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Genomic variant interpretation is a critical step of the diagnostic procedure, often supported by the application of tools that may predict the damaging impact of each variant or provide a guidelines-based classification. We propose the application of Machine Learning methodologies, in particular Penalized Logistic Regression, to support variant classification and prioritization. Our approach combines ACMG/AMP guidelines for germline variant interpretation as well as variant annotation features and provides a probabilistic score of pathogenicity, thus supporting the prioritization and classification of variants that would be interpreted as uncertain by the ACMG/AMP guidelines. We compared different approaches in terms of variant prioritization and classification on different datasets, showing that our data-driven approach is able to solve more variant of uncertain significance (VUS) cases in comparison with guidelines-based approaches and in silico prediction tools.

Despite decades of unequivocal evidence that waist circumference provides both independent and additive information to BMI for predicting morbidity and risk of death, this measurement is not routinely obtained in clinical practice. This Consensus Statement proposes that measurements of waist circumference afford practitioners with an important opportunity to improve the management and health of patients. We argue that BMI alone is not sufficient to properly assess or manage the cardiometabolic risk associated with increased adiposity in adults and provide a thorough review of the evidence that will empower health practitioners and professional societies to routinely include waist circumference in the evaluation and management of patients with overweight or obesity. We recommend that decreases in waist circumference are a critically important treatment target for reducing adverse health risks for both men and women. Moreover, we describe evidence that clinically relevant reductions in waist circumference can be achieved by routine, moderate-intensity exercise and/or dietary interventions. We identify gaps in the knowledge, including the refinement of waist circumference threshold values for a given BMI category, to optimize obesity risk stratification across age, sex and ethnicity. We recommend that health professionals are trained to properly perform this simple measurement and consider it as an important ‘vital sign’ in clinical practice.In this Consensus Statement, the International Atherosclerosis Society and International Chair on Cardiometabolic Risk Working Group on Visceral Obesity recommend that waist circumference be included routinely as a measurement in clinical practice. They summarize the evidence that waist circumference and BMI together can provide improved assessments of cardiometabolic risk compared with either measurement alone.

Changes in endocrine and kidney functions have been associated with spaceflight. Here, we discuss the most relevant evidence about the impact of spaceflight on the cardiometabolic system, the cardiorenal function and the reproductive/gonadal axis. Notably, these changes appear to be interrelated with other organ/system functions, suggesting the need of a systemic approach leading to a more comprehensive understanding of physiological and health-related impacts of the space environment. Therefore, this review will also focus on the need to move space endocrinological research to multi-omics approaches and the implementation of “machine learning” and “data mining” strategies.

Chronic low-grade inflammation, through the specific activation of the NACHT leucine-rich repeat- and PYD-containing (NLRP)3 inflammasome-interleukin (IL)-1β pathway, is an important contributor to the development of atherosclerotic cardiovascular disease (ASCVD), being triggered by intracellular cholesterol accumulation within cells. Within this pathological context, this complex pathway is activated by a number of factors, such as unhealthy nutrition, altered gut and oral microbiota, and elevated cholesterol itself. Moreover, evidence from autoinflammatory diseases, like psoriasis and others, which are also associated with higher cardiovascular disease (CVD) risk, suggests that variants of NLRP3 pathway-related genes (like NLRP3 itself, caspase recruitment domain-containing protein (CARD)8, caspase-1 and IL-1β) may carry gain-of-function mutations leading, in some individuals, to a constitutive pro-inflammatory pattern. Indeed, some reports have recently associated the presence of specific single nucleotide polymorphisms (SNPs) on such genes with greater ASCVD prevalence. Based on these observations, a potential effective strategy in this context may be the identification of carriers of these NLRP3-related SNPs, to generate a genomic score, potentially useful for a better CVD risk prediction, and, possibly, for personalized therapeutic approaches targeted to the NLRP3-IL-1β pathway.

