Identification of key genes and molecular mechanisms of chronic urticaria based on bioinformatics.

Chronic urticaria (CU) is characterized by persistent skin hives, redness, and itching, enhanced by immune dysregulation and inflammation. Our main objective is identifying key genes and molecular mechanisms of chronic urticaria based on bioinformatics. We used the Gene Expression Omnibus (GEO) database and retrieved two GEO datasets, GSE57178 and GSE72540. The raw data were extracted, pre-processed, and analyzed using the GEO2R tool to identify the differentially expressed genes (DEGs). The samples were divided into two groups: healthy samples and CU samples. We defined cut-off values of log2 fold change ≥1 and p < .05. Analyses were performed in the Kyoto Encyclopaedia of Genes and Genomes (KEGG), the Database for Annotation, Visualization and Integrated Discovery (DAVID), Metascape, Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and CIBERSOFT databases. We obtained 1613 differentially expressed genes. There were 114 overlapping genes in both datasets, out of which 102 genes were up-regulated while 12 were down-regulated. The biological processes included activation of myeloid leukocytes, response to inflammations, and response to organic substances. Moreover, the KEGG pathways of CU were enriched in the Nuclear Factor-Kappa B (NF-kB) signaling pathway, Tumor Necrosis Factor (TNF) signaling pathway, and Janus kinase/signal transducers and activators of transcription (JAK-STAT) signaling pathway. We identified 27 hub genes that were implicated in the pathogenesis of CU, such as interleukin-6 (IL-6), Prostaglandin-endoperoxide synthase 2 (PTGS2), and intercellular adhesion molecule-1 (ICAM1). The complex interplay between immune responses, inflammatory pathways, cytokine networks, and specific genes enhances CU. Understanding these mechanisms paves the way for potential interventions to mitigate symptoms and improve the quality of life of CU patients.

Identification of Potential Crucial Biomarkers in STEMI Through Integrated Bioinformatic Analysis. / Identificação de Potenciais Biomarcadores Cruciais em IAMCSST por Meio de Análise Bioinformática Integrada.

BACKGROUND: ST-segment elevation myocardial infarction (STEMI) is one of the leading causes of fatal cardiovascular diseases, which have been the prime cause of mortality worldwide. Diagnosis in the early phase would benefit clinical intervention and prognosis, but the exploration of the biomarkers of STEMI is still lacking. OBJECTIVES: In this study, we conducted a bioinformatics analysis to identify potential crucial biomarkers in the progress of STEMI. METHODS: We obtained GSE59867 for STEMI and stable coronary artery disease (SCAD) patients. Differentially expressed genes (DEGs) were screened with the threshold of |log2fold change| > 0.5 and p <0.05. Based on these genes, we conducted enrichment analysis to explore the potential relevance between genes and to screen hub genes. Subsequently, hub genes were analyzed to detect related miRNAs and DAVID to detect transcription factors for further analysis. Finally, GSE62646 was utilized to assess DEGs specificity, with genes demonstrating AUC results exceeding 75%, indicating their potential as candidate biomarkers. RESULTS: 133 DEGs between SCAD and STEMI were obtained. Then, the PPI network of DEGs was constructed using String and Cytoscape, and further analysis determined hub genes and 6 molecular complexes. Functional enrichment analysis of the DEGs suggests that pathways related to inflammation, metabolism, and immunity play a pivotal role in the progression from SCAD to STEMI. Besides, related-miRNAs were predicted, has-miR-124, has-miR-130a/b, and has-miR-301a/b regulated the expression of the largest number of genes. Meanwhile, Transcription factors analysis indicate that EVI1, AML1, GATA1, and PPARG are the most enriched gene. Finally, ROC curves demonstrate that MS4A3, KLRC4, KLRD1, AQP9, and CD14 exhibit both high sensitivity and specificity in predicting STEMI. CONCLUSIONS: This study revealed that immunity, metabolism, and inflammation are involved in the development of STEMI derived from SCAD, and 6 genes, including MS4A3, KLRC4, KLRD1, AQP9, CD14, and CCR1, could be employed as candidate biomarkers to STEMI.

