DNA sensors play crucial roles in inflammation and have been indicated to be involved in antitumor or tumorigenesis, while it is still unclear whether DNA sensors have potential roles in the prognosis and immunotherapy of hepatocellular carcinoma (HCC). Herein, The Cancer Genome Atlas and Gene Expression Omnibus databases were used to analyze RNA sequencing data and clinical information. A total of 14 DNA sensors were collected and performed consensus clustering to determine their molecular mechanisms in HCC. Two distinct molecular subtypes (Clusters C1 and C2) were identified and were associated with different overall survival (OS). Immune subtype analysis revealed that C1 was mainly characterized by inflammation, while C2 was characterized by lymphocyte depletion. Immune scoring and immunomodulatory function analysis confirmed the different immune microenvironment of C1 and C2. Notably, significant differences in “Hot Tumor” Immunophenotype were observed between the two subtypes. Moreover, the prognostic model based on DNA sensors is capable of effectively predicting the OS of HCC patients. Besides, the chemotherapeutic drug analysis showed the different sensitivity of two subtypes. Taken together, our study shows that the proposed DNA sensors were a reliable signature to predict the prognosis and immunotherapy response with potential application in the clinical decision and treatment of HCC. Primary liver cancer is the sixth most common cancer and the third leading cause of cancer-related death worldwide . PatholoDNA defective mismatch repair (dMMR) and microsatellite instability (MSI) that are driven by gene mutations can induce DNA damage response (DDR) in tumor cells . Pan-canTLR9, ZBP1, AIM2, IFI16, PRKDC, DHX9, DHX36, DDX41, DDX60, cGAS, MRE11, HNRNPA2B1, LRRFIP1 and POLR3A, as DNA sensors [In the present study, we selected 14 genes, including  sensors –16, and Expression of the selected DNA sensors in HCC and normal tissue samples derived from the TCGA-LIHC, HCCDB18 and GSE76427 datasets were comparatively analyzed with ssGSEA. Higher scores were found in HCC tissue samples, and those in the HCCDB18 and GSE76427 datasets were statistically different between the two types of samples . DiffereThe clinical features of the two subtypes (C1 and C2) were comparatively analyzed in the TCGA-LIHC dataset. Significant difference was found in Stage between the C1 and C2 subtypes, whereas the T stage and Grade marginally varied . There aThe pathway analysis of the C1 and C2 subtypes was performed using GSEA in the TCGA-LIHC, HCCDB18 and GSE76427 datasets. Pathways, including chemokine signaling pathway, B cell receptor signaling pathway, T cell receptor signaling pathway, and natural killer cell mediated cytotoxicity, were significantly enriched in C1 subtype . In addiFive groups of genes with immunomodulatory functions, including chemokine, chemokine receptor, immunostimulator, immunoinhibitor and MHC, were confirmed from the literature . ExpressThe “Hot” tumor feature of C1 and C2 subtypes was analyzed with IFN-γ, Merck 18, CD8, PD-L1, T-cell dysfunction score, T-cell exclusion score, MDSC score, CAF score, and TAM.M2 score using the TIDE tool in the TCGA-LIHC datasets. In comparison to the C2 subtype, the C1 subtype had significantly higher levels of IFN-γ, Merck 18, CD8, PD-L1, T-cell dysfunction score, and CAF score –6F. In cTCGA samples were assigned into the training or test set with the method of random sampling (7:3). Univariate analysis in the training set resulted in 69 prognosis-related genes (p < 0.05), while LASSO model was used to obtain 14 genes with the lambda that yields the minimum mean squared error of 0.0423 . UnivariLinkage between the molecular subtypes of HCC and the sensitivity to chemotherapeutic agents was analyzed in the TCGA-LIHC, HCCDB18 and GSE76427 datasets. Consistent in the three datasets, the C1 subtype was more sensitive to Sunitinib, Paclitaxel, TAE684, Crizotinib, S-Trityl-L-cysteine, CGP-60474, BMS-509744 and CP466722, whereas the C2 subtype was more sensitive to Rapamycin, Pyrimethamine, Vinorelbine and AKT inhibitor VIII –8C. UsinIFI16, PRKDC, DHX9, DDX41, cGAS, HNRNPA2B1, DHX36 and DDX60, was examined by RT-qPCR. The result of comet assay indicated that both Olaparib and YU238259 induced DNA damage in HCC cells after 24 hours of treatment , and the degree of DNA damage was determined via the comet assay. The mRNA expression of 8 DNA sensors, including reatment . In cellpression –9I.HCC is a global threat to human health, and its incidence has continued to increase . UnfortuFor the past few years, techniques of conjoint analysis based on genomics , proteomThe combination of ICIs and other therapies has been applied in clinical tumor treatment with satisfactory results, whereas the response rate is still limited . ConsistAccording to the response to immunotherapy, solid tumors can be classified into two categories: “Hot (immune-inflamed)” and “Cold (immune-desert/immune-excluded)” . For “HoPRKACB, RUVBL1, HLA-DPB1, BCL11B, SCL1A5, ADA, ITM2A, to analyze the clinical value of DNA sensors. The model performed well in predicting the 1-, 3- and 5-year OS in HCC patients.Besides, our study constructed a prognostic model based on 7 DNA sensors-related genes, including The current study still has the following limitations that need to be addressed. First, this study only examined the expression of DNA sensors upon DNA damage using RT-qPCR, whereas the underlying molecular mechanism requires further experimental validation. Second, the expression of DNA sensors and their prognostic significance in HCC patients have not been validated in clinical settings.Taken together, this study identified two molecular subtypes of HCC (C1 and C2) based on the DNA sensors, which varied significantly in terms of the immune features, including the inflamed characteristics, immune microenvironment scores, and “Hot” tumor microenvironment property. The findings of the study provide evidence for the potential of the combination of DNA sensors targeting therapy with ICIs immunotherapy as a therapeutic strategy for HCC. In addition, the DNA sensors were also proven with favorable performance in predicting the survival outcomes of HCC patients. In all, this study provides new insights into the markers available for predicting prognosis and immunotherapy response for HCC.https://xenabrowser.net/) and then converted to TPM format. GSE76427 dataset was derived from the GEO database (https://www.ncbi.nlm.nih.gov/geo/). HCCDB18 dataset was obtained from the HCCDB website (http://lifeome.net/database/hccdb/download.html). All the three datasets contained clinical data of HCC patients.RNA-seq data (FPKM format) were downloaded from the TCGA-LIHC dataset with the UCSC Xena browser  of the genes within a gene set and the remaining genes in a single sample, and then provides a normalized value based on the difference between the minimal and maximal expression values. Here, the enrichment scores of the DNA sensors in the TCGA-LIHC, GSE76427 and HCCDB18 datasets were calculated. Pearson correlation was conducted to obtain protein-coding genes associated with the DNA sensors in the TCGA-LIHC, GSE76427 and HCCDB18 datasets, with the threshold set as |Pearson’s R| > 0.5 and p < 0.05. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted to explore potential molecular processes and biological pathways related to DNA sensors in HCC. These analyses were performed using the R package “WebGestaltR”. The p < 0.05 were considered statistically significant.https://biit.cs.ut.ee/methsurv/). It is a web tool to provide survival analysis based on DNA methylation biomarkers using TCGA data. The methylation and prognostic value of DNA sensor genes in LIHC were analyzed by MethSurv online tool.MethSurv online tool was used . Consensus clustering analysis was then performed based on the candidates using the package “ConsensusClusterPlus”, and the optimal number of clusters was determined via CDF. The Kaplan–Meier (K–M) survival curves of overall survival (OS) were used to evaluate the clinical prognostic value of DNA sensors-related genes.Clinical features of different molecular subtypes were comparatively analyzed from T stage, Stage and Grade. There are 6 immune subtypes according to the literature, including C1: wound healing, C2: IFN-γ dominant, C3: inflammatory, C4: lymphocyte depleted, C5: immunologically quiet, and C6: TGF-β dominant . The difThe KEGG gene set c2.cp.kegg.v7.0.symbols.gmt containing 186 signaling pathways was selected, and the significant enriched pathways in different molecular subtypes were revealed by GSEA with the threshold set as p < 0.05 and FDR < 0.25. Immune scores in different molecular subtypes were calculated by EPIC, MCPcounter, TIMER, ESTIMATE and ssGSEA, and the results were shown as a Heatmap. Five groups of genes with immunomodulatory functions, including chemokine, chemokine receptor, immunostimulator, immunoinhibitor, and MHC, were obtained from the literature [http://tide.dfci.harvard.edu/).The “Hot tumor” features in different molecular subtypes were comparatively analyzed according to the levels of T-cell dysfunction score, T-cell exclusion score, IFN-γ, Merck18, CD8, MDSC, CAF and TAM.M2 using the TIDE tool . Candidate signature genes were obtained via LASSO regression and multivariate COX analysis in succession, and the corresponding risk coefficients were acquired. Each sample in the TCGA-LIHC training and test sets, GSE76427 dataset and HCCDB18 dataset were assigned a risk score and divided into the high- or low-risk group. Kaplan-Meier and ROC curves were generated to analyze the survival difference between the two groups.R package “pRRophetic” was used to predict the half maximal inhibitory concentration (IC50) of chemotherapeutic agents in different molecular subtypes in the TCGA-LIHC, HCCDB18 and GSE76427 datasets.https://clue.io/). The small molecule compounds were predicted based on the enrichment value and p-value.To predict the small molecule compounds of HCC, the differentially expressed genes (DEGs) between subtypes C1 and C2 were initially identified using the limma R package with | logFC| > 1.5 and adjusted p < 0.05. Subsequently, these DEGs were uploaded to the Connective Map (CMap) database  medium containing 10% fetal bovine