Synergy of Rapamycin and Methioninase on Colorectal Cancer Cells Requires Simultaneous and Not Sequential Administration: Implications for mTOR Inhibition.

Background/Aim: Rapamycin inhibits the mTOR protein kinase. Methioninase (rMETase), by degrading methionine, targets the methionine addiction of cancer cells and has been shown to improve the efficacy of chemotherapy drugs, reducing their effective doses. Our previous study demonstrated that rapamycin and rMETase work synergistically against colorectal-cancer cells, but not on normal cells, when administered simultaneously in vitro. In the present study, we aimed to further our previous findings by exploring whether  synergy exists between rapamycin and rMETase when used sequentially against HCT-116 colorectal-carcinoma cells, compared to simultaneous administration, in vitro. Materials and Methods: The half-maximal inhibitory concentrations (IC50) of rapamycin alone and rMETase alone against the HCT-116 human colorectal-cancer cell line were previously determined using the CCK-8 cell viability assay (11). We then examined the efficacy of rapamycin and rMETase, both at their IC50, administered simultaneously or sequentially on the HCT-116 cell line, with rapamycin administered before rMETase and vice versa. Results: The IC50 for rapamycin and rMETase, determined from previous experiments (11), was 1.38 nM and 0.39 U/ml, respectively, of HCT-116 cells. When rMETase was administered four days before rapamycin, both at the IC50, there was a 30.46% inhibition of HCT-116 cells. When rapamycin was administered four days before rMETase, both at the IC50, there was an inhibition of 41.13%. When both rapamycin and rMETase were simultaneously administered, both at the IC50, there was a 71.03% inhibition. Conclusion: Rapamycin and rMETase have synergistic efficacy against colorectal-cancer cells in vitro when administered simultaneously, but not sequentially.

Mechanistic Insights from the Crystal Structure and Computational Analysis of the Radical SAM Deaminase DesII.

Radical S-adenosyl-L-methionine (SAM) enzymes couple the reductive cleavage of SAM to radical-mediated transformations that have proven to be quite broad in scope. DesII is one such enzyme from the biosynthetic pathway of TDP-desosamine where it catalyzes a radical-mediated deamination. Previous studies have suggested that this reaction proceeds via direct elimination of ammonia from an α-hydroxyalkyl radical or its conjugate base (i.e., a ketyl radical) rather than 1,2-migration of the amino group to form a carbinolamine radical intermediate. However, without a crystal structure, the active site features responsible for this chemistry have remained largely unknown. The crystallographic studies described herein help to fill this gap by providing a structural description of the DesII active site. Computational analyses based on the solved crystal structure are consistent with direct elimination and indicate that an active site glutamate residue likely serves as a general base to promote deprotonation of the α-hydroxyalkyl radical intermediate and elimination of the ammonia group.

Identification of two archaeal GDGT lipid-modifying proteins reveals diverse microbes capable of GMGT biosynthesis and modification.

Archaea produce unique membrane-spanning lipids (MSLs), termed glycerol dialkyl glycerol tetraethers (GDGTs), which aid in adaptive responses to various environmental challenges. GDGTs can be modified through cyclization, cross-linking, methylation, hydroxylation, and desaturation, resulting in structurally distinct GDGT lipids. Here, we report the identification of radical SAM proteins responsible for two of these modifications-a glycerol monoalkyl glycerol tetraether (GMGT) synthase (Gms), responsible for covalently cross-linking the two hydrocarbon tails of a GDGT to produce GMGTs, and a GMGT methylase (Gmm), capable of methylating the core hydrocarbon tail. Heterologous expression of Gms proteins from various archaea in Thermococcus kodakarensis results in the production of GMGTs in two isomeric forms. Further, coexpression of Gms and Gmm produces mono- and dimethylated GMGTs and minor amounts of trimethylated GMGTs with only trace GDGT methylation. Phylogenetic analyses reveal the presence of Gms homologs in diverse archaeal genomes spanning all four archaeal superphyla and in multiple bacterial phyla with the genetic potential to synthesize fatty acid-based MSLs, demonstrating that GMGT production may be more widespread than previously appreciated. We demonstrate GMGT production in three Gms-encoding archaea, identifying an increase in GMGTs in response to elevated temperature in two Archaeoglobus species and the production of GMGTs with up to six rings in Vulcanisaeta distributa. The occurrence of such highly cyclized GMGTs has been limited to environmental samples and their detection in culture demonstrates the utility of combining genetic, bioinformatic, and lipid analyses to identify producers of distinct archaeal membrane lipids.

