Arabidopsis Argonaute10 specifically sequesters miR166/165 to regulate shoot apical meristem development.

The shoot apical meristem (SAM) comprises a group of undifferentiated cells that divide to maintain the plant meristem and also give rise to all shoot organs. SAM fate is specified by class III HOMEODOMAIN-LEUCINE ZIPPER (HD-ZIP III) transcription factors, which are targets of miR166/165. In Arabidopsis, AGO10 is a critical regulator of SAM maintenance, and here we demonstrate that AGO10 specifically interacts with miR166/165. The association is determined by a distinct structure of the miR166/165 duplex. Deficient loading of miR166 into AGO10 results in a defective SAM. Notably, the miRNA-binding ability of AGO10, but not its catalytic activity, is required for SAM development, and AGO10 has a higher binding affinity for miR166 than does AGO1, a principal contributor to miRNA-mediated silencing. We propose that AGO10 functions as a decoy for miR166/165 to maintain the SAM, preventing their incorporation into AGO1 complexes and the subsequent repression of HD-ZIP III gene expression.

The genome of the stable fly, Stomoxys calcitrans, reveals potential mechanisms underlying reproduction, host interactions, and novel targets for pest control.

BACKGROUND: The stable fly, Stomoxys calcitrans, is a major blood-feeding pest of livestock that has near worldwide distribution, causing an annual cost of over $2 billion for control and product loss in the USA alone. Control of these flies has been limited to increased sanitary management practices and insecticide application for suppressing larval stages. Few genetic and molecular resources are available to help in developing novel methods for controlling stable flies. RESULTS: This study examines stable fly biology by utilizing a combination of high-quality genome sequencing and RNA-Seq analyses targeting multiple developmental stages and tissues. In conjunction, 1600 genes were manually curated to characterize genetic features related to stable fly reproduction, vector host interactions, host-microbe dynamics, and putative targets for control. Most notable was characterization of genes associated with reproduction and identification of expanded gene families with functional associations to vision, chemosensation, immunity, and metabolic detoxification pathways. CONCLUSIONS: The combined sequencing, assembly, and curation of the male stable fly genome followed by RNA-Seq and downstream analyses provide insights necessary to understand the biology of this important pest. These resources and new data will provide the groundwork for expanding the tools available to control stable fly infestations. The close relationship of Stomoxys to other blood-feeding (horn flies and Glossina) and non-blood-feeding flies (house flies, medflies, Drosophila) will facilitate understanding of the evolutionary processes associated with development of blood feeding among the Cyclorrhapha.

Correction: High-Resolution Phenotypic Landscape of the RNA Polymerase II Trigger Loop.

[This corrects the article DOI: 10.1371/journal.pgen.1006321.].

Accurate prediction of maize grain yield using its contributing genes for gene-based breeding.

Accurately predicting the phenotypes of complex traits is crucial to enhanced breeding in plants and livestock, and to enhanced medicine in humans. Here we reports the first study accurately predicting complex traits using their contributing genes, especially their number of favorable alleles (NFAs), genotypes and transcript expressions, with the grain yield of maize, Zea mays L. When the NFAs or genotypes of only 27 SNP/InDel-containing grain yield genes were used, a prediction accuracy of r = 0.52 or 0.49 was obtained. When the expressions of grain yield gene transcripts were used, a plateaued prediction accuracy of r = 0.84 was achieved. When the phenotypes predicted with two or three of the genic datasets were used for progeny selection, the selected lines were completely consistent with those selected by phenotypic selection. Therefore, the genes controlling complex traits enable accurately predicting their phenotypes, thus desirable for gene-based breeding in crop plants.

Bayesian gamma-negative binomial modeling of single-cell RNA sequencing data.