serum at 37° C with 5% CO2. The cells were intervened by Olaparib or YU238259 (inhibitor for HDR) for 24 h and were then harvested for DNA extraction. DNA damage analysis was conducted according to the procedure described by Comet Assay Kit . All samples were stained with SYBR Green I for scoring. The percentage of DNA in tail was scored using CASP software in 90 random nuclei per sample. All experiments were carried out in three replicates.IFI16, PRKDC, DHX9, DDX41, cGAS, HNRNPA2B1, DHX36 and DDX60, were examined, and β-actin was selected as the internal reference gene. All experiments were carried out in three replicates. Sequences of primers were detailed in Total RNA extraction was completed using the RNeasy Mini Kit , and cDNA synthesis was fulfilled with the PrimeScript™ RT Master Mix . Primers were designed and synthesized by Takara. RT-qPCR was ran using the TB Green® Premix Ex Taq™ II  with the LightCycler 480 System . The results were shown as 2-ΔΔCt. The mRNA expression of 8 DNA sensors, including All statistical analysis was conducted using R software (version 4.03) and SPSS20.0 . The experimental data were represented by mean ± standard deviation (SD) from at least three independent experiments. Comparison between two groups was performed using Student t-test, whereas comparison among over two groups was conducted with one-way ANOVA. Kaplan-Meier together with log-rank test was used to generate survival curves. Two-sided p < 0.05 was considered statistically significant.Supplementary FiguresSupplementary Table 1Supplementary Table 2


With both PCR and SUC, a consensus of two or more elevated biomarkers correlated well with microbial densities > 10,000 cells/mL or CFU/mL, respectively. The association between ≥10,000 cells and CFU per mL with elevated biomarkers in symptomatic patients suggests that this lower threshold may be more suitable than 100,000 CFU/mL for diagnosing UTIs.The literature lacks consensus on the minimum microbial density required for diagnosing urinary tract infections (UTIs). This study categorized the microbial densities of urine specimens from symptomatic UTI patients aged ≥ 60 years and correlated them with detected levels of the immune response biomarkers neutrophil gelatinase-associated lipocalin (NGAL), interleukin-8 (IL-8), and interleukin-1-beta (IL-1β). The objective was to identify the microbial densities associated with significant elevation of these biomarkers in order to determine an optimal threshold for diagnosing symptomatic UTIs. Biobanked midstream voided urine samples were analyzed for microbial identification and quantification using standard urine culture (SUC) and multiplex-polymerase chain reaction (M-PCR) testing, while NGAL, IL-8, and IL-1β levels were measured via enzyme-linked immunosorbent assay (ELISA). NGAL, IL-8, and IL-1β levels were all significantly elevated at microbial densities ≥ 10,000 cells/mL when measured via M-PCR ( In published guidelines, there is a pressing need for greater consensus regarding the minimum microbial threshold for diagnosing a urinary tract infection (UTI) ,6,7,8,9.Due to the limitations of standard urine culture (SUC), which make it a flawed gold standard test for diagnosis, it is important to identify cases that have a very high likelihood of being true UTIs in any study evaluating diagnostic tests and thresholds ,16. ThisThe uroepithelium and resident innate immune cells in the bladder quickly protect against microbial threats by first identifying microbial patterns and triggering an immune response that produces antimicrobial peptides and pro-inflammatory cytokines ,18,19,202 CFU/mL , IL-8 [range 7.5–2000 pg/mL], and IL-1β [range 3.9–250 pg/mL] in the urine study specimens per the manufacturer’s instructions. An Infinite M Nano+ microplate reader  measured absorbance at 450 nm and 540 nm, respectively. Frozen supernatants were thawed at room temperature before assaying.® UTI, Pathnostics, Irvine, CA, USA) were used for susceptibility testing for 19 antibiotics, semi-quantitation of 27 pathogens, 3 bacterial groups, the ESBL phenotype, and the identification of 32 antibiotic-resistance genes. This test is intended to be used for the diagnosis of complicated, persistent, or recurrent UTIs and for UTIs in elevated-risk patients. The assay was performed as previously described ; see We examined urine samples from a total of 583 individuals with a mean age of 76.6 years ). Of these, 68.3%  and SUC  for NGAL, IL-8, and IL-1β, respectively. The ≥100,000 cells/mL category also displayed a significantly higher median level of each biomarker according to both M-PCR  and SUC . There was no significant difference in the median levels between specimens with no microbes detected and specimens with microbes detected at <10,000 cells/mL for M-PCR  or CFU/mL for SUC  for NGAL, IL-8, or IL-1β, respectively.In both the M-PCR and SUC methodologies, there was a consistent rise in the median levels of the three biomarkers as the microbial density increased. Compared to the group with no microbes detected, biomarkers showed significantly higher median levels, starting in the 10,000 to 99,999 cells/mL category for M-PCR (n = 193) compared to M-PCR specimens (n = 117).As observed in p = 0.002), IL-8 (p = 0.009), and IL-1β (p = 0.001) when microbial detection was assessed via SUC compared to M-PCR. (n = 364) compared to SUC (n = 263). Several specimens from the “no microorganisms detected” SUC group had high biomarker levels and high microbial densities when tested via M-PCR. Out of the 193 samples that were negative based on SUC, 60 (31%) showed high levels of both microbes, ≥10,000 from M-PCR and NGAL (above threshold); 70 (36.2%) had elevated microbial levels, ≥10,000 from M-PCR, along with high levels of IL-8 (above threshold); and 47 (44.4%) had microbial levels ≥ 10,000 by M-PCR combined with biomarker levels that surpassed the threshold for IL-1β (above threshold).Median biomarker levels in the “no microorganisms detected” group were significantly higher for NGAL (o M-PCR. . There wn = 193), had biomarker consensus-positive results in 46% of cases, which was much higher than M-PCR negative cases (n = 117) with 29% being consensus-positive.The assessment of each specimen for biomarker consensus (defined as two or more biomarkers above the established cutoffs) was conducted and organized based on microbial density and detection method . SpecimeIn this study, we employed the current standard-of-care SUC method and the culture-independent M-PCR assay to identify and quantify microbes from patients with presumptive UTIs. Previous studies have demonstrated a 1:1 correlation for microbial quantitation in the linear range between SUC and M-PCR, allowing for direct comparisons between culture-dependent and culture-independent methods across various microbial densities . AdditioTraditionally, a microbial density of ≥100,000 cells/mL has been deemed diagnostically significant; however, recent clinical reviews and guidelines have proposed lower thresholds that are still clinically relevant ,3,4,5,6.Using a threshold of >100,000 cells/mL as a criterion for initiating antimicrobial therapy carries significant clinical implications. One consequence is the potential for undertreatment of certain UTI patients, allowing microbes to proliferate, infiltrate host cells, develop biofilms, or acquire antibiotic resistance before treatment is administered ,37. DelaE. coli but is less effective for non-E. coli microorganisms and fastidious microbes, which are increasingly recognized as common uropathogens [Previous studies consistently show that M-PCR outperforms SUC in identifying and quantifying more causative uropathogens ,39,40. Tathogens ,43,44,45athogens ,39,40.n = 193) in the SUC group compared to the M-PCR group (n = 117). Interestingly, the specimens with no microorganisms detected via SUC exhibited significantly higher levels of NGAL, IL-8, and IL-1β than those with no microbes detected via M-PCR. Furthermore, SUC specimens in which no organisms were detected demonstrated significantly higher (46%) consensus scores (two or more biomarkers above the established thresholds) when compared to M-PCR negative cases (29%).Building on these findings, our study also observed more specimens in which no microorganisms were detected  and determining the accuracy of a negative diagnostic test result. The inclusion of multiple biomarkers and assays enhances the robustness of the findings and strengthens the overall conclusions.However, there are some limitations to consider. One weakness of the study is that we were unable to measure the biomarker levels based on specific symptoms experienced by the patients. Understanding how the biomarkers correlate with different symptoms could provide valuable insights into the diagnostic and prognostic capabilities of the assays. Additionally, the number of cases in our study was not sufficient to measure biomarker levels across densities for specific microbial species. Further investigations with a larger sample size would enable a more comprehensive analysis of the relationship between microbial densities, biomarker levels, and specific microbial species.Future studies could build upon these findings by assessing the biomarker levels across different species or combinations of species, allowing for a more targeted approach to diagnosing and managing UTIs. Additionally, exploring the association between biomarker levels and specific symptoms of UTIs would provide valuable clinical information for personalized treatment strategies. These potential avenues of research can further enhance our understanding of urinary tract infections and contribute to improved diagnostic accuracy and patient care.In conclusion, our study shows that symptomatic subjects with UTIs exhibit a significant immune response at a microbial density threshold of ≥10,000 cells/mL, regardless of the detection method used. This suggests that a lower diagnostic microbial density threshold is clinically appropriate for UTI diagnosis and management, applicable to both the microbial identification and quantitation techniques used here. Additionally, our study emphasizes the need for closer monitoring when SUC yields a negative result, given the possibility of false negatives and their clinical implications.


