Patient-Specific Variations in Local Strain Patterns on the Surface of a Trussed Titanium Interbody Cage

Introduction: 3D printed trussed titanium interbody cages may deliver bone stimulating mechanobiological strains to cells attached at their surface. The exact size and distribution of these strains may depend on patient-specific factors, but the influence of these factors remains unknown. Therefore, this study aimed to determine patient-specific variations in local strain patterns on the surface of a trussed titanium interbody fusion cage.Materials and Methods: Four patients eligible for spinal fusion surgery with the same cage size were selected from a larger database. For these cases, patient-specific finite element models of the lumbar spine including the same trussed titanium cage were made. Functional dynamics of the non-operated lumbar spinal segments, as well as local cage strains and caudal endplate stresses at the operated segment, were evaluated under physiological extension/flexion movement of the lumbar spine.Results: All patient-specific models revealed physiologically realistic functional dynamics of the operated spine. In all patients, approximately 30% of the total cage surface experienced strain values relevant for preserving bone homeostasis and stimulating bone formation. Mean caudal endplate contact pressures varied up to 10 MPa. Both surface strains and endplate contact pressures varied more between loading conditions than between patients.Conclusions: This study demonstrates the applicability of patient-specific finite element models to quantify the impact of patient-specific factors such as bone density, degenerative state of the spine, and spinal curvature on interbody cage loading. In the future, the same framework might be further developed in order to establish a pipeline for interbody cage design optimizations.

Conformational Flexibility and Local Frustration in the Functional States of the SARS-CoV-2 Spike B.1.1.7 and B.1.351 Variants: Mutation-Induced Allosteric Modulation Mechanism of Functional Dynamics and Protein Stability

The experimental and computational studies of the SARS-CoV-2 spike protein variants revealed an important role of the D614G mutation that is shared across variants of concern(VOCs), linking the effect of this mutation with the enhanced virus infectivity and transmissibility. The recent structural and biophysical studies characterized the closed and open states of the B.1.1.7 (B.1.1.7) and B.1.351 (Beta) spike variants allowing for a more detailed atomistic characterization of the conformational landscapes and functional changes. In this study, we employed coarse-grained simulations of the SARS-CoV-2 spike variant trimers together with the ensemble-based mutational frustration analysis to characterize the dynamics signatures of the conformational landscapes. By combining the local frustration analysis of the conformational ensembles with collective dynamics and residue-based mutational scanning of protein stability, we determine protein stability hotspots and identify potential energetic drivers favoring the receptor-accessible open spike states for the B.1.1.7 and B.1.351 spike variants. Through mutational scanning of protein stability changes we quantify mutational adaptability of the S-G614, S-B.1.1.7 and S-B.1.351 variants in different functional forms. Using this analysis, we found a significant conformational and mutational plasticity of the open states for all studied variants. The results of this study suggest that modulation of the energetic frustration at the inter-protomer interfaces can serve as a mechanism for allosteric couplings between mutational sites, the inter-protomer hinges of functional motions and motions of the receptor-binding domain required for binding of the host cell receptor. The proposed mechanism of mutation-induced energetic frustration may result in the greater adaptability and the emergence of multiple conformational substates in the open form. This study also suggested functional relationships between mutation-induced modulation of protein dynamics, local frustration and allosteric regulation of the SARS-CoV-2 spike protein.

The Geometry of Health

Chapter 5 examines the dynamic nature of interfaces and starts examining their characteristics. The authors posit that just as we might derive a multitude of dimensions to describe biological structure, so too are there many dimensions that describe the functional dynamics in how biological systems vary over time. Current environmental epidemiological methods used in analyzing data on our environment and our physiology treat each measure as if it were an independent dimension, much like a carpenter measuring the height, width, or length of a piece of furniture. However, because there are processes underlying our physiological development, constraints are applied to the forms that we and our environment can take. Knowledge of these can be harnessed to identify the primary dimensions along which we must characterize the systems under study. By doing this we were able to take an important first step in operationalizing Environmental Biodynamics for clinical application.

Long-read metagenomics of soil communities reveals phylum-specific secondary metabolite dynamics

Microbial biosynthetic gene clusters (BGCs) encoding secondary metabolites are thought to impact a plethora of biologically mediated environmental processes, yet their discovery and functional characterization in natural microbiomes remains challenging. Here we describe deep long-read sequencing and assembly of metagenomes from biological soil crusts, a group of soil communities that are rich in BGCs. Taking advantage of the unusually long assemblies produced by this approach, we recovered nearly 3,000 BGCs for analysis, including 712 full-length BGCs. Functional exploration through metatranscriptome analysis of a 3-day wetting experiment uncovered phylum-specific BGC expression upon activation from dormancy, elucidating distinct roles and complex phylogenetic and temporal dynamics in wetting processes. For example, a pronounced increase in BGC transcription occurs at night primarily in cyanobacteria, implicating BGCs in nutrient scavenging roles and niche competition. Taken together, our results demonstrate that long-read metagenomic sequencing combined with metatranscriptomic analysis provides a direct view into the functional dynamics of BGCs in environmental processes and suggests a central role of secondary metabolites in maintaining phylogenetically conserved niches within biocrusts.

