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A comparative genomic analysis was conducted for 171 Salmonella isolates recovered from raw inshell almonds and raw almond kernels between 2001 and 2013 and for 30 Salmonella Enteritidis phage type (PT) 30 isolates recovered between 2001 and 2006 from a 2001 salmonellosis outbreak-associated almond orchard. Whole genome sequencing was used to measure the genetic distance among isolates by single nucleotide polymorphism (SNP) analyses and to predict the presence of plasmid DNA and of antimicrobial resistance (AMR) and virulence genes. Isolates were classified by serovars with Parsnp, a fast core-genome multi aligner, before being analyzed with the CFSAN SNP Pipeline (U.S. Food and Drug Administration Center for Food Safety and Applied Nutrition). Genetically similar (≤18 SNPs) Salmonella isolates were identified among several serovars isolated years apart. Almond isolates of Salmonella Montevideo (2001 to 2013) and Salmonella Newport (2003 to 2010) differed by ≤9 SNPs. Salmonella Enteritidis PT 30 isolated between 2001 and 2013 from survey, orchard, outbreak, and clinical samples differed by ≤18 SNPs. One to seven plasmids were found in 106 (62%) of the Salmonella isolates. Of the 27 plasmid families that were identified, IncFII and IncFIB plasmids were the most predominant. AMR genes were identified in 16 (9%) of the survey isolates and were plasmid encoded in 11 of 16 cases; 12 isolates (7%) had putative resistance to at least one antibiotic in three or more drug classes. A total of 303 virulence genes were detected among the assembled genomes; a plasmid that harbored a combination of pef, rck, and spv virulence genes was identified in 23% of the isolates. These data provide evidence of long-term survival (years) of Salmonella in agricultural environments.

A comparative genomic analysis was conducted for 171 Salmonella isolates recovered from raw inshell almonds and raw almond kernels between 2001 and 2013 and for 30 Salmonella Enteritidis phage type (PT) 30 isolates recovered between 2001 and 2006 from a 2001 salmonellosis outbreak-associated almond orchard. Whole genome sequencing was used to measure the genetic distance among isolates by single nucleotide polymorphism (SNP) analyses and to predict the presence of plasmid DNA and of antimicrobial resistance (AMR) and virulence genes. Isolates were classified by serovars with Parsnp, a fast core-genome multi aligner, before being analyzed with the CFSAN SNP Pipeline (U.S. Food and Drug Administration Center for Food Safety and Applied Nutrition). Genetically similar (≤18 SNPs) Salmonella isolates were identified among several serovars isolated years apart. Almond isolates of Salmonella Montevideo (2001 to 2013) and Salmonella Newport (2003 to 2010) differed by ≤9 SNPs. Salmonella Enteritidis PT 30 isolated between 2001 and 2013 from survey, orchard, outbreak, and clinical samples differed by ≤18 SNPs. One to seven plasmids were found in 106 (62%) of the Salmonella isolates. Of the 27 plasmid families that were identified, IncFII and IncFIB plasmids were the most predominant. AMR genes were identified in 16 (9%) of the survey isolates and were plasmid encoded in 11 of 16 cases; 12 isolates (7%) had putative resistance to at least one antibiotic in three or more drug classes. A total of 303 virulence genes were detected among the assembled genomes; a plasmid that harbored a combination of pef, rck, and spv virulence genes was identified in 23% of the isolates. These data provide evidence of long-term survival (years) of Salmonella in agricultural environments. [ABSTRACT FROM AUTHOR]

Introduction: Persistent symptoms after COVID-19 infection ("long COVID") negatively affects almost half of COVID-19 survivors. Despite its prevalence, its pathophysiology is poorly understood, with multiple host systems likely affected. Here, we followed patients from hospital to discharge and used a systems-biology approach to identify mechanisms of long COVID. Methods: RNA-seq was performed on whole blood collected early in hospital and 4-12 weeks after discharge from 24 adult COVID-19 patients (10 reported post-COVID symptoms after discharge). Differential gene expression analysis, pathway enrichment, and machine learning methods were used to identify underlying mechanisms for post-COVID symptom development. Results: Compared to patients with post-COVID symptoms, patients without post-COVID symptoms had larger temporal gene expression changes associated with downregulation of inflammatory and coagulation genes over time. Patients could also be separated into three patient endotypes with differing mechanistic trajectories, which was validated in another published patient cohort. The "Resolved" endotype (lowest rate of post-COVID symptoms) had robust inflammatory and hemostatic responses in hospital that resolved after discharge. Conversely, the inflammatory/hemostatic responses of "Suppressive" and "Unresolved" endotypes (higher rates of patients with post-COVID symptoms) were persistently dampened and activated, respectively. These endotypes were accurately defined by specific blood gene expression signatures (6-7 genes) for potential clinical stratification. Discussion: This study allowed analysis of long COVID whole blood transcriptomics trajectories while accounting for the issue of patient heterogeneity. Two of the three identified and externally validated endotypes ("Unresolved" and "Suppressive") were associated with higher rates of post-COVID symptoms and either persistently activated or suppressed inflammation and coagulation processes. Gene biomarkers in blood could potentially be used clinically to stratify patients into different endotypes, paving the way for personalized long COVID treatment. [ABSTRACT FROM AUTHOR]

