Revisiting the Central Dogma in the 21st Century

TransUFold: Unlocking the structural complexity of short and long RNA with pseudoknots

The RNA secondary structure is like a blueprint that holds the key to unlocking the mysteries of RNA function and 3D structure. It serves as a crucial foundation for investigating the complex world of RNA, making it an indispensable component of research in this exciting field. However, pseudoknots cannot be accurately predicted by conventional prediction methods based on free energy minimization, which results in a performance bottleneck. To this end, we propose a deep learning-based method called TransUFold to train directly on RNA data annotated with structure information. It employs an encoder-decoder network architecture, named Vision Transformer, to extract long-range interactions in RNA sequences and utilizes convolutions with lateral connections to supplement short-range interactions. Then, a post-processing program is designed to constrain the model's output to produce realistic and effective RNA secondary structures, including pseudoknots. After training TransUFold on benchmark datasets, we outperform other methods in test data on the same family. Additionally, we achieve better results on longer sequences up to 1600 nt, demonstrating the outstanding performance of Vision Transformer in extracting long-range interactions in RNA sequences. Finally, our analysis indicates that TransUFold produces effective pseudoknot structures in long sequences. As more high-quality RNA structures become available, deep learning-based prediction methods like Vision Transformer can exhibit better performance.

"Molecular Biology"-Pleonasm or Denotation for a Discipline of Its Own? Reflections on the Origins of Molecular Biology and Its Situation Today

The disciplinary identity of molecular biology has frequently been called into question. Although the debates might sometimes have been more about creating or debunking myths, defending intellectual territory and the distribution of resources, there are interesting underlying questions about this area of biology and how it is conceptually organized. By looking at the history of molecular biology, its origins and development, I examine the possible criteria for its status as a scientific discipline. Doing so allows us to answer the title question in such a way that offers a reasonable middle ground, where molecular biology can be properly viewed as a viable interdisciplinary program that can very well be called a discipline in its own right, even if no strict boundaries can be established. In addition to this historical analysis, a couple of systematic issues from a philosophy of science perspective allow for some assessment of the current situation and the future of molecular biology.

A revised central dogma for the 21st century:all biology is cognitive information processing

Crick's Central Dogma has been a foundational aspect of 20th century biology, describing an implicit relationship governing the flow of information in biological systems in biomolecular terms. Accumulating scientific discoveries support the need for a revised Central Dogma to buttress evolutionary biology's still-fledgling migration from a Neodarwinian canon. A reformulated Central Dogma to meet contemporary biology is proposed: all biology is cognitive information processing. Central to this contention is the recognition that life is the self-referential state, instantiated within the cellular form. Self-referential cells act to sustain themselves and to do so, cells must be in consistent harmony with their environment. That consonance is achieved by the continuous assimilation of environmental cues and stresses as information to self-referential observers. All received cellular information must be analyzed to be deployed as cellular problem-solving to maintain homeorhetic equipoise. However, the effective implementation of information is definitively a function of orderly information management. Consequently, effective cellular problem-solving is information processing and management. The epicenter of that cellular information processing is its self-referential internal measurement. All further biological self-organization initiates from this obligate activity. As the internal measurement by cells of information is self-referential by definition, self-reference is biological self-organization, underpinning 21st century Cognition-Based Biology.

Self-empowerment of life through RNA networks, cells and viruses

Our understanding of the key players in evolution and of the development of all organisms in all domains of life has been aided by current knowledge about RNA stem-loop groups, their proposed interaction motifs in an early RNA world and their regulative roles in all steps and substeps of nearly all cellular processes, such as replication, transcription, translation, repair, immunity and epigenetic marking. Cooperative evolution was enabled by promiscuous interactions between single-stranded regions in the loops of naturally forming stem-loop structures in RNAs. It was also shown that cooperative RNA stem-loops outcompete selfish ones and provide foundational self-constructive groups (ribosome, editosome, spliceosome, etc.). Self-empowerment from abiotic matter to biological behavior does not just occur at the beginning of biological evolution; it is also essential for all levels of socially interacting RNAs, cells and viruses.

Self-empowerment of life through RNA networks, cells and viruses

Our understanding of the key players in evolution and of the development of all organisms in all domains of life has been aided by current knowledge about RNA stem-loop groups, their proposed interaction motifs in an early RNA world and their regulative roles in all steps and substeps of nearly all cellular processes, such as replication, transcription, translation, repair, immunity and epigenetic marking. Cooperative evolution was enabled by promiscuous interactions between single-stranded regions in the loops of naturally forming stem-loop structures in RNAs. It was also shown that cooperative RNA stem-loops outcompete selfish ones and provide foundational self-constructive groups (ribosome, editosome, spliceosome, etc.). Self-empowerment from abiotic matter to biological behavior does not just occur at the beginning of biological evolution; it is also essential for all levels of socially interacting RNAs, cells and viruses.

