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, and , 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.

Chromosome-scale pearl millet genomes reveal CLAMT1b as key determinant of strigolactone pattern and Striga susceptibility.

The yield of pearl millet, a resilient cereal crop crucial for African food security, is severely impacted by the root parasitic weed Striga hermonthica, which requires host-released hormones, called strigolactones (SLs), for seed germination. Herein, we identify four SLs present in the Striga-susceptible line SOSAT-C88-P10 (P10) but absent in the resistant 29Aw (Aw). We generate chromosome-scale genome assemblies, including four gapless chromosomes for each line.

Genome-wide expression analysis of vegetative organs during developmental and herbicide-induced whole plant senescence in Arabidopsis thaliana.

Background: Whole plant senescence represents the final stage in the life cycle of annual plants, characterized by the decomposition of aging organs and transfer of nutrients to seeds, thereby ensuring the survival of next generation. However, the transcriptomic profile of vegetative organs during this death process remains to be fully elucidated, especially regarding the distinctions between natural programmed death and artificial sudden death induced by herbicide.

Results: Differential genes expression analysis using RNA-seq in leaves and roots of Arabidopsis thaliana revealed that natural senescence commenced in leaves at 45-52 days after planting, followed by roots initiated at 52-60 days.

Exploring Antiviral Drugs on Monolayer Black Phosphorene: Atomistic Theory and Explainable Machine Learning-Assisted Platform.

Favipiravir (FP) and ebselen (EB) belong to a diverse class of antiviral drugs known for their significant efficacy in treating various viral infections. Utilizing molecular dynamics (MD) simulations, machine learning, and van der Waals density functional theory, we accurately elucidate the binding properties of these antiviral drugs on a phosphorene single-layer. To further investigate these characteristics, this study employs four distinct machine learning models-Random Forest, Gradient Boosting, XGBoost, and CatBoost.

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.

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

Introduction: 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 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 isolates from retail meat and patients in Saudi Arabia.

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

In this study, we investigated both meat-derived and methicillin-resistant (MRSA), exploring their genetic relatedness to patient-derived MRSA isolates in Saudi Arabia. We collected 250 meat samples and identified 53 isolates, with 79% being methicillin-sensitive (MSSA) and 21% being MRSA. Moreover, we included 80 clinically confirmed patient-derived MRSA isolates.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Type 2 Diabetes Mellitus and its comorbidity, Alzheimer's disease: Identifying critical microRNA using machine learning.

MicroRNAs (miRNAs) are critical regulators of gene expression in healthy and diseased states, and numerous studies have established their tremendous potential as a tool for improving the diagnosis of Type 2 Diabetes Mellitus (T2D) and its comorbidities. In this regard, we computationally identify novel top-ranked hub miRNAs that might be involved in T2D. We accomplish this two strategies: 1) by ranking miRNAs based on the number of T2D differentially expressed genes (DEGs) they target, and 2) using only the common DEGs between T2D and its comorbidity, Alzheimer's disease (AD) to predict and rank miRNA.

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.