16,529 research outputs found
Classification related to immunogenic cell death predicts prognosis, immune microenvironment characteristics, and response to immunotherapy in lower-grade gliomas
BackgroundImmunogenic cell death (ICD) is a form of cell death that elicits immune responses against the antigens found in dead or dying tumor cells. Growing evidence implies that ICD plays a significant role in triggering antitumor immunity. The prognosis for glioma remains poor despite many biomarkers being reported, and identifying ICD-related biomarkers is imminent for better-personalized management in patients with lower-grade glioma (LGG).Materials and methodsWe identified ICD-related differentially expressed genes (DEGs) by comparing gene expression profiles obtained across Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) cohorts. On the foundation of ICD-related DEGs, two ICD-related clusters were identified through consensus clustering. Then, survival analysis, functional enrichment analysis, somatic mutation analysis, and immune characteristics analysis were performed in the two ICD-related subtypes. Additionally, we developed and validated a risk assessment signature for LGG patients. Finally, we selected one gene (EIF2AK3) from the above risk model for experimental validation.Results32 ICD-related DEGs were screened, dividing the LGG samples from the TCGA database into two distinct subtypes. The ICD-high subgroup showed worse overall survival (OS), greater immune infiltration, more active immune response process, and higher expression levels of HLA genes than the ICD-low subgroup. Additionally, nine ICD-related DEGs were identified to build the prognostic signature, which was highly correlated with the tumor-immune microenvironment and could unambiguously be taken as an independent prognostic factor and further verified in an external dataset. The experimental results indicated that EIF2AK3 expression was higher in tumors than paracancerous tissues, and high-expression EIF2AK3 was enriched in WHO III and IV gliomas by qPCR and IHC, and Knockdown of EIF2AK3 suppressed cell viability and mobility in glioma cells.ConclusionWe established novel ICD-related subtypes and risk signature for LGG, which may be beneficial to improving clinical outcome prediction and guiding individualized immunotherapy
PIKS: A Technique to Identify Actionable Trends for Policy-Makers Through Open Healthcare Data
With calls for increasing transparency, governments are releasing greater
amounts of data in multiple domains including finance, education and
healthcare. The efficient exploratory analysis of healthcare data constitutes a
significant challenge. Key concerns in public health include the quick
identification and analysis of trends, and the detection of outliers. This
allows policies to be rapidly adapted to changing circumstances. We present an
efficient outlier detection technique, termed PIKS (Pruned iterative-k means
searchlight), which combines an iterative k-means algorithm with a pruned
searchlight based scan. We apply this technique to identify outliers in two
publicly available healthcare datasets from the New York Statewide Planning and
Research Cooperative System, and California's Office of Statewide Health
Planning and Development. We provide a comparison of our technique with three
other existing outlier detection techniques, consisting of auto-encoders,
isolation forests and feature bagging. We identified outliers in conditions
including suicide rates, immunity disorders, social admissions,
cardiomyopathies, and pregnancy in the third trimester. We demonstrate that the
PIKS technique produces results consistent with other techniques such as the
auto-encoder. However, the auto-encoder needs to be trained, which requires
several parameters to be tuned. In comparison, the PIKS technique has far fewer
parameters to tune. This makes it advantageous for fast, "out-of-the-box" data
exploration. The PIKS technique is scalable and can readily ingest new
datasets. Hence, it can provide valuable, up-to-date insights to citizens,
patients and policy-makers. We have made our code open source, and with the
availability of open data, other researchers can easily reproduce and extend
our work. This will help promote a deeper understanding of healthcare policies
and public health issues
TERT promoter mutation and H3K27me3 trimethylation loss as focal molecular markers in meningioma aggressiveness
