Clonal hematopoiesis in cardiovascular aging: Insights from the verona heart study.

Clonal hematopoiesis of indeterminate potential (CHIP), marked by the accumulation of somatic mutations in hematopoietic stem cells, significantly elevates the risk of all-cause mortality, mainly due to cardiovascular events. Therefore, investigating this pathophysiological phenomenon is crucial for understanding cardiovascular aging and enhancing both health span and lifespan. In the present study, we examined samples of subjects enrolled within the angiographically controlled Verona Heart Study (VHS), which provides a robust model for cardiovascular aging, particularly regarding coronary artery disease (CAD). We analyzed 44 older subjects diagnosed with coronary artery disease (CAD) and 42 healthy, sex- and age-matched controls (CAD-FREE). Employing deep sequencing and an amplicon-based approach, we focused on 11 key genetic regions in ASXL1, DNMT3A, IDH1, IDH2, JAK2, PPM1D, SF3B1, SRSF2, TET2, TP53, and U2AF1 genes to investigate clonal hematopoiesis. Subjects in the CAD group exhibited a significantly higher variant burden than those in the CAD-FREE group, both in terms of the total number of somatic variants and disruptive variants affecting protein function. This increased mutational load was notably influenced by six specific genetic regions: ASXL1, DNMT3A, IDH2, JAK2, TET2, and U2AF1, which displayed elevated variant rates in the CAD subjects. Moreover, ASXL1, DNMT3A, IDH2, JAK2, SF3B1, TET2, and TP53 exhibited substantially higher levels of disruptive variants in the CAD group. In summary, our findings highlight a correlation between clonal hematopoiesis and the accumulation of disruptive variants in specific genomic regions in the VHS cohort, thereby shedding light on their potential role in cardiovascular aging.

MOSAIC: An Artificial Intelligence-Based Framework for Multimodal Analysis, Classification, and Personalized Prognostic Assessment in Rare Cancers.

PURPOSE: Rare cancers constitute over 20% of human neoplasms, often affecting patients with unmet medical needs. The development of effective classification and prognostication systems is crucial to improve the decision-making process and drive innovative treatment strategies. We have created and implemented MOSAIC, an artificial intelligence (AI)-based framework designed for multimodal analysis, classification, and personalized prognostic assessment in rare cancers. Clinical validation was performed on myelodysplastic syndrome (MDS), a rare hematologic cancer with clinical and genomic heterogeneities. METHODS: We analyzed 4,427 patients with MDS divided into training and validation cohorts. Deep learning methods were applied to integrate and impute clinical/genomic features. Clustering was performed by combining Uniform Manifold Approximation and Projection for Dimension Reduction + Hierarchical Density-Based Spatial Clustering of Applications with Noise (UMAP + HDBSCAN) methods, compared with the conventional Hierarchical Dirichlet Process (HDP). Linear and AI-based nonlinear approaches were compared for survival prediction. Explainable AI (Shapley Additive Explanations approach [SHAP]) and federated learning were used to improve the interpretation and the performance of the clinical models, integrating them into distributed infrastructure. RESULTS: UMAP + HDBSCAN clustering obtained a more granular patient stratification, achieving a higher average silhouette coefficient (0.16) with respect to HDP (0.01) and higher balanced accuracy in cluster classification by Random Forest (92.7% ± 1.3% and 85.8% ± 0.8%). AI methods for survival prediction outperform conventional statistical techniques and the reference prognostic tool for MDS. Nonlinear Gradient Boosting Survival stands in the internal (Concordance-Index [C-Index], 0.77; SD, 0.01) and external validation (C-Index, 0.74; SD, 0.02). SHAP analysis revealed that similar features drove patients' subgroups and outcomes in both training and validation cohorts. Federated implementation improved the accuracy of developed models. CONCLUSION: MOSAIC provides an explainable and robust framework to optimize classification and prognostic assessment of rare cancers. AI-based approaches demonstrated superior accuracy in capturing genomic similarities and providing individual prognostic information compared with conventional statistical methods. Its federated implementation ensures broad clinical application, guaranteeing high performance and data protection.

