mSystems

Background: Oral dysbiosis has been associated with oral squamous cell carcinoma (OSCC); however, most microbiome studies rely on 16S ribosomal RNA (rRNA) gene sequencing, limiting species-level taxonomic resolution. Methods: Dental plaque, saliva, and tumor tissue samples from 10 patients with OSCC and dental plaque and saliva samples from 10 healthy controls were analyzed in this exploratory cross-sectional study. DNA was extracted and subjected to shotgun metagenomic sequencing using the Illumina MiSeq platform. Sequence reads were quality filtered with fastp, taxonomically classified using Kraken2 v2.1.3, and species-level abundances were re-estimated with Bracken v2.9 following the removal of human reads and low abundance taxa. Relative abundances were compared using the Mann Whitney U test with the Benjamini Hochberg false discovery rate correction, while the Bray Curtis principal coordinate analysis was used as an exploratory approach to visualize microbial community patterns. Results: Shotgun metagenomic sequencing revealed distinct bacterial community profiles across the oral microenvironment. Dental plaque exhibited the highest taxonomic diversity and relative abundance. The control plaque was enriched in Streptococcus koreensis, Capnocytophaga sp. oral taxon 878, Treponema sp. Marseille Q4132, and Leptotrichia sp. oral taxon 498, whereas the plaque from patients with OSCC showed a higher relative abundance of Pyramidobacter piscolens, Parvimonas parva, and Gemella sanguinis. Salivary samples displayed lower diversity and a more homogeneous composition, predominantly comprising Capnocytophaga endodontalis, Prevotella jejuni, Aggregatibacter aphrophilus, and Gemella sanguinis. The tumor tissue showed relatively higher abundance of Sellimonas catena, Escherichia coli, Solobacterium moorei, and Lacrimispora sp. HJ 01. Conclusions: This exploratory study provides species-level characterization of the oral microbiome across multiple oral microenvironments in OSCC and generates hypotheses for future integrative metagenomic and functional studies investigating the potential contribution of oral bacterial communities to OSCC pathogenesis.

Plant growth-promoting bacterial endophytes represent a sustainable strategy for enhancing agricultural productivity while reducing reliance on synthetic fertilizers and pesticides. This study focused on the genomic and functional characterization of two endophytic bacterial strains, R11F and R19M, isolated from bean and maize roots, respectively. Comparative analyses based on 16S rRNA gene sequences, average nucleotide identity (ANI), and genome-to-genome distance calculations (GGDC) classified both isolates as Pseudomonas palleroniana. Comparative genomic analyses revealed highly conserved genomes containing genes associated with plant colonization, phosphate solubilization, stress adaptation, heavy metal resistance, and hydrocarbon degradation. Genome mining further identified 17 and 18 biosynthetic gene clusters (BGCs) in R11F and R19M, respectively, including non-ribosomal peptide synthetases (NRPS), pyoverdine, NRP-metallophores, RiPP-like compounds, arylpolyenes, {beta}-lactones, terpenes, NAGGN, and hydrogen cyanide. Strain-specific BGCs associated with syringomycin and viscosin biosynthesis were identified in R11F, whereas R19M harbored clusters related to asplenin and kolossin biosynthesis. In vitro assays confirmed indole production, phosphate solubilization, and siderophore production, as well as the ability of both strains to grow in nitrogen-free medium. Both strains significantly inhibited the growth of Fusarium oxysporum, Phytophthora cinnamomi, and Colletotrichum gloeosporioides. Furthermore, plant inoculation assays demonstrated host-dependent growth promotion, with R11F showing the most consistent improvements in plant growth parameters in tomato, wheat, and lentil. Overall, the integration of comparative genomics and experimental validation demonstrates that P. palleroniana R11F and R19M possess complementary traits associated with plant growth promotion, pathogen suppression, saline stress adaptation, and bioremediation.