Biomarkers to identify high-grade meningiomas have finally been added to the recent 2021 edition of the World Health Organization (WHO) grading scheme. Among them, are the well-known telomerase reverse transcriptase promoter (TERTp) mutation and the recently emerged epigenetic marker, trimethylation of lysine 27 of histone 3 (H3K27me3). Although the presence of TERTp is now associated with WHO grade 3 meningiomas and the loss of trimethylation of H3K27me3 is implicated with potentially worse prognosis, the question remains as to their ability to predict an event rather than an observed association. Furthermore, the standards for H3K27me3 immunohistochemistry (IHC) in meningiomas have not been set and have led to inconsistencies in reporting. To address these critical clinical concerns, I set out to investigate the prevalence of TERTp mutations in a cohort of suspected high-risk meningiomas through Sanger sequencing. I also conducted a meta-analysis of all H3K27me3 publications to qualify the current assessment of H3K27me3 trimethylation loss as a predictor of tumor aggressiveness. Due to complications with the quality of purified DNA from FFPE tissue, I was unable to obtain satisfactory sequencing results to assess the prevalence of TERTp mutation in my cohort. I did, however, develop an optimized DNA purification protocol for FFPE tissues for future research purposes. The pooled data on H3K27me3 did confirm a significant association between the loss of H3K27me3 trimethylation and more aggressive tumors (p5 years old resulted in significantly higher rates of H3K27me3 loss, implying tissue age and quality had a significant effect on the staining for H3K27me3 loss in meningiomas. Although H3K27me3 is associated substantially with meningiomas of more aggressive nature and thus a higher likelihood of recurrence, several criteria must be met first to standardize the process to ensure accuracy in reporting and ease of implementation into standard clinical workups
Establishment of a 7-gene prognostic signature based on oxidative stress genes for predicting chemotherapy resistance in pancreatic cancer
Background: Oxidative stress is involved in regulating various biological processes in human cancers. However, the effect of oxidative stress on pancreatic adenocarcinoma (PAAD) remained unclear.Methods: Pancreatic cancer expression profiles from TCGA were downloaded. Consensus ClusterPlus helped classify molecular subtypes based on PAAD prognosis-associated oxidative stress genes. Limma package filtered differentially expressed genes (DEGs) between subtypes. A multi-gene risk model was developed using Lease absolute shrinkage and selection operator (Lasso)-Cox analysis. A nomogram was built based on risk score and distinct clinical features.Results: Consistent clustering identified 3 stable molecular subtypes (C1, C2, C3) based on oxidative stress-associated genes. Particularly, C3 had the optimal prognosis with the greatest mutation frequency, activate cell cycle pathway in an immunosuppressed status. Lasso and univariate cox regression analysis selected 7 oxidative stress phenotype-associated key genes, based on which we constructed a robust prognostic risk model independent of clinicopathological features with stable predictive performance in independent datasets. High-risk group was found to be more sensitive to small molecule chemotherapeutic drugs including Gemcitabine, Cisplatin, Erlotinib and Dasatinib. The 6 of 7 genes expressions were significantly associated with methylation. Survival prediction and prognostic model was further improved through a decision tree model by combining clinicopathological features with RiskScore.Conclusion: The risk model containing seven oxidative stress-related genes may have a greater potential to assist clinical treatment decision-making and prognosis determination
Short exposure to photo-oxidative damage triggers molecular signals indicative of early retinal degeneration
IntroductionAge-related macular degeneration (AMD) is the leading cause of blindness in the developed world, currently affecting over 350 billion people globally. For the most prevalent late-stage form of this disease, atrophic AMD, there are no available prevention strategies or treatments, in part due to inherent difficulties in early-stage diagnosis. Photo-oxidative damage is a well-established model for studying inflammatory and cell death features that occur in late-stage atrophic AMD, however to date has not been investigated as a potential model for studying early features of disease onset. Therefore, in this study we aimed to determine if short exposure to photo-oxidative damage could be used to induce early retinal molecular changes and advance this as a potential model for studying early-stage AMD.MethodsC57BL/6J mice were exposed to 1, 3, 6, 12, or 24h photo-oxidative damage (PD) using 100k lux bright white light. Mice were compared to dim-reared (DR) healthy controls as well as mice which had undergone long periods of photo-oxidative damage (3d and 5d-PD) as known timepoints for inducing late-stage retinal degeneration pathologies. Cell death and retinal inflammation were measured using immunohistochemistry and qRT-PCR. To identify retinal molecular changes, retinal lysates were sent for RNA sequencing, following which bioinformatics analyses including differential expression and pathway analyses were performed. Finally, to investigate modulations in gene regulation as a consequence of degeneration, microRNA (miRNA) expression patterns were quantified using qRT-PCR and visualized using in situ hybridization.ResultsShort exposure to photo-oxidative damage (1-24h-PD) induced