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Next Generation Probiotics for Neutralizing Obesogenic Effects: Taxa Culturing Searching Strategies

Ana López-Moreno
Inmaculada Acuña
Alfonso Torres-Sánchez
Ángel Ruiz-Moreno
Klara Cerk
Ana Rivas
Antonio Suárez
Mercedes Monteoliva-Sánchez
1,2 and
Margarita Aguilera
Department of Microbiology, Faculty of Pharmacy, Campus of Cartuja, University of Granada, 18071 Granada, Spain
Center of Biomedical Research, Institute of Nutrition and Food Technology “José Mataix”, University of Granada, Armilla, 18016 Granada, Spain
Department of Biochemistry and Molecular Biology, Faculty of Pharmacy, Campus of Cartuja, University of Granada, 18071 Granada, Spain
IBS, Instituto de Investigación Biosanitaria, 18012 Granada, Spain
Department of Nutrition and Food Science, Campus of Cartuja, University of Granada, 18071 Granada, Spain
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Nutrients 2021, 13(5), 1617;
Submission received: 26 February 2021 / Revised: 6 May 2021 / Accepted: 8 May 2021 / Published: 12 May 2021
(This article belongs to the Section Prebiotics and Probiotics)


The combination of diet, lifestyle, and the exposure to food obesogens categorized into “microbiota disrupting chemicals” (MDC) could determine obesogenic-related dysbiosis and modify the microbiota diversity that impacts on individual health–disease balances, inducing altered pathogenesis phenotypes. Specific, complementary, and combined treatments are needed to face these altered microbial patterns and the specific misbalances triggered. In this sense, searching for next-generation beneficial microbes or next-generation probiotics (NGP) by microbiota culturing, and focusing on their demonstrated, extensive scope and well-defined functions could contribute to counteracting and repairing the effects of obesogens. Therefore, this review presents a perspective through compiling information and key strategies for directed searching and culturing of NGP that could be administered for obesity and endocrine-related dysbiosis by (i) observing the differential abundance of specific microbiota taxa in obesity-related patients and analyzing their functional roles, (ii) developing microbiota-directed strategies for culturing these taxa groups, and (iii) applying the successful compiled criteria from recent NGP clinical studies. New isolated or cultivable microorganisms from healthy gut microbiota specifically related to obesogens’ neutralization effects might be used as an NGP single strain or in consortia, both presenting functions and the ability to palliate metabolic-related disorders. Identification of holistic approaches for searching and using potential NGP, key aspects, the bias, gaps, and proposals of solutions are also considered in this review.

Graphical Abstract

1. Introduction

1.1. Microbiota Gut Dysbiosis

The microbiota is a microbial community that lives on and in the human body and it varies according to several factors such as age, diet, and lifestyle [1]. These microorganisms play a very important role in maintaining the health homeostasis or eubiosis [2]. It has been well-demonstrated that gastrointestinal tract (GIT) disorders are linked to microbiota alterations patterns (such as constipation, diarrhea, inflammatory bowel diseases [3,4]) that can be treated with probiotics. Moreover, important metabolic disorders, presenting altered levels of triacylglycerols, lipids, cholesterol, and fasting plasma glucose as clinical outcomes [5] are also linked to GIT dysbiosis. Similarly, fertility disorders such as polycystic ovary syndrome (PCOS) [6], gastrointestinal and reproductive cancers [7], or mental health disorders like depression, anorexia, or anxiety are also connected to microbiota dysbiosis [8].

1.2. Traditional Probiotics vs. NGP in Obesity-Related Interventions and Treatments

Probiotics, known as “live microorganisms, which, when administered in adequate amounts confer a health benefit on the host” by the Food and Agriculture Organization of the United Nations (FAO) and the World Health Organization (WHO) [9], have been empirically selected due to their extensive use in fermented foods for centuries and their safety history. Conversely, because of this broad definition, their use has become widespread, making them less effective against specific diseases [10]. Since then, numerous studies have been published in order to demonstrate the benefits of probiotics in an extensive list of disorders and/or diseases, traditional probiotics corresponding to strains or species generally within Lactobacillus and Bifidobacterium genera, and a few from other genera [11]. Traditional probiotics for clinical interventions in obesity-related disorders have been largely used, with huge differential impact on the clinical parameters and outcomes, depending on the basis of the individual microbiota (Table 1).
Additionally, it is well-known that the functional and specific positive biological effects of probiotics are strain-dependent. Therefore, validated clinical studies should define well the specific strains administered to the subjects as shown in Table 1 [12,13].
However, new advances in high-throughput and -omics technologies allowed scientific community to characterize and identify new microorganisms called next generation probiotics (NGP) according to the beneficial basic definition of a probiotic, but they are better characterized by targeting specific diseases and clinical outcomes. NGPs have been initially well-designed and tested for obesity-related disorders (Table 2). Moreover, according to O’Toole et al. [14], there are substantial differences in the way of investigating traditional probiotics vs. NGP, driven by the high-throughput current technologies available and cumulated data evidence. Traditional probiotics harbor a limited number of microbial genera and species and they were initially selected according to their long history of safe use. Also, these probiotics tend to be searched and marketed by companies targeting general, narrowly defined populations. While NGPs belong to a wide range of genera and species, they are investigated by multidisciplinary approaches with microbiome and clinical expertise, the main goal of which is to obtain effective biosources to palliate specific microbiota dysbiosis and associated phenotypic disorders.
Table 1. Traditional probiotics for obesity-related interventional clinical trials and preclinical studies.
Table 1. Traditional probiotics for obesity-related interventional clinical trials and preclinical studies.
Lactobacillus Strains [15]Study Design, Target SpeciesReference Study
L. bulgaricus Nutricion Medica®ICT—Human [16]
L. casei Shirota ICT—Human [17]
L. gasseri BNR17 ICT—Human [18]
L. reuteri V3401 ICT—Human [19]
L. rhamnosus CGMCC1.3724 ICT—Human [20]
L. acidophilus NS1 PCS—Mice [21]
L. johnsonii JNU3402 PCS—Mice [22]
L. plantarum Ln4 PCS—Mice [23]
L.curvatus HY7601PCS—Mice [24]
L. fermentum CQPC07 PCS—Mice [25]
Bifidobacterium strainsStudy design, Target Species,Reference study
B. animalis subsp. lactis 420 ICT—Human[26]
B. breve B-3 ICT—Human [27]
B. infantis DSM24737 (VSL#3) ICT—Human [28]
B. lactis HN019 ICT—Human [29]
B. longum APC1472 ICT–Human/PCS–Mice[30]
B. adolescentisPCS—Mice[31]
B. bifidum BGN4 PCS—Mice[32]
Bacillus, Enterococcus, Streptococcus strainsStudy design, Target Species,Reference study
Bacillus coagulans Unique IS2 ICT—Human[33]
Bacillus amyloliquefaciens SC06 PCS—Mice[34]
Bacillus spp. PCS—Mice[35]
Enterococcus faecium R0026 PCS—Mice[36]
Enterococcus faecalis AG5 PCS—Rats[37]
Streptococcus thermophiles MN-ZLW-002PCS—Mice[38]
Saccharomyces strainsStudy design, Target Species,Reference study
S. boulardii Biocodex PCS–Mice[39]
S. cerevisiae SFBE PCS–Rats[40]
Traditional probiotics strains with obesity and anti-obesity effects. ICT: interventional clinical trials; PCS: preclinical studies.

