The unicellular green alga Chlamydomonas reinhardtii harbors many types of small RNAs (sRNAs) but little is known about their role(s) in the regulation of endogenous genes and cellular processes. To define functional microRNAs (miRNAs) in Chlamydomonas, we characterized sRNAs associated with an argonaute protein, AGO3, by affinity purification and deep sequencing. Using a stringent set of criteria for canonical miRNA annotation, we identified 39 precursor miRNAs, which produce 45 unique, AGO3-associated miRNA sequences including 13 previously reported miRNAs and 32 novel ones. Potential miRNA targets were identified based on the complementarity of miRNAs with candidate binding sites on transcripts and classified, depending on the extent of complementarity, as being likely to be regulated through cleavage or translational repression. The search for cleavage targets identified 74 transcripts. However, only 6 of them showed an increase in messenger RNA (mRNA) levels in a mutant strain almost devoid of sRNAs. The search for translational repression targets, which used complementarity criteria more stringent than those empirically required for a reduction in target protein levels, identified 488 transcripts. However, unlike observations in metazoans, most predicted translation repression targets did not show appreciable changes in transcript abundance in the absence of sRNAs. Additionally, of three candidate targets examined at the protein level, only one showed a moderate variation in polypeptide amount in the mutant strain. Our results emphasize the difficulty in identifying genuine miRNA targets in Chlamydomonas and suggest that miRNAs, under standard laboratory conditions, might have mainly a modulatory role in endogenous gene regulation in this alga.

Small RNAs (sRNAs; ∼20 to 30 nucleotides in length) play important roles in gene regulation as well as in defense responses against transposons and viruses in eukaryotes. Their biogenesis and modes of action have attracted great attention in recent years. However, many aspects of sRNA function, such as the mechanism(s) of translation repression at postinitiation steps, remain poorly characterized. In the unicellular green alga Chlamydomonas reinhardtii, sRNAs derived from genome-integrated inverted repeat transgenes, perfectly complementary to the 3' untranslated region of a target transcript, can inhibit protein synthesis without or with only minimal mRNA destabilization. Here, we report that the sRNA-repressed transcripts are not altered in their polyadenylation status and they remain associated with polyribosomes, indicating inhibition at a postinitiation step of translation. Interestingly, ribosomes associated with sRNA-repressed transcripts show reduced sensitivity to translation inhibition by some antibiotics, such as cycloheximide, both in ribosome run-off assays and in in vivo experiments. Our results suggest that sRNA-mediated repression of protein synthesis in C. reinhardtii may involve alterations to the function/structural conformation of translating ribosomes. Additionally, sRNA-mediated translation inhibition is now known to occur in a number of phylogenetically diverse eukaryotes, suggesting that this mechanism may have been a feature of an ancestral RNA interference machinery.

SUMMARYMicroRNAs (miRNAs) are 20-24 nt non-coding RNAs that play important regulatory roles in a broad range of eukaryotes by pairing with mRNAs to direct post-transcriptional repression. The mechanistic details of miRNA-mediated post-transcriptional regulation have been well documented in multicellular model organisms. However, this process remains poorly studied in algae such as Chlamydomonas reinhardtii, and specific features of miRNA biogenesis, target mRNA recognition and subsequent silencing are not well understood. In this study, we report on the characterization of a Chlamydomonas miRNA, cre-miR1174.2, , which is processed from a near-perfect hairpin RNA. Using Gaussia luciferase (gluc) reporter genes, we have demonstrated that cre-miR1174.2 is functional in Chlamydomonas and capable of triggering site-specific cleavage at the center of a perfectly complementary target sequence. A mismatch tolerance test assay, based on pools of transgenic strains, revealed that target hybridization to nucleotides of the seed region, at the 5′ end of an miRNA, was sufficient to induce moderate repression of expression. In contrast, pairing to the 3′ region of the miRNA was not critical for silencing. Our results suggest that the base-pairing requirements for small RNAmediated repression in C. reinhardtii are more similar to those of metazoans compared with the extensive complementarity that is typical of land plants. Individual Chlamydomonas miRNAs may potentially modulate the expression of numerous endogenous targets as a result of these relaxed base-pairing requirements.

Accurate and comprehensive transcriptome assemblies lay the foundation for a range of analyses, such as differential gene expression analysis, metabolic pathway reconstruction, novel gene discovery, or metabolic flux analysis. With the arrival of next-generation sequencing technologies, it has become possible to acquire the whole transcriptome data rapidly even from non-model organisms. However, the problem of accurately assembling the transcriptome for any given sample remains extremely challenging, especially in species with a high prevalence of recent gene or genome duplications, those with alternative splicing of transcripts, or those whose genomes are not well studied. In this chapter, we provided a detailed overview of the strategies used for transcriptome assembly. We reviewed the different statistics available for measuring the quality of transcriptome assemblies with the emphasis on the types of errors each statistic does and does not detect. We also reviewed simulation protocols to computationally generate RNAseq data that present biologically realistic problems such as gene expression bias and alternative splicing. Using such simulated RNAseq data, we presented a comparison of the accuracy, strengths, and weaknesses of nine representative transcriptome assemblers including de novo, genome-guided, and ensemble methods.

