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A functional genomic framework to elucidate novel causal metabolic dysfunction-associated fatty liver disease genes.

BACKGROUND AND AIMS: Metabolic dysfunction-associated fatty liver disease (MASLD) is the most prevalent chronic liver pathology in western countries, with serious public health consequences. Efforts to identify causal genes for MASLD have been hampered by the relative paucity of human data from gold standard magnetic resonance quantification of hepatic fat. To overcome insufficient sample size, genome-wide association studies using MASLD surrogate phenotypes have been used, but only a small number of loci have been identified to date. In this study, we combined genome-wide association studies of MASLD composite surrogate phenotypes with genetic colocalization studies followed by functional in vitro screens to identify bona fide causal genes for MASLD. APPROACH AND RESULTS: We used the UK Biobank to explore the associations of our novel MASLD score, and genetic colocalization to prioritize putative causal genes for in vitro validation. We created a functional genomic framework to study MASLD genes in vitro using CRISPRi. Our data identify VKORC1 , TNKS , LYPLAL1 , and GPAM as regulators of lipid accumulation in hepatocytes and suggest the involvement of VKORC1 in the lipid storage related to the development of MASLD. CONCLUSIONS: Complementary genetic and genomic approaches are useful for the identification of MASLD genes. Our data supports VKORC1 as a bona fide MASLD gene. We have established a functional genomic framework to study at scale putative novel MASLD genes from human genetic association studies.

Humans

The relationship between vitamin D levels and depression: a genetically informed study.

BACKGROUND: Low vitamin D (vitD) levels are consistently associated with an increased risk of depression. However, the biological mechanisms underlying this relationship and potential shared genetic overlap remain elusive. METHODS: We investigated the genetic overlap and causal relationships between depression (N = 589,356) and vitD levels (N = 417,580) using genome-wide association study (GWAS) summary statistics. We performed genome-wide and local genetic correlation analyses, followed by quantification of polygenic overlap variants. Shared genetic loci were identified and mapped to genes, which were further analyzed through gene expression and lifespan brain expression trajectory analyses. Bidirectional causal relationships were examined using multiple Mendelian randomization approaches. RESULTS: We observed significant negative genetic correlations (rg = -0.079) and identified genetic overlap (N = 410 variants). Genes mapped to the 13 shared loci showed opposing expression patterns. Tissue- and cell-specific functional enrichment analyses revealed significant signals related to brain development, with distinct patterns emerging between fetal development and adulthood. Shared genes (TRMT61A, ITIH4, RASGRP1, CTNND1, HERC1, IP6K1, FURIN ESR1, ZMYND and GRM5) exhibited notable expression variation in the brian throughout the lifespan, aligning with functional enrichment findings. CONCLUSIONS: Our findings elucidate the shared biological mechanisms underlying the relationship between vitD and depression, suggesting that vitD play an important role in the development of depression through altered early neurodevelopmental processes.

Humans

A custom library construction method for super-resolution ribosome profiling in Arabidopsis.

BACKGROUND: Ribosome profiling, also known as Ribo-seq, is a powerful technique to study genome-wide mRNA translation. It reveals the precise positions and quantification of ribosomes on mRNAs through deep sequencing of ribosome footprints. We previously optimized the resolution of this technique in plants. However, several key reagents in our original method have been discontinued, and thus, there is an urgent need to establish an alternative protocol. RESULTS: Here we describe a step-by-step protocol that combines our optimized ribosome footprinting in plants with available custom library construction methods established in yeast and bacteria. We tested this protocol in 7-day-old Arabidopsis seedlings and evaluated the quality of the sequencing data regarding ribosome footprint length, mapped genomic features, and the periodic properties corresponding to actively translating ribosomes through open resource bioinformatic tools. We successfully generated high-quality Ribo-seq data comparable with our original method. CONCLUSIONS: We established a custom library construction method for super-resolution Ribo-seq in Arabidopsis. The experimental protocol and bioinformatic pipeline should be readily applicable to other plant tissues and species.

