Computational Imaging Biomarker Correlation with Intraocular Cytokine Expression in Diabetic Macular Edema: Radiomics Insights from the IMAGINE Study.

Purpose: Various pathways and cytokines are implicated in pathogenesis of diabetic macular edema (DME). Computational imaging biomarkers (CIBs) of vessel tortuosity from ultra-widefield fluorescein angiography (UWFA) and texture patterns from OCT images have been associated with anti-vascular endothelial growth factor (VEGF) therapy treatment response in DME. This analysis was a radiogenomic assessment of the association between underlying cytokines, UWFA, and OCT-based DME CIBs. Design: Biclustering analysis based on UWFA and OCT CIBs to identify a common imaging phenotype across patients with subsequent assessment of underlying cytokine signatures and treatment response attributes. Participants: The IMAGINE DME study was a post hoc study of cytokine expressions that included 24 eyes with sufficient baseline aqueous humor samples and an in-depth assessment of the imaging studies obtained during the phase I/II DmeAntiVEgf study (DAVE) that measured different cytokine expressions. Methods: A total of 151 graph or morphologic features quantifying leakage shape, size, density, interobject distance, and architecture of leakage spots and 5 vessel tortuosity features were extracted from the baseline UWFA scans, and 494 texture-based radiomics features were extracted from each of the fluid and retinal tissue compartments of OCT images. Biclustering enables simultaneous clustering of patients and features and was used to aggregate patients in terms of their commonality of phenotypes (based on similar imaging attributes) and to identify commonality in terms of cytokine expression and treatment response to anti-VEGF therapy. Main Outcome Measures: Identification of eyes with similar imaging phenotypes to evaluate commonalities of patterns and underlying cytokine expression. Results: Strong correlations between VEGF and 7 UWFA leakage morphologic features (Pearson correlation coefficient [PCC], 0.45-0.51; P < 0.05), 1 vascular tortuosity-based UWFA feature (PCC, 0.45; P = 0.00016), and 2 OCT-derived intraretinal fluid texture features (PCC, 0.58-0.63; P < 0.05) were identified. Strong correlation between intraretinal fluid features and other cytokines (PCC, 0.41-0.59; P < 0.05) were also observed. Conclusions: This study identified groups of eyes with similar imaging phenotypes as defined by UWFA and OCT CIBs that demonstrated similar treatment response patterns and cytokine expression, including a strong association between VEGF with UWFA-derived leakage morphologic and vessel tortuosity features.

PERISCOPE-Opt: Machine learning-based prediction of optimal fermentation conditions and yields of recombinant periplasmic protein expressed in Escherichia coli.

Optimization of the fermentation process for recombinant protein production (RPP) is often resource-intensive. Machine learning (ML) approaches are helpful in minimizing the experimentations and find vast applications in RPP. However, these ML-based tools primarily focus on features with respect to amino-acid-sequence, ruling out the influence of fermentation process conditions. The present study combines the features derived from fermentation process conditions with that from amino acid-sequence to construct an ML-based model that predicts the maximal protein yields and the corresponding fermentation conditions for the expression of target recombinant protein in the Escherichia coli periplasm. Two sets of XGBoost classifiers were employed in the first stage to classify the expression levels of the target protein as high (>50 mg/L), medium (between 0.5 and 50 mg/L), or low (<0.5 mg/L). The second-stage framework consisted of three regression models involving support vector machines and random forest to predict the expression yields corresponding to each expression-level-class. Independent tests showed that the predictor achieved an overall average accuracy of 75% and a Pearson coefficient correlation of 0.91 for the correctly classified instances. Therefore, our model offers a reliable substitution of numerous trial-and-error experiments to identify the optimal fermentation conditions and yield for RPP. It is also implemented as an open-access webserver, PERISCOPE-Opt (http://periscope-opt.erc.monash.edu).

Alterations in 3D chromatin organization contribute to tumorigenesis of EGFR-amplified glioblastoma.

