Penghao Wang

The abundant skin commensal, Staphylococcus epidermidis, is the leading cause of late-onset sepsis (LOS) in preterm infants but rarely causes infections in term infants and adults. Staphylococcal virulence mechanisms and the role of the preterm immune responses in driving these life-threatening infections remain poorly understood. Using an ex vivo sepsis model, we challenged whole blood from very preterm infants (30-32 weeks gestational age, GA; n = 8), term infants (> 37 weeks GA; n = 8), and young adults (18-25 years; n = 8) with either live S. epidermidis or S. aureus (~ 10
colony-forming units, CFU/ml) for 90 min. Dual RNA-sequencing (RNA-seq) was performed to simultaneously assess host and pathogen gene expression profiles, identifying common and pathogen-specific responses across cohorts. We found shared immune processes induced in all age groups upon bacterial challenge, including cytokine (IL1A, IL1B, IL6, IFNB1) and chemokine (CCL20, CCL3, CCL7, CXCL2) signalling. Preterm infants also exhibited unique responses, such as increased platelet activation and fibrin clot formation, Wnt signalling, and hypoxia pathways in response to S. epidermidis challenge. Our findings suggest that bacterial gene co-expression, including iron acquisition and heme biosynthesis genes, are also influenced by the hosts developmental age, highlighting the complexity of host-bacterial interactions in the early stages of neonatal sepsis.

1. Barley PanTs Supplementary Data1-8_10-14.xlsx
Ten Datasets referred to in the main text (Supplementary Data 1-10), presented in tabular format as separate sheets in a joint Excel formatted file.
Supplementary Data 1: Basic statistics of the RTDs
Supplementary Data 2: Ordering of genotypes incorporated into the linear pan-genome.
Supplementary Data 3: Gene categories of GsRTD
Supplementary Data 4: Alternative splicing events for highly expressed transcripts from core-single-copy genes (average TPM > 10)
Supplementary Data 5: Gene copy number variation cluster significantly correlated with the gene expression.
Supplementary Data 6: C-repeat/DRE-Binding Factor (CBF) genes identifier in GsRTD and their location in the genome
Supplementary Data 7: Genotypes with the 141Mb 7H inversion and non-inversion
Supplementary Data 8: Differentially expressed genes in the 7H inversion.
Supplementary Data 10: Detailed example of a split pattern in Golden Promise
Supplementary Data 11: Barley cv. Morex Expression Atlas Metadata
Supplementary Data 12: GA-Pathway gene expression in PanTs experiment
Supplementary Data 13: Yield and agro data for Ga2ox3-7
Supplementary Data 14: Statistics GA2ox and 7

2. Supplementary Data9.csv A csv file containing genotype-specific co-expression network results (modules and community assignments) with annotation and MorexV3 gene IDs.

3. Supplementary Data15.txt This tab delimited text file is too large for inclusion in the main Supplementary Data file and contains the details of how genes/transcripts from the genotype specific RTDs map onto genes in PanBaRT20.

Background
The investigation of ovarian development, dysfunction, and aging is essential for female reproductive health. Despite extensive research on the cellular functions of Brefeldin A (BFA) as an intracellular transport inhibitor, its specific effects and mechanisms on ovarian development/aging remain inadequately understood.

Methods
Mice and porcine oocytes/granulosa cells (GCs) were treated with BFA. Morphological and omics analyses (including Western blot, real-time polymerase chain reaction (RT-PCR), transcriptomics, and metabolomics) were conducted.

Results
In 3-week-old female mice, BFA treatment significantly suppressed oocyte maturation, induced apoptosis, and increased estradiol and LH levels. This treatment upregulated apoptosis-related genes while downregulating proliferation-associated genes. Additionally, BFA elevated senescence markers (p21 and p26) and decreased the activity of the longevity gene SIRT6. In porcine oocytes, BFA reduced the maturation rate and lowered mRNA levels of key maturation-related genes, LHX8 and GDF9. In porcine GCs, BFA increased apoptosis and upregulated genes such as Caspase-3, BAX, and P21, while downregulating genes associated with proliferation and longevity. Similar effects were observed in 12-month-old female mice, indicating consistency across age groups. Metabolomic analysis in these mice revealed that BFA primarily impacted pathways related to steroid biosynthesis, ovarian steroidogenesis, and estrogen signaling. Transcriptomic analysis in 12-month-old female mice further demonstrated that BFA disrupted ovarian function through multiple mechanisms, including modulation of the GnRH signaling pathway, activation of the FOXO pathway, and interference with meiosis-related gene expression.

Conclusion
Our findings are pivotal for advancing the understanding of ovarian aging, dysfunctions, and diseases, and ultimately facilitate addressing BFA's potential adverse effects on reproductive health/aging.

