Genetics and molecular biology research have progressed for over a century; however, no laws of biology resembling those of physics have been identified, despite the expectations of some physicists. It may be that it is not the properties of matter alone but evolved properties of matter in combination with atomic physics and chemistry that gave rise to the origin and complexity of life. It is proposed that any law of biology must also be a product of evolution that co-evolved with the origin and progression of life. It was suggested that molecular complexity and redundancy exponentially increase over time and have the following relationship: DNA sequence complexity (Cd) < molecular complexity (Cm) < phenotypic complexity (Cp). This study presents a law of redundancy, which together with the law of complexity, is proposed as an evolutionary law of biology. Molecular complexity and redundancy are inseparable aspects of biochemical pathways, and molecular redundancy provides the first line of defense against environmental challenges, including those of deleterious mutations. Redundancy can create problems for precision medicine because in addition to the issues arising from the involvement of multiple genes, redundancy arising from alternate pathways between genotypes and phenotypes can complicate gene detection for complex diseases and mental disorders. This study uses cancer as an example to show how cellular complexity, molecular redundancy, and hidden variation affect the ability of cancer cells to evolve and evade detection and elimination. Characterization of alternate biochemical pathways or "escape routes" can provide a step in the fight against cancer.

The HD superfamily has been studied in detail for several decades. The plant-specific HD-Zip I subfamily attracts the most attention because of its involvement in plant development and stress responses. In this review, we provide a comprehensive insight into the evolutionary events responsible for the functional redundancy and diversification of the HD-Zip I genes in regulating various biological processes. We summarized the evolutionary history of the HD-Zip family, highlighting the important role of WGDs in its expansion and divergence of retained duplicates in the genome. To determine the relationship between the evolutionary origin and functional conservation of HD-Zip I in different species, we performed a phylogenetic analysis, compared their expression profiles in different tissues and under stress and traced the role of orthologs and paralogs in regulating developmental processes. We found that HD-Zip I from different species have similar gene structures with a highly conserved HD and Zip, bind to the same DNA sequences and are involved in similar biological processes. However, they exhibit a functional diversity, which is manifested in altered expression patterns. Some of them are involved in the regulation of species-specific leaf morphology and phenotypes. Here, we discuss the role of changes in functional domains involved in DNA binding and protein interaction of HD-Zip I and in cis-regulated regions of its target genes in promoting adaptive innovations through the formation of de novo regulatory systems. Understanding the role of the HD-Zip I subfamily in organism-environment interactions remains a challenge for evolutionary developmental biology (evo-devo).

Hox genes encode Homeodomain-containing transcription factors, which specify segmental identities along the anterior-posterior axis. Functional changes in Hox genes have been directly implicated in the evolution of body plans across the metazoan lineage. The Hox protein Ultrabithorax (Ubx) is expressed and required in developing third thoracic (T3) segments in holometabolous insects studied so far, particularly, of the order Coleoptera, Lepidoptera and Diptera. Ubx function is key to specify differential development of the second (T2) and T3 thoracic segments in these insects. While Ubx is expressed in the third thoracic segment in developing larvae of Hymenopteran Apis mellifera, the morphological differences between T2 and T3 are subtle. To identify evolutionary changes that are behind the differential function of Ubx in Drosophila and Apis, which are diverged for more than 350 million years, we performed comparative analyses of genome wide Ubx-binding sites between these two insects. Our studies reveal that a motif with a TAAAT core is a preferred binding site for Ubx in Drosophila, but not in Apis. Biochemical and transgenic assays suggest that in Drosophila, the TAAAT core sequence in the Ubx binding sites is required for Ubx-mediated regulation of two of its target genes studied here; CG13222, a gene that is normally upregulated by Ubx and vestigial (vg), whose expression is repressed by Ubx in T3. Interestingly, changing the TAAT site to a TAAAT site was sufficient to bring an otherwise unresponsive enhancer of the vg gene from Apis under the control of Ubx in a Drosophila transgenic assay. Taken together, our results suggest an evolutionary mechanism by which critical wing patterning genes might have come under the regulation of Ubx in the Dipteran lineage.

