Insights into the Ancient Adaptation to Intertidal Environments by Red Algae Based on a Genomic and Multiomics Investigation of Neoporphyra haitanensis.

Colonization of land from marine environments was a major transition for biological life on Earth, and intertidal adaptation was a key evolutionary event in the transition from marine- to land-based lifestyles. Multicellular intertidal red algae exhibit the earliest, systematic, and successful adaptation to intertidal environments, with Porphyra sensu lato (Bangiales, Rhodophyta) being a typical example. Here, a chromosome-level 49.67 Mb genome for Neoporphyra haitanensis comprising 9,496 gene loci is described based on metagenome-Hi-C-assisted whole-genome assembly, which allowed the isolation of epiphytic bacterial genome sequences from a seaweed genome for the first time. The compact, function-rich N. haitanensis genome revealed that ancestral lineages of red algae share common horizontal gene transfer events and close relationships with epiphytic bacterial populations. Specifically, the ancestor of N. haitanensis obtained unique lipoxygenase family genes from bacteria for complex chemical defense, carbonic anhydrases for survival in shell-borne conchocelis lifestyle stages, and numerous genes involved in stress tolerance. Combined proteomic, transcriptomic, and metabolomic analyses revealed complex regulation of rapid responses to intertidal dehydration/rehydration cycling within N. haitanensis. These adaptations include rapid regulation of its photosynthetic system, a readily available capacity to utilize ribosomal stores, increased methylation activity to rapidly synthesize proteins, and a strong anti-oxidation system to dissipate excess redox energy upon exposure to air. These novel insights into the unique adaptations of red algae to intertidal lifestyles inform our understanding of adaptations to intertidal ecosystems and the unique evolutionary steps required for intertidal colonization by biological life.

Genomic identification and functional analysis of essential genes in Caenorhabditis elegans.

BACKGROUND: Essential genes are required for an organism's viability and their functions can vary greatly, spreading across many pathways. Due to the importance of essential genes, large scale efforts have been undertaken to identify the complete set of essential genes and to understand their function. Studies of genome architecture and organization have found that genes are not randomly disturbed in the genome. RESULTS: Using combined genetic mapping, Illumina sequencing, and bioinformatics analyses, we successfully identified 44 essential genes with 130 lethal mutations in genomic regions of C. elegans of around 7.3 Mb from Chromosome I (left). Of the 44 essential genes, six of which were genes not characterized previously by mutant alleles, let-633/let-638 (B0261.1), let-128 (C53H9.2), let-511 (W09C3.4), let-162 (Y47G6A.18), let-510 (Y47G6A.19), and let-131 (Y71G12B.6). Examine essential genes with Hi-C data shows that essential genes tend to cluster within TAD units rather near TAD boundaries. We have also shown that essential genes in the left half of chromosome I in C. elegans function in enzyme and nucleic acid binding activities during fundamental processes, such as DNA replication, transcription, and translation. From protein-protein interaction networks, essential genes exhibit more protein connectivity than non-essential genes in the genome. Also, many of the essential genes show strong expression in embryos or early larvae stages, indicating that they are important to early development. CONCLUSIONS: Our results confirmed that this work provided a more comprehensive picture of the essential gene and their functional characterization. These genetic resources will offer important tools for further heath and disease research.

High-throughput capturing and characterization of mutations in essential genes of Caenorhabditis elegans.

BACKGROUND: Essential genes are critical for the development of all organisms and are associated with many human diseases. These genes have been a difficult category to study prior to the availability of balanced lethal strains. Despite the power of targeted mutagenesis, there are limitations in identifying mutations in essential genes. In this paper, we describe the identification of coding regions for essential genes mutated using forward genetic screens in Caenorhabditis elegans. The lethal mutations described here were isolated and maintained by a wild-type allele on a rescuing duplication. RESULTS: We applied whole genome sequencing to identify the causative molecular lesion resulting in lethality in existing C. elegans mutant strains. These strains are balanced and can be easily maintained for subsequent characterization. Our method can be effectively used to analyze mutations in a large number of essential genes. We describe here the identification of 64 essential genes in a region of chromosome I covered by the duplication sDp2. Of these, 42 are nonsense mutations, six are splice signal mutations, one deletion, and 15 are non-synonymous mutations. Many of the essential genes in this region function in cell cycle, transcriptional regulation, and RNA processing. CONCLUSIONS: The essential genes identified here are represented by mutant strains, many of which have more than one mutant allele. The genetic resource can be utilized to further our understanding of essential gene function and will be applicable to the study of C. elegans development, conserved cellular function, and ultimately lead to improved human health.

genBlastG: using BLAST searches to build homologous gene models.

MOTIVATION: BLAST users frequently expect to obtain homologous genes with certain similarity to their query genes. But what they get from BLAST searches are often collections of local alignments called high-scoring segment pairs (HSPs). On the other hand, most homology-based gene finders have been built using computation-intensive algorithms, without taking full advantage of BLAST searches that have been perfected over the last decades. RESULTS: Here we report an efficient algorithm, genBlastG that directly uses the HSPs reported by BLAST to define high-quality gene models. AVAILABILITY: http://genome.sfu.ca/genblast/download.html

Identification of Essential Genes in Caenorhabditis elegans with Lethal Mutations Maintained by Genetic Balancers.

