The potential of enhanced phytoremediation to clean up multi-contaminated soil - insights from metatranscriptomics.

This study aimed to (i) investigate the potential for enhanced phytoremediation to remove contaminants from soil historically co-contaminated with petroleum hydrocarbons (PHs) and heavy metals (HMs) and (ii) analyze the expression of crucial bacterial genes and whole metatranscriptomics profiles for better understanding of soil processes during applied treatment. Phytoremediation was performed using Zea mays and supported by the Pseudomonas qingdaonensis ZCR6 strain and a natural biofertilizer: meat and bone meal (MBM). In previous investigations, mechanisms supporting plant growth and PH degradation were described in the ZCR6 strain. Here, ZCR6 survived in the soil throughout the experiment, but the efficacy of PH removal from all soils fertilized with MBM reached 32 % regardless of the bacterial inoculation. All experimental groups contained 2 % (w/w) MBM. The toxic effect of this amendment on plants was detected 30 days after germination, irrespective of ZCR6 inoculation. Among the 17 genes tested using the qPCR method, only expression of the acdS gene, encoding 1-aminocyclopropane-1-carboxylic acid deaminase, and the CYP153 gene, encoding cytochrome P450-type alkane hydroxylase, was detected in soils. Metatranscriptomic analysis of soils indicated increased expression of methane particulated ammonia monooxygenase subunit A (pmoA-amoA) by Nitrosomonadales bacteria in all soils enriched with MBM compared to the non-fertilized control. We suggest that the addition of 2 % (w/w) MBM caused the toxic effect on plants via the rapid release of ammonia, and this led to high pmoA-amoA expression. In parallel, due to its wide substrate specificity, enhanced bacterial hydrocarbon removal in MBM-treated soils was observed. The metatranscriptomic results indicate that MBM application should be considered to improve bioremediation of soils polluted with PHs rather than phytoremediation. However, lower concentrations of MBM could be considered for phytoremediation enhancement. From a broader perspective, these results indicated the superior capability of metatranscriptomics to investigate the microbial mechanisms driving various bioremediation techniques.

Insights from modelling sixteen years of climatic and fumonisin patterns in maize in South Africa.

Mycotoxin contamination of agricultural commodities is a global public health problem that has remained elusive to various mitigation approaches, particularly in developing countries. Climate change and its impact exacerbates South Africa's vulnerability to mycotoxin contamination, and significantly threatens its's food systems, public health, and agro-economic development. Herein we analyse sixteen years (2005/2006-2020/2021) of annual national meteorological data on South Africa which reveals both systematic and erratic variability in critical climatic factors known to influence mycotoxin contamination in crops. Within the same study period, data on fumonisin (FB) monitoring show clear climate-dependent trends. The strongest positive warming trend is observed between 2018/2019 and 2019/2020 (0.51 °C/year), and a strong positive correlation is likewise established between FB contamination and temperature (r ranging from 0.6 to 0.9). Four machine learning models, viz support vector machines, eXtreme gradient boosting, random forest, and orthogonal partial least squares, are generalized on the historical data with suitable performance (RMSE as low as 0.00). All the adopted models are able to predict future FB contamination patterns with reasonable precision (R2 ranging from 0.34 to 1.00). The most important model feature for predicting average FB contamination (YA) is the historical pattern of average FB contamination in maize within the region (ΣFBs_avg). The two most significant features in modelling maximum FB contamination (YM) are minimum temperature from the CMIP6 data (Pro_tempMIN) and observed precipitation from the CRU data (O_prep). Our study provides strong evidence of the impact of climate change on FB in South Africa and reiterates the significance of machine learning modelling in predicting mycotoxin contamination in light of changing climatic conditions, which could facilitate early warnings and the adoption of relevant mitigation measures that could help in mycotoxin risk management and control.

The quantification of southern corn leaf blight disease using deep UV fluorescence spectroscopy and autoencoder anomaly detection techniques.

