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PubMed2025

Novel photoreceptor-specific promoters for gene therapy in mid- to late-stage retinal degeneration.

Inherited retinal degenerations (IRDs) cause progressive photoreceptor loss, leading to vision impairment. Gene therapy using adeno-associated viral (AAV) vectors holds immense promise for treating these conditions. However, achieving optimal gene expression at mid to late stages of retinal degeneration remains challenging due to scarcity of efficient photoreceptor-specific promoters expressed at these disease stages. This study aimed to identify and validate novel promoters capable of robust and specific transgene expression when ≥50% of photoreceptors are lost. Analysis of transcriptomic data from two naturally occurring canine IRD models, laser capture microdissection of retinal cryosections followed by qPCR, and RNA in situ hybridization identified six promising genes with sustained or upregulated expression in photoreceptors in late-stage disease. Upstream cis-regulatory elements of both canine and human orthologs were identified and characterized using in silico analyses and dual-luciferase assays. Short promoters (≤840 base pairs) derived from GNGT2, IMPG2, and PDE6H genes exhibited robust reporter gene expression in photoreceptors when delivered via AAV to the subretinal space of two non-allelic canine IRD models at mid and late disease stages. These findings provide a strategy to enhance AAV-mediated gene therapy by enabling sustained transgene expression in degenerating retinas, improving treatment outcomes for patients with progressive vision loss.

Retinal Degeneration↗
PubMed2026

Comparative evaluation of probe-capture and conventional metagenomic sequencing across multiple clinical sample types, with analysis of paired bronchoalveolar lavage fluid and blood samples.

Conventional metagenomic next-generation sequencing (mNGS) suffers from host nucleic acid interference and poor performance in low-biomass samples. Probe-capture metagenomic sequencing (PC-mNGS), which enriches microbial targets via hybridization probes, shows superior sensitivity but lacks systematic multi-sample evaluations. This study compared PC-mNGS and mNGS across diverse clinical specimens (bronchoalveolar lavage fluid [BALF], blood, cerebrospinal fluid [CSF]) and assessed the clinical utility of pathogen co-detection in paired BALF-blood samples from sepsis patients. A total of 282 samples (81 BALF, 141 blood, 25 CSF, 35 others) sequenced by both PC-mNGS and mNGS were analyzed. Additionally, 621 paired BALF-blood samples from sepsis patients with pulmonary infections were evaluated. PC-mNGS achieved higher pathogen detection rates (66.67% vs 57.10%, P = 0.000198) than mNGS, particularly in blood (66.67% vs 47.52%, P = 2.5 × 10⁻⁵). PC-mNGS detected more bacteria (19 species exclusive) and fungi (11 species exclusive) than mNGS. Viruses showed comparable detection. BALF and CSF exhibited high overall agreement (OPA: 96.30% and 88%, respectively), while blood had lower concordance (NPA: 54.05%, OPA: 70.92%). A total of 60.55% of BALF-positive samples (PC-mNGS) had co-detected pathogens in blood. Gram-negative bacteria (e.g., Klebsiella pneumoniae) and fungi (e.g., Candida albicans) showed higher blood co-detection rates than viruses. In this study, PC-mNGS detected more pathogens and showed a higher positivity rate than mNGS in blood samples. BALF sequencing data, particularly bacterial reads per million (RPM), may predict bloodstream co-detection, aiding in sepsis management. However, clinical validation and integration with traditional diagnostics are needed to confirm utility. This study highlights PC-mNGS as a promising tool for complex infections but underscores the need for rigorous multi-context validation.IMPORTANCEAccurate and rapid identification of pathogens is critical for effective treatment of severe infectious diseases, such as sepsis. This study demonstrates that probe-capture metagenomic sequencing (PC-mNGS) detected more pathogens in blood samples compared to conventional metagenomic sequencing, especially for bacterial and fungal infections. By analyzing paired lung and blood samples, we show that high pathogen levels in lung fluid may predict bloodstream infection, offering a potential early warning for clinicians. These findings support the use of PC-mNGS as a more sensitive diagnostic tool, which could lead to faster, more targeted therapies and better outcomes for patients with complex infections.

