Target Function Research Articles

Optimization of breeding program design through stochastic simulation with evolutionary algorithms.

The effective planning and allocation of resources in modern breeding programs is a complex task. Breeding program design and operational management have a major impact on the success of a breeding program and changing parameters such as the number of selected/phenotyped/genotyped individuals in the breeding program will impact genetic gain, genetic diversity, and costs. As a result, careful assessment and balancing of design parameters is crucial, taking into account the trade-offs between different breeding goals and associated costs. In a previous study, we optimized the resource allocation strategy in a dairy cattle breeding scheme via the combination of stochastic simulations and kernel regression, aiming to maximize a target function containing genetic gain and the inbreeding rate under a given budget. However, the high number of simulations required when using the proposed kernel regression method to optimize a breeding program with many parameters weakens the effectiveness of such a method. In this work, we are proposing an optimization framework that builds on the concepts of kernel regression but additionally makes use of an evolutionary algorithm to allow for a more effective and general optimization. The key idea is to consider a set of potential parameter settings of the breeding program, evaluate their performance based on stochastic simulations, and use these outputs to derive new parameter settings to test in an iterative procedure. The evolutionary algorithm was implemented in a Snakemake workflow management system to allow for efficient scaling on large distributed computing platforms. The algorithm achieved stabilization around the same optimum with a massively reduced number of simulations. Thereby, the incorporation of class variables and accounting for a higher number of parameters in the optimization framework leads to substantially reduced computing time and better scaling for the desired optimization of a breeding program.

Application of Nanoparticles in the Diagnosis and Treatment of Colorectal Cancer.

Colorectal cancer is a common malignant tumor with high morbidity and mortality rates, imposing a huge burden on both patients and the healthcare system. Traditional treatments such as surgery, chemotherapy and radiotherapy have limitations, so finding more effective diagnostic and therapeutic tools is critical to improving the survival and quality of life of colorectal cancer patients. While current tumor targeting research mainly focuses on exploring the function and mechanism of molecular targets and screening for excellent drug targets, it is crucial to test the efficacy and mechanism of tumor cell therapy that targets these molecular targets. Selecting the appropriate drug carrier is a key step in effectively targeting tumor cells. In recent years, nanoparticles have gained significant interest as gene carriers in the field of colorectal cancer diagnosis and treatment due to their low toxicity and high protective properties. Nanoparticles, synthesized from natural or polymeric materials, are NM-sized particles that offer advantages such as low toxicity, slow release, and protection of target genes during delivery. By modifying nanoparticles, they can be targeted towards specific cells for efficient and safe targeting of tumor cells. Numerous studies have demonstrated the safety, efficiency, and specificity of nanoparticles in targeting tumor cells, making them a promising gene carrier for experimental and clinical studies. This paper aims to review the current application of nanoparticles in colorectal cancer diagnosis and treatment to provide insights for targeted therapy for colorectal cancer while also highlighting future prospects for nanoparticle development.

The Combined Effect of Hypoxia Activation and Radiosensitization by a Multifunctional Nanoplatform System Enhances the Therapeutic Efficacy of Chemoradiotherapy in Pancreatic Cancer

BackgroundPancreatic cancer is a highly malignant tumor, which is still a major global health problem. Chemotherapy and radiotherapy are regularly used in adjuvant therapy for pancreatic cancer but their therapeutic efficacy is limited. MethodsIn the present study, nanoparticle（MSN-AuNPs） was used as a drug carrier loaded with tirapazamine(TPZ) and hyaluronic acid (HA) to synthesize a multifunctional nanoplatform HA@TPZ-MSN-AuNPs (HTMA) for hypoxia activation and radiotherapy sensitization, which can be combined with radiotherapy therapy and synergistically enhance the therapeutic effect in pancreatic cancer. The anti-tumor performance of the nano platform was verified by in vivo and in vitro experiments. ResultFirst, the HA@TPZ-MSN-AuNPs (HTMA) was successfully synthesized. Drug release experiments showed that acidic environment and hyaluronidase promoted drug release in the nanoplatform. In vitro experiments, CCK-8, live-dead staining, clonal formation assay and flow cytometry confirmed the combined anti-tumor effect of hypoxia activation and radiotherapy sensitization with HTMA. In the drug uptake experiment, the nanoplatform showed the function of targeting and binding pancreatic cancer cells. In vivo, HTMA demonstrated good antitumor properties and good biocompatibility. ConclusionsThe nanoplatform had a good targeting effect and synergistic anti-tumor effect. The combination of hypoxia activation and radiotherapy sensitization is a promising strategy for the treatment of pancreatic cancer.

