A marine Chlamydomonas sp. emerging as an algal model.

The freshwater microalga Chlamydomonas reinhardtii, which lives in wet soil, has served for decades as a model for numerous biological processes, and many tools have been introduced for this organism. Here, we have established a stable nuclear transformation for its marine counterpart, Chlamydomonas sp. SAG25.89, by fusing specific cis-acting elements from its Actin gene with the gene providing hygromycin resistance and using an elaborated electroporation protocol. Like C. reinhardtii, Chlamydomonas sp. has a high GC content, allowing reporter genes and selection markers to be applicable in both organisms. Chlamydomonas sp. grows purely photoautotrophically and requires ammonia as a nitrogen source because its nuclear genome lacks some of the genes required for nitrogen metabolism. Interestingly, it can grow well under both low and very high salinities (up to 50 g · L-1 ) rendering it as a model for osmotolerance. We further show that Chlamydomonas sp. grows well from 15 to 28°C, but halts its growth at 32°C. The genome of Chlamydomonas sp. contains some gene homologs the expression of which is regulated according to the ambient temperatures and/or confer thermal acclimation in C. reinhardtii. Thus, knowledge of temperature acclimation can now be compared to the marine species. Furthermore, Chlamydomonas sp. can serve as a model for studying marine microbial interactions and for comparing mechanisms in freshwater and marine environments. Chlamydomonas sp. was previously shown to be immobilized rapidly by a cyclic lipopeptide secreted from the antagonistic bacterium Pseudomonas protegens PF-5, which deflagellates C. reinhardtii.

Constructing networks for comparison of collagen types.

Collagens are structural proteins that are predominantly found in the extracellular matrix of multicellular animals, where they are mainly responsible for the stability and structural integrity of various tissues. All collagens contain polypeptide strands (α-chains). There are several types of collagens, some of which differ significantly in form, function, and tissue specificity. Because of their importance in clinical research, they are grouped into subdivisions, the so-called collagen families, and their sequences are often analysed. However, problems arise with highly homologous sequence segments. To increase the accuracy of collagen classification and prediction of their functions, the structure of these collagens and their expression in different tissues could result in a better focus on sequence segments of interest. Here, we analyse collagen families with different levels of conservation. As a result, clusters with high interconnectivity can be found, such as the fibrillar collagens, the COL4 network-forming collagens, and the COL9 FACITs. Furthermore, a large cluster between network-forming, FACIT, and COL28a1 α-chains is formed with COL6a3 as a major hub node. The formation of clusters also signifies, why it is important to always analyse the α-chains and why structural changes can have a wide range of effects on the body.

Statistical analysis of synonymous and stop codons in pseudo-random and real sequences as a function of GC content.

Knowledge of the frequencies of synonymous triplets in protein-coding and non-coding DNA stretches can be used in gene finding. These frequencies depend on the GC content of the genome or parts of it. An example of interest is provided by stop codons. This is relevant for the definition of Open Reading Frames. A generic case is provided by pseudo-random sequences, especially when they code for complex proteins or when they are non-coding and not subject to selection pressure. Here, we calculate, for such sequences and for all 25 known genetic codes, the frequency of each amino acid and stop codon based on their set of codons and as a function of GC content. The amino acids can be classified into five groups according to the GC content where their expected frequency reaches its maximum. We determine the overall Shannon information based on groups of synonymous codons and show that it becomes maximum at a percent GC of 43.3% (for the standard code). This is in line with the observation that in most fungi, plants, and animals, this genomic parameter is in the range from 35 to 50%. By analysing natural sequences, we show that there is a clear bias for triplets corresponding to stop codons near the 5'- and 3'-splice sites in the introns of various clades.

Dataset search in biodiversity research: Do metadata in data repositories reflect scholarly information needs?

The increasing amount of publicly available research data provides the opportunity to link and integrate data in order to create and prove novel hypotheses, to repeat experiments or to compare recent data to data collected at a different time or place. However, recent studies have shown that retrieving relevant data for data reuse is a time-consuming task in daily research practice. In this study, we explore what hampers dataset retrieval in biodiversity research, a field that produces a large amount of heterogeneous data. In particular, we focus on scholarly search interests and metadata, the primary source of data in a dataset retrieval system. We show that existing metadata currently poorly reflect information needs and therefore are the biggest obstacle in retrieving relevant data. Our findings indicate that for data seekers in the biodiversity domain environments, materials and chemicals, species, biological and chemical processes, locations, data parameters and data types are important information categories. These interests are well covered in metadata elements of domain-specific standards. However, instead of utilizing these standards, large data repositories tend to use metadata standards with domain-independent metadata fields that cover search interests only to some extent. A second problem are arbitrary keywords utilized in descriptive fields such as title, description or subject. Keywords support scholars in a full text search only if the provided terms syntactically match or their semantic relationship to terms used in a user query is known.