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Klein, A.

Publications and source records attributed to Klein, A..

At least 19 recordsLinked to original sources

Giant bacteria in gut microbiomes

The advent of sequencing-based omics has transformed our understanding of the gut microbiome, enabling microbial communities to be characterised at unprecedented scale and resolution. However, these approaches provide limited information about cellular traits such as size and morphology. Giant bacteria remain largely overlooked in gut microbiome research, despite their extraordinary cell size, extreme polyploidy and unusual reproductive strategies. Epulopiscium, one of the largest known heterotrophic bacteria, has been studied primarily as an intestinal symbiont of surgeonfish, leaving its broader host range unresolved. Here we show, by a large-scale analysis of publicly available 16S ribosomal RNA amplicon samples, alongside locally collected human samples, that Epulopiscium spp. occur across diverse hosts, including humans and a broad diversity of animals. Full-length 16S rRNA gene phylogenies resolved multiple Epulopiscium-related lineages with distinct host compositions. Among human samples, Epulopiscium sequences were most abundant during the first year of life. In adults, abundance varied across disease states, increasing in inflammatory bowel disease and colorectal cancer while decreasing in neurological conditions compared with a healthy adult baseline. Similar patterns were also observed in mouse models. Our study broadens the known host range of Epulopiscium and establishes giant bacteria as an overlooked component of gut microbiome diversity. These findings highlight the need to combine sequence-based studies with classical microbiological techniques to identify organisms with unique traits, otherwise hidden by taxonomy or phylogeny.

microbiology↗

MERIT: A No-Code Platform for Unifying High-Precision Cognitive Assessment Across Laboratory and Real-World Settings

Cognitive tasks are moving from the laboratory onto participants' own devices. Browser runtimes make that move easy. Their stimulus timing, however, can vary by tens of milliseconds across devices. We asked how much timing error cognitive and neural measures can tolerate. We injected per-trial stimulus-timing jitter into a public EEG dataset (ERP CORE, N = 40) and re-measured three event-related potential components. Under a prespecified criterion of half the between-participant SD, the N170 tolerated jitter up to 15.8 ms, limited by peak-latency bias. The slower MMN and P3 tolerated more than 20 ms. The full jitter distribution predicted the loss. Heavy-tailed jitter cost less than Gaussian jitter of the same RMS. In simulated reaction-time data, the same tail shape determined whether platform error raised or lowered the ex-Gaussian {tau}, a candidate attentional biomarker. A single RMS timing figure therefore cannot tell whether a platform is adequate for these measures. We built the Mobile Experimental Research Investigative Toolkit (MERIT) to meet these requirements without code. Researchers design tasks in a visual builder that supports reaction-time, continuous-control, drawing, and sequential task families. A Unity engine embedded natively in the Curious iOS and Android apps runs the tasks. Measured with an optical sensor, inter-stimulus-interval SD was 0.71 to 0.96 ms on three current phones and tablets. It was 3.73 ms on a 2019 budget Android phone. Every device measured at least four times below the N170 ceiling. In the laboratory, the same task definition streams event markers to Lab Streaming Layer (LSL) for synchronized EEG and physiological recording. In the field, it runs on ecological momentary assessment schedules. Native mobile players, no-code builders, and EMA platforms each exist as separate systems. MERIT combines them, so the task characterized against brain and body recordings in the laboratory is the same task participants complete at home.

neuroscience↗

CellMAGE: cell-type deconvolution for multi-parent population analysis of gene expression

Single-cell RNA-sequencing remains prohibitively expensive for multiparental population (MPP) studies. Existing deconvolution methods treat bulk RNA-seq as genetically anonymous mixtures, but in MPPs, the proportional contribution of each parental strain to each progeny's transcriptome is already known. CellMAGE (Cell-type deconvolution for Multi-parent Analysis of Gene Expression) weights parental cell-type profiles by each progeny's known genetic composition, requiring no model training and no minimum sample size. Validated in 16 Diversity Outbred mice across 12 prefrontal cortex cell types and 23,116 genes, predicted and measured gene expression were statistically equivalent (0.05) in all cell types (pooled Spearman = 0.923, 95% CI: 0.910, 0.934). CIBERSORTx required 96 additional samples to resolve at most 12.4% of genes and only 3 cell types; CellMAGE outperformed it even within this restricted comparison (per-cell-type median : 0.908-0.974 vs. 0.353-0.641). CellMAGE is applicable to any MPP with parental single-cell data, including diploid crop MAGIC populations.