Weakly Supervised SVM-Enhanced SAM Pipeline for Stone-by-Stone Segmentation of the Masonry of the Loire Valley Castles.

The preservation of historical monuments presents a formidable challenge, particularly in monitoring the deterioration of building materials over time. Chateau de Chambord's facade suffers from common issues such as flaking and spalling, which require meticulous stone and joint mapping from experts manually for restoration efforts. Advancements in computer vision have allowed machine-learning models to help in the automatic segmentation process. In this research, a custom architecture defined as SAM-SVM is proposed, to perform stone segmentation, based on the Segment Anything Model (SAM) and Support Vector Machines (SVM). By exploiting the zero-shot learning capabilities of SAM and its customizable input parameters, we obtain segmentation mask for stones and joints, which are then classified using SVM. Two more SAMs (three in total) are used, depending on how many stones are left to segment. Through extensive experimentation and evaluation, supported by computer vision methods, the proposed architecture achieves a Dice coefficient of 85%. Our results highlight the potential of SAM in cultural heritage conservation, providing a scalable and efficient solution for stone segmentation in historic monuments. This research contributes valuable insights and methodologies to the ongoing conservation efforts of Château de Chambord and could be extrapolated to other monuments.

Leaf: an ultrafast filter for population-scale long-read SV detection.

Advances in sequencing technology have facilitated population-scale long-read structural variant (SV) detection. Arguably, one of the main challenges in population-scale analysis is developing effective computational pipelines. Here, we present a new filter-based pipeline for population-scale long-read SV detection. It better captures SV signals at an early stage than conventional assembly-based or alignment-based pipelines. Assessments in this work suggest that the filter-based pipeline helps better resolve intra-read rearrangements. Moreover, it is also more computationally efficient than conventional pipelines and thus may facilitate population-scale long-read applications.

Accessing and exploring the unusual chemistry by radical SAM-RiPP enzymes.

Radical SAM enzymes involved in the biosynthesis of ribosomally synthesized and post-translationally modified peptides catalyze unusual transformations that lead to unique peptide scaffolds and building blocks. Several natural products from these pathways show encouraging antimicrobial activities and represent next-generation therapeutics for infectious diseases. These systems are uniquely configured to benefit from genome-mining approaches because minimal substrate and cognate modifying enzyme expression can reveal unique, chemically complex transformations that outperform late-stage chemical reactions. This report highlights the main strategies used to reveal these enzymatic transformations, which have relied mainly on genome mining using enzyme-first approaches. We describe the general biosynthetic components for rSAM enzymes and highlight emerging approaches that may broaden the discovery and study of rSAM-RiPP enzymes. The large number of uncharacterized rSAM proteins, coupled with their unpredictable transformations, will continue to be an essential and exciting resource for enzyme discovery.

FNPC-SAM: Uncertainty-Guided False Negative/Positive Control for SAM on Noisy Medical Images.

The Segment Anything Model (SAM) is a recently developed all-range foundation model for image segmentation. It can use sparse manual prompts such as bounding boxes to generate pixel-level segmentation in natural images but struggles in medical images such as low-contrast, noisy ultrasound images. We propose a refined test-phase prompt augmentation technique designed to improve SAM's performance in medical image segmentation. The method couples multi-box prompt augmentation and an aleatoric uncertainty-based false-negative (FN) and false-positive (FP) correction (FNPC) strategy. We evaluate the method on two ultrasound datasets and show improvement in SAM's performance and robustness to inaccurate prompts, without the necessity for further training or tuning. Moreover, we present the Single-Slice-to-Volume (SS2V) method, enabling 3D pixel-level segmentation using only the bounding box annotation from a single 2D slice. Our results allow efficient use of SAM in even noisy, low-contrast medical images. The source code has been released at: https://github.com/MedICL-VU/FNPC-SAM.

