The toxicities of A30P and A53T α-synuclein fibrils can be uniquely altered by the length and saturation of fatty acids in phosphatidylserine.

Progressive degeneration of dopaminergic neurons in the midbrain, hypothalamus, and thalamus is a hallmark of Parkinson's disease (PD). Neuronal death is linked to the abrupt aggregation of α-synuclein (α-syn), a small protein that regulates vesicle trafficking in synaptic clefts. Studies of families with a history of PD revealed several mutations in α-syn including A30P and A53T that are linked to the early onset of this pathology. Numerous pieces of evidence indicate that lipids can alter the rate of protein aggregation, as well as modify the secondary structure and toxicity of amyloid oligomers and fibrils. However, the role of lipids in the stability of α-syn mutants remains unclear. In this study, we investigate the effect of phosphatidylserine (PS), an anionic lipid that plays an important role in the recognition of apoptotic cells by macrophages, in the stability of WT, A30P, and A53T α-syn. We found PS with different lengths and saturation of fatty acids accelerated the rate of WT and A30P aggregation. At the same time, the opposite effect was observed for most PS on A53T. We also found that PS with different lengths and saturation of fatty acids change the secondary structure and toxicities of WT, A30P, and A53T fibrils. These results indicate that lipids can play an important role in the onset and spread of familial PD.

Secondary structure and toxicity of lysozyme fibrils are determined by the length and unsaturation of phosphatidic acid.

A progressive aggregation of misfolded proteins is a hallmark of numerous pathologies including diabetes Type 2, Alzheimer's disease, and Parkinson's disease. As a result, highly toxic protein aggregates, which are known as amyloid fibrils, are formed. A growing body of evidence suggests that phospholipids can uniquely alter the secondary structure and toxicity of amyloid aggregates. However, the role of phosphatidic acid (PA), a unique lipid that is responsible for cell signaling and activation of lipid-gated ion channels, in the aggregation of amyloidogenic proteins remains unclear. In this study, we investigate the role of the length and degree of unsaturation of fatty acids (FAs) in PA in the structure and toxicity of lysozyme fibrils formed in the presence of this lipid. We found that both the length and saturation of FAs in PA uniquely altered the secondary structure of lysozyme fibrils. However, these structural differences in PA caused very little if any changes in the morphology of lysozyme fibrils. We also utilized cell toxicity assays to determine the extent to which the length and degree of unsaturation of FAs in PA altered the toxicity of lysozyme fibrils. We found that amyloid fibrils formed in the presence of PA with C18:0 FAs exerted significantly higher cell toxicity compared to the aggregates formed in the presence of PA with C16:0 and C18:1 FAs. These results demonstrated that PA can be an important player in the onset and spread of amyloidogenic diseases.

Elucidating the mechanisms of α-Synuclein-lipid interactions using site-directed mutagenesis.

α-Synuclein (α-syn) is a small protein that is involved in cell vesicle trafficking in neuronal synapses. A progressive aggregation of this protein is the expected molecular cause of Parkinson's disease, a disease that affects millions of people around the world. A growing body of evidence indicates that phospholipids can strongly accelerate α-syn aggregation and alter the toxicity of α-syn oligomers and fibrils formed in the presence of lipid vesicles. This effect is attributed to the presence of high copies of lysines in the N-terminus of the protein. In this study, we performed site-directed mutagenesis and replaced one out of two lysines at each of the five sites located in the α-syn N-terminus. Using several biophysical and cellular approaches, we investigated the extent to which six negatively charged fatty acids (FAs) could alter the aggregation properties of K10A, K23A, K32A, K43A, and K58A α-syn. We found that FAs uniquely modified the aggregation properties of K43A, K58A, and WT α-syn, as well as changed morphology of amyloid fibrils formed by these mutants. At the same time, FAs failed to cause substantial changes in the aggregation rates of K10A, K23A, and K32A α-syn, as well as alter the morphology and toxicity of the corresponding amyloid fibrils. Based on these results, we can conclude that K10, K23, and K32 amino acid residues play a critical role in protein-lipid interactions since their replacement on non-polar alanines strongly suppressed α-syn-lipid interactions.

Engineering Mixing Properties of Fluids by Spatial Modulations.

