Mulberry and Hippophae-based solid beverage promotes weight loss in rats by antagonizing white adipose tissue PPARγ and FGFR1 signaling.

Obesity, a multifactorial disease with many complications, has become a global epidemic. Weight management, including dietary supplementation, has been confirmed to provide relevant health benefits. However, experimental evidence and mechanistic elucidation of dietary supplements in this regard are limited. Here, the weight loss efficacy of MHP, a commercial solid beverage consisting of mulberry leaf aqueous extract and Hippophae protein peptides, was evaluated in a high-fat high-fructose (HFF) diet-induced rat model of obesity. Body component analysis and histopathologic examination confirmed that MHP was effective to facilitate weight loss and adiposity decrease. Pathway enrichment analysis with differential metabolites generated by serum metabolomic profiling suggests that PPAR signal pathway was significantly altered when the rats were challenged by HFF diet but it was rectified after MHP intervention. RNA-Seq based transcriptome data also indicates that MHP intervention rectified the alterations of white adipose tissue mRNA expressions in HFF-induced obese rats. Integrated omics reveals that the efficacy of MHP against obesogenic adipogenesis was potentially associated with its regulation of PPARγ and FGFR1 signaling pathway. Collectively, our findings suggest that MHP could improve obesity, providing an insight into the use of MHP in body weight management.

Transcriptomic insights into the lipotoxicity of high-fat high-fructose diet in rat and mouse.

Along with the increasing prevalence of obesity worldwide, the deleterious effects of high-calorie diet are gradually recognized through more and more epidemiological studies. However, the concealed and chronic causality whitewashes its unhealthy character. Given an ingenious mechanism orchestrates the metabolic adaptation to high-fat high-fructose (HFF) diet and connive its lipotoxicity, in this study, an experimental rat/mouse model of obesity was induced and a comparative transcriptomic analysis was performed to probe the mystery. Our results demonstrated that HFF diet consumption altered the transcriptomic pattern as well as different high-calorie diet fed rat/mouse manifested distinct hepatic transcriptome. Validation with RT-qPCR and Western blotting confirmed that SREBP1-FASN involved in de novo lipogenesis partly mediated metabolic self-adaption. Moreover, hepatic ACSL1-CPT1A-CPT2 pathway involved in fatty acids ß-oxidation, played a key role in the metabolic adaption to HFF. Collectively, our findings enrich the knowledge of the chronic adaptation mechanisms and also shed light on future investigations. Meanwhile, our results also suggest that efforts to restore the fatty acids metabolic fate could be a promising avenue to fight against obesity and associated steatosis and insulin resistance challenged by HFF diet.

A Novel Ultrafiltrate Extract of Propolis Exerts Anti-inflammatory Activity through Metabolic Rewiring.

Thousands of years ago, humans started to use propolis because of its medicinal properties, and modern science has successfully identified several bioactive molecules within this resinous bee product. However, a natural propolis extract which has been removed the adhesive glue and preserved propolis bioactive compounds is urgently needed to maximise the therapeutic opportunities. In this study, a novel ultrafiltrate fraction from Brazilian green propolis, termed P30K, was demonstrated with anti-inflammatory properties, both in vitro and in vivo. Total flavonoids and total phenolic acids content in P30K were 244.6 mg/g and 275.8 mg/g respectively, while the IC50 value of inhibition of cyclooxygenase-2 (COX-2) was 8.30 µg/mL. The anti-inflammatory activity of P30K was furtherly corroborated in experimental models of lipopolysaccharides (LPS)-induced acute liver and lung injury. Mechanistically, integrated GC-MS and LC-MS based serum metabolomics analysis revealed that P30K modulated citrate cycle (TCA), pyruvate, glyoxylate and dicarboxylate metabolism pathways to inhibit secretion of pro-inflammatory cytokines. Results of network pharmacology and molecular docking suggested that P30K targeted catechol-O-methyltransferases (COMT), 11ß-hydroxysteroid dehydrogenases (HSD11B1), and monoamine oxidases (MAOA and MAOB) to promote cellular metabolomic rewiring. Collectively, our work reveals P30K as an efficient therapeutic agent against inflammatory conditions and its efficacy is related to metabolic rewiring.

