Molecular Classes Research Articles

Coupling Isotopic Shifts with Collision Cross-Section Measurements for Carbohydrate Characterization in High-Resolution Ion Mobility Separations.

Herein, we introduce a two-dimensional strategy to better characterize carbohydrate isomers. In a single experiment, we can derive cyclic ion mobility-mass spectrometry (cIMS-MS)-based collision cross-section (CCS) values in conjunction with measuring isotopic shifts through the relative arrival times of light and heavy isotopologues. These isotopic shifts were introduced by permethylating carbohydrates with either light, CH3, or heavy, CD3, labels at every available hydroxyl group to generate a light/heavy pair of isotopologues for every individual species analyzed. We observed that our calculated CCS values, which were exclusively measured for the light isotopologues, were orthogonal to our measured isotopic shifts (i.e., relative arrival time values between heavy and light permethylated isotopologues). Our permethylation-induced isotopic shifts scaled well with increasing molecular weight, up to ∼m/z 1300, expanding the analysis of isotopic shifts to molecules 3-4 times as large as those previously studied. Our presented use of coupling CCS values with the measurement of isotopic shifts in a single cIMS-MS experiment is a proof-of-concept demonstration that our two-dimensional approach can improve the characterization of challenging isomeric carbohydrates. We envision that our presented 2D approach will have broad utility for varying molecular classes as well as being amenable to many forms of derivatization.

A novel molecular class that recruits HDAC/MECP2 complexes to PU.1 motifs reduces neuroinflammation.

Pervasive neuroinflammation occurs in many neurodegenerative diseases, including Alzheimer's disease (AD). SPI1/PU.1 is a transcription factor located at a genome-wide significant AD-risk locus and its reduced expression is associated with delayed onset of AD. We analyzed single-cell transcriptomic datasets from microglia of human AD patients and found an enrichment of PU.1-binding motifs in the differentially expressed genes. In hippocampal tissues from transgenic mice with neurodegeneration, we found vastly increased genomic PU.1 binding. We then screened for PU.1 inhibitors using a PU.1 reporter cell line and discovered A11, a molecule with anti-inflammatory efficacy and nanomolar potency. A11 regulated genes putatively by recruiting a repressive complex containing MECP2, HDAC1, SIN3A, and DNMT3A to PU.1 motifs, thus representing a novel mechanism and class of molecules. In mouse models of AD, A11 ameliorated neuroinflammation, loss of neuronal integrity, AD pathology, and improved cognitive performance. This study uncovers a novel class of anti-inflammatory molecules with therapeutic potential for neurodegenerative disorders.

A Retrospective Genomic Landscape of 661 Young Adult Glioblastomas Diagnosed Using 2016 WHO Guidelines for Central Nervous System Tumors.

The authors present a cohort of 661 young adult glioblastomas diagnosed using 2016 WHO World Health Organization Classification of Tumors of the Central Nervous System, utilizing comprehensive genomic profiling (CGP) to explore their genomic landscape and assess their relationship to currently defined disease entities. This analysis explored variants with evidence of pathogenic function, common copy number variants (CNVs), and several novel fusion events not described in literature. Tumor mutational burden (TMB) mutational signatures, anatomic location, and tumor recurrence are further explored. Using data collected from CGP, unsupervised machine-learning techniques were leveraged to identify 10 genomic classes in previously assigned young adult glioblastomas. The authors relate these molecular classes to current World Health Organization guidelines and reference current literature to give therapeutic and prognostic descriptions where possible.