We propose a method to change the effective interaction between two fluids by modulation of their local density distributions with external periodic potentials, whereby the mixing properties can be controlled. This method is applied to a mixture of dilute bosonic gases, and binodal and spinodal curves emerge in the phase diagram. Spinodal decomposition into a mixed-bubble state becomes possible, in which one of the coexisting phases has a finite mixing ratio. A metastable mixture is also realized, which undergoes phase separation via nucleation.

Large Unilamellar Vesicles of Phosphatidic Acid Reduce the Toxicity of α-Synuclein Fibrils.

Parkinson's disease (PD) is a severe pathology that is caused by a progressive degeneration of dopaminergic neurons in substantia nigra pars compacta as well as other areas in the brain. These neurodegeneration processes are linked to the abrupt aggregation of α-synuclein (α-syn), a small protein that is abundant at presynaptic nerve termini, where it regulates cell vesicle trafficking. Due to the direct interactions of α-syn with cell membranes, a substantial amount of work was done over the past decade to understand the role of lipids in α-syn aggregation. However, the role of phosphatidic acid (PA), a negatively charged phospholipid with a small polar head, remains unclear. In this study, we examined the effect of PA large unilamellar vesicles (LUVs) on α-syn aggregation. We found that PA LUVs with 16:0, 18:0, and 18:1 FAs drastically reduced the toxicity of α-syn fibrils if were present in a 1:1 molar ratio with the protein. Our results also showed that the presence of these vehicles changed the rate of α-syn aggregation and altered the morphology and secondary structure of α-syn fibrils. These results indicate that PA LUVs can be used as a potential therapeutic strategy to reduce the toxicity of α-syn fibrils formed upon PD.

Endophytic Fungi-Mediated Defense Signaling in Maize: Unraveling the Role of WRKY36 in Regulating Immunity against Spodoptera frugiperda.

Seed priming with beneficial endophytic fungi is an emerging sustainable strategy for enhancing plant resistance against insect pests. This study examined the effects of Beauvaria bassiana Bb20091317 and Metarhizium rileyi MrCDTLJ1 fungal colonization on maize growth, defence signalling, benzoxazinoid levels and gene expression. The colonization did not adversely affect plant growth but reduced larval weights of Spodoptera frugiperda. Maize leaves treated with M. rileyi exhibited higher levels of jasmonic acid, jasmonoyl-Isoleucine, salicylic acid, and indole acetic acid compared to control. B. bassiana and M. rileyi accelerated phytohormone increase upon S. frugiperda herbivory. Gene expression analysis revealed modulation of benzoxazinoid biosynthesis genes. We further elucidated the immune regulatory role of the transcription factor zmWRKY36 using virus-induced gene silencing (VIGS) in maize. zmWRKY36 positively regulates maize immunity against S. frugiperda, likely by interacting with defense-related proteins. Transient overexpression of zmWRKY36 in tobacco-induced cell death, while silencing in maize reduced chitin-triggered reactive oxygen species burst, confirming its immune function. Overall, B. bassiana and M. rileyi successfully colonized maize, impacting larval growth, defense signalling, and zmWRKY36-mediated resistance. This sheds light on maize-endophyte-insect interactions for sustainable plant protection.

Mitochondrial genome and nuclear ribosomal RNA analysis place Alveonasus lahorensis within the Argasinae and suggest that the genus Alveonasus is paraphyletic.

Two major families exist in ticks, the Argasidae and Ixodidae. The Argasidae comprise 2 sub-families, Argasinae and Ornithodorinae. The placement into subfamilies illuminate differences in morphological and molecular systematics and is important since it provides insight into evolutionary divergence within this family. It also identifies fundamental gaps in our understanding of argasid evolution that provide directions for future research. Molecular systematics based on mitochondrial genomics and 18S/28S ribosomal RNA confirmed the placement of various genera and subgenera into the Argasinae: Argas (including Argas and Persicargas), Navis, Ogadenus, Otobius lagophilus, Proknekalia, Secretargas and the Ornithodorinae: Alectorobius, Antricola (including Antricola and Parantricola), Carios, Chiropterargas, Nothoaspis, Ornithodoros (including Microargas, Ornamentum, Ornithodoros sensu strictu, Pavlovskyella), Otobius sensu strictu, Reticulinasus and Subparmatus. The position of Alveonasus remains controversial since traditional taxonomy placed it in the Ornithodorinae, while cladistic and limited molecular analysis placed it in the Argasinae. The current study aimed to resolve the systematic position of Alveonasus using mitochondrial genomic and 18S/28S ribosomal RNA systematics by sequencing the type species Alveonasus lahorensis from Pakistan. In addition, the mitochondrial genomes for Argas reflexus and Alectorobius kelleyi are reported from Germany and the USA, respectively. The systematic data unambiguously place Alveonasus in the Argasinae and also suggest that Alveonasus may be another paraphyletic genus.

