Refine
Year of publication
- 2018 (8) (remove)
Document Type
- Article (4)
- Doctoral Thesis (3)
- Postprint (1)
Is part of the Bibliography
- yes (8)
Keywords
- biomarker (8) (remove)
Parkinson's disease (PD) shows high heterogeneity with regard to the underlying molecular pathogenesis involving multiple pathways and mechanisms. Diagnosis is still challenging and rests entirely on clinical features. Thus, there is an urgent need for robust diagnostic biofluid markers. Untargeted metabolomics allows establishing low-molecular compound biomarkers in a wide range of complex diseases by the measurement of various molecular classes in biofluids such as blood plasma, serum, and cerebrospinal fluid (CSF). Here, we applied untargeted high-resolution mass spectrometry to determine plasma and CSF metabolite profiles. We semiquantitatively determined small-molecule levels (<= 1.5 kDa) in the plasma and CSF from early PD patients (disease duration 0-4 years; n = 80 and 40, respectively), and sex-and age-matched controls (n = 76 and 38, respectively). We performed statistical analyses utilizing partial least square and random forest analysis with a 70/30 training and testing split approach, leading to the identification of 20 promising plasma and 14 CSF metabolites. The semetabolites differentiated the test set with an AUC of 0.8 (plasma) and 0.9 (CSF). Characteristics of the metabolites indicate perturbations in the glycerophospholipid, sphingolipid, and amino acid metabolism in PD, which underscores the high power of metabolomic approaches. Further studies will enable to develop a potential metabolite-based biomarker panel specific for PD
Primary progressive multiple sclerosis (PPMS) shows a highly variable disease progression with poor prognosis and a characteristic accumulation of disabilities in patients. These hallmarks of PPMS make it difficult to diagnose and currently impossible to efficiently treat. This study aimed to identify plasma metabolite profiles that allow diagnosis of PPMS and its differentiation from the relapsing remitting subtype (RRMS), primary neurodegenerative disease (Parkinson’s disease, PD), and healthy controls (HCs) and that significantly change during the disease course and could serve as surrogate markers of multiple sclerosis (MS)-associated neurodegeneration over time. We applied untargeted high-resolution metabolomics to plasma samples to identify PPMS-specific signatures, validated our findings in independent sex- and age-matched PPMS and HC cohorts and built discriminatory models by partial least square discriminant analysis (PLS-DA). This signature was compared to sex- and age-matched RRMS patients, to patients with PD and HC. Finally, we investigated these metabolites in a longitudinal cohort of PPMS patients over a 24-month period. PLS-DA yielded predictive models for classification along with a set of 20 PPMS-specific informative metabolite markers. These metabolites suggest disease-specific alterations in glycerophospholipid and linoleic acid pathways. Notably, the glycerophospholipid LysoPC(20:0) significantly decreased during the observation period. These findings show potential for diagnosis and disease course monitoring, and might serve as biomarkers to assess treatment efficacy in future clinical trials for neuroprotective MS therapies.
Femtosecond-Pulsed laser written and etched fiber bragg gratings for fiber-optical biosensing
(2018)
We present the development of a label-free, highly sensitive fiber-optical biosensor for online detection and quantification of biomolecules. Here, the advantages of etched fiber Bragg gratings (eFBG) were used, since they induce a narrowband Bragg wavelength peak in the reflection operation mode. The gratings were fabricated point-by-point via a nonlinear absorption process of a highly focused femtosecond-pulsed laser, without the need of prior coating removal or specific fiber doping. The sensitivity of the Bragg wavelength peak to the surrounding refractive index (SRI), as needed for biochemical sensing, was realized by fiber cladding removal using hydrofluoric acid etching. For evaluation of biosensing capabilities, eFBG fibers were biofunctionalized with a single-stranded DNA aptamer specific for binding the C-reactive protein (CRP). Thus, the CRP-sensitive eFBG fiber-optical biosensor showed a very low limit of detection of 0.82 pg/L, with a dynamic range of CRP detection from approximately 0.8 pg/L to 1.2 mu g/L. The biosensor showed a high specificity to CRP even in the presence of interfering substances. These results suggest that the proposed biosensor is capable for quantification of CRP from trace amounts of clinical samples. In addition, the adaption of this eFBG fiber-optical biosensor for detection of other relevant analytes can be easily realized.
