Utilizing genomic signatures to gain insights into the dynamics of SARS-CoV-2 through Machine and Deep Learning techniques.

The global spread of the SARS-CoV-2 pandemic, originating in Wuhan, China, has had profound consequences on both health and the economy. Traditional alignment-based phylogenetic tree methods for tracking epidemic dynamics demand substantial computational power due to the growing number of sequenced strains. Consequently, there is a pressing need for an alignment-free approach to characterize these strains and monitor the dynamics of various variants. In this work, we introduce a swift and straightforward tool named GenoSig, implemented in C++. The tool exploits the Di and Tri nucleotide frequency signatures to delineate the taxonomic lineages of SARS-CoV-2 by employing diverse machine learning (ML) and deep learning (DL) models. Our approach achieved a tenfold cross-validation accuracy of 87.88% (± 0.013) for DL and 86.37% (± 0.0009) for Random Forest (RF) model, surpassing the performance of other ML models. Validation using an additional unexposed dataset yielded comparable results. Despite variations in architectures between DL and RF, it was observed that later clades, specifically GRA, GRY, and GK, exhibited superior performance compared to earlier clades G and GH. As for the continental origin of the virus, both DL and RF models exhibited lower performance than in predicting clades. However, both models demonstrated relatively higher accuracy for Europe, North America, and South America compared to other continents, with DL outperforming RF. Both models consistently demonstrated a preference for cytosine and guanine over adenine and thymine in both clade and continental analyses, in both Di and Tri nucleotide frequencies signatures. Our findings suggest that GenoSig provides a straightforward approach to address taxonomic, epidemiological, and biological inquiries, utilizing a reductive method applicable not only to SARS-CoV-2 but also to similar research questions in an alignment-free context.

Identification of Differentially Expressed Genes in Human Colorectal Cancer Using RNASeq Data Validated on the Molecular Level with Real-Time PCR.

Colorectal cancer (CRC) is a prevalent cancer with high morbidity and mortality rates worldwide. Late diagnosis is a significant contributor to low survival rates in a minority of cases. The study aimed to perform a robust pipeline using integrated bioinformatics tools that will enable us to identify potential diagnostic and prognostic biomarkers for early detection of CRC by exploring differentially expressed genes (DEGs). In addition to, testing the capability of replacing chemotherapy with plant extract in CRC treatment by validating it using real-time PCR. RNA-seq data from cancerous and adjacent normal tissues were pre-processed and analyzed using various tools such as FastQC, Kallisto, DESeq@ R package, g:Profiler, GNEMANIA-CytoScape and CytoHubba, resulting in the identification of 1641 DEGs enriched in various signaling routes. MMP7, TCF21, and VEGFD were found to be promising diagnostic biomarkers for CRC. An in vitro experiment was conducted to examine the potential anticancer properties of 5-fluorouracile, Withania somnifera extract, and their combination. The extract was found to exhibit a positive trend in gene expression and potential therapeutic value by targeting the three genes; however, further trials are required to regulate the methylation promoter. Molecular docking tests supported the findings by revealing a stable ligand-receptor complex. In conclusion, the study's analysis workflow is precise and robust in identifying DEGs in CRC that may serve as biomarkers for diagnosis and treatment. Additionally, the identified DEGs can be used in future research with larger sample sizes to analyze CRC survival.

A comprehensive analysis of genetic risk for metabolic syndrome in the Egyptian population via allele frequency investigation and Missense3D predictions.