Smart Resource Allocation in Mobile Cloud Next-Generation Network (NGN) Orchestration with Context-Aware Data and Machine Learning for the Cost Optimization of Microservice Applications.

Mobile cloud computing (MCC) provides resources to users to handle smart mobile applications. In MCC, task scheduling is the solution for mobile users' context-aware computation resource-rich applications. Most existing approaches have achieved a moderate service reliability rate due to a lack of instance-centric resource estimations and task offloading, a statistical NP-hard problem. The current intelligent scheduling process cannot address NP-hard problems due to traditional task offloading approaches. To address this problem, the authors design an efficient context-aware service offloading approach based on instance-centric measurements. The revised machine learning model/algorithm employs task adaptation to make decisions regarding task offloading. The proposed MCVS scheduling algorithm predicts the usage rates of individual microservices for a practical task scheduling scheme, considering mobile device time, cost, network, location, and central processing unit (CPU) power to train data. One notable feature of the microservice software architecture is its capacity to facilitate the scalability, flexibility, and independent deployment of individual components. A series of simulation results show the efficiency of the proposed technique based on offloading, CPU usage, and execution time metrics. The experimental results efficiently show the learning rate in training and testing in comparison with existing approaches, showing efficient training and task offloading phases. The proposed system has lower costs and uses less energy to offload microservices in MCC. Graphical results are presented to define the effectiveness of the proposed model. For a service arrival rate of 80%, the proposed model achieves an average 4.5% service offloading rate and 0.18% CPU usage rate compared with state-of-the-art approaches. The proposed method demonstrates efficiency in terms of cost and energy savings for microservice offloading in mobile cloud computing (MCC).

Internet of Vehicles (IoV)-Based Task Scheduling Approach Using Fuzzy Logic Technique in Fog Computing Enables Vehicular Ad Hoc Network (VANET).

The intelligent transportation system (ITS) relies heavily on the vehicular ad hoc network (VANET) and the internet of vehicles (IoVs), which combine cloud and fog to improve task processing capabilities. As a cloud extension, the fog processes' infrastructure is close to VANET, fostering an environment favorable to smart cars with IT equipment and effective task management oversight. Vehicle processing power, bandwidth, time, and high-speed mobility are all limited in VANET. It is critical to satisfy the vehicles' requirements for minimal latency and fast reaction times while offloading duties to the fog layer. We proposed a fuzzy logic-based task scheduling system in VANET to minimize latency and improve the enhanced response time when offloading tasks in the IoV. The proposed method effectively transfers workloads to the fog computing layer while considering the constrained resources of car nodes. After choosing a suitable processing unit, the algorithm sends the job and its associated resources to the fog layer. The dataset is related to crisp values for fog computing for system utilization, latency, and task deadline time for over 5000 values. The task execution, latency, deadline of task, storage, CPU, and bandwidth utilizations are used for fuzzy set values. We proved the effectiveness of our proposed task scheduling framework via simulation tests, outperforming current algorithms in terms of task ratio by 13%, decreasing average turnaround time by 9%, minimizing makespan time by 15%, and effectively overcoming average latency time within the network parameters. The proposed technique shows better results and responses than previous techniques by scheduling the tasks toward fog layers with less response time and minimizing the overall time from task submission to completion.

ANN-Based Intelligent Secure Routing Protocol in Vehicular Ad Hoc Networks (VANETs) Using Enhanced AODV.