Chemical Probes Review: Choosing the Right Path Towards Pharmacological Targets in Drug Discovery, Challenges and Future Perspectives.

Chemical probes are essential for academic research and target validation for disease identification. They facilitate drug discovery, target function investigation, and translation studies. A chemical probe provides starting material that can accelerate therapeutic values and safety measures for identifying any biological target in drug discovery. Essential read outs depend on their versatility in biochemical testing, proving the hypothesis, selectivity, specificity, affinity towards the target site, and valuable in new therapeutic approaches. Disease management will depend upon chemical probes as a primitive tool to ascertain the physicochemical stability for in vivo and in vitro studies useful for clinical trials and industrial application in the future. For cancer research, bacterial infection, and neurodegenerative disorders, chemical probes are integrated circuits which are on pipeline for the drug discovery process Furthermore, pharmacological modulators incorporate activators, crosslinkers, degraders, and inhibitors. Reports accessed depend on their structural, mechanical, biochemical, and pharmacological characterization in drug discovery research. The perspective for designing any chemical probes concludes with the utilization of drug discovery and identification of the potential target. It focuses mainly on evidence-based studies and produces promising results in successfully delivering novel therapeutics to treat cancers and other disorders at the target site. Moreover, natural product pharmacophores like rapamycin, cephalosporin, and α-lactamase are utilized for drug discovery. Chemical probes revolutionize computational-based study design depending on identifying novel targets within the database framework. Chemical probes are the clinical answers for drug development and goforward tools in solving other riddles for scientists and researchers working in this industries.

In Search of the Role of Three-Finger Starfish Proteins

Three-finger proteins (TFPs), or Ly6/uPAR proteins, are characterized by the beta-structural LU domain containing three protruding “fingers” and stabilized by four conserved disulfide bonds. TFPs were initially characterized as snake alpha-neurotoxins, but later many studies showed their regulatory roles in different organisms. Despite a known expression of TFPs in vertebrates, they are poorly studied in other taxa. The presence of TFPs in starfish was previously shown, but their targets and functional role still remain unknown. Here, we analyzed expression, target, and possible function of the Lystar5 protein from the Asterias rubens starfish using bioinformatics, qPCR, and immunoassay. First, the presence of Lystar5 homologues in all classes of echinoderms was demonstrated. qPCR revealed that mRNA of Lystar5 and LyAr2 are expressed mainly in coelomocytes and coelomic epithelium of Asterias, while mRNA of other TFPs, LyAr3, LyAr4, and LyAr5, were also found in a starfish body wall. Using anti-Lystar5 serum from mice immunized by a recombinant Lystar5, we confirmed that this protein is expressed on the surface of coelomocytes and coelomic epithelium cells. According to ELISA, a recombinant analogue of Lystar5 bound to the membrane fraction of coelomocytes and coelomic epithelium but not to the body wall or starfish arm tip. Analysis by LC-MALDI MS/MS suggested integrin α-8-like protein expressed in the coelomocytes and coelomic epithelium as a target of Lystar5. Thus, our insights propose the important role of TFPs in regulation of starfish physiology and show prospects for their further research.

In silico aquatic toxicity prediction of chemicals toward Daphnia magna and fathead minnow using Monte Carlo approaches

The fast-increasing use of chemicals led to large numbers of chemical compounds entering the aquatic environment, raising concerns about their potential effects on ecosystems. Therefore, assessment of the ecotoxicological features of organic compounds on aquatic organisms is very important. Daphnia magna and Fathead minnow are two aquatic species that are commonly tested as standard test organisms for aquatic risk assessment and are typically chosen as the biological model for the ecotoxicology investigations of chemical pollutants. Herein, global quantitative structure–toxicity relationship (QSTR) models have been developed to predict the toxicity (pEC(LC)50) of a large dataset comprising 2106 chemicals toward Daphnia magna and Fathead minnow. The optimal descriptor of correlation weights (DCWs) is calculated using the notation of simplified molecular input line entry system (SMILES) and is used to construct QSTR models. Three target functions, TF1, TF2, and TF3 are utilized to generate 12 QSTR models from four splits, and their statistical characteristics are also compared. The designed QSTR models are validated using both internal and external validation criteria and are found to be reliable, robust, and excellently predictive. Among the models, those generated using the TF3 demonstrate the best statistical quality with R 2 values ranging from 0.9467 to 0.9607, Q 2 values ranging from 0.9462 to 0.9603 and RMSE values ranging from 0.3764 to 0.4413 for the validation set. The applicability domain and the mechanistic interpretations of generated models were also discussed.