FUNDAMENTO: O infarto do miocárdio com elevação do segmento ST (IAMCSST) é uma das principais causas de doenças cardiovasculares fatais, que têm sido a principal causa de mortalidade em todo o mundo. O diagnóstico na fase inicial beneficiaria a intervenção clínica e o prognóstico, mas ainda falta a exploração dos biomarcadores do IAMCSST. OBJETIVOS: Neste estudo, conduzimos uma análise bioinformática para identificar potenciais biomarcadores cruciais no progresso do IAMCSST. MÉTODOS: Obtivemos GSE59867 para pacientes com IAMCSST e doença arterial coronariana estável (DACE). Genes diferencialmente expressos (GDEs) foram selecionados com o limiar de |log2fold change| > 0,5 e p < 0,05. Com base nesses genes, conduzimos análises de enriquecimento para explorar a relevância potencial entre genes e para rastrear genes centrais. Posteriormente, os genes centrais foram analisados para detectar miRNAs relacionados e DAVID para detectar fatores de transcrição para análise posterior. Finalmente, o GSE62646 foi utilizado para avaliar a especificidade dos GDEs, com genes demonstrando resultados de AUC superiores a 75%, indicando seu potencial como candidatos a biomarcadores. Posteriormente, os genes centrais foram analisados para detectar miRNAs relacionados e DAVID para detectar fatores de transcrição para análise posterior. Finalmente, o GSE62646 foi utilizado para avaliar a especificidade dos GDEs, com genes demonstrando resultados de AUC superiores a 75%, indicando seu potencial como candidatos a biomarcadores. RESULTADOS: 133 GDEs entre DACE e IAMCSST foram obtidos. Em seguida, a rede PPI de GDEs foi construída usando String e Cytoscape, e análises posteriores determinaram genes centrais e 6 complexos moleculares. A análise de enriquecimento funcional dos GDEs sugere que as vias relacionadas à inflamação, metabolismo e imunidade desempenham um papel fundamental na progressão de DACE para IAMCSST. Além disso, foram previstos miRNAs relacionados, has-miR-124, has-miR-130a/b e has-miR-301a/b regularam a expressão do maior número de genes. Enquanto isso, a análise dos fatores de transcrição indica que EVI1, AML1, GATA1 e PPARG são os genes mais enriquecidos. Finalmente, as curvas ROC demonstram que MS4A3, KLRC4, KLRD1, AQP9 e CD14 exibem alta sensibilidade e especificidade na previsão de IAMCSST. CONCLUSÕES: Este estudo revelou que imunidade, metabolismo e inflamação estão envolvidos no desenvolvimento de IAMCSST derivado de DACE, e 6 genes, incluindo MS4A3, KLRC4, KLRD1, AQP9, CD14 e CCR1, poderiam ser empregados como candidatos a biomarcadores para IAMCSST.

Enhancing missense variant pathogenicity prediction with protein language models using VariPred.

Computational approaches for predicting the pathogenicity of genetic variants have advanced in recent years. These methods enable researchers to determine the possible clinical impact of rare and novel variants. Historically these prediction methods used hand-crafted features based on structural, evolutionary, or physiochemical properties of the variant. In this study we propose a novel framework that leverages the power of pre-trained protein language models to predict variant pathogenicity. We show that our approach VariPred (Variant impact Predictor) outperforms current state-of-the-art methods by using an end-to-end model that only requires the protein sequence as input. Using one of the best-performing protein language models (ESM-1b), we establish a robust classifier that requires no calculation of structural features or multiple sequence alignments. We compare the performance of VariPred with other representative models including 3Cnet, Polyphen-2, REVEL, MetaLR, FATHMM and ESM variant. VariPred performs as well as, or in most cases better than these other predictors using six variant impact prediction benchmarks despite requiring only sequence data and no pre-processing of the data.

