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Adrenocortical carcinoma (ACC) is a malignant carcinoma with an extremely poor prognosis, and its pathogenesis remains to be understood to date, necessitating further investigation. This study aims to discover biomarkers and potential therapeutic agents for ACC through bioinformatics, enhancing clinical diagnosis and treatment strategies. Differentially expressed genes (DEGs) between ACC and normal adrenal cortex were screened out from the GSE19750 and GSE90713 datasets available in the GEO database. An online Venn diagram tool was utilized to identify the common DEGs between the two datasets. The identified DEGs were subjected to functional assessment, pathway enrichment, and identification of hub genes by performing the protein-protein interaction (PPI), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. The differences in the expressions of hub genes between ACC and normal adrenal cortex were validated at the GEPIA2 website, and the association of these genes with the overall patient survival was also assessed. Finally, on the QuartataWeb website, drugs related to the identified hub genes were determined. A total of 114 DEGs, 10 hub genes, and 69 known drugs that could interact with these genes were identified. The GO and KEGG analyses revealed a close association of the identified DEGs with cellular signal transduction. The 10 hub genes identified were overexpressed in ACC, in addition to being significantly associated with adverse prognosis in ACC. Three genes and the associated known drugs were identified as potential targets for ACC treatment. [ABSTRACT FROM AUTHOR]

Circular RNAs (circRNAs) are cardinal players in numerous physiological and pathological processes. CircRNAs play dual roles as tumor suppressors and oncogenes in different oncological contexts, including hepatocellular carcinoma (HCC). Their roles significantly impact the disease at all stages, including initiation, development, progression, invasion, and metastasis, in addition to the response to treatment. In this review, we discuss the biogenesis and regulatory functional roles of circRNAs, as well as circRNA–protein–mRNA ternary complex formation, elucidating the intricate pathways tuned by circRNAs to modulate gene expression and cellular processes through a comprehensive literature search, in silico search, and bioinformatics analysis. With a particular focus on the interplay between circRNAs, epigenetics, and HCC pathology, the article sets the stage for further exploration of circRNAs as novel investigational theranostic agents in the dynamic realm of HCC. [ABSTRACT FROM AUTHOR]

Atherosclerosis, a lipid‐driven chronic inflammatory disorder, is a significant global health concern associated with high rates of morbidity and mortality, imposing a substantial societal burden. The purpose of this study is to investigate the possible molecular mechanisms of atherosclerosis and identify potential therapeutic targets. We conducted an integrated bioinformatics analysis using data from peripheral blood mononuclear cell and TISSUE databases obtained from the Gene Expression Omnibus, to identify key genes associated with the progression of atherosclerosis. Here, IRF8 was found to be a key gene in atherosclerosis patients. Silencing IRF8 with small interfering RNA reduced inflammation in endothelial cells. This suggests IRF8 is a crucial biomarker for immune infiltration in atherosclerosis advance. [ABSTRACT FROM AUTHOR]

Stony coral tissue loss disease (SCTLD) has devastated coral reefs off the coast of Florida and continues to spread throughout the Caribbean. Although a number of bacterial taxa have consistently been associated with SCTLD, no pathogen has been definitively implicated in the etiology of SCTLD. Previous studies have predominantly focused on the prokaryotic community through 16S rRNA sequencing of healthy and affected tissues. Here, we provide a different analytical approach by applying a bioinformatics pipeline to publicly available metagenomic sequencing samples of SCTLD lesions and healthy tissues from 4 stony coral species. To compensate for the lack of coral reference genomes, we used data from apparently healthy coral samples to approximate a host genome and healthy microbiome reference. These reads were then used as a reference to which we matched and removed reads from diseased lesion tissue samples, and the remaining reads associated only with disease lesions were taxonomically classified at the DNA and protein levels. For DNA classifications, we used a pathogen identification protocol originally designed to identify pathogens in human tissue samples, and for protein classifications, we used a fast protein sequence aligner. To assess the utility of our pipeline, a species-level analysis of a candidate genus, Vibrio , was used to demonstrate the pipeline's effectiveness. Our approach revealed both complementary and unique coral microbiome members compared with a prior metagenome analysis of the same dataset. [ABSTRACT FROM AUTHOR]

Objective: miR-497/195, located at 17p13.1, is a highly conserved miRNA cluster whose abnormal expression is a key regulator of carcinogenesis. We performed a comprehensive analysis of the miR-497/195 cluster to determine its prognostic utility and role in cervical cancer (CC) using publicly available datasets. Results: In silico analysis and validation revealed that this cluster is downregulated in CC. A total of 60 target genes of miR-497/195 cluster were identified as differentially expressed between normal and CC samples. ShinyGO, STRING, CytoHubba, Timer 2.0, HPA, and HCMBD were used for functional enrichment, PPIN network construction, hub gene identification, immune infiltration correlation, histopathological expression, and determination of the metastatic potential of miR-497/195 cluster and their target genes. PPIN analysis identified CCNE1, CCNE2, ANLN, RACGAP1, KIF23, CHEK1, CDC25A, E2F7, CDK1, and CEP55 as the top 10 hub genes (HGs). Furthermore, the upregulation of RECK, ATD5, and BCL2, downregulation of OSBPL3, RCAN3, and HIST1H3H effected overall survival of CC patients. We identified 6 targets (TFAP2A, CLSPN, RASEF, HIST1H3H, AKT3, and ITPR1) of miR-497/195 cluster to influence metastasis. In addition, 8 druggable genes and 38 potential drugs were also identified. Our study identified miR-497/195 cluster target genes and pathways that could be used for prognostic and therapeutic applications in CC. [ABSTRACT FROM AUTHOR]

Gastric cancer (GC) is a prominent public health issue and ranks as the third most prevalent cause of cancer-related mortality on a global scale. The role of long non-coding RNAs (lncRNAs) in cancer is not yet fully understood, particularly in relation to GC development. The objective of this study was to examine the expression levels of lncRNAs in GC tissues using a bioinformatics-based ranking approach. A bioinformatics methodology was employed to prioritize lncRNAs that are hypothesized to play a role in GC tumorigenesis. Moreover, a selection was made for experimental validation of the highest-ranked lncRNAs, which include HCG18, OIP5-AS1, FGD5-AS1, and NORAD. Additionally, quantitative real-time polymerase chain reaction (qRT-PCR) was employed to confirm the results obtained from bioinformatics analysis in a total of 35 GC samples and their corresponding adjacent non-tumoral samples. Receiver operating characteristic (ROC) curves and the corresponding area under the ROC curve (AUC) were utilized to evaluate the diagnostic efficacy of the lncRNAs. The bioinformatics analysis revealed that the lncRNA HCG18 is the highest-ranked lncRNA associated with GC. Furthermore, the expression levels of HCG18, OIP5-AS1, FGD5-AS1, and NORAD were found to be significantly elevated in GC samples when compared to adjacent non-tumoral samples. The calculated values for the AUC of HCG18, OIP5-AS1, FGD5-AS1, and NORAD were 0.80, 0.74, 0.73, and 0.71, respectively. The results of the study indicate that the lncRNAs HCG18, OIP5-AS1, FGD5-AS1, and NORAD may play a role in the development of GC. Additionally, the present study revealed that utilizing bioinformatic techniques can prove to be a highly effective strategy in identifying potential lncRNAs pertinent to the progression of GC. [ABSTRACT FROM AUTHOR]

Pancreatic cancer (PC) is one of the world's most aggressive and deadly cancers, owing to non-specific early clinical symptoms, late-stage diagnosis, and poor survival. Therefore, it is critical to identify specific biomarkers for its early diagnosis. Annexin A2 (ANXA2) is a calcium-dependent phospholipid-binding protein that has been reported to be upregulated in several cancer types, making it an emerging biomarker and potential cancer therapeutic target. However, the mechanism underlying the regulation of ANXA2 overexpression is still unclear. It is well established that genetic and epigenetic alterations may lead to widespread dysregulation of gene expression. Hence, in this study, we focused on exploring the regulatory mechanism of ANXA2 by investigating the transcriptional profile, methylation pattern, somatic mutation, and prognostic value of ANXA2 in PC using several bioinformatics databases. Our results revealed that the expression levels of ANXA2 were remarkably increased in PC tissues comparing to normal tissues. Furthermore, the high expression of ANXA2 was significantly related to the poor prognosis of PC patients. More importantly, we demonstrated for the first time that the ANXA2 promoter is hypomethylated in PC tissues compared to normal tissues which may result in ANXA2 overexpression in PC. However, more experimental research is required to corroborate our findings. [ABSTRACT FROM AUTHOR]

