ManiNetCluster: a novel manifold learning approach to reveal the functional links between gene networks.

BACKGROUND:The coordination of genomic functions is a critical and complex process across biological systems such as phenotypes or states (e.g., time, disease, organism, environmental perturbation). Understanding how the complexity of genomic function relates to these states remains a challenge. To address this, we have developed a novel computational method, ManiNetCluster, which simultaneously aligns and clusters gene networks (e.g., co-expression) to systematically reveal the links of genomic function between different conditions. Specifically, ManiNetCluster employs manifold learning to uncover and match local and non-linear structures among networks, and identifies cross-network functional links. RESULTS:We demonstrated that ManiNetCluster better aligns the orthologous genes from their developmental expression profiles across model organisms than state-of-the-art methods (p-value <2.2×10-16). This indicates the potential non-linear interactions of evolutionarily conserved genes across species in development. Furthermore, we applied ManiNetCluster to time series transcriptome data measured in the green alga Chlamydomonas reinhardtii to discover the genomic functions linking various metabolic processes between the light and dark periods of a diurnally cycling culture. We identified a number of genes putatively regulating processes across each lighting regime. CONCLUSIONS:ManiNetCluster provides a novel computational tool to uncover the genes linking various functions from different networks, providing new insight on how gene functions coordinate across different conditions. ManiNetCluster is publicly available as an R package at https://github.com/daifengwanglab/ManiNetCluster

Towards a Systems Approach in the Genetic Analysis of Archaea: Accelerating Mutant Construction and Phenotypic Analysis in Haloferax volcanii

With the availability of a genome sequence and increasingly sophisticated genetic tools, Haloferax volcanii is becoming a model for both Archaea and halophiles. In order for H. volcanii to reach a status equivalent to Escherichia coli, Bacillus subtilis, or Saccharomyces cerevisiae, a gene knockout collection needs to be constructed in order to identify the archaeal essential gene set and enable systematic phenotype screens. A streamlined gene-deletion protocol adapted for potential automation was implemented and used to generate 22 H. volcanii deletion strains and identify several potentially essential genes. These gene deletion mutants, generated in this and previous studies, were then analyzed in a high-throughput fashion to measure growth rates in different media and temperature conditions. We conclude that these high-throughput methods are suitable for a rapid investigation of an H. volcanii mutant library and suggest that they should form the basis of a larger genome-wide experiment

Genome-wide analysis on Chlamydomonas reinhardtii reveals the impact of hydrogen peroxide on protein stress responses and overlap with other stress transcriptomes.

Reactive oxygen species (ROS) are produced by and have the potential to be damaging to all aerobic organisms. In photosynthetic organisms, they are an unavoidable byproduct of electron transfer in both the chloroplast and mitochondrion. Here, we employ the reference unicellular green alga Chlamydomonas reinhardtii to identify the effect of H2O2 on gene expression by monitoring the changes in the transcriptome in a time-course experiment. Comparison of transcriptomes from cells sampled immediately prior to the addition of H2O2 and 0.5 and 1 h subsequently revealed 1278 differentially abundant transcripts. Of those transcripts that increase in abundance, many encode proteins involved in ROS detoxification, protein degradation and stress responses, whereas among those that decrease are transcripts encoding proteins involved in photosynthesis and central carbon metabolism. In addition to these transcriptomic adjustments, we observe that addition of H2O2 is followed by an accumulation and oxidation of the total intracellular glutathione pool, and a decrease in photosynthetic O2 output. Additionally, we analyze our transcriptomes in the context of changes in transcript abundance in response to singlet O2 (O2*), and relate our H2O2 -induced transcripts to a diurnal transcriptome, where we demonstrate enrichments of H2O2 -induced transcripts early in the light phase, late in the light phase and 2 h prior to light. On this basis several genes that are highlighted in this work may be involved in previously undiscovered stress remediation pathways or acclimation responses

A Gateway platform for functional genomics in Haloferax volcanii

In part due to the existence of simple methods for its cultivation and genetic manipulation, Haloferax volcanii is a major archaeal model organism. It is the only archaeon for which the whole set of post-transcriptionally modified tRNAs has been sequenced, allowing for an in silico prediction of all RNA modification genes present in the organism. One approach to check these predictions experimentally is via the construction of targeted gene deletion mutants. Toward this goal, an integrative “Gateway vector” that allows gene deletion in H. volcanii uracil auxotrophs was constructed. The vector was used to delete three predicted tRNA modification genes: HVO_2001 (encoding an archaeal transglycosyl tranferase or arcTGT), which is involved in archeosine biosynthesis; HVO_2348 (encoding a newly discovered GTP cyclohydrolase I), which catalyzes the first step common to archaeosine and folate biosynthesis; and HVO_2736 (encoding a member of the COG1444 family), which is involved in N4-acetylcytidine (ac4C) formation. Preliminary phenotypic analysis of the deletion mutants was conducted, and confirmed all three predictions

Exploiting algal NADPH oxidase for biophotovoltaic energy.

