Search

Invasive paper wasps such as Polistes dominula are a major pest and problem for biodiversity around the globe. Safe and highly targeted methods for the control of these and other social wasp populations are needed. We attempted to identify potentially-lethal gene targets that could be used on adult paper wasps in a gene silencing or RNA interference (RNAi) approach. Double-stranded RNA (dsRNA) was designed to target genes for which silencing has proven lethal in other insects. dsRNA was provided either orally to foragers or directly injected into the wasps. We also provided the dsRNA unprotected or protected from degradation by gut nucleases in two different forms (lipofectamine and carbon quantum dots). The effects of oral delivery of 22 different gene targets to forager wasps was evaluated. The expression of five different genes was successfully reduced following dsRNA ingestion or injection. These gene targets included the FACT complex subunit spt16 (DRE4) and RNA-binding protein fusilli (FUSILLI), both of which have been previously shown to have potential as lethal targets for pest control in other insects. However, we found no evidence of significant increases in adult wasp mortality following ingestion or injection of dsRNA for these genes when compared with control treatments in our experiments. The methods we used to protect the dsRNA from digestive degradation altered gene expression but similarly did not influence wasp mortality. Our results indicate that while many of the same gene targets can be silenced and induce mortality in other insects, dsRNA and RNAi approaches may not be useful for paper wasp control. [ABSTRACT FROM AUTHOR]

They describe the structure of the MRN Complex and its function in double-strand break (DSB) repair and analyze the different germline alterations of the MRN complex genes in autosomal recessive syndromes, as well as the association between heterozygous germline alterations in these genes and cancer predisposition. In the rapidly evolving landscape of molecular genetics and genomics, this Special Issue brings together a collection of insightful review articles that delve into the forefront of scientific exploration. The authors also propose a possible explanation of the discrepancies in the published results of the effects of the OXTR gene polymorphisms and methylation on different diseases [[2]]. [Extracted from the article]

Despite significant advances in the diagnosis and treatment of cardiovascular diseases, recent calls have emphasized the unmet need to improve precision-based approaches in cardiovascular disease. Although some studies provide preliminary evidence of the diagnostic and prognostic potential of circulating coding and non-coding RNAs, the complex RNA biology and lack of standardization have hampered the translation of these markers into clinical practice. In this position paper of the CardioRNA COST action CA17129, we provide recommendations to standardize the RNA development process in order to catalyse efforts to investigate novel RNAs for clinical use. We list the unmet clinical needs in cardiovascular disease, such as the identification of high-risk patients with ischaemic heart disease or heart failure who require more intensive therapies. The advantages and pitfalls of the different sample types, including RNAs from plasma, extracellular vesicles, and whole blood, are discussed in the sample matrix, together with their respective analytical methods. The effect of patient demographics and highly prevalent comorbidities, such as metabolic disorders, on the expression of the candidate RNA is presented and should be reported in biomarker studies. We discuss the statistical and regulatory aspects to translate a candidate RNA from a research use only assay to an in-vitro diagnostic test for clinical use. Optimal planning of this development track is required, with input from the researcher, statistician, industry, and regulatory partners. [ABSTRACT FROM AUTHOR]

RNA interference (RNAi) is a useful tool to assess gene function by knocking down expression of a target gene and has been used successfully in domestic and laboratory organisms. However, the use of RNAi for functional genomics has not fully extended into ecological model organisms in natural environments. Assessment of gene function in the wild is important because gene function can be environmentally and context dependent. Here, we present a case study using RNAi to assess gene function in wild paper wasps Polistes metricus, to test roles for two candidate genes ( NADH dehydrogenase ( NADHdh) and retinoid and fatty acid binding protein ( RfaBp)) in the development of reproductive castes. Previous studies have shown that these genes are upregulated in larvae that become queens compared to workers, but this pattern was reversed in the laboratory, making field-based studies necessary. We orally administered dsRNA to larvae in field colonies and found evidence of a short-term knockdown followed by a compensatory rebound in expression for RfaBp. We also observed the predicted worker-like decrease in lipid stores in NADHdh dsRNA treated wasps, suggesting a possible role for NADHdh in caste development. We discuss our results in the context of challenges for using RNAi for functional genomics in ecological model organisms in the field. [ABSTRACT FROM AUTHOR]

Why workers forfeit direct reproduction is a crucial question in eusocial evolution. Worker reproduction provides an excellent opportunity to understand the mechanism of kin conflict resolution between the queen and workers. We evaluated behavioral and physiological differences among females in the paper wasp Polistes chinensis antennalis to examine why some workers reproduce under queenright conditions. Reproductive workers were old and foraged less early in the season; their cuticular hydrocarbon (CHC) profiles overlapped with those of queens but were significantly different. The distinct CHC profile of the eggs of the queen likely represented a cue for policing against those by workers. Juvenile hormone (JH) and dopamine seemed to be associated with gonadotropic function, and the JH level of reproductive workers was similar to that of the queen. The high JH level of reproductive workers likely facilitated their reproduction even under queenright conditions. Gene expression levels of the queen and reproductive workers differed only in vitellogenin. These results suggest that worker reproduction is facilitated by an increase in JH level; however, CHC is not a fertility-linked signal, but a queen-linked signal; consequently, reproductive workers without a queen-linked signal might be allowed to stay within the colony. [ABSTRACT FROM AUTHOR]

The gut microbiota is recognized as having major impact in health and disease. Sample storage is an important aspect to obtain reliable results. Mostly recommended is immediate freezing, however, this is not always feasible. Faecal occult blood test (FOBT) papers are an appealing solution in such situations, and most studies find these to be applicable, showing no major changes within 7 days storage at room temperature (RT). As fieldwork often requires RT storage for longer periods, evaluation of this is warranted. We performed 16S rRNA gene sequencing of 19 paired faecal samples immediately frozen or kept five weeks and five months at RT on FOBT papers. Alpha-diversity evaluation revealed no effect of FOBT storage, and evaluation of beta-diversity showed that host explained 65% of community variation, while storage method explained 5%. Evaluation of community dispersion and the Firmicutes/Bacteroidetes ratio revealed a larger effect of storage time for fresh-frozen samples. Single taxa evaluation (order-to-genus level) showed significant alterations of four (of 37) genera after five weeks and five genera after five months. When comparing the two timepoints, alterations were only detectable for fresh-frozen samples. Our findings reveal that long term storage on FOBT papers is an applicable approach for microbiota research. [ABSTRACT FROM AUTHOR]

Understanding biological mechanisms is fundamental for improving animal production and health to meet the growing demand for high-quality protein. As an emerging biotechnology, single-cell transcriptomics has been gradually applied in diverse aspects of animal research, offering an effective method to study the gene expression of high-throughput single cells of different tissues/organs in animals. In an unprecedented manner, researchers have identified cell types/subtypes and their marker genes, inferred cellular fate trajectories, and revealed cell‒cell interactions in animals using single-cell transcriptomics. In this paper, we introduce the development of single-cell technology and review the processes, advancements, and applications of single-cell transcriptomics in animal research. We summarize recent efforts using single-cell transcriptomics to obtain a more profound understanding of animal nutrition and health, reproductive performance, genetics, and disease models in different livestock species. Moreover, the practical experience accumulated based on a large number of cases is highlighted to provide a reference for determining key factors (e.g., sample size, cell clustering, and cell type annotation) in single-cell transcriptomics analysis. We also discuss the limitations and outlook of single-cell transcriptomics in the current stage. This paper describes the comprehensive progress of single-cell transcriptomics in animal research, offering novel insights and sustainable advancements in agricultural productivity and animal health. [ABSTRACT FROM AUTHOR]

Biosensors have emerged as a valuable tool with high specificity and sensitivity for fast and reliable detection of hazardous substances in drinking water. Numerous substances have been addressed using synthetic biology approaches. However, many proposed biosensors are based on living, genetically modified organisms and are therefore limited in shelf life, usability and biosafety. We addressed these issues by the construction of an extensible, cell-free biosensor. Storage is possible through freeze drying on paper. Following the addition of an aqueous sample, a highly efficient cell-free protein synthesis (CFPS) reaction is initiated. Specific allosteric transcription factors modulate the expression of ‘superfolder’ green fluorescent protein (sfGFP) depending on the presence of the substance of interest. The resulting fluorescence intensities are analyzed with a conventional smartphone accompanied by simple and cheap light filters. An ordinary differential equitation (ODE) model of the biosensors was developed, which enabled prediction and optimization of performance. With an optimized cell-free biosensor based on the Shigella flexneri MerR transcriptional activator, detection of 6 μg/L Hg(II) ions in water was achieved. Furthermore, a completely new biosensor for the detection of gamma-hydroxybutyrate (GHB), a substance used as date-rape drug, was established by employing the naturally occurring transcriptional repressor BlcR from Agrobacterium tumefaciens. [ABSTRACT FROM AUTHOR]

