KEGG for representation and analysis of molecular networks involving diseases and drugs

ToxinPredictor: Computational models to predict the toxicity of molecules

Predicting the toxicity of molecules is essential in fields like drug discovery, environmental protection, and industrial chemical management. While traditional experimental methods are time-consuming and costly, computational models offer an efficient alternative. In this study, we introduce ToxinPredictor, a machine learning-based model to predict the toxicity of small molecules using their structural properties. The model was trained on a curated dataset of 7550 toxic and 6514 non-toxic molecules, leveraging feature selection techniques like Boruta and PCA. The best-performing model, a Support Vector Machine (SVM), achieved state-of-the-art results with an AUROC of 91.7%, F1-score of 84.9%, and accuracy of 85.4%, outperforming existing solutions. SHAP analysis was applied to the SVM model to identify the most important molecular descriptors contributing to toxicity predictions, enhancing interpretability. Despite challenges related to data quality, ToxinPredictor provides a reliable framework for toxicity risk assessment, paving the way for safer drug development and improved environmental health assessments. We also created a user-friendly webserver, ToxinPredictor (https://cosylab.iiitd.edu.in/toxinpredictor) to facilitate the search and prediction of toxic compounds.

Comprehensive analysis of differentially expressed genes in toll-like receptor signalling pathway: Insights into new-onset microscopic polyangiitis

OBJECTIVE

We aimed to elucidate the potential contributions of the toll-like receptor (TLR) signalling pathway and identify promising candidates for new-onset microscopic polyangiitis (MPA) using integrated bioinformatics analysis.

METHODS

A PCR array was used to determine the expression profiles of TLR signalling-related genes in CD4+T lymphocytes of individuals with new-onset MPA and healthy controls. Four genes were selected for validation through real-time quantitative polymerase chain reaction (RT-qPCR). Followed by functional enrichment and pathway analysis, we identified the hub genes with cytoHubba. The differentially expressed miRNAs of the target genes were subsequently predicted and visualized via Cytoscape. Finally, these candidates were validated and evaluated at the expression level and for diagnostic value in public databases.

RESULTS

Nineteen differentially expressed genes were screened, and the levels of the validated genes detected using RT‒qPCR were consistent with the findings obtained through the PCR array. The significantly enriched signalling pathways involved were TLR signalling pathway, IL-17 signalling pathway, and NF-κB signalling pathway. Nine hub genes and nine key miRNAs were identified. Furthermore, analysis of three distinct gene expression datasets validated several key genes (TLR4, MYD88, IRF1, CXCL10, CXCL8, and CSF2), showing significant differences between groups and strong diagnostic value, especially TLR4, MYD88, and IRF1. Interestingly, in contrast to the validation results, our results showed that CXCL10 and CXCL8 expression levels were markedly lower, but CSF2 was highly expressed in patients with MPA compared to controls.

CONCLUSION

Aberrant expression of TLRs may occur in CD4+ T lymphocytes of patients with new-onset MPA, offering insights into the pathogenesis as well as potential biomarkers and novel therapeutic targets.

A novel hypothesis-generating computational workflow utilizing reverse pharmacophore mapping-A drug repurposing perspective of istradefylline towards major depressive disorder

BACKGROUND AND PURPOSE

Drug repurposing (DR) offers a compelling alternative to traditional drug discovery's lengthy, resource-intensive process. DR is the process of identifying alternative clinical applications for pre-approved drugs as a low-risk and low-cost strategy. Computational approaches are crucial during the early hypothesis-generating stage of DR. However, 'large-scale' data retrieval remains a significant challenge. A computational workflow addressing such limitations might improve hypothesis generation, ultimately benefit patients and advance DR research.

EXPERIMENTAL APPROACH

We introduce a novel computational workflow (combining free-accessible computational platforms) to provide 'proof-of-concept' of the pre-approved drug's suitability for repurposing. Three key phases are included: target fishing (via reverse pharmacophore mapping), target identification (via disease- and drug-target pathway identification) and retrospective literature and drug-like analysis (via in silico ADMET properties determination). Istradefylline is a Parkinson's disease-approved drug with literature-attributed antidepressant properties remaining unclear. Practically applied, istradefylline's antidepressant activity was assessed in the context of major depressive disorder (MDD).

KEY RESULTS

Data mining aided by target identification resulted in istradefylline potentially representing a novel antidepressant drug class. Retrieved drug targets (KYNU, MAO-B, ALOX12 and PLCB2) associated with selected MDD pathways (tryptophan metabolism and serotonergic synapse) generated a hypothesis that istradefylline increased extracellular 5-HT levels (MAO-B inhibition) and reduced inflammation (KYNU, ALOX12 and PLCB2 inhibition).

CONCLUSION AND IMPLICATIONS

The practically applied workflow's generated hypothesis aligns with known experimental data, validating the effectiveness of this novel computational workflow. It is a low-risk and low-cost DR computational tool providing a bird's-eye view for exploring alternative clinical applications of pre-approved drugs.

Second-generation BRAF inhibitor Encorafenib resistance is regulated by NCOA4-mediated iron trafficking in the drug-resistant malignant melanoma cells

The current study established the first in vitro Encorafenib resistance protocol in BRAF-mutated malignant melanoma (MM) cells and investigated the resistance-related mechanisms. After establishing Encorafenib-resistant A375-MM cells, resistant-related mechanisms were investigated using WST-1, Annexin V, cell cycle, morphological analysis, live-cell, Western blot, RNA-Seq, transmission electron microscopy-(TEM), oxidative stress and iron colorimetric assay. The most resistant group, called A375-R, was determined in the cells treated with a constant dose of 10 nM over 3 months. The viability, apoptosis, and G0/G1 arrest reflected the acquired chemoresistance. Autophagic Beclin and LC3 proteins, and AKT signaling increased in the A375-R. RNA-Seq results also exhibited altered epigenetic regulation of resistance; particularly ferritin family members, ion transport pathways. Then, increased NCOA4, FTH1, and iron levels detected in A375-R suggest that the iron metabolism-related mechanism, such as ferritinophagy, might be triggered, which was supported by TEM and oxidative stress analysis. Iron storage, transport, and ferritinophagy have the promising potential to be targeted for combining with BRAF-targeted therapy to reverse Encorafenib resistance in MM. Moreover, this is the first study evaluating in vitro Encorafenib resistance mechanisms, and we suggest that our findings contribute to improving new drug combinations targeting BRAF and iron metabolism in different MM cells.

A Multicellular In Vitro Model of the Human Intestine with Immunocompetent Features Highlights Host-Pathogen Interactions During Early Salmonella Typhimurium Infection

Studying the molecular basis of intestinal infections caused by enteric pathogens at the tissue level is challenging, because most human intestinal infection models have limitations, and results obtained from animals may not reflect the human situation. Infections with Salmonella enterica serovar Typhimurium (STm) have different outcomes between organisms. 3D tissue modeling of primary human material provides alternatives to animal experimentation, but epithelial co-culture with immune cells remains difficult. Macrophages, for instance, contribute to the immunocompetence of native tissue, yet their incorporation into human epithelial tissue models is challenging. A 3D immunocompetent tissue model of the human small intestine based on decellularized submucosa enriched with monocyte-derived macrophages (MDM) is established. The multicellular model recapitulated in vivo-like cellular diversity, especially the induction of GP2 positive microfold (M) cells. Infection studies with STm reveal that the pathogen physically interacts with these M-like cells. MDMs show trans-epithelial migration and phagocytosed STm within the model and the levels of inflammatory cytokines are induced upon STm infection. Infected epithelial cells are shed into the supernatant, potentially reflecting an intracellular reservoir of invasion-primed STm. Together, the 3D model of the human intestinal epithelium bears potential as an alternative to animals to identify human-specific processes underlying enteric bacterial infections.

Drug-Drug interactions prediction calculations between cardiovascular drugs and antidepressants for discovering the potential co-medication risks

Predicting Drug-Drug Interactions (DDIs) enables cost reduction and time savings in the drug discovery process, while effectively screening and optimizing drugs. The intensification of societal aging and the increase in life stress have led to a growing number of patients suffering from both heart disease and depression. These patients often need to use cardiovascular drugs and antidepressants for polypharmacy, but potential DDIs may compromise treatment effectiveness and patient safety. To predict interactions between drugs used to treat these two diseases, we propose a method named Multi-Drug Features Learning with Drug Relation Regularization (MDFLDRR). First, we map feature vectors representing drugs in different feature spaces to the same. Second, we propose drug relation regularization to determine drug pair relationships in the interaction space. Experimental results demonstrate that MDFLDRR can be effectively applied to two DDI prediction goals: predicting unobserved interactions among drugs within the drug network and predicting interactions between drugs inside and outside the network. Publicly available evidence confirms that MDFLDRR can accurately identify DDIs between cardiovascular drugs and antidepressants. Lastly, by utilizing drug structure calculations, we ascertained the severity of newly discovered DDIs to mine the potential co-medication risks and aid in the smart management of pharmaceuticals.

