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This thesis presents investigations on sediments from two African lakes which have been recording changes in their surrounding environmental and climate conditions since more than 200,000 years. Focus of this work is the time of the last Glacial and the Holocene (the last ~100,000 years before present [in the following 100 kyr BP]). One important precondition for this kind of research is a good understanding of the present ecosystems in and around the lakes and of the sediment formation under modern climate conditions. Both studies therefore include investigations on the modern environment (including organisms, soils, rocks, lake water and sediments). A 90 m long sediment sequence was investigated from Lake Tswaing (north-eastern South Africa) using geochemical analyses. These investigations document alternating periods of high detrital input and low (especially autochthonous) organic matter content and periods of low detrital input, carbonatic or evaporitic sedimentation and high autochthonous organic matter content. These alternations are interpreted as changes between relatively humid and arid conditions, respectively. Before c. 75 kyr BP, they seem to follow changes in local insolation whereas afterwards they appear to be acyclic and are probably caused by changes in ocean circulation and/or in the mean position of the Inter-Tropical Convergence Zone (ITCZ). Today, these factors have main influence on precipitation in this area where rainfall occurs almost exclusively during austral summer. All modern organisms were analysed for their biomarker and bulk organic and compound-specific stable carbon isotope composition. The same investigations on sediments from the modern lake floor document the mixed input of the investigated individual organisms and reveal additional influences by methanotrophic bacteria. A comparison of modern sediment characteristics with those of sediments covering the time 14 to 2 kyr BP shows changes in the productivity of the lake and the surrounding vegetation which are best explained by changes in hydrology. More humid conditions are indicated for times older than 10 kyr BP and younger than 7.5 kyr BP, whereas arid conditions prevailed in between. These observations agree with the results from sediment composition and indications from other climate archives nearby. The second lake study deals with Lake Challa, a small, deep crater lake on the foot of Mount Kilimanjaro. In this lake form mm-scale laminated sediments which were analyses with micro-XRF scanning for changes in the element composition. By comparing these results with investigations on thin sections, results from ongoing sediment trap studies, meteorological data, and investigations on the surrounding rocks and soils, I develop a model for seasonal variability in the limnology and sedimentation of Lake Challa. The lake appears to be stratified during the warm rain seasons (October – December and March – May) during which detrital material is delivered to the lake and carbonates precipitate. On the lake floor forms a dark lamina with high contents of Fe and Ti and high Ca/Al and low Mn/Fe ratios. Diatoms bloom during the cool and windy season (June – September) when mixing down to c. 60 m depth provides easily bio-available nutrients. Contemporaneously, Fe and Mn-oxides are precipitating which cause high Mn/Fe ratios in the light diatom-rich laminae of the sediments. Trends in the Mn/Fe ratio of the sediments are interpreted to reflect changes in the intensity or duration of seasonal mixing in Lake Challa. This interpretation is supported by parallel changes in the organic matter and biogenic silica content observed in the 22 m long profile recovered from Lake Challa. This covers the time of the last 25 kyr BP. It documents a transition around 16 kyr BP from relatively well-mixed conditions with high detrital input during glacial times to stronger stratified conditions which are probably related to increasing lake levels in Challa and generally more humid conditions in East Africa. Intensified mixing is recorded for the time of the Younger Dryas and the period between 11.4 and 10.7 kyr BP. For these periods, reduced intensity of the SW monsoon and intensified NE monsoon are reported from archives of the Indian-Asian Monsoon region, arguing for the latter as a probable source for wind mixing in Lake Challa. This connection is probably also responsible for contemporaneous events in the Mn/Fe ratios of the Lake Challa sediments and in other records of northern hemisphere monsoon intensity during the Holocene and underlines the close interaction of global low latitude atmospheric circulation.