A vehicular ad hoc network (VANET) is a sophisticated wireless communication infrastructure incorporating centralized and decentralized control mechanisms, orchestrating seamless data exchange among vehicles. This intricate communication system relies on the advanced capabilities of 5G connectivity, employing specialized topological arrangements to enhance data packet transmission. These vehicles communicate amongst themselves and establish connections with roadside units (RSUs). In the dynamic landscape of vehicular communication, disruptions, especially in scenarios involving high-speed vehicles, pose challenges. A notable concern is the emergence of black hole attacks, where a vehicle acts maliciously, obstructing the forwarding of data packets to subsequent vehicles, thereby compromising the secure dissemination of content within the VANET. We present an intelligent cluster-based routing protocol to mitigate these challenges in VANET routing. The system operates through two pivotal phases: first, utilizing an artificial neural network (ANN) model to detect malicious nodes, and second, establishing clusters via enhanced clustering algorithms with appointed cluster heads (CH) for each cluster. Subsequently, an optimal path for data transmission is predicted, aiming to minimize packet transmission delays. Our approach integrates a modified ad hoc on-demand distance vector (AODV) protocol for on-demand route discovery and optimal path selection, enhancing request and reply (RREQ and RREP) protocols. Evaluation of routing performance involves the BHT dataset, leveraging the ANN classifier to compute accuracy, precision, recall, F1 score, and loss. The NS-2.33 simulator facilitates the assessment of end-to-end delay, network throughput, and hop count during the path prediction phase. Remarkably, our methodology achieves 98.97% accuracy in detecting black hole attacks through the ANN classification model, outperforming existing techniques across various network routing parameters.

Geography and past climate changes have shaped the evolution of a widespread lizard in arid Central Asia.

The complex orogenic history and structure of Central Asia, coupled with Pleistocene glacial cycles have generated its stepwise aridification. Such events would have significantly influenced the evolution of many mid-latitude species in arid Central Asia (ACA). In this study, we employed two mitochondrial genes (CO1 and ND2) and genome-wide SNPs, coupled with ecological niche modeling, to investigate the lineage diversification and historical demography within a widespread lizard Phrynocephalus helioscopus, and their associations with geography and past climate change. We obtained the mtDNA dataset for 300 individuals from 96 localities within the known range of the lizard, among which 51 individuals from 27 localities were selected for generating the SNP dataset via the genotyping-by-sequencing approach. Phylogenetic analyses of the concatenated mtDNA dataset revealed eight geographically correlated lineages that diverged by 4.21-10.41% for the CO1 gene, which were estimated to have coalesced ∼4.47 million years ago. However, we observed mito-nuclear discordance pattern regarding the population of Clade V (P. helioscopus sergeevi) from Tajikistan. Ancestral area estimations suggested that P. helioscopus originated from the Fergana Valley and then dispersed into the adjacent areas in ACA along with a history of multiple allopatric divergence processes, suggesting that Fergana may have been the cradle of diversification of P. helioscopus. The intensification of aridification across Central Asia during the Late Pliocene may have facilitated the rapid radiation of this arid-adapted lizard throughout this vast territory. Subsequently, the geological events (e.g., uplift of the Hissar-Alay, transgressions of the Caspian Sea) and geographic barriers (e.g., Amu Darya River, Zeravshan River) during the Pleistocene triggered the progressive diversification of P. helioscopus. Interestingly, Clade VIII (P. helioscopus varius) experienced rapid population growth coupled with range expansion while Clade IV (P. helioscopus cameranoi) underwent drastic population expansion associated with range contraction during the Last Glacial Maximum. In Clade IV, but not in Clade VIII, environmental turnover contributes more to mitochondrial genetic distinctiveness than geographic distance. Overall, the SNP dataset demonstrates that geographic distance plays a greater role than environmental distance. Both the mtDNA dataset and the SNP dataset suggest local-scale genetic differentiation in Clade IV and Clade VIII, revealing potential geographic barriers in the Ili River Valley and the Junggar Basin, respectively. Twenty-seven outlier SNPs associated with environmental factors (precipitation and temperature) were identified, which supports the signature of local adaptation to the arid desert environment. Finally, our finding suggests taxonomic implications, such as support for full species status for P. saidalievi (Clade II) and P. meridionalis (Clade I). Future analyses based on further evidence and increased taxon and geographic sampling should be carried out to corroborate our findings.

Genome-wide SNPs reveal the fine-scale population structure of Laodelphax striatellus in China using double-digest restriction site-associated DNA sequencing.

