BioBBC: a multi-feature model that enhances the detection of biomedical entities.

The rapid increase in biomedical publications necessitates efficient systems to automatically handle Biomedical Named Entity Recognition (BioNER) tasks in unstructured text. However, accurately detecting biomedical entities is quite challenging due to the complexity of their names and the frequent use of abbreviations. In this paper, we propose BioBBC, a deep learning (DL) model that utilizes multi-feature embeddings and is constructed based on the BERT-BiLSTM-CRF to address the BioNER task. BioBBC consists of three main layers; an embedding layer, a Long Short-Term Memory (Bi-LSTM) layer, and a Conditional Random Fields (CRF) layer. BioBBC takes sentences from the biomedical domain as input and identifies the biomedical entities mentioned within the text. The embedding layer generates enriched contextual representation vectors of the input by learning the text through four types of embeddings: part-of-speech tags (POS tags) embedding, char-level embedding, BERT embedding, and data-specific embedding. The BiLSTM layer produces additional syntactic and semantic feature representations. Finally, the CRF layer identifies the best possible tag sequence for the input sentence. Our model is well-constructed and well-optimized for detecting different types of biomedical entities. Based on experimental results, our model outperformed state-of-the-art (SOTA) models with significant improvements based on six benchmark BioNER datasets.

Marine picoplankton metagenomes and MAGs from eleven vertical profiles obtained by the Malaspina Expedition.

The Ocean microbiome has a crucial role in Earth's biogeochemical cycles. During the last decade, global cruises such as Tara Oceans and the Malaspina Expedition have expanded our understanding of the diversity and genetic repertoire of marine microbes. Nevertheless, there are still knowledge gaps regarding their diversity patterns throughout depth gradients ranging from the surface to the deep ocean. Here we present a dataset of 76 microbial metagenomes (MProfile) of the picoplankton size fraction (0.2-3.0 µm) collected in 11 vertical profiles covering contrasting ocean regions sampled during the Malaspina Expedition circumnavigation (7 depths, from surface to 4,000 m deep). The MProfile dataset produced 1.66 Tbp of raw DNA sequences from which we derived: 17.4 million genes clustered at 95% sequence similarity (M-GeneDB-VP), 2,672 metagenome-assembled genomes (MAGs) of Archaea and Bacteria (Malaspina-VP-MAGs), and over 100,000 viral genomic sequences. This dataset will be a valuable resource for exploring the functional and taxonomic connectivity between the photic and bathypelagic tropical and sub-tropical ocean, while increasing our general knowledge of the Ocean microbiome.

Clonal Flux and Spread of Staphylococcus aureus Isolated from Meat and Its Genetic Relatedness to Staphylococcus aureus Isolated from Patients in Saudi Arabia.

In this study, we investigated both meat-derived and methicillin-resistant Staphylococcus aureus (MRSA), exploring their genetic relatedness to patient-derived MRSA isolates in Saudi Arabia. We collected 250 meat samples and identified 53 S. aureus isolates, with 79% being methicillin-sensitive Staphylococcus aureus (MSSA) and 21% being MRSA. Moreover, we included 80 clinically confirmed patient-derived MRSA isolates. We identified the most common S. aureus clone in both patients and retail meat. In meat, ST6 and ST97 were the most common clones in 55% of the MRSA isolates, and ST1153 and ST672 were the most common in 21% and 17% of the MSSA isolates. In patients, ST5 and ST6 were the predominant clones in 46% of the S. aureus isolates. CC5/ST5-SCCmecVc-t311 and CC361/ST672-SCCmecV-t3841 were common MRSA clones in both meat and patients. CC97 and CC361 clones were the second most prevalent S. aureus clones in meat and were relatively common in patients. Furthermore, we sequenced and characterized novel S. aureus strains ST8109, ST8110, and ST8111. The genomic similarities between meat- and patient-derived S. aureus isolates suggest that retail meat might be a reservoir for S.aureus and MRSA transmission. Therefore, a structured One Health approach is recommended for S. aureus dissemination, genetic characterization, antibiotic resistance, and impact on human health.

Glutamate-Sensing Genes Are Conserved among Populations Compared to Glutamate Metabolism Genes.

