Custom Database Research Articles

Construction of Protein Sequence Databases for Metaproteomics: A Review of the Current Tools and Databases.

In metaproteomics studies, constructing a reference protein sequence database that is both comprehensive and not overly large is critical for the peptide identification step. Therefore, the availability of well-curated reference databases and tools for custom database construction is essential to enhance the performance of metaproteomics analyses. In this review, we first provide an overview of metaproteomics by presenting a concise historical background, outlining a typical experimental and bioinformatics workflow, emphasizing the crucial step of constructing a protein sequence database for metaproteomics. We then delve into the current tools available for building such databases, highlighting their individual approaches, utility, and advantages and limitations. Next, we examine existing protein sequence databases, detailing their scope and relevance in metaproteomics research. Then, we provide practical recommendations for constructing protein sequence databases for metaproteomics, along with an overview of the current challenges in this area. We conclude with a discussion of anticipated advancements, emerging trends, and future directions in the construction of protein sequence databases for metaproteomics.

TransAnnot—a fast transcriptome annotation pipeline

Abstract Summary The annotation of deeply sequenced, de novo assembled transcriptomes continues to be a challenge as some of the state-of-the-art tools are slow, difficult to install, and hard to use. We have tackled these issues with TransAnnot, a fast, automated transcriptome annotation pipeline that is easy to install and use. Leveraging the fast sequence searches provided by the MMseqs2 suite, TransAnnot offers one-step annotation of homologs from Swiss-Prot, gene ontology terms and orthogroups from eggNOG, and functional domains from Pfam. Users also have the option to annotate against custom databases. TransAnnot accepts sequencing reads (short and long), nucleotide sequences, or amino acid sequences as input for annotation. When benchmarked with test data sets of amino acid sequences, TransAnnot was 333, 284, and 18 times faster than comparable tools such as EnTAP, Trinotate and eggNOG-mapper respectively. Availability and implementation TransAnnot is free to use, open sourced under GPLv3, and is implemented in C ++ and Bash. Source code, documentation, and pre-compiled binaries are available at https://github.com/soedinglab/transannot. TransAnnot is also available via bioconda (https://anaconda.org/bioconda/transannot). Supplementary information Supplementary data are available at Bioinformatics online.

PSIX-18 Multiomic analysis to identify host and microbiome contributions to digestibility in beef cattle

Abstract This study evaluated beef heifers selected for high (efficient) or low (inefficient) digestible fiber intake (DFI). Initial analysis showed that high DFI animals had reduced methane production versus low-DFI under a high forage diet. Using the same cohort of animals maintained on a further 4 diets of varying forage:concentrate ratios, we employed multi-kingdom amplicon sequencing and metagenome shotgun sequencing of rumen digesta and feces alongside RNA sequencing of rumen epimural samples to evaluate the compositional and functional interplay between different microbial groups, and their relationship with host gene expression in cattle divergent for DFI. Samples were collected from 16 cattle during 2 metabolism trials, comprising 5 diets. Amplicon sequencing analysis was conducted using QIIME2; 16S rRNA and 18S rRNA reads were analyzed using the SILVA database, while LSU (fungal) sequences were analyzed using a custom D1/D2 database. Additional analysis of archaeal 16S rRNA sequences was conducted using the Rumen and Intestinal Methanogens (RIM) database. Metagenome shotgun reads underwent a two-pass classification with Kraken2 using a database of prokaryotic genomes derived from the GTDB taxonomy, with the unclassified output undergoing classification using a custom database containing all NCBI protozoa, fungi, and phage genomes, enriched with selected rumen-specific ciliate and fungal genomes. Downstream analysis of taxonomic data from all microbiome work was conducted in R, and differentially abundant taxa were identified using ANCOM-BC and Aldex2. Functional analysis of metagenome contigs using the CAZY database implemented in dbCAN3 is ongoing. RNA-seq data were analyzed using the ARS-UCD reference genome, with identification of DE genes conducted using DeSeq2. Preliminary results indicate no major effect of DFI ranking on host gene expression, bacterial 16S rRNA, or metagenome compositional profiles. Several bacterial genera were differentially abundant between digestibility groups (P < 0.05), but these were all minor (< 0.01%) members of the microbiome. Fungal and methanogen communities different significantly (P < 0.05) according to DFI group, with efficient (high DFI) containing and more diverse communities under high-grain diet (P < 0.05). The same difference showed a tendency toward significance for the 18S rRNA protozoa data (P < 0.1). These preliminary data indicate that the microbial factors underpinning divergence in efficiency measured by DFI vary according to diet and may be more prominent in the non-bacterial fraction of the microbiome. Ongoing functional analysis of metagenome data as well as integration of multiomic data will provide deeper insight into these relationships and how they contribute to feed digestibility and efficiency in cattle.

