Deletion of chromosomal region 8p21 confers resistance to Bortezomib and is associated with upregulated Decoy trail receptor expression in patients with multiple myeloma

Loss of the chromosomal region 8p21 negatively effects survival in patients with multiple myeloma (MM) that undergo autologous stem cell transplantation (ASCT). In this study, we aimed to identify the immunological and molecular consequences of del(8)(p21) with regards to treatment response and bortezomib resistance. In patients receiving bortezomib as a single first line agent without any high-dose therapy, we have observed that patients with del(8)(p21) responded poorly to bortezomib with 50% showing no response while patients without the deletion had a response rate of 90%. In vitro analysis revealed a higher resistance to bortezomib possibly due to an altered gene expression profile caused by del(8)(p21) including genes such as TRAIL-R4, CCDC25, RHOBTB2, PTK2B, SCARA3, MYC, BCL2 and TP53. Furthermore, while bortezomib sensitized MM cells without del(8)(p21) to TRAIL/APO2L mediated apoptosis, in cells with del(8)(p21) bortezomib failed to upregulate the pro-apoptotic death receptors TRAIL-R1 and TRAIL-R2 which are located on the 8p21 region. Also expressing higher levels of the decoy death receptor TRAIL-R4, these cells were largely resistant to TRAIL/APO2L mediated apoptosis. Corroborating the clinical outcome of the patients, our data provides a potential explanation regarding the poor response of MM patients with del(8)(p21) to bortezomib treatment. Furthermore, our clinical analysis suggests that including immunomodulatory agents such as Lenalidomide in the treatment regimen may help to overcome this negative effect, providing an alternative consideration in treatment planning of MM patients with del(8)(p21)

Inflammation-Associated Nitrotyrosination Affects TCR Recognition through Reduced Stability and Alteration of the Molecular Surface of the MHC Complex

Nitrotyrosination of proteins, a hallmark of inflammation, may result in the production of MHC-restricted neoantigens that can be recognized by T cells and bypass the constraints of immunological self-tolerance. Here we biochemically and structurally assessed how nitrotyrosination of the lymphocytic choriomeningitis virus (LCMV)-associated immunodominant MHC class I-restricted epitopes gp33 and gp34 alters T cell recognition in the context of both H-2Db and H-2Kb. Comparative analysis of the crystal structures of H-2Kb/gp34 and H-2Kb/NY-gp34 demonstrated that nitrotyrosination of p3Y in gp34 abrogates a hydrogen bond interaction formed with the H-2Kb residue E152. As a consequence the conformation of the TCR-interacting E152 was profoundly altered in H-2Kb/NY-gp34 when compared to H-2Kb/gp34, thereby modifying the surface of the nitrotyrosinated MHC complex. Furthermore, nitrotyrosination of gp34 resulted in structural over-packing, straining the overall conformation and considerably reducing the stability of the H-2Kb/NY-gp34 MHC complex when compared to H-2Kb/gp34. Our structural analysis also indicates that nitrotyrosination of the main TCR-interacting residue p4Y in gp33 abrogates recognition of H-2Db/gp33-NY complexes by H-2Db/gp33-specific T cells through sterical hindrance. In conclusion, this study provides the first structural and biochemical evidence for how MHC class I-restricted nitrotyrosinated neoantigens may enable viral escape and break immune tolerance

Diagnosis of Alzheimer Disease Using 2D MRI Slices by Convolutional Neural Network

There are many kinds of brain abnormalities that cause changes in different parts of the brain. Alzheimer’s disease is a chronic condition that degenerates the cells of the brain leading to memory asthenia. Cognitive mental troubles such as forgetfulness and confusion are one of the most important features of Alzheimer’s patients. In the literature, several image processing techniques, as well as machine learning strategies, were introduced for the diagnosis of the disease. This study is aimed at recognizing the presence of Alzheimer’s disease based on the magnetic resonance imaging of the brain. We adopted a deep learning methodology for the discrimination between Alzheimer’s patients and healthy patients from 2D anatomical slices collected using magnetic resonance imaging. Most of the previous researches were based on the implementation of a 3D convolutional neural network, whereas we incorporated the usage of 2D slices as input to the convolutional neural network. The data set of this research was obtained from the OASIS website. We trained the convolutional neural network structure using the 2D slices to exhibit the deep network weightings that we named as the Alzheimer Network (AlzNet). The accuracy of our enhanced network was 99.30%. This work investigated the effects of many parameters on AlzNet, such as the number of layers, number of filters, and dropout rate. The results were interesting after using many performance metrics for evaluating the proposed AlzNet

Emotion Recognition Based on Spatially Smooth Spectral Features of the EEG

The primary aim of this study was to select the optimal feature subset for discrimination of three dimensions of emotions (arousal, valence, liking) from subjects using electroencephalogram (EEG) signals. The EEG signals were collected from 25 channels on 21 healthy subjects whilst they were watching movie segments with emotional content. The band power values extracted from eleven frequency bands, namely delta (0.5-3.5 Hz), theta (4-7.5 Hz), alpha (8-12 Hz), beta (13-30 Hz), gamma (30-50 Hz), low theta (4-6 Hz), high theta (6-8 Hz), low alpha (8-10 Hz), high alpha (10-12 Hz), low beta (13-18 Hz) and high beta (18-30 Hz) bands, were used as EEG features. The most discriminative features for classification of EEG feature sets were selected using sequential floating forward search (SFFS) algorithm and a modified version of SFFS algorithm, which imposes the topographical smoothness of spectral features, along with linear discriminant analysis (LDA) classifier. The best classification accuracies for three emotional dimensions were obtained for liking (72.22%) followed by arousal (67.50%) and valence (66.67%). SFFS-LDA and modified SFFS-LDA algorithms produced slightly different classification accuracies. However, the findings suggested that the use of modified SFFS-LDA algorithm provides more robust feature subsets for understanding of underlying functional neuroanatomic mechanisms corresponding to distinct emotional states

Nitrotyrosination of p4Y in H-2D^b/NY-gp33 directly affects recognition by H-2D^b/gp33-specific TCR.