INTRODUCTION: Glutamate is a representative taste molecule with an umami flavor and is a major nutrient found abundantly in nature. Furthermore, it plays a significant role in the human body as a key metabolic intermediate and neurotransmitter. Therefore, the divergence of glutamate functions among populations during their evolution is of particular interest with a hypothesis that the genetic variation can lead to understanding divergence in taste perception. To elucidate variation in glutamate applications and to deepen our understanding of taste perception, we examined the nucleotide diversity of genes associated with glutamate sensing and metabolism among human populations. METHODS: We first established 67 genes related to glutamate sensing and metabolism based on the database and literature survey. Then, for those genes, we used a population genomics approach based on ten populations over 76,156 human genomes in the gnomAD database. RESULTS: Statistical tests of means and medians of the minor allele frequencies did not show any significant difference among populations. However, we observed substantial differences between two functional groups, glutamate sensing and glutamate metabolism, in populations of Latino/admixed American, Ashkenazi Jewish, and Others. Interestingly, we could find significant differences between the African population and the East Asian population at the single nucleotide polymorphism level of glutamate metabolism genes, but no clear differences were noted in glutamate-sensing genes. These suggest that glutamate-sensing genes are under the functional constraint compared to glutamate metabolism genes. CONCLUSION: Thus, glutamate-sensing genes and metabolism genes have a contrasting mode of the evolution, and glutamate-sensing genes are conservatively evolved, indicating its functional importance.

A deep learning model predicts the presence of diverse cancer types using circulating tumor cells.

Circulating tumor cells (CTCs) are cancer cells that detach from the primary tumor and intravasate into the bloodstream. Thus, non-invasive liquid biopsies are being used to analyze CTC-expressed genes to identify potential cancer biomarkers. In this regard, several studies have used gene expression changes in blood to predict the presence of CTC and, consequently, cancer. However, the CTC mRNA data has not been used to develop a generic approach that indicates the presence of multiple cancer types. In this study, we developed such a generic approach. Briefly, we designed two computational workflows, one using the raw mRNA data and deep learning (DL) and the other exploiting five hub gene ranking algorithms (Degree, Maximum Neighborhood Component, Betweenness Centrality, Closeness Centrality, and Stress Centrality) with machine learning (ML). Both workflows aim to determine the top genes that best distinguish cancer types based on the CTC mRNA data. We demonstrate that our automated, robust DL framework (DNNraw) more accurately indicates the presence of multiple cancer types using the CTC gene expression data than multiple ML approaches. The DL approach achieved average precision of 0.9652, recall of 0.9640, f1-score of 0.9638 and overall accuracy of 0.9640. Furthermore, since we designed multiple approaches, we also provide a bioinformatics analysis of the gene commonly identified as top-ranked by the different methods. To our knowledge, this is the first study wherein a generic approach has been developed to predict the presence of multiple cancer types using raw CTC mRNA data, as opposed to other models that require a feature selection step.

Top abundant deep ocean heterotrophic bacteria can be retrieved by cultivation.

Traditional culture techniques usually retrieve a small fraction of the marine microbial diversity, which mainly belong to the so-called rare biosphere. However, this paradigm has not been fully tested at a broad scale, especially in the deep ocean. Here, we examined the fraction of heterotrophic bacterial communities in photic and deep ocean layers that could be recovered by culture-dependent techniques at a large scale. We compared 16S rRNA gene sequences from a collection of 2003 cultured heterotrophic marine bacteria with global 16S rRNA metabarcoding datasets (16S TAGs) covering surface, mesopelagic and bathypelagic ocean samples that included 16 of the 23 samples used for isolation. These global datasets represent 60 322 unique 16S amplicon sequence variants (ASVs). Our results reveal a significantly higher proportion of isolates identical to ASVs in deeper ocean layers reaching up to 28% of the 16S TAGs of the bathypelagic microbial communities, which included the isolation of 3 of the top 10 most abundant 16S ASVs in the global bathypelagic ocean, related to the genera Sulfitobacter, Halomonas and Erythrobacter. These isolates contributed differently to the prokaryotic communities across different plankton size fractions, recruiting between 38% in the free-living fraction (0.2-0.8 µm) and up to 45% in the largest particles (20-200 µm) in the bathypelagic ocean. Our findings support the hypothesis that sinking particles in the bathypelagic act as resource-rich habitats, suitable for the growth of heterotrophic bacteria with a copiotroph lifestyle that can be cultured, and that these cultivable bacteria can also thrive as free-living bacteria.

