gRNAde: A Geometric Deep Learning Pipeline for 3D RNA Inverse Design.

Fundamental to the diverse biological functions of RNA are its 3D structure and conformational flexibility, which enable single sequences to adopt a variety of distinct 3D states. Currently, computational RNA design tasks are often posed as inverse problems, where sequences are designed based on adopting a single desired secondary structure without considering 3D geometry and conformational diversity. In this tutorial, we present gRNAde, a geometric RNA design pipeline operating on sets of 3D RNA backbone structures to design sequences that explicitly account for RNA 3D structure and dynamics. gRNAde is a graph neural network that uses an SE (3) equivariant encoder-decoder framework for generating RNA sequences conditioned on backbone structures where the identities of the bases are unknown. We demonstrate the utility of gRNAde for fixed-backbone re-design of existing RNA structures of interest from the PDB, including riboswitches, aptamers, and ribozymes. gRNAde is more accurate in terms of native sequence recovery while being significantly faster compared to existing physics-based tools for 3D RNA inverse design, such as Rosetta.

Uncovering the pharmacological mechanisms of Patchouli essential oil for treating ulcerative colitis.

ETHNOPHARMACOLOGICAL RELEVANCE: Pogostemonis Herba has long been used in traditional Chinese medicine to treat inflammatory disorders. Patchouli essential oil (PEO) is the primary component of Pogostemonis Herba, and it has been suggested to offer curative potential when applied to treat ulcerative colitis (UC). However, the pharmacological mechanisms of PEO for treating UC remain to be clarified. AIM OF THE STUDY: To elucidate the pharmacological mechanisms of PEO for treating UC. METHODS AND RESULTS: In the present study, transcriptomic and network pharmacology approaches were combined to clarify the mechanisms of PEO for treating UC. Our results reveal that rectal PEO administration in UC model mice significantly alleviated symptoms of UC. In addition, PEO effectively suppressed colonic inflammation and oxidative stress. Mechanistically, PEO can ameliorate UC mice by modulating gut microbiota, inhibiting inflammatory targets (OPTC, PTN, IFIT3, EGFR, and TLR4), and inhibiting the PI3K-AKT pathway. Next, the 11 potential bioactive components that play a role in PEO's anti-UC mechanism were identified, and the therapeutic efficacy of the pogostone (a bioactive component) in UC mice was partially validated. CONCLUSION: This study highlights the mechanisms through which PEO can treat UC, providing a rigorous scientific foundation for future efforts to develop and apply PEO for treating UC.

Hypotensive effect of potent angiotensin-I-converting enzyme inhibitory peptides from corn gluten meal hydrolysate: Gastrointestinal digestion and transepithelial transportation modifications.

The study examined the antihypertensive effect of peptides derived from pepsin-hydrolyzed corn gluten meal, namely KQLLGY and PPYPW, and their in silico gastrointestinal tract digested fragments, KQL and PPY, respectively. KQLLGY and PPYPW showed higher angiotensin I-converting enzyme (ACE)-inhibitory activity and lower ACE inhibition constant (Ki) values when compared to KQL and PPY. Only KQL showed a mild antihypertensive effect in spontaneously hypertensive rats with -7.83 and - 5.71 mmHg systolic and diastolic blood pressure values, respectively, after 8 h oral administration. During passage through Caco-2 cells, KQL was further degraded to QL, which had reduced ACE inhibitory activity. In addition, molecular dynamics revealed that the QL-ACE complex was less stable compared to the KQL-ACE. This study reveals that structural transformation during peptide permeation plays a vital role in attenuating antihypertensive effect of the ACE inhibitor peptide.

Construction of Coarse-Grained Molecular Dynamics Model of Nuclear Global Chromosomes Dynamics in Mammalian Cells.