Predicting the physical and chemical properties of sustainable aviation fuels using elastic-net-regularized linear models based on extended-wavelength FTIR spectra

A Fourier transform infrared (FTIR) spectra-based prescreening approach was developed for estimating nine important physical and chemical properties of sustainable aviation fuels (SAFs): molecular weight (MW), hydrogen-to-carbon (H/C) ratio, density, net heat of combustion (NHC), derived cetane number (DCN), threshold sooting index (TSI), initial boiling point (IBP), flash point, and kinematic viscosity (KV). A training dataset containing the vapor-phase FTIR absorption spectra of pure hydrocarbons spanning four molecular classes, blends of neat hydrocarbons, conventional jet fuels and SAFs, across the 2–15.38 μm wavelength range, was compiled. The physical and chemical properties of the fuels in the training dataset were sourced either from experimental data reported in the literature or calculated using relevant property blending correlations. Elastic-net-regularized linear models were trained for each property by optimizing the model parameters using a cross-validated grid search. The results from these models were compared with the results from previous models (Lasso-regularized linear models developed previously by Wang et al. at Stanford), which were trained on FTIR absorption spectra across the limited wavelength range of 3.3–3.55 μm. Use of the extended wavelength range and the new model-parameter optimization strategy results in significant improvement in predictive performance of the current models compared to the previous models for all nine properties. The model performance is also evaluated on three candidate SAFs. The current models are found to achieve higher property prediction accuracy than the previous models for these test fuels. Overall, this work demonstrates the utility of predictive models based on extended-wavelength FTIR spectra and their potential use as a low-volume prescreening method for characterizing properties of new sustainable aviation fuel candidates.

Hepatocellular carcinoma cell differentiation trajectory predicts immunotherapy, potential therapeutic drugs, and prognosis of patients.

The aim of this study is to explore a novel classification and investigate the clinical significance of hepatocellular carcinoma (HCC) cells. We analyzed integrated single-cell RNA sequencing and bulk RNA-seq data obtained from HCC samples. Cell trajectory analysis divided HCC cells into three subgroups with different differentiation states: state 1 was closely related to phosphoric ester hydrolase activity, state 2 was involved in eukaryotic initiation factor 4E binding, translation regulator activity and ribosome, and state 3 was associated with oxidoreductase activity and metabolism. Three molecular classes based on HCC differentiation-related genes (HDRGs) from HCC samples were identified, which revealed immune checkpoint gene expression and overall survival (OS) of HCC patients. Moreover, a prognostic risk scoring (RS) model was generated based on eight HDRGs, and the results showed that the OS of the high-risk group was worse than that of the low-risk group. Further, potential therapeutic drugs were screened out based on eight prognostic RS-HDRGs. This study highlights the importance of HCC cell differentiation in immunotherapy, clinical prognosis, and potential molecular-targeted drugs for HCC patients, and proposes a direction for the development of individualized treatments for HCC.

Application of feature-based molecular networking and MassQL for the MS/MS fragmentation study of depsipeptides.

The Feature-based Molecular Networking (FBMN) is a well-known approach for mapping and identifying structures and analogues. However, in the absence of prior knowledge about the molecular class, assessing specific fragments and clusters requires time-consuming manual validation. This study demonstrates that combining FBMN and Mass Spec Query Language (MassQL) is an effective strategy for accelerating the decoding mass fragmentation pathways and identifying molecules with comparable fragmentation patterns, such as beauvericin and its analogues. To accomplish this objective, a spectral similarity network was built from ESI-MS/MS experiments of Fusarium oxysporum at various collision energies (CIDs) and paired with a MassQL search query for conserved beauvericin ions. FBMN analysis revealed that sodiated and protonated ions clustered differently, with sodiated adducts needing more collision energy and exhibiting a distinct fragmentation pattern. Based on this distinction, two sets of particular fragments were discovered for the identification of these hexadepsipeptides: ([M + H]+) m/z 134, 244, 262, and 362 and ([M + Na]+) m/z 266, 284 and 384. By using these fragments, MassQL accurately found other analogues of the same molecular class and annotated beauvericins that were not classified by FBMN alone. Furthermore, FBMN analysis of sodiated beauvericins at 70eV revealed subclasses with distinct amino acid residues, allowing distinction between beauvericins (beauvericin and beauvericin D) and two previously unknown structural isomers with an unusual methionine sulfoxide residue. In summary, our integrated method revealed correlations between adduct types and fragmentation patterns, facilitated the detection of beauvericin clusters, including known and novel analogues, and allowed for the differentiation between structural isomers.

