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The article announces the winners of Development's Outstanding Paper Prize for papers published in 2023. The winning papers focus on developmental and stem cell biology. One paper demonstrates the potential of genome engineering techniques and pharmacological upregulation of the Wnt signaling pathway in treating frizzled 2-associated congenital malformations in mouse models. Another paper explores how Frizzled2 receives WntA signaling during butterfly wing pattern formation, providing insights into the complexity and evolution of signaling pathways. The article also mentions a study on the development of primordial germ cell-like cells using stem cell technologies. Additionally, the article discusses a study on the molecular functioning of WntA in butterfly wing color patterning, which involved collaboration between international teams and faced challenges in rearing and injecting butterflies in the lab. The researchers plan to further investigate butterfly color patterns. [Extracted from the article]

This article features an interview with Jingjing Sun and Angelike Stathopoulos, who conducted a study on the collective migration of caudal visceral mesoderm (CVM) cells in Drosophila embryos. They discovered that cell division coordinates two gene expression programs in migrating CVM cells. The researchers discuss their scientific backgrounds and the questions they are trying to answer in their lab. They also highlight the importance of the Drosophila system for their research. The study's key findings include the identification of two groups of genes that regulate the behaviors of CVM cells during migration, and the role of cell division in controlling gene expression programs. The researchers also discuss their motivations and future directions for their research. [Extracted from the article]

A high mortality rate was found in cancer patients with distant metastasis. Development of targeted drugs for effectively inhibiting cancer metastasis is the key for increasing therapeutic success. In the current study, a folding paper system was developed to mimic a tumor–vascular interface for the study of long-distance cell migration. Correlation between the cell migration distance and metastasis-associated gene was successfully analyzed by disassembling the stacked paper construct. The result revealed that the migration distance and number of migrated cells were highly correlated to cell characteristics. Moreover, immunocytochemistry was directly conducted on the paper layer to study the signaling pathway. Kelch-like and protein tyrosine phosphatase families were examined and the PTPN13 gene was shown to regulate long-distance cell migration. By analyzing the phosphorylated mTOR, the PTPN13 gene was further confirmed to be a tumor suppressor gene that inhibits long-distance cell migration. The folding paper system provides an alternative approach for long-distance cell migration. Metastasis-associated gene expression can be analyzed to potentially develop targeted drugs for cancer metastasis inhibition. [ABSTRACT FROM AUTHOR]

This article is an interview with Charlotte Manser and Ruben Perez-Carrasco, discussing their recent paper on the molecular basis of developmental timing. The authors developed a mathematical framework to explore the tempo of development and identify molecular mechanisms that regulate it. They studied gene expression orbits and used mathematical methods to define function and tempo in a quantitative way. The paper aims to reach experimentalists in the developmental biology community and is published in the journal Development. Manser and Perez-Carrasco plan to continue exploring the role of tempo in different developmental systems and are excited about potential applications in synthetic biology and immunology. Outside of their research, they enjoy playing Dungeons & Dragons and have a shared love for music. [Extracted from the article]

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]

Paper mulberry vein banding virus (PMVBV), a member of the genus Badnavirus in the family Caulimoviridae, infects paper mulberry (Broussonetia papyrifera), a dicotyledonous plant. Putative promoter regions in the PMVBV genome were tested using recombinant plant expression vectors, revealing that the promoter activity of three genome fragments was about 1.5-fold higher than that of the 35S promoter of cauliflower mosaic virus in Nicotiana benthamiana. In transformed transgenic Arabidopsis thaliana plants, these promoter constructs showed constitutive expression. Based on the activity and gene expression patterns of these three promoter constructs, a fragment of 384 bp (named PmVP) was deduced to contain the full-length promoter of the PMVBV genome. The results suggest that the PMVBV-derived promoter can be used for the constitutive expression of transgenes in dicotyledonous plants. [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]

