Assessing patterns of metazoans in the global ocean using environmental DNA.

Documenting large-scale patterns of animals in the ocean and determining the drivers of these patterns is needed for conservation efforts given the unprecedented rates of change occurring within marine ecosystems. We used existing datasets from two global expeditions, Tara Oceans and Malaspina, that circumnavigated the oceans and sampled down to 4000 m to assess metazoans from environmental DNA (eDNA) extracted from seawater. We describe patterns of taxonomic richness within metazoan phyla and orders based on metabarcoding and infer the relative abundance of phyla using metagenome datasets, and relate these data to environmental variables. Arthropods had the greatest taxonomic richness of metazoan phyla at the surface, while cnidarians had the greatest richness in pelagic zones. Half of the marine metazoan eDNA from metagenome datasets was from arthropods, followed by cnidarians and nematodes. We found that mean surface temperature and primary productivity were positively related to metazoan taxonomic richness. Our findings concur with existing knowledge that temperature and primary productivity are important drivers of taxonomic richness for specific taxa at the ocean's surface, but these correlations are less evident in the deep ocean. Massive sequencing of eDNA can improve understanding of animal distributions, particularly for the deep ocean where sampling is challenging.

mtDNA "nomenclutter" and its consequences on the interpretation of genetic data.

Population-based studies of human mitochondrial genetic diversity often require the classification of mitochondrial DNA (mtDNA) haplotypes into more than 5400 described haplogroups, and further grouping those into hierarchically higher haplogroups. Such secondary haplogroup groupings (e.g., "macro-haplogroups") vary across studies, as they depend on the sample quality, technical factors of haplogroup calling, the aims of the study, and the researchers' understanding of the mtDNA haplogroup nomenclature. Retention of historical nomenclature coupled with a growing number of newly described mtDNA lineages results in increasingly complex and inconsistent nomenclature that does not reflect phylogeny well. This "clutter" leaves room for grouping errors and inconsistencies across scientific publications, especially when the haplogroup names are used as a proxy for secondary groupings, and represents a source for scientific misinterpretation. Here we explore the effects of phylogenetically insensitive secondary mtDNA haplogroup groupings, and the lack of standardized secondary haplogroup groupings on downstream analyses and interpretation of genetic data. We demonstrate that frequency-based analyses produce inconsistent results when different secondary mtDNA groupings are applied, and thus allow for vastly different interpretations of the same genetic data. The lack of guidelines and recommendations on how to choose appropriate secondary haplogroup groupings presents an issue for the interpretation of results, as well as their comparison and reproducibility across studies. To reduce biases originating from arbitrarily defined secondary nomenclature-based groupings, we suggest that future updates of mtDNA phylogenies aimed for the use in mtDNA haplogroup nomenclature should also provide well-defined and standardized sets of phylogenetically meaningful algorithm-based secondary haplogroup groupings such as "macro-haplogroups", "meso-haplogroups", and "micro-haplogroups". Ideally, each of the secondary haplogroup grouping levels should be informative about different human population history events. Those phylogenetically informative levels of haplogroup groupings can be easily defined using TreeCluster, and then implemented into haplogroup callers such as HaploGrep3. This would foster reproducibility across studies, provide a grouping standard for population-based studies, and reduce errors associated with haplogroup nomenclatures in future studies.

DES-Amyloidoses "Amyloidoses through the looking-glass": A knowledgebase developed for exploring and linking information related to human amyloid-related diseases.

More than 30 types of amyloids are linked to close to 50 diseases in humans, the most prominent being Alzheimer's disease (AD). AD is brain-related local amyloidosis, while another amyloidosis, such as AA amyloidosis, tends to be more systemic. Therefore, we need to know more about the biological entities' influencing these amyloidosis processes. However, there is currently no support system developed specifically to handle this extraordinarily complex and demanding task. To acquire a systematic view of amyloidosis and how this may be relevant to the brain and other organs, we needed a means to explore "amyloid network systems" that may underly processes that leads to an amyloid-related disease. In this regard, we developed the DES-Amyloidoses knowledgebase (KB) to obtain fast and relevant information regarding the biological network related to amyloid proteins/peptides and amyloid-related diseases. This KB contains information obtained through text and data mining of available scientific literature and other public repositories. The information compiled into the DES-Amyloidoses system based on 19 topic-specific dictionaries resulted in 796,409 associations between terms from these dictionaries. Users can explore this information through various options, including enriched concepts, enriched pairs, and semantic similarity. We show the usefulness of the KB using an example focused on inflammasome-amyloid associations. To our knowledge, this is the only KB dedicated to human amyloid-related diseases derived primarily through literature text mining and complemented by data mining that provides a novel way of exploring information relevant to amyloidoses.

