149 research outputs found
Integration of Multisource Heterogenous Omics Information in Cancer
No full textThis eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contac
Superior performance lithium-ion battery anode based on Co9S8 nanoparticles layered in-situ growth with capacitive synergy
No full textIdentifying new anode materials that possess high energy density, outstanding cycling stability, and superior rate capability has emerged as a pivotal research focus in the development of lithium-ion batteries (LIBs). Herein, we successfully synthesized a novel Co9S8-MoB MBene heterostructure. This innovative material was developed through a space-confined growth process, wherein Co9S8 nanoparticles were incorporated within the interstitial layers of MoB MBene, thereby creating a unique composite with enhanced electrochemical properties. The Co9S8-MoB MBene electrode showed excellent performance, retaining 756.34 mAh/g capacity after 200 cycles at 100 mA/g (initial capacity 828.67 mAh/g), with an impressive retention rate of 91.27 %. Even at a high current density (800 mA/g), the specific capacity of 632.1 mAh/g was maintained with a retention rate of 79.83 % after 700 cycles, and the Coulombic efficiency was consistently around 99 %. The excellent cycling stability and rate performance are attributed to the two-dimensional layered structure of conductive MoB MBene. Density functional theory (DFT) calculations reveal that MoB MBene’s low lithium diffusion barrier significantly decreases the Co9S8’s lithium binding energy, through rapid kinetic charge transfer, improving the efficiency of lithium-ion insertion and extraction. The incorporation of MoB MBene restricts the volume expansion of Co9S8 during lithiation and delithiation, and facilitates the formation of surface capacitance and the development of diffusion-controlled pseudocapacitors. The excellent electrochemical performance suggests that the Co9S8-MoB MBene materials designed in this work can be a rational approach to be applied for high-performance LIBs anodes
Roles of NEIL DNA glycosylases in oxidative stress induced genome regulation
No full textThe stability of the genome is continuously threatened by a range of internal and external stimuli that induce damage to the DNA. DNA glycosylases, such as the NEIL (Nei-like) enzyme family, have a crucial function in the base excision repair (BER) process. They identify and remove damaged bases. This thesis investigates the roles of NEIL DNA glycosylases, particularly NEIL1, NEIL2, and NEIL3, beyond their canonical functions in DNA repair.
We initially highlighted the distinctive role of NEIL DNA glycosylases in an in vitro system exposed to genotoxic agents known for causing oxidative DNA damage. Specifically, HAP1 cells missing NEIL123 showed significant resistance to oxidative stress, indicating that these cells may possess an innate adaptive mechanism characterized by elevated levels of baseline glutathione. We have demonstrated that this phenotype results in evading both apoptotic and ferroptotic cell death mechanisms. The potential explanation may stem from an alternative activity of NEIL DNA glycosylases that goes beyond base excision repair (BER), affecting chromatin dynamics and the regulation of gene expression. Our hypothesis suggests these factors may exploit this function by selectively modulating DNA secondary structures in open chromatin areas, particularly G-quadruplexes (G4s) and i-Motifs (iMs).
Subsequently, we continued our investigation in in vivo mouse models of hypoxia-ischemia (HI), since this pathological condition is characterized by extensive oxidative damage caused by elevated ROS production. Initially, we demonstrated that mice lacking NEIL2, and to a lesser degree NEIL1, displayed reduced brain tissue damage compared to wild-type (WT) animals. Remarkably, we noticed substantial changes in gene expression in Neil2-/- mice, with increased expression of key genes associated with cell survival. Notably, catalytically inactive NEIL1 and NEIL2 mutants showed no major difference in brain injury results, implying that these proteins may have non-canonical roles.