The paper mulberry is one of the multifunctional tree species in agroforestry systems and is also commonly utilized in traditional medicine in China and other Asian countries. However, little is known about its molecular genetics, which hinders research on and exploitation of this valuable resource. To discern the correlation between gene expression and the essential properties of the paper mulberry, we performed a transcriptomics analysis, assembling a total of 37,725 unigenes from 54,638,676 reads generated by RNA-seq. Among these, 22,692 unigenes showed greater than 60% similarity with genes from other species. The lengths of 13,566 annotated unigenes were longer than 1,000 bp. Functional clustering analysis with COG (Cluster of Orthologous Groups) revealed that 17,184 unigenes are primarily involved in transcription, translation, signal transduction, carbohydrate metabolism, secondary metabolism, and energy metabolism. GO (Gene Ontology) annotation suggests enrichment of genes encoding antioxidant activity, transporter activity, biosynthesis, metabolism and stress response, with a total of 30,659 unigenes falling in these categories. KEGG (Kyoto Encyclopedia of Genes and Genomes) metabolic pathway analysis showed that 7,199 unigenes are associated with 119 metabolic pathways. In addition to the basic metabolism, these genes are enriched for plant pathogen interaction, flavonoid metabolism and other secondary metabolic processes. Furthermore, differences in the transcriptomes of leaf, stem and root tissues were analyzed and 7,233 specifically expressed unigenes were identified. This global expression analysis provided novel insights about the molecular mechanisms of the biosynthesis of flavonoid, lignin and cellulose, as well as on the response to biotic and abiotic stresses including the remediation of contaminated soil by the paper mulberry. [ABSTRACT FROM AUTHOR]

Background In social groups, dominant individuals may socially inhibit reproduction of subordinates using aggressive interactions or, in the case of highly eusocial insects, pheromonal communication. It has been hypothesized these two modes of reproductive inhibition utilize conserved pathways. Here, we use a comparative framework to investigate the chemical and genomic underpinnings of reproductive dominance in the primitively eusocial wasp Polistes metricus. Our goals were to first characterize transcriptomic and chemical correlates of reproductive dominance and second, to test whether dominance-associated mechanisms in paper wasps overlapped with aggression or pheromone-related gene expression patterns in other species. To explore whether conserved molecular pathways relate to dominance, we compared wasp transcriptomic data to previous studies of gene expression associated with pheromonal communication and queen-worker differences in honey bees, and aggressive behavior in bees, Drosophila, and mice. Results By examining dominant and subordinate females from queen and worker castes in early and late season colonies, we found that cuticular hydrocarbon profiles and genome-wide patterns of brain gene expression were primarily associated with season/social environment rather than dominance status. In contrast, gene expression patterns in the ovaries were associated primarily with caste and ovary activation. Comparative analyses suggest genes identified as differentially expressed in wasp brains are not related to queen pheromonal communication or caste in bees, but were significantly more likely to be associated with aggression in other insects (bees, flies), and even a mammal (mice). Conclusions This study provides the first comprehensive chemical and molecular analysis of reproductive dominance in paper wasps. We found little evidence for a chemical basis for reproductive dominance in P. metricus, and our transcriptomic analyses suggest that different pathways regulate dominance in paper wasps and pheromone response in bees. Furthermore, there was a substantial impact of season/social environment on gene expression patterns, indicating the important role of external cues in shaping the molecular processes regulating behavior. Interestingly, genes associated with dominance in wasps were also associated with aggressive behavior in bees, solitary insects and mammals. Thus, genes involved in social regulation of reproduction in Polistes may have conserved functions associated with aggression in insects and other taxa. [ABSTRACT FROM AUTHOR]

Simple Summary: Neurofibromatosis Type 1 is an inherited tumour predisposition syndrome with a varied clinical phenotype. Long-term monitoring through imaging is inconsistent and varies in high- and low-income countries. Implementation of a clinical practice guideline through a multidisciplinary clinic is instrumental to the care of adult Neurofibromatosis Type 1 patients. This systematic review aims to evaluate the association between a country's socioeconomic status and diagnostic modalities and strategies used for adult Neurofibromatosis Type 1 patients. Our results show multiple imaging modalities are used in high-income countries; however, there is limited use in low-income countries. The two most common diagnostic modalities used in developed countries are WB MRI and FDG PET/CT. Background: Neurofibromatosis Type 1 is an autosomal dominant tumour-predisposition condition commonly diagnosed in childhood and fully penetrant by adulthood. Long-term monitoring through imaging is inconsistent and varies between high- and low-income countries. Implementation of a clinical practice guideline through a multidisciplinary clinic is instrumental to the care of adult Neurofibromatosis Type 1 patients. We aim to systematically review international diagnostic modalities and strategies to evaluate any association between a country's socioeconomic status and diagnostic modalities or strategies used for Neurofibromatosis Type 1 patients. Methods: We searched PubMed, Embase, Web of Science, and Cochrane. Relevant clinical information on the surveillance of adult Neurofibromatosis Type 1 patients worldwide was reviewed, extracted, and synthesised. Results: We identified 51 papers reporting on 7724 individuals. Multiple imaging modalities are actively employed in high-income and upper-middle-income countries for surveying adult Neurofibromatosis Type 1 patients. We did not find any relevant papers from low- and middle-income countries. Conclusions: This systematic review suggests that there is robust data on diagnostic modalities for adult Neurofibromatosis Type 1 patients in high-income countries, but not for low- and middle-income countries. There is a lack of data on consolidated diagnostic strategies from both high- and low-income countries. Efforts should be made to publish data on usual clinical practice in low- and middle-income countries to develop clinical practice guidelines describing best medical practice to fit a local context. [ABSTRACT FROM AUTHOR]

Gene expression is highly impacted by the environment and can be reflective of past events that affected developmental processes. It is therefore expected that gene expression can serve as a signal of a current or future phenotypic traits. In this paper we identify sets of genes, which we call Prognostic Transcriptomic Biomarkers (PTBs), that can predict firmness in Malus domestica (apple) fruits. In apples, all individuals of a cultivar are clones, and differences in fruit quality are due to the environment. The apples transcriptome responds to these differences in environment, which makes PTBs an attractive predictor of future fruit quality. PTBs have the potential to enhance supply chain efficiency, reduce crop loss, and provide higher and more consistent quality for consumers. However, several questions must be addressed. In this paper we answer the question of which of two common modeling approaches, Random Forest or ElasticNet, outperforms the other. We answer if PTBs with few genes are efficient at predicting traits. This is important because we need few genes to perform qPCR, and we answer the question if qPCR is a cost-effective assay as input for PTBs modeled using high-throughput RNA-seq. To do this, we conducted a pilot study using fruit texture in the 'Gala' variety of apples across several postharvest storage regiments. Fruit texture in 'Gala' apples is highly controllable by post-harvest treatments and is therefore a good candidate to explore the use of PTBs. We find that the RandomForest model is more consistent than an ElasticNet model and is predictive of firmness (r2 = 0.78) with as few as 15 genes. We also show that qPCR is reasonably consistent with RNA-seq in a follow up experiment. Results are promising for PTBs, yet more work is needed to ensure that PTBs are robust across various environmental conditions and storage treatments. [ABSTRACT FROM AUTHOR]

Down syndrome is a well-studied aneuploidy condition in humans, which is associated with various disease phenotypes including cardiovascular, neurological, haematological and immunological disease processes. This review paper aims to discuss the research conducted on gene expression studies during fetal development. A descriptive review was conducted, encompassing all papers published on the PubMed database between September 1960 and September 2022. We found that in amniotic fluid, certain genes such as COL6A1 and DSCR1 were found to be affected, resulting in phenotypical craniofacial changes. Additionally, other genes such as GSTT1, CLIC6, ITGB2, C21orf67, C21orf86 and RUNX1 were also identified to be affected in the amniotic fluid. In the placenta, dysregulation of genes like MEST, SNF1LK and LOX was observed, which in turn affected nervous system development. In the brain, dysregulation of genes DYRK1A, DNMT3L, DNMT3B, TBX1, olig2 and AQP4 has been shown to contribute to intellectual disability. In the cardiac tissues, dysregulated expression of genes GART, ETS2 and ERG was found to cause abnormalities. Furthermore, dysregulation of XIST, RUNX1, SON, ERG and STAT1 was observed, contributing to myeloproliferative disorders. Understanding the differential expression of genes provides insights into the genetic consequences of DS. A better understanding of these processes could potentially pave the way for the development of genetic and pharmacological therapies. [ABSTRACT FROM AUTHOR]