Background: EGFR amplification and/or mutation are found in more than half of the cases with glioblastoma. Yet, the role of chromatin interactions and its regulation of gene expression in EGFR-amplified glioblastoma remains unclear. Methods: In this study, we explored alterations in 3D chromatin organization of EGFR-amplified glioblastoma and its subsequent impact by performing a comparative analysis of Hi-C, RNA-seq, and whole-genome sequencing (WGS) on EGFR-amplified glioblastoma-derived A172 and normal astrocytes (HA1800 cell line). Results: A172 cells showed an elevated chromatin relaxation, and unexpected entanglement of chromosome regions. A genome-wide landscape of switched compartments and differentially expressed genes between HA1800 and A172 cell lines demonstrated that compartment activation reshaped chromatin accessibility and activated tumorigenesis-related genes. Topological associating domain (TAD) analysis revealed that altered TAD domains in A172 also contribute to oncogene activation and tumor repressor deactivation. Interestingly, glioblastoma-derived A172 cells showed a different chromatin loop contact propensity. Genes in tumorigenesis-associated signaling pathways were significantly enriched at the anchor loci of altered chromatin loops. Oncogene activation and tumor repressor deactivation were associated with chromatin loop alteration. Structure variations (SVs) had a dramatic impact on the chromatin conformation of EGFR-amplified glioblastoma-derived tumor cells. Moreover, our results revealed that 7p11.2 duplication activated EGFR expression in EGFR-amplified glioblastoma via neo-TAD formation and novel enhancer-promoter interaction emergence between LINC01446 and EGFR. Conclusions: The disordered 3D genomic map and multi-omics data of EGFR-amplified glioblastoma provide a resource for future interrogation of the relationship between chromatin interactions and transcriptome in tumorigenesis.

Computational epitope binning reveals functional equivalence of sequence-divergent paratopes.

The therapeutic efficacy of a protein binder largely depends on two factors: its binding site and its binding affinity. Advances in in vitro library display screening and next-generation sequencing have enabled accelerated development of strong binders, yet identifying their binding sites still remains a major challenge. The differentiation, or "binning", of binders into different groups that recognize distinct binding sites on their target is a promising approach that facilitates high-throughput screening of binders that may show different biological activity. Here we study the extent to which the information contained in the amino acid sequences comprising a set of target-specific binders can be leveraged to bin them, inferring functional equivalence of their binding regions, or paratopes, based directly on comparison of the sequences, their modeled structures, or their modeled interactions. Using a leucine-rich repeat binding scaffold known as a "repebody" as the source of diversity in recognition against interleukin-6 (IL-6), we show that the "Epibin" approach introduced here effectively utilized structural modelling and docking to extract specificity information encoded in the repebody amino acid sequences and thereby successfully recapitulate IL-6 binding competition observed in immunoassays. Furthermore, our computational binning provided a basis for designing in vitro mutagenesis experiments to pinpoint specificity-determining residues. Finally, we demonstrate that the Epibin approach can extend to antibodies, retrospectively comparing its predictions to results from antigen-specific antibody competition studies. The study thus demonstrates the utility of modeling structure and binding from the amino acid sequences of different binders against the same target, and paves the way for larger-scale binning and analysis of entire repertoires.

Reproducibility and validity of a food frequency questionnaire to assess cardiovascular health-related food intake among Mexican adolescents.

There is a lack of region-adapted tools to evaluate diet as a risk factor for cardiovascular disease (CVD) in adolescents. The study aim was to evaluate the reproducibility and validity of a paper-based and region-adapted food frequency questionnaire (FFQ) designed to assess CVD-related food and nutrient intakes of adolescents from Northwest México. The study design was cross-sectional. The FFQ was developed in a two-step process: prototype designing and a pilot test, with re-tested in a 3-month period, along with two administrations of 24 h-recall (24 hR). Pearson's and intra-class correlation coefficients (PCC and ICC) were assessed. Bland-Altman plots, limits of agreement and quintile classifications were carried out. Participants (n 221) were 53·8 % male, 18·5 ± 0·4 years old. Reproducibility had a median PCC = 0·66 for processed meats, ranging from 0·40 (saturated fat) to 0·74 (fish & shellfish), P = 0·001. ICC ranged from 0·53 (saturated fat) to 0·80 (sodium; and nuts, seeds and legumes), P = 0·001. Validity comparing FFQ1 v. 24 hR mean, PCCs ranged from 0·12 (P = 0·06) to 0·95 (P = 0·001), and ICC from 0·20 (P = 0·048) to 0·88 (P = 0·001); comparing FFQ2 v. 24 hR mean, PCCs ranged from 0·07 (P = 0·25) to 0·46 (P = 0·001), and ICC from 0·15 (P = 0·106) to 0·58 (P = 0·001). The FFQ overestimated the intake of all food groups and nutrients (P < 0·05), while Cohen's κ showed coefficients lower than 0·20. The proposed FFQ represents a moderately validated tool to estimate CVD-related food and nutrient intakes as a risk factor, which can be used in combination with multiple administrations of 24 hRs, as a critical mean in future interventions intended to reduce cardiometabolic risk in adolescents.

ceRNA network development and tumour-infiltrating immune cell analysis of metastatic breast cancer to bone.

