Characterization of an Arginine Decarboxylase from Streptococcus pneumoniae by Ultrahigh-Performance Liquid Chromatography-Tandem Mass Spectrometry.

Polyamines are polycations derived from amino acids that play an important role in proliferation and growth in almost all living cells. In Streptococcus pneumoniae (the pneumococcus), modulation of polyamine metabolism not only plays an important regulatory role in central metabolism, but also impacts virulence factors such as the capsule and stress responses that affect survival in the host. However, functional annotation of enzymes from the polyamine biosynthesis pathways in the pneumococcus is based predominantly on computational prediction. In this study, we cloned SP_0166, predicted to be a pyridoxal-dependent decarboxylase, from the Orn/Lys/Arg family pathway in S. pneumoniae TIGR4 and expressed and purified the recombinant protein. We performed biochemical characterization of the recombinant SP_0166 and confirmed the substrate specificity. For polyamine analysis, we developed a simultaneous quantitative method using hydrophilic interaction liquid chromatography (HILIC)-based liquid chromatography-tandem mass spectrometry (LC-MS/MS) without derivatization. SP_0166 has apparent Km, kcat, and kcat/Km values of 11.3 mM, 715,053 min-1, and 63,218 min-1 mM-1, respectively, with arginine as a substrate at pH 7.5. We carried out inhibition studies of SP_0166 enzymatic activity with arginine as a substrate using chemical inhibitors DFMO and DFMA. DFMO is an irreversible inhibitor of ornithine decarboxylase activity, while DFMA inhibits arginine decarboxylase activity. Our findings confirm that SP_0166 is inhibited by DFMA and DFMO, impacting agmatine production. The use of arginine as a substrate revealed that the synthesis of putrescine by agmatinase and N-carbamoylputrescine by agmatine deiminase were both affected and inhibited by DFMA. This study provides experimental validation that SP_0166 is an arginine decarboxylase in pneumococci.

Impact of Difluoromethylornithine and AMXT 1501 on Gene Expression and Capsule Regulation in Streptococcus pneumoniae.

Streptococcus pneumoniae (Spn), a Gram-positive bacterium, poses a significant threat to human health, causing mild respiratory infections to severe invasive conditions. Despite the availability of vaccines, challenges persist due to serotype replacement and antibiotic resistance, emphasizing the need for alternative therapeutic strategies. This study explores the intriguing role of polyamines, ubiquitous, small organic cations, in modulating virulence factors, especially the capsule, a crucial determinant of Spn's pathogenicity. Using chemical inhibitors, difluoromethylornithine (DFMO) and AMXT 1501, this research unveils distinct regulatory effects on the gene expression of the Spn D39 serotype in response to altered polyamine homeostasis. DFMO inhibits polyamine biosynthesis, disrupting pathways associated with glucose import and the interconversion of sugars. In contrast, AMXT 1501, targeting polyamine transport, enhances the expression of polyamine and glucose biosynthesis genes, presenting a novel avenue for regulating the capsule independent of glucose availability. Despite ample glucose availability, AMXT 1501 treatment downregulates the glycolytic pathway, fatty acid synthesis, and ATP synthase, crucial for energy production, while upregulating two-component systems responsible for stress management. This suggests a potential shutdown of energy production and capsule biosynthesis, redirecting resources towards stress management. Following DFMO and AMXT 1501 treatments, countermeasures, such as upregulation of stress response genes and ribosomal protein, were observed but appear to be insufficient to overcome the deleterious effects on capsule production. This study highlights the complexity of polyamine-mediated regulation in S. pneumoniae, particularly capsule biosynthesis. Our findings offer valuable insights into potential therapeutic targets for modulating capsules in a polyamine-dependent manner, a promising avenue for intervention against S. pneumoniae infections.

Predicting Salmonella MIC and Deciphering Genomic Determinants of Antibiotic Resistance and Susceptibility.