High expression of CASP1 induces atherosclerosis.

Atherosclerosis is a chronic, progressive vascular disease. The relationship between CASP1 gene expression and atherosclerosis remains unclear. The atherosclerosis dataset GSE132651 and GSE202625 profiles were downloaded from gene expression omnibus. Differentially expressed genes (DEGs) were screened. The construction and analysis of protein-protein interaction network, functional enrichment analysis, gene set enrichment analysis, and Comparative Toxicogenomics Database analysis were performed. Gene expression heatmap was drawn. TargetScan was used to screen miRNAs that regulate central DEG. 47 DEGs were identified. According to gene ontology analysis, they were mainly enriched in the regulation of stimulus response, response to organic matter, extracellular region, extracellular region, and the same protein binding. Kyoto Encyclopedia of Gene and Genome analysis results showed that the target cells were mainly enriched in the PI3K-Akt signaling pathway, Ras signaling pathway, and PPAR signaling pathway. In the enrichment project of Metascape, vascular development, regulation of body fluid levels, and positive regulation of cell motility can be seen in the gene ontology enrichment project. Eleven core genes (CASP1, NLRP3, MRC1, IRS1, PPARG, APOE, IL13, FGF2, CCR2, ICAM1, HIF1A) were obtained. IRS1, PPARG, APOE, FGF2, CCR2, and HIF1A genes are identified as core genes. Gene expression heatmap showed that CASP1 was highly expressed in atherosclerosis samples and low expressed in normal samples. NLRP3, MRC1, IRS1, PPARG, APOE, IL13, FGF2, CCR2, ICAM1, HIF1A were low expressed in atherosclerosis samples. CTD analysis showed that 5 genes (CASP1, NLRP3, CCR2, ICAM1, HIF1A) were found to be associated with pneumonia, inflammation, cardiac enlargement, and tumor invasiveness. CASP1 gene is highly expressed in atherosclerosis. The higher the CASP1 gene, the worse the prognosis.

Insights from bioinformatics analysis reveal that lipopolysaccharide induces activation of chemokine-related signaling pathways in human nasal epithelial cells.

Lipopolysaccharide (LPS) is known to elicit a robust immune response. This study aimed to investigate the impact of LPS on the transcriptome of human nasal epithelial cells (HNEpC). HNEpC were cultured and stimulated with LPS (1 µg/mL) or an equivalent amount of normal culture medium. Subsequently, total RNA was extracted, purified, and sequenced using next-generation RNA sequencing technology. Differentially expressed genes (DEGs) were identified and subjected to functional enrichment analysis. A protein-protein interaction (PPI) network of DEGs was constructed, followed by Ingenuity Pathway Analysis (IPA) to identify molecular pathways influenced by LPS exposure on HNEpC. Validation of key genes was performed using quantitative real-time PCR (qRT-PCR). A total of 97 DEGs, comprising 48 up-regulated genes and 49 down-regulated genes, were identified. Results from functional enrichment analysis, PPI, and IPA indicated that DEGs were predominantly enriched in chemokine-related signaling pathways. Subsequent qRT-PCR validation demonstrated significant upregulation of key genes in these pathways in LPS-treated HNEpC compared to control cells. In conclusion, LPS intervention profoundly altered the transcriptome of HNEpC, potentially exacerbating inflammatory responses through the activation of chemokine-related signaling pathways.

UBE2C: A pan-cancer diagnostic and prognostic biomarker revealed through bioinformatics analysis.

