The 2021 Nucleic Acids Research database issue and the online molecular biology database collection

Analysis and identification of potential biomarkers for dysfunctional uterine bleeding

Clinical evidence increasingly suggests that traditional treatments for dysfunctional uterine bleeding (DUB) have limited success. In this study, blood samples from 10 DUB patients and 10 healthy controls were collected for transcriptome sequencing. Then, the differentially expressed genes (DEGs) were screened and crossed with the DUB-related module genes to obtain the target genes. These target genes were analyzed for functional enrichment. Further, the biomarkers were screened by protein-protein interaction (PPI) analysis and analyzed by the gene set enrichment analysis (GSEA) and ingenuity pathway analysis (IPA). To explore the pathogenesis of DUB, immune microenvironment analyses were also performed. Potential drugs targeting these biomarkers were predicted for clinical treatment. The expression of these biomarkers was validated using quantitative real-time polymerase chain reaction (qRT-PCR). The results showed that, a total of 754 target genes were found to be related to cell proliferation and senescence. Five biomarkers-CENPE, KIF11, PIK3R1, SMC3, and SMC4-were identified, all of which were down-regulated in the DUB group, and most of these findings were confirmed by qRT-PCR. Notably, CENPE expression showed a negative association with activated NK cells and a positive association with resting NK cells. In addition, 44 potential drugs were predicted for DUB treatment. In conclusion, five DUB biomarkers were identified, enhancing understanding of gene regulation in DUB and providing a theoretical foundation for clinical diagnosis and treatment.

Integrated network pharmacology and transcriptomics to reveal the mechanism of Passiflora against depressive disorder: An observational study

Relevant studies have pointed out that Passiflora could relieve depressive disorder (DD) related symptoms, such as anxiety and insomnia, but its mechanism in DD has not been reported. In this study, DD-related transcriptome data was extracted from the Gene Expression Omnibus (GEO) database. Subsequently, 50 differentially expressed genes (DEGs) were screened by "limma," and the enrichment analysis of these DEGs revealed that they were associated with neuro-inflammatory-related signaling pathways, including IL-17, TNF, NF-kappa B, etc signaling pathways. Then, CCDC58, CXCL5, EGR1, LOC101929855, SCML1, and THBS1 were screened as biomarkers of DD by the least absolute shrinkage and selection operator (LASSO) analysis. Moreover, Harmaline, Harmine, Quercetin, and Kaempferol were the key chemically active ingredients of Passiflora. Noticeable, THBS1 and Quercetin were connected closely. In addition, the quantitative real-time polymerase chain reaction (qRT-PCR) confirmed that the key biomarkers (EGR1 and THBS1) were significantly lowly expressed in DD samples. In summary, we identified 2 key biomarkers of DD and 4 key chemically active ingredients of Passiflora. The potential mechanism of antidepressant effect of DD associated with neuro-inflammatory responses and neurotransmitter function. These might related to the synergistic activity of its key active ingredients with TNF-α, IL-1β, IL-6, etc, which work with EGR1 and THBS1 to regulate IL-17, NF-kappa B, TNF, etc signaling pathways. These findings might help to deepen the understanding of the mechanism of Passiflora in clinical treatment of DD.

Mechanism of Sophorae Flavescentis Radix against ovarian cancer via new pharmacology, molecular docking, and experimental verification

The study aims to elucidate the pharmacological mechanisms of Sophorae Flavescentis Radix (SFR, Kushen) against ovarian cancer (OV) by employing an integrated approach that encompasses network pharmacology, molecular docking, and experimental validation. The effective components and potential targets of SFR were identified through screening the Traditional Chinese Medicine Systems Pharmacology (TSMSP) public database using network pharmacology. Core anti-OV targets were pinpointed using protein-protein interaction (PPI) networks. Datasets from The Cancer Genome Atlas (TCGA), the Human Protein Atlas (HPA), and Gene Expression Profiling Interactive Analysis (GEPIA) were used to investigate the mRNA and protein expressions of critical target genes in both normal and cancerous ovarian tissues, alongside their relationship to overall ovarian survival. Functional and pathway enrichment assessments of putative targets were carried out with Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). The assessment of stable binding effects was conducted through molecular docking with quercetin, luteolin, and formononetin, and validated by anti-OV cell activity. The investigation identified 22 active SFR components yielding 152 potential targets following the intersection with known OV targets. Analysis of PPI network highlighted 13 crucial target genes, including tumor necrosis factor (TNF) and interleukin-1A (IL-1A). GO enrichment analysis covered 703 biological activities, 72 cellular components, and 144 chemical functions. The KEGG enrichment analysis suggested that anti-cancer effects of SFR are mediated by the TNF, interleukin-17 (IL-17), and AGE-RAGE signaling pathways. Molecular docking demonstrated that TNF and IL-1A were stable and strong binding to quercetin, luteolin, and formononetin, indicating that these stable structures significantly inhibited A2780 OV cell viability. This study demonstrated the ability of TNF and IL-1A combined with quercetin, luteolin, and formononetin to decrease the activity of OV cells, suggesting potential therapeutic effect against OV.

PTGS2 as target of compound Huangbai liquid in the nursing of pressure ulcer

OBJECTIVE

Pressure ulcer refers to ulceration and necrosis caused by local skin and cell tissues being compressed for a long time, continuous ischemia, hypoxia, and malnutrition. However, role of prostaglandin-endoperoxide synthase 2 (PTGS2) in the management of pressure ulcers in with compound Huangbai liquid is still unclear.