Salmonella spp., a leading cause of foodborne illness, is a formidable global menace due to escalating antimicrobial resistance (AMR). The evaluation of minimum inhibitory concentration (MIC) for antimicrobials is critical for characterizing AMR. The current whole genome sequencing (WGS)-based approaches for predicting MIC are hindered by both computational and feature identification constraints. We propose an innovative methodology called the "Genome Feature Extractor Pipeline" that integrates traditional machine learning (random forest, RF) with deep learning models (multilayer perceptron (MLP) and DeepLift) for WGS-based MIC prediction. We used a dataset from the National Antimicrobial Resistance Monitoring System (NARMS), comprising 4500 assembled genomes of nontyphoidal Salmonella, each annotated with MIC metadata for 15 antibiotics. Our pipeline involves the batch downloading of annotated genomes, the determination of feature importance using RF, Gini-index-based selection of crucial 10-mers, and their expansion to 20-mers. This is followed by an MLP network, with four hidden layers of 1024 neurons each, to predict MIC values. Using DeepLift, key 20-mers and associated genes influencing MIC are identified. The 10 most significant 20-mers for each antibiotic are listed, showcasing our ability to discern genomic features affecting Salmonella MIC prediction with enhanced precision. The methodology replaces binary indicators with k-mer counts, offering a more nuanced analysis. The combination of RF and MLP addresses the limitations of the existing WGS approach, providing a robust and efficient method for predicting MIC values in Salmonella that could potentially be applied to other pathogens.

Predicting foodborne pathogens and probiotics taxa within poultry-related microbiomes using a machine learning approach.

BACKGROUND: Microbiomes that can serve as an indicator of gut, intestinal, and general health of humans and animals are largely influenced by food consumed and contaminant bioagents. Microbiome studies usually focus on estimating the alpha (within sample) and beta (similarity/dissimilarity among samples) diversities. This study took a combinatorial approach and applied machine learning to microbiome data to predict the presence of disease-causing pathogens and their association with known/potential probiotic taxa. Probiotics are beneficial living microorganisms capable of improving the host organism's digestive system, immune function and ultimately overall health. Here, 16 S rRNA gene high-throughput Illumina sequencing of temporal pre-harvest (feces, soil) samples of 42 pastured poultry flocks (poultry in this entire work solely refers to chickens) from southeastern U.S. farms was used to generate the relative abundance of operational taxonomic units (OTUs) as machine learning input. Unique genera from the OTUs were used as predictors of the prevalence of foodborne pathogens (Salmonella, Campylobacter and Listeria) at different stages of poultry growth (START (2-4 weeks old), MID (5-7 weeks old), END (8-11 weeks old)), association with farm management practices and physicochemical properties. RESULT: While we did not see any significant associations between known probiotics and Salmonella or Listeria, we observed significant negative correlations between known probiotics (Bacillus and Clostridium) and Campylobacter at the mid-time point of sample collection. Our data indicates a negative correlation between potential probiotics and Campylobacter at both early and end-time points of sample collection. Furthermore, our model prediction shows that changes in farm operations such as how often the houses are moved on the pasture, age at which chickens are introduced to the pasture, diet composition and presence of other animals on the farm could favorably increase the abundance and activity of probiotics that could reduce Campylobacter prevalence. CONCLUSION: Integration of microbiome data with farm management practices using machine learning provided insights on how to reduce Campylobacter prevalence and transmission along the farm-to-fork continuum. Altering management practices to support proliferation of beneficial probiotics to reduce pathogen prevalence identified here could constitute a complementary method to the existing but ineffective interventions such as vaccination and bacteriophage cocktails usage. Study findings also corroborate the presence of bacterial genera such as Caloramator, DA101, Parabacteroides and Faecalibacterium as potential probiotics.

Using Activity-Based Proteomics for the Quantification of Deubiquitinases in Animal Tissue.

Ubiquitination is a post-translational modification, that regulates essential cellular functions, and the enzymes that control the removal of this modification, deubiquitinases (DUBs), have been well described for the model organisms. However, the information about DUBs is still largely lacking for the non-model organisms, such as agriculturally relevant animals. To understand the expression of these enzymes in animal tissues, we have used chemical proteomics which can be used to identify biologically active DUBs present in tissues based on their reactivity with the activity-based probes (ABPs). Here we describe a sample preparation protocol for ABP-based purification of DUBs from animal tissue using two approaches to homogenize and lyse the animal tissue compatible with ABP labeling of DUBs, including an ultrasonication-based tissue processing method and bead-beating method. Both of these methods retain the enzymatic activity of DUBs. In addition, we describe a protocol for ABP labeling of DUBs in tissue lysates and the immunoprecipitation of the probe-reactive DUBs that can be used along with mass spectrometric identification of proteins and the detection of these DUBs by Western blotting.

