High-throughput qPCR and 16S rRNA gene amplicon sequencing as complementary methods for the investigation of the cheese microbiota

High-Throughput Multiplex Detection of Antibiotic-Resistant Genes and Virulence Factors in Escherichia coli Using Digital Multiplex Ligation Assay

Escherichia coli causes >400,000 annual deaths in children aged <5 years worldwide, with morbidity and mortality exacerbated by antimicrobial-resistant strains. A high-throughput multiplexing assay called digital multiplex ligation assay (dMLA) was developed to detect simultaneously 43 priority genes in E. coli related to the following: antibiotic resistance (n = 19), virulence factors (n = 16), and phylogroup markers (n = 6) with controls (uidA, gapdh). Genes are detected via PCR amplification of adjacent probe pairs that ligate in the presence of target gene-specific DNA, followed by sequencing of amplicons on short-read sequencers. The assay was tested in technical replicates on 63 synthetic DNA controls, and applied to 58 E. coli, 2 Staphylococcus aureus, 2 Klebsiella pneumoniae, 1 Klebsiella oxytoca, 1 Vibrio cholera, 1 Pseudomonas lurida, and 1 Salmonella enterica isolates in duplicate. Whole-genome sequencing was used to assess specificity and sensitivity. dMLA showed 100% sensitivity and >99.9% specificity and balanced accuracy on synthetic DNA. Balanced accuracy, calculated as the average of sensitivity and specificity, accounts for imbalanced data sets where negative outcomes are significantly more prevalent than positive ones. dMLA achieved a balanced accuracy of 90% for bacterial isolates. The results underline dMLA's effectiveness in high-throughput characterization of E. coli libraries for antimicrobial resistance genes and virulence factors, leveraging sequencing for massively parallel multiplexing of gene regions on multiple samples simultaneously, and are extendable to targets beyond E. coli.

Fluorescence-based spectrometric and imaging methods and machine learning analyses for microbiota analysis

Most microbiota determination (skin, gut, soil, etc.) are currently conducted in a laboratory using expensive equipment and lengthy procedures, including culture-dependent methods, nucleic acid amplifications (including quantitative PCR), DNA microarray, immunoassays, 16S rRNA sequencing, shotgun metagenomics, and sophisticated mass spectrometric methods. In situ and rapid analysis methods are desirable for fast turnaround time and low assay cost. Fluorescence identification of bacteria and their mixtures is emerging to meet this demand, thanks to the recent development in various machine learning methods. High-dimensional spectroscopic or microscopic imaging data can be obtained to identify the bacterial makeup and its implications for human health and the environment. For example, we can classify healthy versus non-healthy skin microbiome, inflammatory versus non-inflammatory gut microbiome, degraded versus non-degraded soil microbiome, etc. This tutorial summarizes the various machine-learning algorithms used in bacteria identification and microbiota determinations. It also summarizes the various fluorescence spectroscopic methods used to identify bacteria and their mixtures, including fluorescence lifetime spectroscopy, fluorescence resonance energy transfer (FRET), and synchronous fluorescence (SF) spectroscopy. Finally, various fluorescence microscopic imaging methods were summarized that have been used to identify bacteria and their mixtures, including epi-fluorescence microscopy, confocal microscopy, two-photon/multi-photon microscopy, and super-resolution imaging methods (STED, SIM, PALM, and STORM). Finally, it discusses how these methods can be applied to microbiota determinations, what can be demonstrated in the future, opportunities and challenges, and future directions.

Analytical Mapping of Swiss Hard Cheese to Highlight the Distribution of Volatile Compounds, Aroma, and Microbiota

Cheese is one of the most consumed fermented animal-based products globally, rendering its quality assessment and evaluation of substantial economic interest. Understanding the degree of cheese homogeneity is paramount for designing effective sampling strategies, yet this information is largely lacking. This study investigates the homogeneity of a cheese wheel based on the distribution of volatile compounds, microbiota, sodium chloride content, and pH, combined with sensory analyses. The outer zones of the cheese wheel were primarily characterized by the presence of sulfur compounds, esters, pyrazines, ketones, Streptococcus thermophilus, high sodium chloride concentration and high pH. In contrast, the inner zones of the cheese wheel were dominated by lactones, carboxylic acids, aldehydes, Lactobacillus delbrueckii subsp. lactis and Lacticaseibacillus paracasei. The presence of alcohols and Lactobacillus helveticus was observed throughout the cheese wheel. Furthermore, sensory descriptions were found to match predominantly with the aroma of the volatile compounds identified. The cheese wheel was found to be heterogeneous in all investigated characteristics. Our results indicate that the level of cheese homogeneity should be considered when designing sampling strategies, as these significantly impact the accuracy and reproducibility of analytical outcomes.