Southern leaf blight (SLB) is a foliar disease caused by the fungus Cochliobolus heterostrophus infecting maize plants in humid, warm weather conditions. SLB causes production losses to corn producers in different regions of the world such as Latin America, Europe, India, and Africa. In this paper, we demonstrate a non-destructive method to quantify the signs of fungal infection in SLB-infected corn plants using a deep UV (DUV) fluorescence spectrometer, with a 248.6 nm excitation wavelength, to acquire the emission spectra of healthy and SLB-infected corn leaves. Fluorescence emission spectra of healthy and diseased leaves were used to train an Autoencoder (AE) anomaly detection algorithm-an unsupervised machine learning model-to quantify the phenotype associated with SLB-infected leaves. For all samples, the signature of corn leaves consisted of two prominent peaks around 450 nm and 325 nm. However, SLB-infected leaves showed a higher response at 325 nm compared to healthy leaves, which was correlated to the presence of C. heterostrophus based on disease severity ratings from Visual Scores (VS). Specifically, we observed a linear inverse relationship between the AE error and the VS (R2 = 0.94 and RMSE = 0.935). With improved hardware, this method may enable improved quantification of SLB infection versus visual scoring based on e.g., fungal spore concentration per unit area and spatial localization.

Aspergillus flavus pangenome (AflaPan) uncovers novel aflatoxin and secondary metabolite associated gene clusters.

BACKGROUND: Aspergillus flavus is an important agricultural and food safety threat due to its production of carcinogenic aflatoxins. It has high level of genetic diversity that is adapted to various environments. Recently, we reported two reference genomes of A. flavus isolates, AF13 (MAT1-2 and highly aflatoxigenic isolate) and NRRL3357 (MAT1-1 and moderate aflatoxin producer). Where, an insertion of 310 kb in AF13 included an aflatoxin producing gene bZIP transcription factor, named atfC. Observations of significant genomic variants between these isolates of contrasting phenotypes prompted an investigation into variation among other agricultural isolates of A. flavus with the goal of discovering novel genes potentially associated with aflatoxin production regulation. Present study was designed with three main objectives: (1) collection of large number of A. flavus isolates from diverse sources including maize plants and field soils; (2) whole genome sequencing of collected isolates and development of a pangenome; and (3) pangenome-wide association study (Pan-GWAS) to identify novel secondary metabolite cluster genes. RESULTS: Pangenome analysis of 346 A. flavus isolates identified a total of 17,855 unique orthologous gene clusters, with mere 41% (7,315) core genes and 59% (10,540) accessory genes indicating accumulation of high genomic diversity during domestication. 5,994 orthologous gene clusters in accessory genome not annotated in either the A. flavus AF13 or NRRL3357 reference genomes. Pan-genome wide association analysis of the genomic variations identified 391 significant associated pan-genes associated with aflatoxin production. Interestingly, most of the significantly associated pan-genes (94%; 369 associations) belonged to accessory genome indicating that genome expansion has resulted in the incorporation of new genes associated with aflatoxin and other secondary metabolites. CONCLUSION: In summary, this study provides complete pangenome framework for the species of Aspergillus flavus along with associated genes for pathogen survival and aflatoxin production. The large accessory genome indicated large genome diversity in the species A. flavus, however AflaPan is a closed pangenome represents optimum diversity of species A. flavus. Most importantly, the newly identified aflatoxin producing gene clusters will be a new source for seeking aflatoxin mitigation strategies and needs new attention in research.

Maize leaf disease recognition using PRF-SVM integration: a breakthrough technique.

The difficulty of collecting maize leaf lesion characteristics in an environment that undergoes frequent changes, suffers varying illumination from lighting sources, and is influenced by a variety of other factors makes detecting diseases in maize leaves difficult. It is critical to monitor and identify plant leaf diseases during the initial growing period to take suitable preventative measures. In this work, we propose an automated maize leaf disease recognition system constructed using the PRF-SVM model. The PRFSVM model was constructed by combining three powerful components: PSPNet, ResNet50, and Fuzzy Support Vector Machine (Fuzzy SVM). The combination of PSPNet and ResNet50 not only assures that the model can capture delicate visual features but also allows for end-to-end training for smooth integration. Fuzzy SVM is included as a final classification layer to accommodate the inherent fuzziness and uncertainty in real-world image data. Five different maize crop diseases (common rust, southern rust, grey leaf spot, maydis leaf blight, and turcicum leaf blight along with healthy leaves) are selected from the Plant Village dataset for the algorithm's evaluation. The average accuracy achieved using the proposed method is approximately 96.67%. The PRFSVM model achieves an average accuracy rating of 96.67% and a mAP value of 0.81, demonstrating the efficacy of our approach for detecting and classifying various forms of maize leaf diseases.