3-nt periodicity

Development and Validation of Amplicon-Based Protocol for Sequencing of Respiratory Syncytial Virus Genome.

The most prevalent cause of severe respiratory infections in children is the human respiratory syncytial virus (RSV). The advent of next-generation sequencing (NGS) has made it possible to incorporate this technology into pathogen monitoring and surveillance. Whole-genome sequencing (WGS) of RSV has now become a relatively widely used method for tracking viral evolution. Here we report an improved high-throughput RSV-WGS assay performed directly on clinical samples that is suitable for short-read sequencing platforms. A total of 100 RSV-positive samples collected between November 2022 and March 2024 fulfilled the inclusion cycle quantification criteria and were randomly included in the validation process. The WGS protocol was designed to amplify three distinct amplicons to cover the entire RSV genome. The protocol described here can be successfully replicated in several instances (approximately 95%) in samples with a relatively low viral load, typically corresponding to cycle of quantification values of 27-32. The amplicon-based protocol produced meaningful sequencing results in terms of median depth of coverage (more than 12000×) and median of mapped reads (> 1 × 106 reads). The sequences that had passed the filters showed a coverage of at least 98% across the entire genome, with cycle quantification values of 32. Based on the obtained data resulting in an easy-to-perform protocol helpful for the molecular epidemiology surveillance of RSV.

Humans

DNA methylation profiles of quail blood cells by whole-genome bisulfite and Oxford Nanopore sequencing.

Whole Genome Bisulfite Sequencing (WGBS) has been the gold standard DNA methylation mapping and quantification for over a decade. Oxford Nanopore Technologies (ONT) sequencing directly measures nucleotide modifications. In this study, we have compared DNA methylation levels (5-methylcytosine) at CpG sites in the quail genome using WGBS and ONT. Samples were collected to investigate transgenerational DNA methylation changes in Japanese quail following ancestral exposure to a phytoestrogen. Blood samples from 24 third-generation (G3) individuals-descendants of either treated or untreated ancestors-were sequenced after bisulfite conversion. Both methods revealed broadly consistent methylation patterns. ONT reads covered more CpG sites and detected a higher number of differentially methylated cytosines (DMCs). Principal component analyses showed that both sex and ancestral treatment groups accounted for a portion of the observed epigenetic variation, for both technologies. Strong concordance between WGBS and ONT results supports the reliability of ONT sequencing for epigenomic research, including in quails. These data pave the way for further investigation into whether genistein induces epigenetic changes for several generations.

Animals

scnanoseq: an nf-core pipeline for Oxford Nanopore single-cell RNA-sequencing.

MOTIVATION: Recent advancements in long-read single-cell RNA sequencing (scRNA-seq) have facilitated the quantification of full-length transcripts and isoforms at the single-cell level. Historically, long-read data would need to be complemented with short-read single-cell data in order to overcome the higher sequencing errors to correctly identify cellular barcodes and unique molecular identifiers. Improvements in Oxford Nanopore sequencing, and development of novel computational methods have removed this requirement. Though these methods now exist, the limited availability of modular and portable workflows remains a challenge. RESULTS: Here, we present, nf-core/scnanoseq, a secondary analysis pipeline for long-read single-cell and single-nuclei RNA that delivers gene and transcript-level quantification. The scnanoseq pipeline is implemented using Nextflow and is built upon the nf-core framework, enabling portability across computational environments, scalability and reproducibility of results across pipeline runs. The nf-core/scnanoseq workflow follows best practices for analyzing single-cell and single-nuclei data, performing barcode detection and correction, genome and transcriptome read alignment, unique molecular identifier deduplication, gene and transcript quantification, and extensive quality control reporting. AVAILABILITY AND IMPLEMENTATION: The source code, and detailed documentation are freely available at https://github.com/nf-core/scnanoseq and https://nf-co.re/scnanoseq under the MIT License. Documentation for the version of nf-core/scnanoseq used for this paper, including default parameters and descriptions of output files are available at https://nf-co.re/scnanoseq/1.1.0.

Single-Cell Analysis

Global disparities in COVID-19 vaccine coverage associated with trajectories of SARS-CoV-2 adaptation.