2. Information and Criteria for Searching and Culturing Next-Generation Probiotics

The search for NGP that are able to modulate the effects of obesogenic and microbiota disruptor chemicals will request the following information according to the corresponding stepwise criteria (Figure 1).

2.1. Target Diseases, Microbiome Variability Composition, Biomarkers and Clinical Traits

2.1.1. Obesity, Metabolic, and Endocrine Diseases: Variability of Microbiota Composition

Interestingly, multiple convergent clinical studies have found differences between the microbiota of obese and healthy patients [59]. The clearest biomarker related to obesity appears to be Firmicutes-to-Bacteroidetes ratio. A higher ratio has been observed in obese or metabolic syndrome populations compared to the healthy ones [60,61]. Specific taxa seem to contribute to this ratio in obese patients: the genera Staphylococcus [62,63] and Clostridium [64], inside the Firmicutes phylum, have been shown to have a positive association with obesity. Moreover, an increase in butyrate and acetate synthesis may contribute to an increase in energy harvest in obese people, and many butyrate-producing species belong to the Firmicutes phylum [65].
The main variations of microbiota taxa found in patients suffering from obesity, diabetes, metabolic syndrome, liver diseases, and endocrine-related disorders are summarized in Table 3. The present work focused on those species or taxa whose abundance was comparatively different between patients and healthy individuals. Therefore, isolating and culturing these microbial species would allow us to test and verify their biological functions, and if the effects were clinically proved, they could be proposed as beneficial microbial or NGP.
Interestingly, levels of traditional probiotics from the genera Lactobacillus and Bifidobacterium seem to be higher in obesity- and endocrine-related diseases accordint to data retrieved and summarized in Table 3. Conversely, the species of NGP that are recognized and clinically tested, seem to be lower in obesity-related patients. Therefore, species tested from the genera Akkermansia, Faecalibacterium, Eubacterium, Bacteroides, Parabacteroides, and Christensenella could contribute to restore the microbial misbalances observed. In this sense, new beneficial microbes or NGP searching approaches might be successfully based on culturing and isolating those new genera and species that present a differential abundance between patients and healthy subjects and they can be linked to relevant clinical outcome.

2.1.2. Nutrition and Diets, Dietary Exposure to Obesogens, and Microbiome Interactions

Dietary intake is considered one of the determining factors that modulate the microbial composition and diversity of the gut microbiome, which could promote either beneficial or negative effects on host health and physiological functions [92,93]. A Western-style diet, rich in animal-based foods, can increase the patient’s levels of bile-tolerant bacteria, including Bacteroidetes (e.g., Bacteroides and Alistipes), and Proteobacteria (Bilophila), and decrease levels of fiber-degrading bacteria such as Firmicutes (e.g., Eubacterium and Ruminococccus) [94]. Conversely, the Mediterranean diet and plant-based diets can promote fiber-degrading bacteria, mainly including genera of the Firmicutes phylum, together with increased overall diversity of the gut microbiota [95]. There are fewer studies about the associations between dietary habits and the gut microbiota in the Asiatic populations [96,97], which are characterized by higher intakes of several fermented foods containing microorganisms similar to probiotic strains [98,99], which could affect the composition and diversity of the gut microbiota, thus affecting human health [100].
In addition, globalized population has incorporated much more processed foods and artificial products into their diets to keep up with the rapid pace of lifestyles. Therefore, the exposure to dietary contaminants became a cause of health concern worldwide [101,102,103]. Processed foods could contain obesogens derived from endocrine-disrupting chemicals that have also an effect on the gut microbiota, promoting adipogenesis and weight gain, as well as microbiome dysbiosis [104,105], which is linked to multiple diseases and adverse health outcomes [106,107]. The enzymatic arsenal of gut microbiota plays a key role in metabolizing dietary obesogens from processed or cooked food, promoting different outcomes: (i) Gut microbiota could protect against the carcinogenic and genotoxic substances by degrading or biotransforming them to less toxic compounds or facilitating their excretion [108,109]. (ii) Gut microbiota may also detoxify xenobiotics, for example, into genotoxins, or may reverse the detoxification implied by the host metabolism [110]. (iii) Gut microbiota is capable of transforming xenobiotics into less toxic and mutagenic substances, thus it may be able to lessen the chances of cancer and other dysbiosis effects [111]. (iv) Gut microbiome (human/animals) might be negatively affected by several food/feed additives (sweeteners, emulsifiers, preservatives, etc.) and other contaminants (BPA, Parabens, Pesticides, etc.) through triggering microbiota dysbiosis. Consequently, advances in toxicomicrobiomics are needed to study these complex and mutual influences between the ever-changing microbiome and obesogens of various origins, with emphasis on their fate and toxicity, and xenobiotic-modifying enzymes [112].

2.2. Culturing and Isolation of NGP through Combined Methodologies

The search for microbiological differences between the study groups (such as the healthy and the dysbiotic taxa groups) allows us to identify potential probiotics, and even detoxifying microorganisms, which could be used as NGP. However, this is followed by isolation and characterization of potential probiotics, and so far, none of the bacteria in the microbiota can be cultured in vitro yet [113]. This could be due to the difficulties of replicating essential aspects of their anaerobic environment [114] or the need to coculture with other bacteria from the same environment [115]. However, new media and modified procedures, such as improved culturomics, are continuously developing and evolving. They consist of multiple culture conditions with rapid identification of bacteria, raising the level of cultured bacteria and their possible use as bioresources or even NGP [116]. Table 4 summarizes the main putative new species isolated from recent culturing approaches in connection with the highlighted species underrepresented in obesity, which could be restored by a supplemented formula. Moreover, the isolation of strains from human microbiota able to biodegrade xenobiotics is successful through a directed cultivation approach with enriched media containing the specific xenobiotic [117]. BPA-tolerant strains were isolated in 30% of infant fecal microbial culture libraries analyzed. Most isolated strains were phylogenetically related to the operational taxonomic group Bacillus amyloliquefaciens. The culture media most used for cultivation of specific gut microbial strains with success were yeast-extract-casein hydrolysate-fatty acids (YCFA); gifu anaerobic medium (GAM); brain–heart infusion (BHI); eosin methylene blue (EMB); Lactobacillus selection (LBS); gut microbiota medium (GMM); and Man, Rogosa, and Sharpe (MRS).