Primary progressive multiple sclerosis (PPMS) shows a highly variable disease progression with poor prognosis and a characteristic accumulation of disabilities in patients. These hallmarks of PPMS make it difficult to diagnose and currently impossible to efficiently treat. This study aimed to identify plasma metabolite profiles that allow diagnosis of PPMS and its differentiation from the relapsing remitting subtype (RRMS), primary neurodegenerative disease (Parkinson’s disease, PD), and healthy controls (HCs) and that significantly change during the disease course and could serve as surrogate markers of multiple sclerosis (MS)-associated neurodegeneration over time. We applied untargeted high-resolution metabolomics to plasma samples to identify PPMS-specific signatures, validated our findings in independent sex- and age-matched PPMS and HC cohorts and built discriminatory models by partial least square discriminant analysis (PLS-DA). This signature was compared to sex- and age-matched RRMS patients, to patients with PD and HC. Finally, we investigated these metabolites in a longitudinal cohort of PPMS patients over a 24-month period. PLS-DA yielded predictive models for classification along with a set of 20 PPMS-specific informative metabolite markers. These metabolites suggest disease-specific alterations in glycerophospholipid and linoleic acid pathways. Notably, the glycerophospholipid LysoPC(20:0) significantly decreased during the observation period. These findings show potential for diagnosis and disease course monitoring, and might serve as biomarkers to assess treatment efficacy in future clinical trials for neuroprotective MS therapies.
In dieser Arbeit steht die Entwicklung einer Sensorplattform für biochemische Anwendungen, welche auf einem optischen Detektionsprinzips beruht, im Vordergrund. Während der Entwicklung wurden zwei komplementäre Konzeptideen behandelt, zum einen ein Sensor, der auf photonischen Kristallen und Wellenleiterstrukturen basiert und zum anderen einen faserbasierten Sensor, der chemisch modifizierte Faser-Bragg-Gitter enthält. Das optische Detektionsprinzip in beiden Sensorideen ist die resultierende Brechungsindexänderung als messbare physikochemische Kenngröße.
Das aus der Natur bekannte Phänomen der photonischen Kristalle, das u. a. bei Opalen und bei Schmetterlingen zu finden ist, wurde bereits 1887 von Lord Rayleigh beschrieben. Er beschrieb die optischen Eigenschaften von periodischen mehrschichtigen Filmen, welche als vereinfachtes Modell eines eindimensionalen photonischen Kristalls verstanden werden können. Die Periodizität der Brechungsindexänderung resultiert in einem optischen Filter für Frequenzen in einem bestimmten spektralen Bereich, weshalb dann dort keine Lichtausbreitung mehr möglich ist. Wird dieses System aber durch eine Defektstelle in der Brechungsindexperiodizität gestört, sodass daraus zwei perfekt periodische Systeme entstehen, ist die Lichtausbreitung für eine bestimmte Frequenz dennoch möglich. In der Folge resultiert daraus ein schmalbandiges Signal im Transmissionsspektrum. Die erlaubte Frequenz ist dabei u. a. abhängig vom Brechungsindexunterschied des periodischen Systems, d.h. Veränderung des Brechungsindexes einer Schicht führt zu einer spektralen Verschiebung der erlaubten Frequenz, dadurch kann dieses Sensorkonzept für biochemische Sensorik ausgenutzt werden [1]. Diese Entwicklung des auf photonischen Kristallen basierenden Sensors war eine Kooperation mit dem Industriepartner „Nanoplus GmbH“. In der Doktorarbeit wurden Simulationen und praktischen Arbeiten zur Designentwicklung des Sensors und die Arbeiten an einem ersten Modellaufbau für die biochemischen Anwendungen durchgeführt.
