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。

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

The number of sequenced plant genomes and associated genomic resources is growing rapidly with the advent of both an increased focus on plant genomics from funding agencies, and the application of inexpensive next generation sequencing. To interact with this increasing body of data, we have developed Phytozome (http://www.phytozome.net), a comparative hub for plant genome and gene family data and analysis. Phytozome provides a view of the evolutionary history of every plant gene at the level of sequence, gene structure, gene family and genome organization, while at the same time providing access to the sequences and functional annotations of a growing number (currently 25) of complete plant genomes, including all the land plants and selected algae sequenced at the Joint Genome Institute, as well as selected species sequenced elsewhere. Through a comprehensive plant genome database and web portal, these data and analyses are available to the broader plant science research community, providing powerful comparative genomics tools that help to link model systems with other plants of economic and ecological importance.

/pdf/phytozome-a-comparative-platform-for-green-plant-genomics-1ezqiu8w9i.pdf

Phytozome: a comparative platform for green plant genomics

Microalgae represent an exceptionally diverse but highly specialized group of micro-organisms adapted to various ecological habitats. Many microalgae have the ability to produce substantial amounts (e.g. 20-50% dry cell weight) of triacylglycerols (TAG) as a storage lipid under photo-oxidative stress or other adverse environmental conditions. Fatty acids, the building blocks for TAGs and all other cellular lipids, are synthesized in the chloroplast using a single set of enzymes, of which acetyl CoA carboxylase (ACCase) is key in regulating fatty acid synthesis rates. However, the expression of genes involved in fatty acid synthesis is poorly understood in microalgae. Synthesis and sequestration of TAG into cytosolic lipid bodies appear to be a protective mechanism by which algal cells cope with stress conditions, but little is known about regulation of TAG formation at the molecular and cellular level. While the concept of using microalgae as an alternative and renewable source of lipid-rich biomass feedstock for biofuels has been explored over the past few decades, a scalable, commercially viable system has yet to emerge. Today, the production of algal oil is primarily confined to high-value specialty oils with nutritional value, rather than commodity oils for biofuel. This review provides a brief summary of the current knowledge on oleaginous algae and their fatty acid and TAG biosynthesis, algal model systems and genomic approaches to a better understanding of TAG production, and a historical perspective and path forward for microalgae-based biofuel research and commercialization.

https://onlinelibrary.wiley.com/doi/epdf/10.1111/j.1365-313X.2008.03492.x

Microalgal triacylglycerols as feedstocks for biofuel production: perspectives and advances

The use of fossil fuels is now widely accepted as unsustainable due to depleting resources and the accumulation of greenhouse gases in the environment that have already exceeded the “dangerously high” threshold of 450 ppm CO2-e. To achieve environmental and economic sustainability, fuel production processes are required that are not only renewable, but also capable of sequestering atmospheric CO2. Currently, nearly all renewable energy sources (e.g. hydroelectric, solar, wind, tidal, geothermal) target the electricity market, while fuels make up a much larger share of the global energy demand (∼66%). Biofuels are therefore rapidly being developed. Second generation microalgal systems have the advantage that they can produce a wide range of feedstocks for the production of biodiesel, bioethanol, biomethane and biohydrogen. Biodiesel is currently produced from oil synthesized by conventional fuel crops that harvest the sun’s energy and store it as chemical energy. This presents a route for renewable and carbon-neutral fuel production. However, current supplies from oil crops and animal fats account for only approximately 0.3% of the current demand for transport fuels. Increasing biofuel production on arable land could have severe consequences for global food supply. In contrast, producing biodiesel from algae is widely regarded as one of the most efficient ways of generating biofuels and also appears to represent the only current renewable source of oil that could meet the global demand for transport fuels. The main advantages of second generation microalgal systems are that they: (1) Have a higher photon conversion efficiency (as evidenced by increased biomass yields per hectare): (2) Can be harvested batch-wise nearly all-year-round, providing a reliable and continuous supply of oil: (3) Can utilize salt and waste water streams, thereby greatly reducing freshwater use: (4) Can couple CO2-neutral fuel production with CO2 sequestration: (5) Produce non-toxic and highly biodegradable biofuels. Current limitations exist mainly in the harvesting process and in the supply of CO2 for high efficiency production. This review provides a brief overview of second generation biodiesel production systems using microalgae.

https://www.springer.com/cda/content/document/cda_downloaddocument/BioEnergyResearch_Second+Generation+Biofuels+High-Efficiency+Microalgae+for+Biodiesel+Production.pdf?SGWID=0-0-45-1347846-0

Second generation biofuels: high-efficiency microalgae for biodiesel production

Microalgae are considered one of the most promising feedstocks for biofuels. The productivity of these photosynthetic microorganisms in converting carbon dioxide into carbon-rich lipids, only a step or two away from biodiesel, greatly exceeds that of agricultural oleaginous crops, without competing for arable land. Worldwide, research and demonstration programs are being carried out to develop the technology needed to expand algal lipid production from a craft to a major industrial process. Although microalgae are not yet produced at large scale for bulk applications, recent advances—particularly in the methods of systems biology, genetic engineering, and biorefining—present opportunities to develop this process in a sustainable and economical way within the next 10 to 15 years.

https://science.sciencemag.org/content/sci/329/5993/796.full.pdf

An outlook on microalgal biofuels.