Pediococcus, Pantoea, and Weissella were the dominant genera. Saccharomyces and Rhizopus were dominant among the 30 fungal genera. Correlation analysis showed that 102 microorganisms were involved in major flavor substance production during primary fermentation, Lactobacillus, Photobacterium, Hyphodontia, Aquicella, Erysipelothrix, Idiomarina, Paraphaeosphaeria, and Sulfuritalea were most associated with flavoring substances. Four bacteria, Lactobacillus (R1), Photobacterium (R2), Idiomarina (R3), and Pediococcus (R4), were isolated and identified from wheat Qu, which were added to wine Qu to prepare four kinds of fortified Qu . QR1 and QR2 fermentation can enhance the quality of Huangjiu. This work reveals the correlation between microorganisms and volatile flavor compounds and is beneficial for regulating the micro-ecosystem and flavor of the broomcorn millet Huangjiu.Broomcorn millet Huangjiu brewing is usually divided into primary fermentation and post-fermentation. Microbial succession is the major factor influencing the development of the typical Huangjiu flavor. Here, we report the changes in flavor substances and microbial community during the primary fermentation of broomcorn millet Huangjiu. Results indicated that a total of 161 volatile flavor compounds were measured during primary fermentation, and estragole was detected for the first time in broomcorn millet Huangjiu. A total of 82 bacteria genera were identified.  Panicum miliaceum), native to Northern China, is one of the earliest cultivated grains and the most important food crop in the Yellow River [Huangjiu is a traditional fermented food in China with a long history of brewing and rich nutrition. Along with beer and wine, these are the three main ancient alcohols worldwide. Given its pleasant flavor, ease of use, and rich nutrition, Huangjiu is popular among consumers. The raw material in Southern Huangjiu is rice, whereas Northern Huangjiu is brewed from broomcorn millet. Broomcorn millet  correlation network.Broomcorn millet was collected from Zhangjiakou . A Gel Extraction Kit was purchased from Qiagen . The TruSeq DNA PCR-Free Sample Preparation Kit was obtained from Illumina . All other reagents were purchased from Sigma-Aldrich .Broomcorn millet was collected from Zhangjiakou, Hebei Province, China. The primary fermentation was carried out in the winery’s experimental fermentation tank at 30 °C for 6 days. Broomcorn millet was washed and cooked for 30 min before fermentation. Wheat Qu was added according to conventional winery standards. Presaccharification was performed before fermentation using the saccharifying enzyme. Fermented mash (approximately 300 g) was sampled on days 1, 2, 3, 4, 5, and 6 of fermentation. Three parallel samples were prepared. The collected samples were sealed in sterile plastic bags and stored at −20 °C before further analysis.The reduced sugar content was detected using the dinitrosalicylic acid method (DNS) with glucose as the standard substance . AccordiThe volatile flavor compounds were measured using HS-SPME-GC-MS. Each Huangjiu sample (8 mL) was mixed with NaCl (2.5 g NaCl and 5 mL 65.76 mg/L 2-octanol) in a 15 mL SPME glass vial. For extraction, the vials were incubated in a water bath and ultrasonicated for 45 min at 50 °C and then extracted using SPME fibers coated with 50/30 μm DVB/CAR/PDMS . The flavor compounds in the samples were analyzed by QP2010 Plus-GC MS . High-purity helium was used as the carrier gas at a constant flow rate of 1.0 mL/min, and the split ratio was set to 50/1. The temperature program was set to 35 °C for 4 min, increased to 150 °C at 5 °C/min, held at 150 °C for 2 min, then followed by a second increase to 210 °C at 3 °C/min. The temperatures of the detector and injector were both 230 °C, and that of the ion source was 200 °C. The ion energy for the electron impact (EI) ionization source at a fragment voltage of 70 eV with a scanning range of m/z 30–350. The relative content of each volatile compound was counted according to the area normalization method [Total genomic DNA from fermented mash samples was extracted using the cetyl trimethylammonium bromide (CTAB) method, which was further adjusted to a uniform final concentration of 1 ng/μL using sterile water .Hypervariable regions of V3-V4 on the 16SrRNA gene of bacteria were amplified by PCR with specific primers 338F (5′-ACTCCTACGGGAGGCAGCAG-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′). For fungal analysis, the internal transcribed spacer (ITS) regions of the rRNA were amplified using ITS1 (5′-Axxx CTTGGTCATTTAGAGGAAGTAA-3′) and ITS2 (5′-BGCTGCGTTCTTCATCGATGC-3′). All PCR reactions were performed using the Phusion High-Fidelity PCR Master Mix . The PCR products were detected on agarose gels and further purified using a Gel Extraction Kit . A sequencing library was constructed using the TruSeq DNA PCR-Free Sample Preparation Kit  following the manufacturer’s recommendations. Library quality was assessed using the Agilent Bioanalyzer 2100 system . Finally, the library was sequenced using an Illumina HiSeq2500 platform.Paired-end reads were assigned to samples based on their unique barcodes and truncated by cutting off the primer sequences and barcodes. Paired-end sequencing reads were merged using FLASH (V1.2.7) . AccordiSequences were analyzed using the Uparse software (Uparse v7.0.1001) . Sequencp-value was adjusted by FDR based on the Benjamini–Hochberg method. Statistical significance was set at p < 0.05.Correlations between microbial communities and volatile flavor compounds were established using the Pearson correlation coefficient (r) during broomcorn millet Huangjiu fermentation. The R programming language was used to create the correlation matrix. The Lactobacillus), R2 (Photobacterium), and R3 (Idiomarina), respectively, which were associated with flavoring substances, and a dominant bacterial strain (R4) belongs to the Pediococcus. After that, 1 kg of broomcorn millet was cooked with 3 kg of water. After adding 0.15% saccharifying enzyme at 60 °C for 30 min, R1, R2, R3, and R4 were incubated at 37 °C for 48 h, during which they were stirred several times. The fortified Qu was prepared as follows. The wheat was crushed, 20~30% water and 1% R1, R2, R3, and R4 strain expansion solution was added, then stirred, stepped, and stacked, and fermented naturally at 45–50 °C for 30 days to prepare fortified Qu QR1, QR2, QR3, and QR4.Wheat Qu was produced by natural fermentation for 30 days. Three bacteria from isolated wheat Qu were identified as R1 .The content of biogenic amines was detected using HPLC  according to GB 5009.208-2016. The volatile flavor substances in Huangjiu were quantified as described before . The amiEach experiment was carried out three times. All results are expressed as mean values ± standard deviation. SPSS 22.0, Origin 8.0, and Microsoft Office Excel 2019 were used to process the microbial and GC-MS data.Physicochemical parameters, including acid, reducing sugar, and alcohol, were measured during broomcorn millet Huangjiu’s primary fermentation. As shown in A total of 161 volatile flavor compounds, including 43 alkanes, 33 alcohols, 31 esters, 22 ketones, 15 aldehydes, 7 acids, 3 phenols, and 7 other kinds of volatile compounds of fermented broomcorn millet Huangjiu samples, were measured during primary fermentation see . At the Additionally, the acids included butyric, propionic, acetic, and caproic acids, which greatly influence flavor and Huangjiu aging . Seven aFirmicutes dominated at the phylum level, followed by Proteobacteria, with the opposite trend to Firmicutes. Pediococcus, Pantoea, and Weissella were the dominant genera, with over 3/5 peaking on the 5th day. Both Firmicutes and Pediococcus increased gradually in the first 4 days of fermentation, with a decrease on the 5th day and reaching the highest level on the 6th day. Proteobacteria, Pantoea, and Klebsiella showed a decreasing trend and then increased on the 5th day.The bacterial communities affect fermentation efficiency and play a decisive role in the flavor quality of Huangjiu. The bacterial community diversity increases during the initial fermentation stage . The comBacteroides established the most positive correlation with some genera, such as Myroides, Arcobacter, Acinetobacter, Fusobacterium, and Providencia. Bacillus spp. were rarely correlated with the other genera. Among LAB, Lactococcus was only positively correlated with Acinetobacter and Ameromonas. Pediococcus and Weissella were both positively associated with Pantoea and Klebsiella and negatively correlated with Enterobacter , and Candida established the most associations by network analysis.A total of 159,909 sequences were subjected to quality control filtering, and 143 OTUs were obtained after clustering. A total of 5 phyla, 11 classes, 20 orders, 29 families, and 30 genera were identified in this study. Ascomycota and Zygomycota were the dominant phyla. At the genus level, the fungal community was mainly dominated by Ascomycota and Zygomycota. Except for on the 2nd day, the dominant phylum was Ascomycota during the fermentation process . The results showed that a total of 1217 associations were established, involving 102 microorganisms and 101 flavoring substances -1-hexanol, the most complex flavoring substance in terms of association, was associated with 54 microorganisms. It was also shown that over 30 associations were established including nonanoic acid ethyl ester, 1-hydroxy-2-propanone, 2,4-dimethyl-1-heptanol, 5-(hydroxymethyl)-2-furancarboxaldehyde, isoamyl lactate, oxalic acid 2-ethylhexyl hexyl ester, oxalic acid isohexyl neopentyl ester, 2(5H)-furanone, 2-phenylethyl acetic acid ester, 3-methyl-1-butanol, 3-methylthio-1-Propanol, decanoic acid ethyl ester, and estragole.To reveal the role of microbes in the formation of