Differential Patterns Research Articles

Transformer-based representation learning and multiple-instance learning for cancer diagnosis exclusively from raw sequencing fragments of bisulfite-treated plasmacell-free DNA.

Early cancer diagnosis from bisulfite-treated cell-free DNA (cfDNA) fragments requires tedious data analytical procedures. Here, we present a deep-learning-based approach for early cancer interception and diagnosis (DECIDIA) that can achieve accurate cancer diagnosis exclusively from bisulfite-treated cfDNA sequencing fragments. DECIDIA relies on transformer-based representation learning of DNA fragments and weakly supervised multiple-instance learning for classification. We systematically evaluate the performance of DECIDIA for cancer diagnosis and cancer type prediction on a curated dataset of 5389 samples that consist of colorectal cancer (CRC; n = 1574), hepatocellular cell carcinoma (HCC; n = 1181), lung cancer (n = 654), and non-cancer control (n = 1980). DECIDIA achieved an area under the receiver operating curve (AUROC) of 0.980 (95% CI, 0.976-0.984) in 10-fold cross-validation settings on the CRC dataset by differentiating cancer patients from cancer-free controls, outperforming benchmarked methods that are based on methylation intensities. Noticeably, DECIDIA achieved an AUROC of 0.910 (95% CI, 0.896-0.924) on the externally independent HCC testing set in distinguishing HCC patients from cancer-free controls, although there was no HCC data used in model development. In the settings of cancer-type classification, we observed that DECIDIA achieved a micro-average AUROC of 0.963 (95% CI, 0.960-0.966) and an overall accuracy of 82.8% (95% CI, 81.8-83.9). In addition, we distilled four sequence signatures from the raw sequencing reads that exhibited differential patterns in cancer versus control and among different cancer types. Our approach represents a new paradigm towards eliminating the tedious data analytical procedures for liquid biopsy that uses bisulfite-treated cfDNA methylome.

Coupling Relationships and Driving Mechanisms of Water–Energy–Food in China from the Perspective of Supply and Demand Security

The rapid increase in population and economy, coupled with accelerated urbanization, is placing immense pressure on the water–energy–food (WEF) system. In this context, the water–energy–food nexus framework has emerged, recognizing the interdependencies and interactions among water, energy, and food systems, with the aim of optimizing resource management through cross-sectoral collaboration to promote sustainable development. Understanding the spatio-temporal differentiation patterns of the WEF nexus and elucidating the driving mechanisms behind changes in their coupling relationships is essential. This knowledge is crucial for ensuring the security of each subsystem and enhancing the overall sustainability of interconnected systems through coordinated efforts. To address these challenges, this study first established evaluation indicators for water, energy, and food security to quantify their levels and spatio-temporal dynamics. Subsequently, the degrees of coupling coordination within the WEF nexus were calculated. Finally, the WEF nexus’s spatial correlations were analyzed by using a spatial autocorrelation model. Spatial econometric models then identified key factors affecting its coordination. The results revealed significant spatial heterogeneity in water, energy, and food security across mainland China’s provinces. From 2002 to 2022, water security improved substantially in 87% of the provinces, while energy security began to improve in the eastern regions following a phase of high consumption. Food security saw significant enhancements, particularly in Inner Mongolia and the northeastern provinces. The overall coupling coordination of the WEF nexus improved across 30 provinces, progressing toward primary coordination. However, Henan and Anhui provinces experienced fluctuations in WEF nexus coordination. Spatial correlation analysis showed upward trends and increased clustering in WEF nexus coordination. Factors such as economic development and population positively influenced coordination, while economic agglomeration, education, and effective irrigation area had negative effects. This study elucidates the complex interconnections and key influencing factors within the WEF nexus, providing a reference framework and practical recommendations for equitable resource management.