Biomolecules contain various heterogeneities in their structures and local chemical properties, and their functions emerge through the dynamics encoded by these heterogeneities. Molecular dynamics model-based studies will greatly contribute to the elucidation of such chemical/mechanical structure-dynamics-function relationships and the mechanisms that generate them. Coarse-grained molecular dynamics models with appropriately designed nonuniform local interactions play an important role in considering the various phenomena caused by large molecular complexes consisting of various proteins and DNA such as nuclear chromosomes. Therefore, in this chapter, we will introduce a method for constructing a coarse-grained molecular dynamics model that simulates the global behavior of each chromosome in the nucleus of a mammalian cell containing many giant chromosomes.

Integrative Modeling of 3D Genome Organization by Bayesian Molecular Dynamics Simulations with Hi-C Metainference.

Polymer modeling has been playing an increasingly important role in complementing 3D genome experiments, both to aid their interpretation and to reveal the underlying molecular mechanisms. This chapter illustrates an application of Hi-C metainference, a Bayesian approach to explore the 3D organization of a target genomic region by integrating experimental contact frequencies into a prior model of chromatin. The method reconstructs the conformational ensemble of the target locus by combining molecular dynamics simulation and Monte Carlo sampling from the posterior probability distribution given the data. Using prior chromatin models at both 1 kb and nucleosome resolution, we apply this approach to a 30 kb locus of mouse embryonic stem cells consisting of two well-defined domains linking several gene promoters together. Retaining the advantages of both physics-based and data-driven strategies, Hi-C metainference can provide an experimentally consistent representation of the system while at the same time retaining molecular details necessary to derive physical insights.

Role of urea in the retention of DON in soil by clay minerals: Analysis based upon molecular weight.

As a widely used fertilizer, urea significantly promotes the leaching of dissolved organic nitrogen (DON) in soils and aggravates nitrogen contamination in groundwater. Clay minerals are considered the most important factor in retaining DON. However, the effect of urea on the retention of DON with different molecular weights by clay minerals is unknown. In this study, the retention of both low-molecular weight DON (LMWD) and high-molecular weight DON (HMWD) by clay minerals in the presence of urea was investigated. For this purpose, batch adsorption and soil column leaching experiments, characterization analysis (Fourier transform infrared spectroscopy X-ray diffraction, and X-ray photoelectron spectroscopy), and molecular dynamics simulations were carried out. Urea had a positive effect on the adsorption of LMWD, whereas a competitive effect existed for the adsorption of HMWD. The dominant interactions among DON, urea, and clay minerals included H-bonding, ligand exchange, and cation exchange. The urea was preferentially adsorbed on clay minerals and formed a complex, which provided more adsorption sites to LMWD and only a few to HMWD. The presence of urea increased the retention of LMWD and decreased the retention of HMWD in clay minerals. The retention capacity of LMWD increased by 6.9%-12.8%, while that of HMWD decreased by 6.7%-53.1%. These findings suggest that LMWD tended to be trapped in soils, while HMWD was prone to be leached into groundwater, which can be used to evaluate the leaching of DON from soil to groundwater.

PFAS-Biomolecule Interactions: Case Study Using Asclepios Nodes and Automated Workflows in KNIME for Drug Discovery and Toxicology.

The Asclepios suite of KNIME nodes represents an innovative solution for conducting cheminformatics and computational chemistry tasks, specifically tailored for applications in drug discovery and computational toxicology. This suite has been developed using open-source and publicly accessible software. In this chapter, we introduce and explore the Asclepios suite through the lens of a case study. This case study revolves around investigating the interactions between per- and polyfluorinated alkyl substances (PFAS) and biomolecules, such as nuclear receptors. The objective is to characterize the potential toxicity of PFAS and gain insights into their chemical mode of action at the molecular level. The Asclepios KNIME nodes have been designed as versatile tools capable of addressing a wide range of computational toxicology challenges. Furthermore, they can be adapted and customized to accomodate the specific needs of individual users, spanning various domains such as nanoinformatics, biomedical research, and other related applications. This chapter provides an in-depth examination of the technical underpinnings and foundations of these tools. It is accompanied by a practical case study that demonstrates the utilization of Asclepios nodes in a computational toxicology investigation. This showcases the extendable functionalities that can be applied in diverse computational chemistry contexts. By the end of this chapter, we aim for readers to have a comprehensive understanding of the effectiveness of the Asclepios node functions. These functions hold significant potential for enhancing a wide spectrum of cheminformatics applications.