Photosynthetic microbes exhibit light-dependent electron export across the cell membrane, which can generate electricity in biological photovoltaic (BPV) devices. How electrons are exported remains to be determined; the identification of mechanisms would help selection or generation of photosynthetic microbes capable of enhanced electrical output. We show that plasma membrane NADPH oxidase activity is a significant component of light-dependent generation of electricity by the unicellular green alga Chlamydomonas reinhardtii. NADPH oxidases export electrons across the plasma membrane to form superoxide anion from oxygen. The C. reinhardtii mutant lacking the NADPH oxidase encoded by RBO1 is impaired in both extracellular superoxide anion production and current generation in a BPV device. Complementation with the wild-type gene restores both capacities, demonstrating the role of the enzyme in electron export. Monitoring light-dependent extracellular superoxide production with a colorimetric assay is shown to be an effective way of screening for electrogenic potential of candidate algal strains. The results show that algal NADPH oxidases are important for superoxide anion production and open avenues for optimizing the biological component of these devices.We acknowledge the UK EPSRC, EnAlgae (European Regional Development Fund: INTERREG IVB NEW programme) and the US Department of Energy for funding. I.K.B. was supported by a training grant from the National Institutes of Health (T32ES015457). Work in the Merchant laboratory was supported by the U.S. Department of Energy (grant no. DE–FC02– 02ER63421).This is the final published version. It first appeared from Wiley via http://dx.doi.org/10.1111/pbi.1233

Synergistic use of plant-prokaryote comparative genomics for functional annotations

<p>Abstract</p> <p>Background</p> <p>Identifying functions for all gene products in all sequenced organisms is a central challenge of the post-genomic era. However, at least 30-50% of the proteins encoded by any given genome are of unknown or vaguely known function, and a large number are wrongly annotated. Many of these ‘unknown’ proteins are common to prokaryotes and plants. We set out to predict and experimentally test the functions of such proteins. Our approach to functional prediction integrates comparative genomics based mainly on microbial genomes with functional genomic data from model microorganisms and post-genomic data from plants. This approach bridges the gap between automated homology-based annotations and the classical gene discovery efforts of experimentalists, and is more powerful than purely computational approaches to identifying gene-function associations.</p> <p>Results</p> <p>Among Arabidopsis genes, we focused on those (2,325 in total) that (i) are unique or belong to families with no more than three members, (ii) occur in prokaryotes, and (iii) have unknown or poorly known functions. Computer-assisted selection of promising targets for deeper analysis was based on homology-independent characteristics associated in the SEED database with the prokaryotic members of each family. In-depth comparative genomic analysis was performed for 360 top candidate families. From this pool, 78 families were connected to general areas of metabolism and, of these families, specific functional predictions were made for 41. Twenty-one predicted functions have been experimentally tested or are currently under investigation by our group in at least one prokaryotic organism (nine of them have been validated, four invalidated, and eight are in progress). Ten additional predictions have been independently validated by other groups. Discovering the function of very widespread but hitherto enigmatic proteins such as the YrdC or YgfZ families illustrates the power of our approach.</p> <p>Conclusions</p> <p>Our approach correctly predicted functions for 19 uncharacterized protein families from plants and prokaryotes; none of these functions had previously been correctly predicted by computational methods. The resulting annotations could be propagated with confidence to over six thousand homologous proteins encoded in over 900 bacterial, archaeal, and eukaryotic genomes currently available in public databases.</p

Activation of Autophagy by Metals in Chlamydomonas reinhardtii

Autophagy is an intracellular self-degradation pathway by which eukaryotic cells recycle their own material in response to specific stress conditions. Exposure to high concentrations of metals causes cell damage, although the effect of metal stress on autophagy has not been explored in photosynthetic organisms. In this study, we investigated the effect of metal excess on autophagy in the model unicellular green alga Chlamydomonas reinhardtii. We show in cells treated with nickel an upregulation of ATG8 that is independent of CRR1, a global regulator of copper signaling in Chlamydomonas. A similar effect on ATG8 was observed with copper and cobalt but not with cadmium or mercury ions. Transcriptome sequencing data revealed an increase in the abundance of the protein degradation machinery, including that responsible for autophagy, and a substantial overlap of that increased abundance with the hydrogen peroxide response in cells treated with nickel ions. Thus, our results indicate that metal stress triggers autophagy in Chlamydomonas and suggest that excess nickel may cause oxidative damage, which in turn activates degradative pathways, including autophagy, to clear impaired components and recover cellular homeostasisMinisterio de Economía y Competitividad BFU2012-35913Junta de Andalucía CVI-7336National Institutes of Health GM42143, R24 GM09247