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PubMed2026

Comparative and Subtractive Genomics Analysis of Multidrug-Resistant Klebsiella pneumoniae Strains for Novel Target Identification and Drug Repurposing Strategies.

The rapid rise of multidrug-resistant (MDR) Klebsiella pneumoniae has created a major global health challenge due to the limited availability of conserved therapeutic targets effective across diverse resistant strains. In this study, an integrative computational target-discovery and drug-repurposing framework was applied to six clinically relevant K. pneumoniae strains. Comparative genomic analysis identified 3012 conserved genes, which were subsequently filtered to nine essential, non-host homologous proteins. Among these, three conserved cytoplasmic proteins (accD, cpxR, and mraZ) were prioritized for functional analysis, with acetyl-CoA carboxylase subunit beta (accD) emerging as the most promising therapeutic target based on sequence conservation, predicted essentiality, subcellular localization, and pathway association. Structural assessment supported the reliability of the predicted accD model, whereas consensus binding-site analysis identified key residues suitable for ligand interaction. Virtual screening of FDA-approved drugs followed by molecular docking identified several compounds with favorable binding profiles toward accD. Subsequent molecular dynamics simulations, including root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), hydrogen-bond occupancy, principal component analysis (PCA), and PCA-based free energy landscape (FEL) analyses, consistently identified tenapanor, micafungin, deferoxamine, and cobicistat as the most stable protein-ligand complexes, with tenapanor exhibiting the most favorable overall structural and thermodynamic stability profile. These findings identify accD as a promising therapeutic target in MDR K. pneumoniae and suggest several FDA-approved compounds as potential candidates for drug repurposing. Although experimental validation is needed to confirm their biological activity and therapeutic potential, this study demonstrates the potential of integrating comparative genomics with molecular dynamics analyses to support antimicrobial target identification and drug repurposing against MDR bacterial pathogens.

Klebsiella pneumoniae↗

PubMed2024

An integrated in-silico approach for drug target identification in human pathogen Shigella dysenteriae.

Shigella dysenteriae, is a Gram-negative bacterium that emerged as the second most significant cause of bacillary dysentery. Antibiotic treatment is vital in lowering Shigella infection rates, yet the growing global resistance to broad-spectrum antibiotics poses a significant challenge. The persistent multidrug resistance of S. dysenteriae complicates its management and control. Hence, there is an urgent requirement to discover novel therapeutic targets and potent medications to prevent and treat this disease. Therefore, the integration of bioinformatics methods such as subtractive and comparative analysis provides a pathway to compute the pan-genome of S. dysenteriae. In our study, we analysed a dataset comprising 27 whole genomes. The S. dysenteriae strain SD197 was used as the reference for determining the core genome. Initially, our focus was directed towards the identification of the proteome of the core genome. Moreover, several filters were applied to the core genome, including assessments for non-host homology, protein essentiality, and virulence, in order to prioritize potential drug targets. Among these targets were Integration host factor subunit alpha and Tyrosine recombinase XerC. Furthermore, four drug-like compounds showing potential inhibitory effects against both target proteins were identified. Subsequently, molecular docking analysis was conducted involving these targets and the compounds. This initial study provides the list of novel targets against S. dysenteriae. Conclusively, future in vitro investigations could validate our in-silico findings and uncover potential therapeutic drugs for combating bacillary dysentery infection.

Shigella dysenteriae↗

PubMed2026

Natural Product Target Identification of Wheldone, a Fungal Metabolite, as a KIF11 Inhibitor in Ovarian Cancer Using the DiffPOP (Differential Protein Precipitation) Method.