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The unicellular alga Chlamydomonas reinhardtii contains many types of small RNAs (sRNAs) but the biological role(s) of bona fide microRNAs (miRNAs) remains unclear. To address their possible function(s) in responses to nutrient availability, we examined miRNA expression in cells cultured under different trophic conditions (mixotrophic in the presence of acetate or photoautotrophic in the presence or absence of nitrogen). We also reanalyzed miRNA expression data in Chlamydomonas subject to sulfur or phosphate deprivation. Several miRNAs were differentially expressed under the various trophic conditions. However, in transcriptome analyses, the majority of their predicted targets did not show expected changes in transcript abundance, suggesting that they are not subject to miRNA-mediated RNA degradation. Mutant strains, defective in sRNAs or in ARGONAUTE3 (a key component of sRNA-mediated gene silencing), did not display major phenotypic defects when grown under multiple nutritional regimes. Additionally, Chlamydomonas miRNAs were not conserved, even in algae of the closely related Volvocaceae family, and many showed features resembling those of recently evolved, species-specific miRNAs in the genus Arabidopsis. Our results suggest that, in C. reinhardtii, miRNAs might be subject to relatively fast evolution and have only a minor, largely modulatory role in gene regulation under diverse trophic states.

Transcriptome assembly using next-generation sequencing data is an important step in a wide range of biological studies at the molecular level. The quality of computationally assembled transcriptomes affects various downstream analyses, such as gene structure prediction, isoform identification, and gene expression analysis. However, the actual accuracy of assembled transcriptomes is usually unknown. Furthermore, assembly quality depends on various factors such as the method used, the parameters (for example, k-mers) used with the method, and the transcript to be assembled. Users often choose an assembly method based solely on availability without considering differences among methods, as well as choices of the parameters. This is partly due to the lack of suitable benchmarking datasets. In this chapter, we provide a review of computational approaches used for transcriptome assembly (genome-guided, de novo, and ensemble), factors that affect assembly performance including those particularly important for plant transcriptomes, and how the transcriptome assembly performance can be assessed. Using examples from plant transcriptomes, we further illustrate how simulated benchmark datasets can be generated and used to

Systems-level analyses, such as differential gene expression analysis, co-expression analysis, and metabolic pathway reconstruction, depend on the accuracy of the transcriptome. Multiple tools exist to perform transcriptome assembly from RNAseq data. However, assembling high quality transcriptomes is still not a trivial problem. This is especially the case for non-model organisms where adequate reference genomes are often not available. Different methods produce different transcriptome models and there is no easy way to determine which are more accurate. Furthermore, having alternative splicing events could exacerbate such difficult assembly problems. While benchmarking transcriptome assemblies is critical, this is also not trivial due to the general lack of true reference transcriptomes. In this study, we provide a pipeline to generate a set of the benchmark transcriptome and corresponding RNAseq data.Using the simulated benchmarking datasets, we compared the performance of various transcriptome assembly approaches including genome-guided, de novo, and ensemble methods. The results showed that the assembly performance deteriorates significantly when the reference is not available from the same genome (for genome-guided methods) or when alternative transcripts (isoforms) exist. We demonstrated the value of consensus between de novo assemblers in transcriptome assembly. Leveraging the overlapping predictions between the four de novo assemblers, we further present ConSemble, a consensus-based de novo ensemble transcriptome assembly pipeline. Without using a reference genome, ConSemble achieved an accuracy up to twice as high as any de novo assemblers we compared. It matched or exceeded the best performing genome-guided assemblers even when the transcriptomes included isoforms. The RNAseq simulation pipeline, the benchmark transcriptome datasets, and the ConSemble pipeline are all freely available from: Author summaryObtaining the accurate representation of the gene expression is critical in many analyses, such as differential gene expression analysis, co-expression analysis, and metabolic pathway reconstruction. The state of the art high-throughput RNA-sequencing (RNAseq) technologies can be used to sequence the set of all transcripts in a cell, the transcriptome. Although many computational tools are available for transcriptome assembly from RNAseq data, assembling high-quality transcriptomes is difficult especially for non-model organisms. Different methods often produce different transcriptome models and there is no easy way to determine which are more accurate. In this study, we present an approach to evaluate transcriptome assembly performance using simulated benchmarking read sets. The results showed that the assembly performance of genome-guided assembly methods deteriorates significantly when the adequate reference genome is not available. The assembly performance of all methods is affected when alternative transcripts (isoforms) exist. We further demonstrated the value of consensus among assemblers in impr...