Vaccines and Monoclonal Antibodies as Alternative Strategies to Antibiotics to Fight Antimicrobial Resistance.

Antimicrobial resistance (AMR) is one of the most critical threats to global public health in the 21st century, causing a large number of deaths every year in both high-income and low- and middle-income countries. Vaccines and monoclonal antibodies can be exploited to prevent and treat diseases caused by AMR pathogens, thereby reducing antibiotic use and decreasing selective pressure that favors the emergence of resistant strains. Here, differences in the mechanism of action and resistance of vaccines and monoclonal antibodies compared to antibiotics are discussed. The state of the art for vaccine technologies and monoclonal antibodies are reviewed, with a particular focus on approaches validated in clinical studies. By underscoring the scope and limitations of the different emerging technologies, this review points out the complementary of vaccines and monoclonal antibodies in fighting AMR. Gaps in antigen discovery for some pathogens, as well as challenges associated with the clinical development of these therapies against AMR pathogens, are highlighted.

Impact on the Transcriptome of Proton Beam Irradiation Targeted at Healthy Cardiac Tissue of Mice.

Proton beam therapy is considered a step forward with respect to electromagnetic radiation, thanks to the reduction in the dose delivered. Among unwanted effects to healthy tissue, cardiovascular complications are a known long-term radiotherapy complication. The transcriptional response of cardiac tissue from xenografted BALB/c nude mice obtained at 3 and 10 days after proton irradiation covering both the tumor region and the underlying healthy tissue was analyzed as a function of dose and time. Three doses were used: 2 Gy, 6 Gy, and 9 Gy. The intermediate dose had caused the greatest impact at 3 days after irradiation: at 2 Gy, 219 genes were differently expressed, many of them represented by zinc finger proteins; at 6 Gy, there were 1109, with a predominance of genes involved in energy metabolism and responses to stimuli; and at 9 Gy, there were 105, mainly represented by zinc finger proteins and molecules involved in the regulation of cardiac function. After 10 days, no significant effects were detected, suggesting that cellular repair mechanisms had defused the potential alterations in gene expression. The nonlinear dose-response curve indicates a need to update the models built on photons to improve accuracy in health risk prediction. Our data also suggest a possible role for zinc finger protein genes as markers of proton therapy efficacy.

Development of a visual Adhesion/Invasion Inhibition Assay to assess the functionality of Shigella-specific antibodies.

Introduction: Shigella is the etiologic agent of a bacillary dysentery known as shigellosis, which causes millions of infections and thousands of deaths worldwide each year due to Shigella's unique lifestyle within intestinal epithelial cells. Cell adhesion/invasion assays have been extensively used not only to identify targets mediating host-pathogen interaction, but also to evaluate the ability of Shigella-specific antibodies to reduce virulence. However, these assays are time-consuming and labor-intensive and fail to assess differences at the single-cell level. Objectives and methods: Here, we developed a simple, fast and high-content method named visual Adhesion/Invasion Inhibition Assay (vAIA) to measure the ability of anti-Shigellaantibodies to inhibit bacterial adhesion to and invasion of epithelial cells by using the confocal microscope Opera Phenix. Results: We showed that vAIA performed well with a pooled human serum from subjects challenged with S. sonnei and that a specific anti-IpaD monoclonal antibody effectively reduced bacterial virulence in a dose-dependent manner. Discussion: vAIA can therefore inform on the functionality of polyclonal and monoclonal responses thereby supporting the discovery of pathogenicity mechanisms and the development of candidate vaccines and immunotherapies. Lastly, this assay is very versatile and may be easily applied to other Shigella species or serotypes and to different pathogens.

Where are we in the implementation of tissue-specific epigenetic clocks?

