Host Manipulation Mechanisms of SARS-CoV-2.

Viruses are the simplest of pathogens, but possess sophisticated molecular mechanisms to manipulate host behavior, frequently utilizing molecular mimicry. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been shown to bind to the host receptor neuropilin-1 in order to gain entry into the cell. To do this, the virus utilizes its spike protein polybasic cleavage site (PCS), which mimics the CendR motif of neuropilin-1's endogenous ligands. In addition to facilitating cell entry, binding to neuropilin-1 has analgesic effects. We discuss the potential impact of neuropilin-1 binding by SARS-CoV-2 in ameliorating sickness behavior of the host, and identify a convergent evolutionary strategy of PCS cleavage and subsequent neuropilin binding in other human viruses. In addition, we discuss the evolutionary leap of the ancestor of SARS-COV-2, which involved acquisition of the PCS thus faciliting binding to the neuropilin-1 receptor. Acquisition of the PCS by the ancestor of SARS-CoV-2 appears to have led to pleiotropic beneficial effects including enhancement of cell entry via binding to ACE2, facilitation of cell entry via binding to neuropilin-1, promotion of analgesia, and potentially the formation of decoy epitopes via enhanced shedding of the S1 subunit. Lastly, other potential neuromanipulation strategies employed by SARS-CoV-2 are discussed, including interferon suppression and the resulting reduction in sickness behavior, enhanced transmission through neurally mediated cough induction, and reduction in sense of smell.

Genetic Code Error Minimization as a Non-Adaptive But Beneficial Trait.

In a recent Letter, Di Giulio questions the use of the term 'neutral' when describing the process by which error minimization may have arisen as a side-product of genetic code expansion, resulting from the addition of similar amino acids to similar codons (Di Giulio, in J Mol Evol 86(9):593-597, 2018). However, I point out that in this scenario error minimization is non-adaptive, and so 'neutral' is an appropriate term to describe its imperviousness to direct selection. Error minimization is a form of mutational robustness, and so commonly viewed as beneficial. This in turn implies that not all beneficial traits may be adaptations generated by direct selection for that trait.

Strong Amerindian Mitonuclear Discordance in Puerto Rican Genomes Suggests Amerindian Mitochondrial Benefit.

A large discrepancy between the Amerindian contribution to the mitochondrial and nuclear genetic components of 55 Puerto Rican (PR) genomes from the 1000 Genomes Project is identified, with Amerindian mitochondrial haplotypes being highly represented (67.3%), in strong contrast to the Amerindian autosomal contribution (12.9%). I examine the potential causes behind this strong mitonuclear discordance. The Amerindian contribution to the X chromosome is 19.8%, implying assortative mating with Amerindian females during the establishment of the PR population. However, this scenario does not account for the extraordinarily high Amerindian mitochondrial contribution. Demographic simulation of simple assortative mating scenarios during establishment of the PR population indicates that the observed Amerindian mitochondrial contribution is higher than expected. The simulations show that expansion from a small founding population does not produce the observed frequencies, instead producing the frequencies expected under neutrality, with the Amerindian mitochondrial frequencies approximately twice the Amerindian autosomal proportion. In addition, multiple replicated simulations show that drift is an unlikely explanation for the elevated Amerindian mitochondrial frequency, as these are unable to produce the elevated Amerindian mitochondrial frequency observed in the PR genomic dataset, under a range of different starting conditions. I conclude that the mitonuclear discordance appears most consistent with adaptive mitochondrial benefit; however, the molecular mechanism(s) remain to be characterized before this can be confirmed and warrant further investigation. Lastly, I show potential evidence of selection on autosomes and allosomes, using admixture proportions. Interestingly, the major histocompatibility complex locus on chromosome 6 shows greatly elevated single nucleotide polymorphism density but is unaccompanied by strong admixture variance. The observations on mitonuclear discordance may affect the interpretation of apparent assortative mating in recent human admixture events, which should be treated with caution when relying only on mitochondrial haplotype frequencies.

The neutral emergence of error minimized genetic codes superior to the standard genetic code.

