Multi-omics data elucidate parasite-host-microbiota interactions and resistance to Haemonchus contortus in sheep. - Related Documents




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516301.0000Multi-omics data elucidate parasite-host-microbiota interactions and resistance to Haemonchus contortus in sheep. BACKGROUND: The integration of molecular data from hosts, parasites, and microbiota can enhance our understanding of the complex biological interactions underlying the resistance of hosts to parasites. Haemonchus contortus, the predominant sheep gastrointestinal parasite species in the tropics, causes significant production and economic losses, which are further compounded by the diminishing efficiency of chemical control owing to anthelmintic resistance. Knowledge of how the host responds to infection and how the parasite, in combination with microbiota, modulates host immunity can guide selection decisions to breed animals with improved parasite resistance. This understanding will help refine management practices and advance the development of new therapeutics for long-term helminth control. METHODS: Eggs per gram (EPG) of feces were obtained from Morada Nova sheep subjected to two artificial infections with H. contortus and used as a proxy to select animals with high resistance or susceptibility for transcriptome sequencing (RNA-seq) of the abomasum and 50 K single-nucleotide genotyping. Additionally, RNA-seq data for H. contortus were generated, and amplicon sequence variants (ASV) were obtained using polymerase chain reaction amplification and sequencing of bacterial and archaeal 16S ribosomal RNA genes from sheep feces and rumen content. RESULTS: The heritability estimate for EPG was 0.12. GAST, GNLY, IL13, MGRN1, FGF14, and RORC genes and transcripts were differentially expressed between resistant and susceptible animals. A genome-wide association study identified regions on chromosomes 2 and 11 that harbor candidate genes for resistance, immune response, body weight, and adaptation. Trans-expression quantitative trait loci were found between significant variants and differentially expressed transcripts. Functional co-expression modules based on sheep genes and ASVs correlated with resistance to H. contortus, showing enrichment in pathways of response to bacteria, immune and inflammatory responses, and hub features of the Christensenellaceae, Bacteroides, and Methanobrevibacter genera; Prevotellaceae family; and Verrucomicrobiota phylum. In H. contortus, some mitochondrial, collagen-, and cuticle-related genes were expressed only in parasites isolated from susceptible sheep. CONCLUSIONS: The present study identified chromosome regions, genes, transcripts, and pathways involved in the elaborate interactions between the sheep host, its gastrointestinal microbiota, and the H. contortus parasite. These findings will assist in the development of animal selection strategies for parasite resistance and interdisciplinary approaches to control H. contortus infection in sheep.202438429820
841310.9992Investigating mechanisms underlying genetic resistance to Salmon Rickettsial Syndrome in Atlantic salmon using RNA sequencing. BACKGROUND: Salmon Rickettsial Syndrome (SRS), caused by Piscirickettsia salmonis, is one of the primary causes of morbidity and mortality in Atlantic salmon aquaculture, particularly in Chile. Host resistance is a heritable trait, and functional genomic studies have highlighted genes and pathways important in the response of salmon to the bacteria. However, the functional mechanisms underpinning genetic resistance are not yet well understood. In the current study, a large population of salmon pre-smolts were challenged with P. salmonis, with mortality levels recorded and samples taken for genotyping. In parallel, head kidney and liver samples were taken from animals of the same population with high and low genomic breeding values for resistance, and used for RNA-Sequencing to compare their transcriptome profile both pre and post infection. RESULTS: A significant and moderate heritability (h(2) = 0.43) was shown for the trait of binary survival. Genome-wide association analyses using 38 K imputed SNP genotypes across 2265 animals highlighted that resistance is a polygenic trait. Several thousand genes were identified as differentially expressed between controls and infected samples, and enriched pathways related to the host immune response were highlighted. In addition, several networks with significant correlation with SRS resistance breeding values were identified, suggesting their involvement in mediating genetic resistance. These included apoptosis, cytoskeletal organisation, and the inflammasome. CONCLUSIONS: While resistance to SRS is a polygenic trait, this study has highlighted several relevant networks and genes that are likely to play a role in mediating genetic resistance. These genes may be future targets for functional studies, including genome editing, to further elucidate their role underpinning genetic variation in host resistance.202133676414
