Probiotics Strain Identification Using AI

Method and device for identifying specific regions in microorganism target fragments for improved PCR sensitivity and specificity. The method involves finding unique and species-specific sequences within microbial genomes to design primers and probes for PCR detection. This addresses the limitations of using conserved plasmid or rRNA genes for PCR primer design, which can have low specificity and sensitivity due to variability between strains and species. The method involves computational analysis of genome sequences to identify regions that are both unique and conserved within a species, providing improved target specificity for PCR.

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As a promising probiotic strain,

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ABSTRACT The increasing knowledge of microbial ecology in food products relating to quality and safety and the established usefulness of machine learning algorithms for anomaly detection in multiple scenarios suggests that the application of microbiome data in food production systems for anomaly detection could be a valuable approach to be used in food systems. These methods could be used to identify ingredients that deviate from their typical microbial composition, which could indicate food fraud or safety issues. The objective of this study was to assess the feasibility of using shotgun sequencing data as input into anomaly detection algorithms using fluid milk as a model system. Contrastive principal component analysis (PCA), cluster-based methods, and explainable artificial intelligence (AI) were evaluated for the detection of two anomalous sample classes using longitudinal metagenomic profiling of fluid milk compared to baseline (BL) samples collected under comparable circumstances. Traditional methods (alpha and beta diversity, clustering-based contrastive PCA, multidimensional... Read More

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The gut microbiota plays a crucial role in maintaining human health and influencing disease states. Recent advancements in artificial intelligence (AI) have opened new avenues for exploring the intricate functionalities of the gut microbiota. This article aims to provide an overview of the current state-of-the-art applications of AI in microbiome analysis, with examples related to metabolomics, transcriptomics, proteomics, and genomics. It also offers a perspective on the use of such AI solutions in probiotic interventions for various clinical settings. This comprehensive understanding can lead to the development of targeted therapies that modulate the gut microbiota to improve health outcomes. This article explores the innovative application of AI in understanding the complex interactions within the gut microbiota. By leveraging AI, researchers aim to uncover the microbiotas role in human health and disease, particularly focusing on CIDs and probiotic interventions.

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Lactic acid bacteria have a long and extensive application record as probiotic strains. Lately, due to wide-spread use of molecular-genetic, transcirptomic, proteomic, metabolomic etc. studies and the massive accumulation of data on the structure and functions of symbiotic intestinal microbiota, the interest in probiotic microorganisms tends to expand year-by-year. Lactic acid bacteria were the first used probiotic species, are still of sharp market demand and have been recognized as the most thoroughly studied microbes among functional relatives. However, characteristics inherent to probiotic bacteria are strain-specific, as a rule, and for preparations with defined purpose it is essential to select special strains showing a complex of appropriate properties. It accentuates the top relevance of research aimed at selection of lactic acid bacterial strains suitable for diverse probiotic preparations. For several decades we curried out investigations to isolate strains of lactic acid bacteria from various natural sources, to characterize their properties and to derive technologies of p... Read More

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Artificial Intelligence (AI), a dynamic discipline within computer science, stands as a driving force in simulating intelligent behaviors in machines. In the specialized realm of medical microbiology, AI systems leverage the capabilities of machine learning algorithms to navigate extensive datasets, unravel intricate patterns, and conduct predictive analyses. This sophisticated application of AI is pivotal, not only amplifying our comprehension of microbial interactions but also furnishing crucial insights into the intricate landscape of infectious diseases (Goodswen et al., 2021; Shelke, Badge, & Bankar, 2023; Bellini et al., 2022). The scope of AI in medical microbiology transcends conventional methodologies, presenting a revolutionary paradigm for unraveling microbial complexities. Through the analysis of genomic, proteomic, and clinical data, AI systems contribute significantly to a more holistic understanding of infectious diseases. These systems showcase the ability to discern subtle patterns indicative of specific microbial strains, forecast disease trajectories, and pinpo... Read More

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Abstract Probiotics are living microorganisms that provide health benefits to their hosts, potentially aiding in the treatment or prevention of various diseases, including diarrhea, irritable bowel syndrome, ulcerative colitis, and Crohns disease. Motivated by successful applications of link prediction in medical and biological networks, we applied link prediction to the probiotic-disease network to identify unreported relations. Using data from the Probio database and International Classification of Diseases-10th Revision (ICD-10) resources, we constructed a bipartite graph focused on the relationship between probiotics and diseases. We applied customized link prediction algorithms for this bipartite network, including common neighbors, Jaccard coefficient, and Adamic/Adar ranking formulas. We evaluated the results using Area under the Curve (AUC) and precision metrics. Our analysis revealed that common neighbors outperformed the other methods, with an AUC of 0.96 and precision of 0.6, indicating that basic formulas can predict at least six out of ten probable relations correctly. ... Read More