An ensemble learning approach to identify pastured poultry farm practice variables and soil constituents that promote Salmonella prevalence.

Animal sourced foods including contaminated poultry meat and eggs contribute to human non-typhoidal salmonellosis, a foodborne zoonosis. Prevalence of Salmonella in pastured poultry production systems can lead to contamination of the final product. Identification of farm practices that affect Salmonella prevalence is critical for implementing control measures to ensure the safety of these products. In this study, we developed predictive models based predominantly on deep learning approaches to identify key pre-harvest management variables (using soil and feces samples) in pastured poultry farms that contribute to Salmonella prevalence. Our ensemble approach utilizing five different machine learning techniques predicts that physicochemical parameters of the soil and feces (elements such as sodium (Na), zinc (Zn), potassium (K), copper (Cu)), electrical conductivity (EC), the number of years that the farms have been in use, and flock size significantly influence pre-harvest Salmonella prevalence. Egg source, feed type, breed, and manganese (Mn) levels in the soil/feces are other important variables identified to contribute to Salmonella prevalence on larger (≥3 flocks reared per year) farms, while pasture feed and soil carbon-to-nitrogen ratio are predicted to be important for smaller/hobby (<3 flocks reared per year) farms. Predictive models such as the ones described here are important for developing science-based control measures for Salmonella to reduce the environmental, animal, and public health impacts from these types of poultry production systems.

Hematological and gene co-expression network analyses of high-risk beef cattle defines immunological mechanisms and biological complexes involved in bovine respiratory disease and weight gain.

Bovine respiratory disease (BRD), the leading disease complex in beef cattle production systems, remains highly elusive regarding diagnostics and disease prediction. Previous research has employed cellular and molecular techniques to describe hematological and gene expression variation that coincides with BRD development. Here, we utilized weighted gene co-expression network analysis (WGCNA) to leverage total gene expression patterns from cattle at arrival and generate hematological and clinical trait associations to describe mechanisms that may predict BRD development. Gene expression counts of previously published RNA-Seq data from 23 cattle (2017; n = 11 Healthy, n = 12 BRD) were used to construct gene co-expression modules and correlation patterns with complete blood count (CBC) and clinical datasets. Modules were further evaluated for cross-populational preservation of expression with RNA-Seq data from 24 cattle in an independent population (2019; n = 12 Healthy, n = 12 BRD). Genes within well-preserved modules were subject to functional enrichment analysis for significant Gene Ontology terms and pathways. Genes which possessed high module membership and association with BRD development, regardless of module preservation ("hub genes"), were utilized for protein-protein physical interaction network and clustering analyses. Five well-preserved modules of co-expressed genes were identified. One module ("steelblue"), involved in alpha-beta T-cell complexes and Th2-type immunity, possessed significant correlation with increased erythrocytes, platelets, and BRD development. One module ("purple"), involved in mitochondrial metabolism and rRNA maturation, possessed significant correlation with increased eosinophils, fecal egg count per gram, and weight gain over time. Fifty-two interacting hub genes, stratified into 11 clusters, may possess transient function involved in BRD development not previously described in literature. This study identifies co-expressed genes and coordinated mechanisms associated with BRD, which necessitates further investigation in BRD-prediction research.

Mannheimia haemolytica Negatively Affects Bovine Herpesvirus Type 1.1 Replication Capacity In Vitro.

Bovine Respiratory Disease (BRD) is a multifactorial condition affecting cattle worldwide resulting in high rates of morbidity and mortality. The disease can be triggered by Bovine Herpesvirus-1 (BoHV-1) infection, stress, and the subsequent proliferation and lung colonization by commensal bacteria such as Mannheimia haemolytica, ultimately inducing severe pneumonic inflammation. Due to its polymicrobial nature, the study of BRD microbes requires co-infection models. While several past studies have mostly focused on the effects of co-infection on host gene expression, we focused on the relationship between BRD pathogens during co-infection, specifically on M. haemolytica's effect on BoHV-1 replication. This study shows that M. haemolytica negatively impacts BoHV-1 replication in a dose-dependent manner in different in vitro models. The negative effect was observed at very low bacterial doses while increasing the viral dose counteracted this effect. Viral suppression was also dependent on the time at which each microbe was introduced to the cell culture. While acidification of the culture medium did not grossly affect cell viability, it significantly inhibited viral replication. We conclude that M. haemolytica and BoHV-1 interaction is dose and time-sensitive, wherein M. haemolytica proliferation induces significant viral suppression when the viral replication program is not fully established.

