Bioinformatics

DNA variation analysis has become indispensable in many aspects of modern biomedicine, most prominently in the comparison of normal and tumor samples. Thousands of samples are collected in local sequencing efforts and public databases requiring highly scalable, portable, and automated workflows for streamlined processing. Here, we present nf-core/sarek 3, a well-established, comprehensive variant calling and annotation pipeline for germline and somatic samples. It is suitable for any genome with a known reference. We present a full rewrite of the original pipeline showing a significant reduction of storage requirements by using the CRAM format and runtime by increasing intra-sample parallelization. Both are leading to a 70% cost reduction in commercial clouds enabling users to do large-scale and cross-platform data analysis while keeping costs and CO emissions low. The code is available at https://nf-co.re/sarek.

In Peru, 29 292 people were diagnosed with tuberculosis in 2022. Although tuberculosis treatments are effective, 3.4%-13% are associated with significant adverse drug reactions, with drug-induced liver injury (DILI) considered the most predominant. Among the first-line antituberculosis drugs, isoniazid is the main drug responsible for the appearance of DILI. In liver, isoniazid (INH) is metabolized by N-acetyltransferase-2 (NAT2) and cytochrome P450 2E1 (CYP2E1). Limited information exists on genetic risk factors associated with the presence of DILI to antituberculosis drugs in Latin America, and even less is known about these factors in the native and mestizo Peruvian population. The aim of this study was to determine the prevalence of NAT2 and CYP2E1 genotypes in native and mestizo population. An analytical cross-sectional analysis was performed using genetic data from mestizo population in Lima and native participants from south of Peru. NAT2 metabolizer was determined as fast, intermediate and slow, and CYP2E1 genotypes were classified as c1/c1, c1/c2 and c2/c2, from molecular tests and bioinformatic analyses. Of the 472 participants, 36 and 6 NAT2 haplotypes were identified in the mestizo and native population, respectively. In mestizo population, the most frequent NAT2*5B and NAT2*7B haplotypes were associated with DILI risk; while in natives, NAT2*5G and NAT2*13A haplotypes were associated with decreased risk of DILI. For CYP2E1, c1/c1 and c1/c2 genotypes are the most frequent in natives and mestizos, respectively. The linkage disequilibrium of NAT2 single nucleotide polymorphisms (SNPs) was estimated, detecting a block between all SNPs natives. In addition, a block between rs1801280 and rs1799929 for NAT2 was detected in mestizos. Despite the limitations of a secondary study, it was possible to report associations between NAT2 and CYP2E alleles with Peruvian native and mestizo by prevalence ratios. The results of this study will help the development of new therapeutic strategies for a Tuberculosis efficient control between populations.

There is a growing interest in milk oligosaccharides (MOs) because of their numerous benefits for newborns' and long-term health. A large number of MO structures have been identified in mammalian milk. Mostly described in human milk, the oligosaccharide richness, although less broad, has also been reported for a wide range of mammalian species. The structure of MOs is particularly difficult to report as it results from the combination of 5 monosaccharides linked by various glycosidic bonds forming structurally diverse and complex matrices of linear and branched oligosaccharides. Exploring the literature and extracting relevant information on MO diversity within or across species appears promising to elucidate structure-function role of MOs. Currently, given the complexity of these molecules, the main issues in exploring literature to extract relevant information on MO diversity within or across species relate to the heterogeneity in the way authors refer to these molecules. Herein, we provide a thesaurus (MilkOligoThesaurus) including the names and synonyms of MOs collected from key selected articles on mammalian milk analyses. MilkOligoThesaurus gathers the names of the MOs with a complete description of their monosaccharide composition and structures. When available, each unique MO molecule is linked to its ID from the NCBI PubChem and ChEBI databases. MilkOligoThesaurus is provided in a tabular format. It gathers 245 unique oligosaccharide structures described by 22 features (columns) including the name of the molecule, its abbreviation, the chemical database IDs if available, the monosaccharide composition, chemical information (molecular formula, monoisotopic mass), synonyms, its formula in condensed form, and in abbreviated condensed form, the abbreviated systematic name, the systematic name, the isomer group, and scientific article sources. MilkOligoThesaurus is also provided in the SKOS (Simple Knowledge Organization System) format. This thesaurus is a valuable resource gathering MO naming variations that are not found elsewhere for (i) Text and Data Mining to enable automatic annotation and rapid extraction of milk oligosaccharide data from scientific papers; (ii) biology researchers aiming to search for or decipher the structure of milk oligosaccharides based on any of their names, abbreviations or monosaccharide compositions and linkages.

We report a cluster of serogroup B invasive meningococcal disease identified via genomic surveillance in older adults in England and describe the public health responses. Genomic surveillance is critical for supporting public health investigations and detecting the growing threat of serogroup B Neisseria meningitidis infections in older adults.

The aim of this study was to identify novel biomarkers for multiple sclerosis (MS) diagnosis and prognosis, addressing the critical need for specific and prognostically valuable markers in the field.

How genomic differences contribute to phenotypic differences is a major question in biology. The recently characterized genomes, isolation environments, and qualitative patterns of growth on 122 sources and conditions of 1,154 strains from 1,049 fungal species (nearly all known) in the yeast subphylum Saccharomycotina provide a powerful, yet complex, dataset for addressing this question. We used a random forest algorithm trained on these genomic, metabolic, and environmental data to predict growth on several carbon sources with high accuracy. Known structural genes involved in assimilation of these sources and presence/absence patterns of growth in other sources were important features contributing to prediction accuracy. By further examining growth on galactose, we found that it can be predicted with high accuracy from either genomic (92.2%) or growth data (82.6%) but not from isolation environment data (65.6%). Prediction accuracy was even higher (93.3%) when we combined genomic and growth data. After the actose utilization genes, the most important feature for predicting growth on galactose was growth on galactitol, raising the hypothesis that several species in two orders, Serinales and Pichiales (containing the emerging pathogen and the genus , respectively), have an alternative galactose utilization pathway because they lack the genes. Growth and biochemical assays confirmed that several of these species utilize galactose through an alternative oxidoreductive D-galactose pathway, rather than the canonical pathway. Machine learning approaches are powerful for investigating the evolution of the yeast genotype-phenotype map, and their application will uncover novel biology, even in well-studied traits.

Gene expression is known to be affected by interactions between local genetic variation and DNA accessibility, with the latter organized into three-dimensional chromatin structures. Analyses of these interactions have previously been limited, obscuring their regulatory context, and the extent to which they occur throughout the genome. Here, we undertake a genome-scale analysis of these interactions in a genetically diverse population to systematically identify global genetic-epigenetic interaction, and reveal constraints imposed by chromatin structure. We establish the extent and structure of genotype-by-epigenotype interaction using embryonic stem cells derived from Diversity Outbred mice. This mouse population segregates millions of variants from eight inbred founders, enabling precision genetic mapping with extensive genotypic and phenotypic diversity. With 176 samples profiled for genotype, gene expression, and open chromatin, we used regression modeling to infer genetic-epigenetic interactions on a genome-wide scale. Our results demonstrate that statistical interactions between genetic variants and chromatin accessibility are common throughout the genome. We found that these interactions occur within the local area of the affected gene, and that this locality corresponds to topologically associated domains (TADs). The likelihood of interaction was most strongly defined by the three-dimensional (3D) domain structure rather than linear DNA sequence. We show that stable 3D genome structure is an effective tool to guide searches for regulatory elements and, conversely, that regulatory elements in genetically diverse populations provide a means to infer 3D genome structure. We confirmed this finding with CTCF ChIP-seq that revealed strain-specific binding in the inbred founder mice. In stem cells, open chromatin participating in the most significant regression models demonstrated an enrichment for developmental genes and the TAD-forming CTCF-binding complex, providing an opportunity for statistical inference of shifting TAD boundaries operating during early development. These findings provide evidence that genetic and epigenetic factors operate within the context of 3D chromatin structure.

Often referred to as the "second genome", the human microbiome is at the epicenter of complex inter-habitat biochemical networks like the "gut-brain axis", which has emerged as a significant determinant of cognition, overall health and well-being, as well as resistance to antibiotics and susceptibility to diseases. As part of a broader understanding of the nexus between the human microbiome, diseases and microbial interactions, whether encoded secondary metabolites (natural products) play crucial signalling roles has been the subject of intense scrutiny in the recent past. A major focus of these activities involves harvesting the genomic potential of the human microbiome bioinformatics guided genome mining and culturomics. Through these efforts, an impressive number of structurally intriguing antibiotics, with enhanced chemical diversity conventional antibiotics have been isolated from human commensal bacteria, thereby generating considerable interest in their total synthesis and expanding their therapeutic space for drug discovery. These developments augur well for the discovery of new drugs and antibiotics, particularly in the context of challenges posed by mycobacterial resistance and emerging new diseases. The current landscape of various synthetic campaigns and drug discovery initiatives on antibacterial natural products from the human microbiome is captured in this review with an intent to stimulate further activities in this interdisciplinary arena among the new generation.

Parkinson's disease (PD) carries substantial psychosocial burden. Using a database of responses by people with PD reporting up to five "most bothersome problems," we identified 225 fear-based verbatims, which were organized using the framework method into 26 categories. Commonly-reported fears included uncertainty of progression (n = 60, 26.7%), fear of future cognitive impairment (n = 24, 10.7%) and fear of becoming a burden on others (n = 23, 10.2%). Fears in PD are wide-ranging and can constitute the most bothersome aspect of the condition. These data can be used to design interventions to lessen the psychosocial burden of PD.

Circular RNAs (CircRNAs) play an important role in diverse biological processes; however, their origin and functions, especially in plants, remain largely unclear. Here, we used two maize (Zea mays) inbred lines, as well as 14 of their derivative RILs with different drought sensitivity, to systematically characterize 8,790 circRNAs in maize roots under well-watered (WW) and water-stress (WS) conditions. We found that a diverse set of circRNAs expressed at significantly higher levels under WS. Enhanced expression of circRNAs was associated with longer flanking introns and an enrichment of long interspersed nuclear element (LINE) retrotransposable elements. The epigenetic marks found at the back-splicing junctions of circRNA-producing genes were markedly different from canonical splicing, characterized by increased levels of H3K36me3/H3K4me1, as well as decreased levels of H3K9Ac/H3K27Ac. We found that genes expressing circRNAs are subject to relaxed selection. The significant enrichment of trait-associated sites along their genic regions suggested that genes giving rise to circRNAs were associated with plant survival rate under drought stress, implying that circRNAs play roles in plant drought responses. Furthermore, we found that overexpression of circMED16, one of the drought-responsive circRNAs, enhances drought tolerance in Arabidopsis (Arabidopsis thaliana). Our results provide a framework for understanding the intricate interplay of epigenetic modifications and how they contribute to the fine-tuning of circRNA expression under drought stress.

With the generation of spatially resolved transcriptomics of microbial biofilms, computational tools can be used to integrate this data to elucidate the multi-scale mechanisms controlling heterogeneous biofilm metabolism. This work presents a Multi-scale model of Metabolism In Cellular Systems (MiMICS) which is a computational framework that couples a genome-scale metabolic network reconstruction (GENRE) with Hybrid Automata Library (HAL), an existing agent-based model and reaction-diffusion model platform. A key feature of MiMICS is the ability to incorporate multiple -omics-guided metabolic models, which can represent unique metabolic states that yield different metabolic parameter values passed to the extracellular models. We used MiMICS to simulate Pseudomonas aeruginosa regulation of denitrification and oxidative stress metabolism in hypoxic and nitric oxide (NO) biofilm microenvironments. Integration of P. aeruginosa PA14 biofilm spatial transcriptomic data into a P. aeruginosa PA14 GENRE generated four PA14 metabolic model states that were input into MiMICS. Characteristic of aerobic, denitrification, and oxidative stress metabolism, the four metabolic model states predicted different oxygen, nitrate, and NO exchange fluxes that were passed as inputs to update the agent's local metabolite concentrations in the extracellular reaction-diffusion model. Individual bacterial agents chose a PA14 metabolic model state based on a combination of stochastic rules, and agents sensing local oxygen and NO. Transcriptome-guided MiMICS predictions suggested microscale denitrification and oxidative stress metabolic heterogeneity emerged due to local variability in the NO biofilm microenvironment. MiMICS accurately predicted the biofilm's spatial relationships between denitrification, oxidative stress, and central carbon metabolism. As simulated cells responded to extracellular NO, MiMICS revealed dynamics of cell populations heterogeneously upregulating reactions in the denitrification pathway, which may function to maintain NO levels within non-toxic ranges. We demonstrated that MiMICS is a valuable computational tool to incorporate multiple -omics-guided metabolic models to mechanistically map heterogeneous microbial metabolic states to the biofilm microenvironment.

