Quantitative Biology

Homozygous mutations in the progranulin gene (GRN) are associated with neuronal ceroid lipofuscinosis 11 (CLN11), a rare lysosomal-storage disorder characterized by cerebellar ataxia, seizures, retinitis pigmentosa, and cognitive disorders, usually beginning between 13 and 25 years of age. This is a rare condition, previously reported in only four families. In contrast, heterozygous GRN mutations are a major cause of frontotemporal dementia associated with neuronal cytoplasmic TDP-43 inclusions. We identified homozygous GRN mutations in six new patients. The phenotypic spectrum is much broader than previously reported, with two remarkably distinct presentations, depending on the age of onset. A childhood/juvenile form is characterized by classical CLN11 symptoms at an early age at onset. Unexpectedly, other homozygous patients presented a distinct delayed phenotype of frontotemporal dementia and parkinsonism after 50 years; none had epilepsy or cerebellar ataxia. Another major finding of this study is that all GRN mutations may not have the same impact on progranulin protein synthesis. A hypomorphic effect of some mutations is supported by the presence of residual levels of plasma progranulin and low levels of normal transcript detected in one case with a homozygous splice-site mutation and late onset frontotemporal dementia. This is a new critical finding that must be considered in therapeutic trials based on replacement strategies. The first neuropathological study in a homozygous carrier provides new insights into the pathological mechanisms of the disease. Hallmarks of neuronal ceroid lipofuscinosis were present. The absence of TDP-43 cytoplasmic inclusions markedly differs from observations of heterozygous mutations, suggesting a pathological shift between lysosomal and TDP-43 pathologies depending on the mono or bi-allelic status. An intriguing observation was the loss of normal TDP-43 staining in the nucleus of some neurons, which could be the first stage of the TDP-43 pathological process preceding the formation of typical cytoplasmic inclusions. Finally, this study has important implications for genetic counselling and molecular diagnosis. Semi-dominant inheritance of GRN mutations implies that specific genetic counseling should be delivered to children and parents of CLN11 patients, as they are heterozygous carriers with a high risk of developing dementia. More broadly, this study illustrates the fact that genetic variants can lead to different phenotypes according to their mono- or bi-allelic state, which is a challenge for genetic diagnosis.

The antioxidant activity of baked foods is of utmost interest when envisioning enhancing their health benefits. Incorporating functional ingredients is challenging since their bioactivity naturally declines during baking. In this study, 3D food printing and design of experiments are employed to clarify how the antioxidant activity of cookies enriched with encapsulated polyphenols can be maximized. A synergistic effect between encapsulation, time, temperature, number of layers, and infill of the printed cookies was observed on the moisture and antioxidant activity. Four-layer cookies with 30 % infill provided the highest bioactivity and phenolic content if baked for 10 min and at 180 °C. The bioacitivity and total phenolic content improved by 115 % and 173 %, respectively, comparing to free extract cookies. Moreover, the proper combination of the design and baking variables allowed to vary the bioactivity of cooked cookies (moisture 3-5 %) between 300 to 700 {\mu}molTR/gdry. The additive manufacture of foods with interconnected pores could accelerate baking and browning, or reduce thermal degradation. This represents a potential approach to enhance the functional and healthy properties of cookies or other thermal treated bioactive food products.

Liquid-liquid phase separation is the mechanism underlying the formation of biomolecular condensates. Disordered protein regions often drive phase separation, but molecular interactions of disordered protein regions are not well understood, sometimes leading to the conflation that all disordered protein regions drive phase separation. Given the critical role of phase separation in many cellular processes, and that dysfunction of phase separation can lead to debilitating diseases, it is important that we understand the interactions and sequence properties underlying phase behavior. A conceptual framework that divides IDRs into interacting and solvating regions has proven particularly useful, and analytical instantiations and coarse-grained models can test our understanding of the driving forces against experimental phase behavior. Validated simulation paradigms enable the exploration of sequence space to help our understanding of how disordered protein regions can encode phase behavior, which IDRs may mediate phase separation in cells, and which IDRs are in contrast highly soluble.

Comprehensive and confident identifications of metabolites and other chemicals in complex samples will revolutionize our understanding of the role these chemically diverse molecules play in biological systems. Despite recent advances, metabolomics studies still result in the detection of a disproportionate number of features than cannot be confidently assigned to a chemical structure. This inadequacy is driven by the single most significant limitation in metabolomics: the reliance on reference libraries constructed by analysis of authentic reference chemicals. To this end, we have developed the in silico chemical library engine (ISiCLE), a high-performance computing-friendly cheminformatics workflow for generating libraries of chemical properties. In the instantiation described here, we predict probable three-dimensional molecular conformers using chemical identifiers as input, from which collision cross sections (CCS) are derived. The approach employs state-of-the-art first-principles simulation, distinguished by use of molecular dynamics, quantum chemistry, and ion mobility calculations to generate structures and libraries, all without training data. Importantly, optimization of ISiCLE included a refactoring of the popular MOBCAL code for trajectory-based mobility calculations, improving its computational efficiency by over two orders of magnitude. Calculated CCS values were validated against 1,983 experimentally-measured CCS values and compared to previously reported CCS calculation approaches. An online database is introduced for sharing both calculated and experimental CCS values (this http URL), initially including a CCS library with over 1 million entries. Finally, three successful applications of molecule characterization using calculated CCS are described. This work represents a promising method to address the limitations of small molecule identification.

