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The exploitation of drought is a critical worldwide challenge that influences wheat growth and productivity. This study aimed to investigate a synergistic amendment strategy for drought using the single and combined application of plant growth-promoting microorganisms (PGPM) (Trichoderma harzianum) and biogenic silica nanoparticles (SiO2NPs) from rice husk ash (RHA) on Saudi Arabia's Spring wheat Summit cultivar (Triticum aestivum L.) for 102 DAS (days after sowing). The significant improvement was due to the application of 600 ppm SiO2NPs and T. harzianum + 600 ppm SiO2NPs, which enhanced the physiological properties of chlorophyll a, carotenoids, total pigments, osmolytes, and antioxidant contents of drought-stressed wheat plants as adaptive strategies. The results suggest that the expression of the studied genes (TaP5CS1, TaZFP34, TaWRKY1, TaMPK3, TaLEA, and the wheat housekeeping gene TaActin) in wheat remarkably enhanced wheat tolerance to drought stress. We discovered that the genes and metabolites involved significantly contributed to defense responses, making them potential targets for assessing drought tolerance levels. The drought tolerance indices of wheat were revealed by the mean productivity (MP), stress sensitivity index (SSI), yield stability index (YSI), and stress tolerance index (STI). We employed four databases, such as BAR, InterPro, phytozome, and the KEGG pathway, to predict and decipher the putative domains in prior gene sequencing. As a result, we discovered that these genes may be involved in a range of important biological functions in specific tissues at different developmental stages, including response to drought stress, proline accumulation, plant growth and development, and defense response. In conclusion, the sole and/or dual T. harzianum application to the wheat cultivar improved drought tolerance strength. These findings could be insightful data for wheat production in Saudi Arabia under various water regimes. [ABSTRACT FROM AUTHOR]

This study extends our prior research on drought responses in three date palm cultivars (Khalas, Reziz, and Sheshi) under controlled conditions. Here, we investigated their drought stress adaptive strategies under ambient environment. Under natural field drought conditions, three date palm cultivars experienced significantly (p ≤ 0.05) varying regulations in their physiological attributes. Specifically, chlorophyll content, leaf RWC, photosynthesis, stomatal conductance, and transpiration reduced significantly, while intercellular CO2 concentration and water use efficiency increased. Through suppression subtraction hybridization (SSH), a rich repertoire (1026) of drought-responsive expressed sequence tags (ESTs) were identified: 300 in Khalas, 343 in Reziz, and 383 in Sheshi. Functional analysis of ESTs, including gene annotation and KEGG pathways elucidation, unveiled that these cultivars withstand drought by leveraging indigenous and multifaceted pathways. While some pathways aligned with previously reported drought resilience mechanism observed under controlled conditions, several new indigenous pathways were noted, pinpointing cultivar-specific adaptations. ESTs identified in three date palm cultivars were enriched through GSEA analysis. Khalas exhibited enrichment in cellular and metabolic processes, catalytic activity, and metal ion binding. Reziz showed enrichment in biological regulation, metabolic processes, signaling, and nuclear functions. Conversely, Sheshi displayed enrichment in organelle, photosynthetic, and ribosomal components. Notably, ca. 50% of the ESTs were unique and novel, underlining the complexity of their adaptive genetic toolkit. Overall, Khalas displayed superior drought tolerance, followed by Reziz and Sheshi, highlighting cultivar-specific variability in adaptation. Conclusively, date palm cultivars exhibited diverse genetic and physiological strategies to cope with drought, demonstrating greater complexity in their resilience compared to controlled settings. [ABSTRACT FROM AUTHOR]

Abstract This study extends our prior research on drought responses in three date palm cultivars (Khalas, Reziz, and Sheshi) under controlled conditions. Here, we investigated their drought stress adaptive strategies under ambient environment. Under natural field drought conditions, three date palm cultivars experienced significantly (p ≤ 0.05) varying regulations in their physiological attributes. Specifically, chlorophyll content, leaf RWC, photosynthesis, stomatal conductance, and transpiration reduced significantly, while intercellular CO2 concentration and water use efficiency increased. Through suppression subtraction hybridization (SSH), a rich repertoire (1026) of drought-responsive expressed sequence tags (ESTs) were identified: 300 in Khalas, 343 in Reziz, and 383 in Sheshi. Functional analysis of ESTs, including gene annotation and KEGG pathways elucidation, unveiled that these cultivars withstand drought by leveraging indigenous and multifaceted pathways. While some pathways aligned with previously reported drought resilience mechanism observed under controlled conditions, several new indigenous pathways were noted, pinpointing cultivar-specific adaptations. ESTs identified in three date palm cultivars were enriched through GSEA analysis. Khalas exhibited enrichment in cellular and metabolic processes, catalytic activity, and metal ion binding. Reziz showed enrichment in biological regulation, metabolic processes, signaling, and nuclear functions. Conversely, Sheshi displayed enrichment in organelle, photosynthetic, and ribosomal components. Notably, ca. 50% of the ESTs were unique and novel, underlining the complexity of their adaptive genetic toolkit. Overall, Khalas displayed superior drought tolerance, followed by Reziz and Sheshi, highlighting cultivar-specific variability in adaptation. Conclusively, date palm cultivars exhibited diverse genetic and physiological strategies to cope with drought, demonstrating greater complexity in their resilience compared to controlled settings.

Understanding complex biological pathways, including gene–gene interactions and gene regulatory networks, is critical for exploring disease mechanisms and drug development. Manual literature curation of biological pathways cannot keep up with the exponential growth of new discoveries in the literature. Large‐scale language models (LLMs) trained on extensive text corpora contain rich biological information, and they can be mined as a biological knowledge graph. This study assesses 21 LLMs, including both application programming interface (API)‐based models and open‐source models in their capacities of retrieving biological knowledge. The evaluation focuses on predicting gene regulatory relations (activation, inhibition, and phosphorylation) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway components. Results indicated a significant disparity in model performance. API‐based models GPT‐4 and Claude‐Pro showed superior performance, with an F1 score of 0.4448 and 0.4386 for the gene regulatory relation prediction, and a Jaccard similarity index of 0.2778 and 0.2657 for the KEGG pathway prediction, respectively. Open‐source models lagged behind their API‐based counterparts, whereas Falcon‐180b and llama2‐7b had the highest F1 scores of 0.2787 and 0.1923 in gene regulatory relations, respectively. The KEGG pathway recognition had a Jaccard similarity index of 0.2237 for Falcon‐180b and 0.2207 for llama2‐7b. Our study suggests that LLMs are informative in gene network analysis and pathway mapping, but their effectiveness varies, necessitating careful model selection. This work also provides a case study and insight into using LLMs das knowledge graphs. Our code is publicly available at the website of GitHub (Muh‐aza). [ABSTRACT FROM AUTHOR]

Understanding the molecular mechanisms involved in the effects of oxidative stress in humans and animals is important to minimize the damage it causes, leading to various intestinal diseases. Our aim is to study the genes and pathways involved in oxidative stress in the gut using mouse small intestinal epithelial cells (MODE-K) as a model. The MODE-K cell line was divided into two different groups: one group was treated with hydrogen peroxide (H2O2) and the other group was not. To analyze the effects of H2O2 exposure, cell viability, apoptosis rate and reactive oxygen species (ROS) levels were determined. Next, transcriptome sequencing was performed, differentially expressed genes (DEGs) were identified and function annotation was performed, followed by a series of bioinformatics analyses. Real-time PCR was used to confirm the transcriptome data. Our results showed that H2O2-induced oxidative stress significantly increased ROS synthesis and promoted cell apoptosis in mouse small intestinal epithelial cells. During oxidative stress, 1207 DEGs (859 up-regulated, 348 down-regulated) were identified. According to GO analysis, DEGs are annotated into 51 different GO classifications including 22 biological processes, 15 cellular components and 14 molecular functions. In addition, using KEGG, PPI and correlation analysis, the two most significant subnetworks were identified. Ten correlated nodal DEGs of the first subnetwork correspond to MAPK, NF-kappa B and PI3K-AKT signaling pathways, and six correlated DEGs of the second subnetwork are associated with mitochondria. KDM6B was found to link these two subnetworks. The results suggest that oxidative stress affects epithelial growth, metabolism and apoptosis in a mouse model of intestinal cells through signaling pathways such as MAPK and PI3K/AKT/NF-kappa B, and mitochondria-related genes that are interconnected through the PTGS2-KDM6B-MT-ATP6 pathway. [ABSTRACT FROM AUTHOR]

AbstractAcquired chemoresistance remains a significant challenge in the clinics as most of the treated cancers eventually emerge as hard-to-treat phenotypes. Therefore, identifying chemoresistance targets is highly warranted to manage the disease better. In this study, we employed a label-free LC-MS/MS-based quantitative proteomics analysis to identify potential targets and signaling pathways underlying acquired chemoresistance in a sub-cell line (A549DR) derived from the parental lung adenocarcinoma cells (A549) treated with gradually increasing doses of doxorubicin (DOX). Our proteomics analysis identified 146 upregulated and 129 downregulated targets in A549DR cells. The KEGG pathway and Gene ontology (GO) analysis of differentially expressed upregulated and downregulated proteins showed that most abundant upregulated pathways were related to metabolic pathways, cellular senescence, cell cycle, and p53 signaling. Meanwhile, the downregulated pathways were related to spliceosome, nucleotide metabolism, DNA replication, nucleotide excision repair, and nuclear-cytoplasmic transport. Further, STRING analysis of upregulated biological processes showed a protein-protein interaction (PPI) between CDK1, AKT2, SRC, STAT1, HDAC1, FDXR, FDX1, NPC1, ALDH2, GPx1, CDK4, and B2M, proteins. The identified proteins in this study might be the potential therapeutic targets for mitigating DOX resistance. [ABSTRACT FROM AUTHOR]