Characterizing virulence differences in a parasitoid wasp through comparative transcriptomic and proteomic

BackgroundTwo strains of the endoparasitoid Cotesia typhae (Hymenoptera: Braconidae) present a differential parasitism success on the host, Sesamia nonagrioides (Lepidoptera: Noctuidae). One is virulent on both permissive and resistant host populations, and the other only on the permissive host. This interaction provides a very interesting frame for studying virulence factors. Here, we used a combination of comparative transcriptomic and proteomic analyses to unravel the molecular basis underlying virulence differences between the strains.ResultsFirst, we report that virulence genes are mostly expressed during the pupal stage 24 h before adult emergence of the parasitoid. Especially, 55 proviral genes are up-regulated at this stage, while their expression is only expected in the host. Parasitoid gene expression in the host increases from 24 to 96 h post-parasitism, revealing the expression of 54 proviral genes at early parasitism stage and the active participation of teratocytes to the parasitism success at the late stage. Secondly, comparison between strains reveals differences in venom composition, with 12 proteins showing differential abundance. Proviral expression in the host displays a strong temporal variability, along with differential patterns between strains. Notably, a subset of proviral genes including protein-tyrosine phosphatases is specifically over-expressed in the resistant host parasitized by the less virulent strain, 24 h after parasitism. This result particularly hints at host modulation of proviral expression. Combining proteomic and transcriptomic data at various stages, we identified 8 candidate genes to support the difference in reproductive success of the two strains, one proviral and 7 venom genes, one of them being also produced within the host by the teratocytes.ConclusionsThis study sheds light on the temporal expression of virulence factors of Cotesia typhae, both in the host and in the parasitoid. It also identifies potential molecular candidates driving differences in parasitism success between two strains. Together, those findings provide a path for further exploration of virulence mechanisms in parasitoid wasps, and offer insights into host-parasitoid coevolution.

Super-enhancer-driven ZFP36L1 promotes PD-L1 expression in infiltrative gastric cancer

Gastric cancer (GC) is a major cause of cancer-related mortality worldwide. Despite the widespread recognition of tumor immunotherapy in treating unresectable GC, challenges, including ineffective immunotherapy and drug resistance, persist. Therefore, understanding the regulatory mechanisms of PD-L1, particularly in the context of super-enhancers (SEs) and zinc finger protein 36 ring finger protein-like 1 (ZFP36L1) RNA-binding protein, is crucial. In this study, we performed H3K27ac Cleavage Under Targets and Tagmentation (CUT&Tag) sequencing, investigated the heterogeneity of SEs between two GC subtypes with differential growth patterns, and revealed the immune escape signatures driven by ZFP36L1-SE in infiltrative GC through SEs inhibitors treatment. The regulation of ZFP36L1 to PD-L1 was evaluated by quantitative PCR, western blot, flow cytometry, and immunohistochemistry. Furthermore, we explored its regulatory mechanisms using a combination of molecular biology techniques, including luciferase reporter assay, GST/RNA pull-down, chromatin immunoprecipitation (ChIP)/RIP experiments, and in vivo functional assays. We demonstrated that ZFP36L1, driven by an SE, enhances IFN-γ-induced PD-L1 expression, with SPI1 identified as the specific transcription factor binding to ZFP36L1-SE. Mechanistically, ZFP36L1 binds to the adenylate uridylate-rich element in the 3ʹ untranslated region (3ʹUTR) of HDAC3 mRNA, exacerbating its mRNA decay, and thereby facilitating PD-L1 abnormal transcriptional activation. Collectively, our findings provide mechanistic insights into the role of the SPI1-ZFP36L1-HDAC3-PD-L1 signaling axis in orchestrating immune escape mechanisms in GC, thereby offering valuable insights into the potential targets for immune checkpoint therapy in GC management.

Development of a prognostic model related to homologous recombination deficiency in glioma based on multiple machine learning.