The global prevalence of Extended-spectrum {beta}-lactamase-producing Enterobacterales (ESBL-E) colonization is increasing. However, it is unclear whether ESBL-E persist and if that is associated with an altered gut microbial ecology especially in early life where the developing microbiome may not provide the same colonization resistance as in adults. In this study, we collected longitudinal infant gut microbiome samples at delivery and in the nonclinical home setting in Chicago, Illinois, U.S.A, aiming to disentangle how genetic factors pertaining to the ESBL-E, as well as the surrounding gut ecology, influences persistence in the infant gut microbiome. We observed not only a higher-than-expected prevalence of ESBL-E in healthy infant gut microbiomes, but also a trend of ESBL-E persistence once colonized. Microbial communities showed higher dissimilarity between ESBL-E positive and negative infant gut microbiome at earlier time points. Although dissimilarity decreased over time, we present evidence that ESBL-E persist even when traditional detection methods are negative.

Next-generation sequencing (NGS) of the prokaryotic 16S rRNA gene revolutionized gut microbiome research two decades ago. However, short read lengths remain an inherent limitation of platforms such as the widely used Illumina platforms (2 x 150-300 bp). Recent advances in Oxford Nanopore Technologies (ONT) flow cell chemistry (R10.4.1) have substantially improved sequencing accuracy. Combined with a custom multiple-primer strategy that comprehensively targets 16S rRNA gene variants to generate near-full-length amplicons, this approach enables read-by-read taxonomic classification, a feature not feasible with short-read sequencing platforms. Although our multiple-primer strategy could enable parallel sequencing of more than 18,000 samples (192 x 96), current flow cell capacity offers sufficient sequencing depth for approximately 1,000-1,500 samples. To validate the scalability and our per-read classification pipeline, we show that more than a thousand human fecal microbiome samples spiked with two bacterial strains (Imtechella halotolerans and Allobacillus halotolerans), not otherwise present in human fecal samples, can be successfully sequenced on a single flow cell, achieving a per-molecule error rate sufficient for direct per-read classification and at an adequate read depth for downstream analysis. This level of scalability significantly reduces per-sample costs, making the approach more accessible to a broader research community. To embrace these advancements, we have developed RubyRed, a pipeline that processes raw sequencing data and assigns taxonomic classifications on a per-read basis. Using spike-in references (I. halotolerans and A. halotolerans), we demonstrate high mean single-read sequencing accuracy (99% and 98.9%, respectively), with the majority of reads exceeding the canonical threshold required for species-level taxonomic classification based on the 16S rRNA gene.

Biological nitrogen fixation converts dinitrogen gas into ammonia, supplying new bioavailable nitrogen to marine ecosystems, but the evolutionary processes shaping its distribution among heterotrophic bacteria remain unresolved. Thalassolituus, a genus within the family Oceanospirillaceae (order Oceanospirillales), is best known for hydrocarbon degradation, yet nitrogen fixation has been confirmed in only one cultured isolate. We analyzed 74 quality-filtered genomes assigned to Thalassolituus within a broader dataset of 421 Oceanospirillaceae genomes to reconstruct the distribution and evolutionary history of the minimal nifHDKENB gene set. Twenty-five genomes encoded complete or near-complete nif loci and occurred in four well-supported clades interspersed with genomes lacking the pathway. Statistical topology tests rejected the species-tree topology for concatenated NifHDK and NifHDKENB protein alignments, and eleven recombination events across nif loci were supported by at least four detection methods. The core nifHDK gene order remained broadly conserved, but accessory neighborhoods differed among clades, and structural nifHDK genes showed stronger codon adaptation than biosynthesis nifENB genes. Clade 2 combined species-gene tree congruence, conserved gene neighborhoods, and comparatively high nifH codon adaptation, whereas Clades 1 and 4 showed greater phylogenetic discordance, more recombination, and weaker codon adaptation. These results support a reticulate history in Thalassolituus, in which lateral acquisition introduced nitrogen fixation into distinct lineages, vertical inheritance preserved it within some clades, and homologous recombination continued to reshape nif loci. These processes help explain why nitrogen fixation is unevenly distributed among closely related marine heterotrophic bacteria.