Quantifying Treatments as Usual and with Technologies in Neurorehabilitation of Individuals with Spinal Cord Injury.

Several technologies have been introduced into neurorehabilitation programs to enhance traditional treatment of individuals with Spinal Cord Injury (SCI). Their effectiveness has been widely investigated, but their adoption has not been properly quantified. The aim of this study is to assess the distribution of conventional (Treatment As Usual-TAU) and technology-aided (Treatment With Technologies-TWT) treatments conveniently grouped based on different therapeutic goals in a selected SCI unit. Data from 104 individuals collected in 29 months were collected in a custom database and categorized according to both the conventional American Impairment Scale classification and a newly developed Multifactor (MF) clustering approach that considers additional sources of information (the lesion level, the level of independence in the activities of daily living, and the hospitalization duration). Results indicated an average technology adoption of about 30%. Moreover, the MF clusters were less overlapped, and the differences in TWT adoption were more pronounced than in AIS-based clustering. MF clustering was capable of grouping individuals based both on neurological features and functional abilities. In particular, individuals with motor complete injuries were grouped together, whereas individuals with sensorimotor incomplete SCI were collected separately based on the lesion level. As regards TWT adoption, we found that in the case of motor complete SCI, TWT for muscle tone control and modulation was mainly selected (about 90% of TWT), while the other types of TWT were seldom adopted. Even for individuals with incomplete SCI, the most frequent rehabilitation goal was muscle tone modulation (about 75% of TWT), regardless of the AIS level, and technologies to improve walking ability (about 12% of TWT) and balance control (about 10% of TWT) were mainly used for individuals with thoracic or lumbar lesions. Analyzing TAU distribution, we found that the highest adoption of muscle tone modulation strategies was reported in the case of individuals with motor complete SCI (about 42% of TAU), that is, in cases when almost no gait training was pursued (about 1% of TAU). In the case of cervical motor incomplete SCI, compared to thoracic and lumbar incomplete SCI, there was a greater focus on muscle tone control and force recruitment in addition to walking training (38% and 14% of TAU, respectively) than on balance training. Overall, the MF clustering provided more insights than the traditional AIS-based classification, highlighting differences in TWT adoption. These findings suggest that a wider overview that considers both neurological and functional characteristics of individuals after SCI based on a multifactor analysis could enhance the personalization of neurorehabilitation strategies.

MRGM: an enhanced catalog of mouse gut microbial genomes substantially broadening taxonomic and functional landscapes.

