Mirko Ledda

While the concept of the Human Digital Twin has been proposed within the automotive domain, a structured framework to define and operationalize it is currently lacking. This paper interrogates the concept of Human Driver Digital Twins (HDDTs), systematically distinguishing them from Human Driver Digital Models (HDDMs) and Human Driver Digital Shadows (HDDSs) based on data integration levels and bidirectional feedback loops. We examine a broad spectrum of automotive use cases to assess how the HDDT paradigm can enhance existing applications and where conventional driver modeling methodologies encounter functional limitations. Furthermore, we acknowledge engineering challenges, such as data sovereignty, standardized interaction protocols, and modular internal architectures. Ultimately, this work highlights how an HDDT framework allows systems to transcend the boundaries of static driver modeling, enabling persistent, adaptive, and personalized representations of the human driver.

The trafficking chaperone PDE6D (or PDEƍ) was proposed as a surrogate target for K-Ras, leading to the development of a series of inhibitors that block its prenyl-binding pocket. These inhibitors suffered from low solubility and intracellular potency, preventing their clinical development. Here we developed a highly soluble PDE6D inhibitor (PDE6Di), Deltaflexin3, which has the currently lowest off-target activity, as we demonstrate in dedicated assays. We further increased the K-Ras focus, by exploiting that PKG2-mediated phosphorylation of Ser181 lowers K-Ras binding to PDE6D. Thus, the combination of Deltaflexin3 with the approved PKG2-activator Sildenafil synergistically inhibits cell- and microtumor growth. However, the overall cancer survival of the high PDE6D/ low PKG2 target population is higher than of the group with the opposite signature. Our results therefore suggest re-examining the interplay between PDE6D and K-Ras in cancer, while recommending the development of PDE6Di that ’plug’, rather than ’stuff’ the hydrophobic pocket of PDE6D. Significance: Combinations of a novel PDE6D inhibitor with Sildenafil synergistically focus the inhibition on K-Ras, however, survival data of the target population suggest an interplay of K-Ras and PDE6D that needs further exploration.

G3: Genes|Genomes|Genetics, 11, 2021. https://doi.org/10.1093/g3journal/jkab015

The widely recounted story of the origin of cultivated strawberry (Fragaria × ananassa) oversimplifies the complex interspecific hybrid ancestry of the highly admixed populations from which heirloom and modern cultivars have emerged. To develop deeper insights into the three-century-long domestication history of strawberry, we reconstructed the genealogy as deeply as possible—pedigree records were assembled for 8,851 individuals, including 2,656 cultivars developed since 1775. The parents of individuals with unverified or missing pedigree records were accurately identified by applying an exclusion analysis to array-genotyped single-nucleotide polymorphisms. We identified 187 wild octoploid and 1,171 F. × ananassa founders in the genealogy, from the earliest hybrids to modern cultivars. The pedigree networks for cultivated strawberry are exceedingly complex labyrinths of ancestral interconnections formed by diverse hybrid ancestry, directional selection, migration, admixture, bottlenecks, overlapping generations, and recurrent hybridization with common ancestors that have unequally contributed allelic diversity to heirloom and modern cultivars. Fifteen to 333 ancestors were predicted to have transmitted 90% of the alleles found in country-, region-, and continent-specific populations. Using parent–offspring edges in the global pedigree network, we found that selection cycle lengths over the past 200 years of breeding have been extraordinarily long (16.0-16.9 years/generation), but decreased to a present-day range of 6.0-10.0 years/generation. Our analyses uncovered conspicuous differences in the ancestry and structure of North American and European populations, and shed light on forces that have shaped phenotypic diversity in F. × ananassa.

High-throughput structure profiling (SP) experiments that provide information at nucleotide resolution are revolutionizing our ability to study RNA structures. Of particular interest are RNA elements whose underlying structures are necessary for their biological functions. We previously introduced patteRNA, an algorithm for rapidly mining SP data for patterns characteristic of such motifs. This work provided a proof-of-concept for the detection of motifs and the capability of distinguishing structures displaying pronounced conformational changes. Here, we describe several improvements and automation routines to patteRNA. We then consider more elaborate biological situations starting with the comparison or integration of results from searches for distinct motifs and across datasets. To facilitate such analyses, we characterize patteRNA’s outputs and describe a normalization framework that regularizes results. We then demonstrate that our algorithm successfully discerns between highly similar structural variants of the human immunodeficiency virus type 1 (HIV-1) Rev response element (RRE) and readily identifies its exact location in whole-genome structure profiles of HIV-1. This work highlights the breadth of information that can be gleaned from SP data and broadens the utility of data-driven methods as tools for the detection of novel RNA elements.