Protein-coding DNA sequences can be translated into completely different amino acid sequences if the nucleotide triplets used are shifted by a non-triplet amount on the same DNA strand or by translating codons from the opposite strand. Such "alternative reading frames" of protein-coding genes are a major contributor to the evolution of novel protein products. Recent studies demonstrating this include examples across the three domains of cellular life and in viruses. These sequences increase the number of trials potentially available for the evolutionary invention of new genes and also have unusual properties which may facilitate gene origin. There is evidence that the structure of the standard genetic code contributes to the features and gene-likeness of some alternative frame sequences. These findings have important implications across diverse areas of molecular biology, including for genome annotation, structural biology, and evolutionary genomics.

The Aryl hydrocarbon receptor is an ancient transcriptional factor originally discovered as a sensor of dioxin. In addition to its function as a receptor of environmental toxicants, it plays an important role in development. Although a significant amount of research has been carried out to understand the AHR signal transduction pathway and its involvement in species' susceptibility to environmental toxicants, none of them to date has comprehensively studied its evolutionary origins. Studying the evolutionary origins of molecules can inform ancestral relationships of genes. The vertebrate genome has been shaped by two rounds of whole-genome duplications (WGD) at the base of vertebrate evolution approximately 600 million years ago, followed by lineage-specific gene losses, which often complicate the assignment of orthology. It is crucial to understand the evolutionary origins of this transcription factor and its partners, to distinguish orthologs from ancient non-orthologous homologs. In this study, we have investigated the evolutionary origins of proteins involved in the AHR pathway. Our results provide evidence of gene loss and duplications, crucial for understanding the functional connectivity of humans and model species. Multiple studies have shown that 2R-ohnologs (genes and proteins that have survived from the 2R-WGD) are enriched in signaling components relevant to developmental disorders and cancer. Our findings provide a link between the AHR pathway's evolutionary trajectory and its potential mechanistic involvement in pathogenesis.

Genes duplicate, mutate, recombine, fuse or fission to produce new genes, or when genes are formed from de novo, novel functions arise during evolution. Researchers have tried to quantify the causes of these molecular diversification processes to know how these genes increase molecular complexity over a period of time, for instance protein domain organization. In contrast to global sequence similarity, protein domain architectures can capture key structural and functional characteristics, making them better proxies for describing functional equivalence. In Prokaryotes and eukaryotes it has proven that, domain designs are retained over significant evolutionary distances. Protein domain architectures are now being utilized to categorize and distinguish evolutionarily related proteins and find homologs among species that are evolutionarily distant from one another. Additionally, structural information stored in domain structures has accelerated homology identification and sequence search methods. Tools for functional protein annotation have been developed to discover, protein domain content, domain order, domain recurrence, and domain position as all these contribute to the prediction of protein functional accuracy. In this review, an attempt is made to summarise facts and speculations regarding the use of protein domain architecture and modularity to identify possible therapeutic targets among cellular activities based on the understanding their linked biological processes.

Two long-standing challenges in theoretical population genetics and evolution are predicting the distribution of phenotype diversity generated by mutation and available for selection, and determining the interaction of mutation, selection and drift to characterize evolutionary equilibria and dynamics. More fundamental for enabling such predictions is the current inability to causally link genotype to phenotype. There are three major mechanistic mappings required for such a linking - genetic sequence to kinetic parameters of the molecular processes, kinetic parameters to biochemical system phenotypes, and biochemical phenotypes to organismal phenotypes. This article introduces a theoretical framework, the Phenotype Design Space (PDS) framework, for addressing these challenges by focusing on the mapping of kinetic parameters to biochemical system phenotypes. It provides a quantitative theory whose key features include (1) a mathematically rigorous definition of phenotype based on biochemical kinetics, (2) enumeration of the full phenotypic repertoire, and (3) functional characterization of each phenotype independent of its context-dependent selection or fitness contributions. This framework is built on Design Space methods that relate system phenotypes to genetically determined parameters and environmentally determined variables. It also has the potential to automate prediction of phenotype-specific mutation rate constants and equilibrium distributions of phenotype diversity in microbial populations undergoing steady-state exponential growth, which provides an ideal reference to which more realistic cases can be compared. Although the framework is quite general and flexible, the details will undoubtedly differ for different functions, organisms and contexts. Here a hypothetical case study involving a small molecular system, a primordial circadian clock, is used to introduce this framework and to illustrate its use in a particular case. The framework is built on fundamental biochemical kinetics. Thus, the foundation is based on linear algebra and reasonable physical assumptions, which provide numerous opportunities for experimental testing and further elaboration to deal with complex multicellular organisms that are currently beyond its scope. The discussion provides a comparison of results from the PDS framework with those from other approaches in theoretical population genetics.