Wheldone, a fungal metabolite, was identified as a cytotoxic compound in high-grade serous ovarian cancer (HGSOC). Wheldone induced caspase 3/7-dependent apoptosis and reduced migration, invasion, and spheroid growth. Wheldone stimulated apoptosis in chemoresistant HGSOC models. Wheldone treatment caused significant downregulation of HNRNPD, a DNA repair protein, and increased DNA damage that could be blocked by N-acetyl-L-cysteine. In vivo, wheldone displayed minimal toxicity but was rapidly cleared from circulation, despite in vitro metabolic stability. Wheldone treatment in vivo did not demonstrate significant reduction in tumor burden. Therefore, in order to overcome these liabilities, it was necessary to find the protein target of wheldone so that modifications can be made to improve the drug-like characteristics of the compound. Using the drug-target interaction proteomics method, differential precipitation of proteins, wheldone was found to act as an inhibitor of Kinesin superfamily protein 11 (KIF11), a motor protein essential for mitotic spindle formation. An ATPase biochemical cell-free assay confirmed direct binding and functional inhibition of KIF11. Wheldone resulted in G2/M arrest and downstream regulation of mitotic proteins such as TPX2, AURKA, and phospho-histone H3. Proteomics after treatment of wheldone in four different HGSOC cancer cell lines all supported changes consistent with mitotic spindle assembly disruption. Further, KIF11 was one of only 13 proteins upregulated in all 4 cell lines treated. Overall, wheldone was found to be a fungal metabolite that inhibits KIF11 in chemoresistant ovarian cancer, with future studies needed to improve its pharmacokinetics and delivery.

Female↗

PubMed1976

Monaural right-ear advantage in a target-identification task.

Explore the source record for details and available documents.

Adolescent↗

PubMed2025

Accurate serotype identification of Streptococcus pneumoniae using nanopore Cas9-targeted serotype identification (nCATSerotyping).

Streptococcus pneumoniae (pneumococcus) is a leading cause of community-acquired pneumonia and invasive diseases, particularly among children and the elderly. The introduction of pneumococcal conjugate vaccines has significantly reduced invasive pneumococcal disease, but the prevalence of non-vaccine serotypes and newly emerging serotypes is increasing globally. Thus, accurate serotyping is essential for epidemiological surveillance and the development of next-generation multivalent pneumococcal vaccines. Conventional serotyping methods, including multiplex polymerase chain reaction (mPCR), monoclonal antibody (mAb) assays, and Quellung reaction using rabbit antisera, are limited by serotype coverage and cross-reactivity, making the detection of new or emerging serotypes challenging. In this study, we developed a nanopore Cas9-targeted serotyping (nCATSerotyping) platform, which employs Cas9-mediated enrichment of the capsular polysaccharide synthesis locus followed by Oxford Nanopore sequencing. Applying this method to 276 clinical pneumococcal isolates collected in South Korea (2018-2020), we achieved a serotyping success rate of 97.10% (268/276), significantly outperforming conventional methods such as mAb and mPCR, which identified only 76.45% (211/276) of isolates. Whole-genome sequencing of the remaining eight non-typeable isolates revealed them to be non-pneumococcal (oral streptococci), confirming 100% accuracy for S. pneumoniae serotyping. Importantly, our method identified emerging and underrepresented serotypes, including serotype 13 and null capsule clade strains. nCATSerotyping offers a rapid, accurate, and comprehensive solution for pneumococcal serotyping, with significant advantages in identifying novel and non-typeable strains. This scalable platform will be a valuable tool for global serotype surveillance and next-generation multivalent pneumococcal vaccine development.IMPORTANCEAccurate pneumococcal serotyping is critical for vaccine development and epidemiological surveillance, particularly as non-vaccine serotypes emerge following widespread pneumococcal conjugate vaccine implementation. Current serotyping methods face significant limitations in coverage and accuracy, identifying around 76% of pneumococcal isolates and failing to detect emerging serotypes like serotype 13 and null capsule clades. The nanopore Cas9-targeted serotyping platform addresses these critical gaps by achieving 100% serotyping accuracy for confirmed Streptococcus pneumoniae isolates while identifying previously undetectable strains that conventional methods missed. This comprehensive approach is essential for monitoring vaccine effectiveness, understanding serotype replacement patterns, and informing next-generation vaccine development strategies. Furthermore, the identification of misclassified oral streptococci highlights the diagnostic precision needed for accurate pneumococcal surveillance, ensuring that epidemiological data accurately reflect true pneumococcal disease burden and serotype distribution patterns.

Streptococcus pneumoniae↗

PubMed2025

Identification of potential cell surface targets in patient-derived cultures toward photoimmunotherapy of high-grade serous ovarian cancer.