The term "zoonosis" denotes diseases transmissible among vertebrate animals and humans. These diseases constitute a significant public health challenge, comprising 61% of human pathogens and causing an estimated 2.7 million deaths annually. Zoonoses not only affect human health but also impact animal welfare and economic stability, particularly in low- and middle-income nations. Leishmaniasis and schistosomiasis are two important neglected tropical diseases with a high prevalence in tropical and subtropical areas, imposing significant burdens on affected regions. Schistosomiasis, particularly rampant in sub-Saharan Africa, lacks alternative treatments to praziquantel, prompting concerns regarding parasite resistance. Similarly, leishmaniasis poses challenges with unsatisfactory treatments, urging the development of novel therapeutic strategies. Effective prevention demands a One Health approach, integrating diverse disciplines to enhance diagnostics and develop safer drugs. Metalloenzymes, involved in parasite biology and critical in different biological pathways, emerged in the last few years as useful drug targets for the treatment of human diseases. Herein we have reviewed recent reports on the discovery of inhibitors of metalloenzymes associated with zoonotic diseases like histone deacetylases (HDACs), carbonic anhydrase (CA), arginase, and heme-dependent enzymes.

In deep-sea cold seeps, microbial communities thrive on the geological seepage of hydrocarbons and inorganic compounds, differing from photosynthetically driven ecosystems. However, their biosynthetic capabilities remain largely unexplored. Here, we analyzed 81 metagenomes, 33 metatranscriptomes, and 7 metabolomes derived from nine different cold seep areas to investigate their secondary metabolites. Cold seep microbiomes encode diverse and abundant biosynthetic gene clusters (BGCs). Most BGCs are affiliated with understudied bacteria and archaea, including key mediators of methane and sulfur cycling. The BGCs encode diverse antimicrobial compounds that potentially shape community dynamics and various metabolites predicted to influence biogeochemical cycling. BGCs from key players are widely distributed and highly expressed, with their abundance and expression levels varying with sediment depth. Sediment metabolomics reveals unique natural products, highlighting uncharted chemical potential and confirming BGC activity in these sediments. Overall, these results demonstrate that cold seep sediments serve as a reservoir of hidden natural products and sheds light on microbial adaptation in chemosynthetically driven ecosystems.

Nonischemic cardiomyopathy (NICM) is a major cause of advanced heart failure, and the morbidity and mortality associated with NICM are serious medical problems. However, the etiology of NICM is complex and the related mechanisms involved in its pathogenesis remain unclear. The microarray datasets GSE1869 and GSE9128 retrieved from the Gene Expression Omnibus database were used to identify differentially expressed genes (DEGs) between NICM and normal samples. The co-expressed genes were identified using Venn diagrams. Kyoto Encyclopedia of Genes and Genomes pathway analyses and gene ontology enrichment were used to clarify biological functions and signaling pathways. Analysis of protein-protein interaction networks using Search Tool for the Retrieval of Interacting Genes/Proteins online to define the hub genes associated with NICM pathogenesis. A total of 297 DEGs were identified from GSE1869, 261 of which were upregulated genes and 36 were downregulated genes. A total of 360 DEGs were identified from GSE9128, 243 of which were upregulated genes and 117 were downregulated genes. In the 2 datasets, the screening identified 36 co-expressed DEGs. Kyoto Encyclopedia of Genes and Genomes pathway and gene ontology analysis showed that DEGs were mainly enriched in pantothenate and CoA biosynthesis, beta-alanine metabolism, kinetochore, G-protein beta/gamma-subunit complex, and other related pathways. The PPI network analysis revealed that DUSP6, EGR1, ZEB2, and XPO1 are the 4 hub genes of interest in the 2 datasets. Bioinformatics analysis of hub genes and key signaling pathways is an effective way to elucidate the mechanisms involved in the development of NICM. The results will facilitate further studies on the pathogenesis and therapeutic targets of NICM.

Here, we present a protocol for using Skipper, a pipeline designed to process crosslinking and immunoprecipitation (CLIP) data into annotated binding sites. We describe steps for partitioning annotated transcript regions and fitting data to a beta-binomial model to call windows of enriched binding. From raw CLIP data, we detail how users can map reproducible RNA-binding sites to call enriched windows and perform downstream analysis. This protocol supports optional customizations for different use cases. For complete details on the use and execution of this protocol, please refer to Boyle et al..

A pangenome captures the genomic diversity for a species, derived from a collection of genetic sequences of diverse populations. Advances in sequencing technologies have given rise to three primary methods for pangenome construction and analysis: de novo assembly and comparison, reference genome-based iterative assembly and graph-based pangenome construction. Each method presents advantages and challenges in processing varying amounts and structures of DNA sequencing data. With the emergence of high-quality genome assemblies and advanced bioinformatics tools, the graph-based pangenome is emerging as an advanced reference for exploring the biological and functional implications of genetic variations.

Mitogen-activated protein kinases (MAPKs) play important roles in plant stress response. As a major member of the MAPK family, MPK3 has been reported to participate in the regulation of chilling stress. However, the regulatory function of wheat (Triticum aestivum ) mitogen-activated protein kinase TaMPK3 in freezing tolerance remains unknown. Dongnongdongmai No.1 (Dn1) is a winter wheat variety with strong freezing tolerance; therefore, it is important to explore the mechanisms underlying this tolerance. In this study, the expression of TaMPK3 in Dn1 was detected under low temperature and hormone treatment. Gene cloning, bioinformatics and subcellular localisation analyses of TaMPK3 in Dn1 were performed. Overexpressed TaMPK3 in Arabidopsis thaliana was obtained, and freezing tolerance phenotype observations, physiological indices and expression levels of ICE-C-repeat binding factor (CBF)-COR -related genes were determined. In addition, the interaction between TaMPK3 and TaICE41 proteins was detected. We found that TaMPK3 expression responds to low temperatures and hormones, and the TaMPK3 protein is localised in the cytoplasm and nucleus. Overexpression of TaMPK3 in Arabidopsis significantly improves freezing tolerance. TaMPK3 interacts with the TaICE41 protein. In conclusion, TaMPK3 is involved in regulating the ICE-CBF-COR cold resistance module through its interaction with TaICE41, thereby improving freezing tolerance in Dn1 wheat.

Soybean (Glycine max ) is an important oilseed, protein and biodiesel crop. It faces significant threats from bacterial, fungal and viral pathogens, which cause economic losses and jeopardises global food security. In this article, we explore the relationship between soybeans and these pathogens, focusing on the molecular responses that are crucial for soybeans defence mechanisms. Molecular responses involve small RNAs and specific genes, including resistance (R) genes that are pivotal in triggering immune responses. Functional genomics, which makes use of cutting-edge technologies, such as CRISPR Cas9 gene editing, allows us to identify genes that provide insights into the defence mechanisms of soybeans with the focus on using genomics to understand the mechanisms involved in host pathogen interactions and ultimately improve the resilience of soybeans. Genes like GmKR3 and GmVQ58 have demonstrated resistance against soybean mosaic virus and common cutworm, respectively. Genetic studies have identified quantitative trait loci (QTLs) including those linked with soybean cyst nematode, root-knot nematode and Phytophthora root and stem rot resistance. Additionally, resistance against Asian soybean rust and soybean cyst nematode involves specific genes and their variations in terms of different copy numbers. To address the challenges posed by evolving pathogens and meet the demands of a growing population, accelerated soybean breeding efforts leveraging functional genomics are imperative. Targeted breeding strategies based on a deeper understanding of soybean gene function and regulation will enhance disease resistance, ensuring sustainable agriculture and global food security. Collaborative research and continued technological advancements are crucial for securing a resilient and productive agricultural future.

The valorization of lignin, a currently underutilized component of lignocellulosic biomass, has attracted attention to promote a stable and circular bioeconomy. Successful approaches including thermochemical, biological, and catalytic lignin depolymerization have been demonstrated, enabling opportunities for lignino-refineries and lignocellulosic biorefineries. Although significant progress in lignin valorization has been made, this review describes unexplored opportunities in chemical and biological routes for lignin depolymerization and thereby contributes to economically and environmentally sustainable lignin-utilizing biorefineries. This review also highlights the integration of chemical and biological lignin depolymerization and identifies research gaps while also recommending future directions for scaling processes to establish a lignino-chemical industry.

The progression and persistence of myocardial ischemia/reperfusion injury (MI/RI) are strongly linked to local inflammatory responses and oxidative stress. Cyclophilin A (CypA), a pro-inflammatory factor, is involved in various cardiovascular diseases. However, the role and mechanism of action of CypA in MI/RI are still not fully understood.

The T cell receptor (TCR) repertoire is an extraordinarily diverse collection of TCRs essential for maintaining the body's homeostasis and response to threats. In this study, we compiled an extensive dataset of more than 4200 bulk TCR repertoire samples, encompassing 221,176,713 sequences, alongside 6,159,652 single-cell TCR sequences from over 400 samples. From this dataset, we then selected a representative subset of 5 million bulk sequences and 4.2 million single-cell sequences to train two specialized Transformer-based language models for bulk (CVC) and single-cell (scCVC) TCR repertoires, respectively. We show that these models successfully capture TCR core qualities, such as sharing, gene composition, and single-cell properties. These qualities are emergent in the encoded TCR latent space and enable classification into TCR-based qualities such as public sequences. These models demonstrate the potential of Transformer-based language models in TCR downstream applications.

is a plant-pathogenic bacterium associated with a diverse range of plant host species. It has undergone substantial reclassification and currently consists of fourteen different subspecies or pathovars that are responsible for a wide range of plant diseases. Whole genome sequencing (WGS) provides a cutting-edge advantage over other diagnostic techniques in epidemiological and evolutionary studies of . because it has a higher discriminatory power and is replicable across laboratories. Also, WGS allows the improvement of multi-locus sequence typing (MLST) schemes. In this study, we used genome sequences of isolates from the NCBI RefSeq database to develop a seven-gene MLST scheme that yielded 19 sequence types (STs) that correlated with phylogenetic clades of . subspecies/pathovars. Using this MLST scheme, we examined 2,911 assemblies from NCBI GenBank and identified 15 novel STs from 37 isolates that were misclassified in the NCBI. In total, we identified 545 . assemblies from GenBank with 95% average nucleotide identity to the . type strain and all were classified as one of the 34 STs. All MLST classifications correlated with phylogenetic position inferred from alignments using 92 conserved genes. We observed several instances where strains from different pathovars formed closely related monophyletic clades and shared the same ST, indicating that further investigation of the validity of these pathovars is required. Our MLST scheme described here is a robust tool for rapid classification of . pathovars using WGS and a powerful method for further comprehensive taxonomic revision of . pathovars.

We aimed to identify serum biomarkers that predict knee osteoarthritis (OA) before the appearance of radiographic abnormalities in a cohort of 200 women. As few as six serum peptides, corresponding to six proteins, reached AUC 77% probability to distinguish those who developed OA from age-matched individuals who did not develop OA up to 8 years later. Prediction based on these blood biomarkers was superior to traditional prediction based on age and BMI (AUC 51%) or knee pain (AUC 57%). These results identify a prolonged molecular derangement of joint tissue before the onset of radiographic OA abnormalities consistent with an unresolved acute phase response. Among all 24 protein biomarkers predicting incident knee OA, the majority (58%) also predicted knee OA progression, revealing the existence of a pathophysiological "OA continuum" based on considerable similarity in the molecular pathophysiology of the progression to incident OA and the progression of established OA.

Saliva plays a crucial role in shaping the compatibility of piercing-sucking insects with their host plants. Understanding the complex composition of leafhopper saliva is important for developing effective and eco-friendly control strategies for the tea green leafhopper, Fabrecius, a major piercing-sucking pest in Chinese tea plantations. This study explored the saliva proteins of tea green leafhopper adults using a custom collection device, consisting of two layers of Parafilm stretched over a sucrose diet. A total of 152 proteins were identified using liquid chromatography-tandem mass spectrometry (LC-MS/MS) following the filter-aided sample preparation (FASP). These proteins were categorized into six groups based on their functions, including enzymes, transport proteins, regulatory proteins, cell structure proteins, other proteins, and unknown proteins. Bioinformatics analyses predicted 16 secreted proteins, which were successfully cloned and transcriptionally analyzed across various tissues and developmental stages. Genes encoding putative salivary secretory proteins, including , , , , , low-density lipoprotein receptor-related protein (), , and , exhibited high expressions in salivary gland (SG) tissues and feeding-associated expressions at different developmental stages. These findings shed light on the potential elicitors or effectors mediating the leafhopper feeding and defense responses in tea plants, providing insights into the coevolution of tea plants and leafhoppers. The study's conclusions open avenues for the development of innovative leafhopper control technologies that reduce the reliance on pesticides in the tea industry.

Previous studies have indicated that neutrophil extracellular traps (NETs) play a pivotal role in pathogenesis of pulmonary arterial hypertension (PAH). However, the specific mechanism underlying the impact of NETs on pulmonary artery smooth muscle cells (PASMCs) has not been determined. The objective of this study was to elucidate underlying mechanisms through which NETs contribute to progression of PAH.