Artificial Intelligence Models for Zoonotic Pathogens: A Survey.

Zoonotic diseases or zoonoses are infections due to the natural transmission of pathogens between species (animals and humans). More than 70% of emerging infectious diseases are attributed to animal origin. Artificial Intelligence (AI) models have been used for studying zoonotic pathogens and the factors that contribute to their spread. The aim of this literature survey is to synthesize and analyze machine learning, and deep learning approaches applied to study zoonotic diseases to understand predictive models to help researchers identify the risk factors, and develop mitigation strategies. Based on our survey findings, machine learning and deep learning are commonly used for the prediction of both foodborne and zoonotic pathogens as well as the factors associated with the presence of the pathogens.

Preharvest Environmental and Management Drivers of Multidrug Resistance in Major Bacterial Zoonotic Pathogens in Pastured Poultry Flocks.

Due to nutritional benefits and perceived humane ways of treating the animals, the demand for antibiotic-free pastured poultry chicken has continued to be steadily rise. Despite the non-usage of antibiotics in pastured poultry broiler production, antibiotic resistance (AR) is reported in zoonotic poultry pathogens. However, factors that drive multidrug resistance (MDR) in pastured poultry are not well understood. In this study, we used machine learning and deep learning approaches to predict farm management practices and physicochemical properties of feces and soil that drive MDR in zoonotic poultry pathogens. Antibiotic use in agroecosystems is known to contribute to resistance. Evaluation of the development of resistance in environments that are free of antibiotics such as the all-natural, antibiotic-free, pastured poultry production systems described here is critical to understand the background AR in the absence of any selection pressure, i.e., basal levels of resistance. We analyzed 1635 preharvest (feces and soil) samples collected from forty-two pastured poultry flocks and eleven farms in the Southeastern United States. CDC National Antimicrobial Resistance Monitoring System guidelines were used to determine antimicrobial/multidrug resistance profiles of Salmonella, Listeria, and Campylobacter. A combination of two traditional machine learning (RandomForest and XGBoost) and three deep learning (Multi-layer Perceptron, Generative Adversarial Network, and Auto-Encoder) approaches identified critical farm management practices and environmental variables that drive multidrug resistance in poultry pathogens in broiler production systems that represents background resistance. This study enumerates management practices that contribute to AR and makes recommendations to potentially mitigate multidrug resistance and the prevalence of Salmonella and Listeria in pastured poultry.

Difluoromethylornithine (DFMO) and AMXT 1501 inhibit capsule biosynthesis in pneumococci.

Polyamines are small cationic molecules that have been linked to various cellular processes including replication, translation, stress response and recently, capsule regulation in Streptococcus pneumoniae (Spn, pneumococcus). Pneumococcal-associated diseases such as pneumonia, meningitis, and sepsis are some of the leading causes of death worldwide and capsule remains the principal virulence factor of this versatile pathogen. α-Difluoromethyl-ornithine (DFMO) is an irreversible inhibitor of the polyamine biosynthesis pathway catalyzed by ornithine decarboxylase and has a long history in modulating cell growth, polyamine levels, and disease outcomes in eukaryotic systems. Recent evidence shows that DFMO can also target arginine decarboxylation. Interestingly, DFMO-treated cells often escape polyamine depletion via increased polyamine uptake from extracellular sources. Here, we examined the potential capsule-crippling ability of DFMO and the possible synergistic effects of the polyamine transport inhibitor, AMXT 1501, on pneumococci. We characterized the changes in pneumococcal metabolites in response to DFMO and AMXT 1501, and also measured the impact of DFMO on amino acid decarboxylase activities. Our findings show that DFMO inhibited pneumococcal polyamine and capsule biosynthesis as well as decarboxylase activities, albeit, at a high concentration. AMXT 1501 at physiologically relevant concentration could inhibit both polyamine and capsule biosynthesis, however, in a serotype-dependent manner. In summary, this study demonstrates the utility of targeting polyamine biosynthesis and transport for pneumococcal capsule inhibition. Since targeting capsule biosynthesis is a promising way for the eradication of the diverse and pathogenic pneumococcal strains, future work will identify small molecules similar to DFMO/AMXT 1501, which act in a serotype-independent manner.

Use of nCounter mRNA profiling to identify at-arrival gene expression patterns for predicting bovine respiratory disease in beef cattle.

