Activator Sequence Research Articles

Characterization of two Friunavirus phages and their inhibitory effects on biofilms of extremely drug resistant Acinetobacter baumannii in Dakar, Senegal

BackgroundAcinetobacter baumannii is a gram-negative, opportunistic pathogen, that is responsible for a wide variety of infections and is a significant cause of hospital-acquired infections. A. baumannii is listed by the World Health Organization (WHO) as a critical priority pathogen because of its high level of antibiotic resistance and the urgent need for alternative treatment solutions. To address this challenge, bacteriophages have been used to combat bacterial infections for more than a century, and phage research has regained interest in recent years due to antimicrobial resistance (AMR). However, although the vast majority of deaths from the AMR crisis will occur in developing countries in Africa and Asia, few phages’ studies have been conducted in these regions. In this study, we present a comprehensive characterization of the bacteriophages vAbBal23 and vAbAbd25, actives against extremely drug-resistant (XDR) A. baumannii.MethodsPhages were isolated from environmental wastewaters in Dakar, Senegal. The host-range, thermal and pH stabilities, infection kinetics, one step growth assay, antibiofilm activity assay, sequencing, and genomic analysis, were performed to characterize the isolated phages.ResultsComparative genomic and phylogenetic analyses revealed that vAbBal23 and vAbAbd25 belong to the Caudoviricetes class, Autographiviridae family and Friunavirus genus. Both phages demonstrated activity against strains with capsular type KL230. They were stable over a wide pH range (pH 3 to 9) and at temperatures ranging from 25 °C to 40 °C. Additionally, the phages exhibited notable activity against both planktonic and biofilm cells of targeted extremely drug resistant A. baumannii. The results presented here indicate the lytic nature of vAbBal23 and vAbAbd25. This is further supported by the absence of genes encoding toxins, resistance genes and bacterial virulence factors, highlighting their potential for future phage applications.ConclusionPhages vAbBal23 and vAbAbd25 are promising biological agents that can infect A. baumannii, making them suitable candidates for use in phage therapies.

Repeated Shifts in Sociality Are Associated With Fine-tuning of Highly Conserved and Lineage-Specific Enhancers in a Socially Flexible Bee.

Comparative genomic studies of social insects suggest that changes in gene regulation are associated with evolutionary transitions in social behavior, but the activity of predicted regulatory regions has not been tested empirically. We used self-transcribing active regulatory region sequencing, a high-throughput enhancer discovery tool, to identify and measure the activity of enhancers in the socially variable sweat bee, Lasioglossum albipes. We identified over 36,000 enhancers in the L. albipes genome from 3 social and 3 solitary populations. Many enhancers were identified in only a subset of L. albipes populations, revealing rapid divergence in regulatory regions within this species. Population-specific enhancers were often proximal to the same genes across populations, suggesting compensatory gains and losses of regulatory regions may preserve gene activity. We also identified 1,182 enhancers with significant differences in activity between social and solitary populations, some of which are conserved regulatory regions across species of bees. These results indicate that social trait variation in L. albipes is associated with the fine-tuning of ancient enhancers as well as lineage-specific regulatory changes. Combining enhancer activity with population genetic data revealed variants associated with differences in enhancer activity and identified a subset of differential enhancers with signatures of selection associated with social behavior. Together, these results provide the first empirical map of enhancers in a socially flexible bee and highlight links between cis-regulatory variation and the evolution of social behavior.

Studying the Flow Experience in Computer-Supported Collaborative Learning

Computer-Supported Collaborative Learning (CSCL) is recognized as an effective methodology for fostering social interaction mediated by technology in ways that potentially trigger learning. The successful implementation of CSCL hinges on factors such as the scripting mechanics for activity sequencing proposed by Collaborative Learning Flow Patterns (CLFP). Yet, research in CSCL scripts has not studied if CLFPs achieves the so-called notion of “flow experience,” defined as an optimal state in which individuals are engaged and absorbed in an activity. This study proposes an approach to measure flow in the case of the Pyramid CLFP and studies the factors that influence the flow experience in the PyramidApp tool. The study tests a model that uses analysis of the Flow Short Scale and data logs. The findings show that there is a relationship between factors such as the speed of individual contributions and active participation in groups with the flow experience. Notably, the quantity of participation does not exhibit a discernible impact on the flow. The study emphasizes the interest of the modelled factors and the proposed approach for learning analytics to understand the flow experience in CLFP implementations.

