Biology subjects

Tannous, J.

Publications and source records attributed to Tannous, J..

6 recordsLinked to original sources

bioRxiv2026

Emergence of a novel hypervirulent extensively drug-resistant ST383 Klebsiella pneumoniae lineage carrying ICEKp5 in Lebanon

Klebsiella pneumoniae ST383 has emerged as a high-risk clone, characterized by carbapenem resistance and increasing detection of hypervirulence determinants. We describe a novel ST383 lineage in Lebanon, defined by the acquisition of ICEKp5, which carries the yersiniabactin locus. Three ST383 K. pneumoniae clinical isolates (LBN_CAKp91, LBN_CTKp3, LBN_CTKp11) recovered from a Lebanese medical center were subjected to whole-genome sequencing. Comparative genomic analysis included regional ST383 strains and previously characterized Lebanese isolates. The study isolates formed a tight, monophyletic cluster (3-9 SNPs) that is phylogenetically distinct from the previously reported Lebanese ST383 clone (>164 SNPs) and grouped most closely to an Egyptian ST383 strain (59-65 SNPs). All three isolates carried ICEKp5 with yersiniabactin lineage ybt14, a feature absent in the earlier Lebanese ST383 clone. The isolates were the only ST383 strains to harbor the full spectrum of hypervirulence determinants to date, including capsule regulators (rmpA, rmpA2), aerobactin (iucABCD, iutA), yersiniabactin, and the hypervirulence biomarker peg-344. All isolates carried dual carbapenemases (blaOXA-48 and blaNDM-5) in addition to blaCTX-M-15 and blaCTX-M-14b. The genetic environments of blaOXA-48 and blaNDM-5 were highly conserved across geographically diverse ST383 isolates, indicating common plasmid origins. This study documents the emergence of a novel hypervirulent extensively drug-resistant (XDR) ST383 K. pneumoniae lineage in Lebanon. The acquisition of ICEKp5, combined with plasmid-borne hypervirulence and resistance determinants, reveals the concerning convergence of hypervirulence and XDR. Enhanced surveillance and infection control measures are urgently needed to monitor this emerging high-risk clone.

genomics↗

bioRxiv2025

From Nicotiana to apple: effector screening reveals new avenues for durable scab resistance

Scab or black spot disease, caused by the fungus Venturia inaequalis, is an ongoing threat to commercial apple production. Current control methods involve extensive fungicide use and the deployment of disease-resistant apple cultivars. However, fungicide-resistant strains of V. inaequalis are becoming more prevalent, as are strains that can overcome one or more qualitative disease resistance genes in apple. To increase the durability of disease resistance, and thus decrease our reliance on fungicides, one promising approach could involve stacking endogenous and exogenous resistance genes in apple cultivars using genetic modification. As a starting point for the identification of exogenous resistance genes that are effective against V. inaequalis, 137 candidate effector (CE) proteins from this fungus, fused to a signal peptide for extracellular targeting to the apoplast, were screened for recognition by extracellular leucine-rich repeat (LRR)-receptor-like protein and LRR-receptor-like kinase immune receptors in the model angiosperm species Nicotiana benthamiana and Nicotiana tabacum using Agrobacterium tumefaciens-mediated transient transformation assays. Here, a cell death response in wild-type plants, together with a loss of this response in plants lacking the extracellular immune co-receptor NbSOBIR1 or NbBAK1, was used as an indicator of recognition. In total, six CE proteins triggered cell death in one or both Nicotiana species, but only one, a homolog of the VmE02 effector protein from the apple pathogen Valsa mali, did so in an NbSOBIR1- and NbBAK1-dependent manner. The five remaining CE proteins are homologs of other known cell death elicitors from filamentous plant pathogens for which there is evidence that they trigger non-canonical extracellular immunity in plants. One of these is an Alt a 1-like protein that also triggered cell death in apple. Collectively, these findings provide a foundation for the use of a combined set of exogenous and endogenous resistance genes in apple to provide durable protection against scab disease.

microbiology↗

bioRxiv2022

Fungal antibiotics control bacterial community diversity in the cheese rind microbiome

Potent antimicrobial metabolites are produced by filamentous fungi in pure lab cultures, but their ecological functions in nature are often unknown. Using an antibiotic-producing Penicillium isolate and the cheese rind microbial community, we demonstrate that a fungal specialized metabolite can regulate the diversity of bacterial communities. Inactivation of the global regulator, LaeA, resulted in the loss of antibacterial activity in the Penicillium isolate. Cheese rind bacterial communities assembled with the laeA deletion strain had significantly higher bacterial abundances than the wild-type strain. RNA-sequencing and metabolite profiling demonstrated a striking reduction in the expression and production of the natural product pseurotin in the laeA deletion strain. Inactivation of a core gene in the pseurotin biosynthetic cluster restored bacterial community composition, demonstrating the role of pseurotins in mediating bacterial community assembly. Our discovery demonstrates how antibiotic production can drive the assembly of microbiomes and provides an ecological context for widespread fungal specialized metabolites.

microbiology↗

bioRxiv2022

A guidance into the fungal metabolomic abyss: Network analysis for revealing relationships between exogenous compounds and their outputs

