Interactive Database Research Articles

Hidden Treasures of Kediri’s Medicinal Plants: A Collaborative Effort to Map and Validate Authentic Information Using Innovative QR Code Security and Cryptography

This research aims to map the distribution of rare and medicinal plants in Kediri Regency, Indonesia, and develop an interactive database system to provide accurate information on these plants. The study was conducted in collaboration between Balitbangda Kediri and Nusantara University PGRI Kediri. The data collection utilized innovation in creating a web-based information system with QR code security and cryptography validation to ensure the authenticity of the plant data. The results of this research provide a comprehensive database of rare and medicinal plants in Kediri Regency that can support drug information, particularly during the Covid-19 pandemic. The interactive system facilitates the dissemination of accurate information and raises public awareness about the potential of medicinal and rare plants in the region. This research has significant implications for the conservation of rare and medicinal plants and their potential contribution to public health.

Obez Hastalarda Olası İlaç-İlaç Etkileşimlerinin Değerlendirilmesi

Aim: Polypharmacy may cause life-threatening adverse effects due to drug-drug interactions (DDIs). It is possible to observe DDIs due
 to polypharmacy in obese patients who is known to have many co-morbid diseases that necessitates multiple drug use. The aim of the
 present study is to determine the frequency and severity of potential DDIs (pDDIs) in obese patients.
 Material and Methods: This cross-sectional study analyzed the patient charts that admitted to obesity outpatient clinic of tertiary care
 hospital from April 1, 2016 to July 1, 2017. The severity of DDIs was interpreted using the Lexi-comp® drug interaction database. A chisquare
 test was performed for the comparison of the presence of DDIs based on patients’ demographic characteristics [gender (male/
 female), age categories (18-44, 45-64 and ≥65 years) and BMI (30-34.9, 35-39.9 and ≥40 kg/m2)], co-morbid clinical conditions and
 number of drugs. The comparisons were considered as statistically significant at p< 0.05.
 Results: Out of 476 patient data evaluated, a total of 781 drugs were prescribed. Among 190 patients who were prescribed two or more
 drugs, 35 (18.4%) patients had one or more pDDIs. We determined 48 (70.6%) C, 12 (17.6%) B, 7 (10.3%) D and 1 (1.5%) X risk category
 interactions. The most common pDDIs were between metformin and nonsteroidal anti-inflammatory drugs (7.4%). The presence of
 pDDIs was significantly associated with the number of prescribed drugs (p

Automatic mechanistic inference from large families of Boolean models generated by Monte Carlo tree search.

Many important processes in biology, such as signaling and gene regulation, can be described using logic models. These logic models are typically built to behaviorally emulate experimentally observed phenotypes, which are assumed to be steady states of a biological system. Most models are built by hand and therefore researchers are only able to consider one or perhaps a few potential mechanisms. We present a method to automatically synthesize Boolean logic models with a specified set of steady states. Our method, called MC-Boomer, is based on Monte Carlo Tree Search an efficient, parallel search method using reinforcement learning. Our approach enables users to constrain the model search space using prior knowledge or biochemical interaction databases, thus leading to generation of biologically plausible mechanistic hypotheses. Our approach can generate very large numbers of data-consistent models. To help develop mechanistic insight from these models, we developed analytical tools for multi-model inference and model selection. These tools reveal the key sets of interactions that govern the behavior of the models. We demonstrate that MC-Boomer works well at reconstructing randomly generated models. Then, using single time point measurements and reasonable biological constraints, our method generates hundreds of thousands of candidate models that match experimentally validated in-vivo behaviors of the Drosophila segment polarity network. Finally we outline how our multi-model analysis procedures elucidate potentially novel biological mechanisms and provide opportunities for model-driven experimental validation.