METHODS

Traditional Chinese medicine components and related targets of compound Huangbai liquid were collected through traditional Chinese medicine systems pharmacology (TCMSP) and Batman-traditional Chinese medicine database. Disease-related targets were obtained using the Gene Cards database. The protein-protein interaction (PPI) network was constructed using the Search tool for retrieval of interacting genes (STRING) and analyzed by Cytoscape to obtain the core components. To evaluate the clinical efficacy of the compound Huangbai liquid in the treatment of pressure ulcers, 40 patients with pressure ulcers were selected and divided into an observation group and a control group, with 20 individuals in each group. The observation group received treatment with compound Huangbai liquid.

RESULTS

Sixty-five components and 480 targets of compound Huangbai liquid were obtained from TCMSP and Batman - traditional Chinese medicine databases. Two hundred seventy-three pressure ulcer-related targets were obtained. Seventy-two potential targets of compound Huangbai pigment in treatment of pressure ulcer were obtained, and 2 unrelated targets were deleted. There were 70 nodes and 1167 edges in PPI network. Gene ontology (GO) function is involved in biological processes such as reactive oxygen species metabolism and cellular response to chemical stress. Cellular components such as platelet α granules lumen and membrane rafts were involved. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment results showed that compound Huangbai liquid in treatment of pressure ulcer. The clinical results indicate that the compound Huangbai liquid has a good therapeutic effect on pressure ulcers.

CONCLUSION

PTGS2 may be a target for treatment of pressure ulcers with compound Huangbai liquid, providing a new direction for its treatment.

Preliminary Analysis of Aging-Related Genes in Intracerebral Hemorrhage by Integration of Bulk and Single-Cell RNA Sequencing Technology

Background

Aging is recognized as the key risk for intracerebral hemorrhage (ICH). The detailed mechanisms of aging in ICH warrant exploration. This study aimed to identify potential aging-related genes associated with ICH.

Methods

ICH-specific aging-related genes were determined by the intersection of differentially expressed genes (DEGs) between perihematomal tissues and corresponding contralateral parts of four patients with ICH (GSE24265) and 349 aging-related genes obtained from the Aging Atlas database. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Set Enrichment Analysis (GSEA) analyses were performed to identify the potential biological functions and pathways in which these ICH-specific aging-related genes may be involved. Then, PPI network was established to identify the hub genes of ICH-specific aging-related genes. Meanwhile, miRNA-mRNA and transcription factor (TF)-mRNA regulatory networks were constructed to further explore the ICH-specific aging-related genes regulation. The relationship between these hub genes and immune infiltration was also further explored. Additional single-cell RNA-seq analysis (scRNA-seq, GSE167593) was used to locate the hub genes in different cell types. Besides, expression levels of the hub genes were validated using clinical samples from our institute and another GEO dataset (GSE206971).

Results

This study identified 24 ICH-specific aging-related genes, including 22 up-regulated and 2 down-regulated genes. The results of GO and KEGG suggested that the ICH-specific aging-related genes mainly enriched in immunity and inflammation-related pathways, suggesting that aging may affect the ich pathogenesis by regulating inflammatory and immune-related pathways.

Conclusion

Our study revealed 24 ICH-specific aging-related genes and their functions highly pertinent to ICH pathogenesis, providing new insights into the impact of aging on ICH.

Unveiling the role of HIST2H2AC in stroke through single-cell and transcriptome analysis

Stroke is a leading cause of death and disability, and genetic risk factors play a significant role in its development. Unfortunately, effective therapies for stroke are currently limited. Early detection and diagnosis are critical for improving outcomes and developing new treatment strategies. In this study, we aimed to identify potential biomarkers and effective prevention and treatment strategies for stroke by conducting transcriptome and single-cell analyses. Our analysis included screening for biomarkers, functional enrichment analysis, immune infiltration, cell-cell communication, and single-cell metabolism. Through differential expression analysis, enrichment analysis, and protein-protein interaction (PPI) network construction, we identified HIST2H2AC as a potential biomarker for stroke. Our study also highlighted the diagnostic role of HIST2H2AC in stroke, its relationship with immune cells in the stroke environment, and our improved understanding of metabolic pathways after stroke. Overall, our research provided important insights into the pathogenesis of stroke, including potential biomarkers and treatment strategies that can be explored further to improve outcomes for stroke patients.

Whole transcriptome sequencing identifies key lncRNAs,circRNAs, and mRNAs for exploring the pathogenesis and therapeutic target of mouse pneumoconiosis

BACKGROUND

Pneumoconiosis is a kind of lung dysfunction caused by the inhalation of mineral dust. However, the potential molecular mechanism of pneumoconiosis have not been fully elucidated.

METHODS

In this study, the silica-treated pneumoconiosis mice model was constructed and the transcriptome sequencing data including lncRNA, circRNA, and mRNA were obtained. Firstly, differentially expressed lncRNA, circRNA, and mRNA (DElncRNA, DEcircRNA, DEGs) between control and pneumoconiosis/silicosis samples were screened, the target miRNAs (co-pre-miRNAs) were obtained by intersecting the miRNAs predicted by DElncRNA and DEcircRNA, respectively, and the target mRNAs (co-mRNA) were obtained by intersecting the mRNAs predicted by target miRNA and DEGs. Then, the lncRNA/circRNA-miRNA-mRNA networks were constructed by Cytoscape. Next, the key mRNAs were obtained by protein-protein interaction (PPI) analysis, and the key lncRNAs/circRNAs were selected by correlation analysis. Moreover, the expression of the key lncRNAs, circRNAs and mRNAs on chromosome were studied by the "circlize" package. Furthermore, the TFs-miRNA-mRNA network was constructed and the function of DEGs were explored by Ingenuity Pathway Analysis (IPA). To demonstrate the feasibility and value of the constructed ceRNA networks, we validated key genes and mmu-miR-682 pathway. Finally, We used the Drug-Gene Interaction database to predict potential drugs that could interfere with key genes,which may help to find promising treatment.