Transposon delivery for CRISPR-based loss-of-function screen in mice identifies NF2 as a cooperating gene involved with the canonical WNT signaling molecular class of hepatocellular carcinoma

Various molecular subclasses of hepatocellular carcinoma (HCC) exists, with many novel cooperating oncogenes and tumor suppressor genes involved in its tumorigenesis. The emerging importance of WNT signaling in HCC has been established. However, the intricate genetic mechanisms involved in this complex signaling pathway remains to be elucidated. Importantly, while some cooperating genes have been identified, there are still many unknown genes associated with catenin beta 1 (CTNNB1)-induced HCC. Mutations in both oncogenes and tumor suppressor genes are required for HCC tumorigenesis. The emergence of the CRISPR/Cas9 system has allowed researchers now to target both alleles efficiently. In this novel study, the Sleeping Beauty transposon system was used as a gene delivery system in vivo to stably integrate an expression cassette that carry pools of gRNAs and overexpress a mutant version of CTNNB1 into the hepatocyte genome. We identified 206 candidate genes that drive HCC tumorigenesis in the context of WNT signaling activation and, neurofibromin 2 (NF2) gene, a known tumor suppressor gene with clinical relevance was validated in this proof-of-principle study.

Molecular Classification Predicts Response to Radiotherapy in the Randomized PORTEC-1 and PORTEC-2 Trials for Early-Stage Endometrioid Endometrial Cancer.

The molecular classification of endometrial cancer (EC) has proven to have prognostic value and is predictive of response to adjuvant chemotherapy. Here, we investigate its predictive value for response to external beam radiotherapy (EBRT) and vaginal brachytherapy (VBT) in early-stage endometrioid EC (EEC). Data of the randomized PORTEC-1 trial (n = 714) comparing pelvic EBRT with no adjuvant therapy in early-stage intermediate-risk EC and the PORTEC-2 trial (n = 427) comparing VBT with EBRT in early-stage high-intermediate-risk EC were used. Locoregional (including vaginal and pelvic) recurrence-free survival was compared between treatment groups across the four molecular classes using Kaplan-Meier's methodology and log-rank tests. A total of 880 molecularly classified ECs, 484 from PORTEC-1 and 396 from PORTEC-2, were included. The majority were FIGO-2009 stage I EEC (97.2%). The median follow-up was 11.3 years. No locoregional recurrences were observed in EC with a pathogenic mutation of DNA polymerase-ε (POLEmut EC). In mismatch repair-deficient (MMRd) EC, locoregional recurrence-free survival was similar after EBRT (94.2%), VBT (94.2%), and no adjuvant therapy (90.3%; P = .74). In EC with a p53 abnormality (p53abn EC), EBRT (96.9%) had a substantial benefit over VBT (64.3%) and no adjuvant therapy (72.2%; P = .048). In EC with no specific molecular profile (NSMP EC), both EBRT (98.3%) and VBT (96.2%) yielded better locoregional control than no adjuvant therapy (87.7%; P < .0001). The molecular classification of EC predicts response to radiotherapy in stage I EEC and may guide adjuvant treatment decisions. Omitting radiotherapy seems to be safe in POLEmut EC. The benefit of radiotherapy seems to be limited in MMRd EC. EBRT yields a significantly better locoregional recurrence-free survival than VBT or no adjuvant therapy in p53abn EC. VBT is the treatment of choice for NSMP EC as it is as effective as EBRT and significantly better than no adjuvant therapy for locoregional tumor control.

Contribution of LDI and MALDI for the Characterization of a Lignocellulosic-Based Pyrolysis Bio-Oil.