Diabetes mellitus (DM) represents a major health problem in Egypt and worldwide, with increasing numbers of patients with prediabetes every year. Numerous factors, such as obesity, hyperlipidemia, and hypertension, which have recently become serious concerns, affect the complex pathophysiology of diabetes. These metabolic syndrome diseases are highly linked to genetic variability that drives certain populations, such as Egypt, to be more susceptible to developing DM. Here we conduct a comprehensive analysis to pinpoint the similarities and uniqueness among the Egyptian genome reference and the 1000-genome subpopulations (Europeans, Ad-Mixed Americans, South Asians, East Asians, and Africans), aiming at defining the potential genetic risk of metabolic syndromes. Selected approaches incorporated the analysis of the allele frequency of the different populations' variations, supported by genotypes' principal component analysis. Results show that the Egyptian's reference metabolic genes were clustered together with the Europeans', Ad-Mixed Americans', and South-Asians'. Additionally, 8563 variants were uniquely identified in the Egyptian cohort, from those, two were predicted to cause structural damage, namely, CDKAL1: 6_21065070 (A > T) and PPARG: 3_12351660 (C > T) utilizing the Missense3D database. The former is a protein coding gene associated with Type 2 DM while the latter is a key regulator of adipocyte differentiation and glucose homeostasis. Both variants were detected heterozygous in two different Egyptian individuals from overall 110 sample. This analysis sheds light on the unique genetic traits of the Egyptian population that play a role in the DM high prevalence in Egypt. The proposed analysis pipeline -available through GitHub- could be used to conduct similar analysis for other diseases across populations.

Using CADD tools to inhibit the overexpressed genes FAP, FN1, and MMP1 by repurposing ginsenoside C and Rg1 as a treatment for oral cancer.

Oral cancer is one of the most common cancer types. Many factors can express certain genes that cause the proliferation of oral tissues. Overexpressed genes were detected in oral cancer patients; three were highly impacted. FAP, FN1, and MMP1 were the targeted genes that showed inhibition results in silico by ginsenoside C and Rg1. Approved drugs were retrieved from the DrugBank database. The docking scores show an excellent interaction between the ligands and the targeted macromolecules. Further molecular dynamics simulations showed the binding stability of the proposed natural products. This work recommends repurposing ginsenoside C and Rg1 as potential binders for the selected targets and endorses future experimental validation for the treatment of oral cancer.

In silico SNP prediction of selected protein orthologues in insect models for Alzheimer's, Parkinson's, and Huntington's diseases.

Alzheimer's, Parkinson's, and Huntington's are the most common neurodegenerative diseases that are incurable and affect the elderly population. Discovery of effective treatments for these diseases is often difficult, expensive, and serendipitous. Previous comparative studies on different model organisms have revealed that most animals share similar cellular and molecular characteristics. The meta-SNP tool includes four different integrated tools (SIFT, PANTHER, SNAP, and PhD-SNP) was used to identify non synonymous single nucleotide polymorphism (nsSNPs). Prediction of nsSNPs was conducted on three representative proteins for Alzheimer's, Parkinson's, and Huntington's diseases; APPl in Drosophila melanogaster, LRRK1 in Aedes aegypti, and VCPl in Tribolium castaneum. With the possibility of using insect models to investigate neurodegenerative diseases. We conclude from the protein comparative analysis between different insect models and nsSNP analyses that D. melanogaster is the best model for Alzheimer's representing five nsSNPs of the 21 suggested mutations in the APPl protein. Aedes aegypti is the best model for Parkinson's representing three nsSNPs in the LRRK1 protein. Tribolium castaneum is the best model for Huntington's disease representing 13 SNPs of 37 suggested mutations in the VCPl protein. This study aimed to improve human neural health by identifying the best insect to model Alzheimer's, Parkinson's, and Huntington's.

Interactive Web-Based Services for Metagenomic Data Analysis and Comparisons.

Recently, sequencing technologies have become readily available, and scientists are more motivated to conduct metagenomic research to unveil the potential of a myriad of ecosystems and biomes. Metagenomics studies the composition and functions of microbial communities and paves the way to multiple applications in medicine, industry, and ecology. Nonetheless, the immense amount of sequencing data of metagenomics research and the few user-friendly analysis tools and pipelines carry a new challenge to the data analysis.Web-based bioinformatics tools are now being developed to facilitate the analysis of complex metagenomic data without prior knowledge of any programming languages or special installation. Specialized web tools help answer researchers' main questions on the taxonomic classification, functional capabilities, discrepancies between two ecosystems, and the probable functional correlations between the members of a specific microbial community. With an Internet connection and a few clicks, researchers can conveniently and efficiently analyze the metagenomic datasets, summarize results, and visualize key information on the composition and the functional potential of metagenomic samples under study. This chapter provides a simple guide to a few of the fundamental web-based services used for metagenomic data analyses, such as BV-BRC, RDP, MG-RAST, MicrobiomeAnalyst, METAGENassist, and MGnify.