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Traditional microbial diagnostic methods face many obstacles such as sample handling, culture difficulties, misidentification, and delays in determining susceptibility. The advent of artificial intelligence (AI) has markedly transformed microbial diagnostics with rapid and precise analyses. Nonetheless, ethical considerations accompany AI adoption, necessitating measures to uphold patient privacy, mitigate biases, and ensure data integrity. This review examines conventional diagnostic hurdles, stressing the significance of standardized procedures in sample processing. It underscores AIs significant impact, particularly through machine learning (ML), in microbial diagnostics. Recent progressions in AI, particularly ML methodologies, are explored, showcasing their influence on microbial categorization, comprehension of microorganism interactions, and augmentation of microscopy capabilities. This review furnishes a comprehensive evaluation of AIs utility in microbial diagnostics, addressing both advantages and challenges. A few case studies including SARS-CoV-2, malaria, and mycobacte... Read More

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Method for identifying cells from biological samples using PCR and high resolution melting curve analysis. The method involves extracting DNA from a suspected sample, amplifying specific genomic regions using primers, and analyzing the melting curves of the amplicons. The curves are mathematically transformed and compared to a database of known organisms using machine learning algorithms for identification.

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Methods, compositions, kits, and systems for detecting, identifying, and quantifying bacteria and fungi using universal primers that target conserved regions flanking ribosomal ITS and other genes. The system enables simultaneous detection of multiple microorganisms in a single reaction, with sensitivity to detect as few as a single bacterium, and provides a rapid, culture-free alternative to conventional pathogen detection methods.

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A method for simultaneous interpretation of taxonomic distribution and replication rates of microbial communities, comprising: collecting a microbiome sample; extracting bacterial genomic DNA; performing amplicon sequencing targeting two or more phylogenetic marker genes; mapping sequence reads to a reference database; identifying bacterial organisms and assigning taxonomic classification; measuring read coverage at genomic locations; fitting a linear function to the read coverage data; estimating replication rates from the slope of the linear function.

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Abstract Beneficial bacteria remain largely unexplored. Lacking systematic methods, understanding probiotic community traits becomes challenging, leading to various conclusions about their probiotic effects among different publications. We developed language modelbased metaProbiotics to rapidly detect probiotic bins from metagenomes, demonstrating superior performance in simulated benchmark datasets. Testing on gut metagenomes from probiotic-treated individuals, it revealed the probioticity of intervention strainsderived bins and other probiotic-associated bins beyond the training data, such as a plasmid-like bin. Analyses of these bins revealed various probiotic mechanisms and bai operon as probiotic Ruminococcaceaes potential marker. In different healthdisease cohorts, these bins were more common in healthy individuals, signifying their probiotic role, but relevant health predictions based on the abundance profiles of these bins faced cross-disease challenges. To better understand the heterogeneous nature of probiotics, we used metaProbiotics to construct a comprehensive probio... Read More

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The identification and quantification of viable bacteria at the species/strain level in compound probiotic products is challenging now. Molecular biology methods, e.g., propidium monoazide (PMA) combination with qPCR, have gained prominence for targeted viable cell counts. This study endeavors to establish a robust PMA-qPCR method for viable Lacticaseibacillus rhamnosus detection and systematically validated key metrics encompassing relative trueness, accuracy, limit of quantification, linear, and range. The inclusivity and exclusivity notably underscored high specificity of the primers for L. rhamnosus , which allowed accurate identification of the target bacteria. Furthermore, the conditions employed for PMA treatment were fully verified by 24 different L. rhamnosus including type strain, commercial strains, etc., confirming its effective discrimination between live and dead bacteria. A standard curve constructed by type strain could apply to commercial strains to convert qPCR C q values to viable cell numbers. The established PMA-qPCR method was applied to 46 samples including pur... Read More

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Abstract Screening probiotics with specific functions is essential for advancing probiotic research. Current screening methods primarily use animal studies or clinical trials, which are inefficient and costly in terms of time, money, and labor. An intelligent intestineonachip integrating machine learning (ML) is developed to screen reliefenteritis functional probiotics. A highthroughput microfluidic chip combined with environment control systems provides a standardized and scalable intestinal microenvironment for multiple probiotic cocultures. An unsupervised MLbased score analyzer is constructed to accurately, comprehensively, and efficiently evaluate interactions between 12 Bifidobacterium strains and host cells of the colitis model in the intestineonachips. The most effective contender, Bifidobacterium longum 314, is discovered to relieve intestinal inflammation and enhance epithelial barrier function in vitro and in vivo. A distinct advantage of this strategy is that it can intelligently differentiate small therapeutic variations in probiotic strains and prioritize thei... Read More

