Fungal endophytes are quite common in nature and some of them have been shown to have adverse effects against insects, nematodes, and plant pathogens.Our research program is aimed at using fungal endophytes-mediated plant defense as a novel biological control mechanism against the coffee berry borer, the most devastating pest of coffee throughout the world. A survey of fungal endophytes in coffee plants from Hawaii, Colombia, Mexico, and Puerto Rico has revealed the presence of various genera of fungal entomopathogens, including Acremonium, Beauveria, Cladosporium, Clonostachys, and Paecilomyces. Two of these, B. bassiana and Clonostachys rosea, were tested against the coffee berry borer and were shown to be pathogenic. This paper reviews the possible mode of action of entomopathogenic fungal endophytes. Published by Elsevier Inc.

Eighty-seven culturable endophytic bacterial isolates in 19 genera were obtained from coffee plants collected in Colombia (n = 67), Hawaii (n = 17), and Mexico (n = 3). Both Gram positive and Gram negative bacteria were isolated, with a greater percentage (68%) being Gram negative. Tissues yielding bacterial endophytes included adult plant leaves, various parts of the berry (e.g., crown, pulp, peduncle and seed), and leaves, stems, and roots of seedlings. Some of the bacteria also occurred as epiphytes. The highest number of bacteria among the berry tissues sampled was isolated from the seed, and includes Bacillus , Burkholderia , Clavibacter , Curtobacterium , Escherichia , Micrococcus , Pantoea , Pseudomonas , Serratia , and Stenotrophomonas . This is the first survey of the endophytic bacteria diversity in various coffee tissues, and the first study reporting endophytic bacteria in coffee seeds. The possible role for these bacteria in the biology of the coffee plant remains unknown.

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Cronobacter species cause infections in all age groups; however neonates are at highest risk and remain the most susceptible age group for life-threatening invasive disease. The genus contains seven species:Cronobacter sakazakii, Cronobacter malonaticus, Cronobacter turicensis, Cronobacter muytjensii, Cronobacter dublinensis, Cronobacter universalis, and Cronobacter condimenti. Despite an abundance of published genomes of these species, genomics-based epidemiology of the genus is not well established. The gene content of a diverse group of 126 unique Cronobacter and taxonomically related isolates was determined using a pan genomic-based DNA microarray as a genotyping tool and as a means to identify outbreak isolates for food safety, environmental, and clinical surveillance purposes. The microarray constitutes 19,287 independent genes representing 15 Cronobacter genomes and 18 plasmids and 2,371 virulence factor genes of phylogenetically related Gram-negative bacteria. The Cronobacter microarray was able to distinguish the seven Cronobacter species from one another and from non-Cronobacter species; and within each species, strains grouped into distinct clusters based on their genomic diversity. These results also support the phylogenic divergence of the genus and clearly highlight the genomic diversity among each member of the genus. The current study establishes a powerful platform for further genomics research of this diverse genus, an important prerequisite toward the development of future countermeasures against this foodborne pathogen in the food safety and clinical arenas.

In a comparison to the widely used Cronobacter rpoB PCR assay, a highly specific multiplexed PCR assay based on cgcA, a diguanylate cyclase gene, that identified all of the targeted six species among 305 Cronobacter isolates was designed. This assay will be a valuable tool for identifying suspected Cronobacter isolates from food-borne investigations.

Insect pests, such as pantry beetles, are often associated with food contaminations and public health risks. Machine learning has the potential to provide a more accurate and efficient solution in detecting their presence in food products, which is currently done manually. In our previous research, we demonstrated such feasibility where Artificial Neural Network (ANN) based pattern recognition techniques could be implemented for species identification in the context of food safety. In this study, we present a Support Vector Machine (SVM) model which improved the average accuracy up to 85%. Contrary to this, the ANN method yielded ~80% accuracy after extensive parameter optimization. Both methods showed excellent genus level identification, but SVM showed slightly better accuracy  for most species. Highly accurate species level identification remains a challenge, especially in distinguishing between species from the same genus which may require improvements in both imaging and machine learning techniques. In summary, our work does illustrate a new SVM based technique and provides a good comparison with the ANN model in our context. We believe such insights will pave better way forward for the application of machine learning towards species identification and food safety.

Although flies are important vectors of food-borne pathogens, there is little information to accurately assess the food-related health risk of the presence of individual flies, especially in urban areas. This study quantifies the prevalence and the relative risk of food-borne pathogens associated with the body surfaces and guts of individual wild flies. One hundred flies were collected from the dumpsters of 10 randomly selected urban restaurants. Flies were identified using taxonomic keys before being individually dissected. Cronobacter spp., Salmonella spp., and Listeria monocytogenes were detected using the PCR-based BAX system Q7. Positive samples were confirmed by culture on specific media and through PCR amplification and sequencing or ribotyping. Among collected flies were the housefly, Musca domestica (47%), the blowflies, Lucilia cuprina (33%) and Lucilia sericata (14%), and others (6%). Cronobacter species were detected in 14% of flies, including C. sakazakii, C. turicensis, and C. universalis, leading to the proposal of flies as a natural reservoir of this food-borne pathogen. Six percent of flies carried Salmonella enterica, including the serovars Poona, Hadar, Schwarzengrund, Senftenberg, and Brackenridge. L. monocytogenes was detected in 3% of flies. Overall, the prevalence of food-borne pathogens was three times greater in the guts than on the body surfaces of the flies. The relative risk of flies carrying any of the three pathogens was associated with the type of pathogen, the body part of the fly, and the ambient temperature. These data enhance the ability to predict the microbiological risk associated with the presence of individual flies in food and food facilities.

To improve the insecticidal efficacy of the entomopathogen Beauveria bassiana, the fungus was genetically modified with an insect-specific scorpion neurotoxin AAIT and an insect cuticle degrading protease PR1A from another insect pathogen (Metarhizium anisopliae). The wild-type and the transformants were bioassayed against the larvae of Masson's pine caterpillar Dendrolimus punctatus and the wax moth Galleria mellonella. In comparison to the wild-type strain, engineered isolates took fewer spores to kill 50% of pine caterpillars, 15-fold less for the aaIT single transformant Bb13T and eightfold less for the double transformant Bb13TPR1A, respectively. The median lethal times for Bb13T and Bb13TPR1A were reduced by 40% and 36.7%, respectively against D. punctatus and 24.4% and 20.9%, respectively against G. mellonella. Our data showed that the cotransformation of these two genes produced no synergistic effects on virulence improvement. It is evident from this study that AAIT could be degraded by the protease PR1A when they are expressed together, emphasizing that protein interactions need to be evaluated when working with multiple genes, particularly if they include proteases.