volatile flavor compounds during broomcorn millet Huangjiu fermentation, we examined the correlation between bacterial and fungal communities and volatile flavor compounds (bstances . Among tLactobacillus (R1), Photobacterium (R2), Idiomarina (R3), and Pediococcus (R4), were isolated and identified from wheat Qu, which were added to wine Qu to prepare four kinds of fortified Qu. The biogenic amine level was determined by HPLC. As shown in p < 0.05). This indicates that adding R1 and R2 may significantly reduce the amount of biogenic amine in Huangjiu. This may be because the new bacteria R1 and R2 could effectively inhibit the growth and reproduction of amine-producing miscellaneous bacteria, resulting in further reduction of biogenic amines and promotion of the quality of Huangjiu.Four bacteria, p < 0.05). The total flavor content of QR2 also increased by 18.0%, among which alkanes, alcohols, esters, acids, and other flavor substances were observed to be higher than the control group (p < 0.05)  . TherefoThe physicochemical index content of fermented Huangjiu is shown in Pediococcus, Pantoea, Weissella, Rhizopus, Saccharomyces, and Lichtheimia were the principal microorganisms produced during the fermentation of broomcorn millet Huangjiu. Pediococcus spp. are commonly used in silages, cheese, and yogurt [Pediococcus is regarded as a contaminant in brewed beer and wine. Some Pediococcus species can produce diacetyl, which gives a buttery or creamy flavor to wine and beer [Pediococcus has been reported to assist in German boche fermentation, which yields acidity to improve quality but has never been involved in Huangjiu fermentation [Pediococcus spp. was strongly associated with ethyl myristate, while Pantoea showed a positive correlation with ethyl myristate content. Consequently, Pantoea and Pediococcus have a strong coordination effect during broomcorn millet Huangjiu fermentation. A positive correlation between Weissella and ethyl butyrate has been reported in traditional Chinese Xiaoqu rice wine [Rhizopus, with its high amylase capacity, is closely bound to many organic acids such as pentanoic acid, nonanoic acid, and octanoic acid. Most species of Saccharomyces ferment one or more of the following sugars: galactose, xylose, maltose, disaccharide, trehalose, and sucrose. Saccharomyces was the only genus of fermented soluble starch, and it was closely associated with alcohols and esters such as propylene glycol and 2-phenylethyl acetic acid ester.Microorganisms have a complex symbiotic relationship during Huangjiu fermentation, thus, forming a unique flavor . The used yogurt ,32. Exceand beer . Pediocoentation . Correlaice wine . Rhizopukivd and adh2 genes in Escherichia coli, Atsumi et al. showed that the isobutanol is biosynthesized via the Ehrlich pathway using 2-ketoisovaleric acid, an intermediate of amino acid synthesis pathway, as a raw material [Rhizopus, Aspergillus, and over 30 genera of microorganisms are associated with 3-methyl-1-Butanol production. However, whether 3-methyl-1-butanol content can be regulated by these genera has not yet been fully investigated. 2, 3-butanediol, a warm, sweet flavor compound, is usually added to white wine in China to improve its flavor. Some bacteria, such as Klebsiella oxytoca, show a high potential for 2, 3-butanediol production [Pantoea and Kluyveromyces are associated with the synthesis of 2, 3-butanediol during the brewing of broomcorn millet Huangjiu. Furthermore, aromatic esters are broadly produced via the combination of organic acids and alcohols during fermentation, amino acids with alcohols, and decomposition, combination, and oxidation reactions during the aging of Huangjiu [Idiomarina can produce amylase, esterase, trypsin, and other fermentation-friendly enzymes. Fusobacterium usually produces a mixture of organic acids and alcohols using sugars or peptones. Ochrobactrum uses amino acids, organic acids, and carbohydrates as carbon sources.Aromatic esters and higher alcohols are regarded as two of the most important flavor characteristics of Huangjiu. Alcohols are ester precursors, which can enhance the sweetness of Huangjiu . Esters material . The assmaterial . Our resoduction . PantoeaHuangjiu . The 32 Moraxella, Enterovibrio, Lactococcus, and Pseudomonas, according to our study; furthermore, this required further analysis of the interactions between them.In addition to aromatic esters and higher alcohols, a momentous microbial metabolite, Acetoin (3-hydroxy-2-butanone), was reported as an important quality indicator and flavor substance for fermented products by a previous study . In our R1, R2, and R3 were most associated with Huangjiu flavor substances, and R4 was the dominant bacterium. Compared with the wheat qu, there was no significant difference between Huangjiu brewed with QR3 and QR4, while using QR1 and QR2 showed significantly lower biogenic amine content, higher flavor substance, and amino acid nitrogen content, which improved the quality of Huangjiu.Lactobacillus, Photobacterium, Hyphodontia, Aquicella, Erysipelothrix, Idiomarina, Paraphaeosphaeria, and Sulfuritalea. 5-methyl-2-(1-methylethyl)-1-Hexanol is the most complex flavoring substance and is associated with 54 microorganisms. Estragole was first detected in broomcorn millet Huangjiu. Furthermore, the addition of QR1 and QR2 could improve Huangjiu’s quality by increasing flavoring substances, the amino acid nitrogen content, and acidity while decreasing the bioamine content. This work reveals the relationship between microorganisms and volatile flavor compounds and is beneficial for the regulation of the micro-ecosystem and flavor of broomcorn millet Huangjiu.In this study, HS-SPME/GC-MS was used to determine 161 volatile flavor compounds, mainly esters, alcohols, acids, aldehydes, ketones, and phenols. A total of 82 and 30 genera of bacteria and fungi, respectively, were detected using high-throughput sequencing. We found 1217 correlations between microbes and flavor compounds. The results indicated that the dominant genera of flavor compounds were


Genome-wide association study (GWAS) is a widely used approach to identify genetic variants and genomic regions associated with phenotypes. However, there are limited studies on GWAS that have focused on carcass traits in Korean Hanwoo using large sample sizes and employing residual or genomic estimated breeding value (GEBV) as response variables. Given these circumstances, our study aimed to reexamine GWAS in Hanwoo using a substantial population sample. We performed a simple single-marker regression analysis, utilizing the deregressed genomic estimated breeding value (DGEBV), GEBV, and residual values as response variables in genomic best linear unbiased prediction (GBLUP) and Bayes B methods. Our primary focus was on identifying common single nucleotide polymorphisms (SNPs) using both approaches, as this approach has been rarely reported. Therefore, the objective of this study was to uncover the genetic regions strongly associated with carcass traits in Hanwoo using a large cattle population. We identified 129 common SNPs using DGEBV and 118 common SNPs using GEBV, located on chromosomes 6, 13, and 14, demonstrating their significant associations with the studied carcass traits. The identification of these regions, along with the discovery of potential candidate genes, will contribute to a deeper understanding of the genetic and biological mechanisms underlying carcass traits in the Hanwoo population.SLIT2, PACRGL, KCNIP4, RP1, XKR4, LYN, RPS20, MOS, FAM110B, UBXN2B, CYP7A1, SDCBP, NSMAF, TOX, CA8, LAP3, FAM184B, and NCAPG. For EMA, the genes IBSP, LAP3, FAM184B, LCORL, NCAPG, SLC30A9, and BEND4 demonstrated significance. Similarly, CYP7B1, ARMC1, PDE7A, and CRH were associated with BF, while CTSZ, GNAS, VAPB, and RAB22A were associated with MS. This finding offers valuable insights into genomic regions and molecular mechanisms influencing Hanwoo carcass traits, aiding efficient breeding strategies.This study aimed to identify genetic associations with carcass traits in Hanwoo cattle using a genome-wide association study. A total of 9302 phenotypes were analyzed, and all animals were genotyped using the Illumina Bovine 50K v.3 SNP chip. Heritabilities for carcass weight (CWT), eye muscle area (EMA), backfat thickness (BF), and marbling score (MS) were estimated as 0.42, 0.36, 0.36, and 0.47, respectively, using the GBLUP model, and 0.47, 0.37, 0.36, and 0.42, respectively, using the Bayes B model. We identified 129 common SNPs using DGEBV and 118 common SNPs using GEBV on BTA6, BTA13, and BTA14, suggesting their potential association with the traits of interest. No common SNPs were found between the GBLUP and Bayes B methods when using residuals as a response variable in GWAS. The most promising candidate genes for CWT included  The genetic improvement of cattle using DNA information began with the milestone of sequencing the genome of the Hereford cow (L1 Dominette 01449), which was deposited into free public databases in 2009 . With thIn recent times, cattle breeding has incorporated advanced molecular techniques and statistical approaches to identify causal mutations influencing traits. Most of the economic traits in cattle, known as quantitative trait loci (QTL), are polygenic in nature and controlled by multiple genes. Hanwoo, a beef cattle breed originally developed and bred in the Korean peninsula, has gained significant popularity among Korean consumers due to its exceptional marbled meat. This breed has been recognized as a valuable genetic resource for nearly a century. To enhance Hanwoo’s status as a leading marbled beef cattle breed, various breeding strategies and efforts have been implemented by both government and private agencies since the 1960s . As a reAnimal geneticists are currently