A deep learning fusion network trained with clinical and high-frequency ultrasound images in the multi-classification of skin diseases in comparison with dermatologists: a prospective and multicenter study.

Background: Clinical appearance and high-frequency ultrasound (HFUS) are indispensable for diagnosing skin diseases by providing internal and external information. However, their complex combination brings challenges for primary care physicians and dermatologists. Thus, we developed a deep multimodal fusion network (DMFN) model combining analysis of clinical close-up and HFUS images for binary and multiclass classification in skin diseases. Methods: Between Jan 10, 2017, and Dec 31, 2020, the DMFN model was trained and validated using 1269 close-ups and 11,852 HFUS images from 1351 skin lesions. The monomodal convolutional neural network (CNN) model was trained and validated with the same close-up images for comparison. Subsequently, we did a prospective and multicenter study in China. Both CNN models were tested prospectively on 422 cases from 4 hospitals and compared with the results from human raters (general practitioners, general dermatologists, and dermatologists specialized in HFUS). The performance of binary classification (benign vs. malignant) and multiclass classification (the specific diagnoses of 17 types of skin diseases) measured by the area under the receiver operating characteristic curve (AUC) were evaluated. This study is registered with www.chictr.org.cn (ChiCTR2300074765). Findings: The performance of the DMFN model (AUC, 0.876) was superior to that of the monomodal CNN model (AUC, 0.697) in the binary classification (P = 0.0063), which was also better than that of the general practitioner (AUC, 0.651, P = 0.0025) and general dermatologists (AUC, 0.838; P = 0.0038). By integrating close-up and HFUS images, the DMFN model attained an almost identical performance in comparison to dermatologists (AUC, 0.876 vs. AUC, 0.891; P = 0.0080). For the multiclass classification, the DMFN model (AUC, 0.707) exhibited superior prediction performance compared with general dermatologists (AUC, 0.514; P = 0.0043) and dermatologists specialized in HFUS (AUC, 0.640; P = 0.0083), respectively. Compared to dermatologists specialized in HFUS, the DMFN model showed better or comparable performance in diagnosing 9 of the 17 skin diseases. Interpretation: The DMFN model combining analysis of clinical close-up and HFUS images exhibited satisfactory performance in the binary and multiclass classification compared with the dermatologists. It may be a valuable tool for general dermatologists and primary care providers. Funding: This work was supported in part by the National Natural Science Foundation of China and the Clinical research project of Shanghai Skin Disease Hospital.

Assessment of the Diagnostic Performance of Clinical Examinations and High-Frequency Ultrasound in Patients With Pigmented Skin Tumors.

OBJECTIVES: To investigate whether the integration of high-frequency ultrasound (HFUS) to routine clinical examinations could improve diagnostic performance and management decision for pigmented skin tumors. METHODS: Three general practitioners trained previously and a dermatologist independently assessed pigmented skin tumors and rendered management decision based on clinical examinations alone or clinical examinations integrating HFUS. RESULTS: After integrating HFUS, the diagnostic area under the curve (AUC) (0.658-0.693 versus 0.848, all P < .05) and specificity (46.6-58.6% versus 89.7%, all P < .05) for pigmented skin malignancies were improved for general practitioners, meanwhile unnecessary biopsy rate reduced (42.9-53.6% versus 10.7%, P < .001). To the dermatologist, the diagnostic AUC (0.822 versus 0.949, P < .001), sensitivity (81.7% versus 96.7%, P = .012) and specificity (0.828 versus 0.931, P = .031) improved significantly, meanwhile both missed biopsy rate (14.5% versus 4.8%, P = .031) and unnecessary biopsy rate (19.6% versus 7.1%, P = .016) decreased. Additionally, the diagnostic performance of the general practitioner with integrating HFUS could be comparable with the dermatologist based on clinical examinations alone (all P > .05). CONCLUSIONS: As a complementary tool of clinical examinations, HFUS could help physicians differentiate pigmented skin malignancies and manage decision.