Integrative proteomics and metabolomics profiling to understand the biochemical basis of beef muscle darkening at a slightly elevated pH

Previous studies investigated the biochemical basis of dark-cutting conditions at elevated muscle pH (above 6), but the molecular basis at slightly above normal pH (between 5.6 and 5.8) is still unclear. The objective was to determine protein and metabolite profiles to elucidate postmortem muscle darkening at slightly elevated pH. Loins were selected based on the criteria established in our laboratory before sample collections, such as pH less than 5.8, L* values (muscle lightness) less than 38, and not discounted by the grader (high-pH beef with dark color are discounted and not sold in retail stores). Six bright red loins (longissimus lumborum) at normal-pH (average pH = 5.57) and six dark-colored strip loins at slightly elevated pH (average pH = 5.70) from A maturity carcasses were obtained within 72-h postmortem from a commercial beef purveyor. Surface color, oxygen consumption, metmyoglobin reducing activity, protein, and metabolite profiles were determined on normal-pH and dark-colored steaks at slightly elevated pH. Enzymes related to glycogen metabolism and glycolytic pathways were more differently abundant than metabolites associated with these pathways. The results indicated that oxygen consumption and metmyoglobin reducing activity were greater (P < 0.05) in darker steaks than normal-pH steaks. Enzymes involved with glycogen catabolic pathways and glycogen storage disease showed lower abundance in dark beef. The tricarboxylic acid metabolite, aconitic acid, was overabundant in darker-colored beef than normal-pH beef, but glucose derivative metabolites were less abundant. The majority of glycogenolytic proteins and metabolites reported as overabundant in the previous dark-cutting studies at high pH (>6.4) also did not show significant differences in the current study. Therefore, our data suggest enzymes involved in glycogen metabolism, in part, create a threshold for muscle darkening than metabolites.

Chromosome Changes in Soma and Germ Line: Heritability and Evolutionary Outcome

The origin and inheritance of chromosome changes provide the essential foundation for natural selection and evolution. The evolutionary fate of chromosome changes depends on the place and time of their emergence and is controlled by checkpoints in mitosis and meiosis. Estimating whether the altered genome can be passed to subsequent generations should be central when we consider a particular genome rearrangement. Through comparative analysis of chromosome rearrangements in soma and germ line, the potential impact of macromutations such as chromothripsis or chromoplexy appears to be fascinating. What happens with chromosomes during the early development, and which alterations lead to mosaicism are other poorly studied but undoubtedly essential issues. The evolutionary impact can be gained most effectively through chromosome rearrangements arising in male meiosis I and in female meiosis II, which are the last divisions following fertilization. The diversity of genome organization has unique features in distinct animals; the chromosome changes, their internal relations, and some factors safeguarding genome maintenance in generations under natural selection were considered for mammals.

Multi-omics data integration approaches for precision oncology

Next-generation sequencing (NGS) has been pivotal to enhance the molecular characterization of human malignancies, allowing multiple omics data types to be available for cancer researchers and practitioners. In this context,...

Precision medicine: Concept and tools

Precision medicine is the new age medicine and refers to tailoring treatments to a subpopulation who have a common susceptibility to a particular disease or similar response to a particular drug. Although the concept existed even during the times of Sir William Osler, it was given a shot in the arm with the Precision Medicine Initiative launched by Barack Obama in 2015. The main tools of precision medicine are Big data, artificial intelligence, the various omics, pharmaco-omics, environmental and social factors and the integration of these with preventive and population medicine. Big data can be acquired from electronic health records of patients and includes various biomarkers (clinical and omics based), laboratory and radiological investigations and these can be analysed through machine learning by various complex flowcharts setting up an algorithm for the management of specific subpopulations. So, there is a move away from the traditional "one size fits all" treatment to precision-based medicine. Research in "omics" has increased in leaps and bounds and advancements have included the fields of genomics, epigenomics, proteomics, transcriptomics, metabolomics and microbiomics. Pharmaco-omics has also come to the forefront with development of new drugs and suiting a particular drug to a particular subpopulation, thus avoiding their prescription to non-responders, preventing unwanted adverse effects and proving economical in the long run. Environmental, social and behavioural factors are as important or in fact more important than genetic factors in most complex diseases and managing these factors form an important part of precision medicine. Finally integrating precision with preventive and public health makes "precision medicine" a complete final product which will change the way medicine will be practised in future.

CRISPR-Mediated Strand Displacement Logic Circuits with Toehold-Free DNA

DNA nanotechnology, and DNA computing in particular, has grown extensively over the past decade to end with a variety of functional stable structures and dynamic circuits. However, the use as designer elements of regular DNA pieces, perfectly complementary double strands, has remained elusive. Here, we report the exploitation of CRISPR-Cas systems to engineer logic circuits based on isothermal strand displacement that perform with toehold-free double-stranded DNA. We designed and implemented molecular converters for signal detection and amplification, showing good interoperability between enzymatic and nonenzymatic processes. Overall, these results contribute to enlarge the repertoire of substrates and reactions (hardware) for DNA computing.

Computational Analysis Predicts Hundreds of Coding lncRNAs in Zebrafish

Simple Summary

Noncoding RNAs (ncRNAs) regulate a variety of fundamental life processes such as development, physiology, metabolism and circadian rhythmicity. RNA-sequencing (RNA-seq) technology has facilitated the sequencing of the whole transcriptome, thereby capturing and quantifying the dynamism of transcriptome-wide RNA expression profiles. However, much remains unrevealed in the huge noncoding RNA datasets that require further bioinformatic analysis. In this study, we applied six bioinformatic tools to investigate coding potentials of approximately 21,000 lncRNAs. A total of 313 lncRNAs are predicted to be coded by all the six tools. Our findings provide insights into the regulatory roles of lncRNAs and set the stage for the functional investigation of these lncRNAs and their encoded micropeptides.

