Gene networks and systems biology in Alzheimer's disease: Insights from multi-omics approaches.

Despite numerous studies in the field of dementia and Alzheimer's disease (AD), a comprehensive understanding of this devastating disease remains elusive. Bulk transcriptomics have provided insights into the underlying genetic factors at a high level. Subsequent technological advancements have focused on single-cell omics, encompassing techniques such as single-cell RNA sequencing and epigenomics, enabling the capture of RNA transcripts and chromatin states at a single cell or nucleus resolution. Furthermore, the emergence of spatial omics has allowed the study of gene responses in the vicinity of amyloid beta plaques or across various brain regions. With the vast amount of data generated, utilizing gene regulatory networks to comprehensively study this disease has become essential. This review delves into some techniques employed in the field of AD, explores the discoveries made using these techniques, and provides insights into the future of the field.

Spatial and single-nucleus transcriptomic analysis of genetic and sporadic forms of Alzheimer's Disease.

The pathogenesis of Alzheimer's disease (AD) depends on environmental and heritable factors, with remarkable differences evident between individuals at the molecular level. Here we present a transcriptomic survey of AD using spatial transcriptomics (ST) and single-nucleus RNA-seq in cortical samples from early-stage AD, late-stage AD, and AD in Down Syndrome (AD in DS) donors. Studying AD in DS provides an opportunity to enhance our understanding of the AD transcriptome, potentially bridging the gap between genetic mouse models and sporadic AD. Our analysis revealed spatial and cell-type specific changes in disease, with broad similarities in these changes between sAD and AD in DS. We performed additional ST experiments in a disease timecourse of 5xFAD and wildtype mice to facilitate cross-species comparisons. Finally, amyloid plaque and fibril imaging in the same tissue samples used for ST enabled us to directly link changes in gene expression with accumulation and spread of pathology.

hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data.

Biological systems are immensely complex, organized into a multi-scale hierarchy of functional units based on tightly regulated interactions between distinct molecules, cells, organs, and organisms. While experimental methods enable transcriptome-wide measurements across millions of cells, popular bioinformatic tools do not support systems-level analysis. Here we present hdWGCNA, a comprehensive framework for analyzing co-expression networks in high-dimensional transcriptomics data such as single-cell and spatial RNA sequencing (RNA-seq). hdWGCNA provides functions for network inference, gene module identification, gene enrichment analysis, statistical tests, and data visualization. Beyond conventional single-cell RNA-seq, hdWGCNA is capable of performing isoform-level network analysis using long-read single-cell data. We showcase hdWGCNA using data from autism spectrum disorder and Alzheimer's disease brain samples, identifying disease-relevant co-expression network modules. hdWGCNA is directly compatible with Seurat, a widely used R package for single-cell and spatial transcriptomics analysis, and we demonstrate the scalability of hdWGCNA by analyzing a dataset containing nearly 1 million cells.

A systematic review of self-esteem and related factors among burns patients.

Introduction: The present systematic review was conducted to examine self-esteem and related factors in burns patients. Methods: A comprehensive search was conducted from the first to the April 1, 2022 at the international electronic databases such as Scopus, PubMed, Web of Science, and Persian electronic databases such as Iranmedex, and Scientific Information Database using keywords extracted from Medical Subject Headings such as "Burns", "Self-confidence", "Self-perception", "Self-esteem", and "Self-concept". Results: A total of 762 burn patients were included in this review from ten cross-sectional studies. The mean score of self-esteem in burn patients based on Rosenberg Self-Esteem Scale, State Self-Esteem Scale, Cooper Smith's self-esteem questionnaire, and Rifai's self-esteem scale were 17.77 (SD = 5.55) out of 30, 65.91 (SD = 5.49) out of 100, 56.68 (SD = 5.49) out of 100, and 109.77 (SD = 9.55) out of 145, respectively. Factors associated with self-esteem in burns patients were gender, occupation, the location of the burn, type of burn, site of burn, burn scar, and quality of life had a significant relationship with burn patients' self-esteem. Factors such as social support, family support, friends support, and supporting others, had a significant positive relationship with self-esteem of burns patient. However, self-esteem in patients with burn had a significant negative relationship with grade of burn injury, percentage of burn, depth of burn, facial burn, post-traumatic stress disorder, psychiatric morbidity, major depressive, and suicidality factors. Conclusion: Overall, patients with burns had moderate levels of self-esteem. Therefore, it is recommended that health professionals use interdisciplinary approaches to better manage burn patients.

