The oncogenic fusion protein BCR-ABL is the driving force of leukemogenesis in chronic myeloid leukemia (CML). Despite great progress for CML treatment through application of tyrosine kinase inhibitors (TKIs) against BCR-ABL, long-term drug administration and clinical resistance continue to be an issue. Herein, we described the design, synthesis, and evaluation of novel proteolysis-targeting chimeric (PROTAC) small molecules targeting BCR-ABL which connect dasatinib and VHL E3 ubiquitin ligase ligand by extensive optimization of linkers. Our efforts have yielded SIAIS178 (19), which induces proper interaction between BCR-ABL and VHL ligase leading to effective degradation of BCR-ABL protein, achieves significant growth inhibition of BCR-ABL + leukemic cells in vitro, and induces substantial tumor regression against K562 xenograft tumors in vivo. In addition, SIAIS178 also degrades several clinically relevant resistance-conferring mutations.Our data indicate that SIAIS178 as efficacious BCR-ABL degrader warrants extensive further investigation for the treatment of BCR-ABL + leukemia.

Yellow rust is one of the most destructive diseases for winter wheat and has led to a significant decrease in winter wheat quality and yield. Identifying and monitoring yellow rust is of great importance for guiding agricultural production over large areas. Compared with traditional crop disease discrimination methods, remote sensing technology has proven to be a useful tool for accomplishing such a task at large scale. This study explores the potential of the Sentinel-2 Multispectral Instrument (MSI), a newly launched satellite with refined spatial resolution and three red-edge bands, for discriminating between yellow rust infection severities (i.e., healthy, slight, and severe) in winter wheat. The corresponding simulative multispectral bands for the Sentinel-2 sensor were calculated by the sensor’s relative spectral response (RSR) function based on the in situ hyperspectral data acquired at the canopy level. Three Sentinel-2 spectral bands, including B4 (Red), B5 (Re1), and B7 (Re3), were found to be sensitive bands using the random forest (RF) method. A new multispectral index, the Red Edge Disease Stress Index (REDSI), which consists of these sensitive bands, was proposed to detect yellow rust infection at different severity levels. The overall identification accuracy for REDSI was 84.1% and the kappa coefficient was 0.76. Moreover, REDSI performed better than other commonly used disease spectral indexes for yellow rust discrimination at the canopy scale. The optimal threshold method was adopted for mapping yellow rust infection at regional scales based on realistic Sentinel-2 multispectral image data to further assess REDSI’s ability for yellow rust detection. The overall accuracy was 85.2% and kappa coefficient was 0.67, which was found through validation against a set of field survey data. This study suggests that the Sentinel-2 MSI has the potential for yellow rust discrimination, and the newly proposed REDSI has great robustness and generalized ability for yellow rust detection at canopy and regional scales. Furthermore, our results suggest that the above remote sensing technology can be used to provide scientific guidance for monitoring and precise management of crop diseases and pests.