Abstract

Recent studies have demonstrated that numerous long noncoding RNAs (ncRNAs having more than 200 nucleotide base pairs (lncRNAs)) actually encode functional micropeptides, which likely represents the next regulatory biology frontier. Thus, identification of coding lncRNAs from ever-increasing lncRNA databases would be a bioinformatic challenge. Here we employed the Coding Potential Alignment Tool (CPAT), Coding Potential Calculator 2 (CPC2), LGC web server, Coding-Non-Coding Identifying Tool (CNIT), RNAsamba, and MicroPeptide identification tool (MiPepid) to analyze approximately 21,000 zebrafish lncRNAs and computationally to identify 2730–6676 zebrafish lncRNAs with high coding potentials, including 313 coding lncRNAs predicted by all the six bioinformatic tools. We also compared the sensitivity and specificity of these six bioinformatic tools for identifying lncRNAs with coding potentials and summarized their strengths and weaknesses. These predicted zebrafish coding lncRNAs set the stage for further experimental studies.

What are the principles that govern life?

We know that living matter must behave in accordance with the universal laws of physics and chemistry. However, these laws are insufficient to explain the specific characteristics of the vital phenomenon and, therefore, we need new principles, intrinsic to biology, which are the basis for developing a theoretical framework for understanding life. Here I propose what I call the seven commandments of life (the Vital Order, the Principle of Inexorability, the reformulated Central Dogma, the Tyranny of Time, the Evolutionary Imperative, the Conservative Rule, the Cooperating Thrust) as a set of principles that help us explain the vital phenomenon from an evolutionary perspective. In a metaphorical way, we can consider life like an endless race in which living beings are the runners, who are changing as the race goes on (the evolutionary process), and the commandments the rules.

A biochemical network modeling of a whole-cell

All cellular processes can be ultimately understood in terms of respective fundamental biochemical interactions between molecules, which can be modeled as networks. Very often, these molecules are shared by more than one process, therefore interconnecting them. Despite this effect, cellular processes are usually described by separate networks with heterogeneous levels of detail, such as metabolic, protein-protein interaction, and transcription regulation networks. Aiming at obtaining a unified representation of cellular processes, we describe in this work an integrative framework that draws concepts from rule-based modeling. In order to probe the capabilities of the framework, we used an organism-specific database and genomic information to model the whole-cell biochemical network of the Mycoplasma genitalium organism. This modeling accounted for 15 cellular processes and resulted in a single component network, indicating that all processes are somehow interconnected. The topological analysis of the network showed structural consistency with biological networks in the literature. In order to validate the network, we estimated gene essentiality by simulating gene deletions and compared the results with experimental data available in the literature. We could classify 212 genes as essential, being 95% of them consistent with experimental results. Although we adopted a relatively simple organism as a case study, we suggest that the presented framework has the potential for paving the way to more integrated studies of whole organisms leading to a systemic analysis of cells on a broader scale. The modeling of other organisms using this framework could provide useful large-scale models for different fields of research such as bioengineering, network biology, and synthetic biology, and also provide novel tools for medical and industrial applications.

Epigenetic modification of cytosines fine tunes the stability of i-motif DNA

i-Motifs are widely used in nanotechnology, play a part in gene regulation and have been detected in human nuclei. As these structures are composed of cytosine, they are potential sites for epigenetic modification. In addition to 5-methyl- and 5-hydroxymethylcytosine modifications, recent evidence has suggested biological roles for 5-formylcytosine and 5-carboxylcytosine. Herein the human telomeric i-motif sequence was used to examine how these four epigenetic modifications alter the thermal and pH stability of i-motifs. Changes in melting temperature and transitional pH depended on both the type of modification and its position within the i-motif forming sequence. The cytosines most sensitive to modification were next to the first and third loops within the structure. Using previously described i-motif forming sequences, we screened the MCF-7 and MCF-10A methylomes to map 5-methylcytosine and found the majority of sequences were differentially methylated in MCF7 (cancerous) and MCF10A (non-cancerous) cell lines. Furthermore, i-motif forming sequences stable at neutral pH were significantly more likely to be epigenetically modified than traditional acidic i-motif forming sequences. This work has implications not only in the epigenetic regulation of DNA, but also allows discreet tunability of i-motif stability for nanotechnological applications.

Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine

Despite the tremendous success, pitfalls have been observed in every step of a clinical metabolomics workflow, which impedes the internal validity of the study. Furthermore, the demand for logistics, instrumentations, and computational resources for metabolic phenotyping studies has far exceeded our expectations. In this conceptual review, we will cover inclusive barriers of a metabolomics-based clinical study and suggest potential solutions in the hope of enhancing study robustness, usability, and transferability. The importance of quality assurance and quality control procedures is discussed, followed by a practical rule containing five phases, including two additional "pre-pre-" and "post-post-" analytical steps. Besides, we will elucidate the potential involvement of machine learning and demonstrate that the need for automated data mining algorithms to improve the quality of future research is undeniable. Consequently, we propose a comprehensive metabolomics framework, along with an appropriate checklist refined from current guidelines and our previously published assessment, in the attempt to accurately translate achievements in metabolomics into clinical and epidemiological research. Furthermore, the integration of multifaceted multi-omics approaches with metabolomics as the pillar member is in urgent need. When combining with other social or nutritional factors, we can gather complete omics profiles for a particular disease. Our discussion reflects the current obstacles and potential solutions toward the progressing trend of utilizing metabolomics in clinical research to create the next-generation healthcare system.