Unraveling the full impact of SPD_0739: a key effector in S. pneumoniae iron homeostasis.

S. pneumoniae obtains growth essential iron from hemoglobin and other host hemoproteins. Still, the bacterial mechanisms involved are only partially understood, and there are inconsistent reports regarding the function of several transporters implicated in iron uptake. In this study, we clarified the role of PnrA/Spbhp-37, a ligand-binding protein previously linked to nucleoside or heme by different studies. We present data supporting a role in nucleoside scavenging rather than heme import and reveal that PnrA/Spbhp-37 modulates iron and heme uptake, likely by influencing the nucleoside cellular pool. Hence, this work provides a new understanding of a process critical to the pathophysiology of a significant human pathogen. Moreover, PnrA/Spbhp-37 is an abundant and immunogenic surface protein that is highly conserved. Hence, this study also clarifies the function of a promising vaccine target.

Identification and validation of circulating microRNAs as biomarkers for heart failure with preserved ejection fraction

Abstract Background Heart failure (HF) is a syndrome due to cardiac insufficiency, manifested by excessive volume overload, increased sympathetic activity, and circulatory redistribution [1]. Heart failure with preserved ejection fraction (HFpEF) accounts for approximately 40%-50% of the total HF events, and its development is often accompanied by various complications, including hypertension, obesity, and metabolic syndrome. Biomarker associations in heart failure with reduced ejection fraction were mostly cardiac stretch based, while many biomarkers of HFpEF are also associated with inflammation [2]. To date, treatments that effectively improve the clinical prognosis of HFpEF are relatively limited, thus HFpEF remains one of the challenges that needs to be further explored. A full understanding of the molecular mechanisms of HFpEF is essential to improve the early diagnosis, treatment and prognosis of these patients. Purpose The study aims to explore key circulating miRNAs and related molecular pathways in HFpEF patients through bioinformatics analysis and machine learning, and to further validate them using a double-hit HFpEF mouse model. Methods We downloaded HF datasets including circulating miRNA expression profiles from the GEO database. LASSO regression, SVM-RFE, Random Forest and XGboost algorithm were conducted to identify key miRNAs for diagnostic model construction. We used R software, GO, KEGG and Cytoscape to better understand the functions of key miRNA targets. Besides, the mRNA-miRNA interaction networks were predicted by starbase. We also constructed a HFpEF mouse model subjected to high fat diet and L-NAME (0.5 g/L) in drinking water to discover the correlation between the screened circulating miRNA and HFpEF. Results After differential expression analysis of the combined HFpEF dataset, we identified 37 differentially circulating miRNAs, of which 15 were down-regulated and 22 were up-regulated. Subsequently, we screened by machine learning and identified 4 key miRNAs that may be involved in the progression of HFpEF, including hsa-miR-645, hsa-miR-296-5p, hsa-miR-199b-5p, and hsa-miR-509-3-5p. We validated these miRNAs based on their expression, and the ROC plots suggest that these miRNAs may have diagnostic capability in HFpEF. The results of GO and KEGG enrichment analyses showed that the target genes of the differentiated miRNAs were mainly enriched in the MAPK signaling pathway, mTOR signaling pathway, protein processing in endoplasmic reticulum, hippo signaling pathway, cell cycle, Foxo signaling pathway, hypertrophic cardiomyopathy, dilated cardiomyopathy, and circadian rhythm. The mRNA-miRNA interaction networks suggested that TMEM135 was regulated by both has-miR-199b-5p and hsa-miR-296-5p. The RT-PCR results also suggested that miR-199b-5p was upregulated in the HFpEF mice. Conclusion We validated 4 circulating miRNAs with strong correlation to HFpEF, which provide a more reliable basis for pre-symptomatic diagnosis.