Mouse gut microbiome research is pivotal for understanding the human gut microbiome, providing insights into disease modeling, host-microbe interactions, and the dietary influence on the gut microbiome. To enhance the translational value of mouse gut microbiome studies, we need detailed and high-quality catalogs of mouse gut microbial genomes. We introduce the Mouse Reference Gut Microbiome (MRGM), a comprehensive catalog with 42,245 non-redundant mouse gut bacterial genomes across 1,524 species. MRGM marks a 40% increase in the known taxonomic diversity of mouse gut microbes, capturing previously underrepresented lineages through refined genome quality assessment techniques. MRGM not only broadens the taxonomic landscape but also enriches the functional landscape of the mouse gut microbiome. Using deep learning, we have elevated the Gene Ontology annotation rate for mouse gut microbial proteins from 3.2% with orthology to 60%, marking an over 18-fold increase. MRGM supports both DNA- and marker-based taxonomic profiling by providing custom databases, surpassing previous catalogs in performance. Finally, taxonomic and functional comparisons between human and mouse gut microbiota reveal diet-driven divergences in their taxonomic composition and functional enrichment. Overall, our study highlights the value of high-quality microbial genome catalogs in advancing our understanding of the co-evolution between gut microbes and their host.

MRGM: An enhanced catalog of mouse gut microbial genomes substantially broadening taxonomic and functional landscapes.

Mouse gut microbiome research is pivotal for understanding the human gut microbiome, providing insights into disease modeling, host-microbe interactions, and the dietary influence on the gut microbiome. To enhance the translational value of mouse gut microbiome studies, we need detailed and high-quality catalogs of mouse gut microbial genomes. We introduce the Mouse Reference Gut Microbiome (MRGM), a comprehensive catalog with 42,245 non-redundant mouse gut bacterial genomes across 1,524 species. MRGM marks a 40% increase in the known taxonomic diversity of mouse gut microbes, capturing previously underrepresented lineages through refined genome quality assessment techniques. MRGM not only broadens the taxonomic landscape but also enriches the functional landscape of the mouse gut microbiome. Using deep learning, we have elevated the Gene Ontology annotation rate for mouse gut microbial proteins from 3.2% with orthology to 60%, marking an over 18-fold increase. MRGM supports both DNA- and marker-based taxonomic profiling by providing custom databases, surpassing previous catalogs in performance. Finally, taxonomic and functional comparisons between human and mouse gut microbiota reveal diet-driven divergences in their taxonomic composition and functional enrichment. Overall, our study highlights the value of high-quality microbial genome catalogs in advancing our understanding of the co-evolution between gut microbes and their host.

OCTOPUS: Disk-based, Multiplatform, Mobile-friendly Metagenomics Classifier.

Portable genomic sequencers such as Oxford Nanopore's MinION enable real-time applications in clinical and environmental health. However, there is a bottleneck in the downstream analytics when bioinformatics pipelines are unavailable, e.g., when cloud processing is unreachable due to absence of Internet connection, or only low-end computing devices can be carried on site. Here we present a platform-friendly software for portable metagenomic analysis of Nanopore data, the Oligomer-based Classifier of Taxonomic Operational and Pan-genome Units via Singletons (OCTOPUS). OCTOPUS is written in Java, reimplements several features of the popular Kraken2 and KrakenUniq software, with original components for improving metagenomics classification on incomplete/sampled reference databases, making it ideal for running on smartphones or tablets. OCTOPUS obtains sensitivity and precision comparable to Kraken2, while dramatically decreasing (4- to 16-fold) the false positive rate, and yielding high correlation on real-word data. OCTOPUS is available along with customized databases at https://github.com/DataIntellSystLab/OCTOPUS and https://github.com/Ruiz-HCI-Lab/OctopusMobile.

A comparative study of predicting high entropy alloy phase fractions with traditional machine learning and deep neural networks

Predicting phase stability in high entropy alloys (HEAs), such as phase fractions as functions of composition and temperature, is essential for understanding alloy properties and screening desirable materials. Traditional methods like CALPHAD are computationally intensive for exploring high-dimensional compositional spaces. To address such a challenge, this study explored and compared the effectiveness of random forests (RF) and deep neural networks (DNN) for accelerating materials discovery by building surrogate models of phase stability prediction. For interpolation scenarios (testing on the same order of system as trained), RF models generally produce smaller errors than DNN models. However, for extrapolation scenarios (training on lower-order systems and testing on higher order systems), DNNs generalize more effectively than traditional ML models. DNN demonstrate the potential to predict topologically relevant phase composition when data were missing, making it a powerful predictive tool in materials discovery frameworks. The study uses a CALPHAD dataset of 480 million data points generated from a custom database, available for further model development and benchmarking. Experiments show that DNN models are data-efficient, achieving similar performance with a fraction of the dataset. This work highlights the potential of DNNs in materials discovery, providing a powerful tool for predicting phase stability in HEAs, particularly within the Cr-Hf-Mo-Nb-Ta-Ti-V-W-Zr composition space.

