresented in the larger cluster of over 98.52% of genomes. Lineage analysis uncovered that these preferences appear consistently across all phyla. Overall, our work revealed an almost universal clustering pattern based on the relative frequency of A2 versus T2 and its role in functional category preference. These findings will promote the understanding of the rationality of theoretical prediction of functional classes of genes from their nucleotide sequences and how protein function is determined by DNA sequence.

Omicron infections profile and vaccination status among 1881 liver transplant recipients: a multi-centre retrospective cohort.

ABSTRACTA wave of Omicron infections rapidly emerged in China in 2022, but large-scale data concerning the safety profile of vaccines and Coronavirus disease 2019 (COVID-19) infection features in liver transplant (LT) recipients have not been collected. Therefore, the aim of this study was to assess the protectiveness and safety profile of the inactivated vaccines in LT patients against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant infections. A multi-centre retrospective study was conducted in a cohort with a history of liver transplantation. A total of 1881 participants (487 vaccinated and 1394 unvaccinated patients) were enrolled from seven centres in China. Fourteen of the participants were infected by Omicron, and 50% patients had over 14 days of viral shedding duration. The protection rate of COVID-19 vaccinations to Omicron was 2.59%. The three breakthrough infections occurred more than 6 months after fully vaccinated. A total of 96 (19.7%) vaccinated patients had adverse events, including fatigue, myalgia, liver dysfunction, swelling, and scleroma. There were more Grade 3 adverse events in the preoperative vaccination group than those in the postoperative vaccination group. Inactivated whole-virion SARS-CoV-2 vaccines are safe in patients with post-liver transplantation. The efficacy of inactivated vaccines decreases after 6 months of vaccination, it is recommended that liver transplant patients get boosted vaccinations as early as possible even when they are fully vaccinated. Although clinical manifestations of Omicron infections were mild in LT patients, unvaccinated patients might have a higher risk of liver dysfunction during infections.

Quantitative elucidation of associations between nucleotide identity and physicochemical properties of amino acids and the functional insight.

Studies on codon property would deepen our understanding of the origin of primitive life and enlighten biotechnical application. Here, we proposed a quantitative measurement of codon-amino acid association and found that seven out of 13 physicochemical properties have stronger associations with the nucleotide identity at the second codon position, indicating that protein structure and function may associate more closely with it than the other two sites. When extending the effect of codon-amino acid association to protein level, it was found that the correlation between the second codon position (measured by the relative frequencies of nucleobase T and A at this codon site) and hydrophobicity (by the form of GRAVY value) became stronger with 96% genomes having R > 0.90 and p < 1e-60. Furthermore, we revealed that informational genes encoding proteins have lower GRAVY values than operational proteins (p < 3e-37) in both prokaryotic and eukaryotic genomes. The above results reveal a complete link from codon identity (A2 versus T2) to amino acid property (hydrophilic versus hydrophobic) and then to protein functions (informational versus operational). Hence, our work may help to understand how the nucleotide sequence determines protein function.

Quantitatively analyzing the failure processes of rechargeable Li metal batteries.

Practical use of lithium (Li) metal for high­energy density lithium metal batteries has been prevented by the continuous formation of Li dendrites, electrochemically isolated Li metal, and the irreversible formation of solid electrolyte interphases (SEIs). Differentiating and quantifying these inactive Li species are key to understand the failure mode. Here, using operando nuclear magnetic resonance (NMR) spectroscopy together with ex situ titration gas chromatography (TGC) and mass spectrometry titration (MST) techniques, we established a solid foundation for quantifying the evolution of dead Li metal and SEI separately. The existence of LiH is identified, which causes deviation in the quantification results of dead Li metal obtained by these three techniques. The formation of inactive Li under various operating conditions has been studied quantitatively, which revealed a general "two-stage" failure process for the Li metal. The combined techniques presented here establish a benchmark to unravel the complex failure mechanism of Li metal.

Visualizing the growth process of sodium microstructures in sodium batteries by in-situ 23Na MRI and NMR spectroscopy.

The growth of sodium dendrites and the associated solid electrolyte interface (SEI) layer is a critical and fundamental issue influencing the safety and cycling lifespan of sodium batteries. In this work, we use in-situ 23Na magnetic resonance imaging (MRI) and nuclear magnetic resonance (NMR) techniques, along with an innovative analytical approach, to provide space-resolved and quantitative insights into the formation and evolution of sodium metal microstructures (SMSs; that is, dendritic and mossy Na metal) during the deposition and stripping processes. Our results reveal that the growing SMSs give rise to a linear increase in the overpotential until a transition voltage of 0.15 V is reached, at which point violent electrochemical decomposition of the electrolyte is triggered, leading to the formation of mossy-type SMSs and rapid battery failure. In addition, we determined the existence of NaH in the SEI on sodium metal with ex-situ NMR results. The poor electronic conductivity of NaH is beneficial for the growth of a stable SEI on sodium metal.

NMR Study of the Degradation Products of Ethylene Carbonate in Silicon-Lithium Ion Batteries.

Ethylene carbonate (EC) is the most widely used electrolyte solvent in lithium ion batteries, but it fails to form a stable passivation layer on materials such as Si and Li metal, which will enable the long-term cycling of the next-generation high-capacity lithium ion batteries containing these anode materials. High concentrations of soluble degradation products are detected in the electrolyte after prolonged cycling, but the chemical structures of these species remain unclear. Here, we used 1D, 2D, and diffusion NMR techniques combined with mass spectrometry to analyze electrolyte-containing 13C-labeled EC, and we report on the formation of a series of linear oligomers consisting of ethylene oxide and carbonate fragments with methoxide end groups as the major soluble degradation products of EC. Oligomers with methoxide terminals are likely to have weak interaction toward the electrode; thus, they easily detach from the electrode and are unable to passivate the surface, which may explain the origin of the capacity fade for high-capacity Si-based or Li metal anodes.