Hulless barley is a cold-resistant crop widely planted in the northwest plateau of China. It is also the main food crop in this region. Phosphorus (P), as one of the important essential nutrient elements, regulates plant growth and defense. This study aimed to analyze the development and related molecular mechanisms of hulless barley under P deficiency and explore the regulatory genes so as to provide a basis for subsequent molecular breeding research. Transcriptome analysis was performed on the root and leaf samples of hulless barley cultured with different concentrations of KH2PO4 (1 mM and 10 μM) Hoagland solution. A total of 46,439 genes were finally obtained by the combined analysis of leaf and root samples. Among them, 325 and 453 genes had more than twofold differences in expression. These differentially expressed genes (DEGs) mainly participated in the abiotic stress biosynthetic process through Gene Ontology prediction. Moreover, the Kyoto Encyclopedia of Genes and Genomes showed that DEGs were mainly involved in photosynthesis, plant hormone signal transduction, glycolysis, phenylpropanoid biosynthesis, and synthesis of metabolites. These pathways also appeared in other abiotic stresses. Plants initiated multiple hormone synergistic regulatory mechanisms to maintain growth under P-deficient conditions. Transcription factors (TFs) also proved these predictions. The enrichment of ARR-B TFs, which positively regulated the phosphorelay-mediated cytokinin signal transduction, and some other TFs (AP2, GRAS, and ARF) was related to plant hormone regulation. Some DEGs showed different values in their FPKM (fragment per kilobase of transcript per million mapped reads), but the expression trends of genes responding to stress and phosphorylation remained highly consistent. Therefore, in the case of P deficiency, the first response of plants was the expression of stress-related genes. The effects of this stress on plant metabolites need to be further studied to improve the relevant regulatory mechanisms so as to further understand the importance of P in the development and stress resistance of hulless barley. [ABSTRACT FROM AUTHOR]

Abstract Rice metabolomics is widely used for biomarker research in the fields of pharmacology. As a consequence, characterization of the variations of the pigmented and non-pigmented traditional rice varieties of Tamil Nadu is crucial. These varieties possess fatty acids, sugars, terpenoids, plant sterols, phenols, carotenoids and other compounds that plays a major role in achieving sustainable development goal 2 (SDG 2). Gas-chromatography coupled with mass spectrometry was used to profile complete untargeted metabolomics of Kullkar (red colour) and Milagu Samba (white colour) for the first time and a total of 168 metabolites were identified. The metabolite profiles were subjected to data mining processes, including principal component analysis (PCA), Orthogonal Partial Least Square Discrimination Analysis (OPLS-DA) and Heat map analysis. OPLS-DA identified 144 differential metabolites between the 2 rice groups, variable importance in projection (VIP) ≥ 1 and fold change (FC) ≥ 2 or FC ≤ 0.5. Volcano plot (64 down regulated, 80 up regulated) was used to illustrate the differential metabolites. OPLS-DA predictive model showed good fit (R2X = 0.687) and predictability (Q2 = 0.977). The pathway enrichment analysis revealed the presence of three distinct pathways that were enriched. These findings serve as a foundation for further investigation into the function and nutritional significance of both pigmented and non-pigmented rice grains thereby can achieve the SDG 2.

Rice metabolomics is widely used for biomarker research in the fields of pharmacology. As a consequence, characterization of the variations of the pigmented and non-pigmented traditional rice varieties of Tamil Nadu is crucial. These varieties possess fatty acids, sugars, terpenoids, plant sterols, phenols, carotenoids and other compounds that plays a major role in achieving sustainable development goal 2 (SDG 2). Gas-chromatography coupled with mass spectrometry was used to profile complete untargeted metabolomics of Kullkar (red colour) and Milagu Samba (white colour) for the first time and a total of 168 metabolites were identified. The metabolite profiles were subjected to data mining processes, including principal component analysis (PCA), Orthogonal Partial Least Square Discrimination Analysis (OPLS-DA) and Heat map analysis. OPLS-DA identified 144 differential metabolites between the 2 rice groups, variable importance in projection (VIP) ≥ 1 and fold change (FC) ≥ 2 or FC ≤ 0.5. Volcano plot (64 down regulated, 80 up regulated) was used to illustrate the differential metabolites. OPLS-DA predictive model showed good fit (R2X = 0.687) and predictability (Q2 = 0.977). The pathway enrichment analysis revealed the presence of three distinct pathways that were enriched. These findings serve as a foundation for further investigation into the function and nutritional significance of both pigmented and non-pigmented rice grains thereby can achieve the SDG 2. [ABSTRACT FROM AUTHOR]

Determining MPO activity and oxidant/antioxidant (MDA, SOD and CAT) levels in the urine of osteoarthritis patients with urinary infection was also performed for the first time to reveal the targets of gene enrichment network analysis of osteoarthritic and urinary system-related genes. In addition, our other main goal was to learn that the use of computer-aided designs in this study and other studies could help the target. Genes from patients with osteoarthritis and urinary infections were retrieved from DisGENET. A venn Diagram was drawn for genes from DisGENET, and gene network analysis for common genes was done with SRplot. 31 patients with concomitant osteoarthritis and urinary tract infection, 17 patients with osteoarthritis, 15 patients with urinary tract infection and 25 healthy controls were included in the study. Urinary MPO, catalase (CAT), superoxide dismutase (SOD), and malondialdehyde (MDA) levels were examined in a spectrophotometer as indicators of oxidant and antioxidant status. Gene network analysis was performed for the first time for urinary infection and osteoarthritis, and 50 common genes were found. These common genes appear to be associated with arthritis, according to the KEGG pathway result. In the osteoarthritis group, MPO activity and MDA levels were higher, and CAT and SOD levels were lower (P <0.001). CAT, SOD, MPO activity and MDA levels were higher in the urinary tract infection group compared to the control group (P <0.05). MPO activity, and MDA levels were found to be higher in patients with concomitant osteoarthritis and in the group with urinary tract infection, and CAT and SOD activities were found to be significantly lower in the group with only tract infection (P <0.001). This study revealed that the group with osteoarthritis and urinary tract infection showed different inflammatory reactions and caused an imbalance in the oxidant/antioxidant status. In addition, it was evaluated for the first time by network analysis and it was determined that which genes it was associated with and that these genes would lead other studies within the scope of this target. [ABSTRACT FROM AUTHOR]

Common vetch (Vicia sativa L.) is an important annual diploid leguminous forage. In the present study, transcriptomic profiling in common vetch in response to salt stress was conducted using a salt-tolerant line (460) and a salt-sensitive line (429). The common responses in common vetch and the specific responses associated with salt tolerance in 460 were analyzed. Several KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways, including plant hormone and MAPK (mitogen-activated protein kinase) signaling, galactose metabolism, and phenylpropanoid phenylpropane biosynthesis, were enriched in both lines, though some differentially expressed genes (DEGs) showed distinct expression patterns. The roots in 460 showed higher levels of lignin than in 429. α-linolenic acid metabolism, carotenoid biosynthesis, the photosynthesis-antenna pathway, and starch and sucrose metabolism pathways were specifically enriched in salt-tolerant line 460, with higher levels of accumulated soluble sugars in the leaves. In addition, higher transcript levels of genes involved in ion homeostasis and reactive oxygen species (ROS) scavenging were observed in 460 than in 429 in response to salt stress. The transcriptomic analysis in common vetch in response to salt stress provides useful clues for further investigations on salt tolerance mechanism in the future. [ABSTRACT FROM AUTHOR]

Circular RNAs (circRNAs) are involved in several neurological disorders; however, the mechanisms underlying their involvement remain to be clarified. We attempted to explore the expression profiles of circRNAs and their potential functions and mechanisms in the pathogenesis of intracerebral hemorrhage (ICH) in Northern Chinese males. The microarray results showed that 50 circRNAs were significantly upregulated, while 194 circRNAs were significantly downregulated in ICH patients compared with healthy controls (p

Background and aim: N-acetyl-p-benzoquinoneimine (NAPQI), a toxic byproduct of paracetamol (Acetaminophen, APAP), can accumulate and cause liver damage by depleting glutathione and forming protein adducts in the mitochondria. These adducts disrupt the respiratory chain, increasing superoxide production and reducing ATP. The goal of this study was to provide computational proof that succinate dehydrogenase (SDH), a subunit of complex II in the mitochondrial respiratory chain, is a favorable binding partner for NAPQI in this regard. Method: Molecular docking, molecular dynamics simulation, protein-protein interaction networks (PPI), and KEGG metabolic pathway analysis were employed to identify binding characteristics, interaction partners, and their associations with metabolic pathways. A lipid membrane was added to the experimental apparatus to mimic the natural cellular environment of SDH. This modification made it possible to develop a context for investigating the role and interactions of SDH within a cellular ecosystem that was more realistic and biologically relevant. Result: The molecular binding affinity score for APAP and NAPQI with SDH was predicted −6.5 and −6.7 kcal/mol, respectively. Furthermore, RMSD, RMSF, and Rog from the molecular dynamics simulations study revealed that NAPQI has slightly higher stability and compactness compared to APAP at 100 ns timeframe with mitochondrial SDH. Conclusion: This study serves to predict the mechanistic process of paracetamol toxicity by using different computational approaches. In addition, this study will provide information about the drug target against APAP hepatotoxicity.