BACKGROUND: Transcriptomics has identified at-arrival differentially expressed genes associated with bovine respiratory disease (BRD) development; however, their use as prediction molecules necessitates further evaluation. Therefore, we aimed to selectively analyze and corroborate at-arrival mRNA expression from multiple independent populations of beef cattle. In a nested case-control study, we evaluated the expression of 56 mRNA molecules from at-arrival blood samples of 234 cattle across seven populations via NanoString nCounter gene expression profiling. Analysis of mRNA was performed with nSolver Advanced Analysis software (p < 0.05), comparing cattle groups based on the diagnosis of clinical BRD within 28 days of facility arrival (n = 115 Healthy; n = 119 BRD); BRD was further stratified for severity based on frequency of treatment and/or mortality (Treated_1, n = 89; Treated_2+, n = 30). Gene expression homogeneity of variance, receiver operator characteristic (ROC) curve, and decision tree analyses were performed between severity cohorts. RESULTS: Increased expression of mRNAs involved in specialized pro-resolving mediator synthesis (ALOX15, HPGD), leukocyte differentiation (LOC100297044, GCSAML, KLF17), and antimicrobial peptide production (CATHL3, GZMB, LTF) were identified in Healthy cattle. BRD cattle possessed increased expression of CFB, and mRNA related to granulocytic processes (DSG1, LRG1, MCF2L) and type-I interferon activity (HERC6, IFI6, ISG15, MX1). Healthy and Treated_1 cattle were similar in terms of gene expression, while Treated_2+ cattle were the most distinct. ROC cutoffs were used to generate an at-arrival treatment decision tree, which classified 90% of Treated_2+ individuals. CONCLUSIONS: Increased expression of complement factor B, pro-inflammatory, and type I interferon-associated mRNA hallmark the at-arrival expression patterns of cattle that develop severe clinical BRD. Here, we corroborate at-arrival mRNA markers identified in previous transcriptome studies and generate a prediction model to be evaluated in future studies. Further research is necessary to evaluate these expression patterns in a prospective manner.

Identification of active deubiquitinases in the chicken tissues.

The existing protein annotation in chicken is mostly limited to computational predictions based on orthology to other proteins, which often leads to a significant underestimation of the function of these proteins. Genome-scale experimental annotation can provide insight into the actual enzymatic activities of chicken proteins. Amongst post-translational modifications, ubiquitination is of interest as anomalies in ubiquitination are implicated in such diseases as inflammatory disorders, infectious diseases, or malignancies. Ubiquitination is controlled by deubiquitinases (DUBs), which remove ubiquitin from protein substrates. However, the DUBs have not been systematically annotated and quantified in chicken tissues. Here we used a chemoproteomics approach, which is based on active-site probes specific to DUBs, and identified 26 active DUBs in the chicken spleen, cecum, and liver.

No-boundary thinking: a viable solution to ethical data-driven AI in precision medicine.

Today Artificial Intelligence (AI) supports difficult decisions about policy, health, and our personal lives. The AI algorithms we develop and deploy to make sense of information, are informed by data, and based on models that capture and use pertinent details of the population or phenomenon being analyzed. For any application area, more importantly in precision medicine which directly impacts human lives, the data upon which algorithms are run must be procured, cleaned, and organized well to assure reliable and interpretable results, and to assure that they do not perpetrate or amplify human prejudices. This must be done without violating basic assumptions of the algorithms in use. Algorithmic results need to be clearly communicated to stakeholders and domain experts to enable sound conclusions. Our position is that AI holds great promise for supporting precision medicine, but we need to move forward with great care, with consideration for possible ethical implications. We make the case that a no-boundary or convergent approach is essential to support sound and ethical decisions. No-boundary thinking supports problem definition and solving with teams of experts possessing diverse perspectives. When dealing with AI and the data needed to use AI, there is a spectrum of activities that needs the attention of a no-boundary team. This is necessary if we are to draw viable conclusions and develop actions and policies based on the AI, the data, and the scientific foundations of the domain in question.

Multipopulational transcriptome analysis of post-weaned beef cattle at arrival further validates candidate biomarkers for predicting clinical bovine respiratory disease.

