Early in the 1900s, it was proposed that health could be improved and senility delayed by manipulating gut microbiota with the host-friendly bacteria found in yogurt. Later, in 1990, the medical community reconsidered this idea and today probiotics represent a developed area of research with a billion-dollar global industry. As a result, in recent decades, increased attention has been paid to the isolation and characterization of novel probiotic bacteria from fermented foods and dairy products. Most of the identified probiotic strains belong to the lactic acid bacteria group and the genus Bifidobacterium. However, current molecular-based knowledge has allowed the identification and culture of obligatory anaerobic commensal bacteria from the human gut, such as Akkermansia spp. and Faecalibacterium spp., among other human symbionts. We are aware that the identification of new strains of these species does not guarantee their probiotic effects and that each effect must be proved through in vitro and in vivo preclinical studies before clinical trials (before even considering it as a probiotic strain). In most cases, the identification and characterization of new probiotic strain candidates may lack the appropriate set of in vitro experiments allowing the next assessment steps. Here, we address some innovative strategies reported in the literature as alternatives to classical characterization: (i) identification of alternatives using whole-metagenome shotgun sequencing, metabolomics, and multi-omics analysis; and (ii) probiotic characterization based on molecular effectors and/or traits to target specific diseases (i.e., inflammatory bowel diseases, colorectal cancer, allergies, among others).

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Strategies for the Identification and Assessment of Bacterial Strains with Specific Probiotic Traits

Lacticaseibacillus paracasei is an economically important bacterial species, used in the food industry and as a probiotic. Here, we investigate the roles of N6-methyladenine (6mA) modification in L. paracasei using multi-omics and high-throughput chromosome conformation capture (Hi-C) analyses. The distribution of 6mA-modified sites varies across the genomes of 28 strains, and appears to be enriched near genes involved in carbohydrate metabolism. A pglX mutant, defective in 6mA modification, shows transcriptomic alterations but only modest changes in growth and genomic spatial organization. 

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Roles of adenine methylation in the physiology of Lacticaseibacillus paracasei

Drug-resistant bacterial infections pose a serious threat to human public health. Biofilm formation is one of the main factors contributing to the development of bacterial resistance, characterized by a hypoxic and microacidic microenvironment. Traditional antibiotic treatments have been ineffective against multidrug-resistant (MDR) bacteria. Novel monotherapies have had little success. On the basis of the photothermal effect, molybdenum disulfide (MoS2) nanoparticles were used to link quaternized polyethylenimine (QPEI), dihydroporphyrin e6 (Ce6), and Panax notoginseng saponins (PNS) in a zeolitic imidazolate framework-8 (ZIF-8). A multifunctional nanoplatform (MQCP@ZIF-8) was constructed with dual response to pH and near-infrared light (NIR), which resulted in synergistic photothermal and photodynamic antibacterial effects. The nanoplatform exhibited a photothermal conversion efficiency of 56%. It inhibited MDR Escherichia coli (E. coli) and MDR Staphylococcus aureus (S. aureus) by more than 95% and effectively promoted wound healing in mice infected with MDR S. aureus. The nanoplatform induced the death of MDR bacteria by promoting biofilm ablation, disrupting bacterial cell membranes and intracellular DNA, and interfering with intracellular material and energy metabolism. In this study, a multifunctional nanoplatform with good antibacterial effect was developed. The molecular mechanisms of MDR bacteria were also elucidated for possible clinical application.

Biofilm Microenvironment-Mediated MoS2 Nanoplatform with Its Photothermal/Photodynamic Synergistic Antibacterial Molecular Mechanism and Wound Healing Study.