Für den faserbasierten Sensor wurden Faser-Bragg-Gitter in den Faserkern hineingeschrieben. Hill et al. entdeckten 1978, dass solche Gitterstrukturen genau wie photonische Kristalle als optische Filter fungieren [2]. Die Gitter bestehen dabei aus Änderungen des Brechungsindexes im Faserkern. Im Laufe der nächsten vierzig Jahren wurden verschiedene Einschreibetechniken und Gitterstrukturen entwickelt, weshalb die Eigenschaften der jeweiligen Gitterstrukturen variieren. Eine solche Gitterstruktur sind u. a. die Faser-Bragg-Gitter, deren Gitterperiode, d. h. die Abstände der Brechungsindexmodifikationen, sich im Nanometer- bis Mikrometerbereich befinden. Aufgrund der kleinen Gitterperiode wird eine rückwärtsführende Welle im Kern für eine bestimmte Frequenz bzw. Wellenlänge, der Bragg-Wellenlänge, erzeugt. Im Endeffekt resultiert daraus ein schmalbandiges Signal sowohl im Transmissionsspektrum, als auch im Reflexionsspektrum. Die Resonanzwellenlänge ist dabei proportional zu der Gitterperiode und dem effektiven Brechungsindex, welcher vom Brechungsindex des Kerns und des kernumgebenen Materials abhängig ist. Letztlich eignet sich diese Technik für physikochemische Sensorik. Im Rahmen dieser Arbeit wurden die Gitter mit Hilfe einer relativen neuen Herstellungsmethode in die Fasern geschrieben [3]. Anschließend stand die Entwicklung eines Biosensors im Vordergrund, wobei zunächst ein Protokoll zum Ätzen der Faser mit Flusssäure entwickelt worden ist, dass das System sensitiv zum umgebenen Brechungsindex macht. Am Ende wurde ein Modellaufbau realisiert, indem ein Modellsystem, hier die Detektion vom C-reaktiven Protein mittels spezifischen einzelsträngigen DNS-Aptameren, erfolgreich getestet und quantifiziert worden ist.
1 Mandal, S.; Erickson, D. Nanoscale Optofluidic Sensor Arrays. Opt. Express 2008, 16 (3), 1623–1631.
2 Hill, K. O.; Fujii, Y.; Johnson, D. C.; Kawasaki, B. S. Photosensitivity in Optical Fiber Waveguides: Application to Reflection Filter Fabrication. Appl. Phys. Lett. 1978, 32 (10), 647–649.
3 Martínez, A.; Dubov, M.; Khrushchev, I.; Bennion, I. Direct Writing of Fibre Bragg Gratings by Femtosecond Laser. Electron. Lett. 2004, 40 (19), 1170.
Neuroinflammatory and neurodegenerative diseases such as Parkinson's (PD) and multiple sclerosis (MS) often result in a severe impairment of the patient´s quality of life. Effective therapies for the treatment are currently not available, which results in a high socio-economic burden. Due to the heterogeneity of the disease subtypes, stratification is particularly difficult in the early phase of the disease and is mainly based on clinical parameters such as neurophysiological tests and central nervous imaging. Due to good accessibility and stability, blood and cerebrospinal fluid metabolite markers could serve as surrogates for neurodegenerative processes. This can lead to an improved mechanistic understanding of these diseases and further be used as "treatment response" biomarkers in preclinical and clinical development programs. Therefore, plasma and CSF metabolite profiles will be identified that allow differentiation of PD from healthy controls, association of PD with dementia (PDD) and differentiation of PD subtypes such as akinetic rigid and tremor dominant PD patients. In addition, plasma metabolites for the diagnosis of primary progressive MS (PPMS) should be investigated and tested for their specificity to relapsing-remitting MS (RRMS) and their development during PPMS progression.