An Objective Evaluation Method for Driver/Passenger Acceptance of an Autonomous Driving System for Lane Changes

Reliable assessment methods of driver acceptance are needed due to increased interest in high levels of autonomous driving systems. Subjective evaluation methods have mostly been utilized to assess the acceptance of newly developed advanced driver assistance systems because acceptance varies depending on the individual. In this paper, an objective evaluation methodology of driver acceptance for an autonomous driving system was proposed based on objective measurable parameters in the case of automatic lane change situations. To this end, a massive driver–vehicle interaction database was utilized, constructed by a specially designed experimental program. The experiment was carried out with 19 selected drivers (9 experts and 10 novices), supposed as an autonomous driving system. The database consisted of not only various measurable parameters on control commands, vehicle behaviors, and relations with other vehicles but also subjective acceptances. To interpret the driver acceptance, objective parameter sets were derived by two different methods: a statistical significance test and an acceptance sensitivity analysis. Then, a modeling method based on stochastic estimation to evaluate driver acceptance was suggested as an objective evaluation method for the driver acceptance of an automatic lane change system. The data set of the expert drivers was only used for the acceptance evaluation modeling; the other data sets of the novice drivers were used for verifications for the suggested model. The estimation accuracies of the two different models using a significance test and sensitivity analysis were 90.2% and 99.5%, respectively. This objective method for acceptance evaluation can not only be expanded to other functions of an autonomous driving system but also to an entirely autonomous driving vehicle.

SP4.8 Can we risk stratify surgical waiting lists using patient level and demographic data? A novel waiting list analyser tool

Abstract Introduction 7.2 million patients are currently awaiting NHS treatment. It is imperative that trusts are able to interrogate waiting lists in order to understand the impact of ‘long waits’, on both patients and hospital services. An ability to prioritise, optimise and risk stratify care would be invaluable. In collaboration with clinicians, managers and patient level information and costing (PLICS) team we present a big data waiting list analyser tool that allows the delivery of patient focused decision making and analysis. Methods Using Structured Query Language (SQL), the analyser pulls data including waiting times (RTT), patient ethnicity and deprivation deciles directly from trust systems. The analyser cross references associated morbidity and hospital attendances to delineate the impact and resource utilisation of acute attendance whilst awaiting treatment. Coding is used to calculate additional bed or theatre requirements for every patient awaiting treatment. Financial costs, lost bed days and associated morbidity are analysed and used to identify at risk patient groups. Results The analyser itself takes the form of a fully interactive database. It provides dynamic analysis for any surgical procedure across three zones; waiting list times, acute presentation, and resources required to deliver elective activity. There is also a population health management section, which includes ethnicity and deprivation deciles. Conclusions This first of a kind analyser is an exciting new tool that will aid data driven decision making. It will allow us to target patients based not only on their waiting time, re-attendance risk but also based on their socioeconomic risk factors.

Exploring the Role of DKK1 in the Occurrence of Lung Adenocarcinoma Based on the Analysis of Bioinformatics

Lung cancer is the most common malignant tumor in China, lung adenocarcinoma (LUAD) is the main type of lung cancer, which is a serious threat to people's life and health. At present, there are fewer studies on the role of Dikkopf1 (DKK1) in lung adenocarcinoma. The aim of this study was to investigate the role and potential prognostic value of DKK1 in the development of lung adenocarcinoma by bioinformatics methods. Several databases, such as genotype-tissue expression (GTEx), The Cancer Genome Atlas (TCGA) and tumor-immune system interactions database (TISIDB), were used to analyze the expression, clinicopathological features, immune cell infiltration, prognosis and methylation of DKK1 in lung adenocarcinoma. Then, linked immune cell infiltration Omics database was used to analyze the co-expressed genes of DKK1 and their functional enrichment. Finally, 59 pathological samples of paraffin-embedded lung adenocarcinoma patients who underwent surgery at the Affiliated Cancer Hospital of Xinjiang Medical University between 2016 and 2017 were collected for the validation of the prognostic value of expression by immunohistochemistry (IHC) test. The results of bioconfidence analysis showed that the expression level of DKK1 in lung adenocarcinoma tissues was higher than that in normal tissues, the expression in advanced cancers was higher than that in early stages, and the experimental validation revealed that among 59 cases of lung adenocarcinoma, there were 15 cases of negative expression (25.4%), 18 cases of weakly positive expression (30.5%), and 26 cases of strongly positive expression (44.1%). The different expression of DKK1 is related to methylation, prognosis and the activities of various immune cells. Functional enrichment shows that DKK1 may be involved in skin development and cell-substrate junction, and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis shows that DKK1 is related to ABC transporters. Bioinformatics analysis and clinical case specimens showed that high DKK1 expression was associated with poorer prognosis in patients with lung adenocarcinoma. High expression of DKK1 in lung adenocarcinoma is associated with poor prognosis. DKK1 is closely associated with tumor immune cell infiltration and pathways. DKK1 can be considered as a potential prognostic marker and a novel target for immunotherapy of lung adenocarcinoma.

