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At least 1,153 records · Page 64Linked to original sources
PubMed2006

Genes from Chagas susceptibility loci that are differentially expressed in T. cruzi-resistant mice are candidates accounting for impaired immunity.

Variation between inbred mice of susceptibility to experimental Trypanosoma cruzi infection has frequently been described, but the immunogenetic background is poorly understood. The outcross of the susceptible parental mouse strains C57BL/6 (B6) and DBA/2 (D2), B6D2F1 (F1) mice, is highly resistant to this parasite. In the present study we show by quantitative PCR that the increase of tissue parasitism during the early phase of infection is comparable up to day 11 between susceptible B6 and resistant F1 mice. A reduction of splenic parasite burdens occurs thereafter in both strains but is comparatively retarded in susceptible mice. Splenic microarchitecture is progressively disrupted with loss of follicles and B lymphocytes in B6 mice, but not in F1 mice. By genotyping of additional backcross offspring we corroborate our earlier findings that susceptibility maps to three loci on Chromosomes 5, 13 and 17. Analysis of gene expression of spleen cells from infected B6 and F1 mice with microarrays identifies about 0.3% of transcripts that are differentially expressed. Assuming that differential susceptibility is mediated by altered gene expression, we propose that the following differentially expressed transcripts from these loci are strong candidates for the observed phenotypic variation: H2-Ealpha, H2-D1, Ng23, Msh5 and Tubb5 from Chromosome 17; and Cxcl11, Bmp2k and Spp1 from Chromosome 5. Our results indicate that innate mechanisms are not of primary relevance to resistance of F1 mice to T. cruzi infection, and that differential susceptibility to experimental infection with this protozoan pathogen is not paralleled by extensive variation of the transcriptome.

Animals↗
PubMed2026

Comparative genomics reveals genotype-phenotype concordance and cryptic resistomes in clinical Pseudomonas aeruginosa.

BACKGROUND: Pseudomonas aeruginosa (P. aeruginosa) is a major pathogen because of its adaptability. It shows rapid evolution of multidrug resistance (MDR). Phenotype-based diagnostics often fail to detect silent resistance determinants and early adaptive changes. This study integrates phenotypic profiling with whole-genome sequencing (WGS) to examine resistance architecture in clinical isolates from eastern India. METHODS: From 1295 culture-positive P. aeruginosa specimens collected at a tertiary care hospital in eastern India. Using predefined criteria, representative MDR and non-MDR isolates were selected, including distinct resistance phenotypes, specimen-source diversity, and hospital and community-acquired settings; multivariate analysis of resistance profiles illustrated phenotypic diversity. Antimicrobial susceptibility assessed using VITEK-2 and Kirby-Bauer disk diffusion, species identity confirmed by 16 S rRNA sequencing, and genomic analysis processed through a reference-guided workflow. Antimicrobial Resistance (AMR) determinants were identified through CARD, and phylogenetic tree constructed from 454 publicly available P. aeruginosa genomes. RESULTS: MDR exhibited greater sequence divergence relative to PA14 (~ 69,000 variants) than the non-MDR isolate (~ 58,700 variants), with > 92% coverage at ≥ 30X depth. Strong genotype-phenotype concordance observed in MDR isolates across five antibiotic classes, associated with β-lactamase variants (PDC-67, OXA-396) and regulatory adaptations (ArmR, cprS). The non-MDR isolate harboured gyrA (T83I) resistance-associated mutations, PDC-1, and OXA-847 without phenotypic expression, indicating silent resistome. Phylogenetically, MDR isolates clustered tightly within the phylogeny, while the non-MDR isolate formed a distinct lineage. CONCLUSION: Observed genomic differences align with adaptation under antimicrobial selection, though confirmation requires larger collections. The non-MDR isolate retained a silent resistome. Findings highlight limitations of phenotype-only diagnostics, support genomic data integration, and emphasize transcriptomics for hidden resistance expression and regulatory dynamics.

Pseudomonas aeruginosa↗
PubMed2006

Searching for IRES.