The small brown planthopper (SBPH), Laodelphax striatellus (Fallén) is one of the most destructive rice pests and has caused serious economic losses in China. To clarify the genetic differentiation and population genetic structure of this insect pest, we investigated the genomic polymorphisms, genetic differentiation, and phylogeography of 31 SBPH populations from 28 sampling sites from three climatic zones of China using double-digest restriction site-associated DNA sequencing (ddRADseq). In total, 2,813,221,369 high-quality paired-end reads from 306 individuals and 1925 single nucleotide polymorphisms (SNPs) were obtained. Low levels of genetic diversity and significant genetic differentiation were observed among the SBPH populations, and three genetic clusters were detected in China. Neutrality tests and bottleneck analysis provided strong evidence for recent rapid expansion with a severe bottleneck in most populations. Our work provides new insights into the genetics of the SBPH and will contribute to the development of effective management strategies for this pest.

An Adaptive Real-Time Malicious Node Detection Framework Using Machine Learning in Vehicular Ad-Hoc Networks (VANETs).

Modern vehicle communication development is a continuous process in which cutting-edge security systems are required. Security is a main problem in the Vehicular Ad Hoc Network (VANET). Malicious node detection is one of the critical issues found in the VANET environment, with the ability to communicate and enhance the mechanism to enlarge the field. The vehicles are attacked by malicious nodes, especially DDoS attack detection. Several solutions are presented to overcome the issue, but none are solved in a real-time scenario using machine learning. During DDoS attacks, multiple vehicles are used in the attack as a flood on the targeted vehicle, so communication packets are not received, and replies to requests do not correspond in this regard. In this research, we selected the problem of malicious node detection and proposed a real-time malicious node detection system using machine learning. We proposed a distributed multi-layer classifier and evaluated the results using OMNET++ and SUMO with machine learning classification using GBT, LR, MLPC, RF, and SVM models. The group of normal vehicles and attacking vehicles dataset is considered to apply the proposed model. The simulation results effectively enhance the attack classification with an accuracy of 99%. Under LR and SVM, the system achieved 94 and 97%, respectively. The RF and GBT achieved better performance with 98% and 97% accuracy values, respectively. Since we have adopted Amazon Web Services, the network's performance has improved because training and testing time do not increase when we include more nodes in the network.

BBSF: Blockchain-Based Secure Weather Forecasting Information through Routing Protocol in Vanet.

A vehicular ad hoc network (VANET) is a technique that uses vehicles with the ability to sense data from the environment and use it for their safety measures. Flooding is a commonly used term used for sending network packets. VANET may cause redundancy, delay, collision, and the incorrect receipt of the messages to their destination. Weather information is one of the most important types of information used for network control and provides an enhanced version of the network simulation environments. The network traffic delay and packet losses are the main problems identified inside the network. In this research, we propose a routing protocol which can transmit the weather forecasting information on demand based on source vehicle to destination vehicles, with the minimum number of hop counts, and provide significant control over network performance parameters. We propose a BBSF-based routing approach. The proposed technique effectively enhances the routing information and provides the secure and reliable service delivery of the network performance. The results taken from the network are based on hop count, network latency, network overhead, and packet delivery ratio. The results effectively show that the proposed technique is reliable in reducing the network latency, and that the hop count is minimized when transferring the weather information.

Integrated Bulk Segregant Analysis, Fine Mapping, and Transcriptome Revealed QTLs and Candidate Genes Associated with Drought Adaptation in Wild Watermelon.

Drought stress has detrimental effects on crop productivity worldwide. A strong root system is crucial for maintaining water and nutrients uptake under drought stress. Wild watermelons possess resilient roots with excellent drought adaptability. However, the genetic factors controlling this trait remain uninvestigated. In this study, we conducted a bulk segregant analysis (BSA) on an F2 population consisting of two watermelon genotypes, wild and domesticated, which differ in their lateral root development under drought conditions. We identified two quantitative trait loci (qNLR_Dr. Chr01 and qNLR_Dr. Chr02) associated with the lateral root response to drought. Furthermore, we determined that a small region (0.93 Mb in qNLR_Dr. Chr01) is closely linked to drought adaptation through quantitative trait loci (QTL) validation and fine mapping. Transcriptome analysis of the parent roots under drought stress revealed unique effects on numerous genes in the sensitive genotype but not in the tolerant genotype. By integrating BSA, fine mapping, and the transcriptome, we identified six genes, namely L-Ascorbate Oxidase (AO), Cellulose Synthase-Interactive Protein 1 (CSI1), Late Embryogenesis Abundant Protein (LEA), Zinc-Finger Homeodomain Protein 2 (ZHD2), Pericycle Factor Type-A 5 (PFA5), and bZIP transcription factor 53-like (bZIP53-like), that might be involved in the drought adaptation. Our findings provide valuable QTLs and genes for marker-assisted selection in improving water-use efficiency and drought tolerance in watermelon. They also lay the groundwork for the genetic manipulation of drought-adapting genes in watermelon and other Cucurbitacea species.