464220.9990Characterization of antibiotic resistance and host-microbiome interactions in the human upper respiratory tract during influenza infection. BACKGROUND: The abundance and diversity of antibiotic resistance genes (ARGs) in the human respiratory microbiome remain poorly characterized. In the context of influenza virus infection, interactions between the virus, the host, and resident bacteria with pathogenic potential are known to complicate and worsen disease, resulting in coinfection and increased morbidity and mortality of infected individuals. When pathogenic bacteria acquire antibiotic resistance, they are more difficult to treat and of global health concern. Characterization of ARG expression in the upper respiratory tract could help better understand the role antibiotic resistance plays in the pathogenesis of influenza-associated bacterial secondary infection. RESULTS: Thirty-seven individuals participating in the Household Influenza Transmission Study (HITS) in Managua, Nicaragua, were selected for this study. We performed metatranscriptomics and 16S rRNA gene sequencing analyses on nasal and throat swab samples, and host transcriptome profiling on blood samples. Individuals clustered into two groups based on their microbial gene expression profiles, with several microbial pathways enriched with genes differentially expressed between groups. We also analyzed antibiotic resistance gene expression and determined that approximately 25% of the sequence reads that corresponded to antibiotic resistance genes mapped to Streptococcus pneumoniae and Staphylococcus aureus. Following construction of an integrated network of ARG expression with host gene co-expression, we identified several host key regulators involved in the host response to influenza virus and bacterial infections, and host gene pathways associated with specific antibiotic resistance genes. CONCLUSIONS: This study indicates the host response to influenza infection could indirectly affect antibiotic resistance gene expression in the respiratory tract by impacting the microbial community structure and overall microbial gene expression. Interactions between the host systemic responses to influenza infection and antibiotic resistance gene expression highlight the importance of viral-bacterial co-infection in acute respiratory infections like influenza. Video abstract.202032178738
837830.9990Genome mining reveals unlocked bioactive potential of marine Gram-negative bacteria. BACKGROUND: Antibiotic resistance in bacteria spreads quickly, overtaking the pace at which new compounds are discovered and this emphasizes the immediate need to discover new compounds for control of infectious diseases. Terrestrial bacteria have for decades been investigated as a source of bioactive compounds leading to successful applications in pharmaceutical and biotech industries. Marine bacteria have so far not been exploited to the same extent; however, they are believed to harbor a multitude of novel bioactive chemistry. To explore this potential, genomes of 21 marine Alpha- and Gammaproteobacteria collected during the Galathea 3 expedition were sequenced and mined for natural product encoding gene clusters. RESULTS: Independently of genome size, bacteria of all tested genera carried a large number of clusters encoding different potential bioactivities, especially within the Vibrionaceae and Pseudoalteromonadaceae families. A very high potential was identified in pigmented pseudoalteromonads with up to 20 clusters in a single strain, mostly NRPSs and NRPS-PKS hybrids. Furthermore, regulatory elements in bioactivity-related pathways including chitin metabolism, quorum sensing and iron scavenging systems were investigated both in silico and in vitro. Genes with siderophore function were identified in 50% of the strains, however, all but one harboured the ferric-uptake-regulator gene. Genes encoding the syntethase of acylated homoserine lactones were found in Roseobacter-clade bacteria, but not in the Vibrionaceae strains and only in one Pseudoalteromonas strains. The understanding and manipulation of these elements can help in the discovery and production of new compounds never identified under regular laboratory cultivation conditions. High chitinolytic potential was demonstrated and verified for Vibrio and Pseudoalteromonas species that commonly live in close association with eukaryotic organisms in the environment. Chitin regulation by the ChiS histidine-kinase seems to be a general trait of the Vibrionaceae family, however it is absent in the Pseudomonadaceae. Hence, the degree to which chitin influences secondary metabolism in marine bacteria is not known. CONCLUSIONS: Utilizing the rapidly developing sequencing technologies and software tools in combination with phenotypic in vitro assays, we demonstrated the high bioactive potential of marine bacteria in an efficient, straightforward manner - an approach that will facilitate natural product discovery in the future.201525879706
837740.9990Genome-Wide Association Analyses in the Model Rhizobium Ensifer meliloti. Genome-wide association studies (GWAS) can identify genetic variants responsible for naturally occurring and quantitative phenotypic variation. Association studies therefore provide a powerful complement to approaches that rely on de novo mutations for characterizing gene function. Although bacteria should be amenable to GWAS, few GWAS have been conducted on bacteria, and the extent to which nonindependence among genomic variants (e.g., linkage disequilibrium [LD]) and the genetic architecture of phenotypic traits will affect GWAS performance is unclear. We apply association analyses to identify candidate genes underlying variation in 20 biochemical, growth, and symbiotic phenotypes among 153 strains of Ensifer meliloti For 11 traits, we find genotype-phenotype associations that are stronger than expected by chance, with the candidates in relatively small linkage groups, indicating that LD does not preclude resolving association candidates to relatively small genomic regions. The significant candidates show an enrichment for nucleotide polymorphisms (SNPs) over gene presence-absence variation (PAV), and for five traits, candidates are enriched in large linkage groups, a possible signature of epistasis. Many of the variants most strongly associated with symbiosis phenotypes were in genes previously identified as being involved in nitrogen fixation or nodulation. For other traits, apparently strong associations were not stronger than the range of associations detected in permuted data. In sum, our data show that GWAS in bacteria may be a powerful tool for characterizing genetic