Multiset sparse partial least squares path modeling for high dimensional omics data analysis

BACKGROUND

Recent technological developments have enabled the measurement of a plethora of biomolecular data from various omics domains, and research is ongoing on statistical methods to leverage these omics data to better model and understand biological pathways and genetic architectures of complex phenotypes. Current reviews report that the simultaneous analysis of multiple (i.e. three or more) high dimensional omics data sources is still challenging and suitable statistical methods are unavailable. Often mentioned challenges are the lack of accounting for the hierarchical structure between omics domains and the difficulty of interpretation of genomewide results. This study is motivated to address these challenges. We propose multiset sparse Partial Least Squares path modeling (msPLS), a generalized penalized form of Partial Least Squares path modeling, for the simultaneous modeling of biological pathways across multiple omics domains. msPLS simultaneously models the effect of multiple molecular markers, from multiple omics domains, on the variation of multiple phenotypic variables, while accounting for the relationships between data sources, and provides sparse results. The sparsity in the model helps to provide interpretable results from analyses of hundreds of thousands of biomolecular variables.

RESULTS

With simulation studies, we quantified the ability of msPLS to discover associated variables among high dimensional data sources. Furthermore, we analysed high dimensional omics datasets to explore biological pathways associated with Marfan syndrome and with Chronic Lymphocytic Leukaemia. Additionally, we compared the results of msPLS to the results of Multi-Omics Factor Analysis (MOFA), which is an alternative method to analyse this type of data.

CONCLUSIONS

msPLS is an multiset multivariate method for the integrative analysis of multiple high dimensional omics data sources. It accounts for the relationship between multiple high dimensional data sources while it provides interpretable results through its sparse solutions. The biomarkers found by msPLS in the omics datasets can be interpreted in terms of biological pathways associated with the pathophysiology of Marfan syndrome and of Chronic Lymphocytic Leukaemia. Additionally, msPLS outperforms MOFA in terms of variation explained in the chronic lymphocytic leukaemia dataset while it identifies the two most important clinical markers for Chronic Lymphocytic Leukaemia AVAILABILITY: http://uva.csala.me/mspls.https://github.com/acsala/2018_msPLS.

A comparative study of multi-omics integration tools for cancer driver gene identification and tumour subtyping

Oncogenesis and cancer can arise as a consequence of a wide range of genomic aberrations including mutations, copy number alterations, expression changes and epigenetic modifications encompassing multiple omics layers. Integrating genomic, transcriptomic, proteomic and epigenomic datasets via multi-omics analysis provides the opportunity to derive a deeper and holistic understanding of the development and progression of cancer. There are two primary approaches to integrating multi-omics data: multi-staged (focused on identifying genes driving cancer) and meta-dimensional (focused on establishing clinically relevant tumour or sample classifications). A number of ready-to-use bioinformatics tools are available to perform both multi-staged and meta-dimensional integration of multi-omics data. In this study, we compared nine different integration tools using real and simulated cancer datasets. The performance of the multi-staged integration tools were assessed at the gene, function and pathway levels, while meta-dimensional integration tools were assessed based on the sample classification performance. Additionally, we discuss the influence of factors such as data representation, sample size, signal and noise on multi-omics data integration. Our results provide current and much needed guidance regarding selection and use of the most appropriate and best performing multi-omics integration tools.

The N-space Episenome unifies cellular information space-time within cognition-based evolution

Self-referential cellular homeostasis is maintained by the measured assessment of both internal status and external conditions based within an integrated cellular information field. This cellular field attachment to biologic information space-time coordinates environmental inputs by connecting the cellular senome, as the sum of the sensory experiences of the cell, with its genome and epigenome. In multicellular organisms, individual cellular information fields aggregate into a collective information architectural matrix, termed a N-space Episenome, that enables mutualized organism-wide information management. It is hypothesized that biological organization represents a dual heritable system constituted by both its biological materiality and a conjoining N-space Episenome. It is further proposed that morphogenesis derives from reciprocations between these inter-related facets to yield coordinated multicellular growth and development. The N-space Episenome is conceived as a whole cell informational projection that is heritable, transferable via cell division and essential for the synchronous integration of the diverse self-referential cells that constitute holobionts.