Introduction: DNA methylation clocks presents advantageous characteristics with respect to the ambitious goal of identifying very early markers of disease, based on the concept that accelerated ageing is a reliable predictor in this sense. Methods: Such tools, being epigenomic based, are expected to be conditioned by sex and tissue specificities, and this work is about quantifying this dependency as well as that from the regression model and the size of the training set. Results: Our quantitative results indicate that elastic-net penalization is the best performing strategy, and better so when-unsurprisingly-the data set is bigger; sex does not appear to condition clocks performances and tissue specific clocks appear to perform better than generic blood clocks. Finally, when considering all trained clocks, we identified a subset of genes that, to the best of our knowledge, have not been presented yet and might deserve further investigation: CPT1A, MMP15, SHROOM3, SLIT3, and SYNGR. Conclusion: These factual starting points can be useful for the future medical translation of clocks and in particular in the debate between multi-tissue clocks, generally trained on a large majority of blood samples, and tissue-specific clocks.

Deep-learning image analysis for high-throughput screening of opsono-phagocytosis-promoting monoclonal antibodies against Neisseria gonorrhoeae.

Antimicrobial resistance (AMR) is nowadays a global health concern as bacterial pathogens are increasingly developing resistance to antibiotics. Monoclonal antibodies (mAbs) represent a powerful tool for addressing AMR thanks to their high specificity for pathogenic bacteria which allows sparing the microbiota, kill bacteria through complement deposition, enhance phagocytosis or inhibit bacterial adhesion to epithelial cells. Here we describe a visual opsono-phagocytosis assay which relies on confocal microscopy to measure the impact of mAbs on phagocytosis of the bacterium Neisseria gonorrhoeae by macrophages. With respect to traditional CFU-based assays, generated images can be automatically analysed by convolutional neural networks. Our results demonstrate that confocal microscopy and deep learning-based analysis allow screening for phagocytosis-promoting mAbs against N. gonorrhoeae, even when mAbs are not purified and are expressed at low concentration. Ultimately, the flexibility of the staining protocol and of the deep-learning approach make the assay suitable for other bacterial species and cell lines where mAb activity needs to be investigated.

Covering Hierarchical Dirichlet Mixture Models on binary data to enhance genomic stratifications in onco-hematology.

Onco-hematological studies are increasingly adopting statistical mixture models to support the advancement of the genomically-driven classification systems for blood cancer. Targeting enhanced patients stratification based on the sole role of molecular biology attracted much interest and contributes to bring personalized medicine closer to reality. In onco-hematology, Hierarchical Dirichlet Mixture Models (HDMM) have become one of the preferred method to cluster the genomics data, that include the presence or absence of gene mutations and cytogenetics anomalies, into components. This work unfolds the standard workflow used in onco-hematology to improve patient stratification and proposes alternative approaches to characterize the components and to assign patient to them, as they are crucial tasks usually supported by a priori clinical knowledge. We propose (a) to compute the parameters of the multinomial components of the HDMM or (b) to estimate the parameters of the HDMM components as if they were Multivariate Fisher's Non-Central Hypergeometric (MFNCH) distributions. Then, our approach to perform patients assignments to the HDMM components is designed to essentially determine for each patient its most likely component. We show on simulated data that the patients assignment using the MFNCH-based approach can be superior, if not comparable, to using the multinomial-based approach. Lastly, we illustrate on real Acute Myeloid Leukemia data how the utilization of MFNCH-based approach emerges as a good trade-off between the rigorous multinomial-based characterization of the HDMM components and the common refinement of them based on a priori clinical knowledge.

Epigenetic aging differences between Wichí and Criollos from Argentina: Insights from genomic history and ecology.