Proton nuclear magnetic resonance (1H-NMR) is a widely used tool for chemical structural analysis. However, 1H-NMR spectra suffer from natural aberrations that render computer-assisted automated identification of these spectra difficult, and at times impossible. Previous efforts have successfully implemented instrument dependent or conditional identification of these spectra. In this paper, we report the first instrument independent computer-assisted automated identification system for a group of complex carbohydrates known as the xyloglucan oligosaccharides. The developed system is also implemented on the world wide web (this http URL) as part of an identification package called the CCRC-Net and is intended to recognize any submitted 1H-NMR spectrum of these structures with reasonable signal-to-noise ratio, recorded on any 500 MHz NMR instrument. The system uses Artificial Neural Networks (ANNs) technology and is insensitive to the instrument and environment-dependent variations in 1H-NMR spectroscopy. In this paper, comparative results of the ANN engine versus a multidimensional Bayes' classifier is also presented.

Spectropolarimetry of intact plant leaves allows to probe the molecular architecture of vegetation photosynthesis in a non-invasive and non-destructive way and, as such, can offer a wealth of physiological information. In addition to the molecular signals due to the photosynthetic machinery, the cell structure and its arrangement within a leaf can create and modify polarization signals. Using Mueller matrix polarimetry with rotating retarder modulation, we have visualized spatial variations in polarization in transmission around the chlorophyll a absorbance band from 650 nm to 710 nm. We show linear and circular polarization measurements of maple leaves and cultivated maize leaves and discuss the corresponding Mueller matrices and the Mueller matrix decompositions, which show distinct features in diattenuation, polarizance, retardance and depolarization. Importantly, while normal leaf tissue shows a typical split signal with both a negative and a positive peak in the induced fractional circular polarization and circular dichroism, the signals close to the veins only display a negative band. The results are similar to the negative band as reported earlier for single macrodomains. We discuss the possible role of the chloroplast orientation around the veins as a cause of this phenomenon. Systematic artefacts are ruled out as three independent measurements by different instruments gave similar results. These results provide better insight into circular polarization measurements on whole leaves and options for vegetation remote sensing using circular polarization.

Purpose: Pulsed-laser irradiation of light-absorbing gold nanoparticles (AuNPs) attached to cells transiently increases cell membrane permeability for targeted molecule delivery. Here, we targeted EGFR on the ovarian carcinoma cell line OVCAR-3 with AuNPs. In order to optimize membrane permeability and to demonstrate molecule delivery into adherent OVCAR-3 cells, we systematically investigated different experimental conditions. Materials and methods: AuNPs (30 nm) were functionalized by conjugation of the antibody cetuximab against EGFR. Selective binding of the particles was demonstrated by silver staining, multiphoton imaging, and fluorescence-lifetime imaging. After laser irradiation, membrane permeability of OVCAR-3 cells was studied under different conditions of AuNP concentration, cell-incubation medium, and cell - AuNP incubation time. Membrane permeability and cell viability were evaluated by flow cytometry, measuring propidium iodide and fluorescein isothiocyanate - dextran uptake. Results: Adherently growing OVCAR-3 cells can be effectively targeted with EGFR-AuNP. Laser irradiation led to successful permeabilization, and 150 kDa dextran was successfully delivered into cells with about 70% efficiency. Conclusion: Antibody-targeted and laser-irradiated AuNPs can be used to deliver molecules into adherent cells. Efficacy depends not only on laser parameters but also on AuNP:cell ratio, cell-incubation medium, and cell - AuNP incubation time.

In this paper, we aim to synthesize cell microscopy images under different molecular interventions, motivated by practical applications to drug development. Building on the recent success of graph neural networks for learning molecular embeddings and flow-based models for image generation, we propose Mol2Image: a flow-based generative model for molecule to cell image synthesis. To generate cell features at different resolutions and scale to high-resolution images, we develop a novel multi-scale flow architecture based on a Haar wavelet image pyramid. To maximize the mutual information between the generated images and the molecular interventions, we devise a training strategy based on contrastive learning. To evaluate our model, we propose a new set of metrics for biological image generation that are robust, interpretable, and relevant to practitioners. We show quantitatively that our method learns a meaningful embedding of the molecular intervention, which is translated into an image representation reflecting the biological effects of the intervention.

Predicting accurate protein-ligand binding affinity is important in drug discovery but remains a challenge even with computationally expensive biophysics-based energy scoring methods and state-of-the-art deep learning approaches. Despite the recent advances in the deep convolutional and graph neural network based approaches, the model performance depends on the input data representation and suffers from distinct limitations. It is natural to combine complementary features and their inference from the individual models for better predictions. We present fusion models to benefit from different feature representations of two neural network models to improve the binding affinity prediction. We demonstrate effectiveness of the proposed approach by performing experiments with the PDBBind 2016 dataset and its docking pose complexes. The results show that the proposed approach improves the overall prediction compared to the individual neural network models with greater computational efficiency than related biophysics based energy scoring functions. We also discuss the benefit of the proposed fusion inference with several example complexes. The software is made available as open source at this https URL.

Traditional approaches to elucidation of protein structures by NMR spectroscopy rely on distance restraints also known as nuclear Overhauser effects (NOEs). The use of NOEs as the primary source of structure determination by NMR spectroscopy is time consuming and expensive. Residual Dipolar Couplings (RDCs) have become an alternate approach for structure calculation by NMR spectroscopy. In previous works, the software package REDCRAFT has been presented as a means of harnessing the information containing in RDCs for structure calculation of proteins. In this work, we present significant improvements to the REDCRAFT package including: refinement of the decimation procedure, the inclusion of graphical user interface, adoption of NEF standards, and addition of scripts for enhanced protein modeling options. The improvements to REDCRAFT have resulted in the ability to fold proteins that the previous versions were unable to fold. For instance, we report the results of folding of the protein 1A1Z in the presence of highly erroneous data.