The Value of Ultra-High-Frequency Ultrasound for the Differentiation between Superficial Basal Cell Carcinoma and Bowen's Disease.

BACKGROUND: The similar visual appearance of superficial basal cell carcinoma (sBCC) and Bowen's disease (BD) may cause confusion for diagnosis. OBJECTIVE: The aim of the study was to investigate the value of ultra-high-frequency ultrasound (uHFUS) in differentiating sBCC from BD. MATERIALS AND METHODS: This prospective study included a pilot cohort of 110 patients (73 BDs and 37 sBCCs) from November 2016 to October 2020 and a validation cohort of 42 patients (30 BDs and 12 sBCCs) from July 2021 to December 2021. Clinical and uHFUS features of pathologically confirmed sBCC and BD were assessed. A predictive model was developed based on the uHFUS features of the pilot cohort. Subsequently, the model was validated and compared with clinical diagnosis in the validation cohort. RESULTS: uHFUS features with significant differences between sBCC and BD included lesion surface, skin layer involvement, hyperkeratosis, and hyperechoic spots (all p < 0.05). A prediction model based on the above features was established to identify sBCC and BD in the pilot and validation cohorts with areas under the curve (AUC) of 0.908 and 0.923, sensitivity of 82.3% and 83.3%, specificity of 91.9% and 91.7%, and accuracy of 85.5% and 85.7%, respectively, which were significantly higher than those obtained by clinical diagnosis based on photographic pictures of lesions, with the AUC of 0.692, sensitivity of 63.3%, specificity of 75.3%, and accuracy of 66.7% (all p < 0.05). CONCLUSION: uHFUS provides detailed internal features of sBCC and BD, which facilitates the differentiation between sBCC and BD, and its diagnostic performance is superior to clinical diagnosis.

OsRAM2 Function in Lipid Biosynthesis Is Required for Arbuscular Mycorrhizal Symbiosis in Rice.

Arbuscular mycorrhiza (AM) is a mutualistic symbiosis formed between most land plants and Glomeromycotina fungi. During symbiosis, plants provide organic carbon to fungi in exchange for mineral nutrients. Previous legume studies showed that the required for arbuscular mycorrhization2 (RAM2) gene is necessary for transferring lipids from plants to AM fungi (AMF) and is also likely to play a "signaling" role at the root surface. To further explore RAM2 functions in other plant lineages, in this study, two rice (Oryza sativa) genes, OsRAM2 and OsRAM2L, were identified as orthologs of legume RAM2. Examining their expression patterns during symbiosis revealed that only OsRAM2 was strongly upregulated upon AMF inoculation. CRISPR/Cas9 mutagenesis was then performed to obtain three Osram2 mutant lines (-1, -2, and -3). After inoculation by AMF Rhizophagus irregularis or Funneliformis mosseae, all of the mutant lines showed extremely low colonization rates and the rarely observed arbuscules were all defective, thus supporting a conserved "nutritional" role of RAM2 between monocot and dicot lineages. As for the signaling role, although the hyphopodia numbers formed by both AMF on Osram2 mutants were indeed reduced, their morphology showed no abnormality, with fungal hyphae invading roots successfully. Promoter activities further indicated that OsRAM2 was not expressed in epidermal cells below hyphopodia or outer cortical cells enclosing fungal hyphae but instead expressed exclusively in cortical cells containing arbuscules. Therefore, this suggested an indirect role of RAM2 rather than a direct involvement in determining the symbiosis signals at the root surface.[Formula: see text] The author(s) have dedicated the work to the public domain under the Creative Commons CC0 "No Rights Reserved" license by waiving all of his or her rights to the work worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law, 2022.

High-frequency ultrasound for differentiation between high-risk basal cell carcinoma and cutaneous squamous cell carcinoma.

