A high-resolution C. elegans essential gene network based on phenotypic profiling of a complex tissue.

High-content screening for gene profiling has generally been limited to single cells. Here, we explore an alternative approach-profiling gene function by analyzing effects of gene knockdowns on the architecture of a complex tissue in a multicellular organism. We profile 554 essential C. elegans genes by imaging gonad architecture and scoring 94 phenotypic features. To generate a reference for evaluating methods for network construction, genes were manually partitioned into 102 phenotypic classes, predicting functions for uncharacterized genes across diverse cellular processes. Using this classification as a benchmark, we developed a robust computational method for constructing gene networks from high-content profiles based on a network context-dependent measure that ranks the significance of links between genes. Our analysis reveals that multi-parametric profiling in a complex tissue yields functional maps with a resolution similar to genetic interaction-based profiling in unicellular eukaryotes-pinpointing subunits of macromolecular complexes and components functioning in common cellular processes.

The Caenorhabditis elegans TDRD5/7-like protein, LOTR-1, interacts with the helicase ZNFX-1 to balance epigenetic signals in the germline.

LOTUS and Tudor domain containing proteins have critical roles in the germline. Proteins that contain these domains, such as Tejas/Tapas in Drosophila, help localize the Vasa helicase to the germ granules and facilitate piRNA-mediated transposon silencing. The homologous proteins in mammals, TDRD5 and TDRD7, are required during spermiogenesis. Until now, proteins containing both LOTUS and Tudor domains in Caenorhabditis elegans have remained elusive. Here we describe LOTR-1 (D1081.7), which derives its name from its LOTUS and Tudor domains. Interestingly, LOTR-1 docks next to P granules to colocalize with the broadly conserved Z-granule helicase, ZNFX-1. The Tudor domain of LOTR-1 is required for its Z-granule retention. Like znfx-1 mutants, lotr-1 mutants lose small RNAs from the 3' ends of WAGO and mutator targets, reminiscent of the loss of piRNAs from the 3' ends of piRNA precursor transcripts in mouse Tdrd5 mutants. Our work shows that LOTR-1 acts with ZNFX-1 to bring small RNA amplifying mechanisms towards the 3' ends of its RNA templates.

A protein domain-based interactome network for C. elegans early embryogenesis.

Many protein-protein interactions are mediated through independently folding modular domains. Proteome-wide efforts to model protein-protein interaction or "interactome" networks have largely ignored this modular organization of proteins. We developed an experimental strategy to efficiently identify interaction domains and generated a domain-based interactome network for proteins involved in C. elegans early-embryonic cell divisions. Minimal interacting regions were identified for over 200 proteins, providing important information on their domain organization. Furthermore, our approach increased the sensitivity of the two-hybrid system, resulting in a more complete interactome network. This interactome modeling strategy revealed insights into C. elegans centrosome function and is applicable to other biological processes in this and other organisms.

Diatom modulation of select bacteria through use of two unique secondary metabolites.

Unicellular eukaryotic phytoplankton, such as diatoms, rely on microbial communities for survival despite lacking specialized compartments to house microbiomes (e.g., animal gut). Microbial communities have been widely shown to benefit from diatom excretions that accumulate within the microenvironment surrounding phytoplankton cells, known as the phycosphere. However, mechanisms that enable diatoms and other unicellular eukaryotes to nurture specific microbiomes by fostering beneficial bacteria and repelling harmful ones are mostly unknown. We hypothesized that diatom exudates may tune microbial communities and employed an integrated multiomics approach using the ubiquitous diatom Asterionellopsis glacialis to reveal how it modulates its naturally associated bacteria. We show that A. glacialis reprograms its transcriptional and metabolic profiles in response to bacteria to secrete a suite of central metabolites and two unusual secondary metabolites, rosmarinic acid and azelaic acid. While central metabolites are utilized by potential bacterial symbionts and opportunists alike, rosmarinic acid promotes attachment of beneficial bacteria to the diatom and simultaneously suppresses the attachment of opportunists. Similarly, azelaic acid enhances growth of beneficial bacteria while simultaneously inhibiting growth of opportunistic ones. We further show that the bacterial response to azelaic acid is numerically rare but globally distributed in the world's oceans and taxonomically restricted to a handful of bacterial genera. Our results demonstrate the innate ability of an important unicellular eukaryotic group to modulate select bacteria in their microbial consortia, similar to higher eukaryotes, using unique secondary metabolites that regulate bacterial growth and behavior inversely across different bacterial populations.