A cautionary signal from the Red Sea on the impact of increased dust activity on marine microbiota.

BACKGROUND: Global climate change together with growing desertification is leading to increased dust emissions to the atmosphere, drawing attention to possible impacts on marine ecosystems receiving dust deposition. Since microorganisms play important roles in maintaining marine homeostasis through nutrient cycling and carbon flow, detrimental changes in the composition of marine microbiota in response to increased dust input could negatively impact marine health, particularly so in seas located within the Global Dust Belt. Due to its strategic location between two deserts and unique characteristics, the Red Sea provides an attractive semi-enclosed "megacosm" to examine the impacts of large dust deposition on the vastly diverse microbiota in its exceptionally warm oligotrophic waters. RESULTS: We used culture-independent metagenomic approaches to assess temporal changes in the Red Sea microbiota in response to two severe sandstorms, one originated in the Nubian Desert in the summer 2016 and a second one originated in the Libyan Desert in the spring 2017. Despite differences in sandstorm origin and meteorological conditions, both sandstorms shifted bacterial and Archaeal groups in a similar mode. In particular, the relative abundance of autotrophic bacteria declined while those of heterotrophic bacteria, particularly Bacteroidetes, and Archaea increased. The changes peaked within six days from the start of sandstorms, and the community recovered the original assemblage within one month. CONCLUSION: Our results suggest that increased dust emission with expanding desertification could lead to undesirable impacts in ocean function, enhancing heterotrophic processes while reducing autotrophic ones, thereby affecting the marine food web in seas receiving dust deposition.

DTi2Vec: Drug-target interaction prediction using network embedding and ensemble learning.

Drug-target interaction (DTI) prediction is a crucial step in drug discovery and repositioning as it reduces experimental validation costs if done right. Thus, developing in-silico methods to predict potential DTI has become a competitive research niche, with one of its main focuses being improving the prediction accuracy. Using machine learning (ML) models for this task, specifically network-based approaches, is effective and has shown great advantages over the other computational methods. However, ML model development involves upstream hand-crafted feature extraction and other processes that impact prediction accuracy. Thus, network-based representation learning techniques that provide automated feature extraction combined with traditional ML classifiers dealing with downstream link prediction tasks may be better-suited paradigms. Here, we present such a method, DTi2Vec, which identifies DTIs using network representation learning and ensemble learning techniques. DTi2Vec constructs the heterogeneous network, and then it automatically generates features for each drug and target using the nodes embedding technique. DTi2Vec demonstrated its ability in drug-target link prediction compared to several state-of-the-art network-based methods, using four benchmark datasets and large-scale data compiled from DrugBank. DTi2Vec showed a statistically significant increase in the prediction performances in terms of AUPR. We verified the "novel" predicted DTIs using several databases and scientific literature. DTi2Vec is a simple yet effective method that provides high DTI prediction performance while being scalable and efficient in computation, translating into a powerful drug repositioning tool.

DES-Tcell is a knowledgebase for exploring immunology-related literature.