The archaeology of coding RNA

Different theories concerning the origin of RNA (and, in particular, mRNA) point to the concatenation and expansion of proto-tRNA-like structures. Different biochemical and biophysical tools have been used to search for ancient-like RNA elements with a specific structure in genomic viral RNAs, including that of the hepatitis C virus, as well as in cellular mRNA populations, in particular those of human hepatocytes. We define this method as "archaeological," and it has been designed to discover evolutionary patterns through a nonphylogenetic and nonrepresentational strategy. tRNA-like elements were found in structurally or functionally relevant positions both in viral RNA and in one of the liver mRNAs examined, the antagonist interferon-alpha subtype 5 (IFNA5) mRNA. Additionally, tRNA-like elements are highly represented within the hepatic mRNA population, which suggests that they could have participated in the formation of coding RNAs in the distant past. Expanding on this finding, we have observed a recurring dsRNA-like motif next to the tRNA-like elements in both viral RNAs and IFNA5 mRNA. This suggested that the concatenation of these RNA motifs was an activity present in the RNA pools that might have been relevant in the RNA world. The extensive alteration of sequences that likely triggered the transition from the predecessors of coding RNAs to the first fully functional mRNAs (which was not the case in the stepwise construction of noncoding rRNAs) hinders the phylogeny-based identification of RNA elements (both sequences and structures) that might have been active before the advent of protein synthesis. Therefore, our RNA archaeological method is presented as a way to better understand the structural/functional versatility of a variety of RNA elements, which might represent "the losers" in the process of RNA evolution as they had to adapt to the selective pressures favoring the coding capacity of the progressively longer mRNAs.

That is life: communicating RNA networks from viruses and cells in continuous interaction

All the conserved detailed results of evolution stored in DNA must be read, transcribed, and translated via an RNA-mediated process. This is required for the development and growth of each individual cell. Thus, all known living organisms fundamentally depend on these RNA-mediated processes. In most cases, they are interconnected with other RNAs and their associated protein complexes and function in a strictly coordinated hierarchy of temporal and spatial steps (i.e., an RNA network). Clearly, all cellular life as we know it could not function without these key agents of DNA replication, namely rRNA, tRNA, and mRNA. Thus, any definition of life that lacks RNA functions and their networks misses an essential requirement for RNA agents that inherently regulate and coordinate (communicate to) cells, tissues, organs, and organisms. The precellular evolution of RNAs occurred at the core of the emergence of cellular life and the question remained of how both precellular and cellular levels are interconnected historically and functionally. RNA networks and RNA communication can interconnect these levels. With the reemergence of virology in evolution, it became clear that communicating viruses and subviral infectious genetic parasites are bridging these two levels by invading, integrating, coadapting, exapting, and recombining constituent parts in host genomes for cellular requirements in gene regulation and coordination aims. Therefore, a 21st century understanding of life is of an inherently social process based on communicating RNA networks, in which viruses and cells continuously interact.

Environmental Carcinogenesis and Transgenerational Transmission of Carcinogenic Risk: From Genetics to Epigenetics

The dominant pathogenic model, somatic mutation theory (SMT), considers carcinogenesis as a ‘genetic accident’ due to the accumulation of ‘stochastic’ DNA mutations. This model was proposed and accepted by the scientific community when cancer mainly affected the elderly, but it does not explain the epidemiological observation of the continuous increase in cancer incidence among children and young adults. Somatic mutation theory has been proposed for a revision based on the emerging experimental evidence, as it does not fully address some issues that have proven to be crucial for carcinogenesis, namely: the inflammatory context of cancer; the key role played by the stroma, microenvironment, endothelial cells, activated macrophages, and surrounding tissues; and the distorted developmental course followed by the neoplastic tissue. Furthermore, SMT is often not able to consider either the existence of specific mutations resulting in a well-defined cancer type, or a clear relationship between mutations and tumor progression. Moreover, it does not explain the mechanism of action of the non-mutagenic and environmental carcinogens. In the last decade, cancer research has highlighted the prominent role of an altered regulation of gene expression, suggesting that cancer should be considered as a result of a polyclonal epigenetic disruption of stem/progenitor cells, mediated by tumour-inducing genes. The maternal and fetal exposure to a wide range of chemicals and environmental contaminants is raising the attention of the scientific community. Indeed, the most powerful procarcinogenic mechanisms of endocrine disruptors and other pollutants is linked to their potential to interfere epigenetically with the embryo-fetal programming of tissues and organs, altering the regulation of the genes involved in the cell cycle, cell proliferation, apoptosis, and other key signaling pathways. The embryo-fetal exposure to environmental, stressful, and proinflammatory triggers (first hit), seems to act as a ‘disease primer’, making fetal cells and tissues more susceptible to the subsequent environmental exposures (second hit), triggering the carcinogenic pathways. Furthermore, even at the molecular level, in carcinogenesis, ‘epigenetics precedes genetics’ as global DNA hypomethylation, and the hypermethylation of tumor suppressor genes are common both in cancerous and in precancerous cells, and generally precede mutations. These epigenetic models may better explain the increase of cancer and chronic/degenerative diseases in the last decades and could be useful to adopt appropriate primary prevention measures, essentially based on the reduction of maternal-fetal and child exposure to several procarcinogenic agents and factors dispersed in the environment and in the food-chains, as recently suggested by the World Health Organization.