Pheniqs 2.0: accurate, high-performance Bayesian decoding and confidence estimation for combinatorial barcode indexing.

BACKGROUND: Systems biology increasingly relies on deep sequencing with combinatorial index tags to associate biological sequences with their sample, cell, or molecule of origin. Accurate data interpretation depends on the ability to classify sequences based on correct decoding of these combinatorial barcodes. The probability of correct decoding is influenced by both sequence quality and the number and arrangement of barcodes. The rising complexity of experimental designs calls for a probability model that accounts for both sequencing errors and random noise, generalizes to multiple combinatorial tags, and can handle any barcoding scheme. The needs for reproducibility and community benchmark standards demand a peer-reviewed tool that preserves decoding quality scores and provides tunable control over classification confidence that balances precision and recall. Moreover, continuous improvements in sequencing throughput require a fast, parallelized and scalable implementation. RESULTS AND DISCUSSION: We developed a flexible, robustly engineered software that performs probabilistic decoding and supports arbitrarily complex barcoding designs. Pheniqs computes the full posterior decoding error probability of observed barcodes by consulting basecalling quality scores and prior distributions, and reports sequences and confidence scores in Sequence Alignment/Map (SAM) fields. The product of posteriors for multiple independent barcodes provides an overall confidence score for each read. Pheniqs achieves greater accuracy than minimum edit distance or simple maximum likelihood estimation, and it scales linearly with core count to enable the classification of > 11 billion reads in 1 h 15 m using < 50 megabytes of memory. Pheniqs has been in production use for seven years in our genomics core facility. CONCLUSION: We introduce a computationally efficient software that implements both probabilistic and minimum distance decoders and show that decoding barcodes using posterior probabilities is more accurate than available methods. Pheniqs allows fine-tuning of decoding sensitivity using intuitive confidence thresholds and is extensible with alternative decoders and new error models. Any arbitrary arrangement of barcodes is easily configured, enabling computation of combinatorial confidence scores for any barcoding strategy. An optimized multithreaded implementation assures that Pheniqs is faster and scales better with complex barcode sets than existing tools. Support for POSIX streams and multiple sequencing formats enables easy integration with automated analysis pipelines.

The Microbiome of the Lebanese Wild Apple, Malus trilobata, is a Rich Source of Potential Biocontrol Agents for Fungal Post-harvest Pathogens of Apples.

The widespread use of harmful fungicides in the agricultural sector has led to a demand for safer alternatives to protect against crop pathogens. The domestic apple is the second most highly consumed fruit in the world and encounters several pre- and post-harvest fungal and bacterial phytopathogens. The goal of this study was to explore the uncharacterized microbiome of a wild apple, Malus trilobata, as a potential source of novel biocontrol agents for two post-harvest fungi that affect commercial apples: Botrytis cinerea and Penicillium expansum. We sampled microflora associated with the leaves, bulk soil, and roots of Malus trilobata in two regions of Lebanon: Ehden reserve in the north and Dhour EL Choueir near Beirut. The two regions have different soil types Dhour EL Choueir and samples from the two regions showed very different microbial compositions, with greater microbial diversity among those from Ehden reserve. Molecular characterization revealed a wide variety of genera displaying activity against the two fungal pathogens, including several with previously unknown antifungal activity: Bosea, Microlunatus, Microbacterium, Mycetecola, Rhizobium and Paraphoma. In total, 92 strains inhibited Penicillium expansum (39%) and 87 strains inhibited Botrytis cinerea (38%) out of 237 screened. Further chemical and genetic characterization of one or more selected strains could pave the way for future development of new biocontrol agents for post-harvest applications.

miR-122 and Ago interactions with the HCV genome alter the structure of the viral 5' terminus.