CryoSegNet: accurate cryo-EM protein particle picking by integrating the foundational AI image segmentation model and attention-gated U-Net.

Picking protein particles in cryo-electron microscopy (cryo-EM) micrographs is a crucial step in the cryo-EM-based structure determination. However, existing methods trained on a limited amount of cryo-EM data still cannot accurately pick protein particles from noisy cryo-EM images. The general foundational artificial intelligence-based image segmentation model such as Meta's Segment Anything Model (SAM) cannot segment protein particles well because their training data do not include cryo-EM images. Here, we present a novel approach (CryoSegNet) of integrating an attention-gated U-shape network (U-Net) specially designed and trained for cryo-EM particle picking and the SAM. The U-Net is first trained on a large cryo-EM image dataset and then used to generate input from original cryo-EM images for SAM to make particle pickings. CryoSegNet shows both high precision and recall in segmenting protein particles from cryo-EM micrographs, irrespective of protein type, shape and size. On several independent datasets of various protein types, CryoSegNet outperforms two top machine learning particle pickers crYOLO and Topaz as well as SAM itself. The average resolution of density maps reconstructed from the particles picked by CryoSegNet is 3.33 Å, 7% better than 3.58 Å of Topaz and 14% better than 3.87 Å of crYOLO. It is publicly available at https://github.com/jianlin-cheng/CryoSegNet.

A balanced mitral leaflet and large ring strategy avoids systolic anterior motion in Barlow's disease.

OBJECTIVES: Mitral valve repair for Barlow's disease offers good outcomes but excessive and myxomatous valvular tissue is associated with systolic anterior motion. Although valvular disease might progress after repair and cause long-term systolic anterior motion, few reports focus on this aspect. Herein, we will review our 16-year experience with mitral valve repair for Barlow's disease and systolic anterior motion incidence. METHODS: We retrospectively reviewed surgical outcomes of 92 cases of mitral valve repair using a balanced leaflet/large ring strategy plus median sternotomy for Barlow's disease (median age 45.1 ± 12.7 years old [19-72], 37 females) from 2004 to 2019. Concomitant surgeries, except for tricuspid valve or anti-arrhythmic surgeries, were excluded. RESULTS: The follow-up period was 5.8 ± 4.4 years with no deaths. Patients had mitral regurgitation of grade 3/4 (15 cases) or 4/4 (77 cases) due to anterior leaflet (3 cases), posterior leaflet (75 cases), or bileaflet (14 cases) prolapse, with chord elongation (39 cases), chord rupture (22 cases), or a combination of both (14 cases). All cases required ring annuloplasty (median size of 33.0 ± 5.4 mm) combined with leaflet resection (91 cases), chord intervention (12 cases), or indentation closure (2 cases). No case had short- or long-term SAM. The freedom-from-mitral-regurgitation (of greater than grade 2/4) rate was 94.1% over 5 years and 76.0% over 10 years without reoperation. CONCLUSIONS: Our two-pronged strategy for mitral valve repair in Barlow's disease avoids systolic anterior motion over the long-term, with good outcomes.

Breast Delineation in Full-Field Digital Mammography Using the Segment Anything Model.

Breast cancer is a major health concern worldwide. Mammography, a cost-effective and accurate tool, is crucial in combating this issue. However, low contrast, noise, and artifacts can limit the diagnostic capabilities of radiologists. Computer-Aided Diagnosis (CAD) systems have been developed to overcome these challenges, with the accurate outlining of the breast being a critical step for further analysis. This study introduces the SAM-breast model, an adaptation of the Segment Anything Model (SAM) for segmenting the breast region in mammograms. This method enhances the delineation of the breast and the exclusion of the pectoral muscle in both medio lateral-oblique (MLO) and cranio-caudal (CC) views. We trained the models using a large, multi-center proprietary dataset of 2492 mammograms. The proposed SAM-breast model achieved the highest overall Dice Similarity Coefficient (DSC) of 99.22% ± 1.13 and Intersection over Union (IoU) 98.48% ± 2.10 over independent test images from five different datasets (two proprietary and three publicly available). The results are consistent across the different datasets, regardless of the vendor or image resolution. Compared with other baseline and deep learning-based methods, the proposed method exhibits enhanced performance. The SAM-breast model demonstrates the power of the SAM to adapt when it is tailored to specific tasks, in this case, the delineation of the breast in mammograms. Comprehensive evaluations across diverse datasets-both private and public-attest to the method's robustness, flexibility, and generalization capabilities.