The emergence of antimicrobial resistance has prompted the search for antibiotic alternatives, and bacteriocins have emerged as promising alternatives to traditional antibiotics. Bacteriocins refer to a large family of antimicrobial peptides that are ribosomally synthesized by bacteria. The class of bacteriocins called leaderless bacteriocins is a unique group distinguished by the absence of a leader peptide and post-translational modifications, and hence their linear structures. Members of this class have been discovered by a traditional approach of screening bacterial isolates for antimicrobial activity and subsequently identifying the active molecule. This study presents the first extensive genome mining approach for leaderless bacteriocin discovery. In detail, we performed a precursor peptide-based genome mining to search for novel leaderless bacteriocins. Over 400 producer organisms with putative leaderless bacteriocins encoded in their genomes were identified, revealing the widespread occurrence of leaderless bacteriocins. Among the identified putative bacteriocins, 122 are unique peptide sequences. To validate our genome mining results, a novel bacteriocin we termed miticin, encoded in the genome of Streptococcus mitis, was obtained through chemical synthesis. Miticin was indeed found to be active, specifically against a wide range of Gram-positive bacteria. With this study, we provide the foundation for exploring a repertoire of potential novel antimicrobials identified through an innovative bioinformatics-guided approach, representing a significant leap compared to traditional screening and isolation methods for the discovery of bioactive molecules, specifically leaderless bacteriocins. [ABSTRACT FROM AUTHOR]

Lung adenocarcinoma (LUAD) has a malignant characteristic that is highly aggressive and prone to metastasis. There is still a lack of suitable biomarkers to facilitate the refinement of precision-based therapeutic regimens. We used a combination of 10 known clustering algorithms and the omics data from 4 dimensions to identify high-resolution molecular subtypes of LUAD. Subsequently, consensus machine learning-related prognostic signature (CMRS) was developed based on subtypes related genes and an integrated program framework containing 10 machine learning algorithms. The efficiency of CMRS was analyzed from the perspectives of tumor microenvironment, genomic landscape, immunotherapy, drug sensitivity, and single-cell analysis. In terms of results, through multi-omics clustering, we identified 2 comprehensive omics subtypes (CSs) in which CS1 patients had worse survival outcomes, higher aggressiveness, mRNAsi and mutation frequency. Subsequently, we developed CMRS based on 13 key genes up-regulated in CS1. The prognostic predictive efficiency of CMRS was superior to most established LUAD prognostic signatures. CMRS demonstrated a strong correlation with tumor microenvironmental feature variants and genomic instability generation. Regarding clinical performance, patients in the high CMRS group were more likely to benefit from immunotherapy, whereas low CMRS were more likely to benefit from chemotherapy and targeted drug therapy. In addition, we evaluated that drugs such as neratinib, oligomycin A, and others may be candidates for patients in the high CMRS group. Single-cell analysis revealed that CMRS-related genes were mainly expressed in epithelial cells. The novel molecular subtypes identified in this study based on multi-omics data could provide new insights into the stratified treatment of LUAD, while the development of CMRS could serve as a candidate indicator of the degree of benefit of precision therapy and immunotherapy for LUAD. [ABSTRACT FROM AUTHOR]

In the field of bioinformatics, amplicon sequencing of 16S rRNA genes has long been used to investigate community membership and taxonomic abundance in microbiome studies. As we can observe, shotgun metagenomics has become the dominant method in this field. This is largely owing to advancements in sequencing technology, which now allow for random sequencing of the entire genetic content of a microbiome. Furthermore, this method allows profiling both genes and the microbiome's membership. Although these methods have provided extensive insights into various microbiomes, they solely assess the existence of organisms or genes, without determining their active role within the microbiome. Microbiome scholarship now includes metatranscriptomics to decipher how a community of microorganisms responds to changing environmental conditions over a period of time. Metagenomic studies identify the microbes that make up a community but metatranscriptomics explores the diversity of active genes within that community, understanding their expression profile and observing how these genes respond to changes in environmental conditions. This expert review article offers a critical examination of the computational metatranscriptomics tools for studying the transcriptomes of microbial communities. First, we unpack the reasons behind the need for community transcriptomics. Second, we explore the prospects and challenges of metatranscriptomic workflows, starting with isolation and sequencing of the RNA community, then moving on to bioinformatics approaches for quantifying RNA features, and statistical techniques for detecting differential expression in a community. Finally, we discuss strengths and shortcomings in relation to other microbiome analysis approaches, pipelines, use cases and limitations, and contextualize metatranscriptomics as a tool for clinical metagenomics. [ABSTRACT FROM AUTHOR]

Rough‐and‐tumble play in juvenile rats and song in flocks of adult songbirds outside a breeding context (gregarious song) are two distinct forms of non‐sexual social behavior. Both are believed to play roles in the development of sociomotor skills needed for later life‐history events, including reproduction, providing opportunities for low‐stakes practice. Additionally, both behaviors are thought to be intrinsically rewarded and are associated with a positive affective state. Given the functional similarities of these behaviors, this study used RNA‐sequencing to identify commonalities in their underlying neurochemical systems within the medial preoptic area. This brain region is implicated in multiple social behaviors, including song and play, and is highly conserved across vertebrates. DESeq2 and rank–rank hypergeometric overlap analyses identified a shared neurotranscriptomic profile in adult European starlings singing high rates of gregarious song and juvenile rats playing at high rates. Transcript levels for several glutamatergic receptor genes, such as GRIN1, GRIN2A, and GRIA1, were consistently upregulated in highly gregarious (i.e., playful/high singing) animals. This study is the first to directly investigate shared neuromodulators of positive, non‐sexual social behaviors across songbirds and mammals. It provides insight into a conserved brain region that may regulate similar behaviors across vertebrates. [ABSTRACT FROM AUTHOR]

The homologous E6-AP carboxy-terminal structural domain (HECT) contained in E3 ubiquitin ligases (E3s) is a key factor in protein degradation and maintenance of cellular homeostasis in animals. However, the functional roles and evolutionary aspects of the HECT gene family in bivalve mussels remain unclear and warrant further investigation. In this study, we identified 22 HECT genes within the genome of Mytilus coruscus Gould, all containing a conserved HECT structural domain derived from dispersed repeats, distributed unevenly across 11 chromosomes. Phylogenetic analysis classified M. coruscus HECT genes into six major classes, with amino acid sequences within the same evolutionary clade displaying similar conserved motifs. Homology analysis with HECT genes of four bivalve species revealed that M. coruscus and Mytilus galloprovincialis possessed the largest number of homologous gene pairs, showing a significant correlation between the two in the evolution of the HECT gene family. Homology analysis with HECT genes of four bivalve species revealed that M. coruscus and M. galloprovincialis possessed the largest number of homologous gene pairs, showing a significant correlation between the two in the evolution of the HECT gene family. M. coruscus exhibited pronounced and specific expression in gills and blood tissues. Notably, Mco_UPL3 gene expression was significantly upregulated after 12 h of acute heat stress (33 °C) and 24 h of Vibrio injection (0.4 OD). Gene ontology analysis of the HECT genes in M. coruscus revealed that it is primarily enriched in protein modification and degradation functions. This suggests that HECT genes may play a key role in protein degradation and immunomodulation in M. coruscus. These findings offer valuable insights for the breeding of stress-tolerant traits in M. coruscus. In summary, our data shed light on the potential functions of HECT E3 ligases in response to heat stress and Vibrio infection, providing practical guidance for enhancing resilience through breeding in M. coruscus. [ABSTRACT FROM AUTHOR]

