Antimicrobial resistance (AMR) has emerged as a significant global healthcare problem. Antibiotic use has accelerated the physiologic process of AMR, particularly in Gram-negative pathogens. Urinary tract infections (UTIs) are predominantly of a Gram-negative nature. Uropathogens are evolutionarily highly adapted and selected strains with specific virulence factors, suggesting common mechanisms in how bacterial cells acquire virulence and AMR factors. The simultaneous increase in resistance and virulence is a complex and context-dependent phenomenon. Among known AMR mechanisms, the plenitude of different β-lactamases is especially prominent. The risk for AMR in UTIs varies in different patient populations. A history of antibiotic consumption and the physiology of urinary flow are major factors that shape AMR prevalence. The urinary tract is in close crosstalk with the microbiome of other compartments, including the gut and genital tracts. In addition, pharmacokinetic properties and the physiochemical composition of urinary compartments can contribute to the emergence of AMR. Alternatives to antibiotic treatment and a broader approach to address bacterial infections are needed. Among the various alternatives studied, antimicrobial peptides and bacteriophage treatment appear to be highly promising approaches. We herein summarize the present knowledge of clinical and microbiological AMR in UTIs and discuss innovative approaches, namely new risk prediction tools and the use of non-antibiotic approaches to defend against uropathogenic microbes.

SARS-CoV-2, responsible for the ongoing global pandemic, must overcome a conundrum faced by all viruses. To achieve its own replication and spread, it simultaneously depends on and subverts cellular mechanisms. At the early stage of infection, SARS-CoV-2 expresses the viral nonstructural protein 1 (NSP1), which inhibits host translation by blocking the mRNA entry tunnel on the ribosome; this interferes with the binding of cellular mRNAs to the ribosome. Viral mRNAs, on the other hand, overcome this blockade. We show that NSP1 enhances expression of mRNAs containing the SARS-CoV-2 leader. The first stem–loop (SL1) in the viral leader is both necessary and sufficient for this enhancement mechanism. Our analysis pinpoints specific residues within SL1 (three cytosine residues at the positions 15, 19, and 20) and another within NSP1 (R124), which are required for viral evasion, and thus might present promising drug targets. We target SL1 with the antisense oligo (ASO) to efficiently and specifically down-regulate SARS-CoV-2 mRNA. Additionally, we carried out analysis of a functional interactome of NSP1 using BioID and identified components of antiviral defense pathways. Our analysis therefore suggests a mechanism by which NSP1 inhibits the expression of host genes while enhancing that of viral RNA. This analysis helps reconcile conflicting reports in the literature regarding the mechanisms by which the virus avoids NSP1 silencing.

Multimodal imaging by matrix-assisted laser desorption ionisation mass spectrometry imaging (MALDI MSI) and microscopy holds potential for understanding pathological mechanisms by mapping molecular signatures from the tissue microenvironment to specific cell populations. However, existing software solutions for MALDI MSI data analysis are incomplete, require programming skills and contain laborious manual steps, hindering broadly applicable, reproducible, and high-throughput analysis to generate impactful biological discoveries. Here, we present msiFlow, an accessible open-source, platform-independent and vendor-neutral software for end-to-end, high-throughput, transparent and reproducible analysis of multimodal imaging data. msiFlow integrates all necessary steps from raw data import to analytical visualisation along with state-of-the-art and self-developed algorithms into automated workflows. Using msiFlow, we unravel the molecular heterogeneity of leukocytes in infected tissues by spatial regulation of ether-linked phospholipids containing arachidonic acid. We anticipate that msiFlow will facilitate the broad applicability of MSI in multimodal imaging to uncover context-dependent cellular regulations in disease states.

Platelets are components of the blood that are highly reactive, and they quickly respond to multiple physiological and pathophysiological processes. In the last decade, it became clear that platelets are the key components of circulation, linking hemostasis, innate, and acquired immunity. Protein composition, localization, and activity are crucial for platelet function and regulation. The current state of mass spectrometry-based proteomics has tremendous potential to identify and quantify thousands of proteins from a minimal amount of material, unravel multiple post-translational modifications, and monitor platelet activity during drug treatments. This review focuses on the role of proteomics in understanding the molecular basics of the classical and newly emerging functions of platelets. including the recently described role of platelets in immunology and the development of COVID-19.The state-of-the-art proteomic technologies and their application in studying platelet biogenesis, signaling, and storage are described, and the potential of newly appeared trapped ion mobility spectrometry (TIMS) is highlighted. Additionally, implementing proteomic methods in platelet transfusion medicine, and as a diagnostic and prognostic tool, is discussed.

Quorum-sensing (QS) systems play a crucial role in regulating virulence, biofilm formation, and antibiotic resistance in clinically relevant microbes. This review explores the potential of QS systems as targets for developing novel plant-based therapeutic strategies using bioinformatics, aimed at combating highly pathogenic bacteria: uropathogenic Escherichia coli (UPEC) and Pseudomonas aeruginosa. We examine the key components and molecular pathways of QS systems in these microbes, including autoinducer synthases, receptors, and regulatory proteins. In UPEC, we discuss the LuxS-dependent autoinducer (AI)-2 system, while for P. aeruginosa, we analyze the more complex interconnected Las, Rhl, and PQS circuits. We highlight how these systems control the expression of virulence factors and contribute to biofilm formation, emphasizing their importance in pathogenesis. Furthermore, we explore bioinformatics approaches for identifying and characterizing QS components, i.e., by predicting protein structures and interactions. The potential of in silico screening for QS inhibitors is also discussed, along with challenges and opportunities in targeting QS systems for therapeutic interventions. By integrating microbiological, molecular, and computational perspectives, this review aims to provide insights into the application of bioinformatics in understanding and targeting QS systems in these clinically significant pathogens. The goal is to facilitate the development of novel anti-virulence approaches in search of novel antibiotics that could complement or replace traditional antibiotic treatments, addressing the growing concern of antimicrobial resistance in these clinically relevant microbes.