RESULTS

There were 427 DElncRNAs, 107 DEcircRNAs and 1,597 DEGs between silicosis and control groups. Totals of 77 co-pre-miRNAs and 96 co-mRNA were screened, and the lncRNA/circRNA-miRNA-mRNA networks were constructed with 27 lncRNA/25 circRNAs, 74 miRNAs and 96 mRNAs. Then, 6 key mRNAs including Igf1, Klf4, Ptgs2, Epas1, Gnao1, and Il1a were obtained by PPI, and all of these key mRNAs and 10 key lncRNAs and 8 circRNAs were significantly different between the pneumoconiosis and normal groups, in which 10 lncRNAs and 9 circRNA that have not been previously studied in pneumoconiosis/silicosis can be used as new potential therapeutic targets. Moreover, the TFs-miRNA-mRNA network were constructed with 11 TFs, 1 key miRNA (mmu-miR-682) and 3 key mRNAs (Igf1, Epas1, Ptgs2). And the validation of key genes revealing by RNA-seq through experimental approaches shows the the predictive power of this study. Finally, IPA results indicated that 41 pathways were activated and 2 pathways were suppressed in pneumoconiosis/silicosis groups, and Pathogen Induced Cytokine Storm Signaling Pathway was the most significant pathway affected by pneumoconiosis/silicosis. In addition, 93 drugs were screened out by Drug-Gene Interaction database. Among them, Hydroxychloroquine was a kind of drug which associated with Il1a and Ptgs2, may be a promising treatment.

CONCLUSION

This study constructed the lncRNA/circRNA-miRNA-mRNA and TFs-miRNA-mRNA networks, which could deepen the potential molecular regulatory mechanism of pneumoconiosis/silicosis.

Identification of TBK1 inhibitors against breast cancer using a computational approach supported by machine learning

Introduction: The cytosolic Ser/Thr kinase TBK1 is of utmost importance in facilitating signals that facilitate tumor migration and growth. TBK1-related signaling plays important role in tumor progression, and there is need to work on new methods and workflows to identify new molecules for potential treatments for TBK1-affecting oncologies such as breast cancer. Methods: Here, we propose the machine learning assisted computational drug discovery approach to identify TBK1 inhibitors. Through our computational ML-integrated approach, we identified four novel inhibitors that could be used as new hit molecules for TBK1 inhibition. Results and Discussion: All these four molecules displayed solvent based free energy values of -48.78, -47.56, -46.78 and -45.47 Kcal/mol and glide docking score of -10.4, -9.84, -10.03, -10.06 Kcal/mol respectively. The molecules displayed highly stable RMSD plots, hydrogen bond patterns and MMPBSA score close to or higher than BX795 molecule. In future, all these compounds can be further refined or validated by in vitro as well as in vivo activity. Also, we have found two novel groups that have the potential to be utilized in a fragment-based design strategy for the discovery and development of novel inhibitors targeting TBK1. Our method for identifying small molecule inhibitors can be used to make fundamental advances in drug design methods for the TBK1 protein which will further help to reduce breast cancer incidence.

Banxia-Shengjiang drug pair inhibits gastric cancer development and progression by improving body immunity

To investigate the mechanism of action of Banxia-Shengjiang drug pair on the inhibition of gastric cancer (GC) using network pharmacology and bioinformatics techniques. The action targets of the Banxia (Pinellia ternata (Thunb.) Makino) -Shengjiang (Zingiber officinale Roscoe) drug pair obtained from the TCMSP database were intersected with differentially expressed genes (DEGs) and GC-related genes, and the intersected genes were analyzed for pathway enrichment to identify the signaling pathways and core target genes. Subsequently, the core target genes were analyzed for clinical relevance gene mutation analysis, methylation analysis, immune infiltration analysis and immune cell analysis. Finally, by constructing the PPI network of hub genes and corresponding active ingredients, the key active ingredients of the Banxia-Shengjiang drug pair were screened for molecular docking with the hub genes. In this study, a total of 557 target genes of Banxia-Shengjiang pairs, 7754 GC-related genes and 1799 DEGs in GC were screened. Five hub genes were screened, which were PTGS2, MMP9, PPARG, MMP2, and CXCR4. The pathway enrichment analyses showed that the intersecting genes were associated with RAS/MAPK signaling pathway. In addition, the clinical correlation analysis showed that hub genes were differentially expressed in GC and was closely associated with immune infiltration and immunotherapy. The results of single nucleotide variation (SNV) and copy number variation (CNV) indicated that mutations in the hub genes were associated with the survival of gastric cancer patients. Finally, the PPI network and molecular docking results showed that PTGS2 and MMP9 were potentially important targets for the inhibition of GC by Banxia-Shengjiang drug pair, while cavidine was an important active ingredient for the inhibition of GC by Banxia-Shengjiang drug pair. Banxia-Shengjiang drug pair may regulate the immune function and inhibit GC by modulating the expression of core target genes such as RAS/MAPK signaling pathway, PTGS2 and MMP9.