Lastly, we explored how HI alters the transcriptome and epigenome in newborn WT mice. Through extensive analysis of ChIP-seq and RNA-seq data, we were the first to profile H3K4me3, H3K27ac, and G4s in such an in vivo model. Following HI, we observed an increase of H3K27ac and a decrease in the occurrence of G4s at various genomic regulatory sites, including gene promoters, enhancers, and CpG islands. The data were accompanied by a general increase in gene expression, which is strongly correlated with the results obtained from chromatin profiling. The results of this thesis offer useful insights for further investigation into the roles of NEIL DNA glycosylases beyond canonical BER and for the development of treatment strategies in neonatal hypoxia-ischemia
Computational study of associations between histone modification and protein-DNA binding in yeast genome by integrating diverse information
No full textAbstract Background In parallel with the quick development of high-throughput technologies, in vivo (vitro) experiments for genome-wide identification of protein-DNA interactions have been developed. Nevertheless, a few questions remain in the field, such as how to distinguish true protein-DNA binding (functional binding) from non-specific protein-DNA binding (non-functional binding). Previous researches tackled the problem by integrated analysis of multiple available sources. However, few systematic studies have been carried out to examine the possible relationships between histone modification and protein-DNA binding. Here this issue was investigated by using publicly available histone modification data in yeast. Results Two separate histone modification datasets were studied, at both the open reading frame (ORF) and the promoter region of binding targets for 37 yeast transcription factors. Both results revealed a distinct histone modification pattern between the functional protein-DNA binding sites and non-functional ones for almost half of all TFs tested. Such difference is much stronger at the ORF than at the promoter region. In addition, a protein-histone modification interaction pathway can only be inferred from the functional protein binding targets. Conclusions Overall, the results suggest that histone modification information can be used to distinguish the functional protein-DNA binding from the non-functional, and that the regulation of various proteins is controlled by the modification of different histone lysines such as the protein-specific histone modification levels.</p
Quantitative model for inferring dynamic regulation of the tumour suppressor gene p53
No full textBackground: The availability of various "omics" datasets creates a prospect of performing the study of genome-wide genetic regulatory networks. However, one of the major challenges of using mathematical models to infer genetic regulation from microarray datasets is the lack of information for protein concentrations and activities. Most of the previous researches were based on an assumption that the mRNA levels of a gene are consistent with its protein activities, though it is not always the case. Therefore, a more sophisticated modelling framework together with the corresponding inference methods is needed to accurately estimate genetic regulation from "omics" datasets.
Results: This work developed a novel approach, which is based on a nonlinear mathematical model, to infer genetic regulation from microarray gene expression data. By using the p53 network as a test system, we used the nonlinear model to estimate the activities of transcription factor (TF) p53 from the expression levels of its target genes, and to identify the activation/inhibition status of p53 to its target genes. The predicted top 317 putative p53 target genes were supported by DNA sequence analysis. A comparison between our prediction and the other published predictions of p53 targets suggests that most of putative p53 targets may share a common depleted or enriched sequence signal on their upstream non-coding region.
Conclusions: The proposed quantitative model can not only be used to infer the regulatory relationship between TF and its down-stream genes, but also be applied to estimate the protein activities of TF from the expression levels of its target genes
The effect of prior assumptions over the weights in BayesPI with application to study protein-DNA interactions from ChIP-based high-throughput data
No full textAbstract Background To further understand the implementation of hyperparameters re-estimation technique in Bayesian hierarchical model, we added two more prior assumptions over the weight in BayesPI, namely Laplace prior and Cauchy prior, by using the evidence approximation method. In addition, we divided hyperparameter (regularization constants α of the model) into multiple distinct classes based on either the structure of the neural networks or the property of the weights. Results The newly implemented BayesPI was tested on both synthetic and real ChIP-based high-throughput datasets to identify the corresponding protein binding energy matrices. The results obtained were encouraging: 1) there was a minor effect on the quality of predictions when prior assumptions over the weights were altered (e.g. the prior probability distributions to the weights and the number of classes to the hyperparameters) in BayesPI; 2) however, there was a significant impact on the computational speed when tuning the weight prior in the model: for example, BayesPI with a Laplace weight prior achieved the best performance with regard to both the computational speed and the prediction accuracy. Conclusions From this study, we learned that it is absolutely necessary to try different prior assumptions over the weights in Bayesian hierarchical model to design an efficient learning algorithm, though the quality of the final results may not be associated with such changes. In future, the evidence approximation method can be an alternative to Monte Carlo methods for computational implementation of Bayesian hierarchical model.</p