early molecular changes in the retina, with progressive downregulation of homeostatic pathways including metabolism, transport and phototransduction observed across this time-course. Inflammatory pathway upregulation was observed from 3h-PD, preceding observable levels of microglia/macrophage activation which was noted from 6h-PD, as well as significant photoreceptor row loss from 24h-PD. Further rapid and dynamic movement of inflammatory regulator miRNA, miR-124-3p and miR-155-5p, was visualized in the retina in response to degeneration.ConclusionThese results support the use of short exposure to photo-oxidative damage as a model of early AMD and suggest that early inflammatory changes in the retina may contribute to pathological features of AMD progression including immune cell activation and photoreceptor cell death. We suggest that early intervention of these inflammatory pathways by targeting miRNA such as miR-124-3p and miR-155-5p or their target genes may prevent progression into late-stage pathology
Pollution-induced community tolerance in freshwater biofilms – from molecular mechanisms to loss of community functions
Exposure to herbicides poses a threat to aquatic biofilms by affecting their community structure, physiology and function. These changes render biofilms to become more tolerant, but on the downside community tolerance has ecologic costs. A concept that addresses induced community tolerance to a pollutant (PICT) was introduced by Blanck and Wängberg (1988). The basic principle of the concept is that microbial communities undergo pollution-induced succession when exposed to a pollutant over a long period of time, which changes communities structurally and functionally and enhancing tolerance to the pollutant exposure. However, the mechanisms of tolerance and the ecologic consequences were hardly studied up to date. This thesis addresses the structural and functional changes in biofilm communities and applies modern molecular methods to unravel molecular tolerance mechanisms.
Two different freshwater biofilm communities were cultivated for a period of five weeks, with one of the communities being contaminated with 4 μg L-1 diuron. Subsequently, the communities were characterized for structural and functional differences, especially focusing on their crucial role of photosynthesis. The community structure of the autotrophs was assessed using HPLC-based pigment analysis and their functional alterations were investigated using Imaging-PAM fluorometry to study photosynthesis and community oxygen profiling to determine net primary production. Then, the molecular fingerprints of the communities were measured with meta-transcriptomics (RNA-Seq) and GC-based community metabolomics approaches and analyzed with respect to changes in their molecular functions. The communities were acute exposed to diuron for one hour in a dose-response design, to reveal a potential PICT and uncover related adaptation to diuron exposure. The combination of apical and molecular methods in a dose-response design enabled the linkage of functional effects of diuron exposure and underlying molecular mechanisms based on a sensitivity analysis.
Chronic exposure to diuron impaired freshwater biofilms in their biomass accrual. The contaminated communities particularly lost autotrophic biomass, reflected by the decrease in specific chlorophyll a content. This loss was associated with a change in the molecular fingerprint of the communities, which substantiates structural and physiological changes. The decline in autotrophic biomass could be due to a primary loss of sensitive autotrophic organisms caused by the selection of better adapted species in the course of chronic exposure. Related to this hypothesis, an increase in diuron tolerance has been detected in the contaminated communities and molecular mechanisms facilitating tolerance have been found. It was shown that genes of the photosystem, reductive-pentose phosphate cycle and arginine metabolism were differentially expressed among the communities and that an increased amount of potential antioxidant degradation products was found in the contaminated communities. This led to the hypothesis that contaminated communities may have adapted to oxidative stress, making them less sensitive to diuron exposure. Moreover, the photosynthetic light harvesting complex was altered and the photoprotective xanthophyll cycle was increased in the contaminated communities. Despite these adaptation strategies, the loss of autotrophic biomass has been shown to impair primary production. This impairment persisted even under repeated short-term exposure, so that the tolerance mechanisms cannot safeguard primary production as a key function in aquatic systems.:1. The effect of chemicals on organisms and their functions .............................. 1
1.1 Welcome to the anthropocene .......................................................................... 1
1.2 From cellular stress responses to ecosystem resilience ................................... 3
