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Ocimum sanctum Linn (O. sanctum L.), commonly known as Holy Basil or Tulsi, is a fragrant herbaceous plant belonging to the Lamiaceae family. This plant is widely cultivated and found in north-central parts of India, several Arab countries, West Africa and tropical regions of the Eastern World. Tulsi is known to be an adaptogen, aiding the body in adapting to stress by harmonizing various bodily systems. Revered in Ayurveda as the "Elixir of Life", Tulsi is believed to enhance lifespan and foster longevity. Eugenol, the active ingredient present in Tulsi, is a l-hydroxy-2-methoxy-4-allylbenzene compound with diverse therapeutic applications. As concerns over the adverse effects of conventional antibacterial agents continue to grow, alternative therapies have gained prominence. Essential oils (EOs) containing antioxidants have a long history of utilization in traditional medicine and have gained increasing popularity over time. Numerous in vitro, in vivo and clinical studies have provided compelling evidence supporting the safety and efficacy of antioxidant EOs derived from medicinal plants for promoting health. This comprehensive review aims to highlight the scientific knowledge regarding the therapeutic properties of O. sanctum, focusing on its antibacterial, anti-diabetic, anti-carcinogenic, radioprotective, immunomodulatory, anti-inflammatory, cardioprotective, neurogenesis, anti-depressant and other beneficial characteristics. Also, the extracts of O. sanctum L. have the ability to reduce chronic inflammation linked to neurological disorders such as Parkinson's and Alzheimer's disease. The information presented in this review shed light on the multifaceted potential of Tulsi and its derivatives in maintaining and promoting health. This knowledge may pave the way for the development of novel therapeutic interventions and natural remedies that harness the immense therapeutic potential of Tulsi in combating various health conditions, while also providing valuable insights for further research and exploration in this field. [ABSTRACT FROM AUTHOR]

Jeju black cattle (JBC) are recognised as one of Korea's native breeds that are well-suited to the local environmental conditions and known for their excellent meat quality. This study investigated the variance components and genomic prediction accuracy for carcase traits such as carcase weight (CWT), eye muscle area (EMA), backfat thickness (BF) and marbling score (MS) in JBC using various statistical models. We employed single-step genomic best linear unbiased prediction (ssGBLUP) and single-step Bayesian regression (SSBR) models, which utilised imputed genotypes for non-genotyped individuals. Phenotypic data were collected from 2045 JBC, and 3759 JBC individuals were genotyped using the Affymetrix 160K SNP Axiom array. Estimated heritability ranged from 0.24 to 0.40 for both single-trait and multi-trait models. Multi-trait models consistently demonstrated higher genomic prediction accuracy compared to single-trait models, underscoring the value of integrating multiple traits. For instance, CWT accuracy improved from 0.24 in single-trait PBLUP (st-PBLUP) to 0.47 in multi-trait ssGBLUP. Similarly, SSBR models showed significant improvements, with single-trait SSBR Bayes B achieving 0.46 and its multi-trait version reaching 0.49. EMA accuracy improved from 0.21 in st-PBLUP to 0.39 in the multi-trait SSBR Bayes C model. BF and MS also exhibited consistent accuracy improvements with multi-trait Bayesian regression models. These results demonstrate the superior accuracy of ssGBLUP and SSBR models compared to traditional BLUP, particularly for traits with higher heritability values. This findings offer valuable insights for enhancing genomic selection strategies and improving carcase traits in JBC breeding programs, contributing to the breed's economic sustainability and meat production efficiency. [ABSTRACT FROM AUTHOR]