Severely-afflicted COVID-19 patients can exhibit disease manifestations representative of sepsis, including acute respiratory distress syndrome and multiple organ failure. We hypothesized that diagnostic tools used in managing all-cause sepsis, such as clinical criteria, biomarkers, and gene expression signatures, should extend to COVID-19 patients. Here we analyzed the whole blood transcriptome of 124 early (1–5 days post-hospital admission) and late (6–20 days post-admission) sampled patients with confirmed COVID-19 infections from hospitals in Quebec, Canada. Mechanisms associated with COVID-19 severity were identified between severity groups (ranging from mild disease to the requirement for mechanical ventilation and mortality), and established sepsis signatures were assessed for dysregulation. Specifically, gene expression signatures representing pathophysiological events, namely cellular reprogramming, organ dysfunction, and mortality, were significantly enriched and predictive of severity and lethality in COVID-19 patients. Mechanistic endotypes reflective of distinct sepsis aetiologies and therapeutic opportunities were also identified in subsets of patients, enabling prediction of potentially-effective repurposed drugs. The expression of sepsis gene expression signatures in severely-afflicted COVID-19 patients indicates that these patients should be classified as having severe sepsis. Accordingly, in severe COVID-19 patients, these signatures should be strongly considered for the mechanistic characterization, diagnosis, and guidance of treatment using repurposed drugs. [ABSTRACT FROM AUTHOR]

It is easy for today's students and researchers to believe that modern bioinformatics emerged recently to assist next-generation sequencing data analysis. However, the very beginnings of bioinformatics occurred more than 50 years ago, when desktop computers were still a hypothesis and DNA could not yet be sequenced. The foundations of bioinformatics were laid in the early 1960s with the application of computational methods to protein sequence analysis (notably, de novo sequence assembly, biological sequence databases and substitution models). Later on, DNA analysis also emerged due to parallel advances in (i) molecular biology methods, which allowed easier manipulation of DNA, as well as its sequencing, and (ii) computer science, which saw the rise of increasingly miniaturized and more powerful computers, as well as novel software better suited to handle bioinformatics tasks. In the 1990s through the 2000s, major improvements in sequencing technology, along with reduced costs, gave rise to an exponential increase of data. The arrival of 'Big Data' has laid out new challenges in terms of data mining and management, calling for more expertise from computer science into the field. Coupled with an ever-increasing amount of bioinformatics tools, biological Big Data had (and continues to have) profound implications on the predictive power and reproducibility of bioinformatics results. To overcome this issue, universities are now fully integrating this discipline into the curriculum of biology students. Recent subdisciplines such as synthetic biology, systems biology and whole-cell modeling have emerged from the ever-increasing complementarity between computer science and biology. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various reports within the issue on topics including the relationship between protest and ethical and political theory, and social and political philosophy.

Purpose – The purpose of this paper is to explain the antecedents of environmental uncertainty in management using a historical framework. The goal of developing passion and compassion in management practice and research cannot be achieved unless a better understanding is developed of the main challenge facing researchers and practitioners – uncertainty. Design/methodology/approach – The antecedents of uncertainty in management are explored using a historical framework. This enables the generation of insights into the nature and use of uncertainty over the decades. Findings – The importance of environmental uncertainty is escalating. The paper's historical, philosophical and critical view helps scholars explain and interpret uncertainty within their own research and formulate new research questions. Originality/value – Understanding the epistemological assumptions underlying paradigms will better enable researchers and practitioners to face a future filled with uncertainty and equivocality. [ABSTRACT FROM AUTHOR]

An introduction to the journal is presented in which discusses various reports published within the issue including one on the democratic governance's failure on genetically-modified organism (GMO) and another on the influence of the growing urban farming on the marginalization of rural knowledge.