Hashimoto's thyroiditis (HT) is the most common organ-specific autoimmune disease, predominantly affecting women. Although the pathogenesis of HT is incompletely understood, some studies have found that macrophage polarization plays a role. Puerarin is a soy isoflavone compound that has anti-inflammatory and immunomodulatory effects and regulates macrophage immune activity. This study aimed to verify the therapeutic effect of puerarin on HT and explored its regulatory effect on macrophage polarization imbalance in HT. Through bioinformatics analysis and molecular biology methods, it was found that macrophages increased significantly in HT patients and model mice. Immunological staining showed that puerarin intervention could reduce tissue inflammatory cell infiltration. Molecular biological examination displayed that puerarin could inhibit local and systemic inflammation levels, and the expression of marker thyroglobulin and thyroid peroxidase Abs. In vivo experimental results indicated that puerarin regulated macrophage polarity and reduced inflammatory damage, possibly by inhibiting the pyroptosis signaling pathway. In vivo macrophage clearance experiments demonstrated that puerarin relied on macrophages to exert its mechanism of action in treating HT. The results of this study indicate that macrophages are important mediators in the development of HT, and puerarin can regulate macrophage polarity and inflammatory status to provide thyroid tissue protection, which provides a new idea for the treatment of HT.

Accurate annotation to single-nucleotide resolution of the transcribed regions in genomes is key to optimally analyse RNA-seq data, understand regulatory events and for the design of experiments. However, currently most genome annotations provided by GenBank generally lack information about untranslated regions. Additionally, information regarding genomic locations of non-coding RNAs, such as sRNAs, or anti-sense RNAs is frequently missing. To provide such information, diverse RNA-seq technologies, such as Rend-seq, have been developed and applied to many bacterial species. However, incorporating this vast amount of information into annotation files has been limited and is bioinformatically challenging, resulting in UTRs and other non-coding elements being overlooked or misrepresented. To overcome this problem, we present pyRAP (python Rend-seq Annotation Pipeline), a software package that analyses Rend-seq datasets to accurately resolve transcript boundaries genome-wide. We report the use of pyRAP to find novel transcripts, transcript isoforms, and RNase-dependent sRNA processing events. In we uncovered 63 novel transcripts and provide genomic coordinates with single-nucleotide resolution for 2218 5'UTRs, 1864 3'UTRs and 161 non-coding RNAs. In we report 117 novel transcripts, 2429 5'UTRs, 1619 3'UTRs and 91 non-coding RNAs, and in 16 novel transcripts, 664 5'UTRs, 696 3'UTRs, and 81 non-coding RNAs. Finally, we use pyRAP to produce updated annotation files for , , and for use in the wider microbial genomics research community.

Henoch-Schönlein purpura nephritis (HSPN) is the most severe manifestation of Henoch-Schönlein purpura (HSP). This study aimed to determine the role of urine metabolomics in predicting HSPN and explore the potential mechanisms of HSP. A liquid chromatography-tandem mass spectrometry-based untargeted metabolomics analysis was performed to investigate the urinary metabolic profiles of 90 participants, comprising 30 healthy children (group CON) and 60 patients with HSP, including 30 HSP patients without renal involvement (group H) and 30 HSPN patients (group HSPN). The differentially expressed metabolites (DEMs) were identified using orthogonal partial least squares discriminant analysis (OPLS-DA), and subsequent bioinformatics analysis was conducted to elucidate the perturbed metabolic pathways. A total of 43 DEMs between H and HSPN groups were analyzed by the Kyoto Encyclopedia of Gene and Genome (KEGG) database, and the result indicates that glycine, serine and threonine metabolism, and cysteine and methionine metabolism were significantly disturbed. A composite model incorporating propionylcarnitine and indophenol sulfate was developed to assess the risk of renal involvement in pediatric patients with HSP.   Conclusion: This study reveals the metabolic alterations in healthy children, HSPN patients, and HSP patients without renal involvement. Furthermore, propionylcarnitine and indophenol sulfate may be potential predictive biomarkers of the occurrence of HSPN. What is Known: • HSP is the predominant type of vasculitis observed in children. The long-term prognosis of HSP is contingent upon the extent of renal impairment. In severe nephritis, a delay in appropriate treatment may lead to fibrosis progression and subsequent development of chronic kidney disease (CKD), even leading to renal failure. • The application of metabolomics in investigating diverse renal disorders has been documented. Urine is a robust and sensitive medium for metabolomics detection. What is New: • The metabolic profiles were identified in urine samples of healthy children and those with HSP at the early stage of the disease. Different metabolites were identified between HSP patients without nephritis and those who developed HSPN. • These different metabolites may affect oxidative stress in the progression of HSPN.

Homeodomain-leucine ZIPper (HD-ZIP) transcription factors play crucial roles in plant growth, development, and stress responses. The HD-ZIP family is categorised into four groups (HD-ZIP I-IV). While extensive genome-wide studies have been conducted on the HD-ZIP I, III, and IV subfamily in Nicotiana tabacum (tobacco), comprehensive reports on the HD-ZIP II subfamily genes are limited.

The gut microbiota is hypothesized as a prognostic biomarker for cancer immunotherapy. Antibiotic-induced dysbiosis negatively affects the clinical outcomes of immunotherapy. However, the effect of dysbiosis on the efficacy and safety of Chemoimmunotherapy (chemo-IOs), the frontline standard of care, in advanced non-small cell lung cancer (NSCLC) remains unknown. We aimed to compare the efficacy and safety of chemo-IOs in patients exposed to antibiotics before treatment with those of patients who were not exposed.

The objective is to evaluate the performance of blood test results, radiomics, and a combination of the two data types on the prediction of the 24-h oxygenation support need for the Coronavirus disease 2019 (COVID-19) patients. In this retrospective cohort study, COVID-19 patients with confirmed real-time reverse transcription-polymerase chain reaction assay (RT-PCR) test results between February 2020 and August 2021 were investigated. Initial blood cell counts, chest radiograph, and the status of oxygenation support used within 24 h were collected (n = 290; mean age, 45 ± 19 years; 125 men). Radiomics features from six lung zones were extracted. Logistic regression and random forest models were developed using the clinical-only, radiomics-only, and combined data. Ten repeats of fivefold cross-validation with bootstrapping were used to identify the input features and models with the highest area under the receiver operating characteristic curve (AUC). Higher AUCs were achieved when using only radiomics features compared to using only clinical features (0.94 ± 0.03 vs. 0.88 ± 0.04). The best combined model using both radiomics and clinical features achieved highest in the cross-validation (0.95 ± 0.02) and test sets (0.96 ± 0.02). In comparison, the best clinical-only model yielded AUCs of 0.88 ± 0.04 in cross-validation and 0.89 ± 0.03 in test set. Both radiomics and clinical data can be used to predict 24-h oxygenation support need for COVID-19 patients with AUC > 0.88. Moreover, the combination of both data types further improved the performance.

In low-income countries there is insufficient evidence on hematological, clinical, cytogenetic and molecular profiles among new CML patients. Therefore, we performed this study among newly confirmed CML patients at Tikur Anbesa Specialized Hospital (TASH), Ethiopia.

This study provides a new methodology for the rapid analysis of numerous venom samples in an automated fashion. Here, we use LC-MS (Liquid Chromatography-Mass Spectrometry) for venom separation and toxin analysis at the accurate mass level combined with new in-house written bioinformatic scripts to obtain high-throughput results. This analytical methodology was validated using 31 venoms from all members of a monophyletic clade of Australian elapids: brown snakes ( spp.) and taipans ( spp.). In a previous study, we revealed extensive venom variation within this clade, but the data was manually processed and MS peaks were integrated into a time-consuming and labour-intensive approach. By comparing the manual approach to our new automated approach, we now present a faster and more efficient pipeline for analysing venom variation. Pooled venom separations with post-column toxin fractionations were performed for subsequent high-throughput venomics to obtain toxin IDs correlating to accurate masses for all fractionated toxins. This workflow adds another dimension to the field of venom analysis by providing opportunities to rapidly perform in-depth studies on venom variation. Our pipeline opens new possibilities for studying animal venoms as evolutionary model systems and investigating venom variation to aid in the development of better antivenoms.

Pathogenic parasites are responsible for multiple diseases, such as malaria and Chagas disease, in humans and livestock. Traditionally, pathogenic parasites have been largely an evasive topic for vaccine design, with most successful vaccines only emerging recently. To aid vaccine design, the VIOLIN vaccine knowledgebase has collected vaccines from all sources to serve as a comprehensive vaccine knowledgebase. VIOLIN utilizes the Vaccine Ontology (VO) to standardize the modeling of vaccine data. VO did not model complex life cycles as seen in parasites. With the inclusion of successful parasite vaccines, an update in parasite vaccine modeling was needed.

Phthalates, widely utilized in industrial products, are classified as endocrine-disrupting chemicals (EDCs). Although certain phthalate and their metabolites have been implicated in cancer development, the reported findings have exhibited inconsistencies. Therefore, we conducted the comprehensive literature search to assess the association between phthalate and their metabolites and cancer risk by identifying original studies measuring phthalates or their metabolites and reporting their correlation with cancer until July 4, 2023. The Odds Ratios (ORs) and corresponding 95% confidence intervals (CIs) were extracted and analyzed to estimate the risk. Pooled data from eleven studies, including 3101 cancer patients and 6858 controls, were analyzed using a fixed- or random-effects model based on heterogeneity tests. When comparing extreme categories of different phthalates and their metabolites, we observed a significant association between urinary phthalates and phthalate metabolites (MEHHP, MECPP, DBP and MBzP) and cancer risk. The findings of our meta-analysis reinforce the existing evidence that urinary phthalates and phthalate metabolites is strongly associated with cancer development. Further investigations are warranted to elucidate the underlying mechanisms of this association. These results may offer novel insights into cancer development.

The irrational use of antibiotics has favored the emergence of resistant bacteria, posing a serious threat to global health. To counteract antibiotic resistance, this research seeks to identify novel antimicrobials derived from essential oils that operate through several mechanisms. It aims to evaluate the quality and composition of essential oils from and ; test their antimicrobial activity against various strains; explore their synergies with commercial antibiotics; predict the efficacy, toxicity, and stability of compounds; and understand their molecular interactions through docking and dynamic simulations. The essential oils were extracted via hydrodistillation from the flowering tops of oregano in the Middle Atlas Mountains in Morocco. Gas chromatography combined with mass spectrometry (GC-MS) was used to examine their composition. Nine common antibiotics were chosen and tested alone or in combination with essential oils to discover synergistic effects against clinically important and resistant bacterial strains. A comprehensive in silico study was conducted, involving molecular docking and molecular dynamics simulations (MD). oil includes borneol (8.58%), p-cymene (42.56%), thymol (28.43%), and carvacrol (30.89%), whereas oil is mostly composed of γ-terpinene (22.89%), p-cymene (15.84%), thymol (10.21%), and (E)-caryophyllene (3.63%). With proving to be the most potent, these essential oils showed antibacterial action against both Gram-positive and Gram-negative bacteria. Certain antibiotics, including ciprofloxacin, ceftriaxone, amoxicillin, and ampicillin, have been shown to elicit synergistic effects. To fight resistant bacteria, the essential oils of and , particularly those high in thymol and (E)-caryophyllene, seem promising when combined with antibiotics. These synergistic effects could result from their ability to target the same bacterial proteins or facilitate access to target sites, as suggested by molecular docking simulations. Molecular dynamics simulations validated the stability of the examined protein-ligand complexes, emphasizing the propensity of substances like thymol and (E)-caryophyllene for particular target proteins, opening the door to potentially effective new therapeutic approaches against pathogens resistant to multiple drugs.

Neurodegenerative diseases (NDDs) refer to a complex heterogeneous group of diseases which are associated with the accumulation of amyloid fibrils or plaques in the brain leading to progressive loss of neuronal functions. Alzheimer's disease is one of the major NDD responsible for 60-80% of all dementia cases. Currently, there are no curative or disease-reversing/modifying molecules for NDDs except a few such as donepezil, rivastigmine, galantamine, carbidopa and levodopa which treat the disease-associated-symptoms. Similarly, there are few FDA-approved tracers such as flortaucipir for tau fibril-imaging and florbetaben, flutemetamol, and florbetapir for amyloid-imaging available for diagnosis. Recent advances in the cryo-EM reported distinctly different microstructures for tau fibrils associated with different tauopathies highlighting the possibility to develop tauopathy-specific imaging agents and therapeutics. In addition, it is important to identify the proteins that are associated with disease development and progression to know about their 3D structure to develop various diagnostics, therapeutics and theranostic agents. The current article discusses in detail the disease-associated amyloid and non-amyloid proteins along with their structural insights. We discuss various proteins associated with NDDs and their implications in NDD. In addition, we document  chemical compounds developed for diagnosis and therapy of different NDDs with special emphasis on theranostic agents.