Perspectives on label-free microscopy of heterogeneous and dynamic biological systems.

Significance: Advancements in label-free microscopy could provide real-time, non-invasive imaging with unique sources of contrast and automated standardized analysis to characterize heterogeneous and dynamic biological processes. These tools would overcome challenges with widely used methods that are destructive (e.g., histology, flow cytometry) or lack cellular resolution (e.g., plate-based assays, whole animal bioluminescence imaging). Aim: This perspective aims to (1) justify the need for label-free microscopy to track heterogeneous cellular functions over time and space within unperturbed systems and (2) recommend improvements regarding instrumentation, image analysis, and image interpretation to address these needs. Approach: Three key research areas (cancer research, autoimmune disease, and tissue and cell engineering) are considered to support the need for label-free microscopy to characterize heterogeneity and dynamics within biological systems. Based on the strengths (e.g., multiple sources of molecular contrast, non-invasive monitoring) and weaknesses (e.g., imaging depth, image interpretation) of several label-free microscopy modalities, improvements for future imaging systems are recommended. Conclusion: Improvements in instrumentation including strategies that increase resolution and imaging speed, standardization and centralization of image analysis tools, and robust data validation and interpretation will expand the applications of label-free microscopy to study heterogeneous and dynamic biological systems.

Severe mitochondrial encephalomyopathy caused by de novo variants in OPA1 gene.

Background: Mitochondria adjust their shape in response to the different energetic and metabolic requirements of the cell, through extremely dynamic fusion and fission events. Several highly conserved dynamin-like GTPases are involved in these processes and, among those, the OPA1 protein is a key player in the fusion of inner mitochondrial membranes. Hundreds of monoallelic or biallelic pathogenic gene variants have been described in OPA1, all associated with a plethora of clinical phenotypes without a straightforward genotype-phenotype correlation. Methods: Here we report two patients harboring novel de novo variants in OPA1. DNA of two patients was analyzed using NGS technology and the pathogenicity has been evaluated through biochemical and morphological studies in patient's derived fibroblasts and in yeast model. Results: The two patients here reported manifest with neurological signs resembling Leigh syndrome, thus further expanding the clinical spectrum associated with variants in OPA1. In cultured skin fibroblasts we observed a reduced amount of mitochondrial DNA (mtDNA) and altered mitochondrial network characterized by more fragmented mitochondria. Modeling in yeast allowed to define the deleterious mechanism and the pathogenicity of the identified gene mutations. Conclusion: We have described two novel-single OPA1 mutations in two patients characterized by early-onset neurological signs, never documented, thus expanding the clinical spectrum of this complex syndrome. Moreover, both yeast model and patients derived fibroblasts showed mitochondrial defects, including decreased mtDNA maintenance, correlating with patients' clinical phenotypes.

Disease resistance gene count increases with rainfall in Silphium integrifolium.

Intracellular plant defense against pathogens is mediated by a class of disease resistance genes known as NB-LRRs or NLRs (R genes). Many of the diseases these genes protect against are more prevalent in regions of higher rainfall, which provide better growth conditions for the pathogens. As such, we expect a higher selective pressure for the maintenance and proliferation of R genes in plants adapted to wetter conditions. In this study, we enriched libraries for R genes using RenSeq from baits primarily developed from the common sunflower (Helianthus annuus) reference genome. We sequenced the R gene libraries of Silphium integrifolium Michx, a perennial relative of sunflower, from 12 prairie remnants across a rainfall gradient in the Central Plains of the United States, with both Illumina short-read (n = 99) and PacBio long-read (n = 10) approaches. We found a positive relationship between the mean effective annual precipitation of a plant's source prairie remnant and the number of R genes in its genome, consistent with intensity of plant pathogen coevolution increasing with precipitation. We show that RenSeq can be applied to the study of ecological hypotheses in non-model relatives of model organisms.