In recent years, various alternatives to fossil fuels have been developed. One of them involves the production of bio-oils from lignocellulosic-based biomass through pyrolysis. However, bio-oils present numerous heteroatoms and, in particular, oxygen atoms that need to be removed by an upgrading process. To optimize these processes, it is necessary to have good knowledge of the composition of the bio-oils at the molecular level. This work aims to establish the usefulness of laser desorption ionization (LDI) and matrix-assisted laser desorption/ionization (MALDI) techniques on lignocellulosic biomass-based bio-oils. Using a Fourier transform ion cyclotron mass spectrometer (FTICR MS), we showed that MALDI gives more information than LDI. The selectivity of a series of MALDI matrices was investigated, showing that some matrices are selective toward compound families and others ionize a wider range of compounds. In this study, nine proton-transfer matrices and three electron-transfer matrices were used and compared to results obtained in LDI. Dithranol, acetosyringone, and graphene oxide were the three promising matrices selected from all matrices, giving an overall characterization of oxygenated classes in a bio-oil. They allowed the ionization of many more species covering a wide range of polarity, aromaticity, and mass with a homogeneous relative intensity for all molecular classes such as lignin-derivative species, sugars, and lipid-derivative species.

Identifying new small proteins through a molecular biology course-based undergraduate research experience laboratory class.

We developed a curriculum for an upper-level molecular biology course-based undergraduate research laboratory class funded by a National Science Foundation CAREER grant that focuses on identifying new small proteins in the bacterium, Escherichia coli. Our CURE class has been continually offered each semester for the last 10 years, with multiple instructors collaboratively developing and implementing their own pedagogical approach while maintaining the same overall scientific goal and experimental strategy. In this paper, we delineate the experimental strategy for our molecular biology CURE laboratory class, describe a range of pedagogical approaches implemented by multiple instructors, and provide recommendations for teaching the class. The purpose of our paper is to share our experiences both in developing and teaching a molecular biology CURE laboratory class based on small protein identification and in creating a curriculum and support system that allows traditional, non-traditional, and under-represented students to participate in authentic research projects.

Knowledge about foodborne pathogens and PCR‐based microbial detection methods among Brazilian students and professionals: A survey

AbstractPolymerase chain reaction (PCR) is widely used in several research areas and routine laboratory analyses, including foodborne pathogens detection. We aimed to investigate the knowledge about PCR‐based methods and foodborne pathogens among undergraduate and graduate Brazilian students and professionals. A cross‐sectional survey was carried out using an online questionnaire as data collection method. The questionnaire was validated and distributed through email and social networks. Data from 1246 respondents was collected. The knowledge scores were verified by correspondence analysis and discussed, 75.8% of the participants answered that they knew a foodborne pathogen and 71.4% of the participants answered that they did not study molecular biology techniques during undergraduate course. The highest level of knowledge was found among professionals with Masters' and PhD degrees. In conclusion, participants are not knowledgeable about PCR‐based methods and the level of academic training influences the knowledge of analytical foundations. Most participants did not study PCR and its application in detecting foodborne pathogens during undergraduate course. We suggested that undergraduate courses in Food Engineering and in Food Science and Technology include mandatory molecular biology classes in academic programs.

Molecular epidemiology of carbapenem-resistant Enterobacteriaceae isolated from patients in COVID-19 wards and ICUs in a Bulgarian University Hospital.