Hepatitis C virus (HCV) is a positive-sense RNA virus that interacts with the liver-specific microRNA, miR-122. miR-122 binds to two sites in the 5' untranslated region (UTR) and this interaction promotes HCV RNA accumulation, although the precise role of miR-122 in the HCV life cycle remains unclear. Using biophysical analyses and Selective 2' Hydroxyl Acylation analyzed by Primer Extension (SHAPE) we investigated miR-122 interactions with the 5' UTR. Our data suggests that miR-122 binding results in alteration of nucleotides 1-117 to suppress an alternative secondary structure and promote functional internal ribosomal entry site (IRES) formation. Furthermore, we demonstrate that two hAgo2:miR-122 complexes are able to bind to the HCV 5' terminus simultaneously and SHAPE analyses revealed further alterations to the structure of the 5' UTR to accommodate these complexes. Finally, we present a computational model of the hAgo2:miR-122:HCV RNA complex at the 5' terminus of the viral genome as well as hAgo2:miR-122 interactions with the IRES-40S complex that suggest hAgo2 is likely to form additional interactions with SLII which may further stabilize the HCV IRES. Taken together, our results support a model whereby hAgo2:miR-122 complexes alter the structure of the viral 5' terminus and promote formation of the HCV IRES.

NASQAR: a web-based platform for high-throughput sequencing data analysis and visualization.

BACKGROUND: As high-throughput sequencing applications continue to evolve, the rapid growth in quantity and variety of sequence-based data calls for the development of new software libraries and tools for data analysis and visualization. Often, effective use of these tools requires computational skills beyond those of many researchers. To ease this computational barrier, we have created a dynamic web-based platform, NASQAR (Nucleic Acid SeQuence Analysis Resource). RESULTS: NASQAR offers a collection of custom and publicly available open-source web applications that make extensive use of a variety of R packages to provide interactive data analysis and visualization. The platform is publicly accessible at http://nasqar.abudhabi.nyu.edu/ . Open-source code is on GitHub at https://github.com/nasqar/NASQAR , and the system is also available as a Docker image at https://hub.docker.com/r/aymanm/nasqarall . NASQAR is a collaboration between the core bioinformatics teams of the NYU Abu Dhabi and NYU New York Centers for Genomics and Systems Biology. CONCLUSIONS: NASQAR empowers non-programming experts with a versatile and intuitive toolbox to easily and efficiently explore, analyze, and visualize their Transcriptomics data interactively. Popular tools for a variety of applications are currently available, including Transcriptome Data Preprocessing, RNA-seq Analysis (including Single-cell RNA-seq), Metagenomics, and Gene Enrichment.

A novel small molecule that disrupts a key event during the oocyte-to-embryo transition in C. elegans.

The complex cellular events that occur in response to fertilization are essential for mediating the oocyte-to-embryo transition. Here, we describe a comprehensive small-molecule screen focused on identifying compounds that affect early embryonic events in Caenorhabditis elegans We identify a single novel compound that disrupts early embryogenesis with remarkable stage and species specificity. The compound, named C22, primarily impairs eggshell integrity, leading to osmotic sensitivity and embryonic lethality. The C22-induced phenotype is dependent upon the upregulation of the LET-607/CREBH transcription factor and its candidate target genes, which primarily encode factors involved in diverse aspects of protein trafficking. Together, our data suggest that in the presence of C22, one or more key components of the eggshell are inappropriately processed, leading to permeable, inviable embryos. The remarkable specificity and reversibility of this compound will facilitate further investigation into the role and regulation of protein trafficking in the early embryo, as well as serve as a tool for manipulating the life cycle for other studies such as those involving aging.