The Primary Cilia are Associated with the Axon Initial Segment in Neurons

The primary cilia serve as pivotal mediators of environmental signals and play crucial roles in neuronal responses. Disruption of ciliary function has been implicated in neuronal circuit disorders and aberrant neuronal excitability. However, the precise mechanisms remain elusive. To study the link between the primary cilia and neuronal excitability, manipulation of somatostatin receptor 3 (SSTR3) is investigated, as an example of how alterations in ciliary signaling may affect neuronal activity. It is found that aberrant SSTR3 expression perturbed not only ciliary morphology but also disrupted ciliary signaling cascades. Genetic deletion of SSTR3 resulted in perturbed spatial memory and synaptic plasticity. The axon initial segment (AIS) is a specialized region in the axon where action potentials are initiated. Interestingly, loss of ciliary SSTR3 led to decrease of Akt-dependent cyclic AMP-response element binding protein (CREB)-mediated transcription at the AIS, specifically downregulating AIS master organizer adaptor protein ankyrin G (AnkG) expression. In addition, alterations of other ciliary proteins serotonin 6 receptor (5-HT6R)and intraflagellar transport protein 88 (IFT88) also induced length changes of the AIS. The findings elucidate a specific interaction between the primary cilia and AIS, providing insight into the impact of the primary cilia on neuronal excitability and circuit integrity.

Enhancement of Tolerance against Flonicamid in Solenopsis invicta Queens through Overexpression of CYP6AQ83

Solenopsis invicta, an extremely destructive invasive species, has rapidly spread in China, with queens exhibiting chemical tolerance. In this study, bioassays were conducted on S. invicta colonies collected in Nanchong, revealing that the LC50 value of flonicamid for queens (3.91 mg/L) was significantly higher than that for workers (1.07 mg/L). Comparative analysis of transcriptomes of workers and queens treated with flonicamid revealed that differentially expressed genes (DEGs) were significantly enriched in the metabolism of xenobiotics by cytochrome P450 and drug metabolism by cytochrome P450 pathways. Based on the screening of transcriptome data, CYP6AQ83 might be involved in the detoxification metabolism of flonicamid in queens. After RNA interference, the sensitivity of queens to flonicamid was significantly increased by 30% in the treatment of the dsCYP6AQ83 group. Furthermore, heterologous overexpression of CYP6AQ83 in Drosophila melanogaster also significantly enhanced the tolerance against flonicamid. These results indicated that the overexpression of CYP6AQ83 in the queen enhances the tolerance against flonicamid.

Genome Annotation

The hallmark of genome sequencing projects is to provide genetic information on a species with functional annotations of genes and proteins. This process heavily relies on genome annotation based on homology detections from previously known genomic data. The rapid advancement of genome sequencing technologies has made genome sequencing affordable and effective in terms of the time frame for the generation of genomic data. Hence, genome sequencing has become a common practice. The annotation and characterization of newly sequenced genomes are crucial factors for the success of any biological experiment based on genomic data. The proteogenomic sector requires annotated genome further characterization of proteomic-based studies, and these are coupled with genomic and RNA-seq data. This chapter describes the genome annotation process from scratch genome sequencing to general genome annotation and specialized genome annotation using BLAST, BLAT2GO (now OMICSBOX), PANNZER, gene ontology (GO), and KEGG. It also covers different processes like repeat identification and masking, gene prediction, genome-wide annotation process, and RNA-seq protocol. It also focuses on genes of interest such as genes associated with BGCs (biosynthetic gene clusters), carbohydrate-active enzymes (CAZymes), serpins (serine protease inhibitors), membrane transporters, and toxins. Manual annotation is also a critical step for at least some groups of genes, which are often critical for the species in consideration. This chapter also briefly describes the phylogenetic and phylogenomic processes required during genome annotation.

Vaginal metatranscriptome meta-analysis reveals functional BV subgroups and novel colonisation strategies

BACKGROUND

The application of '-omics' technologies to study bacterial vaginosis (BV) has uncovered vast differences in composition and scale between the vaginal microbiomes of healthy and BV patients. Compared to amplicon sequencing and shotgun metagenomic approaches focusing on a single or few species, investigating the transcriptome of the vaginal microbiome at a system-wide level can provide insight into the functions which are actively expressed and differential between states of health and disease.

RESULTS

We conducted a meta-analysis of vaginal metatranscriptomes from three studies, split into exploratory (n = 42) and validation (n = 297) datasets, accounting for the compositional nature of sequencing data and differences in scale between healthy and BV microbiomes. Conducting differential expression analyses on the exploratory dataset, we identified a multitude of strategies employed by microbes associated with states of health and BV to evade host cationic antimicrobial peptides (CAMPs); putative mechanisms used by BV-associated species to resist and counteract the low vaginal pH; and potential approaches to disrupt vaginal epithelial integrity so as to establish sites for adherence and biofilm formation. Moreover, we identified several distinct functional subgroups within the BV population, distinguished by genes involved in motility, chemotaxis, biofilm formation and co-factor biosynthesis. After defining molecular states of health and BV in the validation dataset using KEGG orthology terms rather than community state types, differential expression analysis confirmed earlier observations regarding CAMP resistance and compromising epithelial barrier integrity in healthy and BV microbiomes and also supported the existence of motile vs. non-motile subgroups in the BV population. These findings were independent of the enzyme classification system used (KEGG or EggNOG).

CONCLUSIONS

Our findings highlight a need to focus on functional rather than taxonomic differences when considering the role of microbiomes in disease and identify pathways for further research as potential BV treatment targets. Video Abstract.

Population genomics of a thermophilic cyanobacterium revealed divergence at subspecies level and possible adaptation genes

BACKGROUND

Cyanobacteria are diverse phototrophic microbes with ecological importance and potential for biotechnology applications. One species of thermophilic cyanobacteria, Thermosynechococcus taiwanensis, has been studied for biomass pyrolysis, estrogen degradation, and the production of bioethanol, monosaccharide, and phycocyanin. To better understand the diversity and evolution of this species, we sampled across different regions in Taiwan for strain isolation and genomic analysis.

RESULTS

A total of 27 novel strains were isolated from nine of the 12 hot springs sampled and subjected to whole genome sequencing. Including strains studied previously, our genomic analyses encompassed 32 strains from 11 hot springs. Genome sizes among these strains ranged from 2.64 to 2.70 Mb, with an average of 2.66 Mb. Annotation revealed between 2465 and 2576 protein-coding genes per genome, averaging 2537 genes. Core-genome phylogeny, gene flow estimates, and overall gene content divergence consistently supported the within-species divergence into two major populations. While isolation by distance partially explained the within-population divergence, the factors driving divergence between populations remain unclear. Nevertheless, this species likely has a closed pan-genome comprising approximately 3030 genes, with our sampling providing sufficient coverage of its genomic diversity. To investigate the divergence and potential adaptations, we identified genomic regions with significantly lower nucleotide diversity, indicating loci that may have undergone selective sweeps within each population. We identified 149 and 289 genes within these regions in populations A and B, respectively. Only 16 genes were common to both populations, suggesting that selective sweeps primarily targeted different genes in the two populations. Key genes related to functions such as photosynthesis, motility, and ion transport were highlighted.

CONCLUSIONS

This work provides a population genomics perspective on a hot spring cyanobacterial species in Taiwan. Beyond advancing our understanding of microbial genomics and evolution, the strains collected and genome sequences generated in this work provide valuable materials for future development and utilization of biological resources.

Deciphering molecular landscape of breast cancer progression and insights from functional genomics and therapeutic explorations followed by in vitro validation

Breast cancer is caused by aberrant breast cells that proliferate and develop into tumors. Tumors have the potential to spread throughout the body and become lethal if ignored. Metastasis is the process by which invasive tumors move to neighboring lymph nodes or other organs. Metastasis can be lethal and perhaps fatal. The objective of our study was to elucidate the molecular mechanisms underlying the transition of Ductal Carcinoma In Situ (DCIS) to Invasive Ductal Carcinoma (IDC), with a particular focus on hub genes and potential therapeutic agents. Using Weighted Gene Co-expression Network Analysis (WGCNA), we built a comprehensive network combining clinical and phenotypic data from both DCIS and IDC. Modules within this network, correlated with specific phenotypic traits, were identified, and hub genes were identified as critical markers. Receiver Operating Characteristic (ROC) analysis assessed their potential as biomarkers, while survival curve analysis gauged their prognostic value. Furthermore, molecular docking predicted interactions with potential therapeutic agents. Ten hub genes-CDK1, KIF11, NUF2, ASPM, CDCA8, CENPF, DTL, EXO1, KIF2C, and ZWINT-emerged as pivotal fibroblast-specific genes potentially involved in the DCIS to IDC transition. These genes exhibited pronounced positive correlations with key pathways like the cell cycle and DNA repair, Molecular docking revealed Fisetin, an anti-inflammatory compound, effectively binding to both CDK1 and DTL underscoring their role in orchestrating cellular transformation. CDK1 and DTL were selected for molecular docking with CDK1 inhibitors, revealing effective binding of Fisetin, an anti-inflammatory compound, to both. Of the identified hub genes, DTL-an E3 ubiquitin ligase linked to the CRL4 complex-plays a central role in cancer progression, impacting tumor growth, invasion, and metastasis, as well as cell cycle regulation and epithelial-mesenchymal transition (EMT). CDK1, another hub gene, is pivotal in cell cycle progression and associated with various biological processes. In conclusion, our study offers insights into the complex mechanisms driving the transition from DCIS to IDC. It underscores the importance of hub genes and their potential interactions with therapeutic agents, particularly Fisetin. By shedding light on the interplay between CDK1 and DTL expression, our findings contribute to understanding the regulatory landscape of invasive ductal carcinoma and pave the way for future investigations and novel therapeutic avenues.

