Yunjun Wang

For a long time, NMR chemical shifts have been used to identify protein secondary structures. Currently, this is accomplished through comparing the observed (1)H(alpha), (13)C(alpha), (13)C(beta), or (13)C' chemical shifts with the random coil values. Here, we present a new protocol, which is based on the joint probability of each of the three secondary structural types (beta-strand, alpha-helix, and random coil) derived from chemical-shift data, to identify the secondary structure. In combination with empirical smooth filters/functions, this protocol shows significant improvements in the accuracy and the confidence of identification. Updated chemical-shift statistics are reported, on the basis of which the reliability of using chemical shift to identify protein secondary structure is evaluated for each nucleus. The reliability varies greatly among the 20 amino acids, but, on average, is in the order of: (13)C(alpha)>(13)C'>(1)H(alpha)>(13)C(beta)>(15)N>(1)H(N) to distinguish an alpha-helix from a random coil; and (1)H(alpha)>(13)C(beta) >(1)H(N) approximately (13)C(alpha) approximately (13)C' approximately (15)N for a beta-strand from a random coil. Amide (15)N and (1)H(N) chemical shifts, which are generally excluded from the application, in fact, were found to be helpful in distinguishing a beta-strand from a random coil. In addition, the chemical-shift statistical data are compared with those reported previously, and the results are discussed. A JAVA User Interface program has been developed to make the entire procedure fully automated and is available via http://ccsr3150-p3.stanford.edu.

Objective: In this study, we exploited a VGG-16 deep convolutional neural network (DCNN) model to differentiate papillary thyroid carcinoma (PTC) from benign thyroid nodules using cytological images. Methods: A pathology-proven dataset was built from 279 cytological images of thyroid nodules. The images were cropped into fragmented images and divided into a training dataset and a test dataset. VGG-16 and Inception-v3 DCNNs were trained and tested to make differential diagnoses. The characteristics of tumor cell nucleus were quantified as contours, perimeter, area and mean of pixel intensity and compared using independent Student's t-tests. Results: In the test group, the accuracy rates of the VGG-16 model and Inception-v3 on fragmented images were 97.66% and 92.75%, respectively, and the accuracy rates of VGG-16 and Inception-v3 in patients were 95% and 87.5%, respectively. The contours, perimeter, area and mean of pixel intensity of PTC in fragmented images were more than the benign nodules, which were 61.0117.10 vs 47.0024.08, p=0.000, 134.9921.42 vs 62. 4029.15, p=0.000, 1770.89627.22 vs 1157.27722.23, p=0.013, 165.8426.33 vs 132.9428.73, p=0.000), respectively. Conclusion: In summary, after training with a large dataset, the DCNN VGG-16 model showed great potential in facilitating PTC diagnosis from cytological images. The contours, perimeter, area and mean of pixel intensity of PTC in fragmented images were more than the benign nodules.

Solid tumors have developed robust ferroptosis resistance. The mechanism underlying ferroptosis resistance regulation in solid tumors, however, remains elusive. Here, we report that the hypoxic tumor microenvironment potently promotes ferroptosis resistance in solid tumors in a hypoxia-inducible factor 1α (HIF-1α)-dependent manner. In combination with HIF-2α, which promotes tumor ferroptosis under hypoxia, HIF-1α is the main driver of hypoxia-induced ferroptosis resistance. Mechanistically, HIF-1α-induced lactate contributes to ferroptosis resistance in a pH-dependent manner that is parallel to the classical SLC7A11 and FSP1 systems. In addition, HIF-1α also enhances transcription of SLC1A1, an important glutamate transporter, and promotes cystine uptake to promote ferroptosis resistance. In support of the role of hypoxia in ferroptosis resistance, silencing HIF-1α sensitizes mouse solid tumors to ferroptosis inducers. In conclusion, our results reveal a mechanism by which hypoxia drives ferroptosis resistance and identify the combination of hypoxia alleviation and ferroptosis induction as a promising therapeutic strategy for solid tumors.

In this study, we report nearest neighbor residue effects statistically determined from a chemical shift database. For an amino acid sequence XYZ, we define two correction factors, Delta((X)Y)n,s and Delta(Y(Z))n,s, representing the effects on Y's chemical shifts from the preceding residue (X) and the following residue (Z), respectively, where X, Y, and Z are any of the 20 naturally occurring amino acids, n stands for (1)H(N), (15)N, (1)H(alpha), (13)C(alpha), (13)C(beta), and (13)C' nuclei, and s represents the three secondary structural types beta-strand, random coil, and alpha-helix. A total of approximately 14400 Delta((X)Y)n,s and Delta(Y(Z))n,s, representing nearly all combinations of X, Y, Z, n, and s, have been quantitatively determined. Our approach overcomes the limits of earlier experimental methods using short model peptides, and the resulting correction factors have important applications such as chemical shift prediction for the folded proteins. More importantly, we have found, for the first time, a linear correlation between the Delta((X)Y)n,s (n = (15)N) and the (13)C(alpha) chemical shifts of the preceding residue X. Since (13)C(alpha) chemical shifts of the 20 amino acids, which span a wide range of 40-70 ppm, are largely dominated by one property, the electron density of the side chain, the correlation indicates that the same property is responsible for the effect on the following residue. The influence of the secondary structure on both the chemical shifts and the nearest neighbor residue effect are also investigated.