Glial cell proteome using targeted quantitative methods for potential multi-diagnostic biomarkers

Glioblastoma is one of the most malignant primary brain cancer. Despite surgical resection with modern technology followed by chemo-radiation therapy with temozolomide, resistance to the treatment and recurrence is common due to its aggressive and infiltrating nature of the tumor with high proliferation index. The median survival time of the patients with glioblastomas is less than 15 months. Till now there has been no report of molecular target specific for glioblastomas. Early diagnosis and development of molecular target specific for glioblastomas are essential for longer survival of the patients with glioblastomas. Development of biomarkers specific for glioblastomas is most important for early diagnosis, estimation of the prognosis, and molecular target therapy of glioblastomas. To that end, in this study, we have conducted a comprehensive proteome study using primary cells and tissues from patients with glioblastoma. In the discovery stage, we have identified 7429 glioblastoma-specific proteins, where 476 proteins were quantitated using Tandem Mass Tag (TMT) method; 228 and 248 proteins showed up and down-regulated pattern, respectively. In the validation stage (20 selected target proteins), we developed quantitative targeted method (MRM: Multiple reaction monitoring) using stable isotope standards (SIS) peptide. In this study, five proteins (CCT3, PCMT1, TKT, TOMM34, UBA1) showed the significantly different protein levels (t-test: p value ≤ 0.05, AUC ≥ 0.7) between control and cancer groups and the result of multiplex assay using logistic regression showed the 5-marker panel showed better sensitivity (0.80 and 0.90), specificity (0.92 and 1.00), error rate (10 and 2%), and AUC value (0.94 and 0.98) than the best single marker (TOMM34) in primary cells and tissues, respectively. Although we acknowledge that the model requires further validation in a large sample size, the 5 protein marker panel can be used as baseline data for the discovery of novel biomarkers of the glioblastoma.

Vaccine effectiveness in symptom and viral load mitigation in COVID-19 breakthrough infections in South Korea

OBJECTIVES

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccine effectiveness in coronavirus disease (COVID-19) patients with breakthrough infections has not been established in South Korea. To address this, we assessed the impact of vaccination on symptom occurrence and viral load.

METHODS

We performed a retrospective cohort study of 9,030 COVID-19 patients enrolled between February and November 2021. The impact of vaccination on the incidence of symptoms and viral load as indicated by cycle threshold (Ct) values of RdRp and E genes was evaluated using relative risks (RRs) and 95% confidence intervals (95% CIs).

RESULTS

Compared with unvaccinated patients, fully vaccinated patients were associated with a reduced symptom onset of cough, sputum, and myalgia in COVID-19 patients (RR (95% CI) = 0.86 (0.75-0.99) for cough; RR (95% CI) = 0.74 (0.56-0.98) for sputum; RR (95% CI) = 0.65 (0.53-0.79) for myalgia, respectively). Additionally, lower risk of high viral load, Ct value of RdRp gene <15 or Ct value of E gene <15, was observed especially in fully vaccinated patients younger than 40 years ((RR (95% CI) = 0.69 (0.49-0.96) for RdRp gene; (RR (95% CI) = 0.71 (0.53-0.95) for E gene).

CONCLUSION

SARS-CoV-2 vaccination was associated with a reduced risk of COVID-19 symptoms as well as decreased viral load, especially in patients younger than 40 years.

Normalizing Input-Output Relationships of Cancer Networks for Reversion Therapy

Accumulated genetic alterations in cancer cells distort cellular stimulus-response (or input-output) relationships, resulting in uncontrolled proliferation. However, the complex molecular interaction network within a cell implicates a possibility of restoring such distorted input-output relationships by rewiring the signal flow through controlling hidden molecular switches. Here, a system framework of analyzing cellular input-output relationships in consideration of various genetic alterations and identifying possible molecular switches that can normalize the distorted relationships based on Boolean network modeling and dynamics analysis is presented. Such reversion is demonstrated by the analysis of a number of cancer molecular networks together with a focused case study on bladder cancer with in vitro experiments and patient survival data analysis. The origin of reversibility from an evolutionary point of view based on the redundancy and robustness intrinsically embedded in complex molecular regulatory networks is further discussed.