A Longitudinal Examination of Blood Sugar Dynamics in Diabetes and Non-Diabetes Using Growth Curve Model: The Sabzevar Persian Cohort Study.

Background: Diabetes mellitus is a chronic metabolic disorder with substantial implications for public health. Understanding the factors influencing blood sugar fluctuations is crucial for effective diabetes management and prevention. This study aimed to evaluate factors associated with blood sugar changes in diabetic patients and healthy individuals attending the Sabzevar Persian Cohort Center, employing the growth curve model. Materials and Methods: Data related to 589 diabetic patients and 589 non-diabetic patients participating in the Persian cohort study of Sabzevar were used. Due to the repetition of blood sugar measurements for each individual over time, we use the conditional latent growth curve model to examine intra-individual changes and variables that affect these changes over time. Results: The linear latent growth curve model, fitted with independent variables, exhibited a superior fit. The slope of the line for the diabetic group was measured at 1.78, while for the non-diabetic group, it was estimated to be -0.29. Within the diabetic group, the influence of age, the presence of fatty liver, and history of congenital heart disease (CHD) had a significant impact on the baseline (the intercept), and the effect of body mass index (BMI) on the changing trend of the response variable (slope) was also significant. In the non-diabetic group, significant effects were observed for age variables, BMI, family history of diabetes, and history of stroke in the family. Conclusion: Overall, the linear latent growth curve model showed good performance in the evaluation of the factors related to blood sugar changes in diabetic patients and healthy people.

Human Antigen R -mediated modulation of Transforming Growth Factor Beta 1 expression in retinal pathological milieu.

The fate and stability of messenger RNA (mRNA), from transcription to degradation is regulated by a dynamic shuttle of epigenetic modifications and RNA binding proteins in maintaining healthy cellular homeostasis and disease development. While Transforming Growth Factor Beta 1 (TGFß1) has been implicated as a key regulator for diabetic retinopathy, a microvascular complication of diabetes, the RNA binding proteins post-transcriptionally regulating its expression remain unreported in the ocular context. Further, dysfunction of TGFß1 signalling is also strongly associated with angiogenesis, inflammatory responses and tissue fibrosis in many eye conditions leading to vision loss. In this study, computational and molecular simulations were initially carried out to identify Human Antigen R (HuR) binding sites in TGFß1 mRNA and predict the structural stability of these RNA-protein interactions. These findings were further validated through in vitro experiments utilizing Cobalt Chloride (CoCl2) as a hypoxia mimetic agent in human retinal microvascular endothelial cells (HRMVEC). In silico analysis revealed that HuR preferentially binds to the 5'-UTR of TGFß1 and displayed more stable interaction than the 3'UTR. Consistent with in silico analysis, RNA immunoprecipitation demonstrated a robust association between HuR and TGFß1 mRNA specifically under hypoxic conditions. Further, silencing of HuR significantly reduced TGFß1 protein expression upon CoCl2 treatment. Thus, for the first time in ocular pathological milieu, direct evidence of HuR- TGFß1 mRNA interaction under conditions of hypoxia has been reported in this study providing valuable insights into RNA binding proteins as therapeutic targets for ocular diseases associated with TGFß1 dysregulation.

CHARMM-GUI PDB Reader and Manipulator: Covalent Ligand Modeling and Simulation.

Molecular modeling and simulation serve an important role in exploring biological functions of proteins at the molecular level, which is complementary to experiments. CHARMM-GUI (https://www.charmm-gui.org) is a web-based graphical user interface that generates complex molecular simulation systems and input files, and we have been continuously developing and expanding its functionalities to facilitate various complex molecular modeling and make molecular dynamics simulations more accessible to the scientific community. Currently, covalent drug discovery emerges as a popular and important field. Covalent drug forms a chemical bond with specific residues on the target protein, and it has advantages in potency for its prolonged inhibition effects. Even though there are higher demands in modeling PDB protein structures with various covalent ligand types, proper modeling of covalent ligands remains challenging. This work presents a new functionality in CHARMM-GUI PDB Reader & Manipulator that can handle a diversity of ligand-amino acid linkage types, which is validated by a careful benchmark study using over 1,000 covalent ligand structures in RCSB PDB. We hope that this new functionality can boost the modeling and simulation study of covalent ligands.