BACKGROUND: The similar visual appearance of high-risk basal cell carcinoma (BCC) and cutaneous squamous cell carcinoma (cSCC) may cause confusion for diagnosis. High-frequency ultrasound (HFUS) may provide additional intralesional information and thus help to distinguish them. METHOD: In this retrospective study, we analyzed the clinical characteristics, HFUS grayscale, and color Doppler flow imaging (CDFI) features of pathologically confirmed high-risk BCC and cSCC lesions (n = 65 vs n = 68). Subsequently, discrimination models based on the significant HFUS features were established. RESULTS: Between high-risk BCC and cSCC lesions, the HFUS grayscale features of the lesion size (10.0 mm vs 17.4 mm), thickness (3.1 mm vs 5.9 mm), internal hyperechoic spots (80.0% vs 23.5%), and posterior acoustic shadowing (16.9% vs 66.2%) were statistically different (all p < 0.001). As for the CDFI features, high-risk BCC lesions mainly appeared as pattern II (47.7%), while cSCC lesions mainly appeared as pattern III (66.2%). Based on the above five features, an optimal discrimination model was established with a sensitivity of 91.2%, a specificity of 87.7%, and an accuracy of 89.5%. CONCLUSION: HFUS features, including size, thickness, internal hyperechoic spots, posterior acoustic shadowing, and Doppler vascularity pattern, are useful for differential diagnosis between high-risk BCC and cSCC.

Improving the quality of breast ultrasound examination performed by inexperienced ultrasound doctors with synchronous tele-ultrasound: a prospective, parallel controlled trial.

PURPOSE: This prospective study explored the value of synchronous tele-ultrasound (US) to aid doctors inexperienced in US with breast US examinations. METHODS: In total, 99 patients were enrolled. Two trainee doctors who were inexperienced in US (trainee A [TA] and trainee B [TB]) and one doctor who was an expert in US completed the US examinations sequentially. TA completed the US examinations independently, while TB was instructed by the expert using synchronous tele-US. Subsequently, the expert performed on-site US examinations in person. Separately, they selected the most clinically significant nodule as the target nodule. Consistency with the expert and image quality were compared between TA and TB to evaluate tele-US. Furthermore, TB and the patients evaluated tele-US through questionnaires. RESULTS: TB demonstrated higher consistency with the expert in terms of target nodule selection than TA (93.3% vs. 63.3%, P<0.001). TB achieved good inter-observer agreement (ICC, >0.75) with the expert on five US features (5/9, 55.6%), while TA only did so for one (1/9, 11.1%) (P=0.046). TB's image quality was higher than TA's in gray value, time gain compensation, depth, color Doppler adjustment, and the visibility of key information (P=0.018, P<0.001, P<0.001, P=0.033, and P=0.006, respectively). The comprehensive assessment score was higher for TB than for TA (3.96±0.82 vs. 3.09±0.87, P<0.001). Tele-US was helpful in 69.7% of US examinations and had a training effect in 68.0%. Furthermore, 63.6% of patients accepted tele-US and 60.6% were willing to pay. CONCLUSION: Tele-US can help doctors inexperienced in US to perform breast US examinations.

Quantitative assessment of crystal dissolution in gout during urate-lowering therapy with computer-aided MicroPure imaging: a cohort study.