Humans↗
PubMed2025

N6-methyladenine identification using deep learning and discriminative feature integration.

N6-methyladenine (6 mA) is a pivotal DNA modification that plays a crucial role in epigenetic regulation, gene expression, and various biological processes. With advancements in sequencing technologies and computational biology, there is an increasing focus on developing accurate methods for 6 mA site identification to enhance early detection and understand its biological significance. Despite the rapid progress of machine learning in bioinformatics, accurately detecting 6 mA sites remains a challenge due to the limited generalizability and efficiency of existing approaches. In this study, we present Deep-N6mA, a novel Deep Neural Network (DNN) model incorporating optimal hybrid features for precise 6 mA site identification. The proposed framework captures complex patterns from DNA sequences through a comprehensive feature extraction process, leveraging k-mer, Dinucleotide-based Cross Covariance (DCC), Trinucleotide-based Auto Covariance (TAC), Pseudo Single Nucleotide Composition (PseSNC), Pseudo Dinucleotide Composition (PseDNC), and Pseudo Trinucleotide Composition (PseTNC). To optimize computational efficiency and eliminate irrelevant or noisy features, an unsupervised Principal Component Analysis (PCA) algorithm is employed, ensuring the selection of the most informative features. A multilayer DNN serves as the classification algorithm to identify N6-methyladenine sites accurately. The robustness and generalizability of Deep-N6mA were rigorously validated using fivefold cross-validation on two benchmark datasets. Experimental results reveal that Deep-N6mA achieves an average accuracy of 97.70% on the F. vesca dataset and 95.75% on the R. chinensis dataset, outperforming existing methods by 4.12% and 4.55%, respectively. These findings underscore the effectiveness of Deep-N6mA as a reliable tool for early 6 mA site detection, contributing to epigenetic research and advancing the field of computational biology.

Deep Learning↗
PubMed2026

A leakage-aware genomic prediction pipeline for meropenem resistance in Klebsiella pneumoniae using transformer-based resistome representation learning.

MOTIVATION: Antimicrobial resistance (AMR) in Klebsiella pneumoniae, particularly to carbapenems such as meropenem, is a major global health problem. Machine learning is increasingly used to predict resistance from genomic markers; however, many models fail to capture high-level gene-gene interactions and may exhibit inflated performance due to lineage-biased prediction. Existing genomic prediction models largely rely on flat feature representations that fail to capture epistatic gene interactions, and commonly suffer from inflated performance estimates due to phylogenetic data leakage. To address these limitations simultaneously, a leakage-aware hybrid TabTransformer-CatBoost pipeline was developed, combining self-attention-based resistome representation learning with gradient boosting classification under clade-aware data partitioning. A self-attention encoder converts sparse gene presence-absence profiles into contextualized latent embeddings, which are subsequently classified using gradient boosting to capture lineage-aware AMR patterns. RESULTS: The proposed architecture outperformed classical baselines including Logistic Regression, Random Forest, XGBoost, and optimized CatBoost models. Internal accuracy reached 92.59% for the Chained Hybrid configuration (area under the receiver operating characteristic curve, AUROC = 0.8670, F1 = 0.8537). Performance gains primarily originated from the embedding stage, as confirmed by ablation analysis. External validation across independent multinational cohorts (n = 305) demonstrated generalizability (AUROC = 0.8105; F1 = 0.7552). Permutation testing produced near-zero Matthews Correlation Coefficient (MCC) = 0.0091, indicating predictions reflect genuine biological signal rather than noise. These results establish attention-based genomic embedding with gradient boosting as a scalable, interpretable, and leakage-aware framework for clinical AMR prediction. AVAILABILITY AND IMPLEMENTATION: The source code for the TabTransformer-CatBoost framework, including preprocessing pipelines and pre-trained embeddings, is available at https://github.com/SibelKervanci/kp-meropenem-tabtransformer.