Beav: a bacterial genome and mobile element annotation pipeline.

Annotation of genome features, such as the presence of genes and their predicted function, or larger loci encoding secretion systems or biosynthetic gene clusters, is necessary for understanding the functions encoded by an organism. Genomes can also host diverse mobile genetic elements, such as integrative and conjugative elements and/or phages, that are often not annotated by existing pipelines. These elements can horizontally mobilize genes encoding for virulence, antimicrobial resistance, or other adaptive functions and alter the phenotype of an organism. We developed a software pipeline, called Beav, that combines new and existing tools for the comprehensive annotation of these and other major features. Existing pipelines often misannotate loci important for virulence or mutualism in plant-associated bacteria. Beav includes custom databases and optional workflows for the improved annotation of plant-associated bacteria. Beav is designed to be easy to install and run, making comprehensive genome annotation broadly available to the research community.

A simple BLASTn-based approach generates novel insights into the regulation and biological function of type I toxin-antitoxins.

Bacterial chromosomal type I toxin-antitoxin systems consist of a small protein, typically under 60 amino acids, and a small RNA (sRNA) that represses toxin translation. These gene pairs have gained attention over the last decade for their contribution to antibiotic persistence and phage tolerance in bacteria. However, biological functions for many remain elusive as gene deletions often fail to produce an observable phenotype. For many pairs, it is still unknown when the toxin and/or antitoxin gene are natively expressed within the bacterium. We examined sequence conservation of three type I toxin-antitoxin systems, tisB/istR-1, shoB/ohsC, and zor/orz, in over 2,000 Escherichia coli strains, including pathogenic and commensal isolates. Using our custom database, we found that these gene pairs are widespread across E. coli and have expression potential via BLASTn. We identified an alternative, dominant sequence variant of TisB and confirmed that it is toxic upon overproduction. Additionally, analyses revealed a highly conserved sequence in the zorO mRNA untranslated region that is required for full toxicity. We further noted that over 30% of E. coli genomes contain an orz antitoxin gene only and confirmed its expression in a representative strain: the first confirmed report of a type I antitoxin without its cognate toxin. Our results add to our understanding of these systems, and our methodology is applicable for other type I loci to identify critical regulatory and functional features.IMPORTANCEChromosomal type I toxin-antitoxins are a class of genes that have gained increasing attention over the last decade for their roles in antibiotic persistence which may contribute to therapeutic failures. However, the control of many of these genes and when they function have remained elusive. We demonstrate that a simple genetic conservation-based approach utilizing free, publicly available data yields known and novel insights into the regulation and function of three chromosomal type I toxin-antitoxins in Escherichia coli. This study also provides a framework for how this approach could be applied to other genes of interest.

CalScope: methodology and lessons learned for conducting a remote statewide SARS-CoV-2 seroprevalence study in California using an at-home dried blood spot collection kit and online survey.