Improving transonic performance with adjoint-based NACA 0012 airfoil design optimization

This study delves into the discrete adjoint method, a powerful tool in high-fidelity aerodynamic shape optimization that efficiently computes derivatives of a target function for different design variables. It offers a theoretical exploration of its implementation as an innovative tool for calculating partial derivatives (sensitivities) related to objective functions and design variables. It is implemented to a NACA0012 airfoil at a transonic speed. This designated test case is qualitatively evaluated, considering specified Mach number and Reynolds number values of 0.7 and 15.96 million, respectively. The standard Spalart-Allmaras turbulence model is adapted to improve computational cost efficiency. The results validate the efficiency of Discrete Adjoint with OpenFOAM, showcasing its capability to generate optimal configurations. The achieved optimized performance is evidenced by minimizing the drag coefficient value (Cd) to an impressive value of 0.00871, 62.2 % lower than the previous studies, thus securing a novelty. While this research does not consider the post-processing of sensitivity calculations, it enables the potential for future research. The primary target of this paper is to assess the educational and training needs of researchers, engineers, and graduate students, offering a comprehensive introduction to discrete adjoint aerodynamic design optimization, presenting a novel methodology, and contributing to future advancements in the design optimization field.

Evaluation of influential factors on energy system optimisation

Energy system analysis is gaining more importance in political decisions, science, and society, thus, the basis of interpreting the results is increasingly being questioned. Different approaches have been used in energy system models: Scenario variations, changing base assumptions or sensitivity analyses. A direct comparison of these approaches aims to show that the influence on the results is comparable for all and thus needs to be considered alongside each other. Therefore, this paper uses energy system optimisation within a case study on Germany. It confirms that all approaches greatly influence the results and are thus suitable for discussing uncertainties. Among other things, the sole change of the reference weather year can have a comparable influence on the emissions just like the specification of an entirely different mobility scenario (+44% and +56% respectively). However, the target function (total costs) is significantly more influenced by scenarios. Among the evaluated parameters, the investment costs of renewable technologies and the potential of renewable feedstock affect the system the most. In summary, all three methods provide important insights into the optimisation model and should therefore be used together for validation, discussion and design of energy system models.

Harnessing AlphaFold to reveal hERG channel conformational state secrets.

To design safe, selective, and effective new therapies, there must be a deep understanding of the structure and function of the drug target. One of the most difficult problems to solve has been resolution of discrete conformational states of transmembrane ion channel proteins. An example is K v 11.1 (hERG), comprising the primary cardiac repolarizing current, I kr . hERG is a notorious drug anti-target against which all promising drugs are screened to determine potential for arrhythmia. Drug interactions with the hERG inactivated state are linked to elevated arrhythmia risk, and drugs may become trapped during channel closure. However, the structural details of multiple conformational states have remained elusive. Here, we guided AlphaFold2 to predict plausible hERG inactivated and closed conformations, obtaining results consistent with multiple available experimental data. Drug docking simulations demonstrated hERG state-specific drug interactions in good agreement with experimental results, revealing that most drugs bind more effectively in the inactivated state and are trapped in the closed state. Molecular dynamics simulations demonstrated ion conduction for an open but not AlphaFold2 predicted inactivated state that aligned with earlier studies. Finally, we identified key molecular determinants of state transitions by analyzing interaction networks across closed, open, and inactivated states in agreement with earlier mutagenesis studies. Here, we demonstrate a readily generalizable application of AlphaFold2 as an effective and robust method to predict discrete protein conformations, reconcile seemingly disparate data and identify novel linkages from structure to function.

Selection of aerodynamic characteristics and engine parameters of a maneuverable aircraft under epistemic uncertainty

At the preliminary stage of aircraft design, it is usually required to solve the problem of insufficient initial data for applying traditional algoristic-type mathematical programming models. In many cases, numerous input parameters cannot be accurately specified at the time of making design decisions. If the inaccuracy of parameters is not taken into account, the actual values of target functions may differ significantly from the calculated ones when solving optimization problems. In this regard, the issue of current interest is the development of algorithms to improve the reliability of design decisions under conditions of epistemological uncertainty when experts engage in the formation of initial data. The paper considers the problem of selecting aerodynamic characteristics and engine parameters of maneuverable aircraft under conditions of uncertainty associated with inaccuracy of expert data. The applied problem under consideration is further complicated by the necessity to include “black box” models in the algorithms being developed. The paper proposes algorithms that apply the uncertainty theory together with “black box” models that implement optimization calculation techniques derived from previous engineering practice of aircraft design. Using these algorithms, experts are able to set uncertain parameters whereby the lack of data is factored in using uncertainty distribution functions. In cases of monotonicity of uncertain parameters in target functions, application of the uncertainty theory provides a significant reduction in computational costs compared to the method of statistical modeling in optimization calculations. The paper presents the results of computational studies of the developed algorithms. A genetic algorithm (mathematical optimization solver) is used to optimize the search for design solutions. Pareto frontiers are obtained for different confidence levels enabling to make design decisions including values of aerodynamic characteristics and engine parameters of maneuverable aircraft.