The Unforeseen Non-Coding RNAs in Head and Neck Cancer

Previously ignored non-coding RNAs (ncRNAs) have become the subject of many studies. However, there is an imbalance in the amount of consideration that ncRNAs are receiving. Some transcripts such as microRNAs (miRNAs) or small interfering RNAs (siRNAs) have gained much attention, but it is necessary to investigate other “pieces of the RNA puzzle”. These can offer a more complete view over normal and pathological cell behavior. The other ncRNA species are less studied, either due to their recent discovery, such as stable intronic sequence RNA (sisRNA), YRNA, miRNA-offset RNAs (moRNA), telomerase RNA component (TERC), natural antisense transcript (NAT), transcribed ultraconserved regions (T-UCR), and pseudogene transcript, or because they are still largely seen as non-coding transcripts with no relevance to pathogenesis. Moreover, some are still considered housekeeping RNAs, for instance small nucleolar RNAs (snoRNAs) and TERC. Our review summarizes the biogenesis, mechanism of action and potential role of less known ncRNAs in head and neck cancer, with a particular focus on the installment and progress for this particular cancer type.

The Importance of ncRNAs as Epigenetic Mechanisms in Phenotypic Variation and Organic Evolution

Neo-Darwinian explanations of organic evolution have settled on mutation as the principal factor in producing evolutionary novelty. Mechanistic characterizations have been also biased by the classic dogma of molecular biology, where only proteins regulate gene expression. This together with the rearrangement of genetic information, in terms of genes and chromosomes, was considered the cornerstone of evolution at the level of natural populations. This predominant view excluded both alternative explanations and phenomenologies that did not fit its paradigm. With the discovery of non-coding RNAs (ncRNAs) and their role in the control of genetic expression, new mechanisms arose providing heuristic power to complementary explanations to evolutionary processes overwhelmed by mainstream genocentric views. Viruses, epimutation, paramutation, splicing, and RNA editing have been revealed as paramount functions in genetic variations, phenotypic plasticity, and diversity. This article discusses how current epigenetic advances on ncRNAs have changed the vision of the mechanisms that generate variation, how organism-environment interaction can no longer be underestimated as a driver of organic evolution, and how it is now part of the transgenerational inheritance and evolution of species.

Brain plasticity, cognitive functions and neural stem cells: a pivotal role for the brain-specific neural master gene |-SRGAP2-FAM72-|

Due to an aging society with an increased dementia-induced threat to higher cognitive functions, it has become imperative to understand the molecular and cellular events controlling the memory and learning processes in the brain. Here, we suggest that the novel master gene pair |-SRGAP2-FAM72-| (SLIT-ROBO Rho GTPase activating the protein 2, family with sequence similarity to 72) reveals a new dogma for the regulation of neural stem cell (NSC) gene expression and is a distinctive player in the control of human brain plasticity. Insight into the specific regulation of the brain-specific neural master gene |-SRGAP2-FAM72-| may essentially contribute to novel therapeutic approaches to restore or improve higher cognitive functions.

Biological information systems: Evolution as cognition-based information management

An alternative biological synthesis is presented that conceptualizes evolutionary biology as an epiphenomenon of integrated self-referential information management. Since all biological information has inherent ambiguity, the systematic assessment of information is required by living organisms to maintain self-identity and homeostatic equipoise in confrontation with environmental challenges. Through their self-referential attachment to information space, cells are the cornerstone of biological action. That individualized assessment of information space permits self-referential, self-organizing niche construction. That deployment of information and its subsequent selection enacted the dominant stable unicellular informational architectures whose biological expressions are the prokaryotic, archaeal, and eukaryotic unicellular forms. Multicellularity represents the collective appraisal of equivocal environmental information through a shared information space. This concerted action can be viewed as systematized information management to improve information quality for the maintenance of preferred homeostatic boundaries among the varied participants. When reiterated in successive scales, this same collaborative exchange of information yields macroscopic organisms as obligatory multicellular holobionts. Cognition-Based Evolution (CBE) upholds that assessment of information precedes biological action, and the deployment of information through integrative self-referential niche construction and natural cellular engineering antecedes selection. Therefore, evolutionary biology can be framed as a complex reciprocating interactome that consists of the assessment, communication, deployment and management of information by self-referential organisms at multiple scales in continuous confrontation with environmental stresses.

Two genetic codes: Repetitive syntax for active non-coding RNAs; non-repetitive syntax for the DNA archives

Current knowledge of the RNA world indicates 2 different genetic codes being present throughout the living world. In contrast to non-coding RNAs that are built of repetitive nucleotide syntax, the sequences that serve as templates for proteins share-as main characteristics-a non-repetitive syntax. Whereas non-coding RNAs build groups that serve as regulatory tools in nearly all genetic processes, the coding sections represent the evolutionarily successful function of the genetic information storage medium. This indicates that the differences in their syntax structure are coherent with the differences of the functions they represent. Interestingly, these 2 genetic codes resemble the function of all natural languages, i.e., the repetitive non-coding sequences serve as appropriate tool for organization, coordination and regulation of group behavior, and the non-repetitive coding sequences are for conservation of instrumental constructions, plans, blueprints for complex protein-body architecture. This differentiation may help to better understand RNA group behavioral motifs.

SMT and TOFT: Why and How They are Opposite and Incompatible Paradigms

The Somatic Mutation Theory (SMT) has been challenged on its fundamentals by the Tissue Organization Field Theory of Carcinogenesis (TOFT). However, a recent publication has questioned whether TOFT could be a valid alternative theory of carcinogenesis to that presented by SMT. Herein we critically review arguments supporting the irreducible opposition between the two theoretical approaches by highlighting differences regarding the philosophical, methodological and experimental approaches on which they respectively rely. We conclude that SMT has not explained carcinogenesis due to severe epistemological and empirical shortcomings, while TOFT is gaining momentum. The main issue is actually to submit SMT to rigorous testing. This concern includes the imperatives to seek evidence for disproving one's hypothesis, and to consider the whole, and not just selective evidence.