Growth-promoting bacteria and arbuscular mycorrhizal fungus enhance maize tolerance to saline stress.

Climate change intensifies soil salinization and jeopardizes the development of crops worldwide. The accumulation of salts in plant tissue activates the defense system and triggers ethylene production thus restricting cell division. We hypothesize that the inoculation of plant growth-promoting bacteria (PGPB) producing ACC (1-aminocyclopropane-1-carboxylate) deaminase favors the development of arbuscular mycorrhizal fungi (AMF), promoting the growth of maize plants under saline stress. We investigated the efficacy of individual inoculation of PGPB, which produce ACC deaminase, as well as the co-inoculation of PGPB with Rhizophagus clarus on maize plant growth subjected to saline stress. The isolates were acquired from the bulk and rhizospheric soil of Mimosa bimucronata (DC.) Kuntze in a temporary pond located in Pernambuco State, Brazil. In the first greenhouse experiment, 10 halophilic PGPB were inoculated into maize at 0, 40 and 80 mM of NaCl, and in the second experiment, the PGPB that showed the best performance were co-inoculated with R. clarus in maize under the same conditions as in the first experiment. Individual PGPB inoculation benefited the number of leaves, stem diameter, root and shoot dry mass, and the photosynthetic pigments. Inoculation with PGPB 28-10 Pseudarthrobacter enclensis, 24-1 P. enclensis and 52 P. chlorophenolicus increased the chlorophyll a content by 138%, 171%, and 324% at 0, 40 and 80 mM NaCl, respectively, comparing to the non-inoculated control. We also highlight that the inoculation of PGPB 28-10, 28-7 Arthrobacter sp. and 52 increased the content of chlorophyll b by 72%, 98%, and 280% and carotenoids by 82%, 98%, and 290% at 0, 40 and 80 mM of NaCl, respectively. Co-inoculation with PGPB 28-7, 46-1 Leclercia tamurae, 70 Artrobacter sp., and 79-1 Micrococcus endophyticus significantly increased the rate of mycorrhizal colonization by roughly 50%. Furthermore, co-inoculation promoted a decrease in the accumulation of Na and K extracted from plant tissue, with an increase in salt concentration, from 40 mM to 80 mM, also favoring the establishment and development of R. clarus. In addition, co-inoculation of these PGPB with R. clarus promoted maize growth and increased plant biomass through osmoregulation and protection of the photosynthetic apparatus. The tripartite symbiosis (plant-fungus-bacterium) is likely to reprogram metabolic pathways that improve maize growth and crop yield, suggesting that the AMF-PGPB consortium can minimize damages caused by saline stress.

Stalk rot species diversity and molecular phylogeny associated with diseased maize in India.

Stalk rot disease is a major constraint in maize production and till date reported to be caused by two to three species of phytopathogenic fungi but, in our present study, we disclose the first report of stalk rot is caused by complex species of phytopathogens, which belongs to five different genera. Therefore, to substantiate these findings, a total of 105 diseased samples of maize were collected from 21 different locations in six different geographical locations of India from which 48 isolates were used for the research study. Morphological features such as pigmentation, colony color, type of mycelium and pattern of mycelium was examined using macro and microscopic methods. A total of 11 different spp. of pathogens belonging to the five different genera: Fusarium verticillioides (56.25%), F. equiseti (14.5%), F. andiyazi (6.25%), F. solani (2.08%), F. proliferatum (2.08%), F. incarnatum (2.08%), Lasidioplodia theobrame (6.25%), Exserohilum rostrtum (4.16%), Nigrospora spp. (4.16%). and Schizophyllum commune (2.08%) were identified by different housekeeping genes (ITS, TEF-1α, RPB2 and Actin). Fusarium verticillioides, F. equiseti and F. andiyazi were major pathogens involved in stalk rot. This is the first report on F. proliferatum, F. solani, F. incarnatum, Lasidioplodia theobrame, Exserohilum rostrtum, Nigrospora spp. and Schizophyllum commune causing stalk rot of maize and their distribution in the different states of India. Studies on population dynamics of PFSR will enhance the understanding of pathogen behavior, virulence, or its association with different pathogens across India, which will facilitate the development of resistant maize genotypes against the PFSR.