Comparative sociogenomics has the potential to provide important insights into how social behaviour evolved. We examined brain gene expression profiles of the primitively eusocial wasp Polistes metricus and compared the results with a growing base of brain gene expression information for the advanced eusocial honeybee, Apis mellifera. We studied four female wasp groups that show variation in foraging/provisioning behaviour and reproductive status, using our newly developed microarray representing approximately 3248 P. metricus genes based on sequences generated from high-throughput pyrosequencing. We found differences in the expression of approximately 389 genes across the four groups. Pathways known from Drosophila melanogaster to be related to lipid metabolism, heat and stress response, and various forms of solitary behaviour were associated with behavioural differences among wasps. Forty-five per cent of differentially expressed transcripts showed significant associations with foraging/provisioning status, and 14 per cent with reproductive status. By comparing these two gene lists with lists of genes previously shown to be differentially expressed in association with honeybee division of labour, we found a significant overlap of genes associated with foraging/provisioning, but not reproduction, across the two species. These results suggest common molecular roots for foraging division of labour in two independently evolved social insect species and the possibility of more lineage-specific roots of reproductive behaviour. We explore the implications of these findings for the idea that there is a conserved ‘genetic toolkit’ for division of labour across multiple lineages. [ABSTRACT FROM AUTHOR]

Quantitative measurement of RNA expression levels through RNA-Seq is an ideal replacement for conventional cancer diagnosis via microscope examination. Currently, cancer-related RNA-Seq studies focus on two aspects: classifying the status and tissue of origin of a sample and discovering marker genes. Existing studies typically identify marker genes by statistically comparing healthy and cancer samples. However, this approach overlooks marker genes with low expression level differences and may be influenced by experimental results. This paper introduces "GENESO," a novel framework for pan-cancer classification and marker gene discovery using the occlusion method in conjunction with deep learning. we first trained a baseline deep LSTM neural network capable of distinguishing the origins and statuses of samples utilizing RNA-Seq data. Then, we propose a novel marker gene discovery method called "Symmetrical Occlusion (SO)". It collaborates with the baseline LSTM network, mimicking the "gain of function" and "loss of function" of genes to evaluate their importance in pan-cancer classification quantitatively. By identifying the genes of utmost importance, we then isolate them to train new neural networks, resulting in higher-performance LSTM models that utilize only a reduced set of highly relevant genes. The baseline neural network achieves an impressive validation accuracy of 96.59% in pan-cancer classification. With the help of SO, the accuracy of the second network reaches 98.30%, while using 67% fewer genes. Notably, our method excels in identifying marker genes that are not differentially expressed. Moreover, we assessed the feasibility of our method using single-cell RNA-Seq data, employing known marker genes as a validation test. [ABSTRACT FROM AUTHOR]

MicroRNAs (miRNAs) are small, non-coding RNAs that play a crucial role in regulating gene expression. Dysfunction in miRNAs can lead to various diseases, including cancers, neurological disorders, and cardiovascular conditions. To date, approximately 2000 miRNAs have been identified in humans. These small molecules have shown promise as disease biomarkers and potential therapeutic targets. Therefore, identifying miRNA biomarkers for diseases and developing effective miRNA drug delivery systems are essential. Nanotechnology offers promising new approaches to addressing scientific and medical challenges. Traditional miRNA detection methods include next-generation sequencing, microarrays, Northern blotting, and reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Nanotechnology can serve as an effective alternative to Northern blotting and RT-qPCR for miRNA detection. Moreover, nanomaterials exhibit unique properties that differ from larger counterparts, enabling miRNA therapeutics to more effectively enter target cells, reduce degradation in the bloodstream, and be released in specific tissues or cells. This paper reviews the application of nanotechnology in miRNA detection and drug delivery systems. Given that miRNA therapeutics are still in the developing stages, nanotechnology holds great promise for accelerating miRNA therapeutics development. [ABSTRACT FROM AUTHOR]

Methylation represents a crucial class of modification that orchestrates a spectrum of regulatory roles in plants, impacting ornamental characteristics, growth, development, and responses to abiotic stress. The establishment and maintenance of methylation involve the coordinated actions of multiple regulatory factors. Methyltransferases play a pivotal role by specifically recognizing and methylating targeted sites, which induces alterations in chromatin structure and gene expression, subsequently influencing the release of volatile aromatic substances and the accumulation of pigments in plant petals. In this paper, we review the regulatory mechanisms of methylation modification reactions and their effects on the changes in aromatic substances and pigments in plant petals. We also explore the potential of methylation modifications to unravel the regulatory mechanisms underlying aroma and color in plant petals. This aims to further elucidate the synthesis, metabolism, and regulatory mechanisms of various methylation modifications related to the aroma and color substances in plant petals, thereby providing a theoretical reference for improving the aroma and color of plant petals. [ABSTRACT FROM AUTHOR]

As part of the field of DNA methylation identification, this study tackles the challenge of enhancing recognition performance by introducing a specialized deep learning framework called DeepPGD. DNA methylation, a crucial biological modification, plays a vital role in gene expression analyses, cellular differentiation, and the study of disease progression. However, accurately and efficiently identifying DNA methylation sites remains a pivotal concern in the field of bioinformatics. The issue addressed in this paper is the presence of methylation in DNA, which is a binary classification problem. To address this, our research aimed to develop a deep learning algorithm capable of more precisely identifying these sites. The DeepPGD framework combined a dual residual structure involving Temporal convolutional networks (TCNs) and bidirectional long short-term memory (BiLSTM) networks to effectively extract intricate DNA structural and sequence features. Additionally, to meet the practical requirements of DNA methylation identification, extensive experiments were conducted across a variety of biological species. The experimental results highlighted DeepPGD's exceptional performance across multiple evaluation metrics, including accuracy, Matthews' correlation coefficient (MCC), and the area under the curve (AUC). In comparison to other algorithms in the same domain, DeepPGD demonstrated superior classification and predictive capabilities across various biological species datasets. This significant advancement in algorithmic prowess not only offers substantial technical support, but also holds potential for research and practical implementation within the DNA methylation identification domain. Moreover, the DeepPGD framework shows potential for application in genomics research, biomedicine, and disease diagnostics, among other fields. [ABSTRACT FROM AUTHOR]

Hundreds of microbiota gene expressions are significantly different between healthy and diseased humans. The "bottleneck" preventing a mechanistic dissection of how they affect host biology/disease is that many genes are encoded by nonmodel gut commensals and not genetically manipulatable. Approaches to efficiently identify their gene transfer methodologies and build their gene manipulation tools would enable mechanistic dissections of their impact on host physiology. This paper will introduce a step‐by‐step protocol to identify gene transfer conditions and build the gene manipulation tools for nonmodel gut microbes, focusing on Gram‐negative Bacteroidia and Gram‐positive Clostridia organisms. This protocol enables us to identify gene transfer methods and develop gene manipulation tools without prior knowledge of their genome sequences, by targeting bacterial 16s ribosomal RNAs or expanding their compatible replication origins combined with clustered regularly interspaced short palindromic repeats machinery. Such an efficient and generalizable approach will facilitate functional studies that causally connect gut microbiota genes to host diseases. Highlights: A protocol introducing a step‐by‐step genetic manipulation method would facilitate the investigation of those functional genes encoded by nonmodel gut commensals.The gene‐editing tools could be established in nonmodel gut Bacteroidia and Clostridia without prior knowledge of their genome information.The genetic manipulation pipeline may serve as a high‐throughput genetics screening and manipulating platform for human gut microbes. [ABSTRACT FROM AUTHOR]

Gene co-expression networks are valuable tools for discovering biologically relevant information within gene expression data. However, analysing large datasets presents challenges due to the identification of nonlinear gene–gene associations and the need to process an ever-growing number of gene pairs and their potential network connections. These challenges mean that some experiments are discarded because the techniques do not support these intense workloads. This paper presents pyEnGNet, a Python library that can generate gene co-expression networks in High-performance computing environments. To do this, pyEnGNet harnesses CPU and multi-GPU parallel computing resources, efficiently handling large datasets. These implementations have optimised memory management and processing, delivering timely results. We have used synthetic datasets to prove the runtime and intensive workload improvements. In addition, pyEnGNet was used in a real-life study of patients after allogeneic stem cell transplantation with invasive aspergillosis and was able to detect biological perspectives in the study. [ABSTRACT FROM AUTHOR]