Moving beyond species: fungal function in house dust provides novel targets for potential indicators of mold growth in homes

BACKGROUND

Increased risk of asthma and other respiratory diseases is associated with exposures to microbial communities growing in damp and moldy indoor environments. The exact causal mechanisms remain unknown, and occupant health effects have not been consistently associated with any species-based mold measurement methods. We need new quantitative methods to identify homes with potentially harmful fungal growth that are not dependent upon species. The goal of this study was to identify genes consistently associated with fungal growth and associated function under damp conditions for use as potential indicators of mold in homes regardless of fungal species present. A de novo metatranscriptomic analysis was performed using house dust from across the US, incubated at 50%, 85%, or 100% equilibrium relative humidity (ERH) for 1 week.

RESULTS

Gene expression was a function of moisture (adonis2 p < 0.001), with fungal metabolic activity increasing with an increase in moisture condition (Kruskal-Wallis p = 0.003). Genes associated with fungal growth such as sporulation (n = 264), hyphal growth (n = 62), and secondary metabolism (n = 124) were significantly upregulated at elevated ERH conditions when compared to the low 50% ERH (FDR-adjusted p ≤ 0.001, log2FC ≥ 2), indicating that fungal function is influenced by damp conditions. A total of 67 genes were identified as consistently associated with the elevated 85% or 100% ERH conditions and included fungal developmental regulators and secondary metabolite genes such as brlA (log2FC = 7.39, upregulated at 100% compared to 85%) and stcC (log2FC = 8.78, upregulated at 85% compared to 50%).

CONCLUSIONS

Our results demonstrate that moisture conditions more strongly influence gene expression of indoor fungal communities compared to species presence. Identifying genes indicative of microbial growth under damp conditions will help develop robust monitoring techniques for indoor microbial exposures and improve understanding of how dampness and mold are linked to disease. Video Abstract.

Dormancy-inducing 3D engineered matrix uncovers mechanosensitive and drug-protective FHL2-p21 signaling axis

Solid cancers frequently relapse with distant metastasis, despite local and systemic treatment. Cellular dormancy has been identified as an important mechanism underlying drug resistance enabling late relapse. Therefore, relapse from invisible, minimal residual cancer of seemingly disease-free patients call for in vitro models of dormant cells suited for drug discovery. Here, we explore dormancy-inducing 3D engineered matrices, which generate mechanical confinement and induce growth arrest and survival against chemotherapy in cancer cells. We characterized the dormant phenotype of solitary cells by P-ERKlow:P-p38high dormancy signaling ratio, along with Ki67- expression. As underlying mechanism, we identified stiffness-dependent nuclear localization of the four-and-a-half LIM domain 2 (FHL2) protein, leading to p53-independent high p21Cip1/Waf1 nuclear expression, validated in murine and human tissue. Suggestive of a resistance-causing role, cells in the dormancy-inducing matrix became sensitive against chemotherapy upon FHL2 down-regulation. Thus, our biomaterial-based approach will enable systematic screens for previously unidentified compounds suited to eradicate potentially relapsing dormant cancer cells.

Pathway-based prediction of the therapeutic effects and mode of action of custom-made multiherbal medicines

Multiherbal medicines are traditionally used as personalized medicines with custom combinations of crude drugs; however, the mechanisms of multiherbal medicines are unclear. In this study, we developed a novel pathway-based method to predict therapeutic effects and the mode of action of custom-made multiherbal medicines using machine learning. This method considers disease-related pathways as therapeutic targets and evaluates the comprehensive influence of constituent compounds on their potential target proteins in the disease-related pathways. Our proposed method enabled us to comprehensively predict new indications of 194 Kampo medicines for 87 diseases. Using Kampo-induced transcriptomic data, we demonstrated that Kampo constituent compounds stimulated the disease-related proteins and a customized Kampo formula enhanced the efficacy compared with an existing Kampo formula. The proposed method will be useful for discovering effective Kampo medicines and optimizing custom-made multiherbal medicines in practice.

scToppR: a coding-friendly R interface to ToppGene

MOTIVATION

The scToppR package provides a ToppGene interface from R programs/scripts to fully access/control the database for functional enrichment without the need for active interaction on its Web site (https://toppgene.cchmc.org/).

RESULTS

The library facilitates the functional enrichment analysis and visualization by interacting with ToppGene, downloading the functional enrichment dataframes, and using R environment to visualize the final results.

AVAILABILITY AND IMPLEMENTATION

Code and documentation are currently available at https://github.com/BioinformaticsMUSC/scToppR.

Qualitative and Untargeted Volatilome Fingerprinting of Aspergillus sp. and Bulbithecium sp. by HS-SPME-GCMS and Functional Interactions

Research on fungal volatile organic compounds (VOCs) has increased worldwide in the last 10 years, but marine fungal volatilomes remain underexplored. Similarly, the hormone-signaling pathways, agronomic significance, and biocontrol potential of VOCs in plant-associated fungi make the area of research extremely promising. In the current investigation, VOCs of the isolates-Aspergillus sp. GSBT S13 and GSBT S14 from marine sediments, and Bulbithecium sp. GSBT E3 from Eucalyptus foliage were extracted using Head Space solid phase microextraction, followed by gas chromatography-mass spectrometry, identification, statistical analyses, and prediction of functions by KEGG COMPOUND and STITCH 5.0 databases. The significance of this research is fingerprinting VOCs of the isolates from distinct origins, identification of compounds using three libraries (NIST02, NIST14, and W9N11), and using bioinformatic tools to perform functional analysis. The most important findings include the identification of previously unreported compounds in fungi-1-methoxy naphthalene, diethyl phthalate, pentadecane, pristane, and nonanal; the prediction of the involvement of small molecules in the degradation of aromatic compound pathways and activation, inhibition, binding, and catalysis of metabolites with predicted protein partners. This study has ample opportunity to validate the findings and understand the mechanism or mode of action, the interspecies interactions, and the role of the metabolites in geochemical cycles.

Genome-Wide Scan for Copy Number Variations in Chinese Merino Sheep Based on Ovine High-Density 600K SNP Arrays

Sheep are a vital species in the global agricultural economy, providing essential resources such as meat, milk, and wool. Merino sheep (Junken type) are a key breed of fine wool sheep in China. However, research on fine wool traits has largely overlooked the role of SNPs and their association with phenotypes. Copy number variations (CNVs) have emerged as one of the most important sources of genetic variation, influencing phenotypic traits by altering gene expression and dosage. To generate a comprehensive CNVR map of the ovine genome, we conducted genome-wide CNV detection using genotyping data from 285 fine wool sheep. This analysis revealed 656 CNVRs, including 628 on autosomes and 28 on the X chromosome, covering a total of 43.9 Mbs of the sheep genome. The proportion of CNVRs varied across chromosomes, from 0.45% on chromosome 26 to 3.72% on chromosome 10. Functional annotation through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses highlighted significantly enriched GO terms, including odorant binding, ATP binding, and sulfuric ester hydrolase activity. The KEGG analysis identified involvement in pathways such as neuroactive ligand-receptor interaction, axon guidance, ECM-receptor interaction, the one-carbon pool by folate, and focal adhesion (p < 0.05). To validate these CNVRs, we performed quantitative real-time PCR experiments to verify copy number predictions made by PennCNV software (v1.0.5). Out of 11 selected CNVRs with predicted gain, loss, or gain-loss statuses, 8 (IDs 68, 156, 201, 284, 307, 352, 411, 601) were successfully confirmed. This study marks a significant step forward in mapping CNVs in the ovine genome and offers a valuable resource for future research on genetic variation in sheep.

Insights into the radiation and oxidative stress mechanisms in genus Deinococcus

Deinococcus species, noted for their exceptional resistance to DNA-damaging environmental stresses, have piqued scientists' interest for decades. This study dives into the complex mechanisms underpinning radiation resistance in the Deinococcus genus. We have examined the genomes of 82 Deinococcus species and classified radiation-resistance proteins manually into five unique curated categories: DNA repair, oxidative stress defense, Ddr and Ppr proteins, regulatory proteins, and miscellaneous resistance components. This classification reveals important information about the various molecular mechanisms used by these extremophiles which have been less explored so far. We also investigated the presence or lack of these proteins in the context of phylogenetic relationships, core, and pan-genomes, which offered light on the evolutionary dynamics of radiation resistance. This comprehensive study provides a deeper understanding of the genetic underpinnings of radiation resistance in the Deinococcus genus, with potential implications for understanding similar mechanisms in other organisms using an interactomics approach. Finally, this study reveals the complexities of radiation resistance mechanisms, providing a comprehensive understanding of the genetic components that allow Deinococcus species to flourish under harsh environments. The findings add to our understanding of the larger spectrum of stress adaption techniques in bacteria and may have applications in sectors ranging from biotechnology to environmental research.