Wastewater-based epidemiology (WBE) has emerged as a powerful population-level surveillance tool, but its coverage is structurally concentrated in in-network urban areas, potentially leaving rural populations underrepresented. Routine human movement between sewered (in-network) and unsewered (off-network) areas may, however, cause wastewater treatment plant (WWTP) measurements to reflect infectious disease dynamics beyond sewer boundaries. We evaluated this hypothesis using daily clinical COVID-19 testing data (January 2021-April 2022) across New York State excluding New York City (NYC). We disaggregated weekly cases and tests into in-network (WWTP catchment area) and off-network (outside WWTP catchment area) components applied to two geographic frameworks: administrative counties (N = 53 mixed-coverage) and mobility-defined communities identified through Walktrap community detection applied to census tract-level movement networks (N = 32 mixed-coverage). In/off-network COVID-19 trends were strongly correlated under both frameworks. County-level statewide aggregate correlations were high (incidence r = 0.994, positivity r = 0.996), as were individual county correlations (median r = 0.909 and 0.932, respectively). Mobility-defined community-level statewide correlations were similarly strong (r = 0.990 and 0.992), with comparable unit-level medians (r = 0.877 and 0.894). The mobility-defined community framework provided better population balance between in-network and off-network strata (87.5% vs. 69.8% in balanced range) and a higher floor on representativeness (minimum r = 0.440 vs. 0.177). Population size was the dominant predictor of in-network/off-network alignment at both scales; wastewater infrastructure density and off-network signal variability provided additional explanatory power at the mobility-defined community level. WWTPs broadly represent COVID-19 dynamics in surrounding off-network populations, supporting their use as sentinel surveillance sites. Representativeness weakens in smaller, more rural communities, and mobility-defined communities provide a complementary framework for identifying where this occurs.

Machine-learning models of the human microbiome are trained mostly on stool samples from single cohorts, limiting cross-body-site representation and cross-study generalization. Progress is constrained less by algorithms than by the absence of a harmonized multi-body-site corpus carrying the technical metadata needed to model, rather than ignore, batch structure. Here we release Corpusome, a harmonized two-tier cross-body-site human microbiome corpus for representation learning: a harmonized corpus of 187,546 human microbiome samples integrating standardized profiles from curatedMetagenomicData, the American Gut Project, and the EBI MGnify platform. Corpusome follows a two-tier design preserving both functional depth and cross-body-site breadth: a shotgun tier (22,588 samples, 93 studies) with species- and pathway-level profiles, and a 16S tier (164,958 samples, from a full pull of 708 MGnify studies) with genus-level profiles extending coverage to oral, skin, respiratory, and urogenital sites. It spans six body sites and two modalities, with harmonized metadata for batch-aware modelling. Body-site signal exceeds technical/source variance in the 16S tier by approximately 2.4-fold.