<p>Nitrotyrosination of the main TCR-interacting peptide residue p4Y will affect the structural conformation of both TCR interacting residues on H-2D^b and of the TCR P14. The peptide binding cleft of H-2D^b and the TCR, both colored in white, are annotated. Hydrogen bond interactions appear as dotted lines. <b>A.</b> In H-2D^b/gp33, the side chain of p4Y protrudes out of the H-2D^bpeptide-binding cleft, positioning itself perfectly in the hot spot of the p14 TCR composed of the CDR3 loops from both Vα and Vβ. It forms three hydrogen bonds, two of them directly with Y36(Vα) and G102(Vβ) on the TCR P14. The last hydrogen bond is formed with the side chain of the H-2D^b histidine residue H155, linking this domain of the heavy chain to the TCR. <b>B.</b> The side chain of the nitrotyrosinated p4-NY can not be accommodated within the hot-spot of P14, resulting in sterical clashes with the side chain of the TCR residue Y36(Vα). Furthermore, the negatively charged side chain of the H-2D^b residue E163, important for TCR recognition, would also be repelled by the introduced negatively charged nitrotyrosination. <b>C.</b> Similarly, the other rotamer of the nitrotyrosinated p4-NY would result in sterical clashes with both G102(Vβ)the side chain of H155, abolishing all formed hydrogen bond interactions.</p

Crystal structures of H-2K^b in complex with gp34 and NY-gp34.

<p>Overall schematic views of the H-2K^b/gp34 and H-2K^b/NY-gp34 MHC complexes are presented in the left part of panels A and B. The α1, α2 and α3 domains of the MHC heavy chain are colored in white. The β₂m subunit is colored in green. The peptides are in blue. The 2F_o-F_c electron density maps for the peptides gp34 and NY-gp34 when bound to H-2K^b presented in the right part of panels A and B, respectively, are contoured at 1.0 σ. The final models are displayed for comparison. The peptides, depicted with their N-termini to the left and their C-termini to the right, are displayed ‘from above’ as seen by the TCRs.</p

Nitrotyrosination of peptide residue p3Y results in a conformational change of the side-chain of the H-2K^b residue E152 only, altering TCR recognition.

<p><b>A.</b> Superposition of the peptide-binding clefts of H-2K^b/gp34 and H-2K^b/NY-gp34 demonstrate a subtle shift of the side chain of p3NY when compared to p3Y, resulting in a significant conformational change of the TCR-interacting H-2K^b heavy chain residue E152 (underlined). Both side chains of residues p3Y and p3NY protrude in the D-pocket of H-2K^b consisting of residues W147, E152, R155, L156 and Y159. The gp34 and NY-gp34 peptides are colored cyan and light green, respectively. <b>B.</b> Side view of the peptide gp34 when bound to H-2K^b. Three hydrogen bond interactions are formed between p3Y and the H-2K^b residues E152 and R155. No interactions are observed with the H-2K^b residues Q114 and Y116. <b>C.</b> A novel hydrogen bond and a long ionic range interaction are formed between p3NY and the H-2K^b residues Q114 and Y116, respectively. While two hydrogen bond interactions are maintained between p3NY and R155, all interactions are lost with E152.</p

Protrusion of p1K in H-2K^b/gp33 does not affect the overall conformation of the N-terminal part of the peptide binding cleft and conserves the conformation of the epitopes.

<p><b>A.</b> Superimposed side views of the peptides gp33 (KAVYNFATM) and gp34 (AVYNFATM) depicting how residue p1K in gp33 protrudes out of the peptide binding cleft of H-2K^b. The remaining residues of gp33 take a similar conformation to all the side chains of gp34. The peptides gp33 and gp34, annotated in black and cyan, respectively, are depicted with their N termini to the left and their C termini to the right. The carbon atoms of the peptides gp33 and gp34 are colored in white and cyan, respectively. Carbon, nitrogen and oxygen atoms are in cyan, blue and red, respectively. The peptide-binding cleft of H-2K^b is colored white. <b>B.</b> Conformation of side chain residues interacting with the N-termini of peptides in the crystal structures of H-2K^b/gp33 (in white) and H-2K^b/gp34 (both MHC complexes from the asymmetric unit are displayed in cyan and light green, respectively), following superposition of the α₁α₂ domains. Note that the p2A residue in gp33 occupies the position corresponding to the p1A in gp34. The side chain of p1K in gp33 is not displayed. The orientation of the peptides is depicted by a black arrow (from the N terminus toward the C terminus). The α1 and α2 helices are indicated.</p