Identification of Nitrogen Fixation Genes in Lactococcus Isolated from Maize Using Population Genomics and Machine Learning

Artificial neural network-based gene screening and immune cell infiltration analysis of osteosarcoma feature

BACKGROUND

The present study aimed to construct an artificial neural network (ANN) model that leverages characteristic genes associated with osteosarcoma (OS) to enable accurate prognostication for OS patients.

METHODS

Our research revealed 467 differentially expressed genes (DEGs) via gene expression contrast analysis, consisting of 345 downregulated genes and 122 upregulated genes. Gene Ontology (GO) enrichment analysis illuminated functions primarily encompassing T-cell activation, secretory granule lumen and antioxidant activity, among others. Through Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, we discovered significant correlations between the DEGs and certain pathways, including phagosome, Staphylococcus aureus infection and human T-cell leukemia virus 1 infection. We then screened out 30 characteristic DEGs (CDEGs) based on random forest analysis and constructed the ANN model using the gene score matrix. To verify the credibility and accuracy of the ANN model, we performed internal and external validation processes, which affirmed our model's predictive capabilities.

RESULTS

The study further delved into the analysis of immune cell infiltration and its correlation with the target CDEGs, revealing disparities in the infiltration of 22 types of immune cells across different groups and their interrelationships. Moreover, we probed the expression of the two foremost CDEGs (YES1 and MFNG) in OS and normal tissues. We noted a positive relationship between the expression of YES1 and MFNG in OS tissues and the clinicopathological characteristics of OS patients.

CONCLUSIONS

Collectively, the findings of the present study validate the effectiveness of the CDEGs-based ANN model in predicting OS patients, which might facilitate early diagnosis and treatment of OS.

Conservation and diversity of the pollen microbiome of Pan-American maize using PacBio and MiSeq

Pollen is a vector for diversification, fitness-selection, and transmission of plant genetic material. The extent to which the pollen microbiome may contribute to host diversification is largely unknown, because pollen microbiome diversity within a plant species has not been reported, and studies have been limited to conventional short-read 16S rRNA gene sequencing (e.g., V4-MiSeq) which suffers from poor taxonomic resolution. Here we report the pollen microbiomes of 16 primitive and traditional accessions of maize (corn) selected by indigenous peoples across the Americas, along with the modern U.S. inbred B73. The maize pollen microbiome has not previously been reported. The pollen microbiomes were identified using full-length (FL) 16S rRNA gene PacBio SMRT sequencing compared to V4-MiSeq. The Pan-American maize pollen microbiome encompasses 765 taxa spanning 39 genera and 46 species, including known plant growth promoters, insect-obligates, plant pathogens, nitrogen-fixers and biocontrol agents. Eleven genera and 13 species composed the core microbiome. Of 765 taxa, 63% belonged to only four genera: 28% were Pantoea, 15% were Lactococcus, 11% were Pseudomonas, and 10% were Erwinia. Interestingly, of the 215 Pantoea taxa, 180 belonged to a single species, P. ananatis. Surprisingly, the diversity within P. ananatis ranged nearly 10-fold amongst the maize accessions analyzed (those with ≥3 replicates), despite being grown in a common field. The highest diversity within P. ananatis occurred in accessions that originated near the center of diversity of domesticated maize, with reduced diversity associated with the north-south migration of maize. This sub-species diversity was revealed by FL-PacBio but missed by V4-MiSeq. V4-MiSeq also mis-identified some dominant genera captured by FL-PacBio. The study, though limited to a single season and common field, provides initial evidence that pollen microbiomes reflect evolutionary and migratory relationships of their host plants.

Enhancing maize's nitrogen-fixing potential through ZmSBT3, a gene suppressing mucilage secretion

Maize (Zea mays) requires substantial amounts of nitrogen, posing a challenge for its cultivation. Recent work discovered that some ancient Mexican maize landraces harbored diazotrophic bacteria in mucilage secreted by their aerial roots. To see if this trait is retained in modern maize, we conducted a field study of aerial root mucilage (ARM) in 258 inbred lines. We observed that ARM secretion is common in modern maize, but the amount significantly varies, and only a few lines have retained the nitrogen-fixing traits found in ancient landraces. The mucilage of the high-ARM inbred line HN5-724 had high nitrogen-fixing enzyme activity and abundant diazotrophic bacteria. Our genome-wide association study identified 17 candidate genes associated with ARM across three environments. Knockouts of one candidate gene, the subtilase family gene ZmSBT3, confirmed that it negatively regulates ARM secretion. Notably, the ZmSBT3 knockout lines had increased biomass and total nitrogen accumulation under nitrogen-free culture conditions. High ARM was associated with three ZmSBT3 haplotypes that were gradually lost during maize domestication, being retained in only a few modern inbred lines such as HN5-724. In summary, our results identify ZmSBT3 as a potential tool for enhancing ARM, and thus nitrogen fixation, in maize.