Phage D29 infects Mycobacterium smegmatis mc2 155 and has a non-canonical lysis cassette that encodes two endolysin proteins (Lysin A and Lysin B) and a single two transmembrane domain (TMD) protein, LysA2a similar to F1 cluster phage LysF1a. A 1TMD LysF1b homolog, LysA2b, is encoded by a gene found downstream of the tape measure. Exogenous expression of both LysA2 proteins in tandem is a cytotoxic to M. smegmatis. Deletion of lysA2a produces phages that are lysis competent with a 10-minute triggering delay and 30% plaque size reduction. Deletion of lysA2b results in severe lysis defects manifest by 70% reduced plaque size, delayed lysis timing and reduced burst size. Deletion of both lysA2 genes results in phages that are viable and show lysis phenotypes like the lysF1b deletion. Genetic complementation of lysA2b deleted phage with the lysF1b gene fully complements the lysis phenotypes but alters the triggering time to that of an F1 cluster phage. Energy poisons trigger lysis prematurely in all phages with lysA2 gene deletions. Lysis recovery mutants (LRM) isolated from phages lacking the lysA2b genes generate wild type plaque size and have point mutations that map to TMD1 or the C-terminal region of the lysA2a gene. LRMs isolated from phages lacking both lysA2 genes show premature lysis and have mutations that all map to residue C31 of a novel lipoprotein (gene 64). Deletion of gene 64 does not change wild type D29 lysis phenotypes or rescue the lysis defects of any of the lysA2 mutants. A fitness/competition assay shows that loss of the lysA2 genes imposes a substantial competitive fitness cost. These finding support a lysis regulatory network model where the 2TMD protein is maintained in an inactive state until activated by its cognate 1TMD lysis regulator and the lipoprotein has accessory function that may enhance lysis efficiency.

Public genomic repositories can support antimicrobial resistance (AMR) surveillance, but unequal sampling can bias interpretation. We characterised AMR determinants, multicountry genomic cluster overlap and surveillance gaps across Africa using an NCBI Pathogen Detection snapshot retrieved on 24 August 2026 for 55 African Union member states. Records were validated and deduplicated by BioSample, and complete AMRFinderPlus calls were summarised across five United Nations M49 subregions and eight overlapping regional economic communities (RECs). Country-pair cluster overlap was assessed using the Jaccard index, while project-based and composition-standardised sensitivity analyses evaluated repository bias. The dataset contained 86,829 unique BioSamples from 51 states; South Africa, Malawi and Kenya contributed 55.8%. Complete extended-spectrum {beta}-lactamase calls were detected in 21,513 isolates and carbapenemase calls in 4,642. blaCTX-M-15 dominated the ESBL profile, while NDM and OXA types predominated. Seventy clusters contained carbapenemase-positive isolates from at least two countries. A shared REC covered all participating countries in 38 clusters, while 32 crossed REC boundaries. Normalised country-pair overlap was low, with a maximum Jaccard index of 9.5%. Project balancing reduced the Northern African carbapenemase estimate from 32.3% to 17.9% and the Eastern African ESBL estimate from 36.9% to 12.5%. Public repositories identify determinants and clusters for investigation but do not estimate prevalence or transmission. AMR surveillance should combine national confirmation, regional institution-led investigation where countries share an REC, and continent-wide coordination through Africa CDC for cross-REC signals, supported by representative One Health sampling, standardised metadata and sustained African sequencing capacity.

The global dissemination of antimicrobial resistance is increasingly driven by bacterial clones combining antimicrobial resistance with enhanced virulence and environmental adaptability. Escherichia coli sequence type 131 (ST131) has historically been regarded as a major disseminator of the extended-spectrum {beta}-lactamase (ESBL) blaCTX-M-15. However, the emergence of E. coli ST1193 carrying blaCTX-M-15 may represent an ongoing shift in the epidemiology of this resistance determinant. Here, we investigated the prevalence, genomic characteristics, virulence and antimicrobial resistance potential of ST1193 in comparison with ST131. A total of 1,136 E. coli isolates were recovered from touristic and non-touristic environments, hospital-associated samples, and aircraft toilets in Guadeloupe. Isolates were whole-genome sequenced and analysed for antimicrobial resistance and virulence determinants. Additionally, publicly available genomic data comprising 1,215 blaCTX-M-15-positive ST131 and ST1193 isolates were analysed to assess temporal and geographical trends. ST1193 was significantly associated with aircraft-associated samples and exhibited a higher antimicrobial resistance gene burden than ST131, while maintaining a comparable virulence factor content. Analysis of publicly available genomes revealed similar temporal emergence patterns for blaCTX-M-15-positive ST1193 and ST131, with ST1193 showing a more recent distribution and a higher number of deposited isolates in recent years, consistent with a potential ongoing clonal replacement. Comparative genomic analysis identified numerous virulence and adaptation-associated genes shared between both sequence types, while ST1193 additionally carried distinct determinants, including components of the transmissible locus of stress tolerance. Furthermore, quinolone resistance-associated mutations were strongly linked to blaCTX-M-15 carriage, particularly among ST1193 isolates. Together, these findings identify E. coli ST1193 as an emerging high-risk clone with substantial potential for blaCTX-M-15 dissemination. Its association with aircraft-associated samples further highlights the potential role of air travel in long-distance transmission and underscores the need to reconsider current surveillance strategies focused predominantly on ST131.