Fungicides play an important role in crop protection, but they have also been shown to adversely affect non-target organisms, including those living in the aquatic environment. The aim of the present study is to combine experimental and computational approaches to evaluate the effects of flutriafol, metconazole, myclobutanil, tebuconazole, tetraconazole and triticonazole on aquatic model organisms and to obtain information on the effects of these fungicides on , a freshwater plant, at the molecular level. The EC (the half-maximum effective concentration) values for the growth inhibition of in the presence of the investigated fungicides show that metconazole (EC = 0.132 mg/L) and tetraconazole (EC = 0.539 mg/L) are highly toxic, tebuconazole (EC = 1.552 mg/L), flutriafol (EC = 3.428 mg/L) and myclobutanil (EC = 9.134 mg/L) are moderately toxic, and triticonazole (EC = 11.631 mg/L) is slightly toxic to this plant. The results obtained with the computational tools TEST, ADMETLab2.0 and admetSAR2.0 also show that metconazole and tetraconazole are toxic to other aquatic organisms: , and . A molecular docking study shows that triazole fungicides can affect photosynthesis in because they strongly bind to C43 (binding energies between -7.44 kcal/mol and -7.99 kcal/mol) and C47 proteins (binding energies between -7.44 kcal/mol and -8.28 kcal/mol) in the reaction center of photosystem II, inhibiting the binding of chlorophyll a to these enzymes. In addition, they can also inhibit glutathione S-transferase, an enzyme involved in the cellular detoxification of .

Over the past decade, there have been reports of short novel functional peptides (less than 100 aa in length) translated from so-called non-coding RNAs (ncRNAs) that have been characterized using mass spectrometry (MS) and large-scale proteomics studies. Therefore, understanding the bivalent functions of some ncRNAs as transcripts that encode both functional RNAs and short peptides, which we named ncPEPs, will deepen our understanding of biology and disease. In 2020, we published the first database of functional peptides translated from non-coding RNAs-FuncPEP. Herein, we have performed an update including the newly published ncPEPs from the last 3 years along with the categorization of host ncRNAs. FuncPEP v2.0 contains 152 functional ncPEPs, out of which 40 are novel entries. A PubMed search from August 2020 to July 2023 incorporating specific keywords was performed and screened for publications reporting validated functional peptides derived from ncRNAs. We did not observe a significant increase in newly discovered functional ncPEPs, but a steady increase. The novel identified ncPEPs included in the database were characterized by a wide array of molecular and physiological parameters (i.e., types of host ncRNA, species distribution, chromosomal density, distribution of ncRNA length, identification methods, molecular weight, and functional distribution across humans and other species). We consider that, despite the fact that MS can now easily identify ncPEPs, there still are important limitations in proving their functionality.

Cancer cell lines are frequently used in metabolomics, such as in vitro tumor models. In particular, A2780 cells are commonly used as a model for ovarian cancer to evaluate the effects of drug treatment. Here, we compare the NMR metabolomics profiles of A2780 and cisplatin-resistant A2780 cells with those of cells derived from 10 patients with high-grade serous ovarian carcinoma (collected during primary cytoreduction before any chemotherapeutic treatment). Our analysis reveals a substantial similarity among all primary cells but significant differences between them and both A2780 and cisplatin-resistant A2780 cells. Notably, the patient-derived cells are closer to the resistant A2780 cells when considering the exo-metabolome, whereas they are essentially equidistant from A2780 and A2780-resistant cells in terms of the endo-metabolome. This behavior results from dissimilarities in the levels of several metabolites attributable to the differential modulation of underlying biochemical pathways. The patient-derived cells are those with the most pronounced glycolytic phenotype, whereas A2780-resistant cells mainly diverge from the others due to alterations in a few specific metabolites already known as markers of resistance.

Small data learning problems are characterized by a significant discrepancy between the limited number of response variable observations and the large feature space dimension. In this setting, the common learning tools struggle to identify the features important for the classification task from those that bear no relevant information and cannot derive an appropriate learning rule that allows discriminating among different classes. As a potential solution to this problem, here we exploit the idea of reducing and rotating the feature space in a lower-dimensional gauge and propose the gauge-optimal approximate learning (GOAL) algorithm, which provides an analytically tractable joint solution to the dimension reduction, feature segmentation, and classification problems for small data learning problems. We prove that the optimal solution of the GOAL algorithm consists in piecewise-linear functions in the Euclidean space and that it can be approximated through a monotonically convergent algorithm that presents-under the assumption of a discrete segmentation of the feature space-a closed-form solution for each optimization substep and an overall linear iteration cost scaling. The GOAL algorithm has been compared to other state-of-the-art machine learning tools on both synthetic data and challenging real-world applications from climate science and bioinformatics (i.e., prediction of the El Niño Southern Oscillation and inference of epigenetically induced gene-activity networks from limited experimental data). The experimental results show that the proposed algorithm outperforms the reported best competitors for these problems in both learning performance and computational cost.

The objective of this study was the presentation of quantitative characteristics regarding the scientific content and bibliometric details of the relevant publications. In total, 156 papers were considered. Most papers presented original studies ( = 135), and fewer were reviews ( = 21). Most original articles ( = 101) referred to work involving cattle. Most original articles described work related to the diagnosis ( = 72) or pathogenesis ( = 62) of mastitis. Most original articles included field work ( = 75), whilst fewer included experimental ( = 31) or laboratory ( = 30) work. The tissue assessed most frequently in the studies was milk ( = 59). Milk was assessed more frequently in studies on the diagnosis (61.1% of relevant studies) or pathogenesis (30.6%) of the infection, but mammary tissue was assessed more frequently in studies on the treatment (31.0%). In total, 47 pathogens were included in the studies described; most were Gram-positive bacteria ( = 34). The three bacteria most frequently included in the studies were ( = 55 articles), ( = 31) and ( = 19). The proteomics technology employed more often in the respective studies was liquid chromatography-tandem mass spectrometry (LC-MS/MS), either on its own ( = 56) or in combination with other technologies ( = 40). The median year of publication of articles involving bioinformatics or LC-MS/MS and bioinformatics was the most recent: 2022. The 156 papers were published in 78 different journals, most frequently in the ( = 16 papers) and the ( = 12). The median number of cited references in the papers was 48. In the papers, there were 1143 co-authors (mean: 7.3 ± 0.3 co-authors per paper, median: 7, min.-max.: 1-19) and 742 individual authors. Among them, 15 authors had published at least seven papers (max.: 10). Further, there were 218 individual authors who were the first or last authors in the papers. Most papers were submitted for open access ( = 79). The median number of citations received by the 156 papers was 12 (min.-max.: 0-339), and the median yearly number of citations was 2.0 (min.-max.: 0.0-29.5). The -index of the papers was 33, and the -index was 2. The increased number of cited references in papers and international collaboration in the respective study were the variables associated with most citations to published papers. This is the first ever scientometrics evaluation of proteomics studies, the results of which highlighted the characteristics of published papers on mastitis and proteomics. The use of proteomics in mastitis research has focused on the elucidation of pathogenesis and diagnosis of the infection; LC-MS/MS has been established as the most frequently used proteomics technology, although the use of bioinformatics has also emerged recently as a useful tool.

Preeclampsia (PE) occurs in 5% to 7% of all pregnancies, and the PE that results from abnormal placentation acts as a primary cause of maternal and neonatal morbidity and mortality. The objective of this secondary analysis was to elucidate the pathogenesis of PE by probing protein-protein interactions from in silico analysis of transcriptomes between PE and normal placenta from Gene Expression Omnibus (GSE149812). The pathogenesis of PE is apparently determined by associations of miRNA molecules and their target genes and the degree of changes in their expressions with irregularities in the functions of hemostasis, vascular systems, and inflammatory processes at the fetal-maternal interface. These irregularities ultimately lead to impaired placental growth and hypoxic injuries, generally manifesting as placental insufficiency. These differentially expressed miRNAs or genes in placental tissue and/or in blood can serve as novel diagnostic and therapeutic biomarkers.

Usmani-Riazuddin syndrome (USRISR, MIM# 619548; USRISD, MIM#619467) is a very rare genetic condition. recently associated with deleterious variants in AP1G1 (MIM* 603533). It is characterized by multisystemic involvement including intellectual disability, speech and developmental delay, behavioral anomalies, muscular tone disorders, seizures, limb defects, and unspecified facial gestalt. In this report, we describe this syndrome for the second time, in association to a novel AP1G1 variant identified in a toddler with multisystemic involvement including intellectual disability, speech and developmental delay, behavioral anomalies, arrhythmias, hearing loss, skin changes, and limb defects. Next generation sequencing (NGS) analysis through clinical exome disclosed AP1G1: c.1969C>G (p.Leu657Val), de novo, likely pathogenic variant, according to ACMG classification criteria. Proband's facial features resembled the spectrum of chromatinopathies. Clinical pictures were analyzed and a clinical overlap was supported by DeepGestalt analysis (www.face2gene.com). The system identified 6 chromatin disorders out of 30 possible diagnoses. The remaining 24 included 9 miscellaneous cryptic chromosomal abnormalities (excluded due to normal microarray study). To the best of our knowledge, this is the first description of likely distinctive facial features in a patient with Usmani-Riazuddin syndrome. Further multicentric analyses are needed for a better definition of this aspect.

The human Respiratory Syncytial Virus (hRSV) stands as one of the most common causes of acute respiratory diseases. The infectivity of this virus is intricately linked to its membrane proteins, notably the attachment glycoprotein (G protein). The latter plays a key role in facilitating the attachment of hRSV to respiratory tract epithelial cells, thereby initiating the infection process. The present study aimed to characterize the interaction of the conserved cysteine-noose domain of hRSV G protein (cndG) with the transmembrane CX3C motif chemokine receptor 1 (CX3CR1) isoforms using computational tools of molecular modeling, docking, molecular dynamics simulations, and binding free energy calculations. From MD simulations of the molecular system embedded in the POPC lipid bilayer, we showed a stable interaction of cndG with the canonical fractalkine binding site in the N-terminal cavity of the CX3CR1 isoforms and identified that residues in the extracellular loop 2 (ECL2) region and Glu279 of this receptor are pivotal for the stabilization of CX3CR1/cndG binding, corroborating what was reported for the interaction of the chemokine fractalkine with CX3CR1 and its structure homolog US28. Therefore, the results presented here contribute by revealing key structural points for the CX3CR1/G interaction, allowing us to better understand the biology of hRSV from its attachment process and to develop new strategies to combat it.

The study aimed to identify common differentially expressed lncRNAs from manually curated ulcerative colitis (UC) gene expression omnibus (GEO) datasets. Nine UC transcriptomic datasets of clearly annotated human colonic biopsies were included in the study. The datasets were manually curated to select active UC samples and controls. R packages geneknitR, gprofiler, clusterProfiler were used for gene symbol annotation. The R EdgeR package was used to analyze differential expression. This resulted in a total of nineteen lncRNAs that were differentially expressed in at least three datasets of the nine GEO datasets. Several of the differentially expressed lncRNAs found in UC were associated with promoting colorectal cancer (CRC) through regulating gene expression, epithelial to mesenchymal transition (EMT), cell cycle progression, and by promoting tumor proliferation, invasion, and migration. The expression of several lncRNAs varied between disease states and tissue locations within the same disease state. The identified differentially expressed lncRNAs may function as general markers for active UC independent of biopsy location, age, gender, or treatment, thereby representing a comparative resource for future comparisons using available GEO UC datasets.

Nowadays, the explosion of knowledge in the field of epigenetics has revealed new pathways toward the treatment of multifactorial diseases, rendering the key players of the epigenetic machinery the focus of today's pharmaceutical landscape. Among epigenetic enzymes, DNA methyltransferases (DNMTs) are first studied as inhibition targets for cancer treatment. The increasing clinical interest in DNMTs has led to advanced experimental and computational strategies in the search for novel DNMT inhibitors. Considering the importance of epigenetic targets as a novel and promising pharmaceutical trend, the present study attempted to discover novel inhibitors of natural origin against DNMTs using a combination of structure and ligand-based computational approaches. Particularly, a pharmacophore-based virtual screening was performed, followed by molecular docking and molecular dynamics simulations in order to establish an accurate and robust selection methodology. Our screening protocol prioritized five natural-derived compounds, derivatives of coumarins, flavones, chalcones, benzoic acids, and phenazine, bearing completely diverse chemical scaffolds from FDA-approved "Epi-drugs". Their total DNMT inhibitory activity was evaluated, revealing promising results for the derived hits with an inhibitory activity ranging within 30-45% at 100 µM of the tested compounds.

Sensory processing sensitivity (SPS) is a personality trait that makes certain individuals excessively sensitive to stimuli. People carrying this trait are defined as Highly Sensitive People (HSP). The SPS trait is notably prevalent among nursing students and nurse staff. Although there are HSP diagnostic tools, there is little information about early detection. Therefore, the aim of this work was to develop a prediction model to identify HSP and provide an individualized nursing assessment. A total of 672 nursing students completed all the evaluations. In addition to the HSP diagnosis, emotional intelligence, communication skills, and conflict styles were evaluated. An interpretable machine learning model was trained to predict the SPS trait. We observed a 33% prevalence of HSP, which was higher in women and people with previous health training. HSP were characterized by greater emotional repair ( = 0.033), empathy ( = 0.030), respect ( = 0.038), and global communication skills ( = 0.036). Overall, sex and emotional intelligence dimensions are important to detect this trait, although personal characteristics should be considered. The present individualized prediction model could help to predict the presence of the SPS trait in nursing students, which may be useful in conducting intervention strategies to avoid the negative consequences and reinforce the positive ones of this trait.