BACKGROUND: The diverse and complex attributes of cancer have made it a daunting challenge to overcome globally and remains to endanger human life. Detection of critical cancer-related gene alterations in solid tumor samples better defines patient diagnosis and prognosis, and indicates what targeted therapies must be administered to improve cancer patients' outcome. MATERIALS AND METHODS: To identify genes that have aberrant expression across different cancer types, differential expressed genes were detected within the TCGA datasets. Subsequently, the DEGs common to all pan cancers were determined. Furthermore, various methods were employed to gain genetic alterations, co-expression genes network and protein-protein interaction (PPI) network, pathway enrichment analysis of common genes. Finally, the gene regulatory network was constructed. RESULTS: Intersectional analysis identified UBE2C as a common DEG between all 28 types of studied cancers. Upregulated UBE2C expression was significantly correlated with OS and DFS of 10 and 9 types of cancer patients. Also, UBE2C can be a diagnostic factor in CESC, CHOL, GBM, and UCS with AUC = 100% and diagnose 19 cancer types with AUC ≥90%. A ceRNA network constructed including UBE2C, 41 TFs, 10 shared miRNAs, and 21 circRNAs and 128 lncRNAs. CONCLUSION: In summary, UBE2C can be a theranostic gene, which may serve as a reliable biomarker in diagnosing cancers, improving treatment responses and increasing the overall survival of cancer patients and can be a promising gene to be target by cancer drugs in the future.

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 .

DeepFGRN: inference of gene regulatory network with regulation type based on directed graph embedding.

The inference of gene regulatory networks (GRNs) from gene expression profiles has been a key issue in systems biology, prompting many researchers to develop diverse computational methods. However, most of these methods do not reconstruct directed GRNs with regulatory types because of the lack of benchmark datasets or defects in the computational methods. Here, we collect benchmark datasets and propose a deep learning-based model, DeepFGRN, for reconstructing fine gene regulatory networks (FGRNs) with both regulation types and directions. In addition, the GRNs of real species are always large graphs with direction and high sparsity, which impede the advancement of GRN inference. Therefore, DeepFGRN builds a node bidirectional representation module to capture the directed graph embedding representation of the GRN. Specifically, the source and target generators are designed to learn the low-dimensional dense embedding of the source and target neighbors of a gene, respectively. An adversarial learning strategy is applied to iteratively learn the real neighbors of each gene. In addition, because the expression profiles of genes with regulatory associations are correlative, a correlation analysis module is designed. Specifically, this module not only fully extracts gene expression features, but also captures the correlation between regulators and target genes. Experimental results show that DeepFGRN has a competitive capability for both GRN and FGRN inference. Potential biomarkers and therapeutic drugs for breast cancer, liver cancer, lung cancer and coronavirus disease 2019 are identified based on the candidate FGRNs, providing a possible opportunity to advance our knowledge of disease treatments.

From tradition to innovation: conventional and deep learning frameworks in genome annotation.

Following the milestone success of the Human Genome Project, the 'Encyclopedia of DNA Elements (ENCODE)' initiative was launched in 2003 to unearth information about the numerous functional elements within the genome. This endeavor coincided with the emergence of numerous novel technologies, accompanied by the provision of vast amounts of whole-genome sequences, high-throughput data such as ChIP-Seq and RNA-Seq. Extracting biologically meaningful information from this massive dataset has become a critical aspect of many recent studies, particularly in annotating and predicting the functions of unknown genes. The core idea behind genome annotation is to identify genes and various functional elements within the genome sequence and infer their biological functions. Traditional wet-lab experimental methods still rely on extensive efforts for functional verification. However, early bioinformatics algorithms and software primarily employed shallow learning techniques; thus, the ability to characterize data and features learning was limited. With the widespread adoption of RNA-Seq technology, scientists from the biological community began to harness the potential of machine learning and deep learning approaches for gene structure prediction and functional annotation. In this context, we reviewed both conventional methods and contemporary deep learning frameworks, and highlighted novel perspectives on the challenges arising during annotation underscoring the dynamic nature of this evolving scientific landscape.

DeepReg: a deep learning hybrid model for predicting transcription factors in eukaryotic and prokaryotic genomes.

