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Abstract Lymphoid specification in human hematopoietic progenitors is not fully understood. To better associate lymphoid identity with protein-level cell features, we conduct a highly multiplexed single-cell proteomic screen on human bone marrow progenitors. This screen identifies terminal deoxynucleotidyl transferase (TdT), a specialized DNA polymerase intrinsic to VDJ recombination, broadly expressed within CD34+ progenitors prior to B/T cell emergence. While these TdT+ cells coincide with granulocyte-monocyte progenitor (GMP) immunophenotype, their accessible chromatin regions show enrichment for lymphoid-associated transcription factor (TF) motifs. TdT expression on GMPs is inversely related to the SLAM family member CD84. Prospective isolation of CD84lo GMPs demonstrates robust lymphoid potentials ex vivo, while still retaining significant myeloid differentiation capacity, akin to LMPPs. This multi-omic study identifies human bone marrow lymphoid-primed progenitors, further defining the lympho-myeloid axis in human hematopoiesis.

Abstract Background Horizontal gene transfer (HGT) describes the transmission of DNA outside of direct ancestral lineages. The process is best characterised within the bacterial kingdom and can enable the acquisition of genetic traits that support bacterial adaptation to novel niches. The adaptation of bacteria to novel niches has particular relevance for faecal microbiota transplantation (FMT), a therapeutic procedure which aims to resolve gut-related health conditions of individuals, through transplanted gut microbiota from healthy donors. Results Three hundred eighty-one stool metagenomic samples from a placebo-controlled FMT trial for obese adolescents (the Gut Bugs Trial) were analysed for HGT, using two complementary methodologies. First, all putative HGT events, including historical HGT signatures, were quantified using the bioinformatics application WAAFLE. Second, metagenomic assembly and gene clustering were used to assess and quantify donor-specific genes transferred to recipients following the intervention. Both methodologies found no difference between the level of putative HGT events in the gut microbiomes of FMT and placebo recipients, post-intervention. HGT events facilitated by engrafted donor species in the FMT recipient gut at 6 weeks post-intervention were identified and characterised. Bacterial strains contributing to this subset of HGT events predominantly belonged to the phylum Bacteroidetes. Engraftment-dependent horizontally transferred genes were retained within recipient microbiomes at 12 and 26 weeks post-intervention. Conclusion Our study suggests that novel microorganisms introduced into the recipient gut following FMT have no impact on the basal rate of HGT within the human gut microbiome. Analyses of further FMT studies are required to assess the generalisability of this conclusion across different FMT study designs and for the treatment of different gut-related conditions. Video Abstract

Abstract Objective Toxoplasma gondii is a ubiquitous parasite of medical and veterinary importance; however, there exists no cure for chronic toxoplasmosis. Metabolic enzymes required for the production and maintenance of tissue cysts represent promising targets for novel therapies. Here, we use reverse genetics to investigate the role of Toxoplasma phosphoglucomutase 1, PGM1, in Toxoplasma growth and cystogenesis. Results We found that disruption of pgm1 did not significantly affect Toxoplasma intracellular growth and the lytic cycle. pgm1-defective parasites could differentiate into bradyzoites and produced cysts containing amylopectin in vitro. However, cysts produced in the absence of pgm1 were significantly smaller than wildtype. Together, our findings suggest that PGM1 is dispensable for in vitro growth but contributes to optimal Toxoplasma cyst development in vitro, thereby necessitating further investigation into the function of this enzyme in Toxoplasma persistence in its host.

Abstract Background Musculoskeletal disorders can contribute to injurious falls and incur significant societal and healthcare burdens. Identification of fallers from non-fallers through wearable-based gait analysis can facilitate timely intervention to assist mobility and prevent falls whilst improving care and attention for high fall-risk patients. In this study, we use wearable sensor-based gait analysis to introduce a novel variable to assess walking stability in fallers and non-fallers – the Walking Orientation Randomness Metric. The WORM score quantifies the stability, or ‘figure-of-eight’ motion of a subject’s trunk during walking as an indicator of a falls-predictive (pathological) gait. Methods WORM is calculated as the ‘figure-of-eight’ oscillation mapped out in the transverse-plane by the upper body’s centre-point during a walking bout. A sample of patients presenting to the Prince of Wales Hospital (Sydney, Australia) with a primary diagnosis of “falls for investigation” and age-matched healthy controls (non-fallers) from the community were recruited. Participants were fitted at the sternal angle with the wearable accelerometer, MetaMotionC (Mbientlab Inc., USA) and walked unobserved (at self-selected pace) for 5-50 m along an obstacle-free, carpeted hospital corridor. Results Participants comprised of 16 fallers (mean age: 70 + 17) and 16 non-fallers (mean age: 70 + 9) based on a recent fall(s) history. The (median) WORM score was 17-fold higher (p 0.51 cm) show high sensitivity (88%) and specificity (94%). Conclusion In this pilot study we have introduced the WORM score, demonstrating its discriminative performance in a preliminary sample size of 16 fallers. WORM is a novel gait metric assessing walking stability as measured by truncal way during ambulation and shows promise for objective and clinical evaluation of fallers.