Activated single nucleotide mismatch determination through toehold-embedded hairpin-mediated strand displacement reaction alongside CRISPR-Cas12a for detection of TP53 point mutation

The CRISPR-Cas12a-based diagnostic platform is renowned for its high sensitivity and, when coupled with the toehold-mediated strand displacement (TMSD) reaction, enables the detection of single nucleotide mismatch (SNM). However, conventional TMSD with the CRISPR-Cas12a system suffers from signal leakage due to overlapping activator sequences and low displacement efficiency. Here, we propose a toehold-embedded hairpin-mediated strand displacement (THMSD) method and leverage the magnesium attenuation effect of CRISPR-Cas12a for selective single-point mutation detection in the TP53 gene. This approach effectively mitigates signal leakage as the hairpin DNA retains partial crRNA targeting sequences within its loop, preventing sequence overlap with the trigger and direct Cas12a activation. Additionally, the magnesium attenuation effect reduces background signal and enhances selectivity. We investigated toehold length and mismatch position to detect SNM with a high discrimination factor (DF). This high DF allows the THMSD/CRISPR-Cas12a system to differentiate between TP53 R273H mutant and TP53 wildtype genes for cancer screening at low abundances, down to 0.1 % with a concentration of 20 pM. When used for screening between H1975 and WI38 cells, the statistically significant difference was attainable with polymerase chain reaction (PCR) template loading as low as 4 ng. These results pave the way for developing CRISPR-Cas12a-based methods to effectively detect TP53 mutations as lung cancer biomarkers.

Programming gel automata shapes using DNA instructions

The ability to transform matter between numerous physical states or shapes without wires or external devices is a major challenge for robotics and materials design. Organisms can transform their shapes using biomolecules carrying specific information and localize at sites where transitions occur. Here, we introduce gel automata, which likewise can transform between a large number of prescribed shapes in response to a combinatorial library of biomolecular instructions. Gel automata are centimeter-scale materials consisting of multiple micro-segments. A library of DNA activator sequences can each reversibly grow or shrink different micro-segments by polymerizing or depolymerizing within them. We develop DNA activator designs that maximize the extent of growth and shrinking, and a photolithography process for precisely fabricating gel automata with elaborate segmentation patterns. Guided by simulations of shape change and neural networks that evaluate gel automata designs, we create gel automata that reversibly transform between multiple, wholly distinct shapes: four different letters and every even or every odd numeral. The sequential and repeated metamorphosis of gel automata demonstrates how soft materials and robots can be digitally programmed and reprogrammed with information-bearing chemical signals.

Time matters: a critical multimodal study of an English learning app for children in China

ABSTRACT Time and its management, as evident in the scheduling, sequencing and structuring of activities, is important in early childhood education (ECE). While ECE apps have been changing teaching and learning practices for over a decade, research has yet to examine how they represent and manage time. To address this gap, we analysed the representation and management of time in iHuman ABC, a popular English learning app for children in China. Adopting Djonov and Van Leeuwen's (2018) model for analysing semiotic software and Van Leeuwen’s (2008) critical discourse studies framework, we examined temporal features in the app's design and references to these features in promotional materials and user reviews. Findings reveal that iHuman ABC reflects an instructivist ideology which considerably limits children’s ability to organise their time and contradicts research-based recommendations for teaching English. This study contributes to critical multimodal studies of the potential of software to transform educational practices.

Principle and application of self-transcribing active regulatory region sequencing in enhancer discovery research.

Self-transcribing active regulatory region sequencing (STARR-seq) is a high-throughput sequencing method capable of simultaneously discovering and validating all enhancers within the genome. In this method, candidate sequences are inserted into plasmid vectors and electroporated into cells. Acting as both enhancers and target genes, the self-transcription of these sequences will also be enhanced by themselves. By sequencing the transcriptome and comparing the results with the non-inserted control, the locations and activity of enhancers can be determined. In traditional enhancer discovery strategies, the chromatin open regions and transcription active regions were sequenced and predicted as enhancers. However, the activity of these putative enhancers could only be validated one by one without a high-throughput method. STARR-seq solved this limitation, allowing simultaneous enhancers discovery and activity validation in a high-throughput manner. Since the introduction of STARR-seq, it has been widely used to discover enhancers and validate enhancer activity in a number of organisms and cells. In this review, we present the traditional enhancer prediction methods and the basic principles, development history, specific applications of STARR-seq, and its future prospects, aiming to provide a reference for researchers in related fields conducting enhancer studies.