Tracking Antibiotic Resistance from the Environment to Human Health.

Antimicrobial resistance (AMR) is one of the threats to our world according to the World Health Organization (WHO). Resistance is an evolutionary dynamic process where host-associated microbes have to adapt to their stressful environments. AMR could be classified according to the mechanism of resistance or the biome where resistance takes place. Antibiotics are one of the stresses that lead to resistance through antibiotic resistance genes (ARGs). The resistome could be defined as the collection of all ARGs in an organism's genome or metagenome. Currently, there is a growing body of evidence supporting that the environment is the largest source of ARGs, but to what extent the environment does contribute to the antimicrobial resistance evolution is a matter of investigation. Monitoring the ARGs transfer route from the environment to humans and vice versa is a nature-to-nature feedback loop where you cannot set an accurate starting point of the evolutionary event. Thus, tracking resistome evolution and transfer to and from different biomes is crucial for the surveillance and prediction of the next resistance outbreak.Herein, we review the overlap between clinical and environmental resistomes and the available databases and computational analysis tools for resistome analysis through ARGs detection and characterization in bacterial genomes and metagenomes. Till this moment, there is no tool that can predict the resistance evolution and dynamics in a distinct biome. But, hopefully, by understanding the complicated relationship between the environmental and clinical resistome, we could develop tools that track the feedback loop from nature to nature in terms of evolution, mobilization, and transfer of ARGs.

Comprehensive Guideline for Microbiome Analysis Using R.

The need for a comprehensive consolidated guide for R packages and tools that are used in microbiome data analysis is significant; thus, we aim to provide a detailed step-by-step dissection of the most used R packages and tools in the field of microbiome data integration and analysis. The guideline aims to be a user-friendly simplification and tutorial on five main packages, namely phyloseq, MegaR, DADA2, Metacoder, and microbiomeExplorer due to their high efficiency and benefit in microbiome data analysis.

In-Silico targeting of SARS-CoV-2 NSP6 for drug and natural products repurposing.

Non-Structural Protein 6 (NSP6) has a protecting role for SARS-CoV-2 replication by inhibiting the expansion of autophagosomes inside the cell. NSP6 is involved in the endoplasmic reticulum stress response by binding to Sigma receptor 1 (SR1). Nevertheless, NSP6 crystal structure is not solved yet. Therefore, NSP6 is considered a challenging target in Structure-Based Drug Discovery. Herein, we utilized the high quality NSP6 model built by AlphaFold in our study. Targeting a putative NSP6 binding site is believed to inhibit the SR1-NSP6 protein-protein interactions. Three databases were virtually screened, namely FDA-approved drugs (DrugBank), Northern African Natural Products Database (NANPDB) and South African Natural Compounds Database (SANCDB) with a total of 8158 compounds. Further validation for 9 candidates via molecular dynamics simulations for 100 ns recommended potential binders to the NSP6 binding site. The proposed candidates are recommended for biological testing to cease the rapidly growing pandemic.

Clay chips and beads capture in situ barley root microbiota and facilitate in vitro long-term preservation of microbial strains.

Capturing the diverse microbiota from healthy and/or stress resilient plants for further preservation and transfer to unproductive and pathogen overloaded soils, might be a tool to restore disturbed plant-microbe interactions. Here, we introduce Aswan Pink Clay as a low-cost technology for capturing and storing the living root microbiota. Clay chips were incorporated into the growth milieu of barley plants and developed under gnotobiotic conditions, to capture and host the rhizospheric microbiota. Afterward, it was tested by both a culture-independent (16S rRNA gene metabarcoding) and -dependent approach. Both methods revealed no significant differences between roots and adjacent clay chips in regard total abundance and structure of the present microbiota. Clay shaped as beads adequately supported the long-term preservation of viable pure isolates of typical rhizospheric microbes, i.e. Bacillus circulans, Klebsiella oxytoca, Sinorhizobium meliloti, and Saccharomyces sp., up to 11 months stored at -20°C, 4°C, and ambient temperature. The used clay chips and beads have the capacity to capture the root microbiota and to long-term preserve pure isolates. Hence, the developed approach is qualified to build on it a comprehensive strategy to transfer and store complex and living environmental microbiota of rhizosphere toward biotechnological application in sustainable plant production and environmental rehabilitation.