Abstract Purpose Graph coloring approach has emerged as a valuable problem-solving tool for both theoretical and practical aspects across various scientific disciplines, including biology. In this study, we demonstrate the graph coloring’s effectiveness in computational network biology, more precisely in analyzing protein–protein interaction (PPI) networks to gain insights about the viral infections and its consequences on human health. Accordingly, we propose a generic model that can highlight important hub proteins of virus-associated disease manifestations, changes in disease-associated biological pathways, potential drug targets and respective drugs. We test our model on SARS-CoV-2 infection, a highly transmissible virus responsible for the COVID-19 pandemic. The pandemic took significant human lives, causing severe respiratory illnesses and exhibiting various symptoms ranging from fever and cough to gastrointestinal, cardiac, renal, neurological, and other manifestations. Methods To investigate the underlying mechanisms of SARS-CoV-2 infection-induced dysregulation of human pathobiology, we construct a two-level PPI network and employed a differential evolution-based graph coloring (DEGCP) algorithm to identify critical hub proteins that might serve as potential targets for resolving the associated issues. Initially, we concentrate on the direct human interactors of SARS-CoV-2 proteins to construct the first-level PPI network and subsequently applied the DEGCP algorithm to identify essential hub proteins within this network. We then build a second-level PPI network by incorporating the next-level human interactors of the first-level hub proteins and use the DEGCP algorithm to predict the second level of hub proteins. Results We first identify the potential crucial hub proteins associated with SARS-CoV-2 infection at different levels. Through comprehensive analysis, we then investigate the cellular localization, interactions with other viral families, involvement in biological pathways and processes, functional attributes, gene regulation capabilities as transcription factors, and their associations with disease-associated symptoms of these identified hub proteins. Our findings highlight the significance of these hub proteins and their intricate connections with disease pathophysiology. Furthermore, we predict potential drug targets among the hub proteins and identify specific drugs that hold promise in preventing or treating SARS-CoV-2 infection and its consequences. Conclusion Our generic model demonstrates the effectiveness of DEGCP algorithm in analyzing biological PPI networks, provides valuable insights into disease biology, and offers a basis for developing novel therapeutic strategies for other viral infections that may cause future pandemic.

Abstract Background Genetic variants can contribute differently to trait heritability by their functional categories, and recent studies have shown that incorporating functional annotation can improve the predictive performance of polygenic risk scores (PRSs). In addition, when only a small proportion of variants are causal variants, PRS methods that employ a Bayesian framework with shrinkage can account for such sparsity. It is possible that the annotation group level effect is also sparse. However, the number of PRS methods that incorporate both annotation information and shrinkage on effect sizes is limited. We propose a PRS method, PRSbils, which utilizes the functional annotation information with a bilevel continuous shrinkage prior to accommodate the varying genetic architectures both on the variant-specific level and on the functional annotation level. Results We conducted simulation studies and investigated the predictive performance in settings with different genetic architectures. Results indicated that when there was a relatively large variability of group-wise heritability contribution, the gain in prediction performance from the proposed method was on average 8.0% higher AUC compared to the benchmark method PRS-CS. The proposed method also yielded higher predictive performance compared to PRS-CS in settings with different overlapping patterns of annotation groups and obtained on average 6.4% higher AUC. We applied PRSbils to binary and quantitative traits in three real world data sources (the UK Biobank, the Michigan Genomics Initiative (MGI), and the Korean Genome and Epidemiology Study (KoGES)), and two sources of annotations: ANNOVAR, and pathway information from the Kyoto Encyclopedia of Genes and Genomes (KEGG), and demonstrated that the proposed method holds the potential for improving predictive performance by incorporating functional annotations. Conclusions By utilizing a bilevel shrinkage framework, PRSbils enables the incorporation of both overlapping and non-overlapping annotations into PRS construction to improve the performance of genetic risk prediction. The software is available at https://github.com/styvon/PRSbils .

Purpose: Graph coloring approach has emerged as a valuable problem-solving tool for both theoretical and practical aspects across various scientific disciplines, including biology. In this study, we demonstrate the graph coloring's effectiveness in computational network biology, more precisely in analyzing protein–protein interaction (PPI) networks to gain insights about the viral infections and its consequences on human health. Accordingly, we propose a generic model that can highlight important hub proteins of virus-associated disease manifestations, changes in disease-associated biological pathways, potential drug targets and respective drugs. We test our model on SARS-CoV-2 infection, a highly transmissible virus responsible for the COVID-19 pandemic. The pandemic took significant human lives, causing severe respiratory illnesses and exhibiting various symptoms ranging from fever and cough to gastrointestinal, cardiac, renal, neurological, and other manifestations. Methods: To investigate the underlying mechanisms of SARS-CoV-2 infection-induced dysregulation of human pathobiology, we construct a two-level PPI network and employed a differential evolution-based graph coloring (DEGCP) algorithm to identify critical hub proteins that might serve as potential targets for resolving the associated issues. Initially, we concentrate on the direct human interactors of SARS-CoV-2 proteins to construct the first-level PPI network and subsequently applied the DEGCP algorithm to identify essential hub proteins within this network. We then build a second-level PPI network by incorporating the next-level human interactors of the first-level hub proteins and use the DEGCP algorithm to predict the second level of hub proteins. Results: We first identify the potential crucial hub proteins associated with SARS-CoV-2 infection at different levels. Through comprehensive analysis, we then investigate the cellular localization, interactions with other viral families, involvement in biological pathways and processes, functional attributes, gene regulation capabilities as transcription factors, and their associations with disease-associated symptoms of these identified hub proteins. Our findings highlight the significance of these hub proteins and their intricate connections with disease pathophysiology. Furthermore, we predict potential drug targets among the hub proteins and identify specific drugs that hold promise in preventing or treating SARS-CoV-2 infection and its consequences. Conclusion: Our generic model demonstrates the effectiveness of DEGCP algorithm in analyzing biological PPI networks, provides valuable insights into disease biology, and offers a basis for developing novel therapeutic strategies for other viral infections that may cause future pandemic. [ABSTRACT FROM AUTHOR]

Background: Genetic variants can contribute differently to trait heritability by their functional categories, and recent studies have shown that incorporating functional annotation can improve the predictive performance of polygenic risk scores (PRSs). In addition, when only a small proportion of variants are causal variants, PRS methods that employ a Bayesian framework with shrinkage can account for such sparsity. It is possible that the annotation group level effect is also sparse. However, the number of PRS methods that incorporate both annotation information and shrinkage on effect sizes is limited. We propose a PRS method, PRSbils, which utilizes the functional annotation information with a bilevel continuous shrinkage prior to accommodate the varying genetic architectures both on the variant-specific level and on the functional annotation level. Results: We conducted simulation studies and investigated the predictive performance in settings with different genetic architectures. Results indicated that when there was a relatively large variability of group-wise heritability contribution, the gain in prediction performance from the proposed method was on average 8.0% higher AUC compared to the benchmark method PRS-CS. The proposed method also yielded higher predictive performance compared to PRS-CS in settings with different overlapping patterns of annotation groups and obtained on average 6.4% higher AUC. We applied PRSbils to binary and quantitative traits in three real world data sources (the UK Biobank, the Michigan Genomics Initiative (MGI), and the Korean Genome and Epidemiology Study (KoGES)), and two sources of annotations: ANNOVAR, and pathway information from the Kyoto Encyclopedia of Genes and Genomes (KEGG), and demonstrated that the proposed method holds the potential for improving predictive performance by incorporating functional annotations. Conclusions: By utilizing a bilevel shrinkage framework, PRSbils enables the incorporation of both overlapping and non-overlapping annotations into PRS construction to improve the performance of genetic risk prediction. The software is available at https://github.com/styvon/PRSbils. [ABSTRACT FROM AUTHOR]

Qian Song,1,&ast; Ying Chen,2,3,&ast; JianQing Ma,1 Wei Zhou,4 JunYan Song,1 ChunFu Wu,5 Jie Liu2 1Department of Medical Laboratory, North China Medical & Health Group Xingtai General Hospital, Orthopedic Hospital of Xingtai, Xingtai, People’s Republic of China; 2BGI Genomics, BGI-Shenzhen, Shenzhen, People’s Republic of China; 3College of Life Sciences, University of Chinese Academy of Sciences, Beijing, People’s Republic of China; 4China National Genebank, BGI-Shenzhen, Shenzhen, People’s Republic of China; 5Yantai Harbor Hospital, Yantai, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Jie Liu, BGI Genomics, BGI-Shenzhen, Shenzhen, 518083, People’s Republic of China, Email liujie8@genomics.cn ChunFu Wu, Yantai Harbor Hospital, Yantai, 264000, People’s Republic of China, Email tjnk2009@126.comPurpose: Psoriasis is a chronic, multi-system skin disease that can be influenced by immunological, environmental, and genetic factors. Plasma metabolomic analysis can provide a great deal of information on potential diagnostic biomarkers, pathogenesis and personalized treatment. However, the role of metabolites in psoriasis is unknown.Patients and Methods: We performed an untargeted metabolomic analysis of plasma based on high-resolution liquid chromatography mass spectrometry from 10 plaque psoriasis patients and 10 healthy controls.Results: A total of 301 differential metabolites were detected, of which 10 metabolites were possible potential biomarkers, including vitamins, amino acids, and lipids. At the same time, KEGG pathway enrichment analysis was performed for all detected differential metabolites, and it was found that protein digestion and absorption, amino acid metabolism and lipid metabolism may be jointly involved in regulating the pathogenesis of psoriasis. In addition, the proteins ESR1, OPRM1 and HSD11B1 were identified as possible potential topical therapeutic targets for psoriasis through analysis of the metabolite-protein interaction network.Conclusion: In this study, we identified 10 differential metabolites as possible potential combinatorial biomarkers for the diagnosis of psoriasis. 12 metabolic pathways were significantly enriched that may be closely related to the occurrence and development of psoriasis. Three proteins, ESR1, OPRM1, and HSD11B1, were identified as possible potential therapeutic targets for psoriasis.Keywords: psoriasis, metabolomics, biomarker, KEGG pathway, therapeutic target