RNA pseudouridine modification exists in different RNA types of many species, and it has a significant role in regulating the expression of biological processes. To understand the functional mechanisms for RNA pseudouridine sites, the accurate identification of pseudouridine sites in RNA sequences is essential. Although several fast and inexpensive computational methods have been proposed, the challenge of improving recognition accuracy and generalization still exists. This study proposed a novel ensemble predictor called PseUpred-ELPSO for improved RNA pseudouridine site prediction. After analyzing the nucleotide composition preferences between RNA pseudouridine site sequences, two feature representations were determined and fed into the stacking ensemble framework. Then, using five tree-based machine learning classifiers as base classifiers, 30-dimensional RNA profiles are constructed to represent RNA sequences, and using the PSO algorithm, the weights of the RNA profiles were searched to further enhance the representation. A logistic regression classifier was used as a meta-classifier to complete the final predictions. Compared to the most advanced predictors, the performance of PseUpred-ELPSO is superior in both cross-validation and the independent test. Based on the PseUpred-ELPSO predictor, a free and easy-to-operate web server has been established, which will be a powerful tool for pseudouridine site identification.

Phylogenetic studies now routinely require manipulating and summarizing thousands of data files. For most of these tasks, currently available software requires considerable computing resources and substantial knowledge of command-line applications. We develop an ultrafast and memory-efficient software, SEGUL, that performs common phylogenomic dataset manipulations and calculates statistics summarizing essential data features. Our software is available as standalone command-line interface (CLI) and graphical user interface (GUI) applications, and as a library for Rust, R and Python, with possible support of other languages. The CLI and library versions run native on Windows, Linux and macOS, including Apple ARM Macs. The GUI version extends support to include mobile iOS, iPadOS and Android operating systems. SEGUL leverages the high performance of the Rust programming language to offer fast execution times and low memory footprints regardless of dataset size and platform choice. The inclusion of a GUI minimizes bioinformatics barriers to phylogenomics while SEGUL's efficiency reduces economic barriers by allowing analysis on inexpensive hardware. Our support for mobile operating systems further enables teaching phylogenomics where access to computing power is limited.

Voacangine, a naturally occurring alkaloid, has been testified to display beneficial effects on a variety of human diseases, but its role in ischemic stroke is unclear. The impacts of voacangine on oxygen-glucose deprivation/reoxygenation (OGD/R)-tempted hippocampal neuronal cells are investigated. The bioinformatics analysis found that voacangine is a bioactive ingredient that may have good effects on ischemic stroke. KEGG pathways analysis found that voacangine may regulate ischemic stroke through modulating the PI3K-Akt-FoxO signaling pathway. Voacangine could mitigate OGD/R-tempted cytotoxicity in HT22 cells. Voacangine mitigated OGD/R-tempted oxidative stress in HT22 cells by diminishing reactive oxygen species level and enhancing superoxide dismutase level. Voacangine mitigated OGD/R-tempted ferroptosis in HT22 cells. Voacangine promoted activation of the PI3K-Akt-FoxO signaling in OGD/R-induced HT22 cells. Inactivation of the PI3K-Akt-FoxO signaling pathway reversed the protective effects of voacangine against OGD/R-tempted oxidative stress, cytotoxicity, and ferroptosis in HT22 cells. In conclusion, voacangine protects hippocampal neuronal cells against OGD/R-caused oxidative stress and ferroptosis by activating the PI3K-Akt-FoxO signaling.

The density of tumor-associated macrophages (TAMs) in the tumor microenvironment of anaplastic thyroid cancer (ATC) is associated with poor prognosis. However, the crosstalk between macrophages and ATC cells is poorly understood. This study aimed to examine the impact of macrophages on cancer cell phenotypes. We found a new mediator between M2 macrophages and ATC cells through proteomics analysis.

R-loops are nucleic acid structures composed of a DNA:RNA hybrid with a displaced non-template single-stranded DNA. Current approaches to identify and map R-loop formation across the genome employ either an antibody targeted against R-loops (S9.6) or a catalytically inactivated form of RNase H1 (dRNH1), a nuclease that can bind and resolve DNA:RNA hybrids via RNA exonuclease activity. This overview article outlines several ways to map R-loops using either methodology, explaining the differences and similarities among the approaches. Bioinformatic analysis of R-loops involves several layers of quality control and processing before visualizing the data. This article provides resources and tools that can be used to accurately process R-loop mapping data and explains the advantages and disadvantages of the resources as compared to one another. © 2024 Wiley Periodicals LLC.

The significant role of mast cells in the development of allergic and inflammatory diseases is well-established. Among the various mechanisms of mast cell activation, the interaction of antigens/allergens with IgE and the subsequent binding of this complex to the high-affinity IgE receptor FcεRI stand out as the most studied and fundamental pathways. This activation process leads to the rapid exocytosis of granules containing preformed mediators, followed by the production of newly synthesized mediators, including a diverse array of cytokines, chemokines, arachidonic acid metabolites, and more. While conventional approaches to allergy control primarily focus on allergen avoidance and the use of antihistamines (despite their associated side effects), there is increasing interest in exploring novel methods to modulate mast cell activity in modern medicine. Recent evidence suggests a role for autophagy in mast cell activation, offering potential avenues for utilizing low-molecular-weight autophagy regulators in the treatment of allergic diseases. More specifically, mitochondria, which play an important role in the regulation of autophagy as well as mast cell activation, emerge as promising targets for drug development. This review examines the existing literature regarding the involvement of the molecular machinery associated with autophagy in FcεRI-dependent mast cell activation.

Analysis of blood-based indicators of brain health could provide an understanding of early disease mechanisms and pinpoint possible intervention strategies. By examining lipid profiles in extracellular vesicles (EVs), secreted particles from all cells, including astrocytes and neurons, and circulating in clinical samples, important insights regarding the brain's composition can be gained. Herein, a targeted lipidomic analysis was carried out in EVs derived from plasma samples after removal of lipoproteins from individuals with Alzheimer's disease (AD) and healthy controls. Differences were observed for selected lipid species of glycerolipids (GLs), glycerophospholipids (GPLs), lysophospholipids (LPLs) and sphingolipids (SLs) across three distinct EV subpopulations (all-cell origin, derived by immunocapture of CD9, CD81 and CD63; neuronal origin, derived by immunocapture of L1CAM; and astrocytic origin, derived by immunocapture of GLAST). The findings provide new insights into the lipid composition of EVs isolated from plasma samples regarding specific lipid families (MG, DG, Cer, PA, PC, PE, PI, LPI, LPE, LPC), as well as differences between AD and control individuals. This study emphasizes the crucial role of plasma EV lipidomics analysis as a comprehensive approach for identifying biomarkers and biological targets in AD and related disorders, facilitating early diagnosis and potentially informing novel interventions.

Cervical cancer is a major cause of death in women despite the advancement of current treatment modalities. The conventional therapeutic agent, cisplatin (CCDP), is the standard treatment for CC; however, resistance often develops due to the cancer's heterogeneity. Therefore, a detailed elucidation of the specific molecular mechanisms driving CC is crucial for the development of targeted therapeutic strategies. Retinoblastoma binding protein 6 () is a potential biomarker associated with cell proliferation and is upregulated in cervical cancer sites, exhibiting apoptosis and dysregulated expression. Furthermore, has been demonstrated to sensitize cancer cells to radiation and certain chemotherapeutic agents by regulating the gene, thus suggesting a crosstalk among / oncogenic signatures. The present study, therefore, investigated the relationship between cisplatin and expression in CC cells. Herein, we first explored bioinformatics simulations and identified that the signaling pathway is overexpressed and correlated with CC. For further analysis, we explored the Genomics of Drug Sensitivity in Cancer (GDSC) and found that most of the CC cell lines are sensitive to CCDP. To validate these findings, was silenced in HeLa and Vero cells using RNAi technology, followed by measurement of wild-type and at the mRNA level using qPCR. Cells co-treated with cisplatin and siRBBP6 were subsequently analyzed for apoptosis induction and real-time growth monitoring using flow cytometry and the xCELLigence system, respectively. Cancer cells in the co-treatment group showed a reduction in apoptosis compared to the cisplatin-treated group. Moreover, the real-time growth monitoring revealed a reduced growth rate in knockdown cells treated with cisplatin. Although wild-type remained unchanged in the co-treatment group of cancer cells, was completely repressed, suggesting that is necessary for sensitizing cervical cancer cells to cisplatin treatment by downregulating . The Vero cell population, which served as a non-cancerous control cell line in this study, remained viable following treatment with both siRBBP6 and cisplatin. Findings from this study suggest that expression promotes cisplatin sensitivity in HeLa cells through downregulation. Knockdown of limits apoptosis induction and delays cell growth inhibition in response to cisplatin. The knowledge obtained here has the potential to help improve cisplatin efficacy through personalized administration based on the expression profile of among individual patients.

Weise is one of the dominant pests feeding on spp., a pioneer plant used for windbreaking and sand fixation purposes, and poses a threat to local livestock and ecosystems. To clarify the key olfactory genes of and provide a theoretical basis for attractant and repellent development, the optimal reference genes under two different conditions (tissue and sex) were identified, and the bioinformatics and characterization of the tissue expression profiles of two categories of soluble olfactory proteins (OBPs and CSPs) were investigated. The results showed that the best reference genes were and for comparison among tissues, and and for comparison between sexes. Strong expressions of , , , , , , and were found in antennae, the most important olfactory organ for . These findings not only provide a basis for further in-depth research on the olfactory molecular mechanisms of host-specialized pests but also provide a theoretical basis for the future development of new chemical attractants or repellents using volatiles to control .

The EU-OPENSCREEN (EU-OS) European Research Infrastructure Consortium (ERIC) is a multinational, not-for-profit initiative that integrates high-capacity screening platforms and chemistry groups across Europe to facilitate research in chemical biology and early drug discovery. Over the years, the EU-OS has assembled a high-throughput screening compound collection, the European Chemical Biology Library (ECBL), that contains approximately 100 000 commercially available small molecules and a growing number of thousands of academic compounds crowdsourced through our network of European and non-European chemists. As an extension of the ECBL, here we describe the computational design, quality control and use case screenings of the European Fragment Screening Library (EFSL) composed of 1056 mini and small chemical fragments selected from a substructure analysis of the ECBL. Access to the EFSL is open to researchers from both academia and industry. Using EFSL, eight fragment screening campaigns using different structural and biophysical methods have successfully identified fragment hits in the last two years. As one of the highlighted projects for antibiotics, we describe the screening by Bio-Layer Interferometry (BLI) of the EFSL, the identification of a 35 μM fragment hit targeting the beta-ketoacyl-ACP synthase 2 (FabF), its binding confirmation to the protein by X-ray crystallography (PDB 8PJ0), its subsequent rapid exploration of its surrounding chemical space through hit-picking of ECBL compounds that contain the fragment hit as a core substructure, and the final binding confirmation of two follow-up hits by X-ray crystallography (PDB 8R0I and 8R1V).

High blood cholesterol levels are a major risk factor for cardiovascular diseases. A purified aqueous extract of , rich in phlorotannins and peptides, has been described for its potential to inhibit cholesterol biosynthesis and intestinal absorption. In this work, the effect of this extract on intestinal cells' metabolites and proteins was analysed to gain a deeper understanding of its mode of action on lipids' metabolism, particularly concerning the absorption and transport of exogenous cholesterol. Caco-2 cells, differentiated into enterocytes, were exposed to the extract, and analysed by untargeted metabolomics and proteomics. The results of the metabolomic analysis showed statistically significant differences in glutathione content of cells exposed to the extract compared to control cells, along with an increased expression of fatty acid amides in exposed cells. A proteomic analysis showed an increased expression in cells exposed to the extract compared to control cells of FAB1 and NPC1, proteins known to be involved in lipid metabolism and transport. To the extent of our knowledge, this study is the first use of untargeted metabolomics and a proteomic analysis to investigate the effects of on differentiated Caco-2 cells, offering insights into the molecular mechanism of the extract's compounds on intestinal cells.

The "missing" heritability of complex traits may be partly explained by genetic variants interacting with other genes or environments that are difficult to specify, observe, and detect. We propose a new kernel-based method called Latent Interaction Testing (LIT) to screen for genetic interactions that leverages pleiotropy from multiple related traits without requiring the interacting variable to be specified or observed. Using simulated data, we demonstrate that LIT increases power to detect latent genetic interactions compared to univariate methods. We then apply LIT to obesity-related traits in the UK Biobank and detect variants with interactive effects near known obesity-related genes (URL: https://CRAN.R-project.org/package=lit ).