ARCA: the interactive database for arbovirus reported cases in the Americas

BackgroundAccurate case report data are essential to understand arbovirus dynamics, including spread and evolution of arboviruses such as Zika, dengue and chikungunya viruses. Giving the multi-country nature of arbovirus epidemics in the Americas, these data are not often accessible or are reported at different time scales (weekly, monthly) from different sources.ResultsWe developed a publicly available and user-friendly database for arboviral case data in the Americas: ARCA. ARCA is a relational database that is hosted on the ARCA website. Users can interact with the database through the website by submitting queries through the website, which generates displays results and allows users to download these results in different, convenient file formats. Users can choose to view arboviral case data through a table which containscontaining the number of cases for a particular week, a plot, or through a map.ConclusionOur ARCA database is a useful tool for arboviral epidemiology research allowing for complex queries, data visualization, integration, and formatting.

Crosstalk between cell fate and survival pathways during uterine cervical carcinoma progression: a molecular and clinical perspective

The development of uterine cervical cancer is primarily attributed to infection by high-risk human papillomaviruses (HR-HPVs). E5, E6 and E7, the three early oncoproteins of HR-HPVs, have been implicated in initiation and progression of cervical cancer. The intricate molecular mechanisms that orchestrate aberrant cellular transformations to establish carcinoma of the cervical epithelium following viral infections are poorly understood. Here, we discuss how deregulation of three major cell fate regulatory pathways, Hedgehog, Wnt and Notch, and cell survival strategies involving EGFR signaling and G1/S checkpoint contribute towards cervical cancer development and progression. Further exploration of protein interaction database has revealed several genes that are involved in cervical cancer initiation and progression, and the two crucial "driver” genes, MYC and CTNNB1 (β-catenin), have been identified as major players in protein-protein interaction network. GSK3β emerged as the key mediator of crosstalk between Hedgehog, Wnt and Notch signaling pathways. GSK3β regulates cytoplasmic stabilization and nuclear translocation of β-catenin, which further impacts the expression of MYC, critical for cell cycle progression. Collectively, our analyses suggest that combinatorial therapeutic targeting of these proteins may be more effective in blocking cervical cancer initiation and progression.

Comprehensive analysis of ACKR family members in breast cancer using prognostic values.