Ho, Nyaga et al. develop a machine learning approach for ranking tissue-specific gene regulatory affects, used here for type 1 diabetes SNPs. They identify the lung as a site where these regulatory impacts can be most impactful, which may contribute to understanding the link between respiratory issues and risk of islet autoantibody seroconvernsion.

Abstract Background Walking is a fundamental part of living, and its importance is not limited by age or medical status. Reduced walking speed (WS), or gait velocity, is a sign of advancing age, various disease states, cognitive impairment, mental illness and early mortality. Activity levels, as defined in the literature as “daily step count” (DSC), is also a relevant measure of health status. A deterioration in our walking metrics, such as reduced WS and DSC, is associated with poor health outcomes. These objective measures are of such importance, that walking speed has been dubbed “the 6th vital sign”. We report a new objective measure that scores walking using the relevant metrics of walking speed and daily step count, into an easy-to-understand score from 0 (nil mobility) to 100 (excellent mobility), termed the Simplified Mobility Score (SMoS™). We have provided equal weighting to walking speed and daily step count, using a simple algorithm to score each metric out of 50. Methods Gait data was collected from 182 patients presenting to a tertiary hospital spinal unit with complaints of pain and reduced mobility. Walking speed was measured from a timed walk along an unobstructed pathway. Daily step count information was obtained from patients who had enabled step count tracking on their devices. The SMoS of the sample group were compared to expected population values calculated from the literature using 2-tailed Z tests. Results There were significantly reduced SMoS in patients who presented to the spinal unit than those expected at each age group for both genders, except for the 50–59 age bracket where no statistically significant reduction was observed. Even lower scores were present in those that went on to have surgical management. There was a significant correlation of SMoS scores with subjective disability scores such as the Oswestry Disability Index (ODI) and Visual Analogue Scale (VAS) in this cohort. Conclusions The SMoS is a simple and effective scoring tool which is demonstrably altered in spinal patients across age and gender brackets and correlates well with subjective disability scores. The SMoS has the potential to be used as a screening tool in primary and specialised care settings.

Context: Despite increasing promising reports regarding composite titanium (Ti)/PolyEtherEtherKetone (PEEK) cages, further longer-term, quality research is required. Synthetic bone graft substitutes are another rapidly developing area of spinal surgical research. Aims: The purpose of this study is to evaluate the outcomes of an integral fixation composite Ti/PEEK cage for anterior cervical discectomy and fusion (ACDF) and compare a synthetic bone graft substitute (β-tricalcium phosphate; [βTCP]) with allograft processed using supercritical fluid technology. Methods and Design: Data from 195 consecutive patients were prospectively collected from a single centre. Indications were largely degenerative. Allograft and βTCP were used in a 3:1 randomization protocol. Patients were followed up for a minimum of 6 months and up to 48 months. Clinical outcomes included visual analogue scale and neck oswestry disability index. Radiographic outcomes included fusion rates, subsidence rates and implant complications. Results: Graft sub-cohorts were largely comparable and included 133 and 52 patients in the allograft and βTCP sub-cohorts, respectively. Clinical outcomes overall significantly improved (P < 0.001), with no significant inter-cohort differences. There were no implant-related complications. Overall fusion rate was 94.1% (175/186). The allograft cohort produced a significantly greater fusion rate of 97.7% (126/129) compared to 77.6% (38/49) for the βTCP cohort (P = 0.001). Conclusions: This study demonstrates the viability of an integral fixation composite Ti/PEEK ACDF device in effectively and safely improving patient outcomes and achieving fusion. Allograft is more effective in achieving fusion compared to βTCP, though both were similarly efficacious in improving clinical outcomes.