The Use of Japanese Traditional (Kampo) Medicines Before and During Pregnancy in Japan: The Tohoku Medical Megabank Project Birth and Three-Generation Cohort Study

PURPOSE

Japanese traditional (Kampo) medicines are often used for pregnant women in Japan. However, no comprehensive studies have been conducted regarding the self-reported use of these medicines during pregnancy. This study investigated the use of Kampo medicines during pregnancy in Japan using the Tohoku Medical Megabank Project Birth and Three-Generation Cohort Study (TMM BirThree Cohort Study).

METHODS

Questionnaires were distributed to pregnant women participating in the TMM BirThree Cohort Study (July 2013 to March 2017) at approximately 12 weeks (early pregnancy) and 26 weeks (middle pregnancy). We analysed Kampo medicines use over three periods: (1) 12 months before pregnancy diagnosis, (2) the period between pregnancy diagnosis and around Week 12 of pregnancy and (3) from around Week 12 of pregnancy.

RESULTS

In total, 19 220 women were included in the analysis. The proportions using prescribed Kampo medicines were 4.1% before pregnancy diagnosis, 4.5% from diagnosis to Week 12% and 4.5% after Week 12 of pregnancy. The most frequently prescribed Kampo medicines were tokishakuyakusan (1.0%) before pregnancy diagnosis, shoseiryuto (1.3%) from diagnosis to Week 12 and shoseiryuto (1.5%) Post-week 12. Sixty of the pregnant women used Kampo medicines containing crude drugs, which should be administered cautiously during pregnancy.

CONCLUSION

The proportion of Kampo medicines use before and during pregnancy was 4%-5%. Some pregnant women used Kampo medicines containing crude drugs that should be administered cautiously during pregnancy. Further research is required to determine the safety of Kampo medicines during pregnancy.

The C. elegans Myc-family of transcription factors coordinate a dynamic adaptive response to dietary restriction

Dietary restriction (DR), the process of decreasing overall food consumption over an extended period of time, has been shown to increase longevity across evolutionarily diverse species and delay the onset of age-associated diseases in humans. In Caenorhabditis elegans, the Myc-family transcription factors (TFs) MXL-2 (Mlx) and MML-1 (MondoA/ChREBP), which function as obligate heterodimers, and PHA-4 (orthologous to FOXA) are both necessary for the full physiological benefits of DR. However, the adaptive transcriptional response to DR and the role of MML-1::MXL-2 and PHA-4 remains elusive. We identified the transcriptional signature of C. elegans DR, using the eat-2 genetic model, and demonstrate broad changes in metabolic gene expression in eat-2 DR animals, which requires both mxl-2 and pha-4. While the requirement for these factors in DR gene expression overlaps, we found many of the DR genes exhibit an opposing change in relative gene expression in eat-2;mxl-2 animals compared to wild-type, which was not observed in eat-2 animals with pha-4 loss. Surprisingly, we discovered more than 2000 genes synthetically dysregulated in eat-2;mxl-2, out of which the promoters of down-regulated genes were substantially enriched for PQM-1 and ELT-1/3 GATA TF binding motifs. We further show functional deficiencies of the mxl-2 loss in DR outside of lifespan, as eat-2;mxl-2 animals exhibit substantially smaller brood sizes and lay a proportion of dead eggs, indicating that MML-1::MXL-2 has a role in maintaining the balance between resource allocation to the soma and to reproduction under conditions of chronic food scarcity. While eat-2 animals do not show a significantly different metabolic rate compared to wild-type, we also find that loss of mxl-2 in DR does not affect the rate of oxygen consumption in young animals. The gene expression signature of eat-2 mutant animals is consistent with optimization of energy utilization and resource allocation, rather than induction of canonical gene expression changes associated with acute metabolic stress, such as induction of autophagy after TORC1 inhibition. Consistently, eat-2 animals are not substantially resistant to stress, providing further support to the idea that chronic DR may benefit healthspan and lifespan through efficient use of limited resources rather than broad upregulation of stress responses, and also indicates that MML-1::MXL-2 and PHA-4 may have distinct roles in promotion of benefits in response to different pro-longevity stimuli.

Genetic and genomic analysis of reproduction traits in holstein cattle using SNP chip data and imputed sequence level genotypes

BACKGROUND

Reproductive performance plays an important role in animal welfare, health and profitability in animal husbandry and breeding. It is well established that there is a negative correlation between performance and reproduction in dairy cattle. This relationship is being increasingly considered in breeding programs. By elucidating the genetic architecture of underlying reproduction traits, it will be possible to make a more detailed contribution to this. Our study followed two approaches to elucidate this area; in a first part, variance components were estimated for 14 different calving and fertility traits, and then genome-wide association studies were performed for 13 reproduction traits on imputed sequence-level genotypes with subsequent enrichment analyses.

RESULTS

Variance components analyses showed a low to moderate heritability (h2) for the traits analysed, ranging from 0.014 for endometritis up to 0.271 for stillbirth, indicating variable degrees of variation within the reproduction traits. For genome-wide association studies, we were able to detect genome-wide significant association signals for nine out of 13 analysed traits after Bonferroni correction on chromosome 6, 18 and the X chromosome. In total, we detected over 2700 associated SNPs encircling more than 90 different genes using the imputed whole-genome sequence data. Functional associations were reviewed so far known and potential candidate regions in the proximity of reproduction events were hypothesised.

CONCLUSION

Our results confirm previous findings of other authors in a comprehensive cohort including 13 different traits at the same time. Additionally, we identified new candidate genes involved in dairy cattle reproduction and made initial suggestions regarding their potential impact, with special regard to the X chromosome as a putative information source for further research. This work can make a contribution to reveal the genetic architecture of reproduction traits in context of trait specific interactions.

Polypharmacology prediction: the long road toward comprehensively anticipating small-molecule selectivity to de-risk drug discovery

INTRODUCTION

Small molecules often bind to multiple targets, a behavior termed polypharmacology. Anticipating polypharmacology is essential for drug discovery since unknown off-targets can modulate safety and efficacy - profoundly affecting drug discovery success. Unfortunately, experimental methods to assess selectivity present significant limitations and drugs still fail in the clinic due to unanticipated off-targets. Computational methods are a cost-effective, complementary approach to predict polypharmacology.

AREAS COVERED

This review aims to provide a comprehensive overview of the state of polypharmacology prediction and discuss its strengths and limitations, covering both classical cheminformatics methods and bioinformatic approaches. The authors review available data sources, paying close attention to their different coverage. The authors then discuss major algorithms grouped by the types of data that they exploit using selected examples.

EXPERT OPINION

Polypharmacology prediction has made impressive progress over the last decades and contributed to identify many off-targets. However, data incompleteness currently limits most approaches to comprehensively predict selectivity. Moreover, our limited agreement on model assessment challenges the identification of the best algorithms - which at present show modest performance in prospective real-world applications. Despite these limitations, the exponential increase of multidisciplinary Big Data and AI hold much potential to better polypharmacology prediction and de-risk drug discovery.

Targeting of human cancer stem cells predicts efficacy and toxicity of FDA-approved oncology drugs

Cancer remains the leading cause of death worldwide with approved oncology drugs continuing to have heterogenous patient responses and accompanied adverse effects (AEs) that limits effectiveness. Here, we examined >100 FDA-approved oncology drugs in the context of stemness using a surrogate model of transformed human pluripotent cancer stem cells (CSCs) vs. healthy stem cells (hSCs) capable of distinguishing abnormal self-renewal and differentiation. Although a proportion of these drugs had no effects (inactive), a larger portion affected CSCs (active), and a unique subset preferentially affected CSCs over hSCs (selective). Single cell gene expression and protein profiling of each drug's FDA recognized target provided a molecular correlation of responses in CSCs vs. hSCs. Uniquely, drugs selective for CSCs demonstrated clinical efficacy, measured by overall survival, and reduced AEs. Our findings reveal that while unintentional, half of anticancer drugs are active against CSCs and associated with improved clinical outcomes. Based on these findings, we suggest ability to target CSC targeting should be included as a property of early onco-therapeutic development.

Drivergene.net: A Cytoscape app for the identification of driver nodes of large-scale complex networks and case studies in discovery of drug target genes

There are no tools to identify driver nodes of large-scale networks in approach of competition-based controllability. This study proposed a novel method for this computation of large-scale networks. It implemented the method in a new Cytoscape plug-in app called Drivergene.net. Experiments of the software on large-scale biomolecular networks have shown outstanding speed and computing power. Interestingly, 86.67% of the top 10 driver nodes found on these networks are anticancer drug target genes that reside mostly at the innermost K-cores of the networks. Finally, compared method with those of five other researchers and confirmed that the proposed method outperforms the other methods on identification of anticancer drug target genes. Taken together, Drivergene.net is a reliable tool that efficiently detects not only drug target genes from biomolecular networks but also driver nodes of large-scale complex networks. Drivergene.net with a user manual and example datasets are available https://github.com/tinhpd/Drivergene.git.