Journal Article↗
PubMed2026

Dissecting genetic variance structure and evaluating genomic prediction models for single-cross hybrids derived from Stiff Stalk and Non-Stiff Stalk maize heterotic groups.

The early 20th-century discovery of heterosis and the establishment of heterotic groups transformed maize (Zea mays L.) into a keystone of global agriculture. However, maize breeding faces two significant challenges: the gradual decline of general combining ability (GCA) variance within heterotic groups and the impracticality of testing all possible single crosses in the early stages of a breeding program. Here, we developed genomic best linear unbiased prediction (GBLUP)-based multikernel models, using additive and two alternative nonadditive genomic relationship matrices, to estimate the variance components associated with the general combining ability of Stiff Stalk (SS) and Non-Stiff Stalk (NSS) heterotic groups and the specific combining ability arising from their crosses. We further applied these models to predict the performance of untested single-cross combinations under varying levels of parental information. We showed that the SS and NSS groups retained significant GCA variance across traits in both early- and late-maturity groups. The SS group, in contrast, exhibited no detectable GCA variance in grain yield for the intermediate-flowering subset of hybrids, highlighting a limitation for future genetic improvement. Furthermore, our results showed that GBLUP-based multikernel models effectively identified superior hybrids when parental information was available. In the absence of this information, however, these models underperformed compared to covariance-based approaches. Both nonadditive matrices yielded similar results, indicating that they capture comparable genetic relationship patterns despite their distinct formulations. Overall, this study sheds light on the future use of US maize commercial germplasm and demonstrates how GBLUP-based multikernel models can improve the efficiency of hybrid breeding programs.

Zea mays↗
PubMed2026

Optimizing a culture-enriched hybrid metagenomics pipeline to assess the AMR footprint of livestock manure in anaerobic digestate.

The role of environmental samples from livestock production systems, including manure and anaerobic digestate, as reservoirs of antimicrobial resistance genes (ARGs) is likely underestimated because conventional metagenomic approaches can overlook low-abundance ARGs and often lack the resolution to associate these genes with their microbial hosts and co-localized mobile genetic elements (MGEs). We evaluated whether culture-enriched metagenomics (CEMG), with and without antibiotic selection, enhances ARG detection in anaerobic digestate and improves the resolution of ARG-MGE-host associations using hybrid short- and long-read metagenomic assembly. CEMG increased ARG recovery; mean ARG abundance rose from 15.4 counts per million (CPM) in metagenomic fresh digestate (FD) to 124 CPM in CEMG without antibiotics and 160 CPM in antibiotic-selective CEMG. In FD, only 9 unique ARGs were detected, whereas CEMG recovered 112, including ARGs of clinical importance, such as glycopeptide resistance, beta-lactamase genes, and the cfr 23S rRNA methyltransferase conferring cross-resistance to multiple antibiotic classes. Antibiotic selection induced targeted, class-specific shifts in ARG profiles, with ARGs associated with tetracycline resistance consistently enriched across treatments. Hybrid metagenomic assembly resolved the genomic context of 784 ARGs, of which 59.3% were co-localized with at least one class of MGEs, predominantly plasmids and integrative conjugative elements/integrative mobilizable elements. Biocide and metal resistance genes frequently co-occurred with ARGs on the same contigs. Together, these findings demonstrate that antibiotic-selective culture enrichment enhances resistome surveillance by improving detection of low-abundance ARGs, while hybrid assembly provides critical genomic context for assessing their mobility and host associations.IMPORTANCELivestock manure and its byproducts, such as anaerobic digestate, are recognized as important environmental reservoirs of antimicrobial resistance genes (ARGs) and resistant bacteria, yet current metagenomic approaches may underestimate this risk by failing to detect low-abundance but clinically relevant ARGs. Here, we show that integrating culture enrichment with hybrid metagenomics improves ARG recovery and reveals ARG co-localization with mobile genetic elements and putative bacterial hosts. This approach captures a cultivable and condition-responsive fraction of the resistome that is not readily accessible through direct metagenomic sequencing alone, providing a more informative framework for environmental AMR surveillance.

anaerobic digestion↗
PubMed1979

Use of molecular hybridization to detect type D retrovirus markers in rhesus placentas and other tissues.