Parkinson’s disease (PD) is a complex neurodegenerative disease with a range of causes and clinical presentations. Over 76 genetic loci (comprising 90 SNPs) have been associated with PD by the most recent GWAS meta-analysis. Most of these PD-associated variants are located in non-coding regions of the genome and it is difficult to understand what they are doing and how they contribute to the aetiology of PD. We hypothesised that PD-associated genetic variants modulate disease risk through tissue-specific expression quantitative trait loci (eQTL) effects. We developed and validated a machine learning approach that integrated tissue-specific eQTL data on known PD-associated genetic variants with PD case and control genotypes from the Wellcome Trust Case Control Consortium. In so doing, our analysis ranked the tissue-specific transcription effects for PD-associated genetic variants and estimated their relative contributions to PD risk. We identified roles for SNPs that are connected with INPP5P, CNTN1, GBA and SNCA in PD. Ranking the variants and tissue-specific eQTL effects contributing most to the machine learning model suggested a key role in the risk of developing PD for two variants (rs7617877 and rs6808178) and eQTL associated transcriptional changes of EAF1-AS1 within the heart atrial appendage. Similarly, effects associated with eQTLs located within the Brain Cerebellum were also recognized to confer major PD risk. These findings were replicated in two additional, independent cohorts (the UK Biobank, and NeuroX) and thus warrant further mechanistic investigations to determine if these transcriptional changes could act as early contributors to PD risk and disease development.

Liver disease causes relative compromise of the host immune system through multiple overlapping mechanisms and is an established risk factor for invasive fungal diseases including candidiasis and cryptococcosis. This immunologic derangement also leads to rapid progression of disease with resultant increases in morbidity and mortality. We describe severe coccidioidomycosis cases in the setting of liver dysfunction. Collaborative multi-center epidemiologic studies should be performed to determine the incidence of severe coccidioidomycosis in patients with concurrent liver disease.

Abstract Background Identification of imprinted genes, demonstrating a consistent preference towards the paternal or maternal allelic expression, is important for the understanding of gene expression regulation during embryonic development and of the molecular basis of developmental disorders with a parent-of-origin effect. Combining allelic analysis of RNA-Seq data with phased genotypes in family trios provides a powerful method to detect parent-of-origin biases in gene expression. Results We report findings in 296 family trios from two large studies: 165 lymphoblastoid cell lines from the 1000 Genomes Project and 131 blood samples from the Genome of the Netherlands (GoNL) participants. Based on parental haplotypes, we identified > 2.8 million transcribed heterozygous SNVs phased for parental origin and developed a robust statistical framework for measuring allelic expression. We identified a total of 45 imprinted genes and one imprinted unannotated transcript, including multiple imprinted transcripts showing incomplete parental expression bias that was located adjacent to strongly imprinted genes. For example, PXDC1, a gene which lies adjacent to the paternally expressed gene FAM50B, shows a 2:1 paternal expression bias. Other imprinted genes had promoter regions that coincide with sites of parentally biased DNA methylation identified in the blood from uniparental disomy (UPD) samples, thus providing independent validation of our results. Using the stranded nature of the RNA-Seq data in lymphoblastoid cell lines, we identified multiple loci with overlapping sense/antisense transcripts, of which one is expressed paternally and the other maternally. Using a sliding window approach, we searched for imprinted expression across the entire genome, identifying a novel imprinted putative lncRNA in 13q21.2. Overall, we identified 7 transcripts showing parental bias in gene expression which were not reported in 4 other recent RNA-Seq studies of imprinting. Conclusions Our methods and data provide a robust and high-resolution map of imprinted gene expression in the human genome.

Abstract Introduction The aim of this study was to assess if the complex anatomy of aortic aneurysm and aortic dissection can be accurately reproduced from a contrast‐enhanced computed tomography (CT) scan into a three‐dimensional (3D) printed model. Methods Contrast‐enhanced cardiac CT scans from two patients were post‐processed and produced as 3D printed thoracic aorta models of aortic aneurysm and aortic dissection. The transverse diameter was measured at five anatomical landmarks for both models, compared across three stages: the original contrast‐enhanced CT images, the stereolithography (STL) format computerised model prepared for 3D printing and the contrast‐enhanced CT of the 3D printed model. For the model with aortic dissection, measurements of the true and false lumen were taken and compared at two points on the descending aorta. Results Three‐dimensional printed models were generated with strong and flexible plastic material with successful replication of anatomical details of aortic structures and pathologies. The mean difference in transverse vessel diameter between the contrast‐enhanced CT images before and after 3D printing was 1.0 and 1.2 mm, for the first and second models respectively (standard deviation: 1.0 mm and 0.9 mm). Additionally, for the second model, the mean luminal diameter difference between the 3D printed model and CT images was 0.5 mm. Conclusion Encouraging results were achieved with regards to reproducing 3D models depicting aortic aneurysm and aortic dissection. Variances in vessel diameter measurement outside a standard deviation of 1 mm tolerance indicate further work is required into the assessment and accuracy of 3D model reproduction.