The standard genetic code (SGC) assigns amino acids to codons in such a way that the impact of point mutations is reduced, this is termed 'error minimization' (EM). The occurrence of EM has been attributed to the direct action of selection, however it is difficult to explain how the searching of alternative codes for an error minimized code can occur via codon reassignments, given that these are likely to be disruptive to the proteome. An alternative scenario is that EM has arisen via the process of genetic code expansion, facilitated by the duplication of genes encoding charging enzymes and adaptor molecules. This is likely to have led to similar amino acids being assigned to similar codons. Strikingly, we show that if during code expansion the most similar amino acid to the parent amino acid, out of the set of unassigned amino acids, is assigned to codons related to those of the parent amino acid, then genetic codes with EM superior to the SGC easily arise. This scheme mimics code expansion via the gene duplication of charging enzymes and adaptors. The result is obtained for a variety of different schemes of genetic code expansion and provides a mechanistically realistic manner in which EM has arisen in the SGC. These observations might be taken as evidence for self-organization in the earliest stages of life.

DNA Repair Is Associated with Information Content in Bacteria, Archaea, and DNA Viruses.

The concept of a "proteomic constraint" proposes that DNA repair capacity is positively correlated with the information content of a genome, which can be approximated to the size of the proteome (P). This in turn implies that DNA repair genes are more likely to be present in genomes with larger values of P. This stands in contrast to the common assumption that informational genes have a core function and so are evenly distributed across organisms. We examined the presence/absence of 18 DNA repair genes in bacterial genomes. A positive relationship between gene presence and P was observed for 17 genes in the total dataset, and 16 genes when only nonintracellular bacteria were examined. A marked reduction of DNA repair genes was observed in intracellular bacteria, consistent with their reduced value of P. We also examined archaeal and DNA virus genomes, and show that the presence of DNA repair genes is likewise related to a larger value of P. In addition, the products of the bacterial genes mutY, vsr, and ndk, involved in the correction of GC/AT mutations, are strongly associated with reduced genome GC content. We therefore propose that a reduction in information content leads to a loss of DNA repair genes and indirectly to a reduction in genome GC content in bacteria by exposure to the underlying AT mutation bias. The reduction in P may also indirectly lead to the increase in substitution rates observed in intracellular bacteria via loss of DNA repair genes.

A nonadaptive origin of a beneficial trait: in silico selection for free energy of folding leads to the neutral emergence of mutational robustness in single domain proteins.

Proteins are regarded as being robust to the deleterious effects of mutations. Here, the neutral emergence of mutational robustness in a population of single domain proteins is explored using computer simulations. A pairwise contact model was used to calculate the ΔG of folding (ΔG folding) using the three dimensional protein structure of leech eglin C. A random amino acid sequence with low mutational robustness, defined as the average ΔΔG resulting from a point mutation (ΔΔG average), was threaded onto the structure. A population of 1,000 threaded sequences was evolved under selection for stability, using an upper and lower energy threshold. Under these conditions, mutational robustness increased over time in the most common sequence in the population. In contrast, when the wild type sequence was used it did not show an increase in robustness. This implies that the emergence of mutational robustness is sequence specific and that wild type sequences may be close to maximal robustness. In addition, an inverse relationship between ∆∆G average and protein stability is shown, resulting partly from a larger average effect of point mutations in more stable proteins. The emergence of mutational robustness was also observed in the Escherichia coli colE1 Rop and human CD59 proteins, implying that the property may be common in single domain proteins under certain simulation conditions. The results indicate that at least a portion of mutational robustness in small globular proteins might have arisen by a process of neutral emergence, and could be an example of a beneficial trait that has not been directly selected for, termed a "pseudaptation."

How Mitochondrial Signaling Games May Shape and Stabilize the Nuclear-Mitochondrial Symbiosis.

The eukaryotic lineage has enjoyed a long-term "stable" mutualism between nucleus and mitochondrion, since mitochondrial endosymbiosis began about 2 billion years ago. This mostly cooperative interaction has provided the basis for eukaryotic expansion and diversification, which has profoundly altered the forms of life on Earth. While we ignore the exact biochemical details of how the alpha-proteobacterial ancestor of mitochondria entered into endosymbiosis with a proto-eukaryote, in more general terms, we present a signaling games perspective of how the cooperative relationship became established, and has been maintained. While games are used to understand organismal evolution, information-asymmetric games at the molecular level promise novel insights into endosymbiosis. Using a previously devised biomolecular signaling games approach, we model a sender-receiver information asymmetric game, in which the informed mitochondrial sender signals and the uninformed nuclear receiver may take actions (involving for example apoptosis, senescence, regeneration and autophagy/mitophagy). The simulation shows that cellularization is a stabilizing mechanism for Pareto efficient sender/receiver strategic interaction. In stark contrast, the extracellular environment struggles to maintain efficient outcomes, as senders are indifferent to the effects of their signals upon the receiver. Our hypothesis has translational implications, such as in cellular therapy, as mitochondrial medicine matures. It also inspires speculative conjectures about how an analogous human-AI endosymbiosis may be engineered.

