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The tomato parthenocarpic fruit (pat) mutation associates a strong competence for parthenocarpy with homeotic transformation of anthers and aberrancy of ovules. To dissect this complex floral phenotype, genes involved in the pollination-independent fruit set of the pat mutant were investigated by microarray analysis using wild-type and mutant ovaries. Normalized expression data were subjected to one-way ANOVA and 2499 differentially expressed genes (DEGs) displaying a >1.5 log-fold change in at least one of the pairwise comparisons analyzed were detected. DEGs were categorized into 20 clusters and clusters classified into five groups representing transcripts with similar expression dynamics. The “regulatory function” group (685 DEGs) contained putative negative or positive fruit set regulators, “pollination-dependent” (411 DEGs) included genes activated by pollination, “fruit growth-related” (815 DEGs) genes activated at early fruit growth. The last groups listed genes with different or similar expression pattern at all stages in the two genotypes. qRT-PCR validation of 20 DEGs plus other four selected genes assessed the high reliability of microarray expression data; the average correlation coefficient for the 20 DEGs was 0.90. In all the groups were evidenced relevant transcription factors encoding proteins regulating meristem differentiation and floral organ development, genes involved in metabolism, transport and response of hormones, genes involved in cell division and in primary and secondary metabolism. Among pathways related to secondary metabolites emerged genes related to the synthesis of flavonoids, supporting the recent evidence that these compounds are important at the fruit set phase. Selected genes showing a de-regulated expression pattern in pat were studied in other four parthenocarpic genotypes either genetically anonymous or carrying lesions in known gene sequences. This comparative approach offered novel insights for improving the present molecular understanding of fruit set and parthenocarpy in tomato.

Camellia japonica var. decumbens is a naturally occurring highly cold resistant variety of Camellia japonica which is suitable for snowy and cold regions. However, the underlying cold-adaptive mechanisms associated with gene regulation have been poorly investigated. We analyzed the transcriptomic changes caused by cold stress in a cold-tolerant accession. Samples were collected at the end of each temperature treatment (T1, T3, T5, T7 and T9 represent the temperatures 25°C, 0°C, -4°C, -8°C and -12°C, respectively). Sample T1 at 25°C was used as control. Based on transcriptome analysis, 2828, 2384, 3099 and 3075 differentially expressed genes (DEGs) were up-regulated, and 3184, 2592, 2373 and 2615 DEGs were down-regulated by analyzing T3/T1, T5/T1, T7/T1 and T9/T1, respectively. A gene ontology (GO) analysis revealed an enrichment of GO terms such as response to stimulus, metabolic process, catalytic activity or binding. Out of the larger number of DEGs, 67 functional and regulatory DEGs stood out, since they were functionally characterized in other models. These genes are cold-responsive transcription factors (26) or involved in cold sensor or signal transduction (17) and in the stabilization of the plasma membrane and osmosensing response (24). These results suggest rapid and multiple molecular mechanisms of perception, transduction and responses to cold stress in cold acclimation of Camellia japonica var. decumbens. They could also serve as a valuable resource for relevant research on cold-tolerance and help to explore cold-related genes to foster the understanding of low-temperature tolerance and plant-environment interactions.

The adult mammalian heart has limited capacity for regeneration following injury, whereas the neonatal heart can readily regenerate within a short period after birth. To uncover the molecular mechanisms underlying neonatal heart regeneration, we compared the transcriptomes and epigenomes of regenerative and nonregenerative mouse hearts over a 7-d time period following myocardial infarction injury. By integrating gene expression profiles with histone marks associated with active or repressed chromatin, we identified transcriptional programs underlying neonatal heart regeneration, and the blockade to regeneration in later life. Our results reveal a unique immune response in regenerative hearts and a retained embryonic cardiogenic gene program that is active during neonatal heart regeneration. Among the unique immune factors and embryonic genes associated with cardiac regeneration, we identified Ccl24, which encodes a cytokine, and Igf2bp3, which encodes an RNA-binding protein, as previously unrecognized regulators of cardiomyocyte proliferation. Our data provide insights into the molecular basis of neonatal heart regeneration and identify genes that can be modulated to promote heart regeneration.