Children with cerebral palsy (CP) have motor impairments that make it challenging for them to participate in standard physical activity (PA) interventions. There is a need to evaluate adapted PA interventions for this population. Dance can promote coordination, posture, muscle strength, motor learning, and executive functioning. This pilot study evaluated the feasibility and the effects of a new therapeutic ballet intervention specifically designed for children with CP.Methods: Eight children with CP (9–14 y/o; 75% female) participated in a 6-week therapeutic ballet intervention. Outcomes were measured in multiple domains, including body composition (DXA), muscle strength (hand-grip dynamometer), habitual physical activity, gait and selective motor control functions, and executive functioning. Follow-up assessments of habitual physical activity, gait, and executive functioning were completed 4 to 5 weeks post-intervention.Results: Five of the eight participants were overfat or obese based on DXA percentage of body fat. All participants were below the 50th percentile for their age and gender for bone density. Four participants showed a trend to improve hand-grip strength in one hand only, while one improved in both hands. There were significant improvements in gait across time points (pre, post, and follow-up), specifically in time of ambulation (Xpre = 4.36, Xpost = 4.22, Xfollow−up = 3.72, d = 0.056, p = 0.02), and in step length (cm) on the right: Xpre = 48.29, Xpost = 50.77, Xfollow−up = 52.11, d = 0.22, p = 0.027, and left stride: Xpre = 96.29, Xpost = 102.20, Xfollow−up = 104.20, d = 0.30, p = 0.027, indicating gait changes in bilateral lower extremities. There was improvement in inhibitory control (d = 0.78; 95% Confidence Limit = ±0.71, p < 0.05) with large individual responses primarily among those above the mean at baseline.Conclusions: Therapeutic ballet may prove to be a useful intervention to promote physiological and cognitive functions in children with CP. Results demonstrated feasibility of the physical, physiological, and cognitive assessments and suggested improvements in participants' gait and inhibitory control with large individual responses. Modifications to personalize the intervention may be needed to optimize positive outcomes.Clinical Trial Registration:www.ClinicalTrials.gov, identifier: NCT03681171

MatchIt implements the suggestions of Ho, Imai, King, and Stuart (2007) for improving parametric statistical models by preprocessing data with nonparametric matching methods. MatchIt implements a wide range of sophisticated matching methods, making it possible to greatly reduce the dependence of causal inferences on hard-to-justify, but commonly made, statistical modeling assumptions. The software also easily fits into existing research practices since, after preprocessing data with MatchIt, researchers can use whatever parametric model they would have used without MatchIt, but produce inferences with substantially more robustness and less sensitivity to modeling assumptions. MatchIt is an R program, and also works seamlessly with Zelig.

Abstract The CARLO (cold ablation robot-guided laser osteotome) is a compact device with integrated multiple sensory, steering and safety checking elements. A multi-center study was performed to evaluate the CARLO device for the linear part of midface osteotomy in 28 patients. Feasibility, success rate, safety, performance and experience of the surgeons were assessed and evaluated. All 28 procedures were completed with CARLO without falling back to the conventional methods, giving a technical success rate of 100%. For 27 (96%) cases, procedural success was achieved with cutting lines deviation less than 2 mm. For 25 procedures, the CARLO-cutting was conducted smoothly. In the other 3 cases, some minor difficulties related to the reference markers were reported. For 18 procedures, no change for the cutting path was necessary. For the other 10 cases, cut path was adapted. Intraoperative re-planning was possible, easy and quick without significant delay of the procedures. No CARLO-related adverse events were recorded. Especially, there was no unexpected and unusual bleeding during the CARLO-conducted osteotomy. The time needed for the registration ranged from 1 to 12 min (median = 4). The CARLO-cutting lasted for 5 to 21 min (median = 7). The present study demonstrated feasibility, simplicity, safety, reliability and accuracy of CARLO for the linear part of midface osteotomy.