genomics↗

Single-cell analysis of an adult IBD INCEPTION cohort reveals Galectin-linked disease mechanisms

Background and AimsThe molecular pathogenesis of Inflammatory Bowel Disease (IBD) remains unclear. We aimed to establish a high-resolution immune landscape of treatment-naive IBD to identify central drivers of disease onset and early pathogenic signalling. MethodsWe generated a single-cell atlas using intestinal biopsies from a large adult inception cohort of 137 individuals, including treatment-naive Crohns disease (CD), ulcerative colitis (UC), and symptomatic non-IBD controls. We integrated scRNA-seq (1 million cells) with co-varying neighbourhood analysis (CNA) and unbiased tensor decomposition of cell-cell communication (CCC) networks. Findings were validated in vitro macrophage stimulation model and using serum from patients. ResultsThe inception cohort exhibited significantly more homogenous compartmental diversity compared to benchmark reference studies (p < 0.001). Inflammation in both CD and UC was characterized by a marked expansion of inflammatory monocytes. Unbiased CCC analysis identified a dominant disease-specific signalling module centred on the Galectin family (LGALS1 and LGALS9). Galectin-9 expression was specifically enriched in inflammatory monocytes, which exhibited distinct transcriptional programs linked to antigen presentation and microbial sensing. In vitro, Galectin-9 acted as a potent stimulus, driving macrophages toward a pro-inflammatory phenotype. Clinically, serum Galectin-9 levels were significantly elevated in IBD patients and correlated with systemic inflammatory markers and treatment response. ConclusionsOur data identify a galectin-monocyte signalling axis as a unifying inflammatory hallmark of early IBD. Galectin-9 serves as both a functional driver of mucosal inflammation and a dynamic biomarker, offering new opportunities for therapeutic targeting and disease monitoring from diagnosis.

immunology↗

Data-driven prioritization of mouse strains for improved preclinical modeling of rare and common disease

Choosing an appropriate mouse genetic background is a persistent challenge for successful translation of preclinical disease modeling. We present Strain Recommender, a genomic framework that prioritizes inbred mouse strains as relatively vulnerable or resilient to a disease state using disease-associated gene signatures and strain-specific transcriptome predictions. The method represents disease states as weighted gene scores, ranks 657 strains based on resemblance to the disease state, and estimates uncertainty via a permutation-derived false positive rate (FPR). In a prospective validation of connective tissue disorder predictions, vulnerable and resilient Collaborative Cross strains showed significantly different cardiovascular abnormalities. In a global retrospective validation predicting previously reported strain background effects, Strain Recommender achieved [&ge;] 90% sensitivity for 86.6% of diseases with 94.4% mean sensitivity (95% CI: 94.0-94.8%) across 5,890 diseases, including 92.3% (95% CI: 91.6-93.0%) for 2,598 rare diseases, demonstrating its potential to improve the validity of mouse models of human disease.

bioinformatics↗

The effect of ULV-based mosquito control on target and non-target organisms in Hungary: an experimental field study

Ultra-low volume (ULV) insecticide spraying with deltamethrin as the active ingredient is widely used in mosquito control programs, yet its effectiveness against target mosquitoes and its ecological side effects remain poorly quantified under field conditions in Central Europe. Here, we experimentally evaluated the short-term impact of ground ULV spraying on both mosquito populations and non-target flying insects in Hungary using a paired before-after-control-impact (BACI) design. Mosquitoes were sampled with BG Sentinel traps, while non-target insects were collected using malaise traps. ULV treatment resulted in a significant reduction in mosquito abundance at treated sites, with an average decline of approximately 45%. Native and invasive mosquito species, including Aedes albopictus and Aedes koreicus, showed similar proportional decreases. However, treatment effectiveness varied substantially among sites and was influenced by initial mosquito abundance and wind conditions. In parallel, malaise trap samples revealed a marked decline in non-target flying insects, with reductions exceeding 40% across multiple taxonomic groups, particularly among small- and medium-sized insects, and also when considering pollinator taxa together. Our results indicate that while ULV spraying can temporarily reduce mosquito abundance, it also imposes considerable short-term impacts on non-target insect communities, highlighting trade-offs between vector control and insect conservation within mosquito management programs.

ecology↗

A Multimodal Single-Cell Epigenomic and 3D Genome Atlas of the Human Basal Ganglia