MicroRNA networks linked with BRCA1/2, PTEN, and common genes for Alzheimer's disease and breast cancer share highly enriched pathways that may unravel targets for the AD/BC comorbidity treatment.

MicroRNAs (miRNAs) are involved in the regulation of various cellular processes including pathological conditions. MiRNA networks have been extensively researched in age-related degenerative diseases, such as cancer, Alzheimer's disease (AD), and heart failure. Thus, miRNA has been studied from different approaches, in vivo, in vitro, and in silico including miRNA networks. Networks linking diverse biomedical entities unveil information not readily observable by other means. This work focuses on biological networks related to Breast cancer susceptibility 1 (BRCA1) in AD and breast cancer (BC). Using various bioinformatics approaches, we identified subnetworks common to AD and BC that suggest they are linked. According to our results, miR-107 was identified as a potentially good candidate for both AD and BC treatment (targeting BRCA1/2 and PTEN in both diseases), accompanied by miR-146a and miR-17. The analysis also confirmed the involvement of the miR-17-92 cluster, and miR-124-3p, and highlighted the importance of poorly researched miRNAs such as mir-6785 mir-6127, mir-6870, or miR-8485. After filtering the in silico analysis results, we found 49 miRNA molecules that modulate the expression of at least five genes common to both BC and AD. Those 49 miRNAs regulate the expression of 122 genes in AD and 93 genes in BC, from which 26 genes are common genes for AD and BC involved in neuron differentiation and genesis, cell differentiation and migration, regulation of cell cycle, and cancer development. Additionally, the highly enriched pathway was associated with diabetic complications, pointing out possible interplay among molecules underlying BC, AD, and diabetes pathology.

Current trends in RNA virus detection through metatranscriptome sequencing data.

With advances in sequencing technology, metatranscriptome sequencing from a variety of environmental and biological sources has revealed the existence of various previously unknown RNA viruses. This review presents recent major RNA virome studies sampled from invertebrate and vertebrate species as well as aquatic environments. In particular, we focus on severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and related RNA virus identification through metatranscriptome sequencing analyses. Recently developed bioinformatics software and databases for RNA virus identification are introduced. A relationship between newly identified RNA viruses and endogenous viral elements in host genomes is also discussed.

OncoRTT: Predicting novel oncology-related therapeutic targets using BERT embeddings and omics features.

Late-stage drug development failures are usually a consequence of ineffective targets. Thus, proper target identification is needed, which may be possible using computational approaches. The reason being, effective targets have disease-relevant biological functions, and omics data unveil the proteins involved in these functions. Also, properties that favor the existence of binding between drug and target are deducible from the protein's amino acid sequence. In this work, we developed OncoRTT, a deep learning (DL)-based method for predicting novel therapeutic targets. OncoRTT is designed to reduce suboptimal target selection by identifying novel targets based on features of known effective targets using DL approaches. First, we created the "OncologyTT" datasets, which include genes/proteins associated with ten prevalent cancer types. Then, we generated three sets of features for all genes: omics features, the proteins' amino-acid sequence BERT embeddings, and the integrated features to train and test the DL classifiers separately. The models achieved high prediction performances in terms of area under the curve (AUC), i.e., AUC greater than 0.88 for all cancer types, with a maximum of 0.95 for leukemia. Also, OncoRTT outperformed the state-of-the-art method using their data in five out of seven cancer types commonly assessed by both methods. Furthermore, OncoRTT predicts novel therapeutic targets using new test data related to the seven cancer types. We further corroborated these results with other validation evidence using the Open Targets Platform and a case study focused on the top-10 predicted therapeutic targets for lung cancer.

Synergy of Small Antiviral Molecules on a Black-Phosphorus Nanocarrier: Machine Learning and Quantum Chemical Simulation Insights.