Background and objectives: Epigenetic estimators based on DNA methylation levels have emerged as promising biomarkers of human aging. These estimators exhibit natural variations across human groups, but data about indigenous populations remain underrepresented in research. This study aims to investigate differences in epigenetic estimators between two distinct human populations, both residing in the Gran Chaco region of Argentina, the Native-American Wichí, and admixed Criollos who are descendants of intermarriages between Native Americans and the first European colonizers, using a population genetic approach. Methodology: We analyzed 24 Wichí (mean age: 39.2 ± 12.9 yo) and 24 Criollos (mean age: 41.1 ± 14.0 yo) for DNA methylation levels using the Infinium MethylationEPIC (Illumina) to calculate 16 epigenetic estimators. Additionally, we examined genome-wide genetic variation using the HumanOmniExpress BeadChip (Illumina) to gain insights into the genetic history of these populations. Results: Our results indicate that Native-American Wichí are epigenetically older compared to Criollos according to five epigenetic estimators. Analyses within the Criollos population reveal that global ancestry does not influence the differences observed, while local (chromosomal) ancestry shows positive associations between specific SNPs located in genomic regions over-represented by Native-American ancestry and measures of epigenetic age acceleration (AgeAccelHannum). Furthermore, we demonstrate that differences in population ecologies also contribute to observed epigenetic differences. Conclusions and implications: Overall, our study suggests that while the genomic history may partially account for the observed epigenetic differences, non-genetic factors, such as lifestyle and ecological factors, play a substantial role in the variability of epigenetic estimators, thereby contributing to variations in human epigenetic aging.

Sellar xanthogranuloma as a diagnostic challenge: a report on five cases.

Xanthogranulomas are considered rare tumors, with their sellar and non-sellar frequency ranging from 1.6 to 7% among intracranial lesions, and described as a separate entity by the World Health Organization in 2000. The diagnosis of sellar xanthogranulomas is challenging, given their uncertain origin and clinical course. In addition, the limited reporting of sellar xanthogranuloma cases and the absence of characteristic images make these entities difficult to distinguish from other cystic lesions of the sellar region, such as adamantinomatous craniopharyngiomas, Rathke's cleft cysts, pituitary tumors, arachnoid cysts, epidermoid cysts, and dermoid cysts. Here, we describe the clinical presentation, radiological findings, immunohistochemical/histopathological analysis, and the ultrastructural examination by transmission electron microscopy of five sellar xanthogranulomas cases reported in two care centers in Cordoba, Argentina. Two males and three females between 37 and 73 years of age (average 51.8 years) presented with persistent headaches, generalized endocrine defects, and visual problems. MRI revealed cystic formations in the sellar region, which usually projected into adjacent tissues such as the suprasellar region or cavernous sinuses, and compressed other structures such as the optic chiasm, pituitary gland, and cranial nerves. All patients underwent surgical intervention to remove the tumor tissue. The histopathological analysis of the samples showed cellular tissue with a xanthogranulomatous appearance, inflammatory cellular infiltrate (mainly lymphocytes and macrophages), fibroblasts, abundant collagen fibers, and hemorrhages. An ultrastructural analysis helped to identify cellular infiltrates and granules resulting from tumor cell activity. The data support the hypothesis that sellar xanthogranulomas could occur as an inflammatory reaction secondary to the rupture and hemorrhage of a previous cystic process, thereby generating an expansion of the tumor body toward adjacent tissues. The information obtained from these cases contributes to the current knowledge about this disease's origin and clinical and histological evolution. However, the scarcity of patients and the observed phenotypic heterogeneity make its diagnosis still challenging. Undoubtedly, more investigations are needed to provide additional information in order to be able to achieve a more accurate diagnosis and effective treatment of this rare disease.

High-throughput bactericidal assays for monoclonal antibody screening against antimicrobial resistant Neisseria gonorrhoeae.

Neisseria gonorrhoeae (gonococcus) is an obligate human pathogen and the etiological agent of the sexually transmitted disease gonorrhea. The rapid rise in gonococcal resistance to all currently available antimicrobials has become a significant public health burden and the need to develop novel therapeutic and prophylactic tools is now a global priority. While high-throughput screening methods allowed rapid discovery of extremely potent monoclonal antibodies (mAbs) against viral pathogens, the field of bacteriology suffers from the lack of assays that allow efficient screening of large panels of samples. To address this point, we developed luminescence-based (L-ABA) and resazurin-based (R-ABA) antibody bactericidal assays that measure N. gonorrhoeae metabolic activity as a proxy of bacterial viability. Both L-ABA and R-ABA are applicable on the large scale for the rapid identification of bactericidal antibodies and were validated by conventional methods. Implementation of these approaches will be instrumental to the development of new medications and vaccines against N. gonorrhoeae and other bacterial pathogens to support the fight against antimicrobial resistance.