BACKGROUND: To evaluate whether MicroPure imaging, an ultrasound (US) image-processing technique with computer-aided analysis, can quantitatively detect crystal dissolution during urate-lowering therapy (ULT) in gout. METHODS: This was a prospective study of gout patients requiring ULT. The first metatarsophalangeal joints were examined using US and MicroPure before and after 3 months of ULT. Elementary lesions of gout, including the double contour sign (DCS), aggregates, tophi, erosion, and other US features were recorded at baseline and 3 months. MicroPure imaging features were automatically calculated by a self-developed software. Patients were divided into goal-achieved and goal-not-achieved groups according to their urate levels at 3 months. The US and MicroPure imaging features of the two groups were analyzed at baseline and 3 months. RESULTS: A total of 55 consecutive patients were enrolled (25: goal-achieved group; 30: goal-not-achieved group). US findings demonstrated that the power Doppler signal grade decreased at 3 months, regardless of the group (both P<0.05). From baseline to 3 months, tophi size and the DCS reduced in the goal-achieved group (both P<0.05), while the US aggregate features showed no difference (P=0.250). However, on the MicroPure imaging, the number and density of aggregates at 3 months decreased in the goal-achieved group (both P<0.05). There were no significant changes at 3 months in any of the MicroPure imaging features in the goal-not-achieved group (all P>0.05). CONCLUSIONS: In comparison with B-mode US, computer-aided MicroPure imaging can sensitively and quantitatively detect aggregate dissolution during effective ULT after only 3 months of treatment.

Value of High-Frequency Ultrasound for Differentiating Invasive Basal Cell Carcinoma from Non-invasive Types.

The purpose of the study was to evaluate the value of high-frequency ultrasound (HFUS) for differentiating invasive basal cell carcinomas (BCCs) from non-invasive BCCs. We established a prediction model based on ultrasound features and validated it further. One hundred patients in the pilot cohort and another 43 in the validation cohort were evaluated. All patients underwent HFUS examinations by the same radiologist, and then were divided on the basis of pathology into invasive and non-invasive types. With respect to growth pattern, 60.5% of invasive BCCs had an irregular pattern, whereas 89.5% of non-invasive BCCs had a nodular or crawling pattern (p < 0.001). As for the layers involved, the more invasive BCCs broke through the dermis compared with non-invasive BCCs (23.3% vs. 1.8%) (p < 0.001). With respect to intralesional hyperechoic spot distribution, invasive and non-invasive BCCs tended to be clustered and absent/scattered-like, respectively (55.8% vs. 91.2%) (p < 0.001). On the basis of the aforementioned features, a prediction model was established with accuracies of 84.0% and 76.7%, respectively, in the pilot and validation cohorts. HFUS holds promise for the differentiation of the invasiveness of BCCs and is helpful in its clinical management.

High-frequency ultrasound in the diagnosis of the spectrum of cutaneous squamous cell carcinoma: Noninvasively distinguishing actinic keratosis, Bowen's Disease, and invasive squamous cell carcinoma.

OBJECTIVE: To evaluate high-frequency ultrasound (HFUS) features for diagnosing cutaneous squamous cell carcinoma (cSCC) as a spectrum of progressively advanced malignancies, including precursor actinic keratosis (AK), Bowen's disease (BD), and invasive squamous cell carcinoma (iSCC). METHOD: In this retrospective study, 160 skin lesions diagnosed histopathologically (54 AK, 54 BD, and 52 iSCC) in 160 patients were included. The HFUS features of AK, BD, and iSCC were analyzed. The obtained data were evaluated using univariate and forward multivariate logistic regression analyses. RESULTS: The most significant HFUS features in AK were regular surface (odds ratio [OR], 8.42) and irregular basal border (OR, 6.36). The most significant HFUS features in BD were crumpled surface (OR, 19.62) and layer involvement confined to the epidermis (OR, 3.96). The most significant HFUS features in iSCC were concave surface (OR, 27.06), stratum corneum (SC) detachment (OR, 14.41), irregular basal border (OR, 4.01), and convex surface (OR, 3.73). The characteristics of surface features, basal border, and layer involvement could be valuable HFUS clues in the discrimination of AK, BD, and iSCC. CONCLUSION: High-frequency ultrasound is valuable for the differentiation of AK, BD, and iSCC, which may allow dynamic and noninvasive monitoring in the spectrum of cSCC.

Multimodal Ultrasound Imaging in Breast Imaging-Reporting and Data System 4 Breast Lesions: A Prediction Model for Malignancy.