Tumor-targeted, activatable photoimmunotherapy (taPIT) has shown promise in preclinical models to selectively eliminate drug-resistant micrometastases that evade standard treatments. Moreover, taPIT has the potential to resensitize chemo-resistant tumor cells to chemotherapy, making it a complementary modality for treating recurrent high-grade serous ovarian cancer (HGSOC). However, the established implementation of taPIT relies on the overexpression of EGFR in tumor cells, which is not universally observed in HGSOCs. Motivated by the need to expand taPIT applications beyond EGFR, we conducted mRNA-sequencing and proteomics to identify alternative cell surface targets for taPIT in patient-derived HGSOC cell cultures with weak EGFR expression and lacking expression of other cell surface proteins commonly reported in the literature as overexpressed in ovarian cancers, such as FOLR1 and EpCAM. Our findings highlight TFRC and LRP1 as promising alternative targets. Notably, TFRC was overexpressed in 100% (N = 5) of the patient-derived HGSOC models tested, whereas only 60% of models had high EpCAM expression, suggesting that future larger cohort studies should include TFRC. While this study focuses on target identification, future work will expand the approaches developed here to larger HGSOC biopsy repositories and will also develop and evaluate antibody-photosensitizer conjugates targeting these proteins for taPIT applications.

Humans↗

PubMed2026

Target Antigen Identification for Antibody Drug Conjugate Therapy in Biliary Tract Cancer.

BACKGROUND: Data on antibody-drug conjugates (ADCs) target expression prevalence, intertumoral heterogeneity, genomic concordance, and its effect on clinical outcomes is limited in biliary tract cancers (BTC). METHODS: Resected primary BTC specimens, and when available, matched metastatic samples were assembled into tissue microarrays and tested for CLDN18.2, c-MET, Nectin-4, TROP2, and HER2 expression by immunohistochemistry (IHC). A subset underwent targeted next-generation sequencing using MSK-IMPACT (NCT01775072). Exploratory associations of target expression with clinicopathologic parameters, genomic alterations, recurrence-free (RFS), and overall (OS) survival were evaluated. RESULTS: 65 patients with resected BTC and 18 paired metastatic sites were identified-43% extrahepatic cholangiocarcinoma, 40% intrahepatic cholangiocarcinoma, and 17% gallbladder cancer. All evaluated target antigens were expressed; percent positivity and H-score ≥200 were: TROP2 (83%, 26%), c-MET (75%, 26%), Nectin-4 (66%, 35%), and CLDN18.2 (46%, 7.7%). HER2 overexpression occurred in 3.1% of tumors. Overall agreement among paired primary and metastatic samples on calling either positive or negative ranged from 43% to 75% with the highest observed for HER2 [75%; κ=0.29 (95%CI: -0.32 to 0.91)] and TROP2 (71%; κ not available) and lowest for c-MET, CLDN18.2, and Nectin-4. Frequently altered genes included TP53 (36%), SMAD4 (27%), ELF3 (21%). We observed no significant association between target antigen expression with genomics, RFS, or OS. CONCLUSIONS: BTC displays frequent but heterogeneous expression of multiple ADC targets. These hypothesis generating findings suggest inherent complexity of target protein quantification, target threshold determination, and target sampling discordance. Future studies will be required to refine our understanding the utlitiy of ADCs in BTC.

Journal Article↗

PubMed1976

Tissue interaction in androgen response of embryonic mammary rudiment of mouse: identification of target tissue for testosterone.

In the androgen response of the embryonic mammary rudiment of the mouse, both gland epithelium and surrounding mesenchyme are visibly involved. The question whether this is due to a direct action of testosterone on both tissues was investigated in experimental combination of mammary epithelium and mammary mesenchyme, derived either from normal or from androgen-insensitive (XTfm/Y) embryos. A typical androgen response occurred in combinations of androgen-insensitive epithelium with normal mesenchyme, whereas all combinations of normal epithelium with androgen-insensitive mesenchyme failed to respond. It is therefore concluded that only the mesenchyme of the mammary rudiment is the target tissue for testosterone, and that all changes in the gland epithelium, including its necrosis, are secondarily caused by testosterone-activated mesenchymal cells.

Androgen-Insensitivity Syndrome↗

PubMed1975

Prevention of folate deficiency by food fortification. IV. Identification of target groups in addition to pregnant women in an adult rural population.

