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ABSTRACT Over the course of hundreds of millions of years, biomineralization has evolved independently many times across all kingdoms of life. Among animals, the phylum Mollusca displays a remarkable diversity in biomineral structures, particularly the molluscan shell, which varies greatly in shape, size, pigmentation, and patterning. Shell matrix proteins (SMPs) are key components of these shells, and are thought to drive the precipitation of calcium carbonate minerals and influence shell morphology. However, this structure‐function relationship has rarely been studied directly because tools for knocking out genes did not exist in molluscs until recently. In this study, we report the first successful use of CRISPR/Cas9 gene editing to target an SMP in gastropod molluscs. Using the emerging model gastropodCrepidula atrasolea, we generated knockouts of theSMP1gene. Successful gene editing was confirmed by Sanger and MiSeq sequencing, and loss ofSMP1expression was validated through high‐content imaging of crispant embryos. This study establishesC. atrasoleaas a valuable model for investigating the genetic basis of shell formation and provides a framework for applying CRISPR/Cas9 technology in other molluscan species. Our approach will enable future studies to thoroughly test the role of SMPs in shaping the diverse array of molluscan shell structures. 

Abstract Mollusca is a morphologically diverse phylum, exhibiting an immense variety of calcium carbonate structures. Proteomic studies of adult shells often report high levels of rapidly-evolving, ‘novel’ shell matrix proteins (SMPs), which are hypothesized to drive shell diversification. However, relatively little is known about the phylogenetic distribution of SMPs, or about the function of individual SMPs in shell construction. To understand how SMPs contribute to shell diversification a thorough characterization of SMPs is required. Here, we build tools and a foundational understanding of SMPs in the marine gastropod speciesCrepidula fornicataandCrepidula atrasoleabecause they are genetically-enabled mollusc model organisms. First, we established a staging system of shell development inC. atrasoleafor the first time. Next, we leveraged previous findings inC. fornicatacombined with phylogenomic analyses of 95 metazoan species to determine the evolutionary lineage of its adult SMP repertoire. We found that 55% ofC. fornicata’sSMPs belong to molluscan orthogroups, with 27% restricted to Gastropoda, and only 5% restricted at the species level. The low percentage of species-restricted SMPs underscores the importance of broad-taxon sampling and orthology inference approaches when determining homology of SMPs. From our transcriptome analysis, we found that the majority ofC. fornicataSMPs that were found conserved inC. atrasoleawere expressed in both larval and adult stages. We then selected a subset of SMPs of varying evolutionary ages for spatial-temporal analysis using in situ hybridization chain reaction (HCR) during larval shell development inC. atrasolea. Out of the 18 SMPs analyzed, 12 were detected in the larval shell field. These results suggest overlapping larval vs. adult SMP repertoires. Using multiplexed HCR, we observed five SMP expression patterns and three distinct cell populations within the shell field. These patterns support the idea that modular expression of SMPs could facilitate divergence of shell morphological characteristics. Collectively, these data establish an evolutionary and developmental framework inCrepidulathat enables future comparisons of molluscan biomineralization to reveal mechanisms of shell diversification. 

Electronic materials that exhibit phase transitions between metastable states (e.g., metal-insulator transition materials with abrupt electrical resistivity transformations) are challenging to decode. For these materials, conventional machine learning methods display limited predictive capability due to data scarcity and the absence of features that impede model training. In this article, we demonstrate a discovery strategy based on multi-objective Bayesian optimization to directly circumvent these bottlenecks by utilizing latent variable Gaussian processes combined with high-fidelity electronic structure calculations for validation in the chalcogenide lacunar spinel family. We directly and simultaneously learn phase stability and bandgap tunability from chemical composition alone to efficiently discover all superior compositions on the design Pareto front. Previously unidentified electronic transitions also emerge from our featureless adaptive optimization engine. Our methodology readily generalizes to optimization of multiple properties, enabling co-design of complex multifunctional materials, especially where prior data is sparse. 

The new, quaternary diamond-like semiconductor (DLS) Cu 4 MnGe 2 S 7 was prepared at high-temperature from a stoichiometric reaction of the elements under vacuum. Single crystal X-ray diffraction data were used to solve and refine the structure in the polar space group Cc. Cu 4 MnGe 2 S 7 features [Ge 2 S 7 ] 6− units and adopts the Cu 5 Si 2 S 7 structure type that can be considered a derivative of the hexagonal diamond structure. The DLS Cu 2 MnGeS 4 with the wurtz-stannite structure was similarly prepared at a lower temperature. The achievement of relatively phase-pure samples, confirmed by X-ray powder diffraction data, was nontrival as differential thermal analysis shows an incongruent melting behaviour for both compounds at relatively high temperature. The dark red Cu 2 MnGeS 4 and Cu 4 MnGe 2 S 7 compounds exhibit direct optical bandgaps of 2.21 and 1.98 eV, respectively. The infrared (IR) spectra indicate potentially wide windows of optical transparency up to 25 μm for both materials. Using the Kurtz–Perry powder method, the second-order nonlinear optical susceptibility, χ (2) , values for Cu 2 MnGeS 4 and Cu 4 MnGe 2 S 7 were estimated to be 16.9 ± 2.0 pm V −1 and 2.33 ± 0.86 pm V −1 , respectively, by comparing with an optical-quality standard reference material, AgGaSe 2 (AGSe). Cu 2 MnGeS 4 was found to be phase matchable at λ = 3100 nm, whereas Cu 4 MnGe 2 S 7 was determined to be non-phase matchable at λ = 1600 nm. The weak SHG response of Cu 4 MnGe 2 S 7 precluded phase-matching studies at longer wavelengths. The laser-induced damage threshold (LIDT) for Cu 2 MnGeS 4 was estimated to be ∼0.1 GW cm −2 at λ = 1064 nm (pulse width: τ = 30 ps), while the LIDT for Cu 4 MnGe 2 S 7 could not be ascertained due to its weak response. The significant variance in NLO properties can be reasoned using the results from electronic structure calculations. 

The authors showcase the potential of symbolic regression as an analytic method for use in materials research. First, the authors briefly describe the current state-of-the-art method, genetic programming-based symbolic regression (GPSR), and recent advances in symbolic regression techniques. Next, the authors discuss industrial applications of symbolic regression and its potential applications in materials science. The authors then present two GPSR use-cases: formulating a transformation kinetics law and showing the learning scheme discovers the well-known Johnson–Mehl–Avrami–Kolmogorov form, and learning the Landau free energy functional form for the displacive tilt transition in perovskite LaNiO 3 . Finally, the authors propose that symbolic regression techniques should be considered by materials scientists as an alternative to other machine learning-based regression models for learning from data.