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CRISPR/Cas9 technology has greatly accelerated genome engineering research. The CRISPR/Cas9 complex, a bacterial immune response system, is widely adopted for RNA-driven targeted genome editing. The systematic mapping study presented in this paper examines the literature on machine learning (ML) techniques employed in the prediction of CRISPR/Cas9 sgRNA on/off-target cleavage, focusing on improving support in sgRNA design activities and identifying areas currently being researched. This area of research has greatly expanded recently, and we found it appropriate to work on a Systematic Mapping Study (SMS), an investigation that has proven to be an effective secondary study method. Unlike a classic review, in an SMS, no comparison of methods or results is made, while this task can instead be the subject of a systematic literature review that chooses one theme among those highlighted in this SMS. The study is illustrated in this paper. To the best of the authors’ knowledge, no other SMS studies have been published on this topic. Fifty-seven papers published in the period 2017-2022 (April, 30) were analyzed. This study reveals that the most widely used ML model is the convolutional neural network (CNN), followed by the feedforward neural network (FNN), while the use of other models is marginal. Other interesting information has emerged, such as the wide availability of both open code and platforms dedicated to supporting the activity of researchers or the fact that there is a clear prevalence of public funds that finance research on this topic.Although aging is an increasingly severe healthy, economic, and social global problem, it is far from well-modeling aging due to the aging process’s complexity. To promote the aging modeling, here we did the quantitative measurement based on aging blood transcriptome. Specifically, the aging blood transcriptome landscape was constructed through ensemble modeling in a cohort of 505 people, and 1138 age-related genes were identified. To assess the aging rate in the linear dimension of aging, we constructed a simplified linear aging clock, which distinguished fast-aging and slow-aging populations and showed the differences in the composition of immune cells. Meanwhile, the non-linear dimension of aging revealed the transcriptome fluctuations with a crest around the age of 40 and showed that this crest came earlier and was more vigorous in the fast-aging population. Moreover, the aging clock was applied to evaluate the rejuvenation effect of molecules in vitro, such as Nicotinamide Mononucleotide (NMN) and Metformin. In sum, this study developed a de novo aging clock to evaluate age-dependent precise medicine by revealing its fluctuation nature based on comprehensively mining the aging blood transcriptome, promoting the development of personal aging monitoring and anti-aging therapies.Lipopolysaccharide (LPS), a main component of the outer membrane of Gram-negative bacteria, has crucial implications on both antibiotic resistance and the overstimulation of the host innate immune system. Fighting against these global concerns calls for the molecular understanding of the barrier function and immunostimulatory ability of LPS. Molecular dynamics (MD) simulations have become an invaluable tool for uncovering important findings in LPS research. While the reach of MD simulations for investigating the immunostimulatory ability of LPS has been already outlined, little attention has been paid to the role of MD simulations for exploring its barrier function and synthesis. Herein, we give an overview about the impact of MD simulations on gaining insight into the shield role and synthesis pathway of LPS, which have attracted considerable attention to discover molecules able to surmount antibiotic resistance, either circumventing LPS defenses or disrupting its synthesis. We specifically focus on the enhanced sampling and free energy calculation methods that have been combined with MD simulations to address such research. We also highlight the use of special-purpose MD supercomputers, the importance of appropriate LPS and ions parameterization to obtain reliable results, and the complementary views that MD and wet-lab experiments provide. Thereby, this work, which covers the last five years of research, apart from outlining the phenomena and strategies that are being explored, evidences the valuable insights that are gained by MD, which may be useful to advance antibiotic design, and what the prospects of this in silico method could be in LPS research.Filament formation by cytoskeletal proteins is critical to their involvement in myriad cellular processes. The bacterial actin homolog MreB, which is essential for cell-shape determination in many rod-shaped bacteria, has served as a model system for studying the mechanics of cytoskeletal filaments. Previous molecular dynamics (MD) simulations revealed that the twist of MreB double protofilaments is dependent on the bound nucleotide, as well as binding to the membrane or the accessory protein RodZ, and MreB mutations that modulate twist also affect MreB spatial organization and cell shape. Here, we show that MreB double protofilaments can adopt multiple twist states during microsecond-scale MD simulations. A deep learning algorithm trained only on high- and low-twist states robustly identified all twist conformations across most perturbations of ATP-bound MreB, suggesting the existence of a conserved set of states whose occupancy is affected by each perturbation to MreB. Simulations replacing ATP with ADP indicated that twist states were generally stable after hydrolysis. These findings suggest a rich twist landscape that could provide the capacity to tune MreB activity and therefore its effects on cell shape.Glycolipid metabolism disorder are major threats to human health and life. Genetic, environmental, psychological, cellular, and molecular factors contribute to their pathogenesis. Several studies demonstrated that neuroendocrine axis dysfunction, insulin