In a rural Negro population subsisting on a predominantly maize meal diet, the incidence of folate deficiency was 43.8% in nonanaemic women in late pregnancy, 32.1% in nonpregnant women, and 18.6% in adult males. More than one-third of all subjects older than 60 were deficient. No instance of unequivocal vitamin B12 deficiency was revealed in 431 subjects sampled, and it is considered that the hazards of giving a small daily dose of folic acid in this population are negligible. These findings warrant food fortification with folic acid in this and similar population groups.

Adolescent↗

PubMed2025

Lit-OTAR framework for extracting biological evidences from literature.

SUMMARY: The lit-OTAR framework, developed through a collaboration between Europe PMC and Open Targets, leverages deep learning to revolutionize drug discovery by extracting evidence from scientific literature for drug target identification and validation. This novel framework combines named entity recognition for identifying gene/protein (target), disease, organism, and chemical/drug within scientific texts, and entity normalization to map these entities to databases like Ensembl, Experimental Factor Ontology, and ChEMBL. Continuously operational, it has processed over 39 million abstracts and 4.5 million full-text articles and preprints to date, identifying more than 48.5 million unique associations that significantly help accelerate the drug discovery process and scientific research >29.9 m distinct target-disease, 11.8 m distinct target-drug, and 8.3 m distinct disease-drug relationships. AVAILABILITY AND IMPLEMENTATION: The results are accessible through Europe PMC's SciLite web app (https://europepmc.org/) and its annotations API (https://europepmc.org/annotationsapi), as well as via the Open Targets Platform (https://platform.opentargets.org/). The daily pipeline is available at https://github.com/ML4LitS/otar-maintenance, and the Open Targets ETL processes are available at https://github.com/opentargets.

Drug Discovery↗

PubMed2026

Engineering cold stress resilience in capsicum annuum through functional genomics and precision breeding.

This review synthesizes the molecular mechanisms of cold tolerance in pepper, integrating multi-omics data,genome editing, and precision breeding strategies to accelerate the development of cold-resilient cultivars. Cold stress is a significant environmental factor that affects the growth, productivity, and fruit quality of Capsicum annuum by impairing membrane integrity photosynthesis and cellular redox homeostasis. Although pepper has several endogenous cold-responsive regulators such as CaNAC035 and CabHLH035, along with antioxidant defense systems, its cold tolerance remains limited due to low transcriptional activation of key regulators, functional redundancy among cold-responsive genes, and the polygenicity of cold tolerance. These complexities, combined with low genetic diversity and linkage drag, have hindered the improvement of cold-resistant cultivars through conventional breeding. This review brings together the recent progress in understanding the molecular mechanisms of cold stress perception, signal transduction, transcriptional regulation, metabolic reprogramming, and phytohormone interactions in pepper. Precision Breeding 2.0 is a new innovation that combines the integration of multi-omics-based target identification with next-generation genome-editing techniques, allowing precise and multiplex engineering of complex and interconnected regulatory networks instead of single genes. We cover new approaches such as engineering the DREB/CBF pathway, allele-specific editing and targeted disruption of negative regulators to enhance the pathway(s) involved in cold response. Moreover, we propose a roadmap for integration of transcriptomics, proteomics, metabolomics, high-throughput phenomics, and speed breeding to accelerate the identification, validation, and deployment of superior alleles to boost cold tolerance. This review provides a foundation for developing climate-resilient pepper cultivars by connecting functional genomics with precision genome engineering approaches to maintain productivity under variable environmental conditions.

Capsicum↗

PubMed2025

Identification and external validation of a prognostic signature based on myeloid-derived suppressor cells-related LncRNAs to evaluate survival prognosis and treatment efficacy in invasive breast carcinoma.

