7,635 research outputs found

    Exploring a novel seven-gene marker and mitochondrial gene TMEM38A for predicting cervical cancer radiotherapy sensitivity using machine learning algorithms

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    BackgroundRadiotherapy plays a crucial role in the management of Cervical cancer (CC), as the development of resistance by cancer cells to radiotherapeutic interventions is a significant factor contributing to treatment failure in patients. However, the specific mechanisms that contribute to this resistance remain unclear. Currently, molecular targeted therapy, including mitochondrial genes, has emerged as a new approach in treating different types of cancers, gaining significant attention as an area of research in addressing the challenge of radiotherapy resistance in cancer.MethodsThe present study employed a rigorous screening methodology within the TCGA database to identify a cohort of patients diagnosed with CC who had received radiotherapy treatment. The control group consisted of individuals who demonstrated disease stability or progression after undergoing radiotherapy. In contrast, the treatment group consisted of patients who experienced complete or partial remission following radiotherapy. Following this, we identified and examined the differentially expressed genes (DEGs) in the two cohorts. Subsequently, we conducted additional analyses to refine the set of excluded DEGs by employing the least absolute shrinkage and selection operator regression and random forest techniques. Additionally, a comprehensive analysis was conducted in order to evaluate the potential correlation between the expression of core genes and the extent of immune cell infiltration in patients diagnosed with CC. The mitochondrial-associated genes were obtained from the MITOCARTA 3.0. Finally, the verification of increased expression of the mitochondrial gene TMEM38A in individuals with CC exhibiting sensitivity to radiotherapy was conducted using reverse transcription quantitative polymerase chain reaction and immunohistochemistry assays.ResultsThis process ultimately led to the identification of 7 crucial genes, viz., GJA3, TMEM38A, ID4, CDHR1, SLC10A4, KCNG1, and HMGCS2, which were strongly associated with radiotherapy sensitivity. The enrichment analysis has unveiled a significant association between these 7 crucial genes and prominent signaling pathways, such as the p53 signaling pathway, KRAS signaling pathway, and PI3K/AKT/MTOR pathway. By utilizing these 7 core genes, an unsupervised clustering analysis was conducted on patients with CC, resulting in the categorization of patients into three distinct molecular subtypes. In addition, a predictive model for the sensitivity of CC radiotherapy was developed using a neural network approach, utilizing the expression levels of these 7 core genes. Moreover, the CellMiner database was utilized to predict drugs that are closely linked to these 7 core genes, which could potentially act as crucial agents in overcoming radiotherapy resistance in CC.ConclusionTo summarize, the genes GJA3, TMEM38A, ID4, CDHR1, SLC10A4, KCNG1, and HMGCS2 were found to be closely correlated with the sensitivity of CC to radiotherapy. Notably, TMEM38A, a mitochondrial gene, exhibited the highest degree of correlation, indicating its potential as a crucial biomarker for the modulation of radiotherapy sensitivity in CC

    Infrastructuring educational genomics:Associations, architectures and apparatuses

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    Technoscientific transformations in molecular genomics have begun to influence knowledge production in education. Interdisciplinary scientific consortia are seeking to identify ‘genetic influences’ on ‘educationally relevant’ traits, behaviors, and outcomes. This article examines the emerging ‘knowledge infrastructure’ of educational genomics, attending to the assembly and choreography of organizational associations, epistemic architecture, and technoscientific apparatuses implicated in the generation of genomic understandings from masses of bioinformation. As an infrastructure of datafied knowledge production, educational genomics is embedded in data-centered epistemologies and practices which recast educational problems in terms of molecular genetic associations—insights about which are deemed discoverable from digital bioinformation and potentially open to genetically informed interventions in policy and practice. While scientists claim to be ‘opening the black box of the genome’ and its association with educational outcomes, we open the black box of educational genomics itself as a source of emerging scientific authority. Data-intensive educational genomics does not straightforwardly ‘discover’ the biological bases of educationally relevant behaviors and outcomes. Rather, this knowledge infrastructure is also an experimental ‘ontological infrastructure’ supporting particular ways of knowing, understanding, explaining, and intervening in education, and recasting the human subjects of education as being surveyable and predictable through the algorithmic processing of bioinformation

    Investigating neural differentiation capacity in Alzheimer’s disease iPSC-derived neural stem cells