Cell-free plasma microRNAs that identify patients with glioblastoma.

Glioblastoma (GBM) is still one of the most commonly diagnosed advanced stage primary brain tumors. Current treatments for patients with primary GBM (pGBM) are often not effective and a significant proportion of the patients with pGBM recur. The effective treatment options for recurrent GBM (rGBM) are limited and survival outcomes are poor. This retrospective multicenter pilot study aims to determine potential cell-free microRNAs (cfmiRs) that identify patients with pGBM and rGBM tumors. 2,083 miRs were assessed using the HTG miRNA whole transcriptome assay (WTA). CfmiRs detection was compared in pre-operative plasma samples from patients with pGBM (n = 32) and rGBM (n = 13) to control plasma samples from normal healthy donors (n = 73). 265 cfmiRs were found differentially expressed in plasma samples from pGBM patients compared to normal healthy donors (FDR < 0.05). Of those 193 miRs were also detected in pGBM tumor tissues (n = 15). Additionally, we found 179 cfmiRs differentially expressed in rGBM, of which 68 cfmiRs were commonly differentially expressed in pGBM. Using Random Forest algorithm, specific cfmiR classifiers were found in the plasma of pGBM, rGBM, and both pGBM and rGBM combined. Two common cfmiR classifiers, miR-3180-3p and miR-5739, were found in all the comparisons. In receiving operating characteristic (ROC) curves analysis for rGBM miR-3180-3p showed a specificity of 87.7% and a sensitivity of 100% (AUC = 98.5%); while miR-5739 had a specificity of 79.5% and sensitivity of 92.3% (AUC = 90.2%). This study demonstrated that plasma samples from pGBM and rGBM patients have specific miR signatures. CfmiR-3180-3p and cfmiR-5739 have potential utility in diagnosing patients with pGBM and rGBM tumors using a minimally invasive blood assay.

Assessment of Cell-Free microRNA by NGS Whole-Transcriptome Analysis in Cutaneous Melanoma Patients' Blood.

MicroRNAs (miRs) are small RNA molecules (18-22 nucleotides) that regulate the transcriptome at a post-transcriptional level by affecting the expression of specific genes. This regulatory mechanism is critical to maintain cell homeostasis and specific functions. Aberrant expression of miRs have been associated with pathobiological processes including cancer. There are few technologies available that are able to profile whole-genome miR expression using minimal amounts of blood samples and without the need for time-consuming extraction steps. Here, we describe the HTG EdgeSeq miR Whole-Transcriptome Assay (WTA) in serum and plasma samples. To identify specific cell-free miR (cfmiR) patterns we have first focused on the analysis of normal donor samples and have then compared these to patients with cutaneous melanoma. The identification of specific cfmiR for melanoma patients will allow for better patient surveillance during targeted and/or checkpoint inhibitor immunotherapy (CII) treatment.

Towards a unified open access dataset of molecular interactions.

The International Molecular Exchange (IMEx) Consortium provides scientists with a single body of experimentally verified protein interactions curated in rich contextual detail to an internationally agreed standard. In this update to the work of the IMEx Consortium, we discuss how this initiative has been working in practice, how it has ensured database sustainability, and how it is meeting emerging annotation challenges through the introduction of new interactor types and data formats. Additionally, we provide examples of how IMEx data are being used by biomedical researchers and integrated in other bioinformatic tools and resources.

A Pilot Study Comparing the Efficacy of Lactate Dehydrogenase Levels Versus Circulating Cell-Free microRNAs in Monitoring Responses to Checkpoint Inhibitor Immunotherapy in Metastatic Melanoma Patients.