Bioproduction of methylated phenylpropenes and isoeugenol in Escherichia coli.

Phenylpropenes are a class of natural products that are synthesised by a vast range of plant species and hold considerable promise in the flavour and fragrance industries. Many in vitro studies have been carried out to elucidate and characterise the enzymes responsible for the production of these volatile compounds. However, there is a scarcity of studies demonstrating the in vivo production of phenylpropenes in microbial cell factories. In this study, we engineered Escherichia coli to produce methylchavicol, methyleugenol and isoeugenol from their respective phenylacrylic acid precursors. We achieved this by extending and modifying a previously optimised heterologous pathway for the biosynthesis of chavicol and eugenol. We explored the potential of six S-adenosyl l-methionine (SAM)-dependent O-methyltransferases to produce methylchavicol and methyleugenol from chavicol and eugenol, respectively. Additionally, we examined two isoeugenol synthases for the production of isoeugenol from coniferyl acetate. The best-performing strains in this study were able to achieve titres of 13 mg L-1 methylchavicol, 59 mg L-1 methyleugenol and 361 mg L-1 isoeugenol after feeding with their appropriate phenylacrylic acid substrates. We were able to further increase the methyleugenol titre to 117 mg L-1 by supplementation with methionine to facilitate SAM recycling. Moreover, we report the biosynthesis of methylchavicol and methyleugenol from l-tyrosine through pathways involving six and eight enzymatic steps, respectively.

Ablation of Sam50 is associated with fragmentation and alterations in metabolism in murine and human myotubes.

The sorting and assembly machinery (SAM) Complex is responsible for assembling ß-barrel proteins in the mitochondrial membrane. Comprising three subunits, Sam35, Sam37, and Sam50, the SAM complex connects the inner and outer mitochondrial membranes by interacting with the mitochondrial contact site and cristae organizing system complex. Sam50, in particular, stabilizes the mitochondrial intermembrane space bridging (MIB) complex, which is crucial for protein transport, respiratory chain complex assembly, and regulation of cristae integrity. While the role of Sam50 in mitochondrial structure and metabolism in skeletal muscle remains unclear, this study aims to investigate its impact. Serial block-face-scanning electron microscopy and computer-assisted 3D renderings were employed to compare mitochondrial structure and networking in Sam50-deficient myotubes from mice and humans with wild-type (WT) myotubes. Furthermore, autophagosome 3D structure was assessed in human myotubes. Mitochondrial metabolic phenotypes were assessed using Gas Chromatography-Mass Spectrometry-based metabolomics to explore differential changes in WT and Sam50-deficient myotubes. The results revealed increased mitochondrial fragmentation and autophagosome formation in Sam50-deficient myotubes compared to controls. Metabolomic analysis indicated elevated metabolism of propanoate and several amino acids, including ß-Alanine, phenylalanine, and tyrosine, along with increased amino acid and fatty acid metabolism in Sam50-deficient myotubes. Furthermore, impairment of oxidative capacity was observed upon Sam50 ablation in both murine and human myotubes, as measured with the XF24 Seahorse Analyzer. Collectively, these findings support the critical role of Sam50 in establishing and maintaining mitochondrial integrity, cristae structure, and mitochondrial metabolism. By elucidating the impact of Sam50-deficiency, this study enhances our understanding of mitochondrial function in skeletal muscle.

FusionVision: A Comprehensive Approach of 3D Object Reconstruction and Segmentation from RGB-D Cameras Using YOLO and Fast Segment Anything.

