Biology subjects

Ganda, E.

Publications and source records attributed to Ganda, E..

5 recordsLinked to original sources

bioRxiv2023

Indigenous probiotics Lactobacillus reuteri and Enterococcus faecium exhibit positive growth performance and disease prevention against extended-spectrum cephalosporin and fluoroquinolones resistant Salmonella enterica in broiler chicks.

ABSTRACTThe rapid increase in antibiotic resistance poses a global threat to public health, necessitating the development of effective antimicrobial alternatives. This study compared an indigenous probiotic mix containing Lactobacillus reuteri and two strains of Enterococcus faecium to a commercial probiotic blend ProtexinR on the growth performance, mortality rate, histomorphology, serum immunoglobulins, and intestinal microflora of broiler chickens challenged with two multi drug resistant Salmonella serovars, Typhimurium and Enteritidis. Two hundred and forty day-old broiler chicks were randomly assigned to six treatment groups for 4 weeks: the treatment groups were; birds continuously supplemented with only indigenous probiotic strains (108 CFU/mL) (IPRO-); birds challenged with Salmonella serovars 106 (CFU/mL) (PC+); birds continuously supplemented with indigenous probiotic strains and challenged with Salmonella serovars (IPRO+); birds supplemented with ProtexinR and challenged with Salmonella serovars (CM+); birds supplemented with only ProtexinR (CM-); and birds with no Salmonella challenge or probiotics (negative control; PC-). The results revealed that IPRO- diets significantly improved feed conversion ratio (FCR) and increased body weight (BW) (P [≤] 0.05). No effect of probiotic treatments was observed on IPRO- and CM- on relative organ weights as compared to the negative control (PC-). The Salmonella- challenged group PC+ had the highest (20%) mortality rate and lowest BW. The IPRO- had significantly lower FCR (1.55) compared to PC- (1.86) and PC+ (1.95). The broilers in the IPRO- group showed significantly increased serum concentrations of IgA and IgG relative to both control groups (P [≤] 0.05). Morphological analysis of the ileum revealed significant increases (P [≤] 0.05) in the villus height and villus height/crypt depth in birds fed IPRO- compared with the PC+. Cecal Lactobacillus and Enterococcus counts were the highest (P [≤] 0.05) and Salmonella counts were the lowest (P [≤] 0.05) in the IPRO- group compared to the Salmonella infected group PC+. These results indicated that indigenous probiotic strains Lactobacillus reuteri and Enterococcus faecium can be an effective and low-cost alternative compared to commercial probiotics in the Pakistan poultry industry.

microbiology↗

bioRxiv2023

Changes in the gut mycobiome are associated with Type 2 diabetes mellitus and metformin treatment across populations

The human gut teems with a diverse ecosystem of microbes, yet non-bacterial portions of that community are overlooked in studies of metabolic diseases firmly linked to gut bacteria. Type 2 diabetes mellitus (T2D) associates with compositional shifts in the gut bacterial microbiome and fungal mycobiome, but whether T2D and/or pharmaceutical treatments underpin the community change is unresolved. To differentiate these effects, we curated a gut mycobiome cohort to-date spanning 1,000 human samples across 5 countries and a murine experimental model. We use Bayesian multinomial logistic normal models to show that metformin and T2D both associate with shifts in the relative abundance of distinct gut fungi. T2D associates with shifts in the Saccharomycetes and Sordariomycetes fungal classes, while the genera Fusarium and Tetrapisipora most consistently associate with metformin treatment. We confirmed the impact of metformin on individual gut fungi by administering metformin to healthy mice. Thus, metformin and T2D account for subtle, but significant and distinct variation in the gut mycobiome across human populations. This work highlights for the first time that oral pharmaceuticals can confound associations of gut fungi with T2D and warrants the need to consider pharmaceutical interventions in investigations of linkages between metabolic diseases and gut microbial inhabitants.

microbiology↗

bioRxiv2022

Detecting anomalous content in dairy with whole metagenome sequencing

The increasing knowledge of microbial ecology in food products relating to quality and safety and the established usefulness of machine learning algorithms for anomaly detection in multiple scenarios suggests that the application of microbiome data in food production systems for anomaly detection could be a valuable approach to be used in food systems. These methods could be used to identify ingredients that deviate from their typical microbial composition, which could indicate food fraud or safety issues. The objective of this study was to assess the feasibility of using shotgun sequencing data as input into anomaly detection algorithms using fluid milk as a model system. Contrastive PCA, cluster-based methods, and explainable AI were evaluated for the detection of two anomalous sample classes using longitudinal metagenomic profiling of fluid milk compared to baseline samples collected under comparable circumstances. Traditional methods (alpha and beta diversity, clustering-based contrastive PCA, MDS, and dendrograms) failed to differentiate anomalous sample classes; however, explainable AI was able to classify anomalous vs. baseline samples and indicate microbial drivers in association with antibiotic use. We validated the potential for explainable AI to classify different milk sources using larger publicly available fluid milk 16s rDNA sequencing datasets and demonstrated that explainable AI is able to differentiate between milk storage methods, processing stage, and season. Our results indicate the application of artificial intelligence continues to hold promise in the realm of microbiome data analysis and could present further opportunities for downstream analytic automation to aid in food safety and quality. IMPORTANCEWe evaluated the feasibility of using untargeted metagenomic se-quencing of raw milk for detecting anomalous food ingredient content with artificial intelligence methods in a study specifically designed to test this hypothesis. We also show through analysis of publicly available fluid milk microbial data that our artificial intelligence approach is able to successfully predict milk in different stages of process-ing. The approach could potentially be applied in the food industry for safety and quality control.