Deep learning models (DLMs) have gained importance in predicting, detecting, translating, and classifying a diversity of inputs. In bioinformatics, DLMs have been used to predict protein structures, transcription factor-binding sites, and promoters. In this work, we propose a hybrid model to identify transcription factors (TFs) among prokaryotic and eukaryotic protein sequences, named Deep Regulation (DeepReg) model. Two architectures were used in the DL model: a convolutional neural network (CNN), and a bidirectional long-short-term memory (BiLSTM). DeepReg reached a precision of 0.99, a recall of 0.97, and an F1-score of 0.98. The quality of our predictions, the bias-variance trade-off approach, and the characterization of new TF predictions were evaluated and compared against those produced by DeepTFactor, as well as against experimental data from three model organisms. Predictions based on our DLM tended to exhibit less variance and bias than those from DeepTFactor, thus increasing reliability and decreasing overfitting.

Prioritization of oligogenic variant combinations in whole exomes.

MOTIVATION: Whole exome sequencing (WES) has emerged as a powerful tool for genetic research, enabling the collection of a tremendous amount of data about human genetic variation. However, properly identifying which variants are causative of a genetic disease remains an important challenge, often due to the number of variants that need to be screened. Expanding the screening to combinations of variants in two or more genes, as would be required under the oligogenic inheritance model, simply blows this problem out of proportion. RESULTS: We present here the High-throughput oligogenic prioritizer (Hop), a novel prioritization method that uses direct oligogenic information at the variant, gene and gene pair level to detect digenic variant combinations in WES data. This method leverages information from a knowledge graph, together with specialized pathogenicity predictions in order to effectively rank variant combinations based on how likely they are to explain the patient's phenotype. The performance of Hop is evaluated in cross-validation on 36 120 synthetic exomes for training and 14 280 additional synthetic exomes for independent testing. Whereas the known pathogenic variant combinations are found in the top 20 in approximately 60% of the cross-validation exomes, 71% are found in the same ranking range when considering the independent set. These results provide a significant improvement over alternative approaches that depend simply on a monogenic assessment of pathogenicity, including early attempts for digenic ranking using monogenic pathogenicity scores. AVAILABILITY AND IMPLEMENTATION: Hop is available at https://github.com/oligogenic/HOP.

MGCNSS: miRNA-disease association prediction with multi-layer graph convolution and distance-based negative sample selection strategy.

Identifying disease-associated microRNAs (miRNAs) could help understand the deep mechanism of diseases, which promotes the development of new medicine. Recently, network-based approaches have been widely proposed for inferring the potential associations between miRNAs and diseases. However, these approaches ignore the importance of different relations in meta-paths when learning the embeddings of miRNAs and diseases. Besides, they pay little attention to screening out reliable negative samples which is crucial for improving the prediction accuracy. In this study, we propose a novel approach named MGCNSS with the multi-layer graph convolution and high-quality negative sample selection strategy. Specifically, MGCNSS first constructs a comprehensive heterogeneous network by integrating miRNA and disease similarity networks coupled with their known association relationships. Then, we employ the multi-layer graph convolution to automatically capture the meta-path relations with different lengths in the heterogeneous network and learn the discriminative representations of miRNAs and diseases. After that, MGCNSS establishes a highly reliable negative sample set from the unlabeled sample set with the negative distance-based sample selection strategy. Finally, we train MGCNSS under an unsupervised learning manner and predict the potential associations between miRNAs and diseases. The experimental results fully demonstrate that MGCNSS outperforms all baseline methods on both balanced and imbalanced datasets. More importantly, we conduct case studies on colon neoplasms and esophageal neoplasms, further confirming the ability of MGCNSS to detect potential candidate miRNAs. The source code is publicly available on GitHub https://github.com/15136943622/MGCNSS/tree/master.

Integrated bioinformatics analysis of retinal ischemia/reperfusion injury in rats with potential key genes.