The rapid advancement of high-throughput technologies, particularly next-generation sequencing (NGS), has revolutionized cancer research by enabling the investigation of genetic variations such as SNPs, copy number variations, gene expression, and protein levels. These technologies have elevated the significance of precision oncology, creating a demand for biomarker identification and validation. This review explores the complex interplay of oncology, cancer biology, and bioinformatics tools, highlighting the challenges in statistical learning, experimental validation, data processing, and quality control that underpin this transformative field. This review outlines the methodologies and applications of bioinformatics tools in cancer genomics research, encompassing tools for data structuring, pathway analysis, network analysis, tools for analyzing biomarker signatures, somatic variant interpretation, genomic data analysis, and visualization tools. Open-source tools and repositories like The Cancer Genome Atlas (TCGA), Genomic Data Commons (GDC), cBioPortal, UCSC Genome Browser, Array Express, and Gene Expression Omnibus (GEO) have emerged to streamline cancer omics data analysis. Bioinformatics has significantly impacted cancer research, uncovering novel biomarkers, driver mutations, oncogenic pathways, and therapeutic targets. Integrating multi-omics data, network analysis, and advanced ML will be pivotal in future biomarker discovery and patient prognosis prediction. [ABSTRACT FROM AUTHOR]

Background. Alagille syndrome (ALGS) is a rare autosomal dominant genetic disease caused by pathogenic variants in two genes: Jagged Canonical Notch Ligand 1 (JAG1) and Notch Receptor 2 (NOTCH2). It is characterized by phenotypic variability and incomplete penetrance with multiorgan clinical signs. Methods. Using Next Generation Sequencing (NGS), we analyzed a panel of liver-disease-related genes in a population of 230 patients with cholestasis and hepatopathies. For the rare variants, bioinformatics predictions and pathogenicity classification were performed. Results. We identified eleven rare NOTCH2 variants in 10 patients, two variants being present in the same patient. Ten variants had never been described before in the literature. It was possible to classify only two null variants as pathogenic, whereas the most of variants were missense (8 out of 11) and were classified as uncertain significance variants (USVs). Among patients with ALGS suspicion, two carried null variants, two carried variants predicted to be pathogenic by bioinformatics, one carried a synonymous variant and variants in glycosylation-related genes, and two carried variants predicted as benign in the PEST domain. Conclusions. Our results increased the knowledge about NOTCH2 variants and the related phenotype, allowing us to improve the genetic diagnosis of ALGS. [ABSTRACT FROM AUTHOR]

Inosine is a nucleotide resulting from the deamination of adenosine in RNA. This chemical modification process, known as RNA editing, is typically mediated by a family of double-stranded RNA binding proteins named Adenosine Deaminase Acting on dsRNA (ADAR). While the presence of ADAR orthologs has been traced throughout the evolution of metazoans, the existence and extension of RNA editing have been characterized in a more limited number of animals so far. Undoubtedly, ADAR-mediated RNA editing plays a vital role in physiology, organismal development and disease, making the understanding of the evolutionary conservation of this phenomenon pivotal to a deep characterization of relevant biological processes. However, the lack of direct high-throughput methods to reveal RNA modifications at single nucleotide resolution limited an extended investigation of RNA editing. Nowadays, these methods have been developed, and appropriate bioinformatic pipelines are required to fully exploit this data, which can complement existing approaches to detect ADAR editing. Here, we review the current literature on the "bioinformatics for inosine" subject and we discuss future research avenues in the field. [ABSTRACT FROM AUTHOR]

Allopolyploidy in plants involves the merging of two or more distinct parental genomes into a single nucleus, a significant evolutionary process in the plant kingdom. Transcriptomic analysis provides invaluable insights into allopolyploid plants by elucidating the fate of duplicated genes, revealing evolutionary novelties and uncovering their environmental adaptations. By examining gene expression profiles, scientists can discern how duplicated genes have evolved to acquire new functions or regulatory roles. This process often leads to the development of novel traits and adaptive strategies that allopolyploid plants leverage to thrive in diverse ecological niches. Understanding these molecular mechanisms not only enhances our appreciation of the genetic complexity underlying allopolyploidy but also underscores their importance in agriculture and ecosystem resilience. However, transcriptome profiling is challenging due to genomic redundancy, which is further complicated by the presence of multiple chromosomes sets and the variations among homoeologs and allelic genes. Prior to transcriptome analysis, sub-genome phasing and homoeology inference are essential for obtaining a comprehensive view of gene expression. This review aims to clarify the terminology in this field, identify the most challenging aspects of transcriptome analysis, explain their inherent difficulties, and suggest reliable analytic strategies. Furthermore, bulk RNA-seq is highlighted as a primary method for studying allopolyploid gene expression, focusing on critical steps like read mapping and normalization in differential gene expression analysis. This approach effectively captures gene expression from both parental genomes, facilitating a comprehensive analysis of their combined profiles. Its sensitivity in detecting low-abundance transcripts allows for subtle differences between parental genomes to be identified, crucial for understanding regulatory dynamics and gene expression balance in allopolyploids. [ABSTRACT FROM AUTHOR]

Ammonium transporters (AMTs) are vital plasma membrane proteins facilitating NH4+ uptake and transport, crucial for plant growth. The identification of favorable AMT genes is the main goal of improving ammonium-tolerant algas. However, there have been no reports on the systematic identification and expression analysis of Chlamydomonas reinhardtii (C. reinhardtii) AMT genes. This study comprehensively identified eight CrAMT genes, distributed across eight chromosomes, all containing more than 10 transmembrane structures. Phylogenetic analysis revealed that all CrAMTs belonged to the AMT1 subfamily. The conserved motifs and domains of CrAMTs were similar to those of the AMT1 members of OsAMTs and AtAMTs. Notably, the gene fragments of CrAMTs are longer and contain more introns compared to those of AtAMTs and OsAMTs. And the promoter regions of CrAMTs are enriched with cis-elements associated with plant hormones and light response. Under NH4+ treatment, CrAMT1;1 and CrAMT1;3 were significantly upregulated, while CrAMT1;2, CrAMT1;4, and CrAMT1;6 saw a notable decrease. CrAMT1;7 and CrAMT1;8 also experienced a decline, albeit less pronounced. Transgenic algas with overexpressed CrAMT1;7 did not show a significant difference in growth compared to CC-125, while transgenic algas with CrAMT1;7 knockdown exhibited growth inhibition. Transgenic algas with overexpressed or knocked-down CrAMT1;8 displayed reduced growth compared to CC-125, which also resulted in the suppression of other CrAMT genes. None of the transgenic algas showed better growth than CC-125 at high ammonium levels. In summary, our study has unveiled the potential role of CrAMT genes in high-ammonium environments and can serve as a foundational research platform for investigating ammonium-tolerant algal species. [ABSTRACT FROM AUTHOR]

A genome of Rhodococcus rhodochrous IEGM 1362 was sequenced and annotated. This strain can transform monoterpene alcohol (–)-isopulegol with the formation of two novel pharmacologically promising metabolites. Nine genes encoding cytochrome P450, presumably involved in (–)-isopulegol transformation, were found in the genome of R. rhodochrous IEGM 1362. Primers and PCR conditions for their detection were selected. The obtained data can be used for the further investigation of genes encoding enzymes involved in monoterpene biotransformation. [ABSTRACT FROM AUTHOR]

Extranuclear localization of long noncoding RNAs (lncRNAs) is poorly understood. Based on machine learning evaluations, we propose a lncRNA-mitochondrial interaction pathway where polynucleotide phosphorylase (PNPase), through domains that provide specificity for primary sequence and secondary structure, binds nuclear-encoded lncRNAs to facilitate mitochondrial import. Using FVB/NJ mouse and human cardiac tissues, RNA from isolated subcellular compartments (cytoplasmic and mitochondrial) and cross-linked immunoprecipitate (CLIP) with PNPase within the mitochondrion were sequenced on the Illumina HiSeq and MiSeq, respectively. lncRNA sequence and structure were evaluated through supervised [classification and regression trees (CART) and support vector machines (SVM)] machine learning algorithms. In HL-1 cells, quantitative PCR of PNPase CLIP knockout mutants (KH and S1) was performed. In vitro fluorescence assays assessed PNPase RNA binding capacity and verified with PNPase CLIP. One hundred twelve (mouse) and 1,548 (human) lncRNAs were identified in the mitochondrion with Malat1 being the most abundant. Most noncoding RNAs binding PNPase were lncRNAs, including Malat1. lncRNA fragments bound to PNPase compared against randomly generated sequences of similar length showed stratification with SVM and CART algorithms. The lncRNAs bound to PNPase were used to create a criterion for binding, with experimental validation revealing increased binding affinity of RNA designed to bind PNPase compared to control RNA. The binding of lncRNAs to PNPase was decreased through the knockout of RNA binding domains KH and S1. In conclusion, sequence and secondary structural features identified by machine learning enhance the likelihood of nuclear-encoded lncRNAs binding to PNPase and undergoing import into the mitochondrion. NEW & NOTEWORTHY: Long noncoding RNAs (lncRNAs) are relatively novel RNAs with increasingly prominent roles in regulating genetic expression, mainly in the nucleus but more recently in regions such as the mitochondrion. This study explores how lncRNAs interact with polynucleotide phosphorylase (PNPase), a protein that regulates RNA import into the mitochondrion. Machine learning identified several RNA structural features that improved lncRNA binding to PNPase, which may be useful in targeting RNA therapeutics to the mitochondrion. [ABSTRACT FROM AUTHOR]