Graph Representation Learning Based on Specific Subgraphs for Biomedical Interaction Prediction

Discovering the novel associations of biomedical entities is of great significance and can facilitate not only the identification of network biomarkers of disease but also the search for putative drug targets.Graph representation learning (GRL) has incredible potential to efficiently predict the interactions from biomedical networks by modeling the robust representation for each node.> However, the current GRL-based methods learn the representation of nodes by aggregating the features of their neighbors with equal weights. Furthermore, they also fail to identify which features of higher-order neighbors are integrated into the representation of the central node. In this work, we propose a novel graph representation learning framework: a multi-order graph neural network based on reconstructed specific subgraphs (MGRS) for biomedical interaction prediction. In the MGRS, we apply the multi-order graph aggregation module (MOGA) to learn the wide-view representation by integrating the multi-hop neighbor features. Besides, we propose a subgraph selection module (SGSM) to reconstruct the specific subgraph with adaptive edge weights for each node. SGSM can clearly explore the dependency of the node representation on the neighbor features and learn the subgraph-based representation based on the reconstructed weighted subgraphs. Extensive experimental results on four public biomedical networks demonstrate that the MGRS performs better and is more robust than the latest baselines.

Network medicine analysis for dissecting the therapeutic mechanism of consensus TCM formulae in treating hepatocellular carcinoma with different TCM syndromes

Introduction

Hepatocellular carcinoma (HCC) is a major cause of cancer-related mortality worldwide. Traditional Chinese Medicine (TCM) is widely utilized as an adjunct therapy, improving patient survival and quality of life. TCM categorizes HCC into five distinct syndromes, each treated with specific herbal formulae. However, the molecular mechanisms underlying these treatments remain unclear.

Methods

We employed a network medicine approach to explore the therapeutic mechanisms of TCM in HCC. By constructing a protein-protein interaction (PPI) network, we integrated genes associated with TCM syndromes and their corresponding herbal formulae. This allowed for a quantitative analysis of the topological and functional relationships between TCM syndromes, HCC, and the specific formulae used for treatment.

Results

Our findings revealed that genes related to the five TCM syndromes were closely associated with HCC-related genes within the PPI network. The gene sets corresponding to the five TCM formulae exhibited significant proximity to HCC and its related syndromes, suggesting the efficacy of TCM syndrome differentiation and treatment. Additionally, through a random walk algorithm applied to a heterogeneous network, we prioritized active herbal ingredients, with results confirmed by literature.

Discussion

The identification of these key compounds underscores the potential of network medicine to unravel the complex pharmacological actions of TCM. This study provides a molecular basis for TCM's therapeutic strategies in HCC and highlights specific herbal ingredients as potential leads for drug development and precision medicine.

Medication use before and during pregnancy in Japan: the Tohoku Medical Megabank Project Birth and Three-Generation Cohort Study

PURPOSE

To elucidate the status of medication use among pregnant women in Japan, by means of a multigenerational genome and birth cohort study: the Tohoku Medical Megabank Project Birth and Three-Generation Cohort Study (TMM BirThree Cohort Study).

METHODS

Questionnaires were distributed to pregnant women participating in the TMM BirThree Cohort Study (from July 2013 to March 2017) around 12 weeks (early pregnancy) and 26 weeks (middle pregnancy). We analysed medication use over three periods: (1) 12 months prior to pregnancy diagnosis, (2) the period between pregnancy diagnosis and around week 12 of pregnancy, and (3) post around week 12 of pregnancy.

RESULTS

In total, 19,297 women were included in the analysis. The proportion of pregnant women using medications was 49.0% prior to pregnancy diagnosis, 52.1% from diagnosis to week 12, and 58.4% post week 12 of pregnancy. The most frequently prescribed medications were loxoprofen sodium hydrate (5.5%) prior to pregnancy diagnosis, magnesium oxide (5.9%) from diagnosis to week 12, and ritodrine hydrochloride (10.5%) post week 12 of pregnancy. The number of women who used suspected teratogenic medications during early pregnancy was 96 prior to pregnancy diagnosis, 48 from diagnosis to week 12, and 54 post week 12 of pregnancy.

CONCLUSION

We found that ~ 50% of the pregnant women used medications before and during pregnancy and some took potential teratogenic medications during pregnancy. In birth genomic cohort study, it is expected that investigations into the safety and effectiveness of medications used during pregnancy will advance.

A network-based trans-omics approach for predicting synergistic drug combinations

BACKGROUND

Combination therapy can offer greater efficacy on medical treatments. However, the discovery of synergistic drug combinations is challenging. We propose a novel computational method, SyndrumNET, to predict synergistic drug combinations by network propagation with trans-omics analyses.

METHODS

The prediction is based on the topological relationship, network-based proximity, and transcriptional correlation between diseases and drugs. SyndrumNET was applied to analyzing six diseases including asthma, diabetes, hypertension, colorectal cancer, acute myeloid leukemia (AML), and chronic myeloid leukemia (CML).

RESULTS

Here we show that SyndrumNET outperforms the previous methods in terms of high accuracy. We perform in vitro cell survival assays to validate our prediction for CML. Of the top 17 predicted drug pairs, 14 drug pairs successfully exhibits synergistic anticancer effects. Our mode-of-action analysis also reveals that the drug synergy of the top predicted combination of capsaicin and mitoxantrone is due to the complementary regulation of 12 pathways, including the Rap1 signaling pathway.

CONCLUSIONS

The proposed method is expected to be useful for discovering synergistic drug combinations for various complex diseases.

Transcriptomic Analysis of Arachidonic Acid Pathway Genes Provides Mechanistic Insight into Multi-Organ Inflammatory and Vascular Diseases

Arachidonic acid (AA) metabolites have been associated with several diseases across various organ systems, including the cardiovascular, pulmonary, and renal systems. Lipid mediators generated from AA oxidation have been studied to control macrophages, T-cells, cytokines, and fibroblasts, and regulate inflammatory mediators that induce vascular remodeling and dysfunction. AA is metabolized by cyclooxygenase (COX), lipoxygenase (LOX), and cytochrome P450 (CYP) to generate anti-inflammatory, pro-inflammatory, and pro-resolutory oxidized lipids. As comorbid states such as diabetes, hypertension, and obesity become more prevalent in cardiovascular disease, studying the expression of AA pathway genes and their association with these diseases can provide unique pathophysiological insights. In addition, the AA pathway of oxidized lipids exhibits diverse functions across different organ systems, where a lipid can be both anti-inflammatory and pro-inflammatory depending on the location of metabolic activity. Therefore, we aimed to characterize the gene expression of these lipid enzymes and receptors throughout multi-organ diseases via a transcriptomic meta-analysis using the Gene Expression Omnibus (GEO) Database. In our study, we found that distinct AA pathways were expressed in various comorbid conditions, especially those with prominent inflammatory risk factors. Comorbidities, such as hypertension, diabetes, and obesity appeared to contribute to elevated expression of pro-inflammatory lipid mediator genes. Our results demonstrate that expression of inflammatory AA pathway genes may potentiate and attenuate disease; therefore, we suggest further exploration of these pathways as therapeutic targets to improve outcomes.

Application of Transcriptome-Based Gene Set Featurization for Machine Learning Model to Predict the Origin of Metastatic Cancer

Identifying the primary site of origin of metastatic cancer is vital for guiding treatment decisions, especially for patients with cancer of unknown primary (CUP). Despite advanced diagnostic techniques, CUP remains difficult to pinpoint and is responsible for a considerable number of cancer-related fatalities. Understanding its origin is crucial for effective management and potentially improving patient outcomes. This study introduces a machine learning framework, ONCOfind-AI, that leverages transcriptome-based gene set features to enhance the accuracy of predicting the origin of metastatic cancers. We demonstrate its potential to facilitate the integration of RNA sequencing and microarray data by using gene set scores for characterization of transcriptome profiles generated from different platforms. Integrating data from different platforms resulted in improved accuracy of machine learning models for predicting cancer origins. We validated our method using external data from clinical samples collected through the Kangbuk Samsung Medical Center and Gene Expression Omnibus. The external validation results demonstrate a top-1 accuracy ranging from 0.80 to 0.86, with a top-2 accuracy of 0.90. This study highlights that incorporating biological knowledge through curated gene sets can help to merge gene expression data from different platforms, thereby enhancing the compatibility needed to develop more effective machine learning prediction models.

Legume-rhizobia symbiosis: Translatome analysis

Leguminous plants can establish endosymbiotic relationships with nitrogen-fixing soil rhizobacteria. Bacterial infection and nodule organogenesis are two independent but highly coordinated genetic programs that are active during this interaction. These genetic programs can be regulated along all the stages of gene expression. Most of the studies, for both eukaryotes and prokaryotes, focused on the transcriptional regulation level determining the abundance of mRNAs. However, it has been demonstrated that mRNA levels only sometimes correlate with the abundance or activity of the coded proteins. For this reason, in the past two decades, interest in the role of translational control of gene expression has increased, since the subset of mRNA being actively translated outperforms the information gained only by the transcriptome. In the case of legume-rhizobia interactions, the study of the translatome still needs to be explored further. Therefore, this review aims to discuss the methodologies for analyzing polysome-associated mRNAs at the genome-scale and their contribution to studying translational control to understand the complexity of this symbiotic interaction. Moreover, the Dual RNA-seq approach is discussed for its relevance in the context of a symbiotic nodule, where intricate multi-species gene expression networks occur.