Abstract Background While substantial research has explored rhizosphere and phyllosphere microbiomes, knowledge on flower microbiome, particularly in wild plants remains limited. This study explores into the diversity, abundance, and composition of bacterial and fungal communities on leaves and flowers of wild flowering plants in their natural alpine habitat, considering the influence of environmental factors. Methods We investigated 50 wild flowering plants representing 22 families across seven locations in Austria. Sampling sites encompassed varied soil types (carbonate/silicate) and altitudes (450–2760 m). Amplicon sequencing to characterize bacterial and fungal communities and quantitative PCR to assess microbial abundance was applied, and the influence of biotic and abiotic factors assessed. Results Our study revealed distinct bacterial and fungal communities on leaves and flowers, with higher diversity and richness on leaves (228 fungal and 91 bacterial ASVs) than on flowers (163 fungal and 55 bacterial ASVs). In addition, Gammaproteobacteria on flowers and Alphaproteobacteria on leaves suggests niche specialization for plant compartments. Location significantly shaped both community composition and fungal diversity on both plant parts. Notably, soil type influenced community composition but not diversity. Altitude was associated with increased fungal species diversity on leaves and flowers. Furthermore, significant effects of plant family identity emerged within a subset of seven families, impacting bacterial and fungal abundance, fungal Shannon diversity, and bacterial species richness, particularly on flowers. Conclusion This study provides novel insights into the specific microbiome of wild flowering plants, highlighting adaptations to local environments and plant–microbe coevolution. The observed specificity indicates a potential role in plant health and resilience, which is crucial for predicting how microbiomes respond to changing environments, ultimately aiding in the conservation of natural ecosystems facing climate change pressures.

Abstract Background Robotic-assisted technologies have been developed to increase surgical precision and reduce surgical variability in total knee arthroplasty (TKA). Several different robotic systems have been introduced in the last decade for TKA. The DePuy Synthes VELYS™ Robotic-Assisted Solution (VRAS) is an imageless system designed to eliminate the need for preoperative CT scans and is one of the latest entrants in the rapidly evolving field of robotic technology in TKA. This study compared the clinical and economic outcomes associated with VRAS and other robotic-assisted technologies for primary TKA. Methods A retrospective cohort study using the Premier Healthcare Database included patients who underwent primary TKA with VRAS or other robotic-assisted technologies from January 1, 2022, to April 30, 2023. The primary outcome for the study was hospital follow-up visits (revisits) within 90 days post-TKA. Secondary outcomes included readmission and revision rates within 90 days post-TKA, operating room time, length of stay, discharge status and hospital costs. Cohorts were balanced using propensity score fine stratification, and generalized linear models were constructed to evaluate outcomes. Results This study included 827 VRAS TKA patients and 16,428 TKA patients treated with other robotic-assisted technologies. The 90-day all-cause and knee-related revisit rates were significantly lower for VRAS than for other robotic-assisted technologies (all-cause 13.9% vs. 22.8% and knee-related 2.8% vs. 5.4%, respectively; p value

Abstract Background Perceived stress from schoolwork and perceived teacher care are shown to influence adolescents’ life satisfaction. However, there is a need to further explore how levels of perceived school stress affect life satisfaction of Norwegian adolescents across gender and school levels using nationwide data, and whether this association is moderated by perceived teacher care. Hence, this paper sought to: (1) describe perceived school stress, perceived teacher care and satisfaction with life in Norwegian adolescents stratified by gender and school level, (2) examine the association between perceived school stress and satisfaction with life by testing perceived teacher care as a possible moderator and (3) explore the association between perceived teacher care and adolescents’ satisfaction with life. Methods We utilized cross-sectional data from the Norwegian Ungdata Survey from 2021, encompassing adolescents from lower and upper secondary school. Two-thirds of all Norwegian adolescents participated in the Ungdata Survey from 2021. All data is anonymous. The study variables are presented according to lower and upper secondary school as well as gender. Linear regressions were conducted and adjusted for socioeconomic status (SES) by using SPSS. Results In total, 139,841 adolescents were included. Girls exhibited higher perceived school stress, lower perceived teacher care, and lower life satisfaction than boys in both lower and secondary school (all p