To describe the methodology for conducting the CalScope study, a remote, population-based survey launched by the California Department of Public Health (CDPH) to estimate SARS-CoV-2 seroprevalence and understand COVID-19 disease burden in California. Between April 2021 and August 2022, 666,857 randomly selected households were invited by mail to complete an online survey and at-home test kit for up to one adult and one child. A gift card was given for each completed survey and test kit. Multiple customized REDCap databases were used to create a data system which provided task automation and scalable data management through API integrations. Support infrastructure was developed to manage follow-up for participant questions and a communications plan was used for outreach through local partners. Across 3 waves, 32,671 out of 666,857 (4.9%) households registered, 6.3% by phone using an interactive voice response (IVR) system and 95.7% in English. Overall, 25,488 (78.0%) households completed surveys, while 23,396 (71.6%) households returned blood samples for testing. Support requests (n = 5,807) received through the web-based form (36.3%), by email (34.1%), and voicemail (29.7%) were mostly concerned with the test kit (31.6%), test result (26.8%), and gift card (21.3%). Ensuring a well-integrated and scalable data system, responsive support infrastructure for participant follow-up, and appropriate academic and local health department partnerships for study management and communication allowed for successful rollout of a large population-based survey. Remote data collection utilizing online surveys and at-home test kits can complement routine surveillance data for a state health department.

Clustering Method for Signals in the Wideband RF Spectrum Using Semi-Supervised Deep Contrastive Learning

This paper presents the application of self-supervised deep contrastive learning in clustering signals detected in the wideband RF spectrum, presented in the form of spectrograms. Radio clustering is a method of searching for similar signals within the analyzed part of the radio spectrum. Typically, it is based on one or several specific parameters processed from the signal in a given channel. The authors propose a slightly different, innovative approach; thanks to the self-supervised learning of neural networks, there is no need to define specific parameters, and the feature vector, enabling comparison of Euclidean distances between signals, is generated by a deep neural network trained using a contrastive loss function on a dataset containing different radio modulations. The authors describe self-supervised solutions based on contrastive learning and the methods of signal segmentation and augmentation. The training process utilizes a custom database and the Resnet-50 network with a contrastive cost function. Radio clustering is used for autonomous spectrum analysis across wide frequency ranges and enables, among other things, the detection of tactical radio stations operating with widely dispersed frequency-hopping or a significant reduction in computational power required for real-time analysis of a large number of radio signals.

An in-depth evaluation of metagenomic classifiers for soil microbiomes

BackgroundRecent endeavours in metagenomics, exemplified by projects such as the human microbiome project and TARA Oceans, have illuminated the complexities of microbial biomes. A robust bioinformatic pipeline and meticulous evaluation of their methodology have contributed to the success of these projects. The soil environment, however, with its unique challenges, requires a specialized methodological exploration to maximize microbial insights. A notable limitation in soil microbiome studies is the dearth of soil-specific reference databases available to classifiers that emulate the complexity of soil communities. There is also a lack of in-vitro mock communities derived from soil strains that can be assessed for taxonomic classification accuracy.ResultsIn this study, we generated a custom in-silico mock community containing microbial genomes commonly observed in the soil microbiome. Using this mock community, we simulated shotgun sequencing data to evaluate the performance of three leading metagenomic classifiers: Kraken2 (supplemented with Bracken, using a custom database derived from GTDB-TK genomes along with its own default database), Kaiju, and MetaPhlAn, utilizing their respective default databases for a robust analysis. Our results highlight the importance of optimizing taxonomic classification parameters, database selection, as well as analysing trimmed reads and contigs. Our study showed that classifiers tailored to the specific taxa present in our samples led to fewer errors compared to broader databases including microbial eukaryotes, protozoa, or human genomes, highlighting the effectiveness of targeted taxonomic classification. Notably, an optimal classifier performance was achieved when applying a relative abundance threshold of 0.001% or 0.005%. The Kraken2 supplemented with bracken, with a custom database demonstrated superior precision, sensitivity, F1 score, and overall sequence classification. Using a custom database, this classifier classified 99% of in-silico reads and 58% of real-world soil shotgun reads, with the latter identifying previously overlooked phyla using a custom database.ConclusionThis study underscores the potential advantages of in-silico methodological optimization in metagenomic analyses, especially when deciphering the complexities of soil microbiomes. We demonstrate that the choice of classifier and database significantly impacts microbial taxonomic profiling. Our findings suggest that employing Kraken2 with Bracken, coupled with a custom database of GTDB-TK genomes and fungal genomes at a relative abundance threshold of 0.001% provides optimal accuracy in soil shotgun metagenome analysis.