Per- and polyfluoroalkyl substances (PFASs) are a family of "forever chemicals" including perfluorooctane sulfonate (PFOS). These toxic chemicals do not break down in the environment or in our bodies. In the human body, PFOS and perfluoroctanoic acid (PFOA) have a half-life (T1/2) of about 4–5 yr so low daily consumption of these chemicals can accumulate in the human body to a harmful level over a long period. Although the use of PFOS in consumer products was banned in the United States in 2022/2023, this forever chemical remains detectable in our tap water and food products. Every American tested has a high level of PFAS in their blood (https://cleanwater.org/pfas-forever-chemicals). In this report, we used a Sertoli cell blood-testis barrier (BTB) model with primary Sertoli cells cultured in vitro with an established functional tight junction (TJ)-permeability barrier that mimicked the BTB in vivo. Treatment of Sertoli cells with PFOS was found to perturb the TJ-barrier, which was the result of cytoskeletal disruption across the cell cytoplasm, disrupting actin and microtubule polymerization. These changes thus affected the proper localization of BTB-associated proteins at the BTB. Using RNA-Seq transcriptome profiling, bioinformatics analysis, and pertinent biochemical and cell biology techniques, it was discovered that PFOS -induced Sertoli cell toxicity through the c-Jun N-terminal kinase (JNK; also known as stress-activated protein kinase, SAPK) and its phosphorylated/active form p-JNK signaling pathway. More importantly, KB-R7943 mesylate (KB), a JNK/p-JNK activator, was capable of blocking PFOS-induced Sertoli cell injury, supporting the notion that PFOS-induced cell injury can possibly be therapeutically managed. NEW & NOTEWORTHY: PFOS induces Sertoli cell injury, including disruption of the 1) blood-testis barrier function and 2) cytoskeletal organization, which, in turn, impedes male reproductive function. These changes are mediated by JNK/p-JNK signaling pathway. However, the use of KB-R7943, a JNK/p-JNK activator was capable of blocking PFOS-induced Sertoli cell injury, supporting the possibility of therapeutically managing PFOS-induced reproductive dysfunction. [ABSTRACT FROM AUTHOR]

Amplicon sequencing is an effective and increasingly applied method for studying viral communities in the environment. Here, we present vAMPirus, a user‐friendly, comprehensive, and versatile DNA and RNA virus amplicon sequence analysis program, designed to support investigators in exploring virus amplicon sequencing data and running informed, reproducible analyses. vAMPirus intakes raw virus amplicon libraries and, by default, performs nucleotide‐ and amino acid‐based analyses to produce results such as sequence abundance information, taxonomic classifications, phylogenies and community diversity metrics. The vAMPirus analytical framework leverages 16 different opensource tools and provides optional approaches that can increase the ratio of biological signal‐to‐noise and thereby reveal patterns that would have otherwise been masked. Here, we validate the vAMPirus analytical framework and illustrate its implementation as a general virus amplicon sequencing workflow by recapitulating findings from two previously published double‐stranded DNA virus datasets. As a case study, we also apply the program to explore the diversity and distribution of a coral reef‐associated RNA virus. vAMPirus is streamlined within Nextflow, offering straightforward scalability, standardization and communication of virus lineage‐specific analyses. The vAMPirus framework is designed to be adaptable; community‐driven analytical standards will continue to be incorporated as the field advances. vAMPirus supports researchers in revealing patterns of virus diversity and population dynamics in nature, while promoting study reproducibility and comparability. [ABSTRACT FROM AUTHOR]

Inhibiting breast cancer stem cell (BCSC) signaling pathways is a strategic method for successfully treating breast cancer. Nobiletin (NOB) is a compound widely found in orange peel that exhibits a toxic effect on various types of cancer cells, and inhibits the signaling pathways that regulate the properties of BCSCs; however, the effects of NOB on BCSCs remain elusive. The purpose of this study was to determine the target genes of NOB for inhibiting BCSCs using in vitro three-dimensional breast cancer cell culture (mammospheres) and in silico approaches. We combined in vitro experiments to develop mammospheres and conducted cytotoxicity, next-generation sequencing, and bioinformatics analyses, such as gene ontology, the Reactome pathway enrichment, network topology, gene set enrichment analysis, hub genes selection, genetic alterations, prognostic value related to the mRNA expression, and mRNA and protein expression of potential NOB target genes that inhibit BCSCs. Here, we show that NOB inhibited BCSCs in mammospheres from MCF-7 cells. We also identified CDC6, CHEK1, BRCA1, UCHL5, TOP2A, MTMR4, and EXO1 as potential NOB targets inhibiting BCSCs. NOB decreased G0/G1, but increased the G2/M cell population. These findings showed that NOB is a potential therapeutic candidate for BCSCs treatment by regulating cell cycle. [ABSTRACT FROM AUTHOR]

Background: N6‐methyladenosine (m6A) has been identified as the most abundant modification of RNA molecules and the aberrant m6A modifications have been associated with the development of autoimmune diseases. However, the role of m6A modification in ankylosing spondylitis (AS) has not been adequately investigated. Therefore, we aimed to explore the significance of m6A regulator‐mediated RNA methylation in AS. Methods: The methylated RNA immunoprecipitation sequencing (meRIP‐seq) and digital RNA sequencing (Digital RNA‐seq) were conducted using the peripheral blood mononuclear cells from three AS cases and three healthy controls, to identify genes affected by abnormal RNA methylation. The genes associated with different peaks were cross‐referenced with AS‐related genes obtained from the GeneCards Suite. Subsequently, the expression levels of shared differentially expressed genes (DEGs) and key m6A regulators in AS were evaluated using data from 68 AS cases and 36 healthy controls from two data sets (GSE25101 and GSE73754). In addition, the results were validated through quantitative polymerase chain reaction (qPCR). Results: The meRIP‐seq and Digital RNA‐seq analyses identified 28 genes with upregulated m6A peaks but with downregulated expression, and 52 genes with downregulated m6A peaks but with upregulated expression. By intersecting the genes associated with different peaks with 2184 AS‐related genes from the GeneCards Suite, we identified a total of five shared DEGs: BCL11B, KAT6B, IL1R1, TRIB1, and ALDH2. Through analysis of the data sets and qPCR, we found that BCL11B and IL1R1 were differentially expressed in AS. Moreover, two key m6A regulators, WTAP and heterogeneous nuclear ribonucleoprotein C, were identified. Conclusions: In conclusion, the current study revealed that m6A modification plays a crucial role in AS and might hence provide a new treatment strategy for AS disease. [ABSTRACT FROM AUTHOR]