The Korean Sapsaree dog is a native breed known for its distinctive appearance and historical significance in Korean culture. The accurate estimation of breeding values is essential for the genetic improvement and conservation of such indigenous breeds. This study aimed to evaluate the accuracy of breeding values for body height, body length, chest width, hair length, and distraction index (DI) traits in Korean Sapsaree dogs. Additionally, a genome-wide association study (GWAS) was conducted to identify the genomic regions and nearby candidate genes influencing these traits. Phenotypic data were collected from 378 Korean Sapsaree dogs, and of these, 234 individuals were genotyped using the 170k Illumina CanineHD BeadChip. The accuracy of genomic predictions was evaluated using the traditional BLUP method with phenotypes only on genotyped animals (PBLUP-G), another traditional BLUP method using a pedigree-based relationship matrix (PBLUP) for all individuals, a GBLUP method based on a genomic relationship matrix, and a single-step GBLUP (ssGBLUP) method. Heritability estimates for body height, body length, chest width, hair length, and DI were 0.45, 0.39, 0.32, 0.55, and 0.50, respectively. Accuracy values varied across methods, with ranges of 0.22 to 0.31 for PBLUP-G, 0.30 to 0.57 for PBLUP, 0.31 to 0.54 for GBLUP, and 0.39 to 0.67 for ssGBLUP. Through GWAS, 194 genome-wide significant SNPs associated with studied Sapsaree traits were identified. The selection of the most promising candidate genes was based on gene ontology (GO) terms and functions previously identified to influence traits. Notable genes included CCKAR and DCAF16 for body height, PDZRN3 and CNTN1 for body length, TRIM63, KDELR2, and SUPT3H for chest width, RSPO2, EIF3E, PKHD1L1, TRPS1, and EXT1 for hair length, and DDHD1, BMP4, SEMA3C, and FOXP1 for the DI. These findings suggest that significant QTL, combined with functional candidate genes, can be leveraged to improve the genetic quality of the Sapsaree population. This study provides a foundation for more effective breeding strategies aimed at preserving and enhancing the unique traits of this Korean dog breed. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is a progressive neurological disease that causes both motor and nonmotor symptoms. While our understanding of putative mechanisms has advanced significantly, it remains challenging to verify biomarkers with sufficient evidence for regular clinical use. Clinical symptoms are the primary basis for diagnosing the disease, which can be mild in the early stages and overlap with other neurological disorders. As a result, clinical testing and medical records are mostly relied upon for diagnosis, posing substantial challenges during both the initial diagnosis and the continuous disease monitoring. Recent biochemical, neuroimaging, and genetic biomarkers have helped us understand the pathophysiology of Parkinson's disease. This comprehensive study focuses on these biomarkers, which were chosen based on their relevance, methodological excellence, and contribution to the field. Biochemical biomarkers, including α-synuclein and glial fibrillary acidic protein (GFAP), can predict disease severity and progression. The dopaminergic system is widely used as a neuroimaging biomarker to diagnose PD. Numerous genes and genome wide association study (GWAS) sites have been related to the development of PD. Recent research on the SNCA gene and leucine-rich repeat protein kinase 2 (LRRK2) has shown promising results. By evaluating current studies, this review intends to uncover gaps in biomarker validation and use, while also highlighting promising improvements. It emphasizes the need for dependable and reproducible indicators in improving PD diagnosis and prognosis. These biomarkers may open up new avenues for early diagnosis, disease progression tracking, and the development of personalized treatment programs. [ABSTRACT FROM AUTHOR]

Bladder cancer is among the most prevalent tumors in the urinary system and is known for its high malignancy. Although traditional diagnostic and treatment methods are established, recent research has focused on understanding the molecular mechanisms underlying bladder cancer. The primary objective of this study is to identify novel diagnostic markers and discover more effective targeted therapies for bladder cancer. This study identified differentially expressed genes (DEGs) between bladder cancer tissues and adjacent normal tissues using data from The Cancer Genome Atlas (TCGA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to explore the functional roles of these genes. A protein–protein interaction (PPI) network was also constructed to identify and analyze hub genes within this network. Gene set variation analysis (GSVA) was conducted to investigate the involvement of these genes in various biological processes and pathways. Ten key genes were found to be significantly associated with bladder cancer: IL6, CCNA2, CCNB1, CDK1, PLK1, TOP2A, AURKA, AURKB, FOXM1, and CALML5. GSVA analyses revealed that these genes are involved in a variety of biological processes and signaling pathways, including coagulation, UV-response-down, apoptosis, Notch signaling, and Wnt/beta-catenin signaling. The diagnostic relevance of these genes was validated through ROC curve analysis. Additionally, potential therapeutic drug interactions with these key genes were identified. This study provides valuable insights into key genes and their roles in bladder cancer. The identified genes and their interactions with therapeutic drugs could serve as potential biomarkers, presenting new opportunities for enhancing the diagnosis and prognosis of bladder cancer. [ABSTRACT FROM AUTHOR]

Head and neck cancer ranks as the sixth-most common malignancy worldwide, characterized by high mortality and recurrence rates. Research studies indicate that molecular diagnostics play a crucial role in the early detection and prognostic evaluation of these diseases. This study aimed to identify potential biomarkers for head and neck cancer and elucidate their interactions with miRNAs and possible therapeutic drugs. Four drivers, namely, FN1, IL1A, COL1A1, and MMP9, were identified using network biology and machine learning approaches. Gene set variation analysis (GSVA) showed that these genes were significantly involved in different biological processes and pathways, including coagulation, UV-response-down, apoptosis, NOTCH signaling, Wnt-beta catenin, and other signal pathways. The diagnostic value of these hub genes was validated using receiver operating characteristic (ROC) curves. The top interactive miRNAs, including miR-128-3p, miR-218-5p, miR-214-3p, miR-124-3p, miR-129-2-3p, and miR-1-3p, targeted the key genes. Furthermore, the interaction between the key genes and drugs was also identified. In summary, the key genes and miRNAs or drugs reported in this study might provide valuable information for potential biomarkers to increase the prognosis and diagnosis of head and neck cancer. [ABSTRACT FROM AUTHOR]