A non-canonical site reveals the cooperative mechanisms of microRNA-mediated silencing.

Although strong evidence supports the importance of their cooperative interactions, microRNA (miRNA)-binding sites are still largely investigated as functionally independent regulatory units. Here, a survey of alternative 3΄UTR isoforms implicates a non-canonical seedless site in cooperative miRNA-mediated silencing. While required for target mRNA deadenylation and silencing, this site is not sufficient on its own to physically recruit miRISC. Instead, it relies on facilitating interactions with a nearby canonical seed-pairing site to recruit the Argonaute complexes. We further show that cooperation between miRNA target sites is necessary for silencing in vivo in the C. elegans embryo, and for the recruitment of the Ccr4-Not effector complex. Using a structural model of cooperating miRISCs, we identified allosteric determinants of cooperative miRNA-mediated silencing that are required for both embryonic and larval miRNA functions. Our results delineate multiple cooperative mechanisms in miRNA-mediated silencing and further support the consideration of target site cooperation as a fundamental characteristic of miRNA function.

In Vivo Interaction Proteomics in Caenorhabditis elegans Embryos Provides New Insights into P Granule Dynamics.

Studying protein interactions in whole organisms is fundamental to understanding development. Here, we combine in vivo expressed GFP-tagged proteins with quantitative proteomics to identify protein-protein interactions of selected key proteins involved in early C. elegans embryogenesis. Co-affinity purification of interaction partners for eight bait proteins resulted in a pilot in vivo interaction map of proteins with a focus on early development. Our network reflects known biology and is highly enriched in functionally relevant interactions. To demonstrate the utility of the map, we looked for new regulators of P granule dynamics and found that GEI-12, a novel binding partner of the DYRK family kinase MBK-2, is a key regulator of P granule formation and germline maintenance. Our data corroborate a recently proposed model in which the phosphorylation state of GEI-12 controls P granule dynamics. In addition, we find that GEI-12 also induces granule formation in mammalian cells, suggesting a common regulatory mechanism in worms and humans. Our results show that in vivo interaction proteomics provides unique insights into animal development.

Assembly and analysis of eukaryotic Argonaute-RNA complexes in microRNA-target recognition.

Experimental studies have uncovered a variety of microRNA (miRNA)-target duplex structures that include perfect, imperfect and seedless duplexes. However, non-canonical binding modes from imperfect/seedless duplexes are not well predicted by computational approaches, which rely primarily on sequence and secondary structural features, nor have their tertiary structures been characterized because solved structures to date are limited to near perfect, straight duplexes in Argonautes (Agos). Here, we use structural modeling to examine the role of Ago dynamics in assembling viable eukaryotic miRNA-induced silencing complexes (miRISCs). We show that combinations of low-frequency, global modes of motion of Ago domains are required to accommodate RNA duplexes in model human and C. elegans Ago structures. Models of viable miRISCs imply that Ago adopts variable conformations at distinct target sites that generate distorted, imperfect miRNA-target duplexes. Ago's ability to accommodate a duplex is dependent on the region where structural distortions occur: distortions in solvent-exposed seed and 3'-end regions are less likely to produce steric clashes than those in the central duplex region. Energetic analyses of assembled miRISCs indicate that target recognition is also driven by favorable Ago-duplex interactions. Such structural insights into Ago loading and target recognition mechanisms may provide a more accurate assessment of miRNA function.

Tertiary structure-based analysis of microRNA-target interactions.