architecture and identifying genes responsible for phenotypic variation. However, careful evaluation of candidates is necessary to avoid false signals of association.IMPORTANCE Genome-wide association analyses are a powerful approach for identifying gene function. These analyses are becoming commonplace in studies of humans, domesticated animals, and crop plants but have rarely been conducted in bacteria. We applied association analyses to 20 traits measured in Ensifer meliloti, an agriculturally and ecologically important bacterium because it fixes nitrogen when in symbiosis with leguminous plants. We identified candidate alleles and gene presence-absence variants underlying variation in symbiosis traits, antibiotic resistance, and use of various carbon sources; some of these candidates are in genes previously known to affect these traits whereas others were in genes that have not been well characterized. Our results point to the potential power of association analyses in bacteria, but also to the need to carefully evaluate the potential for false associations.201830355664
471550.9989Genomic and stress resistance characterization of Lactiplantibacillus plantarum GX17, a potential probiotic for animal feed applications. Lactobacilli, recognized as beneficial bacteria within the human body, are celebrated for their multifaceted probiotic functions, including the regulation of intestinal flora, enhancement of body immunity, and promotion of nutrient absorption. This study comprehensively analyzed the genotypic and phenotypic characteristics of Lactiplantibacillus plantarum (L. plantarum) strains isolated from the intestines of healthy chicks and assessed their potential as probiotics. The assembled genome consists of 29,521,986 bp, and a total of 1,771 coding sequences (CDSs) were predicted. Based on the entire genome sequence analysis, 50 stress resistance genes and seven virulence factors were identified. The results of the phenotypic experiments showed that the strain had good resistance to high temperature, low temperature, acid, alkali, salt, artificial gastrointestinal fluid, and strong antioxidant capacity. Additionally, transcriptomic analysis confirmed that under stress conditions, the expression levels of key genes were significantly upregulated. Therefore, the phenotypic characteristics of L. plantarum GX17 align well with its genotypic features, demonstrating promising probiotic properties. This strain holds great potential as a probiotic candidate, and further investigation into its beneficial effects on human health is warranted. IMPORTANCE: In humans, Lactiplantibacillus plantarum may synergize with host microbiota to ameliorate dysbiosis-related pathologies, enhance immunomodulation, and facilitate micronutrient bioavailability. For livestock, its application could improve feed conversion ratios, suppress enteric pathogens through competitive exclusion, and mitigate antibiotic overuse, "a critical strategy in One Health frameworks." Further investigations into strain-specific mechanisms (e.g., postbiotic metabolites, quorum sensing regulation) are warranted to translate these genomic-phenotypic advantages into sustainable health solutions across species.202540919934
510160.9989Identification of Key Features Pivotal to the Characteristics and Functions of Gut Bacteria Taxa through Machine Learning Methods. BACKGROUND: Gut bacteria critically influence digestion, facilitate the breakdown of complex food substances, aid in essential nutrient synthesis, and contribute to immune system balance. However, current knowledge regarding intestinal bacteria remains insufficient. OBJECTIVE: This study aims to discover essential differences for different intestinal bacteria. METHODS: This study was conducted by investigating a total of 1478 gut bacterial samples comprising 235 Actinobacteria, 447 Bacteroidetes, and 796 Firmicutes, by utilizing sophisticated machine learning algorithms. By building on the dataset provided by Chen et al., we engaged sophisticated machine learning techniques to further investigate and analyze the gut bacterial samples. Each sample in the dataset was described by 993 unique features associated with gut bacteria, including 342 features annotated by the Antibiotic Resistance Genes Database, Comprehensive Antibiotic Research Database, Kyoto Encyclopedia of Genes and Genomes, and Virulence Factors of Pathogenic Bacteria. We employed incremental feature selection methods within a computational framework to identify the optimal features for classification. RESULTS: Eleven feature ranking algorithms selected several key features as pivotal to the characteristics and functions of gut bacteria. These features appear to facilitate the identification of specific gut bacterial species. Additionally, we established quantitative rules for identifying Actinobacteria, Bacteroidetes, and Firmicutes. CONCLUSION: This research underscores the significant potential of machine learning in studying gut microbes and enhances our understanding of the multifaceted roles of gut bacteria.202540671232