Exosome lncRNA IFNG-AS1 derived from mesenchymal stem cells of human adipose ameliorates neurogenesis and ASD-like behavior in BTBR mice

BACKGROUND

The transplantation of exosomes derived from human adipose-derived mesenchymal stem cells (hADSCs) has emerged as a prospective cellular-free therapeutic intervention for the treatment of neurodevelopmental disorders (NDDs), as well as autism spectrum disorder (ASD). Nevertheless, the efficacy of hADSC exosome transplantation for ASD treatment remains to be verified, and the underlying mechanism of action remains unclear.

RESULTS

The exosomal long non-coding RNAs (lncRNAs) from hADSC and human umbilical cord mesenchymal stem cells (hUCMSC) were sequenced and 13,915 and 729 lncRNAs were obtained, respectively. The lncRNAs present in hADSC-Exos encompass those found in hUCMSC-Exos and are associated with neurogenesis. The biodistribution of hADSC-Exos in mouse brain ventricles and organoids was tracked, and the cellular uptake of hADSC-Exos was evaluated both in vivo and in vitro. hADSC-Exos promote neurogenesis in brain organoid and ameliorate social deficits in ASD mouse model BTBR T + tf/J (BTBR). Fluorescence in situ hybridization (FISH) confirmed lncRNA Ifngas1 significantly increased in the prefrontal cortex (PFC) of adult mice after hADSC-Exos intraventricular injection. The lncRNA Ifngas1 can act as a molecular sponge for miR-21a-3p to play a regulatory role and promote neurogenesis through the miR-21a-3p/PI3K/AKT axis.

CONCLUSION

We demonstrated hADSC-Exos have the ability to confer neuroprotection through functional restoration, attenuation of neuroinflammation, inhibition of neuronal apoptosis, and promotion of neurogenesis both in vitro and in vivo. The hADSC-Exos-derived lncRNA IFNG-AS1 acts as a molecular sponge and facilitates neurogenesis via the miR-21a-3p/PI3K/AKT signaling pathway, thereby exerting a regulatory effect. Our findings suggest a potential therapeutic avenue for individuals with ASD.

Molecular Dynamics and Docking Simulations of Homologous RsmE Methyltransferases Hints at a General Mechanism for Substrate Release upon Uridine Methylation on 16S rRNA

In this study, molecular dynamics (MD) and docking simulations were carried out on the crystal structure of Neisseria Gonorrhoeae RsmE aiming at free energy of binding estimation (ΔGbinding) of the methyl transfer substrate S-adenosylmethionine (SAM), as well as its homocysteine precursor S-adenosylhomocysteine (SAH). The mechanistic insight gained was generalized in view of existing homology to two other crystal structures of RsmE from Escherichia coli and Aquifex aeolicus. As a proof of concept, the crystal poses of SAM and SAH were reproduced reflecting a more general pattern of molecular interaction for bacterial RsmEs. Our results suggest that a distinct set of conserved residues on loop segments between β12, α6, and Met169 are interacting with SAM and SAH across these bacterial methyltransferases. Comparing molecular movements over time (MD trajectories) between Neisseria gonorrhoeae RsmE alone or in the presence of SAH revealed a hitherto unknown gatekeeper mechanism by two isoleucine residues, Ile171 and Ile219. The proposed gating allows switching from an open to a closed state, mimicking a double latch lock. Additionally, two key residues, Arg221 and Thr222, were identified to assist the exit mechanism of SAH, which could not be observed in the crystal structures. To the best of our knowledge, this study describes for the first time a general catalytic mechanism of bacterial RsmE on theoretical ground.

An Unsupervised Classifier for Whole-Genome Phylogenies, the Maxwell© Tool

The development of phylogenetic trees based on RNA or DNA sequences generally requires a precise and limited choice of important RNAs, e.g., messenger RNAs of essential proteins or ribosomal RNAs (like 16S), but rarely complete genomes, making it possible to explain evolution and speciation. In this article, we propose revisiting a classic phylogeny of archaea from only the information on the succession of nucleotides of their entire genome. For this purpose, we use a new tool, the unsupervised classifier Maxwell, whose principle lies in the Burrows-Wheeler compression transform, and we show its efficiency in clustering whole archaeal genomes.

Web tools support predicting protein-nucleic acid complexes stability with affinity changes

Numerous biological processes, such as transcription, replication, and translation, rely on protein-nucleic acid interactions (PNIs). Demonstrating the binding stability of protein-nucleic acid complexes is vital to deciphering the code for PNIs. Numerous web-based tools have been developed to attach importance to protein-nucleic acid stability, facilitating the prediction of PNIs characteristics rapidly. However, the data and tools are dispersed and lack comprehensive integration to understand the stability of PNIs better. In this review, we first summarize existing databases for evaluating the stability of protein-nucleic acid binding. Then, we compare and evaluate the pros and cons of web tools for forecasting the interaction energies of protein-nucleic acid complexes. Finally, we discuss the application of combining models and capabilities of PNIs. We may hope these web-based tools will facilitate the discovery of recognition mechanisms for protein-nucleic acid binding stability. This article is categorized under: RNA Interactions with Proteins and Other Molecules > Protein-RNA Recognition RNA Interactions with Proteins and Other Molecules > RNA-Protein Complexes RNA Interactions with Proteins and Other Molecules > Protein-RNA Interactions: Functional Implications.