The basal ganglia (BG) underlie motor control, reward processing, and many neurological and psychiatric disorders, but a comprehensive epigenomic and 3D-genome atlas of the human BG is lacking. Here we present a multimodal single-cell atlas profiling DNA methylation and 3D chromatin conformation in 261,331 nuclei (snm3C-seq) across eight subregions, resolving 12 classes, 31 subclasses, and 59 groups. Harmonized under the HMBA basal-ganglia consensus taxonomy, this atlas integrates with matched RNA, ATAC-seq, and histone-modification data across five regulatory layers. We identify millions of cell-type- and region-specific differentially methylated regions enriched for distinct transcription factor motifs and link them to disease-associated heritability. Neuron-specific loops dominate cell-type-specific 3D contact remodeling, while most non-neuron-specific loops are constitutive. Among spiny projection neurons (SPNs), chromatin loops, rather than TAD boundaries, distinguish D1, D2, and eccentric SPN subclasses, with eccentric SPNs showing the most loop-level reorganization among the three. We characterize STR D2 SMYD2-HTR7 SPN, a newly recognized POU6F2 D2-SPN subtype, and reveal region-specific methylation and contact gradients of disease-associated genes, including CADM1 and PDE8B. Integrative gene-regulatory networks reconstruct cell-type-resolved enhancer-promoter links to interpret Parkinsons disease risk variants at SNCA. Finally, MERFISH spatial profiling combined with cross-species Patch-seq identifies non-SPN neuronal subtypes, including a MOXD1 striosomal STR FS PTHLH-PVALB GABA subtype with distinct electrophysiology, partitioning across the striatal matrix-striosome boundary. HighlightsO_LIA multimodal single-cell atlas maps DNA methylation and 3D genome architecture across human basal ganglia cell types and subregions. C_LIO_LINeuron-specific loops dominate cell-type-specific 3D contact remodeling in the human BG, whereas most non-neuron-specific loops are constitutive. C_LIO_LISpiny Projection Neuron (SPN) subtypes exhibit regionally organized epigenomic and 3D genome signatures that align with dorsal-ventral identities. C_LIO_LIChromatin loops are the primary distinguishing feature among D1, D2, and eccentric SPN subclasses, with eccentric SPNs being the most 3D-reorganized. C_LIO_LIIntegrated regulatory maps link cell-type-specific enhancers to disease-associated genetic risk in the human basal ganglia. C_LIO_LINon-SPN interneurons differ in distribution and in transcriptional and epigenetic identity across the matrix-striosome boundary. C_LI

neuroscience↗

Single-cell Multiome Analysis of Chromatin State and Transcriptome in the Human Basal Ganglia

The basal ganglia play essential roles in motor control, emotion, learning and reward processing. Their dysfunction contributes to many neurological and psychiatric disorders. However, the gene regulatory programs defining basal ganglia cell-type identity and function remain poorly understood, limiting interpretation of disease-associated non-coding variants. Here, we present the first single-cell multiome atlas of histone modifications and transcriptomes across eight basal ganglia regions from neurotypical adult human donors. Joint profiling reveals cell-type-specific deployment of active and repressive cis-regulatory elements and gene regulatory networks, and suggests a combinatorial homeobox transcription factor code underlying cell identity. Integration with matched spatial transcriptomic MERFISH data uncovers regional heterogeneity of epigenomic landscapes. Comparative analysis between human and mouse medium spiny neurons uncovers conservation of core gene regulatory features. This atlas interprets non-coding risk variants of neuropsychiatric disorders and supports the development of a deep learning model to predict gene regulation and functional effects of disease-associated variants. HIGHLIGHTSO_LIJoint single-cell profiling of transcriptomes and three histone modifications across eight human basal ganglia regions characterizes active and repressive chromatin states at cell-type resolution. C_LIO_LICell-type-specific gene regulatory programs decode combinatorial homeobox TF grammar governing the identity and diversification of basal ganglia neurons. C_LIO_LIIntergrative analyses link noncoding neuropsychiatric risk variants to specific cell types, regulatory elements, and candidate target genes. C_LIO_LIA sequence-to-function deep-learning model predicts gene regulation from DNA sequence and prioritizes functional disease-associated variants. C_LI

genomics↗

Single-Cell Atlas of Transcription and Chromatin States Reveals Regulatory Programs in the Human Brain