960370.9989Resistance signatures manifested in early drug response across cancer types and species. Aim: Growing evidence points to non-genetic mechanisms underlying long-term resistance to cancer therapies. These mechanisms involve pre-existing or therapy-induced transcriptional cell states that confer resistance. However, the relationship between early transcriptional responses to treatment and the eventual emergence of resistant states remains poorly understood. Furthermore, it is unclear whether such early resistance-associated transcriptional responses are evolutionarily conserved. In this study, we examine the similarity between early transcriptional responses and long-term resistant states, assess their clinical relevance, and explore their evolutionary conservation across species. Methods: We integrated datasets on early drug responses and long-term resistance from multiple cancer cell lines, bacteria, and yeast to identify early transcriptional changes predictive of long-term resistance and assess their evolutionary conservation. Using genome-wide CRISPR-Cas9 knockout screens, we evaluated the impact of genes associated with resistant transcriptional states on drug sensitivity. Clinical datasets were analyzed to explore the prognostic value of the identified resistance-associated gene signatures. Results: We found that transcriptional states observed in drug-naive cells and shortly after treatment overlapped with those seen in fully resistant populations. Some of these shared features appear to be evolutionarily conserved. Knockout of genes marking resistant states sensitized ovarian cancer cells to Prexasertib. Moreover, early resistance gene signatures effectively distinguished therapy responders from non-responders in multiple clinical cancer trials and differentiated premalignant breast lesions that progressed to malignancy from those that remained benign. Conclusion: Early cellular transcriptional responses to therapy exhibit key similarities to fully resistant states across different drugs, cancer types, and species. Gene signatures defining these early resistance states have prognostic value in clinical settings.202541019980
884280.9989Transcriptomic study of Salmonella enterica subspecies enterica serovar Typhi biofilm. BACKGROUND: Typhoid fever is an acute systemic infection of humans caused by Salmonella enterica subspecies enterica serovar Typhi (S. Typhi). In chronic carriers, the bacteria survive the harsh environment of the gallbladder by producing biofilm. The phenotype of S. Typhi biofilm cells is significantly different from the free-swimming planktonic cells, and studies have shown that they are associated with antibiotic resistance, immune system evasion, and bacterial persistence. However, the mechanism of this transition and the events leading to biofilm formation are unknown. High throughput sequencing was performed to identify the genes involved in biofilm formation and to postulate the mechanism of action. RESULTS: Planktonic S. Typhi cells were cultured using standard nutrient broth whereas biofilm cells were cultured in a stressful environment using high shearing-force and bile to mimic the gallbladder. Sequencing libraries were prepared from S. Typhi planktonic cells and mature biofilm cells using the Illumina HiSeq 2500 platform, and the transcriptome data obtained were processed using Cufflinks bioinformatics suite of programs to investigate differential gene expression between the two phenotypes. A total of 35 up-regulated and 29 down-regulated genes were identified. The identities of the differentially expressed genes were confirmed using NCBI BLAST and their functions were analyzed. The results showed that the genes associated with metabolic processes and biofilm regulations were down-regulated while those associated with the membrane matrix and antibiotic resistance were highly up-regulated. CONCLUSIONS: It is proposed that the biofilm phenotype of S. Typhi allows the bacteria to increase production of the membrane matrix in order to serve as a physical shield and to adhere to surfaces, and enter an energy conservation state in response to the stressful environment. Conversely, the planktonic phenotype allows the bacteria to produce flagella and increase metabolic activity to enable the bacteria to migrate and form new colonies of infection. This data provide a basis for further studies to uncover the mechanism of biofilm formation in S. Typhi and to discover novel genes or pathways associated with the development of the typhoid carrier state.201729089020
627890.9989Genome evolution drives transcriptomic and phenotypic adaptation in Pseudomonas aeruginosa during 20 years of infection. The opportunistic pathogen Pseudomonas aeruginosa chronically infects the lungs of patients with cystic fibrosis (CF). During infection the bacteria evolve and adapt to the lung environment. Here we use genomic, transcriptomic and phenotypic approaches to compare multiple isolates of P. aeruginosa collected more than 20 years apart during a chronic infection in a CF patient. Complete genome sequencing of the isolates, using short- and long-read technologies, showed that a genetic bottleneck occurred during infection and was followed by diversification of the bacteria. A 125 kb deletion, an 0.9 Mb inversion and hundreds of smaller mutations occurred during evolution of the bacteria in the lung, with an average rate of 17 mutations per year. Many of the mutated genes are associated with infection or antibiotic resistance. RNA sequencing was used to compare the transcriptomes of an earlier and a later isolate. Substantial reprogramming of the transcriptional network had occurred, affecting multiple genes that contribute to continuing infection. Changes included greatly reduced expression of flagellar machinery and increased expression of genes for nutrient acquisition and biofilm formation, as well as altered expression of a large number of genes of unknown function. Phenotypic studies showed that most later isolates had increased cell adherence and antibiotic resistance, reduced motility, and reduced production of pyoverdine (an iron-scavenging siderophore), consistent with genomic and transcriptomic data. The approach of integrating genomic, transcriptomic and phenotypic analyses reveals, and helps to explain, the plethora of changes that P. aeruginosa undergoes to enable it to adapt to the environment of the CF lung during a chronic infection.202134826267