Abstract Background Visualization approaches transform high-dimensional data from single cell RNA sequencing (scRNA-seq) experiments into two-dimensional plots that are used for analysis of cell relationships, and as a means of reporting biological insights. Yet, many standard approaches generate visuals that suffer from overplotting, lack of quantitative information, and distort global and local properties of biological patterns relative to the original high-dimensional space. Results We present scBubbletree, a new, scalable method for visualization of scRNA-seq data. The method identifies clusters of cells of similar transcriptomes and visualizes such clusters as “bubbles” at the tips of dendrograms (bubble trees), corresponding to quantitative summaries of cluster properties and relationships. scBubbletree stacks bubble trees with further cluster-associated information in a visually easily accessible way, thus facilitating quantitative assessment and biological interpretation of scRNA-seq data. We demonstrate this with large scRNA-seq data sets, including one with over 1.2 million cells. Conclusions To facilitate coherent quantification and visualization of scRNA-seq data we developed the R-package scBubbletree, which is freely available as part of the Bioconductor repository at: https://bioconductor.org/packages/scBubbletree/

Abstract This research explores prospective determinants of trust in the recommendations of artificial agents regarding decisions to kill, using a novel visual challenge paradigm simulating threat-identification (enemy combatants vs. civilians) under uncertainty. In Experiment 1, we compared trust in the advice of a physically embodied versus screen-mediated anthropomorphic robot, observing no effects of embodiment; in Experiment 2, we manipulated the relative anthropomorphism of virtual robots, observing modestly greater trust in the most anthropomorphic agent relative to the least. Across studies, when any version of the agent randomly disagreed, participants reversed their threat-identifications and decisions to kill in the majority of cases, substantially degrading their initial performance. Participants’ subjective confidence in their decisions tracked whether the agent (dis)agreed, while both decision-reversals and confidence were moderated by appraisals of the agent’s intelligence. The overall findings indicate a strong propensity to overtrust unreliable AI in life-or-death decisions made under uncertainty.

The increasing volume of solid waste generated globally necessitates efficient classification systems to enhance recycling and waste management processes. Convolutional Neural Networks (CNNs) have emerged as a powerful tool for image classification tasks, including solid waste identification. However, difficult external variables including changes in illumination, occlusion, and background clutter can have a big impact on CNN performance. Furthermore, pooling procedures frequently cause classic CNNs to lose spatial information, which might impair performance on tasks requiring extremely fine sense of place. This paper presents a comprehensive study on the application of an improved CNN-based models for solid waste classification. In the present study we explored image data resizing, augmentation technique and hyperparameter tuning to improve the performance of the proposed model. The results demonstrate that the improved-CNN model achieved high accuracy of 94.40 % compared to the conventional CNN and other deep learning model such as ResNet-50, Inception-V3 and VGG-19 (81.83, 66.67, 52.83 and 56.00 %).

ABSTRACT In this work, we develop a new analytical turbine induction model that can incorporate complex inflow conditions including cases where the wind velocity and temperature profiles can vary as functions of height. This induction model is derived from the linearized Navier–Stokes and leads to a second‐order ODE that can be solved using a Green's function formulation. The corresponding Green's function for several configurations are found including the infinite domain, semi‐infinite domain with ground plane, and a power law velocity inflow profile. The results of this approach are then compared with simulations of the turbine induction field using the AMR‐Wind CFD solver with a uniformly loaded actuator disk model. These comparisons show that the Green's function approach captures the centerline blockage, three‐dimensional blockage flow field, and streamwise velocity slow down, with very good agreement for lower thrust conditions and at larger distances away from rotor disk. The effects of shear on the turbine blockage were also compared using a power law inflow profile, and we show that this approach matches the CFD predictions for the cases considered.

Abstract Despite that climate change is currently one of the most pervasive challenges, its effects on the plant-associated microbiome is still poorly studied. The aim of this study was to evaluate the impact of the independent and combinatory effect of climate warming and drought on the microbiome assembly of oak from seed to seedling. In a multifactorial experimental set up, acorns were subjected to different temperatures (15 °C, 20 °C, and 25 °C) and soil moisture levels (drought (15%) and control (60%)) from germination until the seedling stage, after which the bacterial and fungal communities associated to the rhizosphere and phyllosphere were characterized by amplicon sequencing and qPCR. The results showed a stronger effect of temperature on fungal than on bacterial diversity and the effect was more pronounced in the phyllosphere. Under drought condition, temperature had a significantly negative effect on phyllosphere fungal diversity. In the rhizosphere, temperature had a significant effect on the fungal community composition which was primarily caused by species turnover. Regardless of temperature, Actinobacteriota was significantly enriched in drought, a group of bacteria known to increase plant drought tolerance. This study provides new insights into the effect of climate change on the plant microbiome in natural ecosystems.