Few animals are known to individually recognize conspecifics, i.e. learn and recall unique individuals during subsequent encounters, and nearly all are social vertebrates. Remarkably, the social paper wasp Polistes fuscatus has recently been discovered to possess this ability, which is useful for remembering identities during competitive social interactions. We analyzed brain gene expression in staged encounters between pairs of individuals to explore potential mechanisms underlying wasps' ability to recall familiar individuals using real-time qRT-PCR. We identified four candidate genes ( IP3K, IP3R, Nckx30C and Su(var)2- 10) that were down-regulated in the presence of familiar individuals compared to single wasps and pairs of wasps meeting for the first time. These candidate genes are related to calcium signaling, therefore, we treated wasps with lithium chloride, a pharmacological agent that inhibits calcium signaling in neurons. This treatment decreased aggression in paper wasps, but did not affect expression of genes related to calcium signaling. The results suggest calcium signaling differences may be related to individual memory recall in wasps, and we present four promising candidate genes for future study. These data suggest genes associated with dominance behavior may be co-opted for individual recognition, but further work is needed to establish a causal association with the behavior. [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]

The article cites a research study that examines the role of MiR- Par3L axis in breast cancer progression. It examined normal tissues and Par3L expressions in breast cancer tissues and cells. Par3L was found to be frequently down-regulated in human breast cancer cells and tissues and Par3L low-expression was associated with aggressive clinicopathological features and poor clinical outcome of breast cancer patients.

DNA methylation is a biochemical process that modifies gene expression without changing the underlying DNA sequence, and this represents the molecular basis for imprinting and epigenetics. Recent reports have revealed alterations in DNA methylation profiles in the placenta of babies born from assisted reproductive technologies (ART). This supports several previous observations that suggested an increase in the prevalence of imprinting diseases following ART treatment, and also fits our observations regarding the metabolism and requirements of early human embryos. Human embryo culture media (HECM) are currently formulated according to requirements based on the mouse embryo model, and in fact need to pass the Mouse Embryo Assay test in order to be accepted by the relevant authorities, despite the fact that physiological (especially the time necessary to reach genomic activation) and biochemical requirements of mouse and human embryos are quite different. This commentary aims to explain some of the discrepancies, and emphasize why human embryo metabolism tells us that the composition of HECM, as well as the role of the MEA as a unique model, should be re-evaluated. [ABSTRACT FROM AUTHOR]

Increasing cellulase production in cellulolytic fungus Trichoderma reesei is of interest for biofuels and biorefineries. Previous studies indicated that secreted protein was occasionally accumulated in vacuoles; this phenomenon has also been reported in T. reesei. Therefore, alleviating vacuolar transport seems to be a promising strategy for improving cellulase production in T. reesei. Herein, we found that knockout of vps10, vps13, and vps21, among 11 vacuolar protein sorting factors, improved cellulase production in T. reesei. The filter paper activity in Δvps10, Δvps13, and Δvps21 increased by 1.28-, 2.45-, and 2.11-fold than that of the parent strain. Moreover, the β-glucosidase activity in Δvps13 and Δvps21 increased by 3.22- and 3.56-fold after 6 days of fermentation. Furthermore, we also found that the vacuolar trafficking towards vacuoles was partially impaired in three knockout mutants, and disruption of vps13 alleviated the autophagy process. These results indicated that alleviated transport and degradation towards vacuole in Δvps10, Δvps13, and Δvps21 might improve cellulase production. Of note, the expression of cellulase genes in Δvps13 and Δvps21 was dramatically increased in the late induction phase compared to the parent. These results suggested that Vps13 and Vps21 might influence the cellulase production at transcription level. And further transcriptome analysis indicated that increased cellulase gene expression might be attributed to the differential expression of sugar transporters. Our study unravels the effect of alleviating vacuolar transport through knockout vps10, vps13, and vps21 for efficient cellulase secretion, providing new clues for higher cellulase production in T. reesei. Key points: • Disruption of vps10, vps13 or vps21 improves cellulase production • Vacuolar transport is impaired in three vps KO mutants • Deletion of vps13 or vps21 increases the transcript of cellulase genes in late stage [ABSTRACT FROM AUTHOR]

