Indole-3-acetic acid (IAA) is an auxin produced by terrestrial plants which influences development through a variety of cellular mechanisms, such as altering cell orientation, organ development, fertility, and cell elongation. IAA is also produced by bacterial pathogens and symbionts of plants and algae, allowing them to manipulate growth and development of their host. They do so by either producing excess exogenous IAA or hijacking the IAA biosynthesis pathway of their host. The endogenous production of IAA by algae, however, remains contentious. Using Emiliania huxleyi, a globally abundant marine haptophyte, we investigated the presence and potential role of IAA in algae. Homologs of genes involved in several tryptophan-dependent IAA biosynthesis pathways were identified in E. huxleyi. This suggests that this haptophyte can synthesize IAA using various precursors derived from tryptophan. Addition of L-tryptophan to E. huxleyi stimulated IAA production, which could be detected using Salkowski’s reagent and GC×GC-TOFMS in the C cell type (coccolith-bearing), but not in the N cell type (bald). Various concentrations of IAA were exogenously added to these two cell types to identify a physiological response in E. huxleyi. The N cell type, which did not produce IAA, was more sensitive to it, showing an increased variation in cell size and membrane permeability, and a corresponding increase in the photosynthetic potential quantum yield of Photosystem II (PSII). A bacterial symbiont of E. huxleyi, Ruegeria sp. R11, previously shown to produce IAA, was co-cultured with E. huxleyi C and N cells. IAA could not be detected from these co-cultures, and even when stimulated by addition of L-tryptophan, they produced less IAA than axenic C type culture similarly induced. This suggests that IAA plays a novel role, signalling between different E. huxleyi cell types, rather than between a bacterial symbiont and its algal host.

Partial least-squares discriminant analysis (PLS-DA) and support vector machine (SVM) techniques were applied to develop a crystal structure predictor for binary AB compounds. Models were trained and validated on the basis of the classification of 706 AB compounds adopting the seven most common structure types (CsCl, NaCl, ZnS, CuAu, TlI, β-FeB, and NiAs), through data extracted from Pearson’s Crystal Data and ASM Alloy Phase Diagram Database. Out of 56 initial variables (descriptors based on elemental properties only), 31 were selected in as unbiased manner as possible through a procedure of forward selection and backward elimination, with the quality of the model evaluated by measuring the cluster resolution at each step. PLS-DA gave sensitivity of 96.5%, specificity of 66.0%, and accuracy of 77.1% for the validation set data, whereas SVM gave sensitivity of 94.2%, specificity of 92.7%, and accuracy of 93.2%, a significant improvement. Radii, electronegativity, and valence electrons, previously chosen intuitively in structure maps, were confirmed as important variables. PLS-DA and SVM could also make quantitative predictions of hypothetical compounds, unlike semiclassical approaches. The new compound RhCd was predicted to have the CsCl-type structure by PLS-DA (0.669 probability) and, at an even stronger confidence level, by SVM (0.918 probability). RhCd was synthesized by reaction of the elements at 800 °C and confirmed by X-ray diffraction to adopt the CsCl-type structure. SVM is thus a superior classification method in crystallography that is fast and makes correct, quantitative predictions; it may be more broadly applicable to help identify the structure of unknown compounds with any arbitrary composition.