Background: Psoriasis is an immune‐mediated skin disease, closely related to immune regulation. The aim was to understand the pathogenesis of psoriasis further, reveal potential therapeutic targets, and provide new clues for its diagnosis, treatment, and prevention. Materials and methods: Expression profiling data were obtained from the Gene Expression Omnibus (GEO) database for skin tissues from healthy population and psoriasis patients. Differentially expressed genes (DEGs) were selected for Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) analysis separately. Machine learning algorithms were used to obtain characteristic genes closely associated with psoriasis. Receiver operating characteristic (ROC) curve was used to assess the diagnostic value of the characteristic genes for psoriasis. The Cell‐type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was used to calculate the proportion of immune cell infiltration. Correlation analysis was used to characterize the connection between gene expression and immune cell, Psoriasis Area and Severity Index (PASI). Results: A total of 254 DEGs were identified in the psoriasis group, including 185 upregulated and 69 downregulated genes. GO was mainly enriched in cytokine‐mediated signaling pathway, response to virus, and cytokine activity. KEGG was mainly focused on cytokine‐cytokine receptor interaction and IL‐17 signaling pathway. GSEA was mainly in chemokine signaling pathway and cytokine‐cytokine receptor interaction. The machine learning algorithm screened nine characteristic genes C10orf99, GDA, FCHSD1, C12orf56, S100A7, INA, CHRNA9, IFI44, and CXCL9. In the validation set, the expressions of these nine genes increased in the psoriasis group, and the AUC values were all > 0.9, consistent with those of the training set. The immune infiltration results showed increased proportions of macrophages, T cells, and neutrophils in the psoriasis group. The characteristic genes were positively or negatively correlated to varying degrees with T cells and macrophages. Nine characteristic genes were highly expressed in the moderate to severe psoriasis group and positively correlated with PASI scores. Conclusion: High levels of nine characteristic genes C10orf99, GDA, FCHSD1, C12orf56, S100A7, INA, CHRNA9, IFI44, and CXCL9 were risk factors for psoriasis, the differential expression of which was related to the regulation of immune system activity and PASI scores, affecting the proportions of different immune cells and promoting the occurrence and development of psoriasis. [ABSTRACT FROM AUTHOR]

Background: Dermatomyositis (DM) is a kind of dermatologically associated autoimmune disease that is notably associated with an increased risk of concurrent malignancies, although the underlying mechanisms remain to be fully elucidated. The purpose of this investigation was to examine the immunological parallels between DM and nasopharyngeal carcinoma (NPC), with the aim of identifying pivotal biomarkers that could facilitate a deeper understanding and enhance the predictive capabilities of NPC in DM patients. Method: Data for DM and NPC were sourced from the Gene Expression Omnibus (GEO) database. Immune infiltration was analyzed using the "cibersort" R package, differentially expressed genes (DEGs) were identified with the "limma" package, and functional pathways were investigated through Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses. Characteristic genes were determined by Utilizing Protein–Protein Interaction (PPI) and Least Absolute Shrinkage and Selection Operator (LASSO), and their features were validated using the GSE53819 dataset. Results: In comparison to normal samples, significant infiltration of macrophage M1 was observed in both DM and NPC. The analysis revealed 77 DEGs in DM and 1051 DEGs in NPC, with 22 genes found to be co‐DEGs. Following PPI and LASSO analysis, six distinctive genes were retained. Notably, CCL8, IFIH1, CXCL10, and CXCL11 exhibited optimal diagnostic efficacy for NPC and displayed significant correlation with macrophage M1 infiltration within the carcinoma. Conclusion: Four characteristic genes, CCL8, IFIH1, CXCL10, and CXCL11 are risk factors for both DM and NPC. They exhibit a robust correlation with the incidence of NPC and offer a commendable diagnostic efficacy. Furthermore, they may serve as prospective predictive biomarkers for the emergence of NPC in DM. [ABSTRACT FROM AUTHOR]

The patterns and biological functions of copper homeostasis-related genes (CHRGs) in acute myeloid leukemia (AML) remain unclear. We explored the patterns and biological functions of CHRGs in AML. Using independent cohorts, including TCGA-GTEx, GSE114868, GSE37642, and clinical samples, we identified 826 common differentially expressed genes. Specifically, 12 cuproptosis-related genes (e.g., ATP7A, ATP7B) were upregulated, while 17 cuproplasia-associated genes (e.g., ATOX1, ATP7A) were downregulated in AML. We used LASSO-Cox, Kaplan-Meier, and Nomogram analyses to establish prognostic risk models, effectively stratifying patients with AML into high- and low-risk groups. Subgroup analysis revealed that high-risk patients exhibited poorer overall survival and involvement in fatty acid metabolism, apoptosis, and glycolysis. Immune infiltration analysis indicated differences in immune cell composition, with notable increases in B cells, cytotoxic T cells, and memory T cells in the low-risk group, and increased monocytes and neutrophils in the high-risk group. Single-cell sequencing analysis corroborated the expression characteristics of critical CHRGs, such as MAPK1 and ATOX1, associated with the function of T, B, and NK cells. Drug sensitivity analysis suggested potential therapeutic agents targeting copper homeostasis, including Bicalutamide and Sorafenib. PCR validation confirmed the differential expression of 4 cuproptosis-related genes (LIPT1, SLC31A1, GCSH, and PDHA1) and 9 cuproplasia-associated genes (ATOX1, CCS, CP, MAPK1, SOD1, COA6, PDK1, DBH, and PDE3B) in AML cell line. Importantly, these genes serve as potential biomarkers for patient stratification and treatment. In conclusion, we shed light on the expression patterns and biological functions of CHRGs in AML. The developed risk models provided prognostic implications for patient survival, offering valuable information on the regulatory characteristics of CHRGs and potential avenues for personalized treatment in AML. [ABSTRACT FROM AUTHOR]

Annotating cell types of single-cell RNA sequencing (scRNA-seq) data is crucial for studying cellular heterogeneity in the tumor microenvironment. Recently, large-scale pre-trained language models (PLMs) have achieved significant progress in cell-type annotation of scRNA-seq data. This approach effectively addresses previous methods' shortcomings in performance and generalization. However, fine-tuning PLMs for different downstream tasks demands considerable computational resources, rendering it impractical. Hence, a new research branch introduces parameter-efficient fine-tuning (PEFT). This involves optimizing a few parameters while leaving the majority unchanged, leading to substantial reductions in computational expenses. Here, we utilize scBERT, a large-scale pre-trained model, to explore the capabilities of three PEFT methods in scRNA-seq cell type annotation. Extensive benchmark studies across several datasets demonstrate the superior applicability of PEFT methods. Furthermore, downstream analysis using models obtained through PEFT showcases their utility in novel cell type discovery and model interpretability for potential marker genes. Our findings underscore the considerable potential of PEFT in PLM-based cell type annotation, presenting novel perspectives for the analysis of scRNA-seq data. • Employ scBERT to investigate three parameter-efficient fine-tuning (PEFT) methods. • Investigation of the PEFT methods in discovering new cell types. • Exploration of the interpretability of models fine-tuned using PEFT methods. [ABSTRACT FROM AUTHOR]

Objectives: This study aimed to identify the optimal protein construction for designing a multiepitope vaccine with both prophylactic and therapeutic effects against cervical cancer, utilizing an immunoinformatics approach. The construction process involved using capsid epitopes L1 and L2, as well as oncoproteins E5, E6, and E7 from human papillomavirus (HPV) types 16 and 18. Methods: An experimental in silico analysis with an immunoinformatics approach was used to develop 2 multi-epitope vaccine constructs (A and B). Further analysis was then conducted to compare the constructs and select the one with the highest potential against cervical cancer. Results: This study produced 2 antigenic, non-allergenic, and nontoxic multi-epitope vaccine constructs (A and B), which exhibited the ideal physicochemical properties for a vaccine. Further analysis revealed that construct B effectively induced both cellular and humoral immune responses. Conclusion: The multi-epitope vaccine construct B for HPV 16 and 18, designed for both prophylactic and therapeutic purposes, met the development criteria for a cervical cancer vaccine. However, these findings need to be validated through in vitro and in vivo experiments. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Cancer Biotherapy is the property of Editorial Office of Chinese Journal of Cancer Biotherapy and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Cancer Biotherapy is the property of Editorial Office of Chinese Journal of Cancer Biotherapy and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Deep learning techniques have recently yielded remarkable results across various fields. However, the quality of these results depends heavily on the quality and quantity of data used during the training phase. One common issue in multi-class and multi-label classification is class imbalance, where one or several classes make up a substantial portion of the total instances. This imbalance causes the neural network to prioritize features of the majority classes during training, as their detection leads to higher scores. In the context of object detection, two types of imbalance can be identified: (1) an imbalance between the space occupied by the foreground and background and (2) an imbalance in the number of instances for each class. This paper aims to address the second type of imbalance without exacerbating the first. To achieve this, we propose a modification of the copy-paste data augmentation technique, combined with weight-balancing methods in the loss function. This strategy was specifically tailored to improve the performance in datasets with a high instance density, where instance overlap could be detrimental. To validate our methodology, we applied it to a highly unbalanced dataset focused on nuclei detection. The results show that this hybrid approach improves the classification of minority classes without significantly compromising the performance of majority classes. [ABSTRACT FROM AUTHOR]