devoting significant efforts to identify the causal genes and mutations responsible for economic beef quality traits. In these studies, single nucleotide polymorphism (SNP) markers are commonly utilized in genome-wide association studies (GWASs) to pinpoint genes that influence specific traits. While SNPs located within the coding region of a gene typically have a direct impact on its phenotypic expression, the majority of SNPs are found in non-coding regions, including intergenic regions. Determining which SNPs affect gene function in these non-coding regions poses a significant challenge. Interestingly, intergenic SNPs have been observed to be significantly associated with phenotypes in GWAS, despite their location outside of coding regions . GWAS ofhttps://www.animalgenome.org/cgi-bin/QTLdb/BT/index accessed on 5 August 2023). Specifically, there have been 2138 QTL identified for carcass weight, 1653 for eye muscle area (Longissimus muscle area), 701 for backfat thickness, and 2436 QTL for marbling score. In the context of Hanwoo populations in Korea, genome-wide association studies have been conducted to map QTL affecting carcass weight, eye muscle area, backfat thickness, and marbling score. However, previous GWAS studies on Hanwoo carcass traits [As of 25 April 2023, significant progress has been made in the field of quantitative trait loci (QTL) mapping, leading to the identification of an impressive total of 193,898 QTL for 680 different base traits, 201 trait variants, and 258 eQTL genes within the cattle genome  from the Hardy–Weinberg Equilibrium (HWE). The identity-by-state (IBS) test was performed to determine if there were similar individuals or genotyping errors in the datasets. Pairs of individuals showing a similarity rate greater than 99% were considered either identical animals or indicative of genotyping errors. The entire QC process and IBS test were performed through the PLINK v1.9 toolset [A total of 9302 Hanwoo from the mainland were genotyped using the Illumina Bovine SNP50K v.3 BeadChip , which contained 52,122 embedded SNPs. SNPs located on sex chromosomes and with unknown and duplicate positions were removed for further quality control (QC) procedures. Several QC thresholds were set to remove poor-quality SNPs for further analysis. SNPs were discarded from the analysis when the SNP call rate was less than 90%, individuals had a genotyping call rate less than 90%, and the minor allele frequency (MAF) was less than 1% (monomorphic). The genotype frequency significantly deviated  was built using the Genome-wide complex trait analysis (GCTA) tools developed by Yang et al. , which eij denotes the genotype of the individual i at marker loci j, ĝj represents the allele substitution effect at locus marker j, and m is the number of markers. The GEBV of an animal i was calculated after estimating the marker effects using the formula below:The DGEBVs were obtained following the Garrick’s method  as folloHere, For GWAS analysis, the de-regressed genomic estimated breeding value (DGEBV), genomic estimated breeding value (GEBV), and the residuals  were used to estimate by fitting the following single-marker regression analysis:p-value for the regression coefficient was estimated. If the SNP marker has a significant effect for the trait, then it can be assumed that the SNP was in a linkage disequilibrium (LD) state with an unobserved QTL. The null hypothesis (H0 = the marker had no effect) was set to test the effect of the marker SNP on the trait and alternative hypothesis vice versa. The significance of the test statistic p-value threshold was set at (−log10P = 4) for each SNP.The PLINK v1.9 toolset  estimateThe genomic prediction methodology with Bayes B was performed using the GenSel v4.90 program . The BayIn order to determine the marker effects, a mixture model was applied, i.e., a fraction of markers (probability π) with zero effect and (probability 1 − π) of markers with non-zero effects, which was used to predict GEBV. Then, the genetic variances of the markers with non-zero effects would have σai2>0 . In thisThe fixed effects and covariate for each trait were fitted under the GBLUP model. For the Bayes B analysis, the estimates of genetic and residual variances that were obtained from ASReml-SA analysis were used as prior values. A total of 41,000 iterations of Markov chain Monte Carlo (MCMC) were run for the analyses, after discarding the first 1000 iterations of the burn-in period, and each of 100 iterations was selected to calculate the posterior mean and variance for the marker effects. i; and e is the vector of random residuals. The GWAS by the Bayes B model using the GenSel v4.90 program  is conduth estimated SNP marker effect.The estimated variance components were used as prior information, and a total of 41,000 MCMC iterations were implemented, with 1000 discarded as burn-in. Genetic variance explained by SNPs was calculated using fixed windows comprising 20 consecutive SNP markers. The genetic variance of SNP windows was determined as the sum of each SNP’s variance, calculated as follows:Bos taurus) QTL database  for searching QTL—release 48, accessed in November 2022. The available BED file with the QTL coordinates according to the cattle genome assembly (Bos_taurus_UMD_3.1.1) was used to check the QTL identified in our significant SNPs on respective chromosomes. The genomic positions and window that did not overlap with previously annotated QTL for meat and other carcass traits were considered as novel QTL affecting the trait.QTL regions were determined based on the location of the significant SNPs. We retrieved the whole genome cattle (http://asia.ensembl.org) accessed on 10 June 2023, NCBI (genome data viewer), Bovinemine V1.6 of the bovine genome database (www.bovinegenome.org) accessed on 10 June 2023, and BGVD  accessed on 12 June 2023. All four online resources were cross-checked to accurately identify all nearby genes in the significant SNP locations.Putative candidate genes within the SNP or genomic window regions and in the neighboring 1 Mb upstream and downstream regions of our mapped significant SNPs were identified based on the Bos_taurus_UMD_3.1.1 genome assembly, using four online resources, such as Biomart tools from Ensembl (www.genecards.org) accessed on 15 June 2023. The candidate genes that seem to functionally relate to our desired traits of interest were assumed as promising candidate genes.The functional roles of the identified genes located within and nearby significant SNPs associated with the four carcass traits, viz., CWT, EMA, BF and MS, were explored through published reports in PMC (NCBI database) journals and other literature surveys. We also found the functional roles of each gene from the online resource of human gene functions at GENECARDS (https://pantherdb.org (accessed on 15 June 2023) [p-value threshold was set at p ≤ 0.05. Manhattan plots were drawn using the R package qqman [Significant GO (gene ontology) terms provide insight into the functional characteristics of annotated genes. The gene ontology analysis was carried out to explore attributes of the genes, including the biological processes (BPs), molecular functions (MFs), and cellular components (CCs) of the candidate genes near our significant SNPs using the PANTHER v14.1  web-based tools available at ne 2023)  and the ne 2023) . We alsoge qqman .The summary statistics for the carcass traits  of a mainland Hanwoo population consisting of 9302 animals, including 7328 steers, are presented in 2, 14.20 mm, and 5.99, respectively, which aligns closely with our steers data records. Additionally, in another recent report by Mehrban et al. [2, 8.60 mm, and 1.38, respectively. The lower marbling score observed in their study was a result of the data being transformed using the natural logarithm to lnMS after adding 1 to all records, as the original MS data exhibited skewness. The phenotypic data in Mehrban’s study were lower than our records, likely due to the lower slaughter age in their study. However, these differences can be attributed to the fact that the Hanwoo carcass yield and quality have improved in recent years due to the implementation of genetic improvement programs by the government and farmers.In another recent report by Haque et al. , the autn et al. , genomic2) values for CWT, EMA, BF, and MS were calculated from these variance estimations and are presented in 2  for the Hanwoo population as follows: CWT , EMA , BF , and MS . These estimates significantly differ from the h2 estimates obtained in recent studies by Haque et al. [2 for CWT, EMA, BF, and MS using the GBLUP method as 0.30, 0.26, 0.26, and 0.34, respectively. Mehrban et al. [2 for these four traits using Bayes C and Bayes L methods and obtained almost similar values. We rechecked the h2 values using a fivefold cross-validated dataset and obtained results consistent with our initial estimates . According to their reports, the four Bayesian models  estimated h2 values for CWT ranging from 0.48 to 0.59, ribeye area (REA) ranging from 0.32 to 0.36, fat thickness (FT) ranging from 0.06 to 0.27, and MS ranging from 0.62 to 0.72. Notably, larger differences in h2 values were observed within breeds. Several studies [2 of traits. It has been consistently observed in real data and simulation studies that higher h2 is associated with increased accuracy of genomic prediction [In addition, Rolf et al.  conductebreed 0.9. Accordi studies  have demediction ,43. Howep-values, and nearby candidate genes with their type of consequence, is presented in p-values for each trait, accompanied by their relevant information. Additional significant SNP IDs are detailed in 10) of each SNP’s p-value. Additionally, we depicted Q-Q plots to illustrate the observed versus expected p-values (−log10P) for each trait in the GBLUP