Detection of Maize Mold Based on a Nanocomposite Colorimetric Sensor Array under Different Substrates.

This study developed a novel nanocomposite colorimetric sensor array (CSA) to distinguish between fresh and moldy maize. First, the headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC/MS) method was used to analyze volatile organic compounds (VOCs) in fresh and moldy maize samples. Then, principal component analysis and orthogonal partial least-squares discriminant analysis (OPLS-DA) were used to identify 2-methylbutyric acid and undecane as key VOCs associated with moldy maize. Furthermore, colorimetric sensitive dyes modified with different nanoparticles were employed to enhance the dye properties used in the nanocomposite CSA analysis of key VOCs. This study focused on synthesizing four types of nanoparticles: polystyrene acrylic (PSA), porous silica nanospheres (PSNs), zeolitic imidazolate framework-8 (ZIF-8), and ZIF-8 after etching. Additionally, three types of substrates, qualitative filter paper, polyvinylidene fluoride film, and thin-layer chromatography silica gel, were comparatively used to fabricate nanocomposite CSA combining with linear discriminant analysis (LDA) and K-nearest neighbor (KNN) models for real sample detection. All moldy maize samples were correctly identified and prepared to characterize the properties of the CSA. Through initial testing and nanoenhancement of the chosen dyes, four nanocomposite colorimetric sensitive dyes were confirmed. The accuracy rates for LDA and KNN models in this study reached 100%. This work shows great potential for grain quality control using CSA methods.

Plant immunity suppression by an ß-1,3-glucanase of the maize anthracnose pathogen Colletotrichum graminicola.

BACKGROUND: Many phytopathogens secrete a large number of cell wall degrading enzymes (CWDEs) to decompose host cell walls in order to penetrate the host, obtain nutrients and accelerate colonization. There is a wide variety of CWDEs produced by plant pathogens, including glycoside hydrolases (GHs), which determine the virulence, pathogenicity, and host specificity of phytopathogens. The specific molecular mechanisms by which pathogens suppress host immunity remain obscure. RESULT: In this study, we found that CgEC124 encodes a glycosyl hydrolase with a signal peptide and a conserved Glyco_hydro_cc domain which belongs to glycoside hydrolase 128 family. The expression of CgEC124 was significantly induced in the early stage of Colletotrichum graminicola infection, especially at 12 hpi. Furthermore, CgEC124 positively regulated the pathogenicity, but it did not impact the vegetative growth of mycelia. Ecotopic transient expression of CgEC124 decreased the disease resistance and callose deposition in maize. Moreover, CgEC124 exhibited the ß-1,3-glucanase activity and suppresses glucan-induced ROS burst in maize leaves. CONCLUSIONS: Our results indicate that CgEC124 is required for full virulence of C. graminicola but not for vegetative growth. CgEC124 increases maize susceptibility by inhibiting host reactive oxygen species burst as well as callose deposition. Meanwhile, our data suggests that CgEC124 explores its ß-1,3-glucanase activity to prevent induction of host defenses.

Reducing transmission of high-risk antibiotic resistance genes in whole-crop corn silage through lactic acid bacteria inoculation and increasing ensiling temperature.