Bovine respiratory disease (BRD) remains the leading infectious disease in post-weaned beef cattle. The objective of this investigation was to contrast the at-arrival blood transcriptomes from cattle derived from two distinct populations that developed BRD in the 28 days following arrival versus cattle that did not. Forty-eight blood samples from two populations were selected for mRNA sequencing based on even distribution of development (n = 24) or lack of (n = 24) clinical BRD within 28 days following arrival; cattle which developed BRD were further stratified into BRD severity cohorts based on frequency of antimicrobial treatment: treated once (treated_1) or treated twice or more and/or died (treated_2+). Sequenced reads (~ 50 M/sample, 150 bp paired-end) were aligned to the ARS-UCD1.2 bovine genome assembly. One hundred and thirty-two unique differentially expressed genes (DEGs) were identified between groups stratified by disease severity (healthy, n = 24; treated_1, n = 13; treated_2+, n = 11) with edgeR (FDR ≤ 0.05). Differentially expressed genes in treated_1 relative to both healthy and treated_2+ were predicted to increase neutrophil activation, cellular cornification/keratinization, and antimicrobial peptide production. Differentially expressed genes in treated_2+ relative to both healthy and treated_1 were predicted to increase alternative complement activation, decrease leukocyte activity, and increase nitric oxide production. Receiver operating characteristic (ROC) curves generated from expression data for six DEGs identified in our current and previous studies (MARCO, CFB, MCF2L, ALOX15, LOC100335828 (aka CD200R1), and SLC18A2) demonstrated good-to-excellent (AUC: 0.800-0.899; ≥ 0.900) predictability for classifying disease occurrence and severity. This investigation identifies candidate biomarkers and functional mechanisms in at arrival blood that predicted development and severity of BRD.

Genes and regulatory mechanisms associated with experimentally-induced bovine respiratory disease identified using supervised machine learning methodology.

Bovine respiratory disease (BRD) is a multifactorial disease involving complex host immune interactions shaped by pathogenic agents and environmental factors. Advancements in RNA sequencing and associated analytical methods are improving our understanding of host response related to BRD pathophysiology. Supervised machine learning (ML) approaches present one such method for analyzing new and previously published transcriptome data to identify novel disease-associated genes and mechanisms. Our objective was to apply ML models to lung and immunological tissue datasets acquired from previous clinical BRD experiments to identify genes that classify disease with high accuracy. Raw mRNA sequencing reads from 151 bovine datasets (n = 123 BRD, n = 28 control) were downloaded from NCBI-GEO. Quality filtered reads were assembled in a HISAT2/Stringtie2 pipeline. Raw gene counts for ML analysis were normalized, transformed, and analyzed with MLSeq, utilizing six ML models. Cross-validation parameters (fivefold, repeated 10 times) were applied to 70% of the compiled datasets for ML model training and parameter tuning; optimized ML models were tested with the remaining 30%. Downstream analysis of significant genes identified by the top ML models, based on classification accuracy for each etiological association, was performed within WebGestalt and Reactome (FDR ≤ 0.05). Nearest shrunken centroid and Poisson linear discriminant analysis with power transformation models identified 154 and 195 significant genes for IBR and BRSV, respectively; from these genes, the two ML models discriminated IBR and BRSV with 100% accuracy compared to sham controls. Significant genes classified by the top ML models in IBR (154) and BRSV (195), but not BVDV (74), were related to type I interferon production and IL-8 secretion, specifically in lymphoid tissue and not homogenized lung tissue. Genes identified in Mannheimia haemolytica infections (97) were involved in activating classical and alternative pathways of complement. Novel findings, including expression of genes related to reduced mitochondrial oxygenation and ATP synthesis in consolidated lung tissue, were discovered. Genes identified in each analysis represent distinct genomic events relevant to understanding and predicting clinical BRD. Our analysis demonstrates the utility of ML with published datasets for discovering functional information to support the prediction and understanding of clinical BRD.

The Effect of Impaired Polyamine Transport on Pneumococcal Transcriptome.