Simple Summary: The journey of discovering melanoma—the most dangerous type of skin cancer—biomarkers is never-ending. Under that assumption, this study attempts to partially fill a gap in that journey by identifying melanoma-related biomarkers. This research paper studied the genetic and molecular profiling of melanoma. A transcriptome-wide association study (TWAS) was conducted to reveal the significant biomarkers associated with melanoma. A molecular network was constructed to represent the significant interactions between genes and microRNAs (miRNAs). The related biological processes, the enriched pathways, and the associated diseases were identified. The hotspot biomarkers were discovered to elucidate further the mechanism of these genes and miRNAs in melanoma pathogenesis. This work reveals the pathogenesis and biologically important molecular interactions of melanoma and provides new insights into the molecular mechanisms of melanoma. A transcriptome-wide association study (TWAS) was conducted on genome-wide association study (GWAS) summary statistics of malignant melanoma of skin (UK Biobank dataset) and The Cancer Genome Atlas-Skin Cutaneous Melanoma (TCGA-SKCM) gene expression weights to identify melanoma susceptibility genes. The GWAS included 2465 cases and 449,799 controls, while the gene expression testing was conducted on 103 cases. Afterward, a gene enrichment analysis was applied to identify significant TWAS associations. The melanoma's gene–microRNA (miRNA) regulatory network was constructed from the TWAS genes and their corresponding miRNAs. At last, a disease enrichment analysis was conducted on the corresponding miRNAs. The TWAS detected 27 genes associated with melanoma with p-values less than 0.05 (the top three genes are LOC389458 (RBAK), C16orf73 (MEIOB), and EIF3CL). After the joint/conditional test, one gene (AMIGO1) was dropped, resulting in 26 significant genes. The Gene Ontology (GO) biological process associated the extended gene set (76 genes) with protein K11-linked ubiquitination and regulation of cell cycle phase transition. K11-linked ubiquitin chains regulate cell division. Interestingly, the extended gene set was related to different skin cancer subtypes. Moreover, the enriched pathways were nsp1 from SARS-CoV-2 that inhibit translation initiation in the host cell, cell cycle, translation factors, and DNA repair pathways full network. The gene-miRNA regulatory network identified 10 hotspot genes with the top three: TP53, BRCA1, and MDM2; and four hotspot miRNAs: mir-16, mir-15a, mir-125b, and mir-146a. Melanoma was among the top ten diseases associated with the corresponding (106) miRNAs. Our results shed light on melanoma pathogenesis and biologically significant molecular interactions. [ABSTRACT FROM AUTHOR]

Simple Summary: This paper reviews the role of ENO2, a protein involved in sugar metabolism, in advanced prostate cancer. Analysing five studies, we found that ENO2 levels tend to be higher in aggressive forms of prostate cancer, particularly those that have spread or become resistant to hormone therapy. This increased presence might be linked to how prostate cancer cells change their energy production as the disease progresses, shifting to rely more on sugar breakdown in advanced stages. The study also suggests that ENO2 can be influenced by the tumour's environment, such as low hormone levels or the presence of bone cells, which is relevant, as prostate cancer often spreads to bones. While not proving a direct causal relationship, the research indicates that ENO2 could be an important marker for aggressive disease and potentially a target for future treatments, warranting further investigation into its role in prostate cancer progression, especially in bone metastasis. Prostate cancer (PCa) is the second leading cause of male cancer deaths in the UK and the fifth worldwide. The presence of distant PCa metastasis can reduce the 5-year survival rate from 100% to approximately 30%. Enolase 2 (ENO2), a crucial glycolytic enzyme in cancer metabolism, is associated with the metastasis of multiple cancers and is also used as a marker for neuroendocrine tumours. However, its role in PCa metastasis remains unclear. In this study, we systematically reviewed the current literature to determine the association between ENO2 and metastatic PCa. Medline, Web of Science, and PubMed were searched for eligible studies. The search yielded five studies assessing ENO2 expression in PCa patients or cell lines. The three human studies suggested that ENO2 expression is correlated with late-stage, aggressive PCa, including castrate-resistant PCa (CRPC), metastatic CRPC, and neuroendocrine PCa (NEPC). This was further supported by two in vitro studies indicating that ENO2 expression can be regulated by the tumour microenvironment, such as androgen deprived conditions and the presence of bone-forming osteoblasts. Therefore, ENO2 may functionally contribute to PCa metastasis, possibly due to the unique metabolic features of PCa, which are glycolysis dependent only at the advanced metastatic stage. [ABSTRACT FROM AUTHOR]

Background: Medulloblastoma is one of the common primary central nervous system (CNS) malignancies in pediatric patients. The main treatment is surgical resection preceded and/or followed by chemoradiotherapy. However, their serious side effects necessitate a better understanding of medulloblastoma biology to develop novel therapeutic options. Main body: Circular RNA (circRNA) and long non-coding RNA (lncRNA) regulate gene expression via microRNA (miRNA) pathways. Although growing evidence has highlighted the significance of circRNA and lncRNA-associated competing endogenous RNA (ceRNA) networks in cancers, no study has comprehensively investigated them in medulloblastoma. For this aim, the Web of Science, PubMed, Scopus, and Embase were systematically searched to obtain the relevant papers published before 16 September 2023, adhering to the PRISMA-ScR statement. HOTAIR, NEAT1, linc-NeD125, HHIP-AS1, CRNDE, and TP73-AS1 are the oncogenic lncRNAs, and Nkx2-2as is a tumor-suppressive lncRNA that develop lncRNA-associated ceRNA networks in medulloblastoma. CircSKA3 and circRNA_103128 are upregulated oncogenic circRNAs that develop circRNA-associated ceRNA networks in medulloblastoma. Conclusion: In summary, this study has provided an overview of the existing evidence on circRNA and lncRNA-associated ceRNA networks and their impact on miRNA and mRNA expression involved in various signaling pathways of medulloblastoma. Suppressing the oncogenic ceRNA networks and augmenting tumor-suppressive ceRNA networks can provide ample opportunities for medulloblastoma treatment. [ABSTRACT FROM AUTHOR]

Boolean networks are largely employed to model the qualitative dynamics of cell fate processes by describing the change of binary activation states of genes and transcription factors with time. Being able to bridge such qualitative states with quantitative measurements of gene expressions in cells, as scRNA-seq, is a cornerstone for data-driven model construction and validation. On one hand, scRNA-seq binarisation is a key step for inferring and validating Boolean models. On the other hand, the generation of synthetic scRNA-seq data from baseline Boolean models provides an important asset to benchmark inference methods. However, linking characteristics of scRNA-seq datasets, including dropout events, with Boolean states is a challenging task. We present scBoolSeq, a method for the bidirectional linking of scRNA-seq data and Boolean activation state of genes. Given a reference scRNA-seq dataset, scBoolSeq computes statistical criteria to classify the empirical gene pseudocount distributions as either unimodal, bimodal, or zero-inflated, and fit a probabilistic model of dropouts, with gene-dependent parameters. From these learnt distributions, scBoolSeq can perform both binarisation of scRNA-seq datasets, and generate synthetic scRNA-seq datasets from Boolean traces, as issued from Boolean networks, using biased sampling and dropout simulation. We present a case study demonstrating the application of scBoolSeq's binarisation scheme in data-driven model inference. Furthermore, we compare synthetic scRNA-seq data generated by scBoolSeq with BoolODE's, data for the same Boolean Network model. The comparison shows that our method better reproduces the statistics of real scRNA-seq datasets, such as the mean-variance and mean-dropout relationships while exhibiting clearly defined trajectories in two-dimensional projections of the data. Author summary: The qualitative and logical modelling of cell dynamics has brought precious insight into gene regulatory mechanisms that drive cellular differentiation and fate decisions by predicting cellular trajectories and mutations for their control. However, the design and validation of these models is impeded by the quantitative nature of experimental measurements of cellular states. In this paper, we provide and assess a new methodology, scBoolSeq for bridging single-cell level pseudocounts of RNA transcripts with Boolean classification of gene activity levels. Our method, implemented as a Python package, enables both to binarise scRNA-seq data in order to match quantitative measurements with states of logical models, and to generate synthetic data from Boolean traces to benchmark inference methods. We show that scBoolSeq accurately captures the main statistical features of scRNA-seq data, including measurement dropouts, improving significantly the state of the art. Overall, scBoolSeq brings a statistically-grounded method for enabling the inference and validation of qualitative models from scRNA-seq data. [ABSTRACT FROM AUTHOR]

Summary Computational cell-type deconvolution is an important analytic technique for modeling the compositional heterogeneity of bulk gene expression data. A conceptually new Bayesian approach to this problem, BayesPrism, has recently been proposed and has subsequently been shown to be superior in accuracy and robustness against model misspecifications by independent studies; however, given that BayesPrism relies on Gibbs sampling, it is orders of magnitude more computationally expensive than standard approaches. Here, we introduce the InstaPrism package which re-implements BayesPrism in a derandomized framework by replacing the time-consuming Gibbs sampling step with a fixed-point algorithm. We demonstrate that the new algorithm is effectively equivalent to BayesPrism while providing a considerable speed and memory advantage. Furthermore, the InstaPrism package is equipped with a precompiled, curated set of references tailored for a variety of cancer types, streamlining the deconvolution process. Availability and implementation The package InstaPrism is freely available at: https://github.com/humengying0907/InstaPrism. The source code and evaluation pipeline used in this paper can be found at: https://github.com/humengying0907/InstaPrismSourceCode. [ABSTRACT FROM AUTHOR]