2.3. Standardize Parameters When Using NGP in Clinical Studies

Traditional probiotics (Table 1) were not regulated as drugs but instead as dietary supplements; they are not subjected to the same rigorous standards and could have quality control issues [124]. As previously described, numerous studies have been carried out to prove the benefits of probiotics in a large number of dysbioses, but without standardized steps on dosages, patterns of administration, and detailed strains.
There is no consensus on the minimum number of microorganisms that should be ingested to obtain a beneficial effect [125]. Since the effective dose of probiotics is influenced by multiple variables, it is difficult to standardize an optical dose [126]. Additionally, there is a need to investigate potential synergistic effects or antagonistic activity between strains in multi-strain vs. single-strain products [127]. Furthermore, it is well- demonstrated that the positive biological effects that the probiotics exert are strain-dependent, so it is necessary to obtain a taxonomic characterization to the strain level [12,13]. In previous reviews [128,129], we have seen an unharmonized broad range of intervention, total dose, and administration patterns of probiotics in obesity and fertility disorders. Finally, another parameter to be harmonized is the target population, since it has been seen that the beneficial effect of a probiotic in a population may not be adequate for another population, even causing potential adverse effects [130].

2.4. Whole Genome Sequencing, Next-Generation Sequencing, and Bioinformatics Analyses

The rapid evolution of cultivation-independent, next-generation sequencing, and meta-omics technologies has allowed for the integration and analyses of large datasets for the study of the diversity, complexity, and functional role of the human gut microbiome in health and disease [131]. A large part of the detected bacteria has never been cultivated [132]. Therefore, an integrative approach using both metagenome and metabolome-based characterizations of the gut microbiome together with bioinformatics and statistical filters and algorithms can provide strain-level taxonomic resolution of the taxa present in microbiomes, assess the potential functions encoded by the microbial community and quantify the metabolic activities within a complex microbiome [133].
The various platforms and reference databases developed for the marker gene (16S rRNA), metagenomics, or metatranscriptomics analysis often use similar stepwise approaches (Figure 2) with different bioinformatic tools (DADA2, Deblur, Kraken, MEGAN, HUMAnN, metaSPAdes, MEGAHIT, QIIME, Mothur, and several R packages (vegan, microbiome, etc.).

2.5. Omics Data Integration: Big Data and Host Clinical Responses

As previously mentioned, microbiomics give us a great insight into the regulation of gut microbiota. However, in order to understand the complex biological pathways behind diseases, the identification of novel -omics biomarkers, such as identification of genes (genomics), gene expressions and phenotype (epigenomics), messenger RNA and micro RNA (transcriptomics), proteins (proteomics), and metabolites (metabolomics, lipidomics, glycomics) could bring forward knowledge on probiotics and their effects on obesity and its modulation of pathophysiological mechanisms that have links with chronic diseases [134,135].
Integrating multi-omics datasets is an innovative assignment, due to the increased complexity and diversity of the collected data [136]. This integration is increasingly reliant on efficient bioinformatics tools and advanced statistical methods [137,138,139]. Multi-omics data integration still poses challenges, but integration of multiple meta-omics datasets lays out a promising approach to comprehensively characterizing the composition, functional, and metabolic activity of microbiomes. This is of particular importance for microbiome research to be translated into clinical applications and further improvement of human health management [140].

2.6. Safety Assessment, Regulatory Frameworks, and Market Labeling

The overview of worldwide regulatory frameworks affecting different food categories is summarized in Table 5.
Overall, in the European Union (EU), most bacteria that will be used in foods for human consumption need to comply with two different regulations [141,142], or if used as life biotherapeutic products, as clarified in the European Pharmacopoeia (Ph. Eur.) [143]. At the same time, in the US, probiotics should be classified as microorganisms with a qualification of “generally recognized as safe” (GRAS) by the Food and Drug Administration (FDA). Both regulatory frameworks largely involve scientific requirements [14]. Furthermore, in order to assess the safety of microorganisms, the European Food Safety Authority (EFSA) introduced the concept of qualified presumption of safety (QPS) to harmonize the safety evaluation of microorganisms used as food or feed additives, food enzymes, novel foods, or pesticides, which has to follow certain criteria [144].
Table 5. Summary of probiotics categorization and regulation frameworks worldwide.
Table 5. Summary of probiotics categorization and regulation frameworks worldwide.
CountryCategoryRegulatory FrameworkClaimsReference
FDAHealth claims
Nutrient claims
Structure claims
Dietary supplementsDSHEAProbiotics considered as foods
Biological productFDA (BLA)Probiotics as a reference product, biosimilar product, or an interchangeable product; solely to be used for medical therapeutic purpose
Life biotherapeutic agentFDAProbiotics as a biological product that contains live organisms and is applicable to the prevention, treatment, or cure of a disease or condition; recombinant life biotherapeutic agent
Medical FoodFDA/DSHAProbiotics specially formulated to be intended for dietary management under supervision; medical foods are exempt from the labeling requirements for nutrient content and health claims
ChinaFunctional foodsSFDAConventional foods mark (the presence of a specific ingredient in the label of regular foodstuffs)
Healthy foods (the presence of health function)
EuropeFunctional Food and nutraceuticalsEFSA (FUFOSE)Health claims, nutrition claims
Life biotherapeutic productsEMAProbiotics as medicinal products containing live microorganisms for human use
JapanFunctional foods and nutraceuticalsMHLW, FOSHUFoods with functional claims
Foods with nutrient functional claims
CanadaNatural health productsFDA (CFIA)Nutrient content claims
Health claims
EFSA: European Food Safety Agency; EMA: European Medicines Agency; FAO/WHO: Food and Agricultural Organization/World Health Organization; MHLW: Ministry of Health and Welfare; FOSHU: food for specified health use; FUFOSE: functional food science in Europe; SFDA: State Food and Drug Administration; DSHEA: Dietary Supplement Health and Education Act; BLA: biologic license application; CFIA: the Canadian Food Inspection Agency.
However, despite all preventive effects, the consumption of probiotics may not be completely safe in certain cases or physiological states [14]. In this context, several bacterial species comprising genera other than Lactobacillus and Bifidobacterium with proven efficacy, which are considered as potential NGP, may be strain-by-strain assessed in order to obtain sufficient research data, and to grant probiotic status on the species and strain levels [152].
Information of beneficial results provided by the NGP will encompass comprehensive understanding of their targeted diseases. On top of these, the underlying molecular mechanisms on how NGP work and interact with the host have to be clarified [153]. It is important to characterize in vitro bacterial physiology, genomic analysis of potential virulence and antimicrobial resistance genes, investigations on the presence or absence of potential genes involved in transferring antibiotic resistance gene, and in vivo acute toxicity studies in both healthy and immunosuppressed mice [154].
The regulation of marketed probiotics applies differently among countries according to their classifications, and the country’s nutritional and dietary habits and lifestyle. Therefore, probiotics can be classified as nutraceuticals, dietary supplements, or food. Regulation and requirements for the safety assessment of beneficial microbes is variable within countries [155,156,157,158]. Probiotics, food supplements, labeling and other information to consumers are regulated under the legislation [159,160]. On the opposite side, the US and its FDA, responsible for quality control of probiotics, has taken the approach of having minimal regulation [161]. Most probiotic products in the US are classified as food or dietary supplements, which have to comply with good manufacturing practice (GMP) guidelines [162]. Harmonization and consensus of all stakeholders involved in the probiotic market could be important since boundaries between differently regulated markets have become minimal [144].
Therefore, next-generation beneficial microbes’ approval procedures should be enforced according to their classifications [154,155,156,157,158,159], stating the general safety of the product and using harmonized descriptions: the genus, species, and strains used, the CFU/g or mL of product (colony-forming units), the recommended use, and the daily dose; as well as quality and market parameters of the products: trademarks, formulae, ingredients, expiration dates, and storage conditions [151].