Favipiravir (FP) and Ebselen (EB) belong to a broad range of antiviral drugs that have shown active potential as medications against many viruses. Employing molecular dynamics simulations and machine learning (ML) combined with van der Waals density functional theory, we have uncovered the binding characteristics of these two antiviral drugs on a phosphorene nanocarrier. Herein, by using four different machine learning models (i.e., Bagged Trees, Gaussian Process Regression (GPR), Support Vector Regression (SVR), and Regression Trees (RT)), the Hamiltonian and the interaction energy of antiviral molecules in a phosphorene monolayer are trained in an appropriate way. However, training efficient and accurate models for approximating the density functional theory (DFT) is the final step in using ML to aid in the design of new drugs. To improve the prediction accuracy, the Bayesian optimization approach has been employed to optimize the GPR, SVR, RT, and BT models. Results revealed that the GPR model obtained superior prediction performance with an R2 of 0.9649, indicating that it can explain 96.49% of the data's variability. Then, by means of DFT calculations, we examine the interaction characteristics and thermodynamic properties in a vacuum and a continuum solvent interface. These results illustrate that the hybrid drug is an enabled, functionalized 2D complex with vigorous thermostability. The change in Gibbs free energy at different surface charges and temperatures implies that the FP and EB molecules are allowed to adsorb from the gas phase onto the 2D monolayer at different pH conditions and high temperatures. The results reveal a valuable antiviral drug therapy loaded by 2D biomaterials that may possibly open a new way of auto-treating different diseases, such as SARS-CoV, in primary terms.

Exploiting machine learning models to identify novel Alzheimer's disease biomarkers and potential targets.

We still do not have an effective treatment for Alzheimer's disease (AD) despite it being the most common cause of dementia and impaired cognitive function. Thus, research endeavors are directed toward identifying AD biomarkers and targets. In this regard, we designed a computational method that exploits multiple hub gene ranking methods and feature selection methods with machine learning and deep learning to identify biomarkers and targets. First, we used three AD gene expression datasets to identify 1/ hub genes based on six ranking algorithms (Degree, Maximum Neighborhood Component (MNC), Maximal Clique Centrality (MCC), Betweenness Centrality (BC), Closeness Centrality, and Stress Centrality), 2/ gene subsets based on two feature selection methods (LASSO and Ridge). Then, we developed machine learning and deep learning models to determine the gene subset that best distinguishes AD samples from the healthy controls. This work shows that feature selection methods achieve better prediction performances than the hub gene sets. Beyond this, the five genes identified by both feature selection methods (LASSO and Ridge algorithms) achieved an AUC = 0.979. We further show that 70% of the upregulated hub genes (among the 28 overlapping hub genes) are AD targets based on a literature review and six miRNA (hsa-mir-16-5p, hsa-mir-34a-5p, hsa-mir-1-3p, hsa-mir-26a-5p, hsa-mir-93-5p, hsa-mir-155-5p) and one transcription factor, JUN, are associated with the upregulated hub genes. Furthermore, since 2020, four of the six microRNA were also shown to be potential AD targets. To our knowledge, this is the first work showing that such a small number of genes can distinguish AD samples from healthy controls with high accuracy and that overlapping upregulated hub genes can narrow the search space for potential novel targets.

Computational network analysis of host genetic risk variants of severe COVID-19.

BACKGROUND: Genome-wide association studies have identified numerous human host genetic risk variants that play a substantial role in the host immune response to SARS-CoV-2. Although these genetic risk variants significantly increase the severity of COVID-19, their influence on body systems is poorly understood. Therefore, we aim to interpret the biological mechanisms and pathways associated with the genetic risk factors and immune responses in severe COVID-19. We perform a deep analysis of previously identified risk variants and infer the hidden interactions between their molecular networks through disease mapping and the similarity of the molecular functions between constructed networks. RESULTS: We designed a four-stage computational workflow for systematic genetic analysis of the risk variants. We integrated the molecular profiles of the risk factors with associated diseases, then constructed protein-protein interaction networks. We identified 24 protein-protein interaction networks with 939 interactions derived from 109 filtered risk variants in 60 risk genes and 56 proteins. The majority of molecular functions, interactions and pathways are involved in immune responses; several interactions and pathways are related to the metabolic and cardiovascular systems, which could lead to multi-organ complications and dysfunction. CONCLUSIONS: This study highlights the importance of analyzing molecular interactions and pathways to understand the heterogeneous susceptibility of the host immune response to SARS-CoV-2. We propose new insights into pathogenicity analysis of infections by including genetic risk information as essential factors to predict future complications during and after infection. This approach may assist more precise clinical decisions and accurate treatment plans to reduce COVID-19 complications.