Bayesian Metamodeling of pancreatic islet architecture and functional dynamics

The pancreatic islet (islet of Langerhans) is a mini-organ comprising several thousand endocrine cells, functioning jointly to maintain normoglycemia. Cellular networks within an islet were shown to influence its function in health and disease, but there are major gaps in our quantitative understanding of such architecture-function relations. Comprehensive modeling of an islet architecture and function requires the integration of vast amounts of information obtained through different experimental and theoretical approaches. To address this challenge, our lab has recently developed Bayesian metamodeling, a general approach for modeling complex systems by integrating heterogeneous input models. Here, we further developed metamodeling and applied it to construct a metamodel of a pancreatic islet. The metamodel relates islet architecture and function by combining a Monte-Carlo model of architecture trained on islet imaging data; and an ordinary differential equations (ODEs) mathematical model of function trained on calcium imaging, hormone imaging, and electrophysiological data. These input models are converted to a standardized statistical representation relying on Probabilistic Graphical Models; coupled by modeling their mutual relations with the physical world; and finally, harmonized through backpropagation. We validate the metamodel using existing data and use it to derive a testable hypothesis regarding the functional effect of varying intercellular connections. Since metamodeling currently requires substantial expert intervention, we also develop an automation tool for converting models to PGMs (step I) using feedforward neural networks. This automation is a first step towards automating the entire metamodeling process, working towards collaborative science through sharing of expertise, resources, data, and models.

Pumpkin (Cucurbita moschata) HSP20 Gene Family Identification and Expression Under Heat Stress

Pumpkin (Cucurbita moschata) is an important cucurbit vegetable crop that has strong resistance to abiotic stress. While heat shock protein 20 (HSP20) has been implicated in vegetable response to heat stress, little is known regarding activity of HSP20 family proteins in C. moschata. Here, we performed a comprehensive genome-wide analysis to identify and characterize the functional dynamics of the Cucurbita moschata HSP20 (CmoHSP20) gene family. A total of 33 HSP20 genes distributed across 13 chromosomes were identified from the pumpkin genome. Our phylogenetic analysis determined that the CmoHSP20 proteins fell into nine distinct subfamilies, a division supported by the conserved motif composition and gene structure analyses. Segmental duplication events were shown to play a key role in expansion of the CmoHSP20 gene family. Synteny analysis revealed that 19 and 18 CmoHSP20 genes were collinear with those in the cucumber and melon genomes, respectively. Furthermore, the expression levels of pumpkin HSP20 genes were differentially induced by heat stress. The transcript level of CmoHSP20-16, 24 and 25 were down-regulated by heat stress, while CmoHSP20-7, 13, 18, 22, 26 and 32 were up-regulated by heat stress, which could be used as heat tolerance candidate genes. Overall, these findings contribute to our understanding of vegetable HSP20 family genes and provide valuable information that can be used to breed heat stress resistance in cucurbit vegetable crops.

Functional single cell selection and annotated profiling of dynamically changing cancer cells

A method connecting single cell genomic or transcriptomic profiles to functional cellular characteristics, in particular time-varying phenotypic changes, would be transformative for single cell and cancer biology. Here, we present fSCS: functional single cell selection. This technology combines a custom-built ultrawide field-of-view optical screening microscope, fast automated image analysis and a new photolabeling method, phototagging, using a newly synthesized visible-light-photoactivatable dye. Using fSCS, we screen, selectively photolabel and isolate cells of interest from large heterogeneous populations based on functional dynamics like fast migration, morphological variation, small molecule uptake or cell division. We combined fSCS with single cell RNA sequencing for functionally annotated transcriptomic profiling of fast migrating and spindle-shaped MCF10A cells with or without TGFβ induction. We identified critical genes and pathways driving aggressive migration as well as mesenchymal-like morphology that could not be detected with state-of-the-art single cell transcriptomic analysis. fSCS provides a crucial upstream selection paradigm for single cell sequencing independent of biomarkers, allows enrichment of rare cells and can facilitate the identification and understanding of molecular mechanisms underlying functional phenotypes.