The Cultured Microbiome of Pollinated Maize Silks Shifts after Infection with Fusarium graminearum and Varies by Distance from the Site of Pathogen Inoculation

Styles transmit pollen-derived sperm nuclei from pollen to ovules, but also transmit environmental pathogens. The microbiomes of styles are likely important for reproduction/disease, yet few studies exist. Whether style microbiome compositions are spatially responsive to pathogens is unknown. The maize pathogen Fusarium graminearum enters developing grain through the style (silk). We hypothesized that F. graminearum treatment shifts the cultured transmitting silk microbiome (TSM) compared to healthy silks in a distance-dependent manner. Another objective of the study was to culture microbes for future application. Bacteria were cultured from husk-covered silks of 14 F. graminearum-treated diverse maize genotypes, proximal (tip) and distal (base) to the F. graminearum inoculation site. Long-read 16S sequences from 398 isolates spanned 35 genera, 71 species, and 238 OTUs. More bacteria were cultured from F. graminearum-inoculated tips (271 isolates) versus base (127 isolates); healthy silks were balanced. F. graminearum caused a collapse in diversity of ~20-25% across multiple taxonomic levels. Some species were cultured exclusively or, more often, from F. graminearum-treated silks (e.g., Delftia acidovorans, Klebsiella aerogenes, K. grimontii, Pantoea ananatis, Stenotrophomonas pavanii). Overall, the results suggest that F. graminearum alters the TSM in a distance-dependent manner. Many isolates matched taxa that were previously identified using V4-MiSeq (core and F. graminearum-induced), but long-read sequencing clarified the taxonomy and uncovered greater diversity than was initially predicted (e.g., within Pantoea). These isolates represent the first comprehensive cultured collection from pathogen-treated maize silks to facilitate biocontrol efforts and microbial marker-assisted breeding.

Pan-Genome-Wide Association Study of Serotype 19A Pneumococci Identifies Disease-Associated Genes

Despite the widespread implementation of pneumococcal vaccines, hypervirulent Streptococcus pneumoniae serotype 19A is endemic worldwide. It is still unclear whether specific genetic elements contribute to complex pathogenicity of serotype 19A isolates. We performed a large-scale pan-genome-wide association study (pan-GWAS) of 1,292 serotype 19A isolates sampled from patients with invasive disease and asymptomatic carriers. To address the underlying disease-associated genotypes, a comprehensive analysis using three methods (Scoary, a linear mixed model, and random forest) was performed to compare disease and carriage isolates to identify genes consistently associated with disease phenotype. By using three pan-GWAS methods, we found consensus on statistically significant associations between genotypes and disease phenotypes (disease or carriage), with a subset of 30 consistently significant disease-associated genes. The results of functional annotation revealed that these disease-associated genes had diverse predicted functions, including those that participated in mobile genetic elements, antibiotic resistance, virulence, and cellular metabolism. Our findings suggest the multifactorial pathogenicity nature of this hypervirulent serotype and provide important evidence for the design of novel protein-based vaccines to prevent and control pneumococcal disease. IMPORTANCE It is important to understand the genetic and pathogenic characteristics of S. pneumoniae serotype 19A, which may provide important information for the prevention and treatment of pneumococcal disease. This global large-sample pan-GWAS study has identified a subset of 30 consistently significant disease-associated genes that are involved in mobile genetic elements, antibiotic resistance, virulence, and cellular metabolism. These findings suggest the multifactorial pathogenicity nature of hypervirulent S. pneumoniae serotype 19A isolates and provide implications for the design of novel protein-based vaccines.

Artificial Intelligence: A Promising Tool in Exploring the Phytomicrobiome in Managing Disease and Promoting Plant Health

There is increasing interest in harnessing the microbiome to improve cropping systems. With the availability of high-throughput and low-cost sequencing technologies, gathering microbiome data is becoming more routine. However, the analysis of microbiome data is challenged by the size and complexity of the data, and the incomplete nature of many microbiome databases. Further, to bring microbiome data value, it often needs to be analyzed in conjunction with other complex data that impact on crop health and disease management, such as plant genotype and environmental factors. Artificial intelligence (AI), boosted through deep learning (DL), has achieved significant breakthroughs and is a powerful tool for managing large complex datasets such as the interplay between the microbiome, crop plants, and their environment. In this review, we aim to provide readers with a brief introduction to AI techniques, and we introduce how AI has been applied to areas of microbiome sequencing taxonomy, the functional annotation for microbiome sequences, associating the microbiome community with host traits, designing synthetic communities, genomic selection, field phenotyping, and disease forecasting. At the end of this review, we proposed further efforts that are required to fully exploit the power of AI in studying phytomicrobiomes.