Breast cancer (BC) is by far the most prevalent malignancy found in the female population. Atypical chemokine receptors (ACKRs) are a subclass of G-protein-coupled receptors, which are characterized by disrupted ligand binding and a breakdown of signaling following ligand binding. The evolution and function of multiple ACKRs in BC have yet to be fully elucidated, although certain findings on this family have been reported in several studies in Homo sapiens and other species. The present study identified that the expression level of ACKRs was significantly lower in breast carcinoma (BRCA) tissues compared with normal breast tissues through searches of the Tumor Immune Estimation Resource, UALCAN and Gene Expression Profiling Interactive Analysis databases. Additionally, when comparing BRCA tissues with normal breast tissues, it was found that there was obvious hypomethylation in the promoters of ACKR1, ACKR3 and ACKR5, as well as a marked hypermethylation in the promoters of ACKR2 and ACKR6. In determining the prognosis of patients with BRCA, the expression levels of ACKR1, ACKR2, ACKR3, ACKR4 and ACKR6 were all found to be important factors. The values for distant metastasis-free survival (DMFS), overall survival (OS) and recurrence-free survival (RFS) were all found to be lower in patients with BRCA who had a low expression level of ACKR1. In addition, the RFS rates for patients with BRCA were lower when the expression of ACKR2 was low, and worse values for DMFS, OS and RFS were found to be highly correlated with higher expression levels of ACKR3. Moreover, the DMFS, OS, RFS and predictive power score values were worse in those patients with low ACKR4 expression, and the RFS values for patients with BRCA were also found to be lower when the expression level of ACKR6 was low. Additionally, dendritic cells, macrophages, neutrophils, T cells with CD4+ status, T cells with CD8+ status and B cells were all substantially linked with ACKR expression, as well as immune cell infiltration. Taken together, the findings of the present study may offer a theoretical foundation for the creation of novel targets and prognostic indicators for BRCA therapy.

CCIDB: a manually curated cell-cell interaction database with cell context information.

Cell-cell interaction (CCI) is a crucial event in the development and function of multicellular organisms. The development of CCI databases is beneficial for researchers who want to analyze single-cell sequencing data or study CCI through molecular experiments. CCIs are known to act differently according to cellular and biological contexts such as cell types, gene mutations or disease status; however, previous CCI databases do not completely provide this contextual information pertaining to CCIs. We constructed a cell-cell interaction database (CCIDB) containing the biological and clinical contexts involved in each interaction. To build a database of cellular and tissue contexts, we collected 38 types of context features, which were categorized into seven categories, including 'interaction', 'cell type', 'cofactor', 'effector', 'phenotype', 'pathology' and 'reference'. CCIs were manually retrieved from 272 studies published recently (less than 6 years ago). In the current version of CCIDB, 520 CCIs and their 38 context features have been manually collected and curated by biodata engineers. We suggest that CCIDB is a manually curated CCI resource that is highly useful, especially for analyzing context-dependent alterations in CCIs. Database URL https://ccidb.sysmed.kr/.

PlantMetSuite: A User-Friendly Web-Based Tool for Metabolomics Analysis and Visualisation.

The advancement of mass spectrometry technologies has revolutionised plant metabolomics research by enabling the acquisition of raw metabolomics data. However, the identification, analysis, and visualisation of these data require specialised tools. Existing solutions lack a dedicated plant-specific metabolite database and pose usability challenges. To address these limitations, we developed PlantMetSuite, a web-based tool for comprehensive metabolomics analysis and visualisation. PlantMetSuite encompasses interactive bioinformatics tools and databases specifically tailored to plant metabolomics data, facilitating upstream-to-downstream analysis in metabolomics and supporting integrative multi-omics investigations. PlantMetSuite can be accessed directly through a user's browser without the need for installation or programming skills. The tool is freely available and will undergo regular updates and expansions to incorporate additional libraries and newly published metabolomics analysis methods. The tool's significance lies in empowering researchers with an accessible and customisable platform for unlocking plant metabolomics insights.

Abstract P2174: Transcriptomic Profiling Of Immune Checkpoint Inhibitor Myocarditis Reveals Novel Therapeutic Targets