Repurposing of known drugs for COVID-19 using molecular docking and simulation analysis

We selected fifty one drugs already known for their potential disease treatment roles in various studies and subjected to docking and molecular docking simulation (MDS) analyses. Five of them showed promising features that are discussed and suggested as potential candidates for repurposing for COVID-19. These top five compounds were boswellic acid, pimecrolimus, GYY-4137, BMS-345541 and triamcinolone hexacetonide that interacted with the chosen receptors 1R42, 4G3D, 6VW1, 6VXX and 7MEQ, respectively with binding energies of -9.2 kcal/mol, -9.1 kcal/mol, -10.3 kcal/mol, -10.1 kcal/mol and -8.7 kcal/mol, respectively. The MDS studies for the top 5 best complexes revealed binding features for the chosen receptor, human NF-kappa B transcription factor as an important drug target in COVID-19-based drug development strategies.

Roadmap to the study of gene and protein phylogeny and evolution-A practical guide

Developments in sequencing technologies and the sequencing of an ever-increasing number of genomes have revolutionised studies of biodiversity and organismal evolution. This accumulation of data has been paralleled by the creation of numerous public biological databases through which the scientific community can mine the sequences and annotations of genomes, transcriptomes, and proteomes of multiple species. However, to find the appropriate databases and bioinformatic tools for respective inquiries and aims can be challenging. Here, we present a compilation of DNA and protein databases, as well as bioinformatic tools for phylogenetic reconstruction and a wide range of studies on molecular evolution. We provide a protocol for information extraction from biological databases and simple phylogenetic reconstruction using probabilistic and distance methods, facilitating the study of biodiversity and evolution at the molecular level for the broad scientific community.

WGS Data Collections: How Do Genomic Databases Transform Medicine?

As a scientific community we assumed that exome sequencing will elucidate the basis of most heritable diseases. However, it turned out it was not the case; therefore, attention has been increasingly focused on the non-coding sequences that encompass 98% of the genome and may play an important regulatory function. The first WGS-based datasets have already been released including underrepresented populations. Although many databases contain pooled data from several cohorts, recently the importance of local databases has been highlighted. Genomic databases are not only collecting data but may also contribute to better diagnostics and therapies. They may find applications in population studies, rare diseases, oncology, pharmacogenetics, and infectious and inflammatory diseases. Further data may be analysed with Al technologies and in the context of other omics data. To exemplify their utility, we put a highlight on the Polish genome database and its practical application.

Interrogating the Metabolomic Profile of Amyotrophic Lateral Sclerosis in the Post-Mortem Human Brain by Infrared Matrix-Assisted Laser Desorption Electrospray Ionization (IR-MALDESI) Mass Spectrometry Imaging (MSI)

Amyotrophic lateral sclerosis (ALS) is an idiopathic, fatal neurodegenerative disease characterized by progressive loss of motor function with an average survival time of 2-5 years after diagnosis. Due to the lack of signature biomarkers and heterogenous disease phenotypes, a definitive diagnosis of ALS can be challenging. Comprehensive investigation of this disease is imperative to discovering unique features to expedite the diagnostic process and improve diagnostic accuracy. Here, we present untargeted metabolomics by mass spectrometry imaging (MSI) for comparing sporadic ALS (sALS) and C9orf72 positive (C9Pos) post-mortem frontal cortex human brain tissues against a control cohort. The spatial distribution and relative abundance of metabolites were measured by infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) MSI for association to biological pathways. Proteomic studies on the same patients were completed via LC-MS/MS in a previous study, and results were integrated with imaging metabolomics results to enhance the breadth of molecular coverage. Utilizing METASPACE annotation platform and MSiPeakfinder, nearly 300 metabolites were identified across the sixteen samples, where 25 were identified as dysregulated between disease cohorts. The dysregulated metabolites were further examined for their relevance to alanine, aspartate, and glutamate metabolism, glutathione metabolism, and arginine and proline metabolism. The dysregulated pathways discussed are consistent with reports from other ALS studies. To our knowledge, this work is the first of its kind, reporting on the investigation of ALS post-mortem human brain tissue analyzed by multiomic MSI.

The challenge of managing the evolution of genomics data over time: a conceptual model-based approach

BACKGROUND

Precision medicine is a promising approach that has revolutionized disease prevention and individualized treatment. The DELFOS oracle is a model-driven genomics platform that aids clinicians in identifying relevant variations that are associated with diseases. In its previous version, the DELFOS oracle did not consider the high degree of variability of genomics data over time. However, changes in genomics data have had a profound impact on clinicians' work and pose the need for changing past, present, and future clinical actions. Therefore, our objective in this work is to consider changes in genomics data over time in the DELFOS oracle.

METHODS

Our objective has been achieved through three steps. First, we studied the characteristics of each database from which the DELFOS oracle extracts data. Second, we characterized which genomics concepts of the conceptual schema that supports the DELFOS oracle change over time. Third, we updated the DELFOS Oracle so that it can manage the temporal dimension. To validate our approach, we carried out a use case to illustrate how the new version of the DELFOS oracle handles the temporal dimension.

RESULTS

Three events can change genomics data, namely, the addition of a new variation, the addition of a new link between a variation and a phenotype, and the update of a link between a variation and a phenotype. These events have been linked to the entities of the conceptual model that are affected by them. Finally, a new version of the DELFOS oracle that can deal with the temporal dimension has been implemented.

CONCLUSION

Huge amounts of genomics data that is associated with diseases change over time, impacting patients' diagnosis and treatment. Including this information in the DELFOS oracle added an extra layer of complexity, but using a model-driven based approach mitigated the cost of implementing the needed changes. The new version handles the temporal dimension appropriately and eases clinicians' work.