T-cells are a subtype of white blood cells circulating throughout the body, searching for infected and abnormal cells. They have multifaceted functions that include scanning for and directly killing cells infected with intracellular pathogens, eradicating abnormal cells, orchestrating immune response by activating and helping other immune cells, memorizing encountered pathogens, and providing long-lasting protection upon recurrent infections. However, T-cells are also involved in immune responses that result in organ transplant rejection, autoimmune diseases, and some allergic diseases. To support T-cell research, we developed the DES-Tcell knowledgebase (KB). This KB incorporates text- and data-mined information that can expedite retrieval and exploration of T-cell relevant information from the large volume of published T-cell-related research. This KB enables exploration of data through concepts from 15 topic-specific dictionaries, including immunology-related genes, mutations, pathogens, and pathways. We developed three case studies using DES-Tcell, one of which validates effective retrieval of known associations by DES-Tcell. The second and third case studies focuses on concepts that are common to Grave's disease (GD) and Hashimoto's thyroiditis (HT). Several reports have shown that up to 20% of GD patients treated with antithyroid medication develop HT, thus suggesting a possible conversion or shift from GD to HT disease. DES-Tcell found miR-4442 links to both GD and HT, and that miR-4442 possibly targets the autoimmune disease risk factor CD6, which provides potential new knowledge derived through the use of DES-Tcell. According to our understanding, DES-Tcell is the first KB dedicated to exploring T-cell-relevant information via literature-mining, data-mining, and topic-specific dictionaries.

KAUST Metagenomic Analysis Platform (KMAP), enabling access to massive analytics of re-annotated metagenomic data.

Exponential rise of metagenomics sequencing is delivering massive functional environmental genomics data. However, this also generates a procedural bottleneck for on-going re-analysis as reference databases grow and methods improve, and analyses need be updated for consistency, which require acceess to increasingly demanding bioinformatic and computational resources. Here, we present the KAUST Metagenomic Analysis Platform (KMAP), a new integrated open web-based tool for the comprehensive exploration of shotgun metagenomic data. We illustrate the capacities KMAP provides through the re-assembly of ~ 27,000 public metagenomic samples captured in ~ 450 studies sampled across ~ 77 diverse habitats. A small subset of these metagenomic assemblies is used in this pilot study grouped into 36 new habitat-specific gene catalogs, all based on full-length (complete) genes. Extensive taxonomic and gene annotations are stored in Gene Information Tables (GITs), a simple tractable data integration format useful for analysis through command line or for database management. KMAP pilot study provides the exploration and comparison of microbial GITs across different habitats with over 275 million genes. KMAP access to data and analyses is available at https://www.cbrc.kaust.edu.sa/aamg/kmap.start .

PATHcre8: A Tool That Facilitates the Searching for Heterologous Biosynthetic Routes.

Developing computational tools that can facilitate the rational design of cell factories producing desired products at increased yields is challenging, as the tool needs to take into account that the preferred host organism usually has compounds that are consumed by competing reactions that reduce the yield of the desired product. On the other hand, the preferred host organisms may not have the native metabolic reactions needed to produce the compound of interest; thus, the computational tool needs to identify the metabolic reactions that will most efficiently produce the desired product. In this regard, we developed the generic tool PATHcre8 to facilitate an optimized search for heterologous biosynthetic pathway routes. PATHcre8 finds and ranks biosynthesis routes in a large number of organisms, including Cyanobacteria. The tool ranks the pathways based on feature scores that reflect reaction thermodynamics, the potentially toxic products in the pathway (compound toxicity), intermediate products in the pathway consumed by competing reactions (product consumption), and host-specific information such as enzyme copy number. A comparison with several other similar tools shows that PATHcre8 is more efficient in ranking functional pathways. To illustrate the effectiveness of PATHcre8, we further provide case studies focused on isoprene production and the biodegradation of cocaine. PATHcre8 is free for academic and nonprofit users and can be accessed at https://www.cbrc.kaust.edu.sa/pathcre8/.

Next-Generation Sequencing at High Sequencing Depth as a Tool to Study the Evolution of Metastasis Driven by Genetic Change Events of Lung Squamous Cell Carcinoma.