A growing number of studies have suggested the involvement of long non-coding RNAs as the key players in not just the initiation and progression of the tumor microenvironment, but also in chemotherapy tolerance. In the present study, generated 5-FU-resistant SW480/DR cells were analyzed via cDNA microarray for its aberrant lncRNAs and mRNAs expression in comparison with the 5-FU-susceptible SW480/DS cells. Among the 126 lncRNAs described, lncRNAs , , and have been identified to be significantly upregulated, while lncRNs , , and have been found to be significantly downregulated. In the meantime, bioinformatic analysis through gene ontology studies of aberrantly expressed mRNAs revealed "regulated exocytosis", among others, as the biological process most impacted in SW480/DR cells. To investigate, exosome purification was then carried out and its characterization were validated via transmission electron microscopy and nanoparticle tracking analysis. Interestingly, it was determined that the 5-FU-resistant SW480/DR cells secretes significantly higher concentration of extracellular vesicles, particularly, exosomes when compared to the 5-FU-susceptible SW480/DS cells. Based on the lncRNA-mRNA interaction network analysis generated, lncRNA and have been identified to be potentially involved in the incidence of 5-FU resistance in SW480 colon cancer cells through promoting increased release of exosomes into the intercellular matrix. Our study hopes not only to provide insights on the list of involved candidate lncRNAs, but also to elucidate the role exosomes play in the initiation and development of 5-FU chemotherapy resistance in colon cancer cells.

The high-consequence pathogen causes human anthrax and often results in lethal infections without the rapid administration of effective antimicrobial treatment. Antimicrobial resistance profiling is therefore critical to inform post-exposure prophylaxis and treatment decisions, especially during emergencies such as outbreaks or where intentional release is suspected. Whole-genome sequencing using a rapid long-read sequencer can uncover antimicrobial resistance patterns if genetic markers of resistance are known. To identify genomic markers associated with antimicrobial resistance, we isolated derived from the avirulent Sterne strain with elevated minimal inhibitory concentrations to clarithromycin. Mutants were characterized both phenotypically through broth microdilution susceptibility testing and observations during culturing, as well as genotypically with whole-genome sequencing. We identified two different in-frame insertions in the L22 ribosomal protein-encoding gene , which were subsequently confirmed to be involved in clarithromycin resistance through the reversion of the mutant gene to the parent (drug-susceptible) sequence. Detection of the insertions was possible with rapid long-read sequencing, with a time-to-answer within 3 h. The mutations associated with clarithromycin resistance described here will be used in conjunction with known genetic markers of resistance for other antimicrobials to strengthen the prediction of antimicrobial resistance in .IMPORTANCEThe disease anthrax, caused by the pathogen , is extremely deadly if not treated quickly and appropriately. Clarithromycin is an antibiotic recommended for the treatment and post-exposure prophylaxis of anthrax by the Centers for Disease Control and Prevention; however, little is known about the ability of to develop resistance to clarithromycin or the mechanism of that resistance. The characterization of clarithromycin-resistant isolates presented here provides valuable information for researchers and clinicians in the event of a release of the resistant strain. Additionally, knowledge of the genetic basis of resistance provides a foundation for susceptibility prediction through rapid genome sequencing to inform timely treatment decisions.

A lactic acid (LA) monitoring system aimed at sweat monitoring was fabricated and tested. The sweat LA monitoring system uses a continuous flow of phosphate buffer saline, instead of chambers or cells, for collecting and storing sweat fluid excreted at the skin surface. To facilitate the use of the sweat LA monitoring system by subjects when exercising, the fluid control system, including the sweat sampling device, was designed to be unaffected by body movements or muscle deformation. An advantage of our system is that the skin surface condition is constantly refreshed by continuous flow. A real sample test was carried out during stationary bike exercise, which showed that LA secretion increased by approximately 10 μg/cm/min compared to the baseline levels before exercise. The LA levels recovered to baseline levels after exercise due to the effect of continuous flow. This indicates that the wristwatch sweat LA monitor has the potential to enable a detailed understanding of the LA distribution at the skin surface.

Fusarium oxysporum f.sp. lycopersici is a phytopathogen which causes vascular wilt disease in tomato plants. The survival tactics of both pathogens and hosts depend on intricate interactions between host plants and pathogenic microbes. Iron-binding proteins (IBPs) and copper-binding proteins (CBPs) play a crucial role in these interactions by participating in enzyme reactions, virulence, metabolism, and transport processes. We employed high-throughput computational tools at the sequence and structural levels to investigate the IBPs and CBPs of F. oxysporum. A total of 124 IBPs and 37 CBPs were identified in the proteome of Fusarium. The ranking of amino acids based on their affinity for binding with iron is Glu > His> Asp > Asn > Cys, and for copper is His > Asp > Cys respectively. The functional annotation, determination of subcellular localization, and Gene Ontology analysis of these putative IBPs and CBPs have unveiled their potential involvement in a diverse array of cellular and biological processes. Three iron-binding glycosyl hydrolase family proteins, along with four CBPs with carbohydrate-binding domains, have been identified as potential effector candidates. These proteins are distinct from the host Solanum lycopersicum proteome. Moreover, they are known to be located extracellularly and function as enzymes that degrade the host cell wall during pathogen-host interactions. The insights gained from this report on the role of metal ions in plant-pathogen interactions can help develop a better understanding of their fundamental biology and control vascular wilt disease in tomato plants.

There is a strong relationship between the content of beneficial fatty acids in milk and milk fat metabolic activity in the mammary gland. To improve milk quality, it is therefore necessary to study fatty acid metabolism in bovine mammary gland tissue. In adipose tissue, peroxisome proliferator-activated receptor gamma (PPARG), the core transcription factor, regulates the fatty acid metabolism gene network and determines fatty acid deposition. However, its regulatory effects on mammary gland fatty acid metabolism during lactation have rarely been reported.

Effective reduction of the allergenicity of instant soy milk powder (ISMP) is practically valuable for expanding its applications. This study optimized the enzymolysis technology of ISMP using single-factor experiments and response surface methodology, combined serological analysis, cellular immunological models, bioinformatics tools, and multiple spectroscopy techniques to investigate the effects of alcalase hydrolysis on allergenicity, spatial conformation, and linear epitopes of ISMP. Under the optimal process, special IgE and IgG1 binding abilities and allergenic activity to induce cell degranulation of alcalase-hydrolyzed ISMP were reduced by (64.72 ± 1.76)%, (56.79 ± 3.72)%, and (73.3 ± 1.19)%, respectively ( < 0.05). Moreover, the spatial conformation of instant soy milk powder hydrolysates (ISMPH) changed, including decreased surface hydrophobicity, a weaker peak of amide II band, lower contents of α-helix and β-sheet, and an enhanced content of random coil. Furthermore, the linear epitopes of major soy allergens, 9 from glycinin and 13 from β-conglycinin, could be directionally disrupted by alcalase hydrolysis. Overall, the structure-activity mechanism of alcalase hydrolysis to reduce ISMP allergenicity in vitro was preliminarily clarified. It provided a new research direction for the breakthrough in the desensitization of ISMP and a theoretical basis for revealing the potential mechanism of alcalase enzymolysis to reduce the allergenicity of ISMP.

To date, there have been no DNA-based metabarcoding studies into airborne fungi in tropical Sub-Saharan Africa. In this initial study, 10 air samples were collected onto Vaseline-coated acrylic rods mounted on drones flown at heights of 15-50 meters above ground for 10-15 min at three sites in Ghana. Purified DNA was extracted from air samples, the internal transcribed spacer (ITS) region was amplified using fungal-specific primers, and MinION third-generation amplicon sequencing was undertaken with downstream bioinformatics analyses utilizing GAIA cloud-based software (at genus taxonomic level). Principal coordinate analyses based on Bray-Curtis beta diversity dissimilarity values found no clear evidence for the structuring of fungal air communities, nor were there significant differences in alpha diversity, based on geographic location (east vs. central Ghana), underlying vegetation type (cocoa vs. non-cocoa), or height above ground level (15-23 m vs. 25-50 m), and despite the short flight times (10-15 min), ~90 operational taxonomic units (OTUs) were identified in each sample. In Ghanaian air, fungal assemblages were skewed at the phylum taxonomic level towards the ascomycetes (53.7%) as opposed to basidiomycetes (24.6%); at the class level, the Dothideomycetes were predominant (29.8%) followed by the Agaricomycetes (21.8%). The most common fungal genus in Ghanaian air was cosmopolitan and globally ubiquitous (9.9% of reads). Interestingly, many fungal genera containing economically important phytopathogens of tropical crops were also identified in Ghanaian air, including , , and Consequently, a novel loop-mediated isothermal amplification (LAMP) assay, based on -1α sequences, was developed and tested for rapid, sensitive, and specific detection of the fungal phytopathogenic genus . Potential applications for improved tropical disease management are considered.

Eugenol exhibits broad-spectrum antibacterial and anti-inflammatory properties. However, cytotoxicity at high concentrations limits the full utilization of eugenol-based drug complexes. Formulations of multidrug-loaded eugenol-based nanoemulsions have reduced cytotoxicity; however, it remains crucial to understand how these eugenol complexes interact with primary human carrier proteins to design and develop therapeutic alternatives. Consequently, this study primarily aims to investigate the impact on Human Serum Albumin (HSA) when it interacts with eugenol-based complexes loaded with first-line anti-tuberculosis drugs.

Seriously ill patients rely on spiritual and existential beliefs to support coping and approach crucial treatment and healthcare decisions. Yet, we lack gold standard, validated approaches to gathering information on those spiritual beliefs. Therefore, we developed I-SPIRIT, a spiritual needs and beliefs inventory for those with serious illness (IIR-10-050).

Serological assays for SARS-CoV-2 play a pivotal role in the definition of whether patients are infected, the understanding of viral epidemiology, the screening of convalescent sera for therapeutic and prophylactic purposes, and in obtaining a better understanding of the immune response towards the virus. The aim of this study was to investigate the performance of a bead-based multiplex assay. This assay allowed for the simultaneous testing of IgG antibodies against SARS-CoV-2 spike, S1, S2, RBD, and nucleocapsid moieties and S1 of seasonal coronaviruses hCoV-22E, hCoV-HKU1, hCoV-NL63, and hCoV-OC43, as well as MERS and SARS-CoV. We compared the bead-based multiplex assay with commercial ELISA tests. We tested the sera of 27 SARS-CoV-2 PCR-positive individuals who were previously tested with different ELISA assays. Additionally, we investigated the reproducibility of the results by means of multiple testing of the same sera. Finally, the results were correlated with neutralising assays. In summary, the concordance of the qualitative results ranged between 78% and 96% depending on the ELISA assay and the specific antigen. Repeated freezing-thawing cycles resulted in reduced mean fluorescence intensity, while the storage period had no influence in this respect. In our test cohort, we detected up to 36% of sera positive for the development of neutralising antibodies, which is in concordance with the bead-based multiplex and IgG ELISA.

Additional Affiliation(s): Updated Affiliation [...].

This study describes clinical manifestations of highly pathogenic avian influenza (HPAI) H5N1, H5N8 and H5N6 outbreaks between 2014 and 2018 and 2020 and 2022 in the Netherlands for different poultry types and age groups. Adult duck (breeder) farms and juvenile chicken (broiler and laying pullet) farms were not diagnosed before 2020. Outbreaks in ducks decreased in 2020-2022 vs. 2014-2018, but increased for meat-type poultry. Neurological, locomotor and reproductive tract signs were often observed in ducks, whereas laying- and meat-type poultry more often showed mucosal membrane and skin signs, including cyanosis and hemorrhagic conjunctiva. Juveniles (chickens and ducks) showed neurological and locomotor signs more often than adults. Diarrhea occurred more often in adult chickens and juvenile ducks. Mortality increased exponentially within four days before notification in chickens and ducks, with a more fluctuating trend in ducks and meat-type poultry than in layers. For ducks, a mortality ratio (MR) > 3, compared to the average mortality of the previous week, was reached less often than in chickens. A lower percentage of laying flocks with MR > 3 was found for 2020-2022 vs. 2014-2018, but without significant differences in clinical signs. This study provides a basis for improvements in mortality- and clinical-sign-based early warning criteria, especially for juvenile chickens and ducks.