The tissue damage caused by transient ischemic injury is an essential component of the pathogenesis of retinal ischemia, which mainly hinges on the degree and duration of interruption of the blood supply and the subsequent damage caused by tissue reperfusion. Some research indicated that the retinal injury induced by ischemia-reperfusion (I/R) was related to reperfusion time.In this study, we screened the differentially expressed circRNAs, lncRNAs, and mRNAs between the control and model group and at different reperfusion time (24h, 72h, and 7d) with the aid of whole transcriptome sequencing technology, and the trend changes in time-varying mRNA, lncRNA, circRNA were obtained by chronological analysis. Then, candidate circRNAs, lncRNAs, and mRNAs were obtained as the intersection of differentially expression genes and trend change genes. Importance scores of the genes selected the key genes whose expression changed with the increase of reperfusion time. Also, the characteristic differentially expressed genes specific to the reperfusion time were analyzed, key genes specific to reperfusion time were selected to show the change in biological process with the increase of reperfusion time.As a result, 316 candidate mRNAs, 137 candidate lncRNAs, and 31 candidate circRNAs were obtained by the intersection of differentially expressed mRNAs, lncRNAs, and circRNAs with trend mRNAs, trend lncRNAs and trend circRNAs, 5 key genes (Cd74, RT1-Da, RT1-CE5, RT1-Bb, RT1-DOa) were selected by importance scores of the genes. The result of GSEA showed that key genes were found to play vital roles in antigen processing and presentation, regulation of the actin cytoskeleton, and the ribosome. A network included 4 key genes (Cd74, RT1-Da, RT1-Bb, RT1-DOa), 34 miRNAs and 48 lncRNAs, and 81 regulatory relationship axes, and a network included 4 key genes (Cd74, RT1-Da, RT1-Bb, RT1-DOa), 9 miRNAs and 3 circRNAs (circRNA_10572, circRNA_03219, circRNA_11359) and 12 regulatory relationship axes were constructed, the subcellular location, transcription factors, signaling network, targeted drugs and relationship to eye diseases of key genes were predicted. 1370 characteristic differentially expressed mRNAs (spec_24h mRNA), 558 characteristic differentially expressed mRNAs (spec_72h mRNA), and 92 characteristic differentially expressed mRNAs (spec_7d mRNA) were found, and their key genes and regulation networks were analyzed.In summary, we screened the differentially expressed circRNAs, lncRNAs, and mRNAs between the control and model groups and at different reperfusion time (24h, 72h, and 7d). 5 key genes, Cd74, RT1-Da, RT1-CE5, RT1-Bb, RT1-DOa, were selected. Key genes specific to reperfusion time were selected to show the change in biological process with the increased reperfusion time. These results provided theoretical support and a reference basis for the clinical treatment.

Identification and bioinformatics analysis of lncRNAs in serum of patients with ankylosing spondylitis.

OBJECTIVES: The aim of this study was to explore the long non-coding RNA (lncRNA) expression profiles in serum of patients with ankylosing spondylitis (AS). The role of these lncRNAs in this complex autoimmune situation needs to be evaluated. METHODS: We used high-throughput whole-transcriptome sequencing to generate sequencing data from three patients with AS and three normal controls (NC). Then, we performed bioinformatics analyses to identify the functional and biological processes associated with differentially expressed lncRNAs (DElncRNAs). We confirmed the validity of our RNA-seq data by assessing the expression of eight lncRNAs via quantitative reverse transcription polymerase chain reaction (qRT-PCR) in 20 AS and 20 NC samples. We measured the correlation between the expression levels of lncRNAs and patient clinical index values using the Spearman correlation test. RESULTS: We identified 72 significantly upregulated and 73 significantly downregulated lncRNAs in AS patients compared to NC. qRT-PCR was performed to validate the expression of selected DElncRNAs; the results demonstrated that the expression levels of MALAT1:24, NBR2:9, lnc-DLK1-35:13, lnc-LARP1-1:1, lnc-AIPL1-1:7, and lnc-SLC12A7-1:16 were consistent with the sequencing analysis results. Enrichment analysis showed that DElncRNAs mainly participated in the immune and inflammatory responses pathways, such as regulation of protein ubiquitination, major histocompatibility complex class I-mediated antigen processing and presentation, MAPkinase activation, and interleukin-17 signaling pathways. In addition, a competing endogenous RNA network was constructed to determine the interaction among the lncRNAs, microRNAs, and mRNAs based on the confirmed lncRNAs (MALAT1:24 and NBR2:9). We further found the expression of MALAT1:24 and NBR2:9 to be positively correlated with disease severity. CONCLUSION: Taken together, our study presents a comprehensive overview of lncRNAs in the serum of AS patients, thereby contributing novel perspectives on the underlying pathogenic mechanisms of this condition. In addition, our study predicted MALAT1 has the potential to be deeply involved in the pathogenesis of AS.