Comparative 16S Metabarcoding of Nile Tilapia Gut Microbiota from the Northern Lakes of Egypt.

Nile tilapia, Oreochromis niloticus, is the principal fish bred in Egypt. A pilot study was designed to analyze the bacterial composition of the Nile tilapia fish guts from two saltwater lakes in Northern Egypt. Fish samples were obtained from two Delta lakes: Manzala (ML) and Borollus (BL). DNA was extracted, and the bacterial communities in the stomach content were classified (down to the species level) using the 16S rRNA-based analysis. From the two metagenomics libraries in this study, 1,426,740 reads of the amplicon sequence corresponding to 508 total taxonomic operational units were recorded. The most prevalent bacterial phyla were Proteobacteria, Firmicutes, Actinobacteria, and Synergistetes in all samples. Some of the strains identified belong to classes of pathogenic zoonotic bacteria. A notable difference was observed between gut bacteria of Nile tilapia fish obtained from BL and ML. There is a remarkable indication that Nile tilapia fish living in BL is heavily burdened with pathogenic microbes most remarkably those involved with methylation of mercury and its accumulation in fish organs. These pathogenic microbes could have clinical implications and correlated with many diseases. This result was also consistent with the metagenomic data's functional prediction that indicated that Nile tilapia species harboring these two Egyptian northern lakes may be exposed to numerous anthropogenic pollutants. The findings show that the host environment has a significant impact on the composition of its microbiota. The first step towards exploring the better management of this profit-making fish is recognizing the structure of the microbiome.

Positive selection as a key player for SARS-CoV-2 pathogenicity: Insights into ORF1ab, S and E genes.

The human ß-coronavirus SARS-CoV-2 epidemic started in late December 2019 in Wuhan, China. It causes Covid-19 disease which has become pandemic. Each of the five-known human ß-coronaviruses has four major structural proteins (E, M, N and S) and 16 non-structural proteins encoded by ORF1a and ORF1b together (ORF1ab) that are involved in virus pathogenicity and infectivity. Here, we performed detailed positive selection analyses for those six genes among the four previously known human ß-coronaviruses and within 38 SARS-CoV-2 genomes to assess signatures of adaptive evolution using maximum likelihood approaches. Our results suggest that three genes (E, S and ORF1ab genes) are under strong signatures of positive selection among human ß-coronavirus, influencing codons that are located in functional important protein domains. The E protein-coding gene showed signatures of positive selection in two sites, Asp 66 and Ser 68, located inside a putative transmembrane α-helical domain C-terminal part, which is preferentially composed by hydrophilic residues. Such Asp and Ser sites substitutions (hydrophilic residues) increase the stability of the transmembrane domain in SARS-CoV-2. Moreover, substitutions in the spike (S) protein S1 N-terminal domain have been found, all of them were located on the S protein surface, suggesting their importance in viral transmissibility and survival. Furthermore, evidence of strong positive selection was detected in three of the SARS-CoV-2 nonstructural proteins (NSP1, NSP3, NSP16), which are encoded by ORF1ab and play vital roles in suppressing host translation machinery, viral replication and transcription and inhibiting the host immune response. These results are insightful to assess the role of positive selection in the SARS-CoV-2 encoded proteins, which will allow to better understand the virulent pathogenicity of the virus and potentially identifying targets for drug or vaccine strategy design.

Author Correction: Genotypic characterization of multiple drug resistant Escherichia coli isolates from a pediatric cancer hospital in Egypt.

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

Genotypic characterization of multiple drug resistant Escherichia coli isolates from a pediatric cancer hospital in Egypt.