Biosynthesis and Characterization of Silver Nanoparticles Using Tribulus terrestris Seeds: Revealed Promising Antidiabetic Potentials.

Green synthesis is the most effective and environmentally friendly way to produce nanoparticles. The present research aimed at the biosynthesizing of silver nanoparticles (AgNPs) using Tribulus terrestris seed extract as the reducing and stabilizing agent and investigating their anti-diabetic properties. Fourier transformation infrared (FTIR), X-ray diffraction (XRD), scanning electron microscopy (SEM), and UV-Vis spectroscopy were used to analyze the synthesized silver nanoparticles from Tribulus terrestris (TT-AgNPs). The spectroscopic characterization revealed a surface Plasmon resonance band at 380 nm, which verified the development of TT-AgNPs. The transmittance peaks were observed at 596, 1450, 1631, 2856, 2921, and 3422 cm-1 through the FTIR spectrophotometer. The XRD spectrum showed four distinct diffraction peaks in the 2θ range at 20° to 60°. Intense peaks were at 26.32°, 30.70°, 44.70°, 56.07°, 53.75°, 66.28°, and 75.32°. The SEM analysis revealed that the prepared TT-AgNPs were clustered loosely with a smooth and spherical structure and were of relatively uniform size. The in vitro antidiabetic potential of TT-AgNPs was assessed by using glucose yeast uptake, glucose adsorption, and alpha-amylase assays. TT-AgNPs showed the highest activity (78.45 ± 0.84%) of glucose uptake by yeast at 80 µg/mL. In the glucose adsorption assay, the highest activity of TT-AgNPs was 10.40 ± 0.52% at 30 mM, while in the alpha-amylase assay, TT-AgNPs exhibited the maximum activity of 75.68 ± 0.11% at 100 µg/mL. The results indicate a substantial anti-diabetic effect of the TT-AgNPs. Furthermore, the in vivo antidiabetic study was performed on TT-AgNPs in streptozotocin-induced diabetic mice. After receiving TT-AgNPs treatment for 30 days, the mice were sacrificed for biochemical and histological analyses of pancreatic and liver samples, which demonstrated a good improvement when compared to the control group. Mice treated with TT-AgNPs showed a significant drop in blood sugar levels, showing that the biosynthesized TT-AgNPs have effective anti-diabetic properties.

Genotoxicity and cytotoxicity potential of organoselenium compounds in human leukocytes in vitro.

In the current work, cytotoxicity and genotoxicity of different organoselenium compounds were examined using Trypan blue exclusion and alkaline comet assays with silver staining respectively. Leukocytes were subjected to a 3-hour incubation with organoselenium compounds at concentrations of 1, 5, 10, 25, 50, and 75 µM, or with the control vehicle (DMSO), at a temperature of 37 °C. The viability of the cells was evaluated using the Trypan blue exclusion method, while DNA damage was analyzed through the alkaline comet assay with silver staining. The exposure of leukocytes to different organoselenium compounds including i.e. (Z)-N-(pyridin-2-ylmethylene)-1-(2-((2-(1-((E)-pyridin-2-ylmethyleneamino)ethyl)phenyl)diselanyl)phenyl)ethanamine (C1), 2,2'(1Z,1'E)-(1,1'-(2,2'-diselanediylbis(2,1-phenylene))bis(ethane-1,1-diyl)) bis(azan-1-yl-1-ylidene)bis -methan-1-yl-1-ylidene)diphenol (C2), and dinaphthyl diselenide (NapSe)2, At concentrations ranging from 1 to 5 µM, no significant DNA damage was observed, as indicated by the absence of a noteworthy increase in the Damage Index (DI). Our results suggest that the organoselenium selenium compounds tested were not genotoxic and cytotoxic to human leukocytes in vitro at lower concentration. This study offers further insights into the genotoxicity profile of these organochalcogens in human leukocytes. Their genotoxicity and cytotoxicity effects at higher concentration are probably mediated through reactive oxygen species generation and their ability to catalyze thiol oxidation.