Many studies report an increase in antimicrobial resistance of Gram - negative bacteria during the COVID-19 pandemic. Our aim was to evaluate the epidemiological relationship between carbapenem-resistant (CR) Enterobacteriaceae isolates from patients in COVID-19 wards and to investigate the main mechanisms of carbapenem resistance in these isolates during the period April 2020-July 2021. A total of 45 isolates were studied: Klebsiella pneumoniae (n = 37), Klebsiella oxytoca (n = 2), Enterobacter cloacae complex (n = 4) and Escherichia coli (n = 2). Multiplex PCR was used for detection of genes encoding carbapenemases from different classes (blaKPC, blaIMP, blaVIM, blaNDM, blaOXA-48). For epidemiological typing and analysis, ERIC PCR was performed. Two clinical isolates of E. cloacae, previously identified as representatives of two dominant hospital clones from the period 2014-2017, were included in the study for comparison. In the CR K. pneumoniae group, 23 (62.2%) carried blaKPC, 13 (35.1%) blaNDM, 10 (27.0%) blaVIM, and 9 (24.3%) were positive for both blaKPC and blaVIM. The blaKPC was identified also in the two isolates of K. oxytoca and blaVIM in all E. cloacae complex isolates. The two CR isolates of E. coli possessed blaKPC and blaOXA-48 genes. Epidemiological typing identified 18 ERIC profiles among K. pneumoniae, some presented as clusters of identical and/or closely related isolates. The carbapenem resistance in the studied collection of isolates is mediated mainly by blaKPC. During the COVID-19 pandemic intrahospital dissemination of CR K. pneumoniae, producing carbapenemases of different molecular classes, as well as continuing circulation of dominant hospital clones of multidrug-resistant E. cloacae complex was documented.

METB-10. EXPLAINABLE ARTIFICIAL INTELLIGENCE REVEALS DNA METHYLATION PATTERNS UNDERLYING BRAIN TUMOR CLASSIFICATION

Abstract Over 100 different molecular classes of brain tumors are recognized across histopathological grades and age groups. Their precise diagnosis is crucial for prognostication and appropriate treatment decisions, particularly in pediatric patients. Recently, a random forest-based classifier trained on genome-wide DNA methylation profiles has been developed to guide the accurate classification of these tumors. Over recent years, this classifier has been applied to tens-of-thousands of patient tumors all over the world. Clinical and basic neuro-oncology research would benefit greatly from a detailed understanding of the underlying artificial intelligence decision process. However, why the algorithm arrives at a certain prediction remains unclear. Here, we develop an interpretable framework to explain the model’s decisions and to uncover DNA methylation patterns distinguishing different tumor classes. Using this approach, we demonstrate both global and local differences across epigenetic landscapes. For instance, the model frequently uses DNA methylation probes in CpG islands to distinguish IDH mutant gliomas from other tumor classes. Tumor classes associated with global hypomethylation are distinguished by probes located in broad heterochromatic domains. At the level of individual genes, we find hundreds of promoter and gene body regions specifically methylated or unmethylated in specific classes. In general, we detect a high degree of redundant information in the dataset, with multiple genes across the genome distinguishing individual tumor classes, and many CpG probes representing each gene. These findings explain the technical robustness of the array-based classifier, and provide a starting point for the design of more compact point of care assays. The results of our approach are provided to the research community via an interactive web interface. We hope that this resource will help to build up trust in the machine learning-based classifier in clinical settings, foster biomarker discovery, and enable cancer epigenome research in the context of the central nervous system tumors.

The RAINBO MMRd-GREEN trial (GCIG/DGOG/ENGOT-EN142): A phase III trial on the addition of adjuvant durvalumab to radiotherapy in patients with high-risk MMRd endometrial cancer.