3. Discussion

The use of fermented food containing beneficial microbes is an ancestral tradition. Moreover, classical probiotics have been administered in several disorders and also specifically in obesity and metabolic diseases. However, they do not always provide harmonized endpoints data [136]. Controversial results have triggered the continuous need for searching and elucidating how to better understand and optimize the use and consumption information of probiotics. The combined impact of differential diets and the complementary probiotic strains should be standardized according to the individual and their microbiota composition and status [130]. Moreover, tested administration patterns and robust evidence of probiotics’ clinical beneficial impact should be well-supported by clinical trials [14].
Therefore, NGP as well as the described new beneficial microbial species and strains [10] constitute a growing trend of searching for biotechnological uses. NGP could be considered as a complementary, preventive and/or therapeutic tool for standardized interventional clinical studies [48,49]. However, NGP searching strategies, culturing research, and clinical implementation still face challenges, and there are specific gaps to be covered regarding bioinformatics and statistical analysis, safety assessment, specific strains, and the frame regulation on marketing and labeling [145,146,147,148]. Regarding the bioinformatics analysis, the limitations are related to the capabilities of the different platforms used. Statistical analysis faced problems of high dimensionality, over-dispersion, sparsity, and zero-inflation of data. Safety assessments lack proven efficacy at species level (in vitro test; genomic analysis for identifying potential virulence and antimicrobial resistance genes; in vivo acute toxicity tests), while the regulations frame lacks global harmonization and consensus from all stakeholders involved in the probiotics market, together with clear, reliable, and truthful labeling, focusing specifically on the level of genus, species, and strain used in the product. The label should clearly state the genus, species, and strain used, CFU/g or mL of product (colony-forming units), and the recommended use and daily dose. Moreover, it should refer to the quality parameters and market conditions [151].
More standardization efforts and research intervention strategies should focus on modulatory microbiota capacities and envisage the development and use of NGP, the formulation of which requires competent preclinical studies to show their efficacy and safety status. In overall terms, such advances and directions could help researchers, clinicians, dietitians, and nutritionists in using harmonized probiotics supplementary recommendations and targeted effects. Moreover, a joint effort to incentivize the reuse of published clinical data as open access (OA) [163] will make available more data for robust comparisons.
Next-generation probiotics are emerging microorganisms with demonstrated clinical impact, well-defined modes of actions, and specific functions impacting target diseases. The microbiota of healthy individuals appeared enriched in microorganisms considered NGP such as A. muciniphila, F. prausnitzii, Eubacterium spp., within other several species that seem to contribute to a balanced intestinal microbiota [48,49]. Interestingly, these same species were lower in obesity-related disorders. Thus, the present work has focused on searching and culturing approaches for other profiled and decreased levels of microbial species in metabolic diseases.
Specific approaches for obtaining specific NGP that neutralize dietary obesogens and their effects have been discussed.

4. Conclusions

Therefore, the present work highlights the taxa culturing pathways and key topics for extrapolating and aligning investigation efforts on searching for NGP to target diseases where the interventional modulation studies of microbiota impact on health status. The present work allowed us to highlight the following needs and conclusions:
  • Culturing of microorganisms from microbiota is the key activity to obtain NGP from healthy individuals, mainly through isolating those microorganisms identified as differentially decreased in the target disease or abundant in healthy microbiota, focusing on candidatus species from metagenomics studies.
  • Screening and selection of the potential NGP in a target-disease population by using in vitro models before clinical interventions.
  • Harmonization on performing exhaustive pre-analysis and post-intervention of individual microbiota composition through representative and validated methodologies (e.g., V3–V4 and Illumina MiSeq technology) is needed before administering NGP.
  • There is a need to standardize bioinformatics and database tools for specifically designing analysis of large and universal microbiome datasets.
  • NGP single strains or taxa consortium should have attributable documented benefits and their safety confirmation statements.
  • Effective doses and well-defined patterns of administration of NGP should become factors for aligning intervention doses since the beginning of clinical translation.
  • International guidelines on NGP and microbiota investigations for targeting obesity-related diseases prevention or treatments are needed. This will allow for more meaningful effect comparisons of harmonized and valuable studies, facilitating more robust meta-analysis.
  • Data reuse and availability of open access interventional clinical trials data will contribute to obtaining significant association of clinical outcomes.

Author Contributions

Conceptualization, A.L.-M., and M.A.; Methodology, all authors; Writing—Original Draft Preparation, A.L.-M., I.A., K.C., and M.A.; Writing—Review and Editing; M.A.; Supervision, M.A. and A.S.; Project Administration, M.A.; Funding Acquisition, M.A. All authors have read and agreed to the published version of the manuscript.