Motivation Gene set enrichment (GSE) analysis allows for an interpretation of gene expression through pre-defined gene set databases and is a critical step in understanding different phenotypes. With the rapid development of single-cell RNA sequencing (scRNA-seq) technology, GSE analysis can be performed on fine-grained gene expression data to gain a nuanced understanding of phenotypes of interest. However, with the cellular heterogeneity in single-cell gene profiles, current statistical GSE analysis methods sometimes fail to identify enriched gene sets. Meanwhile, deep learning has gained traction in applications like clustering and trajectory inference in single-cell studies due to its prowess in capturing complex data patterns. However, its use in GSE analysis remains limited, due to interpretability challenges. Results In this paper, we present DeepGSEA, an explainable deep gene set enrichment analysis approach which leverages the expressiveness of interpretable, prototype-based neural networks to provide an in-depth analysis of GSE. DeepGSEA learns the ability to capture GSE information through our designed classification tasks, and significance tests can be performed on each gene set, enabling the identification of enriched sets. The underlying distribution of a gene set learned by DeepGSEA can be explicitly visualized using the encoded cell and cellular prototype embeddings. We demonstrate the performance of DeepGSEA over commonly used GSE analysis methods by examining their sensitivity and specificity with four simulation studies. In addition, we test our model on three real scRNA-seq datasets and illustrate the interpretability of DeepGSEA by showing how its results can be explained. Availability and implementation https://github.com/Teddy-XiongGZ/DeepGSEA [ABSTRACT FROM AUTHOR]

Spatial transcripome (ST) profiling can reveal cells' structural organizations and functional roles in tissues. However, deciphering the spatial context of gene expressions in ST data is a challenge—the high-order structure hiding in whole transcriptome space over 2D/3D spatial coordinates requires modeling and detection of interpretable high-order elements and components for further functional analysis and interpretation. This paper presents a new method GraphTucker—graph-regularized Tucker tensor decomposition for learning high-order factorization in ST data. GraphTucker is based on a nonnegative Tucker decomposition algorithm regularized by a high-order graph that captures spatial relation among spots and functional relation among genes. In the experiments on several Visium and Stereo-seq datasets, the novelty and advantage of modeling multiway multilinear relationships among the components in Tucker decomposition are demonstrated as opposed to the Canonical Polyadic Decomposition and conventional matrix factorization models by evaluation of detecting spatial components of gene modules, clustering spatial coefficients for tissue segmentation and imputing complete spatial transcriptomes. The results of visualization show strong evidence that GraphTucker detect more interpretable spatial components in the context of the spatial domains in the tissues. Availability and implementation https://github.com/kuanglab/GraphTucker. [ABSTRACT FROM AUTHOR]

The symbiotic relationship between arbuscular mycorrhizal fungi (AMF) and plants is well known for its benefits in enhancing plant growth and stress resistance. Research on whether key components of the AMF colonization process, such as MyC factors, can be directly utilized to activate plant symbiotic pathways and key functional gene expression is still lacking. In this paper, we found that, using a hydroponics system with Lotus japonicus, MyC factor analogue chitin oligomer 5 (CO5) had a more pronounced growth-promoting effect compared to symbiosis with AMF at the optimal concentration. Additionally, CO5 significantly enhanced the resistance of Lotus japonicus to various environmental stresses. The addition of CO5 activated symbiosis, nutrient absorption, and stress-related signaling pathways, like AMF symbiosis, and CO5 also activated a higher and more extensive gene expression profile compared to AMF colonization. Overall, the study demonstrated that the addition of MyC factor analogue CO5, by activating relevant pathways, had a superior effect on promoting plant growth and enhancing stress resistance compared to colonization by AMF. These findings suggest that utilizing MyC factor analogues like CO5 could be a promising alternative to traditional AMF colonization methods in enhancing plant growth and stress tolerance in agriculture. [ABSTRACT FROM AUTHOR]

Simple Summary: Insects, like other animals, must face severe winter low temperature conditions in northern areas. Some insects even freeze entirely and wait out the winter until spring comes. Epigenetic modifications have been suggested to play a vital role in regulating the cold responses of insects. However, the dynamics of the enzymes involved in histone lysine methylation, histone H3 acetylation and methylation in response to cold winters in the freeze-tolerant goldenrod gall fly larvae Eurosta solidaginis and the freeze-avoiding gall moth caterpillars Epiblema scudderiana remains unclear. This paper investigates the modifications of key nuclear proteins that act as gene regulators and mainly focuses on proteins—called histones—that act to depress metabolism. The results indicate that histones are modified in E. solidaginis and Ep. scudderiana during winter. Significant reductions were observed in most of the targets of histone methylation/acetylation for decreasing temperatures of Ep. scudderiana larvae, whereas selected histone methylation/acetylation targets were conversely elevated in E. solidaginis. These epigenetic changes are shown to be an important element in winter survival. Overwintering survival by insects, whether of the freeze-tolerant or freeze-avoiding types, is typically associated with a strong suppression of metabolic rate (e.g., entry into diapause) that involves the differential expression of many genes with regulation at the transcriptional, translational or post-translational levels. Epigenetic modifications have been suggested to play a vital role in regulating cold responses of insects. However, knowledge of the roles of epigenetic mechanisms in modulating gene expression for winter survival of the larvae of two goldenrod gall formers, the freeze-tolerant dipteran Eurosta solidaginis and the freeze-avoiding lepidopteran Epiblema scudderiana, remain unknown. The current study evaluates the role of cold-induced lysine methylation and histone modifications, with enzymes of lysine methylation (SETD8, SETD7, SUV39H1, SMYD2 and ASH2L), as well as relative levels of histone H3 acetylation (H3K9ac, H3K18ac, H3K27ac, H3K56ac) and methylation (H3K4me1, H3K9me3, H3K36me2) examined in two insects. Significant (p < 0.05) reductions were observed in most of the targets of histone methylation/acetylation for decreasing temperatures of Ep. scudderiana larvae, whereas selected histone methylation/acetylation targets were conversely elevated (p < 0.05) in E. solidaginis, particularly under conditions of 5 °C for 4 h. Histone H3 expression was found to be variable without statistical differences in larval goldenrod gall moths and gall flies. These results provide basic information on the patterns of epigenetic regulation involved in insect cold hardiness. [ABSTRACT FROM AUTHOR]

Transcription factors (TFs) are crucial in modulating gene expression and sculpting cellular and organismal phenotypes. The identification of TF–target gene interactions is pivotal for comprehending molecular pathways and disease etiologies but has been hindered by the demanding nature of traditional experimental approaches. This paper introduces a novel web application and package utilizing the R program, which predicts TF–target gene relationships and vice versa. Our application integrates the predictive power of various bioinformatic tools, leveraging their combined strengths to provide robust predictions. It merges databases for enhanced precision, incorporates gene expression correlation for accuracy, and employs pan-tissue correlation analysis for context-specific insights. The application also enables the integration of user data with established resources to analyze TF–target gene networks. Despite its current limitation to human data, it provides a platform to explore gene regulatory mechanisms comprehensively. This integrated, systematic approach offers researchers an invaluable tool for dissecting the complexities of gene regulation, with the potential for future expansions to include a broader range of species. [ABSTRACT FROM AUTHOR]

Simple Summary: Cancer remains one of the leading causes of death worldwide, which emphasizes the need for its early and accurate diagnosis and prognosis. Our review explores AI's potential in this field, analyzing 89 recent studies from 2020 through 2023. Specifically, these studies included AI applications for the analysis of multi-omics data: radiomics, pathomics, clinical records, and lab data. Notably, eight studies combined diverse omics data types (genomics, transcriptomics, epigenomics, and proteomics). Integration of AI for the analysis of clinical and omics data contributes to a significant advancement and is essential for safe clinical implementation. Cancer is one of the leading causes of death, making timely diagnosis and prognosis very important. Utilization of AI (artificial intelligence) enables providers to organize and process patient data in a way that can lead to better overall outcomes. This review paper aims to look at the varying uses of AI for diagnosis and prognosis and clinical utility. PubMed and EBSCO databases were utilized for finding publications from 1 January 2020 to 22 December 2023. Articles were collected using key search terms such as "artificial intelligence" and "machine learning." Included in the collection were studies of the application of AI in determining cancer diagnosis and prognosis using multi-omics data, radiomics, pathomics, and clinical and laboratory data. The resulting 89 studies were categorized into eight sections based on the type of data utilized and then further subdivided into two subsections focusing on cancer diagnosis and prognosis, respectively. Eight studies integrated more than one form of omics, namely genomics, transcriptomics, epigenomics, and proteomics. Incorporating AI into cancer diagnosis and prognosis alongside omics and clinical data represents a significant advancement. Given the considerable potential of AI in this domain, ongoing prospective studies are essential to enhance algorithm interpretability and to ensure safe clinical integration. [ABSTRACT FROM AUTHOR]