Background: Tuberculosis is a serious life-threatening disease among the top global health challenges and rapid and effective diagnostic biomarkers are vital for early diagnosis especially given the increasing prevalence of multidrug resistance. Methods: Two human whole blood microarray datasets, GSE42826 and GSE42830 were retrieved from publicly available gene expression omnibus (GEO) database. Deregulated genes (DEGs) were identified using GEO2R online tool and Gene Ontology (GO), protein-protein interaction (PPI) network analysis was performed using Metascape and STRING databases. Significant genes (n = 8) were identified using T-test/ANOVA and Molecular Complex Detection (MCODE) score ≥10, which was validated in GSE34608 dataset. The diagnostic potential of three biomarkers was assessed using Area Under Curve (AUC) of Receiver Operating Characteristic (ROC) plot. The transcriptional levels of these genes were also examined in a separate dataset GSE31348, to monitor the patterns of variation during tuberculosis treatment. Results: A total of 62 common DEGs (57 upregulated, 7 downregulated genes) were identified in two discovery datasets. GO functions and pathway enrichment analysis shed light on the functional roles of these DEGs in immune response and type-II interferon signaling. The genes in Module-1 (n = 18) were linked to innate immune response, interferon-gamma signaling. The common genes (n = 8) were validated in GSE34608 dataset, that corroborates the results obtained from discovery sets. The gene expression levels demonstrated responsiveness to Mtb infection during anti-TB therapy in GSE31348 dataset. In GSE34608 dataset, the expression levels of three specific genes, GBP5, IFITM3, and EPSTI1, emerged as potential diagnostic makers. In combination, these genes scored remarkable diagnostic performance with 100% sensitivity and 89% specificity, resulting in an impressive Area Under Curve (AUC) of 0.958. However, GBP5 alone showed the highest AUC of 0.986 with 100% sensitivity and 89% specificity. Conclusions: The study presents valuable insights into the critical gene network perturbed during tuberculosis. These genes are determinants for assessing the effectiveness of an anti-TB response and distinguishing between active TB and healthy individuals. GBP5, IFITM3 and EPSTI1 emerged as candidate core genes in TB and holds potential as novel molecular targets for the development of interventions in the treatment of TB. [ABSTRACT FROM AUTHOR]

The economic impact of phytopathogenic bacteria on agriculture is staggering, costing billions of US dollars globally. Pseudomonas syringae is the top most phytopathogenic bacteria, having more than 60 pathovars, which cause bacteria speck in tomatoes, halo blight in beans, and so on. Although antibiotics or a combination of antibiotics are used to manage infectious diseases in plants, they are employed far less in agriculture compared to human and animal populations. Moreover, the majority of antibiotics used in plants are immediately washed away, leading to environmental damage to ecosystems and food chains. Due to the serious risk of antibiotic resistance (AR) and the potential for environmental contamination with antibiotic residues and resistance genes, the use of unchecked antibiotics against phytopathogenic bacteria is not advisable. Despite the significant concern regarding AR in the world today, there are inadequate and outdated data on the AR of phytopathogenic bacteria. This review presents recent AR data on plant pathogenic bacteria (PPB), along with their environmental impact. In light of these findings, we suggest the use of biocontrol agents as a sustainable, eco-friendly, and effective alternative to controlling phytopathogenic bacteria. [ABSTRACT FROM AUTHOR]

Simple Summary: Historically, Korea has been an agrarian society where cattle played a significant role in Korean culture as working animals. However, in recent decades, there has been an increase in meat demand and the expansion of the Korean economy. This shift has led to the predominant use of native Korean Hanwoo cattle for beef production since the 1960s. Genetic improvement programs for Hanwoo cattle traditionally focused on carcass and growth qualities due to the accessibility of trait information and the simplicity of analysis techniques, rather than reproductive traits. However, there has been a recent surge of interest in genetic analyses of the reproductive traits of Hanwoo cattle because these traits significantly impact calf productivity. To enhance beef production and the overall profitability of Hanwoo farming, it is imperative to implement genomic predictions for age at first calving (AFC), calving interval (CI), gestation length (GL), and number of artificial inseminations per conception (NAIPC) to better understand and improve their response to selection. This study aimed to estimate heritability and the accuracy of genomic estimated breeding values (GEBVs) using genomic best linear unbiased prediction (GBLUP) and Bayesian methods (BayesB, BayesLASSO, and BayesR) for traits under study. Our analysis revealed relatively lower heritability values for these traits and indicated that the accuracy of genomic prediction across all methods applied was similarly reduced, likely due to the inherent lower heritability of reproductive traits. As a result, the findings of this study provide valuable insights into the genetic breeding programs of the beef cattle industry. This study aimed to predict the accuracy of genomic estimated breeding values (GEBVs) for reproductive traits in Hanwoo cows using the GBLUP, BayesB, BayesLASSO, and BayesR methods. Accuracy estimates of GEBVs for reproductive traits were derived through fivefold cross-validation, analyzing a dataset comprising 11,348 animals and employing an Illumina Bovine 50K SNP chip. GBLUP showed an accuracy of 0.26 for AFC, while BayesB, BayesLASSO, and BayesR demonstrated values of 0.28, 0.29, and 0.29, respectively. For CI, GBLUP attained an accuracy of 0.19, whereas BayesB, BayesLASSO, and BayesR scored 0.21, 0.24, and 0.25, respectively. The accuracy for GL was uniform across GBLUP, BayesB, and BayesR at 0.31, whereas BayesLASSO showed a slightly higher accuracy of 0.33. For NAIPC, GBLUP showed an accuracy of 0.24, while BayesB, BayesLASSO, and BayesR recorded 0.22, 0.27, and 0.30, respectively. The variation in genomic prediction accuracy among methods indicated Bayesian approaches slightly outperformed GBLUP. The findings suggest that Bayesian methods, notably BayesLASSO and BayesR, offer improved predictive capabilities for reproductive traits. Future research may explore more advanced genomic approaches to enhance predictive accuracy and genetic gains in Hanwoo cattle breeding programs. [ABSTRACT FROM AUTHOR]