Despite advances in neuro-oncology, treatments of glioma and tools for predicting the outcome of patients remain limited. The objective of this research is to construct a prognostic model for glioma using the Homologous Recombination Deficiency (HRD) score and validate its predictive capability for glioma. We consolidated glioma datasets from TCGA, various cancer types for pan-cancer HRD analysis, and two additional glioma RNAseq datasets from GEO and CGGA databases. HRD scores, mutation data, and other genomic indices were calculated. Using machine learning algorithms, we identified signature genes and constructed an HRD-related prognostic risk model. The model's performance was validated across multiple cohorts. We also assessed immune infiltration and conducted molecular docking to identify potential therapeutic agents. Our analysis established a correlation between higher HRD scores and genomic instability in gliomas. The model, based on machine learning algorithms, identified seven key genes, significantly predicting patient prognosis. Moreover, the HRD score prognostic model surpassed other models in terms of prediction efficacy across different cancers. Differential immune cell infiltration patterns were observed between HRD risk groups, with potential implications for immunotherapy. Molecular docking highlighted several compounds, notably Panobinostat, as promising for high-risk patients. The prognostic model based on the HRD score threshold and associated genes in glioma offers new insights into the genomic and immunological landscapes, potentially guiding therapeutic strategies. The differential immune profiles associated with HRD-risk groups could inform immunotherapeutic interventions, with our findings paving the way for personalized medicine in glioma treatment.

Super-enhancer-driven ZFP36L1 promotes PD-L1 expression in infiltrative gastric cancer.

Gastric cancer (GC) is a major cause of cancer-related mortality worldwide. Despite the widespread recognition of tumor immunotherapy in treating unresectable GC, challenges, including ineffective immunotherapy and drug resistance, persist. Therefore, understanding the regulatory mechanisms of PD-L1, particularly in the context of super-enhancers (SEs) and zinc finger protein 36 ring finger protein-like 1 (ZFP36L1) RNA-binding protein, is crucial. In this study, we performed H3K27ac Cleavage Under Targets and Tagmentation (CUT&Tag) sequencing, investigated the heterogeneity of SEs between two GC subtypes with differential growth patterns, and revealed the immune escape signatures driven by ZFP36L1-SE in infiltrative GC through SEs inhibitors treatment. The regulation of ZFP36L1 to PD-L1 was evaluated by quantitative PCR, western blot, flow cytometry, and immunohistochemistry. Furthermore, we explored its regulatory mechanisms using a combination of molecular biology techniques, including luciferase reporter assay, GST/RNA pull-down, chromatin immunoprecipitation (ChIP)/RIP experiments, and in vivo functional assays. We demonstrated that ZFP36L1, driven by an SE, enhances IFN-γ-induced PD-L1 expression, with SPI1 identified as the specific transcription factor binding to ZFP36L1-SE. Mechanistically, ZFP36L1 binds to the adenylate uridylate-rich element in the 3' untranslated region (3'UTR) of HDAC3 mRNA, exacerbating its mRNA decay, and thereby facilitating PD-L1 abnormal transcriptional activation. Collectively, our findings provide mechanistic insights into the role of the SPI1-ZFP36L1-HDAC3-PD-L1 signaling axis in orchestrating immune escape mechanisms in GC, thereby offering valuable insights into the potential targets for immune checkpoint therapy in GC management.

Mobile signals, patterning, and positional information in root development.

Multicellular organisms use mobile intercellular signals to generate spatiotemporal patterns of growth and differentiation. These signals, termed morphogens, arise from localized sources and move by diffusion or directional transport to be interpreted at target cells. The classical model for a morphogen is where a substance diffuses from a source to generate a concentration gradient that provides positional information across a field. This concept, presented by Wolpert and popularized as the "French Flag Model," remains highly influential, but other patterning models, which do not rely on morphogen gradients, also exist. Here, we review current evidence for mobile morphogenetic signals in plant root development and how they fit within existing conceptual frameworks for pattern formation. We discuss how the signals are formed, distributed, and interpreted in space and time, emphasizing the regulation of movement on the ability of morphogens to specify patterns. While significant advances have been made in the field since the first identification of mobile morphogenetic factors in plants, key questions remain to be answered, such as how morphogen movement is regulated, how these mechanisms allow scaling in different species, and how morphogens act to enable plant regeneration in response to damage.