Hulless barley is a cold-resistant crop widely planted in the northwest plateau of China. It is also the main food crop in this region. Phosphorus (P), as one of the important essential nutrient elements, regulates plant growth and defense. This study aimed to analyze the development and related molecular mechanisms of hulless barley under P deficiency and explore the regulatory genes so as to provide a basis for subsequent molecular breeding research. Transcriptome analysis was performed on the root and leaf samples of hulless barley cultured with different concentrations of KH2PO4 (1 mM and 10 μM) Hoagland solution. A total of 46,439 genes were finally obtained by the combined analysis of leaf and root samples. Among them, 325 and 453 genes had more than twofold differences in expression. These differentially expressed genes (DEGs) mainly participated in the abiotic stress biosynthetic process through Gene Ontology prediction. Moreover, the Kyoto Encyclopedia of Genes and Genomes showed that DEGs were mainly involved in photosynthesis, plant hormone signal transduction, glycolysis, phenylpropanoid biosynthesis, and synthesis of metabolites. These pathways also appeared in other abiotic stresses. Plants initiated multiple hormone synergistic regulatory mechanisms to maintain growth under P-deficient conditions. Transcription factors (TFs) also proved these predictions. The enrichment of ARR-B TFs, which positively regulated the phosphorelay-mediated cytokinin signal transduction, and some other TFs (AP2, GRAS, and ARF) was related to plant hormone regulation. Some DEGs showed different values in their FPKM (fragment per kilobase of transcript per million mapped reads), but the expression trends of genes responding to stress and phosphorylation remained highly consistent. Therefore, in the case of P deficiency, the first response of plants was the expression of stress-related genes. The effects of this stress on plant metabolites need to be further studied to improve the relevant regulatory mechanisms so as to further understand the importance of P in the development and stress resistance of hulless barley.

Breast cancer (BC) is the leading cause of death among women across the globe. Abnormal gene expression plays a crucial role in tumour progression, carcinogenesis and metastasis of BC. The alteration of gene expression may be through aberrant gene methylation. In the present study, differentially expressed genes which may be regulated by DNA methylation and their pathways associated with BC have been identified. Expression microarray datasets GSE10780, GSE10797, GSE21422, GSE42568, GSE61304, GSE61724 and one DNA methylation profile dataset GSE20713 were downloaded from Gene Expression Omnibus database (GEO). Differentially expressed-aberrantly methylated genes were identified using online Venn diagram tool. Based on fold change expression of differentially expressed-aberrantly methylated genes were chosen through heat map. Protein–protein interaction (PPI) network of the hub genes was constructed by Search Tool for the Retrieval of Interacting Genes (STRING). Gene expression and DNA methylation level of the hub genes were validated through UALCAN. Overall survival analysis of the hub genes was analysed through Kaplan-Meier plotter database for BC. A total of 72 upregulated-hypomethylated genes and 92 downregulated-hypermethylated genes were obtained from GSE10780, GSE10797, GSE21422, GSE42568, GSE61304, GSE61724, and GSE20713 datasets by GEO2R and Venn diagram tool. PPI network of the upregulated-hypomethylated hub genes (MRGBP, MANF, ARF3, HIST1H3D, GSK3B, HJURP, GPSM2, MATN3, KDELR2, CEP55, GSPT1, COL11A1, and COL1A1) and downregulated-hypermethylated hub genes were constructed (APOD, DMD, RBPMS, NR3C2, HOXA9, AMKY2, KCTD9, and EDN1). All the differentially expressed hub genes expression was validated in UALCAN database. 4 in 13 upregulated-hypomethylated and 5 in 8 downregulated-hypermethylated hub genes to be significantly hypomethylated or hypermethylated in BC were confirmed using UALCAN database (p < 0.05). MANF, HIST1H3D, HJURP, GSK3B, GPSM2, MATN3, KDELR2, CEP55, COL1A1, APOD, RBPMS, NR3C2, HOXA9, ANKMY2, and EDN1 were significantly (p < 0.05) associated with poor overall survival (OS). The identified aberrantly methylated-differentially expressed genes and their related pathways and function in BC can serve as novel diagnostic and prognostic biomarkers and therapeutic targets.Please confirm if the author names are presented accurately and in the correct sequence (given name, middle name/initial, family name). Author 4 Given name: [Jeewan Ram] Last name [Vishnoi]. Also, kindly confirm the details in the metadata are correct.It is correct [ABSTRACT FROM AUTHOR]

Hippophae rhamnoides L. has been widely used in research and application for almost two decades. While significant progress was achieved in the examination of its fruits and seeds, the exploration and utilization of its by-products have received relatively less attention. This study aims to address this research gap by investigating the effects and underlying mechanisms of sea buckthorn seed residues both in vitro and in vivo. The primary objective of this study is to assess the potential of the hydroalcoholic extract from sea buckthorn seed residues (HYD-SBSR) to prevent cell apoptosis and mitigate oxidative stress damage. To achieve this, an H2O2-induced B16F10 cell model and a D-galactose-induced mouse model were used. The H2O2-induced oxidative stress model using B16F10 cells was utilized to evaluate the cellular protective and reparative effects of HYD-SBSR. The results demonstrated the cytoprotective effects of HYD-SBSR, as evidenced by reduced apoptosis rates and enhanced resistance to oxidative stress alongside moderate cell repair properties. Furthermore, this study investigated the impact of HYD-SBSR on antioxidant enzymes and peroxides in mice to elucidate its reparative potential in vivo. The findings revealed that HYD-SBSR exhibited remarkable antioxidant performance, particularly at low concentrations, significantly enhancing antioxidant capacity under oxidative stress conditions. To delve into the mechanisms underlying HYD-SBSR, a comprehensive proteomics analysis was conducted to identify differentially expressed proteins (DEPs). Additionally, a Gene Ontology (GO) analysis and an Encyclopedia of Genes and Genomes (KEGG) pathway cluster analysis were performed to elucidate the functional roles of these DEPs. The outcomes highlighted crucial mechanistic pathways associated with HYD-SBSR, including the PPAR signaling pathway, fat digestion and absorption, glycerophospholipid metabolism, and cholesterol metabolism. The research findings indicated that HYD-SBSR, as a health food supplement, exhibits favorable effects by promoting healthy lipid metabolism, contributing to the sustainable and environmentally friendly production of sea buckthorn and paving the way for future investigations and applications in the field of nutraceutical and pharmaceutical research. [ABSTRACT FROM AUTHOR]

Common vetch (Vicia sativa L.) is an important annual diploid leguminous forage. In the present study, transcriptomic profiling in common vetch in response to salt stress was conducted using a salt-tolerant line (460) and a salt-sensitive line (429). The common responses in common vetch and the specific responses associated with salt tolerance in 460 were analyzed. Several KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways, including plant hormone and MAPK (mitogen-activated protein kinase) signaling, galactose metabolism, and phenylpropanoid phenylpropane biosynthesis, were enriched in both lines, though some differentially expressed genes (DEGs) showed distinct expression patterns. The roots in 460 showed higher levels of lignin than in 429. α-linolenic acid metabolism, carotenoid biosynthesis, the photosynthesis-antenna pathway, and starch and sucrose metabolism pathways were specifically enriched in salt-tolerant line 460, with higher levels of accumulated soluble sugars in the leaves. In addition, higher transcript levels of genes involved in ion homeostasis and reactive oxygen species (ROS) scavenging were observed in 460 than in 429 in response to salt stress. The transcriptomic analysis in common vetch in response to salt stress provides useful clues for further investigations on salt tolerance mechanism in the future.

Introduction: Conservation tillage is a widely used technique worldwide, but the effects of conservation tillage on bacterial community structure are poorly understood. We explored proportional alterations in the bacterial community under different tillage treatments. Methodology: Hence, this study utilized high-throughput sequencing technique to investigate the structure and assembly processes of microbial communities in different tillage treatments. Results and discussion: Tillage treatments included tillage no-straw retention (CntWt), no-tillage with straw retention (CntWntS), tillage with straw retention (CntWtS), no-tillage and no-straw retention (CntWnt). The influence of tillage practices on soil bacterial communities was investigated using Illumina MiSeq sequencing. Different tillage methods and straw retention systems significantly influenced soil parameters such as total potassium and pH were not affected by tillage practices, while straw retention significantly affected soil parameters including nitrogen content, available phosphorus and available potassium. Straw retention decreased bacterial diversity while increased bacterial richness. The effect of straw retention and tillage on bacterial communities was greater than with no tillage. Phylogenetic b-diversity analysis showed that deterministic homogeneous selection processes were dominated, while stochastic processes were more pronounced in tillage without straw retention. Ecological network analysis showed that microbial community correlation was increased in CntWntS and CntWnt. Straw retention treatment significantly increased the relative abundance of bacterial taxa Proteobacteria, Bacteroidetes, and OD1, while Nitrospirae, Actinobacteria, and Verrucomicrobia significantly decreased. Conclusion: The conservation tillage practices significantly affect soil properties, bacterial composition, and assembly processes; however, further studies are required to investigate the impact of different crops, tillage practices and physiological characteristics on bacterial community structure and functions. [ABSTRACT FROM AUTHOR]