The microbial hosts of antibiotic resistance genes (ARGs) found epiphytically on plant materials could grow and flourish during silage fermentation. This study employed metagenomic analysis and elucidated the occurrence and transmission mechanisms of ARGs and their microbial hosts in whole-crop corn silage inoculated with homofermentative strain Lactiplantibacillus plantarum or heterofermentative strain Lentilactobacillus buchneri ensiled under different temperature (20 and 30 °C). The results revealed that the corn silage was dominated by Lactobacillus, Leuconostoc, Lentilactobacillus, and Latilactobacillus. Both the ensiling temperature and inoculation had greatly modified the silage microbiota. However, regardless of the ensiling temperature, L. buchneri had significantly higher ARGs, while it only exhibited significantly higher mobile genetic elements (MGEs) in low temperature treatments. The microbial community of the corn silage hosted highly diverse form of ARGs, which were primarily MacB, RanA, bcrA, msbA, TetA (58), and TetT and mainly corresponded to macrolides and tetracyclines drug classes. Plasmids were identified as the most abundant MGEs with significant correlation with some high-risk ARGs (tetM, TolC, mdtH, and NorA), and their abundances have been reduced by ensiling process. Furthermore, higher temperature and L. buchneri reduced abundances of high-risk ARGs by modifying their hosts and reduced their transmission in the silage. Therefore, ensiling, L. buchneri inoculation and higher storage temperature could improve the biosafety of corn silage.

HacA, a key transcription factor for the unfolded protein response, is required for fungal development, aflatoxin biosynthesis and pathogenicity of Aspergillus flavus.

Aspergillus flavus is a fungus notorious for contaminating food and feed with aflatoxins. As a saprophytic fungus, it secretes large amounts of enzymes to access nutrients, making endoplasmic reticulum (ER) homeostasis important for protein folding and secretion. The role of HacA, a key transcription factor in the unfolded protein response pathway, remains poorly understood in A. flavus. In this study, the hacA gene in A. flavus was knockout. Results showed that the absence of hacA led to a decreased pathogenicity of the strain, as it failed to colonize intact maize kernels. This may be due to retarded vegetable growth, especially the abnormal development of swollen tips and shorter hyphal septa. Deletion of hacA also hindered conidiogenesis and sclerotial development. Notably, the mutant strain failed to produce aflatoxin B1. Moreover, compared to the wild type, the mutant strain showed increased sensitivity to ER stress inducer such as Dithiothreitol (DTT), and heat stress. It also displayed heightened sensitivity to other environmental stresses, including cell wall, osmotic, and pH stresses. Further transcriptomic analysis revealed the involvement of the hacA in numerous biological processes, including filamentous growth, asexual reproduction, mycotoxin biosynthetic process, signal transduction, budding cell apical bud growth, invasive filamentous growth, response to stimulus, and so on. Taken together, HacA plays a vital role in fungal development, pathogenicity and aflatoxins biosynthesis. This highlights the potential of targeting hacA as a novel approach for early prevention of A. flavus contamination.

Aquaporin ZmTIP2;3 Promotes Drought Resistance of Maize through Symbiosis with Arbuscular Mycorrhizal Fungi.

Arbuscular mycorrhizal fungi symbiosis plays important roles in enhancing plant tolerance to biotic and abiotic stresses. Aquaporins have also been linked to improved drought tolerance in plants and the regulation of water transport. However, the mechanisms that underlie this association remain to be further explored. In this study, we found that arbuscular mycorrhiza fungi symbiosis could induce the gene expression of the aquaporin ZmTIP2;3 in maize roots. Moreover, compared with the wild-type plants, the maize zmtip2;3 mutant also showed a lower total biomass, colonization rate, relative water content, and POD and SOD activities after arbuscular mycorrhiza fungi symbiosis under drought stress. qRT-PCR assays revealed reduced expression levels of stress genes including LEA3, P5CS4, and NECD1 in the maize zmtip2;3 mutant. Taken together, these data suggest that ZmTIP2;3 plays an important role in promoting maize tolerance to drought stress during arbuscular mycorrhiza fungi symbiosis.

Bacterial diversity in agricultural drainage ditches shifts with increasing urea-N concentrations.

