A deficiency of the essential macronutrient sulfur leads to stunted plant growth and yield loss; however, an association with a symbiotic fungus can greatly improve nutrient uptake by the host plant. Here, we identified and functionally characterized a high-affinity sulfate transporter from the endophytic fungus Serendipita indica. SiSulT fulfills all the criteria expected of a functional sulfate transporter responding to sulfur limitation: SiSulT expression was induced when S. indica was grown under low-sulfate conditions, and heterologous expression of SiSulT complemented a yeast mutant lacking sulfate transport. We generated a knockdown strain of SiSulT by RNA interference to investigate the consequences of the partial loss of this transporter for the fungus and the host plant (maize, Zea mays) during colonization. Wild-type (WT) S. indica, but not the knockdown strain (kd-SiSulT), largely compensated for low-sulfate availability and supported plant growth. Colonization by WT S. indica also allowed maize roots to allocate precious resources away from sulfate assimilation under low-sulfur conditions, as evidenced by the reduction in expression of most sulfate assimilation genes. Our study illustrates the utility of the endophyte S. indica in sulfur nutrition research and offers potential avenues for agronomically sound amelioration of plant growth in low-sulfate environments.

Summary: Iron (Fe) is a micronutrient required for plant growth and development; however, most Fe forms in soil are not readily available to plants, resulting in low Fe contents in plants and, thereby, causing Fe deficiency in humans. Biofortification through plant‐fungal co‐cultivation might be a sustainable approach to increase crop Fe contents. Therefore, we aimed to examine the role of a Piriformospora indica Fe transporter on rice Fe uptake under low Fe conditions. A high‐affinity Fe transporter ( PiFTR ) from P . indica was identified and functionally characterized. PiFTR fulfilled all criteria expected of a functional Fe transporter under Fe‐limited conditions. Additionally, PiFTR expression was induced when P . indica was grown under low Fe conditions, and PiFTR complemented a yeast mutant lacking Fe transport. A knockdown (KD) P . indica strain was created via RNA interference to understand the physiological role of PiFTR . We observed that the KD‐ PiFTR ‐ P . indica strain transported a significantly lower amount of Fe to colonized rice ( Oryza sativa ) than the wild type (WT) P . indica . WT P . indica ‐colonized rice plants were healthier and performed significantly better than KD‐ PiFTR ‐ P . indica‐colonized rice plants. Our study offers potential avenues for an agronomically sound amelioration of plant growth in low Fe environments.

Phosphate is an indispensable metabolite in a wide variety of cells and is involved in nucleotide and lipid synthesis, signaling, and chemical energy storage. Proton-coupled phosphate transporters within the major facilitator family are crucial for phosphate uptake in plants and fungi. Similar proton-coupled phosphate transporters have been found in different protozoan parasites that cause human diseases, in breast cancer cells with elevated phosphate demand, in osteoclast-like cells during bone reabsorption, and in human intestinal Caco2BBE cells for phosphate homeostasis. However, the mechanism of proton-driven phosphate transport remains unclear. Here, we demonstrate in a eukaryotic, high-affinity phosphate transporter from Piriformospora indica (PiPT) that deprotonation of aspartate 324 (D324) triggers phosphate release. Quantum mechanics/molecular mechanics molecular dynamics simulations combined with free energy sampling have been employed here to identify the proton transport pathways from D324 upon the transition from the occluded structure to the inward open structure and phosphate release. The computational insights so gained are then corroborated by studies of D45N and D45E amino acid substitutions via mutagenesis experiments. Our findings confirm the function of the structurally predicted cytosolic proton exit tunnel and suggest insights into the role of the titratable phosphate substrate. Significance Proton- or sodium-coupled transporters within the major facilitator superfamily are essential for nutrient uptake in all forms of life. We focus on a high-affinity eukaryotic proton-coupled phosphate symporter using extensive classical molecular dynamics (MD) simulations and quantum mechanics/molecular mechanics MD combined with free energy sampling and confirm the role of a key residue, D324, at the molecular level. The results explain the exit direction of the proton once dissociated from D324 and reveal a perspective on proton-coupled transporters, showing that titratable substrates can be involved in the proton transport process as a key aspect of the functional cycle. Mutagenesis and phosphate transport confirm the essential nature of the key D45 residue in the protonation pathway.