Background: The aim of this study is to report tumoral genetic mutations observed at high sequencing depth in a lung squamous cell carcinoma (SqCC) sample. We describe the findings and differences in genetic mutations that were studied by deep next-generation sequencing methods on the primary tumor and liver metastasis samples. In this report, we also discuss how these differences may be involved in determining the tumor progression leading to the metastasis stage. Methods: We followed one lung SqCC patient who underwent FDG-PET scan imaging, before and after three months of treatment. We sequenced 26 well-known cancer-related genes, at an average of ~6,000 × sequencing coverage, in two spatially distinct regions, one from a primary lung tumor metastasis and the other from a distal liver metastasis, which was present before the treatment. Results: A total of 3,922,196 read pairs were obtained across all two samples' sequenced locations. Merged mapped reads showed several variants, from which we selected 36 with high confidence call. While we found 83% of genetic concordance between the distal metastasis and primary tumor, six variants presented substantial discordance. In the liver metastasis sample, we observed three de novo genetic changes, two on the FGFR3 gene and one on the CDKN2A gene, and the frequency of one variant found on the FGFR2 gene has been increased. Two genetic variants in the HRAS gene, which were present initially in the primary tumor, have been completely lost in the liver tumor. The discordant variants have coding consequences as follows: FGFR3 (c.746C>G, p. Ser249Cys), CDKN2A (c.47_50delTGGC, p. Leu16Profs*9), and HRAS (c.182A>C, p. Gln61Pro). The pathogenicity prediction scores for the acquired variants, assessed using several databases, reported these variants as pathogenic, with a gain of function for FGFR3 and a loss of function for CDKN2A. The patient follow-up using imaging with 18F-FDG PET/CT before and after four cycles of treatment shows discordant tumor progression in metastatic liver compared to primary lung tumor. Conclusions: Our results report the occurrence of several genetic changes between primary tumor and distant liver metastasis in lung SqCC, among which non-silent mutations may be associated with tumor evolution during metastasis.

Sequencing effort dictates gene discovery in marine microbial metagenomes.

Massive metagenomic sequencing combined with gene prediction methods were previously used to compile the gene catalogue of the ocean and host-associated microbes. Global expeditions conducted over the past 15 years have sampled the ocean to build a catalogue of genes from pelagic microbes. Here we undertook a large sequencing effort of a perturbed Red Sea plankton community to uncover that the rate of gene discovery increases continuously with sequencing effort, with no indication that the retrieved 2.83 million non-redundant (complete) genes predicted from the experiment represented a nearly complete inventory of the genes present in the sampled community (i.e., no evidence of saturation). The underlying reason is the Pareto-like distribution of the abundance of genes in the plankton community, resulting in a very long tail of millions of genes present at remarkably low abundances, which can only be retrieved through massive sequencing. Microbial metagenomic projects retrieve a variable number of unique genes per Tera base-pair (Tbp), with a median value of 14.7 million unique genes per Tbp sequenced across projects. The increase in the rate of gene discovery in microbial metagenomes with sequencing effort implies that there is ample room for new gene discovery in further ocean and holobiont sequencing studies.

Splice2Deep: An ensemble of deep convolutional neural networks for improved splice site prediction in genomic DNA.

BACKGROUND: The accurate identification of the exon/intron boundaries is critical for the correct annotation of genes with multiple exons. Donor and acceptor splice sites (SS) demarcate these boundaries. Therefore, deriving accurate computational models to predict the SS are useful for functional annotation of genes and genomes, and for finding alternative SS associated with different diseases. Although various models have been proposed for the in silico prediction of SS, improving their accuracy is required for reliable annotation. Moreover, models are often derived and tested using the same genome, providing no evidence of broad application, i.e. to other poorly studied genomes. RESULTS: With this in mind, we developed the Splice2Deep models for SS detection. Each model is an ensemble of deep convolutional neural networks. We evaluated the performance of the models based on the ability to detect SS in Homo sapiens, Oryza sativa japonica, Arabidopsis thaliana, Drosophila melanogaster, and Caenorhabditis elegans. Results demonstrate that the models efficiently detect SS in other organisms not considered during the training of the models. Compared to the state-of-the-art tools, Splice2Deep models achieved significantly reduced average error rates of 41.97% and 28.51% for acceptor and donor SS, respectively. Moreover, the Splice2Deep cross-organism validation demonstrates that models correctly identify conserved genomic elements enabling annotation of SS in new genomes by choosing the taxonomically closest model. CONCLUSIONS: The results of our study demonstrated that Splice2Deep both achieved a considerably reduced error rate compared to other state-of-the-art models and the ability to accurately recognize SS in other organisms for which the model was not trained, enabling annotation of poorly studied or newly sequenced genomes. Splice2Deep models are implemented in Python using Keras API; the models and the data are available at https://github.com/SomayahAlbaradei/Splice_Deep.git.

