Christine Moissl-Eichinger

Bio: Christine Moissl-Eichinger is an academic researcher from Medical University of Graz. The author has contributed to research in topics: Microbiome & Archaea. The author has an hindex of 33, co-authored 101 publications receiving 3281 citations. Previous affiliations of Christine Moissl-Eichinger include University of Regensburg & University of Graz.

Microbial syntrophy: interaction for the common good

Abstract: Classical definitions of syntrophy focus on a process, performed through metabolic interaction between dependent microbial partners, such as the degradation of complex organic compounds under anoxic conditions. However, examples from past and current scientific discoveries suggest that a new, simple but wider definition is necessary to cover all aspects of microbial syntrophy. We suggest the term ‘obligately mutualistic metabolism’, which still focuses on microbial metabolic cooperation but also includes an ecological aspect: the benefit for both partners. By the combined metabolic activity of microorganisms, endergonic reactions can become exergonic through the efficient removal of products and therefore enable a microbial community to survive with minimal energy resources. Here, we explain the principles of classical and non-classical syntrophy and illustrate the concepts with various examples. We present biochemical fundamentals that allow microorganism to survive under a range of environmental conditions and to drive important biogeochemical processes. Novel technologies have contributed to the understanding of syntrophic relationships in cultured and uncultured systems. Recent research highlights that obligately mutualistic metabolism is not limited to certain metabolic pathways nor to certain environments or microorganisms. This beneficial microbial interaction is not restricted to the transfer of reducing agents such as hydrogen or formate, but can also involve the exchange of organic, sulfurous- and nitrogenous compounds or the removal of toxic compounds.

The microbiome of the upper respiratory tract in health and disease

Abstract: The human upper respiratory tract (URT) offers a variety of niches for microbial colonization. Local microbial communities are shaped by the different characteristics of the specific location within the URT, but also by the interaction with both external and intrinsic factors, such as ageing, diseases, immune responses, olfactory function, and lifestyle habits such as smoking. We summarize here the current knowledge about the URT microbiome in health and disease, discuss methodological issues, and consider the potential of the nasal microbiome to be used for medical diagnostics and as a target for therapy.

Archaea on human skin

Abstract: The recent era of exploring the human microbiome has provided valuable information on microbial inhabitants, beneficials and pathogens. Screening efforts based on DNA sequencing identified thousands of bacterial lineages associated with human skin but provided only incomplete and crude information on Archaea. Here, we report for the first time the quantification and visualization of Archaea from human skin. Based on 16 S rRNA gene copies Archaea comprised up to 4.2% of the prokaryotic skin microbiome. Most of the gene signatures analyzed belonged to the Thaumarchaeota, a group of Archaea we also found in hospitals and clean room facilities. The metabolic potential for ammonia oxidation of the skin-associated Archaea was supported by the successful detection of thaumarchaeal amoA genes in human skin samples. However, the activity and possible interaction with human epithelial cells of these associated Archaea remains an open question. Nevertheless, in this study we provide evidence that Archaea are part of the human skin microbiome and discuss their potential for ammonia turnover on human skin.

First Insights into the Diverse Human Archaeome: Specific Detection of Archaea in the Gastrointestinal Tract, Lung, and Nose and on Skin.

Abstract: Human-associated archaea remain understudied in the field of micro-biome research, although in particular methanogenic archaea were found to be regular commensals of the human gut, where they represent keystone species in metabolic processes. Knowledge on the abundance and diversity of human-associated archaea is extremely limited, and little is known about their function(s), their overall role in human health, or their association with parts of the human body other than the gastrointestinal tract and oral cavity. Currently, methodological issues impede the full assessment of the human archaeome, as bacteria-targeting protocols are unsuitable for characterization of the full spectrum of Archaea. The goal of this study was to establish conservative protocols based on specifically archaea-targeting, PCR-based methods to retrieve first insights into the archaeomes of the human gastrointestinal tract, lung, nose, and skin. Detection of Archaea was highly dependent on primer selection and the sequence processing pipeline used. Our results enabled us to retrieve a novel picture of the human archaeome, as we found for the first time Methanobacterium and Woesearchaeota (DPANN superphylum) to be associated with the human gastrointestinal tract and the human lung, respectively. Similar to bacteria, human-associated archaeal communities were found to group biogeographically, forming (i) the thaumarchaeal skin landscape, (ii) the (methano) euryarchaeal gastrointestinal tract, (iii) a mixed skin-gastrointestinal tract landscape for the nose, and (iv) a woesearchaeal lung landscape. On the basis of the protocols we used, we were able to detect unexpectedly high diversity of archaea associated with different body parts. IMPORTANCE In summary, our study highlights the importance of the primers and NGS data processing pipeline used to study the human archaeome. We were able to establish protocols that revealed the presence of previously undetected Archaea in all of the tissue samples investigated and to detect biogeographic patterns of the human archaeome in the gastrointestinal tract, on the skin, and for the first time in the respiratory tract, i.e., the nose and lungs. Our results are a solid basis for further investigation of the human archaeome and, in the long term, discovery of the potential role of archaea in human health and disease.