Synthetic Data Generation by Artificial Intelligence to Accelerate Research and Precision Medicine in Hematology.

PURPOSE: Synthetic data are artificial data generated without including any real patient information by an algorithm trained to learn the characteristics of a real source data set and became widely used to accelerate research in life sciences. We aimed to (1) apply generative artificial intelligence to build synthetic data in different hematologic neoplasms; (2) develop a synthetic validation framework to assess data fidelity and privacy preservability; and (3) test the capability of synthetic data to accelerate clinical/translational research in hematology. METHODS: A conditional generative adversarial network architecture was implemented to generate synthetic data. Use cases were myelodysplastic syndromes (MDS) and AML: 7,133 patients were included. A fully explainable validation framework was created to assess fidelity and privacy preservability of synthetic data. RESULTS: We generated MDS/AML synthetic cohorts (including information on clinical features, genomics, treatment, and outcomes) with high fidelity and privacy performances. This technology allowed resolution of lack/incomplete information and data augmentation. We then assessed the potential value of synthetic data on accelerating research in hematology. Starting from 944 patients with MDS available since 2014, we generated a 300% augmented synthetic cohort and anticipated the development of molecular classification and molecular scoring system obtained many years later from 2,043 to 2,957 real patients, respectively. Moreover, starting from 187 MDS treated with luspatercept into a clinical trial, we generated a synthetic cohort that recapitulated all the clinical end points of the study. Finally, we developed a website to enable clinicians generating high-quality synthetic data from an existing biobank of real patients. CONCLUSION: Synthetic data mimic real clinical-genomic features and outcomes, and anonymize patient information. The implementation of this technology allows to increase the scientific use and value of real data, thus accelerating precision medicine in hematology and the conduction of clinical trials.

Effectiveness of Radiomic ZOT Features in the Automated Discrimination of Oncocytoma from Clear Cell Renal Cancer.

BACKGROUND: Benign renal tumors, such as renal oncocytoma (RO), can be erroneously diagnosed as malignant renal cell carcinomas (RCC), because of their similar imaging features. Computer-aided systems leveraging radiomic features can be used to better discriminate benign renal tumors from the malignant ones. The purpose of this work was to build a machine learning model to distinguish RO from clear cell RCC (ccRCC). METHOD: We collected CT images of 77 patients, with 30 cases of RO (39%) and 47 cases of ccRCC (61%). Radiomic features were extracted both from the tumor volumes identified by the clinicians and from the tumor's zone of transition (ZOT). We used a genetic algorithm to perform feature selection, identifying the most descriptive set of features for the tumor classification. We built a decision tree classifier to distinguish between ROs and ccRCCs. We proposed two versions of the pipeline: in the first one, the feature selection was performed before the splitting of the data, while in the second one, the feature selection was performed after, i.e., on the training data only. We evaluated the efficiency of the two pipelines in cancer classification. RESULTS: The ZOT features were found to be the most predictive by the genetic algorithm. The pipeline with the feature selection performed on the whole dataset obtained an average ROC AUC score of 0.87 ± 0.09. The second pipeline, in which the feature selection was performed on the training data only, obtained an average ROC AUC score of 0.62 ± 0.17. CONCLUSIONS: The obtained results confirm the efficiency of ZOT radiomic features in capturing the renal tumor characteristics. We showed that there is a significant difference in the performances of the two proposed pipelines, highlighting how some already published radiomic analyses could be too optimistic about the real generalization capabilities of the models.

mRNA vaccines and hybrid immunity use different B cell germlines against Omicron BA.4 and BA.5.

Severe acute respiratory syndrome 2 Omicron BA.4 and BA.5 are characterized by high transmissibility and ability to escape natural and vaccine induced immunity. Here we test the neutralizing activity of 482 human monoclonal antibodies isolated from people who received two or three mRNA vaccine doses or from people vaccinated after infection. The BA.4 and BA.5 variants are neutralized only by approximately 15% of antibodies. Remarkably, the antibodies isolated after three vaccine doses target mainly the receptor binding domain Class 1/2, while antibodies isolated after infection recognize mostly the receptor binding domain Class 3 epitope region and the N-terminal domain. Different B cell germlines are used by the analyzed cohorts. The observation that mRNA vaccination and hybrid immunity elicit a different immunity against the same antigen is intriguing and its understanding may help to design the next generation of therapeutics and vaccines against coronavirus disease 2019.