Integrating and formatting biomedical data as pre-calculated knowledge graph embeddings in the Bioteque

Biomedical data is accumulating at a fast pace and integrating it into a unified framework is a major challenge, so that multiple views of a given biological event can be considered simultaneously. Here we present the Bioteque, a resource of unprecedented size and scope that contains pre-calculated biomedical descriptors derived from a gigantic knowledge graph, displaying more than 450 thousand biological entities and 30 million relationships between them. The Bioteque integrates, harmonizes, and formats data collected from over 150 data sources, including 12 biological entities (e.g., genes, diseases, drugs) linked by 67 types of associations (e.g., 'drug treats disease', 'gene interacts with gene'). We show how Bioteque descriptors facilitate the assessment of high-throughput protein-protein interactome data, the prediction of drug response and new repurposing opportunities, and demonstrate that they can be used off-the-shelf in downstream machine learning tasks without loss of performance with respect to using original data. The Bioteque thus offers a thoroughly processed, tractable, and highly optimized assembly of the biomedical knowledge available in the public domain.

Development of a novel database to review and assess the clinical effects of EPA and DHA omega-3 fatty acids

Due to their multiple mechanisms of biological action, eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) have been the focus of ongoing active research for decades. In spite of the resulting body of knowledge, there remain significant gaps in our understanding of EPA/DHA health effects. Further, the volume of existing research makes it challenging to conduct systematic investigations to identify or resolve those gaps. The purpose of this article is to introduce the GOED Clinical Study Database (CSD), a comprehensive, manually-curated relational database that catalogs this research.

Survey of bioinformatics courses and concentrations in ALA-accredited master's programs

Introduction

The interdisciplinary field of bioinformatics is considered an information-based discipline by many. Yet, it is unclear how Master of Library and Information Science (MLIS) degrees prepare librarians to apply their expertise in this unique, often non-textual information environment. The goal of this study is to identify the availability of MLIS-based bioinformatics educational opportunities to provide an update to the current bioinformatics landscape in North American MLIS programs or iSchools.

Methods

We conducted a survey of available bioinformatics courses and program concentrations within 69 ALA-accredited master’s programs. Using course catalogues and program descriptions on department websites, we identified the existence of courses and concentrations specific or related to the field of bioinformatics. We also surveyed the availability of associated certificate programs or degree alternatives.

Results

Only two library and information science (LIS)-based bioinformatics courses are currently offered to MLIS students in ALA-accredited programs. There are no bioinformatics concentrations offered in the programs surveyed, however two graduate certificates could be applied towards an ALA-accredited master’s degree. Students interested in related fields can pursue degree alternatives, including eight dual degree options.

Discussion

The scarcity of LIS-based bioinformatics courses and program concentrations may suggest that LIS has not adopted bioinformatics into their field nor curricula. As a result, students interested in pursuing careers in bioinformatics and related disciplines must actively seek out opportunities for education and professional development. Bioinformatics degree options within MLIS or iSchools points towards an increased dialogue and acceptance of the connection between bioinformatics and information science, but the lack of ALA -accreditation limits possibilities for emerging librarians.

DBHR: a collection of databases relevant to human research

BACKGROUND

The achievement of the human genome project provides a basis for the systematic study of the human genome from evolutionary history to disease-specific medicine. With the explosive growth of biological data, a growing number of biological databases are being established to support human-related research.

OBJECTIVE

The main objective of our study is to store, organize and share data in a structured and searchable manner. In short, we have planned the future development of new features in the database research area.

MATERIALS & METHODS

In total, we collected and integrated 680 human databases from scientific published work. Multiple options are presented for accessing the data, while original links and short descriptions are also presented for each database.

RESULTS & DISCUSSION

We have provided the latest collection of human research databases on a single platform with six categories: DNA database, RNA database, protein database, expression database, pathway database and disease database.

CONCLUSION

Taken together, our database will be useful for further human research study and will be modified over time. The database has been implemented in PHP, HTML, CSS and MySQL and is available freely at https://habdsk.org/database.php.

Building Structural Models of a Whole Mycoplasma Cell

Building structural models of entire cells has been a long-standing cross-discipline challenge for the research community, as it requires an unprecedented level of integration between multiple sources of biological data and enhanced methods for computational modeling and visualization. Here, we present the first 3D structural models of an entire Mycoplasma genitalium (MG) cell, built using the CellPACK suite of computational modeling tools. Our model recapitulates the data described in recent whole-cell system biology simulations and provides a structural representation for all MG proteins, DNA and RNA molecules, obtained by combining experimental and homology-modeled structures and lattice-based models of the genome. We establish a framework for gathering, curating and evaluating these structures, exposing current weaknesses of modeling methods and the boundaries of MG structural knowledge, and visualization methods to explore functional characteristics of the genome and proteome. We compare two approaches for data gathering, a manually-curated workflow and an automated workflow that uses homologous structures, both of which are appropriate for the analysis of mesoscale properties such as crowding and volume occupancy. Analysis of model quality provides estimates of the regularization that will be required when these models are used as starting points for atomic molecular dynamics simulations.