Accurate identification of Helicoverpa armigera-Helicoverpa zea hybrids using genome admixture analysis: implications for genomic surveillance.

Helicoverpa armigera, the cotton bollworm moth, is one of the world's most important crop pests, and is spreading throughout the New World from its original range in the Old World. In Brazil, invasive H. armigera has been reported to hybridize with local populations of Helicoverpa zea. The correct identification of H. armigera-H. zea hybrids is important in understanding the origin, spread and future outlook for New World regions that are affected by outbreaks, given that hybridization can potentially facilitate H. zea pesticide resistance and host plant range via introgression of H. armigera genes. Here, we present a genome admixture analysis of high quality genome sequences generated from two H. armigera-H. zea F1 hybrids generated in two different labs. Our admixture pipeline predicts 48.8% and 48.9% H. armigera for the two F1 hybrids, confirming its accuracy. Genome sequences from five H. zea and one H. armigera that were generated as part of the study show no evidence of hybridization. Interestingly, we show that four H. zea genomes generated from a previous study are predicted to possess a proportion of H. armigera genetic material. Using unsupervised clustering to identify non-hybridized H. armigera and H. zea genomes, 8511 ancestry informative markers (AIMs) were identified. Their relative frequencies are consistent with a minor H. armigera component in the four genomes, however its origin remains to be established. We show that the size and quality of genomic reference datasets are critical for accurate hybridization prediction. Consequently, we discuss potential pitfalls in genome admixture analysis of H. armigera-H. zea hybrids, and suggest measures that will improve such analyses.

Discovery of a novel merbecovirus DNA clone contaminating agricultural rice sequencing datasets from Wuhan, China.

HKU4-related coronaviruses are a group of betacoronaviruses belonging to the same merbecovirus subgenus as Middle Eastern Respiratory Syndrome coronavirus (MERS-CoV), which causes severe respiratory illness in humans with a mortality rate of over 30%. The high genetic similarity between HKU4-related coronaviruses and MERS-CoV makes them an attractive subject of research for modeling potential zoonotic spillover scenarios. In this study, we identify a novel coronavirus contaminating agricultural rice RNA sequencing datasets from Wuhan, China. The datasets were generated by the Huazhong Agricultural University in early 2020. We were able to assemble the complete viral genome sequence, which revealed that it is a novel HKU4-related merbecovirus. The assembled genome is 98.38% identical to the closest known full genome sequence, Tylonycteris pachypus bat isolate BtTp-GX2012. Using in silico modeling, we identified that the novel HKU4-related coronavirus spike protein likely binds to human dipeptidyl peptidase 4 (DPP4), the receptor used by MERS-CoV. We further identified that the novel HKU4-related coronavirus genome has been inserted into a bacterial artificial chromosome in a format consistent with previously published coronavirus infectious clones. Additionally, we have found a near complete read coverage of the spike gene of the MERS-CoV reference strain HCoV-EMC/2012, and identify the likely presence of a HKU4-related-MERS chimera in the datasets. Our findings contribute to the knowledge of HKU4-related coronaviruses and document the use of a previously unpublished HKU4 reverse genetics system in apparent MERS-CoV related gain-of-function research. Our study also emphasizes the importance of improved biosafety protocols in sequencing centers and coronavirus research facilities.

Comparative Transcriptomic Analysis of Insecticide-Resistant Aedes aegypti from Puerto Rico Reveals Insecticide-Specific Patterns of Gene Expression.