BACKGROUND: Single-cell RNA sequencing (scRNA-seq) is a powerful profiling technique at the single-cell resolution. Appropriate analysis of scRNA-seq data can characterize molecular heterogeneity and shed light into the underlying cellular process to better understand development and disease mechanisms. The unique analytic challenge is to appropriately model highly over-dispersed scRNA-seq count data with prevalent dropouts (zero counts), making zero-inflated dimensionality reduction techniques popular for scRNA-seq data analyses. Employing zero-inflated distributions, however, may place extra emphasis on zero counts, leading to potential bias when identifying the latent structure of the data. RESULTS: In this paper, we propose a fully generative hierarchical gamma-negative binomial (hGNB) model of scRNA-seq data, obviating the need for explicitly modeling zero inflation. At the same time, hGNB can naturally account for covariate effects at both the gene and cell levels to identify complex latent representations of scRNA-seq data, without the need for commonly adopted pre-processing steps such as normalization. Efficient Bayesian model inference is derived by exploiting conditional conjugacy via novel data augmentation techniques. CONCLUSION: Experimental results on both simulated data and several real-world scRNA-seq datasets suggest that hGNB is a powerful tool for cell cluster discovery as well as cell lineage inference.

Identifying similar transcripts in a related organism from de Bruijn graphs of RNA-Seq data, with applications to the study of salt and waterlogging tolerance in Melilotus.

BACKGROUND: A popular strategy to study alternative splicing in non-model organisms starts from sequencing the entire transcriptome, then assembling the reads by using de novo transcriptome assembly algorithms to obtain predicted transcripts. A similarity search algorithm is then applied to a related organism to infer possible function of these predicted transcripts. While some of these predictions may be inaccurate and transcripts with low coverage are often missed, we observe that it is possible to obtain a more complete set of transcripts to facilitate possible functional assignments by starting the search from the intermediate de Bruijn graph that contains all branching possibilities. RESULTS: We develop an algorithm to extract similar transcripts in a related organism by starting the search from the de Bruijn graph that represents the transcriptome instead of from predicted transcripts. We show that our algorithm is able to recover more similar transcripts than existing algorithms, with large improvements in obtaining longer transcripts and a finer resolution of isoforms. We apply our algorithm to study salt and waterlogging tolerance in two Melilotus species by constructing new RNA-Seq libraries. CONCLUSIONS: We have developed an algorithm to identify paths in the de Bruijn graph that correspond to similar transcripts in a related organism directly. Our strategy bypasses the transcript prediction step in RNA-Seq data and makes use of support from evolutionary information.

High-Resolution Phenotypic Landscape of the RNA Polymerase II Trigger Loop.

The active sites of multisubunit RNA polymerases have a "trigger loop" (TL) that multitasks in substrate selection, catalysis, and translocation. To dissect the Saccharomyces cerevisiae RNA polymerase II TL at individual-residue resolution, we quantitatively phenotyped nearly all TL single variants en masse. Three mutant classes, revealed by phenotypes linked to transcription defects or various stresses, have distinct distributions among TL residues. We find that mutations disrupting an intra-TL hydrophobic pocket, proposed to provide a mechanism for substrate-triggered TL folding through destabilization of a catalytically inactive TL state, confer phenotypes consistent with pocket disruption and increased catalysis. Furthermore, allele-specific genetic interactions among TL and TL-proximal domain residues support the contribution of the funnel and bridge helices (BH) to TL dynamics. Our structural genetics approach incorporates structural and phenotypic data for high-resolution dissection of transcription mechanisms and their evolution, and is readily applicable to other essential yeast proteins.

A divide-and-conquer algorithm for large-scale de novo transcriptome assembly through combining small assemblies from existing algorithms.

BACKGROUND: While the continued development of high-throughput sequencing has facilitated studies of entire transcriptomes in non-model organisms, the incorporation of an increasing amount of RNA-Seq libraries has made de novo transcriptome assembly difficult. Although algorithms that can assemble a large amount of RNA-Seq data are available, they are generally very memory-intensive and can only be used to construct small assemblies. RESULTS: We develop a divide-and-conquer strategy that allows these algorithms to be utilized, by subdividing a large RNA-Seq data set into small libraries. Each individual library is assembled independently by an existing algorithm, and a merging algorithm is developed to combine these assemblies by picking a subset of high quality transcripts to form a large transcriptome. When compared to existing algorithms that return a single assembly directly, this strategy achieves comparable or increased accuracy as memory-efficient algorithms that can be used to process a large amount of RNA-Seq data, and comparable or decreased accuracy as memory-intensive algorithms that can only be used to construct small assemblies. CONCLUSIONS: Our divide-and-conquer strategy allows memory-intensive de novo transcriptome assembly algorithms to be utilized to construct large assemblies.