Current status of PTMs structural databases: applications, limitations and prospects

Protein 3D structures, determined by their amino acid sequences, are the support of major crucial biological functions. Post-translational modifications (PTMs) play an essential role in regulating these functions by altering the physicochemical properties of proteins. By virtue of their importance, several PTM databases have been developed and released in decades, but very few of these databases incorporate real 3D structural data. Since PTMs influence the function of the protein and their aberrant states are frequently implicated in human diseases, providing structural insights to understand the influence and dynamics of PTMs is crucial for unraveling the underlying processes. This review is dedicated to the current status of databases providing 3D structural data on PTM sites in proteins. Some of these databases are general, covering multiple types of PTMs in different organisms, while others are specific to one particular type of PTM, class of proteins or organism. The importance of these databases is illustrated with two major types of in silico applications: predicting PTM sites in proteins using machine learning approaches and investigating protein structure-function relationships involving PTMs. Finally, these databases suffer from multiple problems and care must be taken when analyzing the PTMs data.

Integrated Genomic and Bioinformatics Approaches to Identify Molecular Links between Endocrine Disruptors and Adverse Outcomes

Exposure to Endocrine Disrupting Chemicals (EDC) has been linked with several adverse outcomes. In this review, we examine EDCs that are pervasive in the environment and are of concern in the context of human, animal, and environmental health. We explore the consequences of EDC exposure on aquatic life, terrestrial animals, and humans. We focus on the exploitation of genomics technologies and in particular whole transcriptome sequencing. Genome-wide analyses using RNAseq provides snap shots of cellular, tissue and whole organism transcriptomes under normal physiological and EDC perturbed conditions. A global view of gene expression provides highly valuable information as it uncovers gene families or more specifically, pathways that are affected by EDC exposures, but also reveals those that are unaffected. Hypotheses about genes with unknown functions can also be formed by comparison of their expression levels with genes of known function. Risk assessment strategies leveraging genomic technologies and the development of toxicology databases are explored. Finally, we review how the Adverse Outcome Pathway (AOP) has exploited this high throughput data to provide a framework for toxicology studies.

Omics Analyses: How to Navigate Through a Constant Data Deluge

Omics data are very valuable for researchers in biology, but the work required to develop a solid expertise in their analysis contrasts with the rapidity with which the omics technologies evolve. Data accumulate in public databases, and despite significant advances in bioinformatics softwares to integrate them, data analysis remains a burden for those who perform experiments. Beyond the issue of dealing with a very large number of results, we believe that working with omics data requires a change in the way scientific problems are solved. In this chapter, we explain pitfalls and tips we found during our functional genomics projects in yeasts. Our main lesson is that, if applying a protocol does not guarantee a successful project, following simple rules can help to become strategic and intentional, thus avoiding an endless drift into an ocean of possibilities.

Molecular Biology in Treatment Decision Processes-Neuro-Oncology Edition

Computational approaches including machine learning, deep learning, and artificial intelligence are growing in importance in all medical specialties as large data repositories are increasingly being optimised. Radiation oncology as a discipline is at the forefront of large-scale data acquisition and well positioned towards both the production and analysis of large-scale oncologic data with the potential for clinically driven endpoints and advancement of patient outcomes. Neuro-oncology is comprised of malignancies that often carry poor prognosis and significant neurological sequelae. The analysis of radiation therapy mediated treatment and the potential for computationally mediated analyses may lead to more precise therapy by employing large scale data. We analysed the state of the literature pertaining to large scale data, computational analysis, and the advancement of molecular biomarkers in neuro-oncology with emphasis on radiation oncology. We aimed to connect existing and evolving approaches to realistic avenues for clinical implementation focusing on low grade gliomas (LGG), high grade gliomas (HGG), management of the elderly patient with HGG, rare central nervous system tumors, craniospinal irradiation, and re-irradiation to examine how computational analysis and molecular science may synergistically drive advances in personalised radiation therapy (RT) and optimise patient outcomes.

A comprehensive review of the analysis and integration of omics data for SARS-CoV-2 and COVID-19

Since the first report of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in December 2019, over 100 million people have been infected by COVID-19, millions of whom have died. In the latest year, a large number of omics data have sprung up and helped researchers broadly study the sequence, chemical structure and function of SARS-CoV-2, as well as molecular abnormal mechanisms of COVID-19 patients. Though some successes have been achieved in these areas, it is necessary to analyze and mine omics data for comprehensively understanding SARS-CoV-2 and COVID-19. Hence, we reviewed the current advantages and limitations of the integration of omics data herein. Firstly, we sorted out the sequence resources and database resources of SARS-CoV-2, including protein chemical structure, potential drug information and research literature resources. Next, we collected omics data of the COVID-19 hosts, including genomics, transcriptomics, microbiology and potential drug information data. And subsequently, based on the integration of omics data, we summarized the existing data analysis methods and the related research results of COVID-19 multi-omics data in recent years. Finally, we put forward SARS-CoV-2 (COVID-19) multi-omics data integration research direction and gave a case study to mine deeper for the disease mechanisms of COVID-19.

Bioinformatic Tools for the Analysis and Prediction of ncRNA Interactions

Noncoding RNAs (ncRNAs) play prominent roles in the regulation of gene expression via their interactions with other biological molecules such as proteins and nucleic acids. Although much of our knowledge about how these ncRNAs operate in different biological processes has been obtained from experimental findings, computational biology can also clearly substantially boost this knowledge by suggesting possible novel interactions of these ncRNAs with other molecules. Computational predictions are thus used as an alternative source of new insights through a process of mutual enrichment because the information obtained through experiments continuously feeds through into computational methods. The results of these predictions in turn shed light on possible interactions that are subsequently validated experimentally. This review describes the latest advances in databases, bioinformatic tools, and new in silico strategies that allow the establishment or prediction of biological interactions of ncRNAs, particularly miRNAs and lncRNAs. The ncRNA species described in this work have a special emphasis on those found in humans, but information on ncRNA of other species is also included.