Metagenomic Characterization of the Maerua crassifolia Soil Rhizosphere: Uncovering Microbial Networks for Nutrient Acquisition and Plant Resilience in Arid Ecosystems

Background/Objectives:Maerua crassifolia, a threatened medicinal species endemic to drylands, exhibits a pronounced drought sensitivity. Despite the critical role of microorganisms, particularly bacteria and fungi, the microbial consortia in M. crassifolia's rhizosphere remain underexplored. Methods: Metagenomic whole genome shotgun sequencing (WGS) was employed to elucidate the taxonomic composition of bacterial and fungal communities inhabiting the soil rhizosphere of M. crassifolia. Results: The data revealed a marked predominance of bacterial genomes relative to fungal communities, as evidenced by non-redundant gene analysis. Notably, arbuscular mycorrhizal fungi (AMF), specifically Rhizophagus clarus, Rhizophagus irregularis and Funneliformis geosporum, are key rhizosphere colonizers. This study confirmed the presence of phosphate-solubilizing bacteria (PSB), such as Sphingomonas spp., Cyanobacteria and Pseudomonadota, underscoring the critical role of these microorganisms in the phosphorus cycle. Additionally, the study uncovered the presence of previously uncharacterized species within the phylum Actinobacteria, as well as unidentified taxa from the Betaproteobacteria, Gemmatimonadota and Chloroflexota phyla, which may represent novel microbial taxa with potential plant growth-promoting properties. Conclusions: Findings suggest a complex, symbiotic network where AMF facilitate phosphorus uptake through plant-root interactions. In a tripartite symbiosis, PSB enhance inorganic phosphorus solubilization, increasing bioavailability, which AMF assimilate and deliver to plant roots, optimizing nutrition. This bacterial-fungal interplay is essential for plant resilience in arid environments. Future investigations should prioritize the isolation and characterization of underexplored microbial taxa residing in the rhizosphere of M. crassifolia, with particular emphasis on members of the Actinobacteria, Betaproteobacteria, Gemmatimonadota and Chloroflexota phyla to uncover their roles in nutrient acquisition and sustainability.

Impact of DNA extraction, PCR amplification, sequencing, and bioinformatic analysis on food-associated mock communities using PacBio long-read amplicon sequencing

BACKGROUND

Long-read 16S rRNA gene amplicon sequencing has a high potential for characterizing food-associated microbiomes. The advantage results from sequencing the full-length (1,500 bp) gene, enabling taxonomic resolution at species level. Here we present a benchmarking study using mock communities representative of milking machine biofilms and raw meat, revealing challenges relevant to food-associated habitats. These were varying species abundances, reliable intra-genus differentiation of species, and detection of novel species with < 98.7% sequence identity to type strains. By using mock communities at different levels of preparation - as mixed whole cells, mixed extracted DNA, and mixed PCR products - we systematically investigated the influence of DNA extraction using two different kits, PCR amplification of 16S rRNA genes, sequencing, and bioinformatics analysis including reference database and gene copy number normalization on bacterial composition and alpha diversity.

RESULTS

We demonstrated that PacBio ccs-reads allowed for correct taxonomic assignment of all species present within the mock communities using a custom Refseq database. However, choice of percent identity values for taxonomic assignment had a strong influence on identification and processing of reads from novel species. PCR amplification of 16S rRNA genes produced the strongest bias on the observed community composition, while sequencing alone reproduced the preset composition well. The PCR bias can in part be attributed to differences in mol% G + C content of 16S rRNA genes resulting in preferred amplification of low mol% G + C-containing taxa.

CONCLUSIONS

This study underlines the importance of benchmarking studies with mock communities representing the habitat of interest to evaluate the methodology prior to analyzing real samples of unknown composition. It demonstrates the advantage of long-read sequencing over short-read sequencing, as species level identification enables in-depth characterization of the habitat. One benefit is improved risk assessment by enabling differentiation between pathogenic and apathogenic species of the same genus.