Diabetes alters the function of ion channels responsible for regulating arterial smooth muscle membrane potential, resulting in vasoconstriction. Our prior research demonstrated an elevation of TMEM16A in diabetic arteries. Here, we explored the mechanisms involved in Transmembrane protein 16A (TMEM16A) gene expression. Our data indicate that a Snail-mediated repressor complex regulates arterial TMEM16A gene transcription. Snail expression was reduced in diabetic arteries while TMEM16A expression was upregulated. The TMEM16A promoter contained three canonical E-box sites. Electrophoretic mobility and super shift assays revealed that the −154 nt E-box was the binding site of the Snail repressor complex and binding of the repressor complex decreased in diabetic arteries. High glucose induced a biphasic contractile response in pressurized nondiabetic mouse hindlimb arteries incubated ex vivo. Hindlimb arteries incubated in high glucose also showed decreased phospho-protein kinase D1 and TMEM16A expression. In hindlimb arteries from nondiabetic mice, administration of a bolus dose of glucose activated protein kinase D1 signaling to induce Snail degradation. In both in vivo and ex vivo conditions, Snail expression exhibited an inverse relationship with the expression of protein kinase D1 and TMEM16A. In diabetic mouse arteries, phospho-protein kinase D1 increased while Akt2 and pGSK3β levels declined. These results indicate that in nondiabetic mice, high glucose triggers a transient deactivation of the Snail repressor complex to increase arterial TMEM16A expression independently of insulin signaling. Conversely, insulin resistance activates GSK3β signaling and enhances arterial TMEM16A channel expression. These data have uncovered the Snail-mediated regulation of arterial TMEM16A expression and its dysfunction during diabetes. NEW & NOTEWORTHY: The calcium-activated chloride channel, TMEM16A, is upregulated in the diabetic vasculature to cause increased vasoconstriction. In this paper, we have uncovered that the TMEM16A gene expression is controlled by a Snail-mediated repressor complex that uncouples with both insulin-dependent and -independent pathways to allow for upregulated arterial protein expression thereby causing vasoconstriction. The paper highlights the effect of short- and long-term glucose-induced dysfunction of an ion channel expression as a causative factor in diabetic vascular disease. [ABSTRACT FROM AUTHOR]

Colorectal cancer is a malignant tumor with high morbidity and mortality worldwide. Histone lactylation, a new protein post-translational modification, can be used as a potential biomarker for the diagnosis and treatment of colorectal cancer by regulating the structure of chromatin and gene transcription. This paper focuses on the role and mechanism of histone lactylation modification in the development of colorectal cancer. Additionally, the latest research progress and clinical experimental results are presented. This paper further shows that histone lactylation manipulates gene expression by affecting nuclear transcription factors and other epigenetic modifications involved in various life processes and subsequently regulates cell physiological function, revealing its regulatory mechanism and the functions of related enzymes. The relationships between histone lactylation and energy metabolic pathways such as glycolysis and oxidative phosphorylation are discussed in detail. The final part of this paper illustrates the important role of this modification in terms of the occurrence and development of colorectal cancer, cell metabolic reprogramming, and epigenetic regulation, supporting the clinical application of the use of histone lactylation as a biomarker. [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]