Development of interactive biological web applications with R/Shiny

Development of interactive web applications to deposit, visualize and analyze biological datasets is a major subject of bioinformatics. R is a programming language for data science, which is also one of the most popular languages used in biological data analysis and bioinformatics. However, building interactive web applications was a great challenge for R users before the Shiny package was developed by the RStudio company in 2012. By compiling R code into HTML, CSS and JavaScript code, Shiny has made it incredibly easy to build web applications for the large R community in bioinformatics and for even non-programmers. Over 470 biological web applications have been developed with R/Shiny up to now. To further promote the utilization of R/Shiny, we reviewed the development of biological web applications with R/Shiny, including eminent biological web applications built with R/Shiny, basic steps to build an R/Shiny application, commonly used R packages to build the interface and server of R/Shiny applications, deployment of R/Shiny applications in the cloud and online resources for R/Shiny.

Possible Roles of tRNA Fragments, as New Regulatory ncRNAs, in the Pathogenesis of Rheumatoid Arthritis

Understanding the pathophysiology of rheumatoid arthritis (RA) has led to the successful development of molecule-targeted drugs for the treatment of RA. However, some RA patients are refractory to these treatments, suggesting that the pathological mechanism of the disease is not entirely understood. Genome and transcriptome analysis is essential for understanding the unknown pathophysiology of human diseases. Rapid and more comprehensive gene analysis technologies have revealed notable changes in the expression of coding RNA and non-coding RNA in RA patients. This review focuses on the current state of non-coding RNA research in relation to RA, especially on tRNA fragments. Interestingly, it has been found that tRNA fragments repress translation and are antiapoptotic. The association between tRNA fragments and various diseases has been studied, and this article reviews the possible role of tRNA fragments in RA.

Identification of different side effects between PARP inhibitors and their polypharmacological multi-target rationale

AIMS

The aim of this study was to determine the differences and potential mechanistic rationale for observed adverse drug reactions (ADRs) between four approved PARP inhibitors (PARPi).

METHODS

The Medicines and Healthcare products Regulatory Authority (MHRA) Yellow Card drug analysis profiles and NHS secondary care medicines database enabled the identification of suspected ADRs associated with the PARPi in the UK from launch to 2020. The polypharmacology of the PARPi were data-mined from several public data sources.

RESULTS

The overall ADRs per 100 000 R_x identified across the four PARPi are statistically significant (χ² test, P < .001). Rucaparib has the greatest relative suspected ADRs, which can be explained by its least clean kinome and physicochemical properties. The suspected gastrointestinal ADRs of rucaparib and niraparib can be ascribed to their kinase polypharmacology. Suspected blood and lymphatic system ADRs of PARPi can be linked to their high volume of distribution (V_d ). The thrombocytopenia rate of niraparib > rucaparib > olaparib tracked with the V_d trend. Hypertension is only associated with niraparib and could be explained by the therapeutically achievable inhibition of DYRK1A and/or transporters. Arrhythmia cases are potentially linked to the structural features of hERG ion-channel inhibition found in rucaparib and niraparib. Enhanced psychiatric/nervous disorders associated with niraparib can be interpreted from the diverse neurotransporter off-targets reported.

CONCLUSIONS

Despite their similar mode of action, the differential polypharmacology of PARP inhibitors influences their ADR profile.

Metagenomics: a path to understanding the gut microbiome

The gut microbiome is a major determinant of host health, yet it is only in the last 2 decades that the advent of next-generation sequencing has enabled it to be studied at a genomic level. Shotgun sequencing is beginning to provide insight into the prokaryotic as well as eukaryotic and viral components of the gut community, revealing not just their taxonomy, but also the functions encoded by their collective metagenome. This revolution in understanding is being driven by continued development of sequencing technologies and in consequence necessitates reciprocal development of computational approaches that can adapt to the evolving nature of sequence datasets. In this review, we provide an overview of current bioinformatic strategies for handling metagenomic sequence data and discuss their strengths and limitations. We then go on to discuss key technological developments that have the potential to once again revolutionise the way we are able to view and hence understand the microbiome.

An innovative user-friendly platform for Covid-19 pandemic databases and resources

BACKGROUND

The current coronavirus disease-19 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a global outbreak of a disease from a new coronavirus. Several databases have been published on this pandemic, but the research community still needs an easy way to get comprehensive information on COVID-19.

OBJECTIVES

COVID-19 pandemic database (CO-19 PDB) aims to provide wonderful insights for COVID-19 researchers with the well-gathered of all the COVID-19 data to one platform, which is a global challenge for the research community these days.

METHODS

We gathered 59 updated databases since December-2019 until May 2021 and divided them into six categories: digital image database, genomic database, literature database, visualization tools database, chemical structure database, and social science database. These categories focus on taking number of functions from the images, information from gene sequences, updates from relevant papers, essays, reports, articles, and books, the data or information in the form of maps, graphs, and charts, information of bonds between atoms, and updates about events of the physical and social environment, respectively.

RESULTS

Users can search the information of interest in two ways including typing the name of the database in the search bar or by clicking the right category directly. Computer languages such as CSS, PHP, HTML, Java, etc. are utilized to construct CO-19 PDB.

CONCLUSION

This article attempts to compile up-to-date appropriate COVID-19 datasets and resources that have not been compiled and given in such an accessible and user-friendly manner. As a result, the CO-19 PDB offers extensive open data sharing for both worldwide research communities and local people. Further, we have planned future development of new features, that will be awesome for future study.