Introduction: Immune checkpoint inhibitor (ICI) therapy has revolutionized the treatment of advanced cancers. However, the increased use of ICI has also resulted in a higher incidence of ICI-related cardiovascular side effects, including myocarditis, which has a high mortality rate of nearly 50%. Understanding the mechanisms underlying ICI-related cardiotoxicity is essential for identifying new therapeutic targets and improving patient survival. Hypothesis: We hypothesized that ICI-myocarditis has a unique transcriptomic profile that could be used to identify new drug targets. Methods: To test our hypothesis, we isolated RNA from our ICI-myocarditis mouse hearts and performed RNA-seq using the Illumina NovaSeq 6000. We also reanalyzed a human IC-myocarditis dataset downloaded from the Gene Expression Omnibus database. Results: Our results demonstrated that in both our animal models and patients, the upregulated pathways in ICI myocarditis heart tissues were enriched for regulation of T cell activation, leukocyte adhesion, and multiple cytokine signaling pathways. Conversely, downregulated pathways included those involved in cellular respiration, oxidative phosphorylation, and cardiac conduction. The most significantly upregulated genes were involved in innate and adaptive immune responses, including the CD8-dependent gene program and response to interferon-gamma of macrophages. By querying the Drug Gene Interaction Database, we identified several novel biologically plausible therapeutic targets. Correspondingly, a number of FDA-approved drugs were revealed, including monoclonal or chimeric antibodies such as Tocilizumab and Alemtuzumab, recombinant proteins like Abatacept and Anakinra, as well as small molecules such as Ruxolitinib and Imiquimod. Conclusions: Our study is the first to use the entire transcriptomic profile of ICI myocarditis in both preclinical models and patients for drug discovery. Our results demonstrate that ICI myocarditis has a distinct gene expression profile compared to healthy and other cardiac diseases. By querying a drug-gene interaction database, we identified several novel drug targets and possible treatments for ICI-related cardiotoxicity.

SLC10A3 Is a Prognostic Biomarker and Involved in Immune Infiltration and Programmed Cell Death in Lower Grade Glioma

The association between SLC10A3 (solute carrier family 10 member 3) and lower grade glioma (LGG) remains unclear. We used public databases and bioinformatics analysis to analyze SLC10A3. These included The Cancer Genome Atlas, Genotype-Tissue Expansion, Chinese Glioma Genome Atlas, Human Protein Atlas, GeneCards, cBioPortal, Search Tool for the Retrieval of Interacting Genes/Proteins, Gene Expression Profiling Interactive Analysis, Tumor Immune Estimation Resource, Tumor-Immune System Interaction Database, receiver operating characteristic curve analysis, Kaplan-Meier analysis, Cox analysis, nomograms, calibration plots, gene ontology/Kyoto Encyclopedia of Genes and Genomes enrichment analysis, gene set enrichment analysis, single-sample gene set enrichment analysis, and Spearman's correlation analysis. SLC10A3 was upregulated in adrenocortical carcinoma, glioblastoma, and LGG and was associated with good overall survival (OS) in adrenocortical carcinoma and poor OS in LGG and glioblastoma. SLC10A3 was increased with increased World Health Organization grade, upregulated in isocitrate dehydrogenase-wild type, 1p/19q (chromosome arms 1p and 19q) non-co-deleted, and higher in astrocytoma. Patients with LGG were grouped by the occurrence of the clinical outcome endpoints (i.e., OS, disease-specific survival [DSS], and progression-free interval events). Genetic alterations in SLC10A3 were associated with poor progression-free survival in LGG. Most of clinical characteristics were associated with the SLC10A3 expression level. SLC10A3 with diagnostic and prognostic value (OS, DSS, and progression-free interval) was an independent prognostic factor in LGG. Moreover, Nomograms (WHO grade, 1p/19q codeletion, age and SLC10A3) had moderately accurate predictive for OS and DSS. Functional analysis showed that SLC10A3 might participate in the transport of multiple substances, neurogenic signaling, immune response, and programmed cell death in LGG. SLC10A3 correlated with immune infiltration in LGG and moderately correlated with the gene signature of pyroptosis, lysosome-dependent cell death, necroptosis, apoptosis, ferroptosis, alkaliptosis, and autophagy-dependent cell death. SLC10A3 is a potential diagnostic and prognostic biomarker for LGG and might be associated with substance transport, neurogenic signaling, immune infiltration, and programmed cell death in LGG.