Leveraging explainable deep learning methodologies to elucidate the biological underpinnings of Huntington’s disease using single-cell RNA sequencing data

BackgroundHuntington’s disease (HD) is a hereditary neurological disorder caused by mutations in HTT, leading to neuronal degeneration. Traditionally, HD is associated with the misfolding and aggregation of mutant huntingtin due to an extended polyglutamine domain encoded by an expanded CAG tract. However, recent research has also highlighted the role of global transcriptional dysregulation in HD pathology. However, understanding the intricate relationship between mRNA expression and HD at the cellular level remains challenging. Our study aimed to elucidate the underlying mechanisms of HD pathology using single-cell sequencing data.ResultsWe used single-cell RNA sequencing analysis to determine differential gene expression patterns between healthy and HD cells. HD cells were effectively modeled using a residual neural network (ResNet), which outperformed traditional and convolutional neural networks. Despite the efficacy of our approach, the F1 score for the test set was 96.53%. Using the SHapley Additive exPlanations (SHAP) algorithm, we identified genes influencing HD prediction and revealed their roles in HD pathobiology, such as in the regulation of cellular iron metabolism and mitochondrial function. SHAP analysis also revealed low-abundance genes that were overlooked by traditional differential expression analysis, emphasizing its effectiveness in identifying biologically relevant genes for distinguishing between healthy and HD cells. Overall, the integration of single-cell RNA sequencing data and deep learning models provides valuable insights into HD pathology.ConclusionWe developed the model capable of analyzing HD at single-cell transcriptomic level.

Improving Stroke Outcome Prediction Using Molecular and Machine Learning Approaches in Large Vessel Occlusion.

Introduction: Predicting stroke outcomes in acute ischemic stroke (AIS) can be challenging, especially for patients with large vessel occlusion (LVO). Available tools such as infarct volume and the National Institute of Health Stroke Scale (NIHSS) have shown limited accuracy in predicting outcomes for this specific patient population. The present study aimed to confirm whether sudden metabolic changes due to blood-brain barrier (BBB) disruption during LVO reflect differences in circulating metabolites and RNA between small and large core strokes. The second objective was to evaluate whether integrating molecular markers with existing neurological and imaging tools can enhance outcome predictions in LVO strokes. Methods: The infarction volume in patients was measured using magnetic resonance diffusion-weighted images, and the 90-day stroke outcome was defined by a modified Rankin Scale (mRS). Differential expression patterns of miRNAs were identified by RNA sequencing of serum-driven exosomes. Nuclear magnetic resonance (NMR) spectroscopy was used to identify metabolites associated with AIS with small and large infarctions. Results: We identified 41 miRNAs and 11 metabolites to be significantly associated with infarct volume in a multivariate regression analysis after adjusting for the confounders. Eight miRNAs and ketone bodies correlated significantly with infarct volume, NIHSS (severity), and mRS (outcome). Through integrative analysis of clinical, radiological, and omics data using machine learning, our study identified 11 top features for predicting stroke outcomes with an accuracy of 0.81 and AUC of 0.91. Conclusions: Our study provides a future framework for advancing stroke therapeutics by incorporating molecular markers into the existing neurological and imaging tools to improve predictive efficacy and enhance patient outcomes.

Tetraspanin 5 orchestrates resilience to salt stress through the regulation of ion and reactive oxygen species homeostasis in rice.