Roses are important plants, and they are cultivated worldwide for their beautiful flowers, remarkable scent, and nutritious hips. In this study, we re-sequenced the whole genomes of 57 rose cultivars and one wild species that originated from different regions around the world and had different scents, aiming to evaluate their genetic structure and to detect the potential signature of the selective sweep between different scent groups with single-nucleotide polymorphism (SNP) and indel markers. The roses were sequenced at an average depth of 6× to the reference genome of Rosa 'Old blush'. A total of 2,375,949 SNPs were obtained. The SNP numbers varied among the 58 samples, with an average of 1,271,906 per sample. The phylogeny and population structure revealed that the roses could be divided into three main clusters; however, the groups were not consistent in terms of geographic origin or scent classification. These indicated that rose cultivars have a complex genetic background due to the extensive hybridization between cultivated roses or wild rosa species worldwide. A selective sweep analysis was conducted to detect the selection signatures in rose scent traits. A total of 2430 candidate genes were identified in the strong scent groups, and were at the top 5% FST compared to the moderate group. These genes were significantly enriched in the KEGG pathways of tyrosine metabolism, cyanoamino acid metabolism, alpha-linolenic acid metabolism (13 genes), and phenylpropanoid biosynthesis (30 genes). When the low-scent group was used as the control, 2604 candidate genes were identified at the top 5% FST that were significantly enriched in the KEGG pathways of alpha-linolenic acid metabolism (15 genes), and glutathione metabolism (23 genes). We also observed genes enriched in pathways including the linoleic acid metabolism (five genes), diterpenoid biosynthesis (six genes), and monoterpenoid biosynthesis (seven genes), although they were not significant. These implied a positive selection of rose cultivars with a strong fragrance in terms of fatty acid derivatives, terpenoids and benzenoids/phenylpropanoids during rose breeding. Our study establishes a foundation for the further large-scale characterization of rose germplasm, improving the genetic knowledge of the background of roses. [ABSTRACT FROM AUTHOR]

Abstract Background Cell homeostasis relies on the concerted actions of genes, and dysregulated genes can lead to diseases. In living organisms, genes or their products do not act alone but within networks. Subsets of these networks can be viewed as modules that provide specific functionality to an organism. The Kyoto encyclopedia of genes and genomes (KEGG) systematically analyzes gene functions, proteins, and molecules and combines them into pathways. Measurements of gene expression (e.g., RNA-seq data) can be mapped to KEGG pathways to determine which modules are affected or dysregulated in the disease. However, genes acting in multiple pathways and other inherent issues complicate such analyses. Many current approaches may only employ gene expression data and need to pay more attention to some of the existing knowledge stored in KEGG pathways for detecting dysregulated pathways. New methods that consider more precompiled information are required for a more holistic association between gene expression and diseases. Results PriPath is a novel approach that transfers the generic process of grouping and scoring, followed by modeling to analyze gene expression with KEGG pathways. In PriPath, KEGG pathways are utilized as the grouping function as part of a machine learning algorithm for selecting the most significant KEGG pathways. A machine learning model is trained to differentiate between diseases and controls using those groups. We have tested PriPath on 13 gene expression datasets of various cancers and other diseases. Our proposed approach successfully assigned biologically and clinically relevant KEGG terms to the samples based on the differentially expressed genes. We have comparatively evaluated the performance of PriPath against other tools, which are similar in their merit. For each dataset, we manually confirmed the top results of PriPath in the literature and found that most predictions can be supported by previous experimental research. Conclusions PriPath can thus aid in determining dysregulated pathways, which applies to medical diagnostics. In the future, we aim to advance this approach so that it can perform patient stratification based on gene expression and identify druggable targets. Thereby, we cover two aspects of precision medicine.

Abstract Background Atopic dermatitis (AD) is a common inflammatory skin disease that compromises the skin's barrier function and capacity to retain moisture. Cnidii Fructus (CF), the dried fruits of Cnidium monnieri, has long been used to treat atopic dermatitis (AD) in China. However, the anti-AD compounds and mechanisms of CF are not fully understood. In this study, we evaluated the active compounds and molecular targets of CF in treating AD. Methods The Traditional Chinese Medicine Systems Pharmacology database was used to acquire information regarding the compounds that occur in the herb. Targets of these compounds were predicted using the SwissTargetPrediction website tool. AD-related genes were collected from the GeneCards database. Gene ontology (GO) enrichment analysis and KEGG pathway analysis of proteins that are targeted by active compounds of CF and encoded by AD-related genes were performed using Database for Annotation, Visualization, and Integrated Discovery Bioinformatics Resources. A “compound-target” network was constructed and analyzed using Cytoscape Software. Molecular docking was performed using BIOVIA Discovery Studio Visualizer and AutoDock Vina. Results We identified 19 active compounds in CF, 532 potential targets for these compounds, and 1540 genes related to AD. Results of GO enrichment indicated that CF affects biological processes and molecular functions, such as inflammatory response and steroid hormone receptor activity, which may be associated with its anti-AD effects. KEGG pathway analyses showed that PI3K-Akt signaling, calcium signaling, Rap1 signaling, and cAMP signaling pathways are the main pathways involved in the anti-AD effects of CF. Molecular docking analyses revealed that the key active compounds in CF, such as (E)-2,3-bis(2-keto-7-methoxy-chromen-8-yl)acrolein, ar-curcumene, and diosmetin, can bind the main therapeutic targets AKT1, SRC, MAPK3, EGFR, CASP3, and PTGS2. Conclusions Results of the present study establish a foundation for further investigation of the anti-AD compounds and mechanisms of CF and provide a basis for developing modern anti-AD agents based on compounds that occur in CF.

Wheat (Triticum aestivum) is one of the major crops worldwide and a primary source of calories for human food. Biotic stresses such as fungi, bacteria, and diseases limit wheat production. Although plant breeding and genetic engineering for biotic stress resistance have been suggested as promising solutions to handle losses caused by biotic stress factors, a comprehensive understanding of molecular mechanisms and identifying key genes is a critical step to obtaining success. Here, a network-based meta-analysis approach based on two main statistical methods was used to identify key genes and molecular mechanisms of the wheat response to biotic stress. A total of 163 samples (21,792 genes) from 10 datasets were analyzed. Fisher Z test based on the p-value and REM method based on effect size resulted in 533 differentially expressed genes (p < 0.001 and FDR < 0.001). WGCNA analysis using a dynamic tree-cutting algorithm was used to construct a co-expression network and three significant modules were detected. The modules were significantly enriched by 16 BP terms and 4 KEGG pathways (Benjamini–Hochberg FDR < 0.001). A total of nine hub genes (a top 1.5% of genes with the highest degree) were identified from the constructed network. The identification of DE genes, gene–gene co-expressing network, and hub genes may contribute to uncovering the molecular mechanisms of the wheat response to biotic stress. [ABSTRACT FROM AUTHOR]

IntroductionConservation tillage is a widely used technique worldwide, but the effects of conservation tillage on bacterial community structure are poorly understood. We explored proportional alterations in the bacterial community under different tillage treatments.MethodologyHence, this study utilized high-throughput sequencing technique to investigate the structure and assembly processes of microbial communities in different tillage treatments.Results and discussionTillage treatments included tillage no-straw retention (CntWt), no-tillage with straw retention (CntWntS), tillage with straw retention (CntWtS), no-tillage and no-straw retention (CntWnt). The influence of tillage practices on soil bacterial communities was investigated using Illumina MiSeq sequencing. Different tillage methods and straw retention systems significantly influenced soil parameters such as total potassium and pH were not affected by tillage practices, while straw retention significantly affected soil parameters including nitrogen content, available phosphorus and available potassium. Straw retention decreased bacterial diversity while increased bacterial richness. The effect of straw retention and tillage on bacterial communities was greater than with no tillage. Phylogenetic β-diversity analysis showed that deterministic homogeneous selection processes were dominated, while stochastic processes were more pronounced in tillage without straw retention. Ecological network analysis showed that microbial community correlation was increased in CntWntS and CntWnt. Straw retention treatment significantly increased the relative abundance of bacterial taxa Proteobacteria, Bacteroidetes, and OD1, while Nitrospirae, Actinobacteria, and Verrucomicrobia significantly decreased.ConclusionThe conservation tillage practices significantly affect soil properties, bacterial composition, and assembly processes; however, further studies are required to investigate the impact of different crops, tillage practices and physiological characteristics on bacterial community structure and functions.