The cell has many ways to regulate the production of proteins. One mechanism is through the changes to the machinery of translation initiation. These alterations favor the translation of one subset of mRNAs over another. It was first shown that internal ribosome entry sites (IRESes) within viral RNA genomes allowed the production of viral proteins more efficiently than most of the host proteins. The RNA secondary structure of viral IRESes has sometimes been conserved between viral species even though the primary sequences differ. These structures are important for IRES function, but no similar structure conservation has yet to be shown in cellular IRES. With the advances in mathematical modeling and computational approaches to complex biological problems, is there a way to predict an IRES in a data set of unknown sequences? This review examines what is known about cellular IRES structures, as well as the data sets and tools available to examine this question. We find that the lengths, number of upstream AUGs, and %GC content of 5'-UTRs of the human transcriptome have a similar distribution to those of published IRES-containing UTRs. Although the UTRs containing IRESes are on the average longer, almost half of all 5'-UTRs are long enough to contain an IRES. Examination of the available RNA structure prediction software and RNA motif searching programs indicates that while these programs are useful tools to fine tune the empirically determined RNA secondary structure, the accuracy of de novo secondary structure prediction of large RNA molecules and subsequent identification of new IRES elements by computational approaches, is still not possible.

5' Untranslated Regions↗
PubMed2026

Genomic and transcriptomic features of relapsed small cell lung cancer.

BACKGROUND: Relapsed small cell lung cancer is characterized by treatment resistance and poor outcomes. Genomic and transcriptomic alterations in relapsed SCLC have not been characterized well. We comprehensively profiled relapsed SCLC samples along with patient-matched treatment-naive samples, when available, using whole-exome (WES), whole-genome (WGS), and RNA-sequencing (RNA-seq) to describe the molecular landscape of relapsed SCLC. Our goal is to identify potential novel pathways for additional functional validation and eventually novel therapeutic options. METHODS: We analyzed 54 relapsed and 27 treatment-naive SCLC samples using WES (with 26 patient-matched paired samples). A subset of the samples was also analyzed by WGS (n=28) and RNA-seq (n=31). Differences in mutational signatures, gene expression, structural variants, splicing, and neoantigen profiles at diagnosis and relapse were investigated. RESULTS: Relapsed SCLC samples demonstrated mutation signatures characteristic of platinum and APOBEC mutagenesis. Furthermore, these samples were characterized by MYC, MYCL and MYCN amplifications. Both treatment-naive and relapsed SCLC samples showed high prevalence of mutation-associated neoantigens (median= 86 in treatment-naive and 90 in relapsed SCLC; p=0.8) and TP53 was the most frequently altered gene to result in a neoantigen (48% of analyzed samples). Potential mechanisms of immune evasion, including amplification of CD24, overexpression of IDO1, increased M2 macrophage presence, and upregulation of HLA-E were also observed in relapse samples. Differences in alternative splicing patterns were observed between treatment-naive and relapsed small cell samples. Retained intron events were significantly enriched in treatment-naive samples and affected genes involved in DNA repair, metabolism, and WNT and MYC pathways. CONCLUSIONS: This study highlights the genomic and transcriptomic features of relapsed SCLC. These samples were characterized by genomic instability, WNT and MYC dysregulation, and splicing aberrations. Additional studies targeting the splicing machinery, WNT signaling, and immune evasion pathways could identify novel therapeutic vulnerabilities in SCLC.

Journal Article↗
PubMed2005

Arabidopsis thaliana full genome longmer microarrays: a powerful gene discovery tool for agriculture and forestry.

Sequenced plant genomes provide a large reservoir of known genes with potential for use in crop and tree improvement, but assignment of specific functions to annotated genes in sequenced plant genomes remains a challenge. Furthermore, most plant genes belong to families encoding proteins with related but distinct functions. In this commentary, we discuss our development of Arabidopsis spotted whole genome longmer oligonucleotide microarrays, and their use in global transcription profiling. We show that longmer array based transcriptome analysis in Arabidopsis can be used as an efficient and effective gene discovery and functional genomics tool, particularly for functional analyses of members of large gene families. We discuss experiments that focus on gene families involved in phenylpropanoid natural product biosynthesis and fiber differentiation. These analyses have helped to elucidate functions of individual gene family members, and have identified new candidate genes involved in fiber development and differentiation. Results obtained by these studies in Arabidopsis can be used as the basis for gene discovery in commercially important plants, and we have focused our attention on Populus trichocarpa (poplar), a species important in forestry and agroforestry for which complete genome sequence information is available.