Genetic balancer systems, which allow effective capture and maintenance of lethal mutations stably, play an important role in identifying essential genes. Whole-genome sequencing (WGS) followed by bioinformatics analysis, combined with genetic mapping data analysis, allows for an efficient and economical means of identifying genomic mutations in essential genes. Using this approach, we successfully identified 104 essential genes on ChrI, ChrIII, and ChrV in C. elegans. In this report, we described a protocol that sequences the genome of prebalanced Caenorhabditis elegans (C. elegans) strains to carry lethal mutations and identifies candidate causal mutations and candidate essential genes using a robust bioinformatics procedure.

Eight soybean reference genome resources from varying latitudes and agronomic traits.

Comparative analysis of multiple reference genomes representing diverse genetic backgrounds is critical for understanding the role of key alleles important in domestication and genetic breeding of important crops such as soybean. To enrich the genetic resources for soybean, we describe the generation, technical assessment, and preliminary genomic variation analysis of eight de novo reference-grade soybean genome assemblies from wild and cultivated accessions. These resources represent soybeans cultured at different latitudes and exhibiting different agronomical traits. Of these eight soybeans, five are from new accessions that have not been sequenced before. We demonstrate the usage of these genomes to identify small and large genomic variations affecting known genes as well as screening for genic PAV regions for identifying candidates for further functional studies.

Identification and characterization of novel human tissue-specific RFX transcription factors.

BACKGROUND: Five regulatory factor X (RFX) transcription factors (TFs)-RFX1-5-have been previously characterized in the human genome, which have been demonstrated to be critical for development and are associated with an expanding list of serious human disease conditions including major histocompatibility (MHC) class II deficiency and ciliaophathies. RESULTS: In this study, we have identified two additional RFX genes-RFX6 and RFX7-in the current human genome sequences. Both RFX6 and RFX7 are demonstrated to be winged-helix TFs and have well conserved RFX DNA binding domains (DBDs), which are also found in winged-helix TFs RFX1-5. Phylogenetic analysis suggests that the RFX family in the human genome has undergone at least three gene duplications in evolution and the seven human RFX genes can be clearly categorized into three subgroups: (1) RFX1-3, (2) RFX4 and RFX6, and (3) RFX5 and RFX7. Our functional genomics analysis suggests that RFX6 and RFX7 have distinct expression profiles. RFX6 is expressed almost exclusively in the pancreatic islets, while RFX7 has high ubiquitous expression in nearly all tissues examined, particularly in various brain tissues. CONCLUSION: The identification and further characterization of these two novel RFX genes hold promise for gaining critical insight into development and many disease conditions in mammals, potentially leading to identification of disease genes and biomarkers.

Genomic Identification and Functional Characterization of Essential Genes in Caenorhabditis elegans.

Using combined genetic mapping, Illumina sequencing, bioinformatics analyses, and experimental validation, we identified 60 essential genes from 104 lethal mutations in two genomic regions of Caenorhabditis elegans totaling ∼14 Mb on chromosome III(mid) and chromosome V(left). Five of the 60 genes had not previously been shown to have lethal phenotypes by RNA interference depletion. By analyzing the regions around the lethal missense mutations, we identified four putative new protein functional domains. Furthermore, functional characterization of the identified essential genes shows that most are enzymes, including helicases, tRNA synthetases, and kinases in addition to ribosomal proteins. Gene Ontology analysis indicated that essential genes often encode for enzymes that conduct nucleic acid binding activities during fundamental processes, such as intracellular DNA replication, transcription, and translation. Analysis of essential gene shows that they have fewer paralogs, encode proteins that are in protein interaction hubs, and are highly expressed relative to nonessential genes. All these essential gene traits in C. elegans are consistent with those of human disease genes. Most human orthologs (90%) of the essential genes in this study are related to human diseases. Therefore, functional characterization of essential genes underlines their importance as proxies for understanding the biological functions of human disease genes.

Module discovery by exhaustive search for densely connected, co-expressed regions in biomolecular interaction networks.

BACKGROUND: Computational prediction of functionally related groups of genes (functional modules) from large-scale data is an important issue in computational biology. Gene expression experiments and interaction networks are well studied large-scale data sources, available for many not yet exhaustively annotated organisms. It has been well established, when analyzing these two data sources jointly, modules are often reflected by highly interconnected (dense) regions in the interaction networks whose participating genes are co-expressed. However, the tractability of the problem had remained unclear and methods by which to exhaustively search for such constellations had not been presented. METHODOLOGY/PRINCIPAL FINDINGS: We provide an algorithmic framework, referred to as Densely Connected Biclustering (DECOB), by which the aforementioned search problem becomes tractable. To benchmark the predictive power inherent to the approach, we computed all co-expressed, dense regions in physical protein and genetic interaction networks from human and yeast. An automatized filtering procedure reduces our output which results in smaller collections of modules, comparable to state-of-the-art approaches. Our results performed favorably in a fair benchmarking competition which adheres to standard criteria. We demonstrate the usefulness of an exhaustive module search, by using the unreduced output to more quickly perform GO term related function prediction tasks. We point out the advantages of our exhaustive output by predicting functional relationships using two examples. CONCLUSION/SIGNIFICANCE: We demonstrate that the computation of all densely connected and co-expressed regions in interaction networks is an approach to module discovery of considerable value. Beyond confirming the well settled hypothesis that such co-expressed, densely connected interaction network regions reflect functional modules, we open up novel computational ways to comprehensively analyze the modular organization of an organism based on prevalent and largely available large-scale datasets. AVAILABILITY: Software and data sets are available at http://www.sfu.ca/~ester/software/DECOB.zip.