We have shown previously that approximately 20% of the Mason-Pfizer virus (MPV) genome is present as endogenous provirus in rhesus monkeys. We report here that several full-term rhesus placentas examined contain additional MPV proviral sequences in their DNA. Competitive molecular hybridization experiments demonstrated that some of these placentas also contain RNA complementary to the entire MPV 60 to 70S RNA genome. Examination of internal organs of rhesus monkeys captured in the wild also revealed the presence of additional MPV proviral sequences and expression of MPV RNA in some tissues. These results provide further evidence that MPV is being transmitted via a non-germ line mechanism in the rhesus population and now demonstrate the placenta as a good source for the identification of retrovirus transcriptional products and proviral DNA.

Animals↗
PubMed2026

Molecular identification and diversity assessment of Tyrrhenian Romulea species (Iridaceae).

Taxonomic assignments based only on morphology are often insufficient for delimiting species, particularly in complexes shaped by hybridization and polyploidy, where species boundaries are unclear. This limitation hinders progress in ecological, biogeographic and conservation research. The genus Romulea, distributed across Africa and the Mediterranean Basin, exemplifies this challenge. Despite its remarkable diversity, Mediterranean Romulea has not received much attention from genetic and molecular studies. Here, we present the first multilocus genotype analysis of Mediterranean Romulea taxa, focusing on the Tyrrhenian biogeographic province. Using target-capture sequencing with the universal Angiosperms353 kit, we generated genomic data for 272 individuals representing 18 putative taxa. Our findings reveal genetic groups that align with current taxonomy, the existence of cryptic divergence, and highlight the role of hybridization. Furthermore, analysis of intra-individual genetic diversity suggests one or several allopolyploid origins for Mediterranean Romulea. Four taxa (R. assumptionis, R. revelieri, R. ligustica, R. rollii) are consistently well differentiated across nuclear and plastid datasets, supporting their recognition as distinct species. In contrast, the widespread species R. ramiflora and R. columnae contain well-differentiated groups that may represent cryptic speciation. Several other taxa, including R. x melitensis, R. corsica, and R. bulbocodium, exhibit genomic signatures consistent with hybrid origins. Plastid and nuclear variation patterns are consistent with a hypothesis of rapid radiation in the Tyrrhenian region. These results provide a primary genomic framework for the integrative taxonomy of Romulea.

Genetic Variation↗
PubMed2025

Extrinsic heterogeneity: Collectivity in isotropic conformational fluctuations of chromosomes.

Eukaryotic interphase chromosomes maintain a three-dimensional conformation within the nucleus and undergo fluctuations. However, the analysis of chromosome conformational fluctuations has been mainly limited to chromosome conformation capture data that record the contact frequencies between chromosomal regions. Herein, we investigated chromosome fluctuations as polymers based on experimental data from sequential fluorescence in situ hybridization using a multiomics methodology. To describe the principal modes of chromosome fluctuations, we applied principal-component analysis to the three-dimensional conformation information of single chromosomes in 446 mouse embryonic stem cells obtained from sequential fluorescence in situ hybridization data analysis for spatial genomics and signals of nuclear factors (histone marks, repeat DNAs, and proteins in interchromosomal nuclear compartments). We found that chromosome fluctuations exhibit both isotropic and anisotropic modes. The isotropic conformational fluctuations of all chromosome types tended to synchronize each other, reflecting extrinsic heterogeneity in chromosome conformation that is independent of the cell cycle. In contrast, anisotropic conformational fluctuations, occurring in a spindle-like shape, were associated with the interactions between repeat DNAs and nuclear factors. These results highlight the importance of dissecting cell-cycle-independent nuclear organization based on the conformational folding of chromosomes and the interactions between genomic regions and nuclear factors.