In the realm of computer vision, the integration of advanced techniques into the pre-processing of RGB-D camera inputs poses a significant challenge, given the inherent complexities arising from diverse environmental conditions and varying object appearances. Therefore, this paper introduces FusionVision, an exhaustive pipeline adapted for the robust 3D segmentation of objects in RGB-D imagery. Traditional computer vision systems face limitations in simultaneously capturing precise object boundaries and achieving high-precision object detection on depth maps, as they are mainly proposed for RGB cameras. To address this challenge, FusionVision adopts an integrated approach by merging state-of-the-art object detection techniques, with advanced instance segmentation methods. The integration of these components enables a holistic (unified analysis of information obtained from both color RGB and depth D channels) interpretation of RGB-D data, facilitating the extraction of comprehensive and accurate object information in order to improve post-processes such as object 6D pose estimation, Simultanious Localization and Mapping (SLAM) operations, accurate 3D dataset extraction, etc. The proposed FusionVision pipeline employs YOLO for identifying objects within the RGB image domain. Subsequently, FastSAM, an innovative semantic segmentation model, is applied to delineate object boundaries, yielding refined segmentation masks. The synergy between these components and their integration into 3D scene understanding ensures a cohesive fusion of object detection and segmentation, enhancing overall precision in 3D object segmentation.

Decision-making under stress: A psychological and neurobiological integrative model.

Understanding the impact of stress on cognitive processes, particularly decision-making, is crucial as it underpins behaviors essential for survival. However, research in this domain has yielded disparate results, with inconsistencies evident across stress-induction paradigms and drug administration protocols designed to investigate specific stress pathways or neuromodulators. Building upon empirical studies, this research identifies a multifaceted matrix of variables contributing to the divergent findings. This matrix encompasses factors such as the temporal proximity between stressors and decision tasks, the nature of stressors and decision contexts, individual characteristics including psychobiological profiles and affective states at the time of decision-making and even cultural influences. In response to these complexities, we propose a comprehensive model that integrates these relevant factors and their intricate interplay to elucidate the mechanisms governing decision-making during stressful events. By synthesizing these insights, our model not only refines existing paradigms but also provides a framework for future study designs, offering avenues for theoretical advancements and translational developments in the field of stress's impact on cognitive functions. This research contributes to a deeper understanding of the nuanced relationship between stress and decision-making, ultimately advancing our knowledge of cognitive processes under challenging conditions.

Surgical-DeSAM: decoupling SAM for instrument segmentation in robotic surgery.

PURPOSE: The recent segment anything model (SAM) has demonstrated impressive performance with point, text or bounding box prompts, in various applications. However, in safety-critical surgical tasks, prompting is not possible due to (1) the lack of per-frame prompts for supervised learning, (2) it is unrealistic to prompt frame-by-frame in a real-time tracking application, and (3) it is expensive to annotate prompts for offline applications. METHODS: We develop Surgical-DeSAM to generate automatic bounding box prompts for decoupling SAM to obtain instrument segmentation in real-time robotic surgery. We utilise a commonly used detection architecture, DETR, and fine-tuned it to obtain bounding box prompt for the instruments. We then empolyed decoupling SAM (DeSAM) by replacing the image encoder with DETR encoder and fine-tune prompt encoder and mask decoder to obtain instance segmentation for the surgical instruments. To improve detection performance, we adopted the Swin-transformer to better feature representation. RESULTS: The proposed method has been validated on two publicly available datasets from the MICCAI surgical instruments segmentation challenge EndoVis 2017 and 2018. The performance of our method is also compared with SOTA instrument segmentation methods and demonstrated significant improvements with dice metrics of 89.62 and 90.70 for the EndoVis 2017 and 2018 CONCLUSION: Our extensive experiments and validations demonstrate that Surgical-DeSAM enables real-time instrument segmentation without any additional prompting and outperforms other SOTA segmentation methods.

Rice husk reuse as a sustainable energy alternative in Tolima, Colombia.