BACKGROUND: Originating in the hematopoietic tissue, myeloid-derived suppressor cells (MDSCs) significantly contribute to tumor-related immunological processes. However, their relationship with long noncoding RNAs (lncRNAs) and breast cancer remains incompletely understood. In this study, we introduced MDSCs-associated lncRNAs as novel prognostic biomarkers to assess outcomes in patients with invasive breast carcinoma (BRCA). METHODS: Information regarding BRCA cases, including clinical and genomic details, was obtained from the TCGA repository. Predictive indicators were discovered, and their reliability underwent thorough verification. A clinically useful nomogram was developed following application-based validation. Additional investigations encompassed functional analysis, TMB assessment, TME profiling, immunotherapy efficacy forecasting, and drug sensitivity testing along with target identification. Long non-coding RNA expression was measured using reverse transcription quantitative PCR. RESULTS: A risk stratification model incorporating eight MDSCs-related lncRNAs effectively predicted patient outcomes. Kaplan-Meier (K-M) survival analysis clearly indicated a much worse prognosis among patients classified as high-risk (p < 0.001). The nomogram accurately forecasted overall survival (OS). Analysis of functional enrichment revealed that pathways associated with epithelial cells showed activity among patients at higher risk. Characterization of the tumor microenvironment showed increased immune cell presence in those classified as low-risk. Conversely, individuals with greater risk displayed higher tumor mutational burden. TIDE and IPS analyses indicated superior immunotherapy responsiveness in the low-risk BRCA subgroup. Among 47 drugs with notable IC50 variations, Ribociclib, PD173074, KU-55933, NU7441, and nutlin-3a exhibited lower IC50 values within the low-risk group, whereas Lapatinib demonstrated greater efficacy among the high-risk group. Moreover, 10 potential therapeutic agents and their targets were predicted for high-risk patients. RT-qPCR validation confirmed the robustness of the model. CONCLUSIONS: We successfully verified a new model of molecular markers of MDSCs-related lncRNAs, offering critical insights for predicting outcomes and guiding therapeutic decisions in BRCA cases.

Bioinformatics↗

PubMed2026

Artificial intelligence for anticancer drug discovery from natural products of macroalgae and sponges: A systematic review.

Marine natural products (MNPs) from macroalgae and marine sponges have inspired clinically important anticancer agents, including the cytarabine pharmacophore and the eribulin scaffold, while cyanobacterial dolastatin chemistry supplies the auristatin payloads of several marine-inspired antibody-drug conjugates (ADCs) such as brentuximab vedotin. Artificial intelligence (AI) methods, encompassing both classical machine learning (ML) with hand-engineered features and modern deep learning (DL) with many-layered neural networks, are increasingly supporting key decisions in natural-product anticancer drug discovery, including bioactivity prediction, target identification, absorption, distribution, metabolism, excretion and toxicity (ADMET) filtering, generative analogue design, and the selection of preclinical candidates. DL architectures relevant to this field include graph neural networks, transformer-based molecular generators, diffusion models for protein-ligand docking, and convolutional networks for mass spectrometry, while classical ML contributes interpretable fingerprint-based bioactivity models and molecular networking for dereplication. This review follows a systematic literature review methodology to organize the landscape of AI methods now applied to MNP anticancer discovery, distinguishing ML and DL approaches where relevant, situating them within the chemical context of macroalgal and sponge-derived oncology leads, and critically examining published case studies, including validation level (computational, in vitro, in vivo, clinical). The principal bottleneck for medical translation has shifted partly from algorithmic capability toward data infrastructure and experimental validation. Sparse, heterogeneous, and taxonomically biased bioactivity records limit what current models can learn and reduce the reliability of AI-prioritized candidates entering the preclinical pipeline. A roadmap is proposed that prioritizes open MNP-specific benchmarks, symbiont-aware modeling, and active learning loops with synthesizability and ADMET constraints. These AI workflows may accelerate the prioritization of marine-derived anticancer leads and support earlier, more evidence-based translational decisions in oncology drug development.

Biological Products↗

PubMed2026

NAViFluX: a visualization‑centric platform for interactive analysis, refinement and design of genome‑scale metabolic networks.

MOTIVATION: Genome-scale metabolic network (GSMN) models enable flux-based metabolite fate discovery, metabolic engineering, drug target identification, and multi-omics integration. However, programming requirements, architectural complexity, and limited visualization support impede its adoption by the broader scientific community. Existing tools exclusively specialize in GSMN analyses or visualization while lacking important features such as pathway-specific views, database-integrated refinement, and comprehensive enrichment and perturbation analyses. RESULTS: Here, we present NAViFluX (metabolic Network Analysis and Visualization of Flux), a visualization-centric, web browser-based tool that unifies native pathway/subsystem map generation, interactive model refinement via KEGG/BiGG, pathway merging and modules for flux computations, topology, and functional enrichment all within network views. Using three independent case studies on Escherichia coli, the utility of NAViFluX for characterization of nutrient-specific metabolic adaptations, enhancing gene essentiality predictions and interpretability, and rational design of an optimized carbon-fixing metabolic state is demonstrated. AVAILABILITY AND IMPLEMENTATION: All source code and supplementary files associated with the case studies are publicly available via Zenodo at https://zenodo.org/records/19107831. NAViFluX can be easily installed as a standalone software through https://github.com/bnsb-lab-iith/NAViFluX.