Animals↗
PubMed2026

Subclonal Complete Loss of CDKN1B as a Common Genomic Alteration in Prostate Cancer: Associations With Race and Prostate Cancer Outcomes.

Homozygous biallelic inactivation of CDKN1B is thought to be rare in cancer, including prostate cancer. In the present study, we report that the prevalence of subclonal genomic loss of CDKN1B, especially among self-reported African-American or Black (AA) individuals, has likely been underestimated in primary prostate cancer. Using immunohistochemistry (IHC) for p27 protein and a large cohort of whole tissue sections from radical prostatectomy (N = 412) from AA and European American (EA) individuals, we discovered an unexpectedly high frequency of regions of intratumoral complete p27 protein loss (IPPL) within larger tumor nodules that otherwise showed intact p27 staining that was more prevalent among prostate cancer in AA individuals (18.1%) than EA individuals (12.2%). Regions of IPPL were tightly associated with loss of CDKN1B messenger RNA by in situ hybridization. Furthermore, these focal regions of IPPL were closely linked to CDKN1B genomic loss as detected by next-generation sequencing panel sequencing of laser-captured regions. The detection of IPPL by IHC was associated with ≥pT3 pathologic stage (extraprostatic extension and seminal vesicle involvement) and pN1 (local lymph node involvement) disease; however, when stratified by race, these associations were only significant among AA participants. IPPL was further associated in both univariate and multivariate analyses with the development of biochemical recurrence and metastasis after primary treatment, specifically in AA individuals. The prevalence of p27 genomic alterations in metastatic disease is higher than that of primary prostate cancer in publicly available data sets as well as in our analysis of autopsy specimens via IHC. Overall, subclonal biallelic loss of CDKN1B resulting in complete p27 protein loss is one of the most commonly occurring biallelic tumor suppressor genomic alterations in primary prostate cancer and could contribute to worse prostate cancer outcomes, specifically in AA individuals. Our findings warrant further exploration into the clinical utility of using IHC for p27 loss as a prognostic biomarker.

CDKN1B cancer disparities↗
PubMed2026

HyLnc: a hybrid deep learning and feature-based approach for long non-coding RNA prediction.

Long non-coding RNAs (lncRNAs) play important roles in gene regulation, development and disease, yet accurate identification of lncRNAs from transcriptomic data remains a major computational challenge. Existing methods often rely either on handcrafted sequence features or deep learning approaches, each with their inherent limitations in capturing the full complexity of RNA sequences. In this study, we proposed HyLnc, a computational framework that integrates transformer-based contextual embeddings with biologically meaningful sequence features for improved lncRNA prediction. A custom BERT-based model was first pre-trained on a large corpus of metazoan RNA sequences using a masked language modelling strategy to learn contextual nucleotide dependencies. The model was subsequently fine-tuned on curated datasets of lncRNAs and protein-coding transcripts and 256-dimensional deep sequence embeddings were extracted. Parallelly, 348 handcrafted features, including ORF characteristics, untranslated region (UTR) properties, nucleotide composition and Fickett scores, were computed. A multi-stage feature selection strategy was applied to identify the most informative features, resulting in optimized hybrid feature sets. Multiple machine learning classifiers were evaluated, with the RF model achieving the best performance. The proposed framework attained an accuracy of 91.30%, F1-score of 91.23% and MCC of 82.60 on an independent validation dataset, outperforming several existing lncRNA prediction tools. Thus, HyLnc demonstrates that integrating deep contextual representations with biologically interpretable features enhances lncRNA prediction. This approach provides a robust and scalable solution for large-scale transcriptome annotation and can be extended to other sequence-based prediction.

RNA, Long Noncoding↗
PubMed2026

Reframing early gastric carcinogenesis through lineage, niche, and evolution.