microbiology↗

bioRxiv2020

DNA extraction and host depletion methods significantly impact and potentially bias bacterial detection in a biological fluid

Untargeted sequencing of nucleic acids present in food can inform the detection of food safety and origin, as well as product tampering and mislabeling issues. The application of such technologies to food analysis could reveal valuable insights that are simply unobtainable by targeted testing, leading to the efforts of applying such technologies in the food industry. However, before these approaches can be applied, it is imperative to verify that the most appropriate methods are used at every step of the process: gathering primary material, laboratory methods, data analysis, and interpretation. The focus of this study is in gathering the primary material, in this case, DNA. We used bovine milk as a model to 1) evaluate commercially available kits for their ability to extract nucleic acids from inoculated bovine milk; 2) evaluate host DNA depletion methods for use with milk, and 3) develop and evaluate a selective lysis-PMA based protocol for host DNA depletion in milk. Our results suggest that magnetic-based nucleic acid extraction methods are best for nucleic acid isolation of bovine milk. Removal of host DNA remains a challenge for untargeted sequencing of milk, highlighting that the individual matrix characteristics should always be considered in food testing. Some reported methods introduce bias against specific types of microbes, which may be particularly problematic in food safety where the detection of Gram-negative pathogens and indicators is essential. Continuous efforts are needed to develop and validate new approaches for untargeted metagenomics in samples with large amounts of DNA from a single host. ImportanceTracking the bacterial communities present in our food has the potential to inform food safety and product origin. To do so, the entire genetic material present in a sample is extracted using chemical methods or commercially available kits and sequenced using next-generation platforms to provide a snapshot of what the relative composition looks like. Because the genetic material of higher organisms present in food (e.g., cow in milk or beef, wheat in flour) is around one thousand times larger than the bacterial content, challenges exist in gathering the information of interest. Additionally, specific bacterial characteristics can make them easier or harder to detect, adding another layer of complexity to this issue. In this study, we demonstrate the impact of using different methods in the ability of detecting specific bacteria and highlight the need to ensure that the most appropriate methods are being used for each particular sample.

molecular biology↗

bioRxiv2020

Complex interactions between weather, and microbial and physiochemical water quality impact the likelihood of detecting foodborne pathogens in agricultural water

Agricultural water is an important source of foodborne pathogens on produce farms. Managing water-associated risks does not lend itself to one-size-fits-all approaches due to the heterogeneous nature of freshwater environments, and because environmental conditions affect the likelihood of pathogen contamination and the relationship between indicator organism levels (e.g., E. coli) and pathogen presence. To improve our ability to develop location-specific risk management practices, a study was conducted in two produce-growing regions to (i) characterize the relationship between E. coli levels and pathogen presence in agricultural water, and (ii) identify environmental factors associated with pathogen detection. Three AZ and six NY waterways were sampled longitudinally using 10-L grab samples (GS) and 24-h Moore swabs (MS). Regression showed that the likelihood of Salmonella detection (Odds Ratio [OR]=2.18), and eaeA-stx codetection (OR=6.49) was significantly greater for MS compared to GS, while the likelihood of detecting L. monocytogenes was not. Regression also showed that eaeA-stx codetection in AZ (OR=50.2) and NY (OR=18.4), and Salmonella detection in AZ (OR=4.4) were significantly associated with E. coli levels, while Salmonella detection in NY was not. Random forest analysis indicated that interactions between environmental factors (e.g., rainfall, temperature, turbidity) (i) were associated with likelihood of pathogen detection and (ii) mediated the relationship between E. coli levels and likelihood of pathogen detection. Our findings suggest that (i) environmental heterogeneity, including interactions between factors, affects microbial water quality, and (ii) E. coli levels alone may not be a suitable indicator of the food safety risks. Instead, targeted methods that utilize environmental and microbial data (e.g., models that use turbidity and E. coli levels to predict when there is a high or low risk of surface water being contaminated by pathogens) are needed to assess and mitigate the food safety risks associated with preharvest water use. By identifying environmental factors associated with an increased likelihood of detecting pathogens in agricultural water, this study provides information that (i) can be used to assess when pathogen contamination of agricultural water is likely to occur, and (ii) facilitate development of targeted interventions for individual water sources, providing an alternative to existing one-size-fits-all approaches.

microbiology↗