BACKGROUND: Vaccination serves as an effective intervention for health promotion and disease prevention across the socioecological systems and has played an important role during the COVID-19 pandemic. However, global disparities in vaccine coverage have increased uncertainty about the trajectories of viral adaptation, and the potential interplay between SARS-CoV-2 adaptation and vaccine rollout warrants further quantification. METHODS: Using over 13 million SARS-CoV-2 genomes across 86 countries from March 2020 to September 2022, we analyzed nonlinear associations between SARS-CoV-2 adaptation and vaccination coverage, considering public health and social measures, international travel, and infection dynamics, before and after the emergence of Omicron. Additionally, we examined the relationship between SARS-CoV-2 adaptation and COVID-19 mortality. RESULTS: During the pre-Omicron period, we found positive associations between nonsynonymous to synonymous divergence (dN/dS) ratios in the S1 subunit and medium levels of adjusted vaccine coverage (effect size: 0.96 [95% CI 0.47, 1.45]), while the association became insignificant at high levels (effect size: -1.89 [95% CI -4.20, 0.43]). However, no significant associations were found when Omicron dominated, possibly due to the immune escape ability of Omicron variants and the complex immune landscape shaped by mass hybrid immunity. Moreover, we observed evidence of dynamic interdependence and positive correlations between COVID-19 mortality and SARS-CoV-2 adaptation, with COVID-19 mortality interpreted as a proxy for uncontrolled viral spread. CONCLUSIONS: Our findings suggest a complex nonlinear relationship between vaccine-induced immunity and SARS-CoV-2 adaptation, with high vaccine coverage potentially linked to lower positive selection. We also observed directional coupling between COVID-19 mortality and SARS-CoV-2 adaptation. This may have implications for fair and fast vaccination in pandemic preparedness and response. CLINICAL TRIAL NUMBER: Not applicable.

Humans

DeepGeSeq: deep learning library for genomic sequence modeling and analysis.

MOTIVATION: Deep learning methods have demonstrated significant potential in genomics, enabling broad applications such as sequence activity prediction, regulatory rule identification, and variant effect quantification. However, their widespread adoption is often hindered by the steep computational learning curve required for model construction, training, and downstream biological interpretation. Here, we introduce DeepGeSeq, a user-friendly Deep-learning library tailored for Genomic Sequence modeling and analysis. RESULTS: By integrating state-of-the-art architectural modules, DeepGeSeq streamlines the entire deep learning workflow, requiring minimal user input via a simple configuration file and an intuitive agentic skill. We comprehensively validate the efficacy of DeepGeSeq through diverse case studies, encompassing pipeline verification using synthetic datasets, the reproduction and application of established models, and model fine-tuning coupled with biological interpretation on user-defined data. Furthermore, we demonstrate DeepGeSeq's versatility in domain-specific applications, including single-cell ATAC-seq modeling for cell-type clustering, and MPRA data modeling coupled with in silico saturation mutagenesis to dissect cis-regulatory elements. Ultimately, DeepGeSeq bridges the gap between computational complexity and biological discovery, providing an accessible resource that facilitates the development and broad application of deep learning methods in genomics research. AVAILABILITY AND IMPLEMENTATION: https://github.com/JiaqiLi1024/DeepGeSeq.

Deep Learning

ChromCall: assigning chromatin status to defined genomic regions using epigenomic profiling data.

MOTIVATION: Chromatin regulation is crucial for modulating gene expression and cellular function by altering DNA accessibility. Defining and understanding chromatin regulation across diverse biological conditions, including health and disease, requires quantification of both the presence and enrichment level of diverse DNA-binding factors and chromatin modifications across defined genomic regions. Existing approaches mainly rely on peak-based or genome-wide models, which identify high-signal regions but do not annotate chromatin status at predefined functional genomic regions, such as promoters or enhancers. This lack of region-based annotation limits downstream comparative and integrative analyses across multiple factors and datasets, prompting us to create ChromCall. RESULTS: ChromCall is an R package for region-based chromatin enrichment analysis that provides a robust and extensible foundation for transparent and reproducible epigenomic profiling at predefined genomic regions. We applied ChromCall to ChIP-seq data from glioblastoma (GBM) brain tumours and found that the promoters of genes implicated in treatment resistance are significantly more likely to exhibit a combination of histone marks associated with phenotypic plasticity. This highlights a potential novel mechanism of therapeutic escape in these deadly tumours. AVAILABILITY AND IMPLEMENTATION: The R package is available on https://github.com/GliomaGenomics/ChromCall and the version used in this paper is archived at https://doi.org/10.5281/zenodo.19580967.