Agriculture↗
PubMed2025

Rapid Divergence of Visual Systems and Signaling Traits to Contrasting Light Regimes During Early Speciation of African Crater Lake Cichlid Fish.

Sensory adaptation is widely hypothesized to drive ecological speciation, yet empirical evidence from natural populations undergoing early stage divergence remains limited. In Lake Masoko, a young crater lake in East Africa, the haplochromine cichlid Astatotilapia calliptera is undergoing early stage sympatric speciation into shallow-water littoral and deep-water benthic ecotypes that experience contrasting light environments. Here, we integrate retinal transcriptomics, phenotypic analyses, and visual modeling to uncover rapid sensory divergence associated with this ecological transition. We find striking shifts in cone opsin expression, with the benthic ecotype exhibiting a switch from short-wavelength sensitive SWS2B to SWS2A and an overall narrowing of cone sensitivity toward the center of the light spectrum, consistent with changes in deep-water light environment. In contrast, coding sequence variation in opsin genes was limited and no significant differences in allele frequencies were detected across nine polymorphic sites, pointing to expression regulation as the primary axis of early divergence in visual systems. In parallel, we observed divergence in male signaling traits, with benthic males displaying deeper red egg-spots, aligning with predictions from visual modeling of signal efficiency in different light environments. These results demonstrate rapid transcriptomic and phenotypic divergence in associated signaling traits-within ∼1,000 years-supporting a potential role for regulatory evolution in sensory adaptation during early ecological speciation.

Animals↗
PubMed2006

Mining the oocyte transcriptome.

Mammalian folliculogenesis and oocyte physiology are complex and not fully understood. However, major advances over the past 15 years in our ability to create and study in vivo models have improved our understanding of these essential physiological processes. More recently, the availability of vast arrays of DNA sequence information in the forms of "complete" genomes, expressed sequence tag libraries and microarray data from reproductive tissues have stimulated the discovery of new information through genome scanning, prediction programs and in silico screening techniques. These technological improvements will help to expand our understanding of folliculogenesis and oocyte physiology and improve human reproductive health.

Bone Morphogenetic Protein 15↗
PubMed2026

Mapping ovarian cellular and molecular landscape across the lifespan of women: a scoping review.

BACKGROUND: With growing interest in ART, fertility preservation, and postmenopausal health of women, reproductive medicine is increasingly focused on characterizing oocytes and ovarian tissue composition, as well as understanding the molecular mechanisms that guide ovarian function throughout its lifecycle. High-throughput omics technologies have enabled the characterization of different molecular layers, leading to substantial advances in our understanding of their complex dynamics. However, not all molecular aspects are studied equally, and studies examining the same modalities often show inconsistencies, underscoring the need for data standardization and highlighting the potential for using transformative artificial intelligence and machine-learning (AI/ML) methods for ovary studies. OBJECTIVE AND RATIONALE: This study aims to evaluate how multi-omic studies have advanced our understanding of the ovarian lifecycle from fetal development to postmenopause. We systematically reviewed published studies that have investigated molecular/omic layers, including the genome, methylome, transcriptome, and proteome throughout ovarian development and aging. Our analysis identified key molecular and cellular patterns, highlighted inconsistencies across studies and addressed gaps in data analysis, interpretation, and reproducibility to guide future research. SEARCH METHODS: We conducted a systematic literature search of Medline (PubMed), Embase (Ovid), and Web of Science Core Collection (Clarivate) using a combination of controlled and free text terms for human ovary, oogenesis, folliculogenesis, ovary development and (epi)genome, transcriptome, proteome, and multi-omic mechanisms to find relevant articles published before August 2025. To focus the scope of the current review, studies of domesticated and farm animals, rodents and other model organisms, non-human primates, as well as those examining various human ovarian pathologies were excluded. OUTCOMES: The search identified 23 546 studies for screening, of which 637 full-text studies were assessed for eligibility. Subsequently, we extracted data from 121 studies. Most studies analyzed the transcriptome of oocytes, granulosa cells, and ovarian tissue from reproductive-age individuals (n = 91), with fewer studies examining samples from individuals of advanced reproductive age (n = 45) and fetal (n = 16) samples. Transcriptome analyses were most common (n = 103, 85%), followed by proteome (n = 19, 16%) and epigenome (n = 14, 12%) studies. We found substantial variation in how studies defined and reported participants' groups as well as in their sequencing technologies and data analysis methods, with a lack of standardized reporting of background clinical information, data analysis methods, and pipeline details. The key findings underscore the prevailing consensus on genes defining major ovarian cell types and their roles throughout the ovarian lifespan, from prenatal development to postmenopausal transformation. This review highlighted the underrepresentation of certain patient groups, particularly prepubertal and peri-/postmenopausal individuals, among researched populations, due to obvious clinical and ethical reasons. WIDER IMPLICATIONS: This scoping review offers a comprehensive overview and benchmark of the current state of high-throughput omics-based research on ovarian cellular composition and molecular dynamics. To address these shortcomings, we propose general recommendations for multi-omics ovary studies and emphasize the necessity for more thorough multi-omic data integration by effectively applying novel AI/ML approaches. They can potentially improve the quality of multi-omics analyses at both single-cell and tissue levels despite limited sample sizes and enable integration of molecular profiling data with clinical and radiology datasets, enabling a more comprehensive understanding of ovarian biology. Such advancements can enhance reproducibility of research findings and guide future research to deepen our understanding of ovarian biology and ultimately support the development of medical technologies for better preserving fertility and alleviating infertility. REGISTRATION NUMBER: A protocol was published a priori on the Open Science Framework (https://osf.io/z38gb/).

