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Land plants are astounding processors of information; due to their sessile nature, they adjust the molecular programs that define their development and physiology in accordance with the environment in which they dwell. Transduction of the external input to the respective internal programs hinges to a large degree on molecular signaling cascades, many of which have deep evolutionary origins in the ancestors of land plants and its closest relatives, streptophyte algae. In this Review, we discuss the evolutionary history of the defining factors of streptophyte signaling cascades, circuitries that not only operate in extant land plants and streptophyte algae, but that also likely operated in their extinct algal ancestors hundreds of millions of years ago. We hope this Review offers a starting point for future studies on the evolutionary mechanisms contributing to the current diversity and complexity of plant signaling pathways, with an emphasis on recognizing potential biases.

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Life codes increase in both number and variety with biological complexity. Although our knowledge of codes is constantly expanding, the evolutionary progression of organic, neural, and cultural codes in response to selection pressure remains poorly understood. Greater clarification of the selective mechanisms is achieved by investigating how major evolutionary transitions reduce spatiotemporal and energetic constraints on transmitting heritable code to offspring. Evolution toward less constrained flows is integral to enduring flow architecture everywhere, in both engineered and natural flow systems. Beginning approximately 4 billion years ago, the most basic level for transmitting genetic material to offspring was initiated by protocell division. Evidence from ribosomes suggests that protocells transmitted comma-free or circular codes, preceding the evolution of standard genetic code. This rudimentary information flow within protocells is likely to have first emerged within the geo-energetic and geospatial constraints of hydrothermal vents. A broad-gauged hypothesis is that major evolutionary transitions overcame such constraints with tri-flow adaptations. The interconnected triple flows incorporated energy-converting, spatiotemporal, and code-based informational dynamics. Such tri-flow adaptations stacked sequence splicing code on top of protein-DNA recognition code in eukaryotes, prefiguring the transition to sexual reproduction. Sex overcame the spatiotemporal-energetic constraints of binary fission with further code stacking. Examples are tubulin code and transcription initiation code in vertebrates. In a later evolutionary transition, language reduced metabolic-spatiotemporal constraints on inheritance by stacking phonetic, phonological, and orthographic codes. In organisms that reproduce sexually, each major evolutionary transition is shown to be a tri-flow adaptation that adds new levels of code-based informational exchange. Evolving biological complexity is also shown to increase the nongenetic transmissibility of code.

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Biological complexity is challenging to define, but can be considered through one or more features, including overall genome size, number of genes, morphological features, multicellularity, number of life cycle stages and the ability to adapt to different environments. Euglena gracilis meets several of these criteria, with a large genome of ∼38,000 protein coding genes and a considerable ability to survive under many different conditions, some of which can be described as challenging or harsh. Potential molecular exemplars of complexity tying these aspects together are signalling pathways, including GTPases, kinases and ubiquitylation, which increase the functionality of the gene-encoded proteome manyfold. Each of these examples can modulate both protein activity and gene expression. To address the connection between genome size and complexity I have undertaken a brief, and somewhat qualitative, survey of the small ras-like GTPase superfamily of E. gracilis. Unexpectedly, apart from Rab-GTPases which control intracellular transport and organelle identify, the size of the GTPase cohort is modest, and, for example, has not scaled with gene number when compared to the close relatives, trypanosomatids. I suggest that understanding the functions of this protein family will be vital to uncovering the complexity of E. gracilis biology.

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The size-complexity rule posits that the evolution of larger cooperative groups should favour more division of labour. Examples include more cell types in larger multicellular organisms, and more polymorphic castes in larger eusocial colonies. However, a correlation between division of labour and group size may reflect a shared response of both traits to resource availability and/or profitability. Here, this possibility was addressed by investigating the evolution of sterile caste number (worker and soldier morphotypes) in termites, a major clade of eusocial insects in which the drivers of caste polymorphism are poorly understood. A novel dataset on 90 termite species was compiled from the published literature. The analysis showed that sterile caste number did increase markedly with colony size. However, after controlling for resource adaptations and phylogeny, there was no evidence for this relationship. Rather, sterile caste number increased with increasing nest-food separation and decreased with soil-feeding, through changes in worker (but not soldier) morphotype number. Further, colony size increased with nest-food separation, thus driving the false correlation between sterile caste number and colony size. These findings support adaptation to higher energy acquisition as key to the rise of complex insect societies, with larger size being a by-product.