Abstract: Dried blood spot samples (DBSS) on filter paper offer several advantages compared to conventional serum/plasma samples: they do not require any phlebotomy or separation of blood by centrifugation; they are less invasive; they allow sample stability and shipment at room temperature; and they pose a negligible risk of infection with blood-borne viruses, such as HIV, HBV and HCV, to those who handle them. Therefore dried blood spot samples (DBSS) on filter paper can be a quick, convenient and inexpensive means of obtaining blood samples for biomarker discovery, disease screening, diagnosis and treatment monitoring in non-hospitalized, public health settings. In this study, we investigated for the first time the potential application of dried blood spot samples (DBSS) in protein expression profiling using antibody array technology. First, optimal conditions for array assay performance using dried blood spot samples (DBSS) was established, including sample elution buffer, elution time, elution temperature and assay blocking buffer. Second, we analyzed dried blood spot samples (DBSS) using three distinct antibody array platforms, including sandwich-based antibody arrays, quantitative antibody arrays and biotin-label-based antibody arrays. In comparison with paired serum samples, detection of circulating proteins in dried blood spot samples (DBSS) correlated well for both low- and high-abundance proteins on all three antibody array platforms. In conclusion, our study strongly indicates the novel application of multiplex antibody array platforms to analyze dried blood spot samples (DBSS) on filter paper represents a viable, cost-effective method for protein profiling, biomarker discovery and disease screening in a large, population-based survey. [Copyright &y& Elsevier]

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]

Various reviews and meta‐analyses have shown the positive effects of mindfulness‐based interventions (MBIs) on the mental health of cancer patients and survivors. Some studies have also investigated the impact of MBIs on physiological markers of health in oncology, but a systematic review has not been conducted in this field. The current paper aims to fill this gap in the literature. Following preferred reporting items for systematic reviews and meta‐analyses 2020 guidelines, data were obtained from the databases of Pubmed, Scopus, Web of Science in May 2022. Twenty‐five studies were included. Globally, 35 biomarkers were employed in these studies and were categorized 8 groups (cortisol; blood pressure (BP), heart rate, and respiratory rate; C‐reactive protein; telomere length and telomerase activity (TA); genetic signature; cytokines and hormones; leucocyte activation; leucocyte count and cell subpopulation analysis). In seven of these categories of biomarkers, positive effects of MBIs were observed. The most promising results were obtained for cortisol, BP, TA and pro‐inflammatory gene expression. However, the generally low number of studies per single biomarker limits the possibility to draw reliable conclusions. The present review presents a comprehensive state‐of‐the‐art for MBIs in oncology on biomarkers, confirming MBIs' potential for improving physiological health in cancer patients and survivors besides those already shown in literature on psychological well‐being. [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]

Genetically identical cell populations growing in the same environment show a large degree of heterogeneity in gene expression profiles, resulting in significant phenotypic consequences. One of the important sources of this heterogeneity is transcriptional bursting. In recent years, single cell transcriptome sequencing (scRNA-seq) data have been widely used to infer the kinetics of transcriptional bursting. Moreover, the increasing evidence showed that genes are jointly regulated by several competitive pathways when activated by external signals, and there is molecular memory in the process of gene state switching. Therefore, a gene expression model considering both gene activation pathway and molecular memory can better reflect the nature of transcriptional bursting. In this paper, we used an approximate Bayesian computation algorithm called ABC-PRC combined with a gene expression model that considered crosstalk and memory, to infer the kinetics of transcriptional bursting. We analyze the internal relationship between transcriptional bursting and gene expression using scRNA-seq data obtained from mouse embryonic stem cells and mouse embryonic fibroblasts. Both model analysis and data simulations demonstrate that the bimodal coefficient increases with the increase of burst size, and the noise intensity decreases with the increase of burst frequency. Additionally, we show that some previously proposed models can be simplified as special cases of the gene expression model and inference algorithm proposed here under certain circumstances. 1. Genome-wide transcriptional bursting kinetics are inferred by approximate Bayesian algorithm ABC-PRC both from scRNA-seq data and gene expression models. 2. This article uses a combination of data-driven and model-driven methods to study the intrinsic relationship between transcriptional bursting and gene expression. 3. The model and inference process used in this article can be simplified and widely applied to the classical telegraph model and the generalized telegraph model. [ABSTRACT FROM AUTHOR]