Chromatin

DNA Methylation Analysis by Bisulfite Pyrosequencing of Mouse Embryonic Fibroblasts with Reprogramming Enhanced by Thyroid Hormones.

DNA methylation is a widely studied epigenetic mark which in mammals involves the incorporation of a methyl group to the fifth carbon of cytosines, mainly those belonging to CpG dinucleotides. It has been linked to context-dependent regulatory functions ranging from gene and repetitive DNA silencing to gene body transcriptional activity. Because of its important roles during embryonic development and cell differentiation, DNA methylation can be used to track cell reprogramming by measuring the methylation levels of pluripotency-associated factors. In this scenario, bisulfite pyrosequencing is a simple, robust, and widely used technique which allows for the quantification of DNA methylation levels at small, specific regions of the genome. It involves the amplification and biotin tagging of bisulfite-converted DNA. Single amplified strands are then purified using streptavidin and finally pyrosequenced using a sequencing primer. Thus, it is an ideal method for the quantitative profiling of specific genomic regions, with applications ranging from biomarker discovery and epigenetic clock tracking to omic validation studies.

Animals

Association between NAFLD and liver cancer: A two-sample Mendelian randomization study.

Observational studies suggest an association between nonalcoholic fatty liver disease (NAFLD) and liver cancer, but its causal nature remains unclear. A 2-sample Mendelian randomization (MR) analysis was performed using NAFLD and liver cancer summary statistics from genome-wide association study databases. Instrumental variables satisfying the 3 core MR assumptions were selected. Causal effects were estimated using inverse-variance weighted, MR-Egger, weighted median, and other methods, followed by sensitivity and power analyses. All 4 MR analyses demonstrated a positive causal association between NAFLD and liver cancer risk [odds ratio&#x2005;>&#x2005;1, inverse-variance weighted P&#x2005;<&#x2005;.001]. Sensitivity analysis indicated no significant level of multiplicity or heterogeneity in the instrumental variables, and individual single nucleotide polymorphisms had no significant impact on the results. However, statistical power was insufficient. This study provides the first MR evidence demonstrating a genetically predicted causal relationship between NAFLD and liver cancer that is consistent across subtypes. Sensitivity analyses confirmed the absence of horizontal pleiotropy or heterogeneity, strengthening the robustness of the findings. These results offer genetic support for early NAFLD intervention to reduce the risk of liver cancer. However, the limited statistical power highlights the need for larger-scale genome-wide association study to identify more and stronger genetic instruments for a more precise quantification of the causal effect of NAFLD on liver cancer risk.

Humans

Biomarkers of metastatic disease in pheochromocytoma and paraganglioma.

Pheochromocytomas and paragangliomas (PPGLs) are rare neuroendocrine tumors with variable metastatic potential. While metastatic disease occurs in approximately 10-20% of cases, its prediction remains a major clinical challenge, as no histological system has been universally validated to reliably identify aggressive tumors at diagnosis. This review aims to provide a comprehensive and updated overview of current and emerging biomarkers of metastatic risk in PPGL, encompassing histopathological scoring systems, genetic and molecular markers, biochemical phenotyping, liquid biopsy approaches, and imaging-based biomarkers. Among established markers, germline SDHB mutation status, loss of SDHB expression by immunohistochemistry, elevated plasma 3-methoxytyramine, and histopathological scoring systems, such as GAPP and COPPS, represent the most clinically validated tools for risk stratification. Emerging biomarkers - including somatic alterations in ATRX and TERT, genomic instability indices, tumor immune microenvironment characterization, circulating tumor DNA, and oncometabolite quantification - show promise in refining prognostic assessment but require prospective validation before routine clinical implementation. Accurate risk stratification in PPGL demands a multiparametric and dynamic approach, integrating clinical, genetic, biochemical, and molecular parameters. Future progress will depend on large prospective international cohorts, standardized biomarker platforms, and biomarker-driven clinical trial designs to translate emerging molecular knowledge into improved patient outcomes.