Antilisterial Properties of Selected Strains from the Autochthonous Microbiota of a Swiss Artisan Soft Smear Cheese

High incidences of the foodborne pathogen Listeria monocytogenes have been reported on smear cheeses, and despite increased hygiene efforts, this incidence has remained stable in recent years. Applying antilisterial strains may increase the safety of smear cheeses. To find and test antilisterial strains, we inoculated fresh soft cheeses from nine dairies with the surrogate species Listeria innocua and assessed its growth under standardized ripening conditions. Acetic acid at day 23 (r = -0.66), lactose in fresh cheese (r = -0.63), and glucose at day 10 (r = -0.62), as well as seven amplicon sequence variants (ASVs), were negatively correlated with L. innocua growth. Two of these ASVs were assigned to the genus Leuconostoc of Lactobacillaceae (r = -0.82 and -0.71). Isolates from this family, from Aerococcaceae, and Carnobacteriaceae were characterized according to their inhibitory properties, and those showing antilisterial properties were applied as protective cultures in challenge tests. The combined application of strains of Leuconostoc mesenteroides, Aerococcaceae, and Carnobacteriaceae successfully eliminated low levels of L. innocua in the final products. This is likely explained by antimicrobial compounds, including mesentericin Y105 and acetate, and competition for carbon sources and iron. This study shows a promising way to improve the safety of soft smear cheeses by applying defined protective cultures.

Chinese guidelines for integrated diagnosis and treatment of intestinal microecology technologies in tumor application (2024 Edition)

ABSTRACT

Intestinal microecology (IM) is the largest and most important microecological system of the human body. Furthermore, it is the key factor for activating and maintaining the physiological functions of the intestine. Numerous studies have investigated the effects of the gut microbiota on the different tissues and organs of the human body as well as their association with various diseases, and the findings are gradually being translated into clinical practice. The gut microbiota affects the occurrence, progression, treatment response, and toxic side effects of tumors. The deepening of research related to IM and tumors has opened a new chapter in IM research driven by methods and technologies such as second-generation sequencing and bioinformatics. The IM maintains the function of the host immune system and plays a pivotal role in tumor-control drug therapy. Increasing evidence has proven that the efficacy of tumor-control drugs largely depends on the IM balance, and strategies based on the IM technology show promising application prospects in the diagnosis and treatment of tumor. The Tumor and Microecology Professional Committee of the Chinese Anti-cancer Association gathered relevant experts to discuss and propose the "Chinese guidelines for integrated diagnosis and treatment of IM technologies in tumor application (2024 Edition)," which was established based on the research progress of the application of the IM technology in tumor to provide a basis for the standardization of the diagnosis and treatment of the IM technology in the tumor.

Exploring the Gut Microbiome and Metabolome in Individuals with Alopecia Areata Disease

In recent years, heightened attention has been devoted to unravelling the intricate interplay between genetic and environmental factors shaping the gut microbiota and its significance for human health. This study delves into exploring the plausible connection between Alopecia Areata (AA), an autoimmune disease, and the dynamics of the gut microbiome. Examining a cohort of healthy adults and individuals with AA, both the gut microbiota composition and volatile organic compound (VOC) metabolites from faeces and urine were analysed. While overall microbiota composition showed no significant differences, intra-individual variability revealed distinctions related to age, gender, and pathology status, with AA individuals exhibiting reduced species richness and evenness. Differential abundance analysis identified microbial biomarkers for AA, notably Firmicutes, Lachnospirales, and Blautia, while Coprococcus stood out for healthy individuals. The Data Integration Analysis for Biomarker discovery using Latent Components (DIABLO) method further supported these findings including metabolite biomarkers, such as esters of branched chain fatty acids and branched chain amino acids as predictors for AA, suggesting potential links to oxidative stress. Despite certain limitations, the study highlights the complexity of the gut microbiome and its metabolites in the context of AA, while the biomarkers identified could be useful starting points for upcoming studies.

Effects of extreme precipitation on bacterial communities and bioaerosol composition: Dispersion in urban outdoor environments and health risks