A scalable and memory-efficient algorithm for de novo transcriptome assembly of non-model organisms.

BACKGROUND: With increased availability of de novo assembly algorithms, it is feasible to study entire transcriptomes of non-model organisms. While algorithms are available that are specifically designed for performing transcriptome assembly from high-throughput sequencing data, they are very memory-intensive, limiting their applications to small data sets with few libraries. RESULTS: We develop a transcriptome assembly algorithm that recovers alternatively spliced isoforms and expression levels while utilizing as many RNA-Seq libraries as possible that contain hundreds of gigabases of data. New techniques are developed so that computations can be performed on a computing cluster with moderate amount of physical memory. CONCLUSIONS: Our strategy minimizes memory consumption while simultaneously obtaining comparable or improved accuracy over existing algorithms. It provides support for incremental updates of assemblies when new libraries become available.

Heuristic pairwise alignment of de Bruijn graphs to facilitate simultaneous transcript discovery in related organisms from RNA-Seq data.

BACKGROUND: The advance of high-throughput sequencing has made it possible to obtain new transcriptomes and study splicing mechanisms in non-model organisms. In these studies, there is often a need to investigate the transcriptomes of two related organisms at the same time in order to find the similarities and differences between them. The traditional approach to address this problem is to perform de novo transcriptome assemblies to obtain predicted transcripts for these organisms independently and then employ similarity comparison algorithms to study them. RESULTS: Instead of obtaining predicted transcripts for these organisms separately from the intermediate de Bruijn graph structures employed by de novo transcriptome assembly algorithms, we develop an algorithm to allow direct comparisons between paths in two de Bruijn graphs by first enumerating short paths in both graphs, and iteratively extending paths in one graph that have high similarity to paths in the other graph to obtain longer corresponding paths between the two graphs. These paths represent predicted transcripts that are present in both organisms. CONCLUSIONS: Our approach generalizes the pairwise sequence alignment problem to allow the input to be non-linear structures, and provides a heuristic to reliably recover similar paths from the two structures. Our algorithm allows detailed investigation of the similarities and differences in alternative splicing between the two organisms at both the sequence and structure levels, even in the absence of reference transcriptomes or a closely related model organism.

A memory-efficient algorithm to obtain splicing graphs and de novo expression estimates from de Bruijn graphs of RNA-Seq data.

BACKGROUND: The recent advance of high-throughput sequencing makes it feasible to study entire transcriptomes through the application of de novo sequence assembly algorithms. While a popular strategy is to first construct an intermediate de Bruijn graph structure to represent the transcriptome, an additional step is needed to construct predicted transcripts from the graph. RESULTS: Since the de Bruijn graph contains all branching possibilities, we develop a memory-efficient algorithm to recover alternative splicing information and library-specific expression information directly from the graph without prior genomic knowledge. We implement the algorithm as a postprocessing module of the Velvet assembler. We validate our algorithm by simulating the transcriptome assembly of Drosophila using its known genome, and by performing Drosophila transcriptome assembly using publicly available RNA-Seq libraries. Under a range of conditions, our algorithm recovers sequences and alternative splicing junctions with higher specificity than Oases or Trans-ABySS. CONCLUSIONS: Since our postprocessing algorithm does not consume as much memory as Velvet and is less memory-intensive than Oases, it allows biologists to assemble large libraries with limited computational resources. Our algorithm has been applied to perform transcriptome assembly of the non-model blow fly Lucilia sericata that was reported in a previous article, which shows that the assembly is of high quality and it facilitates comparison of the Lucilia sericata transcriptome to Drosophila and two mosquitoes, prediction and experimental validation of alternative splicing, investigation of differential expression among various developmental stages, and identification of transposable elements.

A 24-48 h fed Amblyomma americanum tick saliva immuno-proteome.