8400100.9989Transferring knowledge of bacterial protein interaction networks to predict pathogen targeted human genes and immune signaling pathways: a case study on M. tuberculosis. BACKGROUND: Bacterial invasive infection and host immune response is fundamental to the understanding of pathogen pathogenesis and the discovery of effective therapeutic drugs. However, there are very few experimental studies on the signaling cross-talks between bacteria and human host to date. METHODS: In this work, taking M. tuberculosis H37Rv (MTB) that is co-evolving with its human host as an example, we propose a general computational framework that exploits the known bacterial pathogen protein interaction networks in STRING database to predict pathogen-host protein interactions and their signaling cross-talks. In this framework, significant interlogs are derived from the known pathogen protein interaction networks to train a predictive l(2)-regularized logistic regression model. RESULTS: The computational results show that the proposed method achieves excellent performance of cross validation as well as low predicted positive rates on the less significant interlogs and non-interlogs, indicating a low risk of false discovery. We further conduct gene ontology (GO) and pathway enrichment analyses of the predicted pathogen-host protein interaction networks, which potentially provides insights into the machinery that M. tuberculosis H37Rv targets human genes and signaling pathways. In addition, we analyse the pathogen-host protein interactions related to drug resistance, inhibition of which potentially provides an alternative solution to M. tuberculosis H37Rv drug resistance. CONCLUSIONS: The proposed machine learning framework has been verified effective for predicting bacteria-host protein interactions via known bacterial protein interaction networks. For a vast majority of bacterial pathogens that lacks experimental studies of bacteria-host protein interactions, this framework is supposed to achieve a general-purpose applicability. The predicted protein interaction networks between M. tuberculosis H37Rv and Homo sapiens, provided in the Additional files, promise to gain applications in the two fields: (1) providing an alternative solution to drug resistance; (2) revealing the patterns that M. tuberculosis H37Rv genes target human immune signaling pathways.201829954330
6336110.9989Comparative Analysis of Transcriptomic Response of Escherichia coli K-12 MG1655 to Nine Representative Classes of Antibiotics. The use of antibiotics leads to strong stresses to bacteria, leading to profound impact on cellular physiology. Elucidating how bacteria respond to antibiotic stresses not only helps us to decipher bacteria's strategies to resistant antibiotics but also assists in proposing targets for antibiotic development. In this work, a comprehensive comparative transcriptomic analysis on how Escherichia coli responds to nine representative classes of antibiotics (tetracycline, mitomycin C, imipenem, ceftazidime, kanamycin, ciprofloxacin, polymyxin E, erythromycin, and chloramphenicol) was performed, aimed at determining and comparing the responses of this model organism to antibiotics at the transcriptional level. On average, 39.71% of genes were differentially regulated by antibiotics at concentrations that inhibit 50% growth. Kanamycin leads to the strongest transcriptomic response (76.4% of genes regulated), whereas polymyxin E led to minimal transcriptomic response (4.7% of genes regulated). Further GO, KEGG, and EcoCyc enrichment analysis found significant transcriptomic changes in carbon metabolism, amino acid metabolism, nutrient assimilation, transport, stress response, nucleotide metabolism, protein biosynthesis, cell wall biosynthesis, energy conservation, mobility, and cell-environmental communications. Analysis of coregulated genes led to the finding of significant reduction of sulfur metabolism by all antibiotics, and analysis of transcription factor-coding genes suggested clustered regulatory patterns implying coregulation. In-depth analysis of regulated pathways revealed shared and unique strategies of E. coli resisting antibiotics, leading to the proposal of four different strategies (the pessimistic, the ignorant, the defensive, and the invasive). In conclusion, this work provides a comprehensive analysis of E. coli's transcriptomic response to antibiotics, which paves the road for further physiological investigation. IMPORTANCE Antibiotics are among the most important inventions in the history of humankind. They are the ultimate reason why bacterial infections are no longer the number one threat to people's lives. However, the wide application of antibiotics in the last half a century has led to aggravating antibiotic resistance, weakening the efficacy of antibiotics. To better comprehend the ways bacteria deal with antibiotics that may eventually turn into resistance mechanisms, and to identify good targets for potential antibiotics, knowledge on how bacteria regulate their physiology in response to different classes of antibiotics is needed. This work aimed to fill this knowledge gap by identifying changes of bacterial functions at the transcription level and suggesting strategies of bacteria to resist antibiotics.202336853057