Immune response is pivotal in the secondary injury of spinal cord injury (SCI). Polarization of macrophages (MΦ) influences the immune response in the secondary injury, which is regulated by several immune-related proteins. M2Φ plays the immunomodulatory role in the central nervous system. This study used bioinformatic analysis and machine algorithms to screen hub immune-related proteins after SCI and experimentally investigate the role of the target protein in the M2Φ polarization and immunomodulation in rats and in vitro after SCI. We downloaded GSE151371 and GSE45006, hub immune-related genes were screened using machine learning algorithms, and the expression of S100A9 was verified by datasets. Allen's weight-drop injury SCI model in Sprague–Dawley rat and bone marrow-derived rat MΦ with myelin debris model were used to study the effects of S100A9 on M2Φ polarization and immunomodulation at the lesion site and in vitro. Bioinformatic analysis showed that S100A9 acts as a hub immune-related gene in the SCI patients and rats. S100A9 increased at the lesion site in SCI rats, and its inhibition reduced CD206 and ARG-1 expression. Exogenous S100A9 promoted CD206 and ARG-1 expression in MΦ. S100A9 also increased the expression of PD-L1 and decreased MHC II at the lesion site in SCI rats and MΦ with myelin debris, and enhanced mitochondrial activity in rat MΦ with myelin debris. In conclusion, S100A9 is an indispensable factor in the immune process in secondary injury following SCI. [ABSTRACT FROM AUTHOR]

Evidence suggests that herpes virus infection is associated with an increased risk of Alzheimer's disease (AD), and innate and adaptive immunity plays an important role in the association. Although there have been many studies, the mechanism of the association is still unclear. This study aims to reveal the underlying molecular and immune regulatory network through multi-omics data and provide support for the study of the mechanism of infection and AD in the future. Here, we found that the herpes virus infection significantly increased the risk of AD. Genes associated with the occurrence and development of AD and genetically regulated by herpes virus infection are mainly enrichment in immune-related pathways. The 22 key regulatory genes identified by machine learning are mainly immune genes. They are also significantly related to the infiltration changes of 3 immune cell in AD. Furthermore, many of these genes have previously been reported to be linked, or potentially linked, to the pathological mechanisms of both herpes virus infection and AD. In conclusion, this study contributes to the study of the mechanisms related to herpes virus infection and AD, and indicates that the regulation of innate and adaptive immunity may be an effective strategy for preventing and treating herpes virus infection and AD. Additionally, the identified key regulatory genes, whether previously studied or newly discovered, may serve as valuable targets for prevention and treatment strategies. [ABSTRACT FROM AUTHOR]

Despite occupying only a small portion of the Earth's surface, tropical rainforests are some of the most diverse ecosystems on the planet. Given the hyperdiversity of tropical rainforests, these ecosystems are considered essential for the preservation of global biodiversity. However, many of them are located in economically marginalized countries, which frequently suffer from uncontrolled exploitation of their natural resources. In this sense, biodiversity information can help in planning conservation strategies and designing protection measures. Despite this, recording biodiversity is a challenging and time-consuming task that usually requires the involvement of important financial and manpower resources. So, any strategy to facilitate or accelerate this labor could be of significant interest for ecological and conservation purposes. In this review, we highlight why we consider that routinely incorporating metabarcoding strategies can facilitate biodiversity surveillance in tropical rainforests. As insects are key players in tropical ecosystems and are a threatened, hyperdiverse group, here we used them to exemplify the main challenges that the implementation of metabarcoding approaches faces in these complex environments, discussing some methodological obstacles for their implementation. We also emphasize the significant gap between the number of published studies using metabarcoding for biodiversity monitoring in tropical regions with respect to other (often less diverse) regions of the planet. We consider that acknowledging and addressing this disparity will encourage the adoption of these strategies in tropical ecosystems, as these regions are in need—possibly more than any other—of innovative approaches to assess their diversity. [ABSTRACT FROM AUTHOR]

The obvious degeneration of articular cartilage occurs in the late stage of osteonecrosis of the femoral head (ONFH), which aggravates the condition of ONFH. This study aimed to demonstrate aberrant activation of autophagy processes in ONFH chondrocytes through bioinformatics and to predict and identify relevant hub genes and pathways. Differentially expressed genes (DEGs) were identified using R software in the GSE74089 dataset from the GEO database. DEGs were crossed with the Human Autophagy Database (HADb) autophagy genes to screen out autophagy-related differential genes (AT-DEGs). GSEA, GSVA, GO, and KEGG pathway enrichment analyses of AT-DEGs were performed. The STRING database was used to analyze the protein-protein interaction (PPI) of the AT-DEGs network, and the MCODE and CytoHubba plugin in the Cytoscape software was used to analyze the key gene cluster module and screen the hub genes. The PPI network of hub genes was constructed using the GeneMANIA database, and functional enrichment and gene connectivity categories were analyzed. The expression levels of hub genes of related genes in the ONFH patients were verified in the dataset GSE123568, and the protein expression was verified by immunohistochemistry in tissues. The analysis of DEGs revealed abnormal autophagy in ONFH cartilage. AT-DEGs in ONFH have special enrichment in macroautophagy, autophagosome membrane, and phosphatidylinositol-3-phosphate binding. In the GSE123568 dataset, it was also found that ATG2B, ATG4B, and UVRAG were all significantly upregulated in ONFH patients. By immunohistochemistry, it was verified that ATG2B, ATG4B, and UVRAG were significantly overexpressed. These three genes regulate the occurrence and extension of autophagosomes through the PI3KC3C pathway. Finally, we determined that chondrocytes in ONFH undergo positive regulation of autophagy through the corresponding pathways involved in three genes: ATG2B, ATG4B, and UVRAG. [ABSTRACT FROM AUTHOR]

Phytochelatins (PCs) are small cysteine-rich peptides involved in metal detoxification, not genetically encoded but enzymatically synthesized by phytochelatin synthases (PCSs) starting from glutathione. The constitutive PCS expression even in the absence of metal contamination, the wide phylogenetic distribution and the similarity between PCSs and the papain-type cysteine protease catalytic domain suggest a wide range of functions for PCSs. These proteins, widely studied in land plants, have not been fully analyzed in algae and cyanobacteria, although these organisms are the first to cope with heavy-metal stress in aquatic environments and can be exploited for phytoremediation. To fill this gap, we compared the features of the PCS proteins of different cyanobacterial and algal taxa by phylogenetic linkage. The analyzed sequences fall into two main, already known groups of PCS-like proteins. Contrary to previous assumptions, they are not classed as prokaryotic and eukaryotic sequences, but rather as sequences characterized by the alternative presence of asparagine and aspartic/glutamic acid residues in proximity of the catalytic cysteine. The presence of these enzymes with peculiar features suggests differences in their post-translational regulation related to cell/environmental requirements or different cell functions rather than to differences due to their belonging to different phylogenetic taxa. [ABSTRACT FROM AUTHOR]

Casparian strip membrane domain proteins (CASPs), regulating the formation of Casparian strips in plants, serve crucial functions in facilitating plant growth, development, and resilience to abiotic stress. However, little research has focused on the characteristics and functions of AhCASPs in cultivated peanuts. In this study, the genome-wide identification and expression analysis of the AhCASPs gene family was performed using bioinformatics and transcriptome data. Results showed that a total of 80 AhCASPs members on 20 chromosomes were identified and divided into three subclusters, which mainly localized to the cell membrane. Ka/Ks analysis revealed that most of the genes underwent purifying selection. Analysis of cis elements suggested the possible involvement of AhCASPs in hormonal and stress responses, including GA, MeJA, IAA, ABA, drought, and low temperature. Moreover, 20 different miRNAs for 37 different AhCASPs genes were identified by the psRNATarget service. Likewise, transcriptional analysis revealed key AhCASPs responding to various stresses, hormonal processing, and tissue types, including 33 genes in low temperature and drought stress and 41 genes in tissue-specific expression. These results provide an important theoretical basis for the functions of AhCASPs in growth, development, and multiple stress resistance in cultivated peanuts. [ABSTRACT FROM AUTHOR]