New insights into hexavalent chromium exposure in electron donor limited denitrification: bio-electron behavior

The bio-electron behavior (electron production, transmission, and consumption) response to a typical heavy metal, hexavalent chromium, was unraveled in the electron donor limited system (EDLS) and electron donor sufficient system (EDSS). Nicotinamide adenine dinucleotide and adenosine triphosphate production were reduced by 44% and 47%, respectively, due to glucose metabolism inhibition, leading to NO₃^--N declining to 31% in EDLS. The decreased electron carrier contents and denitrifying enzymes activity inhibited electron transmission and consumption in both EDLS and EDSS. Additionally, electron transfer and antioxidant stress abilities were reduced, further hindering the survival of denitrifiers in EDLS. The lack of dominant genera (Comamonas, Thermomonas, and Microbacterium) in EDLS was the primary reason for poor biofilm formation and chromium adaptability. The decreased expression of enzymes related to glucose metabolism caused the imbalance of electron supply, transport, and consumption in EDLS, adversely impacting nitrogen metabolism and inhibiting denitrification performance.

A thermophilic chemolithoautotrophic bacterial consortium suggests a mutual relationship between bacteria in extreme oligotrophic environments

A thermophilic, chemolithoautotrophic, and aerobic microbial consortium (termed carbonitroflex) growing in a nutrient-poor medium and an atmosphere containing N₂, O₂, CO₂, and CO is investigated as a model to expand our understanding of extreme biological systems. Here we show that the consortium is dominated by Carbonactinospora thermoautotrophica (strain StC), followed by Sphaerobacter thermophilus, Chelatococcus spp., and Geobacillus spp. Metagenomic analysis of the consortium reveals a mutual relationship among bacteria, with C. thermoautotrophica StC exhibiting carboxydotrophy and carbon-dioxide storage capacity. C. thermoautotrophica StC, Chelatococcus spp., and S. thermophilus harbor genes encoding CO dehydrogenase and formate oxidase. No pure cultures were obtained under the original growth conditions, indicating that a tightly regulated interactive metabolism might be required for group survival and growth in this extreme oligotrophic system. The breadwinner hypothesis is proposed to explain the metabolic flux model and highlight the vital role of C. thermoautotrophica StC (the sole keystone species and primary carbon producer) in the survival of all consortium members. Our data may contribute to the investigation of complex interactions in extreme environments, exemplifying the interconnections and dependency within microbial communities.

Compositionality, sparsity, spurious heterogeneity, and other data-driven challenges for machine learning algorithms within plant microbiome studies

The plant-associated microbiome is a key component of plant systems, contributing to their health, growth, and productivity. The application of machine learning (ML) in this field promises to help untangle the relationships involved. However, measurements of microbial communities by high-throughput sequencing pose challenges for ML. Noise from low sample sizes, soil heterogeneity, and technical factors can impact the performance of ML. Additionally, the compositional and sparse nature of these datasets can impact the predictive accuracy of ML. We review recent literature from plant studies to illustrate that these properties often go unmentioned. We expand our analysis to other fields to quantify the degree to which mitigation approaches improve the performance of ML and describe the mathematical basis for this. With the advent of accessible analytical packages for microbiome data including learning models, researchers must be familiar with the nature of their datasets.

The potential of lactic acid bacteria in mediating the control of plant diseases and plant growth stimulation in crop production - A mini review

The microbial diseases cause significant damage in agriculture, resulting in major yield and quality losses. To control microbiological damage and promote plant growth, a number of chemical control agents such as pesticides, herbicides, and insecticides are available. However, the rising prevalence of chemical control agents has led to unintended consequences for agricultural quality, environmental devastation, and human health. Chemical agents are not naturally broken down by microbes and can be found in the soil and environment long after natural decomposition has occurred. As an alternative to chemical agents, biocontrol agents are employed to manage phytopathogens. Interest in lactic acid bacteria (LAB) research as another class of potentially useful bacteria against phytopathogens has increased in recent years. Due to the high level of biosafety, they possess and the processes they employ to stimulate plant growth, LAB is increasingly being recognized as a viable option. This paper will review the available information on the antagonistic and plant-promoting capabilities of LAB and its mechanisms of action as well as its limitation as BCA. This review aimed at underlining the benefits and inputs from LAB as potential alternatives to chemical usage in sustaining crop productivity.