Infection with multiple drug resistant (MDR) Escherichia coli poses a life threat to immunocompromised pediatric cancer patients. Our aim is to genotypically characterize the plasmids harbored in MDR E. coli isolates recovered from bacteremic patients of Children's Cancer Hospital in Egypt 57357 (CCHE 57357). In this study, 21 carbapenem-resistant E. coli (CRE) isolates were selected that exhibit Quinolones and Aminoglycosides resistance. Plasmid shot-gun sequencing was performed using Illumina next- generation sequencing platform. Isolates demonstrated resistant to all beta-lactams, carbapenems, aminoglycosides and quinolones. Of the 32 antimicrobial resistant genes identified that exceeded the analysis cutoff coverage, the highest represented genes were aph(6)-Id, sul2, aph(3″)-Ib, aph(3')-Ia, sul1, dfrA12, TEM-220, NDM-11. Isolates employed a wide array of resistance mechanisms including antibiotic efflux, antibiotic inactivation, antibiotic target replacements and antibiotic target alteration. Sequenced isolates displayed diverse insertion sequences, including IS26, suggesting dynamic reshuffling of the harbored plasmids. Most isolates carried plasmids originating from other bacterial species suggesting a possible horizontal gene transfer. Only two isolates showed virulence factors with iroA gene cluster which was found in only one of them. Outside the realms of nosocomial infections among patients in hospitals, our results indicate a transfer of resistant genes and plasmids across different organisms.

Enriched Environmental Conditions Modify the Gut Microbiome Composition and Fecal Markers of Inflammation in Parkinson's Disease.

Recent findings suggest an implication of the gut microbiome in Parkinson's disease (PD) patients. PD onset and progression has also been linked with various environmental factors such as physical activity, exposure to pesticides, head injury, nicotine, and dietary factors. In this study, we used a mouse model, overexpressing the complete human SNCA gene (SNCA-TG mice) modeling familial and sporadic forms of PD to study whether environmental conditions such as standard vs. enriched environment changes the gut microbiome and influences disease progression. We performed 16S rRNA DNA sequencing on fecal samples for microbiome analysis and studied fecal inflammatory calprotectin from the colon of control and SNCA-TG mice kept under standard environment (SE) and enriched environment (EE) conditions. The overall composition of the gut microbiota was not changed in SNCA-TG mice compared with WT in EE with respect to SE. However, individual gut bacteria at genus level such as Lactobacillus sp. was a significant changed in the SNCA-TG mice. EE significantly reduced colon fecal inflammatory calprotectin protein in WT and SNCA-TG EE compared to SE. Moreover, EE reduces the pro-inflammatory cytokines in the feces and inflammation inducing genes in the colon. Our data suggest that an enriched social environment has a positive effect on the induction of SNCA mediated inflammation in the intestine and by modulating anti-inflammatory gut bacteria.

Human Salivary Amylase Gene Copy Number Impacts Oral and Gut Microbiomes.

Host genetic variation influences microbiome composition. While studies have focused on associations between the gut microbiome and specific alleles, gene copy number (CN) also varies. We relate microbiome diversity to CN variation of the AMY1 locus, which encodes salivary amylase, facilitating starch digestion. After imputing AMY1-CN for ∼1,000 subjects, we identified taxa differentiating fecal microbiomes of high and low AMY1-CN hosts. In a month-long diet intervention study, we show that diet standardization drove gut microbiome convergence, and AMY1-CN correlated with oral and gut microbiome composition and function. The microbiomes of low-AMY1-CN subjects had enhanced capacity to break down complex carbohydrates. High-AMY1-CN subjects had higher levels of salivary Porphyromonas; their gut microbiota had increased abundance of resistant starch-degrading microbes, produced higher levels of short-chain fatty acids, and drove higher adiposity when transferred to germ-free mice. This study establishes AMY1-CN as a genetic factor associated with microbiome composition and function.

Soil biochar amendment affects the diversity of nosZ transcripts: Implications for N2O formation.

Microbial nitrogen transformation processes such as denitrification represent major sources of the potent greenhouse gas nitrous oxide (N2O). Soil biochar amendment has been shown to significantly decrease N2O emissions in various soils. However, the effect of biochar on the structure and function of microbial communities that actively perform nitrogen redox transformations has not been studied in detail yet. To analyse the community composition of actively denitrifying and N2O-reducing microbial communities, we collected RNA samples at different time points from a soil microcosm experiment conducted under denitrifying conditions and performed Illumina amplicon sequencing targeting nirK, typical nosZ and atypical nosZ mRNA transcripts. Within 10 days, biochar significantly increased the diversity of nirK and typical nosZ transcripts and resulted in taxonomic shifts among the typical nosZ-expressing microbial community. Furthermore, biochar addition led to a significant increase in transcript production among microbial species that are specialized on direct N2O reduction from the environment. Our results point towards a potential coupling of biochar-induced N2O emission reduction and an increase in microbial N2O reduction activity among specific groups of typical and atypical N2O reducers. However, experiments with other soils and biochars will be required to verify the transferability of these findings to other soil-biochar systems.