This work was carried out within the frame of GP/EFSA/ENCO/380 2018/03/G04: OBEMIRISK: Knowledge platform for assessing the risk of Bisphenols on gut microbiota and its role in obesogenic phenotype: looking for biomarkers. This research was also funded by FEDER-Infrastructure: IE_2019-198. A.L-M Incentivación de la Investigación. Plan Propio-UGR. K. Cerk is collaborating with UGR under the EU-FORA Programme (2020/2021).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available in the article or supplementary material.


Part of the results are from Ana López-Moreno doctoral thesis, Biomedicine Doctorate Program and Inmaculada Acuña doctoral thesis, Nutrition and Food Technology Doctorate Program of the University of Granada.

Conflicts of Interest

The authors declare no conflict of interest.


MDCMicrobiota-disrupting chemicals
NGPNext-generation probiotics
GITGastrointestinal tract
PCOSPolycystic ovary syndrome
FAOFood and Agriculture Organization of the United Nations
WHOWorld Health Organization
ICTInterventional clinical trials
PCSPreclinical studies
DCDendritic cells
TLR2Toll-like receptor 2
TNFTumor necrosis factor
WGSWhole genome sequencing
NGSNew-generation sequencing
ANAnorexia nervosa
HCHealthy control
LHLean healthy
MetSMetabolic syndrome
MHNOMetabolically healthy non-obese
MHOMetabolically healthy obese
MUNOMetabolically unhealthy non-obese
MUOMetabolically unhealthy obese
NAFLDNon-alcoholic fatty liver disease
NASHNon-alcoholic steatohepatitis
OBHObese healthy
OBT2DObese type 2 diabetes
RISK1Patients with only one disease
RISK2Patients with two disease
RISK3Patients with three disease
SSSimple steatosis
T1DType 1 diabetes
T2DType 2 diabetes
TSNOTsumura Suzuki obese diabetes mice
TSODTsumura Suzuki non obesity mice
BPABisphenol A
BPSBisphenol S
YCFAYeast-extract-casein hydrolysate-fatty acids
GAMGifu anaerobic medium
BHIBrain–heart infusion
EMBEosin methylene blue
LBSLactobacillus selection
GMMGut microbiota medium
MRSMan, Rogosa, and Sharpe
RNA Ribonucleic acid
rRNARibosomal ribonucleic acid
DNADeoxyribonucleic acid
OTUOperational taxonomic unit
EUEuropean Union
Ph. Eur.European Pharmacopoeia
USUnited States
GRASGenerally recognized as safe
FDAFood and Drug Administration
EFSAEuropean Food Safety Authority
QPSQualified presumption of safety
EMAEuropean Medicines Agency
MHLWMinistry of Health and Welfare
FOSHUFood for specified health use
FUFOSEFunctional food science in Europe
SFDAState Food and Drug Administration
DSHEADietary Supplement Health and Education Act
BLABiologic license application
CFIAThe Canadian Food Inspection Agency
GMPGood manufacturing practice
CFUColony-forming units
OAOpen access