The Tibetan Plateau is the largest elevated landmass in the world and profoundly influences atmospheric circulation patterns such as the Asian monsoon system. Therefore this area has been increasingly in focus of palaeoenvironmental studies. This thesis evaluates the applicability of organic biomarkers for palaeolimnological purposes on the Tibetan Plateau with a focus on aquatic macrophyte-derived biomarkers. Submerged aquatic macrophytes have to be considered to significantly influence the sediment organic matter due to their high abundance in many Tibetan lakes. They can show highly 13C-enriched biomass because of their carbon metabolism and it is therefore crucial for the interpretation of δ13C values in sediment cores to understand to which extent aquatic macrophytes contribute to the isotopic signal of the sediments in Tibetan lakes and in which way variations can be explained in a palaeolimnological context. Additionally, the high abundance of macrophytes makes them interesting as potential recorders of lake water δD. Hydrogen isotope analysis of biomarkers is a rapidly evolving field to reconstruct past hydrological conditions and therefore of special relevance on the Tibetan Plateau due to the direct linkage between variations of monsoon intensity and changes in regional precipitation / evaporation balances. A set of surface sediment and aquatic macrophyte samples from the central and eastern Tibetan Plateau was analysed for composition as well as carbon and hydrogen isotopes of n-alkanes. It was shown how variable δ13C values of bulk organic matter and leaf lipids can be in submerged macrophytes even of a single species and how strongly these parameters are affected by them in corresponding sediments. The estimated contribution of the macrophytes by means of a binary isotopic model was calculated to be up to 60% (mean: 40%) to total organic carbon and up to 100% (mean: 66%) to mid-chain n-alkanes. Hydrogen isotopes of n-alkanes turned out to record δD of meteoric water of the summer precipitation. The apparent enrichment factor between water and n-alkanes was in range of previously reported ones (≈-130‰) at the most humid sites, but smaller (average: -86‰) at sites with a negative moisture budget. This indicates an influence of evaporation and evapotranspiration on δD of source water for aquatic and terrestrial plants. The offset between δD of mid- and long-chain n-alkanes was close to zero in most of the samples, suggesting that lake water as well as soil and leaf water are affected to a similar extent by those effects. To apply biomarkers in a palaeolimnological context, the aliphatic biomarker fraction of a sediment core from Lake Koucha (34.0° N; 97.2° E; eastern Tibetan Plateau) was analysed for concentrations, δ13C and δD values of compounds. Before ca. 8 cal ka BP, the lake was dominated by aquatic macrophyte-derived mid-chain n-alkanes, while after 6 cal ka BP high concentrations of a C20 highly branched isoprenoid compound indicate a predominance of phytoplankton. Those two principally different states of the lake were linked by a transition period with high abundances of microbial biomarkers. δ13C values were relatively constant for long-chain n-alkanes, while mid-chain n-alkanes showed variations between -23.5 to -12.6‰. Highest values were observed for the assumed period of maximum macrophyte growth during the late glacial and for the phytoplankton maximum during the middle and late Holocene. Therefore, the enriched values were interpreted to be caused by carbon limitation which in turn was induced by high macrophyte and primary productivity, respectively. Hydrogen isotope signatures of mid-chain n-alkanes have been shown to be able to track a previously deduced episode of reduced moisture availability between ca. 10 and 7 cal ka BP, indicated by a 20‰ shift towards higher δD values. Indications for cooler episodes at 6.0, 3.1 and 1.8 cal ka BP were gained from drops of biomarker concentrations, especially microbial-derived hopanoids, and from coincidental shifts towards lower δ13C values. Those episodes correspond well with cool events reported from other locations on the Tibetan Plateau as well as in the Northern Hemisphere. To conclude, the study of recent sediments and plants improved the understanding of factors affecting the composition and isotopic signatures of aliphatic biomarkers in sediments. Concentrations and isotopic signatures of the biomarkers in Lake Koucha could be interpreted in a palaeolimnological context and contribute to the knowledge about the history of the lake. Aquatic macrophyte-derived mid-chain n-alkanes were especially useful, due to their high abundance in many Tibetan Lakes and their ability to record major changes of lake productivity and palaeo-hydrological conditions. Therefore, they have the potential to contribute to a fuller understanding of past climate variability in this key region for atmospheric circulation systems.