ReporType: A Flexible Bioinformatics Tool for Targeted Loci Screening and Typing of Infectious Agents.

In response to the pressing need for continuous monitoring of emergence and circulation of pathogens through genomics, it is imperative to keep developing bioinformatics tools that can help in their rapid characterization and classification. Here, we introduce ReporType, a versatile bioinformatics pipeline designed for targeted loci screening and typing of infectious agents. Developed using the snakemake workflow manager, ReporType integrates multiple software for read quality control and de novo assembly, and then applies ABRicate for locus screening, culminating in the production of easily interpretable reports for the identification of pathogen genotypes and/or screening of specific genomic loci. The pipeline accommodates a range of input formats, from Illumina or Oxford Nanopore Technology (ONT) reads (FASTQ) to Sanger sequencing files (AB1), or FASTA files, making it flexible for application in multiple pathogens and with different purposes. ReporType is released with pre-prepared databases for some viruses and bacteria, yet it remains easily configurable to handle custom databases. ReporType performance and functionality were validated through proof-of-concept exercises, encompassing diverse pathogenic species, including viruses such as measles, Newcastle disease virus (NDV), Dengue virus (DENV), influenza, hepatitis C virus (HCV) and Human T-Cell Lymphotropic virus type 1 (HTLV-1), as well as bacteria like Chlamydia trachomatis and Legionella pneumophila. In summary, ReporType emerges as a simple, dynamic and pan-pathogen tool, poised to evolve in tandem with the ever-changing needs of the fields of pathogen genomics, infectious disease epidemiology, and one health bioinformatics. ReporType is freely available at GitHub.

The astounding exhaustiveness and speed of the Astral mass analyzer for highly complex samples is a quantum leap in the functional analysis of microbiomes

BackgroundBy analyzing the proteins which are the workhorses of biological systems, metaproteomics allows us to list the taxa present in any microbiota, monitor their relative biomass, and characterize the functioning of complex biological systems.ResultsHere, we present a new strategy for rapidly determining the microbial community structure of a given sample and designing a customized protein sequence database to optimally exploit extensive tandem mass spectrometry data. This approach leverages the capabilities of the first generation of Quadrupole Orbitrap mass spectrometer incorporating an asymmetric track lossless (Astral) analyzer, offering rapid MS/MS scan speed and sensitivity. We took advantage of data-dependent acquisition and data-independent acquisition strategies using a peptide extract from a human fecal sample spiked with precise amounts of peptides from two reference bacteria.ConclusionsOur approach, which combines both acquisition methods, proves to be time-efficient while processing extensive generic databases and massive datasets, achieving a coverage of more than 122,000 unique peptides and 38,000 protein groups within a 30-min DIA run. This marks a significant departure from current state-of-the-art metaproteomics methodologies, resulting in broader coverage of the metabolic pathways governing the biological system. In combination, our strategy and the Astral mass analyzer represent a quantum leap in the functional analysis of microbiomes.DtQfodvXmxPWcwvjCbxEj1Video

Comparative evaluation of bioinformatic tools for virus-host prediction and their application to a highly diverse community in the Cuatro Ciénegas Basin, Mexico.