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder with a strong genetic component. This research aims to identify key genes associated with autism spectrum disorder using a hybrid deep learning approach. To achieve this, a protein–protein interaction network is constructedand analyzed through a graph convolutional network, which extracts features based on gene interactions. Logistic regression is then employed to predict potential key regulatorgenes using probability scores derived from these features. To evaluate the infection ability of these potential key regulator genes, a susceptible–infected (SI) model, is performed, which reveals the higher infection ability for the genes identified by the proposed method, highlighting its effectiveness in pinpointing key genetic factors associated with ASD. The performance of the proposed method is compared with centrality methods, showing significantly improved results. Identified key genes are further compared with the SFARI gene database and the Evaluation of Autism Gene Link Evidence (EAGLE) framework, revealing commongenes that are strongly associated with ASD. This reinforces the validity of the method in identifying key regulator genes. The proposed method aligns with advancements in therapeutic systems, diagnostics, and neural engineering, providing a robust framework for ASD research and other neurodevelopmental disorders.

Sulfur is an important macronutrient required for the growth, development of plants and is a key component of many metabolic pathways. We have functionally characterized a high-affinity sulphate transporter (PiSulT) from an endophytic fungusSerendipita indica. ThePiSulTbelongs to the major facilitator superfamily (MFS) of membrane transporter. ThePiSulTfunctionally complements the yeast sulphate transporter mutant HK14.PiSulTis a high-affinity sulphate transporter, havingKm15μM. We found enhanced expression ofPiSulTin external fungal hyphae which helps the fungus in the acquisition of sulphate from the soil. When knockdown (KD)-PiSulT-P.indicacolonized with the plant, it results in an 8-fold reduction in the transfer of sulphate to the colonized plants as compared to the plants colonized with the WTS. indica, which suggests thatPiSulTis playing a role in sulphate transfer from soil to host plant. Further, plants colonized with the WTS. indicawere found to be healthy in comparison to the plants colonized with the KD-PiSulT-P.indica. Additionally,S. indicacolonization provides a positive effect on total sulfur content and on plant metabolites like sulfate ions and glutathione, particularly under low sulphate condition. We observed that the expression of sulfur assimilation pathway genes ofS. indicaand plant is dependent on the availability of sulphate and on the colonization with the plant. Our study highlights the importance ofPiSulTin the improvement of sulfur nutrition of host plant particularly under low sulphate condition and in plant growth development. This study will open new vistas to useS. indicaas a bio-fertilizer in the sulphate deficient field to improve crop production.One-Sentence Summary: High-affinity sulphate transporter ofSerendipita indica(PiSulT) transfer sulphate from soil to plant under low sulphate condition and improve plant growth and development.

Brain tumor classification is a crucial task in medical image analysis due to the complexity of the neurological system. With the rapid advancements in deep learning techniques, particularly in medical imaging, there is growing potential to enhance the accuracy and efficiency of brain tumor diagnosis using the magnetic resonance imaging (MRI). This paper proposes an optimized and low-computation deep learning model built on the backbone MobileNetv2 convolutional neural network architecture to classify the brain tumors into three categories: glioma, meningioma, and pituitary tumors. The model is trained, validated, and tested using a dataset of T1-weighted contrast-enhanced brain MR images (T1W-CE MRI). Preprocessing steps are incorporated to enhance the classification efficiency. We evaluate the model's performance on both equally and unequally distributed classes of the images and achieve an accuracy of 92.23% and 93.59%, with F1 scores of 92.21% and 93.65%, respectively, for both the distributions. The experimental results demonstrate that the proposed model efficiently classifies the brain tumors using the MR images and achieves superior accuracy to the latest literature methods and state-of-the-art models: "ResNet50, VGG16, NASNetMobile, InceptionResNetV2, and InceptionV3". Thus, the proposed model may help assisting the radiologists in fast and better diagnoses.