Polyphenol oxidase (PPO) is a common metalloproteinase in plants with important roles in plant responses to abiotic and biotic stresses. There is evidence that PPOs contribute to stress responses in Paulownia fortunei. In this study, PPO gene family members in P. fortunei were comprehensively identified and characterized using bioinformatics methods as well as analyses of phylogenetic relationships, gene and protein structure, codon usage bias, and gene expression in response to stress. The genome contained 10 PPO gene family members encoding 406–593 amino acids, with a G/C bias. Most were localized in chloroplasts. The motif structure was conserved among family members, and α-helices and random coils were the dominant elements in the secondary structure. The promoters contained many cis-acting elements, such as auxin, gibberellin, salicylic acid, abscisic acid, and photoresponsive elements. The formation of genes in this family was linked to evolutionary events, such as fragment replication. Real-time quantitative PCR results showed that PfPPO7, PfPPO10, PfPPO1, PfPPO2, PfPPO3, PfPPO4, PfPPO5, and PfPPO8 may be key genes in drought stress resistance. PfPPO1, PfPPO3, PfPPO4, and PfPPO10 were resistant stress-sensitive genes. These results provide a reliable basis for fully understanding the potential functions of these genes and the selection of resistance breeding. [ABSTRACT FROM AUTHOR]

Background: NR4A family genes play crucial roles in cancers. However, the role of NR4A family genes in cancers remains paradoxical as they promote or suppress tumorigenesis. Objective: We aimed to conduct comprehensive analyses of the association between the expression of NR4A family genes and tumor microenvironment (TME) based on bioinformatics methods. Methods: We collected RNA-seq data from 33 cancer types and 20 normal tissue sites from the TCGA and GTEx databases. Expression patterns of NR4A family genes and their associations with DNA methylation, miRNA, overall survival, drug responses, and tumor microenvironment were investigated. Results: Significant downregulation of all NR4A family genes was observed in 15 cancer types. DNA promoter methylation and expression of NR4A family genes were negatively correlated in five cancers. The expression of 10 miRNAs targeting NR4A family genes was negatively correlated with the expression of NR4A family genes. High expression of all NR4A family genes was associated with poor prognosis in stomach adenocarcinoma and increased expressions of NR4A2 and NR4A3 were associated with poor prognosis in adrenocortical carcinoma. In addition, we found an elevated expression of NR4A2, which enhances the response to various chemotherapeutic drugs, whereas NR4A3 decreases drug sensitivity. Interestingly, in breast cancer, NR4A3 was significantly associated with C2 (IFN-γ dominant), C3 (inflammatory), and C6 (TGF-β dominant) immune subtypes and infiltrated immune cell types, implying both oncogenic and tumor-suppressive functions of NR4A3 in breast cancer. Conclusion: The NR4A family genes have the potential to serve as a diagnostic, prognostic, and immunological marker of human cancers. [ABSTRACT FROM AUTHOR]

Elevated activity of retrotransposons is increasingly recognized to be implicated in a wide range of neurodegenerative and neurodevelopmental diseases, including Down syndrome (DS), which is the most common chromosomal condition causing intellectual disability globally. Previous research by our group has revealed that treatment with lamivudine, a reverse transcriptase inhibitor, improved neurobehavioral phenotypes and completely rescued hippocampaldependent recognition memory in a DS mouse model, Ts65Dn. We hypothesized that retrotransposition rates would increase in the Ts65Dn mouse model, and lamivudine could block retrotransposons. We analyzed the differentially expressed long interspersed element-1 (LINE-1 or L1) mapping on MMU16 and 17, and showed for the first time that retrotransposition could be associated with Ts65Dn's pathology, as misregulation of L1 was found in brain tissues associated with trisomy. In the cerebral cortex, 6 out of 26 upregulated L1s in trisomic treated mice were located in the telomeric region of MMU16 near Ttc3, Kcnj6, and Dscam genes. In the hippocampus, one upregulated L1 element in trisomic treated mice was located near the Fgd4 gene on MMU16. Moreover, two downregulated L1s rescued after treatment with lamivudine were located in the intronic region of Nrxn1 (MMU17) and Snhg14 (MMU7), implicated in a variety of neurodegenerative disorders. To gain further insight into the mechanism of this improvement, we here analyzed the gene expression profile in the hippocampus and cerebral cortex of trisomic mice treated and no-treated with lamivudine compared to their wild-type littermates. We found that treatment with lamivudine rescued the expression of 24% of trisomic genes in the cortex (located on mouse chromosome (MMU) 16 and 17) and 15% in the hippocampus (located in the human chromosome 21 orthologous regions), with important DS candidate genes such as App and Ets2, rescued in both regions. [ABSTRACT FROM AUTHOR]

Roses, a popular ornamental crop, often face various abiotic stresses during growth and development, such as cold, drought, and salinity. Rosa multiflora is a commonly used rootstock and exhibits strong resistance to both biotic and abiotic stresses, making it an ideal material for studying mechanisms for resistance. Among the largest plant families, MYB transcription factors play a crucial role in plant abiotic stresses. Our previous research has indicated that RmMYB44 could be involved in the low-temperature response of R. multiflora. This study further investigated RmMYB44, revealing that its expression levels were upregulated in response to chilling, drought, and salt stress. The results suggested its potential role as a key transcription factor in plant resistance to abiotic stresses. Additionally, RmMYB44 encoded a nuclear-localized protein without the self-activating function. The overexpression of RmMYB44 in tobacco plants enhanced the resistance to cold, drought, and salt stresses, as evidenced by the improved growth compared to wild-type (WT) plants under conditions of 4 °C, 30% water-holding capacity, and 200 mM of NaCl, respectively. Moreover, in overexpression tobacco plants, the levels of hydrogen peroxide and malondialdehyde (MDA) were significantly reduced; and the activities of superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT); as well as the proline content and the expression levels of NtPOD, NtCAT, and NtCBF; were significantly elevated under abiotic stresses. We assumed that the resistance to abiotic stress in plants conferred by RmMYB44 was associated with the regulation of cell membrane integrity. This study aimed to elucidate the role of the RmMYB44 gene in the resistance mechanism of R. multiflora against abiotic stress, thereby providing a candidate gene for the molecular breeding of abiotic stress resistance in roses and related species. [ABSTRACT FROM AUTHOR]

The serine protease inhibitor clade E member 1 (SERPINE1) is a key modulator of the plasminogen/plasminase system and has been demonstrated to promote tumor progression and metastasis in various tumours. However, although much literature has explored the cancer‐promoting mechanism of SERPINE1, the pan‐cancer analyses of its predictive value and immune response remain unexplored. The differential expression, and survival analysis of SERPINE1 expression in multiple cancers were analysed using The Cancer Genome Atlas and Genotype‐Tissue Expression database. Kaplan–Meier (K–M) plotter and survival data analysis were used to analyze the prognostic value of SERPINE1 expression, including overall survival (OS), disease‐specific survival, disease‐free interval and progression‐free interval and investigated the relationship of SERPINE1 expression with microsatellite instability. We further analysed the correlation between the expression of SERPINE1 and immune infiltration. The Kyoto Encyclopaedia of Genes and Genomes pathway was used for enrichment analysis, and the Gene Set Enrichment Analysis (GSEA) database was used to perform pathway analysis. Finally, in vitro experiments demonstrated that knockdown or overexpression of SERPINE1 could alter the proliferation and migration of gastric cancer (GC) cells. The results indicated that SERPINE1 expression levels different significantly between cancer and normal tissues, meanwhile, it was highly expressed in various cancers. By analysing online data, it has been observed that the gene SERPINE1 exhibits heightened expression levels across a variety of human cancers, significantly impacting patient survival rates. Notably, the presence of SERPINE1 was strongly associated with decrease OS and disease‐free survival in individuals diagnosed with GC. Furthermore, an observed link indicates that higher levels of SERPINE expression are associated with increased infiltration of immune cells in GC. Finally, in vitro experiments showed that knockdown or overexpression of SERPINE1 inhibited the growth, and migration, of GC cells. SERPINE1expression potentially represents a novel prognostic biomarker due to its significant association with immune cell infiltration in GC. This study shows that SERPINE1 is an oncogene that participates in regulating the immune infiltration and affecting the prognosis of patients in multiple cancers, especially in GC. These findings underscore the importance of further investigating the role of SERPINE1 in cancer progression and offer a promising direction for the development of new therapeutic strategies. [ABSTRACT FROM AUTHOR]