Metabolic Networks and Pathways↗

PubMed2026

Sex-specific associations of the plasma-proteome with incident coronary artery disease.

AIMS: The etiology of coronary artery Disease (CAD) appears different for men and women, yet insights into underlying sex-specific biological mechanisms are limited. We integrated genomic and proteomic analyses to investigate sex-specific associations of the plasma-proteome with CAD. METHODS AND RESULTS: In 40,829 UK Biobank participants (free-of-CAD, baseline-365 days thereafter; 55% women; mean age 56.9 ± 8.1 years), we examined associations between 2,922 plasma proteins and incident CAD over a median follow-up of 13.7 years (IQR 13.1-14.4) using multivariable-adjusted Cox proportional hazards models. Sex-specific analyses identified 440 female exclusive and 32 male exclusive proteins associated with incident CAD (FDR-corrected p < 0.05), revealing distinct pathway enrichments, including innate immune response in women and angiogenesis in men. Causality was assessed through combined and sex-stratified two-sample Mendelian randomization (MR) using inverse-variance-weighted analyses with genome wide association summary statistics from 422,108 men (61,969 cases) and 521,695 women (27,128 cases) (UK Biobank, FinnGen freeze 9). Integration of direct sex-protein interaction analyses with sex-combined MR identified 59 proteins with evidence for sex-specific causal effects. Four proteins demonstrated concordant directionality in sex-stratified MR analyses (n = 943,803) and multivariable regression models, namely CDKN2D, MYH9, and SKAP2 (women), and CTSH (men). To assess translational relevance, prioritized targets were further evaluated in secondary major adverse cardiovascular events among carotid endarterectomy patients (MACE; Athero-Express) and acute myocardial infarction (AMI; MISSION!) using plasma proteomics and ELISA. After further top-target identification in the context of MACE and AMI, clinical drug candidates were identified through a machine learning framework, including CTSH (men), and TNFRSF4 (both sexes). CONCLUSIONS: We identified sex-specific associations of proteins and biological pathways with incident CAD. Whereas the majority of proteins had consistent associations in both men and women, our findings suggest a degree of sex-specific pathogenesis with evidence for potential causality, opening new alleys for tailored prevention strategies and clinical cardiovascular risk management.

Journal Article↗

PubMed2025

Stepping out of the dark: how metabolomics shed light on fungal biology.

Metabolomics, a critical tool for analyzing small-molecule metabolites, integrates with genomics, transcriptomics, and proteomics to provide a systems-level understanding of fungal biology. By mapping metabolic networks, it elucidates regulatory mechanisms driving physiological and ecological adaptations. In fungal pathogenesis, metabolomics reveals host-pathogen dynamics, identifying virulence factors like gliotoxin in Aspergillus fumigatus and metabolic shifts, such as glyoxylate cycle upregulation in Candida albicans. Ecologically, it highlights fungal responses to abiotic stressors, including osmolyte production like trehalose, enhancing survival in extreme environments. These insights highlight metabolomics' role in decoding fungal persistence and niche colonization. In drug discovery, it aids target identification by profiling biosynthetic pathways, supporting novel antifungal and nanostructured therapy development. Combined with multi-omics, metabolomics advances insights into fungal pathogenesis, ecological interactions, and therapeutic innovation, offering translational potential for addressing antifungal resistance and improving treatment outcomes for fungal infections. Its progress shed light on complex fungal molecular profiles, advancing discovery and innovation in fungal biology.

Metabolomics↗

PubMed2025

The Salmonella pathogenicity island 1-encoded small RNA InvR mediates post-transcriptional feedback control of the activator HilA in Salmonella.