The Clp1 family proteins, consisting of the Clp1 and Nol9/Grc3 groups, have polynucleotide kinase (PNK) activity at the 5' end of RNA strands and are important enzymes in the processing of some precursor RNAs. However, it remains unclear how this enzyme family diversified in the eukaryotes. We performed a large-scale molecular evolutionary analysis of the full-length genomes of 358 eukaryotic species to classify the diverse Clp1 family proteins. The average number of Clp1 family proteins in eukaryotes was 2.3 ± 1.0, and most representative species had both Clp1 and Nol9/Grc3 proteins, suggesting that the Clp1 and Nol9/Grc3 groups were already formed in the eukaryotic ancestor by gene duplication. We also detected an average of 4.1 ± 0.4 Clp1 family proteins in members of the protist phylum Euglenozoa. For example, in Trypanosoma brucei, there are three genes of the Clp1 group and one gene of the Nol9/Grc3 group. In the Clp1 group proteins encoded by these three genes, the C-terminal domains have been replaced by unique characteristics domains, so we designated these proteins Tb-Clp1-t1, Tb-Clp1-t2, and Tb-Clp1-t3. Experimental validation showed that only Tb-Clp1-t2 has PNK activity against RNA strands. As in this example, N-terminal and C-terminal domain replacement also contributed to the diversification of the Clp1 family proteins in other eukaryotic species. Our analysis also revealed that the Clp1 family proteins in humans and plants diversified through isoforms created by alternative splicing.

The ability to encode and convert heritable information into molecular function is a defining feature of life as we know it. The conversion of information into molecular function is performed by the translation process, in which triplets of nucleotides in a nucleic acid polymer (mRNA) encode specific amino acids in a protein polymer that folds into a three-dimensional structure. The folded protein then performs one or more molecular activities, often as one part of a complex and coordinated physiological network. Prebiotic systems, lacking the ability to explicitly translate information between genotype and phenotype, would have depended upon either chemosynthetic pathways to generate its components-constraining its complexity and evolvability- or on the ambivalence of RNA as both carrier of information and of catalytic functions-a possibility which is still supported by a very limited set of catalytic RNAs. Thus, the emergence of translation during early evolutionary history may have allowed life to unmoor from the setting of its origin. The origin of translation machinery also represents an entirely novel and distinct threshold of behavior for which there is no abiotic counterpart-it could be the only known example of computing that emerged naturally at the chemical level. Here we describe translation machinery's decoding system as the basis of cellular translation's information-processing capabilities, and the four operation types that find parallels in computer systems engineering that this biological machinery exhibits.

The Nucleo-Cytoplasmic Large DNA Viruses (NCLDVs) infect a wide range of eukaryotic species, including amoeba, algae, fish, amphibia, arthropods, birds, and mammals. This group of viruses has linear or circular double-stranded DNA genomes whose size spans approximately one order of magnitude, from 100 to 2500 kbp. The ultimate origin of this peculiar group of viruses remains an open issue. Some have argued that NCLDVs' origin may lie in a bacteriophage ancestor that increased its genome size by subsequent recruitment of eukaryotic and bacterial genes. Others have suggested that NCLDVs families originated from cells that underwent an irreversible process of genome reduction. However, the hypothesis that a number of NCLDVs sequences have been recruited from the host genomes has been largely ignored. In the present work, we have performed pangenomic analyses of each of the seven known NCLDVs families. We show that these families' core- and shell genes have cellular homologs, supporting possible escaping-gene events as part of its evolution. Furthermore, the detection of sequences that belong to two protein families (small chain ribonucleotide reductase and Erv1/Air) and to one superfamily [2OG-Fe(II) oxygenases] that are for distribution in all NCLDVs core and shell clusters encoding for oxygen-dependent enzymes suggests that the highly conserved core these viruses originated after the Proterozoic Great Oxidation Event that transformed the terrestrial atmosphere 2.4-2.3 Ga ago.