Identification of novel immune-related biomarker and therapeutic drugs in Parkinson disease via integrated bioinformatics analysis.

The present study was designed to identify immune-related biomarker and candidate drugs for Parkinson disease (PD) by weighted gene co-expression network analysis. Differentially expressed genes were identified in PD and healthy samples in the Gene Expression Omnibus (GEO) database. Besides, immune-related genes were obtained from the immunology database. Then, a co-expression network was constructed by the weighted gene co-expression network analysis package. Diagnostic model for PD was constructed by Lasso and multivariate Cox regression. Furthermore, differentially expressed genes (DEGs) were used to establish PPI and competing endogenous RNA (ceRNA) networks. Functional enrichment and pathway analysis were performed. Drug-hub gene interaction analysis was performed via DGIdb database. PD samples and normal samples were found to have 220 upregulated genes and 216 downregulated genes in the GSE6613 dataset. The differentially expressed genes contained 50 immune-related genes, with 40 upregulated genes and 10 downregulated genes. We obtained 7 hub genes by intersecting the DEGs and candidate hub genes. As potential diagnostic markers, 2 immune-related DEGs were identified among the 7 hub genes. According to functional enrichment analysis, these DEGs were mainly enriched in immune response, inflammatory response, and cytokine-cytokine receptor interactions. Totally, we obtained 182 drug-gene interaction pairs in Drug-Gene Interaction database (DGIdb). Our results revealed crucial genes and candidate drugs for PD patients and deepen our understanding of the molecular mechanisms involved in PD.

Knockdown of ANLN inhibits the progression of lung adenocarcinoma via pyroptosis activation.

Significant advancements have been achieved in the area of molecular targeted therapy for lung adenocarcinoma (LUAD). However, the complex molecular patterns and high heterogeneity of LUAD confine the efficacy of these therapies to a specific subset of patients; therefore, it is necessary to explore novel targets for LUAD treatment. The expression levels of anillin (ANLN) in LUAD were analyzed using the Gene Expression Profiling Interactive Analysis database. Furthermore, the association between ANLN gene expression and patient survival outcomes was evaluated using the Kaplan‑Meier Plotter. Subsequently, small interfering RNA (siRNA) transfection was performed to knock down ANLN in A549 and H1299 cell lines, after which, TUNEL, colony formation and Transwell assays were conducted to assess cell death, colony formation and migration, respectively. Additionally, western blot analysis was performed to analyze the expression levels of caspase‑1, interleukin (IL)‑18 (IL‑18), IL‑1β, NLR family pyrin domain‑containing 3 (NLRP3), apoptosis‑associated speck‑like protein containing a CARD domain (ASC) and cleaved gasdermin D (GSDMD) following ANLN knockdown. The results revealed that ANLN mRNA expression was significantly increased in LUAD tissues compared with adjacent normal samples. Furthermore, the expression levels of ANLN displayed an increasing trend with advancing clinical stage. Furthermore, patients with high ANLN expression levels exhibited poor overall survival rates compared with those with low ANLN expression levels. Subsequent ANLN knockdown experiments indicated elevated cell death rate, and reduced colony formation and migration in both A549 and H1299 cells. Additionally, ANLN knockdown resulted in increased protein expression levels of pyroptosis‑associated molecules, including caspase‑1, NLRP3, cleaved‑GSDMD, IL‑1β, ASC and IL‑18 in both A549 and H1299 cells. In conclusion, ANLN represents an important gene and a promising therapeutic target for LUAD. Its potential as a therapeutic target makes it an interesting candidate for further exploration in the development of novel treatment strategies for LUAD.