PURPOSE: Advanced breast cancer commonly metastasises to bone; however, the molecular mechanisms underlying the affinity for breast cancer cells to bone remains unclear. Thus, we developed nomograms based on a competing endogenous RNA (ceRNA) network and analysed tumour-infiltrating immune cells to elucidate the molecular pathways that may predict prognosis in patients with breast cancer. METHODS: We obtained the RNA expression profile of 1091 primary breast cancer samples included in The Cancer Genome Atlas database, 58 of which were from patients with bone metastasis. We analysed the differential RNA expression patterns between breast cancer with and without bone metastasis and developed a ceRNA network. Cibersort was employed to differentiate between immune cell types based on tumour transcripts. Nomograms were then established based on the ceRNA network and immune cell analysis. The value of prognostic factors was evaluated by Kaplan-Meier survival analysis and a Cox proportional risk model. RESULTS: We found significant differences in long non-coding RNAs (lncRNAs), 18 microRNAs (miRNAs), and 20 messenger RNAs (mRNAs) between breast cancer with and without bone metastasis, which were used to construct a ceRNA network. We found that the protein-coding genes GJB3, CAMMV, PTPRZ1, and FBN3 were significantly differentially expressed by Kaplan-Meier analysis. We also observed significant differences in the abundance of plasma cell and follicular helper T cell populations between the two groups. In addition, the proportion of mast cells, gamma delta T cells, and plasma cells differed depending on disease location and stage. Our analysis showed that a high proportion of follicular helper T cells and a low proportion of eosinophils promoted survival and that DLX6-AS1, Wnt6, and GABBR2 expression may be associated with bone metastasis in breast cancer. CONCLUSIONS: We developed a bioinformatic tool for exploring the molecular mechanisms of bone metastasis in patients with breast cancer and identified factors that may predict the occurrence of bone metastasis.

Interfaces Between Alpha-helical Integral Membrane Proteins: Characterization, Prediction, and Docking.

Protein-protein interaction (PPI) is an essential mechanism by which proteins perform their biological functions. For globular proteins, the molecular characteristics of such interactions have been well analyzed, and many computational tools are available for predicting PPI sites and constructing structural models of the complex. In contrast, little is known about the molecular features of the interaction between integral membrane proteins (IMPs) and few methods exist for constructing structural models of their complexes. Here, we analyze the interfaces from a non-redundant set of complexes of α-helical IMPs whose structures have been determined to a high resolution. We find that the interface is not significantly different from the rest of the surface in terms of average hydrophobicity. However, the interface is significantly better conserved and, on average, inter-subunit contacting residue pairs correlate more strongly than non-contacting pairs, especially in obligate complexes. We also develop a neural network-based method, with an area under the receiver operating characteristic curve of 0.75 and a Pearson correlation coefficient of 0.70, for predicting interface residues and their weighted contact numbers (WCNs). We further show that predicted interface residues and their WCNs can be used as restraints to reconstruct the structure α-helical IMP dimers through docking for fourteen out of a benchmark set of sixteen complexes. The RMSD100 values of the best-docked ligand subunit to its native structure are <2.5 Šfor these fourteen cases. The structural analysis conducted in this work provides molecular details about the interface between α-helical IMPs and the WCN restraints represent an efficient means to score α-helical IMP docking candidates.

Network-based approach to identify prognostic biomarkers for estrogen receptor-positive breast cancer treatment with tamoxifen.

This study aims to identify effective gene networks and prognostic biomarkers associated with estrogen receptor positive (ER+) breast cancer using human mRNA studies. Weighted gene coexpression network analysis was performed with a complex ER+ breast cancer transcriptome to investigate the function of networks and key genes in the prognosis of breast cancer. We found a significant correlation of an expression module with distant metastasis-free survival (HR = 2.25; 95% CI .21.03-4.88 in discovery set; HR = 1.78; 95% CI = 1.07-2.93 in validation set). This module contained genes enriched in the biological process of the M phase. From this module, we further identified and validated 5 hub genes (CDK1, DLGAP5, MELK, NUSAP1, and RRM2), the expression levels of which were strongly associated with poor survival. Highly expressed MELK indicated poor survival in luminal A and luminal B breast cancer molecular subtypes. This gene was also found to be associated with tamoxifen resistance. Results indicated that a network-based approach may facilitate the discovery of biomarkers for the prognosis of ER+ breast cancer and may also be used as a basis for establishing personalized therapies. Nevertheless, before the application of this approach in clinical settings, in vivo and in vitro experiments and multi-center randomized controlled clinical trials are still needed.