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Figure 1. Next Generation Probiotics (NGP) criteria to be applied for searching strategies, Whole Genome Sequencign (WGS), Next Generation Sequencing (NGS):
Figure 1. Next Generation Probiotics (NGP) criteria to be applied for searching strategies, Whole Genome Sequencign (WGS), Next Generation Sequencing (NGS):
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Figure 2. Multiomics and bioinformatics analysis of microbiome components.
Figure 2. Multiomics and bioinformatics analysis of microbiome components.
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Table 2. Next-generation probiotic strains used in obesity-related clinical trials and preclinical studies.
Table 2. Next-generation probiotic strains used in obesity-related clinical trials and preclinical studies.
NGP Microbial Strains, Target Species,
Study Reference
Study DesignDietary AspectsClinical Effects and Functionality
Akkermansia muciniphila Muc [CIP 107961]—Human [41]
[ Identifier: NCT02637115]
ICT: randomized, double-blind, placebo-controlled pilot study
Live probiotics 1010/day vs. pasteurized probiotics 1010/day vs. placebo in patients with metabolic syndrome
Normal dietary intake and physical activity during the study period↑ Insulin sensitivity, ↓ insulinemia and ↓plasma total cholesterol
Akkermansia muciniphila WST01—Human [42] [ Identifier: NCT04797442]ICT: randomized, double-blind, placebo-controlled, multicenter clinical trial
Probiotics vs. placebo in overweight or obese patients with type 2 diabetes
Intervention added onto lifestyleResults will be available in June 2022
Christensenella minuta Xla1—Human [43]
[ Identifier: NCT04663139]
ICT: randomized, partially placebo-controlled double-blind
Probiotics vs. placebo in healthy volunteers, overweight, and obese adults
Agreement to keep food, drink, physical activities, and alcohol consumption habits unchanged throughout the studyResults will be available in October 2021
Eubacterium hallii—Human [44]
[ Identifier: NCT04529473]
ICT:double-blind, randomized, placebo-controlled
Probiotics vs. placebo
Maintenance of dietary habits and physical activity levels throughout the study periodResults will be available on January 2022
Hafnia alvei HA4597—Human [45]
[ Identifier: NCT03657186]
ICT: multicenter, randomized, double-blind placebo-controlled study.
Probiotics vs. placebo on weight reduction in overweight subjects
−20% hypocaloric diet and maintainance of the usual physical activity↑ Weight loss in overweight subjects, ↑ feeling of fullness,
↑ loss of hip circumference, ↓ fasting glycemia
Lactococcus lactis NRRL-B50571—Human [46]
[ Identifier: NCT02670811]
ICT: double-blind randomized controlled
Probiotics vs. placebo on prehypertensive subjects
Participants were asked not to change their diet or lifestyle during the intervention↓ Systolic and diastolic blood pressure,
↓ Triglyceride, total cholesterol, and low-density lipoprotein
Escherichia coli Nissle 1917—Human [47]
[ Identifier: NCT02144948]
ICT: single group assignment.
Patients with type 2 diabetes
-Results not yet available or posted on
November 2021
Akkermansia muciniphila—Muc [CIP 107961]—Mice [48,49]PCS: probiotics vs. control. ObesityHigh-fat diet/standard diet↓ Fat-mass gain, ↑ insulin sensitivity, restore gut barrier function by acting on TLR2, ↑ mucus later thickness; similar effects by a purified membrane protein alone (Amuc_1100)
Clostridium butyricum CGMCC0313.1—Mice [50]PCS: probiotics vs. control. ObesityHigh-fat diet/standard diet↓ Lipid accumulation in liver and serum, ↓ insulin levels, ↑ glucose tolerance, ↑ insulin sensitivity, ↓ TNF-α and ↑ IL-10 and IL-22 in colon
Faecalibacterium prausnitzii VPI C13-20-A—Mice [51]PCS: probiotics vs. control. ObesityHigh-fat diet/standard diet↑ Hepatic health, ↓ adipose tissue inflammation
Bacteroides uniformis CECT 7771– Mice [52]PCS: probiotics vs. control. ObesityHigh-fat diet/standard diet↓ Weight gain; ↓ dietary fat absorption; ↓ liver steatosis; ↓ serum cholesterol, triglyceride, glucose, insulin and leptin; ↑ glucose tolerance; ↑ TNF-α by DCs after LPS stimulation;↑ phagocytosis
Parabacteroides goldsteinii JCM 13446—Mice [53]PCS: probiotics vs. control. ObesityHigh-fat diet/standard diet↓ Obesity by ↑ adipose tissue thermogenesis, ↑ intestinal integrity ↓ inflammation, ↑ insulin sensitivity
Christensenella minuta—Mice [54]PCS: probiotics vs. control. ObesityHigh-fat diet/standard diet↓ Weight gain, ↓ adiposity. Highly heritable in a lean host phenotype
Eubacterium hallii DSM 17630—Mice [55]PCS: probiotics vs. control. DiabetesHigh-fat diet/standard diet↑ Energy metabolism and ↑ insulin sensitivity through glycerol conversion 3hydroxypropionaldehyde
Hafnia alvei HA4597—Mice [56]PCS: probiotics vs. control. ObesityHigh-fat diet/standard diet↑ Beneficial anti-obesity and metabolic effects, ↓ food intake, ↓ body weight and ↓ fat mass gain
Lactococcus lactis (GMM) LL-pCYT: HSP65-6P277 and LL-pHJ—Mice [57]PCS: probiotics vs. control. ObesityHigh-fat diet/standard diet↓ Antigen-specific of cellular immunity
Escherichia coli Nissle 1917 (EcN-GMM)– Mice [58]PCS: probiotics vs. control. ObesityHigh-fat diet/standard dietModulation of the neuropeptide expression of energy intake and expenditure in the hypothalamus
NGP tested with anti-obesity effects; DC: dendritic cells; IL: interleukin; ICT: interventional clinical trials; LPS: lipopolysaccharide; PCS: preclinical studies; TLR2: toll-like receptor 2; TNF: tumor necrosis factor.
Table 3. Clinical trials and variations of the main microbiota taxa found in specimens from patients suffering metabolic- and endocrine-related diseases.
Table 3. Clinical trials and variations of the main microbiota taxa found in specimens from patients suffering metabolic- and endocrine-related diseases.
ReferenceSubjects and
Dietary AspectsSample Size and Clinical TraitsDetection TechniqueMicrobial Taxa Modifications
Zhong et al. [66]Human
NAN = 382; MHNO n = 191; MUNO n = 61; MHO n = 66; MUO n = 64MiSeq platform (Illumina)
V3–V4 region of the 16S rRNA gene
Lachnospiraceae, Bacteroidaceae, Methanobacteriaceae and Pasteurellaceae in MHNO and MUNO
Jonduo et al. [67]Human
Participant’s predominantly plant-based diet:
vegetables (e.g., sweet potato, cassava, plantain, and beans)
n = 18; OB n = 9; Non-OB n = 9 454 GS FLX platform or 454 GS JUNIOR system (Roche)
V1-V2 region of the 16S rRNA gene
Prevotella in almost all individuals
Thingholm et al. [68]Human
NAn = 1280; LH n = 633; OBH n = 494; OBT2D n = 153MiSeq platform (Illumina)
V1-V2 region of 16S rRNA gene
Akkermansia, Faecalibacterium, Oscillibacter, and Alistipes in obese individuals
Faecalibacterium prausnitzii in obese individuals
Schwiertz et al. [65]Human
Western dietn= 98; HC n = 30; OW n = 35; OB n = 33qPCRBacteroides in overweight vs. HC
Ruminococcus flavefaciens in overweight and obese
Bifidobacterium and Clostridium leptum in obese
Methanobrevibacter in overweight and obese
Gao et al. [69]Human
NAn = 192; HC n = 25; OW n = 22; OB n = 145MiSeq platform (Illumina)
V4 region of the 16S rRNA gene
Lachnoclostridium, Fusobacterium, Escherichia-Shigella, Klebsiella, Bacillus, and Pseudomonas in OW and OB
Clostridia, Faecalibacterium, Ruminococcus, Bifidobacterium, and Lachnospiraceae_UCG_008 in HC
Armougom et al. [70]Human
Anorexia nervosa
NAn= 49; HC n = 20; OB n = 20; AN n = 9qPCRLactobacillus in OB
Horie et al. [71]Mice
Type 2 diabetes
NA5-week-old TSNO mice n = 5; 5-week-old TSOD mice n = 5; 12-week-old TSNO mice n = 5; 12-week-old TSOD mice n = 5 qPCRLactobacillus in TSOD vs. TSNO
Bacteroidales and Lachnospiraceae in TSNO vs. TSOD
Turicibacter and SMB53 in TSOD
Larsen et al. [72]Human
Type 2 diabetes
NAn = 36; HC n = 18; T2D n = 18MiSeq platform (Illumina)
V4 region of the 16S rRNA gene
↑ Firmicutes in HC ↑ Bacteroidetes and Betaproteobacteria in T2D
Clostridia in T2D
Sedighi et al. [73]Human
Type 2 diabetes
NAn = 36; HC n = 18; T2D n = 18qPCRLactobacillus in T2D
Bifidobacterium in HC
Fusobacterium in T2D
Moghadam et al. [74]Human
Tipe 2 diabetes
NAn = 36; HC n = 18; T2D n = 18qPCRFaecalibacterium prausnitzii in HC
Ahmad et al. [75]Human
Type 2 diabetes
Eastern dietary habits (high carbohydrate and fat intake, low fiber intake) n = 60; HC n = 20; Obese-T2D n = 40MiSeq platform (Illumina)