Surrogate-assisted cooperative learning genetic programming for the resource-constrained project scheduling problem with stochastic activity durations and transfer times

For the project scheduling problem with transfer times under an uncertain environment, not only the activity durations are stochastic, but transfer times are often also stochastic. Therefore, we propose a resource-constrained project scheduling problem with stochastic activity durations and transfer times (RCPSP-SDT), which requires complex activity sequencing and resource transfer decisions with an activity priority rule (APR) and a resource transfer priority rule (RTPR) under unpredicted dynamic factors. However, manually designed combination rules of APRs and RTPRs are time-consuming and only for specific scenarios. Therefore, we develop a hyper-heuristic approach based on genetic programming (GP), which has been successfully applied to evolve activity priority rules for project scheduling problems. A new representation of GP individuals was designed to evolve the APR and the RTPR simultaneously. In order to improve the efficiency and solution quality of the approach, we propose surrogate-assisted cooperative learning genetic programming (SCLGP) based on GP. Based on the benchmark data set, computer experiments were conducted under nine variance levels of stochastic distributions. The results show that the proposed algorithm SCLGP performs significantly better than the classical priority rule (PR)-based heuristics. Furthermore, the effectiveness and efficiency of SCLGP were verified by comparing it to four other GP-based algorithms. Finally, the impact of the parameters on the algorithm was investigated, proving that these parameters affect the algorithm’s performance.

Root-soil-microbiome interaction in the rhizosphere of Masson pine (Pinus massoniana) under different levels of heavy metal pollution

Heavy metal pollution of the soil affects the environment and human health. Masson pine is a good candidate for phytoremediation of heavy metal in mining areas. Microorganisms in the rhizosphere can help with the accumulation of heavy metal in host plants. However, studies on its rhizosphere bacterial communities under heavy metal pollution are still limited. Therefore, in this study, the chemical and bacterial characteristics of Masson pine rhizosphere under four different levels of heavy metal pollution were investigated using 16 S rRNA gene sequencing, soil chemistry and analysis of plant enzyme activities. The results showed that soil heavy metal content, plant oxidative stress and microbial diversity damage were lower the farther they were from the mine dump. The co-occurrence network relationship of slightly polluted soils (C1 and C2) was more complicated than that of highly polluted soils (C3 and C4). Relative abundance analysis indicated Sphingomonas and Pseudolabrys were more abundant in slightly polluted soils (C1 and C2), while Gaiella and Haliangium were more abundant in highly polluted soils (C3 and C4). LEfSe analysis indicated Burkholderiaceae, Xanthobacteraceae, Gemmatimonadaceae, Gaiellaceae were significantly enriched in C1 to C4 site, respectively. Mantel analysis showed that available cadmium (Cd) contents of soil was the most important factor influencing the bacterial community assembly. Correlation analysis showed that eight bacterial genus were significantly positively associated with soil available Cd content. To the best of our knowledge, this is the first study to investigate the rhizospheric bacterial community of Masson pine trees under different degrees of heavy metal contamination, which lays the foundation for beneficial bacteria-based phytoremediation using Masson pines in the future.

