Current status and future developments of assessing microbiome composition and dynamics in anaerobic digestion systems using metagenomic approaches.

The metagenomic approach stands as a powerful technique for examining the composition of microbial communities and their involvement in various anaerobic digestion (AD) systems. Understanding the structure, function, and dynamics of microbial communities becomes pivotal for optimizing the biogas process, enhancing its stability and improving overall performance. Currently, taxonomic profiling of biogas-producing communities relies mainly on high-throughput 16S rRNA sequencing, offering insights into the bacterial and archaeal structures of AD assemblages and their correlations with fed substrates and process parameters. To delve even deeper, shotgun and genome-centric metagenomic approaches are employed to recover individual genomes from the metagenome. This provides a nuanced understanding of collective functionalities, interspecies interactions, and microbial associations with abiotic factors. The application of OMICs in AD systems holds the potential to revolutionize the field, leading to more efficient and sustainable waste management practices particularly through the implementation of precision anaerobic digestion systems. As ongoing research in this area progresses, anticipations are high for further exciting developments in the future. This review serves to explore the current landscape of metagenomic analyses, with focus on advancing our comprehension and critically evaluating biases and recommendations in the analysis of microbial communities in anaerobic digesters. Its objective is to explore how contemporary metagenomic approaches can be effectively applied to enhance our understanding and contribute to the refinement of the AD process. This marks a substantial stride towards achieving a more comprehensive understanding of anaerobic digestion systems.

Nitrogen Fixing Azotobacter Species as Potential Soil Biological Enhancers for Crop Nutrition and Yield Stability.

Biological nitrogen fixation (BNF) refers to a microbial mediated process based upon an enzymatic "Nitrogenase" conversion of atmospheric nitrogen (N2) into ammonium readily absorbable by roots. N2-fixing microorganisms collectively termed as "diazotrophs" are able to fix biologically N2 in association with plant roots. Specifically, the symbiotic rhizobacteria induce structural and physiological modifications of bacterial cells and plant roots into specialized structures called nodules. Other N2-fixing bacteria are free-living fixers that are highly diverse and globally widespread in cropland. They represent key natural source of nitrogen (N) in natural and agricultural ecosystems lacking symbiotic N fixation (SNF). In this review, the importance of Azotobacter species was highlighted as both important free-living N2-fixing bacteria and potential bacterial biofertilizer with proven efficacy for plant nutrition and biological soil fertility. In addition, we described Azotobacter beneficial plant promoting traits (e.g., nutrient use efficiency, protection against phytopathogens, phytohormone biosynthesis, etc.). We shed light also on the agronomic features of Azotobacter that are likely an effective component of integrated plant nutrition strategy, which contributes positively to sustainable agricultural production. We pointed out Azotobacter based-biofertilizers, which possess unique characteristics such as cyst formation conferring resistance to environmental stresses. Such beneficial traits can be explored profoundly for the utmost aim to research and develop specific formulations based on inoculant Azotobacter cysts. Furthermore, Azotobacter species still need to be wisely exploited in order to address specific agricultural challenges (e.g., nutrient deficiencies, biotic and abiotic constraints) taking into consideration several variables including their biological functions, synergies and multi-trophic interactions, and biogeography and abundance distribution.

Predicting soil phosphorus and studying the effect of texture on the prediction accuracy using machine learning combined with near-infrared spectroscopy.

The estimation of soil phosphorus is essential for agricultural activity. The laboratory chemical analysis techniques are expensive and labor-intensive. In the last decade, near-infrared spectroscopy has been become used as an alternative for soil attributes analysis. It is a rapid technique, and inexpensive relatively. However, this technique requires a calibration step using different machine learning and chemometrics tools. This study aims to develop predictive models for total soil phosphorus and extractable phosphorus by the Olson method (P-Olson) using three regression methods, namely partial least squares (PLS), regression support vector machine (RSVM) and backward propagation neural network (BPNN), combined with a proposed variable selection algorithm (PARtest) and a genetic algorithm PLS (GA-PAS). Also, it aims to investigate the effect of the texture on the accuracy of the prediction. The results show that PARtest combined with PBNN outperform the other used algorithms with an R2t = 0.86, RMSEt = 1104 mg kg-1, and RPD = 3.23 for the TP. For P-Olson the RSVM coupled with GA-PLS outperforms all other methods with an R2t = 0.77, RMSEt = 20.09 mg kg-1, and RPD = 1.90. The use of hierarchical ascendant clustering (HAC) helps to reduce the heterogeneity of soil and helps to increase the quality of prediction. The obtained results show that the models for clayey and loamy soils yielded an excellent prediction quality with an R2t = 0.88, RMSEt = 857.33 mg kg-1, and RPD = 4.10 using BPNN with PARtest for TP. Furthermore, an R2 = 0.83 RMSE = 8.30 mg kg-1, RPD = 11.00 3.11using RSVM with GA-PLS for P-Olson. Thus, the texture has a significant effect on the prediction accuracy.

Identification by fluorescence spectroscopy of lactic acid bacteria isolated from a small-scale facility producing traditional dry sausages.

Three different fluorescence spectra were recorded following excitation at 250 nm (aromatic amino acids+nucleic acids, AAA+NA), 316 nm (NADH) and 380 nm (FAD) for 20 type strain collections of lactic acid bacteria (LAB). Evaluation of the data using principal component analysis and factorial discriminant analysis showed a good discrimination of considered LAB at the genus, species and genus-species level. AAA+NA fluorophores showed the highest percentage of good classification. From AAA+NA spectra recorded on LAB isolated from a small-scale facility producing traditional dry sausages, we succeeded to identify 28 of 29 wild strains. This method allowed us to discriminate between Lactobacillus sakei subsp. carnosus and Lactobacillus sakei subsp. sakei. Thus, intrinsic fluorescence is an economical and powerful tool for the identification of wild LAB isolated from meat and meat products.