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C-value paradox refers to the lack of correlation between biological complexity and the intuitively expected protein-coding genomic information or DNA content. Here I discuss five questions about this paradox: i) Do biologically complex organisms carry more protein-coding genes? ii) Does variable accumulation of selfish/ junk/ parasitic DNA underlie the c-value paradox? iii) Can nucleoskeletal or nucleotypic function of DNA explain the enigma of orders of magnitude high levels of DNA in some 'lower' taxa or in taxonomically related species? iv) Can the newly understood noncoding but functional DNA explain the c-value paradox? and, v) Does natural selection uniformly apply the anthropocentric parameters for 'optimum' and 'economy'? Answers to Q.1-5 are largely negative. Biology presents numerous 'anomalous' examples where the same end function/ phenotype is attained in different organisms through astoundingly diverse ways that appear 'illogical' in our perceptions. Such evolutionary oddities exist because natural selection, unlike a designer, exploits random and stochastic events to modulate the existing system. Consequently, persistence of the new-found 'solution/s' often appear bizarre, uneconomic, and therefore, paradoxical to human logic. The unexpectedly high c-values in diverse organisms are irreversible evolutionary accidents that persisted, and the additional DNA often got repurposed over the evolutionary time scale. Therefore, the c-value paradox is a redundant issue. Future integrative biological studies should address evolutionary mechanisms and processes underlying sporadic DNA expansions/ contractions, and how the newly acquired DNA content has been repurposed in diverse groups.

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tRNA function is based on unique structures that enable mRNA decoding using anticodon trinucleotides. These structures interact with specific aminoacyl-tRNA synthetases and ribosomes using 3D shape and sequence signatures. Beyond translation, tRNAs serve as versatile signaling molecules interacting with other RNAs and proteins. Through evolutionary processes, tRNA fragmentation emerges as not merely random degradation but an act of recreation, generating specific shorter molecules called tRNA-derived small RNAs (tsRNAs). These tsRNAs exploit their linear sequences and newly arranged 3D structures for unexpected biological functions, epitomizing the tRNA "renovatio" (from Latin, meaning renewal, renovation, and rebirth). Emerging methods to uncover full tRNA/tsRNA sequences and modifications, combined with techniques to study RNA structures and to integrate AI-powered predictions, will enable comprehensive investigations of tRNA fragmentation products and new interaction potentials in relation to their biological functions. We anticipate that these directions will herald a new era for understanding biological complexity and advancing pharmaceutical engineering.

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The origin of eukaryotes was among the most important events in the history of life, spawning a new evolutionary lineage that led to all complex multicellular organisms. However, the timing of this event, crucial for understanding its environmental context, has been difficult to establish. The fossil and biomarker records are sparse and molecular clocks have thus far not reached a consensus, with dates spanning 2.1-0.91 billion years ago (Ga) for critical nodes. Notably, molecular time estimates for the last common ancestor of eukaryotes are typically hundreds of millions of years younger than the Great Oxidation Event (GOE, 2.43-2.22 Ga), leading researchers to question the presumptive link between eukaryotes and oxygen. We obtained a new time estimate for the origin of eukaryotes using genetic data of both archaeal and bacterial origin, the latter rarely used in past studies. We also avoided potential calibration biases that may have affected earlier studies. We obtained a conservative interval of 2.2-1.5 Ga, with an even narrower core interval of 2.0-1.8 Ga, for the origin of eukaryotes, a period closely aligned with the rise in oxygen. We further reconstructed the history of biological complexity across the tree of life using three universal measures: cell types, genes, and genome size. We found that the rise in complexity was temporally consistent with and followed a pattern similar to the rise in oxygen. This suggests a causal relationship stemming from the increased energy needs of complex life fulfilled by oxygen.

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A review of the theories of the existence of sexes, genetic diversity, and the distribution of mutations among organisms shows that all these concepts are not a product of random evolution and cannot be explained within the framework of Darwinism. Most mutations are the result of the genome acting on itself. This is an organized process that is implemented very differently in different species, in different places in the genome. Because of the fact that it is not random, this process must be directed and regulated, albeit with complex and not fully understood laws. This means that an additional reason must be included in order to model such mutations during evolution. The assumption of directionality must not only be explicitly included in evolutionary theory but must also occupy a central place in it. In this study an updated model of partially directed evolution is constructed, which is capable of qualitatively explaining the indicated features of evolution. Experiments are described that can confirm or disprove the proposed model.

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Posttranslational modifications have emerged in recent years as the major biological regulators responsible for the orders of magnitude increase in complexity during gene expression and regulation. These "molecular switches" affect nearly every protein in vivo by modulating their structure, activity, molecular interactions, and homeostasis ultimately regulating their functions. While over 350 posttranslational modifications have been described, only a handful of them have been characterized. Until recently, protein arginylation has belonged to the list of obscure, poorly understood posttranslational modifications, before the recent explosion of studies has put arginylation on the map of intracellular metabolic pathways and biological functions. This chapter contains an overview of all the major milestones in the protein arginylation field, from its original discovery in 1963 to this day.