Theobroma cacao L. agricultural soils with natural low and high cadmium (Cd) in Santander (Colombia), contain a persistent shared bacterial composition shaped by multiple soil variables and bacterial isolates highly resistant to Cd concentrations

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This study report statistically significant differences in beta-diversity correlated with soil properties, including Cd concentrations.

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Culture-dependent techniques allowed the isolation of bacterial strains tolerating high Cd concentrations up to 120 mgL⁻¹ for potencial Cd biosorption or intracellular sequestration.

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The combination of different approaches provides a baseline about the bacterial composition and Cd tolerant strains found in these soils influence for lower Cd accumulation in cocoa crops.

Heavy metals can be found in soil as natural components or as product of contaminations events; plants growing in soils are prone to bioaccumulate heavy metals on their biomass. Theobroma cacao L. can bioaccumulate cadmium (Cd) in the seed and could be in derived food products, it considered a human health risk; therefore, removal of Cd is desirable but not vet technically and economically feasible; only to avoid Cd in cocoa is by selecting lands plots exhibiting lower Cd concentrations in soils, imposing a serious limitation to farmers and regulators. The study of bacterial communities and isolation bacteria with tolerance and mechanisms to counteract the translocation of Cd to the parts of cocoa plant exhibits high relevance in Colombia economy and especially to companies producing chocolate and derivatives. Here, we explore bacterial communities associated with soils having relatively high natural Cd concentrations in a large agricultural cocoa plot located in the Santander region. We characterized the bacterial communities’ compositions by amplicon 16S rRNA sequencing from metagenomics soil DNA and by culturing-based enumeration and isolation approaches.

Culture-dependent techniques allowed the isolation of bacteria tolerant to Cd concentration, complement the information for Colombia, and expand the number of strains characterized with adaptive capacity against Cd with tolerance in a concentration of 120 mg/L, which represents the first capacity for Exiguobacterium sp., Ralstonia sp., Serratia sp., Dermacoccus sp., Klebsiella sp., Lactococcus sp. and Staphylococcus sp. In addition to confirming that there is a greater diversity of Cd-tolerant bacteria present in soils of farms cultivated with cocoa in Colombia. As for the results of new generation sequencing, they revealed that, the alpha-diversity in bacterial composition, according to the ANOVA, there are statistically significant differences of the bacterial communities present in the samples. Regarding Pearson correlation analysis, it was found the Shannon Simpson indices, have a positive correlation against OM, C, pH, Mn, C.E.C.I., Ca, P and negatively correlated with S; respect to bacterial community structure, a principal component analysis, which revealed that independent of the concentration of Cd present in soil samples, separates them according to pH value. Phyla to high abundance relative in all samples were Proteobacteria, Acidobacteriota, Actinobacteriota, Verrucomicrobiota, Myxococcota, Chloroflexi, Plactomycetota, Bacteroidota, Gemmatimonadota, Nitrospirota, Firmicutes and NB1_J; the bacteria genera with higher relative abundance (>0.5%) Nitrospira, candidatus Udaeobacter, Haliangium, Cupriavidus, MND1, Bacillus, Kitasatospora, Niveibacterium, Acidothermus, Burkholderia, Acidibacter, Terrimonas, Gaiella, candidatus Solibacter, Kitasatospora, Sphingomonas, Streptomyces, this genus with a relationship with the Cd tolerance process. After it, redundancy analysis was performed between the variation of the bacterial communities identified by dependent and independent techniques and edaphic soil variables, where their positive correlation was found against K, OM, C, Ca, pH (p<0.01) and P, C.E.C.I (p<0.05). For soil samples, the bacterial genera that make up the core community were identified, which are present in all samples as Nitrospira sp., Cupriavidus sp., Burkholderia sp., Haliangium sp., candidatus Udaeobacter, MND1, Kitasatospora, Acidothermus, Acidibacter, Streptomyces, Gaiella, candidatus Solibacter and Terramonas; the genera identified has a different and fundamental role in ecosystem functioning. The combination of different approaches offers new clues regarding the assessment of bacterial communities in soils cultivated with cocoa in soils with elevated Cd content in Colombia, and the ecological role and interplay of soil components and bacterial communities that contribute to modulate the effect of bioaccumulation in products.