The big and intricate dreams of little organelles: Embracing complexity in the study of membrane traffic

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138421/1/tra12497_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/138421/2/tra12497-sup-0001-EditorialProcess.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/138421/3/tra12497.pd

Characterization of the Extracellular Proteome of a Natural Microbial Community with an Integrated Mass Spectrometric / Bioinformatic Approach

Proteomics comprises the identification and characterization of the complete suite of expressed proteins in a given cell, organism or community. The coupling of high performance liquid chromatography (LC) with high throughput mass spectrometry (MS) has provided the foundation for current proteomic progression. The transition from proteomic analysis of a single cultivated microbe to that of natural microbial assemblages has required significant advancement in technology and has provided greater biological understanding of microbial community diversity and function. To enhance the capabilities of a mass spectrometric based proteomic analysis, an integrated approach combining bioinformatics with analytical preparations and experimental data collection was developed and applied. This has resulted in a deep characterization of the extracellular fraction of a community of microbes thriving in an acid mine drainage system. Among the notable features of this relatively low complexity community, they exist in a solution that is highly acidic (pH \u3c 1) and hot (temperature \u3e 40°C), with molar concentrations of metals. The extracellular fraction is of particular interest due to the potential to identify and characterize novel proteins that are critical for survival and interactions with the harsh environment. The following analyses have resulted in the specific identification and characterization of novel extracellular proteins. In order to more accurately identify which proteins are present in the extracellular space, a combined computational prediction and experimental identification of the extracellular fraction was performed. Among the hundreds of proteins identified, a highly abundant novel cytochrome was targeted and ultimately characterized through high performance MS. In order to achieve deep proteomic coverage of the extracellular fraction, a metal affinity based protein enrichment utilizing seven different metals was developed and employed resulting in novel protein identifications. A combined top down and bottom up analysis resulted in the characterization of the intact molecular forms of extracellular proteins, including the identification of post-translational modifications. Finally, in order to determine the effectiveness of current MS methodologies, a software package was designed to characterize the \u3e 100,000 mass spectra collected during an MS experiment, revealing that specific optimizations in the LC, MS and protein sequence database have a significant impact on proteomic depth

Fucoidan degradation by marine bacteria

The oceans are an important carbon sink that have sequestered about half of all anthropogenic CO2 emissions. Marine carbon cycling is driven by the deposition of photosynthetic micro- and macroalgae in ocean sediments, where carbon is stored over thousands of years. The algal polysaccharide fucoidan is considered to be recalcitrant to microbial degradation and may therefore facilitate long-term carbon storage. Yet, factors that render fucoidan recalcitrant against microbial degradation remain unidentified, hampering our understanding of fucoidans in the carbon cycle. Fucoidans originating from the cell wall of brown algae are often co-extracted with other cell wall components. In Chapter I, I develop a simple step-wise protocol to purify fucoidans from different brown algae. Using mass spectrometry and nuclear magnetic resonance analyses, I describe the highly diverse and branched structures of different fucoidans. In Chapter II, I examine how marine bacteria degrade those complex branched fucoidans. Using genomics, proteomics and biochemistry, I characterize the newly isolated Verrucomicrobium a Lentimonasa sp. CC4 and show that fucoidan degradation requires highly dedicated pathways of over 100 enzymes covering 20% of the a Lentimonasa sp. CC4 proteome. The complexity of these pathways implies that only highly specialized bacteria can effectively degrade fucoidans and gives a clue why it may be recalcitrant. The proteomic analysis of a Lentimonasa sp. CC4 in chapter II suggested that two protein families, S1 15 and GH29, are key in fucoidan degradation. In Chapter III, I biochemically and structurally characterize one S1 15 sulfatase and one GH29 fucosidase, revealing their exo-enzyme activity and a novel catalytic pair of two aspartate residues. This provides insights into the molecular mechanism of exo-enzymatic fucoidan degradation. In Chapter IV, I trace the dynamics of different polysaccharides during a diatom spring bloom in Helgoland. I found that the dominant bloom-forming diatom Chaetoceros socialis secretes fucoidan in dissolved form, which aggregates and accumulates in particles at the end of the bloom. Known enzymes to degrade this polysaccharide are not expressed in the microbial community which indicates that fucoidans are not microbially degraded and act as vector for organic carbon drawdown. To summarize, fucoidans are diverse, highly branched polysaccharides whose degradation requires a large set of enzymes found in very few specialized marine bacteria. Their stability-enhancing properties lead to increased brown algal deposition in coastal sediments and in the open ocean they may acts as aggregation nuclei that enhance aggregation and settling of phytoplankton aggregates. Their abundance, recalcitrant nature and stickiness make fucoidans a likely key players in oceanic carbon sequestration

Bioinformatic and Experimental Approaches for Deeper Metaproteomic Characterization of Complex Environmental Samples

The coupling of high performance multi-dimensional liquid chromatography and tandem mass spectrometry for characterization of microbial proteins from complex environmental samples has paved the way for a new era in scientific discovery. The field of metaproteomics, which is the study of protein suite of all the organisms in a biological system, has taken a tremendous leap with the introduction of high-throughput proteomics. However, with corresponding increase in sample complexity, novel challenges have been raised with respect to efficient peptide separation via chromatography and bioinformatic analysis of the resulting high throughput data. In this dissertation, various aspects of metaproteomic characterization, including experimental and computational approaches have been systematically evaluated. In this study, robust separation protocols employing strong cation exchange and reverse phase have been designed for efficient peptide separation thus offering excellent orthogonality and ease of automation. These findings will be useful to the proteomics community for obtaining deeper non-redundant peptide identifications which in turn will improve the overall depth of semi-quantitative proteomics. Secondly, computational bottlenecks associated with screening the vast amount of raw mass spectra generated in these proteomic measurements have been addressed. Computational matching of tandem mass spectra via conventional database search strategies lead to modest peptide/protein identifications. This seriously restricts the amount of information retrieved from these complex samples which is mainly due to high complexity and heterogeneity of the sample containing hundreds of proteins shared between different microbial species often having high level of homology. Hence, the challenges associated with metaproteomic data analysis has been addressed by utilizing multiple iterative search engines coupled with de novo sequencing algorithms for a comprehensive and in-depth characterization of complex environmental samples. The work presented here will utilize various sample types ranging from isolates and mock microbial mixtures prepared in the laboratory to complex community samples extracted from industrial waste water, acid-mine drainage and methane seep sediments. In a broad perspective, this dissertation aims to provide tools for gaining deeper insights to proteome characterization in complex environmental ecosystems

High Performance Computing Framework for Tera-Scale Database Search of Mass Spectrometry Data

Database peptide search algorithms deduce peptides from mass spectrometry data. There has been substantial effort in improving their computational efficiency to achieve larger and more complex systems biology studies. However, modern serial and high-performance computing (HPC) algorithms exhibit suboptimal performance mainly due to their ineffective parallel designs (low resource utilization) and high overhead costs. We present an HPC framework, called HiCOPS, for efficient acceleration of the database peptide search algorithms on distributed-memory supercomputers. HiCOPS provides, on average, more than tenfold improvement in speed and superior parallel performance over several existing HPC database search software. We also formulate a mathematical model for performance analysis and optimization, and report near-optimal results for several key metrics including strong-scale efficiency, hardware utilization, load-balance, inter-process communication and I/O overheads. The core parallel design, techniques and optimizations presented in HiCOPS are search-algorithm-independent and can be extended to efficiently accelerate the existing and future algorithms and software