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Did human culture arise through an evolutionary transition in individuality (ETI)? To address this question, we examine the steps of biological ETIs to see how they could apply to the evolution of human culture. For concreteness, we illustrate the ETI stages using a well-studied example, the evolution of multicellularity in the volvocine algae. We then consider how those stages could apply to a cultural transition involving integrated groups of cultural traditions and the hominins that create and transmit traditions. We focus primarily on the early Pleistocene and examine hominin carnivory and the cultural change from Oldowan to Acheulean technology. We use Pan behaviour as an outgroup comparison. We summarize the important similarities and differences we find between ETI stages in the biological and cultural realms. As we are not cultural anthropologists, we may overlook or be mistaken in the processes we associate with each step. We hope that by clearly describing these steps to individuality and illustrating them with cultural principles and processes, other researchers may build upon our initial exercise. Our analysis supports the hypothesis that human culture has undergone an ETI beginning with a Pan-like ancestor, continuing during the Pleistocene, and culminating in modern human culture. This article is part of the theme issue 'Human socio-cultural evolution in light of evolutionary transitions'.

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The use of next-generation sequencing (NGS) techniques for variant detection has become increasingly important in clinical research and in clinical practice in oncology. Many cancer patients are currently being treated in clinical practice or in clinical trials with drugs directed against specific genomic alterations. In this scenario, the development of reliable and reproducible bioinformatics tools is essential to derive information on the molecular characteristics of each patient's tumor from the NGS data. The development of bioinformatics pipelines based on the use of machine learning and statistical methods is even more relevant for the determination of complex biomarkers. In this review, we describe some important technologies, computational algorithms and models that can be applied to NGS data from Whole Genome to Targeted Sequencing, to address the problem of finding complex cancer-associated biomarkers. In addition, we explore the future perspectives and challenges faced by bioinformatics for precision medicine both at a molecular and clinical level, with a focus on an emerging complex biomarker such as homologous recombination deficiency (HRD).

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Contemporary scientific knowledge is built on both methodological and epistemological reductionism. The discovery of the limitations of the reductionist paradigm in the mathematical treatment of certain physical phenomena originated the notion of complexity, both as a pattern and process. After clarifying some very general terms and ideas on biological evolution and biological complexity, the article will tackle to seek to summarize the debate on biological complexity and discuss the difference between complexities of living and inert matter. Some examples of the major successes of mathematics applied to biological problems will follow; the notion of an intrinsic limitation in the application of mathematics to biological complexity as a global, relational, and historical phenomenon at the individual and species level will also be advanced.

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Nested hierarchical structure is one of life's most familiar properties and a major component of biological diversity and complexity. However, there is little effort to teach the evolution of the hierarchy of life, as there is little effort to teach biological complexity per se. We propose a framework for teaching biological complexity based on research on evolutionary transitions in individuality (ETI theory). Translating ETI theory into the classroom allows students to see the connections between natural selection, social behavior in groups, and the major landmarks of biodiversity in the hierarchy of life. The translation of ETI theory into pedagogic content and practices involves (i) the new content that must be taught, (ii) the development of general teaching tools to teach this new content, and (iii) connecting the new content and teaching tools to the specific educational context including integrating with learning standards and benchmarks. We show how teaching ETIs aids in the teaching of science practices and in teaching the process of evolutionary change. Evolutionary transitions research provides a way to teach biological complexity that is familiar and engaging to students, leveraging their inherent understanding of social dynamics and group behavior.

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To rapidly prognosticate and generate hypotheses on pathogenesis, leukocyte multi-cellularity was evaluated in SARS-CoV-2 infected patients treated in India or the United States (152 individuals, 384 temporal observations). Within hospital (<90-day) death or discharge were retrospectively predicted based on the admission complete blood cell counts (CBC). Two methods were applied: (i) a "reductionist" one, which analyzes each cell type separately, and (ii) a "non-reductionist" method, which estimates multi-cellularity. The second approach uses a proprietary software package that detects distinct data patterns generated by complex and hypothetical indicators and reveals each data pattern's immunological content and associated outcome(s). In the Indian population, the analysis of isolated cell types did not separate survivors from non-survivors. In contrast, multi-cellular data patterns differentiated six groups of patients, including, in two groups, 95.5% of all survivors. Some data structures revealed one data point-wide line of observations, which informed at a personalized level and identified 97.8% of all non-survivors. Discovery was also fostered: some non-survivors were characterized by low monocyte/lymphocyte ratio levels. When both populations were analyzed with the non-reductionist method, they displayed results that suggested survivors and non-survivors differed immunologically as early as hospitalization day 1.

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We describe an approach to early stage drug discovery that explicitly engages with the complexities of human biology. The combined computational and experimental approach is formulated on a conceptual framework in which network biology is used to bridge between individual molecular entities and the cellular phenotype that emerges when those entities interact in a network. Multiple aspects of early stage discovery are addressed including the data-driven elucidation of biological processes implicated in disease, target identification and validation, phenotypic discovery of active molecules and their mechanism of action, and extraction of genetic target support from human population genetics data. Validation is described via summary of a number of discovery projects and details from a project aimed at COVID-19 disease.