Infections due to Streptococcus pneumoniae, a commensal in the nasopharynx, still claim a significant number of lives worldwide. Genome plasticity, antibiotic resistance, and limited serotype coverage of the available polysaccharide-based conjugate vaccines confounds therapeutic interventions to limit the spread of this pathogen. Pathogenic mechanisms that allow successful adaption and persistence in the host could be potential innovative therapeutic targets. Polyamines are ubiquitous polycationic molecules that regulate many cellular processes. We previously reported that deletion of polyamine transport operon potABCD, which encodes a putrescine/spermidine transporter (ΔpotABCD), resulted in an unencapsulated attenuated phenotype. Here, we characterize the transcriptome, metabolome, and stress responses of polyamine transport-deficient S. pneumoniae. Compared with the wild-type strain, the expression of genes involved in oxidative stress responses and the nucleotide sugar metabolism was reduced, while expression of genes involved in the Leloir, tagatose, and pentose phosphate pathways was higher in ΔpotABCD. A metabolic shift towards the pentose phosphate pathway will limit the synthesis of precursors of capsule polysaccharides. Metabolomics results show reduced levels of glutathione and pyruvate in the mutant. Our results also show that the potABCD operon protects pneumococci against hydrogen peroxide and nitrosative stress. Our findings demonstrate the importance of polyamine transport in pneumococcal physiology that could impact in vivo fitness. Thus, polyamine transport in pneumococci represents a novel target for therapeutic interventions.

Comprehensive at-arrival transcriptomic analysis of post-weaned beef cattle uncovers type I interferon and antiviral mechanisms associated with bovine respiratory disease mortality.

BACKGROUND: Despite decades of extensive research, bovine respiratory disease (BRD) remains the most devastating disease in beef cattle production. Establishing a clinical diagnosis often relies upon visual detection of non-specific signs, leading to low diagnostic accuracy. Thus, post-weaned beef cattle are often metaphylactically administered antimicrobials at facility arrival, which poses concerns regarding antimicrobial stewardship and resistance. Additionally, there is a lack of high-quality research that addresses the gene-by-environment interactions that underlie why some cattle that develop BRD die while others survive. Therefore, it is necessary to decipher the underlying host genomic factors associated with BRD mortality versus survival to help determine BRD risk and severity. Using transcriptomic analysis of at-arrival whole blood samples from cattle that died of BRD, as compared to those that developed signs of BRD but lived (n = 3 DEAD, n = 3 ALIVE), we identified differentially expressed genes (DEGs) and associated pathways in cattle that died of BRD. Additionally, we evaluated unmapped reads, which are often overlooked within transcriptomic experiments. RESULTS: 69 DEGs (FDR<0.10) were identified between ALIVE and DEAD cohorts. Several DEGs possess immunological and proinflammatory function and associations with TLR4 and IL6. Biological processes, pathways, and disease phenotype associations related to type-I interferon production and antiviral defense were enriched in DEAD cattle at arrival. Unmapped reads aligned primarily to various ungulate assemblies, but failed to align to viral assemblies. CONCLUSION: This study further revealed increased proinflammatory immunological mechanisms in cattle that develop BRD. DEGs upregulated in DEAD cattle were predominantly involved in innate immune pathways typically associated with antiviral defense, although no viral genes were identified within unmapped reads. Our findings provide genomic targets for further analysis in cattle at highest risk of BRD, suggesting that mechanisms related to type I interferons and antiviral defense may be indicative of viral respiratory disease at arrival and contribute to eventual BRD mortality.

Arginine Decarboxylase Is Essential for Pneumococcal Stress Responses.

Polyamines such as putrescine, cadaverine, and spermidine are small cationic molecules that play significant roles in cellular processes, including bacterial stress responses and host-pathogen interactions. Streptococcus pneumoniae is an opportunistic human pathogen, which causes several diseases that account for significant morbidity and mortality worldwide. As it transits through different host niches, S. pneumoniae is exposed to and must adapt to different types of stress in the host microenvironment. We earlier reported that S. pneumoniae TIGR4, which harbors an isogenic deletion of an arginine decarboxylase (ΔspeA), an enzyme that catalyzes the synthesis of agmatine in the polyamine synthesis pathway, has a reduced capsule. Here, we report the impact of arginine decarboxylase deletion on pneumococcal stress responses. Our results show that ΔspeA is more susceptible to oxidative, nitrosative, and acid stress compared to the wild-type strain. Gene expression analysis by qRT-PCR indicates that thiol peroxidase, a scavenger of reactive oxygen species and aguA from the arginine deiminase system, could be important for peroxide stress responses in a polyamine-dependent manner. Our results also show that speA is essential for endogenous hydrogen peroxide and glutathione production in S. pneumoniae. Taken together, our findings demonstrate the critical role of arginine decarboxylase in pneumococcal stress responses that could impact adaptation and survival in the host.