Automation of conceptual design and modification of aircraft type unmanned aerial vehicles using multidisciplinary optimization and evolutionary algorithms. Part 1: Methods and models

This paper proposes a method for selecting rational parameters for large-size aircraft-type unmanned aerial vehicles at the initial design stages using an optimization algorithm of differential evolution and numerical mathematical modeling of aerodynamic problems. The method assumes implementation of weight and aerodynamic balance in the main flight modes, it can consider aircraft-type unmanned aerial vehicles with one or two lifting surfaces, applies parallel calculations, and automatically generates a three-dimensional geometric model of the aircraft appearance based on the optimization results. A method for accelerating by more than three times the process of solving the problem of optimizing aircraft takeoff weight parameters by introducing the target function into the set of design variables is proposed and demonstrated. The results of assessing the reliability of the mathematical models used for aerodynamics and the correct calculation of the target function are presented, taking into account various constraints. A comprehensive check of the operability and effectiveness of the method were considered by solving demonstration problems by optimizing more than ten main design parameters of the appearance of two existing heavy-class unmanned aerial vehicles with known characteristics from open sources. Examples of using the optimization results to modify prototypes are provided.

What can be learnt with wide convolutional neural networks?*

Abstract Understanding how convolutional neural networks (CNNs) can efficiently learn high-dimensional functions remains a fundamental challenge. A popular belief is that these models harness the local and hierarchical structure of natural data such as images. Yet, we lack a quantitative understanding of how such structure affects performance, for example the rate of decay of the generalisation error with the number of training samples. In this paper, we study infinitely wide deep CNNs in the kernel regime. First, we show that the spectrum of the corresponding kernel inherits the hierarchical structure of the network, and we characterise its asymptotics. Then, we use this result together with generalisation bounds to prove that deep CNNs adapt to the spatial scale of the target function. In particular, we find that if the target function depends on low-dimensional subsets of adjacent input variables then the decay of the error is controlled by the effective dimensionality of these subsets. Conversely, if the target function depends on the full set of input variables then the error decay is controlled by the input dimension. We conclude by computing the generalisation error of a deep CNN trained on the output of another deep CNN with randomly initialised parameters. Interestingly, we find that, despite their hierarchical structure, the functions generated by infinitely wide deep CNNs are too rich to be efficiently learnable in high dimensions.

Numerical evaluation of wear parameters using meta-models

AbstractWear parameters identified by linear friction tester (LFT) experiments overestimate the wear mass loss when applied on a laboratory abrasion & skid tester 100 (LAT100) simulation. On the other hand, the identification of wear parameters directly from the LAT100 experiment can be challenging since variables such as the contact area and the slip velocity are not measured experimentally and need to be assumed. To improve the identification process, numerical calibration is used as a more suitable approach. This approach relies on meta-models as a target function for minimization. The meta-models are generated using a sample of wear parameters applied to an LAT100 finite element modeling (FEM) to calculate the corresponding wear mass.. In this model, a transport velocity is defined for the rolling simulation, and an arbitrary lagrangian–eulerian (ALE) adaptive meshing approach is adopted for the wear modeling. For the wear model, a combination of archard’s wear model and schallamach’s abrasion law is used. This model contains a pair of wear parameters to be identified. The ALE adaptive meshing technique moves the nodes independently of the material. Since the mesh topology remains the same, failure of the simulation occurs if the wear volume loss exceeds that of the element. Meta-models are created to extend wear modeling beyond this failure. Once the meta-models are created, they are used as a target function for the minimization algorithm. The minimization algorithm aims to find the optimal wear parameters by minimizing the difference between experimentally observed and numerically produced wear mass loss. The minimization algorithm inputs a set of wear parameters into the meta-models which in turn yield a prediction of the wear mass loss. The process is carried out until an optimum parameter set is identified. Such an approach has a lower accuracy if the parameters are identified directly from the experiment using assumptions regarding the contact shear stress and the sliding velocity. Nonetheless, the main advantage of parameters identified using the meta-model approach is the usability of these parameters in an LAT100 model.