Probiotics are currently the subject of intensive research pursuits and also represent a multi-billion-dollar global industry given their vast potential to improve human health. In addition, mental health represents a key domain of healthcare, which currently has limited, adverse-effect prone treatment options, and probiotics may hold the potential to be a novel, customizable treatment for depression. Clinical depression is a common, potentially debilitating condition that may be amenable to a precision psychiatry-based approach utilizing probiotics. Although our understanding has not yet reached a sufficient level, this could be a therapeutic approach that can be tailored for specific individuals with their own unique set of characteristics and health issues. Scientifically, the use of probiotics as a treatment for depression has a valid basis rooted in the microbiota-gut-brain axis (MGBA) mechanisms, which play a role in the pathophysiology of depression. In theory, probiotics appear to be ideal as adjunct therapeutics for major depressive disorder (MDD) and as stand-alone therapeutics for mild MDD and may potentially revolutionize the treatment of depressive disorders. Although there is a wide range of probiotics and an almost limitless range of therapeutic combinations, this review aims to narrow the focus to the most widely commercialized and studied strains, namely Lactobacillus and Bifidobacterium, and to bring together the arguments for their usage in patients with major depressive disorder (MDD). Clinicians, scientists, and industrialists are critical stakeholders in exploring this groundbreaking concept.

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A Microbial-Based Approach to Mental Health: The Potential of Probiotics in the Treatment of Depression

Probiotics are amply studied and applied dietary supplements of greater consumer acceptance. Nevertheless, the emerging evidence on probiotics-mediated potential risks, especially among immunocompromised individuals, necessitates careful and in-depth safety studies. The traditional probiotic safety evaluation methods investigate targeted phenotypic traits, such as virulence factors and antibiotic resistance. However, the rapid innovation in omics technologies has offered an impactful means to ultimately sequence and unknot safety-related genes or their gene products at preliminary levels. Further validating the genome features using an array of phenotypic tests would provide an absolute realization of gene expression dynamics. For safety studies in animal models, the in vivo toxicity evaluation guidelines of chemicals proposed by the Organization for Economic Co-operation and Development (OECD) have been meticulously adopted in probiotic research. Future research should also focus on coupling genome-scale safety analysis and establishing a link to its transcriptome, proteome, or metabolome for a fine selection of safe probiotic strains. Considering the studies published over the years, it can be inferred that the safety of probiotics is strain-host-dose-specific. Taken together, an amalgamation of in silico, in vitro, and in vivo approaches are necessary for a fine scale selection of risk-free probiotic strain for use in human applications.

Emerging pre-clinical safety assessments for potential probiotic strains: a review.

Lactic acid bacteria consortia are commonly present in food, and some of these bacteria possess probiotic properties. However, discovery and experimental validation of probiotics require extensive time and effort. Therefore, it is of great interest to develop effective screening methods for identifying probiotics. Advances in sequencing technology have generated massive genomic data, enabling us to create a machine learning-based platform for such purpose in this work. This study first selected a comprehensive probiotics genome dataset from the probiotic database (PROBIO) and literature surveys. Then, k-mer (from 2 to 8) compositional analysis was performed, revealing diverse oligonucleotide composition in strain genomes and apparently more probiotic (P-) features in probiotic genomes than non-probiotic genomes. To reduce noise and improve computational efficiency, 87 376 k-mers were refined by an incremental feature selection (IFS) method, and the model achieved the maximum accuracy level at 184 core features, with a high prediction accuracy (97.77%) and area under the curve (98.00%). Functional genomic analysis using annotations from gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Rapid Annotation using Subsystem Technology (RAST) databases, as well as analysis of genes associated with host gastrointestinal survival/settlement, carbohydrate utilization, drug resistance and virulence factors, revealed that the distribution of P-features was biased toward genes/pathways related to probiotic function. Our results suggest that the role of probiotics is not determined by a single gene, but by a combination of k-mer genomic components, providing new insights into the identification and underlying mechanisms of probiotics. This work created a novel and free online bioinformatic tool, iProbiotics, which would facilitate rapid screening for probiotics.

https://academic.oup.com/bib/advance-article-pdf/doi/10.1093/bib/bbab477/41357209/bbab477.pdf

Jinzhao Li

Papers

iProbiotics: a machine learning platform for rapid identification of probiotic properties from whole-genome primary sequences.