Early gastric cancer is still commonly conceptualized as the endpoint of a linear sequence from chronic gastritis to intestinal metaplasia, dysplasia, and invasion. Yet recent single-cell, spatial, genomic, and functional studies indicate that this model incompletely captures the biology of early gastric carcinogenesis. Malignant potential is established progressively within a precancerous gastric field already shaped by somatic evolution, chronic inflammatory injury, and epithelial lineage distortion. Within this field, progression is concentrated in a restricted set of precursor states, particularly incomplete, hybrid, and stem-like metaplastic populations that display plasticity, persistence, and increasing compatibility with a supportive microenvironment. Fibroblast niche remodeling, immune protection loss, endothelial rewiring, genomic instability, epigenetic drift, and selective retention of advantageous molecular alterations further promote malignant commitment. In parallel, diffuse gastric cancer appears to follow a distinct route that may arise independently of conventional intestinal metaplasia through E-cadherin-deficient epithelial transformation and downstream chromatin reprogramming. Here, we synthesize recent evidence to propose an updated framework for early gastric carcinogenesis based on field evolution, lineage instability, ecosystem support, and pathway divergence. Rather than replacing the classical Correa cascade, this framework seeks to refine it by shifting the unit of risk assessment from histologic stage alone to biologically defined precursor states shaped by lineage instability, clonal persistence, niche permissiveness, and pathway-specific molecular constraints. This perspective shifts the emphasis of prevention from detecting smaller cancers to identifying and intercepting biologically committed precursor states before invasion occurs.

Humans↗
PubMed2026

Advancing the Deciphering of Host-Microbe Crosstalk with Spatial Omics: A Mini-Review.

Host-microbe crosstalk refers to the reciprocal influences between a host and its resident or invading microorganisms. This crosstalk plays important roles in maintaining host health, regulating physiological functions, and coordinating responses to infection. The rapid rise of spatial omics is transforming how this crosstalk is studied in both animals and plants. Unlike traditional bulk omics, which homogenize tissues and erase spatial context, spatial methods preserve in situ organization and can simultaneously capture molecular information from hosts and microbes. As a result, researchers can characterize the spatial organization of colonization and infection, identify spatial associations between microbial niches and host cell states, and visualize local host response gradients across intact tissues. Current spatial omics technologies encompass sequencing-based, imaging-based, and hybrid platforms. Spatial multi-omics approaches enable the joint measurement or integration of gene expression, protein abundance, and metabolite distributions. Although spatial association alone does not establish causality, spatial omics provides a high-resolution framework for characterizing host-microbe relationships within intact tissues and generating spatially constrained, testable hypotheses. When combined with perturbation experiments and complementary experimental evidence, these hypotheses can contribute to mechanistic interpretation of host-microbe crosstalk. Here, we review spatial omics technologies, compare their suitability and major trade-offs for host-microbe studies, and discuss computational strategies, analytical challenges, and future prospects.

Multiomics↗
PubMed2026

Identification and characterization of ectopic chromosomal amplifications in acute myeloid leukemia cell limes using high-throughput chromosome conformation capture screening.

Despite advanced molecular diagnostics, improving outcomes for refractory acute myeloid leukemia (AML) remains challenging. Although many cancer-related genes are identified, their molecular mechanisms are not fully elucidated. Amplification is a mechanism of cancer-associated gene activation, and ectopic gene amplification may have particularly high pathological significance. However, research on ectopically amplified cancer-associated genes in leukemia remains limited. Here, we evaluated the usefulness of high-throughput chromosomal conformation capture (Hi-C) as a screening method for ectopic gene amplification and assessed whether ectopic amplification of cancer-associated genes may represent a general phenomenon in AML. We screened the U-937 and NB-4 cell lines using in situ Hi-C. Regions appearing as "high-intensity bands" in Hi-C contact maps were identified and validated using fluorescence in situ hybridization (FISH). Additionally, copy number variation analysis was performed using whole-genome sequencing (WGS) to extract cancer-associated genes with ectopic amplification. In the U-937, three genomic regions showing "high-intensity bands" were identified and confirmed as ectopic amplifications-including PDCD1LG2 (PD-L2), CD274 (PD-L1), and JAK2; that is, four copies were detected by WGS, and amplification signals were observed by FISH. In the NB-4, four such regions were detected, including MYC and KRAS, with expression level of 498 transcripts per million (TPM) and 34 TPM, respectively. Copy number variation analysis further identified multiple cancer-associated genes with ectopic amplification. Overall, these findings demonstrate the presence of ectopic amplification of cancer-associated genes in AML cell lines and support the usefulness of Hi-C as a screening method for detecting such genomic alterations.