Concerns about contaminants dispersed by seasonal precipitation have grown due to their potential hazards to outdoor environments and human health. However, studies on the crucial environmental factors influencing dispersion changes in bacterial communities are limited. This research adopted four-season in situ monitoring and sequencing techniques to examine the regional distribution profiles of bioaerosols, bacterial communities, and risks associated with extreme snowfall versus rainfall events in two monsoon cities. In the early-hours of winter snowfall, airborne cultivable bioaerosol concentrations were 4.1 times higher than the reference exposure limit (500 CFU/m3). The concentration of ambient particles (2.5 μm) exceeded 24,910 particles/L (97 μg/m3), positively correlating with the prevalence of cultivable bioaerosols. These bioaerosols contained cultivable bacterial species such as pathogenic Staphylococcus aureus, Staphylococcus epidermidis, Streptococcus pneumoniae, and Escherichia coli. Bioaerosol concentrations increased by 53.0% during 50-mm snow extremes. Taxonomic analysis revealed that Pseudomonas, Staphylococcus, and Veillonella were the most abundant bacterial taxa in the initial snowmelt samples during winter precipitation. However, their abundance decreased by 87.6% as snowing continued (24 h). Reduced water base cation concentration also led to a 1.15-fold increase in the Shannon index, indicating a similar yet heightened bacterial diversity. Seasonally, Pedobacter and Massilia showed higher relative abundance (25% and 18%, respectively), presenting increased bacterial transmission to the soil. Furthermore, Pseudomonas was identified in 60% of spring snowstorm samples, suggesting long-distance dispersal of pathogenic bacteria. When these atmospheric aerosol particles carrying biological entities (0.65-1.1 μm) penetrated human alveoli, the calculated hazard ratio was 0.55, which as observed in inhalation exposures. Consequently, this study underscores the risk of seasonal precipitation-enhanced ambient bacterial transmission.

Identification of synthetic consortia from a set of plant-beneficial bacteria

The use of microbial inoculants in agriculture as biofertilisers and/or biopesticides is an appealing alternative to replace or reduce the practice of agrochemicals. Plant microbiota studies are revealing the different bacterial groups which are populating plant microbiomes re-energising the plant probiotic bacteria (PPB) translational research sector. Some single-microbial strain bioinoculants have proven valid in agriculture (e.g., based on Trichoderma, mycorrhiza or rhizobia); however, it is now recommended to consider multistrain consortia since plant-beneficial effects are often a result of community-level interactions in plant microbiomes. A limiting step is the selection of a fitting combination of microbial strains in order to accomplish the best beneficial effect upon plant inoculation. In this study, we have used a subset of 23 previously identified and characterised rice-beneficial bacterial colonisers to design and test a series of associated experiments aimed to identify potential PPB consortia which are able to co-colonise and induce plant growth promotion. Bacterial strains were co-inoculated in vitro and in planta using several different methods and their co-colonisation and co-persistence monitored. Results include the identification of two 5-strain and one 2-strain consortia which displayed plant growth-promoting features. Future practical applications of microbiome research must include experiments aimed at identifying consortia of bacteria which can be most effective as crop amendments.

Transformation of proteins into reproductive DNA templates for sensitive quantification of PSA

A switch DNA template was designed to transform proteins into linear DNA strands of 97 nt. The linear DNA was subjected to quantitative polymerase chain reaction (qPCR) to determine the initial copy number, which correlated positively with the protein concentration. A restriction endonuclease was used to remove background amplification. Using prostate-specific antigen (PSA) as a protein model, the established method quantified PSA in the range of 10-18-10-13 mol/mL (detection limit = 0.034 pg/mL) with an R2 of 0.974. Good repeatability and specificity of the method were demonstrated. The established method was successfully applied for the quantification of serum PSA levels in women. Significant differences in PSA levels were observed between healthy participants and polycystic ovary syndrome patients.

Alterations to the bovine bacterial ocular surface microbiome in the context of infectious bovine keratoconjunctivitis

BACKGROUND

Infectious bovine keratoconjunctivitis (IBK) is a common cause of morbidity in cattle, resulting in significant economic losses. This study aimed to characterize the bovine bacterial ocular surface microbiome (OSM) through conjunctival swab samples from Normal eyes and eyes with naturally acquired, active IBK across populations of cattle using a three-part approach, including bacterial culture, relative abundance (RA, 16 S rRNA gene sequencing), and semi-quantitative random forest modeling (real-time polymerase chain reaction (RT-PCR)).

RESULTS

Conjunctival swab samples were obtained from eyes individually classified as Normal (n = 376) or IBK (n = 228) based on clinical signs. Cattle unaffected by IBK and the unaffected eye in cattle with contralateral IBK were used to obtain Normal eye samples. Moraxella bovis was cultured from similar proportions of IBK (7/228, 3.07%) and Normal eyes (1/159, 0.63%) (p = 0.1481). Moraxella bovoculi was cultured more frequently (p < 0.0001) in IBK (59/228, 25.88%) than Normal (7/159, 4.40%) eyes. RA (via 16 S rRNA gene sequencing) of Actinobacteriota was significantly higher in Normal eyes (p = 0.0045). Corynebacterium variabile and Corynebacterium stationis (Actinobacteriota) were detected at significantly higher RA (p = 0.0008, p = 0.0025 respectively) in Normal eyes. Rothia nasimurium (Actinobacteriota) was detected at significantly higher RA in IBK eyes (p < 0.0001). Alpha-diversity index was not significantly different between IBK and Normal eyes (p > 0.05). Alpha-diversity indices for geographic location (p < 0.001), age (p < 0.0001), sex (p < 0.05) and breed (p < 0.01) and beta-diversity indices for geographic location (p < 0.001), disease status (p < 0.01), age (p < 0.001), sex (p < 0.001) and breed (p < 0.001) were significantly different between groups. Modeling of RT-PCR values reliably categorized the microbiome of IBK and Normal eyes; primers for Moraxella bovoculi, Moraxella bovis, and Staphylococcus spp. were consistently the most significant canonical variables in these models.