TPS5633 Background: The molecular class of endometrial cancer (EC) is both prognostic and predictive in high-risk EC (HREC). The TransPORTEC research consortium showed that patients with MMRd HREC did not benefit from adjuvant chemotherapy added to radiotherapy. MMRd tumors are particularly sensitive to immunotherapy. We hypothesize that adjuvant durvalumab added to standard radiotherapy will increase recurrence-free survival (RFS) in patients with MMRd HREC. Methods: MMRd-GREEN is an international, open-label, randomized phase III trial and part of the RAINBO program (NCT05255653). Eligible patients will be allocated (1:1) to adjuvant durvalumab 1500 mg once every 4 weeks for 1 year combined with radiotherapy or standard radiotherapy alone. The primary endpoint is RFS at 3 years. Secondary endpoints are pelvic, vaginal and distant RFS, EC-specific survival, overall survival, treatment-related toxicity, health-related quality of life and exploratory translational research . Main inclusion criteria include histologically confirmed stage IB/II with (substantial) lymph-vascular space invasion (LVSI) or stage IIIA-C MMRd EC, after hysterectomy and bilateral salpingo-oophorectomy (regardless of lymphadenectomy or sentinel lymph node biopsy) without macroscopic residual disease. Molecular classification must be performed according to the WHO 2020 algorithm. The target accrual is 316 patients, accounting for a total drop-out rate of 12%. This sample size yields 80% power to detect a 13% increase in the 3-year RFS (hazard ratio 0.58) as a result of the addition of durvalumab, with an accrual duration of 48 months and follow-up period of 30 months. No interim analysis is planned; an independent data monitoring committee will routinely monitor recurrences and adverse events. Enrollment: Since August 2022, the trial is open for recruitment in the Netherlands and per February 10th 2023, the first 3 patients have been enrolled. Activation of more international centers is ongoing. Clinical trial information: NCT05255653 .

Association of MGMT Promoter Methylation With Survival in Low-grade and Anaplastic Gliomas After Alkylating Chemotherapy

O6-methylguanine-DNA methyltransferase (MGMT [OMIM 156569]) promoter methylation (mMGMT) is predictive of response to alkylating chemotherapy for glioblastomas and is routinely used to guide treatment decisions. However, the utility of MGMT promoter status for low-grade and anaplastic gliomas remains unclear due to molecular heterogeneity and the lack of sufficiently large data sets. To evaluate the association of mMGMT for low-grade and anaplastic gliomas with chemotherapy response. This cohort study aggregated grade II and III primary glioma data from 3 prospective cohort studies with patient data collected from August 13, 1995, to August 3, 2022, comprising 411 patients: MSK-IMPACT, EORTC (European Organization of Research and Treatment of Cancer) 26951, and Columbia University. Statistical analysis was performed from April 2022 to January 2023. MGMT promoter methylation status. Multivariable Cox proportional hazards regression modeling was used to assess the association of mMGMT status with progression-free survival (PFS) and overall survival (OS) after adjusting for age, sex, molecular class, grade, chemotherapy, and radiotherapy. Subgroups were stratified by treatment status and World Health Organization 2016 molecular classification. A total of 411 patients (mean [SD] age, 44.1 [14.5] years; 283 men [58%]) met the inclusion criteria, 288 of whom received alkylating chemotherapy. MGMT promoter methylation was observed in 42% of isocitrate dehydrogenase (IDH)-wild-type gliomas (56 of 135), 53% of IDH-mutant and non-codeleted gliomas (79 of 149), and 74% of IDH-mutant and 1p/19q-codeleted gliomas (94 of 127). Among patients who received chemotherapy, mMGMT was associated with improved PFS (median, 68 months [95% CI, 54-132 months] vs 30 months [95% CI, 15-54 months]; log-rank P < .001; adjusted hazard ratio [aHR] for unmethylated MGMT, 1.95 [95% CI, 1.39-2.75]; P < .001) and OS (median, 137 months [95% CI, 104 months to not reached] vs 61 months [95% CI, 47-97 months]; log-rank P < .001; aHR, 1.65 [95% CI, 1.11-2.46]; P = .01). After adjusting for clinical factors, MGMT promoter status was associated with chemotherapy response in IDH-wild-type gliomas (aHR for PFS, 2.15 [95% CI, 1.26-3.66]; P = .005; aHR for OS, 1.69 [95% CI, 0.98-2.91]; P = .06) and IDH-mutant and codeleted gliomas (aHR for PFS, 2.99 [95% CI, 1.44-6.21]; P = .003; aHR for OS, 4.21 [95% CI, 1.25-14.2]; P = .02), but not IDH-mutant and non-codeleted gliomas (aHR for PFS, 1.19 [95% CI, 0.67-2.12]; P = .56; aHR for OS, 1.07 [95% CI, 0.54-2.12]; P = .85). Among patients who did not receive chemotherapy, mMGMT status was not associated with PFS or OS. This study suggests that mMGMT is associated with response to alkylating chemotherapy for low-grade and anaplastic gliomas and may be considered as a stratification factor in future clinical trials of patients with IDH-wild-type and IDH-mutant and codeleted tumors.