1.2.1 The individual pursuit for homeostasis ....................................................... 3
1.2.2 Stability from diversity ................................................................................. 5
1.3 Community ecotoxicology - a step forward in monitoring the effects of chemical
pollution? ................................................................................................................. 6
1.4 Functional ecotoxicological assessment of microbial communities ................... 9
1.5 Molecular tools – the key to a mechanistic understanding of stressor effects
from a functional perspective in microbial communities? ...................................... 12
2. Aims and Hypothesis ......................................................................................... 14
2.1 Research question .......................................................................................... 14
2.2 Hypothesis and outline .................................................................................... 15
2.3 Experimental approach & concept .................................................................. 16
2.3.1 Aquatic freshwater biofilms as model community ..................................... 16
2.3.2 Diuron as model herbicide ........................................................................ 17
2.3.3 Experimental design ................................................................................. 18
3. Structural and physiological changes in microbial communities after chronic
exposure - PICT and altered functional capacity ................................................. 21
3.1 Introduction ..................................................................................................... 21
3.2 Methods .......................................................................................................... 23
3.2.1 Biofilm cultivation ...................................................................................... 23
3.2.2 Dry weight and autotrophic index ............................................................. 23
3.2.4 Pigment analysis of periphyton ................................................................. 23
3.2.4.1 In-vivo pigment analysis for community characterization ....................... 24
3.2.4.2 In-vivo pigment analysis based on Imaging-PAM fluorometry ............... 24
3.2.4.3 In-vivo pigment fluorescence for tolerance detection ............................. 26
3.2.4.4 Ex-vivo pigment analysis by high-pressure liquid-chromatography ....... 27
3.2.5 Community oxygen metabolism measurements ....................................... 28
3.3 Results and discussion ................................................................................... 29
3.3.1 Comparison of the structural community parameters ............................... 29
3.3.2 Photosynthetic activity and primary production of the communities after
selection phase ................................................................................................. 33
3.3.3 Acquisition of photosynthetic tolerance .................................................... 34
3.3.4 Primary production at exposure conditions ............................................... 36
3.3.5 Tolerance detection in primary production ................................................ 37
3.4 Summary and Conclusion ........................................................................... 40
4. Community gene expression analysis by meta-transcriptomics ................... 41
4.1 Introduction to meta-transcriptomics ............................................................... 41
4.2. Methods ......................................................................................................... 43
4.2.1 Sampling and RNA extraction................................................................... 43
4.2.2 RNA sequencing analysis ......................................................................... 44
4.2.3 Data assembly and processing................................................................. 45
4.2.4 Prioritization of contigs and annotation ..................................................... 47
4.2.5 Sensitivity analysis of biological processes .............................................. 48
4.3 Results and discussion ................................................................................... 48
4.3.1 Characterization of the meta-transcriptomic fingerprints .......................... 49
4.3.2 Insights into community stress response mechanisms using trend analysis
(DRomic’s) ......................................................................................................... 51
4.3.3 Response pattern in the isoform PS genes .............................................. 63
4.5 Summary and conclusion ................................................................................ 65
5. Community metabolome analysis ..................................................................... 66
5.1 Introduction to community metabolomics ........................................................ 66