V3–V4 region of the 16S rRNA gene
↑ Firmicutes in Obese-T2D
↑ Clostridia, Negativicutes, Coriobacteria, Acidobacteria, Deferribacteres, and Gemmatimonadetes in obese-T2D
↑ Verrucomicrobia, Bacteroidetes, Proteobacteria, and Elusimicrobia in HC
Prevotella P4_76, Clostridiales, Porphyromonadaceae bacterium DJF B175, Candidatus Alistipes marseilloanorexic AP11, Bacillus sporothermodurans, Staphylococcus SV3, and Iamia in obese-T2D
Ejtahed et al. [76]Human
Type 2 diabetes
Type 1 diabetes
NAn = 110; HC n = 40; T2D n = 49;
T1D n = 21
qPCREscherichia, Prevotella, and Lactobacillus in T1D and T2D
Bifidobacterium, Roseburia, and Bacteroides in HC
Faecalibacterium in T1D vs. HC and T2D
Takagi et al. [77]Human
Type 2 diabetes
NAn = 239; HC n = 54; HT n = 97;
HL n = 96; T2D n = 162
MiSeq platform (Illumina)
V3–V4 region of the 16S rRNA gene
↑ Actinobacteria in HT, HL, T2D, RISK2, and RISK3
Bacteroidetes in HT, HL, T2D and RISK3
Bifidobacterium in HL, T2D, RISK1 and RISK2
Collinsella in HT, HL, T2D, RISK2 and RISK3
Escherichia in RISK 3
Alistipes in HL
Wang et al. [78]Human
Non-alcoholic fatty liver disease
Omnivorous Chinese dietn = 126; HC n = 83; NAFLD n = 43454 Life Sciences Genome Sequencer FLX system (Roche)
V3 region of the 16S rRNA gene
↓ Firmicutes ↑Bacteroidetes in NAFLD
↑ Bacteroidia ↓ Clostridia in NAFLD
Coprococcus, Pseudobutyrivibrio, Moryella, Roseburia, Anaerotruncus, Ruminococcus, Anaerosporobacter, andLactobacillus in NAFLD
Li et al. [79]Human
Non-alcoholic fatty liver disease
No dietary restrictions imposedn = 67; HC n = 37; NAFLD n = 30MiSeq platform (Illumina)
V4 region of the16S rRNA gene
Lactobacillaceae, Peptostreptococcaceae, Veillonellaceae, EtOH8, Coprobacillaceae, and Erysipelotrichaceae in NAFLD
Porphyromonas and Succinivibrio in NAFLD
Odoribacter and Proteus in NAFLD
Shen et al. [80]Human
Non-alcoholic fatty liver disease
NAn = 47; HC n = 22; NAFLD n = 25454 GS-FLX platform (Roche)
V3-V5 region of the
16S rRNA gene
Proteobacteria, Fusobacteria, Lachnospiraceae_Incertae_Sedis and Blautia in NAFLD
↑ Bacteroidetes and Prevotella in HC
Escherichia_Shigella, Clostridium_XVIII, and Staphylococcus in NAFLD
Raman et al. [81]Human
Non-alcoholic fatty liver disease
No dietary restrictions imposedn = 60; HC n = 30; NAFLD n = 30qPCRLactobacillus, Roseburia, Dorea, and Robinsoniella in NAFLD
Oscillibacterin NAFLD
Michail et al. [82]Human
Non-alcoholic fatty liver disease
No dietary restrictions imposedn = 50; HC n = 26; NAFLD n = 13; Obese non-NAFLD n = 11qPCRGammaproteobacteria, Prevotella, and Epsilonproteobacteria in NAFLD
ClostridiaAlphaproteobacteria in obese non-NAFLD
Nistal et al. [83]Human
Non-alcoholic fatty liver disease
NAn = 73; HC n = 20; Obese-NAFLD n = 36; Obese non-NAFLD n = 17MiSeq platform (Illumina)
V3–V4 region of the 16S rRNA gene
Bacilli in obese-NAFLD
Betaproteobacteria in obese-NAFLD vs. obese non-NAFLD
Oscillospira, Akkermansia, and Eubacterium in obese-NAFLD and obese non-NAFLD vs. HC
Megasphaera, Lactobacillus, Acidominococcus in obese-NAFLD, and obese non-NAFLD vs. HC
Blautia, Alkaliphilus, and Flavobacterium in obese-NAFLD
Staphylococcus in obese-NAFLD
Loomba et al. [84]Human
Non-alcoholic fatty liver disease
NAn= 86; NAFLD n = 72;
Fibrosis n = 14
qPCR↑ Firmicutes in NAFLD, ↑ Proteobacteria in fibrosis
Eubacterium rectale and Bacteroides vulgatus in NAFLD
Bacteroides vulgatus and Escherichia coli in fibrosis
Ruminococcus obeum, and Eubacterium rectale in fibrosis
Del Chierico et al.
Non-alcoholic fatty liver disease
Non-alcoholic steatohepatitis
NAn= 115; HC n = 54, OB n = 8;
NAFLD n = 27; NASH n = 26
454- Junior Genome Sequencer FLX system (Roche)
V1-V3 region of the 16S rRNA gene
Bradyrhizobium, Anaerococcus, Peptoniphilus, Propionibacterium acnes, Dorea, and Ruminococcus
Oscillospira and Rikenellaceae in NAFLD
Ruminococcus, Dorea, and Blautia in NASH
Da Silva et al. [86]Human
Non-alcoholic steatohepatitis
Simple steatosis
7-day food recordn = 67; HC n = 28; SS n = 15: NASH n = 24MiSeq platform (Illumina)Ruminococcus, Faecalibacteriumprausnitzii, and Coprococcus in NASH and SS vs. HC
Mouzaki et al. [87]Human
Non-alcoholic steatohepatitis
Simple steatosis
HC patients were consuming more calories per kg compared to patients with NASHn = 50; HC n = 17; SS n = 11; NASH n = 22qPCR↓ Bacteroidetes in NASH vs. SS and HC
Clostridium coccoides in NASH vs. SS
Zhu et al. [88]Human
Non-alcoholic steatohepatitis
NAn= 63; HC n = 16; OB n = 25; NASH n = 22qPCRBacteroides ↓ Firmicutes in NASH and OB
Blautia and Faecalibacterium in NASH and OB
Boursier et al. [89]Human
Non-alcoholic steatohepatitis
NAn = 57; Non-NASH n = 20
NASH n = 10; Fibrosis ≥ 2 n = 27
V4 region of 16S rRNA gene
BacteroidesPrevotella in NASH
Bacteroides and Ruminococcus in fibrosis ≥ 2
Prevotella in fibrosis ≥ 2
Qin et al. [90]Human
NAn= 179; HC n = 83; Cirrhosis n = 96qPCRStreptococcus, Veillonella, Clostridium and Prevotella in cirrhosis
Eubacterium and Alistipes in HC
Bacteroides in cirrhosis
Lim et al. [91]Human
Methabolic syndrome
NAn = 655; Monozygotic twins n = 306; Dizygotic twins n = 74; Siblings n = 275MiSeq platform (Illumina)
V4 region of the 16S
rRNA gene
Lactobacillus, Sutterella and Methanobrevibacter in MetS
Parabacteroides, Bifidobacterium, Odoribacter, Akkermansia and Christensenella in MetS
Genera and species in bold letters highlight the decreased microorganisms to be considered as potential NGP to be searched, cultured and assayed for their anti-obesity modulation effects. AN: anorexia nervosa; HC: healthy control; HL: hyperlipidemia; HT: hypertension; LH: lean healthy; MetS: metabolic syndrome; MHNO: metabolically healthy non-obese; MHO: metabolically healthy obese; MUNO: metabolically unhealthy non-obese; MUO: metabolically unhealthy obese; NA: Not applicable; NAFLD: non-alcoholic fatty liver disease; NASH: non-alcoholic steatohepatitis; OB: obese; OBH: obese healthy; OBT2D: obese type 2 diabetes; OW: overweight; RISK1: patients with only one disease; RISK2: patients with two diseases; RISK3: patients with three diseases; SS: simple steatosis; T1D: type 1 diabetes; T2D: type 2 diabetes; TSNO: Tsumura Suzuki Obese Diabetes mice; TSOD: Tsumura Suzuki, Non-Obesity mice.
Table 4. Culturing approaches to favor specific microbiota species and NGP taxa and candidatus species.
Table 4. Culturing approaches to favor specific microbiota species and NGP taxa and candidatus species.
Reference/SampleCulture MediaCulture Media ModificationsSelected Favored Cultured MicroorganismsOutcome and Observations:
New Species Cultured: Potential NGP
Browne et al. [118]
YCFAGlucose (0.2%), maltose (0.2%), and cellobiose (0.2%)Aero-intolerant genus and species 68 new isolated species: 16S RNA similarity 86–97%
Anaerotruncus colihominis
Blautia luti; B. hydrogenotrophica
Clostridium boltae; C. celerecrescens; C. celerescens; C. clostridioforme; C. cocleatum; C. disporicum; C. ghonii; C. hathewayi; C. innocuum; C. lituseburense; C. methylpentosum; C. nexile; C. oroticum; C. saccharogumia; C. saccharolyticum; C. thermocellum; C. xylanolyticum
Coprococcus eutactus
Oscillibacter valericigenes
Roseburia faecis; R. inulinivorans
Ruminococcus albus; R.bromii; R. flavefaciens; R. gnavus; R.obeum; R. torques
YCFAPre-treatment with ethanol 70% (v/v), glucose (0.2%), maltose (0.2%), cellobiose (0.2%), sodium taurocholate (0.1%).
Spore-forming gut aero-intolerant bacteria
Alistipes finegoldii
Anaerotruncus colihominis
Blautia hydrogenotrophica; B. obeum; B. wexlerae
Clostridum baratti; C. bartlettii; C. clostridioforme; C. disporicum; C. hathewayi; C.innocuum; C. paraputrificum; C.perfringens
Coprococcus comes; C. eutactus
Prevotella copri
Roseburia hominis; R. intestinalis; R. inulinvorans;
Ruminococcus bromii; R. gnavus; R. obeum; R. torques
Chang et al. [119]
YCFAPre-incubation in blood culture bottles supplemented with 10% sheep blood and 10% rumenAero-intolerant bacteria
Alistipes shahii; A. onderdonkii,
Clostridium bifermentans, C. innocuum, C. hiranonis, C. butiricum, C. hathewayi,
C. bolteae, C. sporogenes,
Odoribacter splanchnicus
22% of species isolated increase:
16S RNA similarity 93–97%
3 new species isolated:
Longicatena caemuris
Bacillus alcalophilus Pseudogracilibacillus auburnensis
Gotoh et al.
Microbial bank
GAMNAAero-intolerant bacteria