SDHB

LCR-modules: a collection of workflows for cancer genome analysis.

MOTIVATION: The surge of genomic data from advanced sequencing technologies is outpacing current analytical pipelines. We introduce LCR-modules, an open-source suite of bioinformatics tools designed for flexible and automated cancer genome data analysis. LCR-modules enables reproducible analysis of diverse cancer genomics data at scale. The suite comprises 49 Snakemake-based workflows organized into three levels, facilitating tasks from low-level quality control to complex cohort-level analyses. LCR-modules supports various sequencing types and integrates pipelines such as mutation calling, expression quantification, and cohort-level aggregation, ensuring flexibility and reproducibility. LCR-modules represents a significant advancement in genomic data analysis, reducing barriers in reproducibility and scalability and has already been applied to a combination of exomes and genomes from over 10&#x2009;800 samples. AVAILABILITY: No new data were generated in support of this research. The source code for the LCR-modules is openly available at https://github.com/LCR-BCCRC/lcr-modules.

Software

Dual &#x3b2;-lactam therapy against high-risk Pseudomonas aeruginosa isolates: a dynamic in-vitro infection model study integrating population genomics with quantitative systems pharmacology modelling and simulations.

BACKGROUND: Pseudomonas aeruginosa has an extraordinary capacity for resistance emergence during treatment, even with newer antipseudomonals. There is a gap in understanding how resistance mechanisms affect the time-course of bacterial response to these newer agents. Traditional approaches for predicting pathogen response to an antibiotic do not apply to combination therapy. We aimed to develop a modelling framework to predict treatment response based on resistome information, using isolates of the worldwide-disseminated high-risk clone sequence type (ST) 235 and &#x3b2;-lactam antibiotics as the example. METHODS: In this hollow-fibre in-vitro infection study, we used three extensively drug-resistant ST235 clinical isolates from the national collection of the Clinical Microbiology Department of the Hospital Son Espases (Palma de Mallorca, Spain) that were hospital-acquired, were isolated following routine microbiological procedures from different patients between 2017 and 2022, were susceptible to ceftolozane-tazobactam, and had different levels of meropenem resistance. The selected isolates (ST235-05, ST235-09, and ST235-10) showed classical &#x3b2;-lactam resistance mechanisms pre-treatment. The isolates were investigated in 240-h dynamic hollow-fibre in-vitro infection models (HFIMs). The studies exposed the isolates to pharmacokinetic profiles of ceftolozane-tazobactam (simulating 1 g of ceftolozane and 0&#xb7;5 g of tazobactam as a 3-h infusion every 8 h) and meropenem (simulating 6 g per day continuous infusion) as observed in hospitalised patients, as monotherapy and in combination. Treatment response was assessed through the quantification of the time-courses of viable total and resistant bacteria. Whole-genome sequencing identified the mechanisms of emerging resistance. A quantitative systems pharmacology (QSP) approach was used to model total and resistant bacterial counts and corresponding pharmacokinetic data from the HFIM. Monte Carlo simulations were used to predict treatment responses in 1000 virtual infected patients treated with ceftolozane-tazobactam and meropenem as monotherapies or in combination over 10 days. FINDINGS: In the HFIMs, each antibiotic alone amplified resistance by approximately 48 h for all isolates; that is, monotherapies resulted in a higher concentration of resistant bacteria compared with the control treatment at the respective time, except ceftolozane-tazobactam against ST235-10. Combination of ceftolozane-tazobactam and meropenem was synergistic (bacterial counts &#x2265;2 log10 colony forming units [CFU] per mL lower than the best performing monotherapy and initial inoculum) against all isolates and suppressed resistance. Against ST235-10, ceftolozane-tazobactam monotherapy reduced counts to less than 1 log10 CFU per mL from 192 h onwards, whereas the combination reached less than 1 log10 CFU per mL by 24 h. Across strains, population genomics confirmed monotherapy failures were associated with emerging resistance mechanisms (ceftolozane-tazobactam: ampC &#x3a9;-loop mutations; meropenem: ftsl mutation). The developed QSP model incorporated baseline resistance mechanisms and those emerging in resistant mutant subpopulations. The model explained and predicted the monotherapy failures involving amplification of these subpopulations, and synergistic killing and resistance suppression by the combination. Simulations using the model predicted bacterial regrowth above the initial inoculum for more than 90% of patients after 0 to approximately 3 days for meropenem monotherapy across all strains and for ceftolozane-tazobactam monotherapy against ST235-05 and ST235-09. For ceftolozane-tazobactam monotherapy against ST235-10, regrowth was predicted for approximately 30% of patients. In contrast, the simulations predicted sustained bacterial killing of at least 2 log10 CFU per mL compared with the initial inoculum by the combination for more than 89% of patients across all strains. INTERPRETATION: To our knowledge, this model is the first to characterise and predict the time-course of responses of clinical isolates to antibiotics only by the resistance mechanisms present and their complex interplay, representing a step towards pathogen-specific, personalised medicine. FUNDING: Australian National Health and Medical Research Council.