Acute myeloid leukemia↗
PubMed2026

Implementing Prolonged Exposure Therapy in a Community Substance Use Treatment Program: A Qualitative Study.

INTRODUCTION: Post-traumatic stress disorder (PTSD) commonly co-occurs with substance use disorders (SUD), yet few community-based SUD programs incorporate evidence-based trauma-focused. Prolonged exposure (PE), including its massed format (M-PE) with session frequency of 3-4 times per week, is a gold standard intervention for PTSD; however, concerns about client readiness, logistical demands and relapse risk have limited its adoption within SUD settings. This study examined staff perspectives on the feasibility and acceptability of integrating M-PE into a community-based SUD program. METHODS: Prior to launching a Hybrid Type 1 effectiveness-implementation trial (Project COMET), we conducted semi-structured virtual interviews with 15 community clinic staff: providers (n = 8), administrators (n = 2) and peer specialists (n = 5). Interviews were recorded, transcribed and analysed using a rapid qualitative analysis framework with matrix techniques to compare themes across roles. RESULTS: Four overarching themes captured staff perspectives on integrating M-PE: (Theme 1) Prior Knowledge and Experiences: Most staff were familiar with EMDR, while direct knowledge of PE/M-PE was limited. (Theme 2) Perceptions of M-PE: M-PE was widely viewed as a promising, structured intervention that fits the pacing and duration of SUD care. (Theme 3) Symptom Reduction and Client Impact: Staff anticipated improvements in PTSD and SUD symptoms through trauma-focused treatment. (Theme 4) Barriers and Constraints: Participants identified several potential implementation challenges, including logistical barriers and client readiness. DISCUSSION AND CONCLUSIONS: Findings suggest that staff generally viewed M-PE favourably but emphasised the importance of ensuring client readiness and organisational support. Enhancing feasibility and long-term sustainability may require expanded psychoeducation, targeted provider training and flexible delivery models. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT06968832.

Adult↗
PubMed2026

A Molecularly Anchored Spatial Transcriptomic Framework for Precise CA1-Subiculum Parcellation and Region-Resolved Analysis in Alzheimer's Disease.

BACKGROUND: The precise molecular delineation of the interface between the Subiculum (Sub) and cornu ammonis 1 (CA1) is a challenge in hippocampal research, as conventional cytoarchitectural boundaries are often ambiguous and limit reproducible regional annotation. Here, we developed a molecularly anchored spatial transcriptomic framework to define CA1-Sub regional identities using high-definition spatial transcriptomics (Stereo-seq) and single-nucleus RNA sequencing (snRNA-seq) references. FINDINGS: Using a human hippocampal Stereo-seq dataset from 12 donors, we established a data-driven parcellation framework that defines reproducible molecular features distinguishing CA1 and Sub while capturing the transition between these regions. FN1 was identified as a Sub-enriched marker in a subset of EX_Sub and, together with ETV1 and additional regional markers, enabled molecular assignment of CA1 and Sub identities across datasets. The Sub association of FN1 and ETV1 was further supported by human 10X Genomics spatial transcriptomics, mouse in situ hybridization data, and a mouse spatial transcriptomic dataset. Applying this framework to Alzheimer's disease (AD) tissues revealed region-specific transcriptional alterations across CA1 and Sub, including enrichment of mitochondrial energy metabolism-related transcripts in the Sub, suggesting exploratory transcriptional associations of altered metabolic function. CONCLUSIONS: This study provides a molecularly anchored framework for human CA1-Sub parcellation that complements conventional annotation. By defining regional molecular states while preserving the biological continuum across CA1-Sub interface, this approach enables more consistent regional analysis of human hippocampus tissue across donors, datasets, and disease conditions.