The purpose of this study was to develop, validate and test a prediction model for discriminating malignant from benign breast lesions using conventional ultrasound (US), US elastography of strain elastography and contrast-enhanced ultrasound (CEUS). The study included 454 patients with breast imaging-reporting and data system (BI-RADS) category 4 breast lesions identified on histologic examinations. Firstly, 228 breast lesions (cohort 1) were analyzed by logistic regression analysis to identify the risk factors, and a breast malignancy prediction model was created. Secondly, the prediction model was validated in cohort 2 (84 patients) and tested in cohort 3 (142 patients) by using analysis of the area under the receiver operating characteristic curve (AUC). Univariate regression indicated that age ≥40 y, taller than wide shape on US, early hyperenhancement on CEUS and enlargement of enhancement area on CEUS were independent risk factors for breast malignancy (all p < 0.05). The logistic regression equation was established as follows: p = 1/1+Exp∑[-5.066 + 3.125 x (if age ≥40 y) + 1.943 x (if taller than wide shape) + 1.479 x (if early hyperenhancement) + 4.167 x (if enlargement of enhancement area). The prediction model showed good discrimination performance with an AUC of 0.967 in cohort 1, 0.948 in cohort 2 and 0.920 in cohort 3. By using the prediction model to selectively downgrade category 4a lesions, the re-rated BI-RADS yield an AUC of 0.880 (95% confidence interval [CI], 0.794-0.965) in cohort 2 and 0.870 (95% CI, 0.801-0.939) in cohort 3. The specificity increased from 0.0% (0/35) to 80.0% (28/35) without loss of sensitivity (from 100.0% to 95.9%, p = 0.153) in cohort 2. Similarly, the specificity increased from 0.0% (0/58) to 77.6% (45/58) without loss of sensitivity (from 100.0% to 96.4%, p = 0.081) in cohort 3. Multimodal US showed good diagnostic performance in predicting breast malignancy of BI-RADS category 4 lesions. Although the loss of sensitivity was existing, the addition of multimodal US to US BI-RADS could improve the specificity in BI-RADS category 4 lesions, which reduced unnecessary biopsies.

Predicting Axillary Lymph Node Metastasis in Patients With Breast Invasive Ductal Carcinoma With Negative Axillary Ultrasound Results Using Conventional Ultrasound and Contrast-Enhanced Ultrasound.

OBJECTIVES: The purpose of this study was to establish a scoring system for predicting axillary lymph node metastasis (ALNM) in patients with breast invasive ductal carcinoma with negative axillary ultrasound (US) results. METHODS: In this retrospective study, 156 breast invasive ductal carcinoma lesions from 156 women were retrospectively enrolled. The features of conventional US and contrast-enhanced ultrasound (CEUS) qualitative enhancement patterns and quantitative enhancement parameters were analyzed. Subsequently, a scoring system was created by a multivariate logistic regression analysis. RESULTS: The results found that 60 patients (38%) showed ALNM. A scoring system was defined as risk score = 1.75 × (if lesion size ≥20 mm) + 1.93 × (if uncircumscribed margin shown on conventional US) + 1.77 × (if coarse or twisting penetrating vessels shown on CEUS). When the risk scores were less than 1.75, 1.75 to 1.93, 1.94 to 3.70, and 3.70 or higher, the risk rates of ALNM were 0% (0 of 9), 10.7% (5 of 46), 29.2% (14 of 48) and 77.4% (41 of 53), respectively. In comparison with conventional US alone, the scoring system using the combination of conventional US and CEUS showed better discrimination ability in terms of the area under the curve (0.830 versus 0.777; P = .037). CONCLUSIONS: A scoring system based on conventional US and CEUS may improve the prediction of ALNM.

Molecular mapping of the gene(s) conferring resistance to Soybean mosaic virus and Bean common mosaic virus in the soybean cultivar Raiden.