Colombia has great potential to produce clean energy through the use of residual biomass from the agricultural sector, such as residues obtained from the life cycle of rice production. This document presents a mixed approach methodology study to examine the combustion of rice husks as a possible energy alternative in the Tolima department of Colombia. First, the physicochemical characteristics of the rice husk were analyzed to characterize the raw material. Next, System Advisor Model (SAM) software was used to model a bioenergy plant to obtain biochar, bio-oil, and biogas from the combustion of rice husks and generate performance matrices, such as thermal efficiency, heat rate, and capacity factor. Then, the project was evaluated for financial feasibility using a mathematical model of net present value (NPV) with a planning horizon of 5 years. Finally, a subset of the local population was surveyed to assess perspectives on the project in the region. The results of the rice husk physicochemical analysis were the following: nitrogen content (0.74%), organic carbon (38.04%), silica (18.39%), humidity determination (7.68%), ash (19.4%), presence of carbonates (< 0.01%), and pH (6.41). These properties are adequate for the combustion process. The SAM simulation showed that the heat transferred in the boiler was 3180 kW, maintaining an efficiency between 50 and 52% throughout the 12 months of the year, meaning that the rice husk can generate electricity and thermal energy. The financial analysis showed that the internal rate of return (IRR) was 6% higher than the opportunity interest rate (OIR), demonstrating economic feasibility of the project. The design and creation of a rice husk processing plant is socially and environmentally viable and has the potential to contribute to the economic development of the Tolima community and reduce greenhouse gases. Likewise, this activity has the potential to promote energy security for consumers and environmental sustainability while at the same time being economically competitive.

LncRNA Gm15834 Aggravates Cardiac Hypertrophy by Interacting with Sam68 and Activating NF-κB Mediated Inflammation.

BACKGROUND: Cardiac hypertrophy is the common pathological process of multiple cardiovascular diseases. However, the molecular mechanisms of cardiac hypertrophy are unclear. Long non-coding RNA (lncRNA), a newly discovered type of transcript that has been demonstrated to function as crucial regulators in the development of cardiovascular diseases. This study revealed a novel regulatory pathway of lncRNA in cardiac hypertrophy. METHODS: The cardiac hypertrophy models were established by transverse aortic constriction (TAC) in mice and angiotensin II (Ang II) in HL-1 cardiomyocytes. Adeno-associated virus 9 (AAV9) in vivo and lncRNA Gm15834 and shRNA plasmids in vitro were used to overexpress and knock down lncRNA Gm15834. The myocardial tissue structure, cardiomyocyte area, cardiac function, protein expressions, and binding of lncRNA Gm15834 and Src-associated substrate during mitosis of 68 KDa (Sam68) were detected by hematoxylin and eosin (HE) staining, immunofluorescence staining, echocardiography, western blot and RNA immunoprecipitation (RIP), respectively. RESULTS: In cardiac hypertrophy models, inhibiting lncRNA Gm15834 could decrease Sam68 expression and nuclear factor kappa-B (NF-κB) mediated inflammatory activities in vivo and in vitro, but overexpressing lncRNA Gm15834 showed the opposite results. RIP experiments validated the binding activities between lncRNA Gm15834 and Sam68. Overexpression of Sam68 could counteract the anti-hypertrophy effects of lncRNA Gm15834 knockdown. Meanwhile, in vivo inhibition of lncRNA Gm15834 could inhibit Sam68 expression, reduce NF-κB mediated inflammatory activity and attenuate cardiac hypertrophy. CONCLUSION: Our study revealed a novel regulatory axis of cardiac hypertrophy, which comprised lncRNA Gm15834/Sam68/NF-κB/inflammation, shedding a new light for identifying therapy target of cardiac hypertrophy in clinic.

COMBAT-C: COntrol of Major Bleeding by Application of Tourniquets over Clothing.