Current computational analysis of microRNA interactions is based largely on primary and secondary structure analysis. Computationally efficient tertiary structure-based methods are needed to enable more realistic modeling of the molecular interactions underlying miRNA-mediated translational repression. We incorporate algorithms for predicting duplex RNA structures, ionic strength effects, duplex entropy and free energy, and docking of duplex-Argonaute protein complexes into a pipeline to model and predict miRNA-target duplex binding energies. To ensure modeling accuracy and computational efficiency, we use an all-atom description of RNA and a continuum description of ionic interactions using the Poisson-Boltzmann equation. Our method predicts the conformations of two constructs of Caenorhabditis elegans let-7 miRNA-target duplexes to an accuracy of ∼3.8 Šroot mean square distance of their NMR structures. We also show that the computed duplex formation enthalpies, entropies, and free energies for eight miRNA-target duplexes agree with titration calorimetry data. Analysis of duplex-Argonaute docking shows that structural distortions arising from single-base-pair mismatches in the seed region influence the activity of the complex by destabilizing both duplex hybridization and its association with Argonaute. Collectively, these results demonstrate that tertiary structure-based modeling of miRNA interactions can reveal structural mechanisms not accessible with current secondary structure-based methods.

Combinatorial microRNA target predictions.

MicroRNAs are small noncoding RNAs that recognize and bind to partially complementary sites in the 3' untranslated regions of target genes in animals and, by unknown mechanisms, regulate protein production of the target transcript. Different combinations of microRNAs are expressed in different cell types and may coordinately regulate cell-specific target genes. Here, we present PicTar, a computational method for identifying common targets of microRNAs. Statistical tests using genome-wide alignments of eight vertebrate genomes, PicTar's ability to specifically recover published microRNA targets, and experimental validation of seven predicted targets suggest that PicTar has an excellent success rate in predicting targets for single microRNAs and for combinations of microRNAs. We find that vertebrate microRNAs target, on average, roughly 200 transcripts each. Furthermore, our results suggest widespread coordinate control executed by microRNAs. In particular, we experimentally validate common regulation of Mtpn by miR-375, miR-124 and let-7b and thus provide evidence for coordinate microRNA control in mammals.

Assessing nanobody interaction with SARS-CoV-2 Nsp9.

The interaction between SARS-CoV-2 non-structural protein Nsp9 and the nanobody 2NSP90 was investigated by NMR spectroscopy using the paramagnetic perturbation methodology PENELOP (Paramagnetic Equilibrium vs Nonequilibrium magnetization Enhancement or LOss Perturbation). The Nsp9 monomer is an essential component of the replication and transcription complex (RTC) that reproduces the viral gRNA for subsequent propagation. Therefore preventing Nsp9 recruitment in RTC would represent an efficient antiviral strategy that could be applied to different coronaviruses, given the Nsp9 relative invariance. The NMR results were consistent with a previous characterization suggesting a 4:4 Nsp9-to-nanobody stoichiometry with the occurrence of two epitope pairs on each of the Nsp9 units that establish the inter-dimer contacts of Nsp9 tetramer. The oligomerization state of Nsp9 was also analyzed by molecular dynamics simulations and both dimers and tetramers resulted plausible. A different distribution of the mapped epitopes on the tetramer surface with respect to the former 4:4 complex could also be possible, as well as different stoichiometries of the Nsp9-nanobody assemblies such as the 2:2 stoichiometry suggested by the recent crystal structure of the Nsp9 complex with 2NSP23 (PDB ID: 8dqu), a nanobody exhibiting essentially the same affinity as 2NSP90. The experimental NMR evidence, however, ruled out the occurrence in liquid state of the relevant Nsp9 conformational change observed in the same crystal structure.

ß-actin mediated H3K27ac changes demonstrate the link between compartment switching and enhancer-dependent transcriptional regulation.