Predicting therapeutic and side effects from drug binding affinities to human proteome structures

Evaluation of the binding affinities of drugs to proteins is a crucial process for identifying drug pharmacological actions, but it requires three dimensional structures of proteins. Herein, we propose novel computational methods to predict the therapeutic indications and side effects of drug candidate compounds from the binding affinities to human protein structures on a proteome-wide scale. Large-scale docking simulations were performed for 7,582 drugs with 19,135 protein structures revealed by AlphaFold (including experimentally unresolved proteins), and machine learning models on the proteome-wide binding affinity score (PBAS) profiles were constructed. We demonstrated the usefulness of the method for predicting the therapeutic indications for 559 diseases and side effects for 285 toxicities. The method enabled to predict drug indications for which the related protein structures had not been experimentally determined and to successfully extract proteins eliciting the side effects. The proposed method will be useful in various applications in drug discovery.

Vaginal Lactobacillus crispatus persistence following application of a live biotherapeutic product: colonization phenotypes and genital immune impact

BACKGROUND

Bacterial vaginosis (BV) increases HIV acquisition risk, potentially by eliciting genital inflammation. After BV treatment, the vaginal administration of LACTIN-V, a live biotherapeutic containing the Lactobacillus crispatus strain CTV-05, reduced BV recurrence and vaginal inflammation; however, 3 months after product cessation, CTV-05 colonization was only sustained in 48% of participants.

RESULTS

This nested sub-study in 32 participants receiving LACTIN-V finds that 72% (23/32) demonstrate clinically relevant colonization (CTV-05 absolute abundance > 106 CFU/mL) during at least one visit while 28% (9/32) of women demonstrate colonization resistance, even during product administration. Immediately prior to LACTIN-V administration, the colonization-resistant group exhibited elevated vaginal microbiota diversity. During LACTIN-V administration, colonization resistance was associated with elevated vaginal markers of epithelial disruption and reduced chemokines, possibly due to elevated absolute abundance of BV-associated species and reduced L. crispatus. Colonization permissive women were stratified into sustained and transient colonization groups (31% and 41% of participants, respectively) based on CTV-05 colonization after cessation of product administration. These groups also exhibited distinct genital immune profiles during LACTIN-V administration.

CONCLUSIONS

The genital immune impact of LACTIN-V may be contingent on the CTV-05 colonization phenotype, which is in turn partially dependent on the success of BV clearance prior to LACTIN-V administration.

Toward a unified understanding of drug-drug interactions: mapping Japanese drug codes to RxNorm concepts

OBJECTIVES

Linking information on Japanese pharmaceutical products to global knowledge bases (KBs) would enhance international collaborative research and yield valuable insights. However, public access to mappings of Japanese pharmaceutical products that use international controlled vocabularies remains limited. This study mapped YJ codes to RxNorm ingredient classes, providing new insights by comparing Japanese and international drug-drug interaction (DDI) information using a case study methodology.

MATERIALS AND METHODS

Tables linking YJ codes to RxNorm concepts were created using the application programming interfaces of the Kyoto Encyclopedia of Genes and Genomes and the National Library of Medicine. A comparative analysis of Japanese and international DDI information was thus performed by linking to an international DDI KB.

RESULTS

There was limited agreement between the Japanese and international DDI severity classifications. Cross-tabulation of Japanese and international DDIs by severity showed that 213 combinations classified as serious DDIs by an international KB were missing from the Japanese DDI information.

DISCUSSION

It is desirable that efforts be undertaken to standardize international criteria for DDIs to ensure consistency in the classification of their severity.

CONCLUSION

The classification of DDI severity remains highly variable. It is imperative to augment the repository of critical DDI information, which would revalidate the utility of fostering collaborations with global KBs.

Screening of bioactive compounds from selected mushroom species against putative drug targets in Mycobacterium tuberculosis: a multi-target approach

Mycobacterium tuberculosis (Mtb) is a notorious pathogen that causes one of the highest mortalities globally. Due to a pressing demand to identify novel therapeutic alternatives, the present study aims to focus on screening the putative drug targets and prioritizing their role in antibacterial drug development. The most vital proteins involved in the Biotin biosynthesis pathway and the Lipoarabinomannan (LAM) pathway such as biotin synthase (bioB) and alpha-(1->6)-mannopyranosyltransferase A (mptA) respectively, along with other essential virulence proteins of Mtb were selected as drug targets. Among these, the ones without native structures were modelled and validated using standard bioinformatics tools. Further, the interactions were performed with naturally available lead molecules present in selected mushroom species such as Agaricus bisporus, Pleurotus djamor, Hypsizygus ulmarius. Through Gas Chromatography-Mass Spectrometry (GC-MS), 15 bioactive compounds from the methanolic extract of mushrooms were identified. Further, 4 were selected based on drug-likeness and pharmacokinetic screening for molecular docking analysis against our prioritized targets wherein Benz[e]azulene from Pleurotus djamor illustrated a good binding affinity with a LF rank score of -9.036 kcal mol -1 against nuoM (NADH quinone oxidoreductase subunit M) and could be used as a prospective candidate in order to combat Tuberculosis (TB). Furthermore, the stability of the complex are validated using MD Simulations and subsequently, the binding free energy was calculated using MM-GBSA analysis. Thus, the current in silico analysis suggests a promising role of compounds extracted from mushrooms in tackling the TB burden.Communicated by Ramaswamy H. Sarma.

Cardiac glycosides restore autophagy flux in an iPSC-derived neuronal model of WDR45 deficiency

Beta-Propeller Protein-Associated Neurodegeneration (BPAN) is one of the commonest forms of Neurodegeneration with Brain Iron Accumulation, caused by mutations in the gene encoding the autophagy-related protein, WDR45. The mechanisms linking autophagy, iron overload and neurodegeneration in BPAN are poorly understood and, as a result, there are currently no disease-modifying treatments for this progressive disorder. We have developed a patient-derived, induced pluripotent stem cell (iPSC)-based midbrain dopaminergic neuronal cell model of BPAN (3 patient, 2 age-matched controls and 2 isogenic control lines) which shows defective autophagy and aberrant gene expression in key neurodegenerative, neurodevelopmental and collagen pathways. A high content imaging-based medium-throughput blinded drug screen using the FDA-approved Prestwick library identified 5 cardiac glycosides that both corrected disease-related defective autophagosome formation and restored BPAN-specific gene expression profiles. Our findings have clear translational potential and emphasise the utility of iPSC-based modelling in elucidating disease pathophysiology and identifying targeted therapeutics for early-onset monogenic disorders.

Advancing Drug Safety in Drug Development: Bridging Computational Predictions for Enhanced Toxicity Prediction

The attrition rate of drugs in clinical trials is generally quite high, with estimates suggesting that approximately 90% of drugs fail to make it through the process. The identification of unexpected toxicity issues during preclinical stages is a significant factor contributing to this high rate of failure. These issues can have a major impact on the success of a drug and must be carefully considered throughout the development process. These late-stage rejections or withdrawals of drug candidates significantly increase the costs associated with drug development, particularly when toxicity is detected during clinical trials or after market release. Understanding drug-biological target interactions is essential for evaluating compound toxicity and safety, as well as predicting therapeutic effects and potential off-target effects that could lead to toxicity. This will enable scientists to predict and assess the safety profiles of drug candidates more accurately. Evaluation of toxicity and safety is a critical aspect of drug development, and biomolecules, particularly proteins, play vital roles in complex biological networks and often serve as targets for various chemicals. Therefore, a better understanding of these interactions is crucial for the advancement of drug development. The development of computational methods for evaluating protein-ligand interactions and predicting toxicity is emerging as a promising approach that adheres to the 3Rs principles (replace, reduce, and refine) and has garnered significant attention in recent years. In this review, we present a thorough examination of the latest breakthroughs in drug toxicity prediction, highlighting the significance of drug-target binding affinity in anticipating and mitigating possible adverse effects. In doing so, we aim to contribute to the development of more effective and secure drugs.

Substrate scope expansion of 4-phenol oxidases by rational enzyme selection and sequence-function relations

Enzymes are natures' catalysts and will have a lasting impact on (organic) synthesis as they possess unchallenged regio- and stereo selectivity. On the downside, this high selectivity limits enzymes' substrate range and hampers their universal application. Therefore, substrate scope expansion of enzyme families by either modification of known biocatalysts or identification of new members is a key challenge in enzyme-driven catalysis. Here, we present a streamlined approach to rationally select enzymes with proposed functionalities from the ever-increasing amount of available sequence data. In a case study on 4-phenol oxidoreductases, eight enzymes of the oxidase branch were selected from 292 sequences on basis of the properties of first shell residues of the catalytic pocket, guided by the computational tool A2CA. Correlations between these residues and enzyme activity yielded robust sequence-function relations, which were exploited by site-saturation mutagenesis. Application of a peroxidase-independent oxidase screening resulted in 16 active enzyme variants which were up to 90-times more active than respective wildtype enzymes and up to 6-times more active than the best performing natural variants. The results were supported by kinetic experiments and structural models. The newly introduced amino acids confirmed the correlation studies which overall highlights the successful logic of the presented approach.