Species concepts have been defined through a number of lenses, but are almost entirely empirical in nature. Fundamentally linked to various existing species concepts, an interpretation of genomic data through a species classification filter based upon a theoretical genotype-phenotype map with a monophyly requirement is discussed.

Loss of heterozygosity (LOH) is a mitotic recombination event that converts heterozygous loci to homozygous loci. This mutation event is widespread in organisms that have asexual reproduction like budding yeasts, and is also an important and frequent mutation event in tumorigenesis. Mutation accumulation studies have demonstrated that LOH occurs at a rate higher than the point mutation rate, and can impact large portions of the genome. Laboratory evolution experiments of heterozygous yeasts have revealed that LOH often unmasks beneficial recessive alleles that can confer large fitness advantages. Here, I highlight advances in understanding dominance, fitness, and phenotypes in laboratory evolved heterozygous yeast strains. I discuss best practices for detecting LOH in intraspecific and interspecific evolved clones and populations. Utilizing heterozygous strain backgrounds in laboratory evolution experiments offers an opportunity to advance our understanding of this important mutation type in shaping adaptation and genome evolution in wild, domesticated, and clinical populations.

The standard genetic code determines that in most species, including viruses, there are 20 amino acids that are coded by 61 codons, while the other three codons are stop triplets. Considering the whole proteome each species features its own amino acid frequencies, given the slow rate of change, closely related species display similar GC content and amino acids usage. In contrast, distantly related species display different amino acid frequencies. Furthermore, within certain multicellular species, as mammals, intragenomic differences in the usage of amino acids are evident. In this communication, we shall summarize some of the most prominent and well-established factors that determine the differences found in the amino acid usage, both across evolution and intragenomically.

Adaptive evolution navigates a balance between chance and determinism. The stochastic processes of mutation and drift generate phenotypic variation; however, once mutations reach an appreciable frequency in the population, their fate is governed by the deterministic action of selection, enriching for favorable genotypes and purging the less-favorable ones. The net result is that replicate populations will traverse similar-but not identical-pathways to higher fitness. This parallelism in evolutionary outcomes can be leveraged to identify the genes and pathways under selection. However, distinguishing between beneficial and neutral mutations is challenging because many beneficial mutations will be lost due to drift and clonal interference, and many neutral (and even deleterious) mutations will fix by hitchhiking. Here, we review the best practices that our laboratory uses to identify genetic targets of selection from next-generation sequencing data of evolved yeast populations. The general principles for identifying the mutations driving adaptation will apply more broadly.

In recent years, evolutionary biologists have developed an increasing interest in the use of barcoding strategies to study eco-evolutionary dynamics of lineages within evolving populations and communities. Although barcoded populations can deliver unprecedented insight into evolutionary change, barcoding microbes presents specific technical challenges. Here, strategies are described for barcoding populations of the model bacterium Pseudomonas fluorescens SBW25, including the design and cloning of barcoded regions, preparation of libraries for amplicon sequencing, and quantification of resulting barcoded lineages. In so doing, we hope to aid the design and implementation of barcoding methodologies in a broad range of model and non-model organisms.

Cancer, a disease due to uncontrolled cell proliferation is as ancient as multicellular organisms. A 255-million-years-old fossilized forerunner mammal gorgonopsian is probably the oldest evidence of cancer, to date. Cancer seems to have evolved by adapting to the microenvironment occupied by immune sentinel, modulating the cellular behavior from cytotoxic to regulatory, acquiring resistance to chemotherapy and surviving hypoxia. The interaction of genes with environmental carcinogens is central to cancer onset, seen as a spectrum of cancer susceptibility among human population. Cancer occurs in life forms other than human also, although their exposure to environmental carcinogens can be different. Role of genetic etiology in cancer in multiple species can be interesting with regard to not only cancer susceptibility, but also genetic conservation and adaptation in speciation. The widely used model organisms for cancer research are mouse and rat which are short-lived and reproduce rapidly. Research in these cancer prone animal models has been valuable as these have led to cancer therapy. However, another rewarding area of cancer research can be the cancer-resistant animal species. The Peto's paradox and G-value paradox are evident when natural cancer resistance is observed in large mammals, like elephant and whale, small rodents viz. Naked Mole Rat and Blind Mole Rat, and Bat. The cancer resistance remains to be explored in other small or large and long-living animals like giraffe, camel, rhinoceros, water buffalo, Indian bison, Shire horse, polar bear, manatee, elephant seal, walrus, hippopotamus, turtle and tortoise, sloth, and squirrel. Indeed, understanding the molecular mechanisms of avoiding neoplastic transformation across various life forms can be potentially having translational value for human cancer management. Adapted and Modified from (Hanahan and Weinberg 2011).