Female↗
PubMed2005

Functional analysis and comparative genomics of expressed sequence tags from the lycophyte Selaginella moellendorffii.

BACKGROUND: The lycophyte Selaginella moellendorffii is a member of one of the oldest lineages of vascular plants on Earth. Fossil records show that the lycophyte clade arose 400 million years ago, 150-200 million years earlier than angiosperms, a group of plants that includes the well-studied flowering plant Arabidopsis thaliana. S. moellendorffii has a genome size of approximately 100 Mbp, as small or smaller than that of A. thaliana. S. moellendorffii has the potential to provide significant comparative information to better understand the evolution of vascular plants. RESULTS: We sequenced 2181 Expressed Sequence Tags (ESTs) from a S. moellendorffii cDNA library. One thousand three hundred and one non-redundant sequences were assembled, containing 291 contigs and 1010 singletons. Approximately 75% of the ESTs matched proteins in the non-redundant protein database. Among 1301 clusters, 343 were categorized according to Gene Ontology (GO) hierarchy and were compared to the GO mapping of A. thaliana tentative consensus sequences. We compared S. moellendorffii ESTs to the A. thaliana and Physcomitrella patens EST databases, using the tBLASTX algorithm. Approximately 60% of the ESTs exhibited similarity with both A. thaliana and P. patens ESTs; whereas, 13% and 1% of the ESTs had exclusive similarity with A. thaliana and P. patens ESTs, respectively. A substantial proportion of the ESTs (26%) had no match with A. thaliana or P. patens ESTs. CONCLUSION: We discovered 1301 putative unigenes in S. moellendorffii. These results give an initial insight into its transcriptome that will aid in the study of the S. moellendorffii genome in the near future.

Biological Evolution↗
PubMed2026

Strategies for mosaic variant calling in brain disorders.

The human brain is a genomic mosaic, where postzygotic mutations arising from embryogenesis to senescence drive diverse neurodevelopmental and neurodegenerative diseases. Because of numerous sequencing artifacts at ultralow variant allele frequencies (VAFs), detecting these variants remains a significant analytical challenge. This review focuses on single-nucleotide variants and small indels, summarizing current strategies for aligning sampling methods, including bulk, laser capture microdissection, and single-cell genomics, with the expected clonal architecture of the brain. It emphasizes that mosaic detection sensitivity is fundamentally constrained by sequencing depth, since even the most advanced algorithms cannot identify variants not physically represented in the sequencing library. The review further recommends the selection of variant calling algorithms based on validated VAF detection performance, matching tools like MuTect2 and MosaicForecast to their optimal performance ranges. Furthermore, we discuss how multitissue sampling, as emphasized by the SMaHT project, addresses the matched-control dilemma and supports accurate variant classification via cross-tissue VAF gradients. Integrating these established pipelines with multiomics modalities, including transcriptomic and epigenetic data, could advance the field toward a functional understanding of how the somatic genome impacts human brain health and disease.

