The phosphate-solubilising fungi in sustainable agriculture: unleashing the potential of fungal biofertilisers for plant growth.

Phosphate-solubilising fungi (PSF) are beneficial microorganisms that play a pivotal role in plant growth by increasing the availability of phosphorus (P) in soil. Although phosphorus is an essential nutrient for plants, it often becomes inaccessible as it binds into insoluble forms. PSF effectively facilitate the release of this bound phosphorus through diverse mechanisms. Numerous fungal species demonstrate the ability to solubilise various types of phosphate compounds. Among the commonly researched PSF are Penicillium, Aspergillus, Rhizopus, Fusarium, Trichoderma, and Sclerotium. Moreover, yeasts such as Saccharomyces cerevisiae can potentially be leveraged as PSF. PSF secrete organic acids that chelate phosphate ions, thereby increasing their solubility in the soil. Moreover, PSF contribute to the decomposition of organic phosphorus compounds in soil by employing enzymes such as phosphatases, phytases, and phosphonatases. Furthermore, PSF can interact with other soil microorganisms, including nitrogen-fixing bacteria and arbuscular mycorrhizal fungi (AM-fungi), fostering synergistic effects that further enhance plant growth and nutrient absorption. The utilisation of PSF as biofertilisers offers numerous advantages over chemical fertilisers, including environmental friendliness, cost-effectiveness, and enhanced fertiliser utilisation efficiency. Furthermore, PSF can prove beneficial in challenging environments characterised by high phosphate sorption. Hence, this review serves as an updated study aimed at broadening the understanding of PSF and its potential applications in P solubilisation. This review also focuses on the diversity of PSF, the mechanisms underlying solubilisation, ecological roles of PSF in soil microbiome, and the benefits of sustainable agriculture. By delving into the ecological roles of PSF and their potential as biofertilisers, this study contributes to a deeper understanding of sustainable agriculture practices and addresses challenges in phosphate-scarce environments.

Multimodal analysis of genome-wide methylation, copy number aberrations, and end motif signatures enhances detection of early-stage breast cancer.

Introduction: Breast cancer causes the most cancer-related death in women and is the costliest cancer in the US regarding medical service and prescription drug expenses. Breast cancer screening is recommended by health authorities in the US, but current screening efforts are often compromised by high false positive rates. Liquid biopsy based on circulating tumor DNA (ctDNA) has emerged as a potential approach to screen for cancer. However, the detection of breast cancer, particularly in early stages, is challenging due to the low amount of ctDNA and heterogeneity of molecular subtypes. Methods: Here, we employed a multimodal approach, namely Screen for the Presence of Tumor by DNA Methylation and Size (SPOT-MAS), to simultaneously analyze multiple signatures of cell free DNA (cfDNA) in plasma samples of 239 nonmetastatic breast cancer patients and 278 healthy subjects. Results: We identified distinct profiles of genome-wide methylation changes (GWM), copy number alterations (CNA), and 4-nucleotide oligomer (4-mer) end motifs (EM) in cfDNA of breast cancer patients. We further used all three signatures to construct a multi-featured machine learning model and showed that the combination model outperformed base models built from individual features, achieving an AUC of 0.91 (95% CI: 0.87-0.95), a sensitivity of 65% at 96% specificity. Discussion: Our findings showed that a multimodal liquid biopsy assay based on analysis of cfDNA methylation, CNA and EM could enhance the accuracy for the detection of early- stage breast cancer.

Multimodal analysis of methylomics and fragmentomics in plasma cell-free DNA for multi-cancer early detection and localization.

Despite their promise, circulating tumor DNA (ctDNA)-based assays for multi-cancer early detection face challenges in test performance, due mostly to the limited abundance of ctDNA and its inherent variability. To address these challenges, published assays to date demanded a very high-depth sequencing, resulting in an elevated price of test. Herein, we developed a multimodal assay called SPOT-MAS (screening for the presence of tumor by methylation and size) to simultaneously profile methylomics, fragmentomics, copy number, and end motifs in a single workflow using targeted and shallow genome-wide sequencing (~0.55×) of cell-free DNA. We applied SPOT-MAS to 738 non-metastatic patients with breast, colorectal, gastric, lung, and liver cancer, and 1550 healthy controls. We then employed machine learning to extract multiple cancer and tissue-specific signatures for detecting and locating cancer. SPOT-MAS successfully detected the five cancer types with a sensitivity of 72.4% at 97.0% specificity. The sensitivities for detecting early-stage cancers were 73.9% and 62.3% for stages I and II, respectively, increasing to 88.3% for non-metastatic stage IIIA. For tumor-of-origin, our assay achieved an accuracy of 0.7. Our study demonstrates comparable performance to other ctDNA-based assays while requiring significantly lower sequencing depth, making it economically feasible for population-wide screening.