Background
High blood glucose and diabetes are amongst the conditions causing the greatest losses in years of healthy life worldwide. Therefore, numerous studies aim to identify reliable risk markers for development of impaired glucose metabolism and type 2 diabetes. However, the molecular basis of impaired glucose metabolism is so far insufficiently understood. The development of so called 'omics' approaches in the recent years promises to identify molecular markers and to further understand the molecular basis of impaired glucose metabolism and type 2 diabetes. Although univariate statistical approaches are often applied, we demonstrate here that the application of multivariate statistical approaches is highly recommended to fully capture the complexity of data gained using high-throughput methods.
Methods
We took blood plasma samples from 172 subjects who participated in the prospective Metabolic Syndrome Berlin Potsdam follow-up study (MESY-BEPO Follow-up). We analysed these samples using Gas Chromatography coupled with Mass Spectrometry (GC-MS), and measured 286 metabolites. Furthermore, fasting glucose levels were measured using standard methods at baseline, and after an average of six years. We did correlation analysis and built linear regression models as well as Random Forest regression models to identify metabolites that predict the development of fasting glucose in our cohort.
Results
We found a metabolic pattern consisting of nine metabolites that predicted fasting glucose development with an accuracy of 0.47 in tenfold cross-validation using Random Forest regression. We also showed that adding established risk markers did not improve the model accuracy. However, external validation is eventually desirable. Although not all metabolites belonging to the final pattern are identified yet, the pattern directs attention to amino acid metabolism, energy metabolism and redox homeostasis.
Conclusions
We demonstrate that metabolites identified using a high-throughput method (GC-MS) perform well in predicting the development of fasting plasma glucose over several years. Notably, not single, but a complex pattern of metabolites propels the prediction and therefore reflects the complexity of the underlying molecular mechanisms. This result could only be captured by application of multivariate statistical approaches. Therefore, we highly recommend the usage of statistical methods that seize the complexity of the information given by high-throughput methods.
The lakes in the Kenyan Rift Valley offer the unique opportunity to study a wide range of hydrochemical environmental conditions, ranging from freshwater to highly saline and alkaline lakes. Because little is known about the hydro- and biogeochemical conditions in the underlying lake sediments, it was the aim of this study to extend the already existing data sets with data from porewater and biomarker analyses. Additionally, reduced sulphur compounds and sulphate reduction rates in the sediment were determined. The new data was used to examine the anthropogenic and microbial influence on the lakes sediments as well as the influence of the water chemistry on the degradation and preservation of organic matter in the sediment column. The lakes discussed in this study are: Logipi, Eight (a small crater lake in the region of Kangirinyang), Baringo, Bogoria, Naivasha, Oloiden, and Sonachi.
The biomarker compositions were similar in all studied lake sediments; nevertheless, there were some differences between the saline and freshwater lakes. One of those differences is the occurrence of a molecule related to β-carotene, which was only found in the saline lakes. This molecule most likely originates from cyanobacteria, single-celled organisms which are commonly found in saline lakes. In the two freshwater lakes, stigmasterol, a sterol characteristic for freshwater algae, was found. In this study, it was shown that Lakes Bogoria and Sonachi can be used for environmental reconstructions with biomarkers, because the absence of oxygen at the lake bottoms slowed the degradation process. Other lakes, like for example Lake Naivasha, cannot be used for such reconstructions, because of the large anthropogenic influence. But the biomarkers proved to be a useful tool to study those anthropogenic influences. Additionally, it was observed that horizons with a high concentration of elemental sulphur can be used as temporal markers. Those horizons were deposited during times when the lake levels were very low. The sulphur was deposited by microorganisms which are capable of anoxygenic photosynthesis or sulphide oxidation.
Extra-cellular matrix (ECM) components are important and their stabilization is significant in maintaining normal healthy joint environment. In osteoarthritis (OA), ECM components are altered and indicate disease progression. The joint ECM is composed of proteoglycans (aggrecan, perlecan,inter α-trypsin inhibitor), glycoproteins (fibronectin, lubricin, COMP) and collagen types (most abundantly collagen type II) which represent structural and functional transformation during disease advancement. ECM investigation revealed significant biomarkers of OA that could be used as a diagnostic and therapeutic tool in different canine orthopedic diseases. This review deliberates our current findings of how the components of ECM change at the molecular level during disease progression in canine OA.
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.
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.
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.
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.