In the last decades three-dimensional (3D) in vitro cancer models have been proposed as a bridge between bidimensional (2D) cell cultures and in vivo animal models, the gold standards in the preclinical assessment of anticancer drug efficacy. 3D in vitro cancer models can be generated through a multitude of techniques, from both immortalized cancer cell lines and primary patient-derived tumor tissue. Among them, spheroids and organoids represent the most versatile and promising models, as they faithfully recapitulate the complexity and heterogeneity of human cancers. Although their recent applications include drug screening programs and personalized medicine, 3D in vitro cancer models have not yet been established as preclinical tools for studying anticancer drug efficacy and supporting preclinical-to-clinical translation, which remains mainly based on animal experimentation. In this review, we describe the state-of-the-art of 3D in vitro cancer models for the efficacy evaluation of anticancer agents, focusing on their potential contribution to replace, reduce and refine animal experimentations, highlighting their strength and weakness, and discussing possible perspectives to overcome current challenges.

The global race against antimicrobial resistance requires novel antimicrobials that are not only effective in killing specific bacteria, but also minimize the emergence of new resistances. Recently, CRISPR/Cas-based antimicrobials were proposed to address killing specificity with encouraging results. However, the emergence of target sequence mutations triggered by Cas-cleavage was identified as an escape strategy, posing the risk of generating new antibiotic-resistance gene (ARG) variants. Here, we evaluated an antibiotic re-sensitization strategy based on CRISPR interference (CRISPRi), which inhibits gene expression without damaging target DNA. The resistance to four antibiotics, including last resort drugs, was significantly reduced by individual and multi-gene targeting of ARGs in low- to high-copy numbers in recombinant E. coli . Escaper analysis confirmed the absence of mutations in target sequence, corroborating the harmless role of CRISPRi in the selection of new resistances. E. coli clinical isolates carrying ARGs of severe clinical concern were then used to assess the robustness of CRISPRi under different growth conditions. Meropenem, colistin and cefotaxime susceptibility was successfully increased in terms of MIC (up to > 4-fold) and growth delay (up to 11 h) in a medium-dependent fashion. ARG repression also worked in a pathogenic strain grown in human urine, as a demonstration of CRISPRi-mediated re-sensitization in host-mimicking media. This study laid the foundations for further leveraging CRISPRi as antimicrobial agent or research tool to selectively repress ARGs and investigate resistance mechanisms.

CRISPRi-mediated gene regulation allows simultaneous control of many genes. However, highly specific sgRNA-promoter binding is, alone, insufficient to achieve independent transcriptional regulation of multiple targets. Indeed, due to competition for dCas9, the repression ability of one sgRNA changes significantly when another sgRNA becomes expressed. To solve this problem and decouple sgRNA-mediated regulatory paths, we create a dCas9 concentration regulator that implements negative feedback on dCas9 level. This allows any sgRNA to maintain an approximately constant dose-response curve, independent of other sgRNAs. We demonstrate the regulator performance on both single-stage and layered CRISPRi-based genetic circuits, zeroing competition effects of up to 15-fold changes in circuit I/O response encountered without the dCas9 regulator. The dCas9 regulator decouples sgRNA-mediated regulatory paths, enabling concurrent and independent regulation of multiple genes. This allows predictable composition of CRISPRi-based genetic modules, which is essential in the design of larger scale synthetic genetic circuits. CRISPRi allows for the simultaneous control of many genes, however the sgRNAs compete for binding to dCas9. Here the authors design a dCas9 concentration regulator to allow independent regulation of multiple genes.