The cohesin ATPase cycle is mediated by specific conformational dynamics and interface plasticity of SMC1A and SMC3 ATPase domains.

Cohesin is key to eukaryotic genome organization and acts throughout the cell cycle in an ATP-dependent manner. The mechanisms underlying cohesin ATPase activity are poorly understood. Here, we characterize distinct steps of the human cohesin ATPase cycle and show that the SMC1A and SMC3 ATPase domains undergo specific but concerted structural rearrangements along this cycle. Specifically, whereas the proximal coiled coil of the SMC1A ATPase domain remains conformationally stable, that of the SMC3 displays an intrinsic flexibility. The ATP-dependent formation of the heterodimeric SMC1A/SMC3 ATPase module (engaged state) favors this flexibility, which is counteracted by NIPBL and DNA binding (clamped state). Opening of the SMC3/RAD21 interface (open-engaged state) stiffens the SMC3 proximal coiled coil, thus constricting together with that of SMC1A the ATPase module DNA-binding chamber. The plasticity of the ATP-dependent interface between the SMC1A and SMC3 ATPase domains enables these structural rearrangements while keeping the ATP gate shut. VIDEO ABSTRACT.

Ion adsorption-enrichment effect and its driving mechanism for nano-dots lithography with SPM probe on water-soluble crystal surfaces.

HYPOTHESIS: Water-soluble KDP (KH2PO4) crystals possess excellent optical properties and are employed as frequency converters in clean fusion energy. To improve their performances, there is an immediate necessity to lithograph surface nano-patterns on them. Although the Scanning Probe Microscope (SPM) provides a promising way to achieve this purpose through the water menisci, the driving mechanisms of the lithographic behaviors have not yet been revealed. SIMULATIONS AND EXPERIMENTS: Multi-scale investigations are constructed to explore the underlying driving mechanisms. The SPM probe-induced ion diffusion-transport behaviors are investigated by molecular dynamics. The ion adsorption-enrichment mechanisms are revealed by 18 adsorption models via the ab initio. The SPM probe-induced self-assembly experiments are performed to prove the local heavy concentration. A comprehensive model is developed to describe the lithography mechanisms of the probe-induced self-assembly nano-dots on water-soluble substrates. FINDINGS: It is interestingly found that the KDP growth units (H2PO4-) exhibit obvious adsorption-enrichment effect at 3.16 Å from the probe surface, causing local heavy concentration. The H2PO4- would spontaneously adsorb onto the probe surface, which is dominated by the Si-O bonding reactions. The nano-dots with the height of 27 âˆ¼ 48 nm and diameter of 2.0 âˆ¼ 2.7 µm are lithographed on the KDP substrate. The proposed model further confirms that the lithography processes are driven by the solution supersaturation, solute diffusion, and surface free energy.

Failure in a population: Tauopathy disrupts homeostatic set-points in emergent dynamics despite stability in the constituent neurons.

Homeostatic regulation of neuronal activity is essential for robust computation; set-points, such as firing rate, are actively stabilized to compensate for perturbations. The disruption of brain function central to neurodegenerative disease likely arises from impairments of computationally essential set-points. Here, we systematically investigated the effects of tau-mediated neurodegeneration on all known set-points in neuronal activity. We continuously tracked hippocampal neuronal activity across the lifetime of a mouse model of tauopathy. We were unable to detect effects of disease in measures of single-neuron firing activity. By contrast, as tauopathy progressed, there was disruption of network-level neuronal activity, quantified by measuring neuronal pairwise interactions and criticality, a homeostatically controlled, ideal computational regime. Deviations in criticality correlated with symptoms, predicted underlying anatomical pathology, occurred in a sleep-wake-dependent manner, and could be used to reliably classify an animal's genotype. This work illustrates how neurodegeneration may disrupt the computational capacity of neurobiological systems.