Biological insights into systemic lupus erythematosus through an immune cell-specific transcriptome-wide association study

OBJECTIVE

Genome-wide association studies (GWAS) have identified >100 risk loci for systemic lupus erythematosus (SLE), but the disease genes at most loci remain unclear, hampering translation of these genetic discoveries. We aimed to prioritise genes underlying the 110 SLE loci that were identified in the latest East Asian GWAS meta-analysis.

METHODS

We built gene expression predictive models in blood B cells, CD4+ and CD8+ T cells, monocytes, natural killer cells and peripheral blood cells of 105 Japanese individuals. We performed a transcriptome-wide association study (TWAS) using data from the latest genome-wide association meta-analysis of 208 370 East Asians and searched for candidate genes using TWAS and three data-driven computational approaches.

RESULTS

TWAS identified 171 genes for SLE (p<1.0×10-5); 114 (66.7%) showed significance only in a single cell type; 127 (74.3%) were in SLE GWAS loci. TWAS identified a strong association between CD83 and SLE (p<7.7×10-8). Meta-analysis of genetic associations in the existing 208 370 East Asian and additional 1498 cases and 3330 controls found a novel single-variant association at rs72836542 (OR=1.11, p=4.5×10-9) around CD83. For the 110 SLE loci, we identified 276 gene candidates, including 104 genes at recently-identified SLE novel loci. We demonstrated in vitro that putative causal variant rs61759532 exhibited an allele-specific regulatory effect on ACAP1, and that presence of the SLE risk allele decreased ACAP1 expression.

CONCLUSIONS

Cell-level TWAS in six types of immune cells complemented SLE gene discovery and guided the identification of novel genetic associations. The gene findings shed biological insights into SLE genetic associations.

Bispecific Antibody-Based Immune-Cell Engagers and Their Emerging Therapeutic Targets in Cancer Immunotherapy

Cancer is the second leading cause of death worldwide after cardiovascular diseases. Harnessing the power of immune cells is a promising strategy to improve the antitumor effect of cancer immunotherapy. Recent progress in recombinant DNA technology and antibody engineering has ushered in a new era of bispecific antibody (bsAb)-based immune-cell engagers (ICEs), including T- and natural-killer-cell engagers. Since the first approval of blinatumomab by the United States Food and Drug Administration (US FDA), various bsAb-based ICEs have been developed for the effective treatment of patients with cancer. Simultaneously, several potential therapeutic targets of bsAb-based ICEs have been identified in various cancers. Therefore, this review focused on not only highlighting the action mechanism, design and structure, and status of bsAb-based ICEs in clinical development and their approval by the US FDA for human malignancy treatment, but also on summarizing the currently known and emerging therapeutic targets in cancer. This review provides insights into practical considerations for developing next-generation ICEs.

A Multimodal Multi-Shank Fluorescence Neural Probe for Cell-Type-Specific Electrophysiology in Multiple Regions across a Neural Circuit

Cell-type-specific, activity-dependent electrophysiology can allow in-depth analysis of functional connectivity inside complex neural circuits composed of various cell types. To date, optics-based fluorescence recording devices enable monitoring cell-type-specific activities. However, the monitoring is typically limited to a single brain region, and the temporal resolution is significantly low. Herein, a multimodal multi-shank fluorescence neural probe that allows cell-type-specific electrophysiology from multiple deep-brain regions at a high spatiotemporal resolution is presented. A photodiode and an electrode-array pair are monolithically integrated on each tip of a minimal-form-factor silicon device. Both fluorescence and electrical signals are successfully measured simultaneously in GCaMP6f expressing mice, and the cell type from sorted neural spikes is identified. The probe's capability of combined electro-optical recordings for cell-type-specific electrophysiology at multiple brain regions within a neural circuit is demonstrated. The new experimental paradigm to enable the precise investigation of functional connectivity inside and across complex neural circuits composed of various cell types is expected.

Whole blood RNA sequencing reveals a differential transcriptomic profile associated with cervical insufficiency: a pilot study

BACKGROUND

The uterine cervix is a mechanical and immunological barrier against ascending infection during pregnancy. Cervical insufficiency (CI), a painless cervical dilation that occurs in the mid-trimester, is an important cause of extremely preterm birth. We hypothesized that women with CI have a differential transcriptomic profile. Therefore, we compared the transcriptomic profile of peripheral blood in women with CI and that of controls.