5098120.9988Feature selection and aggregation for antibiotic resistance GWAS in Mycobacterium tuberculosis: a comparative study. INTRODUCTION: Drug resistance (DR) of pathogens remains a global healthcare concern. In contrast to other bacteria, acquiring mutations in the core genome is the main mechanism of drug resistance for Mycobacterium tuberculosis (MTB). For some antibiotics, the resistance of a particular isolate can be reliably predicted by identifying specific mutations, while for other antibiotics the knowledge of resistance mechanisms is limited. Statistical machine learning (ML) methods are used to infer new genes implicated in drug resistance leveraging large collections of isolates with known whole-genome sequences and phenotypic states for different drugs. However, high correlations between the phenotypic states for commonly used drugs complicate the inference of true associations of mutations with drug phenotypes by ML approaches. METHODS: Recently, several new methods have been developed to select a small subset of reliable predictors of the dependent variable, which may help reduce the number of spurious associations identified. In this study, we evaluated several such methods, namely, logistic regression with different regularization penalty functions, a recently introduced algorithm for solving the best-subset selection problem (ABESS) and "Hungry, Hungry SNPos" (HHS) a heuristic algorithm specifically developed to identify resistance-associated genetic variants in the presence of resistance co-occurrence. We assessed their ability to select known causal mutations for resistance to a specific drug while avoiding the selection of mutations in genes associated with resistance to other drugs, thus we compared selected ML models for their applicability for MTB genome wide association studies. RESULTS AND DISCUSSION: In our analysis, ABESS significantly outperformed the other methods, selecting more relevant sets of mutations. Additionally, we demonstrated that aggregating rare mutations within protein-coding genes into markers indicative of changes in PFAM domains improved prediction quality, and these markers were predominantly selected by ABESS, suggesting their high informativeness. However, ABESS yielded lower prediction accuracy compared to logistic regression methods with regularization.202540606161
6290130.9988Transcriptomic profiling of ceftriaxone-tolerant phenotypes of Neisseria gonorrhoeae reveals downregulation of ribosomal genes - a pilot study. Antibiotic tolerance is associated with failure of antibiotic treatment and accelerates the development of antimicrobial resistance. The molecular mechanisms underlying antimicrobial tolerance remain poorly understood. Tolerant bacteria can slow metabolism by extending the lag phase without altering antimicrobial susceptibility. We recently induced ceftriaxone (CRO) tolerance in the Neisseria gonorrhoeae reference strain WHO P. In the current study, we characterized the transcriptomic profiles of these CRO-tolerant phenotypes. To induce tolerance, WHO P strains were grown under 3-h intermittent CRO exposure (10× the MIC), followed by overnight growth in gonococcal (GC) broth for seven consecutive days, with cultures maintained in sextuplicate. Two control cultures were maintained without CRO exposure. The tolerance and CRO susceptibility of the isolates were assessed using a modified tolerance disc (TD) test. Total RNA was isolated from tolerant isolates (n = 12) and control (n = 3) strains, followed by Ribo depletion, Illumina Library preparation, and sequencing. Transcriptomic analysis revealed no differentially expressed genes after 1 day of CRO exposure. However, after 3 days of CRO exposure, 13 genes were found to be significantly downregulated, including tRNA-Ser (C7S06_RS03100) and tRNA-Leu (C7S06_RS04945) and ribosomal RNA genes (16S and 23S rRNA). Following 7 days of exposure, 51 genes were differentially expressed, with most downregulated, such as SecB (Protein-export chaperone SecB) and tRNA-Ser (C7S06_RS01850) and the 16S and 23S ribosomal RNA genes. The development of CRO-tolerance in N. gonorrhoeae was associated with the downregulation of various ribosomal genes and associated genes, reflecting a potential mechanism for bacterial survival under antibiotic stress. IMPORTANCE: Antibiotic tolerance allows some bacteria to survive antibiotic treatment, contributing to treatment failure and creating conditions that promote resistance. In this study, we showed that Neisseria gonorrhoeae, the bacteria that causes gonorrhea, can become tolerant to ceftriaxone-the last-line treatment used. By repeatedly exposing the bacteria to high doses of ceftriaxone, we observed the development of tolerance over several days. Using transcriptomic analysis, we found that tolerant bacteria consistently reduced the activity of genes involved in protein synthesis, including ribosomal RNAs and transfer RNAs. This suggests that N. gonorrhoeae may survive antibiotic stress by entering a low-metabolic state that makes the antibiotic less effective. These findings highlight a survival mechanism that does not rely on genetic resistance. Understanding this tolerance response is vital for improving current treatment approaches and could inform the development of new strategies to prevent antibiotic failure in gonorrhea and other infections.202540622217
8401140.9988LSTrAP-Crowd: prediction of novel components of bacterial ribosomes with crowd-sourced analysis of RNA sequencing data. BACKGROUND: Bacterial resistance to antibiotics is a growing health problem that is projected to cause more deaths than cancer by 2050. Consequently, novel antibiotics are urgently needed. Since more than half of the available antibiotics target the structurally conserved bacterial ribosomes, factors involved in protein synthesis are thus prime targets for the development of novel antibiotics. However, experimental identification of these potential antibiotic target proteins can be labor-intensive and challenging, as these proteins are likely to be poorly characterized and specific to few bacteria. Here, we use a bioinformatics approach to identify novel components of protein synthesis. RESULTS: In order to identify these novel proteins, we established a Large-Scale Transcriptomic Analysis Pipeline in Crowd (LSTrAP-Crowd), where 285 individuals processed 26 terabytes of RNA-sequencing data of the 17 most notorious bacterial pathogens. In total, the crowd processed 26,269 RNA-seq experiments and used the data to construct gene co-expression networks, which were used to identify more than a hundred uncharacterized genes that were transcriptionally associated with protein synthesis. We provide the identity of these genes together with the processed gene expression data. CONCLUSIONS: We identified genes related to protein synthesis in common bacterial pathogens and thus provide a resource of potential antibiotic development targets for experimental validation. The data can be used to explore additional vulnerabilities of bacteria, while our approach demonstrates how the processing of gene expression data can be easily crowd-sourced.202032883264