Jiafa Wu,1 Dongping Luo,2 Shengnan Li3 1School of Food and Bioengineering, Henan University of Science and Technology, Luoyang, People’s Republic of China; 2The First Affiliated Hospital, Henan University of Science and Technology, Luoyang, People’s Republic of China; 3School of Medicine, Henan Polytechnic University, Jiaozuo, People’s Republic of ChinaCorrespondence: Jiafa Wu, School of Food and Bioengineering, Henan University of Science and Technology, Kaiyuan Avenue 263, Luoyang, People’s Republic of China, Email wujiafa@haust.edu.cnBackground: Breast cancer stem cells (BCSCs) are associated with tumor initiation, invasion, metastasis and drug resistance. It is known that many proteins and signaling pathways are involved in the regulation of BCSCs, however, much more efforts are needed to understand BCSCs comprehensively. Tumor-infiltrating immune cells are important in cancer treatment efficacy and patient prognosis. We tried to identify potential suppressor of BCSCs and analyze its correlation with various immune cells infiltration by bioinformatic and experimental methods.Methods: Expression level and methylation state of OVOL2 were analyzed by tools from bc-GenExMiner v4.8 and UALCAN databases. The Kaplan–Meier plotter was applied to evaluate the prognostic values of OVOL2. Gene expression datasets (GSE7515, GSE15192) were selected to analyze differentially expressed genes (DEGs) related to BCSCs. GO and KEGG pathway analyses of DEGs were conducted. MCODE app plugin of Cytoscape was used to screen modules in PPI network of downregulated DEGs. Correlation of OVOL2 expression with infiltrating immune cells was evaluated by TIMER 2.0. Experiments were conducted to verify whether OVOL2 could inhibit stemness traits of breast cancer cell MDA-MB-231.Results: The expression level of OVOL2 in basal/TNBC was significantly lower than that of other subtypes. Survival analyses indicated that high expression of OVOL2 was associated with favorable prognosis. GO and KEGG pathway analyses for upregulated and downregulated DEGs were conducted. The top three clusters of downregulated DEGs showed that tight junction and chemokines may play important roles in BCSCs. OVOL2 is one module of clusters. OVOL2 expression is correlated with various immune cells infiltration. Experiments showed that OVOL2 suppresses CD44+/CD24− ratio and mammospheres formation of MDA-MB-231.Conclusion: OVOL2 may play an important role in the regulation of breast cancer stemness and immune cell infiltration, and is likely to be a target for the treatment of breast cancer.Keywords: OVOL2, gene ontology, KEGG pathway, BCSCs, immune cell infiltration

Background: Cell homeostasis relies on the concerted actions of genes, and dysregulated genes can lead to diseases. In living organisms, genes or their products do not act alone but within networks. Subsets of these networks can be viewed as modules that provide specific functionality to an organism. The Kyoto encyclopedia of genes and genomes (KEGG) systematically analyzes gene functions, proteins, and molecules and combines them into pathways. Measurements of gene expression (e.g., RNA-seq data) can be mapped to KEGG pathways to determine which modules are affected or dysregulated in the disease. However, genes acting in multiple pathways and other inherent issues complicate such analyses. Many current approaches may only employ gene expression data and need to pay more attention to some of the existing knowledge stored in KEGG pathways for detecting dysregulated pathways. New methods that consider more precompiled information are required for a more holistic association between gene expression and diseases. Results: PriPath is a novel approach that transfers the generic process of grouping and scoring, followed by modeling to analyze gene expression with KEGG pathways. In PriPath, KEGG pathways are utilized as the grouping function as part of a machine learning algorithm for selecting the most significant KEGG pathways. A machine learning model is trained to differentiate between diseases and controls using those groups. We have tested PriPath on 13 gene expression datasets of various cancers and other diseases. Our proposed approach successfully assigned biologically and clinically relevant KEGG terms to the samples based on the differentially expressed genes. We have comparatively evaluated the performance of PriPath against other tools, which are similar in their merit. For each dataset, we manually confirmed the top results of PriPath in the literature and found that most predictions can be supported by previous experimental research. Conclusions: PriPath can thus aid in determining dysregulated pathways, which applies to medical diagnostics. In the future, we aim to advance this approach so that it can perform patient stratification based on gene expression and identify druggable targets. Thereby, we cover two aspects of precision medicine. [ABSTRACT FROM AUTHOR]

In this study, wild eggplant germplasm No. M239, which is highly susceptible to Verticillium wilt, was used as the experimental material. The physiological and biochemical indices (SOD, PAL, MDA and soluble protein) of M239 roots were measured at different times (0, 12, 24, 36, 48, 60 and 72 h) post inoculation with Verticillium dahliae, and the key time points for the M239 response to Verticillium wilt infection were screened. Then, RNA-Seq technology was used to screen the differentially expressed genes (DEGs) in M239 roots at 0, 12 and 48 h post-inoculation (hpi). The transcriptional results of M239 were also compared with those resistance genes from some reported wild relative Solanum species (S. sisymbriifolium and S. aculeatissimum). Then some DEGs were chosen for validation by qRT-PCR. The results showed that 12 and 48 hpi were the turning points in the changes in all physiological and biochemical indices. A total of 6,783 DEGs were identified by RNA-Seq, including 6,141 DEGs (3,046 upregulated and 3,095 downregulated) at the M_12 h vs. M_0 h, 1,903 DEGs (792 upregulated and 1,111 downregulated) at M_48 h vs. M_12 h, and 1,261 DEGs that appeared simultaneously in both stages. KEGG enrichment analysis showed that there were 5 metabolic pathways enriched from DEGs, which were mostly related to primary metabolism, such as glycolysis, amino acid and ribosome biogenesis. Compared with the NCBI non-redundant protein (NR) database, one Ve2 homologous gene and 8 PR protein-related genes were screened. Transcription factor analysis showed that there were a large number of DEGs, such as MYB, AP2-EREBP, bHLH, NAC and Orphans in the two stages. Compared with the reported Verticillium wilt-resistant wild eggplant species, it was found that there were fewer genes and enriched metabolic pathways in the M239 response to Verticillium wilt infection, and it also lacked the response of some known key resistance genes. These results proved that the above resistance genes and metabolic pathways played a key role in the wild eggplant response to V. dahliae infection. [ABSTRACT FROM AUTHOR]

BACKGROUND: Our previous study has demonstrated that the egg‐white‐derived peptide RVPSL can lower blood pressure in spontaneously hypertensive rats (SHRs), but its potential action mechanism remains unclear. In this work, the underlying mechanism of the antihypertensive effects of RVPSL in SHRs was elucidated using the widely targeted kidney metabolomics approach. RESULTS: Ten SHRs were divided into two groups: SHR‐Untreated group (0.9% saline) and SHR‐RVPSL group (50 mg kg−1 body weight RVPSL) for 4 weeks. After 4 weeks, kidney samples were collected and widely targeted (liquid chromatography–electrospray ionization–tandem mass spectrometry) metabolomics was used to detect metabolites. Fifty‐six biomarkers were identified that may be associated with hypertension. Among them, 17 biomarkers were upregulated and 39 biomarkers were downregulated. The results suggested that eight potential biomarkers were identified in kidney samples: O‐phospho‐l‐serine, tyramine, citric acid, 3‐hydroxybutyrate, O‐acetyl‐l‐serine, 15‐oxo‐5Z,8Z,11Z,13E‐eicosatetraenoic acid (15‐oxoETE), dopaquinone and 3,3′,5‐triiodo‐l‐thyronine. These potential biomarkers mainly involved carbon metabolism, thyroid hormone signaling pathway, tyrosine metabolism and arachidonic acid metabolism. CONCLUSION: The study suggested that RVPSL may exert antihypertensive effects through upregulation of O‐phospho‐l‐serine, 3‐hydroxybutyrate and 15‐oxoETE, and downregulation of tyramine, citric acid, O‐acetyl‐l‐serine, 3,3′,5‐triiodo‐l‐thyronine and dopaquinone. The antihypertensive effects of RVPSL may be related to carbon metabolism, thyroid hormone signaling pathway, tyrosine metabolism and arachidonic acid metabolism. RVPSL exhibited a potent antihypertensive effect, and the antihypertensive effects were associated with inhibition of vascular smooth muscle cell proliferation, vascular remodeling, vascular endothelium dysfunction, restoring reactive oxygen species, oxidative stress, inflammation and immune reaction. © 2022 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Traumatic brain injury (TBI) causes high rates of worldwide mortality and morbidity due to the complex secondary injury cascade. Recently, circular ribonucleic acids (circRNAs) have attracted significant attention in a variety of diseases. However, their expression characteristics in human TBI are still unclear. In this study, we examined brain injury tissues from six severe TBI patients in Jinling Hospital. The TBI tissues and adjacent brain contusion tissues were used to analyze differential expression signatures of circRNAs through full-length transcriptome sequencing, Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and ceRNA network construction. Our results found that there were 126 differently expressed circRNAs in TBI. Among them, 64 circRNAs were up-regulated and 62 circRNAs were down-regulated. Moreover, GO and KEGG analyses revealed that the aberrantly expressed circRNAs participated in many pathophysiological processes of TBI, especially regarding microglial cell activation, protein transport, protein processing and inflammation. Furthermore, the ceRNA (circRNA-miRNA-mRNA) network predicted that there existed strong relationship among circRNA, miRNA and mRNA. Taken together, our results indicated for the first time that the expression profiles of circRNAs were different after human TBI. In addition, we found the signaling pathways that were related to circRNAs and predicted a ceRNA network, which provided new insight of circRNAs in human TBI. [ABSTRACT FROM AUTHOR]

Background: The diagnosis of Parkinson's disease (PD) is complex and there are no biomarkers for early identification. Many studies have reported altered gut microbiota in patients with PD compared with healthy individuals. However, results from previous studies vary across countries. Aims: The aim of this study was to identify gut microbiota biomarkers that could be used as a marker for the diagnosis of PD. Methods: Firstly, the differential gut microbiota was obtained by metaanalysis, and then the results of meta-analysis were validated through metagenomic cohort. Finally, the ROC curve was drawn based on the metagenomic validation results. Results: The meta-analysis showed a lower relative abundance of Prevotellaceae (p < 0.00001) and Lachnospiraceae (p = 0.002), and a higher of Ruminococcaceae (p < 0.00001), Christensenellaceae (p = 0.03), Bifidobacteriaceae (p < 0.00001), and Verrucomicrobiaceae (p = 0.02) in patients with PD. Only Bifidobacteriaceae was also at high levels in the validation cohort of the metagenome. Meanwhile, three species from the Bifidobacteriaceae, including Scardovia_inopinata (p = 0.022), Bifidobacterium_dentium (p = 0.005), and Scardovia_wiggsiae (p = 0.024) were also high. The ROC curve showed that the three species (71.2%) from Bifidobacteriaceae had good predictive efficiency for PD. Conclusion: Elevated Bifidobacteriaceae may be associated with PD. Elevated three species from the Bifidobacteriaceae, including Scardovia_inopinata, Bifidobacterium_dentium and Scardovia_wiggsiae may provide new potential biomarkers for the diagnosis of PD. [ABSTRACT FROM AUTHOR]