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

The Theory of Island Biogeography

Abstract: Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols Used xiii 1. The Importance of Islands 3 2. Area and Number of Speicies 8 3. Further Explanations of the Area-Diversity Pattern 19 4. The Strategy of Colonization 68 5. Invasibility and the Variable Niche 94 6. Stepping Stones and Biotic Exchange 123 7. Evolutionary Changes Following Colonization 145 8. Prospect 181 Glossary 185 References 193 Index 201

SPAdes, a new genome assembly algorithm and its applications to single-cell sequencing ( 7th Annual SFAF Meeting, 2012)

Abstract: The lion's share of bacteria in various environments cannot be cloned in the laboratory and thus cannot be sequenced using existing technologies. A major goal of single-cell genomics is to complement gene-centric metagenomic data with whole-genome assemblies of uncultivated organisms. Assembly of single-cell data is challenging because of highly non-uniform read coverage as well as elevated levels of sequencing errors and chimeric reads. We describe SPAdes, a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V-SC assembler (specialized for single-cell data) and on popular assemblers Velvet and SoapDeNovo (for multicell data). SPAdes generates single-cell assemblies, providing information about genomes of uncultivatable bacteria that vastly exceeds what may be obtained via traditional metagenomics studies. SPAdes is available online ( http://bioinf.spbau.ru/spades ). It is distributed as open source software.

Metagenomic biomarker discovery and explanation

Abstract: This study describes and validates a new method for metagenomic biomarker discovery by way of class comparison, tests of biological consistency and effect size estimation. This addresses the challenge of finding organisms, genes, or pathways that consistently explain the differences between two or more microbial communities, which is a central problem to the study of metagenomics. We extensively validate our method on several microbiomes and a convenient online interface for the method is provided at http://huttenhower.sph.harvard.edu/lefse/.

Fast Tree: Computing Large Minimum-Evolution Trees with Profiles instead of a Distance Matrix

Abstract: Gene families are growing rapidly, but standard methods for inferring phylogenies do not scale to alignments with over 10,000 sequences. We present FastTree, a method for constructing large phylogenies and for estimating their reliability. Instead of storing a distance matrix, FastTree stores sequence profiles of internal nodes in the tree. FastTree uses these profiles to implement neighbor-joining and uses heuristics to quickly identify candidate joins. FastTree then uses nearest-neighbor interchanges to reduce the length of the tree. For an alignment with N sequences, L sites, and a different characters, a distance matrix requires O(N^2) space and O(N^2 L) time, but FastTree requires just O( NLa + N sqrt(N) ) memory and O( N sqrt(N) log(N) L a ) time. To estimate the tree's reliability, FastTree uses local bootstrapping, which gives another 100-fold speedup over a distance matrix. For example, FastTree computed a tree and support values for 158,022 distinct 16S ribosomal RNAs in 17 hours and 2.4 gigabytes of memory. Just computing pairwise Jukes-Cantor distances and storing them, without inferring a tree or bootstrapping, would require 17 hours and 50 gigabytes of memory. In simulations, FastTree was slightly more accurate than neighbor joining, BIONJ, or FastME; on genuine alignments, FastTree's topologies had higher likelihoods. FastTree is available at http://microbesonline.org/fasttree.