PROteolysis TArgeting Chimera (PROTAC) is an emerging technology in chemical biology and drug discovery. This technique facilitates the complete removal of the target proteins that are "undruggable" or challenging to target through chemical molecules via the Ubiquitin–Proteasome System (UPS). PROTACs have been widely explored and outperformed not only in cancer but also in other diseases. During the past few decades, several academic institutes and pharma companies have poured more efforts into PROTAC-related technologies, setting the stage for several major degrader trial readouts in clinical phases. Despite their promising results, the formation of robust ternary orientation, off-target activity, poor permeability, and binding affinity are some of the limitations that hinder their development. Recent advancements in computational technologies have facilitated progress in the development of PROTACs. Researchers have been able to utilize these technologies to explore a wider range of E3 ligases and optimize linkers, thereby gaining a better understanding of the effectiveness and safety of PROTACs in clinical settings. In this review, we briefly explore the computational strategies reported to date for the formation of PROTAC components and discuss the key challenges and opportunities for further research in this area. [ABSTRACT FROM AUTHOR]

We herein describe the fine mapping of an imprinted QTL with major effect on muscle mass that was previously assigned to distal SSC2p in the pig. The proposed approach exploits linkage disequilibrium in combination with QTL genotyping by marker-assisted segregation analysis. By identifying a haplotype shared by all 'Q' chromosomes, we map the QTL to an ~250-kb chromosome segment containing INS and IGF2 as the only known paternally expressed genes. This considerably reinforces the candidacy of these genes, justifying their detailed analysis.

We herein report on our efforts to improve the mapping resolution of a QTL with major effect on milk yield and composition that was previously mapped to bovine chromosome 20. By using a denser chromosome 20 marker map and by exploiting linkage disequilibrium using two distinct approaches, we provide strong evidence that a chromosome segment including the gene coding for the growth hormone receptor accounts for at least part of the chromosome 20 QTL effect. By sequencing individuals with known QTL genotype, we identify an F to Y substitution in the transmembrane domain of the growth hormone receptor gene that is associated with a strong effect on milk yield and composition in the general population.

Abstract Background Previous work showed that the maize primary root adapts to low Ψw (-1.6 MPa) by maintaining longitudinal expansion in the apical 3 mm (region 1), whereas in the adjacent 4 mm (region 2) longitudinal expansion reaches a maximum in well-watered roots but is progressively inhibited at low Ψw. To identify mechanisms that determine these responses to low Ψw, transcript expression was profiled in these regions of water-stressed and well-watered roots. In addition, comparison between region 2 of water-stressed roots and the zone of growth deceleration in well-watered roots (region 3) distinguished stress-responsive genes in region 2 from those involved in cell maturation. Results Responses of gene expression to water stress in regions 1 and 2 were largely distinct. The largest functional categories of differentially expressed transcripts were reactive oxygen species and carbon metabolism in region 1, and membrane transport in region 2. Transcripts controlling sucrose hydrolysis distinguished well-watered and water-stressed states (invertase vs. sucrose synthase), and changes in expression of transcripts for starch synthesis indicated further alteration in carbon metabolism under water deficit. A role for inositols in the stress response was suggested, as was control of proline metabolism. Increased expression of transcripts for wall-loosening proteins in region 1, and for elements of ABA and ethylene signaling were also indicated in the response to water deficit. Conclusion The analysis indicates that fundamentally different signaling and metabolic response mechanisms are involved in the response to water stress in different regions of the maize primary root elongation zone.