Automated Wound Image Segmentation: Transfer Learning from Human to Pet via Active Semi-Supervised Learning.

Wound management is a fundamental task in standard clinical practice. Automated solutions already exist for humans, but there is a lack of applications regarding wound management for pets. Precise and efficient wound assessment is helpful to improve diagnosis and to increase the effectiveness of treatment plans for chronic wounds. In this work, we introduced a novel pipeline for the segmentation of pet wound images. Starting from a model pre-trained on human-based wound images, we applied a combination of transfer learning (TL) and active semi-supervised learning (ASSL) to automatically label a large dataset. Additionally, we provided a guideline for future applications of TL+ASSL training strategy on image datasets. We compared the effectiveness of the proposed training strategy, monitoring the performance of an EfficientNet-b3 U-Net model against the lighter solution provided by a MobileNet-v2 U-Net model. We obtained 80% of correctly segmented images after five rounds of ASSL training. The EfficientNet-b3 U-Net model significantly outperformed the MobileNet-v2 one. We proved that the number of available samples is a key factor for the correct usage of ASSL training. The proposed approach is a viable solution to reduce the time required for the generation of a segmentation dataset.

Heterogeneity of Cellular Senescence: Cell Type-Specific and Senescence Stimulus-Dependent Epigenetic Alterations.

The aim of the present study was to provide a comprehensive characterization of whole genome DNA methylation patterns in replicative and ionizing irradiation- or doxorubicin-induced premature senescence, exhaustively exploring epigenetic modifications in three different human cell types: in somatic diploid skin fibroblasts and in bone marrow- and adipose-derived mesenchymal stem cells. With CpG-wise differential analysis, three epigenetic signatures were identified: (a) cell type- and treatment-specific signature; (b) cell type-specific senescence-related signature; and (c) cell type-transversal replicative senescence-related signature. Cluster analysis revealed that only replicative senescent cells created a distinct group reflecting notable alterations in the DNA methylation patterns accompanying this cellular state. Replicative senescence-associated epigenetic changes seemed to be of such an extent that they surpassed interpersonal dissimilarities. Enrichment in pathways linked to the nervous system and involved in the neurological functions was shown after pathway analysis of genes involved in the cell type-transversal replicative senescence-related signature. Although DNA methylation clock analysis provided no statistically significant evidence on epigenetic age acceleration related to senescence, a persistent trend of increased biological age in replicative senescent cultures of all three cell types was observed. Overall, this work indicates the heterogeneity of senescent cells depending on the tissue of origin and the type of senescence inducer that could be putatively translated to a distinct impact on tissue homeostasis.

Real-World Validation of Molecular International Prognostic Scoring System for Myelodysplastic Syndromes.

PURPOSE: Myelodysplastic syndromes (MDS) are heterogeneous myeloid neoplasms in which a risk-adapted treatment strategy is needed. Recently, a new clinical-molecular prognostic model, the Molecular International Prognostic Scoring System (IPSS-M) was proposed to improve the prediction of clinical outcome of the currently available tool (Revised International Prognostic Scoring System [IPSS-R]). We aimed to provide an extensive validation of IPSS-M. METHODS: A total of 2,876 patients with primary MDS from the GenoMed4All consortium were retrospectively analyzed. RESULTS: IPSS-M improved prognostic discrimination across all clinical end points with respect to IPSS-R (concordance was 0.81 v 0.74 for overall survival and 0.89 v 0.76 for leukemia-free survival, respectively). This was true even in those patients without detectable gene mutations. Compared with the IPSS-R based stratification, the IPSS-M risk group changed in 46% of patients (23.6% and 22.4% of subjects were upstaged and downstaged, respectively).In patients treated with hematopoietic stem cell transplantation (HSCT), IPSS-M significantly improved the prediction of the risk of disease relapse and the probability of post-transplantation survival versus IPSS-R (concordance was 0.76 v 0.60 for overall survival and 0.89 v 0.70 for probability of relapse, respectively). In high-risk patients treated with hypomethylating agents (HMA), IPSS-M failed to stratify individual probability of response; response duration and probability of survival were inversely related to IPSS-M risk.Finally, we tested the accuracy in predicting IPSS-M when molecular information was missed and we defined a minimum set of 15 relevant genes associated with high performance of the score. CONCLUSION: IPSS-M improves MDS prognostication and might result in a more effective selection of candidates to HSCT. Additional factors other than gene mutations can be involved in determining HMA sensitivity. The definition of a minimum set of relevant genes may facilitate the clinical implementation of the score.