Urea-based fertilizers applied to crop fields can enter the surface waters of adjacent agricultural drainage ditches and contribute to the nitrogen (N) loading in nearby watersheds. Management practices applied in drainage ditches promote N removal by the bacterial communities, but little is known about the impacts of excess urea fertilizer from crop fields on the bacterial diversity in these ditches. In 2017, sediments from drainage ditches next to corn and soybean fields were sampled to determine if fertilizer application and high urea-N concentrations alters bacterial diversity and urease gene abundances. A mesocosm experiment was paired with a field study to determine which bacterial groups respond to high urea-N concentrations. The bacterial diversity in the ditch next to corn fields was significantly different from the other site. The bacterial orders of Rhizobiales, Bacteroidales, Acidobacteriales, Burkholderiales, and Anaerolineales were most abundant in the ditch next to corn and increased after the addition of urea-N (0.5 mg N L-1) during the mesocosm experiment. The results of our study suggests that urea-N concentrations >0.07 mg N L-1, which are higher than concentrations associated with downstream harmful algal blooms, can lead to shifts in the bacterial communities of agricultural drainage ditches.

Exploiting genomic tools for genetic dissection and improving the resistance to Fusarium stalk rot in tropical maize.

KEY MESSAGE: A stable genomic region conferring FSR resistance at ~250 Mb on chromosome 1 was identified by GWAS. Genomic prediction has the potential to improve FSR resistance. Fusarium stalk rot (FSR) is a global destructive disease in maize; the efficiency of phenotypic selection for improving FSR resistance was low. Novel genomic tools of genome-wide association study (GWAS) and genomic prediction (GP) provide an opportunity for genetic dissection and improving FSR resistance. In this study, GWAS and GP analyses were performed on 562 tropical maize inbred lines consisting of two populations. In total, 15 SNPs significantly associated with FSR resistance were identified across two populations and the combinedPOP consisting of all 562 inbred lines, with the P-values ranging from 1.99 × 10-7 to 8.27 × 10-13, and the phenotypic variance explained (PVE) values ranging from 0.94 to 8.30%. The genetic effects of the 15 favorable alleles ranged from -4.29 to -14.21% of the FSR severity. One stable genomic region at ~ 250 Mb on chromosome 1 was detected across all populations, and the PVE values of the SNPs detected in this region ranged from 2.16 to 5.18%. Prediction accuracies of FSR severity estimated with the genome-wide SNPs were moderate and ranged from 0.29 to 0.51. By incorporating genotype-by-environment interaction, prediction accuracies were improved between 0.36 and 0.55 in different breeding scenarios. Considering both the genome coverage and the threshold of the P-value of SNPs to select a subset of molecular markers further improved the prediction accuracies. These findings extend the knowledge of exploiting genomic tools for genetic dissection and improving FSR resistance in tropical maize.

Efficacy of atoxigenic Aspergillus flavus from southern China as biocontrol agents against aflatoxin contamination in corn and peanuts.

Aspergillus flavus is a ubiquitous facultative pathogen that routinely infects important crops leading to formation of aflatoxins during crop development and after harvest. Corn and peanuts in warm and/or drought-prone regions are highly susceptible to aflatoxin contamination. Controlling aflatoxin using atoxigenic A. flavus is a widely adopted strategy. However, no A. flavus genotypes are currently approved for use in China. The current study aimed to select atoxigenic A. flavus endemic to Guangxi Zhuang Autonomous Region with potential as active ingredients of aflatoxin biocontrol products. A total of 204 A. flavus isolates from corn, peanuts, and field soil were evaluated for ability to produce the targeted mycotoxins. Overall, 57.3% could not produce aflatoxins while 17.15% were incapable of producing both aflatoxins and CPA. Atoxigenic germplasm endemic to Guangxi was highly diverse, yielding 8 different gene deletion patterns in the aflatoxin and CPA biosynthesis gene clusters ranging from no deletion to deletion of both clusters. Inoculation of corn and peanuts with both an aflatoxin producer and selected atoxigenic genotypes showed significant reduction (74 to 99%) in aflatoxin B1 (AFB1) formation compared with inoculation with the aflatoxin producer alone. Atoxigenic genotypes also efficiently degraded AFB1 (61%). Furthermore, atoxigenic isolates were also highly efficient at reducing aflatoxin concentrations even when present at lower concentrations than aflatoxin producers. The use of multiple atoxigenics was not always as effective as the use of a single atoxigenic. Effective atoxigenic genotypes of A. flavus with known mechanisms of atoxigenicity are demonstrated to be endemic to Southern China. These A. flavus may be utilized as active ingredients of biocontrol products without concern for detrimental impacts that may result from introduction of exotic fungi. Field efficacy trials in the agroecosystems of Southern China are needed to determine the extent to which such products may allow the production of safer food and feed.