A crucial stage in eukaryote gene expression involves mRNA splicing by a protein assembly known as the spliceosome. This step significantly contributes to generating and properly operating the ultimate gene product. Since non-coding introns disrupt eukaryotic genes, splicing entails the elimination of introns and joining exons to create a functional mRNA molecule. Nevertheless, accurately finding splice sequence sites using various molecular biology techniques and other biological approaches is complex and time-consuming. This paper presents a precise and reliable computer-aided diagnosis (CAD) technique for the rapid and correct identification of splice site sequences. The proposed deep learning-based framework uses long short-term memory (LSTM) to extract distinct patterns from RNA sequences, enabling rapid and accurate point mutation sequence mapping. The proposed network employs one-hot encodings to find sequential patterns that effectively identify splicing sites. A thorough ablation study of traditional machine learning, one-dimensional convolutional neural networks (1D-CNNs), and recurrent neural networks (RNNs) models was conducted. The proposed LSTM network outperformed existing state-of-the-art approaches, improving accuracy by 3% and 2% for the acceptor and donor sites datasets. [ABSTRACT FROM AUTHOR]

The advancement of spatial transcriptomics (ST) technology contributes to a more profound comprehension of the spatial properties of gene expression within tissues. However, due to challenges of high dimensionality, pronounced noise and dynamic limitations in ST data, the integration of gene expression and spatial information to accurately identify spatial domains remains challenging. This paper proposes a SpaNCMG algorithm for the purpose of achieving precise spatial domain description and localization based on a neighborhood-complementary mixed-view graph convolutional network. The algorithm enables better adaptation to ST data at different resolutions by integrating the local information from KNN and the global structure from r -radius into a complementary neighborhood graph. It also introduces an attention mechanism to achieve adaptive fusion of different reconstructed expressions, and utilizes KPCA method for dimensionality reduction. The application of SpaNCMG on five datasets from four sequencing platforms demonstrates superior performance to eight existing advanced methods. Specifically, the algorithm achieved highest ARI accuracies of 0.63 and 0.52 on the datasets of the human dorsolateral prefrontal cortex and mouse somatosensory cortex, respectively. It accurately identified the spatial locations of marker genes in the mouse olfactory bulb tissue and inferred the biological functions of different regions. When handling larger datasets such as mouse embryos, the SpaNCMG not only identified the main tissue structures but also explored unlabeled domains. Overall, the good generalization ability and scalability of SpaNCMG make it an outstanding tool for understanding tissue structure and disease mechanisms. Our codes are available at https://github.com/ZhihaoSi/SpaNCMG. [ABSTRACT FROM AUTHOR]

Background: Small RNA molecules known as microRNAs (miRNAs) regulate gene expression during the post-translational steps. miRNAs are essential for many physiological processes, such as cell division, growth, and proliferation, as well as development and metabolism. Aims: To review the developments in investigations on miR-155 and miR-146a as possible biomarkers for multiple sclerosis (MS) disease diagnosis and prognosis. Methods: A comprehensive analysis of the available literature was carried out by searching databases including PubMed, Scopus, and Web of Science for papers published between 2011 and 2023 years. Only original articles written in the English language were considered for inclusion in this review. Results: A total of 29 studies were initially identified, with 14 meeting the inclusion criteria. Conclusion: The present study underscores the crucial role of microRNAs, particularly miR-155 and miR-146a, in the etiology and progression of multiple sclerosis (MS). Through an extensive analysis of the literature, we have found compelling evidence linking aberrations in the expression and function of these microRNAs to MS pathogenesis. Specifically, our synthesis suggests that miR-155 and miR-146a hold promise as valuable biomarkers for both the diagnosis and prognosis of MS. Despite the challenges posed by the heterogeneity of MS subtypes, the non-invasive accessibility of miRNAs in various bodily fluids, including serum, peripheral blood, cerebrospinal fluid, and extracellular vesicles, presents a promising avenue for the development of robust diagnostic and prognostic tools. By elucidating the intricate roles of miR-155 and miR-146a in MS, our findings contribute to advancing our understanding of the disease mechanisms and pave the way for the development of more effective diagnostic and therapeutic strategies. [ABSTRACT FROM AUTHOR]

The nonconventional yeast Kluyveromyces marxianus has potential for industrial production, but the lack of advanced synthetic biology tools for precise engineering hinders its rapid development. Here, we introduce a CRISPR-Cas9-mediated multilocus integration method for assembling multiple exogenous genes. Using SlugCas9-HF, a high-fidelity Cas9 nuclease, we enhance gene editing precision. Specific genomic loci predisposed to efficient integration and expression of heterologous genes are identified and combined with a set of paired CRISPR-Cas9 expression plasmids and donor plasmids to establish a CRISPR-based biosynthesis toolkit. This toolkit enables genome integration of large gene modules over 12 kb and achieves simultaneous quadruple-locus integration in a single step with 20% efficiency. As a proof-of-concept, we apply the toolkit to screen for gene combinations that promote heme production, revealing the importance of HEM4Km and HEM12Sc. This CRISPR-based toolkit simplifies the reconstruction of complex pathways in K. marxianus, broadening its application in synthetic biology. Fungi has been widely used as protein and drug production factories, in which efficient heterologous gene expression is fundamental. This paper provides a genome editing tool kit for heterologous gene expression in Kluyveromyces marxianus. [ABSTRACT FROM AUTHOR]

Oxidative stress represents a critical facet of the array of abiotic stresses affecting crop growth and yield. In this paper, we investigated the potential differences in the functions of two highly homologous Arabidopsis DSS1 proteins in terms of maintaining genome integrity and response to oxidative stress. In the context of homologous recombination (HR), it was shown that overexpressing AtDSS1(I) using a functional complementation test increases the resistance of the Δdss1 mutant of Ustilago maydis to genotoxic agents. This indicates its conserved role in DNA repair via HR. To investigate the global transcriptome changes occurring in dss1 plant mutant lines, gene expression analysis was conducted using Illumina RNA sequencing technology. Individual RNA libraries were constructed from three total RNA samples isolated from dss1(I), dss1(V), and wild-type (WT) plants under hydrogen peroxide-induced stress. RNA-Seq data analysis and real-time PCR identification revealed major changes in gene expression between mutant lines and WT, while the dss1(I) and dss1(V) mutant lines exhibited analogous transcription profiles. The Kyoto Encyclopedia of Genes and Genomes enrichment analysis revealed significantly enriched metabolic pathways. Notably, genes associated with HR were upregulated in dss1 mutants compared to the WT. Otherwise, genes of the metabolic pathway responsible for the synthesis of secondary metabolites were downregulated in both dss1 mutant lines. These findings highlight the importance of understanding the molecular mechanisms of plant responses to oxidative stress. [ABSTRACT FROM AUTHOR]

Simple Summary: This paper investigates the expression of miR-101-3p, miR-106a-5p, and miR-326 in colon cancer tissues compared to adjacent healthy tissues. This study involves 40 colon cancer patients, whose tissue samples were analyzed using qRT-PCR. Results indicate that these miRNAs are overexpressed in adjacent healthy tissues but decrease in advanced cancer stages. This study suggests a strong correlation between miR-106a-5p and miR-326 with colon cancer severity. The findings propose that miRNA profiling could be useful for early diagnosis and prognosis in colon cancer management. Early diagnosis and prognosis of cancer progression through biomarker profiling are crucial in managing colon cancer patients. Our research aimed to investigate the expression of miR-101-3p, miR-106a-5p, and miR-326 in tumor and adjacent healthy tissues of colon cancer patients and determine their potential diagnostic utility. This study included 40 patients divided into four groups according to the TNM staging classification. MiRNA expression was analyzed using qRT-PCR. The results showed that miR-101-3p, miR-106a-5p, and miR-326 are overexpressed in adjacent healthy tissues but decrease in advanced cancer stages. MiR-106a-5p and miR-326 are strongly correlated with colon cancer severity. These findings suggest that miRNA profiling could be useful for early diagnosis and prognosis in colon cancer management. [ABSTRACT FROM AUTHOR]

Simple Summary: Though chemotherapy is generally known to be effective in the fight against cancer, its application is associated with a number of side effects, including toxic impacts on the ovaries. The most ovotoxic cytostatic chemotherapeutics are the classical alkylating compounds, particularly cyclophosphamide. Thus, it is of utmost importance to find effective means to protect ovaries against the negative influence of chemotherapeutic agents. This review paper presents the results of the current research work on the hitherto proposed agents potentially protecting the functions and state of ovaries exposed to chemotherapy. A large body of promising results have been reported, but as presented, it is necessary to undertake thorough and comprehensive studies aimed at providing an explicit evaluation of the efficacy of selected ovoprotecting agents and their possible clinical use in the future. Chemotherapy is one of the leading cancer treatments. Unfortunately, its use can contribute to several side effects, including gynotoxic effects in women. Ovarian reserve suppression and estrogen deficiency result in reduced quality of life for cancer patients and are frequently the cause of infertility and early menopause. Classic alkylating cytostatics are among the most toxic chemotherapeutics in this regard. They cause DNA damage in ovarian follicles and the cells they contain, and they can also induce oxidative stress or affect numerous signaling pathways. In vitro tests, animal models, and a few studies among women have investigated the effects of various agents on the protection of the ovarian reserve during classic chemotherapy. In this review article, we focused on the possible beneficial effects of selected hormones (anti-Müllerian hormone, ghrelin, luteinizing hormone, melatonin), agents affecting the activity of apoptotic pathways and modulating gene expression (C1P, S1P, microRNA), and several natural (quercetin, rapamycin, resveratrol) and synthetic compounds (bortezomib, dexrazoxane, goserelin, gonadoliberin analogs, imatinib, metformin, tamoxifen) in preventing gynotoxic effects induced by commonly used cytostatics. The presented line of research appears to provide a promising strategy for protecting and/or improving the ovarian reserve in the studied group of cancer patients. However, well-designed clinical trials are needed to unequivocally assess the effects of these agents on improving hormonal function and fertility in women treated with ovotoxic anticancer drugs. [ABSTRACT FROM AUTHOR]