Oat grain is a traditional human food that is rich in dietary fibre and contributes to improved human health1,2. Interest in the crop has surged in recent years owing to its use as the basis for plant-based milk analogues3. Oat is an allohexaploid with a large, repeat-rich genome that was shaped by subgenome exchanges over evolutionary timescales4. In contrast to many other cereal species, genomic research in oat is still at an early stage, and surveys of structural genome diversity and gene expression variability are scarce. Here we present annotated chromosome-scale sequence assemblies of 33 wild and domesticated oat lines, along with an atlas of gene expression across 6 tissues of different developmental stages in 23 of these lines. We construct an atlas of gene-expression diversity across subgenomes, accessions and tissues. Gene loss in the hexaploid is accompanied by compensatory upregulation of the remaining homeologues, but this process is constrained by subgenome divergence. Chromosomal rearrangements have substantially affected recent oat breeding. A large pericentric inversion associated with early flowering explains distorted segregation on chromosome 7D and a homeologous sequence exchange between chromosomes 2A and 2C in a semi-dwarf mutant has risen to prominence in Australian elite varieties. The oat pangenome will promote the adoption of genomic approaches to understanding the evolution and adaptation of domesticated oats and will accelerate their improvement.Oat grain is a traditional human food that is rich in dietary fibre and contributes to improved human health1,2. Interest in the crop has surged in recent years owing to its use as the basis for plant-based milk analogues3. Oat is an allohexaploid with a large, repeat-rich genome that was shaped by subgenome exchanges over evolutionary timescales4. In contrast to many other cereal species, genomic research in oat is still at an early stage, and surveys of structural genome diversity and gene expression variability are scarce. Here we present annotated chromosome-scale sequence assemblies of 33 wild and domesticated oat lines, along with an atlas of gene expression across 6 tissues of different developmental stages in 23 of these lines. We construct an atlas of gene-expression diversity across subgenomes, accessions and tissues. Gene loss in the hexaploid is accompanied by compensatory upregulation of the remaining homeologues, but this process is constrained by subgenome divergence. Chromosomal rearrangements have substantially affected recent oat breeding. A large pericentric inversion associated with early flowering explains distorted segregation on chromosome 7D and a homeologous sequence exchange between chromosomes 2A and 2C in a semi-dwarf mutant has risen to prominence in Australian elite varieties. The oat pangenome will promote the adoption of genomic approaches to understanding the evolution and adaptation of domesticated oats and will accelerate their improvement.

Artificial Intelligence (AI) has emerged as a transformative tool in precision agriculture, facilitating datadriven decision-making and crop improvement. In the context of agricultural crops, data from multiple modalities, such as phenotypic traits, genomic markers, and environmental conditions, offer diverse insights into crop development and yield potential. However, single-modality approaches may fail to capture the complex interplay between genomics, environment, and other factors affecting crop traits. To address this challenge, this study investigates the integration of multimodal data to improve genotype-to-phenotype predictions. Focusing on barley (Hordeum vulgare L.), a globally and nationally important cereal crop, we propose a new barley-Multimodal Deep Learning (barley-MMDL) model to predict flowering time and grain yield using heterogeneous multimodal datasets. The model combines Convolutional Neural Networks (CNNs) to process high-dimensional genomic markers with Long Short-Term Memory (LSTM) networks to capture temporal patterns in environmental data. These modality-specific latent features are then fused to enable joint optimization of feature extraction and prediction in an end-to-end manner. The proposed barleyMMDL model achieved the lowest RMSE values of 8.84 for flowering time and 778.50 for grain yield, outperforming baseline unimodal and multimodal models. These results demonstrate the improved predictive capability of barleyMMDL and underscore the potential of multimodal data integration to advance prediction capability in precision agriculture and contribute to sustainable agricultural practices.

This dataset comprises cross-species phenotype data for barley, oat, lupin, and chickpea. The collected data consisted of RNA-seq from Lupin and phenotype data from barley, oat and chickpea, which included many key agronomic traits, such as flowering time, plant height, grain yield, grain plumpness, growing index and tolerant index. The phenotype data were collected from 2014 to 2025 from various trials across Western Australia, including data from approximately 900 barley, 600 oat and 500 chickpea accessions. This dataset is a combination of outputs from multiple GRDC-funded projects: UMU2404-010RTX, UMU2404-010RTX, UMU2302-007RSX, UMU1606-002RMX, UMU1406-002RTX, UMU1903-004RTX, UMU2303-003RTX and UMU2306-008RSX.

Ovarian aging significantly contributes to the decline of the female reproductive system, adversely affecting fertility and endocrine homeostasis. To address the challenges posed by reproductive aging, natural products have shown promising preventive and therapeutic effects. Here, we investigated the beneficial effects of natural compound celastrol on ovarian development and aging, together with its underlying mechanisms. We found that celastrol administration at a concentration of 3 mg/kg promoted follicle development in young mice and enhanced porcine oocyte maturation, while regulating granulosa cell proliferation and apoptosis. In 12-month-old mice (equivalent to middle-aged adults), celastrol exhibited similar beneficial effects. Transcriptomic analysis revealed that differentially expressed genes post-celastrol treatment were associated with steroid biosynthesis, estrogen signaling pathways, type 2 diabetes, insulin secretion, meiosis, and apoptosis. Additionally, insulin receptor substrate 1 (IRS1), an adapter protein in insulin signaling, was shown to advance puberty in young mice and to facilitate oocyte maturation. Overexpression of IRS1 in oocytes promoted follicular development and oocyte maturation, resulting in enhanced steroid hormone levels, whereas IRS1 knockdown inhibited these processes. Our findings indicate that celastrol may regulate ovarian development and aging by modulating IRS1 expression and its related pathways, suggesting celastrol as a novel small-molecule compound targeting IRS1, and offering new perspectives for potential therapeutic strategies against reproductive aging and infertility.

Phenotype and genotype data collected from the GRDC-funded project UMU2303-003RTX (Developing genetic tools to facilitate breeder use and deployment of high value acid soils tolerant chickpea germplasm). The genotype data consisted of approximately 4,300 high-quality SNP markers of 520 chickpea accessions. The phenotype data, including the tolerance index, plant growth data and yield-related traits, were collected from multiple trials across Australia from 2023 to 2025.