Rasch analysis of quality of life in children with vernal keratoconjunctivitis (QUICK) questionnaire

ABSTRACT Clinical relevance A validated questionnaire measures the construct accurately, helps clinician to support decision making, facilitates assessment and monitoring of patients, guides clinical trial and promotes patient-centred care. Background To assess the psychometric properties of the Quality of Life in Children with Vernal Keratoconjunctivitis (QUICK) questionnaire among Indian children with Vernal Keratoconjuntivitis (VKC) using Rasch analysis. Methods This study used a questionnaire validation study design. The QUICK questionnaire for VKC was translated to two local languages using back translation process and administered to the Indian children (aged 5–15 years) diagnosed with VKC between June 2021 and March 2022. The study took place at a tertiary eye care hospital in Chennai, India. The psychometric properties were assessed based on six indicators namely rating scale behaviour, measurement precision, item fit, unidimensionality, targeting and Differential Item Functioning (DIF). Results Fifty six children with VKC (mean age 10.5 years and 77.8% males) filled the questionnaire. The analysis of overall QUICK questionnaire (16 items) revealed an ordered threshold for the rating scale, no noticeable DIF and excellent person-item targeting (0.15). However, the questionnaire showed inadequate person separation (PSI 1.82 and PSR 0.77), four misfit items and lack of unidimensionality (eigenvalue 2.80). Eliminating misfit items and contrasting items resolved multidimensionality, leaving nine items related to the symptoms domain in the questionnaire. However, the person separation (PSI 1.52 and PSR 0.70) remained below 2.0. Conclusion The resultant 9-item QUICK questionnaire discriminates only low and high performers among VKC patients. Thus, it may be used solely to assess the symptoms domain in Health Related Quality of Life (HRQoL). This questionnaire requires addition of items in other domains pertaining to HRQoL to make it holistic.

Almost pairwise independence and resilience to deep learning attacks

Almost pairwise independence (API) is a quantitative property of a class of functions that is desirable in many cryptographic applications. This property is satisfied by Learning with errors (LWE)-mappings and by special Substitution-Permutation Networks (SPN). API block ciphers are known to be resilient to differential and linear cryptanalysis attacks. Recently, security of protocols against neural network-based attacks became a major trend in cryptographic studies. Therefore, it is relevant to study the hardness of learning a target function from an API class of functions by gradient-based methods. We propose a theoretical analysis based on the study of the variance of the gradient of a general machine learning objective with respect to a random choice of target function from a class. We prove an upper bound and verify that, indeed, such a variance is extremely small for API classes of functions. This implies the resilience of actual LWE-based primitives against deep learning attacks, and to some extent, the security of SPNs. The hardness of learning reveals itself in the form of the barren plateau phenomenon during the training process, or in other words, in a low information content of the gradient about the target function. Yet, we emphasize that our bounds hold for the case of a regular parameterization of a neural network and the gradient may become informative if a class is mildly pairwise independent and a parameterization is non-regular. We demonstrate our theory in experiments on the learnability of LWE mappings.