Background National childhood respiratory pathogen spectra are diversifying nearly everywhere - within-country diversity rose in 203 of 204 countries between 1990 and 2023 - yet whether countries are diversifying toward a common spectrum or along divergent paths is unknown. We quantified between-country compositional distance of national pathogen spectra over the same period. Methods We built national pathogen share vectors from Global Burden of Disease Study 2023 lower respiratory infection etiologic attributions (26 pathogens, 204 countries, ages 0-19 years) at five timepoints spanning 1990-2023. Between-country distance was measured as all pairwise Jensen-Shannon divergences (JSD; primary) and Bray-Curtis dissimilarities, with Baselga and Jaccard decompositions; robustness was assessed across metrics, pathogen panels, low-count thresholds and a balanced panel of 107 countries. Results Mean pairwise JSD rose from 0.0084 in 1990 to 0.0283 in 2023 (+238%; trend p = 0.030), peaking in 2021 (+283%) with a partial 2023 pullback. Bray-Curtis dissimilarity rose +120% and the balanced panel +423%. Divergence was entirely balanced variation (share reallocation), with spectrum richness rising from 18.5 to 21.1 of 26 pathogens. Dispersion rose fastest for influenza (coefficient of variation 0.03 to 0.55) and respiratory syncytial virus (0.08 to 0.48). Within-region distance rose in every computable GBD super-region (five of seven): divergence occurs within regions, not between blocs. Conclusions National spectra are re-sorting along country-specific axes as vaccine-preventable dominance recedes at different speeds. Diversification is universal, but convergence is absent: the transition at the etiologic-spectrum level is asynchronous and path-dependent, with implications for empirical treatment policy and pathogen surveillance.

Reputation plays a major role in supporting cooperation among unrelated individuals through indirect reciprocity. By helping others, individuals build a good personal reputation and receive greater benefits from future partners. Most models of indirect reciprocity assume that a person's reputation reflects only their own behaviour. Yet in many societies, people are also judged by their family's reputation. How family reputation affects the evolution of cooperation, and whether reliance on it can itself evolve, remain unclear. Here we show that reputation inheritance expands the conditions under which indirect reciprocity favours cooperation, increasing helping and favouring greater reciprocity. Greater reciprocity in turn favours stronger reliance on inherited reputation, creating a positive feedback that stabilises cooperation, especially when interactions are infrequent or personal behaviour is difficult to observe. This feedback arises because cooperation generates future benefits both for the individual, through their personal reputation, and for their descendants, through inherited reputation. Reputation inheritance thereby provides a route via which kin selection and reciprocity, often treated as alternative explanations for cooperation, can reinforce one another. Our model helps explain why family-based reputation occurs across diverse human societies and provides an evolutionary framework for studying phenomena organised around family standing, including kin-based institutions, feuds between families and honour-based violence within them.