A growing literature suggests that gene expression can be greatly altered in disease conditions, and identifying those changes will improve the understanding of complex diseases such as cancers or diabetes. A prevailing direction in the analysis of gene expression studies the changes in gene pathways which include sets of related genes. Therefore, introducing structured exploration to differential analysis of gene expression networks may lead to meaningful discoveries. The topic of this paper is differential network analysis, which focuses on capturing the differences between two or more precision matrices. We discuss the connection between the thresholding method and the D-trace loss method on differential network analysis in the case that the precision matrices share the common connected components. Based on this connection, we further propose the cluster D-trace loss method which directly estimates the differential network and achieves model selection consistency. Simulation studies demonstrate its improved performance and computational efficiency. Finally, the usefulness of our proposed estimator is demonstrated by a real-data analysis on non-small cell lung cancer. [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]

The hedgehog signaling pathway plays an important role in early development and growth of most vertebrates. Sonic hedgehog (shh) gene is a critical regulator of embryonic development in many species, including humans. However, it is not clear what roles shh can play in the development of fish. In this paper, shh gene was cloned from Pseudopleuronectes yokohamae. The full‐length complementary DNA (cDNA) of P. yokohamae sonic hedgehog gene (Pyshh) comprises 3194 bp, with a 1317‐bp open reading frame (ORF) that encodes a polypeptide of 438 amino acids with a typical HH‐signal domain and Hint‐N domain. The conserved sequences of the protein among species were predicted by using multiple sequence comparison. The phylogenetic tree construction showed that PySHH is clustered in a branch of Pleuronectidae. To explore the expression of Pyshh gene in various tissues of P. yokohamae, we used real‐time fluorescence quantitative PCR technology to detect it. The results showed that Pyshh gene is widely distributed in various tissues of P. yokohamae juveniles, different tissues of adult males and females, and is particularly expressed in immune organs. The Pyshh gene expression was higher in the muscle and brain of juvenile fish, and higher in bone, gill, and skin of male fish than that of female fish, suggesting that Pyshh might be involved in the formation of immune organs of P. yokohamae. The expression of Pyshh gene significantly upregulated from the gastrula stage to the hatching stage. Western blotting of the expression levels of PySHH during different embryonic development stages revealed that PySHH levels increased gradually during development stages from oosperm stage to hatching stage. These results indicate that Pyshh is highly conserved among species and plays a critical role in the complex process of embryonic development. Its precise regulation is essential for the proper formation of many organs and tissues in the body, and disruptions in its function may have serious consequences for the formation of immune organs in fish. [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]

Introduction: Sports activity increases PGC1α and Nrf2, the regulatory factors of mitochondrial biogenesis. This paper aims to study the impact of two-month sodium citrate supplementation with Moderate-Intensity Continuous Training (MICT) on PGC-1α and Nrf2 expression in diabetic rats. Methods: Forty-five three-month-old male Wistar rats were haphazardly assigned to one of five equal groups (N=9): (1) healthy; (2) diabetic; (3) diabetes + exercise (DE); (4) diabetes+ supplementation (DS); and (5) diabetic + exercise + supplementation (DSE), matched according to their weights. After induction, exercises began on a treadmill for 8 weeks, five days a week. The MICT protocol ran at 70% of their maximum speed for 36 minutes. The rats supplemented with sodium-citrate- at 15 mmol/L in drinking water for two months. PGC-1α and Nrf2 expression were measured through Western blotting in the soleus muscle. Data were analyzed using univariate analysis of variance (ANOVA) and the Tukey post-hoc test. Cohen’s D effect size (ES) was calculated to compare the groups. Results: The results showed that induction of diabetes significantly reduced the expression of PGC-1α (P< 0.001; ES=1.36) and Nrf2 (P<0.088; ES=0.24), while exercise increased PGC-1α expression (P<0.001; ES=0.68). Sodium citrate supplementation, either alone or in combination with MICT activity, did not show a clear advantage for Nrf2 expression. Conclusion: MICT activity and sodium citrate supplementation, by increasing PGC-1α expression, can be considered therapeutic strategies for diabetic patients. However, to increase Nrf2 expression, further studies with different exercise intensities and doses of sodium citrate supplementation are needed. [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]