Environmental control programs the emergence of distinct functional ensembles from unconstrained chemical reactions

Abstract

Many approaches to the origin of life focus on how the molecules found in biology might be made in the absence of biological processes, from the simplest plausible starting materials. Another approach could be to view the emergence of the chemistry of biology as process whereby the environment effectively directs "primordial soups" toward structure, function, and genetic systems over time. This does not require the molecules found in biology today to be made initially, and leads to the hypothesis that environment can direct chemical soups toward order, and eventually living systems. Herein, we show how unconstrained condensation reactions can be steered by changes in the reaction environment, such as order of reactant addition, and addition of salts or minerals. Using omics techniques to survey the resulting chemical ensembles we demonstrate there are distinct, significant, and reproducible differences between the product mixtures. Furthermore, we observe that these differences in composition have consequences, manifested in clearly different structural and functional properties. We demonstrate that simple variations in environmental parameters lead to differentiation of distinct chemical ensembles from both amino acid mixtures and a primordial soup model. We show that the synthetic complexity emerging from such unconstrained reactions is not as intractable as often suggested, when viewed through a chemically agnostic lens. An open approach to complexity can generate compositional, structural, and functional diversity from fixed sets of simple starting materials, suggesting that differentiation of chemical ensembles can occur in the wider environment without the need for biological machinery.

Keywords: chemomics; combinatorial chemistry; origin of life; peptides; systems chemistry.

Fig. 1.

Concept: Uncontrolled condensation reactions make a mess, but they can be steered. Reactions where multifunctional building blocks yield combinatorial explosions may be steered by different environmental conditions to consistently yield different product distributions. These different product ensembles can be shown to have consistently different structural and functional properties.

Fig. 2.

PC-DFA analysis of LC-MS data from condensation of G, A, and H in different environments/conditions: (A) different soluble salts, (B) different minerals, and (C) different mixing orders. Points represent individual measurements (nine measurements: three experimental replicates × three analytical replicates), and shaded bubbles represent a 2-SD space around their mean; see SI Appendix, section 2.2 for full details and other representations. Plots generated with Origin Pro-2016 (OriginLab).

Fig. 3.

Plots revealing the sequence permutation distribution of G₄A pentamers. Distribution of mean peak intensity from EICs (m/z = 318.141) of samples with different mixing histories, with error bars representing 1 SD. Assignment shown in top right inset. (See SI Appendix for peak identification and further details. Peak intensities were extracted using Bruker Data analysis; intensity values displayed are means of three experimental replicates × three analytical repeats.)

Fig. 4.

Different product ensembles differentially influence paranitrophenyl acetate conversion. (Inset) Decomposition of pNPA to release pNP produces a yellow color (measured as absorbance at 405 nm). Box plots comparing rates of pNP release on interaction with ensembles produced in different environments/conditions: an equimolar mixture of G, A, and H with (A) different soluble salts, and (B) different minerals, and (C) different mixing orders of G, A, and H over multiple cycles. In all cases the control experiment was with no product present. (Boxes represent middle quartiles, their middle line represents the mean, whiskers represent outlying quartiles.)

Fig. 5.

Recognition, assembly, and gelation properties differ between product ensembles. (A) ThT assay reveals considerable differences in binding properties of product ensembles from A, V, and D condensation with different mixing orders, and (B) TEM images of the same products reveal assembly of qualitatively different structures. (C) On addition of Ca²⁺ salts, some ensembles form self-supporting gels, which remain in place when vials are inverted (others leave clear solutions, which flow to the bottom of vials on inversion, SI Appendix), and (D) TEM inspection of these samples reveals the assembly of fibers in the gelled samples (“gel”), and discrete globular structures in the clear solutions (“sol”).

Fig. 6.

Minerals can direct emergence of distinct ensembles from a primordial soup model. Complex mixtures (SD Mix) were dehydrated in the presence of different minerals. LC-MS data show almost all ensembles are compositionally different: (A) LC-MS features as PC-DFA analysis; plot generated with Origin Pro-2016 (OriginLab) (B) matrix demonstrating ensemble overlap in PC-DFA [“control (blank),” assay buffer blank; “control NM,” no mineral present; “control NR,” no condensation reaction]. All data are from nine measurements: three experimental replicates × three analytical replicates; Example EICs (C–J, ordered by ascending m/z) selected to demonstrate a range of diversity in the data. Some show almost-complete selectivity between three isobaric species. (C, m/z = 101.0715; D, m/z = 102.0918; E, m/z = 166.0245; F, m/z = 174.0582; G, m/z = 244.1907; H, m/z = 278.0520; I, m/z = 255.0590; J, m/z = 321.0014; see SI Appendix for further examples.) TEM of ensembles produced in the presence of natrolite (K), alumina (L), and quartz (M) reveals marked morphological differences (Scale bar, 2 μm.) (See SI Appendix for full collection of TEM images.) (N) Some ensembles interact differently with ThT.