To study unknown proteins on a large scale, a reference system has been set up for the three better studied eukaryotic kingdoms, built with 36 proteomes as taxonomically diverse as possible. Proteins from 362 other eukaryotic proteomes with no known homologue in this set were then analyzed, focusing noteworthy on singletons, that is, on such proteins with no known homologue in their own proteome. Consistently, for a given species, no more than 12% of the singletons thus found are known at the protein level, according to Uniprot. In addition, since they rely on the information found in the alignment of homologous sequences, predictions of AlphaFold2 for their tridimensional structure are poor. In the case of metazoan species, the number of singletons rarely exceeds 1000 for the species the closest to the reference system (divergence times below 75 Myr). Interestingly, in the cases of viridiplantae and fungi, larger amounts of singletons are found for such species, as if the timescale on which singletons are added to proteomes were different in metazoa and in other eukaryotic kingdoms. In order to confirm this phenomenon, further studies of proteomes closer to those of the reference system are, however, needed.

Random DNA barcodes are a versatile tool for tracking cell lineages, with applications ranging from development to cancer to evolution. Here, we review and critically evaluate barcode designs as well as methods of barcode sequencing and initial processing of barcode data. We first demonstrate how various barcode design decisions affect data quality and propose a new design that balances all considerations that we are currently aware of. We then discuss various options for the preparation of barcode sequencing libraries, including inline indices and Unique Molecular Identifiers (UMIs). Finally, we test the performance of several established and new bioinformatic pipelines for the extraction of barcodes from raw sequencing reads and for error correction. We find that both alignment and regular expression-based approaches work well for barcode extraction, and that error-correction pipelines designed specifically for barcode data are superior to generic ones. Overall, this review will help researchers to approach their barcoding experiments in a deliberate and systematic way.

The long-term evolution experiment (LTEE) with Escherichia coli began in 1988 and it continues to this day, with its 12 populations having recently reached 75,000 generations of evolution in a simple, well-controlled environment. The LTEE was designed to explore open-ended questions about the dynamics and repeatability of phenotypic and genetic evolution. Here I discuss various aspects of the LTEE's experimental design that have enabled its stability and success, including the choices of the culture regime, growth medium, ancestral strain, and statistical replication. I also discuss some of the challenges associated with a long-running project, such as handling procedural errors (e.g., cross-contamination) and managing the expanding collection of frozen samples. The simplicity of the experimental design and procedures have supported the long-term stability of the LTEE. That stability-along with the inherent creativity of the evolutionary process and the emergence of new genomic technologies-provides a platform that has allowed talented students and collaborators to pose questions, collect data, and make discoveries that go far beyond anything I could have imagined at the start of the LTEE.

The advent of next generation sequencing technologies (NGS) has greatly accelerated our understanding of critical aspects of organismal biology from non-model organisms. Bats form a particularly interesting group in this regard, as genomic data have helped unearth a vast spectrum of idiosyncrasies in bat genomes associated with bat biology, physiology, and evolution. Bats are important bioindicators and are keystone species to many eco-systems. They often live in proximity to humans and are frequently associated with emerging infectious diseases, including the COVID-19 pandemic. Nearly four dozen bat genomes have been published to date, ranging from drafts to chromosomal level assemblies. Genomic investigations in bats have also become critical towards our understanding of disease biology and host-pathogen coevolution. In addition to whole genome sequencing, low coverage genomic data like reduced representation libraries, resequencing data, etc. have contributed significantly towards our understanding of the evolution of natural populations, and their responses to climatic and anthropogenic perturbations. In this review, we discuss how genomic data have enhanced our understanding of physiological adaptations in bats (particularly related to ageing, immunity, diet, etc.), pathogen discovery, and host pathogen co-evolution. In comparison, the application of NGS towards population genomics, conservation, biodiversity assessment, and functional genomics has been appreciably slower. We reviewed the current areas of focus, identifying emerging topical research directions and providing a roadmap for future genomic studies in bats.