Transcriptome analysis of the phosphate starvation response sheds light on strigolactone biosynthesis in rice.

Phosphorus (P) is a major element required for plant growth and development. To cope with P shortage, plants activate local and long-distance signaling pathways, such as an increase in the production and exudation of strigolactones (SLs). The role of the latter in mitigating P deficiency is, however, still largely unknown. To shed light on this, we studied the transcriptional response to P starvation and replenishment in wild-type rice and a SL mutant, dwarf10 (d10), and upon exogenous application of the synthetic SL GR24. P starvation resulted in major transcriptional alterations, such as the upregulation of P TRANSPORTER, SYG1/PHO81/XPR1 (SPX) and VACUOLAR PHOSPHATE EFFLUX TRANSPORTER. Gene Ontology (GO) analysis of the genes induced by P starvation showed enrichment in phospholipid catabolic process and phosphatase activity. In d10, P deficiency induced upregulation of genes enriched for sesquiterpenoid production, secondary shoot formation and metabolic processes, including lactone biosynthesis. Furthermore, several genes induced by GR24 treatment shared the same GO terms with P starvation-induced genes, such as oxidation reduction, heme binding and oxidoreductase activity, hinting at the role that SLs play in the transcriptional reprogramming upon P starvation. Gene co-expression network analysis uncovered a METHYL TRANSFERASE that displayed co-regulation with known rice SL biosynthetic genes. Functional characterization showed that this gene encodes an enzyme catalyzing the conversion of carlactonoic acid to methyl carlactonoate. Our work provides a valuable resource to further studies on the response of crops to P deficiency and reveals a tool for the discovery of SL biosynthetic genes.

Computational Studies of Auto-Active van der Waals Interaction Molecules on Ultra-Thin Black-Phosphorus Film.

Using the van der Waals density functional theory, we studied the binding peculiarities of favipiravir (FP) and ebselen (EB) molecules on a monolayer of black phosphorene (BP). We systematically examined the interaction characteristics and thermodynamic properties in a vacuum and a continuum, solvent interface for active drug therapy. These results illustrate that the hybrid molecules are enabled functionalized two-dimensional (2D) complex systems with a vigorous thermostability. We demonstrate in this study that these molecules remain flat on the monolayer BP system and phosphorus atoms are intact. It is inferred that the hybrid FP+EB molecules show larger adsorption energy due to the van der Waals forces and planar electrostatic interactions. The changes in Gibbs free energy at different surface charge fluctuations and temperatures imply that the FP and EB are allowed to adsorb from the gas phase onto the 2D film at high temperatures. Thereby, the results unveiled beneficial inhibitor molecules on two dimensional BP nanocarriers, potentially introducing a modern strategy to enhance the development of advanced materials, biotechnology, and nanomedicine.

The high-dimensional space of human diseases built from diagnosis records and mapped to genetic loci.

Human diseases are traditionally studied as singular, independent entities, limiting researchers' capacity to view human illnesses as dependent states in a complex, homeostatic system. Here, using time-stamped clinical records of over 151 million unique Americans, we construct a disease representation as points in a continuous, high-dimensional space, where diseases with similar etiology and manifestations lie near one another. We use the UK Biobank cohort, with half a million participants, to perform a genome-wide association study of newly defined human quantitative traits reflecting individuals' health states, corresponding to patient positions in our disease space. We discover 116 genetic associations involving 108 genetic loci and then use ten disease constellations resulting from clustering analysis of diseases in the embedding space, as well as 30 common diseases, to demonstrate that these genetic associations can be used to robustly predict various morbidities.

Comparative genomic analysis of antibiotic resistance and virulence genes in Staphylococcus aureus isolates from patients and retail meat.