Rho-dependent transcription termination is the dominant mechanism in Mycobacterium tuberculosis.

Intrinsic and Rho-dependent transcription termination mechanisms regulate gene expression and recycle RNA polymerase in bacteria. Both the modes are well studied in Escherichia coli, and a few other organisms. The understanding of Rho function is limited in most other bacteria including mycobacteria. Here, we highlight the dominance of Rho-dependent termination in mycobacteria and validate Rho as a key regulatory factor. The lower abundance of intrinsic terminators, high cellular levels of Rho, and its genome-wide association with a majority of transcriptionally active genes indicate the pronounced role of Rho-mediated termination in Mycobacterium tuberculosis (Mtb). Rho modulates the termination of RNA synthesis for both protein-coding and stable RNA genes in Mtb. Concordantly, the depletion of Rho in mycobacteria impact its growth and enhances the transcription read-through at 3' ends of the transcription units. We demonstrate that MtbRho is catalytically active in the presence of RNA with varied secondary structures. These properties suggest an evolutionary adaptation of Rho as the efficient and preponderant mode of transcription termination in mycobacteria.

B cell analyses after SARS-CoV-2 mRNA third vaccination reveals a hybrid immunity like antibody response.

The continuous evolution of SARS-CoV-2 generated highly mutated variants able to escape natural and vaccine-induced primary immunity. The administration of a third mRNA vaccine dose induces a secondary response with increased protection. Here we investigate the longitudinal evolution of the neutralizing antibody response in four donors after three mRNA doses at single-cell level. We sorted 4100 spike protein specific memory B cells identifying 350 neutralizing antibodies. The third dose increases the antibody neutralization potency and breadth against all SARS-CoV-2 variants as observed with hybrid immunity. However, the B cell repertoire generating this response is different. The increases of neutralizing antibody responses is largely due to the expansion of B cell germlines poorly represented after two doses, and the reduction of germlines predominant after primary immunization. Our data show that different immunization regimens induce specific molecular signatures which should be considered while designing new vaccines and immunization strategies.

A Targeted Epigenetic Clock for the Prediction of Biological Age.

Epigenetic clocks were initially developed to track chronological age, but accumulating evidence indicates that they can also predict biological age. They are usually based on the analysis of DNA methylation by genome-wide methods, but targeted approaches, based on the assessment of a small number of CpG sites, are advisable in several settings. In this study, we developed a targeted epigenetic clock purposely optimized for the measurement of biological age. The clock includes six genomic regions mapping in ELOVL2, NHLRC1, AIM2, EDARADD, SIRT7 and TFAP2E genes, selected from a re-analysis of existing microarray data, whose DNA methylation is measured by EpiTYPER assay. In healthy subjects (n = 278), epigenetic age calculated using the targeted clock was highly correlated with chronological age (Spearman correlation = 0.89). Most importantly, and in agreement with previous results from genome-wide clocks, epigenetic age was significantly higher and lower than expected in models of increased (persons with Down syndrome, n = 62) and decreased (centenarians, n = 106; centenarians' offspring, n = 143; nutritional intervention in elderly, n = 233) biological age, respectively. These results support the potential of our targeted epigenetic clock as a new marker of biological age and open its evaluation in large cohorts to further promote the assessment of biological age in healthcare practice.