Tetraspanins (TETs) are integral membrane proteins, characterized by four transmembrane domains and a unique signature motif in their large extracellular loop. They form dynamic supramolecular complexes called tetraspanin-enriched microdomains (TEMs), through interactions with partner proteins. In plants, TETs are involved in development, reproduction and immune responses, but their role in defining abiotic stress responses is largely underexplored. We focused on OsTET5, which is differentially expressed under various abiotic stresses and localizes to both plasma membrane and endoplasmic reticulum. Using overexpression and underexpression transgenic lines we demonstrate that OsTET5 contributes to salinity and drought stress tolerance in rice. OsTET5 can interact with itself in yeast, suggesting homomer formation. Immunoblotting of native PAGE of microsomal fraction enriched from OsTET5-Myc transgenic rice lines revealed multimeric complexes containing OsTET5, suggesting the potential formation of TEM complexes. Transcriptome analysis, coupled with quantitative PCR-based validation, of OsTET5-altered transgenic lines unveiled the differential expression patterns of several stress-responsive genes, as well as those coding for transporters under salt stress. Notably, OsTET5 plays a crucial role in maintaining the ionic equilibrium during salinity stress, particularly by preserving an elevated potassium-to-sodium (K+/Na+) ratio. OsTET5 also regulates reactive oxygen species homeostasis, primarily by modulating the gene expression and activities of antioxidant pathway enzymes and proline accumulation. Our comprehensive investigation underscores the multifaceted role of OsTET5 in rice, accentuating its significance in developmental processes and abiotic stress tolerance. These findings open new avenues for potential strategies aimed at enhancing stress resilience and making valuable contributions to global food security.

Comparative Analysis of the Codon Usage Pattern in the Chloroplast Genomes of Gnetales Species

Codon usage bias refers to the preferential use of synonymous codons, a widespread phenomenon found in bacteria, plants, and animals. Codon bias varies among species, families, and groups within kingdoms and between genes within an organism. Codon usage bias (CUB) analysis sheds light on the evolutionary dynamics of various species and optimizes targeted gene expression in heterologous host plants. As a significant order of gymnosperms, species within Gnetales possess extremely high ecological and pharmaceutical values. However, comprehensive analyses of CUB within the chloroplast genomes of Gnetales species remain unexplored. A systematic analysis was conducted to elucidate the codon usage patterns in 13 diverse Gnetales species based on the chloroplast genomes. Our results revealed that chloroplast coding sequences (cp CDSs) in 13 Gnetales species display a marked preference for AT bases and A/T-ending codons. A total of 20 predominantly high-frequency codons and between 2 and 7 optimal codons were identified across these species. The findings from the ENC-plot, PR2-plot, and neutrality analyses suggested that both mutation pressure and natural selection exert influence on the codon bias in these 13 Gnetales species, with natural selection emerging as the predominant influence. Correspondence analysis (COA) demonstrated variation in the codon usage patterns among the Gnetales species and indicated mutation pressure is another factor that could impact CUB. Additionally, our research identified a positive correlation between the measure of idiosyncratic codon usage level of conservatism (MILC) and synonymous codon usage order (SCUO) values, indicative of CUB’s potential influence on gene expression. The comparative analysis concerning codon usage frequencies among the 13 Gnetales species and 4 model organisms revealed that Saccharomyces cerevisiae and Nicotiana tabacum were the optimal exogenous expression hosts. Furthermore, the cluster and phylogenetic analyses illustrated distinct patterns of differentiation, implying that codons, even with weak or neutral preferences, could affect the evolutionary trajectories of these species. Our results reveal the characteristics of codon usage patterns and contribute to an enhanced comprehension of evolutionary mechanisms in Gnetales species.

Exploring the impact of biological sex on intrinsic connectivity networks in PTSD: A data-driven approach