Introduction: Staphylococcus aureus is a significant human pathogen that poses a threat to public health due to its association with foodborne contamination and a variety of infections. The factors contributing to the pathogenicity of S. aureus include virulence, drug resistance, and toxin production, making it essential to monitor their prevalence and genetic profiles. This study investigated and compared the genomic characteristics of S. aureus isolates from retail meat and patients in Saudi Arabia. Methods: A total of 136 S. aureus isolates were obtained between October 2021 and June 2022:84 from patients and 53 from meat samples in Riyadh, Saudi Arabia. S. aureus isolates were identified using conventional methods and MALDI-TOF MS, and methicillin-resistant S. aureus (MRSA) was identified using VITEK2 and BD Phoenix systems. MRSA was confirmed phenotypically using chromogenic agar, and genotypically by detecting mecA. Genomic data were analyzed using BactopiaV2 pipeline, local BLAST, and MLST databases. Results: Antibiotic resistance genes were prevalent in both meat and patient S. aureus isolates, with high prevalence of tet38, blaZ, and fosB. Notably, all S. aureus isolates from patients carried multidrug-resistant (MDR) genes, and a high percentage of S. aureus isolates from meat also harbored MDR genes. Phenotypically, 43% of the S. aureus isolates from meat and 100% of the patients' isolates were MDR. Enterotoxin genes, including selX, sem, and sei, exhibited high compatibility between meat and patient S. aureus isolates. Virulence genes such as cap, hly/hla, sbi, and isd were found in all S. aureus isolates from both sources. Conclusion: Our study established a genetic connection between S. aureus isolates from meat and patients, showing shared antibiotic resistance and virulence genes. The presence of these genes in meat derived isolates underscores its role as a reservoir. Genomic relatedness also suggests potential transmission of resistance between different settings. These findings emphasize the necessity for a comprehensive approach to monitor and control S. aureus infections in both animals and humans.

Lactate-mediated neural plasticity genes emerged during the evolution of memory systems.

The ability to record experiences and learning is present to different degrees in several species; however, the complexity and diversity of memory processes are cognitive function features that differentiate humans from other species. Lactate has recently been discovered to act as a signaling molecule for neuronal plasticity linked to long-term memory. Because lactate is not only an energy substrate for neurons but also a signaling molecule for plasticity (Magistretti and Allaman in Nat Rev Neurosci 19:235-249, 2018. https://doi.org/10.1038/nrn.2018.19 ), it is of particular interest to understand how and when memory-related genes and lactate-mediated neural plasticity (LMNP) genes emerged and evolved in humans. To understand the evolutionary origin and processes of memory and LMNP genes, we first collected information on genes related to memory and LMNP from the literature and then conducted a comparative analysis of these genes. We found that the memory and LMNP genes have different origins, suggesting that these genes may have become established gradually in evolutionarily and functional terms and not at the same time. We also found that memory and LMNP systems have a similar evolutionary history, having been formed with the gradual participation of newly emerging genes throughout their evolution. We propose that the function of LMNP as a signaling process may be evolutionarily associated with memory systems through an unidentified system that is linked by 13 common genes between memory and LMNP gene sets. This study provides evolutionary insight into the possible relationship between memory and the LMNP systems that deepens our understanding of the evolution of memory systems.

Development and Validation of Reverse Transcriptase Loop-Mediated Isothermal Amplification (RT-LAMP) as a Simple and Rapid Diagnostic Tool for SARS-CoV-2 Detection.

Since the COVID-19 pandemic outbreak in the world, many countries have searched for quick diagnostic tools to detect the virus. There are many ways to design diagnostic assays; however, each may have its limitations. A quick, sensitive, specific, and simple approach is essential for highly rapidly transmitted infections, such as SARS-CoV-2. This study aimed to develop a rapid and cost-effective diagnostic tool using a one-step Reverse Transcriptase Loop-Mediated Isothermal Amplification (RT-LAMP) approach. The results were observed using the naked eye within 30-60 min using turbidity or colorimetric analysis. The sensitivity, specificity, and lowest limit of detection (LoD) for SARS-CoV-2 RNA against the RT-LAMP assay were assessed. This assay was also verified and validated against commercial quantitative RT-PCR used by health authorities in Saudi Arabia. Furthermore, a quick and direct sampling from the saliva, or buccal cavity, was applied after simple modification, using proteinase K and heating at 98 °C for 5 min to avoid routine RNA extraction. This rapid single-tube diagnostic tool detected COVID-19 with an accuracy rate of 95% for both genes (ORF1a and N) and an LoD for the ORF1a and N genes as 39 and 25 copies/reaction, respectively. It can be potentially used as a high-throughput national screening for different respiratory-based infections within the Middle East region, such as the MERS virus or major zoonotic pathogens such as Mycobacterium paratuberculosis and Brucella spp., particularly in remote and rural areas where lab equipment is limited.