resistance, oxidative stress, chronic inflammatory response, and gut microbiota dysbiosis are core pathological links associated with it. However, the underlying molecular mechanisms and therapeutic targets of glycolipid metabolism disorder remain to be elucidated. Progress in high-throughput technologies has helped clarify the pathophysiology of glycolipid metabolism disorder. In the present review, we explored the ways and means by which genomics, transcriptomics, proteomics, metabolomics, and gut microbiomics could help identify novel candidate biomarkers for the clinical management of glycolipid metabolism disorder. We also discuss the limitations and recommended future research directions of multi-omics studies on these diseases.Toxoplasma gondii is a common zoonotic protozoan pathogen adapted to intracellular parasitism in many host cells of diverse organisms. Our previous work has identified 18 cyclic nucleotide phosphodiesterase (PDE) proteins encoded by the parasite genome, of which 11 are expressed during the lytic cycle of its acutely-infectious tachyzoite stage in human cells. selleck chemicals Here, we show that ten of these enzymes are promiscuous dual-specific phosphodiesterases, hydrolyzing cAMP and cGMP. TgPDE1 and TgPDE9, with a Km of 18 μM and 31 μM, respectively, are primed to hydrolyze cGMP, whereas TgPDE2 is highly specific to cAMP (Km, 14 μM). Immuno-electron microscopy revealed various subcellular distributions of TgPDE1, 2, and 9, including in the inner membrane complex, apical pole, plasma membrane, cytosol, dense granule, and rhoptry, indicating spatial control of signaling within tachyzoites. Notably, despite shared apical location and dual-catalysis, TgPDE8 and TgPDE9 are fully dispensable for the lytic cycle and show no functional redundancy. In contrast, TgPDE1 and TgPDE2 are individually required for optimal growth, and their collective loss is lethal to the parasite. In vitro phenotyping of these mutants revealed the roles of TgPDE1 and TgPDE2 in proliferation, gliding motility, invasion and egress of tachyzoites. Moreover, our enzyme inhibition assays in conjunction with chemogenetic phenotyping underpin TgPDE1 as a target of commonly-used PDE inhibitors, BIPPO and zaprinast. Finally, we identified a retinue of TgPDE1 and TgPDE2-interacting kinases and phosphatases, possibly regulating the enzymatic activity. In conclusion, our datasets on the catalytic function, physiological relevance, subcellular localization and drug inhibition of key phosphodiesterases highlight the previously-unanticipated plasticity and therapeutic potential of cyclic nucleotide signaling in T. gondii.Single-cell transcriptomics offers opportunities to investigate ligand-receptor (LR) interactions between heterogeneous cell populations within tissues. However, most existing tools for the inference of intercellular communication do not allow prioritization of functional LR associations that provoke certain biological responses in the receiver cells. In addition, current tools do not enable the identification of the impact on the downstream cell types of the receiver cells. We present CommPath, an open-source R package and webserver, to analyze and visualize the LR interactions and pathway-mediated intercellular communication chain with single-cell transcriptomic data. CommPath curates a comprehensive signaling pathway database to interpret the consequences of LR associations and therefore infers functional LR interactions. Furthermore, CommPath determines cell-cell communication chain by considering both the upstream and downstream cells of user-defined cell populations. Applying CommPath to human hepatocellular carcinoma dataset shows its ability to decipher complex LR interaction patterns and the associated intercellular communication chain, as well as their changes in disease versus homeostasis.Nuclear translocation of large proteins is mediated through karyopherins, carrier proteins recognizing specific motifs of cargo proteins, known as nuclear localization signals (NLS). However, only few NLS signals have been reported until now. In the present work, NLS signals for Importins 4 and 5 were identified through an unsupervised in silico approach, followed by experimental in vitro validation. The sequences LPPRS(G/P)P and KP(K/Y)LV were identified and are proposed as recognition motifs for Importins 4 and 5 binding, respectively. They are involved in the trafficking of important proteins into the nucleus. These sequences were validated in the breast cancer cell line T47D, which expresses both Importins 4 and 5. Elucidating the complex relationships of the nuclear transporters and their cargo proteins is very important in better understanding the mechanism of nuclear transport of proteins and laying the foundation for the development of novel therapeutics, targeting specific importins.The hepatitis C virus (HCV) p7 viroporin protein is essential for viral assembly and release, suggesting its unrealised potential as a target for HCV interventions. Several classes of small molecules that can inhibit p7 through allosteric mechanisms have shown low efficacy. Here, we used a high throughput virtual screen to design a panel of eight novel cyclic penta-peptides (CPs) that target the p7 channel with high binding affinity. Further examination of the effects of these CPs in viral production assays indicated that CP7 exhibits the highest potency against HCV among them. Moreover, the IC50 efficacy of CP7 in tests of strain Jc1-S282T suggested that this cyclopeptide could also effectively inhibit a drug-resistant HCV strain. A combination of nuclear magnetic resonance (NMR) spectroscopy and molecular dynamics (MD) simulations revealed that CP7 blocking activity relies on direct binding to the p7 channel lumen at the N-terminal bottleneck region. These findings thus present a promising anti-HCV cyclic penta-peptide targeting p7 viroporin, while also describing an alternative strategy for designing a new class of p7 channel blockers for strains resistant to direct-acting antiviral agents (DAA).