BACKGROUND: Multiple tick saliva proteins, the majority of which are unknown, confer tick resistance in repeatedly infested animals. The objective of this study was to identify the 24-48 h fed Amblyomma americanum tick saliva immuno-proteome. The 24-48 h tick-feeding phase is critical to tick parasitism as it precedes important events in tick biology, blood meal feeding and disease agent transmission. Fed male, 24 and 96 h fed female phage display cDNA expression libraries were biopanned using rabbit antibodies to 24 and 48 h fed A. americanum female tick saliva proteins. Biopanned immuno-cDNA libraries were subjected to next generation sequencing, de novo assembly, and bioinformatic analysis. RESULTS: More than 800 transcripts that code for 24-48 h fed A. americanum immuno-proteins are described. Of the 895 immuno-proteins, 52% (464/895) were provisionally identified based on matches in GenBank. Of these, ~19% (86/464) show high level of identity to other tick hypothetical proteins, and the rest include putative proteases (serine, cysteine, leukotriene A-4 hydrolase, carboxypeptidases, and metalloproteases), protease inhibitors (serine and cysteine protease inhibitors, tick carboxypeptidase inhibitor), and transporters and/or ligand binding proteins (histamine binding/lipocalin, fatty acid binding, calreticulin, hemelipoprotein, IgG binding protein, ferritin, insulin-like growth factor binding proteins, and evasin). Others include enzymes (glutathione transferase, cytochrome oxidase, protein disulfide isomerase), ribosomal proteins, and those of miscellaneous functions (histamine release factor, selenoproteins, tetraspanin, defensin, heat shock proteins). CONCLUSIONS: Data here demonstrate that A. americanum secretes a complex cocktail of immunogenic tick saliva proteins during the first 24-48 h of feeding. Of significance, previously validated immunogenic tick saliva proteins including AV422 protein, calreticulin, histamine release factor, histamine binding/lipocalins, selenoproteins, and paramyosin were identified in this screen, supporting the specificity of the approach in this study. While descriptive, this study opens opportunities for in-depth tick feeding physiology studies.

Higher-order epistasis within Pol II trigger loop haplotypes.

RNA polymerase II (Pol II) has a highly conserved domain, the trigger loop (TL), that controls transcription fidelity and speed. We previously probed pairwise genetic interactions between residues within and surrounding the TL and identified widespread incompatibility between TLs of different species when placed in the Saccharomyces cerevisiae Pol II context, indicating epistasis between the TL and its surrounding context. We sought to understand the nature of this incompatibility and probe higher order epistasis internal to the TL. We have employed deep mutational scanning with selected natural TL variants ("haplotypes"), and all possible intermediate substitution combinations between them and the yeast Pol II TL. We identified both positive and negative higher-order residue interactions within example TL haplotypes. Intricate higher-order epistasis formed by TL residues was sometimes only apparent from analysis of intermediate genotypes, emphasizing complexity of epistatic interactions. Furthermore, we distinguished TL substitutions with distinct classes of epistatic patterns, suggesting specific TL residues that potentially influence TL evolution. Our examples of complex residue interactions suggest possible pathways for epistasis to facilitate Pol II evolution.

Quantitative analysis of transcription start site selection reveals control by DNA sequence, RNA polymerase II activity and NTP levels.

Transcription start site (TSS) selection is a key step in gene expression and occurs at many promoter positions over a wide range of efficiencies. Here we develop a massively parallel reporter assay to quantitatively dissect contributions of promoter sequence, nucleoside triphosphate substrate levels and RNA polymerase II (Pol II) activity to TSS selection by 'promoter scanning' in Saccharomyces cerevisiae (Pol II MAssively Systematic Transcript End Readout, 'Pol II MASTER'). Using Pol II MASTER, we measure the efficiency of Pol II initiation at 1,000,000 individual TSS sequences in a defined promoter context. Pol II MASTER confirms proposed critical qualities of S. cerevisiae TSS -8, -1 and +1 positions, quantitatively, in a controlled promoter context. Pol II MASTER extends quantitative analysis to surrounding sequences and determines that they tune initiation over a wide range of efficiencies. These results enabled the development of a predictive model for initiation efficiency based on sequence. We show that genetic perturbation of Pol II catalytic activity alters initiation efficiency mostly independently of TSS sequence, but selectively modulates preference for the initiating nucleotide. Intriguingly, we find that Pol II initiation efficiency is directly sensitive to guanosine-5'-triphosphate levels at the first five transcript positions and to cytosine-5'-triphosphate and uridine-5'-triphosphate levels at the second position genome wide. These results suggest individual nucleoside triphosphate levels can have transcript-specific effects on initiation, representing a cryptic layer of potential regulation at the level of Pol II biochemical properties. The results establish Pol II MASTER as a method for quantitative dissection of transcription initiation in eukaryotes.