5.2 Methods .......................................................................................................... 68
5.2.1 Sampling, metabolite extraction and derivatisation................................... 68
5.2.2 GC-TOF-MS analysis ............................................................................... 69
5.2.3 Data processing and statistical analysis ................................................... 69
5.3 Results and discussion ................................................................................... 70
5.3.1 Characterization of the metabolic fingerprints .......................................... 70
5.3.2 Difference in the metabolic fingerprints .................................................... 71
5.3.3 Differential metabolic responses of the communities to short-term exposure
of diuron ............................................................................................................ 73
5.4 Summary and conclusion ................................................................................ 78
6. Synthesis ............................................................................................................. 79
6.1 Approaches and challenges for linking molecular data to functional
measurements ...................................................................................................... 79
6.2 Methods .......................................................................................................... 83
6.2.1 Summary on the data ............................................................................... 83
6.2.2 Aggregation of molecular data to index values (TELI and MELI) .............. 83
6.2.3 Functional annotation of contigs and metabolites using KEGG ................ 83
6.3 Results and discussion ................................................................................... 85
6.3.1 Results of aggregation techniques ........................................................... 85
6.3.2 Sensitivity analysis of the different molecular approaches and endpoints 86
6.3.3 Mechanistic view of the molecular stress responses based on KEGG
functions ............................................................................................................ 89
6.4 Consolidation of the results – holistic interpretation and discussion ............... 93
6.4.1 Adaptation to chronic diuron exposure - from molecular changes to
community effects.............................................................................................. 93
6.4.2 Assessment of the ecological costs of Pollution-induced community
tolerance based on primary production ............................................................. 94
6.5 Outlook ............................................................................................................ 9
Die akute Appendizitis im Kindes- und Jugendalter: neue diagnostische Verfahren für die prätherapeutische Differenzierung histopathologischer Entitäten zur Unterstützung konservativer Therapiestrategien
Hintergrund der hier zusammengefassten Studien war die aktuelle Datenlage, die dafür spricht, dass es sich bei der klinisch unkomplizierten, histopathologisch phlegmonösen und der klinisch komplizierten, histopathologisch gangränösen Appendizitis um unabhängige Entitäten handelt. Diese können unterschiedlichen Therapieoptionen (konservativ vs. operativ) zugeführt werden. Vor diesem Hintergrund war es ein Ziel der Arbeiten zu untersuchen, wie die Formen der akuten Appendizitis im Kindes- und Jugendalter bereits prätherapeutisch unterschieden werden können.
Sowohl in der Labordiagnostik (P1 und P2) als auch im Ultraschall (P3) lassen sich Unterschiede zwischen Patient*innen mit unkomplizierter, phlegmonöser und komplizierter (gangränöser und perforierender) Appendizitis aufzeigen. Hierdurch allein kann allerdings aufgrund unzureichender Trennschärfe noch keine ausreichende Entscheidungssicherheit erreicht werden. Mit Verfahren der künstlichen Intelligenz auf Untersucher-unabhängige diagnostische Parameter (P4) konnte die Vorhersagegenauigkeit der akuten Appendizitis weiter gesteigert werden. Interessante Ergebnisse bezüglich der unterschiedlichen Pathomechanismen der beiden inflammatorischen Entitäten ergaben sich durch eine differenzielle Genexpressionsanalyse (P5). In einer Proof-of-Concept-Studie wurden zuvor beschriebene Methoden der künstlichen Intelligenz auf die Genexpressionsdaten angewandt (P6). Hierdurch konnte im Modell eine grundsätzliche Differenzierbarkeit der Entitäten durch die Anwendung der neuen Methode aufgezeigt werden.
Ein mittelfristiges Ziel ist es, eine Biomarkersignatur zu definieren, die ihre Aussagekraft durch einen Computeralgorithmus hat. Hierdurch soll eine schnelle Therapieentscheidung ermöglicht werden. Im Idealfall sollte diese Biomarkersignatur sicher, objektiv und einfach zu bestimmen sein sowie eine höhere diagnostische Sicherheit als die bisherige Diagnostik mittels Anamnese, Untersuchung, Laboranalyse und Ultraschall bieten.