8403150.9988Uncovering virulence factors in Cronobacter sakazakii: insights from genetic screening and proteomic profiling. The increasing problem of antibiotic resistance has driven the search for virulence factors in pathogenic bacteria, which can serve as targets for the development of new antibiotics. Although whole-genome Tn5 transposon mutagenesis combined with phenotypic assays has been a widely used approach, its efficiency remains low due to labor-intensive processes. In this study, we aimed to identify specific genes and proteins associated with the virulence of Cronobacter sakazakii, a pathogenic bacterium known for causing severe infections, particularly in infants and immunocompromised individuals. By employing a combination of genetic screening, comparative proteomics, and in vivo validation using zebrafish and rat models, we rapidly screened highly virulent strains and identified two genes, rcsA and treR, as potential regulators of C. sakazakii toxicity toward zebrafish and rats. Proteomic profiling revealed upregulated proteins upon knockout of rcsA and treR, including FabH, GshA, GppA, GcvH, IhfB, RfaC, MsyB, and three unknown proteins. Knockout of their genes significantly weakened bacterial virulence, confirming their role as potential virulence factors. Our findings contribute to understanding the pathogenicity of C. sakazakii and provide insights into the development of targeted interventions and therapies against this bacterium.IMPORTANCEThe emergence of antibiotic resistance in pathogenic bacteria has become a critical global health concern, necessitating the identification of virulence factors as potential targets for the development of new antibiotics. This study addresses the limitations of conventional approaches by employing a combination of genetic screening, comparative proteomics, and in vivo validation to rapidly identify specific genes and proteins associated with the virulence of Cronobacter sakazakii, a highly pathogenic bacterium responsible for severe infections in vulnerable populations. The identification of two genes, rcsA and treR, as potential regulators of C. sakazakii toxicity toward zebrafish and rats and the proteomic profiling upon knockout of rcsA and treR provides novel insights into the mechanisms underlying bacterial virulence. The findings contribute to our understanding of C. sakazakii's pathogenicity, shed light on the regulatory pathways involved in bacterial virulence, and offer potential targets for the development of novel interventions against this highly virulent bacterium.202337750707
4360160.9988Comparative Genomics Reveals Novel Species and Insights into the Biotechnological Potential, Virulence, and Resistance of Alcaligenes. Alcaligenes is a cosmopolitan bacterial genus that exhibits diverse properties which are beneficial to plants. However, the genomic versatility of Alcaligenes has also been associated with the ability to cause opportunistic infections in humans, raising concerns about the safety of these microorganisms in biotechnological applications. Here, we report an in-depth comparative analysis of Alcaligenes species using all publicly available genomes to investigate genes associated with species, biotechnological potential, virulence, and resistance to multiple antibiotics. Phylogenomic analysis revealed that Alcaligenes consists of at least seven species, including three novel species. Pan-GWAS analysis uncovered 389 species-associated genes, including cold shock proteins (e.g., cspA) and aquaporins (e.g., aqpZ) found exclusively in the water-isolated species, Alcaligenes aquatilis. Functional annotation of plant-growth-promoting traits revealed enrichment of genes for auxin biosynthesis, siderophores, and organic acids. Genes involved in xenobiotic degradation and toxic metal tolerance were also identified. Virulome and resistome profiles provide insights into selective pressures exerted in clinical settings. Taken together, the results presented here provide the grounds for more detailed clinical and ecological studies of the genus Alcaligenes.202337761923
8402170.9988Exploring phage-host interactions in Burkholderia cepacia complex bacterium to reveal host factors and phage resistance genes using CRISPRi functional genomics and transcriptomics. Complex interactions of bacteriophages with their bacterial hosts determine phage host range and infectivity. While phage defense systems and host factors have been identified in model bacteria, they remain challenging to predict in non-model bacteria. In this paper, we integrate functional genomics and transcriptomics to investigate phage-host interactions, revealing active phage resistance and host factor genes in Burkholderia cenocepacia K56-2. Burkholderia cepacia complex species are commonly found in soil and are opportunistic pathogens in immunocompromised patients. We studied infection of B. cenocepacia K56-2 with Bcep176, a temperate phage isolated from Burkholderia multivorans. A genome-wide dCas9 knockdown library targeting B. cenocepacia K56-2 was constructed, and a pooled infection experiment identified 63 novel genes or operons coding for candidate host factors or phage resistance genes. The activities of a subset of candidate host factor and resistance genes were validated via single-gene knockdowns. Transcriptomics of B. cenocepacia