CONCLUSIONS

The results provide further evidence that multiple elements of the bovine bacterial OSM are altered in the context of IBK, indicating the involvement of a variety of bacteria in addition to Moraxella bovis, including Moraxella bovoculi and R. nasimurium, among others. Actinobacteriota RA is altered in IBK, providing possible opportunities for novel therapeutic interventions. While RT-PCR modeling provided limited further support for the involvement of Moraxella bovis in IBK, this was not overtly reflected in culture or RA results. Results also highlight the influence of geographic location and breed type (dairy or beef) on the bovine bacterial OSM. RT-PCR modeling reliably categorized samples as IBK or Normal.

Design and validation of a quantitative polymerase chain reaction test for the identification and quantification of uncultivable bacteria associated with periodontitis

OBJECTIVE

This study aimed to standardize a quantitative polymerase chain reaction (qPCR)-based test to identify and quantify the uncultivable bacteria associated with periodontitis.

METHODS

The standardization of qPCR, the curves for the quantification of Eubacterium saphenum, Eubacterium brachy, Desulfobulbus oralis, and Filifactor alocis were developed by cloning the 16 S rRNA target gene fragment, using the GEMTEasy vector. The qPCRs were validated in 55 subgingival biofilm clinical samples, from different stages of periodontitis and from periodontally healthy/gingivitis individuals, which were previously evaluated by next-generation sequencing (NGS). The results obtained by the two methods were compared by the concordance of Cohen's Kappa index, and sensitivity, specificity, receiver operating characteristic (ROC) curve, and predictive values were established.

RESULTS

obtained by the two methods were compared using the concordance of Cohen's Kappa index, and sensitivity, specificity, predictive values, and ROC curves were generated. The qPCR test was standardized with efficiencies between 90% and 100% and R2: 0.997-0.999. Concordance between the qPCR and NSG was moderate to F. alocis (agreement 78.2%; kappa 0.56, p < 0.05) and fair to the other microorganisms (agreement 67.27%-72.73; kappa 0.37-0.38, p < 0.05). qPCR exhibited a high sensitivity (82.2-100%) and specificity (100%) for E. brachy, E. saphenum, and F. alocis. Sensitivity was lower to D. oralis. Conversely, qPCR demonstrated higher sensitivity to E. saphenum than NSG (100 vs. 68.1).

CONCLUSIONS

The uncultivable microorganisms associated with periodontitis, D. oralis, E. brachy, E. saphenum, and F. alocis can be detected and quantified with the newly developed and validates qPCR test.

Developing diagnostic tools for canine periodontitis: combining molecular techniques and machine learning models

BACKGROUND

Dental plaque microbes play a key role in the development of periodontal disease. Numerous high-throughput sequencing studies have generated understanding of the bacterial species associated with both canine periodontal health and disease. Opportunities therefore exist to utilise these bacterial biomarkers to improve disease diagnosis in conscious-based veterinary oral health checks. Here, we demonstrate that molecular techniques, specifically quantitative polymerase chain reaction (qPCR) can be utilised for the detection of microbial biomarkers associated with canine periodontal health and disease.

RESULTS

Over 40 qPCR assays targeting single microbial species associated with canine periodontal health, gingivitis and early periodontitis were developed and validated. These were used to quantify levels of the respective taxa in canine subgingival plaque samples collected across periodontal health (PD0), gingivitis (PD1) and early periodontitis (PD2). When qPCR outputs were compared to the corresponding high-throughput sequencing data there were strong correlations, including a periodontal health associated taxa, Capnocytophaga sp. COT-339 (rs =0.805), and two periodontal disease associated taxa, Peptostreptococcaceae XI [G-4] sp. COT-019 (rs=0.902) and Clostridiales sp. COT-028 (rs=0.802). The best performing models, from five machine learning approaches applied to the qPCR data for these taxa, estimated 85.7% sensitivity and 27.5% specificity for Capnocytophaga sp. COT-339, 74.3% sensitivity and 67.5% specificity for Peptostreptococcaceae XI [G-4] sp. COT-019, and 60.0% sensitivity and 80.0% specificity for Clostridiales sp. COT-028.