Gene therapy aims to modify or manipulate gene expression and change the biological characteristics of living cells to achieve the purpose of treating diseases. The safe, efficient, and stable expression of exogenous genes in cells is crucial for the success of gene therapy, which is closely related to the vectors used in gene therapy. Currently, gene therapy vectors are mainly divided into two categories: viral vectors and non-viral vectors. Viral vectors are widely used due to the advantages of persistent and stable expression, high transfection efficiency, but they also have certain issues such as infectivity, high immunological rejection, randomness of insertion mutation, carcinogenicity, and limited vector capacity. Non-viral vectors have the advantages of non-infectivity, controllable chemical structure, and unlimited vector capacity, but the transfection efficiency is low. With the rapid development of nanotechnology, the unique physicochemical properties of nanomaterials have attracted increasing attention in the field of drug and gene delivery. Among many nanomaterials, iron-based nanomaterials have attracted much attention due to their superior physicochemical properties, such as Fenton reaction, magnetic resonance imaging, magnetothermal therapy, photothermal therapy, gene delivery, magnetically-assisted drug delivery, cell and tissue targeting, and so on. In this paper, the research progress of iron-based nanomaterials in gene delivery and tumor gene therapy is reviewed, and the future application direction of iron-based nanomaterials is further prospected. [ABSTRACT FROM AUTHOR]

Epigenetics plays a crucial role in various clinical diseases, such as autism, by mediating the impact of environmental variables on genomic regulation. Autism spectrum disorder (ASD) is a neurological disorder influenced by both genetic and environmental factors that affect the developing brain. DNA methylation, histone tail modifications, and noncoding RNA activity can change the function of genes without altering nucleotide sequences. Genes and environment combine to produce the etiology of ASD. One of the main areas of ASD research now being studied is the effects of epigenetic factors on gene expression, such as DNA methylation. Autistic patients exhibit evidence of oxidative stress and impaired methylation, which may reflect the effects of toxic exposure on sulfur metabolism that may lead to cellular damage in the brain and altered expression of epigenetic genes. This review paper summarizes the findings of the supplementary therapy studies of ASD, showing that supplements, including B9, B12, B6, D, E, C, glutathione, omega-3, and choline, are highly effective in modifying methylation in autism, improving many nutrient and metabolic problems, and resulting in significant improvements in symptoms. [ABSTRACT FROM AUTHOR]

Background and Objective: The embryo and the fetus develop in a physiologically hypoxic environment, where vascularization is sustained by HIF-1, VEGF, and the β-adrenergic system. In animals, β3-adrenoceptors (β3-ARs), up-regulated by hypoxia, favor global fetal wellness to such an extent that most diseases related to prematurity are hypothesized to be induced or aggravated by a precocious β3-AR down-regulation, due to premature exposure to a relatively hyperoxic environment. In animals, β3-AR pharmacological agonism is currently investigated as a possible new therapeutic opportunity to counteract oxygen-induced damages. Our goal is to translate the knowledge acquired in animals to humans. Recently, we have demonstrated that fetuses become progressively more hypoxemic from mid-gestation to near-term, but starting from the 33rd–34th week, oxygenation progressively increases until birth. The present paper aims to describe a clinical research protocol, evaluating whether the expression level of HIF-1, β3-ARs, and VEGF is modulated by oxygen during intrauterine and postnatal life, in a similar way to animals. Materials and Methods: In a prospective, non-profit, single-center observational study we will enroll 100 preterm (group A) and 100 full-term newborns (group B). We will collect cord blood samples (T0) and measure the RNA expression level of HIF-1, β3-ARs, and VEGF by digital PCR. In preterms, we will also measure gene expression at 48–72h (T1), 14 days (T2), and 30 days (T3) of life and at 40 ± 3 weeks of post-menstrual age (T4), regardless of the day of life. We will compare group A (T0) vs. group B (T0) and identify any correlations between the values obtained from serial samples in group A and the clinical data of the patients. Our protocol has been approved by the Pediatric Ethical Committee for Clinical Research of the Tuscany region (number 291/2022). Expected Results: The observation that in infants, the HIF-1/β3-ARs/VEGF axis shows similar modulation to that of animals could suggest that β3-ARs also promote fetal well-being in humans. [ABSTRACT FROM AUTHOR]

Simple Summary: Enzymes of the detoxification system are known to play a crucial role in insecticide resistance in insects. The role of P450 monooxygenases in providing resistance to chlorfenapyr is ambiguous and of interest due to possible negative cross-resistance to pyrethroids and other insecticides, the resistance to which is ensured by increased activity of these enzymes. This study aimed to perform a biochemical and molecular evaluation of P450 monooxygenases in susceptible and chlorfenapyr-resistant strains of Musca domestica L. depending on sex. The chlorfenapyr-resistant strain of M. domestica L. did not differ from the susceptible strain in terms of the pattern of overall P450-monooxygenase activity: the larvae of both strains had lower enzymatic activity than the adults. However, the development of resistance to chlorfenapyr in house flies was accompanied by an increase in P450-monooxygenase activity without changes in CYP6D1 expression. PCR-RFLP analysis revealed a previously undescribed mutation in the promoter region of the CYP6D1 gene of M. domestica, the effect of which on the gene expression level was not detected. The house fly Musca domestica L. is one of the most common insects of veterinary and medical importance worldwide; its ability to develop resistance to a large number of insecticides is well known. Many studies support the involvement of cytochrome P-450-dependent monooxygenases (P450) in the development of resistance to pyrethroids, neonicotinoids, carbamates, and organophosphates among insects. In this paper, the monooxygenase activity and expression level of CYP6D1 were studied for the first time in a chlorfenapyr-resistant strain of house fly. Our studies demonstrated that P450 activity in adults of the susceptible strain (Lab TY) and chlorfenapyr-resistant strain (ChlA) was 1.56–4.05-fold higher than that in larvae. In females of the Lab TY and ChlA strains, this activity was 1.53- and 1.57-fold higher, respectively (p < 0.05), than that in males, and in contrast, the expression level of CYP6D1 was 21- and 8-fold lower, respectively. The monooxygenase activity did not vary between larvae of the susceptible strain Lab TY and the chlorfenapyr-resistant strain ChlA. Activity in females and males of the ChlA strain exceeded that in the Lab TY strain specimens by 1.54 (p = 0.08) and 1.83 (p < 0.05) times, respectively, with the same level of CYP6D1 expression. PCR-RFLP analysis revealed a previously undescribed mutation in the promoter region of the CYP6D1 gene in adults of the Lab TY and ChlA strains, and it did not affect the gene expression level. The obtained results show that the development of resistance to chlorfenapyr in M. domestica is accompanied by an increase in P450-monooxygenase activity without changes in CYP6D1 expression. [ABSTRACT FROM AUTHOR]

Rapid advancements in gene technology have raised concerns regarding the potential abuse of techniques, such as gene doping, for enhancing athletic performance. To identify this possibility, a reliable procedure for detecting doping genes is required. Although detection methods for doping genes have been created, there are still areas for further improvement. One significant challenge is the high storage and transport costs of the test samples. For this issue, the dried blood spot (DBS) method can be a cost-effective solution. This study aimed to assess the practicality of incorporating DBSs into the gene doping detection process as a pilot study. Whole-blood samples were initially collected from mice engineered to express human erythropoietin from the rAAV vector. Then, the blood was placed in filter papers and left to dry at room temperature for five hours to form DBSs. These DBSs were subsequently preserved in sealed plastic bags at room temperature. After the extraction of DNA, DBSs were formed, and TaqMan-qPCR was utilized to detect the presence of rAAV vector-derived DNA. The finding confirmed that doping gene-specific fragments were successfully detected in DBSs. This outcome suggests that the DBS method is an effective approach to be considered when developing a comprehensive protocol for gene doping detection. [ABSTRACT FROM AUTHOR]