Directly measuring chromatin states alongside transcription is essential for understanding how cell-type-specific regulatory programs are established and maintained in the adult human brain. We present a large-scale single-cell multimodal atlas generated by jointly profiling transcriptome with active (H3K27ac) and repressive (H3K27me3) histone modifications across 18 brain regions. We profile >750,000 nuclei spanning 160 cell types and integrate these data with chromatin accessibility, DNA methylation, 3D genome architecture, and spatial transcriptome. This framework annotates >500,000 regulatory elements and resolves cell-type-specific chromatin states. We link enhancers to target genes, infer gene regulatory networks, and classify chromatin interactions, revealing neuron-enriched long-range Polycomb repression of developmental genes. Integrating these maps with GWAS data and sequence-based model prioritizes noncoding variants, effector genes, and vulnerable cell types for neuropsychiatric disorders. Finally, cross-species comparisons show conserved activation but more divergent repression. Together, this study provides a functional reference for interpreting noncoding variants, epigenetic memory, and brain organization. HIGHLIGHTSO_LIJoint single-cell profiling of transcriptomes with active or repressive histone modification in >750,000 nuclei across adult human brain. C_LIO_LIChromatin state annotation of >500,000 candidate cis-regulatory elements distinguishes active enhancers from accessible and Polycomb-repressed regions. C_LIO_LICell-type-resolved regulatory networks and sequence-based deep learning model prioritize functional neuropsychiatric risk variants. C_LIO_LISpatial epigenomic imputation reveals laminar layer-specific Polycomb repression programs. C_LIO_LIIntegration with 3D genome architecture reveals neuron-specific super long-range chromatin loops silencing early developmental genes. C_LIO_LIEvolutionary analysis uncovers conserved active regulatory grammar but divergent repressive landscape. C_LI

genomics↗

An Integrated Single-Cell and Epigenomic Resource for Comparative Analysis of the Basal Ganglia

The basal ganglia regulate motor, cognitive, and affective behaviors, and their dysfunction underlies diverse neurological and psychiatric disorders. Comprehensive, accessible multi-omics resources are needed to understand the regulatory mechanisms governing basal ganglia cell types. Here we present an open, interactive web-based platform for exploring single-cell multi-omics datasets from basal ganglia, generated using 10X Multiome, snm3C-seq, and Paired-Tag technologies from the BICAN (NIH BRAIN Initiative Cell Atlas Network) consortium. The platform is available at https://basalganglia.epigenomes.net/ and enables integrated visualization of gene expression, chromatin accessibility, DNA methylation, histone modifications, and chromatin conformation across cell types and human, macaque, marmoset, and mouse species, with direct genome browser support and comparative epigenomic functionality. Representative analyses demonstrate cell-type-specific regulatory landscapes, conserved and species-specific regulatory elements, and links between epigenomic regulation and transcription. This resource provides a scalable, community-oriented foundation for advancing basal ganglia biology and interpreting regulatory mechanisms relevant to brain function and disease. HighlightsO_LIIntegrated single-cell epigenomic resource for basal ganglia C_LIO_LIInteractive genome browser enables multi-omics and cross-species exploration C_LIO_LIReveals cell-type-specific and species-specific regulatory landscapes C_LIO_LISupports community access to complex brain epigenomic datasets C_LI

genomics↗

Circuit specific specialization of human basal ganglia astrocytes

Astrocytes shape synapses and circuits, yet human basal ganglia astrocyte diversity is incompletely defined. We built a multimodal atlas by integrating single-nucleus RNA-sequencing and chromatin accessibility with DNA methylation, 3D chromatin conformation, and spatial transcriptomics, then mapped basal ganglia programs onto a whole-brain reference. Astrocytes segregated into three anatomical subgroups spanning striatal gray matter, extra-striatal gray matter, and white matter, with subgroup-biased neurotransmitter transporters and synapse-associated programs consistent with differences in dominant afferent input. Within striatum, dorsal and ventral astrocyte populations aligned with distinct microcircuits and were conserved in nonhuman primates. A deep learning sequence model identified subgroup-associated enhancer code and, when benchmarked against published enhancer-AAV datasets, supported the design of candidate viral tools to target basal ganglia astrocyte programs in vivo. Together, these data define major axes of human astrocyte specialization and provide a framework for cell type-specific dissection of basal ganglia function.

neuroscience↗

Pantoea in agriculture: Balancing promise and pathogenicity

The genus Pantoea has gained attention as a promising biofertilizer and biocontrol agent, with strains like Pantoea agglomerans and Pantoea dispersa enhancing plant growth and suppressing pathogens. However, the same genus includes pathogenic species such as Pantoea stewartii (causing Stewarts wilt in maize) and Pantoea ananatis (inducing center rot in onions). This duality raises critical concerns about the unregulated adoption of Pantoea in agriculture.