Background: An ischemic stroke can be caused by thrombosis and ischemia, which is a major public health problem around the world, resulting in severe disability and a high death rate. The goal of this work is to examine and target various heat shock proteins (HSPs) via their interacting partners, which may have an anti-ischemic stroke impact. Methods: Various heat shock proteins are identified and used for construction of PPI network through STRING webserver. Networks are analysed and visualized using the cytoscape for checking the protein-protein interactions. Along with this, multiple cytoscape based modules are integrated for the analysis of results, and Gene Ontology results are analysed using GOView. Results: The core PPI network was revealed with 129 nodes and 1174 edges. Through Gene ontology (GO) and KEGG enrichment analysis the promising function of HSPs in two important signaling pathways were mainly recorded, representing the HSPs are necessary for repair and activations of brain cells during ischemic stroke. In addition, the study is revelation for targeting multiple HSPs via their interacting partners, which can provide anti-ischemic stroke effect. Conclusion: Overall, this finding provides a network-based framework for future research on HSP as therapeutic molecules for anti-ischemic stroke related applications.

Abstract Background Microbiome has been shown to substantially contribute to some cancers. However, the diagnostic implications of microbiome in head and neck squamous cell carcinoma (HNSCC) remain unknown. Methods To identify the molecular difference in the microbiome of oral and non-oral HNSCC, primary data was downloaded from the Kraken-TCGA dataset. The molecular differences in the microbiome of oral and non-oral HNSCC were identified using the linear discriminant analysis effect size method. Results In the study, the common microbiomes in oral and non-oral cancers were Fusobacterium, Leptotrichia, Selenomonas and Treponema and Clostridium and Pseudoalteromonas, respectively. We found unique microbial signatures that positively correlated with Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways in oral cancer and positively and negatively correlated KEGG pathways in non-oral cancer. In oral cancer, positively correlated genes were mostly found in prion diseases, Alzheimer disease, Parkinson disease, Salmonella infection, and Pathogenic Escherichia coli infection. In non-oral cancer, positively correlated genes showed Herpes simplex virus 1 infection and Spliceosome and negatively correlated genes showed results from PI3K-Akt signaling pathway, Focal adhesion, Regulation of actin cytoskeleton, ECM-receptor interaction and Dilated cardiomyopathy. Conclusions These results could help in understanding the underlying biological mechanisms of the microbiome of oral and non-oral HNSCC. Microbiome-based oncology diagnostic tool warrants further exploration.

Sweating pretreatment plays a crucial role in the post-harvest processing of Poria cocos. In current work, stress sweating method was proposed to better comprehend the potential health advantages of Poria cocos after pretreatment. An untargeted metabolomics-based approach (UHPLC-QTOF-MS/MS) combined with multivariate statistical analysis was utilized to identify bioactive metabolites and conduct metabolic pathway analysis. The experimental results showed the presence of 683 metabolites from 92 classes in Poria cocos. Detailed studies focused on 59, 104, and 51 metabolites from Poriae Cutis (PC) , Rubra Poria (RP), and White Poria (WP), respectively. The sinapyl alcohol, 7-methylguanine, and 13(S)-HpODE were the main metabolites found in three different parts of Poria cocos. The biosynthesis of phenylpropanoids was a key metabolic pathway. Additionally, stress sweating pretreatment contributed to the preservation of bioactive substances including folate, flavonoids, and furanocoumarins. The findings of this study provided valuable information about the alterations of metabolites in different parts of Poria cocos subjected to stress sweating pretreatment, which could be helpful for guiding the post-harvest processing of Poria cocos and development of Poria cocos pharmaceuticals, foods, and essential components. [Display omitted] • 683 metabolites were identified in Poria cocos to evaluate stress sweating. • 3 mutual differential metabolites were detected in different parts of Poria cocos. • Stress sweating pretreatment may help remain folate, flavonoids, and furanocoumarins. • Biosynthesis of phenylpropanoids is pivotal metabolic pathway. [ABSTRACT FROM AUTHOR]

During acute reperfusion, the expression profiles of long noncoding RNAs in adult rats with focal cerebral ischemia undergo broad changes. However, whether long noncoding RNAs are involved in neuroprotective effects following focal ischemic stroke in rats remains unclear. In this study, RNA isolation and library preparation was performed for long noncoding RNA sequencing, followed by determining the coding potential of identified long noncoding RNAs and target gene prediction. Differential expression analysis, long noncoding RNA functional enrichment analysis, and co-expression network analysis were performed comparing ischemic rats with and without ischemic postconditioning rats. Rats were subjected to ischemic postconditioning via the brief and repeated occlusion of the middle cerebral artery or femoral artery. Quantitative real-time reverse transcription-polymerase chain reaction was used to detect the expression levels of differentially expressed long noncoding RNAs after ischemic postconditioning in a rat model of ischemic stroke. The results showed that ischemic postconditioning greatly affected the expression profile of long noncoding RNAs and mRNAs in the brains of rats that underwent ischemic stroke. The predicted target genes of some of the identified long noncoding RNAs (cis targets) were related to the cellular response to ischemia and stress, cytokine signal transduction, inflammation, and apoptosis signal transduction pathways. In addition, 15 significantly differentially expressed long noncoding RNAs were identified in the brains of rats subjected to ischemic postconditioning. Nine candidate long noncoding RNAs that may be related to ischemic postconditioning were identified by a long noncoding RNA expression profile and long noncoding RNA-mRNA co-expression network analysis. Expression levels were verified by quantitative real-time reverse transcription-polymerase chain reaction. These results suggested that the identified long noncoding RNAs may be involved in the neuroprotective effects associated with ischemic postconditioning following ischemic stroke. The experimental animal procedures were approved by the Animal Experiment Ethics Committee of Kunming Medical University (approval No. KMMU2018018) in January 2018.

Stroke is a genetic condition comprising multiple subtypes and arising from both genic and other multi factors. Genetic basis of stroke is well established through several studies. Advances in integrating sequencing methods and Genome-wide association studies have shown that genetics of stroke is manifested in several genic disorders. Many of the neurodegenerative disorders show aggravated protein aggregation through amyloid formation. Through the protein aggregation prediction, we observed a higher protein disorder in 46 stroke-associated proteins. Also, we observed a large number of aggregation residues distributed as a pattern in multiple regions of these candidate proteins. Overall, we present a study showing that there is a possible interrelationship between protein aggregation and stroke.

Background: Atopic dermatitis (AD) is a common inflammatory skin disease that compromises the skin's barrier function and capacity to retain moisture. Cnidii Fructus (CF), the dried fruits of Cnidium monnieri, has long been used to treat atopic dermatitis (AD) in China. However, the anti-AD compounds and mechanisms of CF are not fully understood. In this study, we evaluated the active compounds and molecular targets of CF in treating AD. Methods: The Traditional Chinese Medicine Systems Pharmacology database was used to acquire information regarding the compounds that occur in the herb. Targets of these compounds were predicted using the SwissTargetPrediction website tool. AD-related genes were collected from the GeneCards database. Gene ontology (GO) enrichment analysis and KEGG pathway analysis of proteins that are targeted by active compounds of CF and encoded by AD-related genes were performed using Database for Annotation, Visualization, and Integrated Discovery Bioinformatics Resources. A "compound-target" network was constructed and analyzed using Cytoscape Software. Molecular docking was performed using BIOVIA Discovery Studio Visualizer and AutoDock Vina. Results: We identified 19 active compounds in CF, 532 potential targets for these compounds, and 1540 genes related to AD. Results of GO enrichment indicated that CF affects biological processes and molecular functions, such as inflammatory response and steroid hormone receptor activity, which may be associated with its anti-AD effects. KEGG pathway analyses showed that PI3K-Akt signaling, calcium signaling, Rap1 signaling, and cAMP signaling pathways are the main pathways involved in the anti-AD effects of CF. Molecular docking analyses revealed that the key active compounds in CF, such as (E)-2,3-bis(2-keto-7-methoxy-chromen-8-yl)acrolein, ar-curcumene, and diosmetin, can bind the main therapeutic targets AKT1, SRC, MAPK3, EGFR, CASP3, and PTGS2. Conclusions: Results of the present study establish a foundation for further investigation of the anti-AD compounds and mechanisms of CF and provide a basis for developing modern anti-AD agents based on compounds that occur in CF. [ABSTRACT FROM AUTHOR]

Background: Diethylstilbestrol (DES) has been shown to disrupt the morphology and proliferation of gubernaculum testis cells. Objective: This study aimed to elucidate the mechanism underlying DES induced damage to gubernaculum testis based on proteome analysis. Results: Neonatal mice were exposed to DES or control vehicle and the gonads were collected for isobaric tags for relative and absolute quantitation based proteomics analysis. We found that at the early and late stages of gubernaculum development, treatment with different concentrations of DES upregulated the expression of proteins which were generally involved in several function categories including single-organism process, cellular process, binding, catalytic activity, cell part and organelle. KEGG pathway analysis showed that cardiac muscle contraction, oxidative phosphorylation, calcium signaling and cGMP-PKG signaling pathway were the most dysregulated pathways associated with DES in the late stage of gubernaculum development, while steroid biosynthesis was the only enriched pathway in the early stage of gubernaculum development. Conclusion: Proteomics profiling and functional analysis reveal new clues on how DES interferes with cellular processes and disrupts the development of gubernaculum. These findings have potential applications in the treatment of genitourinary diseases such as cryptorchidism. [ABSTRACT FROM AUTHOR]