Lead-Time Prediction in Wind Tower Manufacturing: A Machine Learning-Based Approach

This study focuses on estimating the lead times of various processes in wind tower factories. Accurate estimation of these times allows for more efficient sequencing of activities, proper allocation of resources, and setting of realistic delivery dates, thus avoiding delays and bottlenecks in the production flow and improving process quality and efficiency. In addition, accurate estimation of these times contributes to a proper assessment of costs, overcoming the limitations of traditional techniques; this allows for the establishment of tighter quotations. The data used in this study were collected at wind tower manufacturing facilities in Spain and Brazil. Data preprocessing was conducted rigorously, encompassing cleaning, transformation, and feature selection processes. Following preprocessing, machine learning regression analysis was performed to estimate lead times. Nine algorithms were employed: decision trees, random forest, Ridge regression, Lasso regression, Elastic Net, support vector regression, gradient boosting, XGBoost, LightGBM, and multilayer perceptron. Additionally, the performance of two deep learning models, TabNet and NODE, designed specifically for tabular data, was evaluated. The results showed that gradient boosting-based algorithms were the most effective in predicting processing times and optimizing resource allocation. The system is designed to retrain models as new information becomes available.

Antitumor activity and transcriptome sequencing (RNA-seq) analyses of hepatocellular carcinoma cells in response to exposure triterpene-nucleoside conjugates

Fifteen betulonic/betulinic acid conjugated with nucleoside derivatives were synthesized to enhance antitumor potency and water solubility. Among these, the methylated betulonic acid-azidothymidine compound (8c) exhibited a broad-spectrum of antitumor activity against three tested tumor cell lines, including SMMC-7721 (IC50 = 5.02 μM), KYSE-150 (IC50 = 5.68 μM), and SW620 (IC50 = 4.61 μM) and along with lower toxicity (TC50 > 100 μM) estimated by zebrafish embryos assay. Compared to betulinic acid (<0.05 μg/mL), compound 8c showed approximately 40-fold higher water solubility (1.98 μg/mL). In SMMC-7721 cells, compound 8c induced autophagy and apoptosis as its concentration increased. Transcriptomic sequencing analysis was used to understand the potential impacts of the underlying mechanism of 8c on SMMC-7721 cells. Transcriptomic studies indicated that compound 8c could activate autophagy by inhibiting the PI3K/AKT pathway in SMMC-7721 cells. Furthermore, in the xenograft mice study, compound 8c significantly slowed down the tumor growth, as potent as paclitaxel treated group. In conclusion, methylated betulonic acid-azidothymidine compound (8c) not only increases water solubility, but also enhances the potency against hepatocellular carcinoma cells by inducing autophagy and apoptosis, and suppressing the PI3K/Akt/mTOR signaling pathway.

Mixed-integer linear programming for project scheduling under various resource constraints

Project scheduling is an important management task in many companies across different industries. Generally, projects require resources, such as personnel or funds, whose availabilities are limited, giving rise to the challenging problem of resource-constrained project scheduling. In this paper, we consider the scheduling of a project consisting of precedence-related activities that require time and two types of resources for execution: storage resources representing, e.g., the project budget; and renewable resources representing, e.g., personnel or equipment. Storage resources are consumed by activities at their start or produced upon their completion, while renewable resources are allocated to activities at their start and released upon their completion. The resource-constrained project scheduling problem with consumption and production of resources (RCPSP-CPR) consists of determining a minimum-makespan schedule such that all precedence relations are respected, the demand for each renewable resource never exceeds its capacity, and the stock level of each storage resource never falls below a prescribed minimum. Due to the consideration of storage resources, the feasibility variant of this problem is NP-complete. We propose a novel compact mixed-integer linear programming (MILP) model based on a novel type of sequencing variables. These variables enable us to identify which activities are processed in parallel and whether a sequencing of activities is necessary to respect the resource capacities. Our computational results indicate that our novel model significantly outperforms state-of-the-art MILP models for all considered scarcity settings of the storage resources. Additionally, our results indicate a superior performance for instances of the well-known resource-constrained project scheduling problem (RCPSP).

Disruptive market-shaping processes: Exploring market formation for autonomous vehicle solutions

In the race toward autonomous vehicle solutions, providers engage in disruptive market-shaping processes to overcome barriers to commercialization and trigger market formation. This study investigates how disruptive market-shaping processes unfold by employing a multiple case study of 10 AVS providers. Our findings reveal a disruptive market-shaping process comprising four dimensions: shaping the value logic, nurturing the demand side, forging ecosystem alliances, and catalyzing institutional changes. Furthermore, we analyze the temporal unfolding of these activities to illustrate a comprehensive market-shaping process comprising three phases: market exploration, market preparation, and market experimentation. We make significant contributions to the market-shaping and AVS literature. By showing empirically how disruptive market-shaping processes unfold, we refine existing knowledge on the broader market formation stage, anchoring it to the emergence of new markets. Our framework offers micro-level insights into the sequencing of activities and phases embedded in shaping new markets. We argue that successful disruptive market shaping is contingent on creating an attractive market value proposition. Finally, we identify the foundational activities required to overcome market barriers and advance the commercialization of autonomous solutions. We find that granular systems logic reveals AVS's true value, questioning traditional market logic and paving the way for a sustainable, autonomous future.