Risk of Major Congenital Malformations Associated with the Use of Japanese Traditional (Kampo) Medicine Containing Ephedra During the First Trimester of Pregnancy

BACKGROUND

Japanese traditional (Kampo) medicines containing ephedra may be used to treat colds during pregnancy. There are reports that ephedrine, a component of ephedra, has a risk of teratogenicity; however, the evidence remains equivocal.

OBJECTIVE

This study aimed to evaluate the risk of major congenital malformations (MCMs) associated with exposure to Kampo medicines containing ephedra during the first trimester of pregnancy using the Tohoku Medical Megabank Project Birth and Three-Generation Cohort Study (TMM BirThree Cohort Study).

METHODS

To 23,730 mother-infant pairs who participated in the TMM BirThree Cohort Study from July 2013 to March 2017, questionnaires in early and middle pregnancy were distributed approximately at weeks 12 and 26 of pregnancy, respectively. Infants' risk of MCMs in women who used Kampo medicines containing ephedra or acetaminophen during the first trimester was assessed, and the odds ratios (ORs) were estimated with unadjusted and adjusted analyses.

RESULTS

Among 20,879 women, acetaminophen and Kampo medicines containing ephedra were used in 665 (3.19%) and 376 (1.80%) women, respectively, in the first trimester. Among the infants born to the mothers who used acetaminophen or Kampo medicine containing ephedra during the first trimester, 11 (1.65%) and 8 (2.13%), respectively, had overall MCMs. OR of overall MCMs was higher in women who used Kampo medicines containing ephedra than in those who used acetaminophen in the first trimester (adjusted OR, 1.45; 95% confidence interval (CIs), 0.57-3.71); however, the difference was not statistically significant.

CONCLUSIONS

In this study, there was no statistically significant association between the use of Kampo medicines containing ephedra during the first trimester of pregnancy and the risk of MCMs. Although some point estimates of ORs exceeded 1.00, the absolute magnitude of any increased risks would be low.

Sensitivity of substrate translocation in chaperone-mediated autophagy to Alzheimer's disease progression

Alzheimer's disease (AD) is a progressive brain disorder marked by abnormal protein accumulation and resulting proteotoxicity. This study examines Chaperone-Mediated Autophagy (CMA), particularly substrate translocation into lysosomes, in AD. The study observes: (1) Increased substrate translocation activity into lysosomes, vital for CMA, aligns with AD progression, highlighted by gene upregulation and more efficient substrate delivery. (2) This CMA phase strongly correlates with AD's clinical symptoms; more proteotoxicity links to worse dementia, underscoring the need for active degradation. (3) Proteins like GFAP and LAMP2A, when upregulated, almost certainly indicate AD risk, marking this process as a significant AD biomarker. Based on these observations, this study proposes the following hypothesis: As AD progresses, the aggregation of pathogenic proteins increases, the process of substrate entry into lysosomes via CMA becomes active. The genes associated with this process exhibit heightened sensitivity to AD. This conclusion stems from an analysis of over 10,000 genes and 363 patients using two AI methodologies. These methodologies were instrumental in identifying genes highly sensitive to AD and in mapping the molecular networks that respond to the disease, thereby highlighting the significance of this critical phase of CMA.

Heat shock protein HvHSP16.9 from wild barley enhances tolerance to salt stress

Heat shock proteins (HSPs) are known to play a crucial role in the response of plants to environmental stress, particularly heat stress. Nevertheless, the function of HSPs in salt stress tolerance in plants, especially in barley, remains largely unexplored. Here, we aimed to investigate and compare the salt tolerance mechanisms between wild barley EC_S1 and cultivated barley RGT Planet through a comprehensive analysis of physiological parameters and transcriptomic profiles. Results demonstrated that the number of differentially expressed genes (DEGs) in EC_S1 was significantly higher than in RGT Planet, indicating that wild barley gene regulation is more adaptive to salt stress. KEGG enrichment analysis revealed that DEGs were mainly enriched in the processes of photosynthesis, plant hormone signal transduction, and reactive oxygen species metabolism. Furthermore, the application of weighted gene correlation network analysis (WGCNA) enabled the identification of a set of key genes, including small heat shock protein (sHSP), Calmodulin-like proteins (CML), and protein phosphatases 2C (PP2C). Subsequently, a novel sHSP gene, HvHSP16.9 encoding a protein of 16.9 kDa, was cloned from wild barley, and its role in plant response to salt stress was elucidated. In Arabidopsis, overexpression of HvHSP16.9 increased the salt tolerance. Meanwhile, barley stripe mosaic virus-induced gene silencing (BSMV-VIGS) of HvHSP16.9 significantly reduced the salt tolerance in wild barley. Overall, this study offers a new theoretical framework for comprehending the tolerance and adaptation mechanisms of wild barley under salt stress. It provides valuable insights into the salt tolerance function of HSP, and identifies new candidate genes for enhancing cultivated barley varieties.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12298-024-01455-4.

Pharmaco-phosphoproteomic analysis of cancer-associated KIT mutations D816V and V560G

The CD117 mast/stem cell growth factor receptor tyrosine kinase (KIT) is critical for haematopoiesis, melanogenesis and stem cell maintenance. KIT is commonly activated by mutation in cancers including acute myeloid leukaemia, melanoma and gastrointestinal stromal tumours (GISTs). The kinase and the juxtamembrane domains of KIT are mutation hotspots; with the kinase domain mutation D816V common in leukaemia and the juxtamembrane domain mutation V560G common in GISTs. Given the importance of mutant KIT signalling in cancer, we have conducted a proteomic and phosphoproteomic analysis of myeloid progenitor cells expressing D816V- and V560G-KIT mutants, using an FDCP1 isogenic cell line model. Proteomic analysis revealed increased abundance of proteases and growth signalling proteins in KIT-mutant cells compared to empty vector (EV) controls. Pathway analysis identified increased oxidative phosphorylation in D816V- and V560G-mutant KIT cells, which was targetable using the inhibitor IACS010759. Dysregulation of RNA metabolism and cytoskeleton/adhesion pathways was identified in both the proteome and phosphoproteome of KIT-mutant cells. Phosphoproteome analysis further revealed active kinases such as EGFR, ERK and PKC, which were targetable using pharmacological inhibitors. This study provides a pharmaco-phosphoproteomic profile of D816V- and V560G-mutant KIT cells, which reveals novel therapeutic strategies that may be applicable to a range of cancers.

Transcript Profiles of Microglia/Macrophage Cells at the Borders of Chronic Active and Subpial Gray Matter Lesions in Multiple Sclerosis

OBJECTIVE

Microglia/macrophages line the border of demyelinated lesions in both cerebral white matter and the cortex in the brains of multiple sclerosis patients. Microglia/macrophages associated with chronic white matter lesions are thought to be responsible for slow lesion expansion and disability progression in progressive multiple sclerosis, whereas those lining gray matter lesions are less studied. Profiling these microglia/macrophages could help to focus therapies on genes or pathways specific to lesion expansion and disease progression.

METHODS

We compared the morphology and transcript profiles of microglia/macrophages associated with borders of white matter (WM line) and subpial gray matter lesions (GM line) using laser capture microscopy. We performed RNA sequencing on isolated cells followed by immunocytochemistry to determine the distribution of translational products of transcripts increased in WM line microglia.

RESULTS

Cells in the WM line appear activated, with shorter processes and larger cell bodies, whereas those in the GM line appear more homeostatic, with smaller cell bodies and multiple thin processes. Transcript profiling revealed 176 genes in WM lines and 111 genes in GM lines as differentially expressed. Transcripts associated with immune activation and iron homeostasis were increased in WM line microglia, whereas genes belonging to the canonical Wnt signaling pathway were increased in GM line microglia.

INTERPRETATION

We propose that the mechanisms of demyelination and dynamics of lesion expansion are responsible for differential transcript expression in WM lines and GM lines, and posit that increased expression of the Fc epsilon receptor, spleen tyrosine kinase, and Bruton's tyrosine kinase, play a key role in regulating microglia/macrophage function at the border of chronic active white matter lesions. ANN NEUROL 2024;95:907-916.

Solute carrier family 16 member 1 as a potential prognostic factor for pancreatic ductal adenocarcinoma and its influence on tumor immunity

Background

Solute carrier family 16 member 1 (SLC16A1) serves as a biomarker in numerous types of cancer. Tumor immune infiltration has drawn increasing attention in cancer progression and treatment. The objective of our study was to explore the association between SLC16A1 and the tumor immune microenvironment in pancreatic ductal adenocarcinoma (PDAC).