Determining the differential expression of proteins under different conditions is of major importance in proteomics Since mass spectrometry-based proteomics is often used to quantify proteins, several labelling strategies have been developed While these are generally more precise than label-free quantitation approaches, they imply specifically designed experiments which also require knowledge about peptides that are expected to be measured and need to be modified We recently designed the 2DB database which aids storage, analysis, and publication of data from mass spectrometric experiments to identify proteins This database can aid identifying peptides which can be used for quantitation Here an extension to the database application, named MSMAG, is presented which allows for more detailed analysis of the distribution of peptides and their associated proteins over the fractions of an experiment Furthermore, given several biological samples in the database, label-free quantitation can be performed Thus, interesting proteins, which may warrant further investigation, can be identified en passant while performing high-throughput proteomics studies

/pdf/label-free-quantitation-an-extension-to-2db-2r5djgglll.pdf

Label-free quantitation, an extension to 2DB

Faba bean (Vicia faba L.) is an important food legume crop with a huge genome. Development of genetic markers for faba bean is important to study diversity and for molecular breeding. In this study, we used Next Generation Sequencing (NGS) technology for the development of genomic simple sequence repeat (SSR) markers. A total of 14,027,500 sequence reads were obtained comprising 4,208 Mb. From these reads, 56,063 contigs were assembled (16,367 Mb) and 2138 SSRs were identified. Mono and dinucleotides were the most abundant, accounting for 57.5 % and 20.9 % of all SSR repeats, respectively. A total of 430 primer pairs were designed from contigs larger than 350 nucleotides and 50 primers pairs were tested for validation of SSR locus amplification. Nearly all (96 %) of the markers were found to produce clear amplicons and to be reproducible. Thirty-nine SSR markers were then applied to 46 faba bean accessions from worldwide origins, resulting in 161 alleles with 87.5 % polymorphism, and an average of 4.1 alleles per marker. Gene diversity (GD) of the markers ranged from 0 to 0.48 with an average of 0.27. Testing of the markers showed that they were useful in determining genetic relationships and population structure in faba bean accessions.

/pdf/development-of-genomic-simple-sequence-repeat-markers-in-5b162rrcqx.pdf

Development of genomic simple sequence repeat markers in faba bean by next-generation sequencing

A disease phenotype is often due to dysregulation of gene expression. Post-translational regulation of protein abundance by microRNAs (miRNAs) is, therefore, of high importance in, for example, cancer studies. MicroRNAs provide a complementary sequence to their target messenger RNA (mRNA) as part of a complex molecular machinery. Known miRNAs and targets are listed in miRTarBase for a variety of organisms. The experimental detection of such pairs is convoluted and, therefore, their computational detection is desired which is complicated by missing negative data. For machine learning, many features for parameterization of the miRNA targets are available and k-mers and sequence motifs have previously been used. Unrelated organisms like intracellular pathogens and their hosts may communicate via miRNAs and, therefore, we investigated whether miRNA targets from one species can be differentiated from miRNA targets of another. To achieve this end, we employed target information of one species as positive and the other as negative training and testing data. Models of species with higher evolutionary distance generally achieved better results of up to 97% average accuracy (mouse versus Caenorhabditis elegans) while more closely related species did not lead to successful models (human versus mouse; 60%). In the future, when more targeting data becomes available, models can be established which will be able to more precisely determine miRNA targets in hostpathogen systems using this approach.

Distinguishing between MicroRNA Targets from Diverse Species using Sequence Motifs and K-mers.

/pdf/izmir-computational-ab-initio-microrna-detection-56q6sa68bc.pdf

izMiR: computational ab initio microRNA detection

Improvements in genome sequencing technology increased the availability of full genomes and transcriptomes of many organisms. However, the major benefit of massive parallel sequencing is to better understand the organization and function of genes which then lead to understanding of phenotypes. In order to interpret genomic data with automated gene annotation studies, several tools are currently available. Even though the accuracy of computational gene annotation is increasing, a combination of multiple lines of experimental evidences should be gathered. Mass spectrometry allows the identification and sequencing of proteins as major gene products; and it is only these proteins that conclusively show whether a part of a genome is a coding region or not to result in phenotypes. Therefore, in the field of proteogenomics, the validation of computational methods is done by exploiting mass spectrometric data. As a result, identification of novel protein coding regions, validation of current gene models, and determination of upstream and downstream regions of genes can be achieved. In this paper, we present new functionality for our proteogenomic tool, PGMiner which performs all proteogenomic steps like acquisition of mass spectrometric data, peptide identification against preprocessed sequence databases, assignment of statistical confidence to identified peptides, mapping confident peptides to gene models, and result visualization. The extensions cover determining proteotypic peptides and thus unambiguous protein identification. Furthermore, peptides conflicting with gene models can now automatically assessed within the context of predicted alternative open reading frames.

Jens Allmer

Papers

Label-free quantitation, an extension to 2DB

Development of genomic simple sequence repeat markers in faba bean by next-generation sequencing

Distinguishing between MicroRNA Targets from Diverse Species using Sequence Motifs and K-mers.

izMiR: computational ab initio microRNA detection

PGMiner reloaded, fully automated proteogenomic annotation tool linking genomes to proteomes.