IntroductionSex as a biological variable (SABV) may help to account for the differential development and expression of post-traumatic stress disorder (PTSD) symptoms among trauma-exposed males and females. Here, we investigate the impact of SABV on PTSD-related neural alterations in resting-state functional connectivity (rsFC) within three core intrinsic connectivity networks (ICNs): the salience network (SN), central executive network (CEN), and default mode network (DMN). MethodsUsing an independent component analysis (ICA), we compared rsFC of the SN, CEN, and DMN between males and females, with and without PTSD (n = 47 females with PTSD, n = 34 males with PTSD, n = 36 healthy control females, n = 20 healthy control males) via full factorial ANCOVAs. Additionally, linear regression analyses were conducted with clinical variables (i.e., PTSD and depression symptoms, childhood trauma scores) in order to determine intrinsic network connectivity characteristics specific to SABV. Furthermore, we utilized machine learning classification models to predict the biological sex and PTSD diagnosis of individual participants based on intrinsic network activity patterns. ResultsOur findings revealed differential network connectivity patterns based on SABV and PTSD diagnosis. Males with PTSD exhibited increased intra-SN (i.e., SN-anterior insula) rsFC and increased DMN-right superior parietal lobule/precuneus/superior occipital gyrus rsFC as compared to females with PTSD. There were also differential network connectivity patterns for comparisons between the PTSD and healthy control groups for males and females, separately. We did not observe significant correlations between clinical measures of interest and brain region clusters which displayed significant between group differences as a function of biological sex, thus further reinforcing that SABV analyses are likely not confounded by these variables. Furthermore, machine learning classification models accurately predicted biological sex and PTSD diagnosis among novel/unseen participants based on ICN activation patterns. ConclusionThis study reveals groundbreaking insights surrounding the impact of SABV on PTSD-related ICN alterations using data-driven methods. Our discoveries contribute to further defining neurobiological markers of PTSD among females and males and may offer guidance for differential sex-related treatment needs.

Dissecting the Mechanisms of Intestinal Immune Homeostasis by Analyzing T-Cell Immune Response in Crohn's Disease and Colorectal Cancer.

Crohn's disease (CD) and colorectal cancer (CRC) represent a group of intestinal disorders characterized by intricate pathogenic mechanisms linked to the disruption of intestinal immune homeostasis. Therefore, comprehending the immune response mechanisms in both categories of intestinal disorders is of paramount significance in the prevention and treatment of these debilitating intestinal ailments. IIn this study, we conducted single-cell analysis on paired samples obtained from primary colorectal tumors and individuals with Crohn's disease, which was aimed at deciphering the factors influencing the composition of the intestinal immune microenvironment. By aligning T cells across different tissues, we identified various T cell subtypes, such as γδ T cell, NK T cell, and regulatory T (Treg) cell, which maintained immune system homeostasis and were confirmed in enrichment analyses. Subsequently, we generated pseudo-time trajectories for subclusters of T cells in both syndromes to delineate their differentiation patterns and identify key driver genes Result: Furthermore, cellular communication and transcription factor regulatory networks are all essential components of the intricate web of mechanisms that regulate intestinal immune homeostasis. The identified complex cellular interaction suggested potential T-lineage immunotherapeutic targets against epithelial cells with high copy number variation (CNV) levels in CD and CRC. Finally, the analysis of regulon networks revealed several promising candidates for cell-specific transcription factors (TFs). This study focused on the immune molecular mechanism under intestinal diseases. It contributed to the novel insight of depicting a detailed immune landscape and revealing T-cell responding mechanisms in CD and CRC.

P53 mutation biases squamocolumnar junction progenitor cells towards dysplasia rather than metaplasia in Barrett’s oesophagus

BackgroundWhile p53 mutations occur early in Barrett’s oesophagus (BE) progression to oesophageal adenocarcinoma (EAC), their role in gastric cardia stem cells remains unclear.ObjectiveThis study investigates the impact of p53 mutation...

Limited Migration From Physiological Refugia Constrains the Rescue of Native Gastropods Facing an Invasive Predator.

Biological invasions have caused the loss of freshwater biodiversity worldwide. The interplay between adaptive responses and demographic characteristics of populations impacted by invasions is expected to be important for their resilience, but the interaction between these factors is poorly understood. The freshwater gastropod Amnicola limosus is native to the Upper St. Lawrence River and distributed along a water calcium concentration gradient within which high-calcium habitats are impacted by an invasive predator fish (Neogobius melanostomus, round goby), whereas low-calcium habitats provide refuges for the gastropods from the invasive predator. Our objectives were to (1) test for adaptation of A. limosus to the invasive predator and the low-calcium habitats, and (2) investigate if migrant gastropods could move from refuge populations to declining invaded populations (i.e., demographic rescue), which could also help maintain genetic diversity through gene flow (i.e., genetic rescue). We conducted a laboratory reciprocal transplant of wild F0 A. limosus sourced from the two habitat types (high calcium/invaded and low calcium/refuge) to measure adult survival and fecundity in home and transplant treatments of water calcium concentration (low/high) and round goby cue (present/absent). We then applied pooled whole-genome sequencing of 12 gastropod populations from across the calcium/invasion gradient. We identified patterns of life-history traits and genetic differentiation across the habitats that are consistent with local adaptation to low-calcium concentrations in refuge populations and to round goby predation in invaded populations. We also detected restricted gene flow from the low-calcium refugia towards high-calcium invaded populations, implying that the potential for demographic and genetic rescue is limited by natural dispersal. Our study highlights the importance of considering the potentially conflicting effects of local adaptation and gene flow for the resilience of populations coping with invasive predators.