Humans↗
PubMed2004

5'-end SAGE for the analysis of transcriptional start sites.

Identification of the mRNA start site is essential in establishing the full-length cDNA sequence of a gene and analyzing its promoter region, which regulates gene expression. Here we describe the development of a 5'-end serial analysis of gene expression (5' SAGE) that can be used to globally identify transcriptional start sites and the frequency of individual mRNAs. Of the 25,684 5' SAGE tags in the HEK293 human cell library, 19,893 matched to the human genome. Among 15,448 tags in one locus of the genome, 85.8%-96.1% of the 5' SAGE tags were assigned within -500 to +200 nt of mRNA start sites using the RefSeq, UniGene and DBTSS databases. This technique should facilitate 5'-end transcriptome analysis in a variety of cells and tissues.

5' Flanking Region↗
PubMed2003

Profiling ethylene-regulated gene expression in Arabidopsis thaliana by microarray analysis.

Ethylene-regulated gene expression in leaves of Arabidopsis thaliana was investigated with an expressed sequence tag-based microarray containing about 6000 unique genes. Comparing expression profiles of the ethylene-insensitive mutant etr1-1, the ethylene-constitutive mutant ctr1-1, ethylene-treated wild-type and untreated wild-type plants identified ca. 7% of the investigated genes as ethylene-regulated. Exogenous ethylene treatment and ctr1-1 had similar changes in gene expression, but differences were noted. Ethylene-regulated genes involved in its own biosynthesis and signal transduction pathway were identified. A large number of transcription factors and some putative signaling components were highly regulated by ethylene. Chloroplast structural protein and photosynthetic genes were generally down-regulated. Ethylene appeared to regulate other primary metabolic genes. Plant defense and PR protein genes were differentially regulated, with some genes within this class highly up-regulated. Other ethylene-regulated genes identified were known sugar-, auxin-, wounding- and jasmonic acid-related genes, suggesting the existence of coordinated interactions between ethylene and other hormonal and defense signaling pathways. Although hundreds of potentially important transcriptome changes were identified, the functions of many ethylene-regulated genes remain unknown.

Arabidopsis↗
PubMed2026

Integration of single-cell transcriptomics and genomic mutation analysis identifies an immunotherapy-resistant tumor subcluster and validates ARNTL2 as a malignant driver in lung adenocarcinoma.

BACKGROUND: Immunotherapy resistance in lung adenocarcinoma (LUAD) remains a critical clinical challenge, and the mechanisms underlying resistance-associated intratumoral heterogeneity are poorly characterized. METHODS: We performed single-cell RNA sequencing of LUAD patients receiving neoadjuvant immunotherapy (responders vs. non-responders), integrating inferCNV, GSVA, and differential expression analyses. Cluster-specific genes were validated across seven independent cohorts (TCGA-LUAD, GSE13213, GSE26939, GSE29016, GSE30219, GSE31210, GSE42127). A multi-algorithm machine learning framework was used to construct a prognostic model, and the immune microenvironment was characterized using TCIA scoring, seven infiltration algorithms, and ESTIMATE. ARNTL2 function was assessed by CCK-8 and Transwell assays in A549 and H1299 cells. RESULTS: Non-responders showed significant enrichment of epithelial cells, depletion of cytotoxic T/NK cells, and elevated copy number variation burden versus responders (p < 0.0001). A resistance-enriched malignant subcluster (Cluster 2) exhibited hyperproliferative and metabolic reprogramming signatures with upregulated KRT17, S100A2, and CST6, which showed tumor-specific overexpression, adverse prognostic value, and genomic amplification across cohorts. CoxBoost combined with survivalSVM achieved optimal predictive performance (C-index = 0.686), yielding robust risk stratification (HR: 2.54-10.51, all p < 0.05). Low-risk patients showed greater immune infiltration and higher TCIA immunophenoscores. ARNTL2 was an independent prognostic factor (HR: 2.07-4.64) strongly correlated with risk score (r = 0.69), and its knockdown suppressed proliferation and invasion in both LUAD cell lines (all p < 0.05). CONCLUSION: This study identifies a resistance-associated malignant subcluster in LUAD, constructs a validated CoxBoost + survivalSVM prognostic model with robust immune stratification, and establishes ARNTL2 as a core oncogenic driver and therapeutic target.

