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Ice-nucleating proteins (INPs) catalyze ice formation at high subzero temperatures, with major biological and environmental implications. While bacterial INPs have been structurally characterized, their counterparts in other organisms remain unknown. Here, we identify a new class of efficient INPs in fungi. These proteins are membrane-free, adopt β-solenoid folds, and multimerize to form large ice-binding surfaces, showing mechanistic parallels with bacterial INPs. Structural modeling, sequence analysis, and functional assays show they are encoded by orthologs of the bacterial InaZ gene, likely acquired via horizontal gene transfer. Our results demonstrate that distinct lineages have independently converged on a common molecular strategy to overcome the energetic barriers of ice formation. The discovery of cell-free INPs provides tools for freezing applications and reveals biophysical constraints on nucleation across life. 

Bacterial ice nucleating proteins (INPs) are exceptionally effective in promoting the kinetically hindered transition of water to ice. Their efficiency relies on the assembly of INPs into large functional aggregates, with the size of ice nucleation sites determining activity. Experimental freezing spectra have revealed two distinct, defined aggregate sizes, typically classified as class A and C ice nucleators (INs). Despite the importance of INPs and years of extensive research, the precise number of INPs forming the two aggregate classes, and their assembly mechanism have remained enigmatic. Here, we report that bacterial ice nucleation activity emerges from more than two prevailing aggregate species and identify the specific number of INPs responsible for distinct crystallization temperatures. We find that INP dimers constitute class C INs, tetramers class B INs, and hexamers and larger multimers are responsible for the most efficient class A activity. We propose a hierarchical assembly mechanism based on tyrosine interactions for dimers, and electrostatic interactions between INP dimers to produce larger aggregates. This assembly is membrane-assisted: Increasing the bacterial outer membrane fluidity decreases the population of the larger aggregates, while preserving the dimers. Inversely, Dulbecco’s Phosphate-Buffered Saline buffer increases the population of multimeric class A and B aggregates 200-fold and endows the bacteria with enhanced stability toward repeated freeze-thaw cycles. Our analysis suggests that the enhancement results from the better alignment of dimers in the negatively charged outer membrane, due to screening of their electrostatic repulsion. This demonstrates significant enhancement of the most potent bacterial INs. 

Biological ice nucleation plays a key role in the survival of cold-adapted organisms. Several species of bacteria, fungi, and insects produce ice nucleators (INs) that enable ice formation at temperatures above −10 °C. Bacteria and fungi produce particularly potent INs that can promote water crystallization above −5 °C. Bacterial INs consist of extended protein units that aggregate to achieve superior functionality. Despite decades of research, the nature and identity of fungal INs remain elusive. Here, we combine ice nucleation measurements, physicochemical characterization, numerical modeling, and nucleation theory to shed light on the size and nature of the INs from the fungusFusarium acuminatum. We find ice-binding and ice-shaping activity ofFusariumIN, suggesting a potential connection between ice growth promotion and inhibition. We demonstrate that fungal INs are composed of small 5.3 kDa protein subunits that assemble into ice-nucleating complexes that can contain more than 100 subunits.FusariumINs retain high ice-nucleation activity even when only the ~12 kDa fraction of size-excluded proteins are initially present, suggesting robust pathways for their functional aggregation in cell-free aqueous environments. We conclude that the use of small proteins to build large assemblies is a common strategy among organisms to create potent biological INs. 

The heterogeneous nucleation of ice is an importantatmospheric process facilitated by a wide range of aerosols. Drop-freezingexperiments are key for the determination of the ice nucleation activity ofbiotic and abiotic ice nucleators (INs). The results of these experimentsare reported as the fraction of frozen droplets fice(T) as a functionof decreasing temperature and the corresponding cumulative freezing spectraNm(T) computed using Gabor Vali's methodology. The differential freezingspectrum nm(T) is an approximant to the underlying distribution ofheterogeneous ice nucleation temperatures Pu(T) that represents thecharacteristic freezing temperatures of all INs in the sample. However,Nm(T) can be noisy, resulting in a differential form nm(T) that is challenging to interpret. Furthermore, there is no rigorousstatistical analysis of how many droplets and dilutions are needed to obtaina well-converged nm(T) that represents the underlying distributionPu(T). Here, we present the HUB (heterogeneousunderlying-based) method and associated Python codes thatmodel (HUB-forward code) and interpret (HUB-backward code) the results ofdrop-freezing experiments. HUB-forward predicts fice(T) and Nm(T)from a proposed distribution Pu(T) of IN temperatures, allowing itsusers to test hypotheses regarding the role of subpopulations of nuclei infreezing spectra and providing a guide for a more efficient collection offreezing data. HUB-backward uses a stochastic optimization method to computenm(T) from either Nm(T) or fice(T). The differential spectrumcomputed with HUB-backward is an analytical function that can be used toreveal and characterize the underlying number of IN subpopulations ofcomplex biological samples (e.g., ice-nucleating bacteria, fungi, pollen)and to quantify the dependence of these subpopulations on environmentalvariables. By delivering a way to compute the differential spectrum fromdrop-freezing data, and vice versa, the HUB-forward and HUB-backward codesprovide a hub to connect experiments and interpretative physical quantitiesthat can be analyzed with kinetic models and nucleation theory. 

Abstract. Forty years ago, lichens were identified as extraordinary biological icenucleators (INs) that enable ice formation at temperatures close to0 ∘C. By employing INs, lichens thrive in freezing environmentsthat surpass the physiological limits of other vegetation, thus making themthe majority of vegetative biomass in northern ecosystems. Aerosolizedlichen INs might further impact cloud glaciation and have the potential toalter atmospheric processes in a warming Arctic. Despite the ecologicalimportance and formidable ice nucleation activities, the abundance,diversity, sources, and role of ice nucleation in lichens remain poorlyunderstood. Here, we investigate the ice nucleation capabilities of lichenscollected from various ecosystems across Alaska. We find ice nucleatingactivity in lichen to be widespread, particularly in the coastal rainforestof southeast Alaska. Across 29 investigated lichen, all species show icenucleation temperatures above −15 ∘C, and ∼30 %initiate freezing at temperatures above −6 ∘C. Concentrationseries of lichen ice nucleation assays in combination with statisticalanalysis reveal that the lichens contain two subpopulations of INs, similarto previous observations in bacteria. However, unlike the bacterial INs, thelichen INs appear as independent subpopulations resistant to freeze–thawcycles and against temperature treatment. The ubiquity and high stability ofthe lichen INs suggest that they can impact local atmospheric processes andthat ice nucleation activity is an essential trait for their survival incold environments. 

The appearance of ice I in the smallest possible clusters and the nature of its phase coexistence with liquid water could not thus far be unraveled. The experimental and theoretical infrared spectroscopic and free-energy results of this work show the emergence of the characteristic hydrogen-bonding pattern of ice I in clusters containing only around 90 water molecules. The onset of crystallization is accompanied by an increase of surface oscillator intensity with decreasing surface-to-volume ratio, a spectral indicator of nanoscale crystallinity of water. In the size range from 90 to 150 water molecules, we observe mixtures of largely crystalline and purely amorphous clusters. Our analysis suggests that the liquid–ice I transition in clusters loses its sharp 1st-order character at the end of the crystalline-size regime and occurs over a range of temperatures through heterophasic oscillations in time, a process without analog in bulk water.