UNLABELLED: Salmonella Pathogenicity Island 1 (SPI1) encodes a Type-3 secretion system (T3SS) essential for Salmonella invasion of intestinal epithelial cells. Many environmental and regulatory signals control SPI1 gene expression, but in most cases, the molecular mechanisms remain unclear. Many regulatory signals control SPI1 at a post-transcriptional level, and we have identified a number of small RNAs (sRNAs) that control the SPI1 regulatory circuit. The transcriptional regulator HilA activates the expression of the genes encoding the SPI1 T3SS structural and primary effector proteins. Transcription of hilA is controlled by the AraC-like proteins HilD, HilC, and RtsA. The hilA mRNA 5' untranslated region (UTR) is ~350 nucleotides in length and binds the RNA chaperone Hfq, suggesting it is a likely target for sRNA-mediated regulation. We used rGRIL-seq (reverse global sRNA target identification by ligation and sequencing) to identify sRNAs that bind to the hilA 5' UTR. The rGRIL-seq data, along with genetic analyses, demonstrate the SPI1-encoded sRNA invasion gene-associated RNA (InvR) base pairs at a site overlapping the hilA ribosome binding site. HilD and HilC activate both invR and hilA. InvR, in turn, negatively regulates the translation of the hilA mRNA. Thus, the SPI1-encoded sRNA InvR acts as a negative feedback regulator of SPI1 expression. Our results suggest that InvR acts to fine-tune SPI1 expression and prevents overactivation of hilA expression, highlighting the complexity of sRNA regulatory inputs controlling SPI1 and Salmonella virulence. IMPORTANCE: Salmonella Typhimurium infections pose a significant public health concern, leading to illnesses that range from mild gastroenteritis to severe systemic infection. Infection requires a complex apparatus that the bacterium uses to invade the intestinal epithelium. Understanding how Salmonella regulates this system is essential for addressing these infections effectively. Here, we show that the small RNA (sRNA) InvR imposes a negative feedback regulation on the expression of the invasion system. This work underscores the role of sRNAs in Salmonella's complex regulatory network, offering new insights into how these molecules contribute to bacterial adaptation and pathogenesis.

Genomic Islands↗

PubMed2025

Cycle threshold values and SARS-CoV-2 variant associations with breakthrough infections: a retrospective study in Accra, Ghana.

BACKGROUND: Breakthrough infections are defined as SARS-CoV-2 infections occurring ≥ 14 days after completing the primary COVID-19 vaccination series and remain a public health challenge, particularly in regions where immune-evasive variants are circulating. However, data on their virological and clinical profiles in low-resource settings are limited. METHODS: This retrospective study was conducted from July to December 2022 in Accra, Ghana, among individuals testing positive for SARS-CoV-2. Real-time Reverse Transcription Polymerase Chain Reaction (RT-PCR) was performed using the Allplex™ 2019-nCoV Assay. Cycle threshold (Ct) values for the nucleocapsid (N), RNA-dependent RNA polymerase (RdRP), and envelope (E) genes, categorised as < 25, 25–30, or > 30. Variant identification targeted Alpha, Delta, and Omicron mutations using mutation-specific RT-PCR. Logistic regression was used to assess associations between vaccination status and demographic, clinical, and virological factors. RESULTS: Of the 268 samples analysed, 81 tested positive; 43.20% [n = 35] were vaccinated individuals. Median Ct-values for the N [27.13, IQR: 21.59–31.96] and E [24.57, IQR: 19.43–29.43] genes were significantly higher among vaccinated cases, indicating lower viral loads. Breakthrough infections were strongly associated with the Omicron variant [aOR = 4.38, p = 0.034]. Diarrhoea [aOR = 9.67, p = 0.022], sore throat [aOR = 8.99, p = 0.038], headache [aOR = 10.156, p = 0.039] and chills [aOR = 3.316, p = 0.046] were mostly associated with breakthrough infections. Ct-values of 25–30 [aOR = 11.33, p = 0.012] and > 30 [aOR = 4.01, p = 0.047] were significantly associated with breakthrough infection compared to Ct < 25 in breakthrough infections. CONCLUSION: Vaccinated individuals with SARS-CoV-2 infection had lower viral loads and were more likely to be infected with the Omicron variant. These findings reinforce the role of vaccination in reducing viral load and support the adoption of practical surveillance strategies, such as Ct value-based surveillance and variant screening in low middle-income countries facing similar constraints in genomic capacity and vaccine deployment.

Humans↗