The Significance of an Enhanced Concept of the Organism for Medicine

Recent developments in evolutionary biology, comparative embryology, and systems biology suggest the necessity of a conceptual shift in the way we think about organisms. It is becoming increasingly evident that molecular and genetic processes are subject to extremely refined regulation and control by the cell and the organism, so that it becomes hard to define single molecular functions or certain genes as primary causes of specific processes. Rather, the molecular level is integrated into highly regulated networks within the respective systems. This has consequences for medical research in general, especially for the basic concept of personalized medicine or precision medicine. Here an integrative systems concept is proposed that describes the organism as a multilevel, highly flexible, adaptable, and, in this sense, autonomous basis for a human individual. The hypothesis is developed that these properties of the organism, gained from scientific observation, will gradually make it necessary to rethink the conceptual framework of physiology and pathophysiology in medicine.

Cognition, Information Fields and Hologenomic Entanglement: Evolution in Light and Shadow

As the prime unification of Darwinism and genetics, the Modern Synthesis continues to epitomize mainstay evolutionary theory. Many decades after its formulation, its anchor assumptions remain fixed: conflict between macro organic organisms and selection at that level represent the near totality of any evolutionary narrative. However, intervening research has revealed a less easily appraised cellular and microbial focus for eukaryotic existence. It is now established that all multicellular eukaryotic organisms are holobionts representing complex collaborations between the co-aligned microbiome of each eukaryote and its innate cells into extensive mixed cellular ecologies. Each of these ecological constituents has demonstrated faculties consistent with basal cognition. Consequently, an alternative hologenomic entanglement model is proposed with cognition at its center and conceptualized as Pervasive Information Fields within a quantum framework. Evolutionary development can then be reconsidered as being continuously based upon communication between self-referential constituencies reiterated at every scope and scale. Immunological reactions support and reinforce self-recognition juxtaposed against external environmental stresses.

The biocommunication method: On the road to an integrative biology

Although molecular biology, genetics, and related special disciplines represent a large amount of empirical data, a practical method for the evaluation and overview of current knowledge is far from being realized. The main concepts and narratives in these fields have remained nearly the same for decades and the more recent empirical data concerning the role of noncoding RNAs and persistent viruses and their defectives do not fit into this scenario. A more innovative approach such as applied biocommunication theory could translate empirical data into a coherent perspective on the functions within and between biological organisms and arguably lead to a sustainable integrative biology.

Endocrine disruptors and female cancer: Informing the patients (Review)

Pollutants altering the endocrine system, known as endocrine disruptors (ED), may modify the risk of female cancers. The carcinogenic effect of ED on humans has been confirmed by experimental studies for various substances including pesticides, DDT, dioxins, phthalates, bisphenol A, diethylstilbestrol, as well as heavy metals, but it is difficult to quantify precisely for several reasons hereby reviewed. Carcinogenesis is a complex and multifactorial mechanism that manifests itself over a long period of time, making difficult the detection of the specific contribution of the pollutants, whose absorbed dose is often unknown. The combined effect of various substances leads to complex interactions whose outcome is difficult to predict. These substances may accumulate and carry out their harmful effect on critical periods of life, probably also at doses considered harmless to an adult. ED can also have epigenetic adverse effects on the health of future generations. In conclusion, the carcinogenic effects of endocrine disruptors on female cancer types is plausible although additional studies are needed to clarify their mechanisms and entities. In the last part of the review we suggest ways to reduce ED exposure as it is mandatory to implement necessary measures to limit exposure, particularly during those periods of life most vulnerable to the impact of oncogenic environmental causes, such as the embryonic period and puberty.

Integrating -Omics: Systems Biology as Explored Through C. elegans Research

-Omics data have become indispensable to systems biology, which aims to describe the full complexity of functional cells, tissues, organs and organisms. Generating vast amounts of data via such methods, researchers have invested in ways of handling and interpreting these. From the large volumes of -omics data that have been gathered over the years, it is clear that the information derived from one -ome is usually far from complete. Now, individual techniques and methods for integration are maturing to the point that researchers can focus on network-based integration rather than simply interpreting single -ome studies. This review evaluates the application of integrated -omics approaches with a focus on Caenorhabditis elegans studies, intending to direct researchers in this field to useful databases and inspiring examples.

Life is physics and chemistry and communication

Manfred Eigen extended Erwin Schroedinger's concept of "life is physics and chemistry" through the introduction of information theory and cybernetic systems theory into "life is physics and chemistry and information." Based on this assumption, Eigen developed the concepts of quasispecies and hypercycles, which have been dominant in molecular biology and virology ever since. He insisted that the genetic code is not just used metaphorically: it represents a real natural language. However, the basics of scientific knowledge changed dramatically within the second half of the 20th century. Unfortunately, Eigen ignored the results of the philosophy of science discourse on essential features of natural languages and codes: a natural language or code emerges from populations of living agents that communicate. This contribution will look at some of the highlights of this historical development and the results relevant for biological theories about life.