INTRODUCTION: External bleeding is the leading cause of preventable trauma-related death. In certain circumstances, tourniquet application over clothing may be necessary. Therefore, the aim of this study was to assess the effectiveness of tourniquets over different clothing setups. METHODS: Three windlass tourniquets (CAT, SAMXT, SOFTT-W) were applied over nine different clothing setups and without clothing on the Hapmed™ Tourniquet Trainer. We compared each tourniquet in each clothing setup to the tourniquet trainer that was not dressed, and we compared the three tourniquets within each clothing setup concerning blood loss, applied pressure and application time. Regression analysis of the effect of thickness, mean weight, mean deformation, application time, and applied pressure on blood loss was performed. RESULTS: Although blood loss was significantly greater in the CAT and SAMXT tourniquets when they were applied over leather motorcycle trousers, the overall findings showed that the clothing setups significantly reduced or did not affect blood loss. The mean blood loss was the lowest with CAT and the highest with SOFTT-W. The measured mean pressures were lower than 180 mmHg in four out of nine clothing setups with SOFTT-W, but CAT and SAMXT always exceeded this threshold. CAT had the fastest application time. Blood loss was significantly influenced by applied pressure and application time but was influenced to a far lesser degree by clothing parameters. CONCLUSION: The effects of the clothing setups were of little clinical relevance, except for leather motorcycle trousers. The effects of rugged protective equipment, e.g., hazard suits, are conceivable and need to be tested for specific garments with the tourniquet intended for use. No clothing parameter for predicting tourniquet effectiveness could be identified.

Genome-wide identification of SAMS gene family in Cucurbitaceae and the role of ClSAMS1 in watermelon tolerance to abiotic stress.

S-Adenosyl-L-methionine (SAM) is widely involved in plant growth, development, and abiotic stress response. SAM synthetase (SAMS) is the key enzyme that catalyzes the synthesis of SAM from methionine and ATP. However, the SAMS gene family has not been identified and their functions have not been characterized in most Cucurbitaceae plants. Here, a total of 30 SAMS genes were identified in nine Cucurbitaceae species and they were categorized into 3 subfamilies. Physicochemical properties and gene structure analysis showed that the SAMS protein members are tightly conserved. Further analysis of the cis-regulatory elements (CREs) of SAMS genes' promoter implied their potential roles in stress tolerance. To further understand the molecular functions of SAMS genes, watermelon SAMSs (ClSAMSs) were chosen to analyze the expression patterns in different tissues and under various abiotic stress and hormone responses. Among the investigated genes, ClSAMS1 expression was observed in all tissues and found to be up-regulated by abiotic stresses including salt, cold and drought treatments as well as exogenous hormone treatments including ETH, SA, MeJA and ABA. Furthermore, knockdown of ClSAMS1 via virus-induced gene silencing (VIGS) decreased SAM contents in watermelon seedings. The pTRSV2-ClSAMS1 plants showed reduced susceptibility to drought, cold and NaCl stress, indicating a positive role of ClSAMS1 in abiotic stresses tolerance. Those results provided candidate SAMS genes to regulate plant resistance against abiotic stresses in Cucurbitaceae plants.

Essential role of N-terminal SAM regions in STIM1 multimerization and function.

The single-pass transmembrane protein Stromal Interaction Molecule 1 (STIM1), located in the endoplasmic reticulum (ER) membrane, possesses two main functions: It senses the ER-Ca2+ concentration and directly binds to the store-operated Ca2+ channel Orai1 for its activation when Ca2+ recedes. At high resting ER-Ca2+ concentration, the ER-luminal STIM1 domain is kept monomeric but undergoes di/multimerization once stores are depleted. Luminal STIM1 multimerization is essential to unleash the STIM C-terminal binding site for Orai1 channels. However, structural basis of the luminal association sites has so far been elusive. Here, we employed molecular dynamics (MD) simulations and identified two essential di/multimerization segments, the α7 and the adjacent region near the α9-helix in the sterile alpha motif (SAM) domain. Based on MD results, we targeted the two STIM1 SAM domains by engineering point mutations. These mutations interfered with higher-order multimerization of ER-luminal fragments in biochemical assays and puncta formation in live-cell experiments upon Ca2+ store depletion. The STIM1 multimerization impeded mutants significantly reduced Ca2+ entry via Orai1, decreasing the Ca2+ oscillation frequency as well as store-operated Ca2+ entry. Combination of the ER-luminal STIM1 multimerization mutations with gain of function mutations and coexpression of Orai1 partially ameliorated functional defects. Our data point to a hydrophobicity-driven binding within the ER-luminal STIM1 multimer that needs to switch between resting monomeric and activated multimeric state. Altogether, these data reveal that interactions between SAM domains of STIM1 monomers are critical for multimerization and activation of the protein.