Due to the enormous diversity of non-culturable viruses, new viruses must be characterized using culture-independent techniques. The associated host is an important phenotypic feature that can be inferred from metagenomic viral contigs thanks to the development of several bioinformatic tools. Here, we compare the performance of recently developed virus-host prediction tools on a dataset of 1,046 virus-host pairs and then apply the best-performing tools to a metagenomic dataset derived from a highly diverse transiently hypersaline site known as the Archaean Domes (AD) within the Cuatro Ciénegas Basin, Coahuila, Mexico. Among host-dependent methods, alignment-based approaches had a precision of 66.07% and a sensitivity of 24.76%, while alignment-free methods had an average precision of 75.7% and a sensitivity of 57.5%. RaFAH, a virus-dependent alignment-based tool, had the best overall performance (F1_score = 95.7%). However, when predicting the host of AD viruses, methods based on public reference databases (such as RaFAH) showed lower inter-method agreement than host-dependent methods run against custom databases constructed from prokaryotes inhabiting AD. Methods based on custom databases also showed the greatest agreement between the source environment and the predicted host taxonomy, habitat, lifestyle, or metabolism. This highlights the value of including custom data when predicting hosts on a highly diverse metagenomic dataset, and suggests that using a combination of methods and qualitative validations related to the source environment and predicted host biology can increase the number of correct predictions. Finally, these predictions suggest that AD viruses infect halophilic archaea as well as a variety of bacteria that may be halophilic, halotolerant, alkaliphilic, thermophilic, oligotrophic, sulfate-reducing, or marine, which is consistent with the specific environment and the known geological and biological evolution of the Cuatro Ciénegas Basin and its microorganisms.

Investigation of the Relationship Between Akkermansia Genomic Diversity in Gut Microbiota and Parkinson's Disease Dementia

Although it is known that the relative abundance of Akkermansia, a bacterial genus commonly associated with health, increases in the gut microbiota of Parkinson's disease (PD) patients, the exact reason for this increase remains unclear. This study was aimed to identify potential changes in Akkermansia within the gut microbiota of PD patients in Türkiye. For this purpose, shotgun metagenomics and a novel Akkermansia genus-specific amplicon sequencing technique was used to investigate the presence of specific Akkermansia strains associated with cognitive impairment (CI) stages in PD and to examine potential genes within these strains. In this context, four gut microbiota samples from Türkiye -three PD with dementia (PDD) and one healthy control without CI (HC)- were analyzed by shotgun metagenomics and metagenome-assembled genomes assigned to Akkermansia genus were reconstructed. Then, a custom database was created by combining these genomes with the Akkermansia genomes in public databases and next generation sequencing (NGS) compatible primers specific to the genus Akkermansia were designed using this database. After optimization of amplification and library preparation steps for genus-specific next generation sequencing, gut microbiota samples from 64 PD patients [32 PDD and 32 PD with mild CI (PD-MCI)] and 26 HCs were analyzed by genus-specific amplicon sequencing. The results revealed the presence of seven strains assigned to Akkermansia muciniphila in gut microbiota samples, two of which showed significant distribution differences (p< 0.05) between demented (PDD) and non-demented groups (PD-MCI, HC). When gene contents of the detected Akkermansia genomes were examined through comparative genomic analysis, the presence of 12 genes only in Akkermansia genomes specific to non-demented groups were predicted. The annotations of these genes showed that they were not reported before with unknown functions. In this study, for the first time, gut microbiota samples from PD patients in Türkiye were analyzed using shotgun metagenomics, a novel genus-specific amplicon sequencing method was developed specifically for the analysis of Akkermansia genus, and then Akkermansia strains and genes potentially associated with CI stages in PD were identified using this method. The results underscore that investigating the species or strain level differences could help better understanding of the changes associated with PD in the human gut microbiota.

Integrative open workflow for confident annotation and molecular networking of metabolomics MSE/DIA data.