Genome Insights of the Plant-Growth Promoting Bacterium Cronobacter muytjensii JZ38 With Volatile-Mediated Antagonistic Activity Against Phytophthora infestans.

Salinity stress is a major challenge to agricultural productivity and global food security in light of a dramatic increase of human population and climate change. Plant growth promoting bacteria can be used as an additional solution to traditional crop breeding and genetic engineering. In the present work, the induction of plant salt tolerance by the desert plant endophyte Cronobacter sp. JZ38 was examined on the model plant Arabidopsis thaliana using different inoculation methods. JZ38 promoted plant growth under salinity stress via contact and emission of volatile compounds. Based on the 16S rRNA and whole genome phylogenetic analysis, fatty acid analysis and phenotypic identification, JZ38 was identified as Cronobacter muytjensii and clearly separated and differentiated from the pathogenic C. sakazakii. Full genome sequencing showed that JZ38 is composed of one chromosome and two plasmids. Bioinformatic analysis and bioassays revealed that JZ38 can grow under a range of abiotic stresses. JZ38 interaction with plants is correlated with an extensive set of genes involved in chemotaxis and motility. The presence of genes for plant nutrient acquisition and phytohormone production could explain the ability of JZ38 to colonize plants and sustain plant growth under stress conditions. Gas chromatography-mass spectrometry analysis of volatiles produced by JZ38 revealed the emission of indole and different sulfur volatile compounds that may play a role in contactless plant growth promotion and antagonistic activity against pathogenic microbes. Indeed, JZ38 was able to inhibit the growth of two strains of the phytopathogenic oomycete Phytophthora infestans via volatile emission. Genetic, transcriptomic and metabolomics analyses, combined with more in vitro assays will provide a better understanding the highlighted genes' involvement in JZ38's functional potential and its interaction with plants. Nevertheless, these results provide insight into the bioactivity of C. muytjensii JZ38 as a multi-stress tolerance promoting bacterium with a potential use in agriculture.

Author Correction: Bioprospecting desert plant Bacillus endophytic strains for their potential to enhance plant stress tolerance.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Regulation of nitric oxide production in hypothyroidism.

Hypothyroidism is a common endocrine disorder that predominantly occurs in females. It is associated with an increased risk of cardiovascular diseases (CVD), but the molecular mechanism is not known. Disturbance in lipid metabolism, the regulation of oxidative stress, and inflammation characterize the progression of subclinical hypothyroidism. The initiation and progression of endothelial dysfunction also exhibit these changes, which is the initial step in developing CVD. Animal and human studies highlight the critical role of nitric oxide (NO) as a reliable biomarker for cardiovascular risk in subclinical and clinical hypothyroidism. In this review, we summarize the recent literature findings associated with NO production by the thyroid hormones in both physiological and pathophysiological conditions. We also discuss the levothyroxine treatment effect on serum NO levels in hypothyroid patients.

Splice2Deep: An ensemble of deep convolutional neural networks for improved splice site prediction in genomic DNA.