Guide RNA biogenesis involves a novel RNase III family endoribonuclease in Trypanosoma brucei.

The mitochondrial genome of kinetoplastids, including species of Trypanosoma and Leishmania, is an unprecedented DNA structure of catenated maxicircles and minicircles. Maxicircles represent the typical mitochondrial genome encoding components of the respiratory complexes and ribosomes. However, most mRNA sequences are cryptic, and their maturation requires a unique U insertion/deletion RNA editing. Minicircles encode hundreds of small guide RNAs (gRNAs) that partially anneal with unedited mRNAs and direct the extensive editing. Trypanosoma brucei gRNAs and mRNAs are transcribed as polycistronic precursors, which undergo processing preceding editing; however, the relevant nucleases are unknown. We report the identification and functional characterization of a close homolog of editing endonucleases, mRPN1 (mitochondrial RNA precursor-processing endonuclease 1), which is involved in gRNA biogenesis. Recombinant mRPN1 is a dimeric dsRNA-dependent endonuclease that requires Mg(2+), a critical catalytic carboxylate, and generates 2-nucleotide 3' overhangs. The cleavage specificity of mRPN1 is reminiscent of bacterial RNase III and thus is fundamentally distinct from editing endonucleases, which target a single scissile bond just 5' of short duplexes. An inducible knockdown of mRPN1 in T. brucei results in loss of gRNA and accumulation of precursor transcripts (pre-gRNAs), consistent with a role of mRPN1 in processing. mRPN1 stably associates with three proteins previously identified in relatively large complexes that do not contain mRPN1, and have been linked with multiple aspects of mitochondrial RNA metabolism. One protein, TbRGG2, directly binds mRPN1 and is thought to modulate gRNA utilization by editing complexes. The proposed participation of mRPN1 in processing of polycistronic RNA and its specific protein interactions in gRNA expression are discussed.

Widespread epistasis shapes RNA Polymerase II active site function and evolution.

Multi-subunit RNA Polymerases (msRNAPs) are responsible for transcription in all kingdoms of life. At the heart of these msRNAPs is an ultra-conserved active site domain, the trigger loop (TL), coordinating transcription speed and fidelity by critical conformational changes impacting multiple steps in substrate selection, catalysis, and translocation. Previous studies have observed several different types of genetic interactions between eukaryotic RNA polymerase II (Pol II) TL residues, suggesting that the TL's function is shaped by functional interactions of residues within and around the TL. The extent of these interaction networks and how they control msRNAP function and evolution remain to be determined. Here we have dissected the Pol II TL interaction landscape by deep mutational scanning in Saccharomyces cerevisiae Pol II. Through analysis of over 15000 alleles, representing all single mutants, a rationally designed subset of double mutants, and evolutionarily observed TL haplotypes, we identify interaction networks controlling TL function. Substituting residues creates allele-specific networks and propagates epistatic effects across the Pol II active site. Furthermore, the interaction landscape further distinguishes alleles with similar growth phenotypes, suggesting increased resolution over the previously reported single mutant phenotypic landscape. Finally, co-evolutionary analyses reveal groups of co-evolving residues across Pol II converge onto the active site, where evolutionary constraints interface with pervasive epistasis. Our studies provide a powerful system to understand the plasticity of RNA polymerase mechanism and evolution, and provide the first example of pervasive epistatic landscape in a highly conserved and constrained domain within an essential enzyme.

Analysis of the genes controlling cotton fiber length reveals the molecular basis of plant breeding and the genetic potential of current cultivars for continued improvement.