Liquid chromatography coupled with high-resolution mass spectrometry data-independent acquisition (LC-HRMS/DIA), including MSE, enable comprehensive metabolomics analyses though they pose challenges for data processing with automatic annotation and molecular networking (MN) implementation. This motivated the present proposal, in which we introduce DIA-IntOpenStream, a new integrated workflow combining open-source software to streamline MSE data handling. It provides 'in-house' custom database construction, allows the conversion of raw MSE data to a universal format (.mzML) and leverages open software (MZmine 3 and MS-DIAL) all advantages for confident annotation and effective MN data interpretation. This pipeline significantly enhances the accessibility, reliability and reproducibility of complex MSE/DIA studies, overcoming previous limitations of proprietary software and non-universal MS data formats that restricted integrative analysis. We demonstrate the utility of DIA-IntOpenStream with two independent datasets: dataset 1 consists of new data from 60 plant extracts from the Ocotea genus; dataset 2 is a publicly available actinobacterial extract spiked with authentic standard for detailed comparative analysis with existing methods. This user-friendly pipeline enables broader adoption of cutting-edge MS tools and provides value to the scientific community. Overall, it holds promise for speeding up metabolite discoveries toward a more collaborative and open environment for research.

Pangenome databases improve host removal and mycobacteria classification from clinical metagenomic data.

Culture-free real-time sequencing of clinical metagenomic samples promises both rapid pathogen detection and antimicrobial resistance profiling. However, this approach introduces the risk of patient DNA leakage. To mitigate this risk, we need near-comprehensive removal of human DNA sequences at the point of sequencing, typically involving the use of resource-constrained devices. Existing benchmarks have largely focused on the use of standardized databases and largely ignored the computational requirements of depletion pipelines as well as the impact of human genome diversity. We benchmarked host removal pipelines on simulated and artificial real Illumina and Nanopore metagenomic samples. We found that construction of a custom kraken database containing diverse human genomes results in the best balance of accuracy and computational resource usage. In addition, we benchmarked pipelines using kraken and minimap2 for taxonomic classification of Mycobacterium reads using standard and custom databases. With a database representative of the Mycobacterium genus, both tools obtained improved specificity and sensitivity, compared to the standard databases for classification of Mycobacterium tuberculosis. Computational efficiency of these custom databases was superior to most standard approaches, allowing them to be executed on a laptop device. Customized pangenome databases provide the best balance of accuracy and computational efficiency when compared to standard databases for the task of human read removal and M. tuberculosis read classification from metagenomic samples. Such databases allow for execution on a laptop, without sacrificing accuracy, an especially important consideration in low-resource settings. We make all customized databases and pipelines freely available.

Short Antimicrobial Peptide Derived from the Venom Gland Transcriptome of Pamphobeteus verdolaga Increases Gentamicin Susceptibility of Multidrug-Resistant Klebsiella pneumoniae.

Infectious diseases account for nine percent of annual human deaths, and the widespread emergence of antimicrobial resistances threatens to significantly increase this number in the coming decades. The prospect of antimicrobial peptides (AMPs) derived from venomous animals presents an interesting alternative for developing novel active pharmaceutical ingredients (APIs). Small, cationic and amphiphilic peptides were predicted from the venom gland transcriptome of Pamphobeteus verdolaga using a custom database of the arthropod's AMPs. Ninety-four candidates were chemically synthesized and screened against ATCC® strains of Escherichia coli and Staphylococcus aureus. Among them, one AMP, named PvAMP66, showed broad-spectrum antimicrobial properties with selectivity towards Gram-negative bacteria. It also exhibited activity against Pseudomonas aeruginosa, as well as both an ATCC® and a clinically isolated multidrug-resistant (MDR) strain of K. pneumoniae. The scanning electron microscopy analysis revealed that PvAMP66 induced morphological changes of the MDR K. pneumoniae strain suggesting a potential "carpet model" mechanism of action. The isobologram analysis showed an additive interaction between PvAMP66 and gentamicin in inhibiting the growth of MDR K. pneumoniae, leading to a ten-fold reduction in gentamicin's effective concentration. A cytotoxicity against erythrocytes or peripheral blood mononuclear cells was observed at concentrations three to thirteen-fold higher than those exhibited against the evaluated bacterial strains. This evidence suggests that PvAMP66 can serve as a template for the development of AMPs with enhanced activity and deserves further pre-clinical studies as an API in combination therapy.