A mechanistic framework for complex microbe-host symbioses.

Virtually all multicellular organisms on Earth live in symbiotic associations with complex microbial communities: the microbiome. This ancient relationship is of fundamental importance for both the host and the microbiome. Recently, the analyses of numerous microbiomes have revealed an incredible diversity and complexity of symbionts, with different mechanisms identified as potential drivers of this diversity. However, the interplay of ecological and evolutionary forces generating these complex associations is still poorly understood. Here we explore and summarise the suite of ecological and evolutionary mechanisms identified as relevant to different aspects of microbiome complexity and diversity. We argue that microbiome assembly is a dynamic product of ecology and evolution at various spatio-temporal scales. We propose a theoretical framework to classify mechanisms and build mechanistic host-microbiome models to link them to empirical patterns. We develop a cohesive foundation for the theoretical understanding of the combined effects of ecology and evolution on the assembly of complex symbioses.

Oocyte Health and Quality: Implication of Mitochondria-related Organelle Interactions.

Among factors like hormonal imbalance and uterine condition, oocyte quality is regarded as one of the key factors involved in age-related decline in the reproductive capacity. Here, are discussions about the functions played by organelles within the oocyte in forming the next generation that is more suitable for survival. Many insights on the adaptation to aging and maintenance of quality can be obtained from: interactions between mitochondria and other organelles that enable the long life of primordial oocytes; characteristics of organelle interactions after breaking dormancy from primary oocytes to mature oocytes; and characteristics of interactions between mitochondria and other organelles of aged oocytes collected during the ovulatory cycle from elderly individuals and animals. This information would potentially be beneficial to the development of future therapeutic methods or agents.

Aerodynamic optimization of athlete posture using virtual skeleton methodology and computational fluid dynamics.

An athlete's posture has a significant impact on aerodynamic drag. Although aerodynamic drag in different sports has been studied extensively, most studies have analysed only a limited number of positions, and no generalized methods for optimization are available. In this work, we present a methodology to perform athlete posture optimization with respect to aerodynamic drag reduction. The method combines the virtual skeleton methodology to adjust the athlete's posture, CFD simulations to evaluate the drag for a given posture, and efficient global optimization to find the optimum position. We demonstrate the method by optimizing the time trial position for a cyclist. The cyclist position was parameterized with 6 design parameters, and the optimization required 41 CFD simulations to converge. The optimal posture yielded a reduction in drag of 17 % compared to the initial posture (disregarding bicycle drag). The method has potential to make posture optimization more accessible across a wide range of sports, and lead to insight into the aerodynamic influence of posture in general.

Sensing the structural and conformational properties of single-stranded nucleic acids using electrometry and molecular simulations.

Inferring the 3D structure and conformation of disordered biomolecules, e.g., single stranded nucleic acids (ssNAs), remains challenging due to their conformational heterogeneity in solution. Here, we use escape-time electrometry (ETe) to measure with sub elementary-charge precision the effective electrical charge in solution of short to medium chain length ssNAs in the range of 5-60 bases. We compare measurements of molecular effective charge with theoretically calculated values for simulated molecular conformations obtained from Molecular Dynamics simulations using a variety of forcefield descriptions. We demonstrate that the measured effective charge captures subtle differences in molecular structure in various nucleic acid homopolymers of identical length, and also that the experimental measurements can find agreement with computed values derived from coarse-grained molecular structure descriptions such as oxDNA, as well next generation ssNA force fields. We further show that comparing the measured effective charge with calculations for a rigid, charged rod-the simplest model of a nucleic acid-yields estimates of molecular structural dimensions such as linear charge spacings that capture molecular structural trends observed using high resolution structural analysis methods such as X-ray scattering. By sensitively probing the effective charge of a molecule, electrometry provides a powerful dimension supporting inferences of molecular structural and conformational properties, as well as the validation of biomolecular structural models. The overall approach holds promise for a high throughput, microscopy-based biomolecular analytical approach offering rapid screening and inference of molecular 3D conformation, and operating at the single molecule level in solution.