CutAndTagAnalyzer - A New Python Package for CUT&Tag Data Analysis
No full textHelt siden oppfinnelsen av neste generasjons sekvensering har nye metoder for å forstå genuttrykk og kontroll gjennom epigenetiske merker blitt langet. Blant disse har Cut&Tag- analyse vist seg å bli en effektiv epigenetisk profileringsteknikk med høy sensitivitet, lave bakgrunnsverdier, som er kompatibel med små prøvemengder. Selv om våtlabstekknikene er på plass, kan analyse av CUT&Tag data skape problemer for forskere med mindre programmeringserfaringer. Derfor presenterer denne masteroppgaven en ny Python-pakke som forenkler datanalysen av CUT&Tag-data, noe som resulterer i raskere og enklere analyse, slik at flere forskere kan utnytte teknikken. Pipeline-pakken er basert på det originale CUT&Tag- innovasjonsteamests datanalyseprotokoll og inkluderer alle nødvendige trinn fra kvalitetskontroll til annotering og differensialanalyse. Under en testkjøring gjenskapte den nyopprettede pipelinen plottene fra den opprinnelige protokollen, fikset en feil i reprodduserbarhetsvurdering og opprettet nye plot samt filer fra tilleggs funksjonen; annotering og differensialanalyse, som ikke var en del av den opprinnelige protokollen. I tillegg viste funksjonsannoterings analyse av annoterte gener, at prediksjonene var i samsvar med gjeldende litteratur om histon modifikasjonene som var målene i eksperimentet.Ever since the invention of next-generation sequencing, new methods for understanding gene expression and control through epigenetics marks have been created. Among these, CUT&Tag analysis has emerged to become an efficient epigenomic profiling technique with low input requirements, high sensitivity, and lower background signals. Even though the wet-lab techniques are in place, analyzing the data is still a challenge for scientists with less computational skills, such as biologists. Therefore, this master’s thesis presents a new Python package that not only simplifies the data analysis of CUT&Tag sequencing but also allows biomedical scientists to easily interpret the results. The new pipeline package is based on the original CUT&Tag innovation team ́s data analysis protocol. It includes every step necessary from quality control to annotation and differential peak analysis. The package also fixed a few bugs, (e.g., reproducibility assessment) from the original protocol, and added new visualization plots, and features like genome annotation and differential peak analysis. In a demonstration run on a real CUT&Tag data set, the new package successfully recreated the plots from the original study. Additionally, function annotation analysis on annotated genes revealed predictions supporting current literature on the target proteins
BayesPI - a new model to study protein-DNA interactions: a case study of condition-specific protein binding parameters for Yeast transcription factors
No full textAbstract Background We have incorporated Bayesian model regularization with biophysical modeling of protein-DNA interactions, and of genome-wide nucleosome positioning to study protein-DNA interactions, using a high-throughput dataset. The newly developed method (BayesPI) includes the estimation of a transcription factor (TF) binding energy matrices, the computation of binding affinity of a TF target site and the corresponding chemical potential. Results The method was successfully tested on synthetic ChIP-chip datasets, real yeast ChIP-chip experiments. Subsequently, it was used to estimate condition-specific and species-specific protein-DNA interaction for several yeast TFs. Conclusion The results revealed that the modification of the protein binding parameters and the variation of the individual nucleotide affinity in either recognition or flanking sequences occurred under different stresses and in different species. The findings suggest that such modifications may be adaptive and play roles in the formation of the environment-specific binding patterns of yeast TFs and in the divergence of TF binding sites across the related yeast species.</p
A new framework for identifying combinatorial regulation of transcription factors: A case study of the yeast cell cycle
No full textAbstractBy integrating heterogeneous functional genomic datasets, we have developed a new framework for detecting combinatorial control of gene expression, which includes estimating transcription factor activities using a singular value decomposition method and reducing high-dimensional input gene space by considering genomic properties of gene clusters. The prediction of cooperative gene regulation is accomplished by either Gaussian Graphical Models or Pairwise Mixed Graphical Models. The proposed framework was tested on yeast cell cycle datasets: (1) 54 known yeast cell cycle genes with 9 cell cycle regulators and (2) 676 putative yeast cell cycle genes with 9 cell cycle regulators. The new framework gave promising results on inferring TF–TF and TF-gene interactions. It also revealed several interesting mechanisms such as negatively correlated protein–protein interactions and low affinity protein–DNA interactions that may be important during the yeast cell cycle. The new framework may easily be extended to study other higher eukaryotes
Quality versus accuracy: result of a reanalysis of protein-binding microarrays from the DREAM5 challenge by using BayesPI2 including dinucleotide interdependence
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