KEY MESSAGE: In the soybean cultivar Raiden, both a SMV-resistance gene and a BCMV-resistance gene were fine-mapped to a common region within the Rsv1 complex locus on chromosome 13, in which two CC-NBS-LRR resistance genes (Glyma.13g184800 and Glyma.13g184900) exhibited significant divergence between resistant and susceptible cultivars and were subjected to positive selection. Both Soybean mosaic virus (SMV) and Bean common mosaic virus (BCMV) can induce soybean mosaic diseases. To date, few studies have explored soybean resistance against these two viruses simultaneously. In this work, Raiden, a cultivar resistant to both SMV and BCMV, was crossed with a susceptible cultivar, Williams 82, to fine-map the resistance genes. After inoculating ~ 200 F2 individuals with either SMV (SC6-N) or BCMV (HZZB011), a segregation ratio of 3 resistant:1 susceptible was observed, indicating that for either virus, a single dominant gene confers resistance. Bulk segregation analysis (BSA) revealed that the BCMV-resistance gene is also linked to the SMV-resistance Rsv1 complex locus. Genotyping the F2 individuals with 12 simple sequence repeat (SSR) markers across the Rsv1 complex locus then preliminarily mapped the SMV-resistance gene, Rsv1-r, between SSR markers BARCSOYSSR_13_1075 and BARCSOYSSR_13_1161 and the BCMV-resistance gene between BARCSOYSSR_13_1084 and BARCSOYSSR_13_1115. Furthermore, a population of 1009 F2 individuals was screened with markers BARCSOYSSR_13_1075 and BARCSOYSSR_13_1161, and 32 recombinant F2 individuals were identified. By determining the genotypes of these F2 individuals on multiple internal SSR and single nucleotide polymorphism (SNP) markers and assaying the phenotypes of selected recombinant F2:3 lines, both the SMV- and BCMV-resistance genes were fine-mapped to a common region ( ~ 154.5 kb) between two SNP markers: SNP-38 and SNP-50. Within the mapped region, two CC-NBS-LRR genes exhibited significant divergence between Raiden and Williams 82, and their evolution has been affected by positive selection.

A bean common mosaic virus (BCMV)-resistance gene is fine-mapped to the same region as Rsv1-h in the soybean cultivar Suweon 97.

KEY MESSAGE: In the soybean cultivar Suweon 97, BCMV-resistance gene was fine-mapped to a 58.1-kb region co-localizing with the Soybean mosaic virus (SMV)-resistance gene, Rsv1-h raising a possibility that the same gene is utilized against both viral pathogens. Certain soybean cultivars exhibit resistance against soybean mosaic virus (SMV) or bean common mosaic virus (BCMV). Although several SMV-resistance loci have been reported, the understanding of the mechanism underlying BCMV resistance in soybean is limited. Here, by crossing a resistant cultivar Suweon 97 with a susceptible cultivar Williams 82 and inoculating 220 F2 individuals with a BCMV strain (HZZB011), we observed a 3:1 (resistant/susceptible) segregation ratio, suggesting that Suweon 97 possesses a single dominant resistance gene against BCMV. By performing bulked segregant analysis with 186 polymorphic simple sequence repeat (SSR) markers across the genome, the resistance gene was determined to be linked with marker BARSOYSSR_13_1109. Examining the genotypes of nearby SSR markers on all 220 F2 individuals then narrowed down the gene between markers BARSOYSSR_13_1109 and BARSOYSSR_13_1122. Furthermore, 14 previously established F2:3 lines showing crossovers between the two markers were assayed for their phenotypes upon BCMV inoculation. By developing six more SNP (single nucleotide polymorphism) markers, the resistance gene was finally delimited to a 58.1-kb interval flanked by BARSOYSSR_13_1114 and SNP-49. Five genes were annotated in this interval of the Williams 82 genome, including a characteristic coiled-coil nucleotide-binding site-leucine-rich repeat (CC-NBS-LRR, CNL)-type of resistance gene, Glyma13g184800. Coincidentally, the SMV-resistance allele Rsv1-h was previously mapped to almost the same region, thereby suggesting that soybean Suweon 97 likely relies on the same CNL-type R gene to resist both viral pathogens.