CONCLUSIONS

A qPCR-based approach is an accurate, sensitive, and cost-effective method for detection of microbial biomarkers associated with periodontal health and disease. Taken together, the correlation between qPCR and high-throughput sequencing outputs, and early accuracy insights, indicate the strategy offers a prospective route to the development of diagnostic tools for canine periodontal disease.

Selective enrichment of the raw milk microbiota in cheese production: Concept of a natural adjunct milk culture

In cheese production, microorganisms are usually added at the beginning of the process as primary starters to drive curd acidification, while secondary microorganisms, with other pro-technological features important for cheese ripening, are added as selected cultures. This research aimed to investigate the possibilities of influencing and selecting the raw milk microbiota using artisanal traditional methods, providing a simple method to produce a natural supplementary culture. We investigated the production of an enriched raw milk whey culture (eRWC), a natural adjunct microbial culture produced from mixing an enriched raw milk (eRM) with a natural whey culture (NWC). The raw milk was enriched by spontaneous fermentation for 21 d at 10°C. Three milk enrichment protocols were tested: heat treatment before incubation, heat treatment plus salt addition, and no treatment. The eRMs were then co-fermented with NWC (ratio of 1:10) at 38°C for 6 h (young eRWC) and 22 h (old eRWC). Microbial diversity during cultures' preparation was evaluated through the determination of colony forming units on selective growth media, and next-generation sequencing (16S rRNA gene amplicon sequencing). The enrichment step increased the streptococci and lactobacilli but reduced microbial richness and diversity of the eRMs. Although the lactic acid bacteria viable count was not significantly different between the eRWCs, they harbored higher microbial richness and diversity than NWC. Natural adjunct cultures were then tested in cheese making trials, following the microbial development, and assessing the chemical quality of the 120 d ripened cheeses. The use of eRWCs slowed the curd acidification in the first hours of cheese making but the pH 24 h after production settled to equal values for all the cheeses. Although the use of diverse eRWCs contributed to having a richer and more diverse microbiota in the early stages of cheese making, their effect decreased over time during ripening, showing an inferior effect to the raw milk microbiota. Even if more research is needed, the optimization of such a tool could be an alternative to the practice of isolating, geno-pheno-typing, and formulating mixed-defined-strain adjunct cultures that require knowledge and facilities not always available for artisanal cheese makers.

Quantitative PCR Assay as a Tool for the Detection of Lactobacilli in Sicilian Table Olives Produced at an Industrial Scale

Table olives are an important fermented product of the Mediterranean area consumed all over the world. In our era, the food industry requires a safe and stable final product with desirable characteristics for the consumer. In the present study, two different experimental fermentations (L, with Lactiplantibacillus plantarum strains, and LY, with L. plantarum strains and Wickerhamomyces anomalus strain) were conducted and monitored up to 180 days and compared with a spontaneous fermentation, used as control (C). The safety and stability of table olives were determined by applying a plate count and quantitative real-time PCR (qPCR) approach. Compared with the control sample (C), experimental fermentations showed a faster acidification and a good inhibition rate of spoilage bacteria, indicating the safety of the process. Quantitative PCR data confirmed the abundance of the Lactobacillus group in both experimental table olives, confirming the importance of the starter cultures for the stability of the final product. In conclusion, the use of starter cultures ensures the safety of industrially produced table olives, and the application of qPCR seems to be a promising tool to detect and quantify lactobacilli as a positive biomarker of table olive fermentation.

New insights for tracking bacterial community structures in industrial wastewater from textile factories to surface water using phenotypic, 16S rRNA isolates identifications and high-throughput sequencing

