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Two Bacillus PGPB Strains in Wheat and Soybean: Wheat Growth Promotion Without Detectable Rhizosphere Microbiome Restructuring.

Plant growth-promoting bacteria (PGPB) are increasingly deployed as biofertilizers, yet the link between an inoculant's genomic potential and its realized effect on the plant is rarely assessed within an integrative framework that jointly captures the rhizosphere microbiome, plant phenotype, and strain genome. Two Bacillus strains-B. halotolerans 1453 and B. pumilus 630-were applied to wheat and soybean in a factorial pot experiment (2 strains &#xd7; 2 application methods &#xd7; 3 frequencies + control, 3-4 replicates). Rhizosphere samples (n = 67 after filtering) were profiled by 16S rRNA sequencing with PICRUSt2 functional prediction and compositional validation (Aitchison PERMANOVA, ALDEx2, ANCOM-BC2). The PGPB gene repertoire was characterized by genome mining (481 marker genes, 14 categories). Wheat phenotype (six traits) and soybean height were analyzed with models appropriate for count data (Negative Binomial and binomial GLMs) for treatment-vs.-control comparisons, and with factorial ANOVA for decomposition into main effects and interactions. Crop identity was the dominant factor shaping both microbiome structure and function (PERMANOVA R2 = 14.7% taxonomically and R2 = 7.8% functionally, both p < 0.001), with biologically meaningful taxonomic differences between wheat and soybean; strain, application count and method had no significant effect on community composition (R2 < 4% each), and co-occurrence networks showed no reliable differences between crops once read depth and sample size were controlled for. Despite this neutrality at the microbiome level, inoculation significantly increased wheat spike count (NB-GLM, all 12 treatments vs. control, padj 0.0002-0.031), ear weight, and stem count, with application count the strongest source of variability and a pronounced strain &#xd7; application count. Strain 1453 outperformed 630 in spike count (+23.1%, p = 0.012) and ear weight (+20.4%, p = 0.023); we hypothesize that this may be related to its more complete DNRA pathway (narGHI + nirB-nirD) and biocontrol genes (bacE, srfAA). Strain 630 produced a less pronounced effect than strain 1453 but was subject to smaller fluctuations across replicates (CV &#x2248; 16-21% vs. &#x2248;24-26% for 1453), which may reflect better resilience to environmental fluctuations, possibly due to its confirmed rsbV/rsbW stress-tolerance regulon. Rhizosphere microbiome composition differed clearly by crop (wheat vs. soybean) but showed no detectable response to strain, application method, or application count. Despite this lack of a microbiome signal, inoculation significantly increased wheat spike count and ear weight, with the magnitude and stability of this effect differing by strain. We hypothesize that this strain-dependent difference relates to underlying genomic differences-particularly in nitrogen metabolism (DNRA pathway) and stress-tolerance genes-though this link has not been tested directly and remains a hypothesis for future work.

Triticum

PCa Detection in PI-RADS 4 and 5 Lesions: Comparison of [68Ga]Ga-PSMA-11 PET/CT-Guided Robot-Assisted Biopsy Versus mpMRI Cognitive-Fusion TRUS-Guided Prostate Biopsy.

Lesions with a Prostate Imaging-Reporting and Data System (PI-RADS) score of 4 or greater on multiparametric MRI (mpMRI) indicate a high likelihood of prostate cancer (PCa), and guidelines recommend a targeted biopsy. We aimed to compare the diagnostic performance of robotic arm-assisted [68Ga]Ga-PSMA-11 PET/CT-guided prostate biopsy (PGPB) with mpMRI-directed cognitive-fusion transrectal ultrasound-guided biopsy (MCFB) in biopsy-na&#xef;ve men with clinical findings suggestive of PCa. Methods: This prospective, single-center, randomized clinical trial (NCT05137561) enrolled biopsy-na&#xef;ve men age 50-90 y with elevated levels of prostate-specific antigen (&#x2265;4 ng/mL) and abnormal digital rectal examination findings. All participants underwent mpMRI, and those with a PI-RADS score of 4 or greater were randomized into 2 arms. In arm 1, participants underwent PGPB for a [68Ga]Ga-PSMA-avid lesion, and participants in arm 2 underwent MCFB. Participants in arm 1 with PET-negative findings subsequently underwent MCFB, and participants with negative biopsy results underwent PET and PGPB. The primary outcome was the detection of PCa. Secondary outcomes included complication rates and participant-reported pain. Result: Of the 267 participants enrolled, 81.3% (217) had lesions with a PI-RADS score of 4 or greater and were randomized to either PGPB (n = 112) or MCFB (n = 105). PCa was detected in 97.1% of participants (101/104) in arm 1 and 81.0% (85/105) in arm 2 (P < 0.05). PGPB showed higher diagnostic accuracy for PI-RADS 5 lesions (100% vs. 95.1%, P = 0.09). Major complications were observed in arm 2 only (n = 5). Arm 1 had significantly fewer complications (10.8% vs. 51.4%, P < 0.01), a lower median visual analog scale score for pain (3 vs. 5), and shorter procedure times. The core positivity rate was higher in arm 1 (60% &#xb1; 20%), despite obtaining fewer cores. Conclusion: [68Ga]Ga-PSMA-11 PGPB demonstrated higher diagnostic performance, fewer complications, and better tolerability compared with MCFB. This approach enables integrated diagnosis and staging, offering a promising alternative for efficient, safe, and accurate evaluation of prostate cancer.

Humans

Data-driven approaches in green microbiology: strategies for plant growth-promoting bacteria.

Plant growth-promoting bacteria (PGPB) are gaining attention as scalable biological solutions to enhance crop productivity and resilience. However, accurately identifying and characterizing PGPB remains challenging, particularly under variable environmental conditions where microbial functions are context-dependent and shaped by complex plant-microbe interactions. Advances in high-throughput sequencing have shifted the field from culture-dependent approaches to genome-informed strategies, enabling large-scale taxonomic and functional profiling. Although trait-based databases support the prediction of plant-beneficial genes, they capture only a fraction of the underlying biological complexity and often require labor-intensive analyses. Machine learning (ML) and deep learning (DL) have emerged as powerful tools to integrate genomic, physiological, and ecological data, enabling the prioritization of candidate strains with plant growth-promoting potential. To evaluate advances in the field, we conducted a systematic review of studies integrating ML and DL with PGPB characterization, assessing algorithm selection, performance, and target plant systems. Across 248 observations, only 6.0% of studies directly addressed PGPB screening, whereas the majority (77.4%) focused on plant disease detection, revealing a substantial gap in the application of AI to beneficial microorganisms for plant growth. Convolutional neural networks (CNNs) were the most frequently applied algorithms, largely driven by image-based phenotyping tasks. Overall, the field is constrained by limited datasets, high computational demands, and challenges in modeling multispecies and host-associated interactions. We highlight the need for integrative and interpretable ML and DL frameworks that bridge genomic data and functional validation. Such approaches represent a promising path toward scalable, data-driven discovery and deployment of bioinoculants in sustainable agriculture.

Agriculture