Macro-micro exploration on dynamic interaction between aflatoxigenic Aspergillus flavus and maize kernels using Vis/NIR hyperspectral imaging and SEM technology.

Aspergillus flavus and its toxic metabolites-aflatoxins infect and contaminate maize kernels, posing a threat to grain safety and human health. Due to the complexity of microbial growth and metabolic processes, dynamic mechanisms among fungal growth, nutrient depletion of maize kernels and aflatoxin production is still unclear. In this study, visible/near infrared (Vis/NIR) hyperspectral imaging (HSI) combined with the scanning electron microscope (SEM) was used to elucidate the critical organismal interaction at kernel (macro-) and microscopic levels. As kernel damage is the main entrance for fungal invasion, maize kernels with gradually aggravated damages from intact to pierced to halved kernels with A. flavus were cultured for 0-120 h. The spectral fingerprints of the A. flavus-maize kernel complex over time were analyzed with principal components analysis (PCA) of hyperspectral images, where the pseudo-color score maps and the loading plots of the first three PCs were used to investigate the dynamic process of fungal infection and to capture the subtle changes in the complex with different hardness of the maize matrix. The dynamic growth process of A. flavus and the interactions of fungus-maize complexes were explained on a microscopic level using SEM. Specifically, fungus morphology, e.g., hyphae, conidia, and conidiophore (stipe) was accurately captured on the microscopic level, and the interaction process between A. flavus and nutrient loss from the maize kernel tissues (i.e., embryo, and endosperm) was described. Furthermore, the growth stage discrimination models based on PLSDA with the results of CCRC = 100 %, CCRV = 97 %, CCRIV = 93 %, and the prediction models of AFB1 based on PLSR with satisfactory performance (R2C = 0.96, R2V = 0.95, R2IV = 0.93 and RPD = 3.58) were both achieved. In conclusion, the results from both macro-level (Vis/NIR-HSI) and micro-level (SEM) assessments revealed the dynamic organismal interactions in A. flavus-maize kernel complex, and the detailed data could be used for modeling, and quantitative prediction of aflatoxin, which would establish a theoretical foundation for the early detection of fungal or toxin contaminated grains to ensure food security.

The impact of processing technology on microbial community composition and functional properties of Beninese maize ogi.

Traditionally fermented maize starch, called ogi, is produced to prepare akpan, a yoghurt-like street food widely consumed in Benin. Current maize ogi production practices were compared to assess the impact of different processing technologies on the characteristics of the fermented product as a basis to determine best practices. Maize starch slurry samples were collected from processors in five municipalities in southern Benin and analysed before fermentation (starch samples) and after spontaneous fermentation (ogi samples). Four technological pathways for maize starch production were distinguished based on variations in the duration of steeping the grains, which ranged from 6 to 72 h, and whether or not kneading of the wet flour before filtration was practised. Six categories of maize ogi were derived from the four technology groups based on the duration of the fermentation, which lasted from 6 to 24 h. The average pH of maize starch varied from 3.2 to 5.3, with the lowest values for the two technology groups that also had the highest lactate concentrations (9-11.8 g/L). The six maize ogi categories had a pH ranging from 3.1 to 4.0. Viable plate counts of lactic acid bacteria were similar for maize starch samples and for ogi samples, whereas yeast counts showed clear differences. Members of the genera Limosilactobacillus, Lactobacillus, Weissella, Streptococcus and Ligilactobacillus, dominated the bacterial community in maize starch, and were also dominant in maize ogi. The members of the genera dominating the fungal community in maize starch were also dominant in maize ogi, except for Aspergillus and Stenocarpella spp., which decreased in relative abundance by fermentation. The highest total free essential amino acid concentration was 61.6 mg/L in maize starch and 98.7 mg/L in ogi. The main volatile organic compounds in maize starch samples were alcohols, esters, and carboxylic acids, which also prevailed in maize ogi samples. The results indicate that the characteristics of traditional maize ogi depend on the processing technologies used to produce the maize starch before the intentional fermentation into ogi, with no clear-cut connection with the production practices due to high variations between samples from the same technology groups. This revealed the importance of a standardized maize starch production process, which would benefit controlling the starch fermentation and the characteristics of maize ogi. Further research is needed to understand the hidden fermentation during maize starch production for determination of the best practices that support the production of quality maize ogi.