Reactive oxygen species (ROSs) are byproducts of normal cellular metabolism and play pivotal roles in various physiological processes. Disruptions in the balance between ROS levels and the body's antioxidant defenses can lead to the development of numerous diseases. Glutathione peroxidase 3 (GPX3), a key component of the body's antioxidant system, is an oxidoreductase enzyme. GPX3 mitigates oxidative damage by catalyzing the conversion of hydrogen peroxide into water. Beyond its antioxidant function, GPX3 is vital in regulating metabolism, modulating cell growth, inducing apoptosis and facilitating signal transduction. It also serves as a significant tumor suppressor in various cancers. Recent studies have revealed aberrant expression of GPX3 in several non-neoplastic diseases, associating it with multiple pathological processes. This review synthesizes the current understanding of GPX3 expression and regulation, highlighting its extensive roles in noncancerous diseases. Additionally, this paper evaluates the potential of GPX3 as a diagnostic biomarker and explores emerging therapeutic strategies targeting this enzyme, offering potential avenues for future clinical treatment of non-neoplastic conditions. [ABSTRACT FROM AUTHOR]

Simple Summary: Simple Summary: The infectious bursal disease virus (IBDV) is a major pathogen in the poultry industry due to its profound impact on the immune systems of young chickens. Various genetic types with distinct biological characteristics have been described. In this study, we evaluated the expression of different cytokines in the bursa of Fabricius and thymus over a 28-day experimental infection with two strains from the G1a (Classical) and G6 (ITA) genogroups. The G6 strain appeared to induce a more immediate immunosuppression, and unlike the G1a strain, did not show signs of gene expression recovery by the end of the study. This finding aligns with the higher replication level previously reported for the G6 and with the clinical outcome, as this genotype, although subclinical, has often been considered more immunosuppressive. Unlike other studies that focused on shorter periods post-infection, the patterns observed in this study were highly variable and complex, depending on the strain, tissue, and time point. Therefore, this study not only confirms the effect of strain/genogroup on immune system modulation but also highlights the importance of extended monitoring post-infection to better understand the intricate patterns and interactions with the host response. Infectious bursal disease virus (IBDV) is a significant burden for poultry production and market due to both direct disease and induced immunosuppression. In the present study, the expression of different cytokines in the bursa of Fabricius and thymus was evaluated during a 28-day-long experimental infection with two strains classified in the G1a (Classical) and G6 (ITA) genogroups. Although both strains significantly affected and modulated the expression of different molecules, the G6 strain seemed to induce a delayed immune response or suppress it more promptly. A recovery in the expression of several mediators was observed in the G1a-infected group at the end of the study, but not in the G6 one, further supporting a more persistent immunosuppression. This evidence fits with the higher replication level previously reported for the G6 and with the clinical outcome, as this genotype, although subclinical, has often been considered more immunosuppressive. However, unlike other studies focused on shorter time periods after infection, the patterns observed in this paper were highly variable and complex, depending on the strain, tissue, and time point, and characterized by a non-negligible within-group variability. Besides confirming the strain/genogroup effect on immune system modulation, the present study suggests the usefulness of longer monitoring activities after experimental infection to better understand the complex patterns and interactions with the host response. [ABSTRACT FROM AUTHOR]

Spontaneous tumour formation in higher plants can occur in the absence of pathogen invasion, depending on the plant genotype. Spontaneous tumour formation on the taproots is consistently observed in certain inbred lines of radish (Raphanus sativus var. radicula Pers.). In this paper, using Oxford Nanopore and Illumina technologies, we have sequenced the genomes of two closely related radish inbred lines that differ in their ability to spontaneously form tumours. We identified a large number of single nucleotide variants (amino acid substitutions, insertions or deletions, SNVs) that are likely to be associated with the spontaneous tumour formation. Among the genes involved in the trait, we have identified those that regulate the cell cycle, meristem activity, gene expression, and metabolism and signalling of phytohormones. After identifying the SNVs, we performed Sanger sequencing of amplicons corresponding to SNV-containing regions to validate our results. We then checked for the presence of SNVs in other tumour lines of the radish genetic collection and found the ERF118 gene, which had the SNVs in the majority of tumour lines. Furthermore, we performed the identification of the CLAVATA3/ESR (CLE) and WUSCHEL (WOX) genes and, as a result, identified two unique radish CLE genes which probably encode proteins with multiple CLE domains. The results obtained provide a basis for investigating the mechanisms of plant tumour formation and also for future genetic and genomic studies of radish. [ABSTRACT FROM AUTHOR]

This research paper presents a novel approach to identifying biomarkers that can be used to prognosticate patients with triple-negative breast cancer (TNBC) eligible for neoadjuvant therapy. The study utilized survival and RNA sequencing data from a cohort of TNBC patients and identified 276 genes whose expression was related to survival in such patients. The gene expression data were then used to classify patients into two major groups based on the presence or absence of Wingless/Integrated-pathway (Wnt-pathway) and mesenchymal (Mes) markers (Wnt/Mes). Patients with a low expression of Wnt/Mes-related genes had a favorable outcome, with no deaths observed during follow-up, while patients with a high expression of Wnt/Mes genes had a higher mortality rate of 50% within 19 months. The identified gene list could be validated and potentially used to shape treatment options for TNBC patients eligible for neoadjuvant therapy providing valuable insights into the development of more effective treatments for TNBC. Our data also showed significant variation in gene expression profiles before and after chemotherapy, with most tumors switching to a more mesenchymal/stem cell-like profile. To verify this observation, we performed an in silico analysis to classify breast cancer tumors in Prediction Analysis of Microarray 50 (PAM50) molecular classes before treatment and after treatment using gene expression data. Our findings demonstrate that following drug intervention and metastasis, certain tumors undergo a transition to alternative subtypes, resulting in diminished therapeutic efficacy. This underscores the necessity for reevaluation of patients who have experienced relapse or metastasis post-chemotherapy, with a focus on molecular subtyping. Tailoring treatment strategies based on these refined subtypes is imperative to optimize therapeutic outcomes for affected individuals. [ABSTRACT FROM AUTHOR]

The identification of co-regulated genes and their Transcription-Factor Binding Sites (TFBSs) are the key steps toward understanding transcription regulation. In addition to effective laboratory assays, various bi-clustering algorithms for the detection of the co-expressed genes have been developed. Bi-clustering methods are used to discover subgroups of genes with similar expression patterns under to-be-identified subsets of experimental conditions when applied to gene expression data. By building two fuzzy partition matrices of the gene expression data with the Axiomatic Fuzzy Set (AFS) theory, this paper proposes a novel fuzzy bi-clustering algorithm for the identification of co-regulated genes. Specifically, the gene expression data are transformed into two fuzzy partition matrices via the sub-preference relations theory of AFS at first. One of the matrices considers the genes as the universe and the conditions as the concept, and the other one considers the genes as the concept and the conditions as the universe. The identification of the co-regulated genes (bi-clusters) is carried out on the two partition matrices at the same time. Then, a novel fuzzy-based similarity criterion is defined based on the partition matrices, and a cyclic optimization algorithm is designed to discover the significant bi-clusters at the expression level. The above procedures guarantee that the generated bi-clusters have more significant expression values than those extracted by the traditional bi-clustering methods. Finally, the performance of the proposed method is evaluated with the performance of the three well-known bi-clustering algorithms on publicly available real microarray datasets. The experimental results are in agreement with the theoretical analysis and show that the proposed algorithm can effectively detect the co-regulated genes without any prior knowledge of the gene expression data. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to analyze the prognostic and immunological value of CDC45 in kidney renal clear cell carcinoma (KIRC) using single-cell and bulk RNA-sequencing approaches. The expression of CDC45 in KIRC was evaluated by the HPA database, the TCGA-KIRC dataset and verified by PCR analysis and single-cell RNA-sequencing. The ability of CDC45 to independently predict prognosis in KIRC was confirmed by univariate/multivariate regression analysis. Gene set enrichment analysis (GSEA) was employed to explore CDC45-related pathways in KIRC. In addition, Relationships between CDC45 and immunity were also examined. Elevated CDC45 expression in KIRC was demonstrated at mRNA and protein levels. The results of the correlation analysis showed that as CDC45 expression increased, so did the histological grade, clinical stage, and TNM stage of the patients (p < 0.05). Univariate/multivariate regression analysis suggested CDC45 as an independent prognostic factor for KIRC. Seven pathways related to CDC45 were screened through GSEA. Meanwhile, we found that CDC45 was correlated with tumor mutational burden (TMB) and microsatellite instability (MSI) but not tumor neoantigen burden (TNB). Regarding immunity, CDC45 exhibited correlations with the tumor microenvironment, immune cell infiltration, and immune checkpoints. Besides, low CDC45 expression was shown to be associated with a better response to immunotherapy. Single-cell RNA-sequencing revealed that CDC45 was differently expressed in T cells (p < 0.05). CDC45 showed potential as a prognostic biomarker and therapeutic target for KIRC. Meanwhile, the CDC45 low expression group was more sensitive to immunotherapy. [ABSTRACT FROM AUTHOR]