Stagnated crop improvement has raised questions of whether and how current crop cultivars can be further improved. Genes are the core determinants of performance of all cultivars. Here, we report the molecular basis of plant breeding and address these questions by analyzing 226 GFL genes controlling and accurately predicting fiber length, an important breeding objective trait, in cotton (Gossypium sp.). We first identified the favorable allele and the number of favorable alleles (NFAs) of each GFL gene, calculated the total NFAs of the 226 GFL genes accumulated in 198 advanced breeding lines, and analyzed them against fiber lengths. Fiber lengths of the breeding lines were strongly correlated with the total NFAs of the GFL genes (r = 0.85, P < 0.0001), suggesting that accumulation of the favorable alleles of the genes controlling objective traits is the molecular basis of cotton breeding. Surprisingly, a breeding line with a fiber length of present cultivars having the longest fibers contained only about 51% of the total NFAs of the 226 GFL genes. The genetic potentials of current cultivars were then predicted using linear and non-linear models, respectively, revealing that a breeding line or cultivar with a fiber length of 33.8 mm could be further improved in fiber length by up to 118%. Finally, we showed that the genetic potential of such a breeding line can be realized through gene-based breeding. Therefore, these findings shed light on continued crop improvement in general and provide 740 genic biomarkers desirable for enhanced cotton fiber breeding.

Hermetia illucens L. larvae-associated intestinal microbes reduce the transmission risk of zoonotic pathogens in pig manure.

Black soldier fly (BSF) larvae are considered a promising biological reactor to convert organic waste and reduce the impact of zoonotic pathogens on the environment. We analysed the effects of BSF larvae on Staphylococcus aureus and Salmonella spp. populations in pig manure (PM), which showed that BSF larvae can significantly reduce the counts of the associated S. aureus and Salmonella spp. Then, using a sterile BSF larval system, we validated the function of BSF larval intestinal microbiota in vivo to suppress pathogens, and lastly, we isolated eight bacterial strains from the BSF larval gut that inhibit S. aureus. Results indicated that functional microbes are essential for BSF larvae to antagonise S. aureus. Moreover, the analysis results of the relationship between the intestinal microbiota and S. aureus and Salmonella spp. showed that Myroides, Tissierella, Oblitimonas, Paenalcalignes, Terrisporobacter, Clostridium, Fastidiosipila, Pseudomonas, Ignatzschineria, Savagea, Moheibacter and Sphingobacterium were negatively correlated with S. aureus and Salmonella. Overall, these results suggested that the potential ability of BSF larvae to inhibit S. aureus and Salmonella spp. present in PM is accomplished primarily by gut-associated microorganisms.

Accurate prediction of complex traits for individuals and offspring from parents using a simple, rapid, and efficient method for gene-based breeding in cotton and maize.

Accurate, simple, rapid, and inexpensive prediction of complex traits controlled by numerous genes is paramount to enhanced plant breeding, animal breeding, and human medicine. Here we report a novel method that enables accurate, simple, and rapid prediction of complex traits of individuals or offspring from parents based on the number of favorable alleles (NFAs) of the genes controlling the objective traits. The NFAs of 226 cotton fiber length (GFL) genes and nine maize hybrid grain yield related (ZmF1GY) genes were directly used to predict cotton fiber lengths of individual plants and maize grain yields of F1 hybrids from parents, respectively, using prediction model-based methods as controls. The NFAs of the 226 GFL genes predicted cotton fiber lengths at an accuracy of 0.85, as the model methods and outperforming genomic prediction by 82 % - 170 %. The NFAs of the nine ZmF1GY genes predicted grain yields of maize hybrids from parents at an accuracy of 0.80, outperforming genomic prediction by 67 %. Moreover, the prediction accuracies of these traits were consistent across years, environments, and eco-agricultural systems. Importantly, the accurate prediction of these traits directly using the NFAs of the genes allows breeding to be performed in greenhouse, phytotron, or off-season, without the need of the model training and validation steps essential and costly for model-based genomic or genic prediction. Therefore, this new method dramatically outperforms the current model-based genomic methods used for phenotype prediction and streamlines the process of breeding, thus promising to substantially enhance current plant and animal breeding.