BACKGROUND: Recent work has demonstrated that three-dimensional genome organization is directly affected by changes in the levels of nuclear cytoskeletal proteins such as ß-actin. The mechanisms which translate changes in 3D genome structure into changes in transcription, however, are not fully understood. Here, we use a comprehensive genomic analysis of cells lacking nuclear ß-actin to investigate the mechanistic links between compartment organization, enhancer activity, and gene expression. RESULTS: Using HiC-Seq, ATAC-Seq, and RNA-Seq, we first demonstrate that transcriptional and chromatin accessibility changes observed upon ß-actin loss are highly enriched in compartment-switching regions. Accessibility changes within compartment switching genes, however, are mainly observed in non-promoter regions which potentially represent distal regulatory elements. Our results also show that ß-actin loss induces widespread accumulation of the enhancer-specific epigenetic mark H3K27ac. Using the ABC model of enhancer annotation, we then establish that these epigenetic changes have a direct impact on enhancer activity and underlie transcriptional changes observed upon compartment switching. A complementary analysis of fibroblasts undergoing reprogramming into pluripotent stem cells further confirms that this relationship between compartment switching and enhancer-dependent transcriptional change is not specific to ß-actin knockout cells but represents a general mechanism linking compartment-level genome organization to gene expression. CONCLUSIONS: We demonstrate that enhancer-dependent transcriptional regulation plays a crucial role in driving gene expression changes observed upon compartment-switching. Our results also reveal a novel function of nuclear ß-actin in regulating enhancer function by influencing H3K27 acetylation levels.

Evolution of a biological thermocouple by adaptation of cytochrome c oxidase in a subterrestrial metazoan.

In this study we report a naturally evolved temperature-sensing electrical regulator in the cytochrome c oxidase of the Devil Worm, Halicephalobus mephisto. This extremophile metazoan was isolated 1.3 km underground in a South African goldmine, where it adapted to heat and potentially to hypoxia, making its mitochondrial sequence a likely target of adaptational change. We obtained the full mitochondrial genome sequence of this organism, and show through dN/dS analysis statistically robust evidence of positive selection in H. mephisto cytochrome c oxidase subunits. Seventeen of these positively-selected amino acid substitutions were localized in proximity to the H- and K-pathway proton channels of the complex. Surprisingly, the H. mephisto cytochrome c oxidase proton pump completely shuts down at low temperatures (20°C) leading to approximately a 4.8-fold reduction in the transmembrane proton gradient voltage (ΔΨm) compared to optimal temperature (37°C). Direct measurement of oxygen consumption found a corresponding 4.7-fold drop at 20°C compared to 37°C. Correspondingly, the lifecycle of H. mephisto takes four-fold longer at the low temperature compared to higher. This elegant evolutionary adaptation creates a finely-tuned mitochondrial temperature sensor, allowing this ectothermic organism to maximize its reproductive success in varying environmental temperatures. Our study shows that evolutionary innovation may remodel core metabolism to make it more accurately map onto environmental variation.

Empirically controlled mapping of the Caenorhabditis elegans protein-protein interactome network.

To provide accurate biological hypotheses and elucidate global properties of cellular networks, systematic identification of protein-protein interactions must meet high quality standards.We present an expanded C. elegans protein-protein interaction network, or 'interactome' map, derived from testing a matrix of approximately 10,000 x approximately 10,000 proteins using a highly specific, high-throughput yeast two-hybrid system. Through a new empirical quality control framework, we show that the resulting data set (Worm Interactome 2007, or WI-2007) was similar in quality to low-throughput data curated from the literature. We filtered previous interaction data sets and integrated them with WI-2007 to generate a high-confidence consolidated map (Worm Interactome version 8, or WI8). This work allowed us to estimate the size of the worm interactome at approximately 116,000 interactions. Comparison with other types of functional genomic data shows the complementarity of distinct experimental approaches in predicting different functional relationships between genes or proteins

RNAi as a tool to study cell biology: building the genome-phenome bridge.

In the few short years since its discovery, RNA interference (RNAi) has revolutionized the functional analysis of genomes: both technical and conceptual approaches to the investigation of gene function are being transformed as a result of this new technology. Genome-scale RNAi analyses have already been performed in the model organisms Caenorhabditis elegans (in vivo) and Drosophila melanogaster (in cell lines), ushering in a new era of RNAi-based approaches to probing the inner workings of the cell. The transformation of complex phenotypic data into mineable 'digitized' formats is fostering the emergence of a new area of bioinformatics related to the phenome.