Heritable microbiome variation is correlated with source environment in locally adapted maize varieties.

Beneficial interactions with microorganisms are pivotal for crop performance and resilience. However, it remains unclear how heritable the microbiome is with respect to the host plant genotype and to what extent host genetic mechanisms can modulate plant-microbiota interactions in the face of environmental stresses. Here we surveyed 3,168 root and rhizosphere microbiome samples from 129 accessions of locally adapted Zea, sourced from diverse habitats and grown under control and different stress conditions. We quantified stress treatment and host genotype effects on the microbiome. Plant genotype and source environment were predictive of microbiome abundance. Genome-wide association analysis identified host genetic variants linked to both rhizosphere microbiome abundance and source environment. We identified transposon insertions in a candidate gene linked to both the abundance of a keystone bacterium Massilia in our controlled experiments and total soil nitrogen in the source environment. Isolation and controlled inoculation of Massilia alone can contribute to root development, whole-plant biomass production and adaptation to low nitrogen availability. We conclude that locally adapted maize varieties exert patterns of genetic control on their root and rhizosphere microbiomes that follow variation in their home environments, consistent with a role in tolerance to prevailing stress.

Isolation and characterization of Paenibacillus peoriae JC-3jx from Dendrobium nobile.

Dendrobium is a rich source of high-value natural components. Endophytic fungi are well studied, yet bacteria research is limited. In this study, endophytic bacteria from Dendrobium nobile were isolated using an improved method, showing inhibition of pathogens and growth promotion. JC-3jx, identified as Paenibacillus peoriae, exhibited significant inhibitory activity against tested fungi and bacteria, including Escherichia coli. JC-3jx also promoted corn seed rooting and Dendrobium growth, highlighting its excellent biocontrol and growth-promoting potential.

Maize functional requirements drive the selection of rhizobacteria under long-term fertilization practices.

Rhizosphere microbiomes are pivotal for crop fitness, but the principles underlying microbial assembly during root-soil interactions across soils with different nutrient statuses remain elusive. We examined the microbiomes in the rhizosphere and bulk soils of maize plants grown under six long-term (≥ 29 yr) fertilization experiments in three soil types across middle temperate to subtropical zones. The assembly of rhizosphere microbial communities was primarily driven by deterministic processes. Plant selection interacted with soil types and fertilization regimes to shape the structure and function of rhizosphere microbiomes. Predictive functional profiling showed that, to adapt to nutrient-deficient conditions, maize recruited more rhizobacteria involved in nutrient availability from bulk soil, although these functions were performed by different species. Metagenomic analyses confirmed that the number of significantly enriched Kyoto Encyclopedia of Genes and Genomes Orthology functional categories in the rhizosphere microbial community was significantly higher without fertilization than with fertilization. Notably, some key genes involved in carbon, nitrogen, and phosphorus cycling and purine metabolism were dominantly enriched in the rhizosphere soil without fertilizer input. In conclusion, our results show that maize selects microbes at the root-soil interface based on microbial functional traits beneficial to its own performance, rather than selecting particular species.