Industrial wastewater can possibly change the microbial ecological environment. There are few studies that focus on the bacterial variety in textile wastewater effluents and after combination with domestic wastewater. Thus, this study aimed to determine dye degrading bacteria from textile wastewater and environmental water samples using cultural method followed by phenotypic using BIOLOG and genotypic identification (16S rRNA) for dye degrading isolates identifications. Moreover, the bacterial communities in three textile and four environmental samples using Illumina MiSeq high-throughput sequencing were investigated. The findings revealed that in textile water samples, the ratio of dye-degrading bacteria (DDB) to total bacterial counts (TBC) was 27%. The identified DDB genera by 16S rRNA based on the cultural approach were Citrobacter spp., Klebsiella spp., Enterobacter spp., Pseudomonas spp., and Aeromonas spp. Regarding to the metagenomics analyses, the environmental samples had 5,598 Operational Toxanomic Units (OTUs) more than textile wastewater samples (1,463 OTUs). Additionally, the most abundant phyla in the textile wastewater were Proteobacteria (24.45-94.83%), Bacteriodetes (0.5-44.84%) and Firmicutes (3.72-67.40%), while, Proteobacteria (30.8-76.3%), bacteroidetes (8.5-50%) and Acentobacteria (0.5-23.12%) were the most abundant phyla in the environmental samples. The maximum abundant bacteria at species level in environmental samples were Aquabacterium parvum (36.71%), Delftia tsuruhatensis (17.61%), Parabacteriodes chartae (15.39%) and Methylorubrum populi (7.51%) in El-Rahawy Drain water (RDW), River Nile water (RNW), wastewater (RWW) from WWTP in Zennin and El-Rahawy Drain sediment (RDS), respectively, whereas the maximum abundant bacteria at species level in textile wastewater were Alkalibacterium pelagium (34.11%), Enterobacter kobei (26.09%) and Chryseobacterium montanum (16.93%) in factory 1 (HBT) sample, SHB sample (before mixing with domestic wastewater) and SHB sample (after mixing with domestic wastewater), respectively. In conclusion, the microbial communities in textile wastewaters are similar to those in environmental samples at the phylum level but distinct at the genus and species levels because they are exposed to a wider range of environmental circumstances.

VFG-Chip: A high-throughput qPCR microarray for profiling virulence factor genes from the environment

As zoonotic pathogens are threatening public health globally, the virulence factor genes (VFGs) they carry underlie latent risk in the environment. However, profiling VFGs in the environment is still in its infancy due to lack of efficient and reliable quantification tools. Here, we developed a novel high-throughput qPCR (HT-qPCR) chip, termed as VFG-Chip, to comprehensively quantify the abundances of targeted VFGs in the environment. A total of 96 VFGs from four bacterial pathogens including Klebsiella pneumoniae, Acinetobacter baumannii, Escherichia coli, and Salmonella enterica were targeted by 120 primer pairs, which were involved in encoding five types of virulence factors (VFs) like toxin, adherence, secretion system, immune evasion/invasion, and iron uptake. The specificity of VFG-Chip was both verified computationally and experimentally, with high identity of amplicon sequencing and melting curves analysis proving its robust capability. The VFG-Chip also displayed high sensitivity (by plasmid serial dilution test) and amplification efficiency averaging 97.7%. We successfully applied the VFG-Chip to profile the distribution of VFGs along a wastewater treatment system with 69 VFGs detected in total. Overall, the VFG-Chip provides a robust tool for comprehensively quantifying VFGs in the environment, and thus provides novel information in assessing the health risks of zoonotic pathogens in the environment.

Lactic acid bacteria in cow raw milk for cheese production: Which and how many?

Lactic Acid Bacteria (LAB) exert a fundamental activity in cheese production, as starter LAB in curd acidification, or non-starter LAB (NSLAB) during ripening, in particular in flavor formation. NSLAB originate from the farm and dairy environment, becoming natural contaminants of raw milk where they are present in very low concentrations. Afterward, throughout the different cheesemaking processes, they withstand chemical and physical stresses becoming dominant in ripened cheeses. However, despite a great body of knowledge is available in the literature about NSLAB effect on cheese ripening, the investigations regarding their presence and abundance in raw milk are still poor. With the aim to answer the initial question: "which and how many LAB are present in cow raw milk used for cheese production?," this review has been divided in two main parts. The first one gives an overview of LAB presence in the complex microbiota of raw milk through the meta-analysis of recent taxonomic studies. In the second part, we present a collection of data about LAB quantification in raw milk by culture-dependent analysis, retrieved through a systematic review. Essentially, the revision of data obtained by plate counts on selective agar media showed an average higher concentration of coccoid LAB than lactobacilli, which was found to be consistent with meta-taxonomic analysis. The advantages of the impedometric technique applied to the quantification of LAB in raw milk were also briefly discussed with a focus on the statistical significance of the obtainable data. Furthermore, this approach was also found to be more accurate in highlighting that microorganisms other than LAB are the major component of raw milk. Nevertheless, the variability of the results observed in the studies based on the same counting methodology, highlights that different sampling methods, as well as the "history" of milk before analysis, are variables of great importance that need to be considered in raw milk analysis.