Mechanistic patterns and clinical implications of oncogenic tyrosine kinase fusions in human cancers

Tyrosine kinase (TK) fusions are frequently found in cancers, either as initiating events or as a mechanism of resistance to targeted therapy. Partner genes and exons in most TK fusions are followed typical recurrent patterns, but the underlying mechanisms and clinical implications of these patterns are poorly understood. By developing Functionally Active Chromosomal Translocation Sequencing (FACTS), we discover that typical TK fusions involving ALK, ROS1, RET and NTRK1 are selected from pools of chromosomal rearrangements by two major determinants: active transcription of the fusion partner genes and protein stability. In contrast, atypical TK fusions that are rarely seen in patients showed reduced protein stability, decreased downstream oncogenic signaling, and were less responsive to inhibition. Consistently, patients with atypical TK fusions were associated with a reduced response to TKI therapies. Our findings highlight the principles of oncogenic TK fusion formation and selection in cancers, with clinical implications for guiding targeted therapy.

Antibody–drug conjugates (ADCs) in lung cancer treatment

SummaryADCs will likely become another important treatment strategy in advanced, pretreated lung cancer where current treatment options are limited. Both onocogene-directed or biomarker relateted (e.g. EGFR, HER2, MET) and ADCs where no biomarker selection is required (e.g. TROP2, HER3) have either already demonstrated efficacy or under further investigation in NSCLC. Improving the outcomes of patients with metastatic lung cancer is important, thus continued research is necessary to improve understanding of the clinical activity, safety, and optimal sequencing of ADCs in lung cancer treatment.

Data-Driven Heuristic Optimization for Complex Large-Scale Crude Oil Operation Scheduling

This paper addresses the challenging scheduling of crude oil operations (SCOO) problem, characterized by the intricate sequencing of activities involving discrete events and continuous variables. Given the NP-Hard nature of scheduling problems due to their combinatorial complexity, this study employs a data-driven optimization approach. Initially, historical operational data relevant to the SCOO are scrutinized; however, due to data limitations, small-scale instances are solved using a mathematical programming model to generate data. Subsequently, operational solution data are processed using the Apriori algorithm, a renowned data mining technique. The insights gained are translated into heuristic rules, laying the groundwork for a novel data-driven heuristic algorithm tailored for the SCOO problem. This algorithm is then applied to a 45-day scheduling scenario, demonstrating the efficacy of the proposed approach.

Rapid transition from complete nitrification to partial nitrification-anammox at low temperatures via thermal inactivation of nitrite oxidoreductase

The stable and efficient biological nitrogen removal at low temperatures has always posed a challenge for the engineering application of the partial nitrification-anammox (PN/A) process. In this study, a complete nitrification system operating at 15 ± 1 °C was rapidly switched to PN/A mode and stably operated through thermal treatment of thickened sludge and anammox biofilm. Batch test results revealed that the ammonia uptake rate (AUR) and nitrite uptake rate (NUR) of activated sludge, after being treated at 35 °C for 2 days, decreased by 22.88 % and 96.10 %, respectively. Similarly, anammox biofilm exhibited no significant change in anammox activity (ram), but a significant decrease in NUR by 52.7 % after being treated at 40 °C for 2 days. Long-term coupling experiments demonstrated that the complete nitrification system at 15 °C was rapidly switched to PN/A mode with a TN removal rate (TNRR) of 87.91 ± 1.53 % through selective inactivation between ammonia monooxygenase (AMO) and nitrite oxidoreductase (NOR) at 35 °C for 2 days for activated sludge. Furthermore, the coexistence of Nitrospira in the seeding anammox biofilm and the proliferation of Nitrotoga in the biofilm led to the deterioration of the PN/A system. However, the collapsed TNRR could be rapidly restored to 96.42 % through selective inactivation between hydrazine synthase (HDH) and NOR at 40 °C for 2 days for anammox biofilm. The results of key enzymatic activity and Illumina MiSeq sequencing indicated a significant decrease in NOR activity but no significant change in community structure after thermal treatment for both thickened sludge and anammox biofilm. Therefore, the rapid transition of the PN/A system in a complete nitrification system at low temperatures was achieved through the selective inactivation of enzymes between NOR and AMO/HDH, rather than NOB washout.