12439 Investigating The Regulation Of Islet Alpha-cell Function Mediated By The ISL1 And LDB1 Transcriptional Complex

Abstract Disclosure: I. Deeba: None. S. Poole: None. Y. Liu: None. C. Hunter: None. Glucose homeostasis is maintained by the action of insulin-secreting β-cells and glucagon-producing α-cells in the pancreatic islet. Diabetes, traditionally considered a dysglycemia centered on the β-cell, also has clear contributions by dysregulated α-cells. However, little is known of the key transcriptional regulators, including transcription factors (TFs) and interacting co-regulators, driving α-cell development and function. A published pancreas-wide knockout (KO) of the LIM-Homeodomain Islet-1 (ISL1) TF in mice led to a dominant β-cell phenotype, including glucose intolerance and reduced insulin secretion. However, significant reductions of glucagon (Gcg) and Arx mRNA and pancreatic GCG content were also noted, suggesting α-cell impacts. Furthermore, we demonstrated that pancreatic ISL1 activity involves interactions with the LDB1 scaffolding co-regulator. However, nothing is known of the comparative α-cell-specific target genes and functions of these protein partners. To first assess ISL1:LDB1 complexes in α-cells, we performed co-IP and proximity ligation assay (PLA) in mouse α-cell lines and found low-glucose stimulated ISL1 and LDB1 interactions. These observations led us to hypothesize that ISL1 and LDB1 complexes regulate transcription of target genes to drive α-cell function. To assess comparative DNA occupancy, we performed ChIP and observed significant ISL1 and LDB1 binding in conserved Gcg promoter and intronic domains. siRNA-mediated Isl1 or Ldb1 knockdown followed by RNA analysis revealed a similar differential expression of α-cell mRNAs (e.g., Gcg, IL6ra, Sox8, Cox5a, Atp5k, Atp5e), suggesting roles in α-cell function. To compare ISL1 and LDB1 importance in vivo, we developed two novel mouse α-cell ISL1 or LDB1 KO models using Gcg-Cre, termed Isl1Δα and Ldb1Δα, respectively. ISL1 and LDB1 loss was confirmed in α-cells by immunofluorescence. Significant fasting hypoglycemia and hypoglucagonemia were found in postnatal Isl1Δα and Ldb1Δα mice, along with decreased GCG+ cell numbers in Ldb1Δα mice, compared to controls. These findings underscore the crucial roles of these factors in regulating GCG production and secretion. Immunostaining for the MAFB α-cell TF in Ldb1Δα islets revealed potential heterogeneous MAFB expression, marked by MAFB+ cells lacking GCG, or co-expressing INS, each indicating a potential loss of α-cell identity. Our lineage tracing analysis revealed α-cell lineage labeled tomato+ cells in Isl1Δα mice appearing to co-express INS, suggesting transdifferentiation. Currently, inducible α-cell-specific KO mice are in development to investigate the role of ISL1 and LDB1 in adult α-cells. In the future, we will also explore the effects of α-cell-specific ISL1 or LDB1 deficiency during embryogenesis. Overall, our study will benefit efforts in identifying new molecular targets and cell-based therapies to combat diabetes. Presentation: 6/1/2024

Engineering Hyperechogenic Colloids with Clot-Targeting Capabilities from Platelet-Derived Membranes.

Thrombosis-related cardiovascular diseases remain the leading global cause of mortality and morbidity. In this study, we present a pioneering approach in the field of nanobiotechnology, with a focus on clinical translation, aimed at advancing early diagnosis and enhancing treatment options for thrombotic disorders. We introduce the fabrication of Platelet Membrane-Derived Bubbles (PMBs), which exhibit distinctive characteristics compared to conventional nanoparticles. These PMBs possess an average diameter of 700 nm and a negative ζ-potential, mirroring the attributes of parent platelet membranes. Utilizing diagnostic ultrasound imaging, we demonstrated the ability to visualize PMBs as hyperechogenic entities in agarose phantoms in vitro and in live mice in vivo. Furthermore, through confocal laser microscopy, we verified the retention of crucial transmembrane proteins, such as CD41 (GPIIb) and CD42 (GPIb), pivotal in conferring platelet-specific targeting functions. Importantly, our platelet aggregation studies confirmed that PMBs do not induce platelet aggregation but instead adhere to preformed platelet-rich in vitro thrombi. Overall, our work showcases the safe and precise utilization of PMBs to directly target acute thrombosis induced by laser injury in murine mesenteric veins in vivo, as visualized through intravital microscopy. In conclusion, we have successfully developed a rapid method for generating PMBs with unique ultrasound-directed and thrombus-targeting properties. These exceptional attributes of PMBs hold significant promise for advancing the field of ultrasound diagnostic thrombus imaging and clot-targeted therapy in the clinical context.

Protein-RNA interactions mediated by silvestrol-insight into a unique molecular clamp.

Molecular staples or interfacial inhibitors are small molecules that exert their activity through co-association with macromolecules leading to various effects on target functions. Some molecules inhibit target activity, while others generate gain-of-function complexes. We and others have previously identified two structurally distinct classes of molecular staples, pateamine A and rocaglates. These molecules inhibit eukaryotic initiation factor (eIF) 4A, a critical RNA helicase required for translation initiation, by simultaneously interacting with both RNA and protein components. Structural insights from members of these two families indicate that they wedge themselves between RNA bases during engagement. To extend our understanding of rocaglates, we investigated the RNA-binding properties of silvestrol, a natural rocaglate distinguished by the presence of a unique dioxanyloxy ring. Our study demonstrates that silvestrol expands the RNA-binding repertoireof rocaglates due to this structural characteristic, providing a rationale for improving synthetic molecular staples targeting eIF4A.