microbiology↗

In vivo models for rustrela virus (RusV) infection in potential reservoir and spill-over hosts

Rustrela virus (RusV; species Rubivirus strelense) is a recently discovered relative of the human rubella virus (Rubivirus rubellae) and causes fatal non-suppurative meningoencephalomyelitis in a broad range of mammalian domestic, wild and zoo animals, in Europe and the USA, including staggering disease in domestic cats. Based on its reportedly broad host range, a zoonotic potential of RusV cannot be excluded. Apparently healthy yellow-necked field mice (Apodemus flavicollis) and wood mice (Apodemus sylvaticus) were identified as potential reservoir hosts. In this study, we experimentally inoculated wood mice and Lewis rats with brain homogenates of RusV-infected animals. Intracranial as well as combined intranasal/peroral inoculation resulted in apparently persistent RusV infection in both rodent species, while combined intramuscular/subcutaneous injection failed to establish detectable infection. Viral loads were highest in the central nervous system, but viral RNA was prominently found also in mucosal organs of wood mice. Viral RNA was frequently detected in oral swabs from infected wood mice, but only sparsely from rats. Neither wood mice nor rats presented neurological signs during twelve weeks after infection. However, RusV-infected rats showed significantly reduced body weight. A lymphohistiocytic meningoencephalomyelitis was observed in infected rats, whereas infected wood mice infrequently developed minimal lesions in the brain. Our study provides the first infection model for RusV and indicates that natural infection may occur via mucosal routes. Shedding of viral RNA suggests wood mice to serve as natural reservoir hosts. In rats, we could reproduce a lymphohistiocytic meningoencephalomyelitis, as described for diseased cats and other tentative non-reservoir hosts.

microbiology↗

Machine Learning-Driven Discovery of Synergistic Protein Interactions Identifies ATL3 as a Putative Biomarker for Cancer Drug Response

BackgroundResistance to targeted molecular therapies--both primary and acquired--remains a major obstacle to effective cancer treatment. Despite extensive research, the molecular determinants of treatment resistance are still incompletely understood, underscoring the need to identify robust resistance drivers to improve therapeutic outcomes. Recently, the ProCan and Wellcome Sanger Institute released the worlds largest pan-cancer proteomic dataset to date, comprising 949 cancer cell lines treated with 625 anti-cancer agents. Despite progress in identifying single protein markers, scalable methods for detecting synergistic protein interactions driving drug susceptibility remain limited. Machine learning models such as random forests may offer a robust framework for capturing non-linear protein interactions across diverse cancer types. ResultsOur study presents synerOmics, a scalable framework for identifying putative synergistic protein interactions by leveraging parent-child co-occurrences in random forest regression trees to reduce the interaction search space. We validated our approach using simulated data and two independent cancer proteomic datasets, identifying both pan-cancer and breast cancer-specific markers associated with drug susceptibility. Synergistic interactions that consistently replicated across datasets were enriched for endoplasmic reticulum stress pathways. Notably, shared targets included established sensitivity markers for tyrosine kinase inhibitors (TKIs) and revealed a resistance-associated network centred on ATL3, which demonstrated both prognostic and predictive relevance. ConclusionsApplying synerOmics to the largest cancer cell line proteomic dataset to date, we identified ATL3 as a strong candidate biomarker for lapatinib resistance in breast cancer, with superior predictive performance compared to the current gold standard sensitivity biomarker, ERBB2. HighlightsO_LIWe present a machine learning framework for drug response biomarker discovery, leveraging the largest proteomic cancer cell line dataset, ProCan-DepMapSanger C_LIO_LIATL3 is proposed as a candidate predictive biomarker for lapatinib resistance in breast cancer C_LIO_LIElevated expression of a novel resistance-conferring subcluster centered on ATL3 is associated with poor prognosis across 11 TCGA cancer types C_LIO_LIBaseline drug resistance may be broadly driven by stress pathways, with ER stress and protein ATL3 implicated in ER-phagy in response to TKIs. C_LI

cancer biology↗

Developmental senescence profiling reveals Eda2r as a common mediator in the core senescence program