Viral infections have long been proposed as environmental contributors to neurodegenerative diseases, including Parkinson's disease (PD), yet the molecular mechanisms linking infection and neurodegeneration are not well defined. Neuroinflammation and disruption of central nervous system (CNS) homeostasis have emerged as potential mediators. In this study, we used severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of COVID-19, as a model pathogen to investigate convergent molecular pathways between viral infection and PD. Single-nucleus RNA sequencing (snRNA-seq) was performed on post-mortem striatal tissue from 14 individuals stratified into four groups: COVID-19 only (COVID-19), PD only (PD), comorbid PD with COVID-19 (PD/COVID-19), and controls (Control). The PD/COVID-19 group exhibited an expanded astrocytic population and a pronounced interferon-associated molecular signature characterized by increased expression of canonical interferon-stimulated genes, including IFI44L (average log2FC= 3.9; adjusted p=2.3 x 10-373), IFI44 (average log2FC=2.9; adjusted p=8.0 x 10-266), ISG15 (average log2FC=3.1; adjusted p=1.2 x 10-197), and RSAD2 (average log2FC= 3.5; adjusted p=8.6 x 10-111). Pathway analyses demonstrated activation of innate immune and antiviral signaling pathways, particularly within microglia and astrocytes, including interferon signaling, pattern-recognition receptor pathways, and complement-associated responses. In parallel, genes involved in lipid metabolism, cholesterol homeostasis, synaptic maintenance, and neuronal signaling were reduced across disease groups. Proteomic analyses independently confirmed enrichment of antiviral and interferon-associated pathways and identified convergent suppression of sterol, cholesterol, and lipid metabolic processes. Our findings identify a convergent molecular signature linking PD and COVID-19, pronounced in comorbid individuals and characterized by interferon-driven innate immune activation, glial inflammatory responses, and dysregulation of lipid metabolic homeostasis. Collectively, the data support a model in which severe viral infection amplifies biological pathways already implicated in PD pathogenesis.

Motivation: With the scaling frequency and intensity of extreme heat events across the globe, it is critical for public institutions to develop early detection systems and continuous monitoring of these events and their impacts. In Brazil, Rio de Janeiro was the first city to publish its heat protocol, with the Rio Heat Dashboard as a central component of this system. Implementation: The dashboard was implemented using R/Shiny and integrates climatic and health data from multiple sources. General features: The application comprises real-time heat exposure monitoring and automatic alert level classification, which is monitored daily by multiple municipal actors and supports activation of actions specified in the heat protocol. It also features a health impact module, which lists each heat event and its impact on mortality, and primary care and emergency visits. Availability: The source for full reproducibility is available through https://github.com/joaohmorais/RioHeatDashboard.

Summary: Prot2Surf is a software tool designed for the characterization and prediction of protein association to surfaces. In this application note, Prot2Surf was tested using catalytic domains of the lytic polysaccharide monooxygenases (LPMOs), interacting with native surfaces. The results show that the software can efficiently analyze key binding features, including protein-surface distances, distances between catalytically reactive atoms, and the orientation angle between surface chains and the protein. These features are essential for distinguishing productive binding poses in these protein-surface systems and for understanding interaction patterns that provide guidance on protein engineering. Prot2Surf performs these analyses within seconds to a few minutes, providing a fast and accessible framework to post-process and characterize protein-surface encounter complexes. Availability and implementation: Prot2Surf, which is written in Fortran90, is documented and freely available as open source on GitHub: https://github.com/TUNNELING-GROUP/Prot2Surf. In order to run Prot2Surf, users should also install the SDA software package which is freely available at https://www.h-its.org/downloads/sda7/.