method , GEBV, and residual values as response variables. This analysis allowed us to identify the SNP markers that affect the studied traits. Our focus was primarily on the common SNPs that were identified by both the GBLUP and Bayes B methods. In total, we detected 129 common SNPs using the DGEBV and 118 common SNPs using the GEBV on chromosome 6, 13, and 14, indicating their associations with carcass traits in the Hanwoo population under study. It is worth noting that no common SNPs were found between the GBLUP and Bayes B methods when using residuals as a response variable in the GWAS. The reasons behind this discrepancy could be attributed to the complex interplay of various factors influencing the genetic architecture and the specific statistical performance of the GBLUP and Bayes B methods. These factors may include the inherent characteristics of the traits, the modeling assumptions of the methods, and the noise introduced by using residuals as response variables. Further investigation and analysis would be needed to fully understand the reasons behind this discrepancy and to determine the potential biological or statistical factors contributing to the observed differences in SNP associations between the two methods when using residuals as the response variables. The detailed summary of the GWAS results, including significant SNP IDs for the four traits, SNP positions on the respective BTAs, effects on the traits, P method . These rp-value threshold were rs41725159, p = 1.5 × 10−13; rs210258477, p = 1.63 × 10−13; rs42646659, p = 1.68 × 10−13; rs110092040, p = 1.68 × 10−13; and rs41725162, p = 1.72 × 10−13 for carcass weight, located on BTA14 at the position of 24.49 Mb to 26.62 Mb. Among these SNP markers, two SNPs, BTB-01143580 (rs42303720) and Hapmap30932-BTC-011225 (rs41724536), were previously identified in two other Hanwoo GWAS studies, affecting carcass weight on BTA14 (24.3 Mb and 24.8 Mb regions) [In the present study, the GWAS detected a total of 51 significant common SNPs associated with carcass weight when using the de-regressed genomic estimated breeding value (DGEBV) as a response variable. Additionally, 63 significant common SNPs were identified using GEBV as a response variable. For carcass weight in Hanwoo, two major chromosome regions were identified, 41.18 Mb to 41.98 Mb (0.8 Mb) on BTA6 and 24.14 Mb to 26.99 Mb (2.85 Mb) on BTA14, when using DGEBV as the response variable. However, when GEBVs were used without deregression, a novel region of 38.37 Mb to 38.99 Mb (0.62 Mb) on BTA6 was detected, while the position on BTA14 remained similar to that observed with DGEBV as the response variable . The mosregions) . In the p-value thresholds were rs798880362, p = 4.76 × 10−9; rs467005433, p = 5.07 × 10−9; rs432843769, p = 3.01 × 10−8; and rs110834363, p = 4.92 × 10−8 located on BTA6 at the 38.53–38.93 Mb region, and rs210258477, p = 6.02 × 10−8 on BTA14 at the 24.49 Mb region  and 62.61 Mb on BTA6, and a region ranging from 24.14 Mb to 26.99 Mb (2.85 Mb) on BTA14. Both the DGEBV and GEBV models identified a total of 56 and 55 common SNPs, respectively. Additionally, a new region ranging from 38.46 Mb to 38.99 Mb (0.53 Mb) on BTA6 was identified when GEBV was used as the response variable. Among the reported QTL affecting the Longissimus muscle area trait in the cattle QTLdb database, 70 out of 431 QTL were found. Among these, 40 QTL were located on BTA6 and 30 QTL on BTA14. The most significant SNPs (top 5) based on b region . In a prp = 3.49 × 10−8; rs41619147, p = 3.73 × 10−8; rs110366862, p = 6.16 × 10−8; rs41639002, p = 6.50 × 10−8; and rs42743206, p = 6.62 × 10−8. These SNPs are located on BTA4 and were detected in the Bayes B model, contributing to more than 1 % genetic variance in the Bayes B model, but did not reach the significance threshold in the GBLUP model. The cattle database indicated that at least 54 QTL may be associated with fat thickness. Interestingly, one QTL (ID: 10971) located at 31.74 Mb on BTA14 overlapped with the region we reported, providing further support for its potential role in BF in Hanwoo cattle.The GWAS for BF in Hanwoo identified 21 significant SNPs on BTA14, specifically, in the region spanning 31.01 Mb to 31.98 Mb (0.97 Mb), using single-marker regression analysis . In the p = 8.63 × 10−5) located on chromosome region BTA13, which was detected by both the GBLUP and Bayes B methods , making it difficult to determine their gene associations. Candidate genes, located near regions of association, can influence the expression of complex phenotypes due to their known biological and physiological properties. To assign SNPs to genes, it is common practice to use a distance cutoff. Various distance cutoffs have been used in previous GWAS studies to identify causal genes. Brodie et al.  suggesteLOC782905, SLIT2, PACRGL, KCNIP4, LAP3, FAM184B, and NCAPG  were identified. This genomic location has been previously detected by several studies in Hanwoo cattle [In BTA14, we identified 10 positional and functional known candidate genes, namely,  and CA8 . Additioo cattle ,44,51, sRP1 (retinitis pigmentosa-1) gene, associated with sensory functions causing progressive retinal degeneration, has been observed to have frameshift variants in Normande cattle [XKR4  gene has been significantly associated with subcutaneous rump fat thickness in a GWAS of seven breeds of cattle [FAM110B (family with sequence similarity 110) gene plays a specific role in cell cycle progression in yeast cells [CYP7A1 (cholesterol 7α-hydroxylase) gene has a key regulatory function in bile acid synthesis, which is important for the regulation of lipid, glucose, and energy homeostasis [TOX (thymocyte selection-associated HMG-box) gene, which plays a critical role in brain development in mouse models, has been associated with reproductive traits in Nelore cattle [CA8 (carbonic anhydrase related protein 8) gene, which is related to a genetic disease in humans, can cause a novel syndrome characterized by ataxia and mild mental retardation, with a predisposition to quadrupedal gait [CHD7 gene, previously identified in Hanwoo on BTA14, has been associated with carcass weight at the 5% chromosome-wise level [The e cattle . Variatif cattle . The FAMst cells . The CYPeostasis . The TOXe cattle ,57. Mutadal gait . The CHDse level .TGS1, TMEM68, SOX17, MRPL15, LYPLA1, TCEA1, RGS20, ATP6V1H, OPRK1, NPBWR1, RB1CC1, and RAB2A, located on BTA14 within 4.85 Mb intervals. Well-known genes, including PLAG1, CHCHD7, SDR16C5, SDR16C6, PENK, and IMPAD1, previously reported in many studies with several beef cattle breeds, may also have associations with carcass weight [To expand the search for possible candidate genes associated with carcass weight, we extended the physical location by 1 Mb upstream and downstream of our candidate region. This search led us to identify additional genes, such as s weight ,44,59.PLAG1 and CHCHD7, have been associated with carcass weight and stature in several cattle breeds, including Hanwoo [TMEM68 gene, identified in Nelore cattle, has 64 most significant SNPs associated with reproduction traits [LYPLA1 (lysophospholipase 1) gene, previously involved in the regulation of appetite in rat stomach, and the TMEM68 (transmembrane protein 68) gene, expressed in bovine rumen, abomasum, intestine, and adipose tissue, may affect lipid biosynthesis and contribute to variation in average daily gain (ADG), average daily feed intake (ADFI), and residual feed intake (RFI) in crossbred steers [The most significant candidate genes, g Hanwoo ,61,62,63d steers . LYN, RPS20, MOS, PLAG1, and CHCHD7, located on BTA14 (24.87 Mb ~ 25.10 Mb), have been identified as responsible for 1.89% and 2.38% variance in backfat thickness and rump fat thickness in Nelore cattle [SDR16C5, SDR16C6, PENK, and IMPAD1, located in the same genomic region, had significant pleiotropic effects on body weight and milk characteristics in New Zealand Holstein-Friesians, Jerseys, and their crosses. The NCAPG (non-SMC condensin 1 complex subunit G) and LCORL (ligand dependent nuclear receptor corepressor-like) genes are located on BTA6 between 38.78 to 38.93 Mb, based on the Bos taurus UMD 3.1.1 genome assembly. This region aligns with our candidate region captured using the GBLUP method, where GEBVs were used as response variables. Mutations in the NCAPG and LCORL genes have been associated with carcass weight and body frame size, and they are expressed in the adipose and muscle tissues of beef cattle, potentially influencing average daily feed intake (ADFI) and average daily gain (ADG) phenotypes. Various GWAS have indicated that the NCAPG and LCORL genes are linked to growth, body size, carcass weight, and carcass composition phenotypes [NCAPG and LCORL gene loci in carcass weight and carcass fat has also been confirmed in Angus, Charolais, and Limousine cattle breeds [The genes e cattle . Additioe cattle  found thenotypes . Moreovee breeds . IBSP, LAP3, FAM184B, NCAPG, and LCORL) (SLC30A9 and BEND4), along with an additional eight genes . In the mouse model, the IBSP  gene, a major structural component of the bone matrix, has been implicated in bone diseases and skeletal development [ATP8A1 gene has been suggested as a strong candidate gene for feed conversion efficiency (FCE) traits in the Sanga cattle population [FAM13A, HERC3, NAP1L5, PYURF, PIGY, HERC5, HERC6, PPM1K, ABCG2, PKD2, SPP1, MEPE, MED28, and DCAF16, are located within 1 Mb distance from the position on BTA6 (38.37~38.99 Mb). A significant QTL region harboring some of these genes  was reported in the Australian sheep population, associated with gastrointestinal parasitic resistance [ABCG2 (ATP binding cassette subfamily G member 2) gene has been identified as a potential candidate gene for facial eczema in sheep [SPP1 gene encodes a protein involved