Langfristiges Ziel von Folgestudien ist die Identifizierung einer Biomarkersignatur mit der bestmöglichen Vorhersagekraft. Hinsichtlich der routinemäßigen klinischen Diagnostik ist die Anwendung von Point-of-Care Devices auf PCR-Basis denkbar. Hier könnte eine limitierte Anzahl von Primern für eine Biomarkersignatur mit hoher Vorhersagekraft zum Einsatz kommen. Der dadurch ermittelte Biomarker würde seine Aussagekraft durch einen einfach anzuwendenden Computeralgorithmus erhalten. Die Kombination aus Genexpressionsanalyse mit Methoden der künstlichen Intelligenz kann somit die Grundlage für ein neues diagnostisches Instrument zur sicheren Unterscheidung unterschiedlicher Appendizitisentitäten darstellen
Neuroanatomical and gene expression features of the rabbit accessory olfactory system. Implications of pheromone communication in reproductive behaviour and animal physiology
Mainly driven by the vomeronasal system (VNS), pheromone
communication is involved in many species-specific fundamental innate socio-sexual behaviors such as mating and
fighting, which are essential for animal reproduction and survival. Rabbits are a unique model for studying
chemocommunication due to the discovery of the rabbit mammary pheromone, but paradoxically there has been a
lack of knowledge regarding its VNS pathway. In this work, we aim at filling this gap by approaching the system
from an integrative point of view, providing extensive anatomical and genomic data of the rabbit VNS, as well as
pheromone-mediated reproductive and behavioural studies. Our results build strong foundation for further
translational studies which aim at implementing the use of pheromones to improve animal production and welfare
Evolutionary Computation in Action: Feature Selection for Deep Embedding Spaces of Gigapixel Pathology Images
One of the main obstacles of adopting digital pathology is the challenge of
efficient processing of hyperdimensional digitized biopsy samples, called whole
slide images (WSIs). Exploiting deep learning and introducing compact WSI
representations are urgently needed to accelerate image analysis and facilitate
the visualization and interpretability of pathology results in a postpandemic
world. In this paper, we introduce a new evolutionary approach for WSI
representation based on large-scale multi-objective optimization (LSMOP) of
deep embeddings. We start with patch-based sampling to feed KimiaNet , a
histopathology-specialized deep network, and to extract a multitude of feature
vectors. Coarse multi-objective feature selection uses the reduced search space
strategy guided by the classification accuracy and the number of features. In
the second stage, the frequent features histogram (FFH), a novel WSI
representation, is constructed by multiple runs of coarse LSMOP. Fine
evolutionary feature selection is then applied to find a compact (short-length)
feature vector based on the FFH and contributes to a more robust deep-learning
approach to digital pathology supported by the stochastic power of evolutionary
algorithms. We validate the proposed schemes using The Cancer Genome Atlas
(TCGA) images in terms of WSI representation, classification accuracy, and
feature quality. Furthermore, a novel decision space for multicriteria decision
making in the LSMOP field is introduced. Finally, a patch-level visualization
approach is proposed to increase the interpretability of deep features. The
proposed evolutionary algorithm finds a very compact feature vector to
represent a WSI (almost 14,000 times smaller than the original feature vectors)
with 8% higher accuracy compared to the codes provided by the state-of-the-art
methods
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The Epidemiology and Genetic Architecture of Vitamin D Deficiency in African Children
Vitamin D deficiency is a common public health problem worldwide. However, little is known about the epidemiology of vitamin D deficiency in Africa. In this thesis, I aimed to determine: 1) the prevalence of and risk factors associated with vitamin D deficiency in studies conducted in Africa; 2) the prevalence and predictors of vitamin D deficiency in African children; 3) the association between vitamin D and iron deficiency in African children; and 4) genetic variants that influence vitamin D status in Africans.
In a systematic review and meta-analyses of previous vitamin D studies in Africa, the average prevalence of low vitamin D status was 18.5%, 34.2% and 59.5% using cut-offs of 25-hydroxyvitamin D (25(OH)D) levels of <30 nmol/L, <50 nmol/L and <75 nmol/L, respectively. Populations at risk of vitamin D deficiency included newborns, women, and people living in high latitudes or urban areas.
In an epidemiological study of young children living in Africa, the prevalence of low vitamin D status was 0.6%, 7.8% and 44.5% using cut-offs of 25(OH)D levels of GC2 variant of the group-specific component (GC) gene, which encodes vitamin D binding protein.
Vitamin D deficiency was also associated with 80% higher odds of iron deficiency in these children. Adjusted regression models revealed that vitamin D deficiency was associated with higher ferritin and hepcidin levels suggesting lower iron status, and reduced sTfR and transferrin levels and increased TSAT and serum iron levels suggesting improved iron status.
Genome-wide association study (GWAS) in Africans revealed genetic variants that influence vitamin D status in vitamin D metabolism genes: DHCR7/NADSYN1, CYP2R1 and GC. However, the majority of SNPs from previous European GWASs did not replicate in the current GWAS.
Findings from this thesis indicate that vitamin D deficiency is prevalent in many African populations and should be considered in public health strategies in Africa
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