BACKGROUND: The accurate identification of the exon/intron boundaries is critical for the correct annotation of genes with multiple exons. Donor and acceptor splice sites (SS) demarcate these boundaries. Therefore, deriving accurate computational models to predict the SS are useful for functional annotation of genes and genomes, and for finding alternative SS associated with different diseases. Although various models have been proposed for the in silico prediction of SS, improving their accuracy is required for reliable annotation. Moreover, models are often derived and tested using the same genome, providing no evidence of broad application, i.e. to other poorly studied genomes. RESULTS: With this in mind, we developed the Splice2Deep models for SS detection. Each model is an ensemble of deep convolutional neural networks. We evaluated the performance of the models based on the ability to detect SS in Homo sapiens, Oryza sativa japonica, Arabidopsis thaliana, Drosophila melanogaster, and Caenorhabditis elegans. Results demonstrate that the models efficiently detect SS in other organisms not considered during the training of the models. Compared to the state-of-the-art tools, Splice2Deep models achieved significantly reduced average error rates of 41.97% and 28.51% for acceptor and donor SS, respectively. Moreover, the Splice2Deep cross-organism validation demonstrates that models correctly identify conserved genomic elements enabling annotation of SS in new genomes by choosing the taxonomically closest model. CONCLUSIONS: The results of our study demonstrated that Splice2Deep both achieved a considerably reduced error rate compared to other state-of-the-art models and the ability to accurately recognize SS in other organisms for which the model was not trained, enabling annotation of poorly studied or newly sequenced genomes. Splice2Deep models are implemented in Python using Keras API; the models and the data are available at https://github.com/SomayahAlbaradei/Splice_Deep.git.

DTiGEMS+: drug-target interaction prediction using graph embedding, graph mining, and similarity-based techniques.

In silico prediction of drug-target interactions is a critical phase in the sustainable drug development process, especially when the research focus is to capitalize on the repositioning of existing drugs. However, developing such computational methods is not an easy task, but is much needed, as current methods that predict potential drug-target interactions suffer from high false-positive rates. Here we introduce DTiGEMS+, a computational method that predicts Drug-Target interactions using Graph Embedding, graph Mining, and Similarity-based techniques. DTiGEMS+ combines similarity-based as well as feature-based approaches, and models the identification of novel drug-target interactions as a link prediction problem in a heterogeneous network. DTiGEMS+ constructs the heterogeneous network by augmenting the known drug-target interactions graph with two other complementary graphs namely: drug-drug similarity, target-target similarity. DTiGEMS+ combines different computational techniques to provide the final drug target prediction, these techniques include graph embeddings, graph mining, and machine learning. DTiGEMS+ integrates multiple drug-drug similarities and target-target similarities into the final heterogeneous graph construction after applying a similarity selection procedure as well as a similarity fusion algorithm. Using four benchmark datasets, we show DTiGEMS+ substantially improves prediction performance compared to other state-of-the-art in silico methods developed to predict of drug-target interactions by achieving the highest average AUPR across all datasets (0.92), which reduces the error rate by 33.3% relative to the second-best performing model in the state-of-the-art methods comparison.

Antioxidant enzymes expression in lymphocytes of patients undergoing carotid endarterectomy.

To remedy carotid artery stenosis and prevent stroke surgical intervention is commonly used, and the gold standard being carotid endarterectomy (CEA). During CEA cerebrovascular hemoglobin oxygen saturation decreases and when this decrease reaches critical levels it leads to cerebral hypoxia that causes neuronal damage. One of the proposed mechanism that affects changes during CEA and contribute to acute brain ischemia (ABI) is oxidative stress. The increased production of reactive oxygen species and reactive nitrogen species during ABI may cause an unregulated inflammatory response and further lead to structural and functional injury of neurons. Antioxidant activity are involved in the protection against neuronal damage after cerebral ischemia. We hypothesized that neuronal injury and poor outcomes in patients undergoing CEA may be results of oxidative stress that disturbed function of antioxidant enzymes and contributed to the DNA damage in lymphocytes.

Redox control of vascular biology.

Redox control is lost when the antioxidant defense system cannot remove abnormally high concentrations of signaling molecules, such as reactive oxygen species (ROS). Chronically elevated levels of ROS cause oxidative stress that may eventually lead to cancer and cardiovascular and neurodegenerative diseases. In this review, we focus on redox effects in the vascular system. We pay close attention to the subcompartments of the vascular system (endothelium, smooth muscle cell layer) and give an overview of how redox changes influence those different compartments. We also review the core aspects of redox biology, cardiovascular physiology, and pathophysiology. Moreover, the topic-specific knowledgebase DES-RedoxVasc was used to develop two case studies, one focused on endothelial cells and the other on the vascular smooth muscle cells, as a starting point to possibly extend our knowledge of redox control in vascular biology.