Benchmarking bioinformatic virus identification tools using real-world metagenomic data across biomes.

BACKGROUND: As most viruses remain uncultivated, metagenomics is currently the main method for virus discovery. Detecting viruses in metagenomic data is not trivial. In the past few years, many bioinformatic virus identification tools have been developed for this task, making it challenging to choose the right tools, parameters, and cutoffs. As all these tools measure different biological signals, and use different algorithms and training and reference databases, it is imperative to conduct an independent benchmarking to give users objective guidance. RESULTS: We compare the performance of nine state-of-the-art virus identification tools in thirteen modes on eight paired viral and microbial datasets from three distinct biomes, including a new complex dataset from Antarctic coastal waters. The tools have highly variable true positive rates (0-97%) and false positive rates (0-30%). PPR-Meta best distinguishes viral from microbial contigs, followed by DeepVirFinder, VirSorter2, and VIBRANT. Different tools identify different subsets of the benchmarking data and all tools, except for Sourmash, find unique viral contigs. Performance of tools improved with adjusted parameter cutoffs, indicating that adjustment of parameter cutoffs before usage should be considered. CONCLUSIONS: Together, our independent benchmarking facilitates selecting choices of bioinformatic virus identification tools and gives suggestions for parameter adjustments to viromics researchers.

Investigating the potential of Juglans regia phytoconstituents for the treatment of cervical cancer utilizing network biology and molecular docking approach.

The fourth most frequent type of cancer in women and the leading cause of mortality for females worldwide is cervical cancer. Traditionally, medicinal plants have been utilized to treat various illnesses and ailments. The molecular docking method is used in the current study to look into the phytoconstituents of Juglans regia's possible anticancer effects on cervical cancer target proteins. This work uses the microarray dataset analysis of GSE63678 from the NCBI Gene Expression Omnibus database to find differentially expressed genes. Furthermore, protein-protein interactions of differentially expressed genes were constructed using network biology techniques. The top five hub genes (IGF1, FGF2, ESR1, MYL9, and MYH11) are then determined by computing topological parameters with Cytohubba. In addition, molecular docking research was performed on Juglans regia phytocompounds that were extracted from the IMPPAT database versus hub genes that had been identified. Utilizing molecular dynamics, simulation confirmed that prioritized docked complexes with low binding energies were stable.

A probabilistic knowledge graph for target identification.

Early identification of safe and efficacious disease targets is crucial to alleviating the tremendous cost of drug discovery projects. However, existing experimental methods for identifying new targets are generally labor-intensive and failure-prone. On the other hand, computational approaches, especially machine learning-based frameworks, have shown remarkable application potential in drug discovery. In this work, we propose Progeni, a novel machine learning-based framework for target identification. In addition to fully exploiting the known heterogeneous biological networks from various sources, Progeni integrates literature evidence about the relations between biological entities to construct a probabilistic knowledge graph. Graph neural networks are then employed in Progeni to learn the feature embeddings of biological entities to facilitate the identification of biologically relevant target candidates. A comprehensive evaluation of Progeni demonstrated its superior predictive power over the baseline methods on the target identification task. In addition, our extensive tests showed that Progeni exhibited high robustness to the negative effect of exposure bias, a common phenomenon in recommendation systems, and effectively identified new targets that can be strongly supported by the literature. Moreover, our wet lab experiments successfully validated the biological significance of the top target candidates predicted by Progeni for melanoma and colorectal cancer. All these results suggested that Progeni can identify biologically effective targets and thus provide a powerful and useful tool for advancing the drug discovery process.