AO_SCPLOWBSTRACTC_SCPLOWFungal specialized metabolites include many bioactive compounds with potential applications as pharmaceuticals, agrochemical agents, and industrial chemicals. Exploring and discovering novel fungal metabolites is critical to combat antimicrobial resistance in various fields, including medicine and agriculture. Yet, identifying the conditions or treatments that will trigger the production of specialized metabolites in fungi can be cumbersome since most of these metabolites are not produced under standard culture conditions. Here, we introduce a data-driven algorithm comprising various network analysis routes to characterize the production of known and putative specialized metabolites and unknown analytes triggered by different exogenous compounds. We use bipartite networks to quantify the relationship between the metabolites and the treatments stimulating their production through two routes. The first, called the direct route, determines the production of known and putative specialized metabolites induced by a treatment. The second, called the auxiliary route, is specific for unknown analytes. We demonstrated the two routes by applying chitooligosaccharides and lipids at two different temperatures to the opportunistic human fungal pathogen Aspergillus fumigatus. We used various network centrality measures to rank the treatments based on their ability to trigger a broad range of specialized metabolites. The specialized metabolites were ranked based on their receptivity to various treatments. Altogether, our data-driven techniques can track the influence of any exogenous treatment or abiotic factor on the metabolomic output for targeted metabolite research. This approach can be applied to complement existing LC/MS analyses to overcome bottlenecks in drug discovery and development from fungi. NoticeThis manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). Author summaryTriggering silent biosynthetic gene clusters in fungi to produce specialized metabolites is a tedious process that requires assessing various environmental conditions, applications of epigenetic modulating agents, or co-cultures with other microbes. We provide a data-driven solution using network analysis, called "direct route", to characterize the production of known and putative specialized metabolites triggered by various exogenous compounds. We also provide a "auxiliary route" to distinguish unique unknown analytes amongst the abundantly produced analytes in response to these treatments. The developed techniques can assist researchers to identify treatments or applications that could positively influence the production of a targeted metabolite or recognize unique unknown analytes that can be further fractionated, characterized, and screened for their biological activities and hence, discover new metabolites.

systems biology↗

bioRxiv2020

From iron to antibiotics: Identification of conserved bacterial-fungal interactions across diverse partners

Microbial interactions are major determinants in shaping microbiome structure and function. Although fungi are found across diverse microbiomes, the mechanisms through which fungi interact with other species remain largely uncharacterized. In this work, we explore the diversity of ways in which fungi can impact bacteria by characterizing interaction mechanisms across 16 different bacterial-fungal pairs, involving 8 different fungi and 2 bacteria (Escherichia coli and Pseudomonas psychrophila). Using random barcode transposon-site sequencing (RB-TnSeq), we identified a large number of bacterial genes and pathways important in fungal interaction contexts. Within each interaction, fungal partners elicit both antagonistic and beneficial effects. Using a panel of phylogenetically diverse fungi allowed us to identify interactions that were conserved across all species. Our data show that all fungi modulate the availability of iron and biotin, suggesting that these may represent conserved bacterial-fungal interactions. Several fungi also appear to produce previously uncharacterized antibiotic compounds. Generating a mutant in a master regulator of fungal secondary metabolite production showed that fungal metabolites are key shapers of bacterial fitness profiles during interactions. This work demonstrates a diversity of mechanisms through which fungi are able to interact with bacterial species. In addition to many species-specific effects, there appear to be conserved interaction mechanisms which may be important across microbiomes.

microbiology↗

bioRxiv2018

Measures of Possible Allostatic Load in Comorbid Cocaine and Alcohol use Disorder: Brain White Matter Integrity, Telomere Length, and Anti-Saccade Performance

Chronic cocaine and alcohol use impart significant stress on biological and cognitive systems, resulting in changes consistent with an allostatic load model of neurocognitive impairment. The present study measured potential markers of allostatic load in individuals with comorbid cocaine/alcohol use disorders (CUD/AUD) and control subjects. Measures of brain white matter (WM) integrity, telomere length, and impulsivity/attentional bias were obtained. WM integrity (CUD/AUD only) was indexed by diffusion tensor imaging metrics, including radial diffusivity (RD) and fractional anisotropy (FA). Telomere length was indexed by T/S ratio. Impulsivity and attentional bias to drug cues were measured via eye-tracking, and were also modeled using the Hierarchical Diffusion Drift Model (HDDM). Average whole-brain RD and FA were associated with years of cocaine use (R2 = 0.56 and 0.51, both p < .005) but not years of alcohol use. CUD/AUD subjects showed more anti-saccade errors (p < .01), greater attentional bias scores (p < .001), and higher HDDM drift rates on cocaine-cue trials (Bayesian probability CUD/AUD > control = p > 0.99). Telomere length was shorter in CUD/AUD, but the difference was not statistically significant. Within the CUD/AUD group, exploratory regression using an elastic-net model determined that more years of cocaine use, older age, larger HDDM drift rate differences and shorter telomere length were all predictive of white matter integrity as measured by RD (model R2 = 0.79). Collectively, the results provide modest support linking CUD/AUD to putative markers of allostatic load.

neuroscience↗