Forages are vital in maintaining the dietary structures of ruminants, and reducing their costs is important for improving dairy production efficiency. Thus, this study investigated the effects of dietary forage types on dry matter intake, production, rumen fermentation, and the microbial profile in peak-lactating cows. Eight cows (600 ± 25 kg) with days in milk (60 ± 10 days) were assigned to four groups using a replicated 4 × 4 Latin square design: OG (oat hay + alfalfa hay + corn silage + concentrate), CW (Leymus chinensis + alfalfa hay + corn silage + concentrate), AS (alfalfa silage + oat hay + corn silage + concentrate), and AC (alkali-treated corn straw + alfalfa hay + corn silage + concentrate). The ruminal butyrate acid concentration was lower in the OG group than in the AS and AC groups post-feeding (12 h; p < 0.05). Ruminal NH3-N content was higher in the AS group than in the AC and CW groups post-feeding (9 h; p < 0.05). The percentage of ruminal Oscillospira and unknown microbes was higher in the CW group than in the other groups (p < 0.05). The total rumination time and rumination time per dry matter intake of AC were significantly higher than those of the other groups (p < 0.05). Milk lactose content in the AS group was highest among the groups (p < 0.05), and milk fat content was higher in the OG group than in the CW group (all p < 0.05). Overall, our results suggested that the butyrate acid content of forage from oat hay in rumen fermentation was significantly lower than that of calcium oxide and alfalfa silage sources when the dietary nutrition level was similar. The diet derived from L. chinensis increased the number of fibrillation helices related to fiber-decomposing bacteria and simultaneously increased unknown strains. Forage derived from alfalfa silage tended to increase milk protein levels. Alkali-treated corn straw could significantly increase the total rumination time and unit dry matter rumination time of dairy cows, which plays an important role in maintaining rumen health. The rational use of low-quality forage has broad prospects in China.

Purpose: Despite successful clinical management of castration-sensitive prostate cancer (CSPC), the 5-year survival rate for men with castration-resistant prostate cancer is only 32%. Combination treatment strategies to prevent disease recurrence are increasing, albeit in biomarker-unselected patients. Identifying a biomarker in CSPC to stratify patients who will progress on standard-of-care therapy could guide therapeutic strategies. Experimental Design: Targeted deep sequencing was performed for the University of Illinois (UI) cohort (n = 30), and immunostaining was performed on a patient tissue microarray (n = 149). Bioinformatic analyses identified pathways associated with biomarker overexpression (OE) in the UI cohort, consolidated RNA sequencing samples accessed from Database of Genotypes and Phenotypes (n = 664), and GSE209954 (n = 68). Neutralizing antibody patritumab and ectopic HER3 OE were utilized for functional mechanistic experiments. Results: We identified ERBB3 OE in diverse patient populations with CSPC, where it was associated with advanced disease at diagnosis. Bioinformatic analyses showed a positive correlation between ERBB3 expression and the androgen response pathway despite low dihydrotestosterone and stable expression of androgen receptor (AR) transcript in Black/African American men. At the protein level, HER3 expression was negatively correlated with intraprostatic androgen in Black/African American men. Mechanistically, HER3 promoted enzalutamide resistance in prostate cancer cell line models and HER3-targeted therapy resensitized therapy-resistant prostate cancer cell lines to enzalutamide. Conclusions: In diverse patient populations with CSPC, ERBB3 OE was associated with high AR signaling despite low intraprostatic androgen. Mechanistic studies demonstrated a direct link between HER3 and enzalutamide resistance. ERBB3 OE as a biomarker could thus stratify patients for intensification of therapy in castration-sensitive disease, including targeting HER3 directly to improve sensitivity to AR-targeted therapies.

Supplemental Table 2. Prevalence of Black/AA/AFR Patients in Consolidated RNA-seq Datasets.

Supplemental Table 1. Self-reported Race vs. Ancestry Admixture.

Supplemental Figure 3: ERBB3 Expression and Preliminary Clinical Outcomes.

Supplemental Table 2. Prevalence of Black/AA/AFR Patients in Consolidated RNA-seq Datasets.