Aspergillosis is a fungal infection caused by various species of , most notably . This fungus causes a spectrum of diseases, including allergic bronchopulmonary aspergillosis, aspergilloma, chronic pulmonary aspergillosis, and invasive aspergillosis. The clinical manifestations and severity of aspergillosis can vary depending on individual immune status and the specific species of involved. The recognition of involves pathogen-associated molecular patterns (PAMPs) such as glucan, galactomannan, mannose, and conidial surface proteins. These are recognized by the pathogen recognition receptors present on immune cells such as Toll-like receptors (TLR-1,2,3,4, etc.) and C-type lectins (Dectin-1 and Dectin-2). We discuss the roles of cytokines and pathogen recognition in aspergillosis from both the perspective of human and experimental infection. Several cytokines and chemokines have been implicated in the immune response to infection, including interferon-γ (IFN-γ), tumor necrosis factor-α (TNF-α), CCR4, CCR17, and other interleukins. For example, allergic bronchopulmonary aspergillosis (ABPA) is characterized by Th2 and Th9 cell-type immunity and involves interleukin (IL)-4, IL-5, IL-13, and IL-10. In contrast, it has been observed that invasive aspergillosis involves Th1 and Th17 cell-type immunity via IFN-γ, IL-1, IL-6, and IL-17. These cytokines activate various immune cells and stimulate the production of other immune molecules, such as antimicrobial peptides and reactive oxygen species, which aid in the clearance of the fungal pathogen. Moreover, they help to initiate and coordinate the immune response, recruit immune cells to the site of infection, and promote clearance of the fungus. Insight into the host response from both human and animal studies may aid in understanding the immune response in aspergillosis, possibly leading to harnessing the power of cytokines or cytokine (receptor) antagonists and transforming them into precise immunotherapeutic strategies. This could advance personalized medicine.

Epigenetic factors, including microRNAs (miRNAs), play an important role in affecting gene expression and, therefore, are involved in various biological processes including immunity protection against tumors. Marek's disease (MD) is a highly contagious disease of chickens caused by the MD virus (MDV). MD has been primarily controlled by vaccinations. MD vaccine efficacy might, in part, be dependent on modulations of a complex set of factors including host epigenetic factors. This study was designed to identify differentially expressed miRNAs in the primary lymphoid organ, bursae of Fabricius, in response to MD vaccination followed by MDV challenge in two genetically divergent inbred lines of White Leghorns. Small RNA sequencing and bioinformatic analyses of the small RNA sequence reads identified hundreds of miRNAs among all the treatment groups. A small portion of the identified miRNAs was differentially expressed within each of the four treatment groups, which were HVT or CVI988/Rispens vaccinated line 6-resistant birds and line 7-susceptible birds. A direct comparison between the resistant line 6 and susceptible line 7 groups vaccinated with HVT followed by MDV challenge identified five differentially expressed miRNAs. Gene Ontology analysis of the target genes of those five miRNAs revealed that those target genes, in addition to various GO terms, are involved in multiple signaling pathways including MAPK, TGF-β, ErbB, and EGFR1 signaling pathways. The general functions of those pathways reportedly play important roles in oncogenesis, anti-cancer immunity, cancer cell migration, and metastatic progression. Therefore, it is highly likely that those miRNAs may, in part, influence vaccine protection through the pathways.

crops are well known for the accumulation of glucosinolates-secondary metabolites crucial for plants' adaptation to various stresses. Glucosinolates also functioning as defence compounds pose challenges to food quality due to their goitrogenic properties. Their disruption leaves plants susceptible to insect pests and diseases. Hence, a targeted reduction in seed glucosinolate content is of paramount importance to increase food acceptance. () present a promising avenue for selectively reducing glucosinolate concentrations in seeds while preserving biosynthesis elsewhere. In this study, 54 putative GTR protein sequences found in were retrieved, employing Arabidopsis GTR1 and GTR2 templates. Comprehensive bioinformatics analyses, encompassing gene structure organization, domain analysis, motif assessments, promoter analysis, and cis-regulatory elements, affirmed the existence of transporter domains and stress-related regulatory elements. Phylogenetic analysis revealed patterns of conservation and divergence across species. Glucosinolates have been shown to increase under stress conditions, indicating a potential role in stress response. To elucidate the role of GTRs in glucosinolate transportation under NaCl stress in two distinct species, and , plants were subjected to 0, 100, or 200 mM NaCl. Based on the literature, key genes were chosen and their expression across various plant parts was assessed. Both species displayed divergent trends in their biochemical profiles as well as glucosinolate contents under elevated salt stress conditions. Statistical modelling identified significant contributors to glucosinolate variations, guiding the development of targeted breeding strategies for low-glucosinolate varieties. Notably, exhibited pronounced expressions in stems, contributing approximately 52% to glucosinolate content variance, while displayed significant expression in flowers. Additionally, and demonstrated noteworthy expression in roots. This study enhances our understanding of glucosinolate regulation under stress conditions, offering avenues to improve crop quality and resilience.

Electroencephalogram (EEG) analysis plays an indispensable role across contemporary medical applications, which encompasses diagnosis, monitoring, drug discovery, and therapeutic assessment. This work puts forth an end-to-end deep learning framework that is uniquely tailored for versatile EEG analysis tasks by directly operating on raw waveform inputs. It aims to address the challenges of manual feature engineering and the neglect of spatial interrelationships in existing methodologies. Specifically, a spatial channel attention module is introduced to emphasize the critical inter-channel dependencies in EEG signals through channel statistics aggregation and multi-layer perceptron operations. Furthermore, a sparse transformer encoder is used to leverage selective sparse attention in order to efficiently process long EEG sequences while reducing computational complexity. Distilling convolutional layers further concatenates the temporal features and retains only the salient patterns. As it was rigorously evaluated on key EEG datasets, our model consistently accomplished a superior performance over the current approaches in detection and classification assignments. By accounting for both spatial and temporal relationships in an end-to-end paradigm, this work facilitates a versatile, automated EEG understanding across diseases, subjects, and objectives through a singular yet customizable architecture. Extensive empirical validation and further architectural refinement may promote broader clinical adoption prospects.

There has been increasing recognition of heterogeneity in blood platelets and their responses, particularly in recent years, where next-generation technologies and advanced bioinformatic tools that interrogate "big data" have enabled large-scale studies of RNA and protein expression across a growing list of disease states. However, pioneering platelet biologists and clinicians were already hypothesizing upon and investigating heterogeneity in platelet (and megakaryocyte) activity and platelet metabolism and aggregation over half a century ago. Building on their foundational hypotheses, in particular Professor Marian A. Packham's pioneering work and a State of the Art lecture in her memoriam at the 2023 International Society on Thrombosis and Haemostasis Congress by Anandi Krishnan, this review outlines the key features that contribute to the heterogeneity of platelets between and within individuals. Starting with important epidemiologic factors, we move stepwise through successively smaller scales down to heterogeneity revealed by single-cell technologies in health and disease. We hope that this overview will urge future scientific and clinical studies to recognize and account for heterogeneity of platelets and aim to apply methods that capture that heterogeneity. Finally, we summarize other exciting new data presented on this topic at the 2023 International Society on Thrombosis and Haemostasis Congress.

Tandem mass spectrometry (MS/MS) stands as the predominant high-throughput technique for comprehensively analyzing protein content within biological samples. This methodology is a cornerstone driving the advancement of proteomics. In recent years, substantial strides have been made in Data-Independent Acquisition (DIA) strategies, facilitating impartial and non-targeted fragmentation of precursor ions. The DIA-generated MS/MS spectra present a formidable obstacle due to their inherent high multiplexing nature. Each spectrum encapsulates fragmented product ions originating from multiple precursor peptides. This intricacy poses a particularly acute challenge in de novo peptide/protein sequencing, where current methods are ill-equipped to address the multiplexing conundrum. In this paper, we introduce Casanovo-DIA, a deep-learning model based on transformer architecture. It deciphers peptide sequences from DIA mass spectrometry data. Our results show significant improvements over existing STOA methods, including DeepNovo-DIA and PepNet. Casanovo-DIA enhances precision by 15.14% to 34.8%, recall by 11.62% to 31.94% at the amino acid level, and boosts precision by 59% to 81.36% at the peptide level. Integrating DIA data and our Casanovo-DIA model holds considerable promise to uncover novel peptides and more comprehensive profiling of biological samples. Casanovo-DIA is freely available under the GNU GPL license at https://github.com/Biocomputing-Research-Group/Casanovo-DIA.

Breast cancer (BC) is one of the most common cancers in women, significantly contributing to cancer-related death in the modern world. Obesity, as a worldwide epidemic besides the menopausal status, has a paradoxical association with BC.

Epigenomic regulation and dynamic DNA methylation, in particular, are widespread mechanisms orchestrating the genome operation across time and species. Whole-genome bisulfite sequencing (WGBS) is currently the only method for unbiasedly capturing the presence of 5-methylcytosine (5-mC) DNA methylation patterns across an entire genome with single-nucleotide resolution. Bisulfite treatment converts unmethylated cytosines to uracils but leaves methylated cytosines intact, thereby creating a map of all methylated cytosines across a genome also known as a methylome. These epigenomic patterns of DNA methylation have been found to regulate gene expression and influence gene evolution rates between species. While protocols have been optimized for vertebrate methylome production, little adaptation has been done for invertebrates. Creating a methylome reference allows comparisons to be made between rates of transcription and epigenomic patterning in animals. Here we present a method of library construction for bisulfite sequencing optimized for non-bilateral metazoans such as the ctenophore, Mnemiopsis leidyi. We have improved upon our previously published method by including spike-in genomic DNA controls to measure methylation conversion rates. By pooling two bisulfite conversion reactions from the same individual, we also produced sequencing libraries that yielded a higher percentage of sequenced reads uniquely mapping to the reference genome. We successfully detected 5-mC in whole-animal methylomes at CpG, CHG, and CHH sites and visualized datasets using circos diagrams. The proof-of-concept tests were performed both under control conditions and following injury tests with changes in methylation patterns of genes encoding innexins, toxins and neuropeptides. Our approach can be easily adapted to produce epigenomes from other fragile marine animals.

Understanding gene evolution across genomes and organisms, including ctenophores, can provide unexpected biological insights. It enables powerful integrative approaches that leverage sequence diversity to advance biomedicine. Sequencing and bioinformatic tools can be inexpensive and user-friendly, but numerous options and coding can intimidate new users. Distinct challenges exist in working with data from diverse species but may go unrecognized by researchers accustomed to gold-standard genomes. Here, we provide a high-level workflow and detailed pipeline to enable animal collection, single-molecule sequencing, and phylogenomic analysis of gene and species evolution. As a demonstration, we focus on (1) PacBio RNA-seq of the genome-sequenced ctenophore Mnemiopsis leidyi, (2) diversity and evolution of the mechanosensitive ion channel Piezo in genetic models and basal-branching animals, and (3) associated challenges and solutions to working with diverse species and genomes, including gene model updating and repair using single-molecule RNA-seq. We provide a Python Jupyter Notebook version of our pipeline (GitHub Repository: Ctenophore-Ocean-To-Tree-2023 https://github.com/000generic/Ctenophore-Ocean-To-Tree-2023 ) that can be run for free in the Google Colab cloud to replicate our findings or modified for specific or greater use. Our protocol enables users to design new sequencing projects in ctenophores, marine invertebrates, or other novel organisms. It provides a simple, comprehensive platform that can ease new user entry into running their evolutionary sequence analyses.

Experimental discovery of neuropeptides and peptide hormones is a long and tedious task. Mining the genomic and transcriptomic sequence data with robust secretory peptide prediction tools can significantly facilitate subsequent experiments. We describe the application of various in silico neuropeptide discovery methods for the placozoan Trichopax adhaerens as an illustrated example and a powerful experimental paradigm for cellular and evolutionary biology. In total, 33 placozoan (neuro)peptide-like hormone precursors were found using homology-based BLAST search and repeat-based and comparative evolutionary methods. Some of the discovered precursors are homologous to insulins and RFamide precursors from Cnidaria and other animal phyla.

Malignant ascites is one of the severe complications of hepatocellular carcinoma, which can be regarded as a unique tumor microenvironment of hepatocellular carcinoma. The identification of novel biomarkers in malignant ascites could be crucial to differentiate patients with hepatocellular carcinoma and cirrhotic ascites.

Placozoans are morphologically the simplest free-living animals. They represent a unique window of opportunities to understand both the origin of the animal organization and the rules of life for the system and synthetic biology of the future. However, despite more than 100 years of their investigations, we know little about their organization, natural habitats, and life strategies. Here, we introduce this unique animal phylum and highlight some directions vital to broadening the frontiers of the biomedical sciences. In particular, understanding the genomic bases of placozoan biodiversity, cell identity, connectivity, reproduction, and cellular bases of behavior are critical hot spots for future studies.