An introduction is presented in which the editor discusses various reports within the issue on topics including an epistemological justification of democracy, an examination of the theme of civic virtue, and the conflict between virtues of civility and righteous demands for civil and political rights.

Aeromonas salmonicida subsp. salmonicida is a Gram-negative bacterium causing furunculosis, an opportunistic infection of farmed salmonid fish. Current treatment methods against furunculosis rely heavily on antibiotherapy. However, strains of this opportunistic fish pathogen were found to possess genes that confer resistance to major antibiotics including those used to cure furunculosis. Therefore, dispensing bacterial symbionts as probiotics to susceptible hosts appears to be a promising alternative. Here, we present the genomic characterization and in vivo safety assessment of two brook charr (Salvelinus fontinalis) bacterial symbionts that inhibited A. salmonicida subsp. salmonicida growth in vitro (Pseudomonas fluorescens ML11A and Aeromonas sobria TM18) as well as a commercialized probiotic, Pediococcus acidilactici MA18/5M (Bactocell®). The genomic sequences of ML11A and TM18 obtained by whole-genome shotgun sequencing lack key virulence factor genes found in related pathogenic strains. Their genomic sequences are also devoid of genes involved in the inactivation (or target modification of) several key antimicrobial compounds used in salmonid aquaculture. Finally, when administered daily to live brook charr fingerlings, ML11A, TM18 and Bactocell® helped improve several physiological condition metrics such as mean body weight, Fulton's condition factor and blood plasma lysozyme activity (an indicator for innate immune activity). [ABSTRACT FROM AUTHOR]

An introduction to the journal is presented in which the editor discusses report within the issue on topics including a key tension that exists in Catholic universities between a right to sectarian exclusivity on the one hand and theologies of love and compassion that require inclusivity on the other, the problems of knowledge and justice, and the nature of experiential knowledge claims.

Pesticides are causing honeybee mortality worldwide. Research carried out on honeybees indicates that application of pesticides has a significant impact on the core gut community, which ultimately leads to an increase in the growth of harmful pathogens. Disturbances caused by pesticides also affect the way bacterial members interact, which results in gut microbial dysbiosis. Administration of beneficial microbes has been previously demonstrated to be effective in treating or preventing disease in honeybees. The objective of this study was to measure under in vivo conditions the ability of two bacterial strains (the Enterobacter sp. and Pantoea sp.) isolated from honeybee gut to improve survival and mitigate gut microbiota dysbiosis in honeybees exposed to a sublethal clothianidin dose (0.1 ppb). Both gut bacterial strains were selected for their ability to degrade clothianidin in vitro regardless of their host-microbe interaction characteristics (e.g., beneficial, neutral, or harmful). To this end, we conducted cage trials on 4- to 6-day-old newly emerging honeybees. During microbial administration, we jointly monitored the taxonomic distribution and activity level of bacterial symbionts quantifying 16S rRNA transcripts. First, curative administration of the Pantoea sp. strain significantly improved the survival of clothianidin-exposed honeybees compared to sugar control bees (i.e., supplemented with sugar [1:1]). Second, curative administration of the Enterobacter sp. strain significantly mitigated the clothianidin-induced dysbiosis observed in the midgut structural network, but without improving survival., Importance: The present work suggests that administration of bacterial strains isolated from honeybee gut may promote recovery of gut microbiota homeostasis after prolonged clothianidin exposure, while improving survival. This study highlights that gut bacterial strains hold promise for developing efficient microbial formulations to mitigate environmental pesticide exposure in honeybee colonies., Competing Interests: The authors declare no conflict of interest.

The major human bacterial pathogen Pseudomonas aeruginosa causes multidrug-resistant infections in people with underlying immunodeficiencies or structural lung diseases such as cystic fibrosis (CF). We show that a few environmental isolates, driven by horizontal gene acquisition, have become dominant epidemic clones that have sequentially emerged and spread through global transmission networks over the past 200 years. These clones demonstrate varying intrinsic propensities for infecting CF or non-CF individuals (linked to specific transcriptional changes enabling survival within macrophages); have undergone multiple rounds of convergent, host-specific adaptation; and have eventually lost their ability to transmit between different patient groups. Our findings thus explain the pathogenic evolution of P. aeruginosa and highlight the importance of global surveillance and cross-infection prevention in averting the emergence of future epidemic clones.