Cellular senescence is a distinct state of stable cell-cycle arrest coupled to a dynamic secretory phenotype. Although transient senescence contributes to embryonic development and regeneration, its relationship to the chronic forms associated with damage and aging is unclear. Here, we generated a p21-reporter mouse to isolate and transcriptionally profile senescent cells in Apical Ectodermal Ridge of the developing limb. Comparative analysis of these cells with diverse in vitro and in vivo senescence models revealed a conserved gene program encompassing canonical markers and regulators, with previously unassociated factors. Among these, ectodysplasin A2 receptor (Eda2r) emerged as a critical modulator required for senescence-associated transcriptional and functional features. Our findings define a shared molecular framework linking developmental and pathological senescence and identify conserved effectors such as Eda2r as potential targets for therapeutic modulation of senescent cell function.

cell biology↗

Senescent cells exhibit features of developmental signaling centres

Cellular senescence is a state that contributes to tissue patterning during development and tissue repair, but which becomes misregulated in ageing and disease. The senescence-associated secretory phenotype (SASP) mediates many of the context-dependent effects, yet its molecular composition and evolutionary origins remain incompletely understood. Here, we characterize the SASP signature of developmental senescent cells and find that it is enriched for genes encoding major developmental signaling pathways. Integrative analyses of in vitro and in vivo transcriptomes reveal that human and mouse senescent cells across diverse contexts consistently activate these developmental programs, with key morphogens constituting conserved SASP components. Functionally, we show that the SASP is sufficient to induce the expression of central developmental genes and transcription factors across multiple cell types. These findings show that senescent cells resemble developmental signaling centers and uncover developmental signaling reactivation as an evolutionarily conserved feature of senescence, providing conceptual insight into its physiological functions and its pathological impact during ageing.

cell biology↗

Tracheal tuft cell-released leukotrienes promote antibacterial immune responses

Tuft cells act as crucial sentinels in the airways that detect bacterial metabolites. In response, tuft cells release signaling molecules that trigger immune responses essential for clearing the infection. The molecular mechanisms driving immune cell activation following tuft cell stimulation in pneumonia are still not fully understood. Here, we identify tuft cells as the primary source of proinflammatory leukotrienes (LTs), which are released in the presence of pathogenic bacteria in the airways. We show that tracheal tuft cells discriminate pathogenic from non-pathogenic bacteria by sensing adenosine triphosphate (ATP) released from pathogens such as Pseudomonas aeruginosa and Rodentibacter pneumotropicus within the first 4 h of invasion, and recruit neutrophils and macrophages to the trachea and alveolar spaces. Taste signaling through the chemosensory transient receptor potential cation channel subfamily M member 5 (Trpm5) channel was essential for tuft cell activation and LT release. Mice lacking Trpm5 were not capable of detecting bacteria-released ATP and became colonized upon R. pneumotropicus infection. In contrast, Trpm5+/+ mice cleared the pathogen. We uncover a critical tuft cell-dependent sensing mechanism in pneumonia and establish tracheal tuft cells as both detectors of bacterial extracellular ATP and triggers of acute innate immune responses.

immunology↗

Combining phenomic and genomic selection for pea breeding improvement

Pea (Pisum sativum L.) is a strategic crop in the development of sustainable agriculture. However, the genetic gain remains limited despite advances in breeding. Genomic selection holds promise to accelerate varietal improvement, but its high implementation cost restricts its use in crops. Phenomic selection, based on near-infrared spectroscopy data, is a cost-effective alternative demonstrated in various crops, but not yet undertaken in pea. This study aims to assess the predictive ability of phenomic selection, alone and combined with genomic selection, for yield-related traits in a panel of elite spring pea lines evaluated across twelve environments. Three cross-validation scenarios were implemented to simulate predictions across different years and locations. Our results show that phenomic prediction is as effective as genomic selection at predicting yield, and is more accurate for seed protein content. The integrative model, combining spectral and molecular data, consistently achieved the highest accuracy for most traits, particularly for complex traits such as grain yield and seed protein. In temporal prediction scenarios, the most accurate predictions were obtained using the spectra data from the same year as phenotyping. In spatial prediction scenarios, predictive accuracy varied by site and year, nevertheless, integrative phenomic-genomic models consistently outperformed univariate approaches. These findings confirm the potential of phenomic selection in pea and underscore the added value of combining near-infrared spectroscopy and genotyping data to improve the prediction of complex traits in breeding programs. In the face of increasing environmental variability, the integrative approach offers a valuable tool for accelerating genetic gain. Key messageThe integration of spectral data into prediction models enhances the predictive ability for complex traits in pea.

genetics↗