Mechanistic and therapeutic implications of EphA-4 receptor tyrosine kinase in the pathogenesis of Alzheimer's disease.

Erythropoietin-producing hepatoma (Eph) receptors belong to a family of tyrosine kinase receptors that plays a pivotal role in the development of the brain. Eph can be divided broadly into two groups, namely, EphA and EphB, comprising nine and five members, respectively. In recent years, the role of EphA-4 has become increasingly apparent in the onset of Alzheimer's disease (AD). Emerging evidence suggests that EphA-4 results in synaptic dysfunction, which in turn promotes the progression of AD. Moreover, pharmacological or genetic ablation of EphA-4 in the murine model of AD can alleviate the symptoms. The current review summarizes different pathways by which EphA-4 can influence pathogenesis. Since, majority of the studies had reported the protective effect of EphA-4 inhibition during AD, designing therapeutics based on decreasing its enzymatic activity might be necessary for introducing the novel interventions. Therefore, the review described peptide and nanobodies inhibitors of EphA-4 that exhibit the potential to modulate EphA-4 and could be used as lead molecules for the targeted therapy of AD.

Neutralizing antibodies to SARS-CoV-2 variants of concern including Delta and Omicron in subjects receiving mRNA-1273, BNT162b2, and Ad26.COV2.S vaccines.

SARS-CoV-2 vaccines have contributed to the control of COVID-19 in some parts of the world. However, the constant emergence of variants of concern (VOCs) challenges the effectiveness of SARS-CoV-2 vaccines over time. In particular, Omicron contains a high number of mutations in the spike (S) protein gene, on which most vaccines were developed. In this study, we quantitated neutralizing antibodies in vaccine recipients at various times postvaccination using S protein-based pseudoviruses derived from wild type (WT) SARS-CoV-2 and five VOCs including Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1), Delta (B.1.617.2), and Omicron (B.1.1.529). We found that two-dose mRNA-1273 and BNT162b2 vaccines elicited robust neutralizing antibodies against WT, Alpha, Beta, Gamma, and Delta, but wanned after 6 months with a faster decline observed for BNT162b2. Both mRNA-1273 and BNT162b2 elicited weak neutralizing antibodies against Omicron. One dose of Ad26.COV2.S vaccine induced weaker neutralizing antibodies against WT and most VOCs than mRNA-1273 and BNT162b2 did but moderate neutralizing antibodies against Delta and Omicron, which lasted for 6 months. These results support current recommendations of the Centers for Disease Control and Prevention for a booster 5 months after full immunization with an mRNA-based vaccine and the use of an mRNA-based vaccine 2 months after Ad26.COV2.S vaccination.

An allelic variant in the ACS7 gene promotes primary root growth in watermelon.

KEY MESSAGE: Gene mining in a C. lanatus × C. amarus population revealed one gene, ACS7, linked to primary root elongation in watermelon. Watermelon is a xerophytic crop characterized by a long primary root and robust lateral roots. Therefore, watermelon serves as an excellent model for studying root elongation and development. However, the genetic mechanism underlying the primary root elongation in watermelon remains unknown. Herein, through bulk segregant analysis we identified a genetic locus, qPRL.Chr03, controlling primary root length (PRL) using two different watermelon species (Citrullus lanatus and Citrullus amarus) that differ in their root architecture. Fine mapping revealed that xaa-Pro dipeptidase and 1-aminocyclopropane-1-carboxylate synthase 7 (ACS7) are candidate regulators of the primary root growth. Allelic variation in the delimited region among 193 watermelon accessions indicated that the long-root alleles might only exist in C. amarus. Interestingly, the discrepancy in PRL among the C. amarus accessions was clearly associated with a nonsynonymous single nucleotide polymorphism variant within the ACS7 gene. The ACS7 expression and ethylene levels in the primary root tips suggested that ethylene is a negative regulator of root elongation in watermelon, as supported by the application of 1-aminocyclopropane-1-carboxylate (ACC, the ethylene precursor) or 2-aminoethoxyvinyl glycine (AVG, an ACS inhibitor). To the best of our knowledge, these findings provide the first description of the genetic basis of root elongation in watermelon. The detected markers of the ACS7 gene will facilitate marker-assisted selection for the PRL trait to improve water and nutrient use efficacy in watermelon and beyond.