In this study, a low-cost chemically defined (CD) culture medium was proposed and evaluated with the aim of replacing culture media such as yeast mold (YM) and yeast peptone dextrose (YPD), commonly used for growth and carotenoid production by Xanthophyllomyces dendrorhous. Initially, the CD culture medium was compared to the YM and YPD. The growth in optical density (OD) and carotenoid production (mg/L) of the cultures reached 4.88, 6.76, 5.79, and 0.67, 0.92, and 0.69, respectively. The CD culture served as the basis of an improved specific culture medium containing industrial glucose. Additionally, in this new formulation, vitamins, glutamate, and other compounds were evaluated. Industrial glucose more than doubled carotenoid production; however, the addition of vitamins was not essential for X. dendrorhous cultivation. Moreover, glutamate and Na2HPO4 proved to be highly significant factors (p-value < 0.05), increasing carotenoid biosynthesis from 0.67 to 1.33 mg/L. The specific culture was successfully used in a bioreactor at 2 L and 110 L pilot-scale levels, increasing carotenoid production up to 2 mg/L. It was demonstrated that the CD-specific culture medium is an efficient alternative to conventional culture media to carry out carotenoid production at the laboratory and pilot levels, with promising potential for industrial scaling.

Aquaculture offers great potential for fish production in Lake Chapala, but reports of heavy metal contamination in fish have identified a main concern for this activity. In the present study, cultures of the species Cyprinus carpio and Ictalurus punctatus were grown in a net cage in Lake Chapala. The patterns of heavy metal accumulation (Cu, Zn, Cd, Hg, Pb, As) in muscle and liver were monitored in order to evaluate the level of metal incorporation in the fish. Estimates of weekly metal intake (EWI) were made based on the results of the concentrations in edible parts of fish of commercial size. The patterns of metal bioaccumulation between tissues and species showed that liver had a higher concentrating capacity for Zn, Cu, Cd, and Pb. In contrast, similar concentrations of Hg and As were found in the liver and muscle tissue. According to the EWI estimates, the heavy metals in these cultured fish do not represent a risk for human consumption.

The structure of mixed microbial cultures—such as the human gut microbiota—is influenced by a complex interplay of interactions among its community members. The objective of this study was to propose a strategy to characterize microbial interactions between particular members of the community occurring in a simulator of the human gastrointestinal tract used as the experimental system. Four runs were carried out separately in the simulator: two of them were fed with a normal diet (control system), and two more had the same diet supplemented with agave fructans (fructan-supplemented system). The growth kinetics of Lactobacillus spp., Bifidobacterium spp., Salmonella spp., and Clostridium spp. were assessed in the different colon sections of the simulator for a nine-day period. The time series of microbial concentrations were used to estimate specific growth rates and pair-wise interaction coefficients as considered by the generalized Lotka-Volterra (gLV) model. A differential neural network (DNN) composed of a time-adaptive set of differential equations was applied for the nonparametric identification of the mixed microbial culture, and an optimization technique was used to determine the interaction parameters, considering the DNN identification results and the structure of the gLV model. The assessment of the fructan-supplemented system showed that microbial interactions changed significantly after prebiotics administration, demonstrating their modulating effect on microbial interactions. The strategy proposed here was applied satisfactorily to gain quantitative and qualitative knowledge of a broad spectrum of microbial interactions in the gut community, as described by the gLV model. In the future, it may be utilized to study microbial interactions within mixed cultures using other experimental approaches and other mathematical models (e.g., metabolic models), which will yield crucial information for optimizing mixed microbial cultures to perform certain processes—such as environmental bioremediation or modulation of gut microbiota—and to predict their dynamics.

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