An invaluable tool in Bioinformatics, this unique volume provides both theoretical and experimental results, and describes basic principles of computational intelligence and pattern analysis while deepening the reader's understanding of the ways in which these principles can be used for analyzing biological data in an efficient manner.This book synthesizes current research in the integration of computational intelligence and pattern analysis techniques, either individually or in a hybridized manner. The purpose is to analyze biological data and enable extraction of more meaningful information and insight from it. Biological data for analysis include sequence data, secondary and tertiary structure data, and microarray data. These data types are complex and advanced methods are required, including the use of domain-specific knowledge for reducing search space, dealing with uncertainty, partial truth and imprecision, efficient linear and/or sub-linear scalability, incremental approaches to knowledge discovery, and increased level and intelligence of interactivity with human experts and decision makersChapters authored by leading researchers in CI in biology informatics.Covers highly relevant topics: rational drug design; analysis of microRNAs and their involvement in human diseases.Supplementary material included: program code and relevant data sets correspond to chapters.Note: The ebook version does not provide access to the companion files.

Background As large-scale studies of gene expression with multiple sources of biological and technical variation become widely adopted, characterizing these drivers of variation becomes essential to understanding disease biology and regulatory genetics. Results We describe a statistical and visualization framework, variancePartition, to prioritize drivers of variation based on a genome-wide summary, and identify genes that deviate from the genome-wide trend. Using a linear mixed model, variancePartition quantifies variation in each expression trait attributable to differences in disease status, sex, cell or tissue type, ancestry, genetic background, experimental stimulus, or technical variables. Analysis of four large-scale transcriptome profiling datasets illustrates that variancePartition recovers striking patterns of biological and technical variation that are reproducible across multiple datasets. Conclusions Our open source software, variancePartition, enables rapid interpretation of complex gene expression studies as well as other high-throughput genomics assays. variancePartition is available from Bioconductor: http://bioconductor.org/packages/variancePartition .

Circulating in China and 158 other countries and areas, the ongoing COVID-19 outbreak has caused devastating mortality and posed a great threat to public health. However, efforts to identify effectively supportive therapeutic drugs and treatments has been hampered by our limited understanding of host immune response for this fatal disease. To characterize the transcriptional signatures of host inflammatory response to SARS-CoV-2 (HCoV-19) infection, we carried out transcriptome sequencing of the RNAs isolated from the bronchoalveolar lavage fluid (BALF) and peripheral blood mononuclear cells (PBMC) specimens of COVID-19 patients. Our results reveal distinct host inflammatory cytokine profiles to SARS-CoV-2 infection in patients, and highlight the association between COVID-19 pathogenesis and excessive cytokine release such as CCL2/MCP-1, CXCL10/IP-10, CCL3/MIP-1A, and CCL4/MIP1B. Furthermore, SARS-CoV-2 induced activation of apoptosis and P53 signalling pathway in lymphocytes may be the cause of patients' lymphopenia. The transcriptome dataset of COVID-19 patients would be a valuable resource for clinical guidance on anti-inflammatory medication and understanding the molecular mechansims of host response.

In the last decades, transcriptome profiling has been one of the most utilized approaches to investigate human diseases at the molecular level. Through expression studies, many molecular biomarkers and therapeutic targets have been found for several human pathologies. This number is continuously increasing thanks to total RNA sequencing. Indeed, this new technology has completely revolutionized transcriptome analysis allowing the quantification of gene expression levels and allele-specific expression in a single experiment, as well as to identify novel genes, splice isoforms, fusion transcripts, and to investigate the world of non-coding RNA at an unprecedented level. RNA sequencing has also been employed in important projects, like ENCODE (Encyclopedia of the regulatory elements) and TCGA (The Cancer Genome Atlas), to provide a snapshot of the transcriptome of dozens of cell lines and thousands of primary tumor specimens. Moreover, these studies have also paved the way to the development of data integration approaches in order to facilitate management and analysis of data and to identify novel disease markers and molecular targets to use in the clinics. In this scenario, several ongoing clinical trials utilize transcriptome profiling through RNA sequencing strategies as an important instrument in the diagnosis of numerous human pathologies.