Antimicrobial resistance is a public health challenge, driving the need for rapid, cost-effective diagnostic support tools. Artificial intelligence (AI) may enable prediction of susceptibility to untested antibiotics from known susceptibility results, but prospective clinical validation is required before routine use. We evaluated an AI-based decision support method, trained on invasive isolates from the European Surveillance System (TESSy), for prediction of antibiotic susceptibility in clinical Escherichia coli urine isolates. The evaluation included 99 E. coli isolates from urine samples with diversity in age, sex, and antibiotic susceptibility. Predictions were evaluated for 14 antibiotics using patient metadata and susceptibility results for 4-8 antibiotics as input. Prediction uncertainty was handled using conformal prediction, allowing abstention when confidence was insufficient. EUCAST disk diffusion test results were used as reference and genomic sequence data was used to explore mechanisms of the AI performance. Without conformal prediction, 84% of predictions were correct when susceptibility results of six antibiotics were used to predict susceptibility to eight additional antibiotics. Across all predictions generated using susceptibility results for six antibiotics as input, the major and very major error rates were 19% and 12%, respectively. Prediction errors varied between antibiotics and were associated with certain phenotypic and genotypic resistance patterns. Conformal prediction reduced errors but increased abstentions; at confidence levels of 90%, 95%, and 97.5%, the model abstained in 9.6%, 14%, and 22% of instances. The method showed promising performance, but its clinical use remains limited and may require diagnostic data beyond susceptibility test results and demographic variables.

Background Using a population-based cohort from the Calgary Health Zone (CHZ), Canada, we integrated longitudinal antimicrobial susceptibility and prescribing data with the whole genome sequences of five major pathogens. We aimed to assess how antimicrobial resistance (AMR) responds to prescribing changes and determine which bacterial strains shape these dynamics. Methods We analysed antibiotic prescribing rates, clinical and genomic data from 7,271 Staphylococcus aureus, 1,609 Enterococcus faecalis, 801 Enterococcus faecium, 11,363 Escherichia coli, and 2,319 Klebsiella pneumoniae isolates, associated with bacteraemia episodes in the CHZ between 2006-2022. Genomic clusters (referred to as strains) were identified using StrainGST and assigned to known sequence types (STs) or clonal complexes (CCs). Strain-level incidence, stratified by community-onset (isolates collected [&le;]48h after admission) and hospital-onset (>48h after admission), AMR phenotypes, and prescribing rates were modelled using negative-binomial and binomial regression. Temporal trends were quantified using average annual percentage change (AAPC). Findings Between 2010-2022, fluoroquinolone prescribing declined in both community (AAPC=-6.8% [95% CI -8.1, -5.4]; p<0.0001) and hospital settings (AAPC=-5.1% [-6.5, -3.7]; p<0.0001). This was accompanied by a significant reduction in fluoroquinolone resistance among Gram-positive species. Specifically, S aureus bacteraemia resistant to clinically important antibiotics, cloxacillin, ciprofloxacin, erythromycin, and clindamycin, declined from 2006 to 2022, mostly in hospital-onset cases (AAPC=-16.0%, [-19.3%, -12.7%], p<0.0001). In E coli, ceftriaxone and ciprofloxacin resistance were clustered in ST131 and the emerging ST1193; the latter increased steadily, particularly in community-onset cases (AAPC=17.7%, [0.0%, 30.0%], p<0.0001). CTX-M-27-producing E coli ST131 strains increased (AAPC=23.8%, [17.4%, 30.5%], p<0.0001) between 20082022, while CTX-M-14-producing E coli ST131 declined (AAPC=-15.9%, [-21.3%, -10.2%], p<0.0001) between 2013-2022. These trends were paralleled by an increase in community cephalosporin prescribing (AAPC=7.3%, [4.2%, 10.5%], p<0.0001) between 2010-2022. For K pneumoniae, hypervirulent ST23 was most common (N=88) with an increasing trend in incidence (AAPC=3.0%, [-2.8%, 9.2%]) between 2006-2019. Conclusions The contrasting resistance trends between Gram-positive and Gram-negative species underscore the complexity of AMR control efforts. Effective strategies will require stewardship efforts targeting multiple drug classes, genomic surveillance for emerging resistant strains, and interventions extending beyond hospital settings.