The validity of Dawkins's selfish gene theory and the role of the unconscious in decision making

Although the proposed Selfish Goal Theory constitutes a major theoretical tour de force for addressing the issue of inconsistencies in human actions and the role of motivational goals in behavior, as it is based on an unproven biological paradigm (Dawkins's selfish gene theory) and overemphasizes the role of unconscious processes in decision making, it provides a questionable model of the underlying psychological structure of human agency.

Designing synthetic biology

Synthetic biology is frequently defined as the application of engineering design principles to biology. Such principles are intended to streamline the practice of biological engineering, to shorten the time required to design, build, and test synthetic gene networks. This streamlining of iterative design cycles can facilitate the future construction of biological systems for a range of applications in the production of fuels, foods, materials, and medicines. The promise of these potential applications as well as the emphasis on design has prompted critical reflection on synthetic biology from design theorists and practicing designers from many fields, who can bring valuable perspectives to the discipline. While interdisciplinary connections between biologists and engineers have built synthetic biology via the science and the technology of biology, interdisciplinary collaboration with artists, designers, and social theorists can provide insight on the connections between technology and society. Such collaborations can open up new avenues and new principles for research and design, as well as shed new light on the challenging context-dependence-both biological and social-that face living technologies at many scales. This review is inspired by the session titled "Design and Synthetic Biology: Connecting People and Technology" at Synthetic Biology 6.0 and covers a range of literature on design practice in synthetic biology and beyond. Critical engagement with how design is used to shape the discipline opens up new possibilities for how we might design the future of synthetic biology.

Quantitative bioluminescence imaging of transgene expression in intact porcine antral follicles in vitro

BACKGROUND

The porcine oocyte maturation in vivo occurs within the ovarian follicle and is regulated by the interactions between oocytes and surrounding follicular components, including theca, granulosa, and cumulus cells, and follicular fluid. Therefore, the antral follicle is an essential microenvironment for efficient oocyte maturation and its developmental competence. Quantitative bioluminescence imaging of firefly luciferase reporter genes in an intact antral follicle would allow investigation of changes in cellular and molecular events and in the context of the whole follicles. In this study, we investigate factors influencing bioluminescence measurements as a first step towards developing a new bioluminescence imaging system for intact antral follicles.

METHODS

We analyzed the time course of bioluminescence emitted from transfected living intact follicles using a cationic lipid mediated gene transfer method with increasing doses (1-3 μg) of firefly luciferase reporter gene (pGL4). In addition, a standard luciferase assay was used to confirm the luciferase expression in granulosa cells in the transfected intact antral follicles. Finally, the dose effects of substrate, D-luciferin, were determined for optimal quantitative bioluminescence imaging of intact porcine antral follicles in vitro.

RESULTS

The level of luciferase activity of follicles with 3 μg pGL4 was significantly (P < 0.05) greater than the 1 μg and 2 μg groups at 1 min after D-luciferin injection. The bioluminescence intensity of transfected follicles reached a peak at 1 min, and then it was significantly (P < 0.05) reduced within 2 min after injection of D-luciferin; with the level of bioluminescence emission remained constant from 2.5 to 10 min. The bioluminescence emission was maximal with 300 μg of D-luciferin.

CONCLUSIONS

The results of this study suggested that the investigation of factors influencing bioluminescence measurements is a critical step toward developing a new bioluminescence imaging model. This study is the first to demonstrate that reporter genes can be transferred to intact granulosa cells with a lipid-mediated gene transfer method within intact follicles in vitro, and the level of transgene expression can be assessed by bioluminescence imaging in living intact antral follicles.

DNA → RNA: What Do Students Think the Arrow Means?

The central dogma of molecular biology, a model that has remained intact for decades, describes the transfer of genetic information from DNA to protein though an RNA intermediate. While recent work has illustrated many exceptions to the central dogma, it is still a common model used to describe and study the relationship between genes and protein products. We investigated understanding of central dogma concepts and found that students are not primed to think about information when presented with the canonical figure of the central dogma. We also uncovered conceptual errors in student interpretation of the meaning of the transcription arrow in the central dogma representation; 36% of students (n = 128; all undergraduate levels) described transcription as a chemical conversion of DNA into RNA or suggested that RNA existed before the process of transcription began. Interviews confirm that students with weak conceptual understanding of information flow find inappropriate meaning in the canonical representation of central dogma. Therefore, we suggest that use of this representation during instruction can be counterproductive unless educators are explicit about the underlying meaning.

Using a systems-based approach to overcome reductionist strategies in the development of diagnostics

Systems biology is a recent addition to the necessary but insufficient reductionist approach used in biological research. Systems biology is focused on understanding living things as a function of their various interactions at multiple levels: not simply as a sum of all their individual parts at any one level. This integrative approach yields predictive models of the normal state, the disease state and therapeutic actions. Although molecular biology has collected an enormous amount of information, including the sequencing of the entire human genome in the year 2000, few real-world applications have resulted from this molecular approach. The pharmaceutical industry's R&D expenditure has increased substantially since 2000, but the number of approved therapeutics has dropped simultaneously, due in part to over-reliance on reductionist genomic, and not systems, approaches. Instead of using reductionist genomics approaches alone, genomics should be incorporated into a multi-level systems biology approach to develop diagnostics and therapeutics.