By applying untargeted high-resolution metabolomics of PD patient samples and in using random forest and partial least square machine learning algorithms, this study identified 20 plasma metabolites and 14 CSF metabolite biomarkers. These differentiate against healthy individuals with an AUC of 0.8 and 0.9 in PD, respectively. We also identify ten PDD specific serum metabolites, which differentiate against healthy individuals and PD patients without dementia with an AUC of 1.0, respectively. Furthermore, 23 akinetic-rigid specific plasma markers were identified, which differentiate against tremor-dominant PD patients with an AUC of 0.94 and against healthy individuals with an AUC of 0.98. These findings also suggest more severe disease pathology in the akinetic-rigid PD than in tremor dominant PD. In the analysis of MS patient samples a partial least square analysis yielded predictive models for the classification of PPMS and resulted in 20 PPMS specific metabolites. In another MS study unknown changes in human metabolism were identified after administration of the multiple sclerosis drug dimethylfumarate, which is used for the treatment of RRMS. These results allow to describe and understand the hitherto completely unknown mechanism of action of this new drug and to use these findings for the further development of new drugs and targets against RRMS.
In conclusion, these results have the potential for improved diagnosis of these diseases and improvement of mechanistic understandings, as multiple deregulated pathways were identified. Moreover, novel Dimethylfumarate targets can be used to aid drug development and treatment efficiency. Overall, metabolite profiling in combination with machine learning identified as a promising approach for biomarker discovery and mode of action elucidation.
Global climate change is one of the greatest challenges of the 21st century, with influence on the environment, societies, politics and economies. The (semi-)arid areas of Southern Africa already suffer from water scarcity. There is a great variety of ongoing research related to global climate history but important questions on regional differences still exist.
In southern African regions terrestrial climate archives are rare, which makes paleoclimate studies challenging. Based on the assumption that continental pans (sabkhas) represent a suitable geo-archive for the climate history, two different pans were studied in the southern and western Kalahari Desert. A combined approach of molecular biological and biogeochemical analyses is utilized to investigate the diversity and abundance of microorganisms and to trace temporal and spatial changes in paleoprecipitation in arid environments. The present PhD thesis demonstrates the applicability of pan sediments as a late Quaternary geo-archive based on microbial signature lipid biomarkers, such as archaeol, branched and isoprenoid glycerol dialkyl glycerol tetraethers (GDGTs) as well as phospholipid fatty acids (PLFA). The microbial signatures contained in the sediment provide information on the current or past microbial community from the Last Glacial Maximum to the recent epoch, the Holocene. The results are discussed in the context of regional climate evolution in southwestern Africa. The seasonal shift of the Innertropical Convergence Zone (ITCZ) along the equator influences the distribution of precipitation- and climate zones. The different expansion of the winter- and summer rainfall zones in southern Africa was confirmed by the frequency of certain microbial biomarkers. A period of increased precipitation in the south-western Kalahari could be described as a result of the extension of the winter rainfall zone during the last glacial maximum (21 ± 2 ka). Instead a period of increased paleoprecipitation in the western Kalahari was indicated during the Late Glacial to Holocene transition. This was possibly caused by a southwestern shift in the position of the summer rainfall zone associated to the southward movement of the ITCZ.
Furthermore, for the first time this study characterizes the bacterial and archaeal life based on 16S rRNA gene high-throughput sequencing in continental pan sediments and provides an insight into the recent microbial community structure. Near-surface processes play an important role for the modern microbial ecosystem in the pans. Water availability as well as salinity might determine the abundance and composition of the microbial communities. The microbial community of pan sediments is dominated by halophilic and dry-adapted archaea and bacteria. Frequently occurring microorganisms such as, Halobacteriaceae, Bacillus and Gemmatimonadetes are described in more detail in this study.