Constructing eRNA-mediated gene regulatory networks to explore the genetic basis of muscle and fat-relevant traits in pigs

BackgroundEnhancer RNAs (eRNAs) play a crucial role in transcriptional regulation. While significant progress has been made in understanding epigenetic regulation mediated by eRNAs, research on the construction of eRNA-mediated gene regulatory networks (eGRN) and the identification of critical network components that influence complex traits is lacking.ResultsHere, employing the pig as a model, we conducted a comprehensive study using H3K27ac histone ChIP-seq and RNA-seq data to construct eRNA expression profiles from multiple tissues of two distinct pig breeds, namely Enshi Black (ES) and Duroc. In addition to revealing the regulatory landscape of eRNAs at the tissue level, we developed an innovative network construction and refinement method by integrating RNA-seq, ChIP-seq, genome-wide association study (GWAS) signals and enhancer-modulating effects of single nucleotide polymorphisms (SNPs) measured by self-transcribing active regulatory region sequencing (STARR-seq) experiments. Using this approach, we unraveled eGRN that significantly influence the growth and development of muscle and fat tissues, and identified several novel genes that affect adipocyte differentiation in a cell line model.ConclusionsOur work not only provides novel insights into the genetic basis of economic pig traits, but also offers a generalizable approach to elucidate the eRNA-mediated transcriptional regulation underlying a wide spectrum of complex traits for diverse organisms.

Personalized recommendations for learning activities in online environments: a modular rule-based approach

Personalization in online learning environments has been extensively studied at various levels, ranging from adaptive hints during task-solving to recommending whole courses. In this study, we focus on recommending learning activities (sequences of homogeneous tasks). We argue that this is an important yet insufficiently explored area, particularly when considering the requirements of large-scale online learning environments used in practice. To address this gap, we propose a modular rule-based framework for recommendations and thoroughly explain the rationale behind the proposal. We also discuss a specific application of the framework.

Abstract LB212: Transcriptional dosage of oncogenic KRAS drives lung adenocarcinoma cell states, progression and metastasis

Abstract Metastasis is the endpoint of tumorigenesis and is generally diagnosed at an untreatable stage. The molecular mechanisms underlying metastasis and biomarkers for early intervention are largely unknown. Here, combining a mouse model of aggressive Kras-driven non-small-cell lung cancer with in vivo fate mapping and CRISPR activation screens, we show that transcriptional amplification of oncogenic Kras promotes orthotopic lung cancer lesions growth as well as oligoclonal and polyclonal metastasis to distal organs. Mild Kras transcriptional up-regulation by CRISPRa is sufficient to fuel competitive advantage in vitro and in vivo. Parallel CRISPR activation and transcriptome sequencing show that KrasG12D amplification cooperates with sterile inflammation to drive tumor progression and with the TGFß pathway to promote metastasis. Chromatin profiling of Kras-amplifying cells uncovers transcriptional regulators potentially collaborating with oncogenic Kras to drive lung cancer and metastasis in vivo. Transcriptional modulation of KRAS dosage in mouse cell and pharmacological inhibition in human PDXs affect cell stress and differentiation programs. These observations are corroborated by bulk and single-cell RNA-seq analysis in patients. Collectively, our data on a model oncogene advance our current understanding of how KRAS drives tumor evolution, with implications for prognosis and treatment of patients. Citation Format: Michela Serresi, Sonia Kertalli, Yuliia Dramaretska, Matthias Jürgen Schmitt, Heike Naumann, Maria Zschummel, Gaetano Gargiulo. Transcriptional dosage of oncogenic KRAS drives lung adenocarcinoma cell states, progression and metastasis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB212.