DNA-Encoded Macrocyclic Peptide Libraries Enable the Discovery of a Neutral MDM2-p53 Inhibitor.

Synthetic macrocyclic peptides are an emerging molecular class for both targeting intracellular protein-protein interactions (PPIs) and providing an oral modality for drug targets typically addressed by biologics. Display technologies, such as mRNA and phage display, often yield peptides that are too large and too polar to achieve passive permeability or oral bioavailability without substantial off-platform medicinal chemistry. Herein, we use DNA-encoded cyclic peptide libraries to discover a neutral nonapeptide, UNP-6457, that inhibits MDM2-p53 interaction with an IC50 of 8.9 nM. X-ray structural analysis of the MDM2-UNP-6457 complex revealed mutual binding interactions and identified key ligand modification points which may be tuned to enhance its pharmacokinetic profile. These studies showcase how tailored DEL libraries can directly yield macrocyclic peptides benefiting from low MW, TPSA, and HBD/HBA counts that are capable of potently inhibiting therapeutically relevant protein-protein interactions.

Identification of biomarkers for glycaemic deterioration in type 2 diabetes

We identify biomarkers for disease progression in three type 2 diabetes cohorts encompassing 2,973 individuals across three molecular classes, metabolites, lipids and proteins. Homocitrulline, isoleucine and 2-aminoadipic acid, eight triacylglycerol species, and lowered sphingomyelin 42:2;2 levels are predictive of faster progression towards insulin requirement. Of ~1,300 proteins examined in two cohorts, levels of GDF15/MIC-1, IL-18Ra, CRELD1, NogoR, FAS, and ENPP7 are associated with faster progression, whilst SMAC/DIABLO, SPOCK1 and HEMK2 predict lower progression rates. In an external replication, proteins and lipids are associated with diabetes incidence and prevalence. NogoR/RTN4R injection improved glucose tolerance in high fat-fed male mice but impaired it in male db/db mice. High NogoR levels led to islet cell apoptosis, and IL-18R antagonised inflammatory IL-18 signalling towards nuclear factor kappa-B in vitro. This comprehensive, multi-disciplinary approach thus identifies biomarkers with potential prognostic utility, provides evidence for possible disease mechanisms, and identifies potential therapeutic avenues to slow diabetes progression.

Multiplex Detection of Antimicrobial Resistance Genes for Rapid Antibiotic Guidance of Urinary Tract Infections