Journal Article↗
PubMed2025

Drought recovery in plants triggers a cell-state-specific immune activation.

All organisms experience stress as an inevitable part of life, from single-celled microorganisms to complex multicellular beings. The ability to recover from stress is a fundamental trait that determines the overall resilience of an organism, yet stress recovery is understudied. To investigate how plants recover from drought, we examine a fine-scale time series of RNA sequencing starting 15 min after rehydration following moderate drought. We reveal that drought recovery is a rapid process involving the activation of thousands of recovery-specific genes. To capture these rapid recovery responses in different Arabidopsis thaliana (A. thaliana) leaf cell types, we perform a single-nucleus transcriptome analysis at the onset of drought recovery, identifying a cell type-specific transcriptional state developing independently across cell types. To further validate the cell-type specific transcriptional changes observed during drought recovery, we employ spatial transcriptomics using multiplexed error-robust fluorescence in situ hybridization (MERFISH), revealing anatomical localization of recovery-induced gene expression programs across Arabidopsis leaf tissues. Furthermore, we reveal a recovery-induced activation of the immune system that occurs autonomously, and which enhances pathogen resistance in vivo in A. thaliana, wild tomato (Solanum pennellii) and domesticated tomato (Solanum lycopersicum cv. M82). Since rehydration promotes microbial proliferation and thereby increases the risk of infection, the activation of drought recovery-induced immunity may be crucial for plant survival in natural environments. These findings indicate that drought recovery coincides with a preventive defense response, unraveling the complex regulatory mechanisms that facilitate stress recovery in different plant cell types.

Arabidopsis↗
PubMed2026

Benchmarking Assembly-Free K-mer Methods for Species Identification in Complex Plant Groups: A Case Study in Populus.

Species identification in taxonomically complex plant groups is frequently limited by the inadequacy of organellar markers, whose phylogenetic signal is disrupted by cytonuclear discordance and chloroplast capture. Using the taxonomically complex genus Populus as a model, we evaluated an assembly-free k-mer workflow against a curated SNP reference benchmark. Whole-genome resequencing data from 235 Populus individuals were curated to a 202-individual, 34-species reference dataset in which all retained species are strictly monophyletic in a genome-wide SNP analysis. Independent maximum likelihood analyses further confirmed that the 31 non-hybrid backbone species each maintained high-support monophyly, while taxa of documented reticulate origin showed placement patterns consistent with their reticulate histories. ABBA-BABA D-statistics detected widespread residual allele sharing within the backbone, though the strongest signals did not correspond to the species pairs responsible for the few k-mer identification failures. Against this benchmark, complete plastomes showed limited resolution, recovering only 3.0% species monophyly and 71.1% nearest-neighbor assignment. The optimized k-mer workflow, operating directly on raw reads without assembly or alignment, recovered 91.2% species monophyly, 99.0% nearest-neighbor assignment, and 98.0% group-average assignment. K-mer length was the primary accuracy-controlling parameter, with k = 31 falling within a stable accuracy plateau. Distance-based metrics reached near-saturation at 0.2× sequencing depth, indicating that low-coverage genome skimming can support scalable nuclear genome-based identification with standard computational resources. K-mer distance heatmaps also flagged unusual genomic affinities in hybrid-origin and outlier samples, providing a rapid screen for subsequent population genomic analyses. These results support assembly-free k-mer distances as an efficient tool for reference-based species identification and sample screening in complex plant groups, with residual limitations concentrated near recently diverged species boundaries. Model-based phylogenomic, coalescent, and network analyses remain necessary for resolving deeper species relationships and detailed introgression histories.

Populus↗