K56-2 during Bcep176 infection revealed that expression of genes coding for host factor and resistance candidates identified in this screen was significantly altered during infection by 4 h post-infection. Identifying which bacterial genes are involved in phage infection is important to understand the ecological niches of B. cenocepacia and its phages, and for designing phage therapies.IMPORTANCEBurkholderia cepacia complex bacteria are opportunistic pathogens inherently resistant to antibiotics, and phage therapy is a promising alternative treatment for chronically infected patients. Burkholderia bacteria are also ubiquitous in soil microbiomes. To develop improved phage therapies for pathogenic Burkholderia bacteria, or engineer phages for applications, such as microbiome editing, it's essential to know the bacterial host factors required by the phage to kill bacteria, as well as how the bacteria prevent phage infection. This work identified 65 genes involved in phage-host interactions in Burkholderia cenocepacia K56-2 and tracked their expression during infection. These findings establish a knowledge base to select and engineer phages infecting or transducing Burkholderia bacteria.202541036840
8466180.9988Genomic Characterization of Lactiplantibacillus plantarum Strains: Potential Probiotics from Ethiopian Traditional Fermented Cottage Cheese. BACKGROUND: Lactiplantibacillus plantarum is a species found in a wide range of ecological niches, including vegetables and dairy products, and it may occur naturally in the human gastrointestinal tract. The precise mechanisms underlying the beneficial properties of these microbes to their host remain obscure. Although Lactic acid bacteria are generally regarded as safe, there are rare cases of the emergence of infections and antibiotic resistance by certain probiotics. OBJECTIVE: An in silico whole genome sequence analysis of putative probiotic bacteria was set up to identify strains, predict desirable functional properties, and identify potentially detrimental antibiotic resistance and virulence genes. METHODS: We characterized the genomes of three L. plantarum strains (54B, 54C, and 55A) isolated from Ethiopian traditional cottage cheese. Whole-genome sequencing was performed using Illumina MiSeq sequencing. The completeness and quality of the genome of L. plantarum strains were assessed through CheckM. RESULTS: Analyses results showed that L. plantarum 54B and 54C are closely related but different strains. The genomes studied did not harbor resistance and virulence factors. They had five classes of carbohydrate-active enzymes with several important functions. Cyclic lactone autoinducer, terpenes, Type III polyketide synthases, ribosomally synthesized and post-translationally modified peptides-like gene clusters, sactipeptides, and all genes required for riboflavin biosynthesis were identified, evidencing their promising probiotic properties. Six bacteriocin-like structures encoding genes were found in the genome of L. plantarum 55A. CONCLUSIONS: The lack of resistome and virulome and their previous functional capabilities suggest the potential applicability of these strains in food industries as bio-preservatives and in the prevention and/or treatment of infectious diseases. The results also provide insights into the probiotic potential and safety of these three strains and indicate avenues for further mechanistic studies using these isolates.202439596588
4357190.9988Comparative genomic analysis of 255 Oenococcus oeni isolates from China: unveiling strain diversity and genotype-phenotype associations of acid resistance. Oenococcus oeni, the only species of lactic acid bacteria capable of fully completing malolactic fermentation under challenging wine conditions, continues to intrigue researchers owing to its remarkable adaptability, particularly in combating acid stress. However, the mechanism underlying its superior adaptation to wine stresses still remains elusive due to the lack of viable genetic manipulation tools for this species. In this study, we conducted genomic sequencing and acid resistance phenotype analysis of 255 O. oeni isolates derived from diverse wine regions across China, aiming to elucidate their strain diversity and genotype-phenotype associations of acid resistance through comparative genomics. A significant correlation between phenotypes and evolutionary relationships was observed. Notably, phylogroup B predominantly consisted of acid-resistant isolates, primarily originating from Shandong and Shaanxi wine regions. Furthermore, we uncovered a noteworthy linkage between prophage genomic islands and acid resistance phenotype. Using genome-wide association studies, we identified key genes correlated with acid resistance, primarily involved in carbohydrates and amino acid metabolism processes. This study offers profound insights into the genetic diversity and genetic basis underlying adaptation mechanisms to acid stress in O. oeni.IMPORTANCEThis study provides valuable insights into the genetic basis of acid resistance in Oenococcus oeni, a key lactic acid bacterium in winemaking. By analyzing 255 isolates from diverse wine regions in China, we identified significant correlations between strain diversity, genomic islands, and acid resistance phenotypes. Our findings reveal that certain prophage-related genomic islands and specific genes are closely linked to acid resistance, offering a deeper understanding of how O. oeni adapts to acidic environments. These discoveries not only advance our knowledge of microbial stress responses but also pave the way for selecting and engineering acid-resistant strains, enhancing malolactic fermentation efficiency and wine quality. This research underscores the importance of genomics in improving winemaking practices and addressing challenges posed by high-acidity wines.202540261018