Aedes aegypti transmits major arboviruses of public health importance, including dengue, chikungunya, Zika, and yellow fever. The use of insecticides represents the cornerstone of vector control; however, insecticide resistance in Ae. aegypti has become widespread. Understanding the molecular basis of insecticide resistance in this species is crucial to design effective resistance management strategies. Here, we applied Illumina RNA-Seq to study the gene expression patterns associated with resistance to three widely used insecticides (malathion, alphacypermethrin, and lambda-cyhalothrin) in Ae. aegypti populations from two sites (Manatí and Isabela) in Puerto Rico (PR). Cytochrome P450s were the most overexpressed detoxification genes across all resistant phenotypes. Some detoxification genes (CYP6Z7, CYP28A5, CYP9J2, CYP6Z6, CYP6BB2, CYP6M9, and two CYP9F2 orthologs) were commonly overexpressed in mosquitoes that survived exposure to all three insecticides (independent of geographical origin) while others including CYP6BY1 (malathion), GSTD1 (alpha-cypermethrin), CYP4H29 and GSTE6 (lambda-cyhalothrin) were uniquely overexpressed in mosquitoes that survived exposure to specific insecticides. The gene ontology (GO) terms associated with monooxygenase, iron binding, and passive transmembrane transporter activities were significantly enriched in four out of six resistant vs. susceptible comparisons while serine protease activity was elevated in all insecticide-resistant groups relative to the susceptible strain. Interestingly, cuticular-related protein genes (chinase and chitin) were predominantly downregulated, which was also confirmed in the functional enrichment analysis. This RNA-Seq analysis presents a detailed picture of the candidate detoxification genes and other pathways that are potentially associated with pyrethroid and organophosphate resistance in Ae. aegypti populations from PR. These results could inform development of novel molecular tools for detection of resistance-associated gene expression in this important arbovirus vector and guide the design and implementation of resistance management strategies.

How signalling games explain mimicry at many levels: from viral epidemiology to human sociology.

Mimicry is exhibited in multiple scales, ranging from molecular, to organismal, and then to human society. 'Batesian'-type mimicry entails a conflict of interest between sender and receiver, reflected in a deceptive mimic signal. 'Müllerian'-type mimicry occurs when there is perfect common interest between sender and receiver in a particular type of encounter, manifested by an honest co-mimic signal. Using a signalling games approach, simulations show that invasion by Batesian mimics will make Müllerian mimicry unstable, in a coevolutionary chase. We use these results to better understand the deceptive strategies of SARS-CoV-2 and their key role in the COVID-19 pandemic. At the biomolecular level, we explain how cellularization promotes Müllerian molecular mimicry, and discourages Batesian molecular mimicry. A wide range of processes analogous to cellularization are presented; these might represent a manner of reducing oscillatory instabilities. Lastly, we identify examples of mimicry in human society that might be addressed using a signalling game approach.

Searching of code space for an error-minimized genetic code via codon capture leads to failure, or requires at least 20 improving codon reassignments via the ambiguous intermediate mechanism.

The standard genetic code (SGC) has a fundamental error-minimizing property which has been widely attributed to the action of selection. However, a clear mechanism for how selection can give rise to error minimization (EM) is lacking. A search through a space of alternate codes (code space) via codon reassignments would be required, to select a code optimized for EM. There are two commonly discussed mechanisms of codon reassignment; the Codon Capture mechanism, which proposes a loss of the codon during reassignment, and the Ambiguous Intermediate mechanism, which proposes that the codon underwent an ambiguous phase during reassignment. When searching of code space via the Codon Capture mechanism is simulated, an optimized genetic code can rarely be achieved (0-3.2% of the time) with most searches ending in failure. When code space is searched via the Ambiguous Intermediate mechanism, under constraints derived from empirical observations of codon reassignments from extant genomes, the searches also often end in failure. When a local minimum is avoided and optimization is achieved, 20-41 sequential improving codon reassignments are required. Furthermore, the structures of the optimized codes produced by these simulations differ from the structure of the SGC. These data are challenges for the Adaptive Code hypothesis to address, which proposes that the EM property was directly selected for, and suggests that EM is simply a byproduct of the addition of amino acids to the expanding code, as described by the alternative 'Emergence' hypothesis.

How Signaling Games Explain Mimicry at Many Levels: From Viral Epidemiology to Human Sociology.

Mimicry is exhibited in multiple scales, ranging from molecular, to organismal, and then to human society. 'Batesian' type mimicry entails a conflict of interest between sender and receiver, reflected in a deceptive mimic signal. 'Mullerian' type mimicry occurs when there is perfect common interest between sender and receiver, manifested by an honest co-mimic signal. Using a signaling games approach, simulations show that invasion by Batesian mimics will make Mullerian mimicry unstable, in a coevolutionary chase. We use these results to better understand the deceptive strategies of SARS-CoV-2 and their key role in the COVID-19 pandemic. At the biomolecular level, we explain how cellularization promotes Mullerian molecular mimicry, and discourages Batesian molecular mimicry. A wide range of processes analogous to cellularization are presented; these might represent a manner of reducing oscillatory instabilities. Lastly, we identify examples of mimicry in human society, that might be addressed using a signaling game approach.