Transcriptome analysis for the screening of hub genes and potential drugs in hypersensitivity pneumonitis

Hypersensitivity pneumonitis (HP), an immune-mediated subtype of interstitial lung diseases (ILDs), is mediated by repetitive inhalation of certain antigens. Transcriptomic signatures, which provide insight into the genetic alterations associated with a disease, somehow remain obscure for HP. In this study, unique transcriptome fingerprints associated with the pathogenesis of HP were investigated using systems biology approach. Furthermore, the metabolic signatures identified in our earlier study, were correlated with the differentially expressed genes (DEGs) to explore the integrated pathways. Two datasets (GSE150910 and GSE19314) procured from the Gene Expression Omnibus repository were used to identify the DEGs of HP patients. The enriched biological processes, altered pathways and protein-protein interaction (PPI) network associated with the DEGs were determined. Based on topological algorithms, 12 hub genes were screened; among which chemokine (C-X-C motif) ligand 13 (CXCL13), CXCL9 and ribosomal protein large 36A (RPL36A) were identified as the top-ranked hub genes. The expression levels of these three candidate genes were experimentally validated in an HP patient cohort using quantitative reverse transcription polymerase chain reaction (qRT-PCR). The findings indicated a trend similar to that of the bioinformatics observations. We integrated the metabolic signatures identified earlier with these hub genes and found glycolysis and phosphatidylinositol-3-kinase (PI3K/AKT) pathways to be impaired in these patients. Finally, the drug-gene interaction database DGIdb 4.0 indicated rituximab to be a promising drug for the management of HP. The present study is the first of its kind to combine bioinformatics and experimental data to identify and validate the genetic signatures and downstream pathways associated with HP. Our findings open up a new window to probe into the gene-metabolite crosstalk involved in the pathogenesis of HP and highlight promising therapeutic strategies.

Excitatory Neurons Derived from Human-Induced Pluripotent Stem Cells Show Transcriptomic Differences in Alzheimer's Patients from Controls.

The recent advances in creating pluripotent stem cells from somatic cells and differentiating them into a variety of cell types is allowing us to study them without the caveats associated with disease-related changes. We generated induced Pluripotent Stem Cells (iPSCs) from eight Alzheimer's disease (AD) patients and six controls and used lentiviral delivery to differentiate them into excitatory glutamatergic neurons. We then performed RNA sequencing on these neurons and compared the Alzheimer's and control transcriptomes. We found that 621 genes show differences in expression levels at adjusted p < 0.05 between the case and control derived neurons. These genes show significant overlap and directional concordance with genes reported from a single-cell transcriptome study of AD patients; they include five genes implicated in AD from genome-wide association studies and they appear to be part of a larger functional network as indicated by an excess of interactions between them observed in the protein-protein interaction database STRING. Exploratory analysis with Uniform Manifold Approximation and Projection (UMAP) suggests distinct clusters of patients, based on gene expression, who may be clinically different. Our research outcomes will enable the precise identification of distinct biological subtypes among individuals with Alzheimer's disease, facilitating the implementation of tailored precision medicine strategies.

CCT6A dysregulation in surgical prostate cancer patients: association with disease features, treatment information, and prognosis.

Chaperonin-containing tailless complex polypeptide 1 subunit 6A (CCT6A) involves several solid cancers' development and progression, while its clinical utility in prostate cancer management is rarely revealed. Consequently, the present study intended to investigate the linkage of CCT6A with disease features, treatment information, and prognosis of surgical prostate cancer patients. CCT6A in 220 surgical prostate cancer patients was determined via immunohistochemistry. Additionally, survival analyses on data from the public databases were performed to validate the prognostic value of CCT6A further. CCT6A expression was upregulated in tumor tissue than in adjacent tissue (P < 0.001). Increased CCT6A was related to elevated Gleason score (P < 0.001) and pathological T stage (P = 0.029). CCT6A was increased in patients with positive surgical margin status (vs. negative) (P = 0.029) and patients with adjuvant external-beam radiation therapy (vs. no) (P = 0.001). Concerning the prognostic value, high tumor CCT6A was linked with shortened disease-free survival (DFS) (P = 0.009), which was also validated through further Cox's proportional hazard regression model analyses (hazard ratio: 2.695, 95% CI: 1.086-6.683, P = 0.032), whereas CCT6A was not correlated with overall survival (OS) (P > 0.050). Additionally, the Gene Expression Profiling Interactive Analysis database indicated that high tumor CCT6A was related to shortened DFS (P = 0.036), but it was not associated with OS (P > 0.050); meanwhile, the Human Protein Atlas database suggested that high tumor CCT6A was linked with reduced OS (P = 0.048). Tumor CCT6A high expression correlates with the elevated Gleason score, pathological T stage, and shortened DFS in surgical prostate cancer patients.