ARNTL2↗
PubMed2005

Impact of haloperidol and risperidone on gene expression profile in the rat cortex.

Despite the clinical efficacy of the most thoroughly studied conventional neuroleptic agent haloperidol, and the atypical antipsychotic risperidone is well established, little information is available on their molecular effects. Recent advances in high-density DNA microarray techniques allow the possibility to analyze thousands of genes simultaneously for their differential gene expression patterns in various biological processes, and to determine mechanisms of drug action. The aim of this series of experiments was to gain experience in antipsychotic gene-expression profiling and characterize (in the parlance of genomics) the "antipsychotic transcriptome." In this prospective animal study, broad-scale gene expression profiles were characterized for brains of rats treated with antipsychotics and compared with those of sham controls. We used DNA microarrays containing 8000 sequences to measure the expression patterns of multiple genes in rat fronto-temporo-parietal cortex after intraperitoneal treatment with haloperidol or risperidone. A number of transcripts were differentially expressed between control and treated samples, of which only 36 and 89 were found to significantly differ in expression as a result of exposure to haloperidol or risperidone, respectively (P<0.05). Acutely, 13 genes were more highly expressed and 15 transcripts were found to be significantly less abundant, whereas chronically nine genes were up-regulated and none of them was repressed in haloperidol-treated cortices. Risperidone acutely induced 43 and repressed 46 genes, and chronically over-expressed 6 and down-regulated 11 transcripts. Selected genes were assayed by real-time PCR, then normalized to beta-actin. These assays confirmed the significance of the array results for all transcripts tested. Despite their differing receptor affinity and selectivity, our findings indicate that haloperidol and risperidone interfere with cell survival, neural plasticity, signal transduction, ionic homeostasis and metabolism in a similar manner.

Animals↗
PubMed2006

Metabolic engineering of malolactic wine yeast.

Malolactic fermentation is essential for the deacidification of high acid grape must. We have constructed a genetically stable industrial strain of Saccharomyces cerevisiae by integrating a linear cassette containing the Schizosaccharomyces pombe malate permease gene (mae1) and the Oenococcus oeni malolactic gene (mleA) under control of the S. cerevisiae PGK1 promoter and terminator sequences into the URA3 locus of an industrial wine yeast. The malolactic yeast strain, ML01, fully decarboxylated 5.5 g/l of malate in Chardonnay grape must during the alcoholic fermentation. Analysis of the phenotype, genotype, transcriptome, and proteome revealed that the ML01 yeast is substantially equivalent to the parental industrial wine yeast. The ML01 yeast enjoys 'Generally Regarded As Safe' status from the FDA and is the first genetically enhanced yeast that has been commercialized. Its application will prevent the formation of noxious biogenic amines produced by lactic acid bacteria in wine.

Bacterial Proteins↗
PubMed2002

SAGE identification of differentiation responsive genes in P19 embryonic cells induced to form cardiomyocytes in vitro.

Transcriptome profiling facilitates the identification of developmentally regulated genes. To quantify the functionally active genome of P19 embryonic carcinoma (EC) cells induced to form cardiomyocytes, we employed serial analysis of gene expression (SAGE) to sequence and compare a total of 171,735 SAGE tags from three libraries (undifferentiated P19 EC cells, differentiation days 3 + 0.5 and 3 + 3.0). After in vitro differentiation, only 3.1% of the gene products demonstrated significant (P < 0.05) changes in expression. The most highly significant changes (P < 0.01) involved altered expression of 410 genes encoding predominantly transcription factors, differentiation factors and growth regulators. Quantitative polymerase chain reaction analysis and in situ hybridization revealed five growth regulators (Dlk1, Igfbp5, Hmga2, Podxl and Ptn) and two unknown ESTs with expression profiles similar to known cardiac transcription factors, implicating these growth regulators in cardiac differentiation. These SAGE libraries thus serve as a reference resource for understanding the role of differentiation-dependent genes in embryonic stem cell models induced to form cardiomyocytes in vitro.

Animals↗