The proteomic constraint and its role in molecular evolution.

Recently, the concept of a "Proteomic Constraint" was introduced to explain the frequency of genetic code deviations in mitochondrial genomes. The Proteomic Constraint was proposed to be proportional to the size of the mitochondrially encoded proteome, hence small proteomes are expected to experience smaller total numbers of errors resulting from genetic code deviations, leading to less likelihood of causing lethality. The concept is now extended to encompass several other aspects of the genetic information system. When the Proteomic Constraint is small, it is proposed that there is little selective pressure to evolve or maintain error correction mechanisms, as a result of the smaller total number of errors that accumulate. Conversely, a large Proteomic Constraint is proposed to result in a correspondingly large selective pressure to evolve or maintain error correction mechanisms. Differences in the size of the Proteomic Constraint can help to explain differences in replicational, transcriptional, and translational fidelities between genomes. A key piece of evidence is the existence of negative power law relationships between proteome size and error rates; these are demonstrated to be diagnostic of the action of the Proteomic Constraint. The Proteomic Constraint is argued to be a major factor determining mutation rates in a diverse range of DNA genomes, implying that mutation rates are clock like. A small Proteomic Constraint partly explains why RNA viruses possess high mutation rates. A reduced Proteomic Constraint in intracellular pathogenic bacteria predicts a drift upwards in mutation rates. Differences in the Proteomic Constraint also appear to be linked to differences in recombination rates between eukaryotes. In addition, a reduced Proteomic Constraint may explain features of resident genomes, such as loss of DNA repair pathways, increased substitution rates, and AT biases, in addition to the occurrence of genetic code deviations. Thus, it is argued that the Proteomic Constraint is a universal factor that influences a wide range of properties of the genetic information system.

The genome of deep-sea vent chemolithoautotroph Thiomicrospira crunogena XCL-2.

Presented here is the complete genome sequence of Thiomicrospira crunogena XCL-2, representative of ubiquitous chemolithoautotrophic sulfur-oxidizing bacteria isolated from deep-sea hydrothermal vents. This gammaproteobacterium has a single chromosome (2,427,734 base pairs), and its genome illustrates many of the adaptations that have enabled it to thrive at vents globally. It has 14 methyl-accepting chemotaxis protein genes, including four that may assist in positioning it in the redoxcline. A relative abundance of coding sequences (CDSs) encoding regulatory proteins likely control the expression of genes encoding carboxysomes, multiple dissolved inorganic nitrogen and phosphate transporters, as well as a phosphonate operon, which provide this species with a variety of options for acquiring these substrates from the environment. Thiom. crunogena XCL-2 is unusual among obligate sulfur-oxidizing bacteria in relying on the Sox system for the oxidation of reduced sulfur compounds. The genome has characteristics consistent with an obligately chemolithoautotrophic lifestyle, including few transporters predicted to have organic allocrits, and Calvin-Benson-Bassham cycle CDSs scattered throughout the genome.

On the Origin of Biomolecular Networks.

Biomolecular networks have already found great utility in characterizing complex biological systems arising from pairwise interactions amongst biomolecules. Here, we explore the important and hitherto neglected role of information asymmetry in the genesis and evolution of such pairwise biomolecular interactions. Information asymmetry between sender and receiver genes is identified as a key feature distinguishing early biochemical reactions from abiotic chemistry, and a driver of network topology as biomolecular systems become more complex. In this context, we review how graph theoretical approaches can be applied not only for a better understanding of various proximate (mechanistic) relations, but also, ultimate (evolutionary) structures encoded in such networks from among all types of variations they induce. Among many possible variations, we emphasize particularly the essential role of gene duplication in terms of signaling game theory, whereby sender and receiver gene players accrue benefit from gene duplication, leading to a preferential attachment mode of network growth. The study of the resulting dynamics suggests many mathematical/computational problems, the majority of which are intractable yet yield to efficient approximation algorithms, when studied through an algebraic graph theoretic lens. We relegate for future work the role of other possible generalizations, additionally involving horizontal gene transfer, sexual recombination, endo-symbiosis, etc., which enrich the underlying graph theory even further.