Ovarian cancer, often labeled a "silent killer," remains one of the most compelling and challenging areas of cancer research. In 2019 alone, a staggering 222,240 new cases of ovarian cancer were reported, with nearly 14,170 lives tragically lost to this relentless disease. The absence of effective diagnostic methods, increased resistance to chemotherapy, and the heterogeneous nature of ovarian cancer collectively contribute to the unfavorable prognosis observed in the majority of cases. Thus, there is a pressing need to explore therapeutic interventions that offer superior efficacy and safety, thereby enhancing the survival prospects for ovarian cancer patients. Recognizing this potential, our research synergizes bioinformatics with a network pharmacology approach to investigate the underlying molecular interactions of Saudi Arabian flora (, , , , , , and ) in ovarian cancer treatment. At first, phytoconstituents of indigenous flora and their associated gene targets, particularly those pertinent to ovarian cancer, were obtained from open-access databases. Later, the shared targets of plants and diseases were compared to identify common targets. A protein-protein interaction (PPI) network of predicted targets was then constructed for the identification of key genes having the highest degree of connectivity among networks. Following that, a compound-target protein-pathway network was constructed, which uncovered that, namely, hispidulin, stigmasterol, ascorbic acid, octopamine, cyperene, kaempferol, pungenin, citric acid, d-tartaric acid, beta-sitosterol, (-)-epicatechin gallate, and (+)-catechin demonstrably influence cell proliferation and growth by impacting the AKT1 and VEGFA proteins. Molecular docking, complemented by a 20-ns molecular dynamic (MD) simulation, was used, and the binding affinity of the compound was further validated. Molecular docking, complemented by a 20-ns MD simulation, confirmed the binding affinity of these compounds. Specifically, for AKT1, ascorbic acid showed a docking score of -11.1227 kcal/mol, interacting with residues Ser A:240, Leu A:239, Arg A:243, Arg C:2, and Glu A:341. For VEGFA, hispidulin exhibited a docking score of -17.3714 kcal/mol, interacting with Asn A:158, Val A:190, Gln B:160, Ser A:179, and Ser B:176. To sum up, both a theoretical and empirical framework were established by this study, directing more comprehensive research and laying out a roadmap for the potential utilization of active compounds in the formulation of anti-cancer treatments.

In mitochondrial DNA (mtDNA) depletion syndrome (MDS), patients cannot maintain sufficient mtDNA for their energy needs. MDS presentations range from infantile encephalopathy with hepatopathy (Alpers syndrome) to adult chronic progressive external ophthalmoplegia. Most are caused by nucleotide imbalance or by defects in the mtDNA replisome. There is currently no curative treatment available. Nucleoside therapy is a promising experimental treatment for deficiency, where patients are supplemented with exogenous deoxypyrimidines. We aimed to explore the benefits of nucleoside supplementation in POLG and TWNK deficient fibroblasts. We used high-content fluorescence microscopy with software-based image analysis to assay mtDNA content and membrane potential quantitatively, using vital dyes PicoGreen and MitoTracker Red CMXRos respectively. We tested the effect of 15 combinations (A, T, G, C, AT, AC, AG, CT, CG, GT, ATC, ATG, AGC, TGC, ATGC) of deoxynucleoside supplements on mtDNA content of fibroblasts derived from four patients with MDS (POLG1, POLG2, DGUOK, TWNK) in both a replicating (10% dialysed FCS) and quiescent (0.1% dialysed FCS) state. We used qPCR to measure mtDNA content of supplemented and non-supplemented fibroblasts following mtDNA depletion using 20 µM ddC and after 14- and 21-day recovery in a quiescent state. Nucleoside treatments at 200 µM that significantly increased mtDNA content also significantly reduced the number of cells remaining in culture after 7 days of treatment, as well as mitochondrial membrane potential. These toxic effects were abolished by reducing the concentration of nucleosides to 50 µM. In POLG1 and TWNK cells the combination of ATGC treatment increased mtDNA content the most after 7 days in non-replicating cells. ATGC nucleoside combination significantly increased the rate of mtDNA recovery in quiescent POLG1 cells following mtDNA depletion by ddC. High-content imaging enabled us to link mtDNA copy number with key read-outs linked to patient wellbeing. Elevated G increased mtDNA copy number but severely impaired fibroblast growth, potentially by inhibiting purine synthesis and/or causing replication stress. Combinations of nucleosides ATGC, T, or TC, benefited growth of cells harbouring mutations. These combinations, one of which reflects a commercially available preparation, could be explored further for treatment of POLG patients.

Recurrent wheezing disorders including asthma are complex and heterogeneous diseases that affect up to 30% of all children, contributing to a major burden on children, their families, and global healthcare systems. It is now recognized that a dysfunctional airway epithelium plays a central role in the pathogenesis of recurrent wheeze, although the underlying mechanisms are still not fully understood. This prospective birth cohort aims to bridge this knowledge gap by investigating the influence of intrinsic epithelial dysfunction on the risk for developing respiratory disorders and the modulation of this risk by maternal morbidities, exposures, and respiratory exposures in the first year of life.

Accessible and affordable health services and products including medicines, vaccines, and public health are an important health agenda of all countries. It is well understood that without digital health technologies, countries will face difficulties in tackling the needs and demands of their population. Global agencies including the World Health Organization (WHO), United Nations (UN), International Telecommunication Union (ITU), etc. have been instrumental in providing various tools, and guidance through digital health strategies in improving health and digital health maturity of the countries. The Digital Health Platform Handbook (DHPH) is a toolkit published by WHO and ITU to help countries create and implement a digital health platform (DHP) to serve as the underlying infrastructure for an interoperable and integrated national digital health system. We apply the foundational principles of DHPH and provide a perspective of DHP components in a layered, enterprise architecture of a digital health infrastructure. India has rolled out the blueprint of its National Digital Health Mission (NDHM) to address the emerging needs for digitization of healthcare in the country. In this paper, we also illustrate the design and implementation of WHO-ITU DHP components at the national level by exploring India's digital health mission implementation utilizing various digital public goods to build a digital health ecosystem in the country.

Antimicrobial resistance (AMR) is a growing problem in African cattle production systems, posing a threat to human and animal health and the associated economic value chain. However, there is a poor understanding of the resistomes in small-holder cattle breeds in East African countries. This study aims to examine the distribution of antimicrobial resistance genes (ARGs) in Kenya, Tanzania, and Uganda cattle using a metagenomics approach. We used the SqueezeMeta-Abricate (assembly-based) pipeline to detect ARGs and benchmarked this approach using the Centifuge-AMRplusplus (read-based) pipeline to evaluate its efficiency. Our findings reveal a significant number of ARGs of critical medical and economic importance in all three countries, including resistance to drugs of last resort such as carbapenems, suggesting the presence of highly virulent and antibiotic-resistant bacterial pathogens (ESKAPE) circulating in East Africa. Shared ARGs such as aph(6)-id (aminoglycoside phosphotransferase), tet (tetracycline resistance gene), sul2 (sulfonamide resistance gene) and cfxA_gen (betalactamase gene) were detected. Assembly-based methods revealed fewer ARGs compared to read-based methods, indicating the sensitivity and specificity of read-based methods in resistome characterization. Our findings call for further surveillance to estimate the intensity of the antibiotic resistance problem and wider resistome classification. Effective management of livestock and antibiotic consumption is crucial in minimizing antimicrobial resistance and maximizing productivity, making these findings relevant to stakeholders, agriculturists, and veterinarians in East Africa and Africa at large.

We examined the proteomic profiles of three registered opium poppy cultivars ( L.) with varying alkaloid contents. The study was conducted on both the stem and capsule organs. A high number of differentially expressed proteins (DEPs) were identified between the cultivars and the organs. We analyzed DEPs for their contribution in GO terms and KEGG pathways. The upregulated DEPs were significantly enriched in photosynthesis and translation for morphine-rich and noscapine-rich cultivars, respectively. The data indicated that photosynthesis is crucial for benzylisoquinoline alkaloid (BIA) biosynthesis, but different processes are also effective in morphine and noscapine biosynthesis, which occur at different branches in the biosynthetic pathway. The proteomics profiles revealed that energy demand is more effective in morphine biosynthesis, while translational control plays a leading role in noscapine biosynthesis. This study represents the first report demonstrating organ-based and cultivar-based protein expression differences in mature poppy plants.

Post-translational modification by Small Ubiquitin-like MOdifier (SUMO) is an important mechanism to regulate protein activity, protein stability, and localization of substrates. Zbtb21 is a zinc finger and BTB (Broad-complex, Tram-track and Bric à brac) domain-containing transcription factor. Bioinformatic prediction suggests several putative SUMOylated sites in Zbtb21 protein.

Blindness or vision loss due to neuroretinal and photoreceptor degeneration affects millions of individuals worldwide. In numerous neurodegenerative diseases, including age-related macular degeneration, dysregulated immune response-mediated retinal degeneration has been found to play a critical role in the disease pathogenesis. To better understand the pathogenic mechanisms underlying the retinal degeneration, we used a mouse model of systemic immune activation where we infected mice with lymphocytic choriomeningitis virus (LCMV) clone 13. Here, we evaluated the effects of LCMV infection and present a comprehensive discovery-based proteomic investigation using tandem mass tag (TMT) labeling and high-resolution liquid chromatography-tandem mass spectrometry (LC-MS/MS). Changes in protein regulation in the posterior part of the eye, neuroretina, and RPE/choroid were compared to those in the spleen as a secondary lymphoid organ and to the kidney as a non-lymphoid but encapsulated organ at 1, 8, and 28 weeks of infection. Using bioinformatic tools, we found several proteins responsible for maintaining normal tissue homeostasis to be differentially regulated in the neuroretina and the RPE/choroid during the degenerative process. Additionally, in the organs we observed, several important protein pathways contributing to cellular homeostasis and tissue development were perturbed and associated with LCMV-mediated inflammation, promoting disease progression. Our findings suggest that the response to a systemic chronic infection differs between the neuroretina and the RPE/choroid, and the processes induced by chronic systemic infection in the RPE/choroid are not unlike those induced in non-immune-privileged organs such as the kidney and spleen. Overall, our data provide detailed insight into several molecular mechanisms of neuroretinal degeneration and highlight various novel protein pathways that further suggest that the posterior part of the eye is not an isolated immunological entity despite the existence of neuroretinal immune privilege.

Long-read sequencing has proven the necessity for high-quality genomic assemblies of reference species, including enigmatic ctenophores. Obtaining high-molecular-weight genomic DNA is pivotal to this process and has proven highly problematic for many species. Here, we discuss different methodologies for gDNA isolation and present a protocol for isolating gDNA for several members of the phylum Ctenophora. Specifically, we describe a Pacific Biosciences library construction method used in conjunction with gDNA isolation methods that have proven successful in obtaining high-quality genomic assemblies in ctenophores.

This study aimed to explore the relationship between CD276 and clear cell renal carcinoma (ccRCC) and assess the diagnostic value of CD276 in ccRCC.

The emergence and development of single-cell RNA sequencing (scRNA-seq) techniques enable researchers to perform large-scale analysis of the transcriptomic profiling at cell-specific resolution. Unsupervised clustering of scRNA-seq data is central for most studies, which is essential to identify novel cell types and their gene expression logics. Although an increasing number of algorithms and tools are available for scRNA-seq analysis, a practical guide for users to navigate the landscape remains underrepresented. This chapter presents an overview of the scRNA-seq data analysis pipeline, quality control, batch effect correction, data standardization, cell clustering and visualization, cluster correlation analysis, and marker gene identification. Taking the two broadly used analysis packages, i.e., Scanpy and MetaCell, as examples, we provide a hands-on guideline and comparison regarding the best practices for the above essential analysis steps and data visualization. Additionally, we compare both packages and algorithms using a scRNA-seq dataset of the ctenophore Mnemiopsis leidyi, which is representative of one of the earliest animal lineages, critical to understanding the origin and evolution of animal novelties. This pipeline can also be helpful for analyses of other taxa, especially prebilaterian animals, where these tools are under development (e.g., placozoan and Porifera).

Mitochondrial proteomes have been experimentally characterized for only a handful of animal species. However, the increasing availability of genomic and transcriptomic data allows one to infer mitochondrial proteins using computational tools. MitoPredictor is a novel random forest classifier, which utilizes orthology search, mitochondrial targeting signal (MTS) identification, and protein domain content to infer mitochondrial proteins in animals. MitoPredictor's output also includes an easy-to-use R Shiny applet for the visualization and analysis of the results. In this article, we provide a guide for predicting and analyzing the mitochondrial proteome of the ctenophore Mnemiopsis leidyi using MitoPredictor.

Pharmacogenomics (PGx) is a rapidly changing field of genomics in which healthcare professionals play an important role in its implementation in the clinical setting, however PGx level of adoption remains low. This study aims to investigate the attitude, self-confidence, level of knowledge, and their impact on health sciences undergraduate students' intentions to adopt PGx in clinical practice using a questionnaire developed based on the Theory of Planned Behavior (TPB). A model was proposed and a questionnaire was developed that was distributed to 467 undergraduate students of all academic years from four different departments of the University of Sharjah (UoS) including medical, dental, nursing, and pharmacy students from September 2022 to November 2022. Descriptive statistics along with factor analysis and regression analysis were conducted. The proposed model had a good internal consistency and fit. Attitude was the factor with the greatest impact on student's intentions followed by self-confidence and barriers. The level of knowledge had a meaningless impact. The majority of students shared a positive attitude and were aware of PGx benefits. Almost 60% of the respondents showed a high level of knowledge, while 50% of them were confident of implementing PGx in their clinical practice. Many students were prone to adopt PGx in their future careers. PGx testing cost and the lack of reimbursement were the most important barriers. Overall, students shared a positive intention and were prone to adopt PGx. In the future, it would be important to investigate the differences between gender, year of studies, and area of studies studies and their impact on students' intentions.