72% of species of the top 56 species listed in the “human gut microbial gene catalogue” cultured in GAM
Isolated species in GAM:
Anaerotruncus colihominis,
Blautia hansenii,
Clostridium nexile, C. asparagiforme, C. scindens,
Coprococcus comes
Roseburia intestinalis
Ruminococcus torques, R. lactaris, R. obeum, R. gnavus.
Lagier et al.
16-years-old male
BHIPreincubation of the stool with lytic E. coli T1 and T4 phagesNon-fastidious aerobic and facultatively anaerobic bacteriaEnterobactermassiliensis strain JC163T
Bailey and Coe [122]
Rhesus Monkeys
BHINANon-fastidious aerobic and facultatively anaerobic bacteriaNA
EMBNAGram-negative aerobic and facultatively anaerobic bacteriaNA
LBSNAAerobic members of lactobacilli Lactobacillusspp.
Lei et al. [123]
Female mice
GMMNAGut aero-intolerant bacteria
López-Moreno [117]BHISupplemented with Obesogens: BPA, BPSAnaerobic facultative FirmicutesStaphylococcus, Bacillusamyloliquefaciens group, Streptococcussalivarius
López-Moreno [117]MRSSupplemented with Obesogens: BPA, BPSLactobacillus, EnterobacteriaLatilactobacillus sakei, Enterococcus faecium
YCFA: yeast-extract-casein hydrolysate-fatty acids; GAM: gifu anaerobic medium; BHI: brain–heart infusion; EMB: eosin methylene blue; LBS: Lactobacillus selection; GMM: gut microbiota medium; MRS; Man, Rogosa and Sharpe; BPA: Bisphenol A; BPS: Bisphenol S. Genera and species in bold letters highlight the microorganisms to be considered as potential NGP to be searched, cultured and assayed for their anti-obesity modulation effects.
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López-Moreno, A.; Acuña, I.; Torres-Sánchez, A.; Ruiz-Moreno, Á.; Cerk, K.; Rivas, A.; Suárez, A.; Monteoliva-Sánchez, M.; Aguilera, M. Next Generation Probiotics for Neutralizing Obesogenic Effects: Taxa Culturing Searching Strategies. Nutrients 2021, 13, 1617.

AMA Style

López-Moreno A, Acuña I, Torres-Sánchez A, Ruiz-Moreno Á, Cerk K, Rivas A, Suárez A, Monteoliva-Sánchez M, Aguilera M. Next Generation Probiotics for Neutralizing Obesogenic Effects: Taxa Culturing Searching Strategies. Nutrients. 2021; 13(5):1617.

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López-Moreno, Ana, Inmaculada Acuña, Alfonso Torres-Sánchez, Ángel Ruiz-Moreno, Klara Cerk, Ana Rivas, Antonio Suárez, Mercedes Monteoliva-Sánchez, and Margarita Aguilera. 2021. "Next Generation Probiotics for Neutralizing Obesogenic Effects: Taxa Culturing Searching Strategies" Nutrients 13, no. 5: 1617.

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