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Countless informational proposals and models have explored the singular characteristics of biological systems: from the initial choice of information terms in the early days of molecular biology to the current bioinformatic avalanche in this "omic" era. However, this was conducted, most often, within partial, specialized scopes or just metaphorically. In this paper, we attempt a consistent informational discourse, initially based on the molecular recognition paradigm, which addresses the main stages of biological organization in a new way. It considers the interconnection between signaling systems and information flows, between informational architectures and biomolecular codes, between controlled cell cycles and multicellular complexity. It also addresses, in a new way, a central issue: how new evolutionary paths are opened by the cumulated action of multiple variation engines or mutational 'vehicles' evolved for the genomic exploration of DNA sequence space. Rather than discussing the possible replacement, extension, or maintenance of traditional neo-Darwinian tenets, a genuine informational approach to evolutionary phenomena is advocated, in which systemic variation in the informational architectures may induce differential survival (self-construction, self-maintenance, and reproduction) of biological agents within their open ended environment.

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The staghorn fern (Platycerium bifurcatum, Polypodiaceae) is an epiphyte from Australasia that displays many life history characteristics commonly associated with eusocial animals. Here, I hypothesize about the selective advantage of living in cooperative groups by comparing the morphological characteristics of colonies to their solitary congeners.

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This essay aims to define the origin, expansion, and evolution of living matter. The first formations, identified as remains, fossils, traces etc. of life are almost as old as the Earth itself. During four billion years, life on the Earth has continuously existed and been implemented in the range of conditions, ensuring the liquid state of water. During the entire period of life existence, its evolution was proceeding with the tendency of multidirectionality, after each catastrophe tending to the diversity and vastness of distribution, and all the currently living species, regardless of their complexity, have the same evolutionary age. The property of reproductive surplus (multiplication) is inherent in all the living matter. The reproduction of all the living matter is implemented via the "development" - a process of continuous occurrence of something new that did not exist in the previous moment in the reproduced individual at each specific moment of time with the tendency towards the reproduction of a "copy". In its fundamental basis, Life is based on a programme, its material support is implemented and exists not in the field of causative-consecutive events, but in the field of programmed-causative-consecutive events. This predetermines the "biology laws", the behaviour of the material constituent of Life at each time period, and the future of the material constituent of life.

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A wide range of complex in vitro models (CIVMs) are being developed for scientific research and preclinical drug efficacy and safety testing. The hope is that these CIVMs will mimic human physiology and pathology and predict clinical responses more accurately than the current cellular models. The integration of these CIVMs into the drug discovery and development pipeline requires rigorous scientific validation, including cellular, morphological, and functional characterization; benchmarking of clinical biomarkers; and operationalization as robust and reproducible screening platforms. It will be critical to establish the degree of physiological complexity that is needed in each CIVM to accurately reproduce native-like homeostasis and disease phenotypes, as well as clinical pharmacological responses. Choosing which CIVM to use at each stage of the drug discovery and development pipeline will be driven by a fit-for-purpose approach, based on the specific disease pathomechanism to model and screening throughput needed. Among the different CIVMs, biofabricated tissue equivalents are emerging as robust and versatile cellular assay platforms. Biofabrication technologies, including bioprinting approaches with hydrogels and biomaterials, have enabled the production of tissues with a range of physiological complexity and controlled spatial arrangements in multiwell plate platforms, which make them amenable for medium-throughput screening. However, operationalization of such 3D biofabricated models using existing automation screening platforms comes with a unique set of challenges. These challenges will be discussed in this perspective, including examples and thoughts coming from a laboratory dedicated to designing and developing assays for automated screening.

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The gene regulatory network (GRN) architecture plays a key role in explaining the biological differences between species. We aim to understand species differences in terms of some universally present dynamical properties of their gene regulatory systems. A network architectural feature associated with controlling system-level dynamical properties is the bow-tie, identified by a strongly connected subnetwork, the core layer, between two sets of nodes, the in and the out layers. Though a bow-tie architecture has been observed in many networks, its existence has not been extensively investigated in GRNs of species of widely varying biological complexity. We analyse publicly available GRNs of several well-studied species from prokaryotes to unicellular eukaryotes to multicellular organisms. In their GRNs, we find the existence of a bow-tie architecture with a distinct largest strongly connected core layer. We show that the bow-tie architecture is a characteristic feature of GRNs. We observe an increasing trend in the relative core size with species complexity. Using studied relationships of the core size with dynamical properties like robustness and fragility, flexibility, criticality, controllability and evolvability, we hypothesize how these regulatory system properties have emerged differently with biological complexity, based on the observed differences of the GRN bow-tie architectures.

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