Methods

Data were obtained from The Cancer Genome Atlas. The xCell web tool was used to calculate the proportion of immune cells according to SLC16A1 expression. To further explore the mechanism of SLC16A1, immunity-related genes were screened from differentially expressed genes through weighted gene coexpression network analysis, examined via Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses, and filtrated using univariate Cox regression and least absolute shrinkage and selection operator regression model combined correlation analysis (P<0.05). Next, CIBERSORT was used to analyze the correlation between immune cells and five important genes. SLC16A1 expression and its clinical role in pancreatic cancer was clarified via immunohistochemical staining experiments. Finally, the effects of SLC16A1 on the results of cancer immunity were evaluated by in vitro experiments.

Results

SLC16A1 was overexpressed in PDAC tissues and could be an independent prognostic factor. SLC16A1 was significantly negatively correlated with overall survival and suppressed the tumor immunity of PDAC. In clinic, SLC16A1 expression was significantly positively correlated with tumor progression and poor prognosis. We also found that SLC16A1 could suppress the antitumor ability of CD8+ T cells.

Conclusions

SLC16A1 is a biomarker for the prognosis of PDAC and can influence the immune environment of PDAC. These findings provide new insights into the treatment of PDAC.

Plasma miRNA expression profile in pediatric pineal pure germinomas

Background

Pure germinomas account for 40% of pineal tumors and are characterized by the lack of appreciable tumor markers, thus requiring a tumor biopsy for diagnosis. MicroRNAs (miRNA) have emerged as potential non-invasive biomarkers for germ cell tumors and may facilitate the non-invasive diagnosis of pure pineal germinomas.

Material and methods

A retrospective chart review was performed on all patients treated at the Children's Cancer Hospital Egypt diagnosed with a pineal region tumor between June 2013 and March 2021 for whom a research blood sample was available. Plasma samples were profiled for miRNA expression, and DESeq2 was used to compare between pure germinoma and other tumor types. Differentially expressed miRNAs were identified. The area under the curve of the receive;r operating characteristic curve was constructed to evaluate diagnostic performance.

Results

Samples from 39 pediatric patients were available consisting of 12 pure germinomas and 27 pineal region tumors of other pathologies, including pineal origin tumors [n = 17; pineoblastoma (n = 13) and pineal parenchymal tumors of intermediate differentiation (n = 4)] and others [n = 10; low-grade glioma (n = 6) and atypical teratoid rhabdoid tumor (n = 4)]. Using an adjusted p-value <0.05, three miRNAs showed differential expression (miR-143-3p, miR-320c, miR-320d; adjusted p = 0.0058, p = 0.0478, and p = 0.0366, respectively) and good discriminatory power between the two groups (AUC 90.7%, p < 0.001) with a sensitivity of 25% and a specificity of 100%.

Conclusion

Our results suggest that a three-plasma miRNA signature has the potential to non-invasively identify pineal body pure germinomas which may allow selected patients to avoid the potential surgical complications.

Genetically determined metabolites in allergic conjunctivitis: A Mendelian randomization study

Background

Allergic conjunctivitis (AC) afflicts a significant portion of the global populace. Yet, its metabolic foundations remain largely unexplored.

Methods

We applied Mendelian Randomization (MR) and Linkage Disequilibrium Score Regression (LDSC) to scrutinize a cohort comprising 20 958 AC cases and 356 319 controls. Data were amalgamated from the metabolomics GWAS server and the FinnGen project, under strict quality control protocols.

Results

Using two-sample MR analysis, 486 blood metabolites were investigated in relation to AC. The IVW approach highlighted 18 metabolites as closely tied to AC risk; of these, 16 retained significance post sensitivity assessments for heterogeneity and horizontal pleiotropy. LDSC analysis, adopted to bolster our findings and negate confounders from shared genetic markers, revealed 8 metabolites with marked heritability, including: palmitate (OR = 0.614), 3-methoxytyrosine (OR = 0.657), carnitine (OR = 1.368), threonate (OR = 0.828), N-[3-(2-Oxopyrrolidin-1-yl)propyl]acetamide (OR = 1.257), metoprolol acid metabolite (OR = 0.982), oleoylcarnitine (OR = 0.635), and 2-palmitoylglycerophosphocholine (OR = 1.351).

Conclusion

AC is precipitated by ocular responses to environmental allergens. Our study unveils a causal link between 8 blood metabolites and AC. This insight accentuates the role of metabolites in AC onset, suggesting novel avenues for its early prediction, targeted prevention, and tailored therapeutic interventions.

Study of the influence of tributyrin-supplemented diets on the gut bacterial communities of rainbow trout (Oncorhynchus mykiss)

Dietary supplementation with triglyceride tributyrin (TBT), a butyrate precursor, has been associated with beneficial effects on fish health and improvements in the ability of carnivorous fish to tolerate higher levels of plant-based protein. In this study, we aimed to investigate the effects of a plant-based diet supplemented with TBT on the structural diversity and putative function of the digesta-associated bacterial communities of rainbow trout (Oncorhynchus mykiss). In addition to this, we also assessed the response of fish gut digestive enzyme activities and chyme metabolic profile in response to TBT supplementation. Our results indicated that TBT had no significant effects on the overall fish gut bacterial communities, digestive enzyme activities or metabolic profile when compared with non-supplemented controls. However, a more in-depth analysis into the most abundant taxa showed that diets at the highest TBT concentrations (0.2% and 0.4%) selectively inhibited members of the Enterobacterales order and reduced the relative abundance of a bacterial population related to Klebsiella pneumoniae, a potential fish pathogen. Furthermore, the predicted functional analysis of the bacterial communities indicated that increased levels of TBT were associated with depleted KEGG pathways related to pathogenesis. The specific effects of TBT on gut bacterial communities observed here are intriguing and encourage further studies to investigate the potential of this triglyceride to promote pathogen suppression in the fish gut environment, namely in the context of aquaculture.

CDK5R1, GSE1, HSPG2 and WDFY3 as indirect epigenetic-sensitive genes in atrial fibrillation

BACKGROUND

Although mounting evidence supports that aberrant DNA methylation occurs in the hearts of patients with atrial fibrillation (AF), noninvasive epigenetic characterization of AF has not yet been defined.

METHODS

We investigated DNA methylome changes in peripheral blood CD4+ T cells isolated from 10 patients with AF relative to 11 healthy subjects (HS) who were enrolled in the DIANA clinical trial (NCT04371809) via reduced-representation bisulfite sequencing (RRBS).

RESULTS

An atrial-specific PPI network revealed 18 hub differentially methylated genes (DMGs), wherein ROC curve analysis revealed reasonable diagnostic performance of DNA methylation levels found within CDK5R1 (AUC = 0.76; p = 0.049), HSPG2 (AUC = 0.77; p = 0.038), WDFY3 (AUC = 0.78; p = 0.029), USP49 (AUC = 0.76; p = 0.049), GSE1 (AUC = 0.76; p = 0.049), AIFM1 (AUC = 0.76; p = 0.041), CDK5RAP2 (AUC = 0.81; p = 0.017), COL4A1 (AUC = 0.86; p < 0.001), SEPT8 (AUC = 0.90; p < 0.001), PFDN1 (AUC = 0.90; p < 0.01) and ACOT7 (AUC = 0.78; p = 0.032). Transcriptional profiling of the hub DMGs provided a significant overexpression of PSDM6 (p = 0.004), TFRC (p = 0.01), CDK5R1 (p < 0.001), HSPG2 (p = 0.01), WDFY3 (p < 0.001), USP49 (p = 0.004) and GSE1 (p = 0.021) in AF patients vs HS.

CONCLUSIONS

CDK5R1, GSE1, HSPG2 and WDFY3 resulted the best discriminatory genes both at methylation and gene expression level. Our results provide several candidate diagnostic biomarkers with the potential to advance precision medicine in AF.

A leaky gut dysregulates gene networks in the brain associated with immune activation, oxidative stress, and myelination in a mouse model of colitis

The gut and brain are increasingly linked in human disease, with neuropsychiatric conditions classically attributed to the brain showing an involvement of the intestine and inflammatory bowel diseases (IBDs) displaying an ever-expanding list of neurological comorbidities. To identify molecular systems that underpin this gut-brain connection and thus discover therapeutic targets, experimental models of gut dysfunction must be evaluated for brain effects. In the present study, we examine disturbances along the gut-brain axis in a widely used murine model of colitis, the dextran sodium sulfate (DSS) model, using high-throughput transcriptomics and an unbiased network analysis strategy coupled with standard biochemical outcome measures to achieve a comprehensive approach to identify key disease processes in both colon and brain. We examine the reproducibility of colitis induction with this model and its resulting genetic programs during different phases of disease, finding that DSS-induced colitis is largely reproducible with a few site-specific molecular features. We focus on the circulating immune system as the intermediary between the gut and brain, which exhibits an activation of pro-inflammatory innate immunity during colitis. Our unbiased transcriptomics analysis provides supporting evidence for immune activation in the brain during colitis, suggests that myelination may be a process vulnerable to increased intestinal permeability, and identifies a possible role for oxidative stress and brain oxygenation. Overall, we provide a comprehensive evaluation of multiple systems in a prevalent experimental model of intestinal permeability, which will inform future studies using this model and others, assist in the identification of druggable targets in the gut-brain axis, and contribute to our understanding of the concomitance of intestinal and neuropsychiatric dysfunction.