Microorganisms across all domains of life engage in molecular conflict, deploying toxins to inhibit competitors or respond to biological threats. Among these, ribonuclease toxins are particularly widespread and diverse. A substantial fraction is associated with the BECR fold, a compact /{beta} architecture that supports RNase activity despite extensive divergence. Although several canonical members are well characterized, many BECR-fold proteins remain difficult to identify because of low sequence similarity, variation in catalytic residues, and structural elaborations that obscure evolutionary relationships. The growing availability of high-confidence protein structure predictions provides an opportunity to reassess this deeply divergent protein landscape. Here, we integrate iterative profile-HMM searches, profile-similarity networks, structural analyses, active-site mapping, and genomic context to examine BECR proteins across the tree of life. Our analysis resolves an expanded BECR-fold landscape comprising canonical BECR and BECR-like superfamilies, refines the organization of canonical BECR proteins and identifies previously unrecognized families. We further validate BECR-Tox2 as a toxin neutralized by a cognate immunity protein and show that its homologs occur in both Menshen-like anti-phage systems and polymorphic toxin loci. Together, these findings expand and clarify the BECR-fold landscape and provide a framework for identifying and interpreting highly divergent proteins of this fold.

Background & Aims: Fatigue is a central symptom of chronic liver disease (CLD), substantially impacting health-related quality of life (HRQoL). This study aimed to further understand CLD symptomatology, including fatigue, and its impact on HRQoL from a patient perspective. Methods: Abbott Global Assessment of Patients unmet needs (aGAP) was a multinational, cross-sectional survey in adults with compensated CLD in China, India and Mexico, conducted between July and November 2024. Adult participants who self-reported that they had physician-diagnosed CLD and were experiencing fatigue completed a quantitative survey to assess symptom burden and included three HRQoL patient-reported outcome (PRO) questionnaires (Patient-Reported Outcomes Measurement Information System [PROMIS]-29+2, Work Productivity and Activity Impairment - Specific Health Problem version 2.0 [WPAI: SHP], Multidimensional Fatigue Inventory [MFI]). Results: Overall, 505 participants (China: 200; Mexico: 105; India: 200) completed the study. Participants reported that their CLD-related fatigue sometimes, often or always affected their self-esteem/confidence (45.1%) and ability to maintain or acquire new employment (38.6%). Most participants reported moderate (51.3%) or serious (26.9%) fatigue, with 33.5% experiencing fatigue every day or almost every day. Many participants felt their social life was negatively impacted by their fatigue (47.3%) and that there were related financial difficulties (53.9%). Use of validated PRO tools demonstrated severe fatigue (MFI: overall mean [SD] 13.9 [3.4] general fatigue and 13.4 [3.6] physical fatigue) as well as substantial levels of work and activity impairment (WPAI: SHP overall mean [SD] 53.0 [26.4]) and high levels of anxiety, pain interference, depression and sleep interference (PROMIS T-scores [&ge;]54). Conclusions: Fatigue has a substantial impact on HRQoL among adults with CLD across several countries, highlighting a global unmet need for targeted interventions to effectively identify and manage the condition.

Post-acute infection syndromes (PAIS) follow viral syndromes including post-acute sequelae of COVID19 (PASC) which complicates 10-25% of SARS-CoV-2 infections. These syndromes lack precise explanatory mechanisms. We studied 173 human saliva proteomes during respiratory viral syndromes, seeking associations between 44 clinically-relevant protein expression patterns and subsequent sequelae counts. Exploratory models adjusted by extensive clinical annotations found interactions between 23 acutely-responsive proteins and SARS-CoV-2 infection that inversely predicted subsequent neurocognitive sequelae. An overlapping 19 acutely-responsive proteins during any acute respiratory viral syndrome inversely predicted general fatigue-related sequelae. Altogether, 29 proteins, derived from interferon stimulated genes (ISG), were uniformly beneficial, including 13 predictive of both neurocognitive and general sequelae. The proteins suggested both shared early pathobiology and virus-specific protective responses that shaped resolution of acute disease and different PAIS. Acutely elevated protective ISG proteins associated with reduced post-viral symptoms identify investigational starting points for novel mechanisms, diagnostics and therapeutics for PASC and PAIS.