Establishment and Validation of a New Analysis Strategy for the Study of Plant Endophytic Microorganisms

Amplicon sequencing of bacterial or fungal marker sequences is currently the main method for the study of endophytic microorganisms in plants. However, it cannot obtain all types of microorganisms, including bacteria, fungi, protozoa, etc., in samples, nor compare the relative content between endophytic microorganisms and plants and between different types of endophytes. Therefore, it is necessary to develop a better analysis strategy for endophytic microorganism investigation. In this study, a new analysis strategy was developed to obtain endophytic microbiome information from plant transcriptome data. Results showed that the new strategy can obtain the composition of microbial communities and the relative content between plants and endophytic microorganisms, and between different types of endophytic microorganisms from the plant transcriptome data. Compared with the amplicon sequencing method, more endophytic microorganisms and relative content information can be obtained with the new strategy, which can greatly broaden the research scope and save the experimental cost. Furthermore, the advantages and effectiveness of the new strategy were verified with different analysis of the microbial composition, correlation analysis, inoculant content test, and repeatability test.

A proof of concept infant-microbiota associated rat model for studying the role of gut microbiota and alleviation potential of Cutibacterium avidum in infant colic

Establishing the relationship between gut microbiota and host health has become a main target of research in the last decade. Human gut microbiota-associated animal models represent one alternative to human research, allowing for intervention studies to investigate causality. Recent cohort and in vitro studies proposed an altered gut microbiota and lactate metabolism with excessive H₂ production as the main causes of infant colic. To evaluate H₂ production by infant gut microbiota and to test modulation of gut colonizer lactose- and lactate-utilizer non-H₂-producer, Cutibacterium avidum P279, we established and validated a gnotobiotic model using young germ-free rats inoculated with fecal slurries from infants younger than 3 months. Here, we show that infant microbiota-associated (IMA) rats inoculated with fresh feces from healthy (n = 2) and colic infants (n = 2) and fed infant formula acquired and maintained similar quantitative and qualitative fecal microbiota composition compared to the individual donor’s profile. We observed that IMA rats excreted high levels of H₂, which were linked to a high abundance of lactate-utilizer H₂-producer Veillonella. Supplementation of C. avidum P279 to colic IMA rats reduced H₂ levels compared to animals receiving a placebo. Taken together, we report high H₂ production by infant gut microbiota, which might be a contributing factor for infant colic, and suggest the potential of C. avidum P279 in reducing the abdominal H₂ production, bloating, and pain associated with excessive crying in colic infants.

Analysis of Bacterial Diversity in Different Types of Daqu and Fermented Grains From Danquan Distillery

Bacterial communities in high-temperature Daqu and fermented grains are important for brewing Jiang-flavor Baijiu such as Danquan Baijiu. Daqu is a saccharifying and fermenting agent, which has a significant impact on the flavor of Baijiu. However, bacterial communities in three different types of samples from the Danquan distillery (dqjq_ck, dqjqcp, and dqjp3) were still unclear, which limited further development of Danquan Baijiu. “dqjq_ck” and “dqjqcp” indicate high-temperature Daqu at days 45 and 135, respectively. “dqjp3” indicates fermented grains. In this study, the bacterial communities of three samples were analyzed by Illumina Miseq high-throughput sequencing. The bacterial communities of three samples primarily composed of thermophilic bacteria and bacteria with stress resistance. The most abundant species in dqjq_ck, dqjqcp, and dqjp3 were Comamonas, Bacillus, and unclassified Lactobacillales, respectively. The main bacteria included Bacillus, Comamonas, Myroides, Paenibacillus, Acetobacter, Kroppenstedtia, Staphylococcus, Saccharopolyspora, Planifilum, Lactobacillus, Acinetobacter, Oceanobacillus, Enterococcus, Thermoactinomyces, Lactococcus, Streptomyces, Saccharomonospora, Tepidimicrobium, Anaerosalibacter, unclassified_Lactobacillales, unclassified_Thermoactinomycetaceae_1, unclassified_Bacillaceae_2, unclassified_Bacillales, unclassified_Microbacteriaceae, unclassified_Rhodobacteraceae, unclassified_Actinopolysporineae, and unclassified_Flavobacteriaceae in three samples (percentage was more than 1% in one of three samples). In our study, the succession of microbiota in three samples representing three important stages of Danquan Baijiu brewing was revealed. This article lays a good foundation for understanding the fermentation mechanism and screening some excellent indigenous bacteria to improve the quality of Danquan Baijiu in future.