Background Genome-scale metabolic models (GEMs) allow predicting metabolic phenotypes from limited data on uptake and secretion fluxes by defining the space of all the feasible solutions and excluding physio-chemically and biologically unfeasible behaviors. The integration of additional biological information in genome-scale models, e.g., transcriptomic or proteomic profiles, has the potential to improve phenotype prediction accuracy. This is particularly important for metabolic engineering applications where more accurate model predictions can translate to more reliable model-based strain design. Results Here we present a GEM with Enzymatic Constraints using Kinetic and Omics data (GECKO) model of Bacillus subtilis , which uses publicly available proteomic data and enzyme kinetic parameters for central carbon (CC) metabolic reactions to constrain the flux solution space. This model allows more accurate prediction of the flux distribution and growth rate of wild-type and single-gene/operon deletion strains compared to a standard genome-scale metabolic model. The flux prediction error decreased by 43% and 36% for wild-type and mutants respectively. The model additionally increased the number of correctly predicted essential genes in CC pathways by 2.5-fold and significantly decreased flux variability in more than 80% of the reactions with variable flux. Finally, the model was used to find new gene deletion targets to optimize the flux toward the biosynthesis of poly-γ-glutamic acid (γ-PGA) polymer in engineered B. subtilis . We implemented the single-reaction deletion targets identified by the model experimentally and showed that the new strains have a twofold higher γ-PGA concentration and production rate compared to the ancestral strain. Conclusions This work confirms that integration of enzyme constraints is a powerful tool to improve existing genome-scale models, and demonstrates the successful use of enzyme-constrained models in B. subtilis metabolic engineering. We expect that the new model can be used to guide future metabolic engineering efforts in the important industrial production host B. subtilis .

Precision dosing aims to optimize and customize pharmacological treatment at the individual level. The integration of pharmacometric models with Reinforcement Learning (RL) algorithms is currently under investigation to support the personalization of adaptive dosing therapies. In this study, this hybrid technique is applied to the real multiobjective precision dosing problem of givinostat treatment in polycythemia vera (PV) patients. PV is a chronic myeloproliferative disease with an overproduction of platelets (PLT), white blood cells (WBC), and hematocrit (HCT). The therapeutic goal is to simultaneously normalize the levels of these efficacy/safety biomarkers, thus inducing a complete hematological response (CHR). An RL algorithm, Q‐Learning (QL), was integrated with a PK‐PD model describing the givinostat effect on PLT, WBC, and HCT to derive both an adaptive dosing protocol (QLpop‐agent) for the whole population and personalized dosing strategies by coupling a specific QL‐agent to each patient (QLind‐agents). QLpop‐agent learned a general adaptive dosing protocol that achieved a similar CHR rate (77% vs. 83%) when compared to the actual givinostat clinical protocol on 10 simulated populations. Treatment efficacy and safety increased with a deeper dosing personalization by QLind‐agents. These QL‐based patient‐specific adaptive dosing rules outperformed both the clinical protocol and QLpop‐agent by reaching the CHR in 93% of the test patients and completely avoided severe toxicities during the whole treatment period. These results confirm that RL and PK‐PD models can be valid tools for supporting adaptive dosing strategies as interesting performances were achieved in both learning a general set of rules and in customizing treatment for each patient.

Coastal lagoons and brackish ponds are extremely dynamic and temporary ecosystems that follow natural changes throughout their geological history. The correct management of the lagoons ensures their integrity and proper functioning. For this reason, their ecological status should be surveyed for assessing the most appropriate strategies of use. In the present study, historical datasets collected in 2003–2004 are used to investigate the spatiotemporal variation in the species composition and community structure of the macrobenthos of the Tortolì Lagoon (Sardinia, Italy) and to assess their relationship with key environmental variables. Owing to the presence of a riverine runoff at a site and confined areas at some distance from the sea inlet, we hypothesize the marked spatiotemporal changes of the macrobenthic community consistent with the high environmental variability typical of coastal lagoons. The results show a surprisingly high benthic biodiversity for a medium-sized lagoon (250 ha), with 101 species unevenly distributed across the lagoon. The environmental variables did not explain the zonation of the macrobenthic community as that typically found along a lagoonal gradient, due to a marked marine influence. The sampling sites were in fact discriminated by the species distribution according to their ecological affinity; in particular, the most distinctive characteristics of the Tortolì Lagoon emerged from the strictly marine species that represented the most abundant group, consistently with the high marinization of the lagoon. Our results show that the Tortolì Lagoon constitutes a peculiar ecosystem within Mediterranean lagoons, departing from the classic confinement theory.