in the toll-like receptors (TLR) signaling pathway. In the chromosomal region on BTA14, ranging from 23.14 to 27.99 Mb , we identified 20 potential candidate genes for CWT, which were also associated with EMA in our study. This association suggests a possible pleiotropic relationship between the two traits. For the eye muscle area trait, two chromosomal regions on BTA6 were identified. The first region, spanning from 38.37 Mb to 38.99 Mb with 0.62 Mb intervals, harbors five genes (d LCORL) . The secelopment . The ATPpulation . Furthersistance . The ABCin sheep  and is ain sheep . The SPPCYP7B1, ARMC1, and PDE7A  gene plays an important role in bone mineralization and renal tubular phosphate excretion in mouse models [For backfat thickness, three genes were identified in the chromosomal region on BTA14, ranging from 31.01 to 31.98 Mb : nd PDE7A . The CYPn humans . Within ulations . It has ulations . The SGKe models .LOC783163 and C13H20orf85. To explore further, we expanded our search window by approximately 1 Mb in both directions and discovered 18 nearby positional candidate genes, including SYCP2, PHACTR3, EDN3, ZNF831, PRELID3B, ATP5E, TUBB1, CTSZ, NELFCD, GNAS, MIR6123, NPEPL1, STX16, APCDD1L, VAPB, RAB22A, and PMEPA1. Among these genes, CTSZ, GNAS, and RAB22 have been studied (VAPB (Vesicle associated membrane protein associated protein B) gene encodes the VAP protein family, which plays diverse roles in regulating neurotransmitter release, vesicle trafficking, lipid binding and transfer proteins, maintaining ER/golgi architecture, and the unfolded protein response [RAB22A, a member of the proto-oncogene RAS family, plays an important role in the formation, trafficking, and metabolism of exosomes. It is associated with the occurrence and development of multiple human cancers and lymph node metastasis [For the marbling score, we identified one SNP (rs110025998) on BTA13 at the 58.14 Mb physical position. This SNP is located within two intronic variants,  studied  in pig presponse . It has We analyzed a total of 107 candidate genes within and nearby regions associated with gene ontology (GO) terms related to selected molecular functions, biological processes, and cellular components. The GO analysis revealed that out of the 107 genes, 91 were involved in biological process, 76 genes were associated with cellular components, and 76 genes had molecular functions . To gainThe PANTHER pathway analysis identified 19 genes that were part of specific pathways . Some ofBos taurus as the background, are provided in Further details regarding the GO terms and KEGG pathways associated with the identified genes, as obtained through DAVID bioinformatics resources using In conclusion, our study successfully identified significant genetic regions and candidate genes associated with important beef carcass traits in Hanwoo cattle, such as carcass weight, eye muscle area, backfat thickness, and marbling score. Through genome-wide association analysis using a large cattle population, we detected 129 genome-wide significant SNPs, with BTA6, BTA13, and BTA14 being the most prominent genomic regions. We also found a shared genomic region on BTA14 associated with carcass weight and eye muscle area, indicating a genetic relationship between these traits. Our findings emphasize the complex genetic architecture of Hanwoo carcass quality traits, influenced by multiple SNPs in various genes. Further research is needed to validate and confirm these results, providing a deeper understanding of the genetic basis of Hanwoo carcass traits and paving the way for improved breeding and selection strategies in the future.


Given that their applicability depends on their dimensions, crystal morphology, and surface chemistry, Fe3O4 NPs must be synthesized in a controlled, simple, and reproducible manner. Since conventional methods often lack tight control over reaction parameters and produce materials with unreliable characteristics, increased scientific interest has been directed to microfluidic techniques. In this context, the present paper describes the development of an innovative 3D microfluidic platform suitable for synthesizing uniform Fe3O4 NPs with fine-tuned properties. On-chip co-precipitation was performed, followed by microwave-assisted silanization. The obtained nanoparticles were characterized from the compositional and microstructural perspectives by X-ray diffraction (XRD) and transmission electron microscopy (TEM). Moreover, supplementary physicochemical investigations, such as Fourier Transform Infrared Spectroscopy (FT-IR), Kaiser Test, Ultraviolet-Visible (UV-Vis) Spectrophotometry, Dynamic Light Scattering (DLS), and Thermogravimetry and Differential Scanning Calorimetry (TG-DSC) analyses, demonstrated the successful surface modification. Considering the positive results, the presented synthesis and functionalization method represents a fast, reliable, and effective alternative for producing tailored magnetic nanoparticles.Magnetite nanoparticles (Fe Nanotechnology is gaining increasing popularity for advancing various sides of science as it allows matter manipulation on a scale where materials display different features than micro-/macro-scale counterparts. The unique, appealing physicochemical properties  render nanodimensional materials suitable for numerous and broad applications ,3,4,5,6.3O4) has been particularly remarked, as it shows the strongest magnetism compared to other transition metal oxides [3O4 NPs possess superparamagnetism, high saturation field, and high magnetic susceptibility, with their distinctive features being attributed to the transfer of ions from Fe2+ to Fe3+ [3O4 NPs suitable for a plethora of utilizations, including bio-sensing and diagnosis [Among all investigated nanomaterials, magnetic nanoparticles (NPs) have received the most interest, given their attractive characteristics. These materials are especially recognized for their biocompatibility, chemical stability, low price, and size-dependent magnetic activity ,8. From l oxides  while all oxides . Fe3O4 N to Fe3+ ,11. Thesiagnosis ,13,14, ciagnosis ,16,17,18iagnosis ,22,23,24iagnosis ,20,21,25iagnosis ,28,29,30iagnosis ,32,33, biagnosis ,35,36, miagnosis ,38,39, diagnosis ,41,42, aiagnosis ,45,46,473O4 NPs tend to oxidize, quickly transforming into maghemite [Despite being relatively stable at room temperature, Feaghemite . Moreoveaghemite . Magnetiaghemite .3O4 NPs is deeply related to their dimensions, crystal morphology, and surface chemistry, the synthesis process requires simplicity, reproducibility, and repeatability [As the behavior of Fetability ,48,49. Ttability ,51. Desptability ,51,52. Atability ,52.3O4 NPs with specified characteristics and tailored properties must be obtained, conventional synthesis methods do not provide tight control over experimental variables, generating particles with a broad size distribution, large inter-batch variability, irregular structures, and unreliable properties [Thus, when Feoperties ,54,55,56operties ,57,58.To overcome the challenges of conventional syntheses, microfluidic technology has emerged as a promising solution. By fluid manipulation in microscale channels and chambers, microfluidic devices are excellent synthesis platforms for NPs with controlled properties and functions ,59,60,613O4 NPs synthesis, microreactor channels can get clogged due to the high reactivity of iron precursor and the large surface-to-volume ratio of the products [Given their appealing features, microfluidic devices started being involved in numerous syntheses, providing a rapid, low-cost, controllable, sustainable, and reliable method for fabricating a wide range of nanomaterials, including magnetite ,63. Howeproducts . Other iproducts ,64. The products . In partproducts ,64.3O4 NPs synthesis. Specifically, a novel microfluidic device was designed, fabricated, and tested for magnetite production. To avoid undesired surface oxidation and particle agglomeration, the microfluidic-obtained NPs were further functionalized with (3-aminopropyl) triethoxysilane (APTES) through a microwave-assisted method. In addition, Fe3O4 NPs were thoroughly evaluated by a series of physicochemical characterization methods: X-ray diffraction (XRD), Transmission Electron Microscopy (TEM), Selected Area Electron Diffraction (SAED), Fourier Transform Infrared Spectroscopy (FT-IR), Kaiser Test, Ultraviolet-Visible (UV-Vis) Spectrophotometry, Dynamic Light Scattering (DLS), and Thermogravimetry and Differential Scanning Calorimetry (TG-DSC).In this context, this study has focused on developing an innovative 3D microfluidic platform suitable for Fe3), iron sulfate heptahydrate (FeSO4·7H2O), (3-aminopropyl) triethoxysilane (APTES), potassium cyanide (KCN), and phenol were purchased from Sigma Aldrich Merck ; sodium hydroxide (NaOH) was purchased from Lach-Ner ; ethanol and acetic acid were purchased from Emsure Merck Millipore ; pyridine and ninhydrin were purchased from Merck . All the reagents utilized in this study were of analytical purity and used as received. Ultrapure water was used for all experiments.Ferric chloride , with the patterns indicated in 3 and FeSO4·7H2O in a 1 to 6 weight ratio in 300 mL of ultrapure water, while the precipitating agent solution consisted of 300 mL of 1.25 M NaOH aqueous solution. Iron oxide nanoparticles were obtained through the co-precipitation of the iron ions at the contact point with the alkaline solution. Reagent solutions were simultaneously introduced in the experimental setup using a classical osmosis pump with a 90 mL/s flow rate. In more detail, the iron precursor solution and the precipitating solution were circulated through the channels represented with green in The precursor solution was prepared by dissolving FeCl3O4 NPs were separated using a neodymium magnet. Then, they