Characterising the Genomic Landscape of Differentiation Between Annual and Perennial Rye.

Annuality and perenniality represent two different life-history strategies in plants, and an analysis of genomic differentiation between closely related species of different life histories bears the potential to identify the underlying targets of selection. Additionally, understanding the interactions between patterns of recombination and signatures of natural selection is a central aim in evolutionary biology, because patterns of recombination shape the evolution of genomes by affecting the efficacy of selection. Here, our aim was to characterise the landscape of genomic differentiation between weedy annual rye (Secale cereale L.) and wild perennial rye (Secale strictum C. Presl), and explore the extent to which signatures of selection are influenced by recombination rate variation. We used population-level sequence data of annual and perennial rye to analyse population structure and their demographic history. Based on our analyses, annual and perennial rye diverged approximately 26,500 years ago (ya) from an ancestral population size of ~85,000 individuals. We analysed patterns of genetic diversity and genetic differentiation, and found highly differentiated regions located in low-recombination regions, indicative of linked selection. Although all highly differentiated regions, as revealed by F ST-outlier scans, were located in low-recombining regions, not all chromosomes showed this tendency. We therefore performed a gene ontology enrichment analysis, which showed that highly differentiated regions comprise genes involved in photosynthesis. This enrichment was confirmed when F ST outlier scans were performed separately in low- and intermediate-recombining regions, but not in high-recombining regions, suggesting that local recombination rate variation in rye affects outlier scans. Cultivated rye is an annual crop, but the introduction of perenniality may be advantageous in regions with poor soil quality or under low-input farming. Although the resolution of our analysis is limited to a broad-scale, knowledge about the evolutionary divergence between annual and perennial rye might support breeding efforts towards perennial rye cultivation.

Metabolite concentrations and the expression profiles of the corresponding metabolic pathway genes in eggplant (Solanum melongena L.) fruits of contrasting colors.

Eggplant (Solanum melongena L.) ranks fifth in importance among vegetable crops of the Solanaceae family, in part due to the high antioxidant properties and polyphenol content of the fruit. Along with the popular purple-fruited varieties of S. melongena, there are cultivars, the fruits of which are rich in phenolic compounds, but are white-colored due to the lack of anthocyanin biosynthesis. Determination of the amount of anthocyanins and other phenolic compounds, as well as carotenoids and sugars, is included in the assessment of the quality of eggplant fruits of commercial (technical) ripeness. In addition to antioxidant and taste properties, these metabolites are associated with fruit resistance to various stress factors. In this study, a comparative analysis of the content of anthocyanins, carotenoids and soluble sugars (sucrose, glucose, fructose) in the peel and pulp of the fruit of both technical and biological ripeness was carried out in purple-fruited (cv. Vlas) and white-fruited (cv. Snezhny) eggplant accessions of domestic selection. The peel and pulp of biologically ripe fruits of the cvs Vlas and Snezhny were used for comparative transcriptomic analysis. The key genes of the flavonoid and carotenoid metabolism, sucrose hydrolysis, and soluble sugar transport were shown to be differentially expressed between fruit tissues, both within each cultivar and between them. It has been confirmed that the purple color of the peel of the cv. Vlas fruit is due to substantial amounts of anthocyanins. Flavonoid biosynthesis genes showed a significantly lower expression level in the ripe fruit of the cv. Vlas in comparison with the cv. Snezhny. However, in both cultivars, transcripts of anthocyanin biosynthesis genes (DFR, ANS, UFGT) were not detected. Additionally, the purple fruit of the cv. Vlas accumulated more carotenoids and sucrose and less glucose and fructose than the white fruit of the cv. Snezhny. Biochemical data corresponded to the differential expression pattern of the key genes encoding the structural proteins of metabolism and transport of the compounds analyzed.