A simple statistical test of taxonomic or functional homogeneity using replicated microbiome sequencing samples.

One important question in microbiome analysis is how to assess the homogeneity of the microbial composition in a given environment, with respect to a given analysis method. Do different microbial samples taken from the same environment follow the same taxonomic distribution of organisms, or the same distribution of functions? Here we provide a non-parametric statistical "triangulation test" to address this type of question. The test requires that multiple replicates are available for each of the biological samples, and it is based on three-way computational comparisons of samples. To illustrate the application of the test, we collected three biological samples taken from different locations in one piece of human stool, each represented by three replicates, and analyzed them using MEGAN. (Despite its name, the triangulation test does not require that the number of biological samples or replicates be three.) The triangulation test rejects the null hypothesis that the three biological samples exhibit the same distribution of taxa or function (error probability ≤0.05), indicating that the microbial composition of the investigated human stool is not homogenous on a macroscopic scale, suggesting that pooling material from multiple locations is a reasonable practice. We provide an implementation of the test in our open source program MEGAN Community Edition.

A metagenomic-based survey of microbial (de)halogenation potential in a German forest soil.

In soils halogens (fluorine, chlorine, bromine, iodine) are cycled through the transformation of inorganic halides into organohalogen compounds and vice versa. There is evidence that these reactions are microbially driven but the key enzymes and groups of microorganisms involved are largely unknown. Our aim was to uncover the diversity, abundance and distribution of genes encoding for halogenating and dehalogenating enzymes in a German forest soil by shotgun metagenomic sequencing. Metagenomic libraries of three soil horizons revealed the presence of genera known to be involved in halogenation and dehalogenation processes such as Bradyrhizobium or Pseudomonas. We detected a so far unknown diversity of genes encoding for (de)halogenating enzymes in the soil metagenome including specific and unspecific halogenases as well as metabolic and cometabolic dehalogenases. Genes for non-heme, no-metal chloroperoxidases and haloalkane dehalogenases were the most abundant halogenase and dehalogenase genes, respectively. The high diversity and abundance of (de)halogenating enzymes suggests a strong microbial contribution to natural halogen cycling. This was also confirmed in microcosm experiments in which we quantified the biotic formation of chloroform and bromoform. Knowledge on microorganisms and genes that catalyze (de)halogenation reactions is critical because they are highly relevant to industrial biotechnologies and bioremediation applications.

MEGAN Community Edition - Interactive Exploration and Analysis of Large-Scale Microbiome Sequencing Data.

There is increasing interest in employing shotgun sequencing, rather than amplicon sequencing, to analyze microbiome samples. Typical projects may involve hundreds of samples and billions of sequencing reads. The comparison of such samples against a protein reference database generates billions of alignments and the analysis of such data is computationally challenging. To address this, we have substantially rewritten and extended our widely-used microbiome analysis tool MEGAN so as to facilitate the interactive analysis of the taxonomic and functional content of very large microbiome datasets. Other new features include a functional classifier called InterPro2GO, gene-centric read assembly, principal coordinate analysis of taxonomy and function, and support for metadata. The new program is called MEGAN Community Edition (CE) and is open source. By integrating MEGAN CE with our high-throughput DNA-to-protein alignment tool DIAMOND and by providing a new program MeganServer that allows access to metagenome analysis files hosted on a server, we provide a straightforward, yet powerful and complete pipeline for the analysis of metagenome shotgun sequences. We illustrate how to perform a full-scale computational analysis of a metagenomic sequencing project, involving 12 samples and 800 million reads, in less than three days on a single server. All source code is available here: https://github.com/danielhuson/megan-ce.