Machine Learning Algorithm for Predicting Warfarin Dose in Caribbean Hispanics Using Pharmacogenetic Data.

Despite some previous examples of successful application to the field of pharmacogenomics, the utility of machine learning (ML) techniques for warfarin dose predictions in Caribbean Hispanic patients has yet to be fully evaluated. This study compares seven ML methods to predict warfarin dosing in Caribbean Hispanics. This is a secondary analysis of genetic and non-genetic clinical data from 190 cardiovascular Hispanic patients. Seven ML algorithms were applied to the data. Data was divided into 80 and 20% to be used as training and test sets. ML algorithms were trained with the training set to obtain the models. Model performance was determined by computing the corresponding mean absolute error (MAE) and % patients whose predicted optimal dose were within ±20% of the actual stabilization dose, and then compared between groups of patients with "normal" (i.e., > 21 but <49 mg/week), low (i.e., ≤21 mg/week, "sensitive"), and high (i.e., ≥49 mg/week, "resistant") dose requirements. Random forest regression (RFR) significantly outperform all other methods, with a MAE of 4.73 mg/week and 80.56% of cases within ±20% of the actual stabilization dose. Among those with "normal" dose requirements, RFR performance is also better than the rest of models (MAE = 2.91 mg/week). In the "sensitive" group, support vector regression (SVR) shows superiority over the others with lower MAE of 4.79 mg/week. Finally, multivariate adaptive splines (MARS) shows the best performance in the resistant group (MAE = 7.22 mg/week) and 66.7% of predictions within ±20%. Models generated by using RFR, MARS, and SVR algorithms showed significantly better predictions of weekly warfarin dosing in the studied cohorts than other algorithms. Better performance of the ML models for patients with "normal," "sensitive," and "resistant" to warfarin were obtained when compared to other populations and previous statistical models.

Characterizing positive and negative selection and their phylogenetic effects.

Total evidence and the use of large datasets to overcome uncertainty are the state of the art in systematic analysis. This assumes that the only true phylogenetic signal is ancestry and that functional, structural, and other factors will not add an alternative signal. Using gene families, where individual codon positions were sorted into bins based upon average-pairwise dN/dS ratio, we show that standard, common phylogenetic methods that were designed for stochastic, neutral, site-independent processes, generate less robust phylogenetic signal for bins with strong negative or positive selection. This was true for phylogenetic reconstruction with parsimony, distance, and likelihood methods. Further, we present a case for the potential existence of systematic functional or structural signal that competes with ancestral signal. For the example of positive selection, we simulate the evolution of sequences through three dimensional lattice constructs with folding constraint and changing binding functionality and show that total evidence for these lattice genes presents trees with functional signal, but that the neutral synonymous sites in these genes show the true ancestral signal. In this case, sequence convergence is promoted by functional convergence.

Origin of biomolecular games: deception and molecular evolution.

Biological macromolecules encode information: some of it to endow the molecule with structural flexibility, some of it to enable molecular actions as a catalyst or a substrate, but a residual part can be used to communicate with other macromolecules. Thus, macromolecules do not need to possess information only to survive in an environment, but also to strategically interact with others by sending signals to a receiving macromolecule that can properly interpret the signal and act suitably. These sender-receiver signalling games are sustained by the information asymmetry that exists among the macromolecules. In both biochemistry and molecular evolution, the important role of information asymmetry remains largely unaddressed. Here, we provide a new unifying perspective on the impact of information symmetry between macromolecules on molecular evolutionary processes, while focusing on molecular deception. Biomolecular games arise from the ability of biological macromolecules to exert precise recognition, and their role as units of selection, meaning that they are subject to competition and cooperation with other macromolecules. Thus, signalling game theory can be used to better understand fundamental features of living systems such as molecular recognition, molecular mimicry, selfish elements and 'junk' DNA. We show how deceptive behaviour at the molecular level indicates a conflict of interest, and so provides evidence of genetic conflict. This model proposes that molecular deception is diagnostic of selfish behaviour, helping to explain the evasive behaviour of transposable elements in 'junk' DNA, for example. Additionally, in this broad review, a range of major evolutionary transitions are shown to be associated with the establishment of signalling conventions, many of which are susceptible to molecular deception. These perspectives allow us to assign rudimentary behaviour to macromolecules, and show how participation in signalling games differentiates biochemistry from abiotic chemistry.