OA15.04 Multi-Omic Profiling of Paired Non-Small Cell Lung Cancer and Draining Lymph Nodes Reveals Novel T-Cell Differentiation Patterns

OA15.04 Multi-Omic Profiling of Paired Non-Small Cell Lung Cancer and Draining Lymph Nodes Reveals Novel T-Cell Differentiation Patterns

Genome-wide differentiation corresponds to climatic niches in two species of lichen-forming fungi.

Lichens can withstand fluctuating environmental conditions such as hydration-desiccation cycles. Many species distribute across climate zones, suggesting population-level adaptations to conditions such as freezing and drought. Here, we aim to understand how climate affects population genomic patterns in lichenized fungi. We analysed population structure along elevational gradients in closely related Umbilicaria phaea (North American; two gradients) and Umbilicaria pustulata (European; three gradients). All gradients showed clear genomic breaks splitting populations into low-elevation (Mediterranean zone) and high-elevation (cold temperate zone). A total of 3301 SNPs in U. phaea and 138 SNPs in U. pustulata were driven to fixation between the two ends of the gradients. The difference between the species is likely due to differences in recombination rate: the sexually reproducing U. phaea has a higher recombination rate than the primarily asexually reproducing U. pustulata. Cline analysis revealed allele frequency transitions along all gradients at approximately 0°C, coinciding with the transition between the Mediterranean and cold temperate zones, suggesting freezing is a strong driver of population differentiation. Genomic scans further confirmed temperature-related selection targets. Both species showed similar differentiation patterns overall, but different selected alleles indicate convergent adaptation to freezing. Our results enrich our knowledge of fungal genomic functions related to temperature and climate, fungal population genomics, and species responses to environmental heterogeneity.

Characterization of ATP-dependent phosphofructokinase genes during ripening and their modulation by phytohormones during postharvest storage of citrus fruits (Citrus reticulata Blanco.)

The level of sweetness in citrus fruit is crucial for consumer appeal and market competitiveness, determined mainly by soluble sugars and organic acids. ATP-dependent 6-phosphofructokinase is central to regulating sugar metabolism, yet its role in citrus fruit ripening and postharvest storage remains underexplored. We characterized phosphofructokinase genes in citrus, identifying eight genes classified into pyrophosphate-dependent phosphofructokinase (PFP) and ATP-dependent 6-phosphofructokinase (PFK) subgroups using phylogenetic analysis, genomic architectures, and protein motifs. Comparative genomic analysis with other plants highlighted significant protein homology among CitPFKs. The motif analysis indicated conserved phosphofructokinase domains in CitPFK sequences, with upstream promoter regions containing diverse cis-regulatory elements, most notably light-responsive (LREs). The gene expression profiling throughout fruit development and ripening revealed differential patterns, with responses to gibberellic acid and salicylic acid phytohormones during postharvest indicating their roles in regulating CitPFK genes. The analysis of the transcriptome showed high expression of ATP-dependent 6-phosphofructokinase 3 (CitPFK3) during fruit development, indicating a positive role in fruit maturation. Consequently, silencing CitPFK3 through virus-induced gene silencing (VIGS) increased hexose sugar content, suggesting its function in sugar accumulation. These findings improve our understanding of PFKs in citrus, particularly CitPFK3's pivotal role in regulating hexose sugar dynamics and their modulation by exogenous phytohormones after harvest. This study provides a foundation for optimizing soluble sugar regulation to enhance fruit quality and postharvest handling in citrus production.