Identification of antimicrobial resistance markers in urinary tract infections could provide a more targeted approach in the diagnosis and treatment of UTIs while reducing overall public health burdens. We describe a molecular assay as a diagnostic tool for antibiotic resistance characterization to promote faster diagnosis of antibiotic regimens compared to standard microbiology techniques. Targeted antibiotic usage for pathogenic infections remains a main goal for effective antibiotic treatment protocols and reducing the overall public health burden. Rapid identification of the pathogen(s) causing the infection and harboring the antibiotic resistance gene is also a main area of exploration for antibiotic appropriation and stewardship. Urinary tract infections are a common clinical disease and reservoir for pathogenic infection and the development of antibiotic resistance, especially in hospital- and community-acquired settings. Standard methods require urine culture, which is time consuming and relies on phenotypic characterization. A genetic diagnostic method is warranted for the rapid molecular characterization of antibiotic resistance genes to reduce inappropriate exposure to antibiotics while improving the overall treatment model for urinary tract infections. The purpose of this study is to demonstrate logical viability for real-time molecular diagnostics for early identification, active surveillance and overall targeted antibiotic stratification that is proposed as an in vitro rapid and comprehensive tool for assessing proper antibiotic stewardship in UTIs. Here, we describe a multiplex real-time fluorescence polymerase chain reaction (PCR) for probe-based detection of the top 24 antibiotic resistance genes with targeted relationships to target molecular drug classes and administered antibiotics. Multiplexed analysis based on molecular features enables rapid testing while shifting the diagnostic detection paradigm from monocentric infections towards polymicrobial infections. We utilized 366 samples from the FDA-CDC Antimicrobial Resistance Isolate Bank to test the efficacy of the assay and propose a model to infer the identity of bacterial isolates. We found that, in addition to a high level of accuracy in predicting bacterial genus classification, the assay was mostly in agreement with CDC-tested genotypic and phenotypic results. This study provides evidence for using genetic diagnostic methods, such as multiplex qPCR, in the rapid identification of antibiotic resistance (ABR) genes for the characterization and treatment of urinary tract infections.

Evaluation of Autonomic Nervous System Dysfunction in Childhood Obesity and Prader-Willi Syndrome.

The autonomic nervous system (ANS) may play a role in the distribution of body fat and the development of obesity and its complications. Features of individuals with Prader-Willi syndrome (PWS) impacted by PWS molecular genetic classes suggest alterations in ANS function; however, these have been rarely studied and presented with conflicting results. The aim of this study was to investigate if the ANS function is altered in PWS. In this case-control study, we assessed ANS function in 20 subjects with PWS (6 males/14 females; median age 10.5 years) and 27 body mass index (BMI) z-score-matched controls (19 males/8 females; median age 12.8 years). Standardized non-invasive measures of cardiac baroreflex function, heart rate, blood pressure, heart rate variability, quantitative sudomotor axon reflex tests, and a symptom questionnaire were completed. The increase in heart rate in response to head-up tilt testing was blunted (p < 0.01) in PWS compared to controls. Besides a lower heart rate ratio with Valsalva in PWS (p < 0.01), no significant differences were observed in other measures of cardiac function or sweat production. Findings suggest possible altered sympathetic function in PWS.

Prognostic Impact of Pathologic Features in Molecular Subgroups of Endometrial Carcinoma.

The molecular characterization of endometrial carcinoma (EC) has recently been included in the ESGO/ESTRO/ESP guidelines. The study aims to evaluate the impact of integrated molecular and pathologic risk stratification in the clinical practice and the relevance of pathologic parameters in predicting prognosis in each EC molecular subgroup. ECs were classified using immunohistochemistry and next-generation sequencing into the four molecular classes: POLE mutant (POLE), mismatch repair deficient (MMRd), p53 mutant (p53abn), and no specific molecular profile (NSMP). According to the WHO algorithm, 219 ECs were subdivided into the following molecular subgroups: 7.8% POLE, 31% MMRd, 21% p53abn, 40.2% NSMP. Molecular classes as well as ESGO/ESTRO/ESP 2020 risk groups were statistically correlated with disease-free survival. Considering the impact of histopathologic features in each molecular class, stage was found to be the strongest prognostic factor in MMRd ECs, whereas in the p53abn subgroup, only lymph node status was associated with recurrent disease. Interestingly, in the NSMP tumor, several histopathologic features were correlated with recurrence: histotype, grade, stage, tumor necrosis, and substantial lymphovascular space invasion. Considering early-stage NSMP ECs, substantial lymphovascular space invasion was the only independent prognostic factor. Our study supports the prognostic importance of EC molecular classification and demonstrated the essential role of histopathologic assessment in patients' management.