Insulin deficiency in patients with type 2 diabetes mellitus (T2D) is associated with beta-cell dysfunction, a condition increasingly recognized to involve processes such as dedifferentiation and apoptosis. Moreover, emerging research points to a potential role for ferroptosis in the pathogenesis of T2D. In this study, we aimed to investigate the potential involvement of ferroptosis in the dedifferentiation of beta cells in T2D. We performed single-cell RNA sequencing analysis of six public datasets. Differential expression and gene set enrichment analyses were carried out to investigate the role of ferroptosis. Gene set variation and pseudo-time trajectory analyses were subsequently used to verify ferroptosis-related beta clusters. After cells were categorized according to their ferroptosis and dedifferentiation scores, we constructed transcriptional and competitive endogenous RNA networks, and validated the hub genes via machine learning and immunohistochemistry. We found that ferroptosis was enriched in T2D beta cells and that there was a positive correlation between ferroptosis and the process of dedifferentiation. Upon further analysis, we identified two beta clusters that presented pronounced features associated with ferroptosis and dedifferentiation. Several key transcription factors and 2 long noncoding RNAs (MALAT1 and MEG3) were identified. Finally, we confirmed that ferroptosis occurred in the pancreas of high-fat diet-fed mice and identified 4 proteins (NFE2L2, CHMP5, PTEN, and STAT3) that may participate in the effect of ferroptosis on dedifferentiation. This study helps to elucidate the interplay between ferroptosis and beta-cell health and opens new avenues for developing therapeutic strategies to treat diabetes. [ABSTRACT FROM AUTHOR]

Plant genomics and crop breeding are at the intersection of biotechnology and information technology. Driven by a combination of high-throughput sequencing, molecular biology and data science, great advances have been made in omics technologies at every step along the central dogma, especially in genome assembling, genome annotation, epigenomic profiling, and transcriptome profiling. These advances further revolutionized three directions of development. One is genetic dissection of complex traits in crops, along with genomic prediction and selection. The second is comparative genomics and evolution, which open up new opportunities to depict the evolutionary constraints of biological sequences for deleterious variant discovery. The third direction is the development of deep learning approaches for the rational design of biological sequences, especially proteins, for synthetic biology. All three directions of development serve as the foundation for a new era of crop breeding where agronomic traits are enhanced by genome design. [ABSTRACT FROM AUTHOR]

The vast array of omics data in microbiology presents significant opportunities for studying bacterial pathogenesis and creating computational tools for predicting pathogenic potential. However, the field lacks a comprehensive, curated resource that catalogs bacterial strains and their ability to cause human infections. Current methods for identifying pathogenicity determinants often introduce biases and miss critical aspects of bacterial pathogenesis. In response to this gap, we introduce BacSPaD (Bacterial Strains' Pathogenicity Database), a thoroughly curated database focusing on pathogenicity annotations for a wide range of high-quality, complete bacterial genomes. Our rule-based annotation workflow combines metadata from trusted sources with automated keyword matching, extensive manual curation, and detailed literature review. Our analysis classified 5502 genomes as pathogenic to humans (HP) and 490 as non-pathogenic to humans (NHP), encompassing 532 species, 193 genera, and 96 families. Statistical analysis demonstrated a significant but moderate correlation between virulence factors and HP classification, highlighting the complexity of bacterial pathogenicity and the need for ongoing research. This resource is poised to enhance our understanding of bacterial pathogenicity mechanisms and aid in the development of predictive models. To improve accessibility and provide key visualization statistics, we developed a user-friendly web interface. [ABSTRACT FROM AUTHOR]

We sequenced and assembled genomes for 17 isolates of Staphylococcus cohnii isolated from osteomyelitis lesions in young broilers from two separate experiments where we induced lameness using a hybrid wire-litter flooring system. Whole genome comparisons using three different methods support a close relationship of genomes from both S. cohnii and Staphylococcus urealyticus. The data support three different lineages, which we designated as Lineage 1, Lineage 2, and Lineage 3, uniting these two species within an evolving complex. We present evidence for horizontal transfer between lineages of genomic regions from 50–440 kbp. The transfer of a 186 kbp region from Lineage 1 to Lineage 2 appears to have generated Lineage 3. Human-associated isolates appear to be limited to Lineages 2 and 3 but Lineage 2 appears to contain a higher number of human pathogenic isolates. The chicken isolates from our lameness trials included genomically diverse isolates from both Lineage 1 and 2, and isolates from both lineages were obtained from osteomyelitis lesions of individual birds. Our results expand the diversity of Staphylococci associated with osteomyelitis in poultry and suggest a high diversity in the microbiome of day-old chicks. Our data also support a reevaluation and unification of the taxonomic classifications of S. cohnii and S. urealyticus. [ABSTRACT FROM AUTHOR]

Identification of features with high levels of confidence in liquid chromatography–mass spectrometry (LC–MS) lipidomics research is an essential part of biomarker discovery, but existing software platforms can give inconsistent results, even from identical spectral data. This poses a clear challenge for reproducibility in biomarker identification. In this work, we illustrate the reproducibility gap for two open-access lipidomics platforms, MS DIAL and Lipostar, finding just 14.0% identification agreement when analyzing identical LC–MS spectra using default settings. Whilst the software platforms performed more consistently using fragmentation data, agreement was still only 36.1% for MS2 spectra. This highlights the critical importance of validation across positive and negative LC–MS modes, as well as the manual curation of spectra and lipidomics software outputs, in order to reduce identification errors caused by closely related lipids and co-elution issues. This curation process can be supplemented by data-driven outlier detection in assessing spectral outputs, which is demonstrated here using a novel machine learning approach based on support vector machine regression combined with leave-one-out cross-validation. These steps are essential to reduce the frequency of false positive identifications and close the reproducibility gap, including between software platforms, which, for downstream users such as bioinformaticians and clinicians, can be an underappreciated source of biomarker identification errors. [ABSTRACT FROM AUTHOR]

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The rapid advancement of sequencing technologies has led to the identification of numerous mutations in cancer genomes, many of which are variants of unknown significance (VUS). Computational models are increasingly being used to predict the functional impact of these mutations, in both coding and noncoding regions. Integration of these models with emerging genomic datasets will refine our understanding of mutation effects and guide clinical decision making. Future advancements in modeling protein interactions and transcriptional regulation will further enhance our ability to interpret VUS. Periodic incorporation of these developments into VUS reclassification practice has the potential to significantly improve personalized cancer care. [ABSTRACT FROM AUTHOR]

Background: Primary squamous cell carcinoma (SCC) of the thyroid and anaplastic thyroid carcinoma (ATC) show significant clinical and histologic overlap. Their biological behaviors are so similar that the fifthWHO updates SCC as a morphologic pattern of ATC rather than a separate entity. However, molecular genomic evidence that determines them as the same histologic type is limited. We aimed to explore whether they belong to the same classification from a molecular-typing perspective. Methods: A cohort enrolled 15 SCCs and 15 ATCs was collected. Whole exome sequencing (WES) and RNA-sequencing were performed to analyze molecular genetic and gene-expression profiles. Results: Significantly differential-mutant genes were BRAF, DPCR1, PCYOX1L, BRSK2, NRG1, PRR14L, TET1, VAMP4 suggesting differences in mutation level, as well as differences in highfrequency mutated genes, and SCC had a much lower tumor mutation burden than ATC. Mutational co-occurrence and mutual exclusion were less frequent in SCC than in ATC. 2047 differentialexpress genes were screened, indicating differences in gene expression were extremely strong. In principal component analysis, ATC and SCC could be notably clustered together, respectively, meanwhile they could be explicitly distinguished. Unsupervised clustering analysis validated they can indeed be clearly separated from each other which demonstrated that they may be two distinctive entities. Conclusions: It is controversial yet SCC is classified as a morphologic pattern of ATC. We revealed that SCC exhibited molecular genetic characteristics distinct from ATC. Although the fifth WHO categorizes them together, this study may provide strong molecular genetic evidence for the next edition of WHO classification that may allow for the separation of thyroid SCC from ATC. [ABSTRACT FROM AUTHOR]