Pseudomonas aeruginosa

Remodeling of host lipid metabolism by Wolbachia strain wAlbB is associated with lipid accumulation and cardiolipin dysregulation in the Aedes aegypti fat body.

BACKGROUND: The intracellular symbiont Wolbachia, particularly the wAlbB strain, is a promising biocontrol agent against mosquito-borne diseases. Although Wolbachia infection is known to perturb host metabolism, the underlying mechanisms, especially those related to lipid metabolism, remain poorly understood. METHODS: We performed an integrated multi-level analysis of the Aedes aegypti fat body in uninfected and wAlbB-infected mosquitoes, combining histology, biochemistry, untargeted liquid chromatography-mass spectrometry (LC-MS) lipidomics, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analysis, reverse transcription quantitative PCR of key metabolic genes, and quantification of acetyl-coenzyme A (acetyl-CoA) and reduced nicotinamide adenine dinucleotide (NADH) levels. RESULTS: wAlbB infection increased fat body wet weight and thickness, accompanied by accumulation of triglyceride and of lipid droplets. Lipidomic analysis further revealed extensive lipidome remodeling, with elevated free fatty acid, diglyceride, and triglyceride, but broad depletion of glycerophospholipids, particularly cardiolipin. These changes were supported by transcriptional alterations: upregulation of fatty acid synthase 1 and glycerol-3-phosphate acyltransferase 1, and downregulation of adipose triglyceride lipase and carnitine palmitoyltransferase 1. Cardiolipin depletion correlated with downregulation of genes involved in its synthesis and remodeling, including phosphatidylglycerophosphate synthase and calcium-independent phospholipase A2&#x3b3;. These lipid changes were also associated with accumulation of acetyl-CoA and NADH. CONCLUSIONS: Our findings suggest that wAlbB infection is associated with extensive lipid metabolic remodeling in the Aedes aegypti fat body, characterized by accumulation of neutral lipids and cardiolipin depletion, accompanied by transcriptional remodeling of key metabolic enzymes. This study establishes the fat body as a primary tissue-level hub for Wolbachia-associated lipid remodeling and provides a foundational framework for future mechanistic investigations into host-symbiont metabolic interactions.

Animals

Genomic Characterization of Antimicrobial Resistance and Virulence in ST11 Carbapenem-Resistant Klebsiella Pneumoniae Colonizing the Intestinal Tract of Elderly Inpatients.