were washed with ultrapure water, dispersed by ultrasonication, and centrifuged for 40 min at 8000 rpm. These processes were repeated three times. After that, the processes were repeated three more times using ethanol (with a trace amount of acetic acid).The obtained FeThe purified NPs were dispersed in ethanol, APTES solution  was added under continuous stirring, and the resulting mixture was heated using microwave irradiation for 30 min , rated microwave power output 600 W, microwave frequency 2450 MHz). The final nanostructured products were further subjected to several series of washing with ethanol, ultrasonication, and centrifugation to eliminate potential traces of unreacted compounds.An X-ray diffraction analysis of nanomaterial powders was accomplished with the aid of a Panalytical Empyrean diffractometer  equipped with a CuKα radiation source (λ = 1.056 Å) at 40 mA and 45 kV. Samples were scanned at room temperature, with determinations in the Bragg diffraction angle range between 10° and 80°.The sample was dispersed in ethanol through a 15-min ultrasonic treatment. Then, a small amount of it was placed on a carbon-copper grid and dried at room temperature. For TEM micrographs recording, a high-resolution 80–200 Titan Themis transmission electron microscope from ThermoFisher Scientific  was operated in the transmission mode at a 200 kV voltage, with point and line resolutions of 2 Å and 1 Å, respectively. Additional crystallographic data was acquired using the equipment’s SAED accessory .−1, using the resolution of 8 cm−1. All spectra were registered in attenuated total reflectance (ATR) mode using a diamond crystal. OmnicPicta software  was used to co-add and process the 96 scans acquired for each sample.The synthesized nanoparticles were characterized using a Nicolet iS50FT-IR  spectrometer. The measurements were performed at room temperature in the range of 4000–400 cmFor the Kaiser detection, fresh test solutions were prepared and added to a small amount of sample placed in a test tube, as described in reference . The tesThe resulting solutions were further analyzed using an Evolution 300 UV-Vis spectrophotometer . The absorbance values were measured in standard quartz cuvettes between 400 and 800 nm with a bandwidth of 2.0 nm and a scan speed of 240 nm/min. The acquired data were processed using the VISIONpro dedicated software (version 2.0).The synthesized nanoparticles were dispersed in water, sonicated for 5 min, placed in dedicated cuvettes (DTS0012), and subjected to DLS analysis using a Nano ZS Zetasizer . Measurements were performed at a spreading angle of 90° and a temperature of 25 °C; the reported values for average hydrodynamic diameter, polydispersity index, and Zeta potential are the average of five measurements.−1, under a 50 mL min−1 dried airflow. As a reference, an empty alumina crucible was used. In addition, the evolved gases were studied by a thermostat gas-cell-equipped FTIR Tensor 27 from Bruker .For the realization of the thermal analysis, an STA 449C Jupiter device from Netzsch  was employed. A small amount of sample was placed in an open alumina crucible and heated from room temperature up to 900 °C, at a heating rate of 10 °C min3O4 NPs is presented in The X-ray diffractogram of pristine Fe3O4 NPs  with monomodal size distribution e, exclus3O4 NPs d recorde3O4 and Fe3O4@APTES NPs both exhibited a wide absorption band around 3400 cm−1, attributed to O–H stretching vibration, and a small peak around 1640 cm−1, correlated with O–H deformed vibration, which demonstrated the presence of OH groups on the surface of obtained NPs. The absorption bands specific to Fe–O stretching vibrations were also identified in both spectra at 548 cm−1 for bare magnetite and 571 cm−1 for the functionalized sample. The shift to a higher wavenumber noticed in the Fe3O4@APTES can be explained by the formation of Fe–O–Si bonds, reflecting the replacement of Fe–O–H groups on the particle surface with Fe–O–Si(O–)2–(CH2)3–NH2. As –Si(O–) has a greater electronegativity than H, the forces of Fe–O bonds are enhanced, and the absorbance bands are shifted to higher wavenumber values [−1 and 2927 cm−1 attributed to C–H stretching. Additionally, the 1541 cm−1 peak corresponds to the N–H bending vibration.To confirm the successful silanization of the magnetite NPs, FT-IR analysis was performed . The FT-r values . The pre−1 peak to 557 cm−1, demonstrating a modification of surface interaction forces from Fe–O–H to Fe–O–Si correlated with a force constant modification of the Fe–O bond. There is also a considerable absorption difference between non-functionalized magnetite and APTES-functionalized one, related to the significant mitigation of the Fe–O stretching absorption (557 cm−1 shifted peak).Moreover, FAR-IR spectra  prove a 3O4 NPs, and Fe3O4@APTES NPs, leading to the appearance of a violet shade, no color change, and dark brown shade, respectively , pristine Feectively .3O4 NPs and the functionalized ones, the expected blue-violet shade is covered by the color intensity of the dispersed NPs. For better validation of the presence of APTES within the surface-modified sample, UV-Vis analysis was performed on the solutions resulting from the Kaiser test . Moreover, an increase in the Zeta potential was also observed, changing from 39.0 mV (pristine Fe3O4 NPs) to 52.7 mV (Fe3O4@APTES NPs), demonstrating the improved colloidal stability following silanization.DLS analysis allowed the comparison of the fabricated materials in terms of size and colloidal stability . PristinBesides, the increase in size caused by shell addition onto the magnetic cores is visible in TEM micrographs , which a3O4 NPs shows that the sample loses 0.83% of its initial mass up to 115 °C. The process is accompanied by an endothermic effect on the DSC curve, with a minimum at 72.4 °C, indicating the most probable cause of the elimination of residual water molecules from the nanoparticles’ surface. The FTIR 3D diagram and 2D projection confirm that the evolved gases contain water vapors . Performing the co-precipitation on-chip allowed enhanced control over reaction parameters, with the vertical mixing chamber enabling efficient mixing. Compared with existent microfluidic devices for magnetite synthesis , this stMoreover, microfluidic 3D mixing is an emerging technology, and only a few designs have been reported in the literature . The oth3O4-based nanospheres [Based on thorough characterization , it was established that the obtained nanoparticles consisted of crystalline magnetite with spherical morphology. This shape is the most found among microfluidic synthesis methods, with numerous studies reporting on-chip fabricated Feospheres ,75,76,77ospheres , hexagonospheres , and tadospheres .3O4 NPs of diameters below 6.5 nm, a dimension that agrees with the requirements for further applications in biomedicine. Moreover, the proposed synthesis device enabled the fabrication of much smaller magnetite particles than our research group obtained with planar microreactors  [3O4 NPs with a mean particle size of less than 10 nm.Magnetic nanoparticles employed in biomedical applications normally have sizes below 20 nm, a suitable dimension for enabling their superparamagnetic behavior . Our stuo 50 nm) . Other so 50 nm)  managed o 50 nm)  have use3O4 NPs agglomeration through steric repulsion. Moreover, its terminal amine group enables further bioconjugation or can act as a linker in synthesizing composite/hybrid structures made of Fe3O4 NPs and other inorganic materials. Furthermore, the presence of the amino group on the surface of magnetite nanoparticles allows their further modification with other functional groups, including peptides, antibodies, oligonucleotides, or polymers, toward creating vehicles for targeted drug delivery [3O4@APTES NPs, several studies have reported their fabrication [Silanes represent commonly used bifunctional modifiers for metal oxide NPs functionalization . We useddelivery . Considerication ,84,85,863O4@APTES NPs drastically reduced preparation time, required simpler and fewer steps, and did not employ an argon/nitrogen atmosphere. The described functionalization technique resulted in the successful covering of magnetite NPs with an organosilane shell, as demonstrated by FT-IR, Kaiser, UV-Vis, and TG-DSC characterizations. Thus, the obtained materials can be employed in further applications, including composite synthesis, nanostructured catalytic materials, targeted drug delivery vehicles, wastewater treatment, and nanophase synthesis [Compared to literature studies, the proposed fabrication method for Feynthesis ,86,87,883O4 NPs fabrication methods, proposing a 3D microfluidic platform able to generate uniform magnetic NPs. Thorough physicochemical investigations  revealed that the obtained Fe3O4 NPs were crystalline, with ultrasmall sizes (below 6.5 nm) and exclusive spherical morphology. To prevent agglomeration, the pristine particles were further surface-modified by microwave-assisted functionalization with an organosilane . The successful grafting of the coupling agent was confirmed by a series of characterization methods, including FT-IR, Kaiser, UV-Vis, DLS, TEM, and TG-DSC analyses. The proposed method for the fabrication of Fe3O4@APTES NPs required less time, fewer steps, and simpler operations than the previously reported techniques in the literature. Thus, it can be concluded that the developed device provides a reliable synthesis platform for the formation of fine-tuned magnetic materials in a fast and effective manner.Conventional magnetite synthesis methods often lead to the formation of particles with variable features, whereas the utilization of typical microreactor systems is limited by their clogging tendency. Therefore, this study offers an improved alternative to existing Fe