Serum lactate dehydrogenase (LDH) is a standard prognostic biomarker for stage IV melanoma patients. Often, LDH levels do not provide real-time information about the metastatic melanoma patients' disease status and treatment response. Therefore, there is a need to find reliable blood biomarkers for improved monitoring of metastatic melanoma patients who are undergoing checkpoint inhibitor immunotherapy (CII). The objective in this prospective pilot study was to discover circulating cell-free microRNA (cfmiR) signatures in the plasma that could assess melanoma patients' responses during CII. The cfmiRs were evaluated by the next-generation sequencing (NGS) HTG EdgeSeq microRNA (miR) Whole Transcriptome Assay (WTA; 2083 miRs) in 158 plasma samples obtained before and during the course of CII from 47 AJCC stage III/IV melanoma patients' and 73 normal donors' plasma samples. Initially, cfmiR profiles for pre- and post-treatment plasma samples of stage IV non-responder melanoma patients were compared to normal donors' plasma samples. Using machine learning, we identified a 9 cfmiR signature that was associated with stage IV melanoma patients being non-responsive to CII. These cfmiRs were compared in pre- and post-treatment plasma samples from stage IV melanoma patients that showed good responses. Circulating miR-4649-3p, miR-615-3p, and miR-1234-3p demonstrated potential prognostic utility in assessing CII responses. Compared to LDH levels during CII, circulating miR-615-3p levels were consistently more efficient in detecting melanoma patients undergoing CII who developed progressive disease. By combining stage III/IV patients, 92 and 17 differentially expressed cfmiRs were identified in pre-treatment plasma samples from responder and non-responder patients, respectively. In conclusion, this pilot study demonstrated cfmiRs that identified treatment responses and could allow for real-time monitoring of patients receiving CII.

Integrated Assessment of Circulating Cell-Free MicroRNA Signatures in Plasma of Patients with Melanoma Brain Metastasis.

Primary cutaneous melanoma frequently metastasizes to distant organs including the brain. Identification of cell-free microRNAs (cfmiRs) found in the blood can be used as potential body fluid biomarkers for detecting and monitoring patients with melanoma brain metastasis (MBM). In this pilot study, we initially aimed to identify cfmiRs in the blood of MBM patients. Normal donors plasma (healthy, n = 48) and pre-operative MBM patients' plasma samples (n = 36) were compared for differences in >2000 microRNAs (miRs) using a next generation sequencing (NGS) probe-based assay. A 74 cfmiR signature was identified in an initial cohort of MBM plasma samples and then verified in a second cohort of MBM plasma samples (n = 24). Of these, only 58 cfmiRs were also detected in MBM tissues (n = 24). CfmiR signatures were also found in patients who have lung and breast cancer brain metastasis (n = 13) and glioblastomas (n = 36) compared to MBM plasma samples. The 74 cfmiR signature and the latter cfmiR signatures were then compared. We found a 6 cfmiR signature that was commonly upregulated in MBM plasma samples in all of the comparisons, and a 29 cfmiR signature that distinguishes MBM patients from normal donors' samples. In addition, we assessed for cfmiRs in plasma (n = 20) and urine (n = 14) samples collected from metastatic melanoma patients receiving checkpoint inhibitor immunotherapy (CII). Pre- and post-treatment samples showed consistent changes in cfmiRs. Analysis of pre- and post-treatment plasma samples showed 8 differentially expressed (DE) cfmiRs that overlapped with the 35 cfmiR signature found in MBM patients. In paired pre-treatment plasma and urine samples receiving CII 8 cfmiRs overlapped. This study identified specific cfmiRs in MBM plasma samples that may potentially allow for assessment of melanoma patients developing MBM. The cfmiR signatures identified in both blood and urine may have potential utility to assess CII responses after further validation.

Genetic Variants in Immune Related Genes as Predictors of Responsiveness to BCG Immunotherapy in Metastatic Melanoma Patients.

Adjuvant immunotherapy in melanoma patients improves clinical outcomes. However, success is unpredictable due to inherited heterogeneity of immune responses. Inherent immune genes associated with single nucleotide polymorphisms (SNPs) may influence anti-tumor immune responses. We assessed the predictive ability of 26 immune-gene SNPs genomic panels for a clinical response to adjuvant BCG (Bacillus Calmette-Guérin) immunotherapy, using melanoma patient cohorts derived from three phase III multicenter clinical trials: AJCC (American Joint Committee on Cancer) stage IV patients given adjuvant BCG (pilot cohort; n = 92), AJCC stage III patients given adjuvant BCG (verification cohort; n = 269), and AJCC stage III patients that are sentinel lymph node (SLN) positive receiving no immunotherapy (control cohort; n = 80). The SNP panel analysis demonstrated that the responder patient group had an improved disease-free survival (DFS) (hazard ratio [HR] 1.84, 95% CI 1.09-3.13, p = 0.021) in the pilot cohort. In the verification cohort, an improved overall survival (OS) (HR 1.67, 95% CI 1.07-2.67, p = 0.025) was observed. No significant differences of SNPs were observed in DFS or OS in the control patient cohort. This study demonstrates that SNP immune genes can be utilized as a predictive tool for identifying melanoma patients that are inherently responsive to BCG and potentially other immunotherapies in the future.