METHODS

RNA sequencing was used to generate the global gene expression profiles of 11 women with CI and 4 controls, and differential expression analysis was performed to identify genes showing significant expression changes between the CI (n = 11) and control (n = 4) groups as well as between the CI-preterm (n = 7) and CI-term (n = 4) groups. Gene set enrichment was assessed in terms of Gene Ontology processes, and a subset of differentially expressed genes in CI was validated in a different sample-set by qRT-PCR and ELISA.

RESULTS

Thirty genes were differentially expressed between the CI and control groups. Differentially upregulated genes in the CI group included neutrophil-mediated immunity-associated (DEFA3 and ELANE) and bicarbonate transport-related genes. The serum concentration of alpha defensin 3 was significantly higher in women with CI than in controls (P = 0.014). Analysis of differential gene expression according to pregnancy outcomes revealed 338 differentially expressed genes between the CI-term and CI-preterm groups. Immune and defense response to organism-associated genes and influenza A and NOD-like receptor signaling pathways were upregulated in the CI-term group.

CONCLUSIONS

Our results revealed significant differences in the whole blood transcriptomic profiles of women with CI compared to those of controls. Different immune responses in women with CI may affect pregnancy outcomes.

Multi-slice representational learning of convolutional neural network for Alzheimer's disease classification using positron emission tomography

BACKGROUND

Alzheimer's Disease (AD) is a degenerative brain disorder that often occurs in people over 65 years old. As advanced AD is difficult to manage, accurate diagnosis of the disorder is critical. Previous studies have revealed effective deep learning methods of classification. However, deep learning methods require a large number of image datasets. Moreover, medical images are affected by various environmental factors. In the current study, we propose a deep learning-based method for diagnosis of Alzheimer's disease (AD) that is less sensitive to different datasets for external validation, based upon F-18 fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT).

RESULTS

The accuracy, sensitivity, and specificity of our proposed network were 86.09%, 80.00%, and 92.96% (respectively) using our dataset, and 91.02%, 87.93%, and 93.57% (respectively) using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. We observed that our model classified AD and normal cognitive (NC) cases based on the posterior cingulate cortex (PCC), where pathological changes occur in AD. The performance of the GAP layer was considered statistically significant compared to the fully connected layer in both datasets for accuracy, sensitivity, and specificity (p < 0.01). In addition, performance comparison between the ADNI dataset and our dataset showed no statistically significant differences in accuracy, sensitivity, and specificity (p > 0.05).

CONCLUSIONS

The proposed model demonstrated the effectiveness of AD classification using the GAP layer. Our model learned the AD features from PCC in both the ADNI and Severance datasets, which can be seen in the heatmap. Furthermore, we showed that there were no significant differences in performance using statistical analysis.

Effect of diabetes on exosomal miRNA profile in patients with obesity

INTRODUCTION

Obesity is a risk factor for type 2 diabetes mellitus (T2DM) and cardiovascular disease. T2DM increases the risk of cardiovascular-related death. We investigated changes in circulating exosomal microRNA (miRNA) profiles in patients with DM with obesity compared with patients without DM with obesity.

RESEARCH DESIGN AND METHODS

This prospective study involved 29 patients with obesity (patients without DM=16, patients with DM=13) and healthy volunteers (HVs) (n=18). We measured circulating levels of exosomal miRNAs by next-generation sequencing and compared miRNA levels across the three groups.

RESULTS

The expression levels of 25 miRNAs (upregulated=14, downregulated=11) differed between patients with obesity with DM and patients with obesity without DM. Compared with HV, patients with DM with obesity had 53 dysregulated miRNAs. Additionally, moving stepwise from HV to patients with obesity without DM to patients with obesity with DM, there was a consistent increase in expression levels of miR-23a-5p and miR-6087 and a consistent decrease in expressions levels of miR-6751-3p.

CONCLUSIONS

Our data show that the exosomal miRNAs is altered by dysregulated glucose metabolism in patients with obesity. This circulating exosomal miRNA signature in patients with obesity with or without DM is a potential biomarker and therapeutic target in these patients.