Respiratory syncytial virus (RSV) is the leading cause of acute lower respiratory tract infections and hospitalizations in infants worldwide. Known risk factors, however, incompletely explain the variability of RSV disease severity, especially among healthy children. We postulate that the severity of RSV infection is influenced by modulation of the host immune response by the local bacterial ecosystem. To assess whether specific nasopharyngeal microbiota (clusters) are associated with distinct host transcriptome profiles and disease severity in children less than 2 years of age with RSV infection. We characterized the nasopharyngeal microbiota profiles of young children with mild and severe RSV disease and healthy children by 16S-rRNA sequencing. In parallel, using multivariable models, we analyzed whole-blood transcriptome profiles to study the relationship between microbial community composition, the RSV-induced host transcriptional response, and clinical disease severity. We identified five nasopharyngeal microbiota clusters characterized by enrichment of either Haemophilus influenzae, Streptococcus, Corynebacterium, Moraxella, or Staphylococcus aureus. RSV infection and RSV hospitalization were positively associated with H. influenzae and Streptococcus and negatively associated with S. aureus abundance, independent of age. Children with RSV showed overexpression of IFN-related genes, independent of the microbiota cluster. In addition, transcriptome profiles of children with RSV infection and H. influenzae- and Streptococcus-dominated microbiota were characterized by greater overexpression of genes linked to Toll-like receptor and by neutrophil and macrophage activation and signaling. Our data suggest that interactions between RSV and nasopharyngeal microbiota might modulate the host immune response, potentially affecting clinical disease severity.

[This corrects the article DOI: 10.3389/fimmu.2025.1630172.].

Background We used a hybrid machine learning systems (HMLS) strategy that includes the extensive search for the discovery of the most optimal HMLSs, including feature selection algorithms, a feature extraction algorithm, and classifiers for diagnosing breast cancer. Hence, this study aims to obtain a high-importance transcriptome profile linked with classification procedures that can facilitate the early detection of breast cancer. Methods In the present study, 762 breast cancer patients and 138 solid tissue normal subjects were included. Three groups of machine learning (ML) algorithms were employed: (i) four feature selection procedures are employed and compared to select the most valuable feature: (1) ANOVA; (2) Mutual Information; (3) Extra Trees Classifier; and (4) Logistic Regression (LGR), (ii) a feature extraction algorithm (Principal Component Analysis), iii) we utilized 13 classification algorithms accompanied with automated ML hyperparameter tuning, including (1) LGR; (2) Support Vector Machine; (3) Bagging; (4) Gaussian Naive Bayes; (5) Decision Tree; (6) Gradient Boosting Decision Tree; (7) K Nearest Neighborhood; (8) Bernoulli Naive Bayes; (9) Random Forest; (10) AdaBoost, (11) ExtraTrees; (12) Linear Discriminant Analysis; and (13) Multilayer Perceptron (MLP). For evaluating the proposed models' performance, balance accuracy and area under the curve (AUC) were used. Results Feature selection procedure LGR + MLP classifier achieved the highest prediction accuracy and AUC (balanced accuracy: 0.86, AUC = 0.94), followed by an LGR + LGR classifier (balanced accuracy: 0.84, AUC = 0.94). The results showed that achieved AUC for the LGR + LGR classifier belonged to the 20 biomarkers as follows: TMEM212, SNORD115-13, ATP1A4, FRG2, CFHR4, ZCCHC13, FLJ46361, LY6G6E, ZNF323, KRT28, KRT25, LPPR5, C10orf99, PRKACG, SULT2A1, GRIN2C, EN2, GBA2, CUX2, and SNORA66. Conclusions The best performance was achieved using the LGR feature selection procedure and MLP classifier. Results show that the 20 biomarkers had the highest score or ranking in breast cancer detection.