Establishing a link between RNA structure and function remains a great challenge in RNA biology. The emergence of high-throughput structure profiling experiments is revolutionizing our ability to decipher structure, yet principled approaches for extracting information on structural elements directly from these data sets are lacking. We present patteRNA, an unsupervised pattern recognition algorithm that rapidly mines RNA structure motifs from profiling data. We demonstrate that patteRNA detects motifs with an accuracy comparable to commonly used thermodynamic models and highlight its utility in automating data-directed structure modeling from large data sets. patteRNA is versatile and compatible with diverse profiling techniques and experimental conditions.

Motivation: The diverse functionalities of RNA can be attributed to its capacity to form complex and varied structures. The recent proliferation of new structure probing techniques coupled with high-throughput sequencing has helped RNA studies expand in both scope and depth. Despite differences in techniques, most experiments face similar challenges in reproducibility due to the stochastic nature of chemical probing and sequencing. As these protocols expand to transcriptome-wide studies, quality control becomes a more daunting task. General and efficient methodologies are needed to quantify variability and quality in the wide range of current and emerging structure probing experiments.

Structure dictates the function of many RNAs, but secondary RNA structure analysis is either labor intensive and costly or relies on computational predictions that are often inaccurate. These limitations are alleviated by integration of structure probing data into prediction algorithms. However, existing algorithms are optimized for a specific type of probing data. Recently, new chemistries combined with advances in sequencing have facilitated structure probing at unprecedented scale and sensitivity. These novel technologies and anticipated wealth of data highlight a need for algorithms that readily accommodate more complex and diverse input sources. We implemented and investigated a recently outlined probabilistic framework for RNA secondary structure prediction and extended it to accommodate further refinement of structural information. This framework utilizes direct likelihood-based calculations of pseudo-energy terms per considered structural context and can readily accommodate diverse data types and complex data dependencies. We use real data in conjunction with simulations to evaluate performances of several implementations and to show that proper integration of structural contexts can lead to improvements. Our tests also reveal discrepancies between real data and simulations, which we show can be alleviated by refined modeling. We then propose statistical preprocessing approaches to standardize data interpretation and integration into such a generic framework. We further systematically quantify the information content of data subsets, demonstrating that high reactivities are major drivers of SHAPE-directed predictions and that better understanding of less informative reactivities is key to further improvements. Finally, we provide evidence for the adaptive capability of our framework using mock probe simulations.

Various lines of published evidence have already demonstrated the impact of TRPV1 agonists on energetic metabolism through the stimulation of the sympathetic nervous system (SNS). This study presents a trial investigating if stimulation of the two related sensory receptors TRPA1 and TRPM8 could also stimulate the SNS and impact the energetic metabolism of healthy subjects. The trial was designed to be double-blinded, randomized, cross-over, placebo-controlled with healthy subjects and the impact on the energetic metabolism and the autonomic nervous system (ANS) of cinnamaldehyde, capsaicin and a cooling flavor was measured during the 90 min after ingestion. Energy expenditure and substrate oxidation were measured by indirect calorimetry. An exploratory method to measure ANS activity was by facial thermography and power spectral analysis of heart rate variability using ECG was also used. Following cinnamaldehyde ingestion, energy expenditure was increased as compared to placebo. Furthermore, postprandial fat oxidation was maintained higher compared to placebo after cinnamaldehyde and capsaicin ingestion. Similar peripheral thermoregulation was observed after capsaicin and cinnamaldehyde ingestion. Unlike capsaicin, the dose of cinnamaldehyde was not judged to be sensorially ‘too intense’ by participants suggesting that Cinnamaldehyde would be a more tolerable solution to improve thermogenesis via spicy ingredients as compared to capsaicin.