A panel of TDP-43-regulated splicing events verifies loss of TDP-43 function in amyotrophic lateral sclerosis brain tissue

TDP-43 dysfunction is a molecular hallmark of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). A major hypothesis of TDP-43 dysfunction in disease is the loss of normal nuclear function, resulting in impaired RNA regulation and the emergence of cryptic exons. Cryptic exons and differential exon usage are emerging as promising markers of lost TDP-43 function in addition to revealing biological pathways involved in neurodegeneration in ALS/FTD. In this brief report, we identified markers of TDP-43 loss of function by depleting TARDBP from post-mortem human brain pericytes, a manipulable in vitro primary human brain cell model, and identifying differential exon usage events with bulk RNA-sequencing analysis. We present these data in an interactive database (https://www.scotterlab.auckland.ac.nz/research-themes/tdp43-lof-db-v2/) together with seven other TDP-43-depletion datasets we meta-analysed previously, for user analysis of differential expression and splicing signatures. Differential exon usage events that were validated by qPCR were then compiled into a ‘differential exon usage panel’ with other well-established TDP-43 loss-of-function exon markers. This differential exon usage panel was investigated in ALS and control motor cortex tissue to verify whether, and to what extent, TDP-43 loss of function occurs in ALS. We find that profiles of TDP-43-regulated cryptic exons, changed exon usage and changed 3′ UTR usage discriminate ALS brain tissue from controls, verifying that TDP-43 loss of function occurs in ALS. We propose that TDP-43-regulated splicing events that occur in brain tissue will have promise as predictors of disease.

A Machine Learning Approach to Identify Potential miRNA-Gene Regulatory Network Contributing to the Pathogenesis of SARS-CoV-2 Infection.

Worldwide, many lives have been lost in the recent outbreak of coronavirus disease. The pathogen responsible for this disease takes advantage of the host machinery to replicate itself and, in turn, causes pathogenesis in humans. Human miRNAs are seen to have a major role in the pathogenesis and progression of viral diseases. Hence, an in-silico approach has been used in this study to uncover the role of miRNAs and their target genes in coronavirus disease pathogenesis. This study attempts to perform the miRNA seq data analysis to identify the potential differentially expressed miRNAs. Considering only the experimentally proven interaction databases TarBase, miRTarBase, and miRecords, the target genes of the miRNAs have been identified from the mirNET analytics platform. The identified hub genes were subjected to gene ontology and pathway enrichment analysis using EnrichR. It is found that a total of 9 miRNAs are deregulated, out of which 2 were upregulated (hsa-mir-3614-5p and hsa-mir-3614-3p) and 7 were downregulated (hsa-mir-17-5p, hsa-mir-106a-5p, hsa-mir-17-3p, hsa-mir-181d-5p, hsa-mir-93-3p, hsa-mir-28-5p, and hsa-mir-100-5p). These miRNAs help us to classify the diseased and healthy control patients accurately. Moreover, it is also found that crucial target genes (UBC and UBB) of 4 signature miRNAs interact with viral replicase polyprotein 1ab of SARS-Coronavirus. As a result, it is noted that the virus hijacks key immune pathways like various cancer and virus infection pathways and molecular functions such as ubiquitin ligase binding and transcription corepressor and coregulator binding.