BACKGROUND: This study aimed to elucidate the molecular epidemiology and virulence characteristics of ST11 carbapenem-resistant Klebsiella pneumoniae (CRKP) colonizing the intestinal tract of elderly inpatients in the Chongzhou region, providing a basis for controlling the transmission of such resistant bacteria in high-risk populations. METHODS: CRKP strains isolated from the intestines of elderly inpatients in this region between January 2023 and June 2024 were collected. ST11 strains were identified via multilocus sequence typing (MLST). Whole-genome sequencing, antimicrobial susceptibility testing, and string test, serum killing, biofilm formation, capsular polysaccharide quantification were employed to characterize their resistance genes, virulence genes, and molecular typing profiles. RESULTS: Among 58 CRKP isolates, 17 (29.3%) were ST11. ST11-KL64 was the dominant clone (70.6%). All isolates carried the carbapenemase gene bla KPC-2 and exhibited extensive drug resistance, with tigecycline retaining the highest susceptibility (64.7%). The yersiniabactin system genes (ybtS, fyuA, entB) were universally present, whereas the aerobactin gene cluster (iucABCD-iutA) was detected in only 17.6% of isolates. The virulence regulator rmpA2 was incomplete in all carriers. The hypermucoviscosity phenotype was observed in 35.3% of isolates, which correlated with serum resistance in some strains. Biofilm formation was variable. The mortality rate among colonized patients was 35.3%. CONCLUSION: The ST11-KL64 clone is dominant among CRKP strains colonizing the intestinal tract of elderly patients in this region. This clone universally carries the&#xa0;bla KPC-2&#xa0;gene conferring carbapenem resistance and exhibits a unique virulence gene profile characterized by a low carriage rate of classical hypervirulence markers and an incomplete&#xa0;rmpA2&#xa0;regulator gene. This finding clarifies the local epidemic status of this clone and underscores the importance of implementing active surveillance and targeted prevention strategies for high-risk populations.

KL64 serotype

Engineering bubble structures as Cas12a activators for highly sensitive monitoring of WRN helicase function.

The Werner syndrome helicase (WRN) is a critical synthetic lethal target in microsatellite instability cancers, essential for resolving complex genomic structures like replication bubbles and R-loops. However, strategies to simultaneously discriminate WRN activity on DNA versus DNA-RNA substrates in living cells are lacking. Here, we developed a structure-specific CRISPR/Cas12a biosensing strategy to visualize WRN functional activity by engineering bubble-structure probes. These probes were rationally designed to structurally mimic DNA replication bubbles and R-loop associated DNA-RNA hybrids. Upon specific unwinding by WRN, the probes release a sequestered activator strand that triggers Cas12a trans-cleavage, effectively converting the unwinding event into an amplified fluorescent signal. This assay achieves low picomolar sensitivity (LODs: 5.6-6.0 pM) and exceptional selectivity against homologous RecQ helicases. Uniquely, this strategy enables the parallel quantification of WRN activity on both substrate types, providing insights into distinct WRN-mediated pathways for resolving genomic stress. We further demonstrated the strategy's utility by visualizing endogenous WRN dynamics in living cells and profiling the efficacy of small-molecule inhibitors. This work offers a powerful molecular toolkit for dissecting WRN biology and facilitating high-throughput drug screening in targeted cancer therapy.

Werner Syndrome Helicase

Comprehensive quantitative modeling of translation efficiency in a genome-reduced bacterium.

Translation efficiency has been mainly studied by ribosome profiling, which only provides an incomplete picture of translation kinetics. Here, we integrated the absolute quantifications of tRNAs, mRNAs, RNA half-lives, proteins, and protein half-lives with ribosome densities and derived the initiation and elongation rates for 475 genes (67% of all genes), 73 with high precision, in the bacterium Mycoplasma pneumoniae (Mpn). We found that, although the initiation rate varied over 160-fold among genes, most of the known factors had little impact on translation efficiency. Local codon elongation rates could not be fully explained by the adaptation to tRNA abundances, which varied over 100-fold among tRNA isoacceptors. We provide a comprehensive quantitative view of translation efficiency, which suggests the existence of unidentified mechanisms of translational regulation in Mpn.

RNA, Transfer