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    Neurodegeneration in Alzheimer’s disease (AD) may be exacerbated by dysregulated hippocampal neurogenesis. Neural stem cells (NSC) maintain adult neurogenesis and depletion of the NSC niche has been associated with age-related cognitive decline and dementia. We hypothesise that familial AD (FAD) mutations bias NSC toward premature neural specification, reducing the stem cell niche over time and accelerating disease progression. Somatic cells derived from patients with FAD (PSEN1 A246E and PSEN1 M146L heterozygous mutations) and healthy controls were reprogrammed to generate induced pluripotent stem cells (iPSC). Pluripotency for patient and control iPSC lines was confirmed, then cells were amplified and cryopreserved as stores. iPSC were subjected to neural specification to rosette-forming SOX2+/nestin+ NSCs for comparative evaluations between FAD and age-matched controls. FAD patient and control NSC were passaged under defined steady state culture conditions to assess stem cell maintenance using quantitative molecular markers (SOX2, nestin, NeuN, MAP2 and ÎČIII-tubulin). We observed trends towards downregulated expression of the nestin coding gene NES (p=0.051) and upregulated expression of MAP2 (p=0.16) in PSEN1 NSC compared with control NSC, indicative of a premature differentiation phenotype induced by presence of the PSEN1 mutation. Cell cycle analysis of PSEN1 NSC showed that compared with controls, a greater number of PSEN1 NSC were retained in G0/G1 phase of the cell cycle (p=0.39), fewer progressed to S-phase (p=0.11) and fewer still reached G2 phase (p=0.23), suggesting cell cycle progression may be impaired in PSEN1 NSC. Nuclear DNA fragmentation was increased (p=0.10) in FAD NSC compared with controls, indicative of elevated cell death/apoptosis. Flow cytometry-based analysis of live, nestin+ NSC and NPC indicated increased apoptosis (p=0.14) in FAD NSC compared with controls, as well as increasing levels of apoptosis (p=0.33) in FAD NSC as they specified to neural progenitor cells. Global RNA sequencing was used to identify transcriptomic changes occurring during both disease and control neural specification. GO analysis of DEGs between PSEN1 and control NSC at P3 revealed significant upregulation (FDR<0.0000259) of 5 biological processes related to transcription and gene expression as well as significant upregulation (FDR<0.000000725) of 12 molecular functions related to DNA binding and transcription factor activity. These data suggest significant changes in gene expression were occurring in PSEN1 NSC at P3 compared with control NSC at the same stage in neural specification. The number of DEGs (p<0.05) between PSEN1 and control NSC at P3 was 9.92-fold higher than the number of DEGs between PSEN1 and control NSC at P2, suggesting transcriptomic differences between PSEN1 and control NSC become more pronounced as cells specify further down the neural lineage. Gene ontology (GO) analysis of differentially expressed genes (DEGs) specific to AD neural differentiation revealed significant dysregulation (FDR p<0.05) of genes related to neurogenesis, apoptosis, cell cycle, transcriptional control, and cell growth/maintenance as PSEN1 NSC matured from P2 to P3. The number of DEGs (p<0.05) in PSEN1 neural differentiation was 4.7-fold higher than the number of DEGs seen in control neural differentiation, indicating more transcriptional changes occurred in PSEN1 NSC than in controls at the same time point in neural specification. Dysregulation of Notch signalling was specific to PSEN1 neural differentiation and Notch related DEGs significantly upregulated (p<0.05) in PSEN1 NSC at P3 compared with P2 included NCOR2, JAG2, CHAC1 and RFNG. qPCR based validation displayed significant upregulation of RFNG (p=0.04) in PSEN1 NSC at P3 compared with PSEN1 NSC at P2, and indicated a trend towards upregulation of JAG2 expression, correlating with RNA sequencing data. Data generated in this study indicate that presence of the PSEN1 mutation significantly increases the number of transcriptional changes occurring during neural differentiation. It is plausible that transcriptional changes to Notch signalling cause dysregulated neural specification and increased apoptosis in PSEN1 NSC, ultimately resulting in depletion of the NSC niche

    Multi-dimensional omics approaches to dissect natural immune control mechanisms associated with RNA virus infections