Protein-protein interactions (PPIs) are extremely important for gaining mechanistic insights into the functional organization of the proteome. The resolution of PPI functions can help in the identification of novel diagnostic and therapeutic targets with medical utility, thus facilitating the development of newmedications. However, the traditional methods for resolving PPI functions are mainly experimental methods, such as co-immunoprecipitation, pull-down assays, cross-linking, label transfer, and far-Western blot analysis, that are not only expensive but also timeconsuming. In this study, we constructed an integrated feature selection scheme for the large-scale selection of the relevant functions of PPIs by using the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotations of PPI participants. First, we encoded the proteins in each PPI with their gene ontologies andKEGG pathways. Then, the encoded protein featureswere refined as features of both positive and negative PPIs. Subsequently, Boruta was used for the initial filtering of features to obtain 5684 features. Three feature ranking algorithms, namely, least absolute shrinkage and selection operator, light gradient boosting machine, and max-relevance and min-redundancy, were applied to evaluate feature importance. Finally, the top-ranked features derived from multiple datasets were comprehensively evaluated, and the intersection of results mined by three feature ranking algorithms was taken to identify the features with high correlation with PPIs. Some functional terms were identified in our study, including cytokine-cytokine receptor interaction (hsa04060), intrinsic component of membrane (GO:0031224), and protein-binding biological process (GO:0005515). Our newly proposed integrated computational approach offers a novel perspective of the large-scale mining of biological functions linked to PPI. [ABSTRACT FROM AUTHOR]

Uveitis is a typical type of eye inflammation affecting the middle layer of eye (i.e., uvea layer) and can lead to blindness in middle-aged and young people. Therefore, a comprehensive study determining the disease susceptibility and the underlying mechanisms for uveitis initiation and progression is urgently needed for the development of effective treatments. In the present study, 108 uveitis-related genes are collected on the basis of literature mining, and 17,560 other human genes are collected from the Ensembl database, which are treated as non-uveitis genes. Uveitis- and non-uveitis-related genes are then encoded by gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment scores based on the genes and their neighbors in STRING, resulting in 20,681 GO term features and 297 KEGG pathway features. Subsequently, we identify functions and biological processes that can distinguish uveitis-related genes from other human genes by using an integrated feature selection method, which incorporate feature filtering method (Boruta) and four feature importance assessment methods (i.e., LASSO, LightGBM, MCFS, and mRMR). Some essential GO terms and KEGG pathways related to uveitis, such as GO:0001841 (neural tube formation), has04612 (antigen processing and presentation in human beings), and GO:0043379 (memory T cell differentiation), are identified. The plausibility of the association of mined functional features with uveitis is verified on the basis of the literature. Overall, several advanced machine learning methods are used in the current study to uncover specific functions of uveitis and provide a theoretical foundation for the clinical treatment of uveitis. [ABSTRACT FROM AUTHOR]

Oil spills pose a significant threat to marine biodiversity. Crude oil can partition into sediments where it may be persistent, placing benthic species such as decapods at particular risk of exposure. Transcriptomic and histological tools are often used to investigate the effects of hydrocarbon exposure on marine organisms following oil spill events, allowing for the identification of metabolic pathways impacted by oil exposure. However, there is limited information available for decapod crustaceans, many of which carry significant economic value. In the present study, we assess the sublethal impacts of crude oil exposure in the commercially important Australian greentail prawn (Metapenaeus bennettae) using transcriptomic and histological analyses. Prawns exposed to light, unweathered crude oil "spiked" sediments for 90 h were transferred to clean sediments for a further 72 h to assess recovery. Chemical analyses indicated that polycyclic aromatic hydrocarbons increased by approximately 65% and 91% in prawn muscle following 24 and 90 h of exposure, respectively, and significantly decreased during 24‐ and 72‐h recovery periods. Transcriptomic responses followed an exposure and recovery pattern with innate immunity and nutrient metabolism transcripts significantly lowered in abundance after 24 h of exposure and were higher in abundance after 72 h of recovery. In addition, transcription/translation, cellular responses, and DNA repair pathways were significantly impacted after 24 h of exposure and recovered after 72 h of recovery. However, histological alterations such as tubule atrophy indicated an increase in severity after 24 and 72 h of recovery. The present study provides new insights into the sublethal impacts of crude oil exposure in greentail prawns and identifies molecular pathways altered by exposure. We expect these findings to inform future management associated with oil extraction activity and spills. Environ Toxicol Chem 2022;41:2162–2180. © 2022 John Wiley & Sons Ltd. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA. [ABSTRACT FROM AUTHOR]

K. pneumoniae is considered to be the most clinically relevant enterobacteriaceae, including liver abscess. As a physical therapy, HBOT has provided many advantages for patients with infectious diseases either alone or as adjuvant therapy. However, there were still some questions about K. pneumoniae. The purpose of this study was to analyze the effects of HBO on the growth, virulence factors and transcriptome levels of K. pneumoniae. Results showed that HBO treatment may inhibit the growth of K. pneumoniae, inhibit the motility of K. pneumoniae, reduce protease activity and extracellular polysaccharide level, and inhibit biofilm formation. Transcriptional analysis indicated that there are many differentially expressed genes under HBO treatment, which may be related to the change of growth environment. These differentially expressed genes were involved in biological processes such as movement, response to chemicals, response to external stimuli, localization, chemotaxis, oxidation-reduction process, etc. Moreover, these differentially expressed genes might be involved in regulating or affecting the activities of ATPase, pyrophosphatase, hydrolase and nucleoside triphosphatase. Those involved in cellular processes and signaling, metabolism, and gene regulation were enriched in HBO treatment group. Hence, we concluded that the HBO treatment may greatly alter the intestinal microbial growth and physiological metabolism, and may further affect the expression lelves of differentially genes. These findings had suggested that HBO treatment may induce abnormal internal physiological metabolism of K. pneumoniae by regulating these differential genes to participate in relevant metabolic pathways. The results will be helpful to study the mechanism of hyperbaric oxygen inhibition of K. pneumoniae. [ABSTRACT FROM AUTHOR]

Salinization is very detrimental to photosynthetic processes and plant growth, while nanoparticles (NPs) are considered to be the emerging materials to improve plant adaptability to salt stress. Cyclocarya paliurus is being planted on saline-alkali soils to meet the growing demand for its leaves and medicinal products. However, this species exhibits low salt tolerance and little information is available on whether NPs application would mitigate the salt-induced effects. This study explored the influence of three oxide NPs and their application doses on improving salt tolerance in C. paliurus under simulated natural conditions. The results showed that these oxide NPs could modify the salt tolerance in C. paliurus seedlings, but the alleviating effects varied in the NPs types and their application doses. Under the salt stress, foliar applications of SiO 2 -NPs with 500 mg L -1 and MnO 2 -NPs with 50 mg L -1 significantly increased net photosynthetic rate and seedling height by 52.0-59.5 %, and reduced the salt injury index by 67.6-70.7 %. Transcriptomic analysis revealed that the genes related to photosynthesis pathway were well responsive to both salt stress and NPs application, while the applications of high-dose SiO 2 - and MnO 2 -NPs up-regulated the expression of 50 photosynthesis-related genes. Weighted gene co-expression network analysis (WGCNA) indicated there existed a close relationship between physiological parameters and gene expression patterns, and the nine key genes in mitigating salt stress in C. paliurus were identified after the NPs application. Our findings suggested that the effects of NPs on mitigating salt-induced damages depending on the NP type and applied dose. The applications of SiO 2 -NPs and MnO 2 -NPs with an appropriate dose hold great promise for mitigating the salt-induced photosynthetic dysfunction via regulation of related key genes, and ultimately promoting plant growth and ameliorating the salt-tolerance., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Reducing chemical fertilizers and biochar amendment is essential for achieving carbon neutrality, addressing global warming, and promoting sustainable agricultural development. Biochar amendment, a carbon rich soil additive produced through biomass pyrolysis, enhances soil fertility, increases crop yield, and improves soil carbon storage. However, research on the combined effect of fertilizer reduction and biochar amendment on soil mineral associated organic carbon (MAOC) in jasmine gardens is limited. This study aims to determine if biochar can reduce industrial fertilizer usage without compromising soil quality. This study focuses on jasmine cultivation in southeastern China, employing four treatments: conventional fertilization (CK), biochar amendment without fertilizer (BA), fertilizer reduction (FR), and fertilizer reduction with biochar amendment (FRBA). The effects on MAOC, microbial abundance, and enzyme activity were investigated. The FRBA treatment significantly increased MAOC content by 19.98 % compared to CK (P < 0.05). The BA and FRBA treatments enhanced the diversity of soil bacteria, including Lactobacillus, Azospirillum, and Cutibacterium, which are associated with soil organic carbon sequestration and nutrient decomposition. The RandomForest model identified β-N-acetyl-glucosaminidase (NAG), electric conductivity (EC), β-1, 4-Glucosidase (BG), soil potential of Hydrogen (pH), soil bulk density (BD), and β-D-cellobiosidase (CBH) as key soil traits promoting MAOC accumulation (P < 0.05). The results indicate that BA and FRBA improve soil bacterial community structure, enzyme activity, and MAOC content, promoting soil carbon accumulation through environmental factors and dominant bacteria. This study encourages future fertilization protocols that enhance fertilizer efficiency and carbon storage in crop soils., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)