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Microbes, including bacteria, archaea, fungi, and viruses, are fundamental to our ecosystems, health, and industries. Microbial data analysis has become indispensable in understanding their roles and interactions. In this era of big data, advanced techniques, such as high-throughput sequencing, metagenomics, and bioinformatics, have accelerated microbial research. This chapter explores the significance of intelligent techniques, particularly machine learning and artificial intelligence, in revolutionizing microbial data analysis. The aim of this chapter is to showcase the pivotal role of intelligent techniques in microbial data analysis across diverse domains, from ecology and public health to biotechnology. We delve into case studies that highlight the practical applications of these techniques and the transformative impact they have had on microbial research. Several case studies are presented, illustrating the applications of intelligent techniques in microbial research. These include predicting disease risk through gut microbiome analysis, antibiotic resistance prediction, enviro... Read More

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The human gut microbiome is an intricate ecosystem with profound implications for host metabolism, immune function, and neuroendocrine activity. Over the years, studies have strived to decode this microbial universe, especially its interactions with human health and underlying metabolic processes. Traditional analyses often struggle with the complex interplay within the microbiome due to presumptions of microbial independence. In response, machine learning (ML) and deep learning (DL) provide advanced multivariate and non-linear analytical tools that adeptly capture the complex interactions within the microbiota. With the influx of data from metagenomic next-generation sequencing (mNGS), there's an increasing reliance on these artificial intelligence (AI) subsets to derive actionable insights. This review delves deep into the cutting-edge ML techniques tailored for human gut microbiota research. It further underscores the potential of gut microbiota in shaping clinical diagnostics, prognosis, and intervention strategies, pointing to a future where computational methods bridge the gap ... Read More

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Vast shotgun metagenomics data remain an underutilized resource for novel enzymes. Artificial intelligence (AI) has increasingly been applied to protein mining, but its conventional performance evaluation is interpolative in nature, and these trained models often struggle to extrapolate effectively when challenged with unknown data. In this study, we present a framework (DeepMineLys [deep mining of phage lysins from human microbiome]) based on the convolutional neural network (CNN) to identify phage lysins from three human microbiome datasets. When validated with an independent dataset, our method achieved an F1-score of 84.00%, surpassing existing methods by 20.84%. We expressed 16 lysin candidates from the top 100 sequences in E. coli, confirming 11 as active. The best one displayed an activity 6.2-fold that of lysozyme derived from hen egg white, establishing it as the most potent lysin from the human microbiome. Our study also underscores several important issues when applying AI to biology questions. This framework should be applicable for mining other proteins.

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The convergence of artificial intelligence (AI) and microbial therapeutics offers promising avenues for novel discoveries and therapeutic interventions. With the exponential growth of omics datasets and rapid advancements in AI technology, the next generation of AI is increasingly prevalent in microbiology research. In microbial research, AI is instrumental in the classification and functional annotation of microorganisms. Machine learning algorithms facilitate efficient and accurate categorization of microbial taxa, enabling the identification of functional traits and metabolic pathways within microbial communities. Additionally, AI-driven protein design strategies hold promise for engineering enzymes with enhanced catalytic activities and stabilities. By predicting protein structures, functions, and interactions, AI algorithms enable the rational design of proteins and enzymes tailored for specific applications. AI systems are already present in clinical microbiology laboratories in the form of expert rules used by some automated susceptibility testing and identification systems. I... Read More

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<h2>Summary</h2> Whole genome sequencing of bacteria is important to enable strain classification. Using entire genomes as an input to machine learning (ML) models would allow rapid classification of strains while using information from multiple genetic elements. We developed a "bag-of-words" approach to encode, using SentencePiece or k-mer tokenization, entire bacterial genomes and analyze these with ML. Initial model selection identified SentencePiece with 8,000 and 32,000 words as the best approach for genome tokenization. We then classified in <i>Neisseria meningitidis</i> genomes the capsule B group genotype with 99.6% accuracy and the multifactor invasive phenotype with 90.2% accuracy, in an independent test set. Subsequently, in silico knockouts of 2,808 genes confirmed that the ML model predictions aligned with our current understanding of the underlying biology. To our knowledge, this is the first ML method using entire bacterial genomes to classify strains and identify genes considered relevant by the classifier.

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Recent advancements in production and access to large datasets, faster computing and cheaper storage have encouraged the development of machine learning (ML) algorithms with human-like artificial intelligence (AI) capabilities. Such algorithms are emerging as transformative tools in dermatology, enhancing diagnostic accuracy and patient care. Challa et al.1 use these tools in an attempt to validate AI's capacity to diagnose bacterial vaginosis (BV) adopting a training dataset that combines the vaginal microbiome and metabolome of patients and controls. Bacterial vaginosis is one of the most prevalent vaginal disorders among women of childbearing age globally.2 However, its pathogenesis remains elusive, the diagnosis time-consuming and experience-dependent and its treatment highly unsatisfactory, with, 50% rate of relapse within 612 months post-antibiotic therapy, currently the only evidence-based treatment available.2 The diagnosis of BV is particularly challenging because of BV's intricate polymicrobial features and a wide range of clinical presentations. Current diagnostic modalit... Read More

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