EpiCarousel: memory- and time-efficient identification of metacells for atlas-level single-cell chromatin accessibility data.

SUMMARY: Recent technical advancements in single-cell chromatin accessibility sequencing (scCAS) have brought new insights to the characterization of epigenetic heterogeneity. As single-cell genomics experiments scale up to hundreds of thousands of cells, the demand for computational resources for downstream analysis grows intractably large and exceeds the capabilities of most researchers. Here, we propose EpiCarousel, a tailored Python package based on lazy loading, parallel processing, and community detection for memory- and time-efficient identification of metacells, i.e. the emergence of homogenous cells, in large-scale scCAS data. Through comprehensive experiments on five datasets of various protocols, sample sizes, dimensions, number of cell types, and degrees of cell-type imbalance, EpiCarousel outperformed baseline methods in systematic evaluation of memory usage, computational time, and multiple downstream analyses including cell type identification. Moreover, EpiCarousel executes preprocessing and downstream cell clustering on the atlas-level dataset with 707 043 cells and 1 154 611 peaks within 2 h consuming <75 GB of RAM and provides superior performance for characterizing cell heterogeneity than state-of-the-art methods. AVAILABILITY AND IMPLEMENTATION: The EpiCarousel software is well-documented and freely available at https://github.com/biox-nku/epicarousel. It can be seamlessly interoperated with extensive scCAS analysis toolkits.

Riddling Substitution of "hsa" to "has" in the Enigmatic MicroRNA Nomenclature.

This concise review and analysis offers an initial unpacking of a previously under-recognized issue within the microRNA research and communications field regarding the inadvertent use of "has" instead of "hsa" in article titles in the microRNA nomenclature. This subtle change, often the result of grammar auto correction tools, introduces considerable ambiguity and confusion among readers and researchers in reporting of microRNA-related discoveries. The impact of this issue cannot be underestimated, as precise and consistent nomenclature is vital for science communication and computational retrieval of relevant scientific literature and to advance science and innovation. We suggest that the recognition and correction of these often inadvertent "hsa" to "has" substitution errors are timely and important so as to ensure a higher level of accuracy throughout the writing and publication process in the microRNA field in particular. Doing so will also contribute to clarity and consistency in the field of microRNA research, ultimately improving scientific veracity, communication, and progress.

Unraveling the molecular landscape of Ataxia Telangiectasia: Insights into Neuroinflammation, immune dysfunction, and potential therapeutic target.

BACKGROUND: Ataxia Telangiectasia (AT) is a genetic disorder characterized by compromised DNA repair, cerebellar degeneration, and immune dysfunction. Understanding the molecular mechanisms driving AT pathology is crucial for developing targeted therapies. METHODS: In this study, we conducted a comprehensive analysis to elucidate the molecular mechanisms underlying AT pathology. Using publicly available RNA-seq datasets comparing control and AT samples, we employed in silico transcriptomics to identify potential genes and pathways. We performed differential gene expression analysis with DESeq2 to reveal dysregulated genes associated with AT. Additionally, we constructed a Protein-Protein Interaction (PPI) network to explore the interactions between proteins implicated in AT. RESULTS: The network analysis identified hub genes, including TYROBP and PCP2, crucial in immune regulation and cerebellar function, respectively. Furthermore, pathway enrichment analysis unveiled dysregulated pathways linked to AT pathology, providing insights into disease progression. CONCLUSION: Our integrated approach offers a holistic understanding of the complex molecular landscape of AT and identifies potential targets for therapeutic intervention. By combining transcriptomic analysis with network-based methods, we provide valuable insights into the underlying mechanisms of AT pathogenesis.