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    In recent decades, global health has been challenged by emerging and re-emerging viruses such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV2), human immunodeficiency viruses (HIV-1), and Crimean–Congo hemorrhagic fever virus (CCHFV). Studies have shown dysregulations in the host metabolic processes against SARS-CoV2 and HIV-1 infections, and the research on CCHFV infection is still in the infant stage. Hence, understanding the host metabolic re-programming on the reaction level in infectious disease has therapeutic importance. The thesis uses systems biology methods to investigate the host metabolic alterations in response to SARS-CoV2, HIV-1, and CCHFV infections. The three distinct viruses induce distinct effects on human metabolism that, nevertheless, show some commonalities. We have identified alterations in various immune cell types in patients during the infections of the three viruses. Further, differential expression analysis identified that COVID-19 causes disruptions in pathways related to antiviral response and metabolism (fructose mannose metabolism, oxidative phosphorylation (OXPHOS), and pentose phosphate pathway). Up-regulation of OXPHOS and ROS pathways with most changes in OXPHOS complexes I, III, and IV were identified in people living with HIV on treatment (PLWHART). The acute phase of CCHFV infection is found to be linked with OXPHOS, glycolysis, N-glycan biosynthesis, and NOD-like receptor signaling pathways. The dynamic nature of the metabolic process and adaptive immune response in CCHFV-pathogenesis are also observed. Further, we have identified different metabolic flux in reactions transporting TCA cycle intermediates from the cytosol to mitochondria in COVID-19 patients. Genes such as monocarboxylate transporter (SLC16A6) and nucleoside transporter (SLC29A1) and metabolites such as α-ketoglutarate, succinate, and malate were found to be linked with COVID-19 disease response. Metabolic reactions associated with amino acid, carbohydrate, and energy metabolism pathways and various transporter reactions were observed to be uniquely disrupted in PLWHART along with increased production of αketoglutarate (αKG) and ATP molecules. Changes in essential (leucine and threonine) and non-essential (arginine, alanine, and glutamine) amino acid transport were found to be caused by acute CCHFV infection. The altered flux of reactions involving TCA cycle compounds such as pyruvate, isocitrate, and alpha-ketoglutarate was also observed in CCHFV infection. The research described in the thesis displayed dysregulations in similar metabolic processes against the three viral Infections. But further downstream analysis unveiled unique alterations in several metabolic reactions specific to each virus in the same metabolic pathways showing the importance of increasing the resolution of knowledge about host metabolism in infectious diseases

    Determination of antibiotic susceptibility of the bacteria causing urinary tract infections using a novel lab-on-a-chip design

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    Urinary tract infections (UTIs) are one of the most common types of bacterial infection in the UK, and also are expensive to treat costing the National Health Service ~ÂŁ54 million between 2016 and 2017. Culture-based antibiotic susceptibility testing (AST) is used to identify an antibiotic to treat drug-resistant urinary tract infections and takes 48 hours to complete. Faster prescription of effective antibiotics should reduce the risk of sepsis and poor clinical outcomes. To address this need, we developed a Lab-on-a-Chip (LOC) based method to conduct electrochemical AST using screen-printed macroelectrodes (SPEs) and antibiotic-loaded hydrogels. SPEs were fabricated using carbon-graphite based inks, with resazurin bulk modified SPEs (R-SPEs) being fabricated through modification of the SPEs WE. Polyvinyl alcohol (PVA) based hydrogels were loaded with the following antibiotics were used; cephalexin, ceftriaxone, colistin, gentamicin, piperacillin, trimethoprim and vancomycin as well as an antibiotic-free control. LOC devices were then designed to encapsulate both the R-SPEs and the antibiotic hydrogels to enable multiplexed electrochemical AST to occur on a single device. In the initial testing of the R-SPEs and the antibiotic hydrogels independently of a LOC device, antibiotic susceptibility could be determined in 90 minutes for E. coli. After the preliminary work, eight chambered LOC devices were spiked with simulated UTI samples. Each chamber contained an R-SPE and an antibiotic hydrogel. After an incubation step, susceptibility of Escherichia coli and Klebsiella pneumoniae could be established in 85 minutes of testing which is significantly faster than the 48 hours required for conventional culture-based AST. The sensitive detection of resazurin afforded by using the electrochemical detection methodology incorporated onto a LOC device described here offers an inexpensive and simple method for the determination of antibiotic susceptibility that is faster than using a culture-based approach

    Multiplexed High-Resolution Imaging Approach to Decipher the Cellular Heterogeneity of the Kidney and its Alteration in Kidney Disease and Nephrolithiasis

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    Indiana University-Purdue University Indianapolis (IUPUI)Kidney disease and nephrolithiasis both present a major burden on the health care system in the US and worldwide. The cellular and molecular events governing the pathogenesis of these diseases are not fully understood. We propose that defining the cellular heterogeneity and niches in human and mouse kidney tissue specimens from controls and various models of renal disease could provide unique insights into the molecular pathogenesis. For that purpose, a multiplexed fluorescence imaging approach using co-detection by Indexing (CODEX) was used, using a panel of 33 and 38 markers for mouse and human kidney tissues, respectively. A customized computational analytical pipeline was developed and applied to the imaging data using unsupervised and/or semi-supervised machine learning and statistical approaches. The goal was to identify various cell populations present within the tissues, as well as identify unique cellular niches that may be altered with disease and/or injury. In mice, we examined disease models of acute kidney injury (AKI) and in human tissues we analyzed specimens from patients with AKI, IgA nephropathy, chronic kidney disease, systemic lupus erythematosus, and nephrolithiasis. In both mice and humans, the disease and reference samples show similar broad cell populations for the main segments of the nephron, endothelium, as well as similar groups of immune cells, such as resident macrophages and neutrophils. When comparing between health and disease, however, a change in the distribution of few sub-populations occurred. For example, in human kidney tissues, the abundance and distribution of a subpopulation of proximal tubules positive for THY1 (a marker of differentiation and repair), was markedly reduced with disease. Changes observed in mouse tissues included shifts in the immune cell population types and niches with disease. We propose that our analytical workflow and the observed changes in situ will play an important role in deciphering the pathogenesis of kidney disease

    Rapid identification and drug resistance screening of respiratory pathogens based on single-cell Raman spectroscopy

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    Respiratory infections rank fourth in the global economic burden of disease. Lower respiratory tract infections are the leading cause of death in low-income countries. The rapid identification of pathogens causing lower respiratory tract infections to help guide the use of antibiotics can reduce the mortality of patients with lower respiratory tract infections. Single-cell Raman spectroscopy is a “whole biological fingerprint” technique that can be used to identify microbial samples. It has the advantages of no marking and fast and non-destructive testing. In this study, single-cell Raman spectroscopy was used to collect spectral data of six respiratory tract pathogen isolates. The T-distributed stochastic neighbor embedding (t-SNE) isolation analysis algorithm was used to compare the differences between the six respiratory tract pathogens. The eXtreme Gradient Boosting (XGBoost) algorithm was used to establish a Raman phenotype database model. The classification accuracy of the isolated samples was 93–100%, and the classification accuracy of the clinical samples was more than 80%. Combined with heavy water labeling technology, the drug resistance of respiratory tract pathogens was determined. The study showed that single-cell Raman spectroscopy–D2O (SCRS–D2O) labeling could rapidly identify the drug resistance of respiratory tract pathogens within 2 h

    Novel approaches for the control of fungal pathogens

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    Fungal pathogens are a continual threat with potential impacts on human health, agriculture, food and goods security. Despite this, currently used treatments are limited to a handful of drug or fungicide classes. The limited availability of treatment options is further challenged by growing fungal resistance, tightening legislation over drug/fungicide use and evolving public opinion. In this thesis, certain novel approaches were explored for their potential in the control of fungal pathogens of humans or crops. One approach utilised the concept of combinatorial treatments, applied specifically to synergistic interactions among natural product (NP) compounds. NPs have been questioned for their translational applications due to promiscuous activity; this study proposed the potential of synergy for potentiating antifungal activity and improving target specificity. In a high-throughput screening approach, selected NPs were screened pairwise against a wider NP chemical library. Screening of 800 NP combinations revealed 34 pairs that were potentially synergistic in their inhibitory effects on yeast growth. Moreover, scaled-up validation tests for three combinations of particular interest showed that synergy was present against several important pathogens. One synergistic combination was explored mechanistically and found to promote synergistic mitochondrial membrane depolarization and ROS formation. This work indicated the potential for synergistic NP combinations in fungal pathogen control. An additional study focussed on relationships between NP interactions and their underlying mechanisms of synergy, focusing on a particular triangle of NP interactions (involving two synergies but also no interaction). Results indicated that the NP sclareol, found to synergise with a number of other NPs, could also induce synergy between the previously non-synergistic pair of compounds. Results supported that this action of sclareol involved uncoupling of oxidative phosphorylation, which may be an activity that enables synergies against fungal pathogens more widely. An additional approach explored the potential of collateral sensitivity (CS) as a potential drug-repurposing strategy against azole-resistant Candida albicans. CS is where resistance to one drug is linked to sensitivity to another, so offering means to target drug resistant strains. Two azole-resistant clinical isolates of C. albicans showed hypersensitivity to several non-antifungal drugs, particularly aminoglycosides. The mutants were slow growers, but slow growth was not sufficient to explain the hypersensitivity, neither were the isolates’ alleles of erg11, the gene encoding the lanosterol demethylase targeted by azoles. Moreover, the hypersensitivity was not reproduced in other azole-resistant isolates. Mechanistic studies pointed to a possible role for cell wall glycosylation or integrity defects in the original two isolates. Further work expanded the search for CS compounds against azole-resistant C. albicans through a screen of a 1,280-compound library. The results did not identify any hit compounds, but reproducibility and dosage concerns meant that hit compounds could have been missed. A final approach set out to assess mechanistic bases for reported fungal anti-attachment properties of certain polymer materials. One strategy was an accelerated evolution experiment, designed to select C. albicans variants hyper-attaching to polymer. However, attachment propensity did not change, indicating resilience of the anti-attachment material properties. Another strategy examined cell wall properties that may affect anti-attachment, in C. albicans and the plant pathogen Zymoseptoria tritici. Results with selective fluorescent probes highlighted certain cell wall components that were enriched in polymer-attaching or glass-attaching cells. This offers a path for understanding cell properties important for (anti-) attachment to